2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)最新文献

A Very High Speed, High Resolution Dynamic Current Comparator 一个非常高速，高分辨率的动态电流比较器

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691448

Abdel Rahman M. Dawood, F. Farag

引用次数: 0

Performance analysis of Integrating Wireless Sensor Network with Li-Fi Wireless Communication Technology using OptiSystem Simulation Tool 利用OptiSystem仿真工具对无线传感器网络与Li-Fi无线通信技术的集成进行性能分析

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691417

Helmy Milad Helmy Shenoda, Nabil Abd-Rabou Abd-Elazez, Hala Mohamed Abd-El-Qader, A. Hossam

{"title":"Performance analysis of Integrating Wireless Sensor Network with Li-Fi Wireless Communication Technology using OptiSystem Simulation Tool","authors":"Helmy Milad Helmy Shenoda, Nabil Abd-Rabou Abd-Elazez, Hala Mohamed Abd-El-Qader, A. Hossam","doi":"10.1109/JAC-ECC54461.2021.9691417","DOIUrl":"https://doi.org/10.1109/JAC-ECC54461.2021.9691417","url":null,"abstract":"Light Fidelity (Li-Fi) is one of the key areas in wireless communication that works in the same way as optical fiber, but the medium of transmission is free space. The design of a Light-emitting diode (LED) in Li-Fi technology makes changes in the way of communication that can be used for transmitting data by flashing light. On the other hand, sensors nodes in WSNs need photodiodes as a detector which plays an important part in receiving data from a LED and decoding it. Free space optical (FSO) communication has emerged as a feasible technique for broadband wireless applications. In this article, we used two scientific software to analyze the performance of wireless sensor networks with Li-Fi technology: OptiSystem, which is a tool for developing and analyzing optical communication systems, and Matlab which is used to integrate results curves. In this analysis, we consider three models of wireless sensor networks that use photodetectors that receive data from LED throughout the FSO channel: the LOS model, the single LED non-LOS model and the two LED non-LOS model. When compared the models together, the experimental results indicated that the LOS model is the best for WSNs which use Li-Fi technology.","PeriodicalId":354908,"journal":{"name":"2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129365138","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

Accelerated Edge Detection Algorithm for High-Speed Applications 高速应用的加速边缘检测算法

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691429

Aya Saad, Khloud Rafat, A. Soltan, M. Darweesh

引用次数: 0

Extracting Geographic Addresses from Social Media using Deep Recurrent Neural Networks 利用深度递归神经网络从社交媒体中提取地理地址

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691442

Sara Dakrory, Bahgat Abdelhamid Abdelatif, Mohammed Kayed, A. A. Ali

{"title":"Extracting Geographic Addresses from Social Media using Deep Recurrent Neural Networks","authors":"Sara Dakrory, Bahgat Abdelhamid Abdelatif, Mohammed Kayed, A. A. Ali","doi":"10.1109/JAC-ECC54461.2021.9691442","DOIUrl":"https://doi.org/10.1109/JAC-ECC54461.2021.9691442","url":null,"abstract":"The importance of geographical, addresses in people's daily lives cannot be underestimated. People usually use the Internet to search for unfamiliar areas and then use map services to mark locations. Using social media to extract information, particularly geographical addresses, is rapidly increasing worldwide. Social media represents the right choice as a source in identifying the location that people need to find. In this paper, a deep neural network using a Bidirectional Long Short-Term Memory with CRF (BI-LSTM-CRF) model is applied for address extraction. In addition, a Bidirectional Encoder Representations from Transformers (BERT) model is implemented to extract the geographical addresses from Facebook posts. Further, we reveal how to use the BIEO tagging method to apply the sequence labeling technique to Arabic postal address extraction. An Arabic corpus from social media is annotated to evaluate our proposed model. The results show that Arabic postal addresses can be extracted through BI-LSTM-CRF and BERT models with a high F-measure.","PeriodicalId":354908,"journal":{"name":"2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131656436","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

Tomographic Image Reconstruction using Inverse Synthetic Aperture Radar Methods 利用逆合成孔径雷达方法重建层析图像

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691413

Abdel-Aziz Ibrahim Mahmoud Hassanin, Nariman Abdel‐Salam Bauomy

{"title":"Tomographic Image Reconstruction using Inverse Synthetic Aperture Radar Methods","authors":"Abdel-Aziz Ibrahim Mahmoud Hassanin, Nariman Abdel‐Salam Bauomy","doi":"10.1109/JAC-ECC54461.2021.9691413","DOIUrl":"https://doi.org/10.1109/JAC-ECC54461.2021.9691413","url":null,"abstract":"The Tomographic reconstruction of the image using Inverse Synthetic Aperture Radar Methods aims to increase the resolution over the target area. In this paper, the algorithm, theoretical study, methodology, and techniques of tomographic imaging were originally developed in the context of tomographic imaging synthetic aperture radar methods, and have been used in radar imaging system reconstruction. So, the purpose of this presentation is to explore the application of tomographic imaging techniques to learn about the target or familiarize the flight path of aircraft. Tomography image reconstruction methods have been applied to creating three-dimensional models at the range of microwave to several different applications, air defense, and moving targets. Moreover, the goal of this study is to create a comprehensive programming package to work with Inverse Synthetic Aperture Radar Systems. Design and implementation of the system to use for obtaining the form of the object and analysis have been presented. Also, of interest is the improvement in target classification performance afforded by tomographic imaging. In addition to what was mentioned, the performance and critical factors, and identifying promising areas for future research have been presented and achieved.","PeriodicalId":354908,"journal":{"name":"2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131772369","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 Real-Time Approach Based on Deep Learning for Ego-Lane Detection 基于深度学习的自车道实时检测方法

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691421

Yousri Yousri, Mohamed M. R. Mostafa, Rawane Yasser, M. Shawki, Ahmed Khaled, Ziad Mostafa, Ahmed Soltan, M. Darweesh

{"title":"A Real-Time Approach Based on Deep Learning for Ego-Lane Detection","authors":"Yousri Yousri, Mohamed M. R. Mostafa, Rawane Yasser, M. Shawki, Ahmed Khaled, Ziad Mostafa, Ahmed Soltan, M. Darweesh","doi":"10.1109/JAC-ECC54461.2021.9691421","DOIUrl":"https://doi.org/10.1109/JAC-ECC54461.2021.9691421","url":null,"abstract":"Lane detection has been one of the most important tasks of autonomous driving vehicles. The main objective of lane detection is tracking the lane boundaries on a real-time basis. Recently, many researches have shown deep learning models that are capable of detecting road lanes robustly. Yet, providing testing results in the context of real-time is not commonly found. This paper aims to provide a comprehensive real-time evaluation for performing ego-lane detection based on deep learning. The lane detection is recognized here as a semantic segmentation task where a pre-trained ResUNet++ model is adopted from a prior study. The real-time evaluation approaches include testing driving video sequences, CARLA simulator environment, and finally, a hardware kit that runs the deep learning model on the NVIDIA Jetson Nano developer kit. The performance of the model was investigated in various complex environments and dynamic scenarios. Eventually, the experimental results were qualitatively and quantitatively evaluated, showing reliability and promptness of using deep learning models for the lane detection task in a real-time context.","PeriodicalId":354908,"journal":{"name":"2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132693068","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

Towards Leaf Disease Recognition from Individual Lesions Using Deep Learning Techniques 利用深度学习技术识别单个病变的叶片疾病

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691444

Lawrence C. Ngugi, M. Abo-Zahhad, M. Abdelwahab

{"title":"Towards Leaf Disease Recognition from Individual Lesions Using Deep Learning Techniques","authors":"Lawrence C. Ngugi, M. Abo-Zahhad, M. Abdelwahab","doi":"10.1109/JAC-ECC54461.2021.9691444","DOIUrl":"https://doi.org/10.1109/JAC-ECC54461.2021.9691444","url":null,"abstract":"Leaf disease recognition using image processing techniques is presently an active area of research. In recent years, most studies have focused on the use of deep learning techniques for crop disease recognition as these models have consistently outperformed shallow classifiers. When used to classify crop diseases from images taken under controlled lab conditions, deep learning models have achieved near perfect recognition accuracies. However, when used with images captured under field conditions, the deep learning models’ performance dropped considerably. Research showed that complex illumination and background conditions are mainly responsible for this decline in performance. Subsequent studies demonstrated that classifying images of individual lesions rather than images of whole leaves improved disease recognition accuracy while at the same time allowing for the detection of multiple infections presenting on the same leaf. Latest studies have proposed algorithms for automatic extraction and classification of lesions from leaf images. In this paper, the authors present a brief survey of the state-of-art and their contributions towards automatic recognition of disease lesions using deep learning methods. In particular, this paper highlights two deep learning models named KijaniNet and SwapNet which were proposed for use in automatic lesion extraction and classification algorithms. The paper concludes by suggesting some research points to be considered in future studies.","PeriodicalId":354908,"journal":{"name":"2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127416718","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

Quad-Band Patch Antenna for Future Generations of Mobile Handsets 面向未来手机的四波段贴片天线

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691420

M. A. El-Hassan, A. E. Farahat, K. Hussein

引用次数: 0

A robust CNN classification of whole slide thyroid carcinoma images 一种稳健的全片甲状腺癌图像CNN分类方法

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691433

Ahmed S. El-Hossiny, Valid Al-Atabany, Osama N. Hassan, A. Mostafa, Sherif A. Sami

引用次数: 0

An IoT-based Smart Wearable System for Remote Health Monitoring 基于物联网的智能可穿戴远程健康监测系统

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691431

M. Abo-Zahhad, M. Sayed, Ahmed H. Abd El‐Malek, Abdelrahman Fawaz, A. Zakaria, Ahmed E. Wahdan, Moaz Elsayed, Aya Taghian

引用次数: 3