2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)最新文献_第7页

Research on Air Quality Monitoring System Based on STM32 Single Chip Microcomputer 基于STM32单片机的空气质量监测系统研究

2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2022-11-22 DOI: 10.1109/ISPACS57703.2022.10082790

Tingmeng Shi, Pengyu Li, Wu Yang, Ailin Qi, J. Qiao

引用次数: 1

Medical Internet of Things for Classification of Pathological ECG Beats Based on Fractional Fourier Transform and Hyperparameter Tuning 基于分数阶傅立叶变换和超参数调谐的病理性心电心跳分类医疗物联网

2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2022-11-22 DOI: 10.1109/ISPACS57703.2022.10082841

Mohamed Chaabane, Abdessamad Elrharras, A. Chehri, Rachid Saadane, Hicham Sadok

{"title":"Medical Internet of Things for Classification of Pathological ECG Beats Based on Fractional Fourier Transform and Hyperparameter Tuning","authors":"Mohamed Chaabane, Abdessamad Elrharras, A. Chehri, Rachid Saadane, Hicham Sadok","doi":"10.1109/ISPACS57703.2022.10082841","DOIUrl":"https://doi.org/10.1109/ISPACS57703.2022.10082841","url":null,"abstract":"The Medical Internet of Things (MIoT) has recently played a key role in developing functional health systems. As a result, automatic detection and prediction of future risks such as heart valve diseases and arrhythmias are still being researched and studied. Additionally, early detection of heart problems can improve treatment and reduce patient mortality. On the other hand, traditional approaches did not produce good results for accurate diagnosis. This paper proposes electrocardiogram (ECG) beat classification using Deep Transfer Learning (DTL) and hyperparameter tuning. After a frequency domain transformation with the Fractional Fourier Transform, images of ECG signals were captured (FrFT). The framework uses multi-access edge computing technology, allowing end users to access available resources and our DTL Model in the cloud. The proposed automated model incorporates a Convolutional Neural Network (CNN) structure with hyperparameter tuning. Our model is validated using the MIT-BIH database. Finally, we classified heart disease into five categories. According to the experimental results, the developed framework could classify ECG signals with 99.68 percent accuracy. The proposed method is more accurate and efficient than other well-known and popular algorithms when compared to other current methods.","PeriodicalId":410603,"journal":{"name":"2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133860701","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

A Reversible Image Processing Method with Selectable Functions 具有可选函数的可逆图像处理方法

2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2022-11-22 DOI: 10.1109/ISPACS57703.2022.10082795

Yuki Sugimoto, Shoko Imaizumi

引用次数: 0

An Efficient Smoke Detection Approach Based on Dual-Channel Neural Network 一种基于双通道神经网络的烟雾检测方法

2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2022-11-22 DOI: 10.1109/ISPACS57703.2022.10082811

Chengxu Zhou, Dongxia Wang, Haoran Cai

{"title":"An Efficient Smoke Detection Approach Based on Dual-Channel Neural Network","authors":"Chengxu Zhou, Dongxia Wang, Haoran Cai","doi":"10.1109/ISPACS57703.2022.10082811","DOIUrl":"https://doi.org/10.1109/ISPACS57703.2022.10082811","url":null,"abstract":"Smoke is the key feature of fire detection in its early age. Thus, an efficient smoke detection approach (i. e. an accurate and rapid method) is essential important to prevent fires. However, it is difficult to obtain an efficient method due to non-obvious details and monotonous color of smoke images. Moreover, traditional methods of smoke detection based on CNN contains lots of parameters and operations, which severely influents the computing efficiency in practical applications. Thus, we propose an efficient dual-channel neural network (EDCNN) on the basis of the state-of-the-art DCNN. Concretely, we use the linear inverted bottleneck (LIB) to replace the traditional convolution layers on DCNN to build a light weight deep neural network. The introduction of the LIB block can efficiently trade off between accuracy and latency. Moreover, ReLU6 is used as the activation function, because it is more suitable for low-precision hardware devices. We then use some experimental results to demonstrate the effectiveness of EDCNN compared with the competitors for smoke detection in terms of model and computational complexity.","PeriodicalId":410603,"journal":{"name":"2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128562806","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

Continuous Adaptation in Nonstationary Environments Based on Actor-Critic Algorithm 基于actor - critical算法的非平稳环境连续自适应

2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2022-11-22 DOI: 10.1109/ISPACS57703.2022.10082809

Yang Yu, Zhixiong Gan, Chun Xing Li, Hui Luo, Jiashou Wang

引用次数: 0

The Accuracy of Signal-to-Noise Ratio Estimation on a Fully Functional QPSK Transceiver 全功能QPSK收发器信噪比估计的准确性

2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2022-11-22 DOI: 10.1109/ISPACS57703.2022.10082793

Shou-Sheu Lin, Yu-Chi Hsieh, Jyun-Tai Chung, Jia-Ze Wong

引用次数: 0

An Efficient ConvNet for Text-based CAPTCHA Recognition 基于文本的CAPTCHA识别的有效卷积神经网络

2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2022-11-22 DOI: 10.1109/ISPACS57703.2022.10082852

Ke Qing, Rongsheng Zhang

{"title":"An Efficient ConvNet for Text-based CAPTCHA Recognition","authors":"Ke Qing, Rongsheng Zhang","doi":"10.1109/ISPACS57703.2022.10082852","DOIUrl":"https://doi.org/10.1109/ISPACS57703.2022.10082852","url":null,"abstract":"Text-based CAPTCHA is a widely used security mechanism to protect websites from malicious operations. The CAPTCHA recognition based on deep learning is a representative method to verify the security of CAPTCHAs deployed on the website. The ConvNet is a typical model for a wide variety of vision tasks and proves to be effective when it recognizes characters in different scenarios. However, the ConvNet applied to solve CAPTCHAs is still limited to high computation and complicated processing. In this work, we propose an efficient end-to-end network entirely consisting of standard ConvNet modules. According to the property of CAPTCHA, we reduce the redundant convolution calculation in the previous ConvNet and introduce a novel group convolution operation along the width of the image with improved performance and efficiency. The experiment shows our ConvNet successfully solves the CAPTCHAs from the highly-visited website, Sina.com, with a high accuracy above 90%, and the number of trainable parameters of it is less than 1/5 of the model in prior work based on prominent ConvNets such as ResNet and Inception network.","PeriodicalId":410603,"journal":{"name":"2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"23 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120927451","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

Layered Deep learning for Improved Breast Cancer Detection 改进乳腺癌检测的分层深度学习

2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2022-11-22 DOI: 10.1109/ISPACS57703.2022.10082840

Bita Asadi, Q. Memon

引用次数: 0

Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network 基于变压器和周期一致生成对抗网络的视网膜图像恢复

2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2022-11-22 DOI: 10.1109/ISPACS57703.2022.10082822

Alnur Alimanov, Md Baharul Islam

{"title":"Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network","authors":"Alnur Alimanov, Md Baharul Islam","doi":"10.1109/ISPACS57703.2022.10082822","DOIUrl":"https://doi.org/10.1109/ISPACS57703.2022.10082822","url":null,"abstract":"Medical imaging plays a significant role in detecting and treating various diseases. However, these images often happen to be of too poor quality, leading to decreased efficiency, extra expenses, and even incorrect diagnoses. Therefore, we propose a retinal image enhancement method using a vision transformer and convolutional neural network. It builds a cycle-consistent generative adversarial network that relies on unpaired datasets. It consists of two generators that translate images from one domain to another (e.g., low- to high-quality and vice versa), playing an adversarial game with two discriminators. Generators produce indistinguishable images for discriminators that predict the original images from generated ones. Generators are a combination of vision transformer (ViT) encoder and con-volutional neural network (CNN) decoder. Discriminators include traditional CNN encoders. The resulting improved images have been tested quantitatively using such evaluation metrics as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and qualitatively, i.e., vessel segmentation. The proposed method successfully reduces the adverse effects of blurring, noise, illumination disturbances, and color distortions while signifi-cantly preserving structural and color information. Experimental results show the superiority of the proposed method. Our testing PSNR is 31.138 dB for the first and 27.798 dB for the second dataset. Testing SSIM is 0.919 and 0.904, respectively. The code is available at https://github.com/AAleka/Transformer-Cycle-GAN","PeriodicalId":410603,"journal":{"name":"2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128967961","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

A Power Saving Scheme for Smart Phone GPS Tracker Using Kalman Filtering 一种基于卡尔曼滤波的智能手机GPS跟踪器节能方案

2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2022-11-22 DOI: 10.1109/ISPACS57703.2022.10082807

Sun-ting Lin, Shou-Sheu Lin, Kun-You Sun

引用次数: 0