Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal control of nonlinear reaction-diffusion systems using a team of mobile actuators with fractional dynamics

This paper is concerned with the optimal control of nonlinear reaction-diffusion systems by utilizing a team of mobile actuators under fractional dynamics. For this purpose, an optimization problem that is associated with the optimal control of nonlinear reaction-diffusion system and the optimal guidance of mobile actuators is proposed. To solve this optimization problem, we first use Galerkin method to derive a set of reduced ordinary differential equations (ODEs) of the nonlinear reaction-diffusion system. Model predictive control (MPC) method is then presented to numerically solve the proposed optimization problem using the reduced finite-dimensional system approximation, from which we can synthesize the optimal control strategy of nonlinear reaction-diffusion systems and the optimal guidance of mobile actuators. A numerical example is finally included to illustrate the effectiveness of proposed optimal control strategy.
Aspects algorithmiques de la décomposition modulaire

La decomposition modulaire apparait naturellement dans differents domaines de la combinatoire (et en particulier les graphes). Cette decomposition se revele etre un puissant outil de description d'objets discrets. Elle est aussi utilisee comme etape preliminaire a nombreux d'algorithmes. Dans ce memoire, nous nous interessons au calcul de la decomposition modulaire. Malgre la publication d'algorithmes lineaires au milieu des annees 90, la recherche sur ce probleme n'a pas cessee. Nous faisons le point sur les differentes avancees et techniques utilisees.
Violence Detection in Schools Based on Multi Fusion Sensor and Optimized Relief-F Algorithm

In elementary schools, bullying is a widespread social issue. Early school violence is considered more destructive than other school violence around the world. It is advisable to use fusion and advanced relief-F instructions as a multi-censor-based approach for early school violence identity. Two important Motion sensors are collecting data in the detection of violence in elementary schools and our daily practices. There are nine different categories of violence. The qualities and features of the time and frequency of a province are restored and filtered by an improved relief-F system. The authors create a two-level classification next. The first-level assortment separates the random forest jump function from others. In the previous paper, the author used an end wood assortment; However, in the recommended work, a random forest assortment is used, and the next level uses a network with radial basic functions to identify the remaining 9 types of functions. An end layer fusion system is used later to combine the results of the recognition of the two sensors. According to our research, everyday life can be recognized with 98.1% accredited accuracy, while elementary school violence can be recognized with an average of 85.9% accuracy.
Wireless Distributed Consensus in Vehicle to Vehicle Networks for Autonomous Driving

Vital societal and industrial autonomous components are increasingly interconnected through communication networks to complete critical tasks cooperatively. However, as the reliability and trust requirements for connected autonomous systems continue to grow, the centralized communication and decision approaches that are in use today are reaching their limits. Focusing on autonomous driving applications, this paper proposes a resilient and trustworthy framework on wireless distributed consensus networks, where the communication links are less reliable or are even in the presence of incorrect local sensor readings/decisions. To accomplish that, a novel three stages consensus mechanism is proposed based on the practical Byzantine fault tolerance (PBFT), where the veto collection and gossip stages are designed to meet the stringent and complex requirements for a vehicle's maneuvers. A plan tree synthesis is also proposed to make consensus on a series of decisions while adopting network members' decision preferences. A detailed protocol including the distributed consensus, plan tree synthesis, dynamic grouping, etc. is proposed. Simulation results show that the proposed consensus mechanism is able to be reached and propagated through the network under poor wireless communication conditions and the presence of faulty vehicles with incorrect sensor readings. The result can be extended to other autonomous systems to significantly enhance safety in critical industrial applications.
Efficient Block-sparse Signal Recovery with Application to ECG Compression

Compressed sensing enables sparse signals recovery from few linear measurements. For many types of signals, such as those which exhibit sparsity in the wavelet domain, the non-zero elements occur in blocks. Signals of this type are referred to as block-sparse signals. This paper proposes a block-sparse signal reconstruction algorithm, block adaptive matching pursuit (BAMP), which is characterized by high speed and accuracy. In this algorithm, a number of blocks is selected in each iteration, which is adapted according to the average correlation between the signal and blocks of the measurement matrix. Moreover, in each iteration, the support is refined, or pruned, to exclude blocks that do not contribute to the signal. We apply BAMP to ECG signal recovery from compressed measurements. Simulations illustrate significant improvements in accuracy and speed in comparison to other related block-sparse recovery algorithms for random and ECG signals.
A Comparative Study of Anti-Spoofing Detection Based on Deep Learning

In the developing field of biometrics, technology is improving our ability to automatically recognize persons and protect access. The face is an excellent option for an authentication application because most devices can be equipped with a camera. Fraudsters can falsify biometric data by using, for example, photos of faces instead of the real face. So, it is essential to confirm that the biometric is that of a real, live person. Face anti-spoofing is one of the methods to combat this type of fraud. The objective of this work is to propose a comparative study between 5 powerful deep learning architectures for anti-spoofing namely, DenseNet201, DenseNet169, VGG16, MiniVGG, InceptionV3, and ResNet50. We used in this study the ROSE-Youtu Face Liveness Detection dataset. Experimental results show the effectiveness of the use of deep learning and especially the MiniVGG architecture in terms of maximum accuracy and traditional performance evaluation measures.
Machine Learning Integrated Software Defined Networking Architecture for Congestion Control

Data center networks rely upon the excellent throughput of the network to ensure data transfer happens at incredible speeds. Many factors can determine the network’s throughput, such as the design, routing mechanism, and congestion control model. The introduction of the cloud made it more challenging as high traffic can happen and disrupt the data transfer for the cloud applications; this is a challenge in hosting cloud services and applications. The introduction of Software Defined Networking (SDN) made it possible to program the networks and thus provide scope for enhancing the congestion control mechanisms. Separating the control plane from the data plane helps in independence and extracting and processing the data from different perspectives. This paper proposes a Machine Learning approach implementable in an SDN context that helps reduce congestion control by learning data transfer patterns. We propose a modified version of SDN in which an additional layer is implemented above the control plane called ML plane. ML plane takes care of the trend learning in the network that guides the control plane for better decisions. In this paper, we exhibit the architecture of ML plane for the purpose of congestion control in busy networks.
An Experimental Testbed for 5G Network Security Assessment

The Fifth Generation (5G) mobile networks are designed to provide a large range of services with stringent requirements in robustness and security. Thus, it is important to ensure that these networks fulfill these requirements and are resilient against attacks. To meet this challenge, experimental testbeds and tools are required to test and evaluate the security of 5G networks. This work presents an experimentation testbed and support tools for generating and injecting on the fly 5G packets to realize multiple security assessing tasks in particular fuzzing operations for vulnerabilities discovery. Our tool is implemented and tested within a controlled testbed environment built on top of a 5G standalone core server provided by a hardware base station (gNb) and uses an Software Defined Radio(SDR) card for radio transmission. We validate our testbed and the developed tools by successfully injecting at the 5G air interface and the network control levels different messages including RRC and NGAP/NAS over already established communications.
Robust Online Multiuser Detection: A Hybrid Model-Data Driven Approach

We study the problem of robust online multiuser detection in the context of non-orthogonal multiple access. The optimal multiuser detector (in terms of the uncoded bit error rate) is the nonlinear maximum a posteriori (MAP) filter. Learning good nonlinear functions of this type (e.g., with deep neural networks) typically requires a large number of training samples and complicated signal processing, which poses a fundamental problem in dynamic wireless environments. Furthermore, compared with linear approaches, nonlinear filters are generally less robust against changes in the environment. To overcome these problems, we first show that the optimal MAP filter belongs to reproducing kernel Hilbert spaces (RKHSs) associated with Gaussian kernels whose widths satisfy a condition related to the standard deviation of the receiver noise. Second, we show how to approximate the optimal MAP filter with a computationally simple signal processing algorithm using a relatively small number of training samples. Third, to make the filter robust against changes in the wireless environment, we design a partially linear filter in the sum of an RKHS containing the MAP filter and an RKHS of a linear kernel. Finally, based on this partially linear design, we propose a multiuser detection framework that, in contrast to some state-of-the-art approaches, has the following desired features: (i) it has low complexity; (ii) it can work with small sample sets; (iii) it shows better robustness than a purely nonlinear receiver; and (iv) it does not require user parameter estimation (e.g., channels), which is prone to errors and may not be possible in certain scenarios.
Design of Textile Substrates with Desired Air Permeability for E-textiles

The production of textiles with increased conductivity is a challenging process. The textile substrates (TS) for e-textiles or textile sensors are usually made as a standard textile, but it also involves conductivity threads. Another option is the additional coating or embroidering with conductive threads. As conductive yarns are expensive, they must be incorporated into the woven macrostructure without fault. Therefore, the prediction of the air permeability of the textile substrate is essential to minimise the sampling with the participation of expensive conductive threads. Our study deals with the experimental investigation of the air permeability of woven structures used as a textile substrate for the incorporation of conductive yarns to increase fabric conductivity.
AI Meets Entrepreneurship: A Framework of Web Platform for Enhancing Skills, Streamlining Finance and Identifying Multiple Intelligence

This study presents an artificial intelligence-powered online platform that can help aspiring digital entrepreneurs improve their digital skills, organise their finances, and recognise their multiple intelligences. The platform uses an artificial intelligence-based algorithm, namely double auction for optimization, to provide aspiring digital entrepreneurs with a one-stop shop for learning and development. The research was conducted using the following methodology: Literature review, requirements elicitation, platform design and development, user testing, evaluation, and refinement. The results of the evaluation show that the platform has the potential to strengthen the environment for digital entrepreneurship.
Power Compensation and Voltage Frequency Flicker Control of Solar using V-F Droop Controller

Nanogrid is a more advanced idea that lies under the microgrid term that aims to provide an unique power supply framework for future single-family houses and offices. It arises from the requirement for "plug-and-play" renewable energy integration. It’s a type a smart grid that may also serve as a power source. Maintaining voltage and frequency stability in nanogrid islanded and grid connected operation is a major difficulty related with nanogrid control. Alternative power technology solutions are necessary to compensate for Power Quality (PQ) problems. A new V–F droop control method was proposed in this study to compensate for Power Quality Disturbances in a 10KW nanogrid system. The functionality of the droop controller must be improved by lowering Power Quality variations. This study exposes a Solar PV Integrated nanogrid structure and investigates how implementing a V-F Droop controller improves the system’s stable operational limit. The importance of Distributed Generation (DG) grid interaction and the controller’s role in reducing unexpected results and increasing nanogrid reliability. Moreover, the results show a 4.2% increase in transient voltage dependability and a 6.58% reduction in grid harmonic.
Hybrid Attention-Based U-Shaped Network for Remote Sensing Image Super-Resolution

Recently, remote sensing image super-resolution (RSISR) has drawn considerable attention and made great breakthroughs based on convolutional neural networks (CNNs). Due to the scale and richness of texture and structural information frequently recurring inside the same remote sensing images (RSIs) but varying greatly with different RSIs, state-of-the-art CNN-based methods have begun to explore the multiscale global features in RSIs by using attention mechanisms. However, they are still insufficient to explore significant content attention clues in RSIs. In this article, we present a new hybrid attention-based U-shaped network (HAUNet) for RSISR to effectively explore the multiscale features and enhance the global feature representation by hybrid convolution-based attention. It contains two kinds of convolutional attention-based single-scale feature extraction modules (SEM) to explore the global spatial context information and abstract content information, and a cross-scale interaction module (CIM) as the skip connection between different scale feature outputs of encoders to bridge the semantic and resolution gaps between them. Considering the existence of equipment with poor hardware facilities, we further design a lighter HAUNet-S with about 596K parameters. Experimental attribution analysis method LAM results demonstrate that our HAUNet is a more efficient way to capture meaningful content information and quantitative results can show that our HAUNet can significantly improve the performance of RSISR on four remote sensing test datasets. Meanwhile, HAUNET-S also maintains competitive performance. Our code is available at https://github.com/likakakaka/HAUNet_RSISR.
DEEPSATTNET: An End-to-End Deep Neural Network with Self-Attention for EEG-Based Emotion Recognition

Emotion analysis by electroencephalogram (EEG) provides vital information about our overall well-being. Analyzing the change in emotional states in response to changes in brain activity has the potential to anticipate the synchronization of attentional, cognitive, and behavioral reactions to emotionally expressive events and hence detect severe mental disorders. This paper proposes an efficient deep learning architecture, DEEPSATTNET, to classify emotions. In contrast to the traditional method, an additional stage for feature extraction is skipped to reduce computational costs. The CNN block of the DEEPSATTNET network captures spatial characteristics from raw EEG data, whereas the LSTM block efficiently encodes significant features over time steps. To acquire the intra-relationship of the extracted feature, the proposed method employs an efficient self-attention mechanism that effectively assigns greater weights to the most relevant information. Finally, the collected feature vector is fed into the dense classifier, which classifies distinct types of emotions. To validate the performance of the proposed architecture, thorough and extensive experimentations are carried out on a publicly available EEG emotion (DEAP) dataset considering mixed subject analysis. For binary class problems, the mean accuracies found for valence and arousal dimensions are 88.65% and 89.24%, respectively.
Hardware Implementation of PID Autotuning with Efficient Particle Swarm Optimization

Severa1 intelligent control systems these days are utilized by the concept of automatically tuning, especially in proportional-integral-derivative (PID) controller. Furthermore, increasing sensors and actuators disrupt the conventional computing system, which has limited resources and is difficult to meet the timing requirement. This paper develops a hardware implementation of PID auto-tuning based on the particle swarm optimization (PSO) with the parallel architecture of particles and variables so that latency is heuristically minimal. The result shows that the proposed hardware performs 1000x and 800x compared to conventional microprocessors. From the evaluation process, performance and resource utilization are found to be satisfactory compared to conventional PID.
Water Quality Analysis of Gadilam river, Cuddalore based on pollution index and NSF - WQI

Gadilam River is located in Cuddalore district, Tamilnadu nearer to Bay of Bengal. This river is used for agricultural purposes and by a 12-watt Micro hydro power plant. Using the Pollution Index and NSFWQI, this study sought to assess the water quality s tate of the Gadilam River at the Nellikuppam portion of Cuddalore that is impacted by a sugar plant, agriculture, and community activity (National Sanitation Foundation- Water Quality Index). The NSFWQI ranged from 84 to 87, while the pollution index varied from 0.54 to 0.76. As a re sult, the river’s water quality depends on these indexes. In summary, the Gadilam River’s water quality was not negat ively impacted by residents living along the riverbank or the micro-hydro plant.
Energy Efficiency Performance of Millimeter Wave Massive MIMO Systems with Hybrid Precoding

Millimeter wave communication and massive multiple-input multiple-output (MIMO) have emerged as the key enabling technologies for achieving the performance goals of fifth-generation and beyond mobile communication systems. This technology has the ability to remarkably boost the spectral and energy efficiencies of the system utilizing the largely unused millimeter frequency band (30-300 GHz) along with huge multiplexing gain capability realizable with massive number of antennas. Unlike conventional MIMO, fully-digital precoding is infeasible in such systems on account of its huge power consumption, cost and space constraints caused by the RF chains. Hybrid precoding, where signal processing is partitioned into analog and digital domains, has been identified as a promising alternative to fully-digital precoding. This paper presents various precoding/beamforming architectures in MIMO systems. Then, a non-iterative low-complexity hybrid precoding technique suitable for single-user millimeter wave scenarios is presented. The EE performance of millimeter wave massive MIMO systems employing this technique is evaluated and compared with optimal digital precoding and other hybrid precoding techniques. The low-complexity hybrid precoding technique shows promising results in terms of achievable energy efficiency.
A Comprehensive Review on Epilepsy Diagnosis and Prognosis using Machine Learning and Deep Learning Approaches

Epilepsy is an illness that affects the nervous system and may lead to abnormal behavior, sensations, and even loss of consciousness in certain people. When someone is having a seizure, abnormal patterns of brain activity might be seen. About 50 million individuals all over the globe suffer from epilepsy 1. It is essential to begin treatment as soon as possible after an epileptic seizure, and it is also essential to diagnose or anticipate an approaching seizure as early as possible. This will allow for a reduction in the intensity and frequency of any potential bad effects. The rapidly decreasing cost of noninvasive EEG collecting equipment as well as the rapid development of machine learning algorithms have fueled passionate debate in this field of study. This paper investigates the methods that have been applied to the CHB-MIT scalp EEG database of epileptic signals, as well as a number of machine learning and deep learning algorithms that have been developed over the last several years and are based on a variety of characteristics and classifiers. The results of this study will help to choose the best machine learning or deep learning models for predicting epilepsy.
SM and QSM Oriented Codebook Design for Downlink Doubly-irregular SCMA

Sparse Code Multiple Access (SCMA) is a multiple access that has the potential to address the needs of future networks. However, the commonly researched SCMA has a traditional design known as regular SCMA, may have drawbacks in serving massive connection networks and supporting various user services. It has been hypothesized that Doubly-irregular SCMA (DI-SCMA) ameliorates the shortcomings of regular SCMA. In this study, we present codebook designs oriented to Spatial Modulation (SM-oriented) and Quadrature Spatial Modulation (QSM-oriented) with Multiuser Detection (MUD) Q=3 to enhance the spectrum efficiency of downlink DI-SCMA with an overloading factor of 225%. The SM-oriented and QSM-oriented codebook design permits downlink DI-SCMA to transmit more bits from multiple users concurrently. The simulation is performed in a Rayleigh fading channel, demonstrating that our proposed codebook designs are suitable for downlink DI-SCMA.
Design of Robust Integer/Fractional Order PID Controller Based on Bode’s Ideal Transfer Function and H-Infinity Robust Performance Condition

In this paper, we proposed a new fitness function that is minimized by optimization algorithms to tune the parameters of the integer/fractional order PID (proportional-integral-derivative) controllers. The fitness function has been designed by combining H-$\infty$ robust performance condition constraint and Bode’s ideal transfer function as a reference model. Two weighting functions have been used for robust performance condition; multiplicative uncertainty weighting function as a fixed parameters structure, and performance weighting function both as a fixed parameters structure, and optimized adjustable parameters structure. The parameters of the Bode’s ideal transfer function are selected based on desired time response specifications. The particle swarm optimization (PSO) algorithm has been used as an optimization tool to minimize the proposed fitness function to tune the parameters of integer/fractional order PID controllers. The parameters of the controllers are obtained for the given second-order transfer function system as an example. The performance of the systems with obtained controllers has been compared in terms of the reference tracking ability, disturbance rejection capability, and robustness performance against parameter changes in the system. The simulation results have revealed that the proposed fitness function can be used with the aim to tune the parameters of the robust fixed structure controllers such as integer/fractional order PIDs by using any optimization algorithm while retaining the H-$\infty$ robust performance condition.