Convolution - Signals and Systems

In summary, the conversation discusses the use of different variables for discrete and continuous signals in convolution, and how the operation is the same for both cases despite the differences in notation. It is clarified that for a continuous signal, convolution is defined as an integral, while for a discrete signal it is defined as a summation. However, the overall operation remains the same.
  • #1
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I will make this my discussion thread. I have many questions to ask which I will post here. Please keep checking. All help will be appreciated.

My first question is: For discrete signal, we use variable 'n' and for continuous signal, we use variable 't'. But is the convolution integral valid for both. E.g. the only difference would be 'n' and 't'. Tau and integral will be the same?
 
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  • #2
Convolution for a CT signal is defined as
[tex]
y(t) = \int_{-\infty}^{\infty}x(\tau)h(t-\tau)d\tau
[/tex]
and for DT it is defined as
[tex]
y[n] = \sum_{k=-\infty}^{\infty}x[k]h[n-k]
[/tex]

Thus for DT signal we do not have integral but summation.

Just to distinguish for DT case we call it convolution summation and for CT case we call it integral. But the operation is same!

Bhupala!
 
  • #3


The convolution integral is valid for both discrete and continuous signals. However, the variables used may differ. For a discrete signal, the convolution integral is typically expressed as a summation, using the variable 'n'. For a continuous signal, the convolution integral is expressed as an integral, using the variable 't'. The tau and integral will be the same in both cases, as they represent the range of integration for the convolution operation.
 

1. What is convolution and how is it used in signals and systems?

Convolution is a mathematical operation that combines two signals to produce a third signal. It is commonly used in the fields of signal processing and systems analysis to determine the output of a linear time-invariant system given an input signal. It can also be used to analyze the effects of linear filters and to solve differential equations in systems with time-varying inputs.

2. How is convolution different from cross-correlation?

While convolution and cross-correlation are both mathematical operations that involve combining two signals, they differ in their applications and results. Convolution is used to determine the output of a linear system, while cross-correlation is used to find the similarity between two signals. Additionally, convolution is commutative and associative, while cross-correlation is not.

3. What is the meaning of the "convolution integral" in the context of signals and systems?

The convolution integral is a mathematical expression that represents the output of a linear time-invariant system given an input signal. It is defined as the integral of the product of the input signal and the impulse response of the system. This integral is used to calculate the output of a system for any input signal, making it a fundamental tool in the analysis of signals and systems.

4. Can convolution be applied to non-linear systems?

No, convolution is only applicable to linear systems. This is because the principle of superposition, which is a key property of linear systems, does not hold for non-linear systems. In non-linear systems, the output cannot be determined by simply adding the outputs of each individual input signal.

5. What are some real-world applications of convolution in signals and systems?

Convolution has numerous applications in various fields, including signal processing, communication systems, image and audio processing, and control systems. It is used to filter and denoise signals, to design digital filters, to analyze system responses, and to extract features from signals. In everyday life, convolution is used in technologies such as speech recognition, noise cancellation, and image recognition.

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