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Digital signal processing = illustration using Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Digital signal processing/ by S. Esakkirajan, T Veerakumar, Badri N Subudhi.
Reminder of title:	illustration using Python /
Author:	Esakkirajan, S.
other author:	Veerakumar, T.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xviii, 523 p. :ill., digital ;24 cm.
[NT 15003449]:	CHAPTER 1: Generation of Continuous-Time Signals -- CHAPTER 2: Sampling and Quantization of Signals -- CHAPTER 3: Generation and Operation on Discrete-Time Sequence -- CHAPTER 4: Discrete-Time Systems -- CHAPTER 5: Transforms -- CHAPTER 6: Filter Design using Pole-Zero Placement Method-CHAPTER 7: FIR Filter Design-CHAPTER 8: Infinite Impulse Response Filter-CHAPTER 9: Effect of Quantization of Filter Coefficients-CHAPTER 10: Multi-rate Signal Processing-CHAPTER 11: Adaptive Signal Processing Case Studies.
Contained By:	Springer Nature eBook
Subject:	Signal processing - Digital techniques. -
Online resource:	https://doi.org/10.1007/978-981-99-6752-0
ISBN:	9789819967520

Digital signal processing = illustration using Python /
Esakkirajan, S.

Digital signal processingillustration using Python /[electronic resource] :by S. Esakkirajan, T Veerakumar, Badri N Subudhi. - Singapore :Springer Nature Singapore :2024. - xviii, 523 p. :ill., digital ;24 cm.

CHAPTER 1: Generation of Continuous-Time Signals -- CHAPTER 2: Sampling and Quantization of Signals -- CHAPTER 3: Generation and Operation on Discrete-Time Sequence -- CHAPTER 4: Discrete-Time Systems -- CHAPTER 5: Transforms -- CHAPTER 6: Filter Design using Pole-Zero Placement Method-CHAPTER 7: FIR Filter Design-CHAPTER 8: Infinite Impulse Response Filter-CHAPTER 9: Effect of Quantization of Filter Coefficients-CHAPTER 10: Multi-rate Signal Processing-CHAPTER 11: Adaptive Signal Processing Case Studies.

Digital signal processing deals with extraction of useful information from signals. Signal processing algorithms help observe, analyse and transform signals. The objective of this book is to develop signal processing algorithms using Python. Python is an interpreted, object-oriented high-level programming language widely used in various software development fields such as data science, machine learning, web development and more. Digital Signal Laboratory is playing an important role in realizing signal processing algorithms, utilizing different software solutions. The intention of this textbook is to implement signal processing algorithms using Python. Since Python is an open-source language, students, researchers, and faculty can install and work with it without spending money, reducing the financial burden on institutions. Each chapter in this book begins with prelab questions, a set of Python examples to illustrate the concepts, exercises to strengthen the understanding of the concepts, and objective questions to help students prepare for competitive examinations. This book serves as an undergraduate textbook, it can be used for individual study, and it can also be used as the textbook for related courses.

ISBN: 9789819967520

Standard No.: 10.1007/978-981-99-6752-0doiSubjects--Topical Terms:

624853
Signal processing
--Digital techniques.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Digital signal processing = illustration using Python /
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520 $a Digital signal processing deals with extraction of useful information from signals. Signal processing algorithms help observe, analyse and transform signals. The objective of this book is to develop signal processing algorithms using Python. Python is an interpreted, object-oriented high-level programming language widely used in various software development fields such as data science, machine learning, web development and more. Digital Signal Laboratory is playing an important role in realizing signal processing algorithms, utilizing different software solutions. The intention of this textbook is to implement signal processing algorithms using Python. Since Python is an open-source language, students, researchers, and faculty can install and work with it without spending money, reducing the financial burden on institutions. Each chapter in this book begins with prelab questions, a set of Python examples to illustrate the concepts, exercises to strengthen the understanding of the concepts, and objective questions to help students prepare for competitive examinations. This book serves as an undergraduate textbook, it can be used for individual study, and it can also be used as the textbook for related courses.
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