Title of script: Signal processing Using Scilab
|Slide 1|| Dear Friends,
Welcome to the spoken tutorial on “Signal Processing using Scilab”
|Slide 2||Before starting with this tutorial, first we will see the Knowledge prerequisite and software requirement to use this tutorial.|
|Slide 2 & 3|| Ubuntu 11.04 is the operating system used in this tutorial with Scilab 5.3.3
| Scilab can be installed on Ubuntu OS using the Synaptic Package Manager.
|To install Scilab, please refer to the tutorial 'How to install Scilab in Ubuntu using Synaptic Package Manager' available on spoken-tutorial.org|
|Slide 4|| Prerequisite:Listen to basic Level tutorials in
|Slide 5|| What is a Signal?
A signal is any time-varying or spatial-varying quantity.
|Slide 6|| Objective of this tutorial-
In this tutorial i will show the operations on or analysis of signals, in either discrete or continuous time domain.
|Slide 7|| In this slide i will be describing Signal Basics
|Demonstrate|| Let us start first plotting continuous sine wave. This can be done as follows:
t = 0:0.1:2*%pi;
x = sin(t);
|Demonstrate|| A discrete sine wave can be plotted as :
|Demonstrate||I have written the codes to generate step and ramp signal in the file signals.sce. I will execute it and show how the signals are being generated.|
|Slide 8|| In this slide let us learn Convolution
i.e. Linear convolution of two vectors by using the inbuilt command convol()
|Demonstrate|| First step is to take an discrete input signal x=[1,2 ,3,4]
Second step is to take an another input signal h=[1,1,1]
And the the third step is to apply the convolution using convol()
|Slide 9||In this slide we will learn Discrete fourier transform for a discrete sequence by using the inbuilt command dft().|
|Demonstrate|| Calling sequence to calculate DFT is
[xf]=dft(x,flag); Where x is the input vector and flag value is -1 for DFT.
Lets take the input vector is x=[1,2,3,4]
The output dft will be calculated by xf=dft(x,-1)
- 2. + 2.i
- 2. - 9.797D-16i
- 2. - 2.i
|Slide 10||In this slide i will be showing how to calculate inverse discrete fourier transform. This can be found by using the same inbuilt command dft().|
|Demonstrate|| Here the calling sequence for this function is defined.
Here the flag value is 1 for idft.
|Demonstrate|| Let the input sequence xf = [10,-2+2*%i,-2,-2-2*%i]
The output is given by x = dft(y,1)
2. + 5.551D-17i
3. - 1.225D-16i
4. - 5.551D-16i
|Slide 11||In this slide lets have a look how to calculate discrete fourier transform and inverse discrete fourier transform by using the inbuilt function fft().|
|Demonstrate|| I will be calculating dft using fft:
Let the input vector is x= [1,2,3,4];
The output is given by y = fft(x,-1)
|Demonstrate|| I will be calculating idft using fft:
x = fft(y,1)
|Slide 12||In this slide we will be using corr() to find the correlation between two vectors.|
|Demonstrate|| Lets take two input signals are
x1 = [1,2,3,4]; and x2 = [1,3,1,5];
The cross correlation can be found by
Rx1x2 = corr(x1,x2,4)
|Slide 13||In this slide let us show Sampling of a given signal using intdec ()|
|Demonstrate|| The calling sequence for sampling is
I have written the code in sampling.sce and will execute it by clicking on the file.
|I will summarize the things we learnt in this tutorial.|
|Slide 14||In this tutorial we learnt|
|Slide 14||Signal basics and how to plot continuous and discrete sine wave,step and ramp signal.|
|Slide 15||Linear convolution of two vectors by using the inbuilt command convol()|
|Slide 16||Discrete fourier transform and inverse fourier transform for a discrete sequence by using the inbuilt command dft().|
|Slide 17||FFT by fft()|
|Slide 18||Correlation between two signals by using inbuilt command corr().|
|Slide 19||Sampling of a given signal using intdec().|
|Slide 20|| Spoken Tutorial is a part of the
Talk to a Teacher project
| It is supported by the National Mission on
Education through ICT (NMEICT),
MHRD, Government of India
| More information on this mission is
|Slide 21,22 & 23|| About the Spoken Tutorial Project
and watch it.
|Slide 24,25 & 26|| Acknowledgement
|Slide 27,28 & 29|| Acknowledgement
|Slide 30,31 & 32|| * Thanks for joining.
|Hope you found this Spoken Tutorial useful.|
| This script has been contributed by Manas Das.
This is Anuradha Amrutkar from IIT Bombay, signing off
|Thanks for joining us,Good Bye|