000 03929nam a22006135i 4500
001 978-3-030-49395-0
003 DE-He213
005 20220801214811.0
007 cr nn 008mamaa
008 200701s2020 sz | s |||| 0|eng d
020 _a9783030493950
_9978-3-030-49395-0
024 7 _a10.1007/978-3-030-49395-0
_2doi
050 4 _aTK5102.9
072 7 _aTJF
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aTJF
_2thema
072 7 _aUYS
_2thema
082 0 4 _a621.382
_223
100 1 _aHinders, Mark K.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_940538
245 1 0 _aIntelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint
_h[electronic resource] /
_cby Mark K. Hinders.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXIV, 346 p. 208 illus., 143 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aSignal processing.
_94052
650 0 _aBiomedical engineering.
_93292
650 0 _aMaterials—Analysis.
_940539
650 0 _aControl engineering.
_931970
650 0 _aRobotics.
_92393
650 0 _aAutomation.
_92392
650 0 _aComputer science.
_99832
650 1 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aBiomedical Engineering and Bioengineering.
_931842
650 2 4 _aMaterials Characterization Technique.
_933115
650 2 4 _aControl, Robotics, Automation.
_931971
650 2 4 _aComputer Science.
_99832
710 2 _aSpringerLink (Online service)
_940540
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030493943
776 0 8 _iPrinted edition:
_z9783030493967
776 0 8 _iPrinted edition:
_z9783030493974
856 4 0 _uhttps://doi.org/10.1007/978-3-030-49395-0
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c76765
_d76765