CT Convolutions and their bounds

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Tt + T^2.To summarize, when evaluating the convolution integral, it is important to consider the different bounds given in the problem and substitute the corresponding values of x(t) and h(t) into the integral. This will give the correct answer and help in understanding and applying the concept of convolution.
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ktpr2
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This question deals with an example found in Signals and Systems, 2ed, page 99, ex 2.7. But I can summarize it here:

EDIT - my latex doesn't seem to work ...

[tex]

x(t) = (1, 0 < t < T) or (0, otherwise)

[/tex]

[tex]

h(t) = (t, 0 < t < 2T) or (0, otherwise)
[/tex]

I realize there are five bounds to consider: (t<0, 0 <t <T, T < t < 2T, 2T < t <3T, 3T < t)

However, for instance,

[tex]

\\int_T^0 x(\tau) h(t-\tau) d\tau = \\int_T^0 h(t-\tau) d\tau = t\tau - \tau^2/2 (from 0 to T)
[/tex]

does not equal the example's answer of
[tex]
t^2/2
[/tex]

nor does

(integrate from 2T to 3T)
[tex]

\\int_2*T^3*T x(\tau) h(t-\tau) d\tau = \\int_T^0 h(t-\tau) d\tau = t\tau - \tau^2/2 (from 2T to 3T)
[/tex]

does not equal the example's answer of
[tex]
-t^2/2 + Tt + 3/2*T^2
[/tex]

So my question is this:

Once I realize the bounds that the convolution must be evaluated for, how do i convert the bounds so that I can integrate them and derive the correct answer?

edit- x(tau) is a square pulse of height 1, from 0 to T (not tau but T)

and

h(t-tau) is a downward sloping right trianlge (90degree corner on the left) with a base from t-2T to t.

Thank you for your time.
 
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it is important to understand and accurately interpret mathematical concepts such as convolution. In this case, the example from Signals and Systems, 2ed, page 99, ex 2.7 introduces the concept of convolution with a specific example of two signals, x(t) and h(t). It is important to note that the bounds given in the problem are crucial in correctly evaluating the convolution integral.

First, let's clarify the given signals x(t) and h(t). x(t) is a square pulse of height 1, from 0 to T, which means that x(t) is equal to 1 for t values between 0 and T, and equal to 0 otherwise. Similarly, h(t) is a downward sloping right triangle with a base from t-2T to t, which means that h(t) is equal to t for t values between 0 and 2T, and equal to 0 otherwise.

Now, let's look at the bounds given in the problem. We have t < 0, 0 < t < T, T < t < 2T, 2T < t < 3T, and 3T < t. These bounds correspond to the different intervals where the convolution integral needs to be evaluated. For example, in the first interval t < 0, both x(t) and h(t) are equal to 0, so the integral will also be equal to 0.

In the second interval, 0 < t < T, we have x(t) = 1 and h(t) = t. Substituting these values into the integral, we get:

\\int_0^T x(\tau) h(t-\tau) d\tau = \\int_0^T 1 \cdot t d\tau = t\tau (from 0 to T) = Tt

This matches the example's answer of t^2/2. Similarly, in the third interval T < t < 2T, we have x(t) = 1 and h(t) = t. Substituting these values into the integral, we get:

\\int_T^0 x(\tau) h(t-\tau) d\tau = \\int_T^0 1 \cdot t d\tau = t\tau (from T to 2T) = Tt - T^2

This matches the example's answer of -
 

FAQ: CT Convolutions and their bounds

1. What are CT convolutions?

CT convolutions are mathematical operations used to combine two functions to produce a third function, where the resulting function represents the amount of overlap between the two original functions at each point. In the context of signal processing, CT convolutions are often used to describe the relationship between input and output signals in a linear time-invariant (LTI) system.

2. How are CT convolutions calculated?

The formula for calculating a CT convolution involves integrating one of the functions over a range of values, while the other function is shifted in time. This process is repeated for each value of the shift, resulting in a continuous function that describes the overlap between the two original functions at each point.

3. What are the bounds of CT convolutions?

The bounds of CT convolutions depend on the properties of the functions being convolved. In general, the bounds of the resulting convolution will be the sum or difference of the bounds of the two original functions. However, the bounds may be infinite if one or both of the original functions have infinite bounds.

4. What are the applications of CT convolutions?

CT convolutions have a wide range of applications in signal processing, including filtering, deconvolution, and signal analysis. They are also used in other fields such as image processing, probability theory, and differential equations.

5. What are the limitations of CT convolutions?

One limitation of CT convolutions is that they can only be applied to functions that are defined over a continuous range of values. Additionally, they are only applicable to linear time-invariant systems, which may not accurately represent all real-world systems. Finally, the calculation of CT convolutions can be computationally intensive, making them difficult to apply in real-time systems.

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