Ivan Basile Kabiena, Eric Michel Deussom Djomadji, E. Tonyé
Cardiovascular diseases are the origin of many causes of death worldwide. They impose on practitioners optimal diagnostic methods such as telemedicine in order to be able to quickly detect anomalies for daily care and monitoring of patients. The Electrocardiogram (ECG) is an examination that can detect abnormal functioning of the heart and generates a large number of digital data which can be stored or transmitted for further analysis. For storage or transmission purposes, one of the challenges is to reduce the space occupied by ECG signal and for that, it is important to offer more and more efficient algorithms capable of achieving high compression rates, while offering a good quality of reconstruction in a relatively short time. We propose in this paper a new ECG compression scheme that is based on a subset of signal splitting and 2D processing, the wavelet transform (DWT) and SPIHT coding which has proved their worth in the field of signal processing and compression. They are exploited for decorrelation and coding of the signal. The re-sults obtained are significant and offer many perspectives.
{"title":"New Ecg Signal Compression Model Based on Set Theory Applied to Images","authors":"Ivan Basile Kabiena, Eric Michel Deussom Djomadji, E. Tonyé","doi":"10.4236/jcc.2023.118003","DOIUrl":"https://doi.org/10.4236/jcc.2023.118003","url":null,"abstract":"Cardiovascular diseases are the origin of many causes of death worldwide. They impose on practitioners optimal diagnostic methods such as telemedicine in order to be able to quickly detect anomalies for daily care and monitoring of patients. The Electrocardiogram (ECG) is an examination that can detect abnormal functioning of the heart and generates a large number of digital data which can be stored or transmitted for further analysis. For storage or transmission purposes, one of the challenges is to reduce the space occupied by ECG signal and for that, it is important to offer more and more efficient algorithms capable of achieving high compression rates, while offering a good quality of reconstruction in a relatively short time. We propose in this paper a new ECG compression scheme that is based on a subset of signal splitting and 2D processing, the wavelet transform (DWT) and SPIHT coding which has proved their worth in the field of signal processing and compression. They are exploited for decorrelation and coding of the signal. The re-sults obtained are significant and offer many perspectives.","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70939097","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}
In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.
{"title":"Research on Automatic Elimination of Laptop Computer in Security CT Images Based on Projection Algorithm and YOLOv7-Seg","authors":"Fei Wang, Baosheng Liu, Yijun Tang, Lei Zhao","doi":"10.4236/jcc.2023.119001","DOIUrl":"https://doi.org/10.4236/jcc.2023.119001","url":null,"abstract":"In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135595411","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}
In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability.
{"title":"A Novel Scheme for Separate Training of Deep Learning-Based CSI Feedback Autoencoders","authors":"Lusheng Xi, Yanan Yu, Jianzhong Yi, Chao Dong, Kai Niu, Qiuping Huang, Qiubin Gao, Yongqiang Fei","doi":"10.4236/jcc.2023.119009","DOIUrl":"https://doi.org/10.4236/jcc.2023.119009","url":null,"abstract":"In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability.","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135838120","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}
Pub Date : 2023-01-01DOI: 10.4236/jcc.2023.1110008
Doaa E. Elmatary, Doaa M. Maher, Areeg Tarek Ibrahim
A multidisciplinary approach for developing an intelligent sign multi-language recognition system to greatly enhance deaf-mute communication will be discussed and implemented. This involves designing a low-cost glove-based sensing system, collecting large and diverse datasets, preprocessing the data, and using efficient machine learning models. Furthermore, the glove is integrated with a user-friendly mobile application called “Life-sign” for this system. The main goal of this work is to minimize the processing time of machine learning classifiers while maintaining higher accuracy performance. This is achieved by using effective preprocessing algorithms to handle noisy and inconsistent data. Testing and iterating approaches have been applied to various classifiers to refine and improve their accuracy in the recognition process. Additionally, the Extra Trees (ET) classifier has been identified as the best algorithm, with results proving successful gesture prediction at an average accuracy of about 99.54%. A smart optimization feature has been implemented to control the size of data transferred via Bluetooth, allowing for fast recognition of consecutive gestures. Real-time performance has been measured through extensive experimental testing on various consecutive gestures, specifically referring to Arabic Sign Language (ArSL). The results have demonstrated that the system guarantees consecutive gesture recognition with a lower delay of 50 milliseconds.
本文将讨论并实现一种多学科方法来开发智能手语多语言识别系统，以极大地提高聋哑人的交流能力。这包括设计一个低成本的基于手套的传感系统，收集大量不同的数据集，预处理数据，以及使用高效的机器学习模型。此外，该手套还集成了一个名为“Life-sign”的用户友好移动应用程序。这项工作的主要目标是最小化机器学习分类器的处理时间，同时保持更高的准确率性能。这是通过使用有效的预处理算法来处理噪声和不一致的数据来实现的。测试和迭代方法已应用于各种分类器，以改进和提高其识别过程中的准确性。此外，Extra Trees (ET)分类器被认为是最好的算法，其结果证明成功的手势预测平均准确率约为99.54%。一个智能优化功能已经实现，以控制通过蓝牙传输的数据大小，允许快速识别连续的手势。实时性能通过对各种连续手势的广泛实验测试来衡量，特别是指阿拉伯手语(ArSL)。结果表明，该系统可以保证连续的手势识别，延迟较低，为50毫秒。
{"title":"Intelligent Sign Multi-Language Real-Time Prediction System with Effective Data Preprocessing","authors":"Doaa E. Elmatary, Doaa M. Maher, Areeg Tarek Ibrahim","doi":"10.4236/jcc.2023.1110008","DOIUrl":"https://doi.org/10.4236/jcc.2023.1110008","url":null,"abstract":"A multidisciplinary approach for developing an intelligent sign multi-language recognition system to greatly enhance deaf-mute communication will be discussed and implemented. This involves designing a low-cost glove-based sensing system, collecting large and diverse datasets, preprocessing the data, and using efficient machine learning models. Furthermore, the glove is integrated with a user-friendly mobile application called “Life-sign” for this system. The main goal of this work is to minimize the processing time of machine learning classifiers while maintaining higher accuracy performance. This is achieved by using effective preprocessing algorithms to handle noisy and inconsistent data. Testing and iterating approaches have been applied to various classifiers to refine and improve their accuracy in the recognition process. Additionally, the Extra Trees (ET) classifier has been identified as the best algorithm, with results proving successful gesture prediction at an average accuracy of about 99.54%. A smart optimization feature has been implemented to control the size of data transferred via Bluetooth, allowing for fast recognition of consecutive gestures. Real-time performance has been measured through extensive experimental testing on various consecutive gestures, specifically referring to Arabic Sign Language (ArSL). The results have demonstrated that the system guarantees consecutive gesture recognition with a lower delay of 50 milliseconds.","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135212199","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}
Pub Date : 2023-01-01DOI: 10.4236/jcc.2023.1110005
Aytun Onay, Gökhan Ertürk, Cem Kıranlı, Hande Ateş, Yunus E. Isıkdemir
Energy demand will continue to rise as a result of predicted population growth. In this work, a user-friendly home energy monitoring system based on IoT is described, which is capable of collecting, analyzing, and displaying data. Users register their sensors and devices on the monitoring platform. PostgreSQL and Elasticsearch databases are used to store the resulting measurements. In a smart home, the wireless sensor ACS712 was used to monitor the flow of electricity (current and voltage) for a household device. The user can share data about electricity consumption and costs with a third party via the private IPFS (InterPlanetary File System) network. A third party can download all the energy consumption data for a device or many devices from the platform for 1 day, 3 months, 6 months, and 1 year. The studies on the development of energy-efficient technology for home devices benefit greatly from the gathered data. For security in the system, it is preferred to run Keyrock Idm, Wilma Pep Proxy, and Orion Context Broker in HTTPS mode, and MQTTS is used to retrieve sensor data. The experimental results showed that the energy monitoring system accurately records voltage, current, active power, and the total amount of power used and offers low-cost solutions to the users using household devices in a day.
由于预计的人口增长，能源需求将继续上升。本文介绍了一种基于物联网的用户友好型家庭能源监测系统，该系统具有采集、分析和显示数据的功能。用户在监控平台上注册他们的传感器和设备。PostgreSQL和Elasticsearch数据库用于存储结果测量。在智能家居中，无线传感器ACS712用于监测家用设备的电流(电流和电压)。用户可以通过私人IPFS(星际文件系统)网络与第三方共享有关电力消耗和成本的数据。第三方可以从平台下载1天、3个月、6个月、1年的一台或多台设备的全部能耗数据。收集到的数据对家用设备节能技术发展的研究有很大的帮助。为了系统中的安全性，建议在HTTPS模式下运行Keyrock Idm、Wilma Pep Proxy和Orion Context Broker，并使用MQTTS检索传感器数据。实验结果表明，该能量监测系统能够准确记录电压、电流、有功功率和总用电量，为使用家用设备的用户在一天内提供低成本的解决方案。
{"title":"A Smart Home Energy Monitoring System Based on Internet of Things and Inter Planetary File System for Secure Data Sharing","authors":"Aytun Onay, Gökhan Ertürk, Cem Kıranlı, Hande Ateş, Yunus E. Isıkdemir","doi":"10.4236/jcc.2023.1110005","DOIUrl":"https://doi.org/10.4236/jcc.2023.1110005","url":null,"abstract":"Energy demand will continue to rise as a result of predicted population growth. In this work, a user-friendly home energy monitoring system based on IoT is described, which is capable of collecting, analyzing, and displaying data. Users register their sensors and devices on the monitoring platform. PostgreSQL and Elasticsearch databases are used to store the resulting measurements. In a smart home, the wireless sensor ACS712 was used to monitor the flow of electricity (current and voltage) for a household device. The user can share data about electricity consumption and costs with a third party via the private IPFS (InterPlanetary File System) network. A third party can download all the energy consumption data for a device or many devices from the platform for 1 day, 3 months, 6 months, and 1 year. The studies on the development of energy-efficient technology for home devices benefit greatly from the gathered data. For security in the system, it is preferred to run Keyrock Idm, Wilma Pep Proxy, and Orion Context Broker in HTTPS mode, and MQTTS is used to retrieve sensor data. The experimental results showed that the energy monitoring system accurately records voltage, current, active power, and the total amount of power used and offers low-cost solutions to the users using household devices in a day.","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135158163","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}
{"title":"Evaluation of the Global Horizontal Irradiation (GHI) on the Ground from the Images of the Second Generation European Meteorological Satellites MSG","authors":"Ahmed El Ouiqary, E. M. Kheddioui, M. F. Smiej","doi":"10.4236/jcc.2023.111001","DOIUrl":"https://doi.org/10.4236/jcc.2023.111001","url":null,"abstract":"","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70935042","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}
{"title":"Ranking of Web Pages in a Personalized Search","authors":"M. Ghaly","doi":"10.4236/jcc.2023.112007","DOIUrl":"https://doi.org/10.4236/jcc.2023.112007","url":null,"abstract":"","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70935551","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}
Han Lei, Hu Wang, Linli Wang, Yuhang Dong, Jingjie Cheng, Kui Cai
{"title":"An Analysis of the Evolution of Online Public Opinion on Public Health Emergencies by Combining CNN-BiLSTM + Attention and LDA","authors":"Han Lei, Hu Wang, Linli Wang, Yuhang Dong, Jingjie Cheng, Kui Cai","doi":"10.4236/jcc.2023.114009","DOIUrl":"https://doi.org/10.4236/jcc.2023.114009","url":null,"abstract":"","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70936451","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：