In fractal image compression the encoding step is computationally expensive. A large number of sequential searches through a list of domain from the domain pool are carried out while trying to find the best matches for image portions called ranges. Vector Quantization using Linde Buzo Gray algorithm helps in reducing the redundant domain blocks. The mean image constructed using the range blocks was used as the domain pool for the construction of the synthetic codebook. In order to reduce the time consumption the mean, edge strength and texture feature of each range block are determined and compared with the domains in the codebook in order to reduce the redundant domain blocks for each range block. Genetic Algorithm (GA) and Simulated Annealing (SA) are optimization techniques, hence it is proposed to use GA and SA for finding the best match of the domain block to the range block. It is observed that the proposed technique using GA achieves excellent performance in image quality and SA with reduction in computation time. The performance analysis of all the three proposed techniques in iteration-free fractal image compression is reported and discussed in detail in this book.
Gaussian processes can be viewed as a far-reaching infinite-dimensional extension of classical normal random variables. Their theory presents a powerful range of tools for probabilistic modelling in various academic and technical domains such as Statistics, Forecasting, Finance, Information Transmission, Machine Learning - to mention just a few. The objective of these Briefs is to present a quick and condensed treatment of the core theory that a reader must understand in order to make his own independent contributions. The primary intended readership are PhD/Masters students and researchers working in pure or applied mathematics. The first chapters introduce essentials of the classical theory of Gaussian processes and measures with the core notions of reproducing kernel, integral representation, isoperimetric property, large deviation principle. The brevity being a priority for teaching and learning purposes, certain technical details and proofs are omitted. The later chapters touch important recent issues not sufficiently reflected in the literature, such as small deviations, expansions, and quantization of processes. In university teaching, one can build a one-semester advanced course upon these Briefs.
Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. Soft computing covers a wide range of application areas, including optimisation, data analysis and data mining, computer graphics and vision, prediction and diagnosis, design, intelligent control, and traffic and transportation systems. Bioinformatics is the application of computer technology to the management and manipulation of biological information. Soft computing offers a promising approach to achieve efficient and reliable heuristic solution for the bioinformatics problem areas such as clustering, pattern recognition and prediction related domains. In this book, soft computing methodologies for cluster analysis of microarray data using Kohonen's Self Organising Maps and prediction of genes using Back Propagation Network and Learning Vector Quantization Network are discussed.
This book discusses the evolution of the notion of coherent states, from the early works of Schrödinger to the most recent advances, including signal analysis. An integrated and modern approach to the utility of coherent states in many different branches of physics. This self-contained introduction discusses the evolution of the notion of coherent states, from the early works of Schrödinger to the most recent advances, including signal analysis. An integrated and modern approach to the utility of coherent states in many different branches of physics, it strikes a balance between mathematical and physical descriptions.Split into two parts, the first introduces readers to the most familiar coherent states, their origin, their construction, and their application and relevance to various selected domains of physics. Part II, mostly based on recent original results, is devoted to the question of quantization of various sets through coherent states, and shows the link to procedures in signal analysis.
The self-organizing map (SOM) is an unsupervised learning algorithm which has been successfully applied to various applications. In the last several decades, there have been variants of SOM used in many application domains. In this work, two new SOM algorithms are developed for image quantization and compression. The first algorithm is a sample-size adaptive SOM algorithm that can be used for color quantization of images to adapt to the variations of network parameters and training sample size. Based on the sample-size adaptive self-organizing map, we use the sampling ratio of training data, rather than the conventional weight change between adjacent sweeps, as a stop criterion. As a result, it can significantly speed up the learning process. The second algorithm is a novel classified SOM method for edge preserving quantization of images using an adaptive subcodebook and weighted learning rate. The subcodebook sizes of two classes are automatically adjusted in training iterations that can be estimated incrementally. The proposed weighted learning rate updates the neuron efficiently no matter how large the weighting factor is.
Automorphic and cusp forms on a complex bounded symmetric domain are a classical field of research in mathematics with fundamental applications to representation and number theory. Also for physicists they are of interest since the space of cusp forms is a quantization space of the underlying symmetric domain treated as a phase space. Aim of this book is to present a theory of super automorphic forms and a new geometric approach based on the frame flow of the underlying bounded symmetric domain constructing spanning sets for the space of cusp forms on symmetric domains of higher rank resp. on symmetric super domains. For this reason a general introduction to the geometry of complex bounded symmetric domains and to super structures is also included. In particular we present a new concept of 'parametrization' with far reaching applications within the theory of super manifolds and indispensable to a proper notion of super automorphic forms. The book, which has been developped from the author's PhD thesis, will fascinate every person generally interested in geometric quantization and in super structures, for young researchers it will be a great help.
This book introduces audio watermarking methods for copyright protection, which has drawn extensive attention for securing digital data from unauthorized copying. The book is divided into two parts. First, an audio watermarking method in discrete wavelet transform (DWT) and discrete cosine transform (DCT) domains using singular value decomposition (SVD) and quantization is introduced. This method is robust against various attacks and provides good imperceptible watermarked sounds. Then, an audio watermarking method in fast Fourier transform (FFT) domain using SVD and Cartesian-polar transformation (CPT) is presented. This method has high imperceptibility and high data payload and it provides good robustness against various attacks. These techniques allow media owners to protect copyright and to show authenticity and ownership of their material in a variety of applications.· Features new methods of audio watermarking for copyright protection and ownership protection· Outlines techniques that provide superior performance in terms of imperceptibility, robustness, and data payload· Includes applications such as data authentication, data indexing, broadcast monitoring, fingerprinting, etc.
A practical and accessible guide to understanding digital signal processing Introduction to Digital Signal Processing and Filter Design was developed and fine-tuned from the author's twenty-five years of experience teaching classes in digital signal processing. Following a step-by-step approach, students and professionals quickly master the fundamental concepts and applications of discrete-time signals and systems as well as the synthesis of these systems to meet specifications in the time and frequency domains. Striking the right balance between mathematical derivations and theory, the book features: * Discrete-time signals and systems * Linear difference equations * Solutions by recursive algorithms * Convolution * Time and frequency domain analysis * Discrete Fourier series * Design of FIR and IIR filters * Practical methods for hardware implementation A unique feature of this book is a complete chapter on the use of a MATLAB(r) tool, known as the FDA (Filter Design and Analysis) tool, to investigate the effect of finite word length and different formats of quantization, different realization structures, and different methods for filter design. This chapter contains material of practical importance that is not found in many books used in academic courses. It introduces students in digital signal processing to what they need to know to design digital systems using DSP chips currently available from industry. With its unique, classroom-tested approach, Introduction to Digital Signal Processing and Filter Design is the ideal text for students in electrical and electronic engineering, computer science, and applied mathematics, and an accessible introduction or refresher for engineers and scientists in the field.