Intelligent Feature Selection For Machine Learning Using The Dynamic Wavelet Fingerprint
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Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint
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.
Wavelets In Soft Computing (Second Edition)
The comprehensive compendium furnishes a quick and efficient entry point to many multiresolution techniques and facilitates the transition from an idea into a real project. It focuses on methods combining several soft computing techniques (fuzzy logic, neural networks, genetic algorithms) in a multiresolution framework.Illustrated with numerous vivid examples, this useful volume gives the reader the necessary theoretical background to decide which methods suit his/her needs.New materials and applications for multiresolution analysis are added, including notable research topics such as deep learning, graphs, and network analysis.
A Data Engineering Approach to Wave Scattering Analysis with Applications in Radar, Sonar, Medical Diagnostics, Structural Flaw Detection and Intelligent Robotics
Comprehensive resource exploring how recent advancements in computational capabilities open doors to new applications in wave scattering A Data Engineering Approach to Wave Scattering Analysis applies scattering analysis to many applications including radar, sonar, medical diagnosis, intelligent robotics, and more, enabling readers to implement new and better measurements with both novel instrumentation and artificial intelligence that automates the interpretation of various (and multiple) imaging data streams. Composed of 10 chapters, this book brings together separate scientific topics that share a common basis of knowledge and their unchanged mathematical techniques to ensure successful results. Through periodic exercises, this book reinforces the importance of revisiting derivations and reproducing established results. It also delves into the individuals who shaped scientific methods and technologies, exploring 81 notable names and providing insights into their professional journeys. Classic results from scattering are included in each chapter, and rather than simply pasting in plots from classic papers, these results have largely been reproduced for a more coherent reader experience. Written by an established academic in the field, A Data Engineering Approach to Wave Scattering Analysis includes information on various topics: Field equations, covering strain as a dimensionless measure of deformation, generalized Hooke's Law, and elastic and acoustic waves Reflection and refraction, covering reflection from a free surface and surface waves as well as the wave model of acoustic microscopy Guided waves, covering torsional modes, longitudinal waves, and flexural waves in rods, as well as data engineering for lamb wave tomography Inverse scattering, covering wavelet transforms and fingerprinting as well as applications of wavelet fingerprints such as roof fall detection A Data Engineering Approach to Wave Scattering is an essential up-to-date reference on the subject for researchers interested in radar, sonar, medical imaging, structural health monitoring, manufacturing process control, and autonomous vehicles, as well as upper-level undergraduates and graduate students in related programs of study.