Author

# John G. Proakis

Other affiliations: Northeastern University

Bio: John G. Proakis is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Communication channel & Orthogonal frequency-division multiplexing. The author has an hindex of 37, co-authored 137 publications receiving 13806 citations. Previous affiliations of John G. Proakis include Northeastern University.

##### Papers published on a yearly basis

##### Papers

More filters

•

01 Jan 1992TL;DR: This paper presents a meta-analysis of the Z-Transform and its application to the Analysis of LTI Systems, and its properties and applications, as well as some of the algorithms used in this analysis.

Abstract: 1. Introduction. 2. Discrete-Time Signals and Systems. 3. The Z-Transform and Its Application to the Analysis of LTI Systems. 4. Frequency Analysis of Signals and Systems. 5. The Discrete Fourier Transform: Its Properties and Applications. 6. Efficient Computation of the DFT: Fast Fourier Transform Algorithms. 7. Implementation of Discrete-Time Systems. 8. Design of Digital Filters. 9. Sampling and Reconstruction of Signals. 10. Multirate Digital Signal Processing. 11. Linear Prediction and Optimum Linear Filters. 12. Power Spectrum Estimation. Appendix A. Random Signals, Correlation Functions, and Power Spectra. Appendix B. Random Numbers Generators. Appendix C. Tables of Transition Coefficients for the Design of Linear-Phase FIR Filters. Appendix D. List of MATLAB Functions. References and Bibliography. Index.

3,911 citations

•

01 Mar 1993

TL;DR: The preface to the IEEE Edition explains the background to speech production, coding, and quality assessment and introduces the Hidden Markov Model, the Artificial Neural Network, and Speech Enhancement.

Abstract: Preface to the IEEE Edition. Preface. Acronyms and Abbreviations. SIGNAL PROCESSING BACKGROUND. Propaedeutic. SPEECH PRODUCTION AND MODELLING. Fundamentals of Speech Science. Modeling Speech Production. ANALYSIS TECHNIQUES. Short--Term Processing of Speech. Linear Prediction Analysis. Cepstral Analysis. CODING, ENHANCEMENT AND QUALITY ASSESSMENT. Speech Coding and Synthesis. Speech Enhancement. Speech Quality Assessment. RECOGNITION. The Speech Recognition Problem. Dynamic Time Warping. The Hidden Markov Model. Language Modeling. The Artificial Neural Network. Index.

2,761 citations

•

01 Jan 1994TL;DR: This book discusses Elements of an Electrical Communication System, a manual for the design of Communication Channels and their Characteristics, and Random Processes: Basic Concepts, which describes random processes in the Frequency Domain.

Abstract: (NOTE: Each chapter concludes with Further Reading and Problems.) 1. Introduction. Historical Review. Elements of an Electrical Communication System. Communication Channels and Their Characteristics. Mathematical Models for Communication Channels. Organization of the Book. 2. Frequency Domain Analysis of Signals and Systems. Fourier Series. Fourier Transforms. Power and Energy. Sampling of Bandlimited Signals. Bandpass Signals. 3. Analog Signal Transmission and Reception. Introduction to Modulation. Amplitude Modulation (AM). Angle Modulation. Radio and Television Broadcasting. Mobile Radio Stations. 4. Random Processes. Probability and Random Variables. Random Processes: Basic Concepts. Random Processes in the Frequency Domain. Gaussian and White Processes. Bandlimited Processes and Sampling. Bandpass Processes. 5. Effect of Noise on Analog Communication Systems. Effect of Noise on Linear-Modulation Systems. Carrier-Phase Estimation with a Phase-Locked Loop (PLL). Effect of Noise on Angle Modulation. Comparison of Analog-Modulation Systems. Effects of Transmission Losses and Noise in Analog Communication Systems. 6. Information Sources and Source Coding. Modeling of Information Sources. Source-Coding Theorem. Source-Coding Algorithms. Rate-Distortion Theory. Quantization. Waveform Coding. Analysis-Synthesis Techniques. Digital Audio Transmission and Digital Audio Recording. The JPEG Image-Coding Standard. 7. Digital Transmission through the Additive White Gaussian Noise Channel. Geometric Representation of Signal Waveforms. Pulse Amplitude Modulation. Two-Dimensional Signal Waveforms. Multidimensional Signal Waveforms. Optimum Receiver for Digitally Modulated Signals in Additive White Gaussian Noise. Probability of Error for Signal Detection in Additive White Gaussian Noise. Performance Analysis for Wireline and Radio Communication Channels. Symbol Synchronization. 8. Digital Transmission through Bandlimited AWGN Channels. Digital Transmission through Bandlimited Channels. The Power Spectrum of Digitally Modulated Signals. Signal Design for Bandlimited Channels. Probability of Error in Detection of Digital PAM. Digitally Modulated Signals with Memory. System Design in the Presence of Channel Distortion. Multicarrier Modulation and OFDM. 9. Channel Capacity and Coding. Modeling of Communication Channels. Channel Capacity. Bounds on Communication. Coding for Reliable Communication. Linear Block Codes. Cyclic Codes. Convolutional Codes. Complex Codes Based on Combination of Simple Codes. Coding for Bandwidth-Constrained Channels. Practical Applications of Coding. 10. Wireless Communications. Digital Transmission on Fading Multipath Channels. Continuous Carrier-Phase Modulation. Spread-Spectrum Communication Systems. Digital Cellular Communication Systems. Appendix A: The Probability of Error for Multichannel Reception of Binary Signals. References. Index.

1,029 citations

•

01 Jan 1996

785 citations

•

01 Jan 1988

TL;DR: This book progresses rapidly through the fundamentals to advanced topics such as iterative least squares design of IIR filters, inverse filters, power spectral estimation, and multidimensional applications--all in one concise volume.

Abstract: An Introduction to Digital Signal Processing is written for those who need to understand and use digital signal processing and yet do not wish to wade through a multi-semester course sequence. Using only calculus-level mathematics, this book progresses rapidly through the fundamentals to advanced topics such as iterative least squares design of IIR filters, inverse filters, power spectral estimation, and multidimensional applications--all in one concise volume.

552 citations

##### Cited by

More filters

••

01 May 2005TL;DR: In this paper, several fundamental key aspects of underwater acoustic communications are investigated and a cross-layer approach to the integration of all communication functionalities is suggested.

Abstract: Underwater sensor nodes will find applications in oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. Moreover, unmanned or autonomous underwater vehicles (UUVs, AUVs), equipped with sensors, will enable the exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area. In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the characteristics of the underwater channel are detailed. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed and a cross-layer approach to the integration of all communication functionalities is suggested. Furthermore, open research issues are discussed and possible solution approaches are outlined. � 2005 Published by Elsevier B.V.

2,864 citations

••

TL;DR: This paper considers four different sets of allowed distortions in blind audio source separation algorithms, from time-invariant gains to time-varying filters, and derives a global performance measure using an energy ratio, plus a separate performance measure for each error term.

Abstract: In this paper, we discuss the evaluation of blind audio source separation (BASS) algorithms. Depending on the exact application, different distortions can be allowed between an estimated source and the wanted true source. We consider four different sets of such allowed distortions, from time-invariant gains to time-varying filters. In each case, we decompose the estimated source into a true source part plus error terms corresponding to interferences, additive noise, and algorithmic artifacts. Then, we derive a global performance measure using an energy ratio, plus a separate performance measure for each error term. These measures are computed and discussed on the results of several BASS problems with various difficulty levels

2,855 citations

••

TL;DR: The EM (expectation-maximization) algorithm is ideally suited to problems of parameter estimation, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation.

Abstract: A common task in signal processing is the estimation of the parameters of a probability distribution function Perhaps the most frequently encountered estimation problem is the estimation of the mean of a signal in noise In many parameter estimation problems the situation is more complicated because direct access to the data necessary to estimate the parameters is impossible, or some of the data are missing Such difficulties arise when an outcome is a result of an accumulation of simpler outcomes, or when outcomes are clumped together, for example, in a binning or histogram operation There may also be data dropouts or clustering in such a way that the number of underlying data points is unknown (censoring and/or truncation) The EM (expectation-maximization) algorithm is ideally suited to problems of this sort, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation The EM algorithm is presented at a level suitable for signal processing practitioners who have had some exposure to estimation theory

2,573 citations

••

TL;DR: The new effect of phase synchronization of weakly coupled self-sustained chaotic oscillators is presented, and a relation between the phase synchronization and the properties of the Lyapunov spectrum is studied.

Abstract: We present the new effect of phase synchronization of weakly coupled self-sustained chaotic oscillators. To characterize this phenomenon, we use the analytic signal approach based on the Hilbert transform and partial Poincar\'e maps. For coupled R\"ossler attractors, in the synchronous regime the phases are locked, while the amplitudes vary chaotically and are practically uncorrelated. Coupling a chaotic oscillator with a hyperchaotic one, we observe another new type of synchronization, where the frequencies are entrained, while the phase difference is unbounded. A relation between the phase synchronization and the properties of the Lyapunov spectrum is studied.

2,424 citations