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020			_a9783662642153
024	7		_a10.1007/978-3-662-64215-3 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
050		4	_aQA280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
072		7	_aPBT _2thema
090			_aQA280EBK
100	1		_aDeppe, Sahar. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aDiscovery of Ill–Known Motifs in Time Series Data _h[electronic resource] / _cby Sahar Deppe.
250			_a1st ed. 2022.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer Vieweg, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aTechnologien für die intelligente Automation, Technologies for Intelligent Automation, _x2522-8587 ; _v15
505	0		_aIntroduction -- Preliminaries -- General Principles of Time Series Motif Discovery -- State of the Art in Time Series Motif Discovery -- Distortion-Invariant Motif Discovery -- Evaluation -- Conclusion and Outlook -- Appendices A-D.
520			_aThis book includes a novel motif discovery for time series, KITE (ill-Known motIf discovery in Time sEries data), to identify ill-known motifs transformed by affine mappings such as translation, uniform scaling, reflection, stretch, and squeeze mappings. Additionally, such motifs may be covered with noise or have variable lengths. Besides KITE’s contribution to motif discovery, new avenues for the signal and image processing domains are explored and created. The core of KITE is an invariant representation method called Analytic Complex Quad Tree Wavelet Packet transform (ACQTWP). This wavelet transform applies to motif discovery as well as to several signal and image processing tasks. The efficiency of KITE is demonstrated with data sets from various domains and compared with state-of-the-art algorithms, where KITE yields the best outcomes. The Author Sahar Deppe studied Electrical Engineering and Information Technology at Halmstad University (Halmstad, Sweden) and the OWL University of Applied Sciences and Arts (Lemgo, Germany), where she received her Master degree. From 2013 to 2020 she was employed at the Institute Industrial IT (inIT) as a research associate and during this time she completed her doctorate (Dr. rer. nat.) in cooperative graduation with Paderborn University. Since 2020 she is employed at the Fraunhofer Institute IOSB-INA as a research associate with project management responsibilities. In her dissertation, she proposed a novel method to detect motifs in time series data based on mathematical theories suited to represent and handle ill-known motifs such as invariant theory and theories in signal processing such as wavelet theory. Her research interests include but are not limited to the area of motif discovery and time series analysis, pattern recognition, and machine learning. She has published and presented her research at numerous conferences and journals such as IEEE, IARIA, PESARO where she got the best paper award for her research in motif discovery in image data.
650		0	_aTime-series analysis.
650		0	_aSignal processing.
650		0	_aImage processing.
650	1	4	_aTime Series Analysis.
650	2	4	_aDigital and Analog Signal Processing.
650	2	4	_aImage Processing.
710	2		_aSpringerLink (Online service)
830		0	_aTechnologien für die intelligente Automation, Technologies for Intelligent Automation, _x2522-8587 ; _v15
856	4	0	_uhttps://doi.org/10.1007/978-3-662-64215-3 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK