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

Foreword. Preface. 1: Introduction. 1. Processor Categories. 2. Advent of ASIPs in System-on-Chip Design. 3. Organization of this Book. 2: Traditional Asip Design Methodology. 1. Related Work. 2. Motivation of this Work. 3: Processor Models For Asip Design. 1. LISA Language. 2. Model Requirements of Tools. 3. Abstraction Levels. 4. Concluding Remarks. 4: Lisa Processor Design Platform. 1. Hardware Designer Platform. 2. Software Designer Platform. 3. System Integrator Platform. 4. Concluding Remarks. 5: Architecture Exploration. 1. From Specification to Implementation. 2. Architecture Exploration Using LISA. 3. Concluding Remarks. 6: Architecture Implementation. 1. The ICORE Architecture. 2. Architecture Generation from LISA. 3. Case Study. 4. Concluding Remarks. 7: Software Tools For Application Design. 1. Code Generation Tools. 2. Simulation. 3. Debugging. 4. Case Studies. 5. Concluding Remarks. 8: System Integration And Verification. 1. Platform-Based Design. 2. Enabling Platform-Based Design. 3. Software Simulator Integration. 4. Case Study: CoCentric System Studio. 5. Concluding Remarks. 9: Summary And Outlook. 1. Processor Modeling. 2. Architecture Exploration. 3. Software Development Tools. 4. Architecture Implementation. 5. Concluding Remarks. Appendices: Abbreviations.Grammar of the LISA Language. Sample ARM7 LISA Model. The ICORE Architecture. List of Figures. List of Examples. List of Tables. Bibliography. About the Authors.

Architecture Exploration for Embedded Processors with LISA

Digital systems, especially those for mobile applications are sensitive to power consumption, chip size and costs. Therefore they are realized using fixed-point architectures, either dedicated HW or programmable DSPs. On the other hand, system design starts from a floating-point description. These requirements have been the motivation for FRIDGE (Fixed-point pRogrammIng DesiGn Environment), a design environment for the specification, evaluation and implementation of fixed-point systems. FRIDGE offers a seamless design flow from a floating- point description to a fixed-point implementation. Within this paper we focus on two core capabilities of FRIDGE: (1) the concept of an interactive, automated transformation of floating-point programs written in ANSI-C into fixed-point specifications, based on an interpolative approach. The design time reductions that can be achieved make FRIDGE a key component for an efficient HW/SW-CoDesign. (2) a fast fixed-point simulation that performs comprehensive compile-time analyses, reducing simulation time by one order of magnitude compared to existing approaches.

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FRIDGE: a fixed-point design and simulation environment

The problem of optimal carrier recovery and detection of digitally phase modulated signals on fading channels by using a nonstructured approach is presented, i.e. no constraint is placed on the receiver structure. First, the optimal receiver is derived for digitally phase-modulated signals when transmitted over a frequency-nonselective fading channel with memory. The memory results from the fact that usually the coherence time of the channel is larger than the symbol period. Symbols adjacent in time cannot be detected independently and therefore the well-known quadratic receiver is not optimal in this case. A maximum a posteriori (MAP) detector is derived and explicitly utilizes the channel memory for carrier recovery. The derivation shows that the optimal carrier recovery is, under certain conditions, a Kalman filter. Some attractive properties of this carrier recovery unit (including the absence of hang up) are discussed. Then the error rate of several digital modulation schemes is calculated taking the performance of the filter into account. The differences in susceptibility of the modulation schemes to carrier phase jitter are specified. >

A systematic approach to carrier recovery and detection of digitally phase modulated signals of fading channels

In the past few years, MPSoC has become the most popular solution for embedded computing. However, the challenge of programming MPSoCs also comes as the biggest side-effect of the solution. Especially, when designers have to face the legacy C code accumulated through the years, the tool support is mostly unsatisfactory. In this paper, we propose an integrated framework, MAPS, which aims at parallelizing C applications for MPSoC platforms. It extracts coarse-grained parallelism on a novel granularity level. A set of tools have been developed for the framework. We will introduce the major components and their functionalities. Two case studies will be given, which demonstrate the use of MAPS on two different kinds of applications. In both cases the proposed framework helps the programmer to extract parallelism efficiently.

MAPS: an integrated framework for MPSoC application parallelization

We consider the joint sequence estimation, timing and phase recovery for linear modulation. The paper differs from the classical ones in the sense that time-discrete algorithms suitable for fully digital receivers are discussed. Sufficient conditions are given such that the signal samples represent sufficient statistics. These conditions involve signal bandwidth, sampling/symbol rate and the analog prefilter characteristics. It is shown that the sampling rate need not be an exact multiple of the symbol rate, i.e., the samples can be taken from a free-running oscillator. All subsequent signal processing operations in the receiver then operate with the clock of this free-running oscillator. Timing recovery is then performed by a time-variant linear digital interpolator and a decimator. Carrier recovery and sequence estimation are performed at an average rate of one symbol per sample. The digital matched filter for this case is derived for an arbitrary colored noise spectrum. >

Heinrich Meyr

Papers

Architecture Exploration for Embedded Processors with LISA

FRIDGE: a fixed-point design and simulation environment

A systematic approach to carrier recovery and detection of digitally phase modulated signals of fading channels

MAPS: an integrated framework for MPSoC application parallelization

On sampling rate, analog prefiltering, and sufficient statistics for digital receivers