Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint
Autor Mark K. Hindersen Limba Engleză Hardback – 2 iul 2020
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.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 928.46 lei 43-57 zile | |
| Springer International Publishing – 2 iul 2021 | 928.46 lei 43-57 zile | |
| Hardback (1) | 934.33 lei 43-57 zile | |
| Springer International Publishing – 2 iul 2020 | 934.33 lei 43-57 zile |
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Specificații
ISBN-13: 9783030493943
ISBN-10: 3030493946
Ilustrații: XIV, 346 p. 208 illus., 143 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.68 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030493946
Ilustrații: XIV, 346 p. 208 illus., 143 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.68 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
Background and history.- Intelligent structural health monitoring with ultrasonic lamb waves.- Automatic detection of flaws in recorded music.- Pocket depth determination with an ultrasonographic periodontal probe.- Spectral intermezzo: Spirit security systems.- Lamb wave tomographic rays in pipes.- Classification of RFID tags with wavelet fingerprinting.- Pattern classification for interpreting sensor data from a walking-speed robot.- Cranks and charlatans and deepfakes.
Notă biografică
Professor Mark K. Hinders holds BS, MS and PhD degrees in Aerospace and Mechanical Engineering from Boston University, and is currently a Professor of Applied Science at the College of William & Mary in Virginia. Before coming to Williamsburg in 1993, Professor Hinders served as a Senior Scientist at Massachusetts Technological Laboratory, Inc., and as a Research Assistant Professor at Boston University. Before that he was an Electromagnetics Research Engineer at USAF Rome Laboratory located at Hanscom AFB, MA. Professor Hinders is currently conducting research in wave propagation and scattering phenomena as applied to medical imaging, intelligent robotics, security screening, remote sensing and nondestructive evaluation. He and his students are studying the interactions of acoustic, ultrasonic, elastic, thermal, electromagnetic and optical waves with various materials, tissues and structures. He is a founding member of the Applied Science Department and former Graduate Director and Department Chair.
Textul de pe ultima copertă
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.
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.
Caracteristici
Presents the dynamic wavelet fingerprint technique of identifying machine learning features Discusses numerous real-world applications, including in the medical, vehicle and wireless technology Structures chapters in a self-contained way, allowing for chapters to be read individually