Introduction. 1. Two Dimensional Systems and Mathematical Preliminaries. 2. Image Perception. 3. Image Sampling and Quantization. 4. Image Transforms. 5. Image Representation by Stochastic Models. 6. Image Enhancement. 7. Image Filtering and Restoration. 8. Image Analysis and Computer Vision. 9. Image Reconstruction From Projections. 10. Image Data Compression.

Fundamentals of digital image processing

A summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented in this tutorial. An examination of the underlying time series model assumed by each technique serves as the common basis for understanding the differences among the various spectrum analysis approaches. Techniques discussed include the classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods. A summary table in the text provides a concise overview for all methods, including key references and appropriate equations for computation of each spectral estimate.

Spectrum analysis&#8212;A modern perspective

Adaptive Signal Processing

1. Basic Concepts. 2. Nonparametric Methods. 3. Parametric Methods for Rational Spectra. 4. Parametric Methods for Line Spectra. 5. Filter Bank Methods. 6. Spatial Methods. Appendix A: Linear Algebra and Matrix Analysis Tools. Appendix B: Cramer-Rao Bound Tools. Appendix C: Model Order Selection Tools. Appendix D: Answers to Selected Exercises. Bibliography. References Grouped by Subject. Subject Index.

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Spectral analysis of signals

Introduction to linear and nonlinear programming

New distributed forms for optimal estimation and detection problems that arise for instance in a distributed sensor network (DSN) are proposed. One of the prime motivations in a DSN is to try to minimize the communication load between different sites, This is achieved by preprocessing or summarizing the observations locally, e.g., at the sensor location, thus geographically "distributing" or "decentralizing" the algorithm. A batch method is described, as well as an on-line or real-time algorithm when sensor parameters are a priori unknown. After sensor failure optimality is preserved under these new constraints.

Distributed processing in estimation and detection

Recently, various fast "doubling" procedures have been sketched or developed for the inversion of matrices with low shift-rank, e.g., Toeplitz matrices. The subclass of symmetric positive definite matrices is of particular interest in linear estimation, these matrices having the interpretation of covariances of finite shift-rank processes. This paper describes a doubling procedure for such covariance matrices. The procedure evaluates in O(n log2n) operations, both the inverse of an order n covariance and an associated set of parameters of great importance in linear filtering (the reflection coefficients if the covariance is Toeplitz).

Normalized doubling algorithms for finite shift-rank processes

A unified derivation for fast estimation algorithms by the conjugate direction method

This note provides a connection between the (block) Levinson algorithm and the conjugate direction method (CDM). We first present an extension of the scalar CDM to the block matrix case. Then we show how the (block) Levinson recursions can be derived as a special case of the (extended) CDM. This gives a new derivation and interpretation for Levinson's algorithm.

http://www.researchgate.net/profile/Arye_Nehorai/publication/3177275_A_relationship_between_the_Levinson_algorithm_and_the_conjugate_direction_method/links/02e7e5294b638a6c6a000000.pdf

A relationship between the Levinson algorithm and the conjugate direction method

A decision-directed receiver uses the estimate of the rate of the discrete-time point process (DTPP) output of the (0-1) detector as the unknown prior probability of the Bernoulli input sequence. This correspondence applies the results of Segall [5] to permit the estimation of a time-varying rate.

https://ieeexplore.ieee.org/iel5/9/24207/01103404.pdf

Martin Morf

Papers

Distributed processing in estimation and detection

Normalized doubling algorithms for finite shift-rank processes

A unified derivation for fast estimation algorithms by the conjugate direction method

A relationship between the Levinson algorithm and the conjugate direction method

A new decision-directed algorithm for nonstationary priors