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

Soft output Viterbi decoding has evolved as a key technology for advanced decoding systems during the recent years. The article presents a modification of the soft output Viterbi algorithm and the resulting hardware architecture. The new approach leads to storage savings while maintaining the low computational complexity of former approaches and is thus advantageous for hardware as well as for software implementations. The complexity of soft output Viterbi decoding with the new approach is clearly less than twice that of hard decision Viterbi decoding.

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A new postprocessing architecture for soft output Viterbi decoding

Optimal Carrier Recovery and Detection on Frequency-Nonselective Fading Channels

In this work, we derive a new upper bound on the achievable rate of stationary Rayleigh flat-fading channels with i.i.d. input symbols. The novelty lies in the fact that this bound is not restricted to peak power constrained input symbols like known bounds, e.g., in [1] or [2]. Therefore, the derived upper bound can also be used to evaluate the achievable rate with i.i.d. proper Gaussian input symbols, which are capacity achieving in the coherent case. The derivation of the upper bound is based on the prediction error variance of the one-step channel predictor.

The achievable rate of stationary rayleigh flat-fading channels with IID input symbols

During the design of an accelerator board with two different ASICs, a methodology for the concurrent design of a chipset and a software system working as a runtime environment has been developed The software system is implemented in the most appropriate language, eg C, and the board is modeled in a hardware description language, eg VHDL A portable software library in C and VHDL was developed to allow the VHDL simulation process and the runtime system to exchange stimuli and results concurrently This approach addresses the limitations of the VHDL standard testbench concept >

Heinrich Meyr

Papers

A new postprocessing architecture for soft output Viterbi decoding

Optimal Carrier Recovery and Detection on Frequency-Nonselective Fading Channels

The achievable rate of stationary rayleigh flat-fading channels with IID input symbols

Concurrent design of a chipset and its runtime environment

DSP System Implementation