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 optimal phase synchronization and automatic gain control (AGC) scheme for coherent reception of linearly modulated signals on frequency-flat mobile fading channels is presented. The channel model and receiver performance are described. It is shown that using the technique allows the irreducible error floors (due to random FM) known from the noncoherent methods to be practically eliminated. Depending on the fastness of the fading, large power gains over the noncoherent methods are achieved. Unfavorable analog signal processing and/or the high bandwidth inefficiency of the FDM-pilot coherent methods are also avoided. >

A new method for phase synchronization and automatic gain control of linearly modulated signals on frequency-flat fading channels

Unified modeling and estimation of the MD (multiplicative distortion) in finite-alphabet digital communication systems is presented. A simple form of MD is the carrier phase exp(j theta ), which has to be estimated and compensated for in a coherent receiver. A more general case with fading must, however, allow for amplitude as well as phase variations of the MD. The authors assume a state-variable model for the MD and generally obtain a nonlinear estimation problem with additional randomly varying system parameters such as received signal power, frequency offset, and Doppler spread. An extended Kalman filter is then applied as a near-optimal solution to the adaptive MD and channel parameter estimation problem. Examples are given to show the use and some advantages of this scheme. >

Adaptive synchronization and channel parameter estimation using an extended Kalman filter

For the design of complex digital signal processing systems, block diagram oriented simulation has become a widely accepted standard Current research is concerned with the coupling of heterogenous simulation engines and the transition from simulation to the implementation of digital signal processing systems Due to the difficulty in mastering complex design spaces high level hardware and software synthesis is becoming increasingly important The authors concentrate on the block diagram oriented software synthesis of digital signal processing systems for programmable processors, such as digital signal processors (DSP) They present the synthesis environment DESCARTES illustrating novel optimization strategies Furthermore they discuss goal directed software synthesis, by which code is interactively or automatically generated, which can be adapted to the application specific needs imposed by constraints on memory space, sampling rate or latency >

High level software synthesis for signal processing systems

For the design of complex digital signal processing systems, block diagram oriented synthesis of real time software for programmable target processors has become an important design aid. The synthesis approach discussed in the paper is based on multirate block diagrams with scalable synchronous dataflow (SSDF) semantics. For this class of dataflow graphs the authors present scheduling techniques for optimum data memory compaction. These techniques can be employed to map signals of a block diagram onto a minimum data memory space. In order to formalize the data memory compaction problem, they first derive appropriate implementation measures. Based on these implementation measures it can be shown that optimum data memory compaction consists of optimum scheduling as well as optimum memory allocation. For the class of single appearance (SA) block diagrams with SSDF semantics, scheduling can be reduced to an integer linear programming (ILP) problem. Due to the computational complexity of ILP, the authors also present a suboptimum scheduling selection criterion, which call be used for SA and non SA-schedulers.

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Scheduling for optimum data memory compaction in block diagram oriented software synthesis

The design process for fixed-point implementations eitherin software or in hardware requires a bit-true specificationof the algorithm in order to analyze quantization effectson an algorithmical level, abstracting from implementationaldetailsOn the other hand, system design starts froma floating-point description into a fixed-point description becomesnecessaryWithin this paper we present a tool thatallows an automated, interactive transformation from floating-pointANSI-C into a bit-true specification based ona new data type fixed that is introduced as an extensionto ANSI-CThe concept is rooted in a sophisticated datadependency analysis that allows to handle control structuresas well as pointersIt is part of the fixed-point designenvironment FRIDGE which includes an advanced simulatorthat covers the extended ANSI-C syntax as well astarget specific compilers which allow to generate efficientfixed-point implementations either for HW or for SW, startingfrom the bit-true algorithm specification

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Heinrich Meyr

Papers

A new method for phase synchronization and automatic gain control of linearly modulated signals on frequency-flat fading channels

Adaptive synchronization and channel parameter estimation using an extended Kalman filter

High level software synthesis for signal processing systems

Scheduling for optimum data memory compaction in block diagram oriented software synthesis

System level fixed-point design based on an interpolative approach