MATLAB
1. Problem statement
a) Write the function discrete_pulse_train() to have the following function prototype:
function [x,n] = discrete_pulse_train(W,M,Np,D,N)
% [x,n] = discrete_pulse_train(W,M,Np,D,N)
% Generate a discrete-time pulse train
%================ Inputs =========================================
% W = pulse width in samples
% M = period in samples, M > W
% Np = Number of periods to create (if 0 full out to N samples)
% D = turn-on delay in samples of the first period
% N = total number of samples to create
%================ Outputs ========================================
% x = signal vector (a row vector)
% n = integer time axis corresponding to x
%////////////////////////////////////////////////////////////////
An example plot from this function obtained using the command line
>> [x,n] = discrete_pulse_train(5,18,4,13,200);
Show entire document
1. Problem statement
a) Write the function discrete_pulse_train() to have the following function prototype:
function [x,n] = discrete_pulse_train(W,M,Np,D,N)
% [x,n] = discrete_pulse_train(W,M,Np,D,N)
% Generate a discrete-time pulse train
%================ Inputs =========================================
% W = pulse width in samples
% M = period in samples, M > W
% Np = Number of periods to create (if 0 full out to N samples)
% D = turn-on delay in samples of the first period
% N = total number of samples to create
%================ Outputs ========================================
% x = signal vector (a row vector)
% n = integer time axis corresponding to x
%////////////////////////////////////////////////////////////////
An example plot from this function obtained using the command line
>> [x,n] = discrete_pulse_train(5,18,4,13,200);
MATLAB Project 1
This project is to be treated as a take-home exam, meaning each student is to due his/her
own work. To work the project you will need access to M
ATLAB
and at minimum the
signal processing toolbox.
Introduction
This first MATLAB DSP project will get you acquainted/reacquainted with M
ATLAB
and
then move into the exploration of
• Discrete-time signal generation
• Sequence convolution and the convolution sum
• Linear constant coefficient difference equations (LCCDEs)
• The discrete-time Fourier transform (DTFT)
A Quick M
ATLAB
Review Recall that M
ATLAB
stands for matrix laboratory, so it goes
without saying that M
ATLAB
is very efficient at doing matrix oriented numerical
calculations. M
ATLAB
supports many data types, but the two data types we will work with
the most, are scalar numbers (real and complex), and matrices (real and complex). For
our signal processing needs the matrices we create will most often be vectors, that is
matrices of dimension
1
×
m
or
n
×
1
. The default data type for real and complex numbers
is double precision. Complex numbers are handled transparently in M
ATLAB
, so there is
no need to worry about these details, for the most part. In fact, when M
ATLAB
starts-up, it
has both
i
and
j
defined as
1
-
, i.e., at the command window, as shown in Figure 1,
enter
>> j
ans = 0 + 1.0000i
Note that if you use
i
and
j
for something else, these definitions will be overwritten.
The matrix specialization of M
ATLAB
means that the most efficient programming requires
vectorizing. This is an aspect of using M
ATLAB
that most students master over time.
Getting your code to work in some fashion is first and foremost. Writing tight code is
something that requires practice.
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