From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

A summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented in this tutorial. An examination of the underlying time series model assumed by each technique serves as the common basis for understanding the differences among the various spectrum analysis approaches. Techniques discussed include the classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods. A summary table in the text provides a concise overview for all methods, including key references and appropriate equations for computation of each spectral estimate.

Spectrum analysis&#8212;A modern perspective

The recent advances in radio and em beddedsystem technologies have enabled the proliferation of wireless microsensor networks. Such wirelessly connected sensors are released in many diverse environments to perform various monitoring tasks. In many such tasks, location awareness is inherently one of the most essential system parameters. It is not only needed to report the origins of events, but also to assist group querying of sensors, routing, and to answer questions on the network coverage. In this paper we present a novel approach to the localization of sensors in an ad-hoc network. We describe a system called AHLoS (Ad-Hoc Localization System) that enables sensor nodes to discover their locations using a set distributed iterative algorithms. The operation of AHLoS is demonstrated with an accuracy of a few centimeters using our prototype testbed while scalability and performance are studied through simulation.

Dynamic fine-grained localization in Ad-Hoc networks of sensors

A vector quantizer is a system for mapping a sequence of continuous or discrete vectors into a digital sequence suitable for communication over or storage in a digital channel. The goal of such a system is data compression: to reduce the bit rate so as to minimize communication channel capacity or digital storage memory requirements while maintaining the necessary fidelity of the data. The mapping for each vector may or may not have memory in the sense of depending on past actions of the coder, just as in well established scalar techniques such as PCM, which has no memory, and predictive quantization, which does. Even though information theory implies that one can always obtain better performance by coding vectors instead of scalars, scalar quantizers have remained by far the most common data compression system because of their simplicity and good performance when the communication rate is sufficiently large. In addition, relatively few design techniques have existed for vector quantizers. During the past few years several design algorithms have been developed for a variety of vector quantizers and the performance of these codes has been studied for speech waveforms, speech linear predictive parameter vectors, images, and several simulated random processes. It is the purpose of this article to survey some of these design techniques and their applications.

Vector quantization

Information Theory and Reliable Communication

Lossy coding of speech, high-quality audio, still images, and video is commonplace today. However, in 1948, few lossy compression systems were in service. Shannon introduced and developed the theory of source coding with a fidelity criterion, also called rate-distortion theory. For the first 25 years of its existence, rate-distortion theory had relatively little impact on the methods and systems actually used to compress real sources. Today, however, rate-distortion theoretic concepts are an important component of many lossy compression techniques and standards. We chronicle the development of rate-distortion theory and provide an overview of its influence on the practice of lossy source coding.

Lossy source coding

From the Publisher:
The Mobile Communications Handbook covers the entire field - from principles of analog and digital communications to cordless telephones, wireless local area networks (LANs), and international technology standards. The amazing scope of the handbook ensures that it will be the primary reference for every aspect of mobile communications.

Organized in two sections, The Mobile Communications Handbook first introduces the basic principles of analog and digital communications. Consisting of tutorial articles, this section provides background information and technical details, offering a solid foundation for the spectrum of mobile communications technology. The second part of the handbook consists of articles covering such topics as cellular mobile radio, personal communication systems, user location and addressing, wireless data, wireless LANs, and technology standards. New to the Second Edition:
Discussions on:
Rayleigh fading channelsSpace-time processingRadiolocation techniquesEnhancements in second generation systemsWireless video communicationsWireless ATMEvolving third generation standards

Plus updates on:
Wireless dataPower controlChannel assignmentDiversity techniquesError correction codingPseudonoise sequences

Mobile Communications Handbook

1. "Analysis and Optimization of Weighted Order Statistics and Stack Filters" S. Siren, P. Kuosmainen, K. Egiazarian, M. Gabbouj and J. Astola 2. "Image Enhancement and Analysis with Weighted Medians" G. Arce and J. L. Paredes 3. "Spatial-Rank Order Selection Filters" R. Hardie and K. Barner 4. "Signal-Dependent Rank-Ordered Mean (SD-ROM) Filter" E. Abreu 5. "Nonlinear Mean Filters and Their Applications in Image Filtering and Edge Detection" M. Pappas and I. Pitas 6. "Two-Dimensional Teager Filters" S. Thurnhofer 7. "Polynomial and Rational Operators for Image Processing and Analysis" G. Ramponi 8. "Nonlinear Partial Differential Equations in Image Processing" G. Sapiro 9. "Region-Based Filtering of Images and Video Sequences: a Morphological Viewpoint" P. Salembier 10. "Differential Morphology" P. Maragos 11. "Coordinate Logic Filters" B.G. Mertzios and K. Tsirikolias 12. "Nonlinear Filters Based on Fuzzy Models" F. Russo 13. "Digital Halftoning" G. Arce and D. Lau 14. "Intrinsic Dimensionality: Nonlinear Image Operators and Higher Order Statistics" C. Zetzsche and G. Krieger

Nonlinear image processing

The design of speech coders that produce high-quality highly intelligible speech at 6 to 16 kb/s while retaining robustness to background and transmission impairments is an area of current research interest Differential encoding structures employing adaptive quantization and adaptive prediction constitute one of the most promising approaches to achieving these design objectives This paper focuses on the design and analysis of adaptive predictors for differential encoders Several differential encoding systems, including adaptive predictive coding, differential pulse-code modulation, noise feedback coding, direct feedback coding, and prediction error coding, are described and related Adaptive quantizers are briefly discussed and quantitative and qualitative indicators of speech coder performance are defined The channel model, the speech model, and the research problem statements used in the design of differential encoders and adaptive predictors are presented The nomenclature and theory of forward and backward adaptive prediction are developed, and several new backward adaptive algorithms based on various assumptions are presented A detailed survey of theoretical and simulation results on adaptive prediction for speech differential encoders is given, and the effects of background and transmission impairments on these systems are discussed, Finally, the impact of adaptive predictors on rate distortion theory motivated coders is indicated Numerous areas for future research are highlighted

Adaptive prediction in speech differential encoding systems

A report is presented on experiments using a colored-noise assumption Kalman filter to enhance speech additively contaminated by colored noise, such as helicopter noise and jeep noise, with a particular application to linear predictive coding (LPC) of noisy speech. The results indicate that the colored-noise Kalman filter provides a significant gain in SNR, a clear improvement in the sound spectrogram, and an audible improvement in output speech quality. The authors demonstrate that such gains are unavailable with white noise assumption Kalman and Wiener filters. The colored-noise prefilter greatly enhances the quality and intelligibility of LPC output speech for noisy inputs. >

Jerry D. Gibson

Papers

Lossy source coding

Mobile Communications Handbook

Nonlinear image processing

Adaptive prediction in speech differential encoding systems

Filtering of colored noise for speech enhancement and coding