Discrete Time Convolution of Sums

In summary, discrete time convolution is a mathematical operation commonly used in signal processing and systems analysis to model the behavior of systems. It differs from continuous time convolution in the type of signals being convolved and can be calculated using the formula y[n] = ∑<sub>k=-∞</sub><sup>∞</sup> x[k]*h[n-k]. Some applications of discrete time convolution include digital filtering, audio and image processing, and data compression. However, it has limitations such as assuming finite signal lengths and high computational complexity for large signals.
  • #1
Mr.Tibbs
24
0
Evaluate the following discrete-time convolution:

y[n] = cos([itex]\frac{1}{2}[/itex][itex]\pi[/itex]n)*2[itex]^{n}[/itex]u[-n+2]

Here is my sloppy attempt:

y[n] = [itex]\sum[/itex]cos([itex]\frac{1}{2}[/itex][itex]\pi[/itex]k)2[itex]^{n-k}[/itex]u[-n-k+2] from k = -∞ to ∞

= [itex]\sum[/itex]cos([itex]\frac{1}{2}[/itex][itex]\pi[/itex]k)2[itex]^{n-k}[/itex] from k = -∞ to 2

We can re-write the cos as [e[itex]^{0.5j\pi}[/itex]-e[itex]^{-0.5j\pi}[/itex]]0.5

using the property of summation of geometric series:

0.5[itex]\sum[/itex]2[itex]^{n-k}[/itex](e[itex]^{0.5j\pi k}[/itex]-e[itex]^{-0.5j \pi k}[/itex])

from k = -∞ to 2


so more or less am I on the right track?
 
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  • #2
Yes, you are on the right track. You can simplify the expression further by noting that the sum of the geometric series is equal to 2k+1. So your final expression should look like this: y[n] = 0.5\times(2^{n-2}(e^{0.5j\pi(2)} - e^{-0.5j\pi(2)}) + \sum_{k=-\infty}^{-2}2^{n-k}(e^{0.5j\pi k} - e^{-0.5j \pi k}))
 

1. What is discrete time convolution?

Discrete time convolution is a mathematical operation that combines two discrete signals to produce a third signal. It is commonly used in signal processing and systems analysis to model the behavior of systems.

2. How is discrete time convolution different from continuous time convolution?

The main difference between discrete time convolution and continuous time convolution is the type of signals that are being convolved. Discrete time convolution deals with signals that are defined at discrete time intervals, while continuous time convolution deals with signals that are defined at all points in time.

3. What is the formula for calculating discrete time convolution?

The formula for discrete time convolution is:
y[n] = ∑k=-∞ x[k]*h[n-k]
where x[n] and h[n] are the input signals and y[n] is the output signal at time n.

4. What are some applications of discrete time convolution?

Discrete time convolution has various applications in signal processing, such as digital filtering, audio and image processing, and data compression. It is also used in system analysis to model the behavior of linear time-invariant systems.

5. Are there any limitations to using discrete time convolution?

One limitation of discrete time convolution is that it assumes the signals being convolved are finite in length. This means that it may not accurately model systems with infinite or continuously varying input signals. Additionally, the computational complexity of discrete time convolution can be high for large signals, making it challenging to implement in real-time systems.

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