Hands-on Signal Analysis with Python - An Introduction

Haslwanter, Thomas:

ISBN 10: 3030579026 / ISBN 13: 9783030579029

Published by Springer International Publishing, 2021

About this Item

267 Seiten 1st ed. 2021 - Text Englisch -Leichte äußere Mängel - Buch ansonsten in sehr gutem und ungelesenem Zustand - Jeder Lieferung liegt eine ordentliche Rechnung mit ausgewiesener MwSt. bei Sprache: Englisch Gewicht in Gramm: 1010.

Seller Inventory # 327837

Bibliographic Details

• Title: Hands-on Signal Analysis with Python - An Introduction
• Publisher: Springer International Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 3030579026
• ISBN 13: 9783030579029
• Binding: gebundene Ausgabe
• Condition: Sehr gut
• Weight: 1010 grams
• Seller catalogs:
  • Technik

About this title

Synopsis

This 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.

About the Author

Thomas Haslwanter is a Professor at the Department of Medical Engineering of the University of Applied Sciences Upper Austria in Linz, and lecturer at the ETH Zurich in Switzerland. He also worked as a researcher at the University of Sydney, Australia and the University of Tuebingen, Germany. He has extensive experience in medical research, with a focus on the diagnosis and treatment of vertigo and dizziness and on rehabilitation. After 15 years of extensive use of Matlab, he discovered Python, which he now uses for statistical data analysis, sound and image processing, and for biological simulation applications. He has been teaching in an academic environment for more than 10 years.