ABSTRACT Emotion recognition has become a key component of intelligent systems, enabling improved human–computer interaction across domains such as healthcare, education, and robotics. Progress has been achieved using facial expressions and physiological signals, particularly Electroencephalography (EEG) and Electrocardiogram (ECG), supported by advances in deep learning. This paper presents a comprehensive review of unimodal emotion recognition based on facial expressions or physiological signals, with each modality analysed independently to understand signal-specific characteristics and modelling strategies. In addition to commonly studied modalities, this review covers physiological signals including Galvanic Skin Response (GSR), Photoplethysmography (PPG), Electrooculography (EOG), Electromyography (EMG), Respiration Rate (RR), Skin Temperature (SKT), and functional near-infrared spectroscopy (fNIRS). The review emphasises the design and evaluation of deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and emerging approaches such as transformer-based models, Vision Transformers (ViTs), and transfer learning techniques. Studies are analysed using a structured evaluation framework considering model design, preprocessing strategies, datasets, evaluation protocols, and performance. Unlike existing surveys, this work provides a critical analysis of prior studies, highlighting strengths, limitations, and trade-offs, with emphasis on generalisation capability and evaluation strategies. The review identifies risks associated with improper data partitioning, where data leakage can lead to overestimated performance. Key challenges include limited dataset sizes, lack of standardised evaluation protocols, and inconsistencies in performance reporting. This study provides a structured understanding of current research trends and outlines future directions for developing more robust, reliable, and generalisable emotion recognition systems.

Motion cueing algorithm aims to replicate realistic motion sensations for drivers while adhering to the physical limitations of the simulation platform. Model Predictive Control has been extensively employed within the domain of motion cueing algorithms for vehicle and flight simulators due to its ability to handle system constraints and optimise motion fidelity. However, traditional Model Predictive Control-based motion cueing algorithm rely on manually tuned cost function weights, which can be suboptimal and difficult to determine for different operating conditions. This suboptimal tuning can cause discrepancies between the visual input perceived by the simulator driver and the motion cues processed by their vestibular system, potentially resulting in motion sensation errors and increased risk of motion sickness. In this paper, we propose a reinforcement learning weight optimisation approach for the model predictive control-based motion cueing algorithm, leveraging Deep Q-Networks to determine an optimal set of cost function weights through training in a simulated environment. The optimised weights aim to minimise the cost function, thereby maximising the reward function. Simulation results indicate that the proposed method outperforms the traditional approach, leading to a reduction in motion sensation errors between the simulator and real vehicle driver and improving platform utilisation. The reinforcement learning-based control achieves better correlation between the reference and simulated signals for both sensed specific force and angular velocity, enhancing overall motion fidelity. It also reduces the root mean square error for sensed specific force, ensuring more accurate replication of target motion cues. Additionally, the method enables broader use of the simulator's linear displacement range, confirming the effectiveness of reinforcement learning in tuning control parameters for superior simulation performance.

Vehicle trajectory prediction plays a pivotal role in enhancing advanced driver assistance systems (ADAS) and autonomous vehicles (AVs), crucial for collision avoidance, path planning, and traffic management. Traditional models often fail to account for variations in driver behaviour, such as eye movement patterns, which can substantially influence trajectory predictions. Our research presents an advanced trajectory prediction model that integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks with both eye movement data and traditional vehicle dynamic information. The accuracy of these models was evaluated in a simulated environment designed to mimic real-world driving conditions, capturing extensive data on vehicle dynamics, including position, rotation, acceleration, speed, and eye movement patterns. Data collection was rigorously conducted with 17 drivers, each using a driving simulator that ran the Euro Truck Simulator software. The models were implemented and validated using Python 3.9 and Google Colab, chosen for their effectiveness in handling deep learning tasks. Our findings demonstrate that the inclusion of eye movement data alongside vehicle dynamics enhances the accuracy of trajectory predictions, significantly reducing both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as well as Mean Absolute Error Percentage (MAPE), compared to models based solely on vehicle dynamics. This improvement not only bolsters the precision of trajectory predictions for ADAS and AV systems but also significantly elevates their safety and operational efficiency.

Simulator sickness remains a major challenge in immersive simulation systems, particularly in high-fidelity driving environments. While previous research has utilized machine learning with multimodal physiological data, it often depends on restricted feature sets and overlooks comprehensive eye movement and head motion data. In this study, we propose a deep learning framework using a hybrid 1CNN-BiLSTMAttention model for real-time quantification of simulator sickness severity. Data were collected using Deakin University’s Genesis Simulator—an immersive 360° environment with a six degrees-of-freedom motion platform. Eye-tracking and head movement features were extracted, and the Fast Motion Sickness (FMS) scale was used for severity labeling. The proposed model achieved 87.6% accuracy and an F1-score of 0.91 for high-severity detection. A 5-fold cross-validation demonstrated a significant benefit of attention over baseline models (p = 0.0019). This study offers a scalable solution for adaptive simulation and intelligent vehicle systems through integrated eye and head movement data.

This paper addresses the challenge of real-time control of multirotors subjected to partially known and unmodeled dynamics. A physics-informed Koopman operator framework is proposed, where the known physical dynamics and unknown residual effects are separated using a Strang splitting approach. The continuous-time Koopman operator is trained on physics-derived trajectories, while the discrete-time Koopman operator is learned from real-world trajectory data, enabling a data-efficient and globally linearizable model of the multirotor dynamics. The learned linear model is subsequently used to design a discrete-time Sequential Action Control (SAC) policy for real-time trajectory tracking. Experimental validation on a quadrotor platform tracking a lemniscate trajectory demonstrates that the proposed PI-EDMD-based SAC controller achieves superior tracking accuracy and up to 67% lower control energy consumption compared to baseline nonlinear SAC and LQR controllers. These results highlight the effectiveness of the proposed framework in enhancing both trajectory fidelity and actuation efficiency for multirotors.

Enabling multiple autonomous machines to perform reliably requires the development of efficient cooperative control algorithms. This paper presents a survey of algorithms that have been developed for controlling and coordinating autonomous machines in complex environments. We especially focus on task allocation methods using computational intelligence (CI) and deep reinforcement learning (RL). The advantages and disadvantages of the surveyed methods are analysed thoroughly. We also propose and discuss in detail various future research directions that shed light on how to improve existing algorithms or create new methods to enhance the employability and performance of autonomous machines in real-world applications. The findings indicate that CI and deep RL methods provide viable approaches to addressing complex task allocation problems in dynamic and uncertain environments. The recent development of deep RL has greatly contributed to the literature on controlling and coordinating autonomous machines, and it has become a growing trend in this area. It is envisaged that this paper will provide researchers and engineers with a comprehensive overview of progress in machine learning research related to autonomous machines. It also highlights underexplored areas, identifies emerging methodologies, and suggests new avenues for exploration in future research within this domain.

The increasing demand for autonomous systems in complex and dynamic environments has driven significant research into intelligent path planning methodologies. For decades, graph-based search algorithms, linear programming techniques, and evolutionary computation methods have served as foundational approaches in this domain. Recently, deep reinforcement learning (DRL) has emerged as a powerful method for enabling autonomous agents to learn optimal navigation strategies through interaction with their environments. This survey provides a comprehensive overview of traditional approaches as well as the recent advancements in DRL applied to path planning tasks, focusing on autonomous vehicles, drones, and robotic platforms. Key algorithms across both conventional and learning-based paradigms are categorized, with their innovations and practical implementations highlighted. This is followed by a thorough discussion of their respective strengths and limitations in terms of computational efficiency, scalability, adaptability, and robustness. The survey concludes by identifying key open challenges and outlining promising avenues for future research. Special attention is given to hybrid approaches that integrate DRL with classical planning techniques to leverage the benefits of both learning-based adaptability and deterministic reliability, offering promising directions for robust and resilient autonomous navigation.

Motion simulation can substantially improve the immersion of any form of vehicle simulation. However, inadequate motion simulation can fully break the immersion or even induce adverse effects, such as discomfort, motion sickness, or other harm to the simulator occupants. The selection of a stable and safe motion cueing algorithm (MCA) is therefore essential. In particular, complex and simultaneously real-time capable MCAs can carry the risk of instability. This phenomenon can be observed in non-linear model predictive control-based MCAs when the prediction horizon is defined too short, and in learning-based MCAs when there is insufficient utilization of training data. Specifically, the employment of artificial neural networks in the modeling of MCAs can lead to problems such as lack of generalization or overfitting, which, combined with the difficult interpretability due to the black-box character, makes analytical guarantees difficult. The problem is further intensified when the MCA is in a control loop with a motion platform that has a highly non-linear behavior.This work proposes a sample-based framework that utilizes simulative modeling of the deployed simulator platform to investigate the behavior of MCAs. The proposed framework is structured to initialize the system in random states, which allows for a comprehensive investigation of the behavior of any non-specific MCA. The framework is applied to two variations of one state-of-the-art MCA, and the results are compared. It is shown that the framework can identify deficiencies in the trajectory planning of MCAs. Thus, it is able to contribute significantly to the safety and stability of motion simulation.

Traffic accidents are a major public safety challenge around the world and are often influenced by the cognitive and physiological states of drivers. Among the multiple in-vehicle factors, listening to music has shown complex effects on driver behavior, particularly in relation to music tempo. This study proposes TransBiNet, a novel deep learning architecture that integrates Transformer-based attention mechanisms with Bidirectional Long Short-Term Memory layers to classify driver states under different auditory conditions using internal biometric signals. Data were collected from 26 participants driving in a simulated environment, where each subject completed scenarios involving fast-tempo music, slow-tempo music, and no music. Physiological signals (heart rate, breathing rate, galvanic skin response, and skin temperature) and head motion data (gyroscope and accelerometer) were gathered via wearable sensors and used as input to the model. The architecture was optimized through Hyperband-based hyperparameter tuning and showed a test accuracy of 97.62% as well as strong precision and recall across all classes. The results showed that internal physiological and motion-based signals are sufficient for robust classification of music-induced driver states, supporting the potential for real-time, sensor-driven driver monitoring systems in intelligent transportation.

Abstract Traffic accidents are a major global health and economic concern. As such, research into understanding driving behaviors becomes essential to minimize the associated risks. Among various factors that can influence driving behaviors, listening to music while driving is a complex task that needs investigation. Music can enhance arousal and manage stress, which could potentially improve one’s driving performance. Listening to music, however, also competes for cognitive resources, increasing one’s mental workload and potentially degrading the driving ability. This study investigates the impact of listening to music with different tempos on drivers and their performance using an Artificial Intelligence (AI) approach. A total of 26 participants are subjected to three driving scenarios in a unique simulated experiment, while utilizing a motion platform. The conditions are driving while listening to slow-tempo music, and fast-tempo music, and with no music. A dataset is created by collecting data through Tobii eye-tracking glasses, Equivital sensor belts, and a software tool. This dataset is preprocessed and used to train a convolutional neural network-long short-term memory (CNN-LSTM) model. This model’s performance is optimized through hyperparameter tuning and Chi-squared feature selection, in order to maximize accuracy and minimize computation time. The model performance is compared with those from a densely layered deep learning model and several classical machine learning models. The devised CNN-LSTM model outperforms other machine learning models, achieving an average accuracy rate of 99.29% with minimal variance across multiple evaluations, demonstrating its effectiveness and consistency in classifying drivers’ behaviors under varying auditory conditions.

This paper explores the application of driving simulator across multiple sectors, highlighting the challenges associated with refining motion cueing algorithms (MCA) through model predictive control (MPC). Through these platforms, drivers can simulate the sensation of motion. The implementation of MPC-based MCA, while advantageous for its precision in controlling motion simulations, encounters significant hurdles such as the requirement for highly accurate system models and the extensive parameter tuning needed for each specific control scenario. These issues create a critical gap in achieving optimal simulation fidelity and efficiency with lower computational time, necessitating a novel approach to improve the MCA domain. Addressing these challenges, the study pioneers the use of Deep QNetwork (DQN), a reinforcement learning (RL) technique, to optimise the horizons of MPC within the MCA domain. This innovation is significant as it introduces, for the first time, a method to dynamically adjust MPC-based MCA horizons using DQN, which learns through continuous interaction with the simulation environment. This approach is set to overcome the limitations of traditional meta-heuristic optimisation methods, such as the Grasshopper Optimisation Algorithms (GOA) and Butterfly Optimisation Algorithms (BOA), by offering a more flexible and adaptable solution. The overarching goal of this research is to minimise the system's cost function by maximising a reward function that encompasses key performance metrics such as specific force sensation, angular velocity, linear displacement, linear velocity, and angular displacement. By integrating DQN into the MPC-based MCA environment, this study demonstrates a faster computational running time and improves the precision and efficiency of the simulations. This innovative approach enhances the efficiency of the horizon determination process, showcasing promising implications for the MCA domain's advancement.

This paper presents a robust decentralised control approach for modular aerial parcel delivery using persistently excited physics-informed neural networks (PE-PINNs). The proposed method enables each propeller module to independently generate control efforts based solely on its local state information and that of its 1-hop neighbors, without requiring global system knowledge. The PE-PINN is trained to approximate the optimal centralized control policy by incorporating the nominal system dynamics and accounting for modeling uncertainties. Key innovations include estimating the Lipschitz constant to ensure persistent excitation during training, and a decentralised control formulation that minimizes the difference between the learned and optimal control efforts. Experimental results on a modular aerial testbed demonstrate the PE-PINN's ability to achieve high-accuracy fixed-point hover and trajectory tracking performance, outperforming a prior decentralised control approach by 8.57% and 24.17% respectively. The proposed framework enables scalable and robust control of modular aerial systems for parcel delivery applications.

CiRA CORE is a central hub designed to connect AI technology creation with practical application, making it easier to work with ROS (Robot Operating System) and link different systems through a user-friendly drag-and-drop interface. This approach removes the need for extensive coding, making the platform accessible to those with minimal programming experience. CiRA CORE offers a comprehensive suite of features for AI development and robot control, including algorithm creation, AI model training, and device integration commonly used in industrial settings. It supports tasks like image recognition and facilitates data storage, labeling, and integration with other systems for data-driven AI development. Overall, CiRA CORE aims to democratize AI development and robot control, simplifying AI development for Industry 4.0 applications, and leading to increased efficiency, reduced costs, and improved safety in industrial processes. This paper reports the progress of the CiRA CORE training modules funded by the SMCS TEAM Program Award. The project has completed the design of a 6-axis robot 3D training kit and simulation models for CiRA CORE training modules. The next steps involve developing 3D-printed robots and training materials. The main goal is to democratize advanced robotics and AI by simplifying integration through a visual, node-based programming interface. This approach reduces the need for complex coding, making these technologies accessible to users with limited programming experience. This initiative aims to foster widespread adoption in business and industrial settings, aligning with IEEE SMC's mission to promote professional growth and innovation in robotics and AI.

Cognitive load theory suggests that overloading of working memory may negatively affect the performance of human in cognitively demanding tasks. Evaluation of cognitive load is a difficult task; it is often assessed through feedback and evaluation from experts. Cognitive load classification based on Functional Near-InfraRed Spectroscopy (fNIRS) is now one of the key research areas in recent years, due to its resistance of artefacts, cost-effectiveness, and portability. To make fNIRS more practical in various applications, it is necessary to develop robust algorithms that can automatically classify fNIRS signals and less reliant on trained signals. Many of the analytical tools used in cognitive sciences have used Deep Learning (DL) modalities to uncover relevant information for mental workload classification. This review investigates the research questions on the design and overall effectiveness of DL as well as its key characteristics. We have identified 38 studies published between 2011 and 2022, that specifically proposed Machine Learning (ML) models for classifying cognitive load using data obtained from fNIRS devices. Those studies were analyzed based on type of feature selection methods, input, and DL model architectures. Most of the existing cognitive load studies are based on ML algorithms, which follow signal filtration and hand-crafted features. It is observed that hybrid DL architectures that integrate convolution and LSTM operators performed significantly better in comparison with other models. However, DL models especially hybrid models have not been extensively investigated for the classification of cognitive load captured by fNIRS devices. The current trends and challenges are highlighted to provide directions for the development of DL models pertaining to fNIRS research.

In this research, the effective indexes in the cold roll-forming procedure that can affect the energy utilization and required maximum torque of the forming line have been investigated and optimised using NSGA-II and type-2 fuzzy neural networks. The effective parameters were strip thickness, bending angle increment, flange width, inter-distance between the rolling stands and bending radius. Recently, traditional machine-learning applications have been employed in roll-forming technology for different purposes, such as prediction of web-warping, energy efficiency, and strip breakage. A finite element model (FEM) roll-forming procedure was utilised to extract the appropriate datasets for this study. type-2 fuzzy neural network (T2FNN) is not employed in cold roll-forming technology. In this study, T2FNN is employed to imitate the dynamic model of the cold roll-forming procedure to estimate energy consumption and torque. In the following, the NSGA-II extracts the optimal cold roll-forming procedure parameters to reach the lowest energy consumption and maximum torque as the process’s most economical solution. The proposed model is designed and developed under MATLAB software. Fourteen optimal solutions are suggested based on the extracted Pareto-Front of the NSGA-II using the T2FNN of the process.Note to Practitioners—In this research, a hybrid machine-learning method is designed and developed with a combination of T2FNN and NSGA-II to extract the optimal roll-forming procedure parameters to reach the lowest usage of energy as well as the lowest requirement of maximum torque. Implementing the proposed method in cold roll-forming production lines can save millions of dollars in massive factories by reducing the usage of energy and the emission of greenhouse gases. The proposed algorithm is quite fast. Then there is no need for high graphic computers for real-time implementation of the proposed method.

This paper presents a singularity free fast terminal sliding mode control (SFFTSMC) for position and attitude tracking of a quadrotor with unknown dynamics. The contribution of this work is threefold, namely: devising an SFFTSMC strategy using an auxiliary function to eliminate the singularity in the control law, a cost-saving minimum parameter update scheme using a fully connected recurrent neural network (FCRNN) to handle the unknown dynamics of quadrotor and development of an adaptive control law to compensate for the loss of effectiveness of the actuator and saturation effects, considered explicitly, without the requirement of any fault detection and diagnosis (FDD) unit. The proposed approach offers a direct singularity free control action in the presence of unknown dynamics, actuator faults, and saturation. Overall closed-loop stability is guaranteed in finite-time using Lyapunov stability theory and update laws for minimum parameters of FCRNN and fault compensator are derived using the same. Extensive simulations are presented for the trajectory tracking tasks using the proposed method and compared with the existing piece-wise fast terminal sliding mode controller (PFTSMC) and Radial basis function network (RBFN) based fault compensation approach. The proposed method is also validated in the Gazebo simulator via Pixhawk autopilot to demonstrate the feasibility in real-time. Note to Practitioners—This research is motivated by a cost-effective control scheme for quadrotors in cases of unknown/uncertain dynamics, external disturbances, actuator faults, and saturation. Precise quadrotor dynamics are difficult to obtain in real time. Also, there may be parametric uncertainties in quadrotor parameters like mass, inertia, and aerodynamic coefficients. External disturbances like wind gusts are always present in outdoor environments, and it may be the case that quadrotor motors lose their effectiveness due to defective motors or propeller damage while flying. To address these issues, first, it requires a precise control scheme to control the quadrotor while guaranteeing stability to ensure the safety of the quadrotor itself and people in the surrounding environment. Second, the approach should not be computationally heavy, which may sluggish the overall response of the quadrotor. Thus, we propose a control scheme to address these two issues simultaneously using a new fast terminal sliding mode control scheme augmented with FCRNN by updating minimum parameters instead of all the weight parameters of FCRNN. A compensator is introduced to mitigate the effect of unexpected actuator fault and saturation. The proposed approach can be helpful in various quadrotor applications, like pesticide spraying in agriculture, payload transportation, search and rescue, construction, etc., where unknown quadrotor dynamics and unknown payload situations occur.

In recent years, the development of robotics and artificial intelligence (AI) systems has been nothing short of remarkable. As these systems continue to evolve, they are being utilized in increasingly complex and unstructured environments, such as autonomous driving, aerial robotics, and natural language processing. As a consequence, programming their behaviors manually or defining their behavior through the reward functions [as done in reinforcement learning (RL)] has become exceedingly difficult. This is because such environments require a high degree of flexibility and adaptability, making it challenging to specify an optimal set of rules or reward signals that can account for all the possible situations. In such environments, learning from an expert's behavior through imitation is often more appealing. This is where imitation learning (IL) comes into play - a process where desired behavior is learned by imitating an expert's behavior, which is provided through demonstrations.This article aims to provide an introduction to IL and an overview of its underlying assumptions and approaches. It also offers a detailed description of recent advances and emerging areas of research in the field. Additionally, this article discusses how researchers have addressed common challenges associated with IL and provides potential directions for future research. Overall, the goal of this article is to provide a comprehensive guide to the growing field of IL in robotics and AI.

The motion cueing algorithm (MCA) enables lifelike motion in simulators resembling real driving. Regenerated motions must adhere to workspace constraints. Vehicle motion signals (linear acceleration, angular velocity) are generated in a simulated vehicle environment utilised in MCA for motion cues. These signals are categorised into levels (slow, medium, fast) based on frequency and amplitude. The commonly used MCA, the classical washout filter, is typically fine-tuned using worst-case (fast-driving) scenarios to meet the simulator’s requirements across various situations. However, this approach reduces the MCA’s effectiveness in handling slower driving scenarios, resulting in conservatism in platform workspace usage for slow and medium driving. Consequently, a noticeable motion sensation error arises between real vehicle drivers and motion simulator users. To rectify this issue, a novel neural network-based MCA is developed in this study. Three distinct classical washout filters are meticulously tuned to cater to slow, medium, and fast driving scenarios. These filters generate precise motion cues for simulator users at corresponding levels of driving scenarios. The neural network-based MCA is constructed using the synthesised signals from these classical washout filters. This proposed method is thoroughly validated through the utilisation of MATLAB software. In direct comparison with the standard classical washout filter, the proposed MCA significantly reduces the motion sensation error, enriches motion fidelity, and optimises the utilisation of the simulator’s workspace.

Uncertainty quantification (UQ) for predictions generated by neural networks (NNs) is of vital importance in safety-critical applications. An ideal model is supposed to generate low uncertainty for correct predictions and high uncertainty for incorrect predictions. The main focus of state-of-the-art training algorithms is to optimize the NN parameters to improve the accuracy-related metrics. Training based on uncertainty metrics has been fully ignored or overlooked in the literature. This article introduces a novel uncertainty-aware training algorithm for classification tasks. A novel predictive uncertainty estimate-based objective function is defined and optimized using the stochastic gradient descent method. This new multiobjective loss function covers both accuracy and uncertainty accuracy (UA) simultaneously during training. The performance of the proposed training framework is compared from different aspects with other UQ techniques for different benchmarks. The obtained results demonstrate the effectiveness of the proposed framework for developing the NN models capable of generating reliable uncertainty estimates.

Vehicle trajectory prediction is essential in ensuring the safe and efficient operation of advanced driver assistance systems (ADAS) and autonomous vehicles (AVs), as it enables highly efficient collision avoidance, path planning, and traffic control. However, existing models for vehicle trajectory prediction predominantly focus on limited driving scenarios, resulting in limited applicability. To address this limitation, we present a novel vehicle trajectory prediction approach that employs a Convolutional Long Short-Term Memory (CNN-LSTM) model, incorporating simulated environments and vehicle dynamic time series data, including longitudinal, vertical, and latitudinal position and acceleration. Our approach is distinguished by its ability to handle diverse urban driving scenarios, such as highways, roundabouts, intersections, and turns, which enhances its applicability and generalizability. We experimented and collected vehicle data from 17 drivers using a stationary driving simulator and the Euro Truck Simulator software. For the model implementation and validation, we utilized Python 3.9 and Google Colab, as well as the Scikit-learn library for Deep learning algorithms. The proposed CNN-LSTM model leverages a convolutional layer to learn local patterns and an LSTM layer to capture long-term temporal dependencies, improving performance in predicting vehicle trajectories. The experimental results demonstrate that the CNN-LSTM model provides more accurate predictions for longitudinal and lateral positions compared to traditional vehicle trajectory prediction methods that employ LSTM and Recurrent Neural Network (RNN). This research contributes to developing robust and reliable vehicle trajectory prediction systems vital for ADAS and AVs' safe and efficient operation. The proposed approach broadens the applicability of trajectory prediction models, enabling better-informed decision-making in various driving conditions and ultimately improving road safety and efficiency in the rapidly evolving field of autonomous transportation.

The growing prevalence of cybersecurity threats has increased the demand for robust intrusion detection systems (IDSs). Deep learning techniques have shown promising results in detecting and mitigating these threats, making them an increasingly popular choice in IDS design. However, evaluating the performance of deep learning-based IDSs can be challenging due to the complexity of the models and the lack of standardized evaluation metrics. This review paper presents an overview of the most common evaluation metrics used in deep learning-based IDSs, including precision, confusion metrics, accuracy, F1 score, Area Under Curve (AUC), and recall. Several studies have applied machine-learning classic algorithms like Random Forest, Decision Tree, Logistic Regression, and others, but for this paper, we used a Convolutional Neural Network (CNN) that would be independent of the features in the dataset. The studied papers did not provide AUC and none of them balanced the dataset based on the feature's proportion. The dataset utilized in this study is the CSE-CIC-IDS2018 dataset, which underwent meticulous cleansing and normalization procedures to ensure the inclusion of legitimate and useful data. Furthermore, a weighting mechanism was introduced to balance the dataset and mitigate the potential for bias in the Machine Learning process.

Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art performance for many applications, they still provide unreliable point predictions, which lack information about uncertainty estimates. Among various methods to enable neural networks to estimate uncertainty, Monte Carlo (MC) dropout has gained much popularity in a short period due to its simplicity. In this study, we present a new version of the traditional dropout layer where we are able to fix the number of dropout configurations. As such, each layer can take and apply the new dropout layer in the MC method to quantify the uncertainty associated with NN predictions. We conduct experiments on both toy and realistic datasets and compare the results with the MC method using the traditional dropout layer. Performance analysis utilizing uncertainty evaluation metrics corroborates that our dropout layer offers better performance in most cases.

This study reports the robust sideslip angle estimation of vehicles with an uncertain tire cornering stiffness and fading measurements. The missing measurement and possible inaccuracy in the measurement of the vehicle's yaw rate are considered by using a random variable distributed over [0, 1]. Norm-bounded uncertainties are considered in the vehicle's tire cornering stiffness. Next, the Lyapunov stability theory is used to design a sideslip angle estimator such that the filtering error dynamics is stochastically stable and the$H_{\infty}$performance criterion is met. The desired parameters of the proposed$H_{\infty}$sideslip angle estimator are gained by solving a linear matrix inequality (LMI) problem. Simulation results show that the proposed novel estimator can efficiently estimate the sideslip angle while it demonstrates robust performance to uncertainties and fading measurements.

Transcranial Magnetic Stimulation (TMS) is a non-invasive and painless technique used in both clinical trials and research on cortical activity and brain networks. TMS involves the use of an electromagnetic coil, which can induce powerful but brief magnetic pulses. When the coil is headed against the scalp, it can induce electrical activity in underlying brain tissue. For effective results, the TMS coil should be in appropriate contact with patients' scalp and positioned for consistent stimulation. In many cases, it requires researchers and clinicians to not only hold and position the coil on subjects' head, also to take care to ensure appropriate and consistent contact between the TMS coil and subject's scalp. This task is noticeably tiresome for the operators considering weight of the coil and a dense cable attached to it. On the other side, the patient or participant has to sit motionless; otherwise, the contact will be lost and the stimulation will have a reduced impact. In this paper, we propose and develop a haptically-enabled teleoperated robotic platform that removes all those limitations and burdensome from both TMS operators and patients/participants. The operator, through a haptic interface, remotely controls a robotic arm holding the coil. This system provides the operator with the sense of touch to feel the contact force between the coil and patient/participant's head. Therefore, operators and patients/participants do not need to be in the same location while conducting TMS, including the “motor thresholding” procedure. This will offer a huge benefit to the healthcare services in rural areas. Experimental evaluations carried out to demonstrate the effectiveness of the proposed robotic system.

Precise image segmentation is one of the dominant factors in disease diagnosis. A typical application is the segmentation of breast ultrasound images, allowing radiologists to suggest what to do next. After emerging deep learning technology especially convolutional neural networks (CNNs), the image segmentation model achieved state-of-the-art performance in various medical applications such as cancer detection and classification, lung node segmentation, cell segmentation and so on. However, despite these successes, a big question arises: to what extent is the model certain about the predicted result? Generally, most deep learning models focus on high accuracy but not on uncertainty of predicted results, which is not enough to make a critical real-life decision such as a disease diagnosis, where a wrong decision can be life-threatening. Hence for making a crucial decision, it is essential that the predicted result will provide not only accuracy but also estimate model uncertainty. Our contribution to this research is to build a system that predicts pixel-wise semantic segmentation and provides uncertainty estimation of the predicted results. It is achieved by adding a dropout layer during training and using Monte Carlo dropout in inference. We evaluate our model with the breast ultrasound image dataset (BUSI) and compare the results with a few other state-of-the-art methods where our method outperforms others in terms of IoU.

Autonomous vehicles, commonly known as self-driving cars, are rapidly gaining popularity due to their numerous advantages, such as reducing traffic, pollution, and emissions while increasing safety, convenience, and transportation connectivity. In order to accurately track the motion signal, these vehicles are now utilising advanced control techniques, such as model predictive control (MPC). However, the efficiency of MPCs heavily relies on properly tuning their weights. The primary function of the MPC is to recalculate the optimal values for the vehicle control commands, such as desired speed, steering angle, etc., while considering the dynamic model of the autonomous vehicle. The existing linear MPC models cannot reach higher efficiency because of using fixed weights without considering the error. This paper introduces a novel approach for developing an MPC model with a time-varying weights algorithm for autonomous vehicles. The study aims to minimise motion tracking errors such as lateral position and yaw angle errors. Relevant MPC weights are calculated online using fuzzy logic-based units considering the lateral position and yaw angle errors. The proposed linear time-varying MPC was designed and developed using MATLAB software, resulting in improved motion tracking performance with 31.62% and 20.89% reduction of the root means square error of lateral position and yaw angle.

The increased use of commercial Unmanned Aerial Vehicles (UAVs) has generated a great interest in their potential to be used for transporting loads and other equipment. However, as the attraction of a UAV is its versatility and cost effectiveness, constraints are placed on the size and capability of a single UAV. Therefore, using multiple UAVs in flight formation has become an elegant solution to these limitations. The formation control of a team of load bearing UAVs is far from trivial. The UAV itself is an underactuated nonlinear system posing significant control challenges. Recent research on this topic has shown promising results and interesting modelling methods such as the Udwadia-Kalaba method has been proposed, to model the loaded system. This research will explore this problem using this method while seeking to bring in robust control techniques for low-level UAV stabilization by designing a sliding mode control system. The proposed low level controller will be combined with the formation controller and the stability demonstrated through simulations.

The issue of cognitive distraction during driving has been identified as a major cause of road accidents. Detecting cognitive distraction in real-time can be a valuable strategy for preventing accidents. In this study, a novel approach is presented for the purpose of detecting cognitive distraction in real-time using artificial intelligence while taking into account eye-tracking and head movement data, combined with driving performance measures. This methodology involved collecting data from participants in a driving simulator, on a motion platform, while they performed a cognitive task as well as a control driving scenario. The data collected included eye-tracking data, head movement data, driving performance measures, and subjective ratings of distraction. To develop an accurate model for detecting cognitive distraction, a Deep Feedforward Neural Network (D-FFNN) model was employed while considering binocular gaze direction, pupil diameter, orientation of each eye, head rotational velocities, and head acceleration. The developed model was trained using the collected data and achieved an accuracy of 96.09% in detecting cognitive distraction. The results of our study demonstrate the effectiveness of the proposed method in identifying cognitive distraction in real-time. Also, the accuracy of this model was compared with other AI based classification algorithms. The proposed method has significant implications for preventing vehicle accidents caused by cognitive distraction. The proposed method can be integrated into existing driver-assistance systems to alert drivers and assist them in returning their focus to the road.

Abstract Reinforcement learning (RL) has emerged as an effective approach for building an intelligent system, which involves multiple self-operated agents to collectively accomplish a designated task. More importantly, there has been a renewed focus on RL since the introduction of deep learning that essentially makes RL feasible to operate in high-dimensional environments. However, there are many diversified research directions in the current literature, such as multi-agent and multi-objective learning, and human-machine interactions. Therefore, in this paper, we propose a comprehensive software architecture that not only plays a vital role in designing a connect-the-dots deep RL architecture but also provides a guideline to develop a realistic RL application in a short time span. By inheriting the proposed architecture, software managers can foresee any challenges when designing a deep RL-based system. As a result, they can expedite the design process and actively control every stage of software development, which is especially critical in agile development environments. For this reason, we design a deep RL-based framework that strictly ensures flexibility, robustness, and scalability. To enforce generalization, the proposed architecture also does not depend on a specific RL algorithm, a network configuration, the number of agents, or the type of agents.

Automatic segmentation of salient objects in real-world images has gained increasing interests owing to its popularity in diverse real-world applications, such as autonomous driving, medical diagnosis, aviation security, and underwater surveillance. In this research, we propose Firefly Algorithm (FA)-enhanced evolving ensemble deep networks for semantic segmentation and visual saliency prediction. An improved FA model is proposed to optimize network hyper-parameters. Specifically, it employs mutation operators and a neighbouring search strategy with granular search steps to establish search intensification. It also emphasizes search diversification by adopting multiple dynamic hybrid leaders and diverse adaptive sine and cosine search trajectories in full and randomly selected sub-dimensions to overcome stagnation. Because of its competent segmentation performance, DeepLabV3+ is fine-tuned using transfer learning with FA-based hyper-parameter identification. We optimize the learning rate, momentum and weight decay of the transfer learning network. A number of optimized DeepLabV3+ networks with distinguishing learning configurations are yielded. An ensemble model is subsequently constructed by incorporating three optimized base networks to further strengthen segmentation performance. Evaluated using diverse challenging semantic segmentation and saliency prediction tasks using underwater and medical image data sets, our evolving ensemble deep network illustrates significant superiority over other state-of-the-art deep networks and existing studies. The proposed FA model also outperforms other search methods in solving diverse mathematical landscapes with statistical significance.

We consider the event-triggered state and disturbance simultaneous estimation problem for Lipschitz nonlinear systems with an unknown time-varying delay in the state vector. For the first time, state and disturbance can be robustly estimated by using an event-triggered state observer. Our method uses only information of the output vector when an event-triggered condition is satisfied. This contrasts with previous methods of simultaneous state and disturbance estimation based on augmented state observers where the information of the output vector was assumed to be always continuously available. This salient feature, thus, lessens the stress on communication resources while can still maintain an acceptable estimation performance. First, to solve the new problem of event-triggered state and disturbance estimation, and to tackle unknown time-varying delays, we propose a novel event-triggered state observer and establish a sufficient condition for its existence. Then to overcome some technical difficulties in synthesizing observer parameters, we introduce some algebraic transformations and use inequalities, such as the Cauchy matrix inequality and the Schur complement lemma to establish a convex optimization problem in which observer parameters and optimal disturbance attenuation levels can be systematically derived. Finally, we demonstrate the applicability of the method by using two numerical examples.

The automatic and accurate analysis of medical images (e.g., segmentation,detection, classification) are prerequisites for modern disease diagnosis and prognosis. Computer-aided diagnosis (CAD) systems empower accurate and effective detection of various diseases and timely treatment decisions. The past decade witnessed a spur in deep learning (DL)-based CADs showing outstanding performance across many health care applications. Medical imaging is hindered by multiple sources of uncertainty ranging fromnteasurement (aleatoric) errors, physiological variability, and limited medical knowledge (epistemic errors). However, uncertainty quantification (UQ) in most existing DL methods is insufficiently investigated, particularly in medical image analysis. Therefore, to address this gap, in this article, we propose a simple yet novel hierarchical attentive multilevel feature fusion model with an uncertainty-aware module for medical image classification coinedHercules. This approach is tested on several real medical image classification challenges. The proposedHerculesmodel consists of two main feature fusion blocks, where the former concentrates on attention-based fusion with uncertainty quantification module and the latter uses the raw features.Herculeswas evaluated across three medical imaging datasets, i.e., retinal OCT, lung CT, and chest X-ray.Herculesproduced the best classification accuracy in retinal OCT (94.21%), lung CT (99.59%), and chest X-ray (96.50%) datasets, respectively, against other state-of-the-art medical image classification methods.

Nonlinear model predictive control has been used in motion cueing algorithms recently to consider the nonlinear dynamics model of the system. The entire motion cueing algorithm indexes, including the physical and dynamical constraints of the actuators and physical constraints of passive joints, can be controlled with precision using nonlinear model predictive control. However, several weighting parameters in the nonlinear model predictive control-based motion cueing algorithm (including driving sensation, motion description of the actuators, and passive joints) require proper and laborious tuning to attain an optimal design structure. In this work, the optimal weighting parameters of a nonlinear predictive control-based motion cueing algorithm model are calculated using cascade optimisation and human interaction. A cascade optimisation method consisting of a particle swarm optimisation and genetic algorithm is designed to identify the best weighting parameters compared to those from one optimiser. In addition, the human decision-making units are added to the two-level cascade optimiser to determine the best solution from a Pareto front. The proposed cascade optimiser decreases the run-time with better extraction of the optimal weighting parameters to increase the motion fidelity compared to a single optimiser. It should be noted that the proposed methodology is applied along longitudinal channel. While the same methodology can be applied along lateral, heave and yaw channels for further evaluation of the proposed method. The proposed model is simulated utilising the MATLAB software and the results prove the efficiency of the newly proposed model compared to those from the previous single optimiser in reproducing more accurate motion signals with better usage of the driving motion platform workspace.

In this article, we propose a novel stochastic event-driven near-optimal sliding-mode controller design for addressing the consensus of a multiagent system in a network. The system is prone to external disturbances and network uncertainties, such as losses and delays of data packets. The randomness of network uncertainties introduces stochasticity in the system. The design starts with the formulation of control-affine dynamics based on a single integrator robot model, formation error, and sliding surface dynamics. An event-triggering condition is then derived for an update of control input for each agent. These input updates guarantee desired consensus in finite time with reaching time of each agent's sliding surface having an upper bound. The admissibility of event-driven near-optimal control updates is also ensured for each agent. The near-optimal control design for each agent has achieved through neural-network-based actor-critic architecture. The implementation of Pioneer P3-DX mobile robots illustrates threefold efficacy of the proposed design: 1) advantages of event-driven approach and higher order sliding mode controller; 2) robustness to network uncertainties; and 3) near-optimality in system performance.

As the computer networks growing up rapidly, network engineers and administrators are going through a lot of difficulties to keep the network under their management properly and troubleshoot the issues pretty quick. Also they need to keep thinking about the future growth and compatibility to new features and technologies. Therefor manual network management and monitoring is not efficient anymore and most of the companies are trying to adapt the new technology for automation either in monitoring and/or troubleshooting.Software Defined Networking (SDN) is one of the new technologies which is trying to help the network engineers or administrators to have their networks under more control. Also deep-learning and Artificial Intelligent are some technologies which can help to prevent issues or improve the network control performance.

As 5G, new generation of wireless technology, started to be implemented all around the world, scientists and engineers have been working to use more of its benefits in their fields. One of the most interesting areas that 5G, and ultra fast communications in general, is being devised is the networked systems. Amongst network-based systems, telerobotic has attracted most attention thanks to its numerous applications in medicine, under water explorations, rescue systems, and outer space discovery. However, there are challenges and considerations in developing such systems based on 5G technology. In this paper we are going to review some of these challenges also talk about the key benefits of 5G and its use case in robotics. This paper introduces main novel features and properties of 5G networks, such as network slicing, eMBB, uRLLC, mMTC, and more. An immediate utilisation of 5G is due to its ultra low latency capabilities, and is discussed in latency critical services. As an outcome, 0.25−100 ms is the range of delays experienced in such services utilising 5G with error rates less than 10−3.

In the absence of physical motion, people sometimes experience the illusory sensation of self-motion which is known as vection. Vection research could contribute to the improvement of the fidelity of simulators as vection and presence appear to be positively related. However, when utilizing virtual reality technology for simulators, visually-induced motion sickness (VIMS) in the form of Cybersickness (CS) sometimes co-occurs with the experience of vection. Nonetheless, the relationship between vection and CS is not evident. Past research mainly investigated the relationship between the vection and CS using environments with a certain level of ecological relevance. Herein we investigated whether CS negatively affects the relationship between vection and presence across different virtual environments with varying levels of ecological relevance. We immersed twenty-nine participants visually and audibly in virtual environments and after each trial participants reported their vection intensity, CS, and presence. Our results showed that the relationship between vection intensity and presence was unaffected by CS. We conclude that the relationship between vection and presence is unaffected by CS with low levels of discomfort.

Human Activity Recognition (HAR) is one of the essential building blocks of so many applications like security, monitoring, the internet of things and human-robot interaction. The research community has developed various methodologies to detect human activity based on various input types. However, most of the research in the field has been focused on applications other than human-in-the-centre applications. This paper focused on optimising the input signals to maximise the HAR performance from wearable sensors. A model based on Convolutional Neural Networks (CNN) has been proposed and trained on different signal combinations of three Inertial Measurement Units (IMU) that exhibit the movements of the dominant hand, leg and chest of the subject. The results demonstrate k-fold cross-validation accuracy between 99.77 and 99.98% for signals with the modality of 12 or higher. The performance of lower dimension signals, except signals containing information from both chest and ankle, was far inferior, showing between 73 and 85% accuracy.

Advances in utilisng motion simulators for skill training and related applications have yielded numerous benefits, such as safety, availability, and serviceability, environmentally friendly, and economically beneficial. To give simulator users a sense of realistic feeling of driving, an accurate motion cueing algorithm (MCA) is essential, in order to respect the simulator platform limitation and avoid motion sickness. The use of Model Predictive Control (MPC) in MCA designs leads to respecting the constraints and considering the future dynamic behaviors of the simulator. However, the tuning process of the MPC prediction horizon and control horizon still need to be improved. These horizons are normally selected manually by the designer. Previous studies on meta-heuristic algorithms produce a large prediction horizon with a heavy computational load or a small prediction horizon that sacrifices the stability and accuracy of the simulator system. In this study, the Grasshopper Optimization Algorithm (GOA) is adopted to yield optimal prediction and control horizons in MPC-based MCA models. The results are compared with those from the Butterfly Optimization Algorithm (BOA) and Genetic Algorithm (GA) in terms of sensation error and computation time. The GOA technique depicts the fastest process time to promptly detect proper MPC horizons. It does not affect the simulator's efficiency in utilising the workspace, as evidenced by the correlation coefficient and root mean square error between sensation from a real-world vehicle and the simulator.

Driving simulators are utilized for many applications including basic driver training, human factor studies, human-machine interaction, and vehicle prototyping in automobile industries. The main purpose of using driving simulator is to provide realistic driving experience. Since simulator platforms have physical limitations, Motion Cueing Algorithms (MCAs) are used to generate driving sensation for the simulator user while considering the simulator's physical and dynamical constraints. When using a model predictive control (MPC)-based MCA, the principle of MPC is leveraged to predict an optimized future behavior of the simulator where a series of control actions is developed across a defined future horizon using the explicitly specified process model. Corresponding to the pre-positioning or time-varying reference MPC, it is crucial to predict the future vehicle motion signals for the simulator accurately. The existing methods for predicting vehicle motion signals do not excel in predicting time-series of a long sequence due to the missing feedback loop or limited memory size. To address this issue, the Long Short-Term Memory (LSTM) model is developed to predict motion signals using Python. The performance of LSTM is compared with those from different traditional methods using several measurements criteria, which include the root mean squared error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (r). The results indicate that LSTM outperforms RNN by producing more accurate motion allowing the MCA to deliver realistic motion sensations, the LSTM model can be employed in a wide range of applications including autonomous vehicles trajectory prediction, and other prediction problems.

A Motion Cueing Algorithm (MCA) is an algorithm that transforms the movement of a simulated vehicle into movement that can be reproduced with a Motion Simulator (MS) while respecting its physical constraints. Crucially, MCAs aim to provide a realistic driving experience to simulator users. Adaptive MCAs are a type of MCA that is flexible, computationally light and designed to adjust behaviour based on the current MS state. However, adaptive MCAs require extensive manual tuning which is difficult, time consuming and a sub-optimal process. This paper presents an optimisation-based method using Particle Swarm Optimisation (PSO) for automatically tuning the free parameters of the Adaptive Gradient Descent-based MCA (AGDA) while accounting for MS physical constraints and motion fidelity. The cost function of the tuning routine considers the RMSE, correlation coefficient (CC) and error oscillation of the motion sensation signals of the MS driver with respect to those of the simulated vehicle driver. The displacement, velocity and acceleration of the MS are also considered. The proposed method was implemented using MATLAB and Simulink and the effectiveness of the approach was tested with a Rigs of Rods simulation of a ground vehicle. Compared to the existing manually tuned AGDA, the optimally tuned AGDA obtained with the proposed method performs 32.6% and 23.7% better in terms of RMSE and CC of the motion sensation signals, respectively. The observed performance improvement and moderate computational load of the AGDA renew its relevance in the context of modern MCAs.

This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very complicated and time-consuming process because of the complexity of image recognition applications. On the other hand, transfer learning is a relatively new learning method that has been employed in many sectors to achieve good performance with fewer computations. In this research, the PyTorch pre-trained models (VGG19_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers. The employed PyTorch pre-trained models were previously trained in ImageNet. The proposed model is developed and verified in the Kaggle notebook, and it reached the outstanding accuracy of 99.77% without taking a huge computational time during the training process of the network. We also applied the same methodology to the SIIM-FISABIO-RSNA COVID-19 Detection dataset and achieved 80.01% accuracy. In contrast, the previous methods need a huge compactional time during the training process to reach a high-performing model. Codes are available at the following link: github.com/dipuk0506/Spina1Net

The illusory sensation of self-motion is defined as vection. Vection research can help enhance Virtual Reality applications and improve simulator fidelity as vection appears to be a desired sensation in motion simulators. The experience of vection can be modulated by cognitive factors and potentially personal traits, such as the vividness of imagination. Previous research investigating the relationship between auditory vection and kinesthetic imagery presented conflicting findings. However, the relationship between visually-induced vection and imagination has not been investigated. Herein we investigated the relationship between kinesthetic imagery and unimodal visual and bimodal visual-auditory vection. Twenty-nine participants were visually and audibly immersed in virtual environments with varying degrees of ecological relevance wherein they reported on their vection experience. No differences were found for vection intensity and latency measures between participants with high and low kinesthetic imagery. We conclude that imagery does not appear to play a role in the experience of visually-induced vection.

Vection is commonly defined as the illusory sensation of self-motion. Research on vection can assist in improving the fidelity of motion simulators. Vection can be influenced through top-down factors, such as attention, but previous research on the effect of a secondary task on vection presented conflicting findings. We investigated the effect of a visual discrimination reaction time task on vection. Twenty-nine participants were visually and audibly immersed in virtual environments with different levels of ecological relevance wherein they used a joystick to continuously report on their vection experience. In contrast to previous research, our results showed no significant effect of a secondary task on vection measures nor an effect of sensory cues and environment context on secondary task performance. We conclude that participants’ ability to report their vection experience was unaffected whilst performing a visual attention reaction time task.

High-G Centrifuge Systems (HCSs) are valuable tools for training aircrews and research on aviation medicine. Providing a safe and controlled environment, they are an enabler for protecting aircrews and air assets. Despite their vast applications, development of a human-rated HCS is a costly and challenging engineering project. One of the most critical steps in the development of HCSs is the experimental validation. This step has not received enough attention within the published research. This study reports evaluation and validation of an operational HCS located in the Institute for Intelligent Systems Research and Innovation (IISRI) at Deakin University, Australia. The system, owning a low-cost structure with an effective arm length of over 5m, is capable of generating a maximum sustained acceleration of 9G with an onset rate of 5G/sec. The experimental validation of system is implemented using an Advanced Wireless Human Dummy (AWHD) which is fully instrumented. The results of experimental validation show that the system can reliably generate the reference centripetal acceleration with lowest error at the human spine location.

Self-driving vehicles, also known as Autonomous Vehicles (AVs), are steadily becoming very popular due to their huge benefits. They can improve safety, convenience and transport interconnectivity as well as reduce congestion, pollution and emissions. The generation of the comfort motion signal for AVs passenger via the calculation of accurate motion cues with lower motion discomforts is important to promote the adoption of Avs in society. Model predictive control (MPC) is currently used in AVs for tracking the motion signal with good accuracy. However, the higher efficiency of MPC is directly related to the right setting of the weights. In addition, the tracking of time-varying longitudinal velocity is not possible without using linear time-varying (LTV) MPC. In this study, an LTV MPC system is designed and developed as a highly efficient motion tracking mechanism for AVs to reduce the motion tracking error and motion discomfort. In addition, biogeography-based optimisation (BBO) is employed to determine the optimal weights of the LTV MPC controller, which further reduces the motion tracking error and increases the motion comfort for users. The empirical study demonstrates that a BBO-tuned LTV MPC controller decreases the mean square error of motion tracking by 4.79% as compared with that of a manually-tuned version. Moreover, the mean square errors of the lateral deviation and relative yaw decrease by 91.22% and 19.14% as compared with those from a manually-tuned LTV MPC counterpart, respectively.

Motion signal should be generated via the AV control system targeting the maximum motion comfort for the users. Nonlinear model predictive control (MPC) is recently used in AVs to achieve this critical task. However, nonlinear MPC has lots of hyperparameters, including weights and MPC horizons, that should be tuned systematically to reach the system’s high efficiency. The energy usage and motion comfort have a direct relationship. The generation of high-fidelity motion cues for AV users leads to higher energy usage. Hence, there is a need for the use of a multi-objective optimisation technique to tune the weights wisely to satisfy the appropriate energy usage and motion comfort for the AV users. In this study, multi-objective NSGA-II is employed, for the first time, to tune the weights of a nonlinear MPC-based controller in AVs. The proposed method is designed and developed using MATLAB/SIMULINK software. The simulation results show minimum energy usage by generation of smooth motion signals, delivering maximum comfort to AV users.

The autonomous vehicle (AV) has been studied by many researchers recently because of its valuable points in transportation, aviation, military, smart city, and aerospace. The model predictive control (MPC) is employed to track the artificial intelligent regenerated motion signals with higher accuracy than other error-and model-based controllers as it can consider the constraints of the system in extracting the optimal solution. However, the accuracy and applicability of the MPC rely on the MPC horizons, including prediction and control horizons. The higher prediction horizons mean a higher computational load of the system, which reduces the real-time applicability of the system. On the other hand, a higher prediction horizon increases the system’s stability in facing abrupt motion signals. In addition, higher control horizons mean more dexterity in the system facing an unknown situation. On the other hand, a longer control horizon increases the computational load of the system exponentially. This study employs particle swarm optimisation (PSO) to extract the optimal MPC horizons considering the accuracy and computational load. The cost function is defined to increase the accuracy of the longitudinal time-varying velocity tracking, decrease the lateral deviation, decrease the relative yaw angle and decrease the computational load of the system. It should be noted that the lateral deviation and relative yaw angle are extracted using the vehicle four wheels dynamic model in order to evaluate the AVs’ passenger motion comfort. The proposed method is designed and developed under MATLAB/Simulink. The extracted optimal MPC horizon is compared with some other arrangements of the MPC horizons to prove the efficiency of the proposed method compared with the trial-and-error method.

The motion signals are generated for a simulator user based on the visual understanding of the environment using virtual reality. In this respect, a motion cueing algorithm (MCA) is employed to reproduce the motion signals based on the real driving motion scenarios. Advanced MCAs are required to predict precise driving motion scenarios. Nonetheless, investigations on effective methods for predicting the driving motion scenarios accurately are limited. Current state-of-the-art studies mainly focus on the averaged motion signals from several simulator users pertaining to a specific map or from feedforward neural network and non-linear autoregressive. The existing methods are unable to yield precise predictions of the driving scenarios. In this research, the echo state network and long short-term memory models are employed for the first time in MCA to forecast the driving motion signals. Our evaluation proves the efficiency of our proposed methods in comparison with existing methods.

Industry 5.0 is here, and human interaction experts claim that in the process of augmenting a high production/manufacturing workplace, a safety critical situation is created with the introduction of “Cobots”. A Multi-Agent Safety System (MASS) is presented as a solution in this paper which uses commercial, wearable technologies with high data sharing acceptance rates such as the Apple watch to collect and share real time ECG signals with the Cobot. Principal Component Analysis (PCA) is selected as a dimension reduction tool because it is well established and meets the requirements for reliability in the development of a human-centric, safety system. Five Machine Learning (ML) classifiers (KNN, NB, RF, DT and GBM) are used with binary classification to predict whether the human is Distracted (Event 1) or Not Distracted (Event 0) to determine if this will pose a safety risk to the Human Robot Collaboration (HRC) System. Decision Tree (DT) classifier with 4 Principal Components (PCs) is evaluated at 98% Accuracy and 99%AUC and is the recommended model for future development of the MASS. A road map is also presented to ensure the longevity of MASS while signifying the inclusion of real time data which can close the demographic data gap and help to improve the privacy, efficiency and contextual reliability of the MASS model in the Industry 5.0 workplace.

Predicting image saliency has many potential useful applications across several industries, including film production, creative marketing content, product design, and quality control in manufacturing. Although image saliency prediction, in general, has made substantial progress with the advent of deep learning on benchmark datasets, it still has room to improve when applied to novel scenarios. This paper presents our experimental findings in dataset building and model development for predicting saliency of cinematic images in a real-world context. To evaluate the proposed model, we conducted experiments on both benchmark datasets (i.e., SALICON, MIT1003, MIT300, CAT2000) as well as a private dataset. The results demonstrate the superior performance of our proposed method. We highlight a film production use case, but the model and methods explored here may also generalize to other areas relevant to image saliency.

Abstract Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z‐Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper‐parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries.

This work proposes a novel event-triggered exponential supertwisting algorithm (ESTA) for path tracking of a mobile robot. The proposed work is divided into three parts. In the first part, a fractional-order sliding surface-based exponential supertwisting event-triggered controller has been proposed. Fractional-order sliding surface improves the transient response, and the exponential supertwisting reaching law reduces the reaching phase time and eliminates the chattering. The event-triggering condition is derived using the Lipschitz method for minimum actuator utilization, and the interexecution time between two events is derived. In the second part, a fault estimator is designed to estimate the actuator fault using the Lyapunov stability theory. Furthermore, it is shown that in the presence of matched and unmatched uncertainty, event-trigger-based controller performance degrades. Hence, in the third part, an integral sliding-mode controller (ISMC) has been clubbed with the event-trigger ESTA for filtering of the uncertainties. It is also shown that when fault estimator-based ESTA is clubbed with ISMC, then the robustness of the controller increases, and the tracking performance improves. This novel technique is robust toward uncertainty and fault, offers finite-time convergence, reduces chattering, and offers minimum resource utilization. Simulations and experimental studies are carried out to validate the advantages of the proposed controller over the existing methods.

Learning to drive in urban areas is an open challenge for autonomous vehicles (AVs), as complex decision making requirements are needed in multi-task co-ordinations environments. In this paper, we propose a hybrid framework with a new perception model involving affordance learning to simplify the surrounding urban scenes for training an AV agent, along with a planned trajectory and the associated driving measurements. Our proposed solution encompasses two main aspects. Firstly, a supervised learning network is used to map the input sensory data into affordance predictions. The predicted affordances provide a low-dimensional representation of surrounding scenes of the AV in the form of key perception indicators, e.g., true or false with respect to a traffic light signal. Secondly, a deep deterministic policy gradient model that maps the perception information into a series of actions is devised. We evaluate the proposed solution using the CARLA driving simulator in an urban town and evaluate the performance in a new, unseen town under different weather conditions. The quantitative and qualitative results indicate that our proposed solution can generalize well to cope with different traffic solutions and environmental conditions. Our proposed solution also outperforms other baseline methods in a comparative study in handling various AV driving tasks with different levels of difficulty. In addition, the model trained with simulated scenes yields promising prediction results when testing on recorded video streams on real-world highway and suburb environments with varying traffic and weather conditions.

The motion cueing algorithm (MCA) is playing the most critical role in motion simulation platform (MSP) to reproduce the realistic motion sensation of the real car for the MSP’s users while taking into cogitation of the physical boundaries of the platform. Recently, the model predictive control (MPC) is employed for designing MCAs which led to the formation of MPC-based MCAs. The purpose of the MPC-based MCA is to recalculate the optimal values of the input signals with consideration of the MSP’s physical restrictions. All the current MPC-based MCAs have fix weights that can cause the conservative and inefficient utilization of the MSP’s workspace limitations as they have been tuned based on the worst-case scenarios to keep the platforms within their physical limitations. Then, the error of motion sensation between the real car and MSP users increases due to the conservative utilization of the MSP’s workspace boundaries. The main objective of this study is to provide more efficient workspace utilization to minimise the error of motion feeling between the real car and MSP users while respecting the workspace boundaries. A procedure according to the optimised fuzzy logic-based units is employed to calculate the appropriate MPC weights online while considering the sensed specific force error, sensed angular velocity error and the current motion status of the MSP including linear position, linear velocity and angular position of the cockpit. The proposed MPC-based MCA is designed and developed using MATLAB. The outcomes show a better motion feeling compared with the current MPC-based MCAs.

The motion cueing algorithm (MCA) is the main algorithm in motion simulators in charge of generating vehicle motions within the platform’s constraints. The classical washout filter is one of the popular types of MCA, which is used in air and land vehicle motion simulators. The fixed home position of the simulator platform is always cogitated in the MCA to washout the motion simulator after generating each motion. Unfortunately, considering the fixed home position reduces the efficient consumption of the workspace in the linear directions. The linear motion of the motion simulator is due to the production of the high-pass frequency part of the motion scenarios. Prepositioning is used to tackle this assumption by varying the home position rather than the fixed position. The linear motion limitations of the motion simulator can virtually be enlarged using the prepositioning method. The efficient regeneration of the high-pass motion cues using a new propositioning technique is the main goal of this study to increase the motion realism of the simulator and remove any false motion cues due to the platform limitations. The proposed model utilised the recurrent neural network (RNN) to estimate the motion scenario along the prediction horizon. The nonlinear model predictive control (MPC) uses the estimated motion signals to extract the best optimal off-centre position of the motion simulator platform. The newly developed prepositioning technique is developed in the simulation environment of MATLAB to validate the proposed technique in terms of efficiency and applicability. The outcomes prove the capability of the proposed technique against the recently developed prepositioning technique using fuzzy logic and RNN.

This article studies dissipativity analysis of fractional-order quaternion-valued neural networks (FOQVNNs) with time delays. Two specific activation functions are considered along with common bounded and activation functions of Lipschitz-kind. Since quaternion multiplication is not commutative, we must divide the model, which is evaluated by quaternion, into four elements that are real-valued elements. On the basis of the construction of novel Lyapunov functional, and applying fractional-calculus theory, new criteria for the test of the global dissipativity and exponential stability of FOQVNNs model are established. FOQVNNs have also been suggested to provide global dissipativity and exponential stability, whereas nonlinear complex activation functions are constrained by the usage of linear matrix inequality methods, which utilize quaternion matrices and positive quaternion definite matrices. Finally, the effectiveness and superiority of the proposed approach is validated through numerical examples.

Accurate prediction of solar energy is an important issue for photovoltaic power plants to enable early participation in energy auction industries and cost-effective resource planning. This article introduces a new deep learning-based multistep ahead approach to improve the forecasting performance of global horizontal irradiance (GHI). A deep convolutional long short-term memory is used to extract optimal features for accurate prediction of the GHI. The performance of such deep neural networks directly depends on their architectures. To deal with this problem, a swarm evolutionary optimization method, called the sine-cosine algorithm, is applied and advanced to automatically optimize the network architecture. A three-phase modification model is proposed to increase the diversity of population and avoid premature convergence in the optimization mechanism. The performance of the proposed method is investigated using three datasets collected from three solar stations in the east of the United States. The experimental results demonstrate the superiority of the proposed method in comparison to other forecasting models.

This article focuses on developing an adaptive fast terminal sliding-mode controller (AFTSMC) with power rate proportional reaching law for the position and altitude tracking of a quadrotor in the presence of parametric uncertainties and bounded external disturbance. A nonlinear fast terminal sliding surface is proposed for the fast and finite-time convergence of the tracking error despite having the system states far away from the equilibrium point. Also, a power rate proportional reaching law has been proposed that ensures fast and finite-time convergence of the sliding manifold while attenuating the chattering phenomena in the sliding phase. To avoid the problem associated with over-estimation of the unknown disturbance bound, which eventually leads to chattering, an adaptive tuning law for gain adaptation is developed based on the Lyapunov’s stability theory that completely eradicates the necessity of knowing the upper bound of the disturbancea priori. The finite-time stability of a closed-loop system is investigated using the Lyapunov theory. The effectiveness of the proposed scheme is compared with an adaptive sliding-mode controller (ASMC) using extensive simulation and validated on the DJI Matrice 100 quadrotor as a proof of concept on the hardware platform.

Prediction with uncertainty quantification (UQ) is becoming a vital part of the grid management with the increased uncertainty caused by the recent installation of renewable power sources. Specialized algorithms are developed and applied to predict electricity demand, renewable generations and other non-fossil-fuel generations in point predictions. The required fossil fuel generation (RFFG) of a grid is predicted from the subtraction of other generations from the demand. A prediction interval (PI) constructed from intervals of different components results in a higher coverage probability with much higher width. This paper presents the direct construction of smart RFFG PIs, maintains a narrower width with the expected coverage probability. The time series array of the RFFG is obtained from the subtraction of the sum of other generations from the electricity demand. A modified NN based Lower Upper Bound Estimation (LUBE) method is applied with a continuous cost function to construct PIs; as the LUBE method can construct smart PIs for an asymmetric and heteroscedastic probability distribution. Many inter-related uncertainties between the electricity demand and renewables are canceled out with the difference; result in a narrower and smarter PI with less computation.

Recommender systems are known as intelligent systems which have many applications in enormous domains such as social networks, e-commerce services, and online shopping. Deep neural networks have shown significant improvement in the performance of recommender systems by learning the latent features of users/items based on input data. However, it is a challenging issue to how to apply deep neural networks on different resources and how to integrate their results. In this regard, we propose a recommender system in this paper based on deep sparse autoencoder and particle swarm optimization. In particular, a deep sparse autoencoder is utilized to learn latent features based on the ratings matrix, trust relationships, and tag information. Then, particle swarm optimization is used to find the optimal weights of these latent features in calculating unknown ratings. Experiments on two datasets show the superiority of the proposed method in comparison with state of the art recommender algorithms.

Learning to drive in highly crowded urban areas with multi-agent interaction is a challenging task. Most of the current methods rely on hand-crafting the policy required for the decision-making process. In contrast, reinforcement learning (RL) offers proper methodologies to automatically formulate the best policy without manual intervention. However, the baseline RL algorithms face a challenge in handling simulated driving situations (e.g., keep driving in the current lane center, and keep a safe distance from a leading vehicle) in high fidelity and complex urban areas. In this study, we propose an end-to-end autonomous driving system using a Deep Q-Network (DQN) and long-short-term memory (LSTM) with a new observation input to enable the RL agent to learn driving in complex environments i.e. CARLA simulator. The observation input comprises a tuple of RGB (Red, Green, Blue) image data from the forward-facing camera, vehicle speed, and vehicle angle from the road center. The output comprises the steering, brake, and acceleration commands. Our proposed model, in conjunction with formulating the proper reward function, results in a rapid convergence with a better performance and safer driving behaviors from an autonomous driving agent. The results indicate that the LSTM-DQN model enables the autonomous agent in controlling the vehicle in a lane while keeping a safe distance from the leading vehicle in varied unseen road conditions.

The conflicts between the perceived sensation of the different sensory systems can cause adverse effects which is known as motion sickness (MS) and the side effects of MS include nausea, dizziness, stomach awareness etc. Virtual reality sickness (also called Cybersickness or visually induced motion sickness (VIMS)) happens during exposure to a virtual environment when senses transfer conflicting sensation signals to the brain. The symptoms of Cybersickness are similar to motion sickness symptoms. The adverse effects of this common phenomenon can negatively affect the training outcome and benefits using VR, undermine users’ health and usefulness of simulators as it involves health risk and contributes to the increase of dropout rates. Therefore, to mitigate these issues, MS should be detected and measured. The primary objective of this study is to subjectively and objectively detect and quantify cybersickness level using a helicopter simulator. This study has also investigated the change in cybersickness self-reported scores in different weather conditions such as clear and stormy. Simulator sickness questionnaire (SSQ) has been employed for subjective scoring. This research also aimed to correlate SSQ scores with physiological data such as Galvanic Skin Response (GSR). The findings demonstrated that the SSQ total score (TS) has increased significantly from clear weather to stormy for the participants. There is also a positive correlation found between the change in TS and the amount of GSR but not significant.

This study aims to explore different pre-trained models offered in the Torchvision package which is available in the PyTorch library. And investigate their effectiveness on fine-grained images classification. Transfer Learning is an effective method of achieving extremely good performance with insufficient training data. In many real-world situations, people cannot collect sufficient data required to train a deep neural network model efficiently. Transfer Learning models are pre-trained on a large data set, and can bring a good performance on smaller datasets with significantly lower training time. Torchvision package offers us many models to apply the Transfer Learning on smaller datasets. Therefore, researchers may need a guideline for the selection of a good model. We investigate Torchvision pre-trained models on four different data sets: 10 Monkey Species, 225 Bird Species, Fruits 360, and Oxford 102 Flowers. These data sets have images of different resolutions, class numbers, and different achievable accuracies. We also apply their usual fully-connected layer and the Spinal fully-connected layer to investigate the effectiveness of SpinalNet. The Spinal fully-connected layer brings better performance in most situations. We apply the same augmentation for different models for the same data set for a fair comparison. This paper may help future Computer Vision researchers in choosing a proper Transfer Learning model.

People perform most of their activities while being in an upright position. Nonetheless, there are some circumstances where they are required to adapt to different positions other than the upright position as in air manoeuvres and sport gymnastics. In these unexpected scenarios, the physiological signals are likely to change which can affect their performance. While some studies investigated the correlation between physiological signals and different body positions, to our best knowledge, these studies were limited to a rotating chair (1 or 2 degree of freedoms). Here, we investigated and evaluated human physiological responses (such as pupil diameter, skin temperature, heart rate, and breathing rate) to different seated positions including seated supine, seated side, seated inverted, and seated upright using Universal Motion Simulator (UMS), a 6 degree of freedom simulator with the most realistic acceleration and motion sensation. Open loop acrobatic flight motion sensation for the 11 participants were created and accompanied with a series of pre- and post-questionnaires to subjectively assess the physical wellbeing of each participant. The results of the study based on the objective assessment of collected physiological data showed that the mean heart rate decreases during an inverted position (82 beats per minutes bpm) and increased by an average of 7 beats per minute in an upright position 89.5 bpm. Moreover, the mean breathing rate in an upright position was 18.3 respirations per minutes (rpm) which is higher than mean breathing rate in the side position 19.8 rpm. Furthermore, it was found that the mean pupil diameter (PD) in an upright position was 4.33 mm which is higher compared to other positions. Independent from the motion scenarios and body positions, the Skin Temperature kept increasing which might be because of excitement and other emotional factors.

Structural design optimisation can significantly contribute to the identification of the best shape and geometry of a structure that results in lighter, stronger, and more affordable to manufacture materials for both large scale manufacturing and one-off bespoke performance components. Being both modern (evolutionary) and classic optimisation methods have had extensive focus and application in this field, an evaluation study on the performance of these methods has not been reported. This study reports a systematic comparison of the modern and classic optimisation approaches for a benchmark design optimisation problem. One algorithm will be a classic gradient-based optimisation, the second being a general Genetic Algorithm (GA) type optimisation. The results of two optimisation methods will be compared through the application of Finite Element Analysis (FEA) to evaluate both the performance of each algorithm and the real word translation of their effectiveness. The outcomes reveal that, although the gradient-based method shows better statistical results, GA can result in a superior minimum FoS of 3.5 satisfying the requirements for most common structural design applications.

This paper proposes a novel 3 DoF vision augmented Quadrotor model for visual servoing. The proposed model eliminates the necessity of deploying a separate visual-servoing controller and a robot controller, thereby reducing the on-board computational load drastically. The proposed model, as opposed to the conventional PBVS and IBVS approaches, helps in the use of torque control strategies. To utilize this feature of the model, a non-singular fast-terminal sliding mode modified super-twisting controller (NSFTSM-MSTC) is proposed. The non-singular fast-terminal sliding manifold ensures the fast and finite time convergence of the error between the desired and actual points of interest, while ensuring smoother transitions in the quadrotor states. The modified super-twisting reaching law ensures that the control input is continuous thereby ensuring chattering attenuation. The overall system stability is presented using Lyapunov’s stability criteria and an expression for convergence time is also derived. The proposed theory is validated using numerical simulations and is compared with the existing conventional sliding mode based visual servoing approach (CSMVS).

The purpose of the motion cueing algorithm is to reproduce the motion sensation for the drivers considering the physical limitations of this platform. Newly, the model predictive control-based methods have been used in motion cueing algorithms. This control respects the constraints and considers the future dynamics of the model for finding the optimum solution to the problem. However, the tuning of the weights for model predictive control is incredibly challenging to reduce the motion sensation errors. In this paper, a whale optimisation algorithm is used to gain the optimised weights of the model predictive control. The weights are optimized to reduce the cost function which is defined based on the motion inputs, input rates, and outputs. The recalculated weights via the whale optimisation algorithm should consider the limitations of the applications such as maximum tolerated error of motion sensation via the motion platform user, maximum linear and angular displacements, and maximum linear velocity. The proposed method is simulated seven times to demonstrate the accuracy and repeatability of the algorithm. The results show that the whale optimisation algorithm reaches the best solution quickly with minimised motion sensation error compared with the genetic algorithm.

The usage of autonomous vehicles in the transportation sector can achieve the objective of a safe environment. To increase riding comfort in an autonomous vehicle, one main challenge is to implement motion scenarios according to the passenger’s driving behaviours. This leads to customization of the driving style of an autonomous vehicle according to the preference of its passenger. The main disadvantage of the current autonomous vehicles is the regeneration of driving motion signals without taking into consideration the comfort/discomfort of the passengers according to their driving behaviours and preferred driving styles such as acceleration/deceleration rate and steering styles. In this paper, a nonlinear autoregressive network model is developed and trained based on the generated motion scenarios of the passenger and the position of the autonomous vehicle, in order to predict and replicate the motion signals based on the passenger’s driving behaviours. The MATLAB toolbox is used to train the network and forecast the motion signals. The results show the usefulness of the proposed method in terms of a higher shape similarity level and a lower mean square error rate between the actual and forecasted motion signals. These regenerated motion signals can increase the riding comfort of autonomous vehicle’s passengers as it is able to imitate the behaviour of the passengers.

The motion simulator platform can be used in many sectors, including transportation, aviation, and education. The motion cueing algorithm (MCA) is the main component of the motion simulator with the responsibility of motion cues regeneration while respecting the motion simulator’s joint limitations. Recently, a model predictive control (MPC) method has been introduced in the MCA, which is able to extract an optimum input signal within the model constraints. The washout speed of the end-effector using the existing MPC-based MCA model is not considered because the integral of linear displacement of the platform is omitted as an output. As a result, the motion simulator platform returns to the neutral position without the consideration of the motion behavior. In this study, the integral of the end-effector linear displacement is consider inside the MPC-based MCA model to select the best washout speed of the end-effector. Moreover, a grey wolf optimizer is utilized to identify the best MPC weighting indexes, in order to increase the model efficiency for both existing and proposed models. The proposed method outperforms the MPC-based MCA model in producing better regeneration of the motion cues. It yields a higher correlation coefficient and a lower root means square error between the motion sensation signal pertaining to the real vehicle and motion simulator platform users.

The motorized antenna mechanism is the central part of healthy satellite communication using a real-time motion tracking system. Typically, parallel manipulators are employed in the space industry for tracking the satellites with the antenna mounted on the end-effector. However, the workspace limitations of the parallel manipulators are highly restricted in terms of the end-effector’s linear and angular motions compared with the serial manipulators. In order to take the privilege of a serial manipulator advantages, the antenna is mounted on the ABB irb6600 manipulator’s end-effector while only generating 2-degree-of-freedom (2-DoF) motions for simplification. Unfortunately, the current system cannot track the satellite motion accurately as it cannot generate the smooth motion while following a path, especially when the end-effector is in the vertical position. In this study, a real-time motion control tracking system using the 6-DoF motion of the ABB irb6600 robot is designed and developed using TCP/IP communication technique between MATLAB and IRC5 controller aiming to accurately track the satellite path with the ability to decrease the jerkiness of the motion. The proposed method has been tested in simulation environment using the small prototype of the ABB robot (irb120) while Simulink Desktop Real-Time and RoboStudio are used. The inertial measurement unit (IMU) sensor is used to prove the tracking accuracy of the proposed method and elimination of the jerky motions using the proposed algorithm.

The real-time motion satellite tracking mechanism of an antenna, allowing communication to and from the satellite is critical for space applications. Mostly, parallel-based mechanisms are employed in space fields for motorizing the antenna due to the cost-effectiveness, high stiffness, easiness of inverse kinematic solution, and high reachable acceleration. Unfortunately, the angular displacements of the end-effector using parallel-based mechanisms are highly limited due to the existence of the passive joints. More recently, serial-based mechanisms are being used to motorize the antenna for tracking the satellite motions in larger horizons compared with parallel-based mechanisms. The inverse kinematic solution complexity, the iso-metric configurations of the joints for a specific position of the end-effector, and highly advanced controller mechanisms are the main difficulties of serial-based mechanisms that should be considered in their implementations. The existing system is using a traditional proportional–integral–derivative (PID) controller along with the kinematic modelling which can cause tracking error, inaccuracies and consequently loss in receiving data from satellite. In this study, the ABB irb120 robot is employed as a serial-based mechanism with the attached antenna to efficiently track the low-, medium-, and high-altitudes orbiting satellites. The inverse kinematic model of the proposed robot has been incorporated to extract the robot joints’ configurations with a combination of the new fuzzy logic controller compared with a PID controller to increase the motion tracking performance. The simulation study is conducted using MATLAB/SimMechanic to model the mechanism with consideration of the constraints. The results prove satellite motion can be tracked with higher accuracy using the fuzzy logic controller compared to the existing PID controller.

Model Predictive Control (MPC) is a high-performing solution for Autonomous Vehicle’s (AV) control. This technique can tailor balance between various aspects of vehicle dynamics such as vehicle’s speed, acceleration and jerk. This study proposes a longitudinal controller for AV using a data-driven MPC based on human driving demonstration. A novel parameterised cost function-based MPC is designed in order to provide a general solution for different driving scenarios. This parametric cost function provides a customisable approach towards longitudinal motion generation by learning a proper set of parameter values from the user’s driving style. Instead of using any classification technique for identifying driving styles, we asked human drivers to drive with different styles and use that data directly to learn the values of the parameters. The Bayesian Optimisation (BO) approach is used to learn an optimised set of parameters minimising the gap between some carefully chosen feature values of the controller and human-generated motion. The observations of simulation show that the proposed controller is capable of generating customisable longitudinal vehicle speed, acceleration, jerk, as well as headway distance between vehicles based on a specific human driving style.

System-of-Systems design has focused on integrated service offerings. The underlying expectation is that services have a reliance on each other to achieve a greater level of service and outcomes. Within this context, Cloud Computing provides a service consideration that extends system-of-systems design and thinking. Integration with cross provider services, operational and architectural design, systems operations, trust, and cost analysis must all be considered when addressing complex, cloud-based application design. This paper opens a discussion on these key attributes and provides a framework for future research whilst introducing key operational requirements to enable successful implementation of cross hyperscaler system-of-systems architectural models.

Deep learning-based collaborative filtering methods are studied in recommendation systems as efficient feature mapping techniques. The aim of these methods is to project the users and items to a common representation space and obtain their latent features. Although these methods have been widely used in the literature, they suffer from the limited expressiveness of Dot product function. In other words, Dot product cannot describe different impacts of various latent factors. To solve this issue, we propose a novel recommender system named Deep-MSR which exploits the multilayer perceptron (MLP) neural network and stacked auto-encoder network (SAN) to extract item latent factors and user latent factors from user-item interaction matrix. The obtained latent factors are used in the proposed rating prediction module which integrates user preferences and item features in the recommendation process. Our experiments on two well-known datasets show that our method can outperform the competitive baseline recommendation methods.

This paper proposes a new approach to produce classification rules based on evolutionary computation with novel crossover and mutation operators customized for execution on graphics processing unit (GPU). Also, a novel method is presented to define the fitness function, i.e. the function which measures quantitatively the accuracy of the rule. The proposed fitness function is benefited from parallelism due to the parallel execution of data instances. To this end, two novel concepts; coverage matrix and reduction vectors are used and an altered form of the reduction vector is compared with previous works. Our CUDA program performs operations on coverage matrix and reduction vector in parallel. Also these data structures are used for evaluation of fitness function and calculation of genetic operators in parallel. We proposed a vector called average coverage to handle crossover and mutation properly. Our proposed method obtained a maximum accuracy of 99.74% for Hepatitis C Virus (HCV) dataset, 95.73% for Poker dataset, and 100% for COVID-19 dataset. Our speedup is higher than 20% for HCV and COVID-19, and 50% for Poker, compared to using single core processors.

The problem of electricity load forecasting has emerged as an essential topic for power systems and electricity markets seeking to minimize costs. However, this topic has a high level of complexity. Over the past few years, convolutional neural networks (CNNs) have been used to solve several complex deep learning challenges, making substantial progress in some fields and contributing to state of the art performances. Nevertheless, CNN architecture design remains a challenging problem. Moreover, designing an optimal architecture for CNNs leads to improve their performance in the prediction process. This article proposes an effective approach for the electricity load forecasting problem using a deep neuroevolution algorithm to automatically design the CNN structures using a novel modified evolutionary algorithm called enhanced grey wolf optimizer (EGWO). The architecture of CNNs and its hyperparameters are optimized by the novel discrete EGWO algorithm for enhancing its load forecasting accuracy. The proposed method is evaluated on real time data obtained from datasets of Australian Energy Market Operator in the year 2018. The simulation results demonstrated that the proposed method outperforms other compared forecasting algorithms based on different evaluation metrics.

The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries, and also, there are genuine concerns about their reliability and performance. Motivated by these shortcomings, this article proposes a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images. Four popular convolutional neural networks (CNNs), including VGG16, ResNet50, DenseNet121, and InceptionResNetV2, are first applied to extract deep features from chest X-ray and computed tomography (CT) images. Extracted features are then processed by different machine learning and statistical modeling techniques to identify COVID-19 cases. We also calculate and report the epistemic uncertainty of classification results to identify regions where the trained models are not confident about their decisions (out of distribution problem). Comprehensive simulation results for X-ray and CT image data sets indicate that linear support vector machine and neural network models achieve the best results as measured by accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). Also, it is found that predictive uncertainty estimates are much higher for CT images compared to X-ray images.

This work was partly supported by the MINECO/ FEDER under the RTI2018-098913-B100, CV20-45250 and A-TIC-080-UGR18 projects.

This paper is concerned with the motor skill learning from human demonstrations using the framework of dynamic regressive models (DRMs). The DRM-based motion planner is preferred as it generates the end-effector trajectory dynamically based on the current state of the end-effector. Within existing frameworks, a single DRM can learn a single motion profile. In addition, such learned DRMs from the data may not be stable. This paper addresses both these issues in a comprehensive manner. In this paper a single DRM has been used to encode human demonstrations involving multitask profiles and multiple task-equilibriums which is novel. We have introduced the idea that the learned DRM will generate human-like stable motion if the energy dissipation rate (EDR) of the generated trajectory follows that of the human demonstration. Thus, the DRM structure has been modified by adding a continuous guiding signal which can be called as the control signal. This signal has been derived using control theoretic principle to ensure asymptotic stability while maintaining the EDR equivalent to that of the human demonstration. The asymptotic stability of the learned DRM has been established by involving a nonmonotonic Lyapunov function consisting of first derivative of a quadratic function and the energy function associated with the DRM. The proposed framework can be learned using many existing regression techniques in this paper Gaussian mixture regression, locally weighted projection regression, and support vector regression techniques have been used successfully. During the pick and place tasks, human demonstrations involving multiple task profiles and multiple task-equilibriums are generated using a 7 DOF commercial robot manipulator. Experimental validations show that the DRMs learned using these three regression schemes are able to guide the robot along the multitask profiles in a stable manner.

The hexapod manipulator is the most common motion platform, which is widely used as a simulation-based motion platform (SBMP). As the hexapod manipulator has a limited workspace, it is not physically possible to regenerate the real vehicle motion signals using the SBMP. The motion cueing algorithm (MCA) is responsible for regenerating a realistic vehicle motion sensation for the user when the SBMP operates within its physical and dynamical limitations. Recently, model predictive control (MPC) has been introduced to extract the optimal input motion signals while considering the SBMP limitations in the Cartesian coordinate space, which leads to a linear time-invariant (LTI) MPC-based MCA methods. Unfortunately, the existing LTI MPC-based MCA methods are still not able to consider the parameters of the SBMP's hexapod mechanisms inside their models. In general, the current studies only consider the constraints in the Cartesian coordinate system of the hexapod mechanism, instead of its design parameters. This consideration results in a poor usage of the hexapod workspace due to the conservative assumptions; consequently, the SBMP users do not experience realistic motions. The main contribution of this article is to take the SBMP's physical limitations into account in the MPC model such that more precise motion cues can be extracted for the users. A linear time-varying (LTV) MPC-based MCA method is designed for the first time in this article to consider the parameters of the hexapod mechanism in the MPC model. The proposed model (LTV MPC-based MCA) is validated using the MATLAB software, and the results depict better motion sensation with more accurate motion signals as compared with those from the existing LTI MPC-based MCA methods.

The Motion Cueing Algorithm (MCA) oversees regenerating the motion feeling of the real vehicle for the simulation-based motion platform (SBMP) within the physical limitations. Model Predictive Control (MPC) is recently employed as an MCA, which is called MPC-based MCA due to the consideration of the plant's boundaries in finding the optimal input signal. The computational load of the MPC directly relates to the control horizon and prediction horizon of the MPC. In this paper, a new optimisation method using butterfly optimisation algorithm is developed to find the optimal control horizon and prediction horizon of MPC-based MCA. The proposed method reduces the time of the tuning process of the MPC-based MCA, which is usually carried out via trial-and-error and genetic algorithm methods. Also, the trial-and-error method increases the motion sensation error and insufficient usage of the SBMP. The model is validated using MATLAB simulation environment, and the outcomes show that the developed butterfly optimisation algorithm will lead better motion sensation with less wrong motion signals and low computational burden compared with the trial-and-error and genetic algorithm method.

Parallel manipulators are recently used in most motion simulation laboratories as they can easily generate six degrees of freedom motion. Recently, fuzzy logic-based adaptive motion cueing algorithms (MCAs) have been employed to reproduce the motion signals. The usage of fuzzy logic-based adaptive MCA reduces the movement sensation error between the real vehicle and the simulation-based motion platform (SBMP) user considering the end-effector limitations in Cartesian space.. In this paper, a new fuzzy logic-based adaptive MCA is introduced to generate motion signals based on the joints' limitations and the movement sensation error between the real vehicle and the SBMP user. Considering the parallel SBMP, joint limits enhance the ability of the introduced adaptive motion cueing algorithm to generate more accurate movement feelings with high fidelity. The simulation results prove that the proposed adaptive motion cueing algorithm can effectively use the large workspace of the parallel SBMP whilst reducing the motion sensation error.

Uncertainty quantification (UQ) from similar events brings transparency. However, the presence of an irrelevant event may degrade the performance of similarity-based algorithms. This paper presents a UQ technique from similarity and sensitivity. A traditional neural network (NN) for the point prediction is trained at first to obtain the sensitivity of different input parameters at different points. The relative range of each input parameter is set based on sensitivity. When the sensitivity of one parameter is very high, a small deviation in that parameter may result in a large deviation in output. While selecting similar events, we allow a small deviation in highly sensitive parameters and a large deviation in less sensitive parameters. Uncertainty bounds are computed based on similar events. Similar events contain exact matches and slightly different samples. Therefore, we train a NN for bound correction. The bound-corrected uncertainty bounds (UB) provide a fair and domain-independent uncertainty bound. Finally, we train NNs to compute UB directly. The end-user need to run the final NN to obtain UB, instead of following the entire process. The code of the proposed method is also uploaded to Github. Also, users need to run only the fifth script to train a NN of a different UB.

This paper investigates a novel synchronisation strategy for controlling Internet-based teleoperation systems. These kinds of systems considerably suffer from network-induced latencies. Random time-varying delays resulted by the Internet deteriorate the stability and performance of teleoperation processes. Moreover, uncertain dynamic elements, including human operators and partially known remote environments introduce further difficulties to the control design of such systems. Utilising the learning capabilities of artificial neural networks, this paper develops an adaptive algorithm to deal with time-delays and uncertainties negatively affecting an Internet-based teleoperation process. The stable convergence of the proposed control algorithm is proved by Lyapunov-Krasovskii stability criteria. Moreover, the robust performance of the controller is also verified via experimental evaluations.

Automatic classification algorithms are an important component of expert decision support systems that are used in a number of medical applications including diagnostic radiology and disease detection. This study proposes a deep learning-based framework for medical image classification using wavelet features. Convolutional neural networks are incorporated to discover informative latent patterns and features from a set of X-ray images pertaining to human body parts. The features are then passed to a classifier for labelling the respective X-ray images. The experimental results show that the low-pass filter wavelet-based convolutional model outperforms the original convolutional network and some models for classifying X-ray images. The performance of the proposed method implies that it can be implemented effectively in practice for disease detection using radiological images.

End to end learning for autonomous navigation and driving has become a growing research trend in both industry and academia in recent years. Its promise is in treating the whole driving pipeline as the development of a deep neural network (DNN). Its Achilles' heel is access to thousands of images required for training of the DNN. This paper comprehensively investigates the applicability of the deep transfer learning for the specific task of end to end learning of autonomous navigation. Five state of the art DNNs including ResNet, AlexNet, and Densenet are applied here for extracting features from images taken by the front-facing camera of a mobile robot. Extracted features have different information values as DNNs have different architectures and learning capabilities. These features are then processed by a multilayer fully connected neural network to estimate the robot angular velocity. Obtained results for different DNNs indicate that the transfer learning-based models show a promising performance for accurately estimating the angular velocity purely using visual information. According to obtained results, AlexNet-base model outperforms others in terms of the estimation accuracy and the performance consistency.

Communication delays are known to create stability and performance issues in multilateral teleoperation systems. Multilateral teleoperation configurations usually include more than two communication channels, which can become problematic for robot control when limitations in network bandwidth results in delays and uncertainties in data transmission routes. This study develops a sliding surface based on the synchronization errors characterized between each sides of the considered multilateral teleoperation system. Here, two slave robots receive commands from the master system to cooperatively execute the desired teleoperation task in the remote, shared workspace. Lyapunov stability analysis approach guarantees the performance of the proposed controller. Moreover, the effectiveness of the controller is experimentally evaluated through a real-world Internet-based double-slave teleoperation system.

Path tracking controller of Autonomous Vehicles (AVs) plays an important role in improving the dynamic behaviour of the vehicle. Model Predictive Control (MPC) is one the most capable controllers that can handle multiple optimisation objectives, and accommodate the physical limits of the actuators and vehicle states to ensure safety and the other desired behaviour. As a high-potential solution, learning cost function from human demonstration can be integrated into an MPC. By learning the cost function from human demonstrations, extensive parameters tuning can be avoided, and more importantly, the controllers can be adjusted to provide desired control actions which are more natural to the human. In this study, an innovative Inverse Optimal Control (IOC) algorithm is proposed to learn a suitable cost function for the control task using collected data from human demonstration. The objective is to design a controller that generates motion which matches specific features of human-generated motion. These features include lateral acceleration, lateral velocity and deviation from the center of the lane. From the results, it is observed that the designed controller is capable of learning the desired features of human driving and implementing them while generating the appropriate control actions.

The chest computed tomography (CT) images have been used for COVID-19 detection. Automating the process of analyzing can save great amount of time and energy. In this paper a deep bayesian ensembling framework is proposed for automatic detection of COVID-19 cases using the chest CT scans. Data augmentation is applied to increase the size and quality of training data available. Transfer learning is utilized to extract informative features. The extracted features are used to train the three different bayesian classifiers. The uncertainty of the neural network predictions is estimated by anchored, unconstrained and regularized bayesian ensembling methods. The reliability of predictions is then delineated. The epistemic and aleatoric uncertainties are estimated and different bayesian classifiers are compared from different perspectives. We use a small dataset containing only 275 CT images of positive COVID-19 cases. The results sounds promising and they can be improved in the future, as the performance of deep neural networks is reliant to big datasets. Prediction accuracy and predictive uncertainty estimates for unseen chest CT images indicate that the deep bayesian ensembling is a promising framework for COVID-19 detection.

This paper proposes a robust adaptive algorithm that effectively copes with time-varying delay and uncertainties in Internet-based teleoperation systems. Time-delay induced by the communication network, as a major problem in teleoperation systems, along with uncertainties in modeling of robotic manipulators and remote environment warn the stability and performance of the system. A robust adaptive control algorithm is developed to deal with the system uncertainties and to provide a smooth estimation of delayed reference signals. The proposed control algorithm generates chattering-free torques which is one of the practical considerations for robotic applications. In addition, the achieved input-to-state stability gains do not necessarily require high gain control torques to retain the system's stability. Experimental simulation studies validate the effectiveness of the proposed control strategy on a teleoperation system consisting of a Phantom Omni Haptic device and SimMechanics model of the industrial manipulator UR10. The validation of the proposed control methodology was executed through a real-time Internet-based communication established over 4G mobile networks between Australia and Scotland.

Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms, however, have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and capable agents, which can perform efficiently in these challenging environments. This article addresses an important aspect of deep RL related to situations that require multiple agents to communicate and cooperate to solve complex tasks. A survey of different approaches to problems related to multiagent deep RL (MADRL) is presented, including nonstationarity, partial observability, continuous state and action spaces, multiagent training schemes, and multiagent transfer learning. The merits and demerits of the reviewed methods will be analyzed and discussed with their corresponding applications explored. It is envisaged that this review provides insights about various MADRL methods and can lead to the future development of more robust and highly useful multiagent learning methods for solving real-world problems.

Interacting with human operators, remote environment, and communication networks, teleoperation systems are considerably suffering from complexities and uncertainties. Managing these is of paramount importance for safe and smooth performance of teleoperation systems. Among the countless solutions developed by researchers, type-2 fuzzy (T2F) algorithms have shown an outstanding performance in modeling complex systems and tackling uncertainties. Moreover, artificial neural networks (NNs) are well known for their adaptive learning potentials. This article proposes an adaptive interval type-2 fuzzy neural-network control scheme for teleoperation systems with time-varying delays and uncertainties. The T2F models are developed based on the experimental data collected from a teleoperation setup over a local computer network. However, the resulted controller is evaluated on an intercontinental communication network through the Internet between Australia and Scotland. Moreover, the slave robot and the remote workspace are completely different and unforeseen. Stability and performance of the proposed control is analyzed by Lyapunov-Krasovskii method. Comprehensive comparative studies demonstrate that the proposed controller outperforms traditional techniques in experimental evaluations.

Recently, the problem of intent and trajectory prediction of vulnerable road users (VRUs) in urban traffic environments has got some attention from the intelligent transportation research community. One of the main challenges that make this problem even harder is the uncertainty exists in the actions of pedestrians in urban traffic environments, as well as the difficulty in inferring their end goals. In this paper, we are proposing a data-driven framework based on inverse reinforcement learning (IRL) and the bidirectional recurrent neural network architecture (B-LSTM) for long-term prediction of VRUs' intention. We evaluated our framework on real-life datasets for agent behavior modeling in traffic environments, and it has achieved an overall average displacement error of only 2.93 and 4.12 pixels over 2.0 and 3.0 s ahead prediction horizons, respectively. In addition, we compared our framework against other baseline models based on sequence prediction models and planning-based approaches. We have outperformed these approaches with the lowest margin of average displacement error of more than 5 pixels. Furthermore, the performance of the proposed framework was evaluated on an additional vehicle-based video sequence dataset for path prediction of pedestrians and it continued to achieve robust results with higher generalization capabilities.

Traditional point prediction systems compute a most probable value without representing the uncertainty. The point prediction is a value close to the mean or the median. A person or an autonomous system may require a prediction corresponds to a different cumulative probability (CP), known as the uncertainty bound. Therefore, in this paper, we present a probability density computing neural network (NN) training procedure. To overcome the limitation of an effective cost function, example uncertainty bounds are constructed with the help of correlation. Similar occurrences are selected through the correlation and weights are assigned to each similar occurrence based on both shape-similarities and ratio based similarities. Then example results from similar samples are considered. The normalized weighted distribution of examples is the probability distribution. Finally, a shallow NN with the example probability density is trained. The NN receives input circumstances and the cumulative probability. The NN returns the value corresponds to the given circumstances and the cumulative probability. Proposed cumulative probability computation point from a shallow NN is less computation extensive compared to the correlation-based similarity analysis. Moreover, we propose a probability density computation NN for the first time. We also upload an example code to the GitHub.

Understanding the behaviors and intentions of humans are one of the main challenges autonomous ground vehicles still faced with. More specifically, when it comes to complex environments such as urban traffic scenes, inferring the intentions and actions of vulnerable road users such as pedestrians become even harder. In this paper, we address the problem of intent action prediction of pedestrians in urban traffic environments using only image sequences from a monocular RGB camera. We propose a real-time framework that can accurately detect, track and predict the intended actions of pedestrians based on a tracking-by-detection technique in conjunction with a novel spatio-temporal DenseNet model. We trained and evaluated our framework based on real data collected from urban traffic environments. Our framework has shown resilient and competitive results in comparison to other baseline approaches. Overall, we achieved an average precision score of 84.76% with real-time performance at 20 FPS.

Human posture estimation is a fundamental challenge in computer vision research. This is a task that has received substantial interest due to the importance of evaluating the human performance in several disciplines. The ultimate goal for the vision-based pose estimation task is the markerless accurate prediction of necessary postural information. This paper proposes a single shot deep human posture detection and estimation network. The proposed SSDPose architecture increments standard object detection networks to feature posture estimation. SSDPose is an end-to-end trainable model that detects and estimates the body posture from a single image. Further, our network has been trained to predict joint angles which are essential information for several domains such as biomechanic and ergonomic posture analysis. The reference joint angles have been generated using motion capture sequences and a novel inverse kinematics method. Experimental results demonstrate that SSDPose effectively detects and estimates the posture by achieving person mean average precision (mAP) of 98.2%, an average joint angles MAE of 3.16 ± 1.23 deg and an RMSE of 4.22 ± 1.73 deg at up to 30 FPS.

Adversarial examples form a major threat to incorporating machine learning (ML) models in critical applications. The existence and generalisation of these attacks have been attributed to the linear nature of ML models, deep neural network models in particular, in the high dimensional space. This paper presents a new nonlinear computational layer to the deep convolutional neural network architectures. This layer performs a set of comprehensive convolution operations that mimics the overall function of the human visual system (HVS) via focusing on learning structural information. The core of its computations is evaluating the components of the structural similarity metric (SSIM) in a setting that allows the kernels to learn to match structural information. The proposed SSIMLayer is inherently nonlinear. Experiments conducted on CIFAR-10 benchmark demonstrate that the SSIMLayer provides high learning capacity and shows more robustness against adversarial attacks.

Scenario generation-based training in simulated environments has recently gained importance, since real life training environments can be costly, risky, time consuming, and requires substantial resources. In this paper, we propose a narrative-based scenario generation methodology for training, measuring, and analysing the trainee performance level in simulation. We use a driving simulator as an application domain for the training process. Furthermore, we utilised an autonomous Artificial Intelligence (AI) agent for the training experiments. Using the AI agent for practising the generated scenarios offers benefits in many aspects. Firstly, the AI agent simulates the behaviour of a human (trainee). Secondly, it is easy to collect the performance data with the AI agent, as compared with recruiting many trainees for data collection, particularly for the early stage of validation and verification of the proposed scenario generation methodology. We formulate an experimental study to measure and assess the AI agent's behaviours in scenarios with different levels of complexity. The collected performance metrics are used to evaluate the efficiency of the designed scenarios and its capabilities in capturing the variations in performance levels. The empirical results depict that the AI agent's behaviours pertain to the level of scenario complexity including varying weather conditions.

Driving motion simulators are widely used for their reliable, safe and cost-effective abilities to replicate real vehicle driving experience for simulator drivers in virtual environment. As all motion simulators have physical limitations, Motion Cueing Algorithm (MCA) is the most necessary algorithm for transformation of the real vehicle's linear and rotational motions to motion platform aiming to regenerate realistic driving sensation. Model Predictive Control (MPC)-based MCA has recently become one of the most popular MCAs. Scaling and limiting is an important unit of MPC-based MCA to reduce the amplitude of motion signal uniformly aiming to improve the realism of produced motion within the physical limitations of workspace. The current implementations of MPC use a basic form of scaling. In this paper, a novel MPC-based MCA is developed using an optimised nonlinear scaling unit and Genetic Algorithm (GA). The goal is to reproduce accurate motion sensation for the motion simulator drivers as close as possible to real vehicle within the platform's physical constraints. This is achieved via a polynomial scaling unit which is optimized by GA. The aim is to overcome the disadvantages associated with the tuning based on trial-and-error for MPC-based MCA scaling unit which is the main cause of inefficient platform workspace usage and motion sensation error between real vehicle driver and motion simulator driver. The proposed optimization-based method enhances the function of the nonlinear scaling units by considering some important factors such as the motion simulator's physical constraints and motion sensation error between the drivers in a real vehicle and a motion simulator platform. The proposed method is verified via simulation results which show the superiority of the optimised nonlinear scaling compared with the current trial and error based scaling method for MPC-based MCA as it is able to reduce the sensation error between the motion simulator and real vehicle drivers, enhance motion fidelity, and use the platform workspace more wisely to reduce sensation error while respecting the platform's physical boundaries.

Fingerprint recognition and indexing were addressed extensively in the literature. However, the number of the datasets that are used for research and validation is limited. Due to privacy laws and acts in several countries, it is challenging to release finger prints to the public. Consequently, this imposes a challenge on validating these search techniques on larger datasets that can be couple of hundreds of millions. To overcome this limitation, synthetic fingerprints datasets have been introduced as an alternative solution. In this paper we propose a generative model for synthesising fingerprint datasets. In this present work, the synthetic fingerprints are generated from the latent space representation using variational auto encoder. The network is trained to generate random samples that have same distribution as real finger print using latent vectors. By examining the generated synthetic fingerprints images, the ridge patterns were recognisable in most of cases. The unrecognisable synthetic images are reflecting the presence of low quality images in the training samples of the original dataset. Moreover, the extraction of minutiae relies on the quality of the input fingerprint images. In conclusion, the proposed method was able to generate synthetic image that can be further processed to accurately extract the finger minutiae and orientation field.

In this paper we propose accelerating live rendering of flame using generative adversarial neural networks. The proposed method targets entertainment and simulation-based training industries whose demands for high fidelity and high frame rate increases steadily. The proposed approach takes image frames rendered with low voxel resolution (8 × 8 × 8 voxels at 90 FPS) and produces image frames equivalent to imagery produced from high voxel resolution (64 × 64 × 64 voxels) typically rendered at 3 FPS. The error was evaluated using the structural similarity image metric (SSIM). The average error between generated image frames and the ground truth recorded 92:7%±4:6%.

VR user identification is of utmost importance especially with the increased applications of VR that will include e-payment among other applications that requires a high level of security. Biometric identification through eye movement, has been used previously due to the intrinsic characteristics of eye movement that characterises a person uniquely. In this paper, we propose using eye movement along with extraocular muscle activations in VR user verification. The muscle activations are calculated using an ocular biomechanical model. The k-NN classification results showed approximately 90% accuracy when using a feature set with eye movement parameters (3 joint angles), muscle activations for all 6 muscles along with the VR object position in 3D. The classifier is a biometric VR user verification tool that provides an easy and non-intrusive methods that can be easily integrated in different VR applications that require user verification.

Virtual and Augmented reality (VR/AR) are being extensively used in many applications that extends from entertainment, training to rehabilitation and treatment of disorders. Studies on the effects of extended use of VR immersion has been performed. However, the change of pupil diameter with the change of VR simulated depth has not been investigated. Pupil dilation is an indicative measure of cognitive overload. In this paper, we investigate the relationship between VR simulated depth and the pupil diameter change. Results showed a significant difference in pupil diameter change with simulated depth and also a strong negative correlation. This indicates that as the depth of the VR object increase (distance from the VR user increase), the VR user's pupil diameter decreases. These results show that change in pupil diameter can be an indicative of change in the depth of the observed virtual object. This can be an effective VR/AR scene scanning and understanding tool.

The field of neuroevolution has received great attention in recent years due to its promising capability for developing well-performing models. It has been applied to many real-world problems ranging from medical diagnosis to autonomous robots. The choice of the evolutionary algorithm (EA) has a huge impact on the neuroevolution overall performance. Despite recent progress in the field, it is not clear what the best choice of EA is. The problem becomes more severe considering a dozen of EAs available for neuroevolution applications. In this paper, six state of the art EAs are applied for the task of autonomous robot navigation. These EAs are MultiVerse optimizer (MVO), moth-flame optimization (MFO), particle swarm optimization (PSO), cuckoo search (CS), Grey wolf optimizer (GWO) and bat algorithm. MLP networks are trained using these six evolutionary algorithms to solve the classification task related to the autonomous robot navigation. Comprehensive experiments are conducted using three datasets and obtained results are visually and statistically compared. To the best knowledge of the authors, comparison among the aforementioned algorithms has not been considered in the literature. It is found that neuroevolution methods perform well for the task of autonomous robot navigation. Amongst investigated EAs, MVOtrained achieves the highest and most consistent performance metrics.

Cardiovascular diseases are one of the main causes of death among individuals over the last decade. Early diagnosis and recognizing of warning signs of this disease facilitate medical treatment for patients. Angiography is considered a reliable tool to diagnose coronary artery disease (CAD), however, it has some demerits such as complications and costs. Data mining techniques are considered as reliable and powerful tools for early diagnosis of diseases and are widely used in the medicine filed for recent years. In this paper, we use these techniques for early detection of CAD by applying them on a well-known CAD dataset named Z-Alizadeh sani. Thus, an effective nature-inspired optimization algorithm named Multi-verse optimizer (MVO) based on Multilayer perceptron (MLP) training as well as nine states of the art supervised learning techniques are employed for CAD prediction. As this dataset has 54 features, before applying the supervised learning algorithms, we used a feature selection method to identify the most effective features. This procedure enhances the prediction capability of the utilized algorithms. The classification rates of all algorithms are compared with each other using the most usable evaluation metrics including accuracy and area under the curve. Eventually, the experimental results show that the most appropriate model to classify CAD patients is the MLP model trained by MVO among all other nine supervised learning methods.

Imitation learning is an efficient paradigm for teaching and controlling intelligent autonomous cars. Obtaining a set of suitable demonstrations to learn an end-to-end policy from raw pixels is a challenging task in imitation learning problems. Deep neural networks have recently shown outstanding results in learning from raw high dimensional data for solving a wide range of real-world applications. The success of deep neural networks depends on finding suitable hyperparameters for constructing network architecture. Besides, designing hand-crafted deep architectures is not an efficient way for achieving the best performance. To address this issue, this paper performs a neuro-evolution method based on genetic algorithm for finding the optimal deep neural networks architecture in terms of hyperparameters. The experimental results show the effectiveness of the proposed approach for training an autonomous vehicle.

This paper presents generalized noise patterns for (N+1)/N-factor non-linear down-sampling. In linear downsampling of two or three-factor, one sample is kept and the next one or two samples are discarded. N-samples are kept and the next one is discarded in the discussed (N+1)/N-factor non-linear down-sampling. The paper contains the mathematical reasoning of the generalized noise-patterns of (N+1)/N-factor non-linear down-sampling. The mathematical analysis provides the reason for the generalized behaviors and the simulation results bolster those findings. The knowledge of the generalized error pattern may help future researchers in understanding the noise pattern due to missing samples, interpolation, predictive coding, and noise suppression.

Deep neural networks have proven themselves as a strong approach in image classification and object detection with high accuracy. However, they are computationally demanding and the trained networks contain millions of active parameters and connections. Two recent trends of having deeper and dense architectures and the deployment of trained networks on resource-constrained devices such as smart phones and portable tablets bring new challenges. Instead of deploying an ensemble of smaller networks, we propose a pruning methodology on a trained network so that a smaller version of a fully trained network has the same and even better accuracy in comparison to the original one. We achieve two objectives with the pruning scheme. First, we have a smaller network with a better accuracy level, and we make the trained model avoids overfitting. Accordingly, an evolutionary based framework including three steps is defined to perform further tuning on trained deep network using dropping nodes and connections. This study shows that implementing genetic algorithm, after preprocessing and training stages, not only results in partially connected networks, but also increases performance and reduces overfitting specially when the depth and width of fully connected networks are investigated in small datasets.

The regeneration of the motion signals of a real vehicle is not physically possible because of the workspace limitations of the platforms. The motion cueing algorithms (MCAs) are in charge of reproduction of the motion sensation for the drivers of simulation platforms as realistic as possible to the real vehicles. The model predictive control-based motion cueing algorithms (MPC-based MCAs) are recently used to find the optimal value of the input signals with consideration of the linear constraints of the platform in the Cartesian coordinate system of the mechanism. A new time-varying MPC-based MCA is introduced for the first time in this research by considering the joints' limitations of the mechanism inside the MPC model for longitudinal channel. The proposed model can consider the physical limitation of the active joints instead of substituting the limitation in the Cartesian coordinate system. The validation of the proposed model is performed using MATLAB software and the results prove that the proposed time-varying MPC-based MCA leads better motion sensation compared with the existing MPC-based MCA.

Summary A novel security awareness surgical navigation system has been proposed for the accurate minimally invasive surgery with machine learning algorithms, haptic‐enabled devices, and customized surgical tools to guide the surgery with real‐time force and visual navigation. To provide a direct and simplified user interface during the operation, we combined traditional surgical guide images with AR‐based view and implemented a 3D reconstructed patient‐specific surgical environment includes with all surgical requisite details. In particular, we trained the surgical collected biomechanics haptic data by employed LSTM‐based RNN algorithm, and residual network for the intraoperative force manipulation prediction and classification, respectively. Experiments evaluation results on percutaneous therapy surgery demonstrated a higher performance and distinguished accuracy by the visual and haptic combined than the traditional navigation system. These preliminary study findings may suggested a new framework in the minimally invasive surgical navigation application and hint at the possibility integration of haptic, AR, and machine learning algorithms implementation in medical simulation. In addition, we take security into account when implementation this new framework.

Driving simulators are effective tools for training, virtual prototyping, and safety assessment which can minimize the cost and maximize road safety. Despite the aim of a realistic motion generation for the impression of real-world driving, motion simulators are bound in a limited workspace. Motion cueing algorithms (MCAs) aim to plan an acceptable motion feeling for drivers, without infringing the simulated boundaries. Recently, model predictive control (MPC) has been widely used in MCAs; however, the tuning process for finding the best weights of the MPC optimization is still a challenge. As there are several objectives for the optimization without any standard weighting for solution evaluations, a nonbiased scalarization of solutions for the purpose of comparison is impossible. In this paper, a clear method for obtaining the best MPC weighting has been proposed. This method searches for the best tune of MPC cost function weights, reduces the user burden for weight tuning while receiving feedback from the user satisfaction. The MPC-based MCA weights are optimized using a multiobjective genetic algorithm (GA) considering objectives, such as minimization of motion inputs (linear acceleration and angular velocity), input rates, output displacements and the sensed motion errors. Any process based on trial-and-error has been omitted. The adjusted weights have to satisfy a set of predefined conditions related to maximum tolerated error and maximum displacement. The obtained Pareto-front is used for decision making via an interactive GA (IGA), aiming for maximization of the decision maker's satisfaction. A Web interface is developed to interact with the IGA and to influence the region of searching. Simulation results show the superiority of the proposed method compared with the previous empirical tuning method. The sensed motion error is minimized using the proposed method and with the same available workspace, a more realistic motion can be rendered to the driver.

Stability and task accomplishment of Internet-based teleoperation systems are greatly susceptible to the network latency and uncertainty. Control of a teleoperation system aims to provide satisfactory performance of the remote task and in some cases to provide the operator with sensory feedback. This paper reviews the recent control methodologies used for teleoperation systems with model uncertainty, unknown time-varying delay, and Internet-based communication. The focus is on control algorithms that are suitable for nonlinear uncertain systems to decrease restrictions and increase application scope. The key features of these control algorithms are highlighted, and their advantages and disadvantages are comparatively discussed.

In forecasting, the challenge of predicting river flows in time series was amongst the earliest to attract scientific interests. A broad range of mathematical approaches, from simple linear to complex non-linear methods, have been proposed in the literature for this kind of modeling. This paper introduces a hybrid method based on a soft computing fusion for river flow time series forecasting. For the experimental results reported here, this approach consistently outperformed traditional modeling methods. Findings from this specific research promise utility in the water resources and environment sector management where soft computing methods can be applied to various studies for which time series data are available.

An ideal noninvasive electroencephalography (EEG) based brain-computer interface (BCI) is a user-friendly plug and play system where a new user does not need to go through the long training data collection process. To reduce the amount of training data required for a new user, active learning inspired informative instance transfer is investigated in this work as one of the potential solutions. In this informative transfer learning, query by committee is applied as query method to find informative samples from subjects own domain. On the other hand, percentile range around the mean of center distance (PRMCD) query method is introduced in this work as an alternative to existing entropy criterion to find informative samples from the past user's domain. The newly introduced PRMCD algorithm has reached the benchmark performance using only average 12% of whole subjective training set while the existing entropy-based algorithm has achieved the benchmark performance using average 17% of the whole subjective training set in case of 7 out of 9 subjects. For PRMCD algorithm, a new user can achieve the intended mean benchmark performance using reduced (only 50 which is 12.5%) amount of training data in general irrespective of subjects. Therefore, incorporation of PRMCD algorithm has added an important step towards the zero training BCI. It is a significant advancement for the practical application of motor imagery based BCI.

In this paper, an optimization methodology for the weighted autocorrelation based prediction interval is proposed and applied for the prediction of the wind power generation. The Coverage Width Based Criterion (CWC) is applied as the optimization criterion. The improved execution steps are as follows- At first, the string of 20 recent samples is autocorrelated with the similar strings of previous samples. Then, the samples next to the highest normalized correlation values and corresponding indexes are selected. After that, the amplitudes of the matched samples are adjusted by multiplying the value with the amplitude of recent string and by dividing by the amplitude of matched strings. These amplitude-adjusted samples are the prediction value for the next sample. Each prediction values are given a weight depending on the ratio of the amplitude of the string and the value of normalized correlation. The weight equation is trained with the CWC equation to find the optimum relation between amplitude and correlation values. The probability density distribution is derived from the weighted autocorrelation values. Finally, least relevant areas from corners are discarded to achieve the required coverage with smaller PI width. However, as the level of uncertainty changes over time, discarding historical percentile may result in a different coverage on later targets. Therefore, the percentage of discarding is also optimized with the CWC. Wind power generation is predicted and different weight equation and discarding percentages are achieved.

Neural network (NN) based prediction or detection systems often perform excellently with easy problems without considering 1-5% difficult problems. This work proposes an adversarial NN training method for constructing the prediction interval (PI). The proposed training method considers adverse situations where traditional NN based PIs frequently fail. First, the conventional lower upper bound estimation (LUBE) method is applied in parallel for initial training of NNs with different initialization. Each NN based PI fails to cover a few samples. Input combinations of those samples are adversely changed by a small amount to generate the adverse samples. A new dataset is generated by appending adverse samples. Finally, an NN is trained with the adverse dataset. The method is applied to construct the NN for wind power prediction. According to the result analysis, the proposed method performs better in adverse situations.

Steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) systems has attracted paramount amount of attention due to their higher signal to noise ratio and high information transfer rate. In this paper a SSVEP-BCI-based on a convolutional neural network (CNN) classifier is presented. The visual stimulation is provided to the participants with with LED matrices blinking at 6, 7, 8 and 9 Hz respectively. A wireless EEG amplifier, the g.Nautilus was used to acquire the electroencephalogram (EEG) signals from eight parietal and occipital electrodes. The features were derived using Fast Fourier Transformation (FFT) of the 8 channels using a 2s moving window in the form of 8 × 8 grey scale images. The proposed CNN architecture has provided superior average accuracy of 94.7% for four subjects, compared to the average accuracy of 87.4% of the state of the art canonical correlation analysis (CCA) performance.

Throughout the last few decades, a breakthrough took place in the field of autonomous robotics. They have been introduced to perform dangerous, dirty, difficult, and dull tasks, to serve the community. They have been also used to address health-care related tasks, such as enhancing the surgical skills of the surgeons and enabling surgeries in remote areas. This may help to perform operations in remote areas efficiently and in timely manner, with or without human intervention. One of the main advantages is that robots are not affected with human-related problems such as: fatigue or momentary lapses of attention. Thus, they can perform repeated and tedious operations. In this paper, we propose a framework to establish trust in autonomous medical robots based on mutual understanding and transparency in decision making.

Cognitive load is an often-discussed important topic with regards to human performance. Currently, many psychophysiological measures are used to quantify the level of perceived cognitive load under different tasks and environments. Heart rate (HR) is reported in literature as one of the physiological parameters that is influenced by varying cognitive load levels. Electrocardiography (ECG) is the gold-standard measure of HR measurement, however the use of traditional ECG measurement systems limits the applicability of the system to a lab environment. Recent advancements in wearable devices have provided a step towards bringing the physiological signal based human performance measuring system into real-world applications. In this study we are investigating the usability of the Polar OH1, a HR monitoring device initially used for the purpose of physical activity monitoring to use in an arithmetic cognitive load task. With a study carried out with a dataset of 10 subjects, we are able to conclude that the Polar OH1 can be used in place of ECG monitored HR, at varying cognitive load levels.

Brain-computer interface (BCI) using machine learning has the requirement for a large number of training data. This requirement makes the long training session inevitable for a new user. Many approaches including transfer learning (TL) already have been reported in the literature to abridge the long training data collection session. One of them is transferring informative instances using active learning (AL) which was approached in our previous attempts for both binary and multiclass BCI. It was associated with the classic common spatial pattern (CSP) feature extraction method. It showed the potential to obtain the benchmark performance using a reduced amount of training data. However, it has subject dependent performance and was not up to the expectation particularly for multiclass BCI. For binary BCI, it is addressed by selecting the best subject-specific features from subjective narrow frequency window using filter bank CSP (FBCSP). Since multiclass BCI has different characteristics in terms of output performance and nature of features, this work investigates the incorporation of FBCSP into informative transfer learning with AL (ITAL) for multiclass BCI. Comparing with existing direct transfer with AL (DTAL) and ITAL with CSP for multiclass BCI, ITAL with FBCSP reaches the benchmark performance for six out of nine subjects using average 42% of the full training set which is significant at 5% (p <; 0.05) significance level. For multiclass BCI as well, ITAL combined with discriminating feature extraction ensures better transfer which yields to effective reduction of the training session without sacrificing the benchmark robustness.

Visual ergonomics through ocular biomechanical analysis is an inviting, non-invasive method to add insights into the effect of immersion on our ocular system. It can also add insights into the mental and cognitive state, due to the tight coupling of eye movement and mental state. Eye movement tracking has been used in studying eye movement in normal activities and with the use of embedded eye trackers into virtual reality headsets, this can be easily extended into virtual environments. In this paper, we present a biomechanical analysis of eye movements recorded from subjects during immersion. Our objective is to validate the ocular biomechanical model used, through comparing recorded and muscle-driven eye movement. The regression analysis between the recorded eye movement and the muscle-driven eye movement shows a strong significant positive correlation. In addition, the computed extra-ocular muscle controls show agonist-antagonist relationships which is in accordance with the normal realistic eye movement. Insights into the different eye-head coordination styles performed by subjects are highlighted, too.

Multi-screens or wide screen setup is becoming increasingly popular in many work places and training environments. However, there has been limited studies of their effect on human health and performance. In this study, we investigate individuals performance and eye movement characteristics while performing a visual task on multi-screen setup consisting of three monitors. During the task, subjects had to share their attention among three screens to identify the location of the visual stimulus and respond accordingly. Subjects' score was calculated based on validity of input to stimulus and response time, while fixation characteristics were investigated with respect to eye movements. The results show that the use of 3-screens added extra demand on the individual causing a decrease on the score and decrease in reaction time. In a further investigation, we found statistically significant negative correlation between the task score and the participant's age while a statistically significant positive correlation between the response time and the participant's age. In addition, the use of multi-screens to perform the tasks caused both fixation occurrences and duration to decrease, denoting an increased alertness since respond was given with less fixation duration and occurrences and less response time.

Autonomous vehicles (AVs) are considered one of the most promising solutions for enhancing road safety, saving individuals' time, and reducing energy consumption. Autonomous vehicles are still in their early stage to be publicly accepted and gain a high level of trust. They need to be comprehensively and continuously evaluated and improved through road tests which are risky, costly, and time-consuming. Motion simulators are capable of contributing to these tests by providing an immersive virtual environment and high fidelity ride experiences for subjective and objective evaluations of AVs' performance. This paper provides a simulation study on the capability of a serial motion platform, known as the Universal Motion Simulator (UMS), for emulation of an AVs' path tracking capabilities. For this purpose, a versatile path tracking model of an AV is initially introduced. The computational Multi-Body System (MBS) approach is then used to implement inverse kinematics and dynamics analyses of the UMS. The UMS is also equipped with an optimal Motion Cueing Algorithm (MCA) to emulate the motion sensation of the AV performing different manoeuvres. The results show that the UMS is an efficient tool for regenerating a realistic and high-fidelity AV ride experience when (1) curvature of the trajectory is not large and the AV does not experience large turning angles when it negotiates the path, and (2) the human motion sensation (vestibular system mathematical model) is taken into consideration in developing the MCA of the motion simulator.

In 2015, Google's Deepmind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of 49 Atari games. However, the current manifestation of DRL is still immature, and has significant drawbacks. One of DRL's imperfections is its lack of "exploration" during the training process, especially when working with high-dimensional problems. In this paper, we propose a mixed strategy approach that mimics behaviors of human when interacting with environment, and create a "thinking" agent that allows for more efficient exploration in the DRL training process. The simulation results based on the Breakout game show that our scheme achieves a higher probability of obtaining a maximum score than does the baseline DRL algorithm, i.e., the asynchronous advantage actor-critic method. The proposed scheme therefore can be applied effectively to solving a complicated task in a real-world application.

Robotics and intelligent sensing methods are experiencing a new wave applications through the use of machine learning systems. Intelligence is being introduced in robots and sensor platforms by utilizing machine learning techniques such as classification. In the field of robotics, generating training data can be very complex and often, expensive. In this set-up, transfer learning can greatly improve the performance of a classifier wherever and whenever enough labeled data is not available in a domain of interest (target domain), but ample labeled data can be found in a different but related domain (source domain). A new optimized method is proposed in this work to transform the observation from source domain along with a new label transfer mechanism. The transformed, or adapted, domain has the same number of features as the target domain and the same number of observations from the source domain. Labels are transferred from source to target domain using a multivariate Gaussian mixture model (GMM). Genetic algorithm is used to optimize the transformation process by minimizing a cost function that addresses both distribution difference and accuracy. Experiments show that the proposed method outperforms any classifier trained only with source or target domain data.

Imitation learning have been recently applied to a number of robotic-related tasks. In this work, a novel approach based on imitation learning will be applied to the local motion planning problem for mobile robots. Based solely on a monocular RGB camera and set of expert demonstrations, our learned model can predict an accurate steering angle actions to be performed by the local motion planner of a mobile robot. In order to train and validate our model, we collected large amount of labelled RGB images from a monocular camera mounted on a ground mobile robot platform while driving the robot manually by human demonstrator. The proposed approach has achieved resilient results in capturing the behavior of a human demonstrator and provided a smooth and safe steering trajectories. Quantitatively, the model has also achieved lower mean absolute error of on a step-by-step basis of steering angular velocity with only 0.1 radians/s error over all the testing dataset.

The development of eye tracking-based applications has witnessed a number of advancements over the past few years. As a result, a number of low cost commercial remote vision-based eye trackers started to appear in the market. Consequently, a number of research communities started to explore the feasibility of extending the eye-tracking capabilities beyond single computer screen and utilize it in multi-screen setup. One of the main challenges for the wide adoption of such eye trackers in multi-screen setup, is their limitations when it comes to an intuitive and reliable way for tracking human eye movements across these multiple screens without losing much of the eye tracking data itself. In this work, a novel data-driven approach based on deep recurrent neural networks for a reliable and responsive switching mechanism between low cost multi-screen eye trackers is proposed. Our approach has achieved a competent results in terms of higher accuracy and lower positive rate in detecting accurately the screen the subject is attending to with F1 measure score of 85%.

In this work a novel approach for the problem of indoor navigation assistance for the visually impaired people is proposed based solely on a monocular camera. In our formulation for the problem, we cast it as an image classification problem and tackle it holistically in an end-to-end fashion via state-of-the-art deep convolutional residual networks. Given an input RGB image of an indoor scene, our model can accurately guide the visually impaired people to navigate around the obstacles in the scene using four discrete navigational directions. Our model has achieved resilient results in terms of higher classification accuracies with a lower rate of false alarms. Moreover, we compared the performance of our model against two baseline approaches and it has outperformed them with more than 25% improvements with respect to the F1measure evaluation score.

Video-assisted thoracoscopic surgery (VATS), referred to as the commonest minimum invasive excision for located T1 or T2 lung carcinomas, requires a steep learning curve for the novice residents to acquire highly deliberate skills to achieve surgical competence. The aim of this study is to propose a virtual reality-based (VR) surgical educative simulator with realistic performance in both visual and haptic sensation for the VAST procedures. To provide an immersive and perceptual user interface, we combined the customized haptic-enabled thoracoscopic instruments with HTC VIVE helmet set in our simulation system. In particular, position based deformation (PBD) method on the GPU and a novel haptic rendering algorithm of surgical grasps and stapling operations are also been implemented for the surgical scene, respectively for the soft tissue deformation and intraoperative force manipulation simulation. Experiments by thoracic surgery professors and novices' evaluation results on our framework demonstrated a high performance and distinguished accurately. These study findings suggested a new cognitive model for the VATS surgical education integrate with haptic and VR implementation.

The purpose of this study is to investigate the possibility to use eye movements to control the laparoscope during a laparoscopic surgery. Laparoscopic surgery usually needs at least two doctors, a surgeon and a laparoscope assistant. The view of the operating surgeon is provided by the laparoscope assistant. As misunderstandings or conflicts of cooperation may happen, an ideal way is that the surgeon has a full control of all the instruments including the surgical tools and laparoscope. To achieve it, an eye based interaction method is introduced in this paper that allows surgeons to control the view by themselves. With recent developments in the eye tracker platforms and associated eye tracking technologies, many non-contact eye tracking systems are available. It can record where a person is looking at any time and a sequence of eye movements. This information can be used to know where is the attention and interest of the person on a display. As such, surgeon's attention can be captured and then be followed by moving the laparoscope to the region of interest. To have a safe and efficient evaluation on the usability, a virtual reality based laparoscopic surgery simulation is built. It is based on Unity with two haptic devices simulating the surgical tools, a 3D mouse providing 6 degrees-of-freedom control of the camera and an eye tracker capturing eyes' positions on a display. Experiments on moving a camera left, right, up, down, in, out and to specified locations using eyes are conducted, and moreover the performances of the proposed eye based self-control and the 3D mouse based other-control are compared. The results are promising where the proposed pointing method leads to 43.6% faster completion of the tasks against the traditional other-control method using the 3D mouse.

Partial state estimation has numerous applications in practice. Nevertheless, designing delay-dependent functional observers (FOs) for systems with unknown time delays is rigorous and still an open dilemma. This paper addresses the problem for linear time-invariant systems with state time-varying delays. The delay is assumed to be bounded in an interval with a bounded derivative. A sliding mode FO structure that is robust against the delay uncertainties is established to this aim. The structure employs an auxiliary delay function that can be defined based on the existing knowledge on the actual delay values. Delay-dependent sufficient conditions for the stability of the observer are obtained using the Lyapunov Krasovskii approach, and are expressed in terms of a linear matrix inequality and two rank conditions. The delay-free observer structure is additionally studied and the necessary and sufficient conditions for its stability are obtained. Two descriptive numerical examples and simulation results demonstrate the design procedure and emphasize the effectiveness of the proposed observer design algorithm.

This paper presents a novel approach to automatically detect and count cars in different aerial images, which can be satellite or unmanned aerial vehicle (UAV) images. Variations in satellite and/or UAV data make it particularly challenging to have a robust method that works properly on a variety of images. A solution based on the bag-of-words (BoW) model is explored in this paper due to its invariance characteristic and highly stable performance in object/scene categorization. Different from categorization tasks, vehicle detection needs to localize the positions of cars in images. To make BoW suitable for this purpose, we extensively improve the methodology in three aspects, namely, by introducing a recently proposed feature representation, i.e., the local steering kernel descriptor, adding spatial structure constraints, and developing an orientation aware scanning mechanism to produce detection with “one-window-one-car” results. Experiments are conducted on various aerial images with large variations, which consist of data from two public databases, e.g., the Overhead Imagery Research Data Set and Vehicle Detection in Aerial Imagery, as well as other satellite and UAV images. The results demonstrate the effectiveness and robustness of the proposed method. Compared with existing techniques, the proposed method is applicable to a wider range of aerial images.

In this paper, synchronization of an inertial neural network with time-varying delays is investigated. Based on the variable transformation method, we transform the second-order differential equations into the first-order differential equations. Then, using suitable Lyapunov-Krasovskii functionals and Jensen's inequality, the synchronization criteria are established in terms of linear matrix inequalities. Moreover, a feedback controller is designed to attain synchronization between the master and slave models, and to ensure that the error model is globally asymptotically stable. Numerical examples and simulations are presented to indicate the effectiveness of the proposed method. Besides that, an image encryption algorithm is proposed based on the piecewise linear chaotic map and the chaotic inertial neural network. The chaotic signals obtained from the inertial neural network are utilized for the encryption process. Statistical analyses are provided to evaluate the effectiveness of the proposed encryption algorithm. The results ascertain that the proposed encryption algorithm is efficient and reliable for secure communication applications.

One of the key design parameters in tendon-driven continuum robots is the number of tendons and the tendon loading distribution. A load model is also helpful for avoiding slack in tendons that causes control inefficiency and inaccuracy. A quasistatic model of n-tendon continuum robots is derived using the Euler-Lagrange formulation. The model is employed to derive an analytical loading model for equidistant tendon tensions for any given beam configuration within the workspace. The model accounts for the bending and axial compliance of the manipulator as well as tendon compliance. Features of the proposed model are discussed and some of the potential applications are explained. Based on the proposed model, a slack avoidance algorithm with analytical formulation is developed to dynamically optimize the tendon loads while preventing slack in tendons for a given configuration. The proposed model is experimentally validated in a multitendon continuum robot system for four case studies of three- to six-tendon arrangements in open-loop control architecture. A stereo vision-based three-dimensional reconstruction system measures the beam configuration and properties for each of the threeto six-tendon continuum robots. The effect of number of tendons on the tension loads in n-tendon continuum robots is studied. A quantitative dimensionless relationship between the number of tendons, the maximum tendon loads, and the bending angles is developed that may be used as a design tool for tradeoff among the complexity and required force and size.

This paper addresses the synchronization problem of telerobotic systems, in which the master and slave robots are assumed to be serial manipulators and the communication-delays are assumed to be time-varying and nonsymmetric with known lower and upper bounds. Proportional derivative, proportional, and position-force control structures are considered for passive and nonpassive human operator. Using the Lyapunov-Krasovskii methodology, delay-dependent stability conditions of the closed-loop system are established in the form of linear matrix inequalities. The stability criteria derived in this paper is shown to be less conservative than some of the existing results within the literature, and they amend the calculation of the control parameters and ensure the stability and transparency of the system for larger bounds of communication-delays. Simulation studies are performed to demonstrate the effectiveness of the proposed stability criteria in obtaining a larger stability region for the system.

Facial emotion recognition (FER) is a critical task for both human-human (HHI) and human-computer interactions (HCl). In this paper, a brain-inspired neural basis computational model of FER is proposed based on emotional neural networks (ENN), fuzzy c-means (FCM) and genetic algorithms (GA). The proposed model can be applied in both HHI and HCI applications. In HHI, it can be used for improving communication skills, and in HCI it can be used in various treatment processes e.g. anxiety treatment, cancer radiation treatment and remote children/elderlies monitoring systems. The proposed model consists of main modules of emotional brain which recognize the facial emotions. In the experimental studies, the proposed model is examined on children's facial sad recognition as a case study. The results show that our model is valid and can be applied for various FER tasks.

This paper presents an approach to analysis of multiclass EEG data obtained from the brain computer interface (BCI) applications. The proposed approach comprises two stages including feature extraction using the common spatial pattern (CSP) and classification using fuzzy logic systems (FLS). CSP is used to extract significant features that are then fed into FLS as inputs for classification. The metaheuristic population-based particle swarm optimization method is used to train parameters of the FLS. The multiclass motor imagery dataset IIa from the BCI competition IV is used for experiments to highlight the superiority of the proposed approach against competing methods, which include linear discriminant analysis, naïve bayes, k-nearest neighbour, ensemble learning AdaBoost and support vector machine. Results from experiments show the great accuracy of the combination of CSP and FLS. Therefore, the proposed approach can be implemented effectively in the practical BCI systems, which would be helpful for people with impairments and rehabilitation.

Denial of service flood attacks are among the most common and powerful attacks which abuse the computational resources and the bandwidth of a network. In this paper, a heterogeneous defense method is proposed based on a combination of the Software Defined controller and fuzzy decision making. Numerical results show that the proposed method has a lower computational load and response time compared to the traditional methods centralized in the controller.

A control approach for automated highway driving is proposed in this study, which can learn from human driving data, and is applied to the longitudinal trajectory of an autonomous car. Naturalistic driving data are used as samples to train the model offline. Then, the model is used online to emulate what a human driver would do by computing acceleration. This reference acceleration is tracked by a predictive controller, which enforces a set of comfort and safety constraints before applying the final acceleration. The controller is designed to balance between maintaining vehicle safety and following the model's commands. Thus, the proposed controller can handle dynamic traffic situations while performing like a human driver. This approach is validated on two different scenarios using MATLAB simulations.

Collision avoidance is an essential component in advanced driving assistance systems, as it ensures the safety of the vehicle in near crash or crash scenarios. In this study, a collision avoidance system for lane change events is proposed which plans the trajectory based on the level of danger. The danger level is computed by a fuzzy inference system developed with naturalistic driving data to better capture the real-world factors, which may cause an accident. In addition, a fault determination classifier is introduced in order to determine the responsible driver in a near crash event. This system is evaluated on simulated naturalistic near crash events and the results demonstrate good performance of the proposed system.

In this paper, an open source C++ Genetic Algorithm library is proposed called openGA. This library is capable of optimization in each of single objective, multi-objective and interactive modes. The main motivation for proposing this library is to provide freedom to users for designing their custom solution data model without limitations which many currently available software/libraries suffer from such as forcing a user to define the solutions as vectors or limiting the output of evaluation functions to a predefined format. In addition, the user has the entire control over genetic operations such as solution creation, mutation and crossover. The multi-object mode performs a Non-dominated Sorting Genetic Algorithm known as NSGA-III to obtain the pareto-optimal front while preserving the solution diversity. This library can handle multi-threading computations for single and multi-objective problems to increase the speed of the calculations significantly. The interactive mode is suitable for applications where human subjectivity is involved for evaluation of the cost function. Several simulation and tests are performed to verify the effectiveness of this library for calculations of optimization problems.

Human posture analysis is a task of utmost importance for several disciplines. For ergonomists, extracting postural information such as joint angles is necessary to evaluating ergonomie assessment metrics. This allows the early identification of potential work-related musculoskeletal disorders in manufacturing industries, and thus providing adequate interventions. In this paper, we present a holistic posture analysis system that estimates body joint angles from an input depth image. The proposed method utilizes the low cost Kinect sensor for data acquisition and a deep convolutional neural network model for joint angles regression. Further, we rely on learning from synthetic training images to allow simulating several physical tasks by different workers and obtain a highly generalizable learning model. The corresponding ground truth joint angles have been generated using a novel inverse kinematic stage. The proposed method achieves high joint angels prediction rate by recording an average MAE of 4.67 deg and RMSE of 6.64 deg.

Surgical tool segmentation is used for detection, tracking and pose estimation of the tools in the vicinity of surgical scenes. It is considered as an essential task in surgical phase recognition and flow identification. Surgical flow identification is an unresolved task in the domain of context-aware surgical systems, which is used extensively on computer assisted intervention (CAI). CAI is used for staff assignment, automated guidance during intervention, surgical alert systems, automatic indexing of surgical video databases and optimisation of the real-time scheduling of operating room. Semantic segmentation is used for accurate delineation of surgical tools from the background. In semantic segmentation, each label is assigned to a class as a tool or a background. In this presented work, we applied a hybrid method utilising both recurrent and convolutional networks to achieve higher accuracy of surgical tools segmentation. The proposed method is trained and tested using a public dataset MICCAI 2016 Endoscopic Vision Challenge Robotic Instruments dataset "EndoVis". We achieved better performance using the proposed method compared to state-of-the-art methods on the same dataset for benchmarking. We achieved a balanced accuracy of 93.3% and Jaccard index of 82.7%.

Motor imagery based brain computer interface (BCI) has drawback of long subject dependent calibration session times. This can be a very exhausting and a time consuming process. In order to alleviate it, transfer learning and active learning approaches can be utilised. Informative instances are selected by applying active learning concept from other subjects under similar circumstances. Then, they are transferred to target user domain which has low number of training data. This informative transfer learning approach is associated with common spatial pattern (CSP) as feature extraction method in our previous attempt. CSP features are widely used for motor imagery-based BCI systems. However, the classical CSP algorithm will perform poorly when operational frequency bands are inadequately selected. Therefore, in the present study, filter bank common spatial pattern (FBCSP) algorithm has been applied for extracting features from the multi-class motor imagery data. FBCSP algorithm selects subject-specific operational frequency bands for extracting discriminative features. We incorporated FBCSP features into informative instance transfer learning framework to investigate the effect of subject specific feature selection. Results show that performance of new users can be improved with reduced number of training samples when FBCSP features are used compared to the classical CSP-based features.

The main objective of all eye movements is to keep the image of the object of interest focused. Head motion makes this objective more complex. However, the vestibular system is responsible for eye movement that compensate head motion. Simulating and analysing eye movement in coordination with head and neck movements is useful for assessing visual system contribution to discomfort in different tasks especially related to motion such as simulation sickness. In this paper, we present an ocular biomechanic model integrated into a head and neck model and used to simulate and analyse eye-head coordination movement during smooth pursuit. The proposed model is based on the physiological and the kinematics properties of the human eye provided from clinical trials performed by Robinson and Collins and also magnetic resonance imaging studies. The model incorporates six extraocular muscles (EOMs) and their connective tissues, known as pulleys. The pulleys are modelled as moving points on the muscle path according to the Active Pulley Hypothesis (APH) provided by Kono et al. Dynamic simulations of smooth pursuit with head movement is presented. The model achieved root mean square error (RMSE) of 0.77°, 0.29°and 0.14°for the horizontal, vertical and torsional rotation angles. It can be further used to simulate and analyse the vestibulo-ocular reflex of the eye.

Transparency and stability issues have been a major concern for haptic tele-operation. Researchers in this area have already shown that a minimum refresh rate of 1 kHz is required to achieve smoothness for human perception in force-feedback experiments. This rate requires the highest priority for real-time applications to achieve transparent haptic tele-presence. As a result, this rate leads to a round-trip time delay requirement of less than 1ms, which consequently constrain the transmission delay to less than 500us for sample packets in each direction. On the other hand, emerging haptic cooperative and collaborative applications, such as network gaming are typically implemented on loosely-coupled packet-switched networks. However, the transmission delay of UDP packets in such network lower-bounded to 1.5ms, which is higher than haptic applications constraint. This research proposes a new method of event-based haptic data sample transmission, which significantly reduces the number of sample packets required for transparent haptic tele-presence. Prioritizing the events, an event synchronization scheme, and utilization of a haptic-specific PID controller for smooth position adjustment within slave side are the major techniques elaborating this method. Furthermore, an experimental study with a virtual impedance device has been set up, and the results have analyzed. A compression ratio of 94% in the master device, and 97% in slave device has been achieved by the proposed technique.

This paper presents the dimensional design methodology for a hexarot-based simulator with high-G centrifugal motions. The dimensional synthesis of the mechanism is performed, and the optimal kinematic parameters of the manipulator for the translational and rotational motions are obtained. The maximum forces and moments exerted to each joint of the mechanism are calculated based on the dynamics of the mechanism which has been recently developed based on the Newton-Euler approach. Finally, dimensional parameters for the application of this research are presented.

In this paper we are presenting a novel approach for the problem of vulnerable road users (VRUs) attribute prediction which play such critical role for the intent prediction models of VRUs. We formulated the problem as a multi-task learning (MTL) image classification problem and we utilized a convolution neural network (ConvNet) based technique to exploit the commonality between two of the most important attributes of VRUs for intent prediction models (i.e, head orientation and body posture). We achieved classification accuracy scores of 83% and 76% for the body posture and head orientation attributes respectively. We compared the performance of our proposed solution against individual single task learning ConvNet models for each attribute and achieved significant overall accuracy over the two attribute classification tasks. Furthermore, we compared our proposed MTL-ConvNet model against other MTL approaches and achieved more than 18% AP score improvement in the classification of body posture attribute.

In this paper a novel approach for navigational path detection problem for the visually impaired was presented. A deep learning model based on state-of-the-art fully convolution neural networks have been proposed that can accurately semantically segment any navigational areas on pixel-wise level in different scenes without any prior assumptions about the environment of the scene such as textures or specific appearance cues. The proposed approach have been evaluated on two different publicly available dataset and have achieved a pixel accuracy of 91% over the testing images dataset. Furthermore, the performance of the proposed approach have been compared against other commonly used approach for the problem of predicting navigational areas in input RGB images, and the proposed approach outperformed it with more than 14%, 11% and 10% on the mean intersection over union, mean accuracy and pixel accuracy evaluation metrics respectively.

Percutaneous therapy is a common clinical operation in minimally invasive surgery. Yet, learning curve of this skillful manual operation is steep, which imposes negative impacts on its further advances. In this paper, we proposed a novel workflow to simulate percutaneous therapy through visuo-haptic rendering based on the clinical trials. Intraoperative puncture data, obtained by our 6DOF force recording system in the operating room, is fitted as the original force model for the haptic rendering. Patient-specific medical images were also segmented and reconstructed for the highly immersive virtual training scenario. Last but not least, medical professors and novices have also been invited to practice on our training scenario by employed the Global Rating Scale (GRS) questionnaire and parameter metrics recording to validate framework's performance. Posttest values in experts and novices' groups after training showed great progress with respect to pretest values in both GRS scores and objective evaluation.

Debriefing is the practice of after session review of training performance to enhance self-reflection through feedback. It has been considered as a vital and crucial part of simulation-based medical training. However an accurate, objective and in-depth evaluation of trainee performance has been a significant challenge. To address this we developed a knowledge-based framework in which the criteria of performance for clinical training are distilled into expert rules. These rules are then matched to data streams from a training session to evaluate the strengths and weaknesses of a trainee. We applied the evaluation technology to a dataset collected from two medical cohorts. The cohort characteristics are calculated, visualised, and validated by the medical experts. The cohort analysis results inform decision making at the levels of both the trainers and the enterprise. Trainers can compare and characterise the performance of different student cohorts or the same cohort over a period of time. The course coordinators can use the cohort analysis result to adjust the course design to target the identified common problems in trainee cohorts.

With the development of unmanned aerial vehicles (UAVs) and the relevant techniques, UAVs become common and popular for civilian applications such as remote sensing tasks. The reason is because they are cheap, flexible, and easy to set up. Car park occupancy analysis is important for authorities to make decisions on the design, plan and management of car parks. To have a quick knowledge of current parking situations, we proposed to use UAV images to count how many cars are parked during different periods. In this paper, our major contribution is a novel car counting approach for UAV images. Different from traditional detection- or segmentation-based counting techniques, the proposed counting method is density estimation based that does not need intense collection and learning procedures. We transform the car counting problem into the estimation of density values over pixels of an image. Experimental results have been conducted on real car park scenarios and all the results show that our method can provide a promising estimation of car numbers.

Automatic detection of road regions in aerial images remains a challenging research topic. Most existing approaches work well on the requirement of users to provide some seedlike points/strokes in the road area as the initial location of road regions, or detecting particular roads such as well-paved roads or straight roads. This paper presents a fully automatic approach that can detect generic roads from a single unmanned aerial vehicles (UAV) image. The proposed method consists of two major components: automatic generation of road/nonroad seeds and seeded segmentation of road areas. To know where roads probably are (i.e., road seeds), a distinct road feature is proposed based on the stroke width transformation (SWT) of road image. To the best of our knowledge, it is the first time to introduce SWT as road features, which show the effectiveness on capturing road areas in images in our experiments. Different road features, including the SWT-based geometry information, colors, and width, are then combined to classify road candidates. Based on the candidates, a Gaussian mixture model is built to produce road seeds and background seeds. Finally, starting from these road and background seeds, a convex active contour model segmentation is proposed to extract whole road regions. Experimental results on varieties of UAV images demonstrate the effectiveness of the proposed method. Comparison with existing techniques shows the robustness and accuracy of our method to different roads.

This brief is mainly concerned with a series of dynamical analyses of the Hindmarsh-Rose (HR) neuron with state-dependent time delays. The dynamical analyses focus on stability, Hopf bifurcation, as well as chaos and chaos control. Through the stability and bifurcation analysis, we determine that increasing the external current causes the excitable HR neuron to exhibit periodic or chaotic bursting/spiking behaviors and emit subcritical Hopf bifurcation. Furthermore, by choosing a fixed external current and varying the time delay, the stability of the HR neuron is affected. We analyze the chaotic behaviors of the HR neuron under a fixed external current through time series, bifurcation diagram, Lyapunov exponents, and Lyapunov dimension. We also analyze the synchronization of the chaotic time-delayed HR neuron through nonlinear control. Based on an appropriate Lyapunov-Krasovskii functional with triple integral terms, a nonlinear feedback control scheme is designed to achieve synchronization between the uncontrolled and controlled models. The proposed synchronization criteria are derived in terms of linear matrix inequalities to achieve the global asymptotical stability of the considered error model under the designed control scheme. Finally, numerical simulations pertaining to stability, Hopf bifurcation, periodic, chaotic, and synchronized models are provided to demonstrate the effectiveness of the derived theoretical results.

The aim of this paper is to design and develop an optimal motion cueing algorithm (MCA) based on the genetic algorithm (GA) that can generate high-fidelity motions within the motion simulator's physical limitations. Both, angular velocity and linear acceleration are adopted as the inputs to the MCA for producing the higher order optimal washout filter. The linear quadratic regulator (LQR) method is used to constrain the human perception error between the real and simulated driving tasks. To develop the optimal MCA, the latest mathematical models of the vestibular system and simulator motion are taken into account. A reference frame with the center of rotation at the driver's head to eliminate false motion cues caused by rotation of the simulator to the translational motion of the driver's head as well as to reduce the workspace displacement is employed. To improve the developed LQR-based optimal MCA, a new strategy based on optimal control theory and the GA is devised. The objective is to reproduce a signal that can follow closely the reference signal and avoid false motion cues by adjusting the parameters from the obtained LQR-based optimal washout filter. This is achieved by taking a series of factors into account, which include the vestibular sensation error between the real and simulated cases, the main dynamic limitations, the human threshold limiter in tilt coordination, the cross correlation coefficient, and the human sensation error fluctuation. It is worth pointing out that other related investigations in the literature normally do not consider the effects of these factors. The proposed optimized MCA based on the GA is implemented using the MATLAB/Simulink software. The results show the effectiveness of the proposed GA-based method in enhancing human sensation, maximizing the reference shape tracking, and reducing the workspace usage.

In this paper, the concept of prediction granules (PGs) is introduced for the real world application problems. The PGs are constructed by prediction intervals (PIs) and a learning-based method. Specifically, a granular emotional neural network (GENN) is proposed and the resulting network is examined on real world wind farm power generation dataset, obtained from New South Wales of Australia. A traditional artificial neural network (ANN) is also applied for comparison purposes. Numerical results indicate that PGs can improve the prediction results and can provide useful information for prediction tasks of real world uncertain data.

Prediction interval (PI) has been appeared as a promising tool to quantify the uncertainties and disturbances associated with point forecasts. Despite of its numerous applications in prediction problems, the use of PIs in control application is still limited. In this paper, a PI-based ANFIS controller is proposed and designed for nonlinear systems. In the proposed algorithm, a PI-based neural network model (PI-NN) is developed to construct the PIs, and this model is used as an online estimator of PIs for the controller. The PIs along with other traditional inputs are used to train the inverse ANFIS model. The developed PI-based ANFIS model is then used as a nonlinear PI-based controller (PIC). The performance of the proposed PIC is examined for a nonlinear numerical plant. Simulation results revealed that the proposed PIC performance is superior over the traditional ANFIS-based controller.

Non-invasive EEG signal based brain computer interface (BCI) for motor imagery task - classification requires large number of subject specific training samples for each user session that reduces the user feasibility of BCI. A generalized classifier using few subject specific sample will ease the real world implementation of motor imagery based BCI. At first, this paper applies an improved active transfer learning (ATL) on motor imagery based BCI. Then, it proposes a noble method of transferring selective instances (selected by few new subject specific data) from other subjects to new subject combining with selecting most informative subject specific data determined by active learning. Experimental results on BCI competition IV 2B dataset show that improved ATL works well on six out of nine subjects and proposed SIITAL method overcomes ATL limitation for other subjects. This means, it can achieve similar or better accuracy with a lower quantity of subject specific training data. Thus, it reduces the calibration effort.

In this paper, a tensegrity structure (TS) design is formulated as a nonlinear programming (NLP) problem, and a winner-take-all artificial emotional neural network (WTA-ENN) is proposed to solve the resulting NLP. The main feature of proposed WTA-ENN is related to low number of learning weights and simplicity of its learning rules that make it a suitable model for complicated TS design problems. Numerical results indicate that WTA-ENN can effectively solve NLP problem obtained from basic module of a typical TS Tower. The proposed method can be effectively used in architectural, structural and robotics design.

This paper introduces an approach to classification of RNA-seq read count data using Gaussian process (GP) models. RNA-seq data are transformed into microarray-like data before applying the statistical two-sample t-test for gene selection. GP is designed as a classifier that takes discriminant genes selected by the t-test method as inputs. The proposed approach is verified by using two benchmark real datasets and the five-fold cross-validation strategy. Various performance metrics that include accuracy rate, F-measure, area under the ROC curve and mutual information are used to evaluate the classifiers. Experimental results show the significant dominance of the GP classifier against its competing methods including k-nearest neighbors, multilayer perceptron, support vector machine and ensemble learning AdaBoost. The proposed approach therefore can be implemented effectively in real practice for RNA-seq data analysis, which is useful in many applications related to disease diagnosis and monitoring at the molecular level.

A motion simulator is an effective tool for training a driver in a safe environment by mimicking motion similar to the real world. To give a realistic feeling of driving and avoid motion sickness, an accurate motion cueing algorithm is required to restrict the platform within the allowed workspace range while regenerating an appropriate motion feeling for the simulator driver. Recently, employing Model Predictive Control (MPC) in the motion cueing algorithm has become popular. In this control method, by predicting future dynamics, an input is optimized to minimize a cost function over a prediction horizon while respecting the constraints. Reducing the prediction horizon is desirable to minimize the computational burden; however it draws the system toward instability. In this research, applying a nonuniform weighting method is proposed to stabilize the motion cueing algorithm using MPC with short prediction horizon and optimized weighting adjustment. Simulation results show the effectiveness of the proposed method.

Human pose estimation is a well-known computer vision problem that receives intensive research interest. The reason for such interest is the wide range of applications that the successful estimation of human pose offers. Articulated pose estimation includes real time acquisition, analysis, processing and understanding of high dimensional visual information. Ensemble learning methods operating on hand-engineered features have been commonly used for addressing this task. Deep learning exploits representation learning methods to learn multiple levels of representations from raw input data, alleviating the need to hand-crafted features. Deep convolutional neural networks are achieving the state-of-the-art in visual object recognition, localization, detection. In this paper, the pose estimation task is formulated as an offset joint regression problem. The 3D joints positions are accurately detected from a single raw depth image using a deep convolutional neural networks model. The presented method relies on the utilization of the state-of-the-art data generation pipeline to generate large, realistic, and highly varied synthetic set of training images. Analysis and experimental results demonstrate the generalization performance and the real time successful application of the proposed method.

The washout filter for a driving simulator is able to regenerate high fidelity vehicle translational and rotational motions within the simulator's physical limitations and return the simulator platform back to its initial position. The classical washout filter provides a popular solution that has been broadly utilized in different commercial simulators due to its simplicity, short processing time, and reasonable performance. One limitation of the classical washout filter is its sub-optimal parameter tuning process, which is based on the trial-and-error method. This leads to an inefficient workspace usage and, consequently, generation of false motion cues that lead to simulator sickness. Ignorance of a human sensation model in its design is another drawback of classical washout filters. The purpose of this study is to use Particle Swarm Optimization (PSO) to design and tune the washout filter parameters, in order to increase motion fidelity, decrease the human sensation error, and improve efficiency of the workspace usage. The proposed PSO-based washout filter is designed and implemented using the MATLAB/Simulink software package. The results indicate the effectiveness of the PSO-based washout filter in reducing the human sensation error, increasing the capability of reference shape tracking, and improving efficiency of the workspace usage.

Continuum robots offer better maneuverability and inherent compliance and are well-suited for surgical applications as catheters where gentle interaction with the environment is desired. However, sensing their shape and tip position is a challenge as traditional sensors cannot be employed in the same way that they are in rigid robotic manipulators. In this paper, a vision-based shape sensing algorithm for real-time 3D reconstruction of catheters based on the views of two arbitrary positioned cameras is presented. Customized high-speed algorithms are developed for the segmentation and feature extraction from the images. The algorithm is experimentally validated for accuracy by measuring the tip position, bending and orientation angles and for precision by estimating known 3D circular and elliptical shapes of the catheter. Experimental results demonstrate good accuracy and performance of the proposed high speed algorithms.

Although marker-less human pose estimation and tracking is important in various systems, nowadays many applications tend to detect animals while performing a certain task. These applications are multidisciplinary including robotics, computer vision, safety, and animal healthcare. The appearance of RGB-D sensors such as Microsoft Kinect and its successful applications in tracking and recognition made this area of research more active, especially with their affordable price. In this paper, a data synthesis approach for generating realistic and highly varied animal corpus is presented. The generated dataset is used to train a machine learning model to semantically segment animal body parts. In the proposed framework, foreground extraction is applied to segment the animal, dense representations are obtained using the depth comparison feature extractor and used for training a supervised random decision forest. An accurate pixel-wise classification of the parts will allow accurate joint localization and hence pose estimation. Our approach records classification accuracy of 93% in identifying the different body parts of an animal using RGB-D images.

One of the biggest challenges of RGB-D posture tracking is separating appendages such as briefcases, trolleys, and backpacks from the human body. Markerless motion tracking relies on segmenting each depth frame to a finite set of body parts. This is achieved via supervised learning by assigning each pixel to a certain body part. The training image set for the supervised learning are usually synthesised using popular motion capture databases and an ensemble of 3D models covering a wide range of anthropometric characteristics. In this paper, we propose a novel method for generating training data of human postures with attached objects. The results have shown a significant increase in body-part classification accuracy for subjects with props from 60% to 94% using the generated image set.

Skin lesions screening is an effective method for early detection of melanoma. Mostly, melanoma appears as hyper-pigmented area relative to the surrounding skin. Lesion segmentation is an indispensable step for skin lesions analysis. Automated segmentation is used to assist the dermatologist to isolate the suspicious lesion from the surrounding background. Iterative Otsu's method is state-of-art segmentation technique and it has acceptable accuracy. However, iterative methods suffer same drawback, they are time consuming and no guarantee for convergence to the best solution before the maximum iterations limit reached. This paper presents a novel segmentation algorithm using GLCM (Gray Level Co-occurrence Matrix). Segmentation masks extracted by proposed method are compared to human-expert extracted ground truth. The proposed method consists of three major stages, preprocessing, segmentation, and post processing. The proposed method achieved a specificity rate of 98.62%, precision of 96.25% and sensitivity of 80.8%.

Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour. Linear and nonlinear techniques have been used to solve this problem, and they provide good results presented in the performance of the current autonomous cars. However, they lack the ability to adapt to abruptness that happens because of the human factor. In this paper, we introduce a method to extract persistent homology barcode statistics. These statistics are useful as a representative of the driving process including the human behaviour. Human factor identification requires finding features that preserve certain properties against scalability, deformation, and abruptness. Topological Data Analysis (TDA) using persistent homology provides these features for driver behaviour prediction. We captured a driver's head motion as an experimental behavioural cue, combined it with captured simulated vehicle data (location and velocities). Barcodes are extracted using JavaPlex, then we extracted descriptive statistics to show the significance of these barcode as features for driver behaviour prediction. The correlation between the extracted features shows a promising start for a behavioural tracking applications using TDA.

Kinematic and dynamic modelling of UR5 manipulator

Kinematics and dynamics modelling of manipulators are essential for analysis, optimisation, control system design, and motion planning of the manipulators. Deriving these models is a time consuming task and it involves tedious mathematical calculations specifically for manipulators with more than two degrees of freedom. In this paper, development and evaluation of a symbolic modelling tool for the kinematic and dynamic equations of serial manipulators with revolute and prismatic joints are presented. The tool allows a quick access to the full kinematics and dynamics equations of the manipulators. The user only requires to provide the DH parameters for obtaining the kinematic model and the centre of the gravity, mass parameters and momentum of inertia matrices of the links to be able to obtain the dynamic model. The tool is shared for public access and it is aimed to benefit researchers or graduate students in the area of robotics. Evaluation of the models generated by the tool is demonstrated through its accuracy for control design of PUMA 560.

Recent efforts in the evolutionary multi-objective optimization (EMO) community focus on addressing shortcomings of current solution techniques adopted for solving many-objective optimization problems (MaOPs). One such challenge faced by classical multi-objective evolutionary algorithms is diversity preservation in optimization problems with more than three objectives, namely MaOPs. In this vein, NSGA-III has replaced the crowding distance measure in NSGA-II with reference points in the objective space to ensure diversity of the converged solutions along the pre-determined solutions in the environmental selection phase. NSGA-III uses the Pareto-dominance principle to obtain the non-dominated solutions in the environmental selection phase. However, the Pareto-dominance principle loses its selection pressure in high-dimensional optimization problems, because most of the obtained solutions become non-dominated. Inspired by θ-DEA, we address the selection pressure issue in NSGA-III, by exploiting the decomposition principle of MOEA/D using reference points for multiple single-objective optimization problems. Moreover, similar to MOEA/D, the parent selection process is restricted to the neighboring solutions, as opposed to random selection of parent solutions from the entire population in NSGA-III. The effectiveness of the proposed method is demonstrated on different well-known benchmark optimization problems for 3- to 10-objectives. The results compare favorably with those from MOEA/D, NSGA-III, and θ-DEA.

Designing functional observers for time-delay systems is an important practical research topic. However, the performance regulation of those observers and their robustness against the delays upper-bounds, have been fairly overlooked. In this brief, the problem of minimum order multi-functional observer design for Linear-Time-Invariant (LTI) systems with single state delay is revisited. Lyapunov Krasovskii approach is used to design the observer parameters in conjunction with the solution of some interconnected equations. A new methodology based on the descriptor transformation is proposed to construct a less conservative stability criterion compared with some other existing methods in designing delay-dependent functional observers. In addition, the exponential convergence of the observer with a specified convergence rate, is guaranteed. A numerical example shows the performance and the efficacy of the proposed design scheme.

Weber's law has been widely used by researchers for perceptual analysis in data reduction algorithms proposed for haptic applications. The law in its basic definition suggests a constant coefficient k or Just Noticeable Difference (JND). This constant is the percentage of actual stimulus around which the human sensory system cannot notice a change. Moreover, research studies have been conducted to modify the rule to be more efficient and appropriate for haptic applications. Among these studies, the effect of the master operator's velocity on the force-JND used in the slave device's transmission method has been previously discussed for constant velocity within the trajectory. The aim of this research is to overcome some of the limitations within existing research on velocity-adaptive JND, and to investigate the effects of the master operator's acceleration on the force-feedback dead-band threshold. Further, user studies were completed, and the results were investigated to clarify the influence of involving dynamic factors to the JND calculation.

This paper proposes a feature selection approach for RNA-seq read counts modelling based on grey relational analysis (GRA). Read counts are transformed to microarray-like data to facilitate normal-based statistical methods. GRA is designed to select differentially expressed genes by integrating outcomes of five individual feature selection methods including two-sample t-test, entropy test, Bhattacharyya distance, Wilcoxon test and receiver operating characteristic curve. GRA performs as an aggregate filter method through combining advantages of the individual methods to produce significant feature subsets that are then fed into classifiers for evaluation. The proposed approach is verified by using two benchmark real datasets and the five-fold cross-validation method. Experimental results show the performance dominance of the GRA-based feature selection method against its competing methods. This implies that the proposed method can be implemented effectively in real practice for medical applications such as disease diagnosis using RNA-seq data analysis.

Percutaneous surgery is a typical minimally invasive surgery. Featuring minimization in trauma and infection rate as well as rapid recovery time to patients, percutaneous therapy has replaced various traditional open surgery approaches and has become an essential approach for a series of clinic operations over the past decades. However, the practice and training for such a vocational manual skill is both difficult and expensive, which imposes negative impacts on its further advances. In this paper, we conducted an immersive needle insertion simulator for percutaneous surgery through visuo-haptic rendering. Multi-layered deformable tissue model with human anatomic textures are simulated and rendered. Mass-spring based force model and algorithm are also employed for realistic trocar needle insertion. Last but not least, a highly immersive virtual training scenario, integrated with a desktop haptic device is implemented to facilitate perceptive and hands-on experiences. Medical professional and trainees have also been invited to practice on the training scenario and provide subjective opinions in refining our implementation.

This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows us to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test, and signal-to-noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy, and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, and prostate. Rather than the traditional cross validation, leave-one-out cross-validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but of all other classifiers as well. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners.

This correspondence paper proposes a fast orientation estimation approach of natural images without the help of semantic information. Different from traditional low-level features, our low-level features are extracted inspired by the biological simple cells of the visual cortex. Two approximated receptive fields to mimic the biological cells are presented, and a local rotation operator is introduced to determine the optimal output and local orientation corresponding to an image position, which serve as the low-level feature employed in this paper. To generate the low-level features, a bisection method is applied to the first derivative of the model of receptive fields. Moreover, the feature screener is introduced to eliminate too much useless low-level features, which will speed up the processing time. After all the valuable low-level features are combined, the overall image orientation is estimated. The proposed approach possesses several features suitable for real-time applications. First, it avoids the tedious training procedure of some conventional methods. Second, no specific reference such as the horizon is assumed and no a priori knowledge of image is required. The proposed approach achieves a real-time orientation estimation of natural images using only low-level features with a satisfactory resolution. The effectiveness of our proposed approach is verified on real images with complex scenes and strong noises.

An interval type-2 fuzzy logic system is introduced for cancer diagnosis using mass spectrometry-based proteomic data. The fuzzy system is incorporated with a feature extraction procedure that combines wavelet transform and Wilcoxon ranking test. The proposed feature extraction generates feature sets that serve as inputs to the type-2 fuzzy classifier. Uncertainty, noise and outliers that are common in the proteomic data motivate the use of type-2 fuzzy system. Tabu search is applied for structure learning of the fuzzy classifier. Experiments are performed using two benchmark proteomic datasets for the prediction of ovarian and pancreatic cancer. The dominance of the suggested feature extraction as well as type-2 fuzzy classifier against their competing methods is showcased through experimental results. The proposed approach therefore is helpful to clinicians and practitioners as it can be implemented as a medical decision support system in practice.

As an integral part of interval type-2 fuzzy logic system (IT2FLS), type reduction (TR) plays a vital role in determining the performance of IT2FLS. Out of many type reduction algorithms, only Karnik-Mendel type TR algorithms capture the essence of interval type-2 fuzzy sets in type reduction. Enhanced Karnik-Mendel (EKM) algorithm is the most commonly used TR algorithm. In this work, we propose three new initializations for EKM algorithm. It is shown they are performing better than EKM and one of the proposed initializations significantly outperforms others. The performance gain can be upto 40% as per comprehensive simulation results demonstrated in this paper. Our findings are justified by computational time savings and iteration requirement for switch point search.

Karnik-Mendel (KM) algorithm is the most used and researched type reduction (TR) algorithm in literature. This algorithm is iterative in nature and despite consistent long term effort, no general closed form formula has been found to replace this computationally expensive algorithm. In this research work, we demonstrate that the outcome of KM algorithm can be approximated by simple linear regression techniques. Since most of the applications will have a fixed range of inputs with small scale variations, it is possible to handle those complexities in design phase and build a fuzzy logic system (FLS) with low run time computational burden. This objective can be well served by the application of regression techniques. This work presents an overview of feasibility of regression techniques for design of data-driven type reducers while keeping the uncertainty bound in FLS intact Simulation results demonstrates the approximation error is less than 2%. Thus our work preserve the essence of Karnik-Mendel algorithm and serves the requirement of low computational complexities.

Karnik-Mendel (KM) algorithm is the most widely used type reduction (TR) method in literature for the design of interval type-2 fuzzy logic systems (IT2FLS). Its iterative nature for finding left and right switch points is its Achilles heel. Despite a decade of research, none of the alternative TR methods offer uncertainty measures equivalent to KM algorithm. This paper takes a data-driven approach to tackle the computational burden of this algorithm while keeping its key features. We propose a regression method to approximate left and right switch points found by KM algorithm. Approximator only uses the firing intervals, rules centroids, and FLS structural features as inputs. Once training is done, it can precisely approximate the left and right switch points through basic vector multiplications. Comprehensive simulation results demonstrate that the approximation accuracy for a wide variety of FLSs is 100%. Flexibility, ease of implementation, and speed are other features of the proposed method.

Prediction interval (PI) has been extensively used to predict the forecasts for nonlinear systems as PI-based forecast is superior over point-forecast to quantify the uncertainties and disturbances associated with the real processes. In addition, PIs bear more information than point-forecasts, such as forecast accuracy. The aim of this paper is to integrate the concept of informative PIs in the control applications to improve the tracking performance of the nonlinear controllers. In the present work, a PI-based controller (PIC) is proposed to control the nonlinear processes. Neural network (NN) inverse model is used as a controller in the proposed method. Firstly, a PI-based model is developed to construct PIs for every sample or time instance. The PIs are then fed to the NN inverse model along with other effective process inputs and outputs. The PI-based NN inverse model predicts the plant input to get the desired plant output. The performance of the proposed PIC controller is examined for a nonlinear process. Simulation results indicate that the tracking performance of the PIC is highly acceptable and better than the traditional NN inverse model-based controller.

Proposing efficient methods for fire protection is becoming more and more important, because a small flame of fire may cause huge problems in social safety. In this paper, an effective fire flame detection method is investigated. This fire detection method includes four main stages: in the first step, a linear transformation is applied to convert red, green, and blue (RGB) color space through a 3*3 matrix to a new color space. In the next step, fuzzy c-mean clustering method (FCM) is used to distinguish between fire flame and non-fire flame pixels. Particle Swarm Optimization algorithm (PSO) is also utilized in the last step to decrease the error value measured by FCM after conversion. Finally, we apply Otsu threshold method to the new converted images to make a binary picture. Empirical results show the strength, accuracy and fast-response of the proposed algorithm in detecting fire flames in color images.

An approach to EEG signal classification for brain-computer interface (BCI) application using fuzzy standard additive model is introduced in this paper. The Wilcoxon test is employed to rank wavelet coefficients. Top ranking wavelets are used to form a feature set that serves as inputs to the fuzzy classifiers. Experiments are carried out using two benchmark datasets, Ia and Ib, downloaded from the BCI competition II. Prevalent classifiers including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system are also implemented for comparisons. Experimental results show the dominance of the proposed method against competing approaches.

Utility companies provide electricity to a large number of consumers. These companies need to have an accurate forecast of the next day electricity demand. Any forecast errors will result in either reliability issues or increased costs for the company. Because of the widespread roll-out of smart meters, a large amount of high resolution consumption data is now accessible which was not available in the past. This new data can be used to improve the load forecast and as a result increase the reliability and decrease the expenses of electricity providers. In this paper, a number of methods for improving load forecast using smart meter data are discussed. In these methods, consumers are first divided into a number of clusters. Then a neural network is trained for each cluster and forecasts of these networks are added together in order to form the prediction for the aggregated load. In this paper, it is demonstrated that clustering increases the forecast accuracy significantly. Criteria used for grouping consumers play an important role in this process. In this work, three different feature selection methods for clustering consumers are explained and the effect of feature extraction methods on forecast error is investigated.

Ordinary differential equations are used for modelling a wide range of dynamic systems. Even though there are many graphical software applications for this purpose, a fully customised solution for all problems is code-level programming of the model and solver. In this project, a free and open source C++ framework is designed to facilitate modelling in native code environment and fulfill the common simulation needs of control and many other engineering and science applications. The solvers of this project are obtained from ODEINT and specialised for Armadillo matrix library to provide an easy syntax and a fast execution. The solver code is minimised and its modification for users have become easier. There are several features added to the solvers such as controlling maximum step size, informing the solver about sudden input change and forcing custom times into the results and calling a custom method at these points. The comfort of the model designer, code readability, extendibility and model isolation have been considered in the structure of this framework. The application manages the output results, exporting and plotting them. Modifying the model has become more practical and a portion of corresponding codes are updated automatically. A set of libraries is provided for generation of output figures, matrix hashing, control system functions, profiling, etc. In this paper, an example of using this framework for a classical washout filter model is explained.

Brain Computer Interface (BCI) is playing a very important role in human machine communications. Recent communication systems depend on the brain signals for communication. In these systems, users clearly manipulate their brain activity rather than using motor movements in order to generate signals that could be used to give commands and control any communication devices, robots or computers. In this paper, the aim was to estimate the performance of a brain computer interface (BCI) system by detecting the prosthetic motor imaginary tasks by using only a single channel of electroencephalography (EEG). The participant is asked to imagine moving his arm up or down and our system detects the movement based on the participant brain signal. Some features are extracted from the brain signal using Mel-Frequency Cepstrum Coefficient and based on these feature a Hidden Markov model is used to help in knowing if the participant imagined moving up or down. The major advantage in our method is that only one channel is needed to take the decision. Moreover, the method is online which means that it can give the decision as soon as the signal is given to the system. Hundred signals were used for testing, on average 89 % of the up down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial prosthetic limbs and wheelchairs due to it's high speed and accuracy.

Event related potential (ERP) analysis is one of the most widely used methods in cognitive neuroscience research to study the physiological correlates of sensory, perceptual and cognitive activity associated with processing information. To this end information flow or dynamic effective connectivity analysis is a vital technique to understand the higher cognitive processing under different events. In this paper we present a Granger causality (GC)-based connectivity estimation applied to ERP data analysis. In contrast to the generally used strictly causal multivariate autoregressive model, we use an extended multivariate autoregressive model (eMVAR) which also accounts for any instantaneous interaction among variables under consideration. The experimental data used in the paper is based on a single subject data set for erroneous button press response from a two-back with feedback continuous performance task (CPT). In order to demonstrate the feasibility of application of eMVAR models in source space connectivity studies, we use cortical source time series data estimated using blind source separation or independent component analysis (ICA) for this data set.

Adaptive autoregressive (AAR) modeling of the EEG time series and the AAR parameters has been widely used in Brain computer interface (BCI) systems as input features for the classification stage. Multivariate adaptive autoregressive modeling (MVAAR) also has been used in literature. This paper revisits the use of MVAAR models and propose the use of adaptive Kalman filter (AKF) for estimating the MVAAR parameters as features in a motor imagery BCI application. The AKF approach is compared to the alternative short time moving window (STMW) MVAAR parameter estimation approach. Though the two MVAAR methods show a nearly equal classification accuracy, the AKF possess the advantage of higher estimation update rates making it easily adoptable for on-line BCI systems.

Driving phenomenon is a repetitive process, that permits sequential learning under identifying the proper change periods. Sequential filtering is widely used for tracking and prediction of state dynamics. However, it suffers at abrupt changes, which cause sudden incremental prediction error. We provide a sequential filtering approach using online Bayesian detection of change points to decrease prediction error generally, and specifically at abrupt changes. The approach learns from optimally detected segments for identifying driving behaviour. Change points detection is done by the Pruned Exact Linear Time algorithm. Computational cost of our approach is bounded by the cost of the implemented sequential filter. This computational performance is suitable to the online nature of motion simulator's delay reduction. The approach was tested on a simulated driving scenario using Vortex by CM Labs. The state dimensions are simulated 2D space coordinates, and velocity. Particle filter was used for online sequential filtering. Prediction results show that change-point detection improves the quality of state estimation compared to traditional sequential filters, and is more suitable for predicting behavioural activities.

It is not uncommon in many image acquisition solutions to balance a trade off between obtaining high resolution images at very low frame rates or acquiring a burst of low resolution images at higher frame rates. This paper introduces a novel image fusion framework for producing a high resolution video by augmenting analysed motion in a low resolution video to a single high resolution image. Many application domains such as remote sensing, low radiation medical imaging and battlefield automation will benefit from this novel fusion framework. The results show that a captured high resolution 30 frames per second video can be produced with 95% cost reduction while maintaining 94% structural similarity.

In this paper we propose a framework for communicating performance art to deaf, blind and deaf blind audiences and artists haptically through the sense of touch. This research opens doors for novel artistic trends relying mainly on the sense of touch. The paper investigates the design considerations dictated by solo and group dances as well as stage setup. Implementation scenarios for deaf blind audiences and performers are also discussed.

Pixel color has proven to be a useful and robust cue for detection of most objects of interest like fire. In this paper, a hybrid intelligent algorithm is proposed to detect fire pixels in the background of an image. The proposed algorithm is introduced by the combination of a computational search method based on a swarm intelligence technique and the Kemdoids clustering method in order to form a Fire-based Color Space (FCS), in fact, the new technique converts RGB color system to FCS through a 3*3 matrix. This algorithm consists of five main stages:(1) extracting fire and non-fire pixels manually from the original image. (2) using K-medoids clustering to find a Cost function to minimize the error value. (3) applying Particle Swarm Optimization (PSO) to search and find the best W components in order to minimize the fitness function. (4) reporting the best matrix including feature weights, and utilizing this matrix to convert the all original images in the database to the new color space. (5) using Otsu threshold technique to binarize the final images. As compared with some state-of-the-art techniques, the experimental results show the ability and efficiency of the new method to detect fire pixels in color images.

Just-Noticeable-Differences (JND) as a dead-band in perceptual analysis has been widely used for more than a decade. This technique has been employed for data reduction in hap tic data transmission systems by several researchers. In fact, researchers use two different JND coefficients that are JNDV and JNDF for velocity and force data respectively. For position data, they usually rely on the resolution of hap tic display device to omit data that are unperceivable to human. In this paper, pruning undesirable position data that are produced by the vibration of the device or subject and/or noise in transmission line is addressed. It is shown that using inverse JNDV for position data can prune undesirable position data. Comparison of the results of the proposed method in this paper with several well known filters and some available methods proposed by other researchers is performed. It is shown that combination of JNDV could provide lower error with desirable curve smoothness, and as little as possible computation effort and complexity. It also has been shown that this method reduces much more data rather than using forward-JNDV.

Immersion is of paramount importance for virtual training tasks, especially in the area of medical training simulations. Although haptic interaction has been the major boost for immersion in visuo-haptic rendering, the way how visual rendering represent detailed information can greatly affect haptic pipeline and therefore is also crucial for an engaging and effective training simulation. In this paper, we discuss a few details in visuo-haptic rendering of optometry training simulation, and demonstrate two implementations which provide detailed and accurate rendering results and increase the immersion of the training procedure. The effectiveness of these implementations have been validated both visually and haptically by medical professionals through a few user studies. The work discussed in this paper may serve as foundations for future improvements in visuo-haptic medical training simulation tasks.

Lung segmentation in thoracic computed tomography (CT) scans is an important preprocessing step for computer-aided diagnosis (CAD) of lung diseases. This paper focuses on the segmentation of the lung field in thoracic CT images. Traditional lung segmentation is based on Gray level thresholding techniques, which often requires setting a threshold and is sensitive to image contrasts. In this paper, we present a fully automated method for robust and accurate lung segmentation, which includes a enhanced thresholding algorithm and a refinement scheme based on a texture-aware active contour model. In our thresholding algorithm, a histogram based image stretch technique is performed in advance to uniformly increase contrasts between areas with low Hounsfield unit (HU) values and areas with high HU in all CT images. This stretch step enables the following threshold-free segmentation, which is the Otsu algorithm with contour analysis. However, as a threshold based segmentation, it has common issues such as holes, noises and inaccurate segmentation boundaries that will cause problems in future CAD for lung disease detection. To solve these problems, a refinement technique is proposed that captures vessel structures and lung boundaries and then smooths variations via texture-aware active contour model. Experiments on 2,342 diagnosis CT images demonstrate the effectiveness of the proposed method. Performance comparison with existing methods shows the advantages of our method.

Monitoring marine object is important for understanding the marine ecosystem and evaluating impacts on different environmental changes. One prerequisite of monitoring is to identify targets of interest. Traditionally, the target objects are recognized by trained scientists through towed nets and human observation, which cause much cost and risk to operators and creatures. In comparison, a noninvasive way via setting up a camera and seeking objects in images is more promising. In this paper, a novel technique of object detection in images is presented, which is applicable to generic objects. A robust background modelling algorithm is proposed to extract foregrounds and then blob features are introduced to classify foregrounds. Particular marine objects, box jellyfish and sea snake, are successfully detected in our work. Experiments conducted on image datasets collected by the Australian Institute of Marine Science (AIMS) demonstrate the effectiveness of the proposed technique.

A new multi-output interval type-2 fuzzy logic system (MOIT2FLS) is introduced for protein secondary structure prediction in this paper. Three outputs of the MOIT2FLS correspond to three structure classes including helix, strand (sheet) and coil. Quantitative properties of amino acids are employed to characterize twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Three clustering tasks are performed using the adaptive vector quantization method to construct an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS. The genetic fitness function is designed based on the Q3 measure. Experimental results demonstrate the dominance of the proposed approach against the traditional methods that are Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.

A new portfolio risk measure that is the uncertainty of portfolio fuzzy return is introduced in this paper. Beyond the well-known Sharpe ratio (i.e., the reward-to-variability ratio) in modern portfolio theory, we initiate the so-called fuzzy Sharpe ratio in the fuzzy modeling context. In addition to the introduction of the new risk measure, we also put forward the reward-to-uncertainty ratio to assess the portfolio performance in fuzzy modeling. Corresponding to two approaches based on TMand TWfuzzy arithmetic, two portfolio optimization models are formulated in which the uncertainty of portfolio fuzzy returns is minimized, while the fuzzy Sharpe ratio is maximized. These models are solved by the fuzzy approach or by the genetic algorithm (GA). Solutions of the two proposed models are shown to be dominant in terms of portfolio return uncertainty compared with those of the conventional mean-variance optimization (MVO) model used prevalently in the financial literature. In terms of portfolio performance evaluated by the fuzzy Sharpe ratio and the reward-to-uncertainty ratio, the model using TWfuzzy arithmetic results in higher performance portfolios than those obtained by both the MVO and the fuzzy model, which employs TMfuzzy arithmetic. We also find that using the fuzzy approach for solving multiobjective problems appears to achieve more optimal solutions than using GA, although GA can offer a series of well-diversified portfolio solutions diagrammed in a Pareto frontier.

An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground-vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut-based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 × 595 videos on average.

This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.

This paper proposes a novel general framework for line segment perception, which is motivated by a biological visual cortex, and requires no parameter tuning. In this framework, we design a model to approximate receptive fields of simple cells. More importantly, the structure of biological orientation columns is imitated by organizing artificial complex and hypercomplex cells with the same orientation into independent arrays. Besides, an interaction mechanism is implemented by a set of self-organization rules. Enlightened by the visual topological theory, the outputs of these artificial cells are integrated to generate line segments that can describe nonlocal structural information of images. Each line segment is evaluated quantitatively by its significance. The computation complexity is also analyzed. The proposed method is tested and compared to state-of-the-art algorithms on real images with complex scenes and strong noises. The experiments demonstrate that our method outperforms the existing methods in the balance between conciseness and completeness.

This paper introduces a new multi-output interval type-2 fuzzy logic system (MOIT2FLS) that is automatically constructed from unsupervised data clustering method and trained using heuristic genetic algorithm for a protein secondary structure classification. Three structure classes are distinguished including helix, strand (sheet) and coil which correspond to three outputs of the MOIT2FLS. Quantitative properties of amino acids are used to characterize the twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Amino acid sequences are parsed into learnable patterns using a local moving window strategy. Three clustering tasks are performed using the adaptive vector quantization method to derive an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS with the purpose of maximizing the Q3 measure. Comprehensive experimental results demonstrate the strong superiority of the proposed approach over the traditional methods including Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.

This paper proposes a combination of fuzzy standard additive model (SAM) with wavelet features for medical diagnosis. Wavelet transformation is used to reduce the dimension of high-dimensional datasets. This helps to improve the convergence speed of supervised learning process of the fuzzy SAM, which has a heavy computational burden in high-dimensional data. Fuzzy SAM becomes highly capable when deployed with wavelet features. This combination remarkably reduces its computational training burden. The performance of the proposed methodology is examined for two frequently used medical datasets: the lump breast cancer and heart disease. Experiments are deployed with a five-fold cross validation. Results demonstrate the superiority of the proposed method compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. Faster convergence but higher accuracy shows a win-win solution of the proposed approach.

There are three different approaches for functional observer design for Linear Time-Invariant (LTI) systems within the literature. One of the most common methods has been proposed by Aldeen [1] and further developed by others. We found several examples in which the necessary and sufficient conditions for the existence of a functional observer are actually not sufficient for this methodology. This finding motivated us to develop a new methodology for designing functional observers. Our new method provides enough degrees of freedom for the observer design parameter and it improves the weakness within the Aldeen's method in solving the observer coupled matrix equations. In this paper, we present the reason and an example to show the insufficiency of the former method. Furthermore, we present our new developed methodology. An illustrative algorithm also describes the design procedure step by step. A numerical example and simulation results support our findings and performance of the proposed method.

A delay-dependent functional observer is designed for linear time-invariant (LTI) systems with time-varying input delay. Compared to delay-free observers, delay-dependent functional observers are less conservative and cover more systems. The designed functional observer is with minimum possible order (minimal). Necessary and sufficient conditions of the existence of the observer and asymptomatic stability of it are illustrated. The proposed observer is extended to multiple input delayed systems with time-varying delays. An algorithm is developed for designing of the minimal order observer based on the methodology of this paper. Two numerical examples and simulations are used to support our proposed methodology.

Determining an analytical solution to the inverse kinematics problem for a parallel manipulator is typically a straightforward problem. However, lower mobility parallel manipulators with 2-5 degrees of freedom (DOFs) often suffer from an unwanted parasitic motion in one or more DOFs. For such manipulators, the inverse kinematics problem can be significantly more difficult. This paper contains an analysis of the inverse kinematics problem for a class of 3-DOF parallel manipulators with axis-symmetric arm systems. All manipulators in the studied class exhibit parasitic motion in one DOF. For manipulators in the studied class, the general solution to the inverse kinematics problem is reduced to solving a univariate equation, while analytical solutions are presented for several important special cases.

In contrast to point forecast, prediction interval-based neural network offers itself as an effective tool to quantify the uncertainty and disturbances that associated with process data. However, single best neural network (NN) does not always guarantee to predict better quality of forecast for different data sets or a whole range of data set. Literature reported that ensemble of NNs using forecast combination produces stable and consistence forecast than single best NN. In this work, a NNs ensemble procedure is introduced to construct better quality of Pis. Weighted averaging forecasts combination mechanism is employed to combine the Pi-based forecast. As the key contribution of this paper, a new Pi-based cost function is proposed to optimize the individual weights for NN in combination process. An optimization algorithm, named simulated annealing (SA) is used to minimize the PI-based cost function. Finally, the proposed method is examined in two different case studies and compared the results with the individual best NNs and available simple averaging Pis aggregating method. Simulation results demonstrated that the proposed method improved the quality of Pis than individual best NNs and simple averaging ensemble method.

The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.

Spike sorting plays an important role in analysing electrophysiological data and understanding neural functions. Developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. This paper proposes an automatic unsupervised spike sorting method using the landmark-based spectral clustering (LSC) method in connection with features extracted by the locality preserving projection (LPP) technique. Gap statistics is employed to evaluate the number of clusters before the LSC can be performed. Experimental results show that LPP spike features are more discriminative than those of the popular wavelet transformation (WT). Accordingly, the proposed method LPP-LSC demonstrates a significant dominance compared to the existing method that is the combination between WT feature extraction and the superparamagnetic clustering. LPP and LSC are both linear algorithms that help reduce computational burden and thus their combination can be applied into realtime spike analysis.

Although parallel manipulators possess the benefits of high acceleration and accuracy, they typically suffer from a limited workspace to footprint ratio. This paper presents a workspace analysis of two recently proposed axis-symmetric parallel manipulators designed to overcome this problem. The studied manipulators are parametrized, their inverse kinematic models are derived and an in-depth singularity analysis is performed. Next, the architectural parameters are generated for one of the manipulators, using a meta-heuristic algorithm to produce the maximum singularity-free workspace. Thereafter, a second parameter calculation is performed to examine the relationship between maximizing the global conditioning index (GCI) and the resultant achievable workspace for the manipulators.

We propose a fast approach for detecting and tracking a specific road in aerial videos. It combines adaptive Gaussian Mixture Models (GMMs) to describe road colour distributions, and homography based tracking to track road geometries, where an efficient technique is developed to estimate homography transformations between two frames. Experiments are conducted on videos captured by our unmanned aerial vehicles. All the results demonstrate the effectiveness of our proposed method. We test 1755 frames from 5 videos. Our approach can achieve 0.032 seconds per frame and 2.64% segmentation error for images with 908 × 513 resolutions, on average.

The lack of haptic feedback has negatively affected the surgeon's ability to palpate and diagnose tissue and differentiate its stiffness during surgical operations with commercially available robotic assisted surgical systems. A modular surgical instrument capable of non-invasive measurement of sideways tip/tissue interaction forces for direct application in robotic assisted minimally invasive surgery systems is presented in this paper. The proposed force measurement technique enables the actual non-invasive measurement of the sideways interaction forces at the tip jaws. The instrument has two actuation degrees of freedom (DOF) for the tip operation and grasping orientation. The tip functionality type (e.g., grasping, cutting, and dissecting) can also be changed quickly and easily. Experiments were conducted to evaluate functionalities of the proposed instrument in palpating tissues. The results are presented and analysed here that verify the capability of the proposed instrument in accurately measuring lateral tip/tissue interaction forces.

This paper presents a modular design for a robot-assisted biological cell microinjection system. The proposed design is composed of injection, vision, force measurement and control units that provides sufficient flexibility to observe and control cell microinjection by monitoring and regulating position and force simultaneously. Methodologies have been presented for automation of the laborious tasks associated with microinjection to improve the repeatability and reliability of the process. The system is capable of automatic positioning and focusing of the microcapillary tip as well as automatic realization of the cell piercing during the microinjection process with vision-based approaches. The proposed methods were tested for 100 zebrafish embryos micromanipulation experiments at Blastula stage. 97% success rate was achieved which shows high capability of the proposed design and methods.

CHAI3D is a widely accepted haptic SDK in the society because it is open-source and provides support to devices from different vendors. In many cases, CHAI3D and its related demos are used for benchmarking various haptic collision and rendering algorithms. However, CHAI3D is designed for off-the-shelf single-point haptic devices only, and it does not provide native support to customised multi-point haptic devices. In this paper, we aim to extend the existing CHAI3D framework and provide a standardized routine to support customised, single/multi-point haptic devices. Our extension aims at two issues: Intra-device communication and Inter-device communication. Therefore, our extension includes an HIP wrapper layer to concurrently handle multiple HIPs of a single device, and a communication layer to concurrently handle multiple position, orientation and force calculations of multiple haptic devices. Our extension runs on top of a custom-built 8-channel device controller, although other off-the shelf controllers can also be integrated easily. Our extension complies with the CHAI3D design framework and advanced provide inter-device communication capabilities for multi-device operations. With straightforward conversion routines, existing CHAI3D demos can be adapted to multi-point demos, supporting real-time parallel collision detection and force rendering.

Haptic rendering of complex models is usually prohibitive due to its much higher update rate requirement compared to visual rendering. Previous works have tried to solve this issue by introducing local simulation or multi-rate simulation for the two pipelines. Although these works have improved the capacity of haptic rendering pipeline, they did not take into consideration the situation of heterogeneous objects in one scenario, where rigid objects and deformable objects coexist in one scenario and close to each other. In this paper, we propose a novel idea to support interactive visuo-haptic rendering of complex heterogeneous models. The idea incorporates different collision detection and response algorithms and have them seamlessly switched on and off on the fly, as the HIP travels in the scenario. The selection of rendered models is based on the hypothesis of “parallel universes”, where the transition of rendering one group of models to another is totally transparent to users. To facilitate this idea, we proposed a procedure to convert the traditional single universe scenario into a “multiverse” scenario, where the original models are grouped and split into each parallel universe, depending on the scenario rendering requirement rather than just locality. We also proposed to add simplified visual objects as background avatars in each parallel universe to visually maintain the original scenario while not overly increase the scenario complexity. We tested the proposed idea in a haptically-enabled needle thoracostomy training environment and the result demonstrates that our idea is able to substantially accelerate visuo-haptic rendering with complex heterogeneous scenario objects.

This paper aims at optimally adjusting a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. Fuzzy logic system (FLS) is the method applied to develop the intelligent traffic timing controller. For this purpose, an intersection is considered and simulated as an intelligent agent that learns how to set green times in each cycle based on the traffic information. The FLS controller (FLC) uses genetic algorithm to tune its parameters during learning phase. Finally, The performance of the intelligent FLC is compared with the performance of a FLC with predefined parameters and three simple fixed-time controller. Simulation results indicate that intelligent FLC significantly reduces the total delay in the network compared to the fixed-time method and FLC with manual parameter setting.

This paper proposes a method to improve motion smoothness and decrease latency using existing ABB IRC5 robot controllers without access to any low level interface. The proposed control algorithm includes a high-level PID controller used to dynamically generate reference velocities for different travel ranges of the tool centre point (TCP) of the robot. Communication with the ABB IRC5 controller was performed utilising the ABB PC software development kit (SDK). The multitasking feature of the IRC5 controller was used in order to enhance the communication frequency between the controller and the remote application. Trajectory tracking experiments of a predefined 3D trajectory were carried out and the benefits of the proposed algorithm was demonstrated. The robot was intentionally installed on a wobbly table and its vibrations were recorded using a six degrees of freedom (DOF) force/torque sensor fitted to the tool mounting interface of the robot. The robot vibrations were used as a measure of the smoothness of the tracking movements. Experimental results demonstrating the robot tool centre point (TCP), tracking errors, and robot vibrations for different control approaches were provided and analysed. It was demonstrated that the proposed approach results in the smoothest motion with less than 0.2 mm tracking errors.

Microsoft Kinect sensor was introduced with the XBOX gaming console. It features a simple and portable motion capturing system. Kinect nowadays presents a point of interest in many fields of study and areas of research where its affordable price compared to its capabilities. The Kinect sensor has the capability to capture and track detected 3D objects with accuracy comparable to that captured by state of the art commercial systems. Human safety is considered one of the highest concerns, specially nowadays where the existence of machines and robots is widely used. In this paper we present using the Kinect technology for enhancing the safety of equipment and operations in seven different applications. These applications include 1) positioning of child's car seat to optimise the child's position in respected to front and side air-bags; 2) board positioning system to improve the teacher's arm reach posture; 3) gas station safety to prevent children from accessing the gas pump; 4) indoor pool safety to avoid children access to deep pool area; 5) robot safety emergency stop; 6) Workplace safety; and 7) older adults fall prediction.

This paper aims to comprehensively investigate performance of evolutionary algorithms for design optimization of shell and tube heat exchangers (STHX). Genetic algorithm (GA) and firefly algorithm (FA) are implemented for finding the optimal values for seven key design variables of the STHX model. ε-NTU method and Bell-Delaware procedure are used for thermal modelling of STHX and calculation of shell side heat transfer coefficient and pressure drop. The purpose of STHX optimization is to maximize its thermal efficiency. Obtained results for several simulation optimizations indicate that GA is unable to find permissible and optimal solutions in the majority of cases. In contrast, design variables found by FA always lead to maximum STHX efficiency. As per optimization results, maximum efficiency (83.8%) can be achieved using several design configurations. However, these designs are bearing different dollar costs. Also it is found that the behaviour of the majority of decision variables remain consistent in different runs of the FA optimization process.

Emotional neural network (ENN) is a recently developed methodology that uses simulated emotions aiding its learning process. ENN is motivated by neurophysiological knowledge of the human's emotional brain. In this paper, ENNs are developed and examined for prediction tasks. Genetic algorithm is applied for optimal tuning of crisp numerical parameters of ENN. The performance of the proposed ENN is examined using data sets for a couple of synthetic (with constant and variable noise) and real world (wind farm power generation data) case studies. A traditional artificial neural network (ANN) is also implemented for comparison purposes. Numerical results indicate the superiority of ENN over ANN in terms of accuracy and stability.

Understanding neural functions requires the observation of the activities of single neurons that are represented via electrophysiological data. Processing and understanding these data are challenging problems in biomedical engineering. A microelectrode commonly records the activity of multiple neurons. Spike sorting is a process of classifying every single action potential (spike) to a particular neuron. This paper proposes a combination between diffusion maps (DM) and mean shift clustering method for spike sorting. DM is utilized to extract spike features, which are highly capable of discriminating different spike shapes. Mean shift clustering provides an automatic unsupervised clustering, which takes extracted features from DM as inputs. Experimental results show a noticeable dominance of the features extracted by DM compared to those selected by wavelet transformation (WT). Accordingly, the proposed integrated method is significantly superior to the popular existing combination of WT and superparamagnetic clustering regarding spike sorting accuracy.

Some illustrative examples are provided to identify the ineffective and unrealistic characteristics of existing approaches to solving fuzzy linear programming (FLP) problems (with single or multiple objectives). We point out the error in existing methods concerning the ranking of fuzzy numbers and thence suggest an effective method to solve the FLP. Based on the consistent centroid-based ranking of fuzzy numbers, the FLP problems are transformed into non-fuzzy single (or multiple) objective linear programming. Solutions of FLP are then crisp single or multiple objective programming problems, which can respectively be obtained by conventional methods.

Manikin-based medical simulation has been shown to benefit the knowledge, skills and attitudes of the learner, and to impart favourable patient effects. A vital component of any training simulation is the after-session discussion with trainees to debrief their performance. In this study we develop a rule-based debriefing tool for improving the efficacy of medical training sessions. Unlike most existing de-briefing tools, the tool presented here has been designed to reduce medical trainer assessment time and to improve evaluation accuracy through a largely automated evaluation of trainee performance. The developed tool is acknowledged by the School of Medicine of Deakin University as an important advancement in assisting medical trainers carry out the debriefing process effectively and efficiently.

This study developed and tested a research model which examined the impact of user perceptions of self-efficacy (SE) and virtual environment (VE) efficacy on the effectiveness of VE training systems. The model distinguishes between the perceptions of one's own capability to perform trained tasks effectively and the perceptions of system performance, regarding the established parameters from literature. Specifically, the model posits that user perceptions will have positive effects on task performance and memory. Seventy-six adults participated in a VE in a controlled experiment, designed to empirically test the model. Each participant performed a series of object assembly tasks. The task involved selecting, rotating, releasing, inserting and manipulating 3D objects. Initially, the results of factor analysis demonstrated dimensionality of two user perception measures and produced a set of empirical validated factors underlining the VE efficacy. The results of regression analysis revealed that SE had a significant positive effect on perceived VE efficacy. No significant effects were found of perceptions on performance and memory. Furthermore, the study provided insights into the relationships between the perception measures and performance measures for assessing the efficacy of VE training systems. The study also addressed how well users learn, perform, adapt to and perceive the VE training, which provides valuable insight into the system efficacy. Research and practical implications are presented at the end of the paper.

This paper presents a patchwork-based audio watermarking method to resist de-synchronization attacks such as pitch-scaling, time-scaling, and jitter attacks. At the embedding stage, the watermarks are embedded into the host audio signal in the discrete cosine transform (DCT) domain. Then, a set of synchronization bits are implanted into the watermarked signal in the logarithmic DCT (LDCT) domain. At the decoding stage, we analyze the received audio signal in the LDCT domain to find the scaling factor imposed by an attack. Then, we modify the received signal to remove the scaling effect, together with the embedded synchronization bits. After that, watermarks are extracted from the modified signal. Simulation results show that at the embedding rate of 10 bps, the proposed method achieves 98.9% detection rate on average under the considered de-synchronization attacks. At the embedding rate of 16 bps, it can still obtain 94.7% detection rate on average. So, the proposed method is much more robust to de-synchronization attacks than other patchwork watermarking methods. Compared with the audio watermarking methods designed for tackling de-synchronization attacks, our method has much higher embedding capacity.

High performance for face recognition systems occurs in controlled environments and degrades with variations in illumination, facial expression, and pose. Efforts have been made to explore alternate face modalities such as infrared (IR) and 3-D for face recognition. Studies also demonstrate that fusion of multiple face modalities improve performance as compared with singlemodal face recognition. This paper categorizes these algorithms into singlemodal and multimodal face recognition and evaluates methods within each category via detailed descriptions of representative work and summarizations in tables. Advantages and disadvantages of each modality for face recognition are analyzed. In addition, face databases and system evaluations are also covered.

This paper aims at using interval type-2 fuzzy logic systems (IT2FLSs) for one-day ahead load forecasting task. It introduces an optimal type reduction (TR) algorithm for IT2FLSs to improve their approximation capability. Flexibility and adaptiveness are the key features of the proposed nonparametric optimal TR algorithm. Lower and upper firing strengths of rules as well as their consequent coefficients are fed into a neural network (NN). NN output is a crisp value that corresponds to the optimal defuzzified output of IT2FLSs. The NN type reducer is trained through minimization of an error-based cost function with the purpose of improving forecasting performance of IT2FLS models. Once the optimal NN-based type reducer is trained, IT2FLS models can be straightforwardly forecast the next-day load demand. Numerical testing using real load datasets indicate IT2FLS models equipped with the new optimal TR algorithm outperform IT2FLS models using traditional TR algorithms in terms of forecast accuracies. This benefit is achieved in no cost, as the computational requirement of the proposed optimal TR algorithm is the same as for traditional TR algorithms.

Optometry is an essential health care profession that has existed for many centuries and is still evolving. However, the training approaches for optometrists are not yet on par with the latest technological evolution. The traditional supervisor-student training mode could not provide good immersion and repeatability, while most existing vision-based computer-assisted simulations provide even worse immersion on screens. In this paper, we propose an effective system for optometry training simulation with two major components: augmented reality and haptics. These components are integrated with the actual slit lamp and are able to greatly enhance the immersion for typical optometry training tasks such as foreign body removal. Medical doctors are also involved in suggesting configurations and validating visual and haptic rendering results. Preliminary user studies show very positive feedbacks from optometry students.

The current practice of designing microfluidic Lab-on-a-Chip (LoCs) limits reusing designs and makes sharing tasks among researchers difficult. One way to achieve that objective is to borrow best practices from engineering. Also it takes a lot of skills to design LoCs. Design-by-assembly in which a LoC can be designed by configuring, laying out subsystems can help new researchers to develop custom chips. Flexible, reusable, and rapid-prototyping-feasible LoC designs can be achieved by fabricated modular microfluidic blocks. However, challenging problems still persist, which limit the usefulness of prefabricated blocks. We propose software microfluidic modules (SoftMABs) based design technique to solve issues fabricated modules face. By configuring SoftMABs, integrating them, the new assembly of SoftMABs can form a 3D LoC design ready to be prototyped. The proposed method can make designing a complex LoC less challenging, and collaborating among laboratories easier. We created SoftMABs and designed a custom microfluidic chip by assembling SoftMABs like LEGOs, dragging-and-dropping them. Later we reconfigured them - by replacing a SoftMAB with another module - to make a new LoC. We believe this computer-aided method is an interesting and useful LoC design technique.

A variety of type reduction (TR) algorithms have been proposed for interval type-2 fuzzy logic systems (IT2 FLSs). The focus of existing literature is mainly on computational requirements of TR algorithm. Often researchers give more rewards to computationally less expensive TR algorithms. This paper evaluates and compares five frequently used TR algorithms from a forecasting performance perspective. Algorithms are judged based on the generalization power of IT2 FLS models developed using them. Four synthetic and real world case studies with different levels of uncertainty are considered to examine effects of TR algorithms on forecasts accuracies. It is found that Coupland-Jonh TR algorithm leads to models with a better forecasting performance. However, there is no clear relationship between the width of the type reduced set and TR algorithm.

This paper introduces a new type reduction (TR) algorithm for interval type-2 fuzzy logic systems (IT2 FLSs). Flexibility and adaptiveness are the key features of the proposed non-parametric algorithm. Lower and upper firing strengths of rules as well as their consequent coefficients are fed into a neural network (NN). NN output is a crisp value that corresponds to the defuzzified output of IT2 FLSs. The NN type reducer is trained through minimization of an error-based cost function with the purpose of improving modelling and forecasting performance of IT2 FLS models. Simulation results indicate that application of the proposed TR algorithm greatly enhances modelling and forecasting performance of IT2 FLS models. This benefit is achieved in no cost, as the computational requirement of the proposed algorithm is less than or at most equivalent to traditional TR algorithms.

Stock price forecast has long been received special attention of investors and financial institutions. As stock prices are changeable over time and increasingly uncertain in modern financial markets, their forecasting becomes more important than ever before. A hybrid approach consisting of two components, a neural network and a fuzzy logic system, is proposed in this paper for stock price prediction. The first component of the hybrid, i.e. a feedforward neural network (FFNN), is used to select inputs that are highly relevant to the dependent variables. An interval type-2 fuzzy logic system (IT2 FLS) is employed as the second component of the hybrid forecasting method. The IT2 FLS's parameters are initialized through deployment of the k-means clustering method and they are adjusted by the genetic algorithm. Experimental results demonstrate the efficiency of the FFNN input selection approach as it reduces the complexity and increase the accuracy of the forecasting models. In addition, IT2 FLS outperforms the widely used type-1 FLS and FFNN models in stock price forecasting. The combination of the FFNN and the IT2 FLS produces dominant forecasting accuracy compared to employing only the IT2 FLSs without the FFNN input selection.

The introduction of robotics technology within endovascular surgical procedures has realised accurate catheter navigation and reduced surgeon irradiation. Insufficient force information from the catheter remains a limitation for significant improvement in the performance of these procedures. While recent developments of catheter tip-force measurement sensors have witnessed a notable outcome, packaging complexities and sensor size constraints limit their application. This work presents an approach to catheter tip-force measurement during endovascular surgery simulation. Image processing and photoelasticity are utilized to measure arterial stress and morphological deformation in a carotid artery segment model. Deformation is measured in real-time and conveyed to the user as haptic feedback during tele-operation of a robotic catheter insertion system. A preliminary single user study was completed to realise the potential of force feedback as it is correlated to the level of deformation and stress in the artery model. The results of this haptic feedback are compared with experimental results of the same operator manually inserting the catheter, and also using a computer graphical-user-interface to control the catheter insertion system.

Fault tolerance is critical for various situations where robotic manipulators are applied, such as for hazardous waste disposal, exploring remote environments, or medical procedures. Metrics that are frequently applied to measure the degree of fault tolerance that a manipulator possesses have been focused on local kinematic properties of the Jacobian matrix, e.g., the worst-case manipulability (or relative manipulability) following a locked joint failure. These measures are useful for characterizing the manipulator's dexterity at a given configuration, however, they do not provide a measure for completion of a task within a specified workspace. This paper extends the use of local fault tolerance measures by using them to compute a global measure using a probabilistic analysis. Specifically, we compute cumulative density functions (CDF) that can be used to specify a desired fault tolerance threshold for completion of a task within a specified workspace region. We further show how this CDF can be improved by utilizing redundancy to optimize manipulator configurations throughout this region, using a modified nine degree of freedom Mitsubishi PA10-7C.

Infectious disease prevention and control are important in improving, promoting and protecting the health of communities. Epidemiological data analysis plays a crucial role in disease prevention and control. Conventional methods such as moving average or autoregressive analysis normally require the assumption of stationarity, which is often violated in epidemiologic time series. This paper proposes the fusion of neural networks, fuzzy systems and genetic algorithms, with the aim to strengthen the modeling power for epidemiological dynamics. We deploy an additive fuzzy system into a neural network architecture in order to incorporate recurrent nodes to enable the fuzzy system to handle temporal data. The genetic algorithm is employed to optimize the fuzzy rule structure before supervised training is applied to adjust parameters. As epidemiological time series exhibit complex behavior and possibly cyclic patterns, the addition of recurrent nodes to the fuzzy system improves the modeling capability. The proposed model dominates the benchmark feedforward neural network and adaptive neuro-fuzzy inference system model regarding modeling performance. Through real applications for epidemiologic time series modeling, the fusion of soft computing techniques offer accurate forecasts that have considerable meaning in planning infectious disease-control activities.

This paper addresses a major challenge in data-driven haptic modeling of deformable objects. Data-driven modeling is done for specific objects and is difficult to generalize for nearly isometric objects that have similarities in semantics or topology. This limitation prevents the wide use of the data-driven modeling techniques when compared with parametric methods such as finite element methods. The proposed solution is to incorporate deformation transfer methods when processing similar instances. The contributions of this work are focused on the novel automatic shape correspondence method that overcomes the problems of symmetry and semantics presence requirement. The results shows that the proposed algorithm can efficiently calculate the correspondence and transfer deformations for a range of similar 3D objects.

Coal handling is a complex process involving different correlated and highly dependent operations such as selecting appropriate product types, planning stockpiles, scheduling stacking and reclaiming activities and managing train loads. Planning these operations manually is time consuming and can result in non-optimized schedules as future impact of decisions may not be appropriately considered. This paper addresses the operational scheduling of the continuous coal handling problem with multiple conflicting objectives. As the problem is NP-hard in nature, an effective heuristic is presented for planning stockpiles and scheduling resources to minimize delays in production and the coal age in the stockyard. A model of stockyard operations within a coal mine is described and the problem is formulated as a Bi-Objective Optimization Problem (BOOP). The algorithm efficacy is demonstrated on different real-life data scenarios. Computational results show that the solution algorithm is effective and the coal throughput is substantially impacted by the conflicting objectives. Together, the model and the proposed heuristic, can act as a decision support system for the stockyard planner to explore the effects of alternative decisions, such as balancing age and volume of stockpiles, and minimizing conflicts due to stacker and reclaimer movements.

The current marker-based augmented reality (AR) rendering has demonstrated good results for online and special purpose applications such as computer-assisted tasks and virtual training. However, it fails to deliver a solution for off-line and generic applications such as augmented books, newspapers, and scientific articles. These applications feature too many markers that imposes a serious challenge on the recognition module. This paper proposes a novel design for augmented reality markers. The proposed marker design employs multi-view orthographic projection to derive dense depth maps and relies on splats rendering for visualisation. The main objective is to interpret the marker rather than recognising it. The proposed marker design stores six depth map projections of the 3D model along with their coloured textures in the marker.

The understanding of cell manipulation, for example in microinjection, requires an accurate model of the cells. Motivated by this important requirement, a 3D particle-based mechanical model is derived for simulating the deformation of the fish egg membrane and the corresponding cellular forces during micro robotic cell injection. The model is formulated based on the kinematic and dynamic of spring-damper configuration with multi-particle joints considering the visco-elastic fluidic properties. It simulates the indentation force feedback as well as cell visual deformation during microinjection. A preliminary simulation study is conducted with different parameter configurations. The results indicate that the proposed particle-based model is able to provide similar deformation profiles as observed from a real microinjection experiment of the zebra fish embryo published in the literature. As a generic modelling approach is adopted, the proposed model also has the potential in applications with different types of manipulation such as micropipette cell aspiration.

In this research, we study the effect of feature selection in the spike detection and sorting accuracy. We introduce a new feature representation for neural spikes from multichannel recordings. The features selection plays a significant role in analyzing the response of brain neurons. The more precise selection of features leads to a more accurate spike sorting, which can group spikes more precisely into clusters based on the similarity of spikes. Proper spike sorting will enable the association between spikes and neurons. Different with other threshold-based methods, the cepstrum of spike signals is employed in our method to select the candidates of spike features. To choose the best features among different candidates, the Kolmogorov-Smirnov (KS) test is utilized. Then, we rely on the super paramagnetic method to cluster the neural spikes based on KS features. Simulation results demonstrate that the proposed method not only achieve more accurate clustering results but also reduce computational burden, which implies that it can be applied into real-time spike analysis.

Control of polymerization reactors is a challenging issue for researchers due to the complex reaction mechanisms. A lot of reactions occur simultaneously during polymerization. This leads to a polymerization system that is highly nonlinear in nature. In this work, a nonlinear advanced controller, named fuzzy logic controller (FLC), is developed for monitoring the batch free radical polymerization of polystyrene (PS) reactor. Temperature is used as an intermediate control variable to control polymer quality, because the products quality and quantity of polymer are directly depends on temperature. Different FLCs are developed through changing the number of fuzzy membership functions (MFs) for inputs and output. The final tuned FLC results are compared with the results of another advanced controller, named neural network based model predictive controller (NN-MPC). The simulation results reveal that the FLC performance is better than NN-MPC in terms of quantitative and qualitative performance criterion.

Uncertainty of data affects decision making process as it increases the risk and the costs of the decision. One of the challenges in minimizing the impact of the bounded uncertainty on any scheduling algorithm is the lack of information, as only the upper bound and the lower bound are provided without any known probability or membership function. On the contrary, probabilistic uncertainty can use probability distributions and fuzzy uncertainty can use the membership function. McNaughton's algorithm is used to find the optimum schedule that minimizes the make span taking into consideration the preemption of tasks. The challenge here is the bounded inaccuracy of the input parameters for the algorithm, namely known as bounded uncertain data. This research uses interval programming to minimise the impact of bounded uncertainty of input parameters on McNaughton's algorithm, it minimises the uncertainty of the cost function estimate and increase its optimality. This research is based on the hypothesis that doing the calculations on interval values then approximate the end result will produce more accurate results than approximating each interval input then doing numerical calculations.

Making decision usually occurs in the state of being uncertain. These kinds of problems often expresses in a formula as optimization problems. It is desire for decision makers to find a solution for optimization problems. Typically, solving optimization problems in uncertain environment is difficult. This paper proposes a new hybrid intelligent algorithm to solve a kind of stochastic optimization i.e. dependent chance programming (DCP) model. In order to speed up the solution process, we used support vector machine regression (SVM regression) to approximate chance functions which is the probability of a sequence of uncertain event occurs based on the training data generated by the stochastic simulation. The proposed algorithm consists of three steps: (1) generate data to estimate the objective function, (2) utilize SVM regression to reveal a trend hidden in the data (3) apply genetic algorithm (GA) based on SVM regression to obtain an estimation for the chance function. Numerical example is presented to show the ability of algorithm in terms of time-consuming and precision.

In this paper, we apply a computational intelligence method for tunnelling settlement prediction. A supervised feed forward back propagation neural network is used to predict the surface settlement during twin-tunnelling while surface buildings are considered in the models. The performance of the statistical neural network structure is tested on a dataset provided by numerical parametric studies conducted by ABAQUS software based on Shiraz line 1 metro data. Six input variables are fed to neural network model for predicting the surface settlement. These include tunnel center depth, distance between centerlines of twin tunnels, buildings width and building bending stiffness, and building weight and distance to tunnel centerline. Simulation results indicate that the proposed NN models are able to accurately predict the surface settlement.

The fact that medical images have redundant information is exploited by researchers for faster image acquisition. Sample set or number of measurements were reduced in order to achieve rapid imaging. However, due to inadequate sampling, noise artefacts are inevitable in Compressive Sensing (CS) MRI. CS utilizes the transform sparsity of MR images to regenerate images from under-sampled data. Locally sparsified Compressed Sensing is an extension of simple CS. It localises sparsity constraints for sub-regions rather than using a global constraint. This paper, presents a framework to use local CS for improving image quality without increasing sampling rate or without making the acquisition process any slower. This was achieved by exploiting local constraints. Localising image into independent sub-regions allows different sampling rates within image. Energy distribution of MR images is not even and most of noise occurs due to under-sampling in high energy regions. By sampling sub-regions based on energy distribution, noise artefacts can be minimized. Experiments were done using the proposed technique. Results were compared with global CS and summarized in this paper.

In planning an s-curve speed profile for a computer numerical control (CNC) machine, centripetal acceleration and its derivative have to be considered. In a CNC machine, these quantities dictate how much voltage and current should be applied to servo motor windings. In this paper, the necessity of considering centripetal jerk in speed profile generation especially in the look-ahead mode is explained. It is demonstrated that the magnitude of centripetal jerk is proportional to the curvature derivative of the path known as "sharpness". It is also explained that a proper limited jerk motion is only possible when a G2-continuous machining path is planned. Then using a simplified mathematical representation of clothoids, a novel method for approximating a given path with a sequence of clothoid segments is proposed. Using this method, a semi-parallel G2-continuous path with adjustable deviation from the original shape for a sample machining contour is generated. Maximum permissible feed rate for the generated path is also calculated.

This work proposes a novel framework to extract compact and discriminative features from Electrocardiogram (ECG) signals for human identification based on sparse representation of local segments. Specifically, local segments extracted from an ECG signal are projected to a small number of basic elements in a dictionary, which is learned from training data. A final representation is extracted by performing a max pooling procedure over all the sparse coefficient vectors in the ECG signal. Unlike most of existing methods for human identification from ECG signals which require segmentation of individual heartbeats or extraction of fiducial points, the proposed method does not need to segment individual heartbeats or detect any fiducial points. The method achieves an 99.48% accuracy on a 100 subjects dataset constructed from a publicly available database, which demonstrates that both local and global structural information are well captured to characterize the ECG signals.

This paper investigates a new approach for solving the multiobjective job shop scheduling problem, namely the Cuckoo Search (CS) approach. The requirement is to schedule jobs on a single machine so that the total material waste is minimised as well as the total tardiness time. The material waste is quantified in terms of saving factors to show the reduction in material that can be achieved when producing two jobs with the same materials in sequence. The estimated saving factor is used to calculate a cost savings for each job based on its material type. A formulation of multiobjective optimisation problems is adopted to generate the set of schedules that maximise the overall cost savings and minimise the total tardiness time, where all trade-offs are considered for the two conflicting objectives. A Pareto Archived Multiobjective Cuckoo Search (PAMOCS) is developed to find the set of nondom-inated Pareto optimal solutions. The solution accuracy of PAMOCS is shown by comparing the closeness of the obtained solutions to the true Pareto front generated by the complete enumeration method. Results show that CS is a very effective and promising technique to solve job shop scheduling problems.

Bounded uncertainty is a major challenge to real life scheduling as it increases the risk and cost depending on the objective function. Bounded uncertainty provides limited information about its nature. It provides only the upper and the lower bounds without information in between, in contrast to probability distributions and fuzzy membership functions. Bratley algorithm is usually used for scheduling with the constraints of earliest start and due-date. It is formulated as 1|rj, dj|Cmax. The proposed research uses interval computation to minimize the impact of bounded uncertainty of processing times on Bratley's algorithm. It minimizes the uncertainty of the estimate of the objective function. The proposed concept is to do the calculations on the interval values and approximate the end result instead of approximating each interval then doing numerical calculations. This methodology gives a more certain estimate of the objective function.

This study proposes a novel non-parametric method for construction of prediction intervals (PIs) using interval type-2 Takagi-Sugeno-Kang fuzzy logic systems (IT2 TSK FLSs). The key idea in the proposed method is to treat the left and right end points of the type-reduced set as the lower and upper bounds of a PI. This allows us to construct PIs without making any special assumption about the data distribution. A new training algorithm is developed to satisfy conditions imposed by the associated confidence level on PIs. Proper adjustment of premise and consequent parameters of IT2 TSK FLSs is performed through the minimization of a PI-based objective function, rather than traditional error-based cost functions. This new cost function covers both validity and informativeness aspects of PIs. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Quantitative measures are applied for assessing the quality of PIs constructed using IT2 TSK FLSs. The demonstrated results for four benchmark case studies with homogenous and heterogeneous noise clearly show the proposed method is capable of generating high quality PIs useful for decision-making.

Pedestrian steering activity is a perception-based decision making process that involves interaction with the surrounding environment and insight into environmental stimuli. There are many stimuli within the environment that influence pedestrian wayfinding behaviour during walking activities. However, compelling factors such as individual physical and psychological characteristics and trip intention cause the behaviour become a very fuzzy concept. In this paper pedestrian steering behaviour is modelled using a fuzzy logic approach. The objective of this research is to simulate pedestrian walking paths in indoor public environments during normal and non-panic situations. The proposed algorithm introduces a fuzzy logic framework to predict the impact of perceived attractive and repulsive stimuli, within the pedestrian's field of view, on movement direction. Environmental stimuli are quantified using the social force method. The algorithm is implemented in a simulated area of an office corridor consist of a printer and exit door. Stochastic simulation using the proposed fuzzy algorithm generated realistic walking trajectories, contour map of dynamic change of environmental effects in each step of movement and high flow areas in the corridor.

This paper introduces a novel 5-DOF parallel manipulator with a rotation-symmetric arm system. The manipulator is unorthodox since one degree of freedom of its manipulated platform is unconstrained. Such a manipulator is still useful in a wide range of applications utilizing a rotation-symmetric tool. The manipulator workspace is analyzed for singularities and collisions. The rotation-symmetric arm system leads to a large positional workspace in relation to the footprint of the manipulator. With careful choice of structural parameters, the rotational workspace of the tool is also sizeable.

Super-resolution is a method of post-processing image enhancement that increases the spatial resolution of video or images. Existing super-resolution techniques apply only to images captured of a planar scene. This paper aims to extend super-resolution concepts from the 2D domain to the 3D domain, drawing on ideas from both super-resolution and multi-view geometry, two fields of research that until now have predominantly been studied in isolation. 2D super-resolution methods are not without their complexities and limitations. However, once multiple views of a scene are considered within a super-resolution framework, a new range of issues arise that must also be resolved. For example, when input images of a scene with variation in depth are considered, it is no longer clear how and where the images should be registered. This paper describes the use of sparse 3D reconstruction in order to `register' the input images, which are then transferred to a novel image plane and combined to increase the perceived detail in the scene. Experimental results using real images captured from generally positioned input cameras are presented.

Accurate forecasting of wind farm power generation is essential for successful operation and management of wind farms and to minimize risks associated with their integration into energy systems. However, due to the inherent wind intermittency, wind power forecasts are highly prone to error and often far from being perfect. The purpose of this paper is to develop statistical methods for quantifying uncertainties associated with wind power generation forecasts. Prediction intervals (PIs) with a prescribed confidence level are constructed using the delta and bootstrap methods for neural network forecasts. The moving block bootstrap method is applied to preserve the correlation structure in wind power observations. The effectiveness and efficiency of these two methods for uncertainty quantification is examined using two month datasets taken from a wind farm in Australia. It is demonstrated that while all constructed PIs are theoretically valid, bootstrap PIs are more informative than delta PIs, and are therefore more useful for decision-making.

Training of optometrists is traditionally achieved under close supervision of peers and superiors. With the rapid advancement in technology, medical procedures are performed more efficiently and effectively, resulting in faster recovery times and less trauma to the patient. However, application of this technology has made it difficult to effectively demonstrate and teach these manual skills as the education is now a combination of not only the medical procedure but also the use of the technology. In this paper we propose to increase the training capabilities of optometry students through haptically-enabled single-point and multi-point training tools as well as augmented reality techniques. Haptics technology allows a human to touch and feel virtual computer models as though they are real. Through physical connection to the operator, haptic devices are considered to be personal robots that are capable of improving the human-computer interaction with a virtual environment. These devices have played an increasing role in developing expertise, reducing instances of medical error and reducing training costs. A haptically-enabled virtual training environment, integrated with an optometry slit lamp instrument can be used to teach cognitive and manual skills while the system tracks the performance of each individual. These interactions would ideally replicate every aspect of the real procedure, consequently preparing the trainee for every possible scenario, without risking the health of a real patient.

Computer haptics has so far been performed on a personal computer (PC). Off the shelf haptic devices provide only PC interfaces and software drivers for control and communication. The new wave of high capable tablet PCs and high end smart phones introduced new platforms for haptic applications. The major problem was to communicate wirelessly to provide user convenience and support mobility which is an essential feature for these platforms. In this paper we provide a wireless layered communication protocol and a hardware setup that enables off the shelf haptic devices to communicate wirelessly with a mobile device. The layers in the protocol enable the change of any hardware components without affecting the data flow. However, the adoption of the wireless interface instead of the wired one comes with the price of speed. Haptic refresh loops require a relatively high refresh rate of 1000 Hz compared to graphics loop which require between 30 and 60 only. An interpolation algorithm was demonstrated to compensate the latency and secure a stable user experience. The introduced setup was tested against portable environments and the users could perform similar functionalities to what are available on a wired setup to a PC.

Airport baggage handling systems are a critical infrastructure component within major airports, and essential to ensure smooth luggage transfer while preventing dangerous material being loaded onto aircraft. This paper proposes a standard set of measures to assess the expected performance of a baggage handling system through discrete event simulation. These evaluation methods also have application in the study of general network systems. Results from the application of these methods reveal operational characteristics of the studied BHS, in terms of metrics such as peak throughput, in-system time and system recovery time.

Magnetic Resonance Imaging (MRI) is one of the prominent medical imaging techniques. This process is time-consuming and can take several minutes to acquire one image. The aim of this research is to reduce the imaging process time of MRI. This issue is addressed by reducing the number of acquired measurements using theory of Compressive Sensing (CS). Compressive Sensing exploits sparsity in MR images. Randomly under sampled k-space generates incoherent noise which can be handled using a nonlinear image reconstruction method. In this paper, a new framework is presented based on the idea to exploit non-uniform nature of sparsity in MR images, where local sparsity constrains were used instead of traditional global constraint, to further reduce the sample set. Experimental results and comparison with CS using global constraint are demonstrated.

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy may drop due to presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with extra degrees of freedom, are an excellent tool for handling prevailing uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models appropriately approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks used in this study.

The design of locally optimal fault-tolerant manipulators has been previously addressed via adding constraints on the bases of a desired null space to the design constraints of the manipulators. Then by algebraic or numeric solution of the design equations, the optimal Jacobian matrix is obtained. In this study, an optimal fault-tolerant Jacobian matrix generator is introduced from geometric properties instead of the null space properties. The proposed generator provides equally fault-tolerant Jacobian matrices in R3that are optimally fault tolerant for one or two locked joint failures. It is shown that the proposed optimal Jacobian matrices are directly obtained via regular pyramids. The geometric approach and zonotopes are used as a novel tool for determining relative manipulability in the context of fault-tolerant robotics and for bringing geometric insight into the design of optimal fault-tolerant manipulators.

The bootstrap method is one of the most widely used methods in literature for construction of confidence and prediction intervals. This paper proposes a new method for improving the quality of bootstrap-based prediction intervals. The core of the proposed method is a prediction interval-based cost function, which is used for training neural networks. A simulated annealing method is applied for minimization of the cost function and neural network parameter adjustment. The developed neural networks are then used for estimation of the target variance. Through experiments and simulations it is shown that the proposed method can be used to construct better quality bootstrap-based prediction intervals. The optimized prediction intervals have narrower widths with a greater coverage probability compared to traditional bootstrap-based prediction intervals.

Evolving artificial neural networks has attracted much attention among researchers recently, especially in the fields where plenty of data exist but explanatory theories and models are lacking or based upon too many simplifying assumptions. Financial time series forecasting is one of them. A hybrid model is used to forecast the hourly electricity price from the California Power Exchange. A collaborative approach is adopted to combine ANN and evolutionary algorithm. The main contributions of this thesis include: Investigated the effect of changing values of several important parameters on the performance of the model, and selected the best combination of these parameters; good forecasting results have been obtained with the implemented hybrid model when the best combination of parameters is used. The lowest MAPE through a single run is 5.28134%. And the lowest averaged MAPE over 10 runs is 6.088%, over 30 runs is 6.786%; through the investigation of the parameter period, it is found that by including “future values” of the homogenous moments of the instant being forecasted into the input vector, forecasting accuracy is greatly enhanced. A comparison of results with other works reported in the literature shows that the proposed model gives superior performance on the same data set.

This paper focuses on the choice of a supervised learning algorithm and possible data preprocessing in the domain of data-driven haptic simulation. This is done through a comparison of the performance of different supervised learning techniques with and without data preprocessing. The simulation of haptic interactions with deformable objects using data-driven methods has emerged as an alternative to parametric methods. The accuracy of the simulation depends on the empirical data and the learning method. Several methods were suggested in the literature and here we provide a comparison between their performance and applicability to this domain. We selected four examples to be compared: singular learning mechanism which is artificial neural networks (ANN), attribute selection followed by ANN learning process, ensemble of multiple learning techniques, and attribute selection followed by the learning ensemble. These methods performance was compared in the domain of simulating multiple interactions with a deformable object with nonlinear material behavior.

Artificial skins exhibit different mechanical properties in compare to natural skins. This drawback makes physical interaction with artificial skins to be different from natural skin. Increasing the performance of the artificial skins for robotic hands and medical applications is addressed in the present paper. The idea is to add active controls within artificial skins in order to improve their dynamic or static behaviors. This directly results into more interactivity of the artificial skins. To achieve this goal, a piece-wise linear anisotropic model for artificial skins is derived. Then a model of matrix of capacitive MEMS actuators for the control purpose is coupled with the model of artificial skin. Next an active surface shaping control is applied through the control of the capacitive MEMS actuators which shapes the skin with zero error and in a desired time. A simulation study is presented to validate the idea of using MEMS actuator for active artificial skins. In the simulation, we actively control 128 capacitive micro actuators for an artificial fingertip. The fingertip provides the required shape in a required time which means the dynamics of the skin is improved.

Mission and path planning for multiple robots in dynamic environments is required when multiple mobile robots or unmanned vehicles are used for geographically distributed tasks. Assigning tasks and paths for robots for cooperatively accomplishing a mission of reaching to number of target points are addressed in this paper. The methodology that is proposed is based on using an adjustable force field which is suitable for dynamic environment. From the force field analysis, the decisions to assign tasks for each robot are then made. The force field is also used to plan a collision free path for each robot. Adjustable weights for the force field model are proposed to satisfy the constraints of the motion. In this research, the constraints are the cooperation of the robots, the precedence between the targets and between robots, and the discrimination between different obstacles. Two simulations for mission and path planning in 2D and 3D dynamic spaces with multiple robots are presented based on the proposed adjustable force filed. The result of the mission and path planning for three robots cooperatively doing eight target points are shown.

Design of locally optimal fault tolerant manipulators has been recently addressed via using the constraints of the desired null space for the Jacobian matrix of the manipulators. In the present paper the Jacobian matrices for optimal fault tolerance are presented based on geometric properties of column vectors instead of the null space. They are equally fault tolerant to a single joint failure from the worst-case relative manipulability and worst-case dexterity points of view. The optimality is achieved through a symmetric distribution of points on spheres.

Firstly, this paper introduces the OzTug mobile robot developed to autonomously manoeuvre large loads within a manufacturing environment. The mobile robot utilises differential drive and necessary design criteria includes low-cost, mechanical robustness, and the abPility to manoeuvre loads ranging up to 2000kg. The robot is configured to follow a predefined trajectory while maintaining the forward velocity of a user-specified velocity profile. A vision-based fuzzy logic line following controller enables the robot to track the paths on the floor of the manufacturing environment. Secondly, in order to tow large loads along predefined paths three different robot-load configurations are proposed. Simulation within the Webots environment was performed in order to empirically evaluate the three different robot-load configurations. The simulation results demonstrate the cost-performance trade-off of two of the approaches.

Waqaa! And welcome to Anchorage, Alaska USA. It is our distinct pleasure to greet you on behalf of the IEEE Systems, Man, and Cybernetics Society and its annual flagship conference, the IEEE International Conference on Systems, Man, and Cybernetics. This year the conference brings us all to the City of Lights and Flowers where we invite your enthusiastic participation in our yearly international forum on latest innovations, state-of-the-art, ideas, and advances in all aspects of systems science & engineering, human-machine systems, and cybernetics. With the conference fully contained within the Hilton Anchorage you are in great hands of Alaskan hospitality. While in Anchorage, we encourage you to also partake in the “Big Wild Life,” that it offers, both within the city and in the vast wilderness outside.

Human action recognition has been attracted lots of interest from computer vision researchers due to its various promising applications. In this paper, we employ Pyramid Histogram of Orientation Gradient (PHOG) to characterize human figures for action recognition. Comparing to silhouette-based features, the PHOG descriptor does not require extraction of human silhouettes or contours. Two state-space models, i.e., Hidden Markov Model (HMM) and Conditional Random Field (CRF), are adopted to model the dynamic human movement. The proposed PHOG descriptor and the state-space models with respect to different parameters are tested using a standard dataset. We also testify the robustness of the method with respect to various unconstrained conditions and viewpoints. Promising experimental result demonstrates the effectiveness and robustness of our proposed method.

The majority of commercially available haptic devices offer a single point of haptic interaction. These devices are limited when it is desirable to grasp with multiple fingers in applications including virtual training, telesurgery and telemanipulation. Multipoint haptic devices serve to facilitate a greater range of interactions. This paper presents a gripper attachment to enable multi-point haptic grasping in virtual environments. The approach employs two Phantom Omni haptic devices to independently render forces to the user's thumb and other fingers. Compared with more complex approaches to multi-point haptics, this approach provides a number of advantages including low-cost, reliability and ease of programming. The ability of the integrated multi-point haptic platform to interact within a CHAI 3D virtual environment is also presented.

The performance of an adaptive neurofuzzy inference system (ANFIS) significantly drops when uncertainty exists in the data or system operation. Prediction intervals (PIs) can quantify the uncertainty associated with ANFIS point predictions. This paper first presents a methodology to adapt the delta technique for the construction of PIs for outcomes of the ANFIS models. As the ANFIS models are linear in their consequent part, the ANFIS-based PIs are computationally less expensive than neural network (NN)-based PIs. Second, this paper proposes a method to optimize ANFIS-based PIs. A new PI-based cost function is developed for the training of the ANFIS models. A simulated annealing-based algorithm is applied to minimize the new nonlinear cost function and adjust the premise and consequent parameters of the ANFIS model. Using three real-world case studies, it is shown that ANFIS-based PIs are computationally less expensive than NN-based PIs. The application of the proposed optimization algorithm leads to better quality PIs than optimized NN-based PIs.

The accurate prediction of travel times is desirable but frequently prone to error. This is mainly attributable to both the underlying traffic processes and the data that are used to infer travel time. A more meaningful and pragmatic approach is to view travel time prediction as a probabilistic inference and to construct prediction intervals (PIs), which cover the range of probable travel times travelers may encounter. This paper introduces the delta and Bayesian techniques for the construction of PIs. Quantitative measures are developed and applied for a comprehensive assessment of the constructed PIs. These measures simultaneously address two important aspects of PIs: 1) coverage probability and 2) length. The Bayesian and delta methods are used to construct PIs for the neural network (NN) point forecasts of bus and freeway travel time data sets. The obtained results indicate that the delta technique outperforms the Bayesian technique in terms of narrowness of PIs with satisfactory coverage probability. In contrast, PIs constructed using the Bayesian technique are more robust against the NN structure and exhibit excellent coverage probability.

Prediction intervals (PIs) have been proposed in the literature to provide more information by quantifying the level of uncertainty associated to the point forecasts. Traditional methods for construction of neural network (NN) based PIs suffer from restrictive assumptions about data distribution and massive computational loads. In this paper, we propose a new, fast, yet reliable method for the construction of PIs for NN predictions. The proposed lower upper bound estimation (LUBE) method constructs an NN with two outputs for estimating the prediction interval bounds. NN training is achieved through the minimization of a proposed PI-based objective function, which covers both interval width and coverage probability. The method does not require any information about the upper and lower bounds of PIs for training the NN. The simulated annealing method is applied for minimization of the cost function and adjustment of NN parameters. The demonstrated results for 10 benchmark regression case studies clearly show the LUBE method to be capable of generating high-quality PIs in a short time. Also, the quantitative comparison with three traditional techniques for prediction interval construction reveals that the LUBE method is simpler, faster, and more reliable.

This paper evaluates the four leading techniques proposed in the literature for construction of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian, bootstrap, and mean-variance estimation (MVE) methods are reviewed and their performance for generating high-quality PIs is compared. PI-based measures are proposed and applied for the objective and quantitative assessment of each method's performance. A selection of 12 synthetic and real-world case studies is used to examine each method's performance for PI construction. The comparison is performed on the basis of the quality of generated PIs, the repeatability of the results, the computational requirements and the PIs variability with regard to the data uncertainty. The obtained results in this paper indicate that: 1) the delta and Bayesian methods are the best in terms of quality and repeatability, and 2) the MVE and bootstrap methods are the best in terms of low computational load and the width variability of PIs. This paper also introduces the concept of combinations of PIs, and proposes a new method for generating combined PIs using the traditional PIs. Genetic algorithm is applied for adjusting the combiner parameters through minimization of a PI-based cost function subject to two sets of restrictions. It is shown that the quality of PIs produced by the combiners is dramatically better than the quality of PIs obtained from each individual method.

Correspondence estimation in one of the most active research areas in the field of computer vision and number of techniques has been proposed, possessing both advantages and shortcomings. Among the techniques reported, multiresolution analysis based stereo correspondence estimation has gained lot of research focus in recent years. Although, the most widely employed medium for multiresolution analysis is wavelets and multiwavelets bases, however, relatively little work has been reported in this context. In this work we have tried to address some of the issues regarding the work done in this domain and the inherited shortcomings. In the light of these shortcomings, we propose a new technique to overcome some of the flaws that could have significantly impact on the algorithm performance and has not been addressed in the earlier propositions. Proposed algorithm uses multiresolution analysis enforced with wavelets/multiwavelts transform modulus maxima to establish correspondences between the stereo pair of images. Variety of wavelets and multiwavelets bases, possessing distinct properties such as orthogonality, approximation order, short support and shape are employed to analyse their effect on the performance of correspondence estimation. The idea is to provide knowledge base to understand and establish relationships between wavelets and multiwavelets properties and their effect on the quality of stereo correspondence estimation.

In manual cell injection the operator relies completely on visual information for task feedback and is subject to extended training times as well as poor success rates and repeatability. From this perspective, enhancing human-in-the-loop intracellular injection through haptic interaction offers significant benefits. This paper outlines two haptic virtual fixtures aiming to assist the human operator while performing cell injection. The first haptic virtual fixture is a parabolic force field designed to assist the operator in guiding the micropipette's tip to a desired penetration point on the cell's surface. The second is a planar virtual fixture which attempts to assist the operator from moving the micropipette's tip beyond the deposition target location inside the cell. Preliminary results demonstrate the operation of the haptically assisted microrobotic cell injection system.

This paper presents a three-dimensional (3D) computational fluid dynamic simulation of a biomimetic robot fish. Fluent and user-defined function (UDF) is used to define the movement of the robot fish and the Dynamic Mesh is used to mimic the fish swimming in water. Hydrodynamic analysis is done in this paper too. The aim of this study is to get comparative data about hydrodynamic properties of those guidelines to improve the design, remote control and flexibility of the underwater robot fish.

Mobile robots are providing great assistance operating in hazardous environments such as nuclear cores, battlefields, natural disasters, and even at the nano-level of human cells. These robots are usually equipped with a wide variety of sensors in order to collect data and guide their navigation. Whether a single robot operating all sensors or a swarm of cooperating robots operating their special sensors, the captured data can be too large to be transferred across limited resources (e.g. bandwidth, battery, processing, and response time) in hazardous environments. Therefore, local computations have to be carried out on board the swarming robots to assess the worthiness of captured data and the capacity of fused information in a certain spatial dimension as well as selection of proper combination of fusion algorithms and metrics. This paper introduces to the concepts of Type-I and Type-II fusion errors, fusion capacity, and fusion worthiness. These concepts together form the ladder leading to autonomous fusion systems.

This study aims at developing abstract metamodels for approximating highly nonlinear relationships within a metal casting plant. Metal casting product quality nonlinearly depends on many controllable and uncontrollable factors. For improving the productivity of the system, it is vital for operation planners to predict in advance the amount of high quality products. Neural networks metamodels are developed and applied in this study for predicting the amount of saleable products. Training of metamodels is done using the Levenberg-Marquardt and Bayesian learning methods. Statistical measures are calculated for the developed metamodels over a grid of neural network structures. Demonstrated results indicate that Bayesian-based neural network metamodels outperform the Levenberg-Marquardt-based metamodels in terms of both prediction accuracy and robustness to the metamodel complexity. In contrast, the latter metamodels are computationally less expensive and generate the results more quickly.

The heart muscle of a cardiac arrest victim continues to accumulate damage throughout its lifetime. This reduces the heart's ability to pump sufficient oxygen and nutrient blood to meet the body's needs. Medical researchers have shown that direct injection of pre-harvested skeletal myoblast cells into the heart can restore some muscle function [1]. This operative procedure usually necessitates the surgeon to open a patient's chest. The open chest procedure is usually a lengthy process and often extends the recovery time of the patient. Alternatively, a high accuracy surgical aid robotic system can be used to assist the thoracoscopic surgery [2][3]. While the robotic surgical method aids faster patient recovery, a less experienced surgeon can potentially cause damage to surrounding tissue. This paper presents a study into the development of a virtual haptically-enabled heart myoblast injection simulation environment, which can be used to train new surgeons to get hands on experience with the process. The paper also discusses the development of a generic constraint motion technique for needle insertion. Experiments on human performance measures and efficacy, while interacting with haptic feedback training models, are also presented. The experiment involved 10 operators, with each person repeating the needle insertion and injection 10 times. A notable improvement in the task execution time with the number of repetitions was observed. Operators improved their time by up to 300% compared to their first training attempt for a static heart scenario. Under a dynamic heart motion, operator's performance was slightly lower, with the successful rate of completing the experiment reduced from 84% to 75%.

One well acknowledged drawback of traditional parallel kinematic machines (PKMs) is that the ratio of accessible workspace to robot footprint is small for these structures. This is most likely a contributing reason why relatively few PKMs are used in industry today. The SCARA-Tau structure is a parallel robot concept designed with the explicit goal of overcoming this limitation and developing a PKM with a workspace similar to that of a serial type robot of the same size. This paper shows for the first time how a proposed variant of the SCARA-Tau PKM can improve the usability of this robot concept further by significantly reducing the dependence between tool platform position and orientation of the original concept. The inverse kinematics of the proposed variant is derived and a comparison is made between this structure and the original SCARA-Tau concept, both with respect to platform orientation changes and workspace.

When an assistant robotic manipulator cooperatively performs a task with a human and the task is required to be highly reliable, then fault tolerance is essential. To achieve the fault tolerance force within the human robot cooperation, it is required to map the effects of the faulty joint of the robot into the manipulator's healthy joints' torque space and the human force. The objective is to optimally maintain the cooperative force within the human robot cooperation. This paper aims to analyze the fault tolerant force within the cooperation and two frameworks are proposed. Then they have been validated through a fault scenario. Finally, the minimum force jump which is the optimal fault tolerance has been achieved.

This study contributes to work in baggage handling system (BHS) control, specifically dynamic bag routing. Although studies in BHS agent-based control have examined the need for intelligent control, but there has not been an effort to explore the dynamic routing problem. As such, this study provides additional insight into how agents can learn to route in a BHS. This study describes a BHS status-based routing algorithm that applies learning methods to select criteria based on routing decisions. Although numerous studies have identified the need for dynamic routing, little analytic attention has been paid to intelligent agents for learning routing tables rather than manual creation of routing rules. We address this issue by demonstrating the ability of agents to learn how to route based on bag status, a robust method that is able to function in a variety of different BHS designs.

This paper discusses the duality between image fusion algorithms and quality metrics. It discusses odd cases where some quality metrics fail to estimate the added information and proposes a duality index that measures how suitable the metrics are to fusion algorithms. The proposed duality index serves as an objective function against which combinations of fusion algorithms, metrics, and their parameters are tested.

Multiscale image fusion fuses information from two source images at different levels of detail. This paper discusses the limits of multiscale image fusion from an algebraic perspective and discusses the constraints that guide the identification of zero and infinity images and also their impact on fusion algorithms and metrics.

A rich literature discussing techniques for adopting neural networks for metamodelling of complex systems exists. The main focus in many studies conducted so far has been on training and utilising neural networks as point estimators/predictors. Uncertainties prevailing within complex systems and dependencies amongst constituent entities are real threats for prediction performance of these types of metamodels. From a practical point of view, an indication of prediction accuracy is necessary before making a decision based on results yielded by a metamodel. In this paper we adopt neural network metamodels for constructing prediction intervals of stochastic system performance measures. Upper and lower bounds of a prediction interval are computed such that the real system performance will lie between them with a high probability. Demonstrated results for a real world case study show that the constructed prediction intervals cover the targets, are more informative and more suited for decision making, when compared with point predictions.

Finding optimal structures for neural networks is remains an open problem, despite the rich array of literature on the application of neural networks in different areas of science and engineering. The stochastic nature of operations common in complex systems makes point prediction performance of neural network metamodels an additional challenge. We propose a method for selecting the best structure of a neural network metamodel. For selecting the network structure, the new method uses interval prediction capability of neural networks and chooses a topology that yields the narrowest prediction band for targets. This is an improvement on traditional criteria, such as mean square error or mean absolute percentage error. As a case study, the interval prediction method is applied to a metamodel of a complex system composed of many inextricably interconnected entities and stochastic processes. The demonstrated results expressly show that selecting the network structure based on the proposed method yields more reliable estimates.

This paper introduces a novel approach to multi-point multi-hand haptic teleoperation of a mobile robot. The work extends upon existing approaches to provide the teleoperator with the ability to utilise one hand to achieve intuitive haptic control of the mobile robot while utilising the other hand to intuitively control the orientation (and corresponding visual information) of the robot's onboard camera. This work begins with the introduction of the intuitive haptic conical control surface which extends upon existing approaches to provide the teleoperator with an intuitive method for issuing robot motion commands whilst simultaneously displaying real-time task-dependent haptic augmentation. A novel multi-point haptic gripper prototype is then introduced providing the basis for the teleoperator to haptically utilise the camera-in-hand metaphor for intuitive control of the visual information provided by the robot's onboard camera. The distinct advantages justifying the individual approaches are discussed and it is suggested that using dual haptic modalities the teleoperator can utilise both approaches simultaneously for intuitive haptic mobile robotic teleoperation. This work represents the first stage of a continuing research project and provides innovative contributions facilitating the presented approach to mobile robotic teleoperation. The realisation of this capability enables future research to fully investigate the human factors and efficacy of the approach.

This paper investigates the capacity of a light-weight haptic grasping interface to convey event-based high-frequency transient forces within a virtual environment. The addition of vibrations based on contact with real-world objects over traditional position-based feedback has shown to significantly enhance the feel of hard surfaces. We describe the design of our prototype grasping system, and experimentally demonstrate the utility of this type of haptic interface. The frequency response of the device was obtained to demonstrate its ability to display high-frequency signals which meet realistic contact requirements. To determine the ability of the proposed device to display the required high frequency vibrations, empirical waveforms were measured by tapping on real surfaces with a high-bandwidth instrumented version of the two finger grasping interface. Empirical models, based on decaying sinusoids were fit to the measured acceleration waveforms to understand the required bandwidth of the proposed device for the particular material properties.

This paper presents the design and numerical simulation of a T-shape microfluidic electroosmotic micromixer. It is equipped with six microelectrodes that are embedded in the side surfaces of the microchannel. The electrode array consists of two sets of three 20 ¿m and 60 ¿m microelectrodes arranged in the form of two opposing triangles. Numerical analysis of electric potential and frequency effects on mixing efficiency of the micromixer is carried out by means of two sets of simulations. First, the electric potential is kept at 2 V while the frequency is varied within 10-50 Hz. The highest achieved mixing efficiency is 96% at 22 Hz. Next, the frequency is kept at 30 Hz whilst the electric potential is varied within 1-5 V. The best achieved mixing efficiency is 97% at 3 V.

Controlled mobility in wireless sensor networks provides many benefits towards enhancing the network performance and prolonging its lifetime. Mobile elements, acting as mechanical data carriers, traverse the network collecting data using single-hop communication, instead of the more energy demanding multi-hop routing to the sink. Scaling up from single to multiple mobiles is based more on the mobility models and the coordination methodology rather than increasing the number of mobile elements in the network. This work addresses the problem of designing and coordinating decentralized mobile elements for scheduling data collection in wireless sensor networks, while preserving some performance measures, such as latency and amount of data collected. We propose two mobility models governing the behaviour of the mobile element, where the incoming data collection requests are scheduled to service according to bidding strategies to determine the winner element. Simulations are run to measure the performance of the proposed mobility models subject to the network size and the number of mobile elements.

This paper proposes a kinematic modeling method for a bio-inspired robotic fish based on single joint. Lagrangian function of freely swimming robotic fish is built based on a simplified geometric model. In order to build the kinematic model, the fluid force acting on the robotic fish is divided into three parts: the pressure on links, the approach stream pressure and the frictional force. By solving Lagrange's equation of the second kind and the fluid force, the movement of robotic fish is obtained. The robotic fish's motion, such as propelling and turning are simulated, and experiments are taken to verify the model.

Haptic technology provides the ability for a system to recreate the sense of touch to a human operator, and as such offers wide reaching advantages. The ability to interact with the human's tactual modality introduces haptic human-machine interaction to replace or augment existing mediums such as visual and audible information. A distinct advantage of haptic human-machine interaction is the intrinsic bilateral nature, where information can be communicated in both directions simultaneously. This paper investigates the bilateral nature of the haptic interface in controlling the motion of a remote (or virtual) vehicle and presents the ability to provide an additional dimension of haptic information to the user over existing approaches (Park et al., 2006; Lee et al., 2002; and Horan et al., 2007). The 3D virtual haptic cone offers the ability to not only provide the user with relevant haptic augmentation pertaining to the task at hand, as do existing approaches, however, to also simultaneously provide an intuitive indication of the current velocities being commanded.

A multiresolution technique based on multiwavelets scale-space representation for stereo correspondence estimation is presented. The technique uses the well-known coarse-to-fine strategy, involving the calculation of stereo correspondences at the coarsest resolution level with consequent refinement up to the finest level. Vector coefficients of the multiwavelets transform modulus are used as corresponding features, where modulus maxima defines the shift invariant high-level features (multiscale edges) with phase pointing to the normal of the feature surface. The technique addresses the estimation of optimal corresponding points and the corresponding 2D disparity maps. Illuminative variation that can exist between the perspective views of the same scene is controlled using scale normalization at each decomposition level by dividing the details space coefficients with approximation space. The problems of ambiguity, explicitly, and occlusion, implicitly, are addressed by using a geometric topological refinement procedure. Geometric refinement is based on a symbolic tagging procedure introduced to keep only the most consistent matches in consideration. Symbolic tagging is performed based on probability of occurrence and multiple thresholds. The whole procedure is constrained by the uniqueness and continuity of the corresponding stereo features. The comparative performance of the proposed algorithm with eight famous existing algorithms, presented in the literature, is shown to validate the claims of promising performance of the proposed algorithm.

The adoption of simulation as a powerful enabling method for knowledge management is hampered by the relatively high cost of model construction and maintenance. A two-step procedure, based on a divide and conquer strategy, is proposed in this paper. First, a simulation program is partitioned based on a reinterpretation of the model-view-controller architecture. Individual parts are then connected, in terms of abstraction, to guard against possible changes that resulted from shifting user requirements. We explore the applicability of these design principles through a detailed discussion of an industry case study. The knowledge-based perspective guides the design of architecture to accommodate the need of emulation without compromising the integrity of the simulation program. The synergy between simulation and a knowledge management perspective, as shown in the case study, has the potential to achieve the objectives of rapid development of models, with low maintenance cost. This could, in turn, facilitate an extension of the use of simulation in the knowledge management domain.

Teleoperated robotic systems provide a valuable solution for the exploration of hazardous environments. The ability to explore dangerous environments from the safety of a remote location represents an important progression towards the preservation of human safety in the inevitable response to such a threat. While the benefits of removing physical human presence are clear, challenges associated with remote operation of a robotic system need to be addressed. Removing direct human presence from the robot's operating environment introduces telepresence as an important consideration in achieving the desired objective. The introduction of the haptic modality represents one approach towards improving operator performance subject to reduced telepresence. When operating in an urban environment, teleoperative stair climbing is not an uncommon scenario. This work investigates the operation of an articulated track mobile robot designed for ascending stairs under teleoperative control. In order to assist the teleoperator in improved navigational capabilities, a fuzzy expert system is utilised to provide the teleoperator with intelligent haptic augmentation with the aim of improving task performance.

The study of the communication networks for distributed systems is very important, since the overall performance of these systems is often depends on the effectiveness of its communication network. In this paper, we address the problem of networks modeling for heterogeneous large-scale cluster systems. We consider the large-scale cluster systems as a typical cluster of clusters system. Since the heterogeneity is becoming common in such systems, we take into account network as well as cluster size heterogeneity to propose the model. To this end, we present an analytical network model and validate the model through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions

The paper describes some details of the mechanical and kinematics design of a five-axis mechanism. The design has been utilized to physically realize an industrial-scale five-axis milling machine that can carry a three KW spindle. However, the mechanism could be utilized in other material processing and factory automation applications. The mechanism has five rectilinear joints/axes. Two of these axes are arranged traditionally, i.e. in series, and the other three axes utilize the concept of parallel kinematics. This combination results in a design that allows three translational and two rotational two-mode degrees of freedom (DOFs). The design provides speed, accuracy and cost advantages over traditional five-axis machines. All axes are actuated using linear motors.

We address the problem of adaptive blind source separation (BSS) from instantaneous multi-input multi- output (MIMO) channels. In this paper, we propose a new constant modulus (CM)-based algorithm which employ nonlinear function as the de-correlation term. Moreover, it is shown by theoretical analysis that the proposed algorithm has less mean square error (MSE), i.e., better separation performance, in steady state than the cross-correlation and constant modulus algorithm (CC-CMA). Numerical simulations show the effectiveness of the proposed result.

In this paper we discuss the design and development of a novel intelligent headrest system. Developed to reduce neck injuries resulting from up to 63% of rear end accidents, this system uses inductive sensing technology to establish the position of a driver or passenger's skull in a vehicle. Once detected, the system autonomously places the vehicle's headrest in a position that best support an occupant's head in the case of an accident. Sensor construction, mechatronic design and controller selection and real world tests of the system under various conditions are covered

This paper reports a method of controlling a user's hand through the process of writing. Developed predominantly for enabling users to re-learn the skill of writing after a stroke, the process could also be used for teaching children hand/eye coordination, motor skills, movement and position awareness in writing. Utilising low cost haptic technology and custom control software, the system has the potential to increase writing skills in stroke sufferers in the privacy and comfort of their own home.

A decision support tool for production planning is discussed in this paper to perform the job of machine grouping and labour allocation within a machining line. The production plans within the industrial partner have been historically inefficient because the relationship between the cycle times, the machine group size, and the operator's utilisation hasn't been properly understood. Starting with a simulation model, a rule-base has been generated to predict the operator's utilisation for a range of production settings. The resource allocation problem is then solved by breaking the problem into a series of smaller sized tasks. The objective is to minimise the number of operators and the difference between the maximum and minimum cycle times of machines within each group. The results from this decision support tool is presented for the particular case study.

The increasing usage of discrete event simulation packages for modeling and analyzing manufacturing and logistics has led to a need for connecting simulation models together at runtime. One such methodology for linking discrete event simulation models together has been developed for this research and this paper demonstrates the usage of this linking method. A unified simulation model is developed from two submodels developed using different simulation packages.

We present a novel ant colony algorithm integrating genetic algorithms and simplex algorithms. This method is able to not only speed up searching process for optimal solutions, but also improve the quality of the solutions. The proposed method is applied to set up a learning model for the "tuned" mask, which is used for texture classification. Experimental results on real world images and comparisons with genetic algorithms and genetic simplex algorithms are presented to illustrate the merit and feasibility of the proposed method.

A simple and reliable method for controlling the relative orientation between the two magnetic fields of a permanent magnet synchronous motor is presented. Finding the initial (at motor powering-up time) value of this relative location is essential for the proper operation of the motor. The feedback control loop used finds this initial relative orientation quickly. Further, using the proposed method allows considerable cost saving, as a transducer that is usually used for this purpose can be eliminated. The cost saving is most obvious in the case of linear motors and angle motors with large diameters. The way the problem is posed is an essential part of this work and it is the reason behind the apparent simplicity of the solution. The method relied upon a single sensor, and it has been tested when a relative encoder was used.

The positioning error of a large cantilevered mass that is actuated at its supported end is minimized as this mass travels at challenging high speeds and accelerations. An integrated approach is adopted to realize the task. After selecting the appropriate actuator that would provide higher rigidity, the system is viewed as a multi-degree of freedom system, and hence the concept of system-generated disturbance is introduced. This allows the use of appropriate mechanical design considerations and a proper generation of the kinematics commands to minimize such disturbance. A disturbance observer is then designed to detect and compensate the remaining disturbance, hence minimizing the positioning error.

The high pressure die casting (HPDC) process is normally referred to as the cold chamber process which solves the materials problem by separating the molten metal reservoir from the actuator for most of the process cycle. In this process, the thermal effects of molten metal flow in the die are a major factor in determining casting surface quality, die life, and many internal quality parameters such as porosity. Therefore, development of an effective technology to promptly evaluate the effect of changes in thermal process variables is vital to the quality control and improvement of productivity. Image processing technology has been applied to analyse the thermal images. The information is correlated to thermal energy stored in the die to develop a thermal control system and improve the quality of castings.

A person identification system is presented in this paper. The system exploits the localised self-similarity in face images in order to develop an identification method. The identification method is insensitive to partial image variations that are due to translation, rotation or scale. The performance of the system is evaluated by studying the results of its application to ensembles of face images.

Presents a method for the representation of facial images. The proposed method consists of two modules: face-image matching and face-image morphing. In the first module, the correspondence between two images are calculated for all pixel locations. A novel area-based matching method is proposed that makes use of the concept of the fractal dimension, and develops a non-parametric local transform as a basis for establishing the correspondence between two face images. In the second module, a mapping is performed for deformation of the source face image on to the target face image. This is done to map the pixels in the source face image to the location of their corresponding pixels in the target image.