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The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain's electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor's lifespan and creates doubt regarding the application's feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples. [ABSTRACT FROM AUTHOR]

The availability of low-cost embedded devices for multimedia sensing has encouraged their integration with low-power wireless sensors to create systems that enable advanced services and applications referred to as the Internet of Multimedia Things. Image-based sensing applications are challenged by energy efficiency and resource availability. Mainly, image sensing and transmission in Internet of Multimedia Things severely deplete the sensor energy and overflow the network bandwidth with redundant data. Some solutions presented in the literature, such as image compression, do not efficiently solve this problem because of the algorithms' computational complexities. Thus, detecting the event of interest locally before the communication using shape-based descriptors would avoid useless data transmission and would extend the network lifetime. In this article, we propose a new approach of distributed event-based sensing scheme over a set of nodes forming a processing cluster to balance the processing load. This approach is intended to reduce per-node energy consumption in one sensing cycle. The conducted experiments show that our novel method based on the general Fourier descriptor decreases the energy consumption in the camera node to only 2.4 mJ, which corresponds to 75.32% of energy-saving compared to the centralized approach, promising to prolong the network lifetime significantly. In addition, the scheme achieved more than 95% accuracy in target recognition. [ABSTRACT FROM AUTHOR]

The widely adopted paradigm in brain–computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, which lead to a complex process of classifying and finding the potential tasks of a specific participant. Another issue is that BCI systems can result in noisy data and redundant channels, which in turn can lead to increased equipment and computational costs. To address these problems, the optimal channel selection of a multiclass MI classification based on a Fusion convolutional neural network with Attention blocks (FCNNA) is proposed. In this study, we developed a CNN model consisting of layers of convolutional blocks with multiple spatial and temporal filters. These filters are designed specifically to capture the distribution and relationships of signal features across different electrode locations, as well as to analyze the evolution of these features over time. Following these layers, a Convolutional Block Attention Module (CBAM) is used to, further, enhance EEG signal feature extraction. In the process of channel selection, the genetic algorithm is used to select the optimal set of channels using a new technique to deliver fixed as well as variable channels for all participants. The proposed methodology is validated showing 6.41% improvement in multiclass classification compared to most baseline models. Notably, we achieved the highest results of 93.09% for binary classes involving left-hand and right-hand movements. In addition, the cross-subject strategy for multiclass classification yielded an impressive accuracy of 68.87%. Following channel selection, multiclass classification accuracy was enhanced, reaching 84.53%. Overall, our experiments illustrated the efficiency of the proposed EEG MI model in both channel selection and classification, showing superior results with either a full channel set or a reduced number of channels. [ABSTRACT FROM AUTHOR]

Wireless acoustic sensor networks represent an attractive solution that can be deployed for animal detection and recognition in a monitored area. A typical configuration for this application would be to transmit the whole acquired audio signal through multi-hop communication to a remote server for recognition. However, continuous data streaming can cause a severe decline in the energy of the sensors, which consequently reduces the network lifetime and questions the viability of the application. An efficient solution to reduce the sensor's radio activity would be to perform the recognition task at the source sensor then to communicate the result to the remote server. This approach is intended to save the energy of the acoustic source sensor and to unload the network from carrying, probably, useless data. However, the validity of this solution depends on the energy efficiency of performing on-sensor detection of a new acoustic event and accurate recognition. In this context, this paper proposes a new scheme for on-sensor energy-efficient acoustic animal recognition based on low-complexity methods for feature extraction using the Haar wavelet transform. This scheme achieves more than 86% in recognition accuracy while saving 71.59% of the sensor energy compared with the transmission of the raw signal. [ABSTRACT FROM AUTHOR]

Applications based on video and image in wireless sensor network are highly attractive due to their wealth of information. In this context, application for object recognition and tracking using image and video information is one the attractive approaches that can be applied for event detection and localization, security processes, following of the rare animal species and control of road traffic, and so forth. However, the implementation of such approach with WMSN requires a specific image processing scheme and efficient transmission protocol. In fact, because of the limited energy of the batteries embedded in motes, the power consumption is the major constraint facing network life time and reliability in WMSN. The efficiency and the validity of these multimedia applications over wireless sensor networks are then dependent on the capabilities of the designer to provide low-power scheme for data processing and energy-aware transmission protocols. This paper presents a contribution to the design of low complexity scheme based on object identification for efficient sensing of multimedia information in wireless multimedia sensor networks. It proposes a new solution and explores the associated performances of this scheme. The presented results in this paper attest the high efficiency to achieve low-power objects identification when implemented in wireless motes. [ABSTRACT FROM AUTHOR]

Embedded distributed multimedia applications based on the use of on-chip networks for communication and messages exchange requires specific and enhanced quality of service (QoS) management. To reach the desired performances at the application level, the network-on-chip (NoC) router should implement per flit handling strategy with wide granularity. This purpose requires an enhanced internal architecture that ensures from one hand a specific management according to a service classification and from the other hand, it enhances the routing process. In this context, this paper proposes a new mechanism for QoS management in NoC. This mechanism is based on the use of central memory where flits are in-queued according to their class of service. This scheme enables an optimal flit scheduling phase and provides more capabilities to drop low important flits when the router shows congestion state symptoms. The paper presents, also, a protocol structure that fills with this architecture and introduces a signaling mechanism to make efficient the QoS management through the proposed architecture. The circuit performances and its adaptability to achieve QoS with low power processing and high bandwidth in on chip multiprocessor systems will be studied in this paper. [ABSTRACT FROM AUTHOR]

Abstract: Software implementation costs of most algorithms, designed for image compression in wireless sensor networks, do not justify their use to reduce the energy consumption and delay transmission of images. Even though the hardware solution looks to be very attractive for this problem, a specific care should be paid when designing a low power algorithm for image compression and transmission over these systems. The aim of this paper is to present and evaluate a hardware implementation for user-driven image compression scheme designed to respect the energy constraints of image transmission over wireless sensor networks (WSNs). The proposed encoder will be considered as a co-processor for tasks related with image compression and data packetization. In this paper, we discuss both of the hardware architecture and the features of this encoder circuit when prototyped on FPGA (field-programmable gate array) and ASIC (application-specific integrated circuit) circuits. [Copyright &y& Elsevier]

Abstract: Demand for low-power sensing devices with integrated image processing capabilities is increasing, especially for resource-constrained systems such as WSNs. CMOS technology enables the integration of image sensing and image processing, which improves the overall system performance. The aim of this paper is to present and evaluate a smart image sensor integrating a user-driven video compression scheme designed to respect the energy constraints of image processing and transmission over WSNs. The interest of our solution is twofold. First, compression settings can be changed at run time depending on video characteristics. Second, compression is applied only on blocks that change significantly over time. This paper presents in details the internal hardware architecture of the proposed system and describes its performance, with relevant comparisons to some related works. [Copyright &y& Elsevier]

Abstract: The performances of multimedia applications built on wireless systems depend on bandwidth availability that might heavily affect the quality of service. The IEEE 802.11 standards do not provide performed mechanism for bandwidth management through data load distribution among different APs of the network. Then, an AP can be heavily overloaded causing throughput degradation. Load Balancing Algorithms (LBAs) was been considered as one of the attractive solution to share the traffic through the available access points bandwidths. However, applying the load balancing algorithm and shifting a mobile connection from an access point to another without considering the received signal strength indicator of the concerned APs might causes worst communication performances. This paper is a contribution to check the performance’s limits of the LBA algorithm through experimental evaluation of communication metrics for MPEG-4 video transmission over IEEE 802.11 network. Then, the paper focuses on the proposition of a new LBA algorithm structure with the consideration of the RSS level. [Copyright &y& Elsevier]

Abstract: Systems mapped on CMOS architectures are often expected to achieve high processing bandwidth and low energy consumption. However, a specific care should be paid to adequate the algorithm structure to the circuit architecture when designing multimedia wireless embedded networking systems. This paper addresses the problem of low power consumption and real time constraints for image communication in wireless camera sensor networks (WCSN). It presents a low-complexity hardware implementation of JPEG-like encoder for image compression and paquetization. The designed circuit is planned to be embedded in the camera sensor node to relieve the main processor of the data processing tasks. This encoder combines the best lifting DCT algorithm of the literature with a zonal coding approach. The former reduces the number of operations required per DCT coefficient while the latter reduces the number of coefficients to be computed, quantized and encoded. We study the tradeoff between the size of the zonal mask (a square zone of size k) and the visual image quality as a function of the compression bitrate, then we describe the hardware features of the JPEG-like circuit when implemented on different FPGAs and ASIC prototypes. Performance evaluation is provided for several ranges of compression bitrate, accordingly with the right value of k. Considering a grayscale image compressed to 0.25bpp for example, k=4 is the best choice. In this case, and for an image of 128×128 pixels, the CMOS circuit of the proposed encoder, synthesized using 45nm integration technology, clocks at 360MHz and consumes 18.02mW. It outperforms most of similar circuits being presented in the literature. [Copyright &y& Elsevier]

Abstract: In this paper, we present and evaluate a hardware solution for user-driven and packet loss tolerant image compression, especially designed to enable low power image compression and communication over wireless camera sensor networks (WCSNs). The proposed System-on-Chip is intended to be designed as a hardware coprocessor embedded in the camera sensor node. The goal is to relieve the node microcontroller of the image compression tasks and to achieve high-speed and low power image processing. The interest of our solution is twofold. First, compression settings can be changed at runtime (upon reception of a request message sent by an end user or according to the internal state of the camera sensor node). Second, the image compression chain includes a (block of) pixel interleaving scheme which significantly improves the robustness against packet loss in image communication. We discuss in depth the internal hardware architecture of the encoder chip which is planned to reach high performance running in FPGAs and in ASIC circuits. Synthesis results and relevant performance comparisons with related works are presented. [Copyright &y& Elsevier]

Abstract: In this paper, we address the problem of the end-to-end QoS management for multimedia and real-time applications. We would focus on the presentation of a multi-profile communication environment and its reliability in ensuring end-to-end QoS management in the network communication processes. This communication environment, called Virtual Communication Support (VCS), provides a dynamic assignment of application requirements to the network resources reservation and protocol layers invocation. The advantage of this model is that it performs the capabilities of the network by abstracting the details of all communication stacks-layers that must be invoked in the communication process from application specificities and needs. A great attention is devoted to the end-to-end QoS management in the VCS communication model. We would present our approach for the development of a transport middleware for end-to-end QoS control. This middleware performs the QoS management with an adaptive and predictive algorithm based on the network jitter measurement. The main contribution of this paper is that it provides a solution to predict congestion during communication process and then it can avoid network overloading. This is of several importances when we are streaming multimedia real-time packets. In fact, when real-time application losses some packets it may lead to unfavorable situations. Our approach for the specification and the design of the middleware is based on the use of the Specification and Description Language (SDL). This is a formal description technique that enables model validation extension and reusability. The dynamic behavior of this model is being checked with the Message Sequence Chart (MSC). The simulation and validation of this middleware was performed for the VCS. It was simulated by The Network Simulator (NS). Some profiles with the JAVA language on the Fast-Ethernet Local Area Network. Its testing was performed for a real-time manufacturing process and multimedia supervision for control processes. [Copyright &y& Elsevier]

The development of wireless acoustic sensor networks has driven the use of acoustic signals for target monitoring. Most monitoring applications require continuous network connectivity and data transfers, which can rapidly exhaust nodes' energy. Consequently, sensors must collaborate in an adequate architecture to perform target recognition and localization tasks and then to send the results to a remote server with a reduced data volume. The design of an energy-efficient scheme that achieves acoustic target recognition and localization remains an open research problem. Accordingly, this article proposes a low-energy acoustic-based sensing scheme for target recognition and localization to be implemented in a cluster-based sensing approach designed to appropriately balance energy consumption and local processing performed by sensor nodes. A reduced set of low-complexity feature extraction methods in the time domain signal are used in the recognition process. The scheme uses the received energy of the acoustic signals for the target localization. This article details the network architecture, the scheme specification, and its implementation. The results show that the scheme can classify targets with 81.34% accuracy. It requires 3.2 mJ of energy when executed in MICAz, achieving 99% energy savings compared to streaming 3 s of an acoustic signal to a remote server. [ABSTRACT FROM AUTHOR]

Scoring polysomnography for obstructive sleep apnea diagnosis is a laborious, long, and costly process. Machine learning approaches, such as deep neural networks, can reduce scoring time and costs. However, most methods require prior filtering and preprocessing of the raw signal. Our work presents a novel method for diagnosing obstructive sleep apnea using a transformer neural network with learnable positional encoding, which outperforms existing state-of-the-art solutions. This approach has the potential to improve the diagnostic performance of oximetry for obstructive sleep apnea and reduce the time and costs associated with traditional polysomnography. Contrary to existing approaches, our approach performs annotations at one-second granularity. Allowing physicians to interpret the model's outcome. In addition, we tested different positional encoding designs as the first layer of the model, and the best results were achieved using a learnable positional encoding based on an autoencoder with structural novelty. In addition, we tried different temporal resolutions with various granularity levels from 1 to 360 s. All experiments were carried out on an independent test set from the public OSASUD dataset and showed that our approach outperforms current state-of-the-art solutions with a satisfactory AUC of 0.89, accuracy of 0.80, and F1-score of 0.79. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias, and it is an indication of high-risk factors for stroke, myocardial ischemia, and other malignant cardiovascular diseases. Most of the existing AF detection methods typically convert one-dimensional time-series electrocardiogram (ECG) signals into two-dimensional representations to train a deep and complex AF detection system, which results in heavy training computation and high implementation costs. In this paper, a multiscale signal encoding scheme is proposed to improve feature representation and detection performance without the need for using any transformation or handcrafted feature engineering techniques. The proposed scheme uses different kernel sizes to produce the encoded signal by using multiple streams that are passed into a one-dimensional sequence of blocks of a residual convolutional neural network (ResNet) to extract representative features from the input ECG signal. This also allows networks to grow in breadth rather than in depth, thus reducing the computing time by using the parallel processing capability of deep learning networks. We investigated the effects of the use of a different number of streams with different kernel sizes on the performance. Experiments were carried out for a performance evaluation using the publicly available PhysioNet CinC Challenge 2017 dataset. The proposed multiscale encoding scheme outperformed existing deep learning-based methods with an average F1 score of 98.54%, but with a lower network complexity. [ABSTRACT FROM AUTHOR]

With the 802.11 WLAN multimedia applications (Video, Audio, real-time voice over IP,…) increasing, providing Quality of Service (QoS) support becomes very important since the original standard doesn't take QoS into account. The standard offers access to the wireless users only regarding physical considerations. This can lead to overloaded access points (AP) and considerable degradation of the QoS. This paper deals with this problem. It focuses on the presentation of a QoS management solution for wireless communication systems. It mainly defends that a balanced distribution of mobile stations among the available access points leads to better performances of the Wireless LAN. Some OPNET simulations of the proposed approach are presented to show a better resources allocation and efficiency on QoS metrics. A protocol structure between mobiles and APs is also specified for the implementation of this approach. An SDL description and MSC simulation of this protocol is provided as a first step in its development. [ABSTRACT FROM AUTHOR]