Why Is n = 3 Not Considered in Convolution?

In summary, the problem involves finding the convolution of two discrete signals, x[n] and h[n]. After graphing the two signals and using the convolution formula, it is determined that the value at n=3 is zero, making it unnecessary to include in the final solution. The solution is y[n] = 2x[n+1] + 2x[n-1].
  • #1
Larrytsai
228
0

Homework Statement


δ = dirac delta
x[n] = δ[n] + 2δ[n-1] - δ[n-3]
h[n] = 2δ[n+1] + 2δ[n-1]

y[n] = x[n]*h[n]


Homework Equations



y[n] = x[n]h[n] = [tex]\sum[/tex]h[k]x[n-k]

The Attempt at a Solution



I have graphed the x[-k] and h[n], the solution saids

y[n] = h[-1]*x[n+1] + h[1]*x[n-1]
= 2x[n+1] + 2x[n-1]

I do not understand why, it seems like the solution forgot about the value at n = 3 for x[n].

can anyone help explain to me what happened in the solution and why n = 3 is not considered in the convolution.
 
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  • #2
It is hard to visualize convolution problems. Did you solve it by hand?
 
  • #3
y[n] is longer than what's shown in the answer but most of them are zero. that's why n=3 is not in the answer because that prduct is zero. you can solve the product of sum with [itex]-1 \leq n \leq 4[/itex] and see what you get
 
  • #4
Would you mind posting your solution? Graphing convolution problems - particular with a system consisting of delays - generally shows the solution relatively simply. Try showing the x, h, and then as they overlap. You'll see, as Jaynte pointed out, that the product at n=3 is zero, therefore negating the need to write it out.
 
  • #5


Hello,

I understand your confusion and I will try my best to explain the solution. Let's break it down step by step.

First, let's look at the equation for convolution:

y[n] = x[n]*h[n] = \sumh[k]x[n-k]

Here, x[n] and h[n] represent the input and impulse response, respectively. The convolution operation involves sliding h[n] over x[n] and multiplying the overlapping values. So, in this case, we have:

y[n] = h[-1]*x[n+1] + h[0]*x[n] + h[1]*x[n-1]

Now, let's substitute the given values for x[n] and h[n]:

y[n] = (2δ[n+1] + 2δ[n-1])*(-δ[n] + 2δ[n-1] - δ[n-3]) + 2δ[n+1] + 2δ[n-1]

Next, we can simplify this expression by expanding the multiplication and grouping the terms:

y[n] = -2δ[n+1]*δ[n] + 4δ[n+1]*δ[n-1] - 2δ[n+1]*δ[n-3] + 4δ[n-1]*δ[n] - 8δ[n-1]*δ[n-1] + 4δ[n-1]*δ[n-3] + 2δ[n+1] + 2δ[n-1]

Now, we can use the properties of the dirac delta function to simplify further. Remember that δ[n] = 0 for all n ≠ 0 and δ[n]*δ[n] = δ[n]. Using these properties, we can simplify the above equation to:

y[n] = -2δ[n+1] + 4δ[n-1] + 4δ[n+1] - 8δ[n-1] + 2δ[n+1] + 2δ[n-1]

Finally, we can combine like terms and get the solution mentioned in the problem:

y[n] = 2δ[n+1] + 2δ[n-1]

I hope this explanation helps you understand the solution better. The reason why n = 3 is not considered is because δ[n-3] is multiplied with δ[n+1] and δ[n-1],
 

FAQ: Why Is n = 3 Not Considered in Convolution?

1. What is convolution with discrete time?

Convolution with discrete time is a mathematical operation that combines two discrete signals to produce a third signal. It is commonly used in signal processing and other fields of engineering and science.

2. How is convolution with discrete time performed?

Convolution with discrete time is performed by multiplying and summing the values of two discrete signals, one of which is reversed and shifted. This process is repeated for all possible shifts, resulting in a new discrete signal.

3. What is the significance of convolution with discrete time in signal processing?

Convolution with discrete time is significant in signal processing because it allows us to analyze and manipulate signals in both time and frequency domains. It is also used in filtering, smoothing, and noise reduction.

4. Can convolution with discrete time be used in real-world applications?

Yes, convolution with discrete time is commonly used in real-world applications such as image processing, speech recognition, and audio processing. It is also used in various scientific and engineering fields for data analysis.

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

One limitation of convolution with discrete time is that it assumes linearity and time invariance, which may not always hold true in real-world scenarios. Additionally, it can be computationally intensive for larger data sets.

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