• We propose WEViT, the first framework that integrates Neural Architecture Search (NAS) with vision transformers for Weakly Supervised Semantic Segmentation (WSSS). • A one-shot weight-sharing strategy is employed to efficiently train an overparameterized supernet, enabling fast and effective transformer architecture search. • The framework utilizes multi-class tokens to extract class-specific attention from transformers, enhancing the precision of localization maps. • We introduce a novel Refinement Patch Affinity strategy that suppresses background noise and improves class focus in multi-class images. • A regularization loss function is designed to promote class-discriminative attention, with experiments highlighting the importance of transformer layer selection for maximizing segmentation accuracy. Weakly Supervised Semantic Segmentation (WSSS) is a challenging task in computer vision, as it relies on limited supervision to generate precise object localization maps, often using Class Activation Maps (CAMs). Traditional methods struggle with balancing localization accuracy and scalability due to their reliance on fixed network architectures and handcrafted strategies. Neural Architecture Search (NAS), despite its proven success in optimizing network designs across tasks, has not yet been explored in WSSS due to the need for efficient weight sharing. To address these limitations, we propose WEViT, a novel framework that integrates NAS with transformers to optimize network architectures and generate accurate and class-specific object localization maps for WSSS. Our approach leverages the weight entanglement strategy, enabling the supernet to train multiple subnets simultaneously while ensuring high-quality weight inheritance. This eliminates the need for retraining subnets from scratch, significantly reducing computational cost. The best-performing architecture, obtained through the evolutionary algorithm, is then utilized to extract attention weights from transformer heads. These weights are further refined using a Refinement Patch Affinity strategy, effectively removing background noise and enhancing focus on relevant classes in multi-class images. We also incorporate a regularization loss function during training to enhance the generation of class-discriminative localization maps, with experiments highlighting the critical role of transformer layer selection in this process. WEViT achieves state-of-the-art performance on PASCAL VOC 2012 and MS COCO, demonstrating the efficacy of applying NAS to WSSS for the first time and paving the way for scalable, efficient, and accurate segmentation solutions.

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.

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.

ABSTRACT Object detection is a critical aspect of computer vision (CV) applications, especially within autonomous driving systems (AVs), where it is fundamental to ensuring safety and reducing traffic accidents. Recent advancements in computational resources have enabled the widespread adoption of Deep Learning (DL) techniques, significantly enhancing the efficiency and accuracy of object detection tasks. However, the technology for autonomous driving has yet to reach a level of maturity that guarantees consistent performance, reliability, and safety, with several challenges remaining unresolved. This study specifically focuses on 2D image‐based object detection methods, which offer several advantages over other modalities, such as cost‐effectiveness and the ability to capture visual features like colour and texture that are not detectable by LiDAR. We provide a comprehensive survey of DL‐based strategies for detecting vehicles and pedestrians using 2D images, analysing both one‐stage and two‐stage detection frameworks. Additionally, we review the most commonly used publicly available datasets in autonomous driving research and highlight their relevance to 2D detection tasks. The paper concludes by discussing the current challenges in this domain and proposing potential future directions, aiming to bridge the gap between the capabilities of 2D image‐based models and the requirements of real‐world autonomous driving applications. Comparative tables are included to facilitate a clear understanding of the different approaches and datasets.

Simulator sickness poses a significant challenge to the effectiveness of training programs in virtual environments. Early diagnosis of this syndrome can significantly prevent its onset and enhance user safety by enabling the management of symptoms. In our study, thirty-one participants were exposed to a recorded video of a flight in the Helimond simulator. Simulator sickness was then quantified using the Simulator Sickness Questionnaire, a Forced Binary Choice method, and an Equivital sensor. We analyzed the correlations between physiological signals and self-assessment scores, as well as evaluated the performance of various machine learning algorithms in predicting simulator sickness. The results indicated a strong correlation between SSQ scores and forced choice responses. Among the machine learning algorithms tested, Random Forest outperformed the others, achieving the highest accuracy (0.937) and AUC (0.932). Decision Trees followed in performance, while Logistic Regression showed the worst performance. Furthermore, based on our dataset, heart rate and Inter-Beat Interval were identified as the most significant factors for predicting simulator sickness. Our approach emphasizes the importance of instant assessment and prediction of simulator sickness, providing a simple yet effective method to improve the evaluation process. This method facilitates prompt evaluation and proactive symptom prevention, benefiting virtual environment design and user safety.

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.

As robots become more prevalent in society and applied in various workplace sectors, individuals must have an appropriate amount of trust that aligns with robots' or automated systems' actual capabilities, facilitating optimal and safe human-robot interaction. Appropriately calibrated trust levels can enhance robots' safe and successful adoption into our society and their unique applied environments. The current research aims to assess individuals' self-reported trust levels in a triadic human-robot-human interaction concerning a collaborative haptically enabled “sonography” style robot (having tele-operative capabilities) to assess moderators of trust unique to this specific domain. The objectives of the current research are to identify participants' trust levels in a triadic interaction focusing on the operator's proxemic location while operating the robot (1) and to compare self-reported trust levels across conditions suggested by the literature to have an influence (2). A repeated measures ANDVA revealed a significant association between the replicated traditional sonography assessment and participants possessing higher trust levels than all robot-related conditions. Further, participants had greater trust for the smooth and slow-functioning robot than the non-smooth functioning robot. Lastly, the current study's findings suggest that, compared to the other robot-related conditions, the experimenter's location operating the tele-operative robot does not significantly influence participants' trust levels. Future research should consider exploring humans' qualitative perceptions of their interactions with sonography robots and whether trust can be more accurately calibrated over time. Doing so may assist in developing an in-depth understanding of the discrepancies between human-human interaction and human-robot interactions unique to this setting.

Neural Architecture Search (NAS) has significantly improved the accuracy of image classification and segmentation. However, these methods concentrate on finding segmentation structures for natural or medical applications. In this study, we introduce a NAS approach based on gradient optimization to identify ideal cell designs for road segmentation. To the best of our knowledge, this work represents the first application of gradient-based NAS to road extraction. Taking insight from the U-Net model and its successful variations in different image segmentation tasks, we propose NAS-enhanced U-Net, illustrated by an equal number of cells in both encoder and decoder levels. While cross-entropy combined with dice loss is commonly used in many segmentation tasks, road extraction brings up a unique challenge due to class imbalance. To address this, we introduce a combination of loss function. This function merges cross-entropy with weighted Dice loss, focusing on elevating the importance of the road class by assigning it a weight (⍵), while background Dice values are disregarded. The results indicate that the optimal weight for the proposed model equals 2. Additionally, our work challenges the assumption that increased model parameters or depth inherently leads to improved performance. Therefore, we establish search spaces 2,3,4,5,6,7 and 8 to automatically choose the optimal depth for model. We present promising segmentation results for our proposed method, achieved without any pretraining on the Massachusetts road dataset. Furthermore, these results are compared with those of 14 models categorized into four groups: U-Net, Segnet, FCN8, and Nas-U-Net.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.