Fast photovoltaic IncCond-MPPT and backstepping control, using DC-DC boost converter

In this paper, we present our contribution in photovoltaic energy optimization subject. In this research work, the goal is to determinate fastly the optimal PV Module working point, allowing maximum power extraction. In this work we use DC-DC Boost converter to control the working point, by adjusting PV voltage trough duty cycle. In order to achieve our goal, we use the combination of incremental conductance MPPT technique and DC-DC Boost converter backstepping control. The validation of this control is made by Matlab simulation; the obtained results prove its effectiveness and its good maximum power tracking dynamics for different irradiance and temperature profiles

Referenced Approximation Technique for a Rom-Less Sweep Frequency Synthesizer

The main goal of this paper is to present a novel ROM-less direct digital frequency synthesizer for sweep instrumentation systems. It provides a main sweep channel for frequency analysis and a reference channel for phase and amplitude measurement block operating at constant frequency. For phase to amplitude converter, we propose a new trigonometric approximation technique based on a set of reference angles. In addition, we present the design of the proposed synthesizer and its evaluation in Matlab-Simulink environment. The simulation results illustrate the performances and demonstrate the effectiveness of our proposed circuit

Solution analytique et généralisation d'un contraste à référence

- Cet article traite du problème de la séparation aveugle de sources par la considération d'un système à référence. À partir d'un contraste à référence, on construit une famille de contrastes puis on développe une solution analytique. Les résultats de simulation présentent une comparaison de notre proposition avec le contraste de base et en tenant compte de plusieurs valeurs du paramètre de généralisation

Design and SystemC-AMS Modeling of a Parallel Direct Digital Synthesizer

This paper presents a new parallel direct digital frequency synthesis (PDDS) circuit providing simultaneous multichannel. The proposed circuit has several applications in instrumentation, identification and data communications. After analysis of a set of design architectures and their limitations, we opted for a rapid and optimized architecture using Wave Arithmetic Unit (WAU) to operate in a largest frequency range. The effectiveness of our proposed circuit is demonstrated by a high-level design of digital blocks in SystemC and analog blocks in SytsemC-AMS language. Finally, the analysis of result responses of all modules confirms the correct PDDS performances for an open microelectronic integration technology

ECG-Waves: Analysis and Detection by Continuous Wavelet Transform

In this work, we have developed a new algorithm for electrocardiogram (ECG) features extraction. This algorithm was based on continuous wavelet transform (CWT). The core of the process involved analyzing the signal using the CWT coefficients with a selection of scale parameter corresponding to each ECG wave. The entry point of our method was the R peak detection. The next step was the Q and S point localization, after we identified the P and T waves. We evaluated our algorithm on apnea and MIT-BIH databases recording. The algorithm achieved a good performance with the sensitivity of 99.84 % and the positive predictive value of 99.53 %

A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS

In 2019, the whole world is facing a health emergency due to the emergence of the coronavirus (COVID-19). About 223 countries are affected by the coronavirus. Medical and health services face difficulties to manage the disease, which requires a significant amount of health system resources. Several artificial intelligence-based systems are designed to automatically detect COVID-19 for limiting the spread of the virus. Researchers have found that this virus has a major impact on voice production due to the respiratory system's dysfunction. In this paper, we investigate and analyze the effectiveness of cough analysis to accurately detect COVID-19. To do so, we performed binary classification, distinguishing positive COVID patients from healthy controls. The records are collected from the Coswara Dataset, a crowdsourcing project from the Indian Institute of Science (IIS). After data collection, we extracted the MFCC from the cough records. These acoustic features are mapped directly to the Decision Tree (DT), k-nearest neighbor (kNN) for k equals to 3, support vector machine (SVM), and deep neural network (DNN), or after a dimensionality reduction using principal component analysis (PCA), with 95 percent variance or 6 principal components. The 3NN classifier with all features has produced the best classification results. It detects COVID-19 patients with an accuracy of 97.48 percent, 96.96 percent f1-score, and 0.95 MCC. Suggesting that this method can accurately distinguish healthy controls and COVID-19 patients

Myopathy Detection and Classification Based on the Continuous Wavelet Transform

Electromyography (EMG) is the study of the electrical activity of the muscle. This technique is often used in the diagnosis of neuromuscular diseases. Myopathy is one of these cases, which affect the muscle and causes many changes in the electromyography signal characteristics. This paper presents a new method for analysis and classification of normal and myopathy EMG signals based on continuous wavelet transform (CWT). Classification algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Discriminant Analysis (DA) and Naïve Bayes (NB) were used as classifiers in our study. Five Features were extracted from the continuous wavelet analysis and used as inputs to the mentioned classifiers. Comparison between different classification methods developed in this study was made by evaluation of their results based on multiple scalar performances, mainly accuracy, sensitivity, and specificity. Different combinations of features with different kernel functions were discussed to achieve better performances. Results showed that k-NN classifier achieved the best performances with an accuracy value of 93.68%

ECG Generator for Educational Biomedical Engineering Laboratory

The aim of this work is to design and implement an ECG generator for didactic biomedical engineering laboratory. The proposed generator provides analog ECG signals using synthesized or experimental records. The technique used in this work consists to generate the desired ECG waveforms through the PWM outputs of an Arduino board and low-pass filter. To provide many educational functions in both analog instrumentation and digital processing, the generator supplies output voltages in asymmetric or differential mode. To allow the user to setup the ECG signal to be generated, a LabVIEW application has been implemented. Experimentations on proposed generator and results were accomplished using the NI USB 6009 acquisition module and NI MAX software

ECG Signal Denoising by Discrete Wavelet Transform

The denoising of electrocardiogram (ECG) represents the entry point for the processing of this signal. The widely algorithms for ECG denoising are based on discrete wavelet transform (DWT). In the other side the performances of denoising process considerably influence the operations that follow. These performances are quantified by some ratios such as the output signal on noise (SNR) and the mean square error (MSE) ratio. This is why the optimal selection of denoising parameters is strongly recommended. The aim of this work is to define the optimal wavelet function to use in DWT decomposition for a specific case of ECG denoising. The choice of the appropriate threshold method giving the best performances is also presented in this work. Finally the criterion of selection of levels in which the DWT decomposition must be performed is carried on this paper. This study is applied on the electromyography (EMG), baseline drift and power line interference (PLI) noises