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

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Alhussein Abouzeid Rensselaer Polytechnic Institute Raviraj Adve University of Toronto Dharma Agrawal University of Cincinnati Walid Ahmed Tyco M/A-COM Sonia Aissa University of Quebec, INRSEMT Huseyin Arslan University of South Florida Nallanathan Arumugam National University of Singapore Saewoong Bahk Seoul National University Claus Bauer Dolby Laboratories Brahim Bensaou Hong Kong University of Science and Technology Rick Blum Lehigh University Michael Buehrer Virginia Tech Antonio Capone Politecnico di Milano Javier Gómez Castellanos National University of Mexico Claude Castelluccia INRIA Henry Chan The Hong Kong Polytechnic University Ajit Chaturvedi Indian Institute of Technology Kanpur Jyh-Cheng Chen National Tsing Hua University Yong Huat Chew Institute for Infocomm Research Tricia Chigan Michigan Tech Dong-Ho Cho Korea Advanced Institute of Science and Tech. Jinho Choi University of New South Wales Carlos Cordeiro Philips Research USA Laurie Cuthbert Queen Mary University of London Arek Dadej University of South Australia Sajal Das University of Texas at Arlington Franco Davoli DIST University of Genoa Xiaodai Dong, University of Alberta Hassan El-sallabi Helsinki University of Technology Ozgur Ercetin Sabanci University Elza Erkip Polytechnic University Romano Fantacci University of Florence Frank Fitzek Aalborg University Mario Freire University of Beira Interior Vincent Gaudet University of Alberta Jairo Gutierrez University of Auckland Michael Hadjitheodosiou University of Maryland Zhu Han University of Maryland College Park Christian Hartmann Technische Universitat Munchen Hossam Hassanein Queen's University Soong Boon Hee Nanyang Technological University Paul Ho Simon Fraser University Antonio Iera University "Mediterranea" of Reggio Calabria Markku Juntti University of Oulu Stefan Kaiser DoCoMo Euro-Labs Nei Kato Tohoku University Dongkyun Kim Kyungpook National University Ryuji Kohno Yokohama National University Bhaskar Krishnamachari University of Southern California Giridhar Krishnamurthy Indian Institute of Technology Madras Lutz Lampe University of British Columbia Bjorn Landfeldt The University of Sydney Peter Langendoerfer IHP Microelectronics Technologies Eddie Law Ryerson University in Toronto

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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

In this paper we review the most peculiar and interesting information-theoretic and communications features of fading channels. We first describe the statistical models of fading channels which are frequently used in the analysis and design of communication systems. Next, we focus on the information theory of fading channels, by emphasizing capacity as the most important performance measure. Both single-user and multiuser transmission are examined. Further, we describe how the structure of fading channels impacts code design, and finally overview equalization of fading multipath channels.

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Fading channels: information-theoretic and communications aspects

We describe an algorithm for joint timing and carrier phase recovery with CPM signaling. The proposed method may be employed with any CPM format and with either full or reduced state detectors. It can be implemented in digital form with a limited computational complexity. Simulation results show that its tracking performance is excellent. However, when operated in its simplest form, it may exhibit false locks during acquisition. In particular, this happens with multilevel partial response formats. A simple solution to false locks is proposed.

Joint phase and timing recovery with CPM signals

This paper provides an approximate closed form solution to the problem of data-aided joint maximum likelihood estimation of the carrier frequency offset and of the channel impulse response in an Orthogonal Frequency Division Multiplexing transmission over a multipath fading channel. This results in a novel pilot-aided feedforward estimator, that can be employed for pilot-based receiver training. It is also shown how the novel joint estimation strategy can be exploited in an iterative (turbo) receiver structure to track the fast channel and frequency offset changes occurring during the transmission of information symbols. The performance of this structure is assessed by computer simulations and is compared with that provided by other estimation/detection strategies.

Iterative ML-based estimation of carrier frequency offset, channel impulse response and data in OFDM transmissions

An algorithm, based on previous work [Vitetta and Taylor 1994, 1995], for the detection of coded and uncoded PSK sequences transmitted on a frequency-flat fading channel is investigated. It is based on the Viterbi algorithm and processes more than one signal sample per signaling interval. Its performance is evaluated by means of computer simulations for both uncoded and coded systems.

Multisampling receivers for uncoded and coded PSK signal sequences transmitted over Rayleigh frequency-flat fading channels

In this paper, novel statistical models for the representation of the periodic impulsive noise generated by power loads connected to power grids in indoor scenarios are developed. Their derivation is based on a set of experimental results acquired in a measurement campaign and on deseasonalized autoregressive moving average modeling of cyclostationary random processes. Numerical results are evidence that the proposed models can provide an accurate stochastic representation of the periodic impulsive noise generated by specific appliances in the 1-30 MHz band, at the price of limited computational complexity.

Statistical Modeling of Periodic Impulsive Noise in Indoor Power-Line Channels

We derive a novel error probability formula for noncoherent detection of orthogonal binary frequency-shift keying with dual diversity and equal gain combining in the presence of slow, correlated, Rician time-selective fading. Numerical results indicate that: (1) fading correlation may produce a substantial performance loss and (2) the error performance of the noncoherent detector improves uniformly as the intensity of the line-of-sight signal component increases.

Giorgio M. Vitetta

Papers

Joint phase and timing recovery with CPM signals

Iterative ML-based estimation of carrier frequency offset, channel impulse response and data in OFDM transmissions

Multisampling receivers for uncoded and coded PSK signal sequences transmitted over Rayleigh frequency-flat fading channels

Statistical Modeling of Periodic Impulsive Noise in Indoor Power-Line Channels

An error probability formula for noncoherent orthogonal binary FSK with dual diversity on correlated Rician channels