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.

/pdf/from-theory-to-practice-an-overview-of-mimo-space-time-coded-431vesj02q.pdf

From theory to practice: an overview of MIMO space-time coded wireless systems

/pdf/modeling-embedded-processors-and-generating-fast-simulators-3643nubdbe.pdf

Modeling embedded processors and generating fast simulators using the machine description language LISA

Future wireless communication systems require efficient and flexible baseband receivers. Meaningful efficiency metrics are key for design space exploration to quantify the algorithmic and the implementation complexity of a receiver. Most of the current established efficiency metrics are based on counting operations, thus neglecting important issues like data and storage complexity. In this paper we introduce suitable energy and area efficiency metrics which resolve the afore-mentioned disadvantages. These are decoded information bit per energy and throughput per area unit. Efficiency metrics are assessed by various implementations of turbo decoders, LDPC decoders and convolutional decoders. New exploration methodologies are presented, which permit an appropriate benchmarking of implementation efficiency, communications performance, and flexibility trade-offs. These exploration methodologies are based on efficiency trajectories rather than a single snapshot metric as done in state-of-the-art approaches.

On Complexity, Energy- and Implementation-Efficiency of Channel Decoders

The proposed idea of software defined radios (SDRs) offers the potential to cope with the complexity and flexibility demands of future wireless communication devices. Unfortunately, the tight interaction of software and hardware as well as the high computational requirements make development of SDRs to one of the most challenging tasks system architects are facing today. The main challenge is to select the optimal or a sub-optimal solution within the large design space spread by the many design options. This paper introduces a novel design space exploration framework for particular early development stages. The key contribution is a pre-simulation based mathematical analysis based on synchronous data flow (SDF) graphs in order to take the right software and hardware design decisions. This analysis can be utilized right from the start of the design cycle with only limited knowledge of the final implementation. In addition, the proposed technique seamlessly integrates into an electronic system level (ESL) based simulation framework. This allows for a smooth transition from pure mathematical analysis to the simulation of the final implementation. The practical usage of the framework and its capabilities are highlighted by a case study from a typical SDR design.

Analytical and Simulation-based Design Space Exploration of Software Defined Radios

This paper addresses performance analysis and optimization of receive filters in a GSM system using an MLSE detection algorithm. In order to include the effects of adjacent and co-channel interference on the sequence estimation, appropriate signal models are derived. These models are used to calculate a performance measure for a system operating under these impairments. The resulting performance measure which depends on the interfering signals and the receiver filter characteristics is used to optimize the receive filter bandwidth with respect to minimum bit error probability.

Receive-filtering for GSM receivers in interference limited environments

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, to the best of our knowledge, the first high speed VLSI implementation of the soft output Viterbi algorithm, a low complexity soft output algorithm The 43mm2 chip is designed for a 16 state convolutional code, and tested samples achieved a throughput of 50 Mbit/s The chip is roughly twice as big as a 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

http://ieeexplore.ieee.org/iel5/5468478/5468479/05468496.pdf

Heinrich Meyr

Papers

Modeling embedded processors and generating fast simulators using the machine description language LISA

On Complexity, Energy- and Implementation-Efficiency of Channel Decoders

Analytical and Simulation-based Design Space Exploration of Software Defined Radios

Receive-filtering for GSM receivers in interference limited environments

A 40Mbit/s Soft-Output Viterbi Decoder