Convolution of two discrete sequence

In summary, the task is to find the convolution between $y=x*h$ where $x=u_n-u_{n-N}$ and $h_n=u_n-u_{n-M}$ with positive integers $M\ge N$. The general formula for convolution is $y=x*h=\sum\limits_{m=-\infty}^\infty x_m h_{n-m}$. Using this, the expression for $y_n$ is $\sum\limits_{m=-\infty}^\infty (u_m-u_{m-N})(u_{n-m}-u_{n-m-M})$. By applying the definition of convolution, this can be simplified to $(u*u)_{n+N}$.
  • #1
nacho-man
171
0
Hi,

New to this topic, and need some help.

My task is to find the convolution between
$ y= x ∗ h$

where $x = u_n - u_{n-N}$ and $h_n = u_n - u_{n-M}$ and $M\ge N$ are positive integers

My understanding is that

in general, $ y= x ∗ h = \sum\limits_{m=-\infty}^\infty x_m h_{n-m} $

so for my question i get

$\sum\limits_{m=-\infty}^\infty (u_m-u_{m-N})(u_{m-n}-u_{m-M})$
is there anything further i can do here? It doesn't feel complete, and to be honest, the idea of convolution still seems vague to me.

$u_n$ is the step function
 
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  • #2
nacho said:
Hi,

New to this topic, and need some help.

My task is to find the convolution between
$ y= x ∗ h$

where $x = u_n - u_{n-N}$ and $h_n = u_n - u_{n-M}$ and $M\ge N$ are positive integers

My understanding is that

in general, $ y= x ∗ h = \sum\limits_{m=-\infty}^\infty x_m h_{n-m} $

so for my question i get

$\sum\limits_{m=-\infty}^\infty (u_m-u_{m-N})(u_{m-n}-u_{m-M})$
is there anything further i can do here? It doesn't feel complete, and to be honest, the idea of convolution still seems vague to me.

$u_n$ is the step function

Hi nacho!

First off, it should be:
$$y_n = (x*h)_n = \sum\limits_{m=-\infty}^\infty (u_m-u_{m-N})(u_{n-m}-u_{n-m-M})$$

Can you eliminate the parentheses?From the definition of convolution, we have:
$$(u*u)_n = \sum\limits_{m=-\infty}^\infty u_m u_{n-m}$$

Then, with $k=m-N \Rightarrow m=k+N$, this implies for instance:
$$\sum_{m=-\infty}^\infty u_{m-N} u_{n-m}
=\sum_{k=-\infty}^\infty u_{k} u_{n-(k+N)}
=\sum_{k=-\infty}^\infty u_{k} u_{(n+N)-k}
= (u*u)_{n+N}$$

Can you write $y_n$ as a combination of such self-convolutions?
 

FAQ: Convolution of two discrete sequence

What is the definition of convolution of two discrete sequences?

The convolution of two discrete sequences is a mathematical operation that combines the elements of two sequences to create a new sequence. It involves multiplying each element of one sequence by the elements of the other sequence and then summing the results.

What is the purpose of performing convolution of two discrete sequences?

The purpose of performing convolution of two discrete sequences is to analyze and process signals or data in the time domain. It is often used in signal processing, image processing, and digital filtering to extract useful information or remove unwanted noise from a signal.

How is convolution of two discrete sequences different from multiplication?

Convolution of two discrete sequences is similar to multiplication in that it involves multiplying two sequences. However, in convolution, the multiplication is done at each time step and the results are summed together, whereas in multiplication, the entire sequences are multiplied together at once.

What are the properties of convolution of two discrete sequences?

The properties of convolution of two discrete sequences include commutativity, associativity, distributivity, and the convolution property. Commutativity means that the order of the two sequences can be switched without changing the result. Associativity means that the grouping of the sequences can be changed without changing the result. Distributivity means that convolution is distributive over addition. The convolution property states that the convolution of two sequences is equivalent to the inverse Fourier transform of the product of the Fourier transforms of the two sequences.

Can convolution of two discrete sequences be performed using a computer?

Yes, convolution of two discrete sequences can be easily performed using a computer. There are various algorithms and software programs available that can perform convolution efficiently and accurately. Many programming languages also have built-in functions for performing convolution.

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