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William C. Lindsey

Bio: William C. Lindsey is an academic researcher from University of Southern California. The author has contributed to research in topics: Phase-locked loop & Phase-shift keying. The author has an hindex of 25, co-authored 86 publications receiving 3692 citations. Previous affiliations of William C. Lindsey include Titan Corporation & Jet Propulsion Laboratory.


Papers
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Journal ArticleDOI
01 Apr 1981
TL;DR: A systematic survey of the theoretical/experimental work accomplished in the area of digital phase-locked loops (DPLL's) during the period of 1960 to 1980, thereby offering speedy access to the techniques and hardware developments which have been presented in a scattered literature.
Abstract: The purpose of this paper is to present a systematic survey of the theoretical/experimental work accomplished in the area of digital phase-locked loops (DPLL's) during the period of 1960 to 1980. The DPLL represents the heart of the Building blocks required in the implementation of coherent (all digital) communications and tracking receivers. This survey is particularly motivated by the fact that microprocessor technology is advancing rapidly to the extent that sophisticated and flexible signal processing algorithms for communications and control can be realized in the digital domain. In fact, it is anticipated that the use of this signal processing technology will continue to expand rapidly in the development of advanced communications and tracking receivers, e.g., all digital modems. Consequently, one major purpose of this paper is to provide the reader with a survey and an overview of the theoretical and experimental work accomplished to date, thereby offering speedy access to the techniques and hardware developments which have been presented in a scattered literature. In addition, the authors feel that a tutorial article revealing the various theories, their relationships to one another, their shortcomings, their advantages and the assumptions on which each is based, would be of tremendous value to the engineer trying to decide what particular analysis procedure is applicable to his peculiar problem. Consequently, a byproduct of this presentation will be to point out unsolved problems of practical interest. A broad class of digital modulation techniques, viz. I-Q modulations and demodulation, are studied in a rather general way.

568 citations

Book
01 Jan 1995
TL;DR: This chapter discusses Scalar and Vector Communications Over the Discrete Memoryless Channel, and Coherent Communication with Waveforms, and Convolutional-Coded Digital Communications.
Abstract: 1. Introduction to Telecommunications. 2. Power Spectral Density of Digital Modulations. 3. Scalar and Vector Communications Over the Discrete Memoryless Channel. 4. Coherent Communication with Waveforms. 5. Noncoherent Communication with Waveforms. 6. Partially Coherent Communication with Waveforms. 7. Differentially Coherent Communication with Waveforms. 8. Double Differentially Coherent Communication with Waveforms. 9. Communication over Bandlimited Channels. 10. Demodulation and Detection of Other Digital Modulations. 11. Coded Digital Communications. 12. Block-Coded Digital Communications. 13. Convolutional-Coded Digital Communications. Index.

467 citations

Book
01 Jan 1973
TL;DR: This classic graduate- and research-level texty by two leading experts in the field of telecommunications is essential reading for anyone workign today in space and satellite digital communicatiions and those seeking a wider background in statistical communication theory and its applications.
Abstract: From the Publisher: This classic graduate- and research-level texty by two leading experts in the field of telecommunications is essential reading for anyone workign today in space and satellite digital communicatiions and those seeking a wider background in statistical communication theory and its applications. Ideal for practicing engineers as well as graduate students in communication systems courses, the book clearly presents and develops theory that can be used in the design and planning of telecommunication systems operating with either small or large performance margins. The book includes in its coverage a theory for use in the design of one-way and two-way phase-coherent and communication systems; and analysis and comparison of carrier and suppressed carrier synchronization techniques; treatment of the band-pass limiter theory; unification of phase-coherent detection with perfect and noisy synchronization reference signals. Convolutional codes, symbol synchronization, and noncoherent detection of M-ary signals are among the otehr subjects addressed in this comprehensive study. Dr. Lindsey, who is with the Communication Sciences Institute at the University of Southern California, and Dr. Simon, who is with the Jet Propulsion Laboratory at the California Institute of Technology, include at the end of each chapter a comprehensive set of problems that demonstrate the application of the theory developed. Unabridged Dover (1991) republication of the edition published by Prentice-Hall, Inc., Englewood Cliffs, N.J., 1973.265 line illustrations. 3 photographs. References at chapter ends. Problems. Index. xviii + 574pp. 5 3/8 x 8 1/2. Paperbound.

387 citations

Journal ArticleDOI
TL;DR: A survey of known results on certain aspects of the level-crossing properties of random processes is presented and provides a basis for further study in the area.
Abstract: In a variety of practical problems involving random processes, it is necessary to have statistical information on their level-crossing properties. This paper presents a survey of known results on certain aspects of this problem and provides a basis for further study in the area. The goal has been to give a broad view of the problems considered in the literature and a brief indication of the techniques used in their solution. Much material of a more or less historical nature has been included since, to the authors' knowledge, no other survey of this nature exists.

302 citations


Cited by
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Book
23 Nov 2005
TL;DR: The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
Abstract: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

11,357 citations

Journal ArticleDOI
David J. Thomson1
01 Sep 1982
TL;DR: In this article, a local eigenexpansion is proposed to estimate the spectrum of a stationary time series from a finite sample of the process, which is equivalent to using the weishted average of a series of direct-spectrum estimates based on orthogonal data windows to treat both bias and smoothing problems.
Abstract: In the choice of an estimator for the spectrum of a stationary time series from a finite sample of the process, the problems of bias control and consistency, or "smoothing," are dominant. In this paper we present a new method based on a "local" eigenexpansion to estimate the spectrum in terms of the solution of an integral equation. Computationally this method is equivalent to using the weishted average of a series of direct-spectrum estimates based on orthogonal data windows (discrete prolate spheroidal sequences) to treat both the bias and smoothing problems. Some of the attractive features of this estimate are: there are no arbitrary windows; it is a small sample theory; it is consistent; it provides an analysis-of-variance test for line components; and it has high resolution. We also show relations of this estimate to maximum-likelihood estimates, show that the estimation capacity of the estimate is high, and show applications to coherence and polyspectrum estimates.

3,921 citations

Journal ArticleDOI
TL;DR: A unified framework for the design and the performance analysis of the algorithms for solving change detection problems and links with the analytical redundancy approach to fault detection in linear systems are established.
Abstract: This book is downloadable from http://www.irisa.fr/sisthem/kniga/. Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established. We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.

3,830 citations

Journal ArticleDOI
TL;DR: Performance of time-hopping spread-spectrum multiple-access systems employing impulse signal technology for both analog and digital data modulation formats under ideal multiple- access channel conditions is estimated.
Abstract: Attractive features of time-hopping spread-spectrum multiple-access systems employing impulse signal technology are outlined, and emerging design issues are described. Performance of such communications systems in terms of achievable transmission rate and multiple-access capability are estimated for both analog and digital data modulation formats under ideal multiple-access channel conditions.

2,693 citations

Journal Article
TL;DR: In this paper, the authors describe the EM algorithm for finding the parameters of a mixture of Gaussian densities and a hidden Markov model (HMM) for both discrete and Gaussian mixture observation models.
Abstract: We describe the maximum-likelihood parameter estimation problem and how the ExpectationMaximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) finding the parameters of a hidden Markov model (HMM) (i.e., the Baum-Welch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical rigor.

2,455 citations