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

We propose an efficient iterative multiple-input multiple-output (MIMO) detection algorithm based on the local search. Specifically, since the MIMO channel matrix twists the lattice structure of the received symbols, the proposed channel-aware local search (CA-LS) defines its search neighborhood according to the instantaneous channel realization. Such channel-dependent neighborhood can be efficiently identified by using the sphere decoder in a set that comprises the differences between pairs of QAM vectors, which is termed as delta vectors. The delta vectors with small quadratic norms with respect to the channel matrix are identified and then used as the search directions of the CA-LS. Features like sparsity and non-uniformity of delta vectors are exploited to reduce the SD complexity. Furthermore, by reformulating the detection criterion, the log-likelihood ratio (LLR) computations and searches of the CA-LS are greatly simplified. Numerical simulations demonstrate that compared with other practical iterative MIMO detectors, e.g., the list sphere decoder, the CA-LS achieves superior performance in both error rate and complexity aspects.

Channel-aware local search (CA-LS) for iterative MIMO detection

In this paper, the application of beamforming in a single frequency network (SFN) is examined. A single frequency network is characterized by the transmission of the same signal from multiple base stations simultaneously. Since a user receives the signal from many base stations, the beamforming weights for many different base stations need to be jointly optimized for the transmission. An optimality criterion is given and three different beamforming strategies with varying computational complexity and performance are investigated. Their performances are compared to the case of SFN transmission without beamforming, showing significant gains for realistic numbers of transmit antennas at the base stations and numbers of users in the SFN.

/pdf/evaluation-of-beamforming-for-broadcast-applications-in-2821b33f8g.pdf

Evaluation of Beamforming for Broadcast Applications in Single Frequency Networks

Integrating the simulation and implementation of complex digital signal processing systems based on block diagram oriented simulation and code generation is described. The basic concept consists of a generic, i.e., reusable, block diagram with different underlying semantics. This allows the generation of software adapted to the special needs of both simulation and implementation. In order to achieve application-specific implementations, the code generator supports tradeoffs between implementation constraints such as code size, throughput, or latency. Therefore, a wide spectrum of different optimization strategies has been incorporated in the code generator. >

A novel approach to the integration of simulation and implementation to digital signal processing systems

Hardware implementations of digital signal processing tasks are typically considered inflexible and difficult to reuse. The current case study focuses on the design of a universal reusable coprocessor with application specific configurations to accelerate computational intensive tasks. The design methodology and the architecture is described for essential parts of an ADSL transceiver.

https://ieeexplore.ieee.org/iel5/6550/17479/00806502.pdf

A universal coprocessor and its application for an ADSL modem

Modern application-specific instruction-set processors (ASIPs) face the daunting task of delivering high performance for a wide range of applications. For enhancing the performance, architectural features, for example, pipelining, VLIW, are often employed in ASIPs, leading to high design complexity. Integrated ASIP design environments, like template-based approaches and language-driven approaches, provide an answer to this growing design complexity. At the same time, increasing hardware design costs have motivated the processor designers to introduce high flexibility in the processor. Flexibility, in its most effective form, can be introduced to the ASIP by coupling a reconfigurable unit to the base processor. Because of its obvious benefits, several reconfigurable ASIPs (rASIPs) have been designed for years. This design paradigm gained momentum with the advent of coarse-grained FPGAs, where the lack of domain-specific performance common in general-purpose FPGAs are largely overcome by choosing application-dependent basic functional units. These rASIP designs lack a generic flow from high-level specification, resulting in intuitive design decisions and hard-to-retarget processor design tools. Although partial, template-based approaches for rASIP design is existent, a clear design methodology especially for the prefabrication architecture exploration is not present. In order to address this issue, a high-level specification and design methodology for partially reconfigurable VLIW processors is proposed in this article. To show the benefit of this approach, a commercial VLIW processor is used as the base architecture and two domains of applications are studied for potential performance gain.

Heinrich Meyr

Papers

Channel-aware local search (CA-LS) for iterative MIMO detection

Evaluation of Beamforming for Broadcast Applications in Single Frequency Networks

A novel approach to the integration of simulation and implementation to digital signal processing systems

A universal coprocessor and its application for an ADSL modem

Prefabrication and postfabrication architecture exploration for partially reconfigurable VLIW processors