Efficient real-time resource allocation for electric vehicle (EV) charging in Fog computing environments demands fast and intelligent decision-making under strict quality-of-service constraints. Traditional metaheuristics like genetic algorithms and differential evolution yield high-quality solutions but incur prohibitive computational costs, limiting their applicability in real-time systems. This paper introduces the Committee-Based Active Learning Surrogate-Assisted Particle Swarm Optimization (QBC-SA-PSO) framework, which combines multiple surrogate models with a Query by Committee (QBC) strategy to intelligently approximate fitness evaluations. By balancing exploration and exploitation, the framework drastically reduces the need for expensive exact simulations while maintaining near-optimal solution quality. Experimental validation on EV charging datasets demonstrates that PSO-SA-QBC converges within only 33 iterations, achieving a 66% reduction compared to traditional simulation techniques replaced with exact fitness evaluations, while preserving over 99% solution quality.

The rapid adoption of electric vehicles (EVs) raises critical challenges in managing charging stations, requiring efficient resource allocation to balance demand, optimize energy use, and maintain grid stability. This paper proposes FL-PSO, a hybrid strategy combining Federated Learning (FL) and Particle Swarm Optimization (PSO) for resource management in fog-cloud computing environments. FL-PSO is applied to EV charging stations and benchmarked against standard PSO, Genetic Algorithm (GA), and Cat Swarm Optimization (CSO). Evaluation is conducted using Quality of Service (QoS) metrics, including latency, energy consumption, and load balancing. Results show that FL-PSO achieves a fitness value of 0.1489, reduced energy consumption (0.2644 W), and improved load balancing (4.29), consistently outperforming baseline methods under both fixed weights and optimal QoS trade-offs. By leveraging the synergy between FL and PSO, FL-PSO provides a scalable and intelligent solution for distributed fog-cloud systems supporting smart EV charging infrastructures.

The fast expansion of the Internet of Things (IoT) creates new computational and storage challenges for both service providers and end users. IoT devices, interconnected and cooperative within Edge computing environments, generate massive amounts of data that often require real-time processing. Fog computing, a technology created to overcome the limitations of cloud computing brings computational resources closer to the edge of the network, reducing latency and enabling real-time services. It serves as a bridge between IoT devices and the cloud, collecting and processing IoT data locally, while offloading intensive tasks to the cloud when necessary. In an Edge-Fog-Cloud environment, efficient workflow scheduling is essential to optimize available resources, minimize makespan, reduce costs and save energy. However, finding an optimal solution for these conflicting objectives is a complex challenge. This paper present a Multi-Objective Monarch Butterfly Optimization Algorithm (MO-MBO) for efficient workflow scheduling in an Edge-fog-cloud environment. MO-MBO optimizes makespan, cost, and energy consumption simultaneously using Pareto dominance. Evaluations using FogWorkflowSim show the effectiveness of MO-MBO compared to traditional Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA).

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.

Falls represent the main risk of injury for elderly people. One-third of adults aged over 65 and half of people over 80 will have at least one fall a year. People at risk should visit a clinical service to detect gait difficulties. Solutions for detecting daily activities are being studied more and more, aiming to develop a complementary method to early detect this type of health risk as effectively as possible. Non-intrusiveness in the person's life for this type of problem is an important criterion, which is why current research is focusing on solutions involving non-conventional imagery such as radar systems. This paper presents an embedded system for classifying daily activities based on the processing of micro-Doppler images. The implementation of the pre-processing chain with a filter enables the acquisition of detailed spectrograms, which proves to be effective in detecting walking. Additionally, by porting it onto the Jetson Orin, it could be possible to accelerate the inference phase of the classification model. We used the ResNet-18 classification method to classify six human activities: Walking, Sitting, Standing, Picking up objects, Drinking water, and Fall events. The results showed that the model is capable of recognising most of the activities on real data.

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting conditions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.

The correlation between an ageing population glob- ally and the increased risk of falling is a real challenge for health care infrastructures. This calls for the development of new ways to monitor the elderly at home. The confidentiality of radar data coupled with its richness of information can address weaknesses of existing technologies, namely, privacy and acceptance. The radar data produce a large quantity of data that needs to be processed in real-time to ensure a timely detection of fall/critical events necessary for the well-being of the elderly. We introduce a new embedded architecture using a G PU allowing a gain in processing time compared to CPU alone. We used an off- the-shelf frequency-modulated continuous-wave (FMCW) radar (Ancortek model SDR 980AD2). It is followed by a pre-processing chain consisting of a Fast Fourier Transform, Filter and Short Time Fourier Transform (STFT) to obtain time-velocity maps or spectrograms to extract characteristics of human activities such as walking. An implementation with cuFFT on Jetson Xavier increases the performance margin for the downstream of the processing chain, the acceleration factor being 10.49 compared to state-of-the-art CPU architecture. Continuous monitoring of the subject will save lives, minimize injuries, reduce anxiety and prevent post-fall syndrome (PDS).

The growing need for speed of recent embedded systems leads to the adoption of the high speed communication PCIe protocol (Peripheral Component Interconnect Express) as an internal data bus. This technology is used in some recent smartphones, and will be probably adopted by the others in the next few years. The communication between the SoC and its memory through the PCIe bus represent an important source of information for criminal investigations. In this paper, we present a new reliable attack vector on PCIe. We chose to perform a hardware Man-in-the-Middle attack, allowing real-time data analysis, data-replay and a copy technique inspired by the shadow-copy principle. Through this attack, we will be able to locate, duplicate and replay sensitive data. The main challenge of this article is to develop an architecture compliant with PCIe protocol constraints such as response time, frequency and throughput, in order to be invisible to the communication parts. We designed a proof of concept of an emulator based on a computer with PCIe 3.0 bus and a Stratix 5 FPGA with an endpoint PCIe port as development target.

Pulse oximetry is one of the most commonly employed monitoring modalities in critical care setting. Conventional pulse oximeters use two leds at different wavelengths and a photodiode to estimate blood oxygen saturation noninvasively, based in the difference of absorption coefficients between hemoglobin and deoxyhemoglobin. Nevertheless, factors such as low oxygen saturations, skin melanin content, nail polish presence or led wavelength displacement, modify differently the expected light absorbance for both LEDs. To address these issues, a novel approach combining a single led and a Buried Quad Junction photodetector is proposed. With this fundamental modification of the pulse oximetry principle, errors associated with the aforementioned modifying effects are expected to be reduced. The preliminary results show that there is a correlation of 0.84, -0.93, 0.91 and -0.88 for channels 1, 2, 3 and 4 respectively between our proposed method and the response from the measuring reference.

Fibrosis represents an open issue for medium to long-term active implants given that this biological medium surrounds the stimulation electrodes and can impact or modify the performances of the system. For this reason, Embedded Impedance Spectroscopy techniques has been investigated these last years to sense the fibrosis. The following article introduces a new paradigm for Electrochemical Impedance Spectroscopy (EIS) derived from multi-carrier digital communication methods. Due to its properties of flat spectrum and fast generation the Orthogonal-Frequency Division Multiplexing (OFDM) technique for EIS seems to be a real alternative to traditional ones. This article focuses on this approach and defines its performances on gold electrodes used for in-vitro experiments. An embedded implementation is also presented. This designed prototype allows to measure a medium with an error between 2% and 3%, when stimulating with a 16 or 32 tones multitone signal and a sampling frequency of 12KHz.

The following article introduces a wireless and portable system for Electrochemical Impedance Spectroscopy (EIS) sensing based on the Orthogonal Frequency-Division Modulation (OFDM). This technique is derived from multi-carrier digital communication methods and due to its properties of flat spectrum, fast generation and low-foot print memory, the OFDM technique for EIS seems to be a real alternative to traditional ones. The manufacture of the OFDM-EIS system is under the framework of the electrical sensing of fibrosis induced by medium to long-term active implants, given that the detection of fibrous tissues surrounding the electrode, that usually affect the operation of the implant, is an open research problem. Because of this, this article defines its performance on living cells under in-vitro experimentation where the proliferation of cells are matched with the results. Furthermore, traditional EIS techniques, such as frequency sweep and multi-sine are compared to OFDM-EIS.

In this paper, we propose an approach to tackle the localization challenge for autonomous vehicles by taking inspiration from biological models. We present a neural architecture based on a neurobotic model of the place cells found in the hippocampus of mammals. This model is based on an attentional mechanism and only takes into account visual information from a mono-camera and the orientation information to self-localize. Such a localization model has already been integrated in a robot control architecture which allows for successful navigation both in indoor and small outdoor environments. The contribution of this paper is to study how it passes the scale change by evaluating the performance of this model over much larger outdoor environments. Six experiments, taken from the KITTI datasets, using real data (image and orientation) grabbed by a moving vehicle are studied. The results show the strong adaptability of the model to different kinds of environments.

This paper discusses the place of radar for assisted living in the context of IoT for Health and beyond. First, the context of assisted living and the urgency to address the problem is described. The second part gives a literature review of existing sensing modalities for assisted living and explains why radar is an upcoming preferred modality to address this issue. The third section presents developments in machine learning that helps improve performances in classification especially with deep learning with a reflection on lessons learned from it. The fourth section introduces recent published work from our research group in the area that shows promise with multimodal sensor fusion for classification and long short-term memory applied to early stages in the radar signal processing chain. Finally, we conclude with open challenges still to be addressed in the area and open to future research directions in animal welfare.

Real-time monitoring of hematophagous diptera (such as mosquitoes) populations in the field is a crucial challenge to foresee vaccination campaigns and to restrain potential diseases spreading. However, current methods heavily rely on costly DNA extraction which is destructive, costly, time consuming and requires experts. The contributions of this work are: 1) the usage of a new type of imaging, named Wing Interference Patterns (WIPs), which is non-destructive and easier to produce during in the field experiments; 2) a deep learning architecture which is optimized for very low computation cost, memory usage and a short inference time; 3) the use of a dataset of more than 50 medically important species of hematophagous diptera with more than 3000 images of WIPs. With these contributions, we demonstrate that WIPs are an excellent medium to automatically recognize a large amount of hematophagous diptera species with very high accuracy and low computational cost convolutional neural network.

In this article, our concern is the early diagnosis of colorectal cancer from a computeraided detection point of view in order to help physicians in their diagnosis during the gold standard examination: optical video colonoscopy. Since many years, some methods and materials have been developed to reduce the polyp miss rate and to improve detection capabilities. Nevertheless, the real challenge lies in the real-time use of these methods. In this context, more precisely, we focus our attention on the hardware implementation of a previous method we recently introduced in the literature for real-time detection of colorectal polyps, lesions that may degenerate into cancer. This implementation is subject to three performance criteria: real-time processing capabilities, detection rate and necessary computational resources. Six different platforms were tested and compared. If we noticed that only workstation computers are able to perform the detection with a good tradeoff between the three aforementioned criteria, possibilities of architecture optimizations are also identified and discussed in order to achieve real-time performance on platforms with low available computational resources like Raspberry Pi for instance. This latter issue is of major importance for possible integration of the detection algorithm inside smallconnected object like videocapsule, a promising alternative to standard colonoscopy.

A wireless and portable system, consisting in an electronic board and a computer software graphical interface, is presented in this article as a feasible way to do in-vitro electric bioimpedance spectroscopy of cells. It is designed to work inside a culture chamber performing impedance measurement in the frequency range of 64Hz to 200KHz. The board is equipped with a Bluetooth Low Energy (BLE) module allowing it to be wirelessly controlled. The software interface (also called ISMI) is coded with all required functionalities to manage the parameters of the board such as start frequency and sweep, measuring intervals, data acquisition and visualization and also a function to perform automatic measuring between desired time intervals during whole day for many days. In addition, the electrodes used for the measurements of cells are characterized giving an impedance magnitude between 103 to 105 ohms in a frequency range of 300Hz to 100KHz and a maximum voltage without considerable electrode deterioration of 120mVpeak. This information is used in the calibration of the ISMI system giving a measurement accuracy above 98% when compared with simulation results and with a reference instrument. The proposed system is an introductory step in the study of cells related to fibrous tissue induced by implants showing to be a viable and reproducibility-improving approach for such analysis. This first prototype provides information regarding the requirements for the design of an integrated version for embedded applications.

FPGAs are very sensitive to their operating conditions which can induce runtime errors. To prevent timing errors, FPGA manufacturers propose static timing analysis tools to ensure that the application to be implemented in the FPGA will work correctly at the expected frequency. However, that static timing analysis is corner-based and is thus valid for a set of optimal or recommended operating conditions. In the same time, the real operating conditions can be outside these corners when the FPGA is used in a harsh environment. In this paper, we propose a static timing analysis enhancement technique based on the real operating conditions that the FPGA will encounter. Thus, the static timing analysis can be done outside the predefined corners to ensure that the FPGA application will execute correctly in the real operating conditions. We also present some results showing the accuracy of our method and its application to an automotive application intended to be deployed in an aggressive environment.

Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.

This paper presents an empirical method to model the impact of industrial harsh electromagnetic environment on a Field-Programmable Gate Array (FPGA). The methodology focuses principally on the magnetic field effects and is based on three phases: first, an assessment of the radiated magnetic field of the considered source is performed by measurements or by simulations based on Finite Element Method (FEM). Then, the sensitivity of the FPGA to radiated magnetic field is experimentally evaluated. Finally, by combining these results, a parametric model characterizing the effects of a magnetic field on an FPGA is established. This model can then be integrated into a CAD tool in order to take these effects into account at the design stage of the FPGA-based system. This method is applied to investigate the effects of an electric car's high-power electric motor on its control system implemented in FPGA.

This paper introduces flexible radio techniques inside integrated circuits in order to tackle the interconnect issue for many-core chips. We propose to take benefits from OFDMA for a RF-interconnect associated to a carrier allocation policy and adaptive modulation. A 20 GHz bandwidth is shared between 32 tile sets made of 32 tiles of 4 cores each, for a 4096 cores chip. We adopt a cognitive radio approach in order to dynamically share 1024 carriers, which avoids inter-cluster communication contention and decreases latency compared to conventional static approaches.

A paradigm shift is apparent in Chip Multiprocessor (CMP) design, as the new performance bottleneck is becoming communication rather than computation. It is widely provisioned that number of cores on a single chip will reach thousands in a decade. Thus, new high rate interconnects such as optical or RF have been proposed by various researchers. However, these interconnect structures fail to provide essential requirements of heterogeneous on-chip traffic; bandwidth reconfigurability and broadcast support with a low complex design. In this paper we investigate the feasibility of a new Orthogonal Frequency Division Multiple Access (OFDMA) RF interconnect for the first time to the best of our knowledge. In addition we provide a novel dynamic bandwidth arbitration and modulation order selection policy, that is designed regarding the bimodal on-chip packets. The proposed approach decreases the average latency up to 3.5 times compared to conventional static approach.

Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the identification of polyps. In this paper, a new multimodal embeddable method for polyp detection and classification in wireless capsule endoscopic images was developed and tested. The multimodal wireless capsule used both 2D and 3D data to identify possible polyps and to deliver cancerous information of the polyps based on 3D geometric features. Possible polyps within the image (2D) were extracted using simple geometric shape features and, in a second step, the candidate regions of interest (ROI) were evaluated with a boosting-based method using textural features. Once the 2D identification of polyps has been performed, the two-class (“malignant” or “begnin”) classification of the polyps is achieved using the 3D parameters computed from the preselected ROI using an active stereo vision system. At this stage, a Support Vector Machine (SVM) classifier is used to proceed to the final classification and to make possible a pre diagnosis. The new proposed multimodal approach based on 2D-3D feature extraction improves WCE capabilities to identify and classify polyps: The boosting-based polyp classification demonstrated a sensitivity of 91%, a specificity of 95% and a false detection rate of 4.8% on a database composed of 300 hundred positive examples and 1200 negative ones; Considering the 3D performance, a large scale demonstrator was evaluated and tested to perform in vitro experiments on an ad hoc polyp database. The performance of the 3D approach achieved a correct classification rate (malignant or benin) of approximately 95%.

This paper presents an FPGA platform for the design and study of network of coupled All-Digital Phase Locked Loops (ADPLLs), destined for clock generation in large synchronous System on Chip (SoC). An implementation of a programmable and reconfigurable 4×4 ADPLL network is described. The paper emphasizes the difference between the FPGA and ASIC-based implementation of such a system, in particular, implementation of digitally controlled oscillators and phase-frequency detector. The FPGA-implemented network allows studying complex phenomena related to coupled ADPLL operation and exploiting stability issues and nonlinear behavior. A dynamic setup mechanism has been proposed for the network, allowing selecting the desirable synchronized state. Experimental results demonstrate the global synchronization of network and performance of the network for different configurations.

We introduce an empirical method for power consumption modeling of analog components at system level. The principal step of this method uses neural networks to approximate the mathematical curve of the power consumption as a function of the inputs and parameters of the analog component. For a node of a wireless sensors network, we found an average error of 1.53% with a maximum error of 3.06% between our estimation and the measured power consumption. This novel method is suitable for Platform-Based Design and has three key features for architecture exploration purposes. Firstly, the method is generic as it can be applied to any analog component in any modeling and simulation environment. Secondly, the method is suitable for the total (analog and digital) power consumption estimation of a heterogeneous system. Thirdly, the method provides an online estimation of the instantaneous power consumption of analog blocks.

The paper describes the prototype of a broadcast media indexing engine based on a 64-point weighted-overlap-add DFT filter bank implemented in an FPGA. In its present configuration, the device simultaneously demodulates and displays a speech/music flag for 5 FM radio stations, with audio output routed to a loudspeaker via a user-operated switch. Extension to the full FM radio band will be possible using a larger FPGA.

In this paper, we present MMJPEG2000 (mask motion JPEG2000), a new video compression algorithm based on the motion-JPEG2000 standard, it operates through the masking of a difference image by a binary motion map resulting from a Markovian process. This algorithm performs self-adaptation to the context variation of the image sequence. The achieved results show that it reduces the data rate while significantly improving the visual quality of motion JPEG2000. It will thus enable the transmission of video sequences on different wireless networks with good visual quality.

This paper describes an original stereoscopic sensor acquisition of color spherical images derived from panoramic images taken at different 3D space positions. The sensor principle is based on the rotation, under the control of two stepping motors, of two monochromatic linear CCD cameras around two perpendicular axes. The first axis, horizontal gets through the optical centers of the two cameras and allows acquiring color stereo-panoramas. The second axis, vertical gets through the middle of the optical centers. It extends the acquisition to the whole scene. The mechanical sensor architecture allows us to obtain cylindrical stereoscopic images along a ring path. A specific orthographic projection from the color panoramic images provides a couple of color spherical images. Experimental results show the high precision of the acquisition and high quality of the color spherical pictures. This sensor is particularly well adapted for the acquisition of scenes over 360/spl times/360 degrees for multimedia and motion pictures.

This paper describes the development of an original stereoscopic sensor suitable for 3D spherical reconstruction Unlike the existing sensors, it uses four acquisition channels coupled with an additional structured light projector to obtain an accurate 3D spherical reconstruction with faithful colors. Real experiments demonstrate the feasibility of this new spherical sensor. This original sensor is dedicated to real scenes reconstruction for multimedia and motion pictures applications over 360/spl times/360 degrees.