WSEAS TRANSACTIONS ON SIGNAL PROCESSING最新文献_第3页

Learning Texture Features from GLCM for Classification of Brain Tumor MRI Images using Random Forest Classifier 从随机森林分类器中学习GLCM纹理特征用于脑肿瘤MRI图像分类

WSEAS TRANSACTIONS ON SIGNAL PROCESSING Pub Date : 2022-04-19 DOI: 10.37394/232014.2022.18.8

A. Aggarwal

引用次数: 20

Signal Analysis Algorithms and Artificial Neural Network for Electromechanical Fault Detection 机电故障检测的信号分析算法和人工神经网络

WSEAS TRANSACTIONS ON SIGNAL PROCESSING Pub Date : 2022-04-02 DOI: 10.37394/232014.2022.18.7

Pascal Doré, Saad Chakkor, A. El Oualkadi

{"title":"Signal Analysis Algorithms and Artificial Neural Network for Electromechanical Fault Detection","authors":"Pascal Doré, Saad Chakkor, A. El Oualkadi","doi":"10.37394/232014.2022.18.7","DOIUrl":"https://doi.org/10.37394/232014.2022.18.7","url":null,"abstract":"Fault detection is a strategy that can be easily implemented. To ensure acceptable levels of reliability and safety, effective diagnostic methods (at the earliest stage of fault occurrence), fault monitoring, and fault handling are mandatory to avoid any production downtime or loss and to reduce additional repair costs. The detection of these faults by MCSA (Motor Current Signature Analysis) and Principal Component Analysis (PCA) has been widely explored and applied. The remarkable limitations of these approaches have prompted researchers to improve their accuracy and to enhance their complexity. In this work, we propose to study the application of ANN-GA (Artificial Neural Networks-Genetic Algorithm) combined with ESPRIT method variants for efficient faults recognizing in real-time. Computer simulations in Matlab demonstrated that the ESPRIT method variant allows satisfactory precision in discriminating bearing fault even with a noisy signal. Moreover, this algorithm is suitable for application in dataset preparation and in ANN training for the development of a classification model. According to the study finding, the Genetic Algorithm optimizes ANN architecture for identifying each fault type with very good accuracy in time or frequency domains.","PeriodicalId":305800,"journal":{"name":"WSEAS TRANSACTIONS ON SIGNAL PROCESSING","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123814594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vehicle Control with DC Motor in Android Based Systems 基于Android系统的直流电机车辆控制

WSEAS TRANSACTIONS ON SIGNAL PROCESSING Pub Date : 2022-03-26 DOI: 10.37394/232014.2022.18.6

Cem Sonmez, M. Kahriman, O. Coskun

引用次数: 0

Design of Convolutional Neural Network Based on FPGA 基于FPGA的卷积神经网络设计

WSEAS TRANSACTIONS ON SIGNAL PROCESSING Pub Date : 2022-03-23 DOI: 10.37394/232014.2022.18.5

Hasnae El Khoukhi, Y. Filali, M. A. Sabri, A. Aarab

{"title":"Design of Convolutional Neural Network Based on FPGA","authors":"Hasnae El Khoukhi, Y. Filali, M. A. Sabri, A. Aarab","doi":"10.37394/232014.2022.18.5","DOIUrl":"https://doi.org/10.37394/232014.2022.18.5","url":null,"abstract":"Recently with the rapid development of artificial intelligence AI, various deep learning algorithms represented by Convolutional Neural Networks (CNN) have been widely utilized in various fields, showing their unique advantages; especially in Skin Cancer (SC) imaging Neural networks (NN) are methods for performing machine learning (ML) and reside in what's called deep learning (DL). DL refers to the utilization of multiple layers during a neural network to perform the training and classification of data. The Convolutional Neural Networks (CNNs), a kind of neural network and a prominent machine learning algorithm go through multiple phases before they get implemented in hardware to perform particular tasks for a specific application. State-of-the-art CNNs are computationally intensive, yet their parallel and modular nature make platforms like Field Programmable Gate Arrays (FPGAs) compatible with the acceleration process. The objective of this paper is to implement a hardware architecture capable of running on an FPGA platform of a convolutional neural network CNN, for that, a study was made by describing the operation of the concerned modules, we detail them then we propose a hardware architecture with RTL scheme for each of these modules using the software ISE (Xilinx). The main objective is to show the efficiency of such a realization compared to a GPU based execution. An experimental study is accomplished for the PH2 database set of benchmark images. The proposed FPGA-based CNN design gives competitive results and shows well its efficiency.","PeriodicalId":305800,"journal":{"name":"WSEAS TRANSACTIONS ON SIGNAL PROCESSING","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130927071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Algorithm with the Even-odd Splitting of the Wavelet Transform of Non-Hermitian Splines of the Seventh Degree 七次非厄米样条小波变换的奇偶分裂算法

WSEAS TRANSACTIONS ON SIGNAL PROCESSING Pub Date : 2022-03-02 DOI: 10.37394/232014.2022.18.4

B. Shumilov

引用次数: 1

Encryption Algorithms on BMP and JPEG Images BMP和JPEG图像的加密算法

WSEAS TRANSACTIONS ON SIGNAL PROCESSING Pub Date : 2022-02-21 DOI: 10.37394/232014.2022.18.3

Sara Chillali, L. Oughdir

引用次数: 0

Bounding Box Stabilization for Visual Object Tracking Using Kalman and FIR Filters 用卡尔曼滤波器和FIR滤波器稳定视觉目标跟踪的边界盒

WSEAS TRANSACTIONS ON SIGNAL PROCESSING Pub Date : 2022-02-11 DOI: 10.37394/232014.2022.18.2

Eli Pale-Ramon, Y. Shmaliy, L. Morales-Mendoza, M. González-Lee

引用次数: 0

Faster R-CNN based Traffic Sign Detection and Classification 更快的基于R-CNN的交通标志检测和分类

WSEAS TRANSACTIONS ON SIGNAL PROCESSING Pub Date : 2022-02-10 DOI: 10.37394/232014.2022.18.1

Monira Islam, Md. Salah Uddin Yusuf

{"title":"Faster R-CNN based Traffic Sign Detection and Classification","authors":"Monira Islam, Md. Salah Uddin Yusuf","doi":"10.37394/232014.2022.18.1","DOIUrl":"https://doi.org/10.37394/232014.2022.18.1","url":null,"abstract":"Traffic sign is the key aspect in road and also for the autonomous car. Detection and classification of these sign plays a vital role for the invention of driverless vehicles. Convolutional neural network (CNN) has the ability to learn local features using series of convolutional and pooling layer observing the image sequences. In this work, traffic sign detection and classification has been performed based on deep learning approach. The experiment conducted on Germen Traffic Sign Detection Benchmark (GTSDB) and Recognition Benchmark (GTSRB) for detection and recognition. For traffic sign detection a two-stage detector, Faster R-CNN with ResNet 50 backbone structure is used where the CNN layers extracted the features of traffic signs from the images and the region proposal network (RPN) filter the object from the image to create bounding box based on the extracted feature map. The classification network classifies the traffic signs and predict the proposal confidence score. A general deep learning model is transferred into a specific output with weights with transfer learning by tuning the pretrained model based on COCO image dataset. The performance is compared with ResNet 152, MobileNet v3 and RetinaNet based on the confidence score and mean average precision (mAP). Faster R-CNN with ResNet-50 shows better detection performance comparing with other backbone structure. In addition, a series of convolution layer with batch normalization followed by max pooling layer is used to build a classifier and softmax is used in the output for 43 class classification and 97.89% test accuracy has been obtained.","PeriodicalId":305800,"journal":{"name":"WSEAS TRANSACTIONS ON SIGNAL PROCESSING","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121481169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cooperative Spatial Multiplexing for CR Users Sharing a Common Channel with Primary Users* CR用户与主用户共享公共信道的协同空间复用*

WSEAS TRANSACTIONS ON SIGNAL PROCESSING Pub Date : 2021-12-31 DOI: 10.37394/232014.2021.17.18

Yang Xiao, Jinfeng Kou

引用次数: 2

A Spatial Coding Approach for MIMO Cognitive Radio Networks’ Bandwidth Sharing 一种MIMO认知无线网络带宽共享的空间编码方法

WSEAS TRANSACTIONS ON SIGNAL PROCESSING Pub Date : 2021-12-31 DOI: 10.37394/232014.2021.17.17

Yan Qiao, Yang Xiao

引用次数: 2