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

An all digital implementation of a receiver for bandwidth-efficient communication

The complexity of DSP systems grows at an ever-increasing rate while the implementation of these designs must meet criteria like minimum cost and a short time to market. Hence there is a growing need for efficient design automation and a seamless design flow that allows the execution of the design steps at the highest suitable level of abstraction.

Efficient Design Flow for Fixed-Point Systems

When an iterative receiver has more than one iteration loop, scheduling the iterative decoding process is an important issue. To find the optimal scheduling, extrinsic information transfer (EXIT) function was the main tool in the literature. However, it is only very efficient when the codeword is significantly long. In this paper, under the consideration of a packet-based transmission scenario, we propose modeling the scheduling search problem as the foraging problem of ant colony. And then, an ant colony optimization (ACO) algorithm, labeled as max-min ant system (MMAS), is adopted and tailored for our problem. Simulation results show MMAS outperforms the EXIT function-based method when practical length codewords are used.

Searching for optimal scheduling of MIMO doubly iterative receivers: An ant colony optimization-based method

A new approach to traceback processing in Viterbi decoders is presented. The approach reduces memory requirements as compared to previous approaches by using different speeds during acquisition of the best trellis path and the subsequent decoding of a block of data. This dual timescale approach allows in-place updating of the stored information and matches the constraints of commodity semi-custom technologies, where (at the considered high dock rates) write accesses to a RAM usually require more than one clock cycle.

Viterbi decoding with dual timescale traceback processing

This contribution describes design methodology and implementation of a single-chip timing and carrier synchronizer and channel decoder for digital video broadcasting over satellite (DVB-S). The device consists of an A /D converter with AGC, timing and carrier synchronizer with matched filter, Viterbi decoder including node synchronization, byte and frame synchronizer, convolutional de-interleaver, Reed Solomon decoder, and a descrambler.

The system was designed in accordance with the DVB specifications. It is able to perform Viterbi decoding at data rates up to 56 Mbit /s and to sample the analog input values with up to 88 MHz. The chip allows automatic acquisition of the convolutional code rate and the position of the puncturing mask. The symbol synchronization is performed fully digitally by means of interpolation and controlled decimation. Hence, no external analog clock recovery circuit is needed.

For algorithm design, system performance evaluation, co-verification of the building blocks, and functional hardware verification an advanced design methodology and the corresponding tool framework are presented which guarantee both short design time and highly reliable results. The chip has been fabricated in a 0.5 µm CMOS technology with three metal layers. A die photograph is included.

Heinrich Meyr

Papers

An all digital implementation of a receiver for bandwidth-efficient communication

Efficient Design Flow for Fixed-Point Systems

Searching for optimal scheduling of MIMO doubly iterative receivers: An ant colony optimization-based method

Viterbi decoding with dual timescale traceback processing

Design Methodology for a DVB Satellite Receiver ASIC