Pub Date : 2022-06-17DOI: 10.21608/mjeer.2022.138003.1057
M. Berbar
One of the complications of diabetes disease is diabetic retinopathy (DR). Diabetic patients may suffer from total loss of sight. That's if it is not detected and medicated early enough. The early detection of DR is very important during funds screening on a regular basis. Detection and grading of DR are difficult because most fundus images suffer from undersaturation and noise. This paper proposes a new enhancement process as a solution to the poor quality of fundus images. It also proposes two architectures for convolutional neural network (CNN) models. The first one is the binary classifier of DR images into normal and abnormal. The second CNN architecture to classify the severity grades of DR. In this study, we also utilized different pre-trained convolutional neural network models to show the impact on the performance of the use of transfer learning from pre-trained CNN models vs newly defined architectures. The pre-trained CNN models and the two new proposed CNN models are tested using Messidor1, Messidor2, and Kaggle EyePACS datasets. The proposed binary classifier model results in F1-scores of 0.9387, 0.9629, and 0.9430 on the Messidor-1, Messidor-2, and EyePACS datasets, respectively. The proposed second model classifies the five grades with an F1-score of 0.9133, 0.9226, and 0.9393 on the Messidor1, Messidor2, and Kaggle EyePACS datasets, respectively. The new proposed CNN model proved its reliability and efficiency in detecting DR and classifying severity grades of DR in fundus images. Preprocessing techniques enhanced the performance by 10.83% of accuracy and 0.13037 in AUC using the binary model. Keywords— Diabetic Retinopathy; Convolutional Neural Network; Fundus images; Deep learning.
{"title":"Diabetic Retinopathy Detection and Grading using Deep learning","authors":"M. Berbar","doi":"10.21608/mjeer.2022.138003.1057","DOIUrl":"https://doi.org/10.21608/mjeer.2022.138003.1057","url":null,"abstract":"One of the complications of diabetes disease is diabetic retinopathy (DR). Diabetic patients may suffer from total loss of sight. That's if it is not detected and medicated early enough. The early detection of DR is very important during funds screening on a regular basis. Detection and grading of DR are difficult because most fundus images suffer from undersaturation and noise. This paper proposes a new enhancement process as a solution to the poor quality of fundus images. It also proposes two architectures for convolutional neural network (CNN) models. The first one is the binary classifier of DR images into normal and abnormal. The second CNN architecture to classify the severity grades of DR. In this study, we also utilized different pre-trained convolutional neural network models to show the impact on the performance of the use of transfer learning from pre-trained CNN models vs newly defined architectures. The pre-trained CNN models and the two new proposed CNN models are tested using Messidor1, Messidor2, and Kaggle EyePACS datasets. The proposed binary classifier model results in F1-scores of 0.9387, 0.9629, and 0.9430 on the Messidor-1, Messidor-2, and EyePACS datasets, respectively. The proposed second model classifies the five grades with an F1-score of 0.9133, 0.9226, and 0.9393 on the Messidor1, Messidor2, and Kaggle EyePACS datasets, respectively. The new proposed CNN model proved its reliability and efficiency in detecting DR and classifying severity grades of DR in fundus images. Preprocessing techniques enhanced the performance by 10.83% of accuracy and 0.13037 in AUC using the binary model. Keywords— Diabetic Retinopathy; Convolutional Neural Network; Fundus images; Deep learning.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125781529","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 : 2022-06-17DOI: 10.21608/mjeer.2022.137937.1056
M. Berbar
Computational power in deep convolutional neural networks has made it possible to have robust classifiers for faces and facial gender for many security issues and computer vision problems. This paper proposes two convolutional neural network (CNN) models for face recognition and facial gender classification. The models consist of an image input layer, followed by three blocks of convolutional, normalization, activation, and max-pooling layers, and three fully connected layers. The performance of the proposed CNN solutions is evaluated using five publicly available face datasets. Two greyscale face datasets: Sheffield and AT & T. Three color face datasets, Faces94, Ferret, and Celebrity Face Images from Kaggle. The achieved classification accuracy ranged between 99.0% and 100% on the Faces94, Ferret, Sheffield, and AT&T datasets, and classification accuracy of 93.6% to 95.0% on the Kaggle dataset. The proposed CNN can process and classify a smallsize face image 32 × 32-pixel from the Faces94, Sheffield, and AT&T datasets and 100 × 100 pixels from the Ferret and Kaggle datasets. The obtained results prove that the proposed CNN models are an effective solution for face image recognition and facial gender image classification. The proposed model produces competitive accuracy compared to several state-of-the-art methods. Keywords— Face recognition; facial gender classification; convolutional neural network; max pooling layer, fully
深度卷积神经网络的计算能力使得在许多安全问题和计算机视觉问题上对面部和面部性别进行鲁棒分类成为可能。本文提出了两种卷积神经网络(CNN)人脸识别和人脸性别分类模型。该模型包括一个图像输入层，随后是卷积层、归一化层、激活层和最大池化层的三个块，以及三个完全连接的层。使用五个公开可用的人脸数据集评估了所提出的CNN解决方案的性能。两种灰度人脸数据集:Sheffield和at&t。三种颜色的人脸数据集，Faces94, Ferret和Celebrity face Images来自Kaggle。在Faces94、Ferret、Sheffield和AT&T数据集上实现的分类准确率在99.0%到100%之间，在Kaggle数据集上实现的分类准确率在93.6%到95.0%之间。提出的CNN可以处理和分类来自Faces94、Sheffield和AT&T数据集的32 × 32像素的小尺寸人脸图像，以及来自Ferret和Kaggle数据集的100 × 100像素的小尺寸人脸图像。实验结果表明，本文提出的CNN模型是人脸图像识别和人脸性别图像分类的有效解决方案。与几种最先进的方法相比，所提出的模型具有相当的准确性。关键词:人脸识别;面部性别分类;卷积神经网络;最大池化层，完全
{"title":"Faces Recognition and Facial Gender Classification using Convolutional Neural Network","authors":"M. Berbar","doi":"10.21608/mjeer.2022.137937.1056","DOIUrl":"https://doi.org/10.21608/mjeer.2022.137937.1056","url":null,"abstract":"Computational power in deep convolutional neural networks has made it possible to have robust classifiers for faces and facial gender for many security issues and computer vision problems. This paper proposes two convolutional neural network (CNN) models for face recognition and facial gender classification. The models consist of an image input layer, followed by three blocks of convolutional, normalization, activation, and max-pooling layers, and three fully connected layers. The performance of the proposed CNN solutions is evaluated using five publicly available face datasets. Two greyscale face datasets: Sheffield and AT & T. Three color face datasets, Faces94, Ferret, and Celebrity Face Images from Kaggle. The achieved classification accuracy ranged between 99.0% and 100% on the Faces94, Ferret, Sheffield, and AT&T datasets, and classification accuracy of 93.6% to 95.0% on the Kaggle dataset. The proposed CNN can process and classify a smallsize face image 32 × 32-pixel from the Faces94, Sheffield, and AT&T datasets and 100 × 100 pixels from the Ferret and Kaggle datasets. The obtained results prove that the proposed CNN models are an effective solution for face image recognition and facial gender image classification. The proposed model produces competitive accuracy compared to several state-of-the-art methods. Keywords— Face recognition; facial gender classification; convolutional neural network; max pooling layer, fully","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126179209","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 : 2022-01-11DOI: 10.21608/mjeer.2021.70512.1034
Ahmed Ahmed Arafa, M. Radad, M. Badawy, Nawal A. El-Fishawy
In machine learning, optimization of hyperparameters aims to find the best values of model hyperparameters yielding an optimal model with minimum prediction error. It is the most important step that directly affects the performance of learned model. Many techniques have been proposed to optimize hyperparameters for different predictive models. In this paper, the performance of grid search, random search, Bayesian Tree Parzen Estimator (TPE) and Simulated Annealing (SA) optimization techniques is evaluated to determine the best hyperparameters for a logistic regression model when used in cancer classification. Wisconsin Breast Cancer Dataset (WBCD) has been used to evaluate the previously mentioned optimization techniques. The results show that Bayesian TPE outperformed other techniques in terms of number of iterations and running time. The number of iterations to get optimal parameters in TPE is less than SA by 75.75 %, and random search by 77.1%. While the time taken by TPE is better than SA, random search and grid search by 79.9%, 86.1% and 99.9% respectively. The resulted optimal hyperparameter values have been utilized to learn a logistic regression model to classify cancer using WBCD dataset. The optimized model succeeded in classifying cancer with 98.2% for test accuracy, 0.962 for kappa statistic and 0.963 for MCC metrics when evaluated using 10-fold cross validation. Keywords— Hyperparameter Optimization, Random Search Grid Search, Tree Parzen Estimator, Simulated Annealing
{"title":"Logistic Regression Hyperparameter Optimization for Cancer Classification","authors":"Ahmed Ahmed Arafa, M. Radad, M. Badawy, Nawal A. El-Fishawy","doi":"10.21608/mjeer.2021.70512.1034","DOIUrl":"https://doi.org/10.21608/mjeer.2021.70512.1034","url":null,"abstract":"In machine learning, optimization of hyperparameters aims to find the best values of model hyperparameters yielding an optimal model with minimum prediction error. It is the most important step that directly affects the performance of learned model. Many techniques have been proposed to optimize hyperparameters for different predictive models. In this paper, the performance of grid search, random search, Bayesian Tree Parzen Estimator (TPE) and Simulated Annealing (SA) optimization techniques is evaluated to determine the best hyperparameters for a logistic regression model when used in cancer classification. Wisconsin Breast Cancer Dataset (WBCD) has been used to evaluate the previously mentioned optimization techniques. The results show that Bayesian TPE outperformed other techniques in terms of number of iterations and running time. The number of iterations to get optimal parameters in TPE is less than SA by 75.75 %, and random search by 77.1%. While the time taken by TPE is better than SA, random search and grid search by 79.9%, 86.1% and 99.9% respectively. The resulted optimal hyperparameter values have been utilized to learn a logistic regression model to classify cancer using WBCD dataset. The optimized model succeeded in classifying cancer with 98.2% for test accuracy, 0.962 for kappa statistic and 0.963 for MCC metrics when evaluated using 10-fold cross validation. Keywords— Hyperparameter Optimization, Random Search Grid Search, Tree Parzen Estimator, Simulated Annealing","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130301384","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 : 2022-01-01DOI: 10.21608/mjeer.2022.218776
Eslam Omara, Mervat Mosa, Nabil A. Ismail
The main characteristic of deep learning approaches is the ability to learn differentiating and discriminating features. These techniques can discover complex relations and structures within high-dimensional data. For feature extraction, deep learning models employ several layers of nonlinear processing units. One of the fields that have applied deep architectures with a noticeable breakthrough in performance measures is Natural Language Processing (NLP). Recurrent neural networks (RNNs) and their variants Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are commonly used for NLP applications as they are efficient at processing sequential data. Unlike RNNs, LSTMs and GRUs can combat vanishing and exploding gradients. In Addition, Convolutional Neural Network (CNN) is another deep architecture that has been widely used in language processing. On the other side, sentiment analysis (SA) is an NLP task concerned with opinions, attitudes, emotions, and feelings. Sentiment analysis deduces the author's attitude regarding a topic and classifies the attitude polarity according to a set of predefined classes. Application of SA in business analytics helps to gain insight into consumer behaviour and needs. In the proposed work deep LSTM, GRU, and CNN are applied for Arabic sentiment analysis. The models are implemented and tested employing character-level representation. Also, deep hybrid models that combine multiple layers of CNN with LSTM or GRU are studied. The application aims at investigating the capability of deep LSTM, GRU, and hybrid architectures to learn and extract features from characterlevel representation. Results show that combining different architectures can boost performance in SA tasks. The CNNLSTM/GRU combinations registered higher accuracy compared to deep LSTM and GRU. Keywords— Deep learning; Sentiment analysis; LSTM; GRU; CNN-LSTM; CNN-GRU.
{"title":"Applying Recurrent Networks For Arabic Sentiment Analysis","authors":"Eslam Omara, Mervat Mosa, Nabil A. Ismail","doi":"10.21608/mjeer.2022.218776","DOIUrl":"https://doi.org/10.21608/mjeer.2022.218776","url":null,"abstract":"The main characteristic of deep learning approaches is the ability to learn differentiating and discriminating features. These techniques can discover complex relations and structures within high-dimensional data. For feature extraction, deep learning models employ several layers of nonlinear processing units. One of the fields that have applied deep architectures with a noticeable breakthrough in performance measures is Natural Language Processing (NLP). Recurrent neural networks (RNNs) and their variants Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are commonly used for NLP applications as they are efficient at processing sequential data. Unlike RNNs, LSTMs and GRUs can combat vanishing and exploding gradients. In Addition, Convolutional Neural Network (CNN) is another deep architecture that has been widely used in language processing. On the other side, sentiment analysis (SA) is an NLP task concerned with opinions, attitudes, emotions, and feelings. Sentiment analysis deduces the author's attitude regarding a topic and classifies the attitude polarity according to a set of predefined classes. Application of SA in business analytics helps to gain insight into consumer behaviour and needs. In the proposed work deep LSTM, GRU, and CNN are applied for Arabic sentiment analysis. The models are implemented and tested employing character-level representation. Also, deep hybrid models that combine multiple layers of CNN with LSTM or GRU are studied. The application aims at investigating the capability of deep LSTM, GRU, and hybrid architectures to learn and extract features from characterlevel representation. Results show that combining different architectures can boost performance in SA tasks. The CNNLSTM/GRU combinations registered higher accuracy compared to deep LSTM and GRU. Keywords— Deep learning; Sentiment analysis; LSTM; GRU; CNN-LSTM; CNN-GRU.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124171354","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 : 2022-01-01DOI: 10.21608/mjeer.2022.109707.1043
Ahmed A. Asaker, Zeinab F. Elsharkawy, Sabry S. Nassar, N. Ayad, O. Zahran, F. El-Sayed
With the increasing demands for biometric systems in our daily life for automatic identification of individuals, recently iris recognition system has gained a lot of attention attributed to its reliability, uniqueness, difficulty to be imitated and high accuracy in comparison with other available biometric recognition systems. Unfortunately, templates in traditional iris recognition system are unprotected and vulnerable to various threats, such as attacks at the iris reader level or at the database level. Hence, there is a need for developing a system for securing the existing iris recognition system for keeping the original biometrics safe and secure. In this paper, we introduce a hybrid model for protecting iris recognition system through combining an elliptic curve cryptosystem with a new salting-based cancelable iris recognition scheme. The obtained experimental results on CASIA-IrisV3 database proved that the proposed system guarantees a high degree of security and privacy protection without affecting the accuracy. Keywords—Iris Recognition System, Cancelable Biometrics, Elliptic Curve Cryptography, Hamming Distance, Normalized Cross Correlation, Receiver Operating Characteristics.
{"title":"Efficient Implementation of An Elliptic Curve Cryptosystem for Cancelable Biometrics","authors":"Ahmed A. Asaker, Zeinab F. Elsharkawy, Sabry S. Nassar, N. Ayad, O. Zahran, F. El-Sayed","doi":"10.21608/mjeer.2022.109707.1043","DOIUrl":"https://doi.org/10.21608/mjeer.2022.109707.1043","url":null,"abstract":"With the increasing demands for biometric systems in our daily life for automatic identification of individuals, recently iris recognition system has gained a lot of attention attributed to its reliability, uniqueness, difficulty to be imitated and high accuracy in comparison with other available biometric recognition systems. Unfortunately, templates in traditional iris recognition system are unprotected and vulnerable to various threats, such as attacks at the iris reader level or at the database level. Hence, there is a need for developing a system for securing the existing iris recognition system for keeping the original biometrics safe and secure. In this paper, we introduce a hybrid model for protecting iris recognition system through combining an elliptic curve cryptosystem with a new salting-based cancelable iris recognition scheme. The obtained experimental results on CASIA-IrisV3 database proved that the proposed system guarantees a high degree of security and privacy protection without affecting the accuracy. Keywords—Iris Recognition System, Cancelable Biometrics, Elliptic Curve Cryptography, Hamming Distance, Normalized Cross Correlation, Receiver Operating Characteristics.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"363 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114500475","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 : 2021-09-06DOI: 10.21608/mjeer.2021.193088
Wael Badawy
This paper presents a novel metric to assess remote learning. It demonstrates an analysis of 4000+ hours of Nile University in addition to 15,000+ hours of YouTube courses. The results validate the requirement of training of remote learning delivery. It also evidences the lack of ILO alignment between the Courses and the programs.
{"title":"COVID-19 Ad-hoc Remote Learning – Quality Assessment, “Seven Stars” Analysis and Lessons Learned","authors":"Wael Badawy","doi":"10.21608/mjeer.2021.193088","DOIUrl":"https://doi.org/10.21608/mjeer.2021.193088","url":null,"abstract":"This paper presents a novel metric to assess remote learning. It demonstrates an analysis of 4000+ hours of Nile University in addition to 15,000+ hours of YouTube courses. The results validate the requirement of training of remote learning delivery. It also evidences the lack of ILO alignment between the Courses and the programs.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130870154","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 : 2021-07-01DOI: 10.21608/mjeer.2021.193083
Hend Nooreldeen, S. Badawy, M. El-Brawany
{"title":"EEG Signal Analysis Based Brain-Computer","authors":"Hend Nooreldeen, S. Badawy, M. El-Brawany","doi":"10.21608/mjeer.2021.193083","DOIUrl":"https://doi.org/10.21608/mjeer.2021.193083","url":null,"abstract":"","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123283390","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 : 2021-07-01DOI: 10.21608/mjeer.2021.193084
Doaa Elnady, Gamal mahrouce, Gaber Allam
Due to the serious effectiveness of congestion problem in the Internet performance, congestion control has the most concern in the network community. Several End-to- End mechanisms were developed to overcome this problem. However, most of the existing mechanisms adapt the sending rate at the sender, when detecting congestion, without considering the network status. This behavior degrades the Internet performance. This paper presents a new fuzzy controller to adjust the sending rate at the sender dynamically based on the network load. The intended controller is employed to enhance the TCP-Vegas and the performance is evaluated by using the well-known Network Simulator NS-2. The results indicate that the intended controller the AT&T real network increases the throughput and decreases both the packet loss and packet delay.
{"title":"Fuzzy Controller based TCP-Vegas Enhancement for Congestion Control","authors":"Doaa Elnady, Gamal mahrouce, Gaber Allam","doi":"10.21608/mjeer.2021.193084","DOIUrl":"https://doi.org/10.21608/mjeer.2021.193084","url":null,"abstract":"Due to the serious effectiveness of congestion problem in the Internet performance, congestion control has the most concern in the network community. Several End-to- End mechanisms were developed to overcome this problem. However, most of the existing mechanisms adapt the sending rate at the sender, when detecting congestion, without considering the network status. This behavior degrades the Internet performance. This paper presents a new fuzzy controller to adjust the sending rate at the sender dynamically based on the network load. The intended controller is employed to enhance the TCP-Vegas and the performance is evaluated by using the well-known Network Simulator NS-2. The results indicate that the intended controller the AT&T real network increases the throughput and decreases both the packet loss and packet delay.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127534614","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 : 2020-09-13DOI: 10.21608/MJEER.2021.146087
S. Ramzy
In this paper, we introduced another methodology for the cooperative network using the maximum posterior (MAP) and the Alamouti code decoding scheme for the multiple-input single-output (MISO) wireless networks that use decode and forward (DF) protocol as a cooperation protocol. Without loss of generality, the considered network consists of one source, one relay and one receiver. A closed-form expression for the upper bound of the bit error probability is derived. The obtained upper bound expression can be utilized in the power optimization problems, relay positioning issue. The results that are shown in this paper clear that the proposed scheme has two advantages over the related work. The first advantage is that it has less complexity. The second one is that it has better spectrum efficiency by using less number of channels. Therefore, our contribution can be summarized in improving the spectrum efficiency and reducing the complexity of the cooperative network, but the paid price is that the bit error rate increases by a little ratio
{"title":"Performance enhancement of cooperative networks utilizing MAP decoder and Alamouti code","authors":"S. Ramzy","doi":"10.21608/MJEER.2021.146087","DOIUrl":"https://doi.org/10.21608/MJEER.2021.146087","url":null,"abstract":"In this paper, we introduced another methodology for the cooperative network using the maximum posterior (MAP) and the Alamouti code decoding scheme for the multiple-input single-output (MISO) wireless networks that use decode and forward (DF) protocol as a cooperation protocol. Without loss of generality, the considered network consists of one source, one relay and one receiver. A closed-form expression for the upper bound of the bit error probability is derived. The obtained upper bound expression can be utilized in the power optimization problems, relay positioning issue. The results that are shown in this paper clear that the proposed scheme has two advantages over the related work. The first advantage is that it has less complexity. The second one is that it has better spectrum efficiency by using less number of channels. Therefore, our contribution can be summarized in improving the spectrum efficiency and reducing the complexity of the cooperative network, but the paid price is that the bit error rate increases by a little ratio","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115126160","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 : 2020-08-30DOI: 10.21608/MJEER.2021.146073
M. Shouman, Amany S. Saber, M. Shaat, A. El-Sayed, Hanaa Torkey
Reliability assessment of a digital dynamic system using traditional Fault Tree Analysis (FTA) is difficult. This paper addresses the dynamic modeling of safety-critical complex systems such as the digital Reactor Protection System (RPS) in Nuclear Power Plants (NPPs). The digital RPS is a safety system utilized in the NPPs for safe operation and shut-down of the reactor in emergency events. A quantitative evaluation reliability analysis for the digital RPS with 2-out-of-4 architecture using the state transition diagram is presented in this paper. The study assesses the effects of independent hardware failures, Common Cause Failures (CCFs), and software failures on the failure of the RPS through calculating Probability of Failure on Demand (PFD). The results prove the validity of the proposed method in analyzing and evaluating reliability of the digital RPS and also show that the CCFs and longer detection time are the main contributions to the PFD of digital RPS.
{"title":"Dynamic Modeling of Reactor Protection System in Nuclear Power Plant for Reliability Evaluation Based on State Transition Diagram","authors":"M. Shouman, Amany S. Saber, M. Shaat, A. El-Sayed, Hanaa Torkey","doi":"10.21608/MJEER.2021.146073","DOIUrl":"https://doi.org/10.21608/MJEER.2021.146073","url":null,"abstract":"Reliability assessment of a digital dynamic system using traditional Fault Tree Analysis (FTA) is difficult. This paper addresses the dynamic modeling of safety-critical complex systems such as the digital Reactor Protection System (RPS) in Nuclear Power Plants (NPPs). The digital RPS is a safety system utilized in the NPPs for safe operation and shut-down of the reactor in emergency events. A quantitative evaluation reliability analysis for the digital RPS with 2-out-of-4 architecture using the state transition diagram is presented in this paper. The study assesses the effects of independent hardware failures, Common Cause Failures (CCFs), and software failures on the failure of the RPS through calculating Probability of Failure on Demand (PFD). The results prove the validity of the proposed method in analyzing and evaluating reliability of the digital RPS and also show that the CCFs and longer detection time are the main contributions to the PFD of digital RPS.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121807374","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}
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