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100   $a20200701d2020      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aIntelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint /$fby Mark K. Hinders
205   $a1st ed. 2020.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020.
215   $a1 online resource (XIV, 346 p. 208 illus., 143 illus. in color.) 
311 08$a3-030-49394-6 
327   $aBackground 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.
330   $aThis 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.
606   $aSignal processing
606   $aImage processing
606   $aSpeech processing systems
606   $aBiomedical engineering
606   $aMaterials science
606   $aAutomatic control
606   $aRobotics
606   $aMechatronics
606   $aComputer science
606   $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051
606   $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X
606   $aMaterials Science, general$3https://scigraph.springernature.com/ontologies/product-market-codes/Z00000
606   $aControl, Robotics, Mechatronics$3https://scigraph.springernature.com/ontologies/product-market-codes/T19000
606   $aComputer Science, general$3https://scigraph.springernature.com/ontologies/product-market-codes/I00001
615  0$aSignal processing.
615  0$aImage processing.
615  0$aSpeech processing systems.
615  0$aBiomedical engineering.
615  0$aMaterials science.
615  0$aAutomatic control.
615  0$aRobotics.
615  0$aMechatronics.
615  0$aComputer science.
615 14$aSignal, Image and Speech Processing.
615 24$aBiomedical Engineering and Bioengineering.
615 24$aMaterials Science, general.
615 24$aControl, Robotics, Mechatronics.
615 24$aComputer Science, general.
676   $a006.31
700   $aHinders$b Mark K$4aut$4http://id.loc.gov/vocabulary/relators/aut$053566
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910411936303321
996   $aIntelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint$92543440
997   $aUNINA