State prediction from noisy time series measurements is a challenging task found in areas like intelligent transport, structural health monitoring, and environmental monitoring. This paper proposes a Spatio-Temporal Deep Kernel Gaussian Process (STDK GP) approach, which leverages the feature extraction capabilities of convolutional neural networks with the uncertainty quantification of Gaussian Process (GP) methods. The model features a composite spatio-temporal kernel that operates on learned deep features. A key aspect of our approach is that this kernel is learned end-to-end with the feature extractor, allowing it to effectively capture complex spatial and temporal patterns and enabling robust uncertainty quantification. Evaluated on a real-world vehicular traffic forecasting task, the proposed STDK GP demonstrates superior performance. Specifically, it achieves a root mean square error of 2.67105 km/h and improves prediction accuracy by approximately 15.28% over the standard GP approach, 21.23% over the state-of-the-art structural recurrent neural network and by more than 53% over stand-alone deep neural networks.

People with physical impairments often face difficulties in performing daily tasks such as dressing, leading to dependence on caregivers. While robotic manipulators can provide valuable assistance, close physical interaction raises concerns over safety, comfort and trust, which can limit adoption. This article introduces the Assistive Robot Twin (ART) framework, a real-time digital twin that models both the user and the robot to enhance transparency during robot-assisted dressing. ART integrates two visual safety features: Bounding Boxes (BBs), which define static or dynamic protective zones around critical regions, and Trajectory Visualisation (TV), which displays planned robot movements in real time. We conducted a within-subject study with 36 participants mimicking stroke-related mobility impairment, evaluating six BB/TV configurations using validated interaction quality and system usability questionnaires. The results show that BBs significantly improved perceived safety ( \(\textrm{p} < 0.001\) ), reduced discomfort ( \(\textrm{p} < 0.001\) ) and increased trust ( \(\textrm{p} < 0.001\) ), with dynamic BBs providing the greatest safety benefits. TV significantly enhanced overall system usability ( \(\textrm{p}=0.007\) ), confidence and predictability of robot actions. While the study focuses on perceived interaction quality in a controlled setting with healthy participants, the results provide foundational evidence for the design of transparent assistive systems prior to clinical deployment.

Variational Gaussian filter (VGF) approximates the intractable posterior of the state of a non-linear non-Gaussian system using a single Gaussian density normally found through Kullback-Leibler divergence minimization. This paper focuses on the VGFs whose measurement update is realized by employing the natural gradient descent (NGD). Under the assumption that the state predictive distribution is also Gaussian, we re-examine the iterative NGD-based measurement update under two different parameterizations of the Gaussian posterior. The first one consists of the mean and covariance, while the other comprises the mean and precision matrix (i.e., the inverse of the covariance). Their NGD-based update rules are derived in an alternative but unified way using matrix calculus. They are compared against each other and with the one developed using the natural parameterization of the Gaussian density. Important new insights are obtained. Modifications to the established update rules, which guarantee the positive definiteness of the covariance/precision matrix of the Gaussian posterior, are re-visited as well. Simulations are used to corroborate the theoretical results and evaluate the performance of the developed algorithms in range-bearing tracking.

This paper proposes a deep learning approach for contactless detection of sleep apnoea using pulse and blood oxygen saturation (SPO2) data. Three convolutional neural network architectures are adopted for apnoea classification purposes by fusing different features of the available time series signals. A conventional convolutional neural network (CNN), a CNN with a support vector machine (CNN-SVM), and a CNN combined with a recurrent neural network (CNN-RNN) are compared. The RNN includes Gated Recurrent Units (GRU) and Bidirectional GRU (BiGRU). The CNN is utilised to extract features, whilst the SVM and RNN are used for classification. In addition, we compare two different fusion methods, signal-level and feature-level fusion. The performance is validated and evaluated on a public dataset obtain from St. Vincent University Hospital. The results show that the concatenation of SPO2 and pulse signal at the signal level enhances the classification performance compared to using the individual signal. In addition, the classification sensitivity with signal-level fusion is higher than that with feature-level fusion. Overall, the proposed CNN-RNN with GRU (CNN-GRU) architecture gives the best performance with an accuracy of 85.4%, a sensitivity of 61.5%, a specificity of 91.9%, an$F_{1}$score of 0.64, and a$\kappa$score of 0.551 with a dropout rate of 0.5 and a 20-second overlap. The results demonstrate that the proposed deep learning approach offers a promising solution for non-invasive detection of sleep apnoea using affordable physiological signals.

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements.

Active sensing plays an essential role in searching and tracking a target without initial target state information. This paper studies the active sensing approach for sensor management problems using multiple unmanned aerial vehicles based on the received signal strength measurements of the target. A Bayesian optimisation-based approach is proposed which adopts the Gaussian process method to model the received signal strength in an area over time and then the expected improvement acquisition function is leveraged to decide where to take new measurements considering the uncertainty of the Gaussian process. A unique contribution of this paper consists of the designed spatial-temporal composite kernel function that accounts for the time-varying nature of the signal strength. Numerical results obtained from different measurement noise levels and varying initial Bayesian optimisation settings demonstrate that the proposed approach can efficiently schedule multiple unmanned aerial vehicles to locate the target within a minimum number of initial data. Particularly, it achieves at most $57 \%$ lower tracking error and $46 \%$ lower lost-track probability as compared to the benchmark approach.

This paper explores the application of centralised and distributed Gaussian process algorithms to real-time target tracking and compares their performance. By embedding the algorithms into the Stone Soup, the focus is on the innovative implementation of Gaussian process methods with learning hyperparameters and implementation with a factorised variance of the Gaussian kernel. The performance of the methods with different kernels was evaluated, not only with the Gaussian kernel. Extensive experiments with various kernel configurations demonstrate their importance in enhancing prediction accuracy and efficiency, especially in real-time tracking. The case studies with manoeuvring targets show significant advancements in tracking capabilities, particularly in wireless sensor networks, using optimised Gaussian process methods. This work advances Stone Soup’s capabilities and lays the groundwork for future investigations into adaptive Gaussian Process applications in tracking and sensor data analysis.

In this paper we propose a Text-To-Text Transfer Transformer-based (T5) model to generate concise, human-like synopses of Merger and Acquisition (M&A) economic news. Leveraging recent advancements in artificial intelligence and natural language processing, we have focused our resources on the creation of a specific summarizer, opting for a translation of all news into English to ensure consistency and mitigate complexity. The implementation of Graphics processing unit (GPU) resources for model training played a crucial role in optimizing the learning process, allowing us to effectively utilize the time window provided by platforms such as Google Colab Pro and Free. Through iterative fine-tuning, our model was trained to generate high-quality, human-like synopses of M&A events. Our findings underscore the enhanced transparency and interpretability of the proposed generative AI-driven text summariser, suggesting promising avenues for future exploration and development in this field.

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target.

Fine-grained plant pathology classification is an important task for precision agriculture, but at the same time, it is challenging due to the subtle difference in plant categories. Variances in the lighting conditions, position, and stages of disease symptoms usually lead to degradation of classification accuracy. Knowledge distillation is a popular method to improve the model performance to deal with the indistinguishable image classification problem. It aims to have a well-optimised small student network guided by a large teacher network. Existing knowledge distillation methods mainly consider training a teacher network that needs a high storage space and considerable computing resources. Self-knowledge distillation methods have been proposed to distil knowledge from the same network. Although self-knowledge distillation saves time and space compared with knowledge distillation, it only learns label knowledge. In this paper, we propose a novel self-distillation method to recognize the fine-grained plant category, which considers holistic knowledge based on the Squeeze and Excitation Network. We label this new method as holistic self-distillation because it captures knowledge through spatial features and labels. The performance validation of the proposed approach is performed on two public fine-grained plant datasets: Plant Pathology 2021 and Plant Pathology 2020 with the accuracy of 98.22% and 90.72% respectively. We also present experiments on the state-of-the-art algorithm (ResNet-50). The classification results demonstrate the effectiveness of the proposed approach with respect to accuracy.

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset.

This paper presents a novel approach called the Interacting Multiple Model (IMM)-based Maximum Correntropy Student's T Filter (MCStF), which addresses the challenges posed by non-Gaussian measurement noises. The MCStF demonstrates superior performance compared to the IMM algorithm based on Kalman Filters (KFs) in both simulation environments and real-time systems. The Crazyflie 2.0 nano Unmanned Air Vehicle (UAV) model is used in the simulation validation, and results from 3000 independent Monte Carlo runs are shown. After getting the simulation results under monotonously changed non-Gaussian distribution, their performance results have been compared to each other. The same scenario has been applied in the real-time system using Crazyflie 2.0. Next, results from real-time tests are presented in which the position of Crazyflie 2.0 is estimated online.

Computer vision and deep learning have made substantial progress in the areas of agriculture and smart farming, particularly for enhancing crop production using image segmentation techniques for crop yield prediction. Further improvements to crop yield prediction results can be achieved by developing accurate and efficient methods. In response to such demands, this paper proposes a novel convolutional neural network architecture, called densely connected SegNet (D-SegNet) and demonstrates its advantages on plant segmentation using a new morning glory plant dataset, and also on a complimentary publicly available dataset to promote research in this direction. The D-SegNet is evaluated using 10-fold cross validation. It achieves performance better than the state-of-the-art SegNet algorithm. The evaluated precision, recall and F1-score values are 98.20%, 90.64% and 94.26%, respectively, for the morning glory plant dataset. The intersection over union (IoU) value in the image segmentation tasks is 90.56%. A series of experiments on the morning glory plant dataset as well as on the publicly available dataset were conducted. The results show that the proposed method achieves accurate segmentation results and can be useful for assessing the plant weight during harvesting. In summary, this new plant segmentation network, D-SegNet, could form an important component of future cloud-based machine learning systems to predict crop yield from noisy smartphone images taken in the field.

This paper proposes an edge-assisted crowdsourced live video transcoding approach where the transcoding capabilities of the edge transcoders are unknown and dynamic. The resilience and trustworthiness of highly unstable transcoders in decision making are characterized with mean-variance-based measures to avoid making highly risky decisions. The risk level of each device’s situation is assessed and two upper confidence bounds of the variance of transcoding performance are presented. Based on the derived bounds and by leveraging the contextual information of devices, two risk-aware contextual learning schemes are developed to efficiently estimate the transcoding capabilities of the edge devices. Combining context awareness and risk sensitivity, a novel transcoding task assignment and viewer association algorithm is proposed. Simulation results demonstrate that the proposed algorithm achieves robust task offloading with superior network utility performance as compared to the linear upper confidence bound and the risk-aware mean-variance upper confidence bound-based algorithms. In particular, an epoch-based task assignment strategy is designed to reduce the task switching costs incurred in assigning the same transcoding task to different transcoders over time. This strategy also reduces the computational time needed. Numerical results confirm that this strategy achieves up to 86.8% switching costs reduction and 92.3% computational time reduction.

Audio-visual tracking of multiple speakers requires to estimate the state (e.g. velocity and location) of each speaker by leveraging the information of both audio and visual modalities. Estimating the number of speakers and their states jointly remains a challenging problem. We propose an Audio-Visual Possion Multi-Bernoulli Mixture Filter (AV-PMBM) that can not only predict the number of speakers but also give accurate estimation of their states. We also propose a novel sound source localization technique based on DOA information and a deep learning based object detector to provide reliable audio measurements for the AV tracker. To our knowledge, this represents the first attempt using PMBM for multi-speaker tracking with audio visual modalities. Experiments on the AV16.3 dataset demonstrate that AV-PMBM achieves state-of-the-art performance in optimal sub-pattern assignment (OSPA).

This paper compares a conventional interacting multiple model Kalman filter (IMM-KF) filter and an interacting multiple models with maximum correntropy Kalman filter (IMM-MCKF). A nonlinear UAV dynamics model was used to compare these two methods. The compared filters estimated the position of the UAV under the noise distribution. Although KF has reliable accuracy, MCKF has got better results under non-Gaussian or mixed distributions. At this point, these filters have been investigated under maneuver and non-maneuver motion, and it is known that better advantages will be shown when both filters are used in the IMM. These filters have been compared under non-Gaussian distributions, and the Student’s-T distribution has been selected as a non-Gaussian type. The performance validation and testing stages are carried out with variable degrees of freedom, and scaling matrix factors for the Student’s-T distributions have been used. Results from simulation tests from 3000 independent Monte-Carlo runs are presented. In these experiments, UAV models and UAV trajectory results have been used.

The performance of the state estimation for Gaussian state space models can be degraded if the models are affected by the non-Gaussian process and measurement noises with uncertain degree of non-Gaussianity. In this paper, we propose a flexible robust Student's t-based multimodel approach. More specifically, the degrees of freedom parameter from the Student's t-distribution is assumed unknown and modelled by a Markov chain of state values. In order to capture more information of the Student's t-distributions propagated through multiple models, we establish a model-based Versoria cost function in the form of a weighted mixture rather than the original form, and maximize the function to interact and fuse the multiple models. Simulated results prove the flexibility of the robustness of the proposed Student's t-basedmultimodel approach when the existence probability of the outliers is uncertain.

This paper presents a non-random weight initialisation scheme for convolutional neural network layers. It builds upon previous work that was limited to perceptron layers, but in that work repeatable determinism was achieved with equality in categorisation accuracy between the established random scheme and a linear ramp non-random scheme. This work however, is in Convolutional layers and are the layers that have been responsible for better than human performance in image recognition. The previous perceptron work found that number range was more important rather than the gradient. However, that was due to the fully connected nature of dense layers. Although, in convolutional layers by contrast, there is an order direction implied, and the weights relate to filters rather than image pixel positions, so the weight initialisation is more complex. However, the paper demonstrates a better performance, over the currently established random schemes with convolutional layers. The proposed method also induces earlier learning through the use of striped forms, and as such has less unlearning of the traditionally speckled random forms. That proposed scheme also provides a higher performing accuracy in a single learning session, with improvements of: 3.35% un-shuffled, 2.813% shuffled in the first epoch and 0.521% over the 5 epochs of the model. Of which the first epoch is more relevant as it is the epoch after initialisation. Also the proposed method is repeatable and deterministic, which is also a desirable quality for safety critical applications within image classification. The proposed method is also robust to He initialisation values too, and scored 97.55% accuracy compared to 96.929% accuracy with the Glorot/ Xavier in the traditional random forms, of which the benchmark model was originally optimised with.

The application of multiple target tracking algorithms has exponentially increased during the last two decades. Recently, model-free approaches, such as Gaussian process regression and convolutional neural networks, have been developed for target tracking. This paper presents a simulation-based study on the practical aspects of a very promising and recently proposed Gaussian process method, namely the Gaussian process motion tracker [1]. The paper also provides design guidelines on the various aspects of the above-mentioned tracking method.

Sleep apnea is a sleep disorder which is common in many children and adults. It is characterised by abnormal breath pauses or shallow breathing during sleep. Traditional diagnosis of apnea requires special equipment for data collection in clinical conditions and manual analysis by clinicians which is expensive and time-consuming. This paper presents a framework for autonomous detection of sleep apnea, using peripheral blood haemoglobin oxygen saturation (SpO2) data based on the fusion of multiple features and Dirichlet process mixture model. The SpO2signals are segmented into overlapping sub-sequences and several features are extracted from each segment. The distributions of features extracted from disorder and normal segments are modelled by two Gaussian mixture models, respectively, with the Dirichlet process as the prior. The advantage of the framework is that the number of clusters within mixture models can be learned from training data without strong assumptions, which contributes to accurate estimation of the distributions. The proposed framework is subject-independent and it is trained and tested on two publicly available databases with 10-fold cross-validation. It obtains accuracy of 84.89% on the St. Vincent's University Hospital Sleep Apnea Database and accuracy of 97.01% on the Apnea-ECG Database, outperforming state-of-the-art approaches. The results show that the proposed model is capable of representing the distributions of features independently of subjects and can accurately classify segmented signals from patients with symptoms of different severity. The results show the potential of the developed classification framework to support clinicians in their decision making.

The Schmidt-Kalman filter (SKF) achieves filtering consistency in the presence of biases in system dynamic and measurement models through accounting for their impacts when updating the state estimate and covariance. However, the performance of the SKF may break down when the measurements are subject to non-Gaussian and heavy-tail noise. To address this, we impose the Wishart prior distribution on the precision matrix of measurement noise, such that the measurement likelihood now has heavier tails than the Gaussian distribution to deal with the potential occurrence of outliers. Variational inference is invoked to establish analytically tractable methods for computing the posterior of the system state, system biases, and the measurement noise precision matrix. The principle of the SKF considers the effect of system biases but does not actively estimate them when two variants of outlier-robust SKFs are incorporated. We evaluate their performance in terms of estimation accuracy and filtering consistency using simulations and real-world data. Promising results are obtained.

This paper proposes a computer vision framework aimed to segment hot steel sections and contribute to rolling precision. The steel section dimensions are calculated for the purposes of automating a high temperature rolling process. A structured forest algorithm along with the developed steel bar edge detection and regression algorithms extract the edges of the high temperature bars in optical videos captured by a GoPro® camera. To quantify the impact of noises that affect the segmentation process and the final diameter measurements, a weighted variance is calculated, providing a level of trust in the measurements. The results show an accuracy which is in line with the rolling standards, i.e. with a root mean square error less than 2.5 mm.

Many types of cancers such as multiple myeloma cause bone destruction, resulting in pain and fractures in patients and increased fatality. To quantify the degree of bone disease caused by cancer and analyse treatment response for bone repairing, accurate knowledge of the volumetry of all lesions is needed. To this end, this study proposes to apply two main approaches to the segmentation of bone lesions in cancer-induced bone disease from Micro Computed Tomography (μCT) images - structured forest-based edge detection approach and deep learning approach. A fast edge detection approach with structured forest, an extension of [1], is applied to identify the volumetry of all lesions in mice tibia, where the obtained results are evaluated against the manually labelled data, demonstrating the efficiency of the compared approaches. The Gaussian processes (Convnet GP) approach has achieved the best performance among the compared approaches, with 99.6% intersection of union and 99.7% precision. Our results demonstrate that the developed approach provides a reasonable delineation of the samples, showing the great potential towards fully automatic bone tumour segmentation.

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.

Common spatial patterns (CSP) is one of the most popular feature extraction algorithms for brain-computer interfaces (BCI). However, CSP is known to be very sensitive to artifacts and prone to overfitting. This paper proposes a novel dynamic time warping (DTW)-based approach to improve CSP covariance matrix estimation and hence improve feature extraction. Dynamic time warping is widely used for finding an optimal alignment between two time-dependent signals under predefined conditions. The proposed approach reduces within class temporal variations and non-stationarity by aligning the training trials to the average of the trials from the same class. The proposed DTW-based CSP approach is applied to the support vector machines (SVM) classifier and evaluated using one of the publicly available motor imagery datasets. The results showed that the proposed approach, when compared to the classical CSP, improved the classification accuracy from 78% to 83% on average. Importantly, for some subjects, the improvement was around 10%.

The selection of step sizes in the progressive Gaussian approximate filter (PGAF) is important, and it is difficult to select optimal values in practical applications. Furthermore, in the PGAF, significant integral approximation errors are generated by the repeated approximate calculations of the Gaussian weighted integrals, which results in an inaccurate measurement noise covariance matrix (MNCM). To solve these problems, in this paper, the step sizes and the MNCM are jointly estimated based on the variational Bayesian (VB) approach. By incorporating the adaptive estimates of step sizes and the MNCM into the PGAF framework, a novel PGAF with variable step size is proposed. Simulation results illustrate that the proposed filter has higher estimation accuracy than existing state-of-the-art nonlinear Gaussian approximate filters.

One of the major limitations of current electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the long calibration time. Due to a high level of noise and non-stationarity inherent in EEG signals, a calibration model trained using limited number of train data may not yield an accurate BCI model. To address this problem, this paper proposes a novel subject-to-subject transfer learning framework that improves the classification accuracy using limited training data. The proposed framework consists of two steps: The first step identifies if the target subject will benefit from transfer learning using cross-validation on the few available subject-specific training data. If transfer learning is required a novel algorithm for measuring similarity, called the Jensen-Shannon ratio (JSR) compares the data of the target subject with the data sets from previous subjects. Subsequently, the previously calibrated BCI subject model with the highest similarity to the target subject is used as the BCI target model. Our experimental results using the proposed framework obtained an average accuracy of 77% using 40 subject-specific trials, outperforming the subject-specific BCI model by 3%.

Deep neural networks have recently demonstrated the traffic prediction capability with the time series data obtained by sensors mounted on road segments. However, capturing spatio-temporal features of the traffic data often requires a significant number of parameters to train, increasing computational burden. In this work we demonstrate that embedding topological information of the road network improves the process of learning traffic features. We use a graph of a vehicular road network with recurrent neural networks (RNNs) to infer the interaction between adjacent road segments as well as the temporal dynamics. The topology of the road network is converted into a spatio-temporal graph to form a structural RNN (SRNN). The proposed approach is validated over traffic speed data from the road network of the city of Santander in Spain. The experiment shows that the graph-based method outperforms the state-of-the-art methods based on spatio-temporal images, requiring much fewer parameters to train.

Deep learning is actively used in the area of sparse coding. In current deep sparse coding methods uncertainty of predictions is rarely estimated, thus providing the results that lack the quantitative justification. Bayesian learning provides the way to estimate the uncertainty of predictions in neural networks (NNs) by imposing the prior distributions on weights, propagating the resulting uncertainty through the layers and computing the posterior distributions of predictions. We propose a novel method of propagating the uncertainty through the sparsity-promoiting layers of NNs for the first time. We design a Bayesian Learned Iterative Shrinkage-Thresholding network (BayesLIsTA). An efficient posterior inference algorithm based on probabilistic backpropagation is developed. Experiments on sparse coding show that the proposed framework provides both accurate predictions and sensible estimates of uncertainty in these predictions.

Location-based applications attract more and more attention in recent years. Examples of such applications include commercial advertisements, social networking software and patient monitoring. The received signal strength (RSS) based location fingerprinting is one of the most popular solutions for indoor localization. However, there is a big challenge in collecting and maintaining a relatively large RSS fingerprint database. In this work, we propose and compare two algorithms namely, the Gaussian process (GP) and Gaussian process with variogram, to estimate and construct the RSS fingerprints with incomplete data. The fingerprint of unknown reference points is estimated based on measurements at a limited number of surrounding locations. To validate the effectiveness of both algorithms, experiments using Bluetooth-low-energy (BLE) infrastructure have been conducted. The constructed RSS fingerprints are compared to the true measurements, and the result is analyzed. Finally, using the constructed fingerprints, the localization performance of a probabilistic fingerprinting method is evaluated.

Extended object tracking has become an integral part of various autonomous systems in diverse fields. Although it has been extensively studied over the past decade, many complex challenges remain in the context of extended object tracking. In this paper, a new method for tracking multiple irregularly shaped extended objects using surface measurements is proposed. The Gaussian Process Convolution Particle Filter proposed in [1], designed to track a single extended/group object, is enhanced for tracking multiple extended objects. A convolution kernel is proposed to estimate the multi-object likelihood. A target birth/death model based on the proposed method is also introduced for automatic initiation and deletion of the objects. The proposed approach is validated on real-world LiDAR data which shows that the method is efficient in tracking multiple irregularly shaped extended objects in challenging scenarios involving occlusion, dense clutter and low object detection.

Vehicle logo recognition is an important part of vehicle identification in intelligent transportation systems. State-of-the-art vehicle logo recognition approaches use automatically learned features from Convolutional Neural Networks (CNNs). However, CNNs do not perform well when images are rotated and very noisy. This paper proposes an image recognition framework with a capsule network. A capsule is a group of neurons, whose length can represent the existence probability of an entity or part of an entity. The orientation of a capsule contains information about the instantiation parameters such as positions and orientations. Capsules are learned by a routing process, which is more effective than the pooling process in CNNs. This paper, for the first time, develops a capsule learning framework in the field of intelligent transportation systems. By testing with the largest publicly available vehicle logo dataset, the proposed framework gives a quick solution and achieves the highest accuracy (100%) on this dataset. The learning capsules have been tested with different image changes such as rotation and occlusion. Image degradations including blurring and noise effects are also considered, and the proposed framework has proven to be superior to CNNs.

In this paper, a novel robust Rauch-Tung-Striebel smoother is proposed based on the Slash and generalized hyperbolic skew Student's t-distributions. A novel hierarchical Gaussian state-space model is constructed by formulating the Slash distribution as a Gaussian scale mixture form and formulating the generalized hyperbolic skew Student's t-distribution as a Gaussian variance-mean mixture form, based on which the state trajectory, mixing parameters and unknown noise parameters are jointly inferred using the variational Bayesian approach. The posterior probability density functions of mixing parameters of the Slash and generalized hyperbolic skew Student's t-distributions are, respectively, approximated as truncated Gamma and generalized inverse Gaussian. Simulation results illustrate that the proposed robust Rauch-Tung-Striebel smoother has better estimation accuracy than existing state-of-the-art smoothers.

Sign language is a visually oriented, natural, nonverbal communication medium. Having shared similar linguistic properties with its respective spoken language, it consists of a set of gestures, postures and facial expressions. Though, sign language is a mode of communication between deaf people, most other people do not know sign language interpretations. Therefore, it would be constructive if we can translate the sign postures artificially. In this paper, a capsule-based deep neural network sign posture translator for an American Sign Language (ASL) fingerspelling (posture), has been presented. The performance validation shows that the approach can successfully identify sign language, with accuracy like 99%. Unlike previous neural network approaches, which mainly used fine-tuning and transfer learning from pre-trained models, the developed capsule network architecture does not require a pre-trained model. The framework uses a capsule network with adaptive pooling which is the key to its high accuracy. The framework is not limited to sign language understanding, but it has scope for non-verbal communication in Human-Robot Interaction (HRI) also.

Bayesian filters can be made robust to outliers if the solutions are developed under the assumption of heavy-tailed distributed noise. However, in the absence of outliers, these robust solutions perform worse than the standard Gaussian assumption based filters. In this work, we develop a novel robust filter that adopts both Gaussian and multivariate t-distributions to model the outliers contaminated measurement noise. The effects of these distributions are combined within a Bayesian Model Averaging (BMA) framework. Moreover, to reduce the computational complexity of the proposed algorithm, a restricted variational Bayes (RVB) approach handles the multivariate t-distribution instead of its standard iterative VB (IVB) counterpart. The performance of the proposed filter is compared against a standard cubature Kalman filter (CKF) and a robust CKF (employing IVB method) in a representative simulation example concerning target tracking using range and bearing measurements. In the presence of outliers, the proposed algorithm shows a 38 % improvement over CKF in terms of root-mean-square-error (RMSE) and is computationally 2.5 times more efficient than the robust CKF.

Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computational complexity, as in a basic form they scale cubically with the number of observations. Several approaches based on inducing points were proposed to handle this problem in a static context. These methods though face challenges with real-time tasks and when the data is received sequentially over time. In this paper, a novel online algorithm for training sparse Gaussian process models is presented. It treats the mean and hyperparameters of the Gaussian process as the state and parameters of the ensemble Kalman filter, respectively. The online evaluation of the parameters and the state is performed on new upcoming samples of data. This procedure iteratively improves the accuracy of parameter estimates. The ensemble Kalman filter reduces the computational complexity required to obtain predictions with Gaussian processes preserving the accuracy level of these predictions. The performance of the proposed method is demonstrated on the synthetic dataset and real large dataset of UK house prices.

For several economical, financial and operational reasons, forecasting energy demand becomes a key instrument in energy system management. This paper develops a natural gas forecasting approach, which consists of two major phases: 1) it classifies the natural gas consumption daily pattern sequences into different groups with similar attributes. 2) the design and training of multiple autoregressive Gaussian Process models phase is carried out using the Algerian natural gas market data together with exogenous inputs consisting in weather (temperature) and calendar (day of the week, hour indicator) factors. The main novelty in this work consists of the investigation of multiple different clustering techniques for better analysis and clustering of natural gas consumption data. The impact of the obtained clusters, by each technique, is then summarized and evaluated with respect to the prediction accuracy.

The Gaussian mixture model (GMM) has been extensively investigated in nonlinear/non-Gaussian filtering problems. This paper presents two enhancements for GMM-based nonlinear filtering techniques, namely, the adaptive ordering of the measurement update and normalized innovation square (NIS)-based mixture component management. The first technique selects the order of measurement update by maximizing the marginal measurement likelihood to improve performance. The second approach takes the filtering history of a mixture component into account and prunes those components with NIS larger than a threshold to eliminate their impact on the filtering posterior. The advantage of the proposed enhancements is illustrated via simulations that consider source tracking using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements received at two unmanned aerial vehicles (UAVs). A GMM-cubature quadrature Kalman filter (CQKF) is implemented and its performances with different measurement update and mixture component management strategies are compared. The superior performance obtained via the use of the two proposed techniques is demonstrated.

Synthetic Aperture Radar (SAR) satellite systems are very efficient in oil spill monitoring due to their capability to operate under all weather conditions. This paper presents a framework using Gaussian process (GP) to fuse SAR images of different modalities and to segment dark areas (assumed oil spill) for oil spill detection. A new covariance function; a product of an intrinsically sparse kernel and a Rational Quadratic Kernel (RQK) is used to model the prior of the estimated image allowing information to be transferred. The accuracy performance evaluation demonstrates that the proposed framework has 37% less RMSE per pixel and a compelling enhancement visually when compared with existing methods.

Traffic prediction approaches face challenges when presented with sparse or missing data. This can be caused by numerous factors such as: i) sensors not being operational; ii) communication issues; iii) cost prohibiting full monitoring of a road network. This present work adds to existing body of knowledge by proposing a particle based framework for dealing with these challenges. An expression of the likelihood function is derived for the case when the missing value is calculated based on Kriging interpolation. With the Kriging interpolation, the missing values of the measurements are predicted, which are subsequently used in the computation of likelihood terms in the particle filter algorithm. The results show 23% to 36.34% improvement in RMSE values for the synthetic data used.

One of the major limitations of brain computer interface (BCI) is its long calibration time. Due to between sessions/subjects nonstationarity, typically a big amount of training data needs to be collected at the beginning of each session in order to tune the parameters of the system for the target user. In this paper, a number of novel weighted multi-task transfer learning algorithms are proposed in the classification domain to reduce the calibration time without sacrificing the classification accuracy of the BCI system. The proposed algorithms use data from other subjects and combine them to estimate the classifier parameters for the target subject. This combination is done based on how similar the data from each subject is to the few trials available from the target subject. The proposed algorithms are evaluated using dataset 2a from BCI competition IV. According to the results, the proposed algorithms lead to reduce the calibration time by 75% and enhance the average classification accuracy at the same time.

This paper proposes a flame detection framework based on the color, dynamics, and flickering properties of flames. The distribution of flame colors is modeled by a Gaussian mixture model whose number of Gaussian components is estimated by a Dirichlet process from training data rather than set empirically. The proposed approach estimates the flame color distribution more accurately as it can determine the number of Gaussian components of the mixture model automatically. Additionally, a probabilistic saliency analysis method and a one-dimensional wavelet transform are used to extract motion saliency and filtered temporal series as features, describing the dynamics and flickering properties of flames. The developed Dirichlet process Gaussian mixture model based approach for autonomous flame detection is tested on various videos and achieves framewise accuracy higher than 95%.

Semisupervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators' load. In the light of the necessity to process large volumes of video data and provide autonomous decisions, this paper proposes new learning algorithms for activity analysis in video. The activities and behaviors are described by a dynamic topic model. Two novel learning algorithms based on the expectation maximization approach and variational Bayes inference are proposed. Theoretical derivations of the posterior estimates of model parameters are given. The designed learning algorithms are compared with the Gibbs sampling inference scheme introduced earlier in the literature. A detailed comparison of the learning algorithms is presented on real video data. We also propose an anomaly localization procedure, elegantly embedded in the topic modeling framework. It is shown that the developed learning algorithms can achieve 95% success rate. The proposed framework can be applied to a number of areas, including transportation systems, security, and surveillance.

Vehicle logo recognition is an important part of vehicle identification in intelligent transportation systems. State-of-the-art vehicle logo recognition approaches typically consider training models on large datasets. However, there might only be a small training dataset to start with and more images can be obtained during the real-time applications. This paper proposes an online image recognition framework which provides solutions for both small and large datasets. Using this recognition framework, models are built efficiently using a weight updating scheme. Another novelty of this work is that the Cauchy prior logistic regression with conjugate gradient descent is proposed to deal with the multinomial classification tasks. The Cauchy prior results in a quicker convergence speed for the weight updating process which could decrease the computational cost for both online and offline methods. By testing with a publicly available dataset, the Cauchy prior logistic regression deceases the classification time by 59%. An accuracy of up to 98.80% is achieved when the proposed framework is applied.

Advances in sensor systems have resulted in the availability of high resolution sensors, capable of generating massive amounts of data. For complex systems to run online, the primary focus is on computationally efficient filters for the estimation of latent states related to the data. In this paper a novel method for efficient state estimation with the unscented Kalman Filter is proposed. The focus is on applications consisting of a massive amount of data. From a modelling perspective, this amounts to a measurement vector with dimensionality significantly greater than the dimensionality of the state vector. The efficiency of the filter is derived from a parallel filter structure which is enabled by the expectation propagation algorithm. A novel parallel measurement processing expectation propagation unscented Kalman filter is developed. The primary advantage of the novel algorithm is in the ability to achieve computational improvements with negligible loses in filter accuracy. An example of robot localization with a high resolution laser rangefinder sensor is presented. A 47.53% decrease in computational time was exhibited for a scenario with a processing platform consisting of 4 processors, with a negligible loss in accuracy.

Traffic control and vehicle route planning require accurate estimates of the traffic state in order to be successfully implemented. This estimation problem can be solved by using particle filters in conjunction with macroscopic traffic models such as the stochastic compositional model. The accuracy of the estimates can be decreased for road segments where there are no measurements available. However, the inclusion of measurements for all segment boundaries carries a computational cost associated with the evaluation of the likelihood function required by the particle filter. To solve this problem, this paper proposes using the column based matrix decomposition method to select the most significant locations in the road network. This results in the particle filter being applied over a reduced measurement space, allowing a trade-off between computational efficiency and estimation accuracy to be achieved. A performance evaluation based on a simulated stretch of road is provided to validate the proposed method. It shows that by selecting half the original number of measurements, the computational time is reduced by approximately 9% without significantly decreasing the estimation accuracy. A more significant improvement in terms of savings in computational complexity can be expected when considering larger urban road networks.

In this paper we present a method for the tracking of interacting targets disregarding whether or not the targets are close to each other. The method relies on parametric modeling of assumptions about targets interactive motion. Our filtering solution incorporates the parameters of the model in the state vector to perform on-line parameter estimation and exploitation. The proposed method is applied in a simulated Multiple Target Tracking application with radar track-before-detect measurements. Numerical experiments show that this approach results in estimation error reduction, allows detection of interactive target behaviors and reduce labeling uncertainty in closely-spaced targets tracking.

This paper considers locating a static source on Earth using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained by a dual-satellite geolocation system. The TDOA and FDOA from the source are subject to unknown time and frequency offsets because the two satellites are imperfectly time-synchronized or frequency-locked. The satellite locations are not known accurately as well. To make the source position identifiable and mitigate the effect of satellite location errors, calibration stations at known positions are used. Achieving the maximum likelihood (ML) geolocation performance usually requires jointly estimating the source position and extra variables (i.e., time and frequency offsets as well as satellite locations), which is computationally intensive. In this paper, a novel closed-form geolocation algorithm is proposed. It first fuses the TDOA and FDOA measurements from the source and calibration stations to produce a single pair of TDOA and FDOA for source geolocation. This measurement fusion step eliminates the time and frequency offsets while taking into account the presence of satellite location errors. The source position is then found via standard TDOA-FDOA geolocation. The developed algorithm has low complexity and performance analysis shows that it attains the Cramér-Rao lower bound (CRLB) under Gaussian noises and mild conditions. Simulations using a challenging scenario with a short-baseline dual-satellite system verify the theoretical developments and demonstrate the good performance of the proposed algorithm.

The IPv6 over low-power wireless personal area network (6LoWPAN) protocol stack is a key part of the Internet of Things (IoT) where the 6LoWPAN motes will account for the majority of the IoT “things.” In 6LoWPAN networks, heavy network traffic causes congestion which significantly effects the network performance and the quality of service metrics. Generally, two main strategies are used to control and alleviate congestion in 6LoWPAN networks: 1) resource control and 2) traffic control. All the existing work of congestion control in 6LoWPAN networks use one of these. In this paper, we propose a novel congestion control algorithm called optimizationbased hybrid congestion alleviation (OHCA) which combines both strategies into a hybrid solution. OHCA utilizes the positive aspects of each strategy and efficiently uses the network resources. The proposed algorithm uses a multiattribute optimization methodology called gray relational analysis for resource control by combining three routing metrics (buffer occupancy, expected transmission count, and queuing delay) and forwarding packets through noncongested parents. Also, OHCA uses optimization theory and network utility maximization framework to achieve traffic control when the noncongested parent is not available where the optimal nodes' sending rate are computed by using Lagrange multipliers and Karush-Kuhn-Tucker conditions. The proposed algorithm is aware of node priorities and application priorities to support the IoT application requirements where the applications' sending rate allocation is modeled as a constrained optimization problem. OHCA has been tested and evaluated through simulation by using Contiki OS and compared with comparative algorithms. Simulation results show that OHCA improves performance in the presence of congestion by an overall average of 28.36%, 28.02%, 48.07%, 31.97%, and 90.35% in terms of throughput, weighted fairness index, end-toend delay, energy consumption, and buffer dropped packets as compared to duty cycle-aware congestion control for 6LoWPAN networks and queue utilization-based-IPv6 routing protocol for low-power and lossy network.

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.

Electroencephalography (EEG)-based brain computer interface (BCI) is the most studied noninvasive interface to build a direct communication pathway between the brain and an external device. However, correlated noises in EEG measurements still constitute a significant challenge. Alternatively, building BCIs based on filtered brain activity source signals instead of using their surface projections, obtained from the noisy EEG signals, is a promising and not well-explored direction. In this context, finding the locations and waveforms of inner brain sources represents a crucial task for advancing source-based noninvasive BCI technologies. In this paper, we propose a novel multicore beamformer particle filter (multicore BPF) to estimate the EEG brain source spatial locations and their corresponding waveforms. In contrast to conventional (single-core) beamforming spatial filters, the developed multicore BPF considers explicitly temporal correlation among the estimated brain sources by suppressing activation from regions with interfering coherent sources. The hybrid multicore BPF brings together the advantages of both deterministic and Bayesian inverse problem algorithms in order to improve the estimation accuracy. It solves the brain activity localization problem without prior information about approximate areas of source locations. Moreover, the multicore BPF reduces the dimensionality of the problem to half compared with the PF solution, thus alleviating the curse of dimensionality problem. The results, based on generated and real EEG data, show that the proposed framework recovers correctly the dominant sources of brain activity.

Localization of the brain neural generators that create Electroencephalographs (EEGs) has been an important problem in clinical, research and technological applications related to the brain. The active regions in the brain are modeled as equivalent current dipoles, and the positions and moments of these dipoles or brain sources are estimated. So far, the brain dipoles are assumed to be fixed or time-invariant. However, recent neurological studies are showing that brain sources are not static but vary (in terms of location and moment) depending on various internal and external stimuli. This paper presents a shift in the current paradigm of brain source localization by considering dynamic sources in the brain. We formulate the brain source estimation problem from EEG measurements as a (nonlinear) state-space model. We use the Particle Filter (PF), essentially a sequential Monte Carlo method, to track the trajectory of the moving dipoles in the brain. We further address the “curse of dimensionality,” issue of the PF by taking advantage of the structure of the EEG state-space model, and marginalizing out the linearly evolving states. A Kalman Filter is used to optimally estimate the linear elements, whereas the PF is used to track only the non-linear components. This technique reduces the dimension of the problem; thus exponentially reducing the computational cost. Our simulation results show that, where the PF fails, the Marginalized PF is able to successfully track two dipoles in the brain with only 500 particles.

In this paper, we propose a statistical approach to reconstruct the brain neuronal activity based only on recorded EEG data. The brain zones with the strongest activity are expressed at a macro level by a few number of active brain dipoles. Normally, for solving the EEG inverse problem, fixed dipole locations are assumed, independently of the different stimuli that excite the brain. The proposed particle filter (PF) framework presents a shift in the current paradigm by estimating dynamic brain dipoles, which may vary from one location to another in the brain depending on internal/external stimuli that may affect the brain. Also, in contrast to previous solutions, the proposed PF algorithm estimates simultaneously, the number of the active dipoles, their moving locations and their respective oscillations in the three dimensional head geometry.

A novel two stage data association technique for multi-target tracking is proposed which assigns multiple measurements to a target to mitigate information loss. At the first stage a variational Bayesian (VB) clustering technique is used which groups the measurements automatically into a determined number of clusters. In the second stage a belief propagation (BP) based cluster to target association method is proposed to assign multiple clusters to a target. This is achieved by exploiting the inter-cluster dependency information. The proposed technique is suitable to accommodate non-rigid targets such as humans. Both location and features of clusters are used to re-identify the targets when they emerge from occlusions. The proposed technique is compared with state of the art method due to Laet et al. and evaluations are presented on a real data set.

In this paper we present a novel compressed sensing (CS) algorithm for the recovery of compressible, possibly time-varying, signal from a sequence of noisy observations. The newly derived scheme is based on the acclaimed unscented Kalman filter (UKF), and is essentially self reliant in the sense that no peripheral optimization or CS algorithm is required for identifying the underlying signal support. Relying exclusively on the UKF formulation, our method facilitates sequential processing of measurements by employing the familiar Kalman filter predictor corrector form. As distinct from other CS methods, and by virtue of its pseudo-measurement mechanism, the CS-UKF, as we termed it, is non iterative, thereby maintaining a computational overhead which is nearly equal to that of the conventional UKF.

This is a proof of concept work that proposes a solution to the inverse problem of EEG source estimation by combining two techniques, namely a Particle Filter (PF) for geometrical (3D) localization of the most active brain zones (expressed by two dipoles) and a beamformer (BF) as a spatial filter for estimation of the oscillations that have originated the recorded EEG data. The estimation is reliable for uncorrelated brain sources.

In this paper, we propose a novel approach for video distribution over IEEE 802.16 networks for mobile Healthcare (m-Health) applications. The technique incorporates resource distribution, scheduling, and content-aware video streaming taking advantage of a flexible quality of service functionality offered by IEEE 802.16/WiMAX technology. The proposed technique is thoroughly investigated using network simulator software under various real-life m-Health scenarios, which include streaming video over medium access control layer service connections. It is shown that the technique is fully compatible with the WiMAX standard specification and allows a 9-16% increase in the overall network throughput, which is dependent upon the initial system configuration and the selection of WiMAX user parameters.

Large-scale traffic systems require techniques that are able to 1) deal with high amounts of data and heterogenous data coming from different types of sensors, 2) provide robustness in the presence of sparse sensor data, 3) incorporate different models that can deal with various traffic regimes, and 4) cope with multimodal conditional probability density functions (pdfs) for the states. Often, centralized architectures face challenges due to high communication demands. This paper develops new estimation techniques that are able to cope with these problems of large traffic network systems. These are parallelized particle filters (PPFs) and a parallelized Gaussian sum particle filter (PGSPF) that are suitable for online traffic management. We show how complex pdfs of the high-dimensional traffic state can be decomposed into functions with simpler forms and how the whole estimation problem solved in an efficient way. The proposed approach is general, with limited interactions, which reduce the computational time and provide high estimation accuracy. The efficiency of the PPFs and PGSPFs is evaluated in terms of accuracy, complexity, and communication demands and compared with the case where all processing is centralized.

The Structural SIMilarity Measure (SSIM) combined with the sequential Monte Carlo approach has been shown to achieve more reliable video object tracking performance, compared with similar methods based on colour and edge histograms and Bhattacharyya distance. However, the combined use of the structural similarity and a particle filter results in increased computational complexity of the algorithm. In this paper, a novel fast approach for video tracking based on the structural similarity measure is presented. The tracking algorithm proposed determines the state of the target (location, size) based on the gradient ascent procedure applied to the structural similarity surface of the video frame, thus avoiding computationally expensive sampling of the state space. The new method, while being computationally less expensive, has shown higher accuracy compared with the standard mean shift algorithm and the SSIM Particle Filter (SSIM-PF) and its performance is illustrated over real video sequences.

We briefly present the current state-of-the-art approaches for group and extended object tracking with an emphasis on particle methods which have high potential to handle complex structured scenarios. The big dimensionality attributed to the group tracking problem poses a major difficulty to particle filters (PFs). This in turn has motivated researchers to devise many alternatives and variants over the past decade. In this work, we corroborate and extend a single promising direction for alleviating the dimensionality problem. Our derived scheme endows a recently introduced Markov chain Monte Carlo (MCMC) PF algorithm with an improved proposal distribution. We demonstrate the performance of our approach using a nonlinear system with up to 40 states.

The development of real-time traffic models is of paramount importance for the purposes of optimizing traffic flow. Inspired by the compositional model (CM) and the METANET model, this paper proposes an interval approach for macroscopic traffic modeling. We develop an interval CM (ICM) and an interval implementation of the METANET model (IMETANET) that provide a natural way of predicting traffic flows without the assumption of uniform distribution of vehicles in a cell. The interval macroscopic models are suitable for real-time applications in road networks and can be part of road traffic surveillance and control systems. The performances of the interval approaches are investigated for both the ICM and the IMETANET models. The efficiency of the interval models is demonstrated over simulated data, and as well as over real traffic data from Motorway Incident Detection and Automatic Signalling (MIDAS) data sets from the United Kingdom.

Wireless sensor networks are an inherent part of decision making, object tracking, and location awareness systems. This work is focused on simultaneous localization of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multimodel auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localization accuracy is demonstrated.

Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. These assumptions restrict the applicability of BS methods to real-time object detection in video. In this letter, we propose an extended cluster BS technique with a mixture of symmetric alpha-stable (SαS) distributions. An online self-adaptive mechanism is presented that allows automated estimation of the model parameters using the log moment method. Results over real video sequences from indoor and outdoor environments, with data from static and moving video cameras are presented. The SαS mixture model is shown to improve the detection performance compared with a cluster BS method using a Gaussian mixture model and the method of Li et al.

In this article we investigate the application of IEEE 802.11 wireless standard to QoS (quality of service) support within wireless e-health/telemedicine services. The stringent requirements and real-time nature of medical application introduce the need for QoS provisioning in wireless medical networks. The quick delivery of a patient's measurements is an extremely important as well as reliability in terms of data delivery in the emergency care. Another important issue for wireless e-health applications is the mobility support. An ambulance, which is moving through different e-health domains, supporting different e-health applications the connectivity between the monitoring applications with the medical data source may be assured by the different wireless technologies. An overview of the e-health technologies and wireless telemedicine systems are presented and some open research problems are identified. A survey on QoS provisioning in wireless e-health, handoff schemes for QoS support in wireless networks, the scheme of priority for telemedicine/e-health services and techniques/methods for IEEE 802.11 to guarantee QoS is also provided. The simple e-health scenario is when the user has the possibility to have 'communication anywhere' within the mobile network. The technology which is in use has to provide services that would reduce patient journeys, hospital visits and hospital admissions, and would save the time of healthcare professionals, as well as would support individuals living at home and improve the quality or effectiveness of the care or treatment that is delivered. The basic requirements for the different types of application are presented spatially for the e-health/telemedicine services. To this end, we present emergency situation scenarios, designed as a possible e-health emergency scenario. Resource allocation for e-health application is inherited in many aspects including the offered services, traffic requirements, propagation's characteristics and network structure. Within medical networks delay is a critical factor, since there are many cases in which a communication should be timely in order to save the life of critical patient. This paper presents architecture and novel scenarios for QoS provisioning in emergency telemedicine. Some performance evaluation results are obtained by simulations.

Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of mobile station's (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an extended Kalman filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cramer Rao lower bound (PCRLB) is calculated and it is compared with the filters' root- mean-square error performance.

This paper addresses the problem of object tracking in video sequences. The use of a structural similarity measure for tracking is proposed. The measure reflects the distance between two images by comparing their structural and spatial characteristics and has shown to be robust to illumination and contrast changes. As a result it guarantees robustness of the tracking process under changes in the environment. The previously used Bhattacharyya distance is not robust to such changes. Additionally, when a tracker is run with the Bhattacharyya distance, histograms should be calculated in order to find the likelihood function of the measurements. With the new function there is no need to calculate histograms. A particle filter (PF) is implemented where this measure is used for computing the distance between the reference and current frame. The algorithm performance has been tested and evaluated over real-world video sequences, and has been shown to outperform methods based on colour and edge histograms

This paper addresses the problem of position localisation of mobile nodes in ad hoc wireless networks based on received signal strength indicator measurements. Node mobility is modelled as a linear system driven by a discrete command Markov process. Self-localisation of mobile nodes is performed via an interacting multiple model filter consisting of a bank of unscented Kalman filters (IMM-UKF). Estimation of the mobility state, which comprises the position, speed and acceleration of the mobile nodes is accomplished. The performance of the IMM- UKF filter is investigated and compared to a multiple model particle filter (MM PF) by Monte Carlo simulation

This paper presents a multisine approach for trajectory optimization based on information gain, with distance and orientation sensing to known beacons. It addresses the problem of active sensing, i.e. the selection of a robot motion or sequence of motions, which make the robot arrive in its desired goal configuration (position and orientation) with maximum accuracy, given the available sensor information. The optimal trajectory is parameterized as a linear combination of sine functions. An appropriate optimality criterion is selected which takes into account various requirements (e.g. maximum accuracy and minimum time). Several constraints can be formulated, e.g. with respect to collision avoidance. The optimal trajectory is then determined by numerical optimization techniques. The approach is applicable to both nonholonomic and holonomic robots. Its effectiveness is illustrated here for a nonholonomic wheeled mobile robot (WMR) in an environment with and without obstacles.