A code excited linear predictive coder: using a moments algorithm

A speech coding algorithm was developed which was based on a new method of selecting the excitation signal from a codebook of residual error sequences. The residual error sequences in the codebook were generated from 512 frames of real speech signals. L.P.C. inverse filtering was used to obtain the residual signal. Each residual error signal was assigned an index. The index was generated using a moments algorithm. These indices were stored on a Graded Binary Tree. A Binary Search was then used to select the correct index. The use of a Graded Binary Tree in the coding algorithm reduced the search time. The algorithm faithfully reproduced the original speech when the test residual error signal was chosen from the training data. When the test residual error signal was outside the training data, synthetic speech of a recognisable quality was produced. Finally, the fundamentals of speech coders are discussed in detail and various developments are suggested

Video Super Resolution

Note: appendices for this title available here. Advances in digital signal processing technology have created a wide variety of video rendering devices from mobile phones and portable digital assistants to desktop computers and high definition television. This has resulted in wide diversity of video content with spatial and temporal properties fitting into their intended rendering devices. However the sheer ubiquity of video content creation and distribution mechanisms has effectively blurred the classification line resulting in the need for interchangeable rendering of video content across devices of varying spatio-temporal properties. This results in a need for efficient and effective conversion techniques; mostly to increase the resolution (referred to as super resolution) in-order to enhance quality of perception, user satisfaction and overall the utility of the video content

Image Compression Techniques: A Survey in Lossless and Lossy algorithms

The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System. This paper provides a survey on various image compression techniques, their limitations, compression rates and highlights current research in medical image compression

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Combined optimization algorithms applied to pattern classification

Accurate classification by minimizing the error on test samples is the main goal in pattern classification. Combinatorial optimization is a well-known method for solving minimization problems, however, only a few examples of classifiers axe described in the literature where combinatorial optimization is used in pattern classification. Recently, there has been a growing interest in combining classifiers and improving the consensus of results for a greater accuracy. In the light of the "No Ree Lunch Theorems", we analyse the combination of simulated annealing, a powerful combinatorial optimization method that produces high quality results, with the classical perceptron algorithm. This combination is called LSA machine. Our analysis aims at finding paradigms for problem-dependent parameter settings that ensure high classifica, tion results. Our computational experiments on a large number of benchmark problems lead to results that either outperform or axe at least competitive to results published in the literature. Apart from paxameter settings, our analysis focuses on a difficult problem in computation theory, namely the network complexity problem. The depth vs size problem of neural networks is one of the hardest problems in theoretical computing, with very little progress over the past decades. In order to investigate this problem, we introduce a new recursive learning method for training hidden layers in constant depth circuits. Our findings make contributions to a) the field of Machine Learning, as the proposed method is applicable in training feedforward neural networks, and to b) the field of circuit complexity by proposing an upper bound for the number of hidden units sufficient to achieve a high classification rate. One of the major findings of our research is that the size of the network can be bounded by the input size of the problem and an approximate upper bound of 8 + √2n/n threshold gates as being sufficient for a small error rate, where n := log/SL and SL is the training set