Work place: Department of Electrical and Electronic Engineering, Assiut University, Assiut, Egypt
Research Interests: Wireless Networks, Wireless Communication, Signal Processing, Sensor, Graph and Image Processing, Intelligent Systems, Computer Networks
Prof. Mohammed Abo-Zahhad (Senior Member, IEEE, 2000) received the B.Sc. and M.Sc. degrees from Assiut University (AU), Egypt in 1979 and 1983, respectively. In 1988, he received the Ph. D. degree from the University of Kent at Canterbury, UK and AU, Egypt through the channel system. He worked as a research fellow of Telecommunications and Media Informatics at the Department of Communications and Acoustics Engineering, Technical University of Budapest, Hungary during 1989-1992. He was promoted to the rank of professor of wireless communications and multimedia processing, in Jan.1999. He has been elected the positions of vice-dean for graduate studies, from August 2006 till June 2012 and the chair of department of electrical and electronics engineering from Nov. 2013 till Dec. 2016 both in the faculty of engineering, AU. During the period 1996-2003, he joined Yarmouk University, Jordan where he taught graduate and undergraduate courses in biomedical signal processing and electronic circuits. From 2016-2022, he elected the position of Dean of school of electronics, communications and computer engineering, Egypt-Japan University of Science and Technology (E-JUST), Egypt. In addition, he acted as the director of the information and communication technology centers at AU for ten years and E-JUST for two years.
His research interests include: biomedical, and multimedia signal processing, genetic and immune algorithms, wireless sensor nodes, massive MIMO and millimeter wave communications, and internet of medical things (IoMT). He has earned many national and international research awards, among which is the Encouragement State Prize in Engineering, from the Egyptian Academy of Scientific Research and Technology (ASRT); Hijjawi Foundation Engineering and Technology Scientist Prize, Jordan; and the University of Assiut Excellence in Engineering Sciences Prize. He is also a member of the national electronics and communication promotion committee, and an accredited reviewer of the national quality assurance and accreditation authority (NAQQA), Egypt. Prof. Abo-Zahhad has published more than 200 reviewed scientific papers in national and international conferences and impacted journals. He served as a referee for IEEE Transactions on Signal Processing, IEEE Transactions on Circuits and Systems, IEEE Access and numerous other professional publications. In addition, he was the chair of the last five rounds (2017-2021) of the international Japan-Africa conference on electronics, communications and computations (JAC-ECC), organized in cooperation between E-JUST, and Kyushu University, Japan and also chaired technical sessions in many international conferences. He has been promoted to the IEEE Senior Member in 2000 and served as an editor of the Journal of Engineering Sciences (JES), Egypt and as an associate editor of the Journal of Engineering and Applied Science (JEAS), Springer, and the Journal published by MDPI.
He has been the supervisor of more than 41 M. Sc. and PhD theses, from which 35 have been defended in the fields of: biomedical and genomic signal processing, multimedia processing, genetic and immune algorithms, wireless sensor nodes, massive MIMO and millimeter wave communications, and internet of medical things. Currently Prof. Abo-Zahhad is performing his duties as an Emeritus Professor at the Department of Electronics and Communications Engineering, Egypt-Japan University of Science and Technology (E-JUST), Egypt.
DOI: https://doi.org/10.5815/ijisa.2015.06.05, Pub. Date: 8 May 2015
In this paper, a new acquisition protocol is adopted for identifying individuals from electroencephalogram signals based on eye blinking waveforms. For this purpose, a database of 10 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted from brain wave recordings and used for the identification task. The feature extraction stage includes fitting the extracted eye blinks to auto-regressive model. Two algorithms are implemented for auto-regressive modeling namely; Levinson-Durbin and Burg algorithms. Then, discriminant analysis is adopted for classification scheme. Linear and quadratic discriminant functions are tested and compared in this paper. Using Burg algorithm with linear discriminant analysis, the proposed system can identify subjects with best accuracy of 99.8%. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for person identification methods.[...] Read more.
DOI: https://doi.org/10.5815/ijitcs.2014.08.07, Pub. Date: 8 Jul. 2014
Human Genome Project has led to a huge inflow of genomic data. After the completion of human genome sequencing, more and more effort is being put into identification of splicing sites of exons and introns (donor and acceptor sites). These invite bioinformatics to analysis the genome sequences and identify the location of exon and intron boundaries or in other words prediction of splicing sites. Prediction of splice sites in genic regions of DNA sequence is one of the most challenging aspects of gene structure recognition. Over the last two decades, artificial neural networks gradually became one of the essential tools in bioinformatics. In this paper artificial neural networks with different numerical mapping techniques have been employed for building integrated model for splice site prediction in genes. An artificial neural network is trained and then used to find splice sites in human genes. A comparison between different mapping methods using trained neural network in terms of their precision in prediction of donor and acceptor sites will be presented in this paper. Training and measuring performance of neural network are carried out using sequences of the human genome (GRch37/hg19- chr21). Simulation results indicate that using Electron-Ion Interaction Potential numerical mapping method with neural network yields to the best performance in prediction.[...] Read more.
DOI: https://doi.org/10.5815/ijitcs.2014.04.02, Pub. Date: 8 Mar. 2014
Signals that represent information may be classified into two forms: numeric and symbolic. Symbolic signals such as DNA symbolic sequences cannot be directly processed with digital signal processing (DSP) techniques. The only way to apply DSP in genomic field is the mapping of DNA symbolic sequences to numerical sequences. Hence, biological properties are reflected in a numerical domain. This opens a field to present a set of tools for solving genomic problems. In literature many techniques have been developed for numerical representation of DNA sequences. The main drawback of these techniques is that each nucleotide is represented by a numerical value depending on nucleotide type only ignoring its position in codon and DNA sequence. In this paper a new approach for DNA symbolic to numeric representation called Circular Mapping (CM) is introduced. It’s based on graphical representation of DNA sequence that maps each nucleotide by a complex numerical value depending not only on nucleotide type but also on its position in codons. The main applications of this method are the gene prediction that aims to locate the protein-coding regions and the classification of exons and introns in DNA sequences. The proposed approach showed significant improvement in exons and introns classification as compared with the existing techniques. The efficiency of this method in classification depends on the right choice of the mapping angle (θ) as indicated by the power spectral analysis results over the sequences of the human genome (GRch37/hg19).[...] Read more.
DOI: https://doi.org/10.5815/ijitcs.2012.08.03, Pub. Date: 8 Jul. 2012
Using digital signal processing in genomic field is a key of solving most problems in this area such as prediction of gene locations in a genomic sequence and identifying the defect regions in DNA sequence. It is found that, using DSP is possible only if the symbol sequences are mapped into numbers. In literature many techniques have been developed for numerical representation of DNA sequences. They can be classified into two types, Fixed Mapping (FM) and Physico Chemical Property Based Mapping (PCPBM (. The open question is that, which one of these numerical representation techniques is to be used? The answer to this question needs understanding these numerical representations considering the fact that each mapping depends on a particular application. This paper explains this answer and introduces comparison between these techniques in terms of their precision in exon and intron classification. Simulations are carried out using short sequences of the human genome (GRch37/hg19). The final results indicate that the classification performance is a function of the numerical representation method.[...] Read more.
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