Rakeness with block sparse Bayesian learning for efficient ZigBee‐based EEG telemonitoring

Future healthcare systems are shifted toward long‐term patient monitoring using embedded ultra‐low power devices. In this paper, the strengths of both rakeness‐based compressive sensing (CS) and block sparse Bayesian learning (BSBL) are exploited for efficient electroencephalogram (EEG) transmission/reception over wireless body area networks. A binary sensing matrix based on the rakeness concept is used to find the most energetic signal directions. A balance is achieved between collecting energy and enforcing restricted isometry property to capture the underlying signal structure. Correct presentation of the EEG oscillatory activity, EEG wave shape, and main signal characteristics is provided using the discrete cosine transform based BSBL, which models the intra‐block correlation. The IEEE 802.15.4 wireless communication technology (ZigBee) is employed, since it targets low data rate communications in an energy efficient manner. To alleviate noise and channel multipath effects, a recursive least square based equalizer is used, with an adaptation algorithm that continually updates the filter weights using successive input samples. For the same compression ratio (CR), results indicate that the proposed system permits a higher reconstruction quality compared with the standard CS algorithm. For higher CRs, lower dimensional projections are allowed, meanwhile guaranteeing a correct reconstruction. Thus, low computational high quality data compression/reconstruction are achieved with minimal energy expenditure at the sensors nodes.     

IoTScal‐H : Hybrid monitoring solution based on cloud computing for autonomic middleware‐level scalability management within IoT systems and different SLA traffic requirements

Wireless sensor network (WSN) technologies have enabled ubiquitous sensing to intersect many areas of modern day living. The creation of these devices offers the ability to get, gather, exchange, and consume environmental measurement from the physical world in a communicating‐actuating network, called the Internet of Things (IoT). As the number of physical world objects from heterogeneous network environments grows, the data produced by these objects raise uncontrollably, bringing a delicate challenge into scalability management in the IoT networks. Cloud computing is a much more mature technology, offering unlimited virtual capabilities in terms of storage capacity and processing power. Ostensibly, it seems that cloud computing and IoT are evolving independently on their own paths, but in reality, the integration of clouds with IoT will lead to deal with the inability to scale automatically depending on the overload caused by the drastic growth of the number of connected devices and/or by the huge amount of exchanged data in the IoT networks. In this paper, our objective is to promote the scalability management, using hybrid mechanism that will combine traffic‐oriented mechanism and resources‐oriented mechanism, with adaption actions. By the use of autonomic middleware within IoT systems, we seek to improve the monitoring components's architectural design, based on cloud computing‐oriented scalability solution. The intention is to maximize the number of satisfied requests, while maintaining at an acceptable QoS level of the system performances (RTT of the system, RAM, and CPU of the middleware). In order to evaluate our solution performance, we have performed different scenarios testbed experiments. Generally, our proposed results are better than those mentioned as reference.     

Bispectral analysis-based approach for steady-state visual evoked potentials detection

Multimedia Tools and Applications - Brain-Computer Interface (BCI) systems are widely based on steady-state visual evoked potentials (SSVEP) detection using electroencephalography (EEG) signals....     

A new thermal infrared and visible spectrum images-based pedestrian detection system

In this paper, we propose a hybrid system for pedestrian detection, in which both thermal and visible images of the same scene are used. The proposed method is achieved in two basic steps: (1) Hypotheses generation (HG) where the locations of possible pedestrians in an image are determined and (2) hypotheses verification (HV), where tests are done to check the presence of pedestrians in the generated hypotheses. HG step segments the thermal image using a modified version of OTSU thresholding technique. The segmentation results are mapped into the corresponding visible image to obtain the regions of interests (possible pedestrians). A post-processing is done on the resulting regions of interests to keep only significant ones. HV is performed using random forest as classifier and a color-based histogram of oriented gradients (HOG) together with the histograms of oriented optical flow (HOOF) as features. The proposed approach has been tested on OSU Color-Thermal, INO Video Analytics and LITIV data sets and the results justify its effectiveness.

     

Color image segmentation by combining the convex active contour and the Chan Vese model

Pattern Analysis and Applications - In this paper, we present a robust and computationally efficient image segmentation technique based on a hybrid convex active contour and the Chan–Vese...     

Region-based facial representation for real-time Action Units intensity detection across datasets

Pattern Analysis and Applications - Most research on facial expressions recognition has focused on binary Action Units (AUs) detection, while graded changes in their intensity have rarely been...     

H∞ switching fuzzy control of solar power generation systems with asymmetric input constraint

This paper aims at presenting a maximum power point tracking (MPPT) controller for photovoltaic (PV) systems subject to asymmetric input constraint. Indeed, the output voltage of the DC‐DC converter used for adjusting the photovoltaic output power can be controlled by means of variation of duty ratio limited between 1 and 0. The control design goal is to improve the efficiency of PV systems under asymmetric saturation of duty ratio. To achieve this goal, first, a Takagi‐Sugeno (T‐S) fuzzy model is used to represent the nonlinear behavior of the PV system. A T–S reference model is employed to give the ideal state direction which must be followed. To achieve a good steady state tracking, the integral of the state tracking error is used to define an extended system state vector. Second, the input characteristic is partitioned into several regions. In each region, the asymmetric saturation function can be considered as a symmetric saturation function. Furthermore, H∞ stabilization conditions for the resulting switching fuzzy control of the PV system under actuator saturation are formulated in term of linear matrix inequalities (LMI) using the Lyapunov approach. Simulation results are exhibited to demonstrate the effectiveness of the proposed design method.     

Perception,navigation, and manipulation in the team KAUST approach to the MBZIRC ground robotics challenge

The ground robotics challenge in the Mohammed Bin Zayed International Robotics Challenge required a ground vehicle equipped with a robotic arm to autonomously locate a panel, select a proper size wrench among several options mounted on the panel, and use the wrench to rotate a valve. Autonomy was the critical factor in this challenge, which required the teams to devise algorithms that can operate successfully in a semistructured environment without human supervision. This paper presents the approaches taken by team KAUST to meet this challenge, ranging from in‐house hardware designs to algorithm integration and customization. We separated the whole objective into three interconnected tasks: Navigation, perception, and manipulation. For the navigation task, we developed a basic robotic exploration scheme to find the panel front side where the wrenches were present. For the perception task, we integrated common object detection algorithms with neural networks to identify the proper size wrench precisely. For successful manipulation, we designed and built a custom gripper, which was inspired by the common grasping behavior of a human hand under tight clearance conditions. The modular structure of the proposed approach allowed the team to progress in several subtasks simultaneously. However, the interconnection between the subtasks necessitated a reliable integration framework between these modules for effective implementation. We tuned our algorithms in extensive experimental studies and eventually obtained 10 consecutive successful navigation runs, 96% true wrench detection rate, and high success rate in wrench grasping. Furthermore, successful complete tests proved the reliability and repeatability of our system.     

Combined passivity based control and optimal control for energy management of fuel cell/battery hybrid system

The energy management of hybrid electric vehicles is becoming an interesting topic for many researchers. Furthermore, the wise choice of the energy management strategy allows not only the best distribution of the power between the used sources, but also it allows reduction of consumption, increase in the lifetime of the sources, and improves the autonomy of the hybrid electric vehicle. The autonomy is guaranteed by the optimization of the embedded sources. In this study, the hybrid system consists of combining the fuel cell as the main source with the battery as the auxiliary source. The novelty of the proposed energy management strategy for the studied hybrid system is the combination between interconnection and damping assignment‐passivity based control and the Hamiltonian Jacobi Bellman method. The stability proof is given and the efficiency of the proposed strategy is proved by the experimental work, where the obtained results show the good and adequate results to the proposed scenario.     

Hybrid control design for limit cycle stabilisation of planar switched systems

This paper addresses the control problem of an important class of hybrid dynamical systems where the desired state does not belong to the set of subsystem equilibria. Beyond the practical stabilisation where the system trajectory has to be driven to a neighbourhood of a desired state, a control law is designed to solve the limit cycle stabilisation problem for planar hybrid systems. Using the hybrid Poincaré map, two hybrid controllers are developed, which guarantee a local asymptotic/finite-time stability of the desired limit cycle. Illustrative examples are provided to highlight the effectiveness of the derived results.