A multi-relay cooperative diversity protocol based on the concept of Generalized Spatial Modulation (GSM) scheme is proposed in this paper, assuming that decode-and-forward relaying protocol is adopted at relays. This scheme is referred to as Distributed Generalized Spatial Modulation (DGSM) with activating more than one relay. The system performance of the proposed diversity protocol in terms of the Symbol Error Rate (SER) is evaluated and compared to the performance of GSM and Distributed Spatial Modulation (DSM) schemes. Simulation results show that DGSM systems with activating more than one relay perform almost the same as DSM systems for the same spectral efficiency. It is also demonstrated that a performance enhancement of about 3 dB is achieved over GSM schemes for the same modulation order, which increases the energy efficiency and the reachability using the proposed model. Therefore, the proposed scheme can be effectively used in various 5G wireless networks.
Taissir Y. Elganimi, Fatima I. Alwerfly, Akram A. Marseet(10-2020)

With the growth of modern digital technologies, demand for transmission multimedia and digital images, which require more storage space and transmission bandwidth, has been increased rapidly. Hence, developing new image compression techniques for reducing data size without degrading the quality of the image, has gained a lot of interest recently. In this study, an adaptive multiresolution image decomposition (AMID) algorithm is proposed and its application for image compression is explored. The developed algorithm is capable of decomposing an image along the vertical, horizontal, and diagonal directions using the pyramidal multiresolution scheme. Compared to the wavelet transform, the AMID can be used for decimation with the guarantees of perfect signal reconstruction. Furthermore, the application of the AMID for image compression is explored and its performance is compared with the state-of-the-art image compression techniques. The performance of compression method is evaluated using peak signal-to-noise ratio and compression ratio. Experimental results have shown promising performance compared with the results of using other image compression approaches
Osama A. Alkishriwo(9-2020)

Technology is all around us and it’s changing rapidly, expanding Internet access has had huge impacts on everyday lives as people do everything on their phones and computers. The widespread growth in the use of digital computers, have an increasing need to be able to communicate with machines in a simpler manner. One of the main tasks that can simplify communication with machines is speech recognition. In this work, we introduce the Arabic speech commands database that contains six Arabic control order words and Arabic spoken digits. The created database is used to analyze and compare the recognition accuracy and performance of three recognition techniques which are, Wavelet Time Scattering feature extraction with Support Vector Machine (SVM) classifier, Wavelet Time Scattering feature extraction with Long Short-Term Memory (LSTM) classifier, and Mel-Frequency Cepstrum Coefficients (MFCC) feature extraction with K-Nearest Neighbor (KNN) classifier. Finally, the experimental results show that the most accurate prediction of the database commands was 98.1250% given by Wavelet Time Scattering feature extraction and LSTM classifier and the fastest training time for the database was 144 minutes given by MFCC and KNN classifier. arabic 5 English 42
Osama A. Alkishriwo, Lina Tarek Benamer(12-2020)