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)

Modeling, Specification & Evaluation Language (MOSEL), tool used for the performance and reliability modeling of communication systems, computers, and manufacturing systems, once the system is specified using this language. The modeling language is part of the evaluation environment. Once the system is specified using the language, the evaluation environment takes place by executing the performance analyses of the model, and calculating the steady state probabilities . After this stage, results can be collected in the result file or in graphics mode using the Intermediate Graphical Language (IGL), where the aim of this paper is to give an overview of mosel and to show a real example under windows platform.
wael saleh mohamed abughres, Mohamed Ahmed Ramadan Mgheder, Ahmed B. Abdurrman (8-2017)

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)