Studying And Analyzing the modulators and demodulators of PCM

Modulating signals of transmission stations to reception stations is a key factor to guarantee the best possible transmission and reception of these signals .Digital modulation represents a huge evolution in communication field and modulation, which used to depend on analog signal modulation of one parameter-Amplitude . frequency or phase. Digital modulation depends on transforming the transmitted data signal (Bits) and then sending it as samples, and changed back into an analog signals in reception station . In digital systems, digital data are transformed into analog data in the transmitter and does the reverse in the receiver. In digital transmission, on the other hand, as in wired local area networks (WLAN), Digital data are transmitted in their digital state.

Performance Analysis of G.711, G.723.1, G.729 Codecs Over VoIP Apps

Voice over IP Protocol is an important Internet voice connection, characterized by high quality of service. In this work, we will assess how today's Internet service matches its expectations by examining the performance of the Voice over IP protocol and its quality of service. We have relied on the method of selection of encoders first within some parameters to obtain the simulation result of the comparison and analysis (QoS). Which we adopted on VoIP protocols in the case of multiple users with three algorithms for the symbols, and after determining the problem in this range, we took a number of factors into account due to their impact on sound performance, such as jitter and Delay. This action simulates three of the most common encoders (analog audio conversion and packet compression), G.711, G.723.1 and G.729. The main objective is to achieve high-quality sound performance by making the appropriate decision in the choice of sound encoder.

A Study of The Effectiveness and Sound Quality in Audio Compression Algorithms

The sound is an essential component of multimedia, and due to the needto be used in many life applications such as television broadcasting andcommunication programs, so it was necessary for the existence of audio signal processing techniquessuch as compressing, improving, and noisereduction. Data compression process aims to reduce the bit rate used, by doing encoding information using fewer bits than the original representation for transmitting and storing. By this process,the unnecessary information is determined and removed, that means it gives the compressed information for useable compression, which we need as a fundamental, not the minutest details. This research aims to study how to process sound and musical signal. It's a process that consists of a wide range of applications like coding and digital compression for the effective transport and storage on mobile phones and portable music players, modeling and reproduction of the sound of musical instruments and music halls and the harmonics of digital music, editing digital music, and classification of music content, and other things.

Application to Topic Modeling of Time- Stamped Documents and apply that for Algorithms

We have introduced a new applications for Dynamic Factor Graphs, consisting in topic modeling, text classification and information retrieval. DFGs are tailored here to sequences of time-stamped documents. Based on the auto-encoder architecture, our nonlinear multi-layer model is trained stage-wise to produce increasingly more compact representations of bags-ofwords at the document or paragraph level, thus performing a semantic analysis. It also incorporates simple temporal dynamics on the latent representations, to take advantage of the inherent (hierarchical) structure of sequences of documents, and can simultaneously perform a supervised classification or regression on document labels, which makes our approach unique. Learning this model is done by maximizing the joint likelihood of the encoding, decoding, dynamical and supervised modules, and is possible using an approximate and gradient-based maximum-a-posteriori inference. We demonstrate that by minimizing a weighted cross-entropy loss between his tograms of word occurrences and their reconstruction, we directly minimize the topic model perplexity, and show that our topic model obtains lower perplexity than the Latent Dirichlet Allocation on the NIPS and State of the Union datasets. We illustrate how the dynamical constraints help the learning while enabling to visualize the topic trajectory.