Convolution exercise with block diagram and cases

In summary: First, you can find the Fourier transform of x(t) and h(t). Then, you can take the product of the two Fourier transforms and find the inverse Fourier transform to get y(t). This will give you the solution for all values of t.In summary, the problem involves finding the output y(t) given the input x(t) and the functions h(t) and δ(t-1)*h(t). Using the convolution theorem, the problem can be simplified by finding the Fourier transform of x(t) and h(t), taking the product of the two and finding the inverse Fourier transform to get y(t). This eliminates the need to consider multiple cases with different limits and provides a solution for all values of t.
  • #1
coutnoob
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1. First of all, a block diagram is given where x(t) is the input and y(t) the output.The y(t) is asked when given the h(t) and x(t).We have in parallel h(t) and δ(t-1)*h(t) and those 2 go through a summer (the second one with minus and the first one with a plus) and give y(t).

2. I know from a previous question that x(t) is a rectangular signal with length from -1 to 2.
And also, h(t)=e^{2-t}u(t-2).

3. i find that y(t)=x(t)*h(t)-x(t)*h(t-1) , cause δ(t-1)*h(t)=h(t-1) (time shift).
When i have to find first the x(t)*h(t) i get 3 cases and get :
1.0 for t<1
2.1-e^{1-t} for t>=1 and t<4
3.e^{4-t}-e^{1-t} for t>4.

Of course i could do the same for x(t)*h(t-1) and get another 3 cases.
Here's my main problem : the limits of the 2 convolutions are totally diferrent : should i get a lot of cases for y(t) with all the limits from the 2 convolutions or is a smarter-more accurate way of calculating y(t) ?
I've been working this for hours and tomorrow i must have it solved with another 2 exercises :(
Any help would be much appreciated.Thanks in advance !
 
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  • #2
The best way to solve this problem is to use the convolution theorem. The convolution theorem states that the convolution of two functions, f and g, is equal to the product of the Fourier transforms of the two functions. By doing this, you can simplify the problem and avoid having to consider multiple cases with different limits.
 

Related to Convolution exercise with block diagram and cases

1. What is a convolution exercise?

A convolution exercise is a mathematical operation used in signal processing to combine two signals and produce a third signal. It is represented by a block diagram and can be used to model real-world scenarios such as filters and image processing.

2. How does a convolution exercise work?

In a convolution exercise, the two signals are multiplied together and then integrated over a specific interval. This process is repeated for different intervals, resulting in a new output signal that represents the combination of the two original signals.

3. What are the different cases in a convolution exercise?

There are three main cases in a convolution exercise: continuous-time, discrete-time, and digital. Continuous-time convolution is used for signals that vary continuously over time, while discrete-time convolution is used for signals that are sampled at specific time intervals. Digital convolution is used for signals that are represented by digital data.

4. What are the applications of convolution exercises?

Convolution exercises have a wide range of applications in various fields such as signal processing, image and audio processing, and communication systems. They are used to filter signals, remove noise, and extract information from signals. They are also used in machine learning and artificial intelligence algorithms.

5. Are there any real-world examples of convolution exercises?

Yes, there are many real-world examples of convolution exercises. One example is the use of convolution in image processing to blur or sharpen images. Another example is the use of convolution in digital audio processing to apply effects such as reverb or echo. Convolution is also used in radar and sonar systems to detect and locate objects.

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