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

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.

/pdf/sequential-monte-carlo-methods-in-practice-3kfxeebn8e.pdf

Sequential Monte Carlo methods in practice

Fundamentals Of Statistical Signal Processing

Information Theory and Reliable Communication

物件導向軟體之架構(Object-Oriented Software Construction)探討

Phase-Locked Loop Fundamentals. Phase-Locked Loop Tracking Performance in the Presence of Noise. Unaided Acquisition. Aided Acquisition. Loop Threshold. Amplitude Control. Automatic Frequency Control. Brief Review of Some Mathematical Fundamentals. Relaxation Times, Meantime Between Cycle Slips, Transition Rates, and Eigenvalues of Fokker-Planck Operators. Renewal Process Approach. The Matrix Eigenvalue Approach. Epilogue: Unexplored Topics. Index.

Phase-, frequency-locked loops, and amplitude control

This paper proposes a new concept of multiple-input multiple-output (MIMO) detection, aiming at minimizing the average computational cost. The detection methods, according to the estimated channel state information, are adapted to deliver just acceptable error rate. Performance of two popular MIMO detectors is analyzed. Based on these analyses, a channel adaptive MIMO (CA-MIMO) receiver with a novel method- selection strategy is investigated. Simulation results demonstrate that the proposed CA-MIMO detector, operating at as low SNR as the sphere search does, achieves huge average complexity saving.

Low-Complexity Channel-Adaptive MIMO Detection with Just-Acceptable Error Rate

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

/pdf/system-level-fixed-point-design-based-on-an-interpolative-ft1vnbg63a.pdf

System level fixed-point design based on an interpolative approach

This paper presents a technique for simulating processors and attached hardware using the principle of compiled simulation. Unlike existing, inhouse and off-the-shelf hardware/software co-simulators, which use interpretive processor simulation, the proposed technique performs instruction decoding and simulation scheduling at compile time. The technique offers up to three orders of magnitude faster simulation. The high speed allows the user to explore algorithms and hardware/software trade-offs before any hardware implementation. In this paper, the sources of the speedup and the limitations of the technique are analyzed and the realization of the simulation compiler is presented.

Compiled HW/SW co-simulation

Digital communication receivers

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