Pub Date : 2019-07-01DOI: 10.21608/mjeer.2019.62774
Mahmoud Aboalneel, M. Abd-Elnaby, A. El-Sayed
In Wireless Sensor Networks (WSNs), the main challenge is the design of an efficient routing protocol which has a major impact on the reduction of energy consumption. In this paper, the proposed routing protocol is based on Signal to Noise Ratio (SNR) to improve the performance of WSNs. In the proposed routing protocol, the residual energy is used to elect the Primary Cluster Head (P-CH), while the SNR of received signal, residual energy and the distance from a node to a sink are used to elect Secondary Cluster Head(S-CH). This proposed election mechanism helps the S-CH to use a better-quality RF channel and provides a good packet delivery ratio with minimum energy consumption. The simulation results demonstrate in static mode and Gauss-Markov Mobility Model (GMMM) as, mobile mode. The proposed routing protocol extends the network lifetime by decreasing the energy consumption and reduces overhead data. Also, it compares with C-LEACH, TL-LEACH, and A-TEEN protocols.
{"title":"Quality-based LEACH protocol with enhanced cluster-head selection for wireless sensor networks","authors":"Mahmoud Aboalneel, M. Abd-Elnaby, A. El-Sayed","doi":"10.21608/mjeer.2019.62774","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62774","url":null,"abstract":"In Wireless Sensor Networks (WSNs), the main challenge is the design of an efficient routing protocol which has a major impact on the reduction of energy consumption. In this paper, the proposed routing protocol is based on Signal to Noise Ratio (SNR) to improve the performance of WSNs. In the proposed routing protocol, the residual energy is used to elect the Primary Cluster Head (P-CH), while the SNR of received signal, residual energy and the distance from a node to a sink are used to elect Secondary Cluster Head(S-CH). This proposed election mechanism helps the S-CH to use a better-quality RF channel and provides a good packet delivery ratio with minimum energy consumption. The simulation results demonstrate in static mode and Gauss-Markov Mobility Model (GMMM) as, mobile mode. The proposed routing protocol extends the network lifetime by decreasing the energy consumption and reduces overhead data. Also, it compares with C-LEACH, TL-LEACH, and A-TEEN protocols.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129921768","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 : 2019-07-01DOI: 10.21608/mjeer.2019.62763
A. Hamad, T. Taha, S. El-Rabaie, A. El-Fishawy, T. Alotaiby, S. Alshebeili, F. E. El-Samie
{"title":"Sub-band Decomposition for Epileptic Seizure Prediction","authors":"A. Hamad, T. Taha, S. El-Rabaie, A. El-Fishawy, T. Alotaiby, S. Alshebeili, F. E. El-Samie","doi":"10.21608/mjeer.2019.62763","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62763","url":null,"abstract":"","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"339 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115886214","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 : 2019-07-01DOI: 10.21608/mjeer.2019.62770
Mohamed Shalaby, M. Shokair, N. Messiha
{"title":"Interference Mitigation Techniques Applied to LTE Femtocells Systems","authors":"Mohamed Shalaby, M. Shokair, N. Messiha","doi":"10.21608/mjeer.2019.62770","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62770","url":null,"abstract":"","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126298428","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 : 2019-07-01DOI: 10.21608/mjeer.2019.62761
H. Shawky, Mohamed Abd-Elnaby, M. Rihan, M. A. Nassar, A. El-Fishawy, F. El-Samie
This paper investigates an approach for speaker identification in a remote access system based on coded speech signals. The aim of using the coding process is to decrease the amount of transmitted data via the channel. In this proposed system, the speech signal is coded by two different coding techniques. It can be coded either by linear predictive coding or compressive sensing. The coded speech signal is transmitted into the receiver via the wireless communication channel. At the receiver, the received signal is decoded, and then speaker identification system is applied on the decoded signal. During the transmission process, the channel errors affect on the transmitted signal, so they should be taken into account. The speaker identification process is used to achieve the security needed for the remote access system. In speaker identification system, the feature vectors are captured from different discrete transforms such as discrete wavelet transform, discrete cosine transform, and discrete sine transform, besides the time domain. The recognition rate for all transforms is computed to evaluate the effect of coded signals on the performance of the speaker identification system. The results proved that the discrete cosine transform and discrete wavelet transform are the best. In addition to the proposed system gives close recognition results to those obtained from real speech signals revealing a simple degradation effect due to the speech coding.
{"title":"Efficient Remote Access System Based on Coded Speech Signals","authors":"H. Shawky, Mohamed Abd-Elnaby, M. Rihan, M. A. Nassar, A. El-Fishawy, F. El-Samie","doi":"10.21608/mjeer.2019.62761","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62761","url":null,"abstract":"This paper investigates an approach for speaker identification in a remote access system based on coded speech signals. The aim of using the coding process is to decrease the amount of transmitted data via the channel. In this proposed system, the speech signal is coded by two different coding techniques. It can be coded either by linear predictive coding or compressive sensing. The coded speech signal is transmitted into the receiver via the wireless communication channel. At the receiver, the received signal is decoded, and then speaker identification system is applied on the decoded signal. During the transmission process, the channel errors affect on the transmitted signal, so they should be taken into account. The speaker identification process is used to achieve the security needed for the remote access system. In speaker identification system, the feature vectors are captured from different discrete transforms such as discrete wavelet transform, discrete cosine transform, and discrete sine transform, besides the time domain. The recognition rate for all transforms is computed to evaluate the effect of coded signals on the performance of the speaker identification system. The results proved that the discrete cosine transform and discrete wavelet transform are the best. In addition to the proposed system gives close recognition results to those obtained from real speech signals revealing a simple degradation effect due to the speech coding.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130648310","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 : 2019-07-01DOI: 10.21608/mjeer.2019.62773
Tamer S. Mostafa, El-Sayed M. El-Rabaie
The all-optical encoder (AOE) based on photonic crystals (Ph.Cs.) is one of the most important devices in computing systems. The essential related parameters are the delay time, the switching speed and the contrast ratio (CR). Moreover, the design simplicity, the compact size and the multi-wavelength operation have come as a fabrication and functional relevant attributes. Throughout the upcoming lines, an introduction for the important assessment factors and definitions will be presented. Finite difference time domain (FDTD) and plane wave expansion (PWE) methods were used for analyzing all structures. An intensive overview of the photonic crystals (AOE) was achieved for the recently published (4x2) and (8x3) types. The corresponding functional parameters for each design were explored, and comparison tables were organized. Finally, numerical methods were discussed with the accompanying commercial software packages; then a future view for the higher-performance operation was attained.
{"title":"Literature Review on All-Optical Photonic Crystal Encoders and Some Novel Trends","authors":"Tamer S. Mostafa, El-Sayed M. El-Rabaie","doi":"10.21608/mjeer.2019.62773","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62773","url":null,"abstract":"The all-optical encoder (AOE) based on photonic crystals (Ph.Cs.) is one of the most important devices in computing systems. The essential related parameters are the delay time, the switching speed and the contrast ratio (CR). Moreover, the design simplicity, the compact size and the multi-wavelength operation have come as a fabrication and functional relevant attributes. Throughout the upcoming lines, an introduction for the important assessment factors and definitions will be presented. Finite difference time domain (FDTD) and plane wave expansion (PWE) methods were used for analyzing all structures. An intensive overview of the photonic crystals (AOE) was achieved for the recently published (4x2) and (8x3) types. The corresponding functional parameters for each design were explored, and comparison tables were organized. Finally, numerical methods were discussed with the accompanying commercial software packages; then a future view for the higher-performance operation was attained.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132263300","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 : 2019-07-01DOI: 10.21608/mjeer.2019.62767
A. Ellakany, M. Abouelatta, I. Hafez, S. El-Rabaie, C. Gontrand
{"title":"Towards 3D Nuclear Detectors","authors":"A. Ellakany, M. Abouelatta, I. Hafez, S. El-Rabaie, C. Gontrand","doi":"10.21608/mjeer.2019.62767","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62767","url":null,"abstract":"","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128493167","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 : 2019-07-01DOI: 10.21608/mjeer.2019.62785
A. Ghozia, G. Attiya, N. El-Fishawy
Billions of active online users are continuously feeding the world with multimedia Big Data through their smart phones and PCs. These heterogenous productions are existing in different social media platforms, such as Facebook and Twitter, delivering a composite message in the form of audio, visual and textual signals. Analyzing multimedia Big Data to understand the intended delivered message, had been a challenge to audio, image, video and text processing researchers. Thanks to the recent advances in deep learning algorithms, researchers had been able to improve the performance of multimedia Big Data analytics and understanding techniques This paper presents a survey on how a multimedia file is analyzed, key challenges facing multimedia analysis, and how deep learning is helping conquer and advance beyond those challenges. Future directions of multimedia analysis are also addressed. The aim is to stay objective all through this study, bringing both empowering enhancements and in addition inescapable shortcomings, wishing to bring up fresh questions and stimulating new research frontiers for the reader.
{"title":"Towards the Conceptual Retrieval of Multimedia Documentary: A Survey","authors":"A. Ghozia, G. Attiya, N. El-Fishawy","doi":"10.21608/mjeer.2019.62785","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62785","url":null,"abstract":"Billions of active online users are continuously feeding the world with multimedia Big Data through their smart phones and PCs. These heterogenous productions are existing in different social media platforms, such as Facebook and Twitter, delivering a composite message in the form of audio, visual and textual signals. Analyzing multimedia Big Data to understand the intended delivered message, had been a challenge to audio, image, video and text processing researchers. Thanks to the recent advances in deep learning algorithms, researchers had been able to improve the performance of multimedia Big Data analytics and understanding techniques This paper presents a survey on how a multimedia file is analyzed, key challenges facing multimedia analysis, and how deep learning is helping conquer and advance beyond those challenges. Future directions of multimedia analysis are also addressed. The aim is to stay objective all through this study, bringing both empowering enhancements and in addition inescapable shortcomings, wishing to bring up fresh questions and stimulating new research frontiers for the reader.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125795008","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 : 2019-07-01DOI: 10.21608/mjeer.2019.62781
Sally Abdulaziz, Essam Nabil, G. Zaki, Galal A. M. Atlam
{"title":"Tuning the Parameters of TSK Neuro-Fuzzy System by Particle Swarm Optimization","authors":"Sally Abdulaziz, Essam Nabil, G. Zaki, Galal A. M. Atlam","doi":"10.21608/mjeer.2019.62781","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62781","url":null,"abstract":"","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"297 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132193704","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 : 2019-07-01DOI: 10.21608/mjeer.2019.62778
A. Ghozia, G. Attiya, N. El-Fishawy
Deep learning, in general, is about multi layered neural networks copying the structure and intellectual procedure of the human mind. Rather than handcrafted features, it permits the procurement of knowledge straightforwardly from information. They relapse mind boggling target works in a nested system, where more complex forms with bigger receptive fields are estimated using less abstract ones. Deep learning additionally makes it conceivable to consider formal domain knowledge and supplant an extensive collection of traditional algorithmic methods with flexible differentiable modules. These all strengthen and empower deep learning and make it adaptable while establishing the connection between the input information and target yield. Research frontiers are presently moving toward the rest of the difficulties. This paper presents a complete overview about deep learning. It illustrates where did deep learning initiate from, what had been accomplished using deep learning, What research areas are currently being investigated via deep learning, and most importantly What are the challenges and open problems of deep learning - as those are the issues, once handled, will lead to achieve the general conscious Artificial Intelligence (AI). The purpose is to empower graduates, practitioners, researchers and fans toward a powerful cooperation in the field of deep learning.
{"title":"The Power of Deep Learning: Current Research and Future Trends","authors":"A. Ghozia, G. Attiya, N. El-Fishawy","doi":"10.21608/mjeer.2019.62778","DOIUrl":"https://doi.org/10.21608/mjeer.2019.62778","url":null,"abstract":"Deep learning, in general, is about multi layered neural networks copying the structure and intellectual procedure of the human mind. Rather than handcrafted features, it permits the procurement of knowledge straightforwardly from information. They relapse mind boggling target works in a nested system, where more complex forms with bigger receptive fields are estimated using less abstract ones. Deep learning additionally makes it conceivable to consider formal domain knowledge and supplant an extensive collection of traditional algorithmic methods with flexible differentiable modules. These all strengthen and empower deep learning and make it adaptable while establishing the connection between the input information and target yield. Research frontiers are presently moving toward the rest of the difficulties. This paper presents a complete overview about deep learning. It illustrates where did deep learning initiate from, what had been accomplished using deep learning, What research areas are currently being investigated via deep learning, and most importantly What are the challenges and open problems of deep learning - as those are the issues, once handled, will lead to achieve the general conscious Artificial Intelligence (AI). The purpose is to empower graduates, practitioners, researchers and fans toward a powerful cooperation in the field of deep learning.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116121816","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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