Miracle-dz/UniFAR-FTUs huggingface dataset:
Biometric Facial Security System with Telemetry Module

Over the years the human being has sought greater security for their personal things, this has led them to develop automatic systems of detection, recognition, and fast and reliable monitoring, becoming an important tool for many people because they can identify or verify through biometric data the identity of a person and thereby guarantee access to a place. Traditional methods such as passwords, cards, among others, involve some risks such as loss, forgetfulness, cloning or theft, which is why they are not very secure. Currently, biometric facial security systems have contributed to the identification of people, access to private sites and all places that need security and order with the help of computerized computers that perform facial biometric recognition, exclusively based on images of human faces for their function, being a technology that is still being developed in the field of research. In view of this problem, this article will implement a biometric facial security system with telemetry module to detect the face of users and according to a previous evaluation allow or not access to a certain area or information safely. By implementing the facial security system, you have a more accurate detection of people's faces with an efficiency of 97.75%, so the system works in the best way by identifying each person when entering.
Risk management in a Tunisian commercial company's logistic distribution process

Risk is defined in the context of organizations as any unanticipated state or occurrence that has an impact on one or more obj ectives. The risk management strategy has arisen to deal with these uncertainties, and it is used in all sorts of companies. Risk management is a systematic approach to identifying and analyzing hazards. There are two categories of risk: those that have a good effect are called opportunities, and those that have a negative effect are termed risks. The paper describes a risk and opportunity analysis technique that was used to a commercial company's logistic distribution operation. We follow the risk management measures suggested by ISO 31000 v2018, an international standard organization. The case study shows the efficiency of the risk management which allows to elaborate an action plan which is pursue in order to decrease or eliminate the recognized risks as well as the expansion or appearance of identified opportunities. The case study demonstrates the effectiveness of risk management, which allows for the reduction or elimination of recognized hazards as well as the expansion or appearance of identified opportunities.
Prediction of Maintenance Time and IoT Device Failures using Artificial Intelligence

The real-time mechatronic system is critical in today's industry for increasing productivity and product quality to meet consumer demands. The quality of a product is largely determined by the quality of the machines employed in the manufacturing process. The reliability prediction model's accuracy is improved by sorting the submodules systematically and feeding the qualitative and quantitative fault data acquired into it. Fault detection and reliability forecasting modules are included in this model. Predictive maintenance aims to reduce equipment downtime and lessen the impact of failures by scheduling maintenance activities prior to the commencement of failures. This hastened the implementation of Genetic algorithms based on artificial intelligence and machine learning to predict equipment problems. For software fault prediction, a Bayes Decision classifier is used in this study to find error probabilities and integrals using feature and classifier data, this work explains how to make basic predictions about software errors. Chernoff Bound and Bhattacharyya Bound are also discussed in the suggested software error prediction using fault-predictable zone. Software errors can be predicted using a new Bayesian decision procedure that incorporates error probabilities and integrals from a machine learning model.
A Nonlinear Titanium Dioxide Memristor Model for Memory Crossbars Analysis

In this work, a modified and highly nonlinear ionic drift titanium dioxide memristor model and its operation in passive and hybrid memory crossbars are presented. In a combination with a differentiable and continuous step-like function, its application and analysis in LTSPICE are considered. The described memristive model is with an enhanced nonlinearity, it includes activation thresholds and successfully represents the boundary effects for hard-switching mode. The terminal state issues are also resolved by the used modified window function. The obtained results are compared with those, derived by applying the classical Lehtonen-Laiho, Joglekar and Biolek memristor models. The improved window function, used in the suggested model, allows altering the time derivative of the state in a broad range and thus the applied memristor model is able to properly operate at higher-frequency signals, retaining its good switching properties. A comparison between the used models according to several significant criteria is conducted. The correct operation of the proposed model in complex electronic schemes is confirmed.
1.5-MHz High-Performance 380-V/12-V LLC Resonant Converter

This study proposed a core optimization technique to explore DC-DC converters at a high frequency of 1.5 MHz. In addition, the optimization of the width of PCB winding is proposed by targeting the direct current resistance of the winding; the optimization successfully lowered the conductance loss of the winding under a limited area condition. Conventionally, a winding placement method is applied to a multiple-leg core to achieve magnetic flux offset. A mathematical model was used to describe the effects of additional effective core volume on core loss reduction. In addition, a finite element analysis 3D simulation was used to verify the degree to which additional effective volume reduced core loss. A 1-kW experimental prototype using GaN switches was implemented with an input voltage of 380 V, an output voltage of 12 V, and a power density of 41.1 W/cm3. The LLC converter operated at 1.5 MHz with a maximum efficiency of 96.02 %.
Enhancement of Spectrum Efficiency in Satellite Communication Applying Prediction Model of Machine Learning Technique

Spectrum sensing is critical to the advancement of cognitive radio technology in next-generation wireless communication systems. According to FCC policy, the frequency coexistence of GEO (geostationary) as the main user satellite network and NGEO (non-geostationary) as the secondary user satellite network, NGEO as the interference user system, shall not cause harmful interference to the GEO system. In this situation, spectrum sensing is shown as a viable approach to avoid interference. With the increasing number of NGEO satellites in orbit, a NGEO system detecting signals from one GEO system may be affected by another NGEO system. Several sensing technologies such as energy detection, cyclo-stationary features and matching filters have been introduced in the last decade. However, these methods have several drawbacks. Consequently, all of these strategies require the establishment of a threshold, which requires previous knowledge of the noise distribution. As a result, spectrum sensing dependence in wireless communications research remains an unresolved question. In this study, we present a spectrum sensing approach for cognitive radio networks based on machine learning technique. The spectrum sensing problem is studied systematically, and as a result large-scale, exhaustive data collections are created. This dataset is used to train, validate, and test a variety of machine learning algorithms, including random forests, support vector machines, decision trees, k-nearest neighbors, and others. The model was thoroughly tested and evaluated using matrices such as detection probability, false alarm, and missed detection, sensing time, delay, throughput and classification accuracy. Thus, according to the simulation data, random forest models and neural networks outperform all other machine learning methods.
An Interleaved Switched-Boost Common-Ground Five-Level Inverter

Transfomerless grid-connected inverters with common-grounded circuit architectures and single-stage dy-namic voltage boosting gain are promising candidates for PV energy conversion applications. In this work, a new five-level (5L) variant of this category of inverters is introduced. Key features of the presented inverter are the reduced current stress profile, modularity, uniform peak voltage stress across the switches, higher power handling capability, and bi-directional power flow operation. The proposed topology is comprised of two input inductors, two capacitors, and ten power switches. Through a modular design with a phase-shifted SPWM technique, the injected grid current can be shared among the modules, while the size of the grid-interface filters can be reduced. The working principle of the converter is discussed, and some simulation and experimental results are presented to validate its feasibility.
Global Impedance Identification of Inverter-Based Power Systems Using Grid-Forming-Inverter-Based Current Perturbation Injections at Single Node

Stability of the conventional synchronous generator-based power systems can be deteriorated by the increasingly-penetrated grid-following inverters. It is thus crucial to identify the weak point such that the problematic controller can be re-tuned or stability enhancement devices can be installed at the appropriate place to improve the system stability margin. To this end, this paper presents a cost-effective grid-forming-inverter-based global impedance identification method, which can obtain the stability margins at all of the nodes of interest by only injecting the current perturbations into single node. The effectiveness of the proposed method is validated on both a passive single-Π-based RLC circuit and a four-inverter-paralleled power system.
OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices

This article presents a novel over-the-air (OTA) technique to remotely deploy tiny ML models over Internet of Things (IoT) devices and perform tasks, such as machine learning (ML) model updates, firmware reflashing, reconfiguration, or repurposing. We discuss relevant challenges for OTA ML deployment over IoT both at the scientific and engineering level. We propose OTA-TinyML to enable resource-constrained IoT devices to perform end-to-end fetching, storage, and execution of many TinyML models. OTA-TinyML loads the C source file of ML models from a web server into the embedded IoT devices via HTTPS. OTA-TinyML is tested by performing remote fetching of six types of ML models, storing them on four types of memory units, then loading and executing on seven popular MCU boards.
Deep Learning with Attention on Hand Gesture Recognition Based on sEMG

The human forearm Surface Electromyography signal (sEMG) is related to gesture activities, and the human body movement intention can be predicted by analyzing and identifying the forearm sEMG signal. Deep learning has been widely used in gesture recognition research because of its ability to extract deep features. On this basis, this paper introduces an attention mechanism to assign weights to different channels, so that the acquisition of the model is more dependent on some explicit channels to obtain a model with better performance. Compared with other models, the model proposed in this paper not only has fewer parameters, but the experimental accuracy rate on private datasets can reach up to 99.6%, which is comparable to some current CNN network models with good classification effects; In the case of the smaller datasets, the model can still maintain more than 95% accuracy and has good adaptability.
An Efficient Model Fusion Method for Bearing Fault Diagnosis

Nowadays, machinery and equipment are becoming more complex and precise, and many machinery problems are caused by bearing faults. To ensure the safety of the equipment，a reliable fault detection system is essential, which requires that automatic fault detection algorithms must be accurate and stable. In this paper, a multi-classification model fusion method based on stacking is proposed. Firstly, features extracted by Multi-Layer Perceptron, Gradient Boosting Decision Tree and Support Vector Machine are concatenated into a probability matrix. Then, Logistic Regression works as a meta-classifier based on the probability matrix to implement the final prediction. The simulation results show that the stacking-based model fusion method can achieve significant improvement under different noise levels, sample data length and size of train dataset. To further evaluate the effectiveness of this stacking-based method, t-distributed Stochastic Neighbor Embedding (t-SNE) is applied to reduce dimensions and visualize the distribution of raw bearing data, meta-classifier input and meta-classifier output. And the performance of feature extracting is verified by computing the Kullback-Leibler divergence of the probability distribution. In conclusion, the stacking-based model fusion method possesses superior classification performance since it can efficiently use prior data information for bearing fault diagnosis by fusing diverse classification methods in a data-driven way.
An Acoustic Analysis of Speech for Emotion Recognition using Deep Learning

Speech emotion recognition has shown several advancements as a result of advancements in Deep Learning algorithms. These algorithms can easily extract the features from the data and learn to recognize patterns from them. Although these algorithms can successfully recognize emotions, their efficiency is often argued. The main objective of this paper is to efficiently classify the emotional state of a person from speech signals using traditional machine learning and deep learning techniques and to present a comparative analysis. We have considered eight different types of emotions, and have analyzed them in the following two ways: First, by considering the male and female emotions combinedly (gender-neutral) where they are classified into eight classes, and second, separately for the male and female emotions (gender-based) for a total of 16 classes. We have performed experimentation and have tested several architectures like K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), One Dimensional Convolutional Neural Network + Long Short-Term Memory (ID CNN+LSTM) by efficiently tuning the hyperparameters to classify the emotional states. Best results are obtained with the ID CNN + LSTM model. We have obtained an accuracy of 87.4% for gender-neutral cases and 82.78% for gender-based cases. This model outperforms existing techniques.
Integration of Hough Transform and Inter-Frame Clustering for Road Lane Detection and Tracking

This paper proposes a lane detection and tracking algorithm using vision-based measurement techniques. A framework consisting of color space conversion, region of interest (ROI), edge detection, Hough transform (HT), slope filtering and inter-frame clustering are used in the lane detection algorithm. An efficient ROI selection is obtained through vanishing point determination. Any conventional edge detection algorithm can be used in this algorithm with no trade-off in functional performance. HT detects all lines in the road images, but the potential lane candidate selection is achieved with slope filtering and inter-frame clustering. Further increasing the robustness of the lane detection algorithm, Kalman filter-based estimation technique is employed for lane tracking. The proposed algorithm is tested on the Caltech dataset, and its performance parameters are evaluated in terms of correct rate and false rate. It has shown a promising results in scenario of shadow, illumination variation, multiple lane markings, markings on the road, no lane markings, with road curb, and multiple lanes. The proposed algorithm is implemented on National Instruments compact Reconfigurable input-output (NI cRIO) real-time processor.
Analytical Computation of Inductance for High-Speed Spoke-Type Permanent Magnet Synchronous Motor Accounting for Saturation

This paper investigates the analytical computation of inductance in high-speed spoke-type permanent magnet synchronous motor. This method considers the effect of harmonics and stator saturation. Based on the subdomain model, the flux linkage and inductance can be firstly calculated regardless of saturation. When current increases, the stator is easy to saturate. The lumped parameter magnetic circuit model is used to solve the saturation problem. The validity of the proposed method is verified by the finite element method.
Towards a Multi-Pixel Time-of-Flight Indoor Navigation System for Nano-Drone Applications

Unmanned aerial vehicles (UAVs) have recently attracted the industry’s attention due to their numerous civilian and potential commercial applications. A promising UAV subclass includes nano and micro UAVs, characterized by centimeter size, few grams of payload and extremely limited on-board computational resources. Those features pose major challenges to enable autonomous navigation or even more basic relevant subtasks, such as reliable obstacle avoidance. This paper explores and characterizes a multi-zone Time of Flight (ToF) sensor to enhance autonomous navigation with a significantly lower computational load than most common visual-based solutions. In particular, the state-of-the-art integrated ToF sensor is characterized for the first time in literature in-field using an ad hoc lightweight PCB and the Crazyflie nano-UAV. The paper focuses, on the 8x8 pixel configuration, to detect obstacles up to 3m with centimeter accuracy and a frame rate up to 15 fps. The paper presents a solution for computing the approaching angle, crucial for many UAV tasks, with a maximum error of ±6°. Furthermore, relying on empirical data, the paper proposes a lightweight approach to calculate collision probability from each ToF sensor frame. This work aims to pave the way for future nano-UAV camera-less compact navigation solutions capable of extracting complex environmental features directly on-board.
Detection of Halyomorpha Halys Using Neural Networks

The paper's goal was to create some neural networks-based models for the detection and classification of insects such as Halyomorpha Halys in ecological orchards, from acquired images in the trees. The detecting operations were performed using models from two of the most efficient deep learning families in this area: R-CNN and YOLO. Using the proposed models, (Faster R-CNN, YOLOv5-s, YOLOv5-m, and YOLOv5-1) to early detection of harmful insects, a real contribution to anticipating damage in orchards is possible. The dataset is composed of images taken from the Maryland Biodiversity dataset. All training and testing operations were performed with the help of GPU processors provided by Google, the resulting models being saved on Google Drive Cloud. The images were evaluated from the detection and the classification perspective based on specific metrics such as precision, recall, and mAP. The best results were obtained for YOLOv5-m.
Optimized diagnostic strategy for embedded memories of Automotive Systems-on-Chip

Embedded memories in Automotive Systems-on-Chip usually occupy a large die area portion. Consequently, their defectivity can strongly impact production yield for any automotive device. Along with the technology ramp-up phase and for statistical process control reasons during volume production, it is a good automotive industry practice to collect diagnostic information in addition to pure testing data. Designers and technology experts must receive accurate diagnostic results from failing devices to react to misbehavior by identifying and correcting the related issues at their source and drawing correct repair strategy conclusions. A commonly used approach resorts to the generation of failure bitmaps based on collecting all failing bits coordinates to be sent one by one to the tester. More efficiently, the encountered faults can be compacted or compressed in on-chip memory resources to be retrieved by the tester at the end of the memory test.This paper presents an on-chip method to compact diagnostic information during embedded memory testing. More specifically, the method is applied to diagnose embedded FLASH memories. This strategy permits the reconstruction of failure bitmaps without any loss, while compression approaches obtain an approximation. The proposed method uses a fraction of the memory requested by a coordinate-based bit mapping approach and is comparable to compression methods. At the cost of a moderate test time overhead, the proposed strategy permits dramatically increasing the number of devices that can be fully diagnosed without any bitmap reconstruction loss. Most failing devices in a real embedded FLASH production scenario were diagnosed after a single transfer from on-chip to the tester host computer.
Cost-efficient Analysis of Core and PM Eddy Current Loss Considering Current Harmonics

The analysis of AC losses, including the core and permanent magnet (PM) eddy current losses, requires a long computational time because of the large number of analysis steps and elements for the overall consideration of current harmonics. Therefore, this paper proposes a cost-efficient analysis of the AC losses considering current harmonics. First, the permeability of all the elements at each step is obtained via a nonlinear static finite element analysis (FEA), using the fundamental component of the current. Then, the harmonics of AC losses are calculated using a linear static FEA and a two-dimensional time-harmonic T-Ω method, which are well-known for their fast computation. At each analysis step, the permeability of each element is interpolated from the obtained permeability map. The cost-efficiency of the proposed analysis is validated with a PM synchronous motor used for vehicle traction.
TATCN: Time Series Prediction Model Based on Time Attention Mechanism and TCN

Prediction is an important research task of time series data analysis. As a powerful tool to solve the problem of time series prediction, Temporal Convolutional Networks (TCN) shows good performance in the prediction task. However, TCN model lacks the consideration of the influence of different historical segments on the prediction value, which limits the prediction accuracy of the model to a certain extent. Therefore, this paper combines the attention mechanism with the data characteristics of time series, proposes a Time Attention mechanism (TA), and integrates it into the TCN model framework to build a prediction model (called TATCN). In TATCN, the TCN output vector of each layer is convoluted, and the sigmoid function is used to generate the weight coefficient, and then the weight coefficient is multiplied by the original output vector to form a new output vector. The new output vector and the input vector of the current layer are added by residual connection as the final output vector of the current layer and input to the next layer network. The experimental results on EEG data and Yanbian electricity fees data show that the Time Attention mechanism in this paper can effectively represent the importance of different historical data to the current prediction. The proposed TATCN model has a significant improvement in the prediction accuracy compared with TCN model, and is also better than RNN prediction models such as LSTM and GRU.