Equalization and System Identification Batch Processing Algorithms Performance and Applications

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Contents

  1. Equalization and System Identification Batch Processing Algorithms Performance and Applications
  2. Chong-Yung Chi - Google 學術搜尋引用文獻

Discrete-time signal processing has had a momentous impact on advances in engineering and science over recent decades. The rapid progress of digital and mixed-signal integrated circuits in processing speed, functionality and cost-effectiveness has led to their ubiquitous employment in signal processing and transmission in diverse milieux.

Equalization and System Identification Batch Processing Algorithms Performance and Applications

The absence of training or pilot signals from many kinds of transmission — in, for example, speech analysis, seismic exploration and texture image analysis — necessitates the widespread use of blind equalization and system identification. There have been a great many algorithms developed for these purposes, working with one- or two-dimensional 2-d signals and with single-input single-output SISO or multiple-input multiple-output MIMO , real or complex systems.


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It is now time for a unified treatment of this subject, pointing out the common characteristics and the sometimes close relations of these algorithms as well as learning from their different perspectives. Blind Equalization and System Identification provides such a unified treatment presenting theory, performance analysis, simulation, implementation and applications.

It is the intention of the organizers to promote SYSID as a meeting place where scientists and engineers from several research communities can meet to discuss issues related to these areas. Relevant topics for the symposium program include: Identification of linear and multivariable systems, identification of nonlinear systems, including neural networks, identification of hybrid and distributed systems, Identification for control, experimental modelling in process control, vibration and modal analysis, model validation, monitoring and fault detection, signal processing and communication, parameter estimation and inverse modelling, statistical analysis and uncertainty bounding, adaptive control and data-based controller tuning, learning, data mining and Bayesian approaches, sequential Monte Carlo methods, including particle filtering, applications in process control systems, motion control systems, robotics, aerospace systems, bioengineering and medical systems, physical measurement systems, automotive systems, econometrics, transportation and communication systems.

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Chong-Yung Chi - Google 學術搜尋引用文獻

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Mapping Signal Processing Algorithms to Architectures - Course Introduction

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