Abstract Voice over IP (VoIP) uses the Internet Protocol (IP) to transmit voice as packets over an IP network, like the Internet, Intranets and Local Area Networks (LAN). Here the voice signal is digitized, compressed and converted to IP packets and then transmitted over the IP network. However, at the receiving end, some packets may be missed in its way due to network congestion. This packets loss degrades the quality of speech at the receiving end of a voice transmission system in the IP network. Since the voice transmission is a real time process, the receiver cannot request for retransmission of the missing packets. High speed networks provide real time variable bit rate service with packet loss requirements. The burstiness of the correlated traffic makes dynamic buffer management highly desirable to satisfy the Quality of Service (QoS) requirements. This thesis presents an algorithm to improve and optimize the Adaptive Buffer Allocation Scheme to deal with input traffic based on loss of consecutive packets in data streams and buffer occupancy levels. Buffer is designed to allow the input traffic to be partitioned into different priority classes, and based on the input traffic behavior it controls the threshold dynamically. This scheme allows input packets to enter into buffer if its occupancy level is less than the threshold value for priority of that packet. The threshold is dynamically varied in runtime based on packet loss behavior. The performance evaluation is carried out using simulation and is carried out for two and multiple priority classes of the input traffic "real time and non real time classes". The simulation results show that the Modified Adaptive Partial Buffer Sharing (ADPBS) has better performance than Adaptive Partial Buffer Sharing under the same traffic conditions.
اسماء احمد الكيش (2010)

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)