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)

Abstract In this thesis, we present a method for the design of multidimensional digital filters. This method is based on the use of genetic algorithm (GA). GA algorithm is proposed to optimize the coefficients of magnitude frequency response of digital filter design. GA algorithm is used to minimize a cost function representing the difference between the frequency response magnitudes of an ideal and obtained digital filters. Two types of filters namely, Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) are implemented.The digital FIR filter exhibits a wide transition bands and ripples in the pass band which are considered as disadvantages in digital image processing. Therefore, the only way to obtain a steep transition FIR is to use high order which translates to high cost. An alternative way to avoid high cost filters is to use IIR digital filters. Such digital filters can provide steeper transition bands with lower order than equivalent FIR digital filters. Although FIR digital filters have the disadvantages of high cost in term of hardware realizations, they have the advantage of stability and linear phase. Since IIR digital filters have infinite impulse response, they are prone to unstability. Linear phase which is easy to obtain in FIR digital filters is hard to come by IIR digital filters. The low cost and fast filtering make the IIR digital filters attractive. The proposed GA is utilizing for the design of stable IIR filter with near linear phase constraints. Experimental results are presented for 1-D and 2-D digital IIR filter. From the magnitude-frequency response, the convergence of the proposed approach will be able to obtain global minima in a faster time.
ميسون محمد الزرقاني (2010)