The authors study dynamical effects of incident compressional and distortional elastic waves on a layer of planar, cylindrical, or spherical geometry, especially focusing on the stress fields surrounding the layer. These results are derived from the exact solutions for elastic wave scattering from such interfaces developped in the first part of the book. Comparisons of numerical solutions of special problems with the analytical solutions are given and it is shown how the latter help to simplify the numerical treatment. The material presented in this monograph will help in developing composite materials with improved chemical and physical properties and in non-destructive testing of such materials. Engineers, physicists, and workers in applied mathematics will welcome this well written text. It may also be used for additional reading in a course on elasto-mechanics.
This book discusses various applications of machine learning using a new approach, the dynamic wavelet fingerprint technique, to identify features for machine learning and pattern classification in time-domain signals. Whether for medical imaging or structural health monitoring, it develops analysis techniques and measurement technologies for the quantitative characterization of materials, tissues and structures by non-invasive means. Intelligent Feature Selection for Machine Learning using the Dynamic Wavelet Fingerprint begins by providing background information on machine learning and the wavelet fingerprint technique. It then progresses through six technical chapters, applying the methods discussed to particular real-world problems. Theses chapters are presented in such a way that they can be read on their own, depending on the reader’s area of interest, or read together to provide a comprehensive overview of the topic. Given its scope, the book will be of interest to practitioners, engineers and researchers seeking to leverage the latest advances in machine learning in order to develop solutions to practical problems in structural health monitoring, medical imaging, autonomous vehicles, wireless technology, and historical conservation.
Mobile robots require the ability to make decisions such as "go through the hedges" or "go around the brick wall." Mobile Robot Navigation with Intelligent Infrared Image Interpretation describes in detail an alternative to GPS navigation: a physics-based adaptive Bayesian pattern classification model that uses a passive thermal infrared imaging system to automatically characterize non-heat generating objects in unstructured outdoor environments for mobile robots. The resulting classification model complements an autonomous robot’s situational awareness by providing the ability to classify smaller structures commonly found in the immediate operational environment.
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