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020 _a9783030579036
_9978-3-030-57903-6
024 7 _a10.1007/978-3-030-57903-6
_2doi
050 4 _aTK5102.9
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082 0 4 _a621.3822
_223
100 1 _aHaslwanter, Thomas.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_950261
245 1 0 _aHands-on Signal Analysis with Python
_h[electronic resource] :
_bAn Introduction /
_cby Thomas Haslwanter.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXVI, 267 p. 156 illus., 106 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 _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.
520 _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.
650 0 _aSignal processing.
_94052
650 0 _aTelecommunication.
_910437
650 0 _aMathematics—Data processing.
_931594
650 0 _aEngineering mathematics.
_93254
650 0 _aEngineering—Data processing.
_931556
650 0 _aCompilers (Computer programs).
_93350
650 1 4 _aDigital and Analog Signal Processing.
_936907
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aComputational Science and Engineering.
_950262
650 2 4 _aMathematical and Computational Engineering Applications.
_931559
650 2 4 _aCompilers and Interpreters.
_931853
710 2 _aSpringerLink (Online service)
_950263
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030579029
776 0 8 _iPrinted edition:
_z9783030579043
776 0 8 _iPrinted edition:
_z9783030579050
856 4 0 _uhttps://doi.org/10.1007/978-3-030-57903-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c78554
_d78554