Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Fundamentals Of Statistical Signal Processing

Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.

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Sequential Monte Carlo methods in practice

物件導向軟體之架構(Object-Oriented Software Construction)探討

This paper presents an overview of progress in the area of multiple input multiple output (MIMO) space-time coded wireless systems. After some background on the research leading to the discovery of the enormous potential of MIMO wireless links, we highlight the different classes of techniques and algorithms proposed which attempt to realize the various benefits of MIMO including spatial multiplexing and space-time coding schemes. These algorithms are often derived and analyzed under ideal independent fading conditions. We present the state of the art in channel modeling and measurements, leading to a better understanding of actual MIMO gains. Finally, the paper addresses current questions regarding the integration of MIMO links in practical wireless systems and standards.

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From theory to practice: an overview of MIMO space-time coded wireless systems

From the Publisher:
Digital Communication Receivers offers a complete treatment on the theoretical and practical aspects of synchronization and channel estimation from the standpoint of digital signal processing. The focus on these increasingly important topics, the systematic approach to algorithm development, and the linked algorithm-architecture methodology in digital receiver design are unique features of this book. The material is structured according to different classes of transmission channels. In Part C, baseband transmission over wire or optical fiber is addressed. Part D covers passband transmission over satellite or terrestrial wireless channels. Part E deals with transmission over fading channels. Designed for the practicing communication engineer and the graduate student, the book places considerable emphasis on helpful examples, summaries, illustrations, and bibliographies. Contents include basic material, baseband communications, passband transmission, receiver structure for PAM signals, synthesis of synchronization algorithms, performance analysis of synchronizers, bit error degradation caused by random tracking errors, frequency estimation, timing adjustment by interpolation, DSP system implementation, characterization, modeling, and simulation of linear fading channels, detection and parameter synchronization on fading channels, receiver structures for fading channels, parameter synchronization for flat fading channels, and parameter synchronization for selective fading channels.

Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing

Orthogonal frequency-division multiplexing (OFDM) is the technique of choice in digital broad-band applications that must cope with highly dispersive transmission media at low receiver implementation cost. In this paper, we focus on the inner OFDM receiver and its functions necessary to demodulate the received signal and deliver soft information to the outer receiver for decoding. The effects of relevant nonideal transmission conditions are thoroughly analyzed: imperfect channel estimation, symbol frame offset, carrier and sampling clock frequency offset, time-selective fading, and critical analog components. Through an appropriate optimization criterion (signal-to-noise ratio loss), minimum requirements on each receiver synchronization function are systematically derived. An equivalent signal model encompassing the effects of all relevant imperfections is then formulated in a generalized framework. The paper concludes with an outline of synchronization strategies.

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Optimum receiver design for wireless broad-band systems using OFDM. I

The digital realization of timing recovery circuits for digital data transmission is considered. A digital algorithm is proposed that can be implemented very efficiently even at high data rates. The resulting timing jitter has been computed and verified by simulations. In contrast to other known algorithms, the one presented here allows free-running sampling oscillators and a novel planar filtering method that prevents synchronization hangups. >

Digital filter and square timing recovery

This paper details on the design of OFDM receivers. Special attention is paid to the OFDM-specific receiver functions necessary to demodulate the received signal and deliver soft information to the outer receiver for decoding. In part I of the paper, the effects of nonideal transmission conditions have been thoroughly analyzed. To show the impact of the synchronization algorithms-which are most critical in OFDM-on system performance and complexity we consider the design of a complete receiver consisting of symbol synchronization, carrier/sampling clock synchronization and channel estimation. The performance of the algorithms is analyzed and a qualitative estimate of the resulting complexity is given. This allows one to draw conclusions concerning the achievable system performance under realistic complexity assumptions.

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Heinrich Meyr

Papers

Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing

Optimum receiver design for wireless broad-band systems using OFDM. I

Digital filter and square timing recovery

Optimum receiver design for OFDM-based broadband transmission .II. A case study

Digital communication receivers