http://library.utia.cas.cz/prace/20030125.pdf

Image registration methods: a survey

There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

Robust Model-Based Fault Diagnosis for Dynamic Systems

1 Introduction to Wavelets 2 A Multiresolution Formulation of Wavelet Systems 3 Filter Banks and the Discrete Wavelet Transform 4 Bases, Orthogonal Bases, Biorthogonal Bases, Frames, Tight Frames, and Unconditional Bases 5 The Scaling Function and Scaling Coefficients, Wavelet and Wavelet Coefficients 6 Regularity, Moments, and Wavelet System Design 7 Generalizations of the Basic Multiresolution Wavelet System 8 Filter Banks and Transmultiplexers 9 Calculation of the Discrete Wavelet Transform 10 Wavelet-Based Signal Processing and Applications 11 Summary Overview 12 References Bibliography Appendix A Derivations for Chapter 5 on Scaling Functions Appendix B Derivations for Section on Properties Appendix C Matlab Programs Index

Introduction to Wavelets and Wavelet Transforms: A Primer

We survey the quadratic eigenvalue problem, treating its many applications, its mathematical properties, and a variety of numerical solution techniques. Emphasis is given to exploiting both the structure of the matrices in the problem (dense, sparse, real, complex, Hermitian, skew-Hermitian) and the spectral properties of the problem. We classify numerical methods and catalogue available software.

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The Quadratic Eigenvalue Problem

Numerical methods are described for determining robust, or well-conditioned, solutions to the problem of pole assignment by state feedback. The solutions obtained are such that the sensitivity of the assigned poles to perturbations in the system and gain matrices is minimized. It is shown that for these solutions, upper bounds on the norm of the feedback matrix and on the transient response are also minimized and a lower bound on the stability margin is maximized. A measure is derived which indicates the optimal conditioning that may be expected for a particular system with a given set of closed-loop poles, and hence the suitability of the given poles for assignment.

Robust pole assignment in linear state feedback

Feature point (FP) detection is an important pre- processing step in image registration, data fusion, objec- t recognition and in many other tasks. This paper deals with multiframe FP detection, i.e. detection in two or more images of the same scene which are supposed to be blurred, noisy, rotated and shifted with respect to each other. We present a new method invariant under rotation that can handle differ- ently blurred images. Thanks to this, the point sets extracted from different frames have relatively high number of common elements. This property is highly desirable for further multi- frame processing. The performance of the method is demon- strated experimentally on satellite images and application on medical data is

/pdf/feature-point-detection-in-multiframe-images-3zmffndb4v.pdf

Feature point detection in multiframe images

A class of so-called shift products of wavelet matrices is introduced. These products are based on circulations of columns of orthogonal banded block circulant matrices arising in applications of discrete orthogonal wavelet transforms (or paraunitary multirate filter banks) or, equivalently, on augmentations of wavelet matrices by zero columns (shifts). A special case is no shift; a product which is closely related to the Pollen product is then obtained. Known decompositions using factors formed by two blocks are described and additional conditions such that uniqueness of the factorization is guaranteed are given. Next it is shown that when nonzero shifts are used, an arbitrary wavelet matrix can be factorized into a sequence of shift products of square orthogonal matrices. Such a factorization, as well as those mentioned earlier, can be used for the parameterization and construction of wavelet matrices, including the costruction from the first row. Moreover, it is also suitable for efficient implementations of discrete orthogonal wavelet transforms and paraunitary filter banks.

Shift products and factorizations of wavelet matrices

Robust multiple eigenvalue assignment by state feedback in linear systems

It is shown that given a one-dimensional interpolatory integration rule with positive weights based on a set S of n points, it is possible to find a subset T of S containing n-1 points such that the weights of the interpolatory integration rule based on T are all nonnegative. Consequently, given any one-dimensional positive interpolatory integration rule R, we can generate a sequence of imbedded positive interpolatory rules starting with R. This idea of starting with a rule R and generating a sequence of imbedded rules, which is due to Patterson, can be applied to generate sequences of imbedded multidimensional integration rules. In this case, we do not aim for positive rules but for efficient rules. Some examples are given as to what can be attained.

Jaroslav Kautsky

Papers

Feature point detection in multiframe images

Shift products and factorizations of wavelet matrices

Robust multiple eigenvalue assignment by state feedback in linear systems

On Sequences of Imbedded Integration Rules

Rotation of 2D orthogonal polynomials