About this paper

Appears in:
Pages: 6376-6383
Publication year: 2018
ISBN: 978-84-697-9480-7
ISSN: 2340-1079
doi: 10.21125/inted.2018.1502

Conference name: 12th International Technology, Education and Development Conference
Dates: 5-7 March, 2018
Location: Valencia, Spain

PROGRAMMING ENVIRONMENTS FOR LEARNING DIGITAL SIGNAL PROCESSING

I.B. Pavaloiu, M. Raducanu, I. Petrescu, G. Dragoi

University POLITEHNICA of Bucharest (ROMANIA)
Digital Signal Processing (DSP) represents the processing of signals in digital form. While many times it is a convenient way to deal with the natural analog signals, its use has been generalized in the last years along with observations of economic, social, biologic systems which proffer numerical data. The generalized use of DSP made it a subject that exceeds the studies in electronics engineering, where it is used for many years. We will describe in this paper some of the most important software products used to learn DSP in the laboratory by the undergraduate students. We will focus on design, analysis and application of digital filters and we will compare the results obtained using Matlab, LabVIEW and Python. Matlab (MATrix LABoratory) is a very popular software package built by Mathworks around the philosophy to operate primarily at matrices and arrays level. Programs (script files) can be written or commands entered interactively, and the graphics are very good. It has a number of "toolboxes", collections of routines dedicated to particular domains and one of them handles authoritatively the Signal Processing domain. LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench) is a graphical programming environment developed by National Instruments. LabVIEW-subroutines are termed Virtual Instruments (VIs) and the execution flow is given by a graphical block diagram. LabVIEW is the interface of choice for a large part of data acquisition hardware and this support its use for DSP. Python is an interpreted programming language with accent on the readability and the simplicity of the code. Scipy, a large library containing open-source software for mathematics, science and engineering, includes scipy.signal, a dedicated signal processing toolbox. The popularity of Python as a coding language has a positive effect on its use in a multitude of domains. The paper will present the strong and the delicate points of the above mentioned environments for the practical study of DSP in the laboratory and our considerations regarding this subject.
@InProceedings{PAVALOIU2018PRO,
author = {Pavaloiu, I.B. and Raducanu, M. and Petrescu, I. and Dragoi, G.},
title = {PROGRAMMING ENVIRONMENTS FOR LEARNING DIGITAL SIGNAL PROCESSING},
series = {12th International Technology, Education and Development Conference},
booktitle = {INTED2018 Proceedings},
isbn = {978-84-697-9480-7},
issn = {2340-1079},
doi = {10.21125/inted.2018.1502},
url = {http://dx.doi.org/10.21125/inted.2018.1502},
publisher = {IATED},
location = {Valencia, Spain},
month = {5-7 March, 2018},
year = {2018},
pages = {6376-6383}}
TY - CONF
AU - I.B. Pavaloiu AU - M. Raducanu AU - I. Petrescu AU - G. Dragoi
TI - PROGRAMMING ENVIRONMENTS FOR LEARNING DIGITAL SIGNAL PROCESSING
SN - 978-84-697-9480-7/2340-1079
DO - 10.21125/inted.2018.1502
PY - 2018
Y1 - 5-7 March, 2018
CI - Valencia, Spain
JO - 12th International Technology, Education and Development Conference
JA - INTED2018 Proceedings
SP - 6376
EP - 6383
ER -
I.B. Pavaloiu, M. Raducanu, I. Petrescu, G. Dragoi (2018) PROGRAMMING ENVIRONMENTS FOR LEARNING DIGITAL SIGNAL PROCESSING, INTED2018 Proceedings, pp. 6376-6383.
User:
Pass: