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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.