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008			220226s2021 sz | s |||| 0|eng d
020			_a9783030579036
024	7		_a10.1007/978-3-030-57903-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5102.9 _b2021 EB
100	1		_aHaslwanter, Thomas _eautor _4 _4http://id.loc.gov/vocabulary/relators/aut _9682573 _d1964-
245	1	0	_aHands-on Signal Analysis with Python : _bAn Introduction _cby Thomas Haslwanter
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XVI, 267 páginas) _b 156 ilustraciones, 106 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aEngineering (SpringerNature-11647)
490	0		_aEngineering (R0) (SpringerNature-43712)
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	3		_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.
988			_aSpringer_Engineering_2021
650		7	_2embne _9150608 _aProceso de señales
650		7	_2embne _9161036 _aPython (Lenguaje de programación)
776	0	8	_iPrinted edition: _z9783030579029
776	0	8	_iPrinted edition: _z9783030579043
776	0	8	_iPrinted edition: _z9783030579050
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-57903-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eIG _zSI