
Digital Fir Filter Design Using DSP Processor
LAP Lambert Academic Publishing
Published on 29. September 2025
Book
Paperback/Softback
76 pages
978-620-9-05069-5 (ISBN)
Description
This book is your essential guide to designing and implementing digital filters using the Texas Instruments TMS320C6713 DSP platform. This work explores a reliable solution where filters can be reconfigured with a simple program change, not a hardware overhaul.Through a combination of theoretical foundations and practical implementation, you will learn to design, test, and deploy algorithms directly on the DSK board. The book covers concepts of Adaptive Signal Processing and demonstrates the design of various Digital Filters like LPF, HPF, BPF, and BSF. Coding is done within the Code Composer Studio IDE, and the practical effect of filtering can be observed by connecting an audio input and a speaker to the DSK board.This book places a special emphasis on optimum digital filter design and Adaptive Filters, showcasing their use in real-world applications such as Active Noise Cancellation, System Identification, and Inverse System Modeling. Whether you are a student, researcher, or professional in electrical engineering, electronics, or computer science, this book provides a hands-on blueprint to unlock the full potential of digital signal processing.
More details
Language
English
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 220 mm
Width: 150 mm
Thickness: 5 mm
Weight
131 gr
ISBN-13
978-620-9-05069-5 (9786209050695)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Dr. Saurabh R. Prasad, Ph.D. in Electronics Engineering, is an Assistant Professor at DKTE Society's Textile and Engineering Institute in Ichalkaranji, India, with over 22 years of teaching and 8 years of research experience. His expertise includes Signal Processing, Microwave Engineering, Artificial Intelligence, and Machine Learning.