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010   $a3-030-57903-4
024 7 $a10.1007/978-3-030-57903-6
035   $a(CKB)4100000011950074
035   $a(MiAaPQ)EBC6635037
035   $a(Au-PeEL)EBL6635037
035   $a(OCoLC)1253475720
035   $a(DE-He213)978-3-030-57903-6
035   $a(PPN)255886039
035   $a(EXLCZ)994100000011950074
100   $a20210531d2021      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aHands-on Signal Analysis with Python $eAn Introduction /$fby Thomas Haslwanter
205   $a1st ed. 2021.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021.
215   $a1 online resource (276 pages)
311   $a3-030-57902-6 
320   $aIncludes bibliographical references.
327   $aIntroduction -- Python -- Data Input -- Data Display -- Data Filtering -- Event- and Feature-Finding -- Statistics -- Parameter Fitting -- Spectral Signal Analysis -- Solving Equations of Motion -- Machine Learning -- Useful Programming Tools.
330   $aThis book provides the tools for analyzing data in Python: different types of filters are introduced and explained, such as FIR-, IIR- and morphological filters, as well as their application to one- and two-dimensional data. The required mathematics are kept to a minimum, and numerous examples and working Python programs are included for a quick start. The goal of the book is to enable also novice users to choose appropriate methods and to complete real-world tasks such as differentiation, integration, and smoothing of time series, or simple edge detection in images. An introductory section provides help and tips for getting Python installed and configured on your computer. More advanced chapters provide a practical introduction to the Fourier transform and its applications such as sound processing, as well as to the solution of equations of motion with the Laplace transform. A brief excursion into machine learning shows the powerful tools that are available with Python. This book also provides tips for an efficient programming work flow: from the use of a debugger for finding mistakes, code-versioning with git to avoid the loss of working programs, to the construction of graphical user interfaces (GUIs) for the visualization of data. Working, well-documented Python solutions are included for all exercises, and IPython/Jupyter notebooks provide additional help to get people started and outlooks for the interested reader.
606   $aSignal processing
606   $aTelecommunication
606   $aMathematics$xData processing
606   $aEngineering mathematics
606   $aEngineering$xData processing
606   $aCompilers (Computer programs)
606   $aDigital and Analog Signal Processing
606   $aSignal, Speech and Image Processing 
606   $aCommunications Engineering, Networks
606   $aComputational Science and Engineering
606   $aMathematical and Computational Engineering Applications
606   $aCompilers and Interpreters
615  0$aSignal processing.
615  0$aTelecommunication.
615  0$aMathematics$xData processing.
615  0$aEngineering mathematics.
615  0$aEngineering$xData processing.
615  0$aCompilers (Computer programs).
615 14$aDigital and Analog Signal Processing.
615 24$aSignal, Speech and Image Processing .
615 24$aCommunications Engineering, Networks.
615 24$aComputational Science and Engineering.
615 24$aMathematical and Computational Engineering Applications.
615 24$aCompilers and Interpreters.
676   $a005.133
700   $aHaslwanter$b Thomas$f1964-$01082460
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910484917703321
996   $aHands-on signal analysis with Python$92597898
997   $aUNINA