2021 International Conference on Electronic Engineering (ICEEM)最新文献_第6页

A Design for An Efficient Hybrid Compression System for EEG Data 一种高效的脑电数据混合压缩系统设计

2021 International Conference on Electronic Engineering (ICEEM) Pub Date : 2021-07-03 DOI: 10.1109/ICEEM52022.2021.9480377

Retaj Yousri, Madyan Alsenwi, M. Saeed Darweesh, T. Ismail

{"title":"A Design for An Efficient Hybrid Compression System for EEG Data","authors":"Retaj Yousri, Madyan Alsenwi, M. Saeed Darweesh, T. Ismail","doi":"10.1109/ICEEM52022.2021.9480377","DOIUrl":"https://doi.org/10.1109/ICEEM52022.2021.9480377","url":null,"abstract":"The Electroencephalography (EEG) signals that indicate the electrical activity of the brain are acquired with a high sampling rate. Consequently, the size of the recorded EEG data is large. For storing and transmitting these data, large space and bandwidth are demanded. Therefore, preprocessing and compressing EEG data are important for efficient data transmission and storage. The purpose of this approach is to design an efficient EEG data compression system in terms of time and space complexities. The proposed system consists of three main units: preprocessing unit, compression unit, and reconstruction unit. The core of the compression process occurs in the compression unit. Different combinations of hybrid lossy/lossless compression techniques were tried in the compression process. In this study, both the Discrete Cosine Transform and the Discrete Wavelet Transform techniques were experimented for the lossy compression algorithm. The Arithmetic Coding and the Run Length Encoding were experimented then for the lossless compression algorithm. The final results showed that combining both the Discrete Cosine Transform and the Run Length Encoding yields the most optimal system complexity and compression ratio. This approach achieved up to CR = 94% at RMSE = 0.188.","PeriodicalId":352371,"journal":{"name":"2021 International Conference on Electronic Engineering (ICEEM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130637342","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

Compressed Sensing for SAR Image Reconstruction 基于压缩感知的SAR图像重建

2021 International Conference on Electronic Engineering (ICEEM) Pub Date : 2021-07-03 DOI: 10.1109/ICEEM52022.2021.9480655

Alaa El-Ashkar, H. Shendy, W. El-shafai, T. Taha, A. El-Fishawy, Mohamed Abd El-Nabi, F. El-Samie

引用次数: 6

Implementation of Network Attack Detection using Convolutional Neural Network 利用卷积神经网络实现网络攻击检测

2021 International Conference on Electronic Engineering (ICEEM) Pub Date : 2021-07-03 DOI: 10.1109/ICEEM52022.2021.9480645

Youssef F. Sallam, HossamEl-din H. Ahmed, A. Saleeb, Nirmeen A. El-Bahnasawy, F. El-Samie

{"title":"Implementation of Network Attack Detection using Convolutional Neural Network","authors":"Youssef F. Sallam, HossamEl-din H. Ahmed, A. Saleeb, Nirmeen A. El-Bahnasawy, F. El-Samie","doi":"10.1109/ICEEM52022.2021.9480645","DOIUrl":"https://doi.org/10.1109/ICEEM52022.2021.9480645","url":null,"abstract":"The Internet obviously has a major impact on the global economy and human life every day. This boundless use pushes the attack programmers to attack the data frameworks on the Internet. Web attacks influence the reliability of the Internet and its administrations. These attacks are classified as User-to-Root (U2R), Remote-to-Local (R2L), Denial-of-Service (DoS) and Probing (Probe). Subsequently, making sure about web framework security and protecting data are pivotal. The conventional layers of safeguards like antivirus scanners, firewalls and proxies, which are applied to treat the security weaknesses are insufficient. So, Intrusion Detection Systems (IDSs) are utilized to screen PC and data frameworks for security shortcomings. IDS adds more effectiveness in securing networks against attacks. This paper presents an IDS model based on Deep Learning (DL) with Convolutional Neural Network (CNN) hypothesis. The model has been evaluated on the NSLKDD dataset. It has been trained by Kddtrain+ and tested twice, once using kddtrain+ and the other using kddtest+. The achieved test accuracies are 99.7% and 98.43% with 0.002 and 0.02 wrong alert rates for the two test scenarios, respectively.","PeriodicalId":352371,"journal":{"name":"2021 International Conference on Electronic Engineering (ICEEM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132441093","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

Performance Analysis of Genetic Algorithm and Ant Colony Optimization Dependent on PID Controller for Matrix Converter 基于PID控制器的遗传算法和蚁群优化在矩阵变换器中的性能分析

2021 International Conference on Electronic Engineering (ICEEM) Pub Date : 2021-07-03 DOI: 10.1109/ICEEM52022.2021.9480640

Mahmoud Ibrahim Mohamed, G. El-Saady, A. Yousef

引用次数: 3

Handwritten Chemical Formulas Classification Model Using Deep Transfer Convolutional Neural Networks 基于深度传递卷积神经网络的手写化学配方分类模型

2021 International Conference on Electronic Engineering (ICEEM) Pub Date : 2021-07-03 DOI: 10.1109/ICEEM52022.2021.9480627

Ahmed M. Hagag, Ibrahim Omara, A. N. K. Alfarra, Fahd Mekawy

{"title":"Handwritten Chemical Formulas Classification Model Using Deep Transfer Convolutional Neural Networks","authors":"Ahmed M. Hagag, Ibrahim Omara, A. N. K. Alfarra, Fahd Mekawy","doi":"10.1109/ICEEM52022.2021.9480627","DOIUrl":"https://doi.org/10.1109/ICEEM52022.2021.9480627","url":null,"abstract":"With the spread of the COVID19 pandemic, blended learning has become one of the most used methods in educational organizations such as universities, community colleges, and schools. In blended learning, the students’ practical activities are done in more than one way, including simulation software and the place of study. For chemical experiment programs, the classification of handwritten chemical formulas plays an important role in determining the simulation software’s efficiency. Accordingly, in this study, we propose a model for handwritten chemical formula classification. First, this paper describes a handwritten chemical formulas dataset that contains eight classes (HCFD8). Second, convolutional neural networks (CNNs) with pre-trained weights are used as a deep feature extractor to extract features from the images. Third, due to limited training images per class, the proposed model uses data augmentation techniques to expand the training images. Then, an enhanced multilayer perceptron (EMLP) strategy is used to classify the image. Finally, we provide a performance analysis of typical deep learning approaches on HCFD8, which shows that the proposed model performs good accuracy results.","PeriodicalId":352371,"journal":{"name":"2021 International Conference on Electronic Engineering (ICEEM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123982465","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: 2

Performance Improvement of Fog Environment Using Deadline Based Scheduling Algorithm 基于截止日期调度算法的雾环境性能改进

2021 International Conference on Electronic Engineering (ICEEM) Pub Date : 2021-07-03 DOI: 10.1109/ICEEM52022.2021.9480629

Amal El-Nattat, Sahar Elkazzaz, Nirmeen A. El-Bahnasawy, A. El-Sayed

引用次数: 1

Employment of Task Scheduling based on Water Wave Optimization in Multi Robot System 基于水波优化的任务调度在多机器人系统中的应用

2021 International Conference on Electronic Engineering (ICEEM) Pub Date : 2021-07-03 DOI: 10.1109/ICEEM52022.2021.9480617

Dina A. Amer, G. Attiya, Ibrahim Zeidan, Aida A. Nasr

引用次数: 2

Stabilization enhancement of the ball on the plate system (BOPS) based on Takagi-Sugeno (T-S) fuzzy modeling 基于Takagi-Sugeno (T-S)模糊模型的板上球系统稳定化增强

2021 International Conference on Electronic Engineering (ICEEM) Pub Date : 2021-07-03 DOI: 10.1109/ICEEM52022.2021.9480612

Mohamed R. Elshamy, Essam Nabil, Amged Sayed Abdelmageed, B. Abozalam

{"title":"Stabilization enhancement of the ball on the plate system (BOPS) based on Takagi-Sugeno (T-S) fuzzy modeling","authors":"Mohamed R. Elshamy, Essam Nabil, Amged Sayed Abdelmageed, B. Abozalam","doi":"10.1109/ICEEM52022.2021.9480612","DOIUrl":"https://doi.org/10.1109/ICEEM52022.2021.9480612","url":null,"abstract":"After applying the ball on plate stabilization system in practice, a big problem appeared, which is that the servo motors used must be very fast and have large-angle but these motors are rare and expensive Therefore, commonly used servo motors that have large angles and low speed are used in the practical experiment, and this makes the angle of the plate large and this increases the nonlinearity in this system, Therefore, a model was developed that deals with the large nonlinearity in the system, depending on Takagi-Sugeno (T-S) fuzzy to improve the tracking and stabilization of the ball on the plate system (BOPS) and this model not presented in any scientific paper before. This paper introduces the mathematical model of the two degree of freedom BOPS using state space and modeling through Takagi-Sugeno (T-S) fuzzy. The T-S fuzzy is utilized for improving the controller performance by aiding the state feedback controller. The determination of the gains of the controller is performed through the linear quadratic regulator (LQR) method. The proposed controller gives more precisely tracking the desired trajectory of the ball position.","PeriodicalId":352371,"journal":{"name":"2021 International Conference on Electronic Engineering (ICEEM)","volume":"69 Suppl 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130071467","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

Hybrid Method for Contrast Enhancement of Industrial Videoscope Images 工业内窥镜图像对比度增强的混合方法

2021 International Conference on Electronic Engineering (ICEEM) Pub Date : 2021-07-03 DOI: 10.1109/ICEEM52022.2021.9480610

Reda Ammar, F. A. Abd El-Samie, W. El-shafai, Noha A. El-Hag, Atef Abou Elazm, Ashraf A M Khalaf, Sahar Aboshosha, Ghada M. El-Banby, Amir El-Safrawey

引用次数: 0

Plant Seedlings Classification using Transfer Learning 利用迁移学习进行植物幼苗分类

2021 International Conference on Electronic Engineering (ICEEM) Pub Date : 2021-07-03 DOI: 10.1109/ICEEM52022.2021.9480654

Esraa Hassan, M. Shams, N. A. Hikal, S. Elmougy

{"title":"Plant Seedlings Classification using Transfer Learning","authors":"Esraa Hassan, M. Shams, N. A. Hikal, S. Elmougy","doi":"10.1109/ICEEM52022.2021.9480654","DOIUrl":"https://doi.org/10.1109/ICEEM52022.2021.9480654","url":null,"abstract":"Agriculture is essential for human survival and remains a major economic driver in many countries around the world. Most of the living things around the world feed on vegetation produced by agriculture. Therefore, the researchers should work on developing agriculture using the most recent artificial intelligence approaches. The diagnosis of the plant diseases based on the leaf detection are currently utilized based on machine vision systems. The selective of weeding are more helpful and struggled to identify weeds on a reliable and accurate manner compared with the traditional classification workflows that are sluggish and error-prone results from classification expertise given small number of expert taxonomists. In this paper, an overview of recent attempts to classify species using computer vision and machine learning techniques are realized. It concentrates on identifying plant species using leaf images. We used a dataset containing 4,275 images of 12 species at various growth stages. Furthermore, we present an architecture for plant seedling classification-based machine learning. Convolutional Neural Network (CNN) and transfer learning are utilized as a classification algorithm. The experimentations results based on these classifiers indicated that the proposed model achieved 0.9754, 0.9742, 0.9766, and 0.9754 in terms of Accuracy, Sensitivity, Specificity, and F-score, respectively.","PeriodicalId":352371,"journal":{"name":"2021 International Conference on Electronic Engineering (ICEEM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115255602","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