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)

Denoising of chirp based signals is a challenging problem in signal processing and communications. In this paper, we propose a suitable denoising algorithm based on the discrete linear chirp transform (DLCT), which provides local signal decomposition in terms of linear chirps. Analytical expression for the optimal filter response is derived. The method relies on the ability of the DLCT for providing a sparse representation to a wide class of broadband signals like chirp signals. Simulation results show the efficiency of the proposed method, especially for mono-component chirp signals.
Osama A. Alkishriwo(12-2020)

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)