Multiple Input Multiple Output (MIMO) systems with limited Radio Frequency (RF) chains play a pivotal role in the Fifth Generation (5G) of wireless networks. However, the transmitter of Generalized Spatial Modulation MIMO (GSM-MIMO) systems that characterized by a single RF chain and multiple active antennas is capable of reducing both the energy consumption and the transmitter cost. In this paper, combining GSM-MIMO systems with the fully digital Geometric Mean Decomposition (GMD)-based precoding scheme and Analog Beamforming (ABF) into Hybrid Beamforming (HBF) regime is presented for Millimeter-wave (mmWave) massive MIMO systems which is a key enabler for 5G networks. In addition, applying the norm-based user selection algorithm in GSM-MIMO scheme with GMD-based hybrid precoding is proposed, and referred to as Multiuser Steered GSM-MIMO (MUS-GSM-MIMO) scheme. Simulation results demonstrate that the proposed schemes are capable of achieving considerable performance gains over the conventional GSM-MIMO schemes, while avoiding the overwhelming costs of multiple RF chains. Therefore, the proposed schemes are very efficient and highly suitable for large-scale and 5G wireless networks.
Taissir Y. Elganimi, Amani A. Aturki(9-2020)

التصوير باستخدام طريقة المعاوقة الكهربائية كتقنية تستخدم لأخذ صورة مقطعية وذلك من خلال قياس الموصلية على سطح الجسم , بحيث تكون القيمة التقريبية لتوزيع المقاومة النوعية الداخلية مبنية عن طريق حساب فرق الجهد والتيار الكهربائي. التصوير المقطعي بالمعاوقة الكهربائية عند تردد معين يسمح بتصوير التغيرات فقط في الجزء الحقيقي من الموصلية ويهمل الجزء التخيلي منها والقيم المطلقة. وقد أقترح حلاً لهذه المشكلة باستخدام التصوير المقطعي متعدد التردد التصوير المقطعي بالمعوقة الكهربائية متعددة التردد يعطي صورة ثابتة بواسطة تكوين صورة لتغيير مقاومة الأنسجة مع التردد, ولغرض الحصول على كل المعلومات تم الأخذ في الاعتبار الجزء الحقيقي والتخيلي للموصلية في هذا البحت تم تطوير طريقة جديدة للتصوير الطبقي بالمعاوقة الكهربائية متعددة التردد باستخدام دوال لاجير الثنائية , وحيث أن تحسين صورة التصوير المقطعي بالمعاوقة الكهربائية يعتمد على كمية المعلومات التي يمكن الحصول عليها من القياسات الحدودية تم الأخذ في الاعتبار الجزء الحقيقي والتخيلي للموصلية لقد تم تطبيق هذه الطريقة والحصول على نتائج جيدة من حيث جودة الصورة مقارنة بنتائج تم الحصول عليها في أبحاث سابقة. Abstract Electrical Impedance Tomography (EIT) is a technique which allows cross- sectional image related to the electrical impedance within an object to be reconstructed from a set of measurements made on the object surface. EIT aims to image the conductivity distribution within a test object by making electrical measurements on the surface of the object. In single frequency Electrical Impedance Tomography only the change in the real part of the conductivity can be imaged and not those of the imaginary part of the conductivity or of the absolute values. Multi-frequency Electrical Impedance Tomography has been proposed to solve this problem. In this thesis, there is a new method using Laguerre function in multi-frequency Electrical Impedance Tomography which has been developed, and full information contained in the complex tissue conductivity can be obtained.
جلال مفتاح مهلهل (2009)

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)