Explains procedures for analyzing signals and characterizing systems in civil engineering, tasks that are important in such quickly growing areas as non-destructive testing, system identification, image processing, local or remote monitoring, and smart structures. Presents step-by-step implementatio

Discrete Signals and Inverse Problems examines fundamental concepts necessary to engineers and scientists working with discrete signal processing and inverse problem solving, and places emphasis on the clear understanding of algorithms within the context of application needs. Based on the original ‘Introduction to Discrete Signals and Inverse Problems in Civil Engineering’, this expanded and enriched version: combines discrete signal processing and inverse problem solving in one book covers the most versatile tools that are needed to process engineering and scientific data presents step-by-step ‘implementation procedures’ for the most relevant algorithms provides instructive figures, solved examples and insightful exercises Discrete Signals and Inverse Problems is essential reading for experimental researchers and practicing engineers in civil, mechanical and electrical engineering, non-destructive testing and instrumentation. This book is also an excellent reference for advanced undergraduate students and graduate students in engineering and science.

The interaction phenomenon is very common between different components of a mechanical system. It is a natural phenomenon and is found with the impact force in aircraft landing; the estimation of degree of ripeness of an apple from impact on a beam; the interaction of the magnetic head of a computer disk leading to miniature development of modern c

Theories of surface waves develop since the end of XIX century and many fundamental problems like existence, phase and group velocities, attenuation (quality factor), mode conversion, etc. have been, in part successfully, solved within the framework of such simple models as ideal fluids^ or linear elasticity. However, a sufficiently complete presentation of this subject, particularly for solids, is still missing in the literature. The sole exception is the book of I. A. Viktorov^ which contains an extensive discussion of fundamental properties of surface waves in homogeneous and stratified linear elastic solids with particular emphasis on contributions of Russian scientists. Unfortunately, the book has never been translated to English and its Russian version is also hardly available. Practical applications of surface waves develop intensively since a much shorter period of time than theories even though the motivation of discoverers of surface waves such as Lord Rayleigh stems from their appearance in geophysics and seismology. Nowadays the growing interest in practical applications of surface waves stem from the following two main factors: surface waves are ideal for developing relatively cheap and convenient methods of nondestructive testing of various systems spanning from nanomaterials (e.g.

This unique book extends mechatronics to spatially distributed systems. Issues regarding remote measurements and indirect monitoring and control of distributed systems is presented in the general framework of the recently developed ill-posed inverse problems. The book starts with an overview of the main results in the inverse problem theory and continues with the presentation of basic results in discrete inverse theory. The second part presents various forward and inverse problems resulting from modeling, monitoring and controlling mechanical, acoustic, fluid and thermal systems. Finally, indirect and remote monitoring and control issues are analyzed as cases of ill-posed inverse problems. Numerous numerical examples illustrate current approaches used for solving practical inverse problems.

The term “soft computing” applies to variants of and combinations under the four broad categories of evolutionary computing, neural networks, fuzzy logic, and Bayesian statistics. Although each one has its separate strengths, the complem- tary nature of these techniques when used in combination (hybrid) makes them a powerful alternative for solving complex problems where conventional mat- matical methods fail. The use of intelligent and soft computing techniques in the field of geo- chanical and pavement engineering has steadily increased over the past decade owing to their ability to admit approximate reasoning, imprecision, uncertainty and partial truth. Since real-life infrastructure engineering decisions are made in ambiguous environments that require human expertise, the application of soft computing techniques has been an attractive option in pavement and geomecha- cal modeling. The objective of this carefully edited book is to highlight key recent advances made in the application of soft computing techniques in pavement and geo- chanical systems. Soft computing techniques discussed in this book include, but are not limited to: neural networks, evolutionary computing, swarm intelligence, probabilistic modeling, kernel machines, knowledge discovery and data mining, neuro-fuzzy systems and hybrid approaches. Highlighted application areas include infrastructure materials modeling, pavement analysis and design, rapid interpre- tion of nondestructive testing results, porous asphalt concrete distress modeling, model parameter identification, pavement engineering inversion problems, s- grade soils characterization, and backcalculation of pavement layer thickness and moduli.

Ill-posedness. Regularization. Stability. Uniqueness. To many engineers, the language of inverse analysis projects a mysterious and frightening image, an image made even more intimidating by the highly mathematical nature of most texts on the subject. But the truth is that given a sound experimental strategy, most inverse engineering problems can b