AbstractThis paper examines the process of sending an image over wireless mobile Channels with errors while maintaining bandwidth, transmission speed and image quality. With the tremendous development in networks, new wireless channels have been opened that threaten the security of data transmitted through networks and communication channels, and from this emerged Encryption techniques to increase the security of the transmitted data, and since the digital image compression algorithm is a process to reassemble the parts of the image so that it takes less space, but this increases the impact of the error extension in sending compressed digital images and greatly reduces the quality of the received image. This prompts us in this paper to study the performance of HQAM technology for the purpose of transmitting image data over wireless communication channels containing errors. In this paper, image quality performance has been evaluated by the BER, SNR, PSNR, SSIM, and Correlation evaluation criteria. The simulated results show that there is an improvement in the image quality received using the proposed technique.
wael saleh mohamed abughres, , , Ashrf Alzrouk Almagdob1, Ahmed B. Abdurrhman, Ibrahim A.Nasir, (9-2020)

In this paper, a customized Auto-Encoder Complex Valued Convolutional Neural Network (AE-CV-CNN) that has been developed in a prior work is applied to Single Symbol Generalized Spatial Modulation (SS-GSM) scheme with new extracted features. The achieved reductions in the computational complexity at the receiver is at least 63.64% for M-PSK schemes compared to the complexity of Maximum Likelihood (ML) detection algorithm. This Fast detection algorithm is based on a proposed Low Complexity ML (LC-ML) detector that affords a complexity reduction of at least 40.91%. With these proposed algorithms, the complexity is reduced as the spatial constellation size increases. Furthermore, in comparison to other sub optimal detection algorithms, the computational complexity in terms of real valued multiplications of the AE-CV-CNN applied to LC-ML is independent of the spatial spectrum efficiency which means that the total spectrum efficiency increases with larger spatial constellation size at no additional complexity.
Akram A. Marseet , Taissir Y. Elganimi(10-2019)

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)