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

Instruction set simulators are indispensable tools for both the design of programmable architectures and software development. However, due to a constantly increasing processor complexity and the frequent demand for cycle-accurate models, such simulators have become defectively slow. The principle of compiled simulation addresses this shortcoming. Compiled simulators make use of a priori knowlegde to accelerate simulation, with the highest efficiency achieved by employing static scheduling techniques.In the past, such statically scheduled simulators have only been implemented for specific DSP architectures. The approach presented here discusses the application of static scheduling techniques to retargetable simulation tools based on the processor description language LISA. Principles and implementation issues are discussed in this paper, and results are presented for two selected processor architectures.

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Using static scheduling techniques for the retargeting of high speed, compiled simulators for embedded processors from an abstract machine description

Soft-output decoding has evolved as a key technology for new error correction approaches with unprecedented performance as well as for improvement of well established transmission techniques. In this paper, we present a high-speed VLSI implementation of the soft-output Viterbi algorithm, a low complexity soft-output algorithm, for a 16-state convolutional code. The 43 mm/sup 2/ standard cell chip achieves a simulated throughput of 40 Mb/s, while tested samples achieved a throughput of 50 Mb/s. The chip is roughly twice as big as a 16-state Viterbi decoder without soft outputs. It is thus shown with the design that transmission schemes using soft-output decoding can be considered practical even at very high throughput. Since such decoding systems are more complex to design than hard output systems, special emphasis is placed on the employed design methodology. >

A 40 Mb/s soft-output Viterbi decoder

This paper presents a complete framework for Verilog-based fault injection and evaluation. In contrast to existing approaches, the proposed solution is the first one based on the Verilog programming interface (VPI). Due to standardization of the VPI, the framework is—in contrast to simulator command based techniques—independent from the used simulator. Additionally, it does not require recompilation for different fault injection experiments like techniques modifying the Verilog code for fault injection. The feasibility of the VPI-based approach is shown in a case study.

SSIRI 2009 Student Doctoral Program A fast and flexible Platform for Fault Injection and Evaluation in Verilog-based Simulations

Using the Schnorr-Euchner (SE) order for soft-input sphere decoders is inefficient for implementation, because it requires exhaustive calculation and sorting of partial metrics of all constellation points. Instead, low-complexity methods can be applied by separating the partial metric into channel information and a priori information and solely enumerating based on one of them. With such an orthogonalization, this paper presents an algorithm that effectively combines these two enumerations to deliver an order close to the SE one. Mathematical analyses and simulation results demonstrate that this is the first algorithm allowing for a low-complexity implementation with optimal error rate performance for any number of iterations.

Combining orthogonalized partial metrics: Efficient enumeration for soft-input sphere decoder

Digital mobile systems are sensitive to power consumption, chip size and costs. Therefore they are realized using fixed-point architectures, either dedicated hardware (HW) or fixed-point processors. On the other hand, system design starts from a floating-point description. These requirements have been the motivation for FRIDGE, 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. We focus on the FRIDGE-concept of an interactive, automated transformation of floating-point programs written in ANSI-C into fixed-point specifications, based on an interpolative approach. Since HW and software (SW) implementations of the same functionality in general require different fixed-point specifications, the design time reductions that can be achieved by using FRIDGE make it a key component for an efficient HW/SW-codesign.

Heinrich Meyr

Papers

Using static scheduling techniques for the retargeting of high speed, compiled simulators for embedded processors from an abstract machine description

A 40 Mb/s soft-output Viterbi decoder

SSIRI 2009 Student Doctoral Program A fast and flexible Platform for Fault Injection and Evaluation in Verilog-based Simulations

Combining orthogonalized partial metrics: Efficient enumeration for soft-input sphere decoder

FRIDGE: an interactive code generation environment for HW/SW codesign