A CMOS-based Analog Function Generator: HSPICE Modeling and Simulation

In many Engineering applications, analog circuits present many advantagesover their digital counterparts and have recently been particularly used in awide range of signal processor circuits. In this paper, an analog non-linearfunction synthesizer is presented based on a polynomial expansion model.The proposed function synthesizer model is based on a 10th orderpolynomial approximation of any of the required non-linear functions. Thepolynomial approximations of these functions can then be implemented usingbasic CMOS circuit blocks. The proposed circuit model can simultaneouslysynthesize and generate many different mathematical functions. The circuitmodel is designed and simulated with HSPICE and its performance isdemonstrated through the simulation of a number of non-linear functions.DOI:http://dx.doi.org/10.11591/ijece.v4i4.598

SVM Classification of MRI Brain Images for Computer-Assisted Diagnosis

Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. MRI Image pre-processing followed by detection of brain abnormalities, such as brain tumors, are considered in this work. These images are often corrupted by noise from various sources. The Discrete Wavelet Transforms (DWT) with details thresholding is used for efficient noise removal followed by edge detection and threshold segmentation of the denoised images. Segmented image features are then extracted using morphological operations. These features are finally used to train an improved Support Vector Machine classifier that uses a Gausssian radial basis function kernel. The performance of the classifier is evaluated and the results of the classification show that the proposed scheme accurately distinguishes normal brain images from the abnormal ones and benign lesions from malignant tumours. The accuracy of the classification is shown to be 100% which is superior to the results reported in the literature

Improvement of crankshaft MAC protocol for wireless sensor networks: a simulation study

Due to the dramatic growth in the use of Wireless Sensor Network (WSN) applications ranging from environment and habitat monitoring to tracking and surveillance, network research in WSN protocols has been very active in the last decade. With battery-powered sensors operating in unattended environments, energy conservation becomes the key technique for improving WSN lifetimes. WSN Medium Access Control (MAC) protocols address energy awareness and reduced duty cycles. The focus of this study is to investigate, through simulation, the effect of variations in various factors that influence the performance results of WSNs. Using MiXiM framework with OMNeT++ simulator, this simulation study proposes modifications in Crankshaft MAC protocol in order to improve its performance. The impact of duration and number of slots, degree of connectivity among the nodes, mobility speed and mobility update interval and also, the impact of sending data packets without preambles are investigated. Based on the simulation results, an improved version of the Crankshaft protocol for WSN is suggested and a comparative study of the performances of the original and improved protocol is presented. The results clearly indicate the superiority of the improved protocol over its original version

Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete

International audienceThis paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough, i.e. well before they are visible on the concrete surface, in order to implement adequate maintenance actions on civil engineering structures. The key element of this original approach is the wavelet-based multiresolution analysis of the ultrasonic signal received from a sample or a specimen of the studied material subjected to several types of solicitation. This analysis is finally coupled to an automatic identification scheme of the types of cracks based on artificial neural networks (ANNs), and in particular deep learning by convolutional neural networks (CNNs); a technology today at the cutting edge of machine learning, in particular for all applications of pattern recognition. Wavelet-based multiresolution analysis does not add any value in detecting fractures in concrete visible by optical inspection. However, the results of its implementation coupled with different CNN architectures show cracks in concrete can be identified at an early stage with a very high accuracy, i.e. around 98%, and a loss function of less than 0.1, regardless of the implemented learning architecture

Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis

International audienceIn this paper, we propose a new methodology for crack detection and monitoring in concrete structures. This approach is based on a multiresolution analysis of a sample or a specimen of concrete material subjected to several types of solicitation. The image obtained by ultrasonicinvestigation and processed by a customized wavelet is analyzed at various scales in order to detect internal cracks and crack initiation. The ultimate objective of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible. This is achieved through the combination of two major data analysis tools which are wavelets and deep learning. This original procedure is shown to yield a high accuracyclose to 90%. In order to evaluate the performance of the proposed CNN architectures, we also used an open access database, SDNET2018, for the automatic detection of external cracks

Fault detection in robots based on discrete wavelet transformation and eigenvalue of energy

This article addresses the problem of fault detection in robot manipulator systems. In the production field, online detection and prevention of unexpected robot stops avoids disruption to the entire manufacturing line. A number of researchers have proposed fault diagnosis architectures for electrical systems such as induction motor, DC motor, etc..., utilising the technique of discrete wavelet transform. The results obtained from the use of this technique in the field of diagnosis are very encouraging. Inspired by previous work, The objective of this paper is to present a methodology that enables accurate fault detection in the actuator of a two-degree of freedom robot arm to avoid system performance degradation. A partial reduction in joint torque constitutes the actuator fault, resulting in a deviation from the desired end-effector motion. The actuator fault detection is carried out by analysing the torques signals using the wavelet transform. The stored energy at each level of the transform contains information which can be used as a fault indicator. A Matlab/Simulink simulation of the manipulator robot demonstrates the effectiveness of the proposed technique

Decoupled Unknown Input Observer for Takagi-Sugeno Systems: Hardware-in-the-Loop Validation to Synchronous Reluctance Motor

This paper introduces a decoupled unknown input observer (DUIO) for Takagi-Sugeno (T-S) systems, designed specifically for the synchronous reluctance motor (SynRM). The proposed DUIO method demonstrates enhanced robustness and accuracy in state estimation by effectively decoupling the influence of unknown inputs from the estimation error dynamics. Furthermore, the DUIO exhibits superior performance compared to the proportional integral observer (PIO) and the proportional multi-integral observer (PMIO) presented in previous studies, without the need for prior knowledge of the unknown input form or assumptions regarding its boundedness. Stability conditions, achieved using the quadratic Lyapunov function, are expressed as linear matrix inequalities (LMIs), which ensure asymptotic convergence of the estimation error. The effectiveness of the DUIO method is further validated in various scenarios through hardware-in-the-loop (HIL) implementation. This innovative approach significantly enhances the accuracy and reliability of SynRM state estimations and unknown input detections

Stability and Stabilization of TS Fuzzy Systems via Line Integral Lyapunov Fuzzy Function

This paper is concerned with the stability and stabilization problem of a Takagi-Sugeno fuzzy (TSF) system. Using a non-quadratic function (well-known integral Lyapunov fuzzy candidate (ILF)) and some lemmas, new sufficient conditions are established as linear matrix inequalities (LMIs), which are solved with a stochastic fractal search (SFS). The main advantage of the technique used is its small conservatives. Motivated by the mean value theorem, a state feedback controller based on a non-quadratic Lyapunov function is designed. Unlike other approaches based on poly-quadratic Lyapunov candidates, stability conditions of the closed loop are obtained in LMI regions. It is important to highlight that the time derivatives of membership functions do not appear in the used line integral Lyapunov function, which is the well-known problem of poly-quadratic Lyapunov functions. A numerical example is given to show the advantages and the utility of the integral Lyapunov fuzzy candidate, which provides a wider feasibility region than other Lyapunov functions

Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation

International audienceSegmentation of brain tumor images, to refine the detection and understanding of abnormal masses in the brain, is an important research topic in medical imaging. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed, to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied, to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block, to classify it as a tumor or not. A thorough evaluation of the proposed algorithm was performed, using: (1) a private MRI database provided by the Kouba imaging center—Algiers (KICA); (2) the multimodal brain tumor segmentation challenge (BraTS) 2015 database. Estimates of the selected fitness function were first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion, to demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm was then compared to the results of several state-of-the-art techniques. The results obtained, by using the Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements, demonstrated the superiority of the proposed optimized segmentation algorithm over equivalent technique