Convolve & Crosscorrelate Delayed Signals Example

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In summary: Your name]In summary, time delays in convolution and cross-correlation are handled differently. When convolving two signals with time delays, the resulting signal will have a delay equal to the sum of the individual delays. However, when cross-correlating the two signals, the resulting signal will have a delay equal to the difference of the individual delays. This can be seen in an example where two signals with delays of tl = 2 seconds and t2 = 3 seconds result in a delay of 5 seconds in convolution, but a delay of -1 second in cross-correlation.
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Homework Statement



Consider two signals gl(t) and g2(t). These two signals are delayed by amounts
equal to tl and t2 seconds respectively. Show that the time delays are additive
in convolving the pair of delayed signals, whereas they are subtractive in crosscorrelating
them. Explain with an example.

Homework Equations



I tried Convolution of delayed ex and u(t)

The Attempt at a Solution



Pretty much lost when I tried.
 
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Hello there,

I would like to provide some clarification on how time delays are handled in convolution and cross-correlation.

First, let's define convolution and cross-correlation. Convolution is a mathematical operation that combines two signals to produce a third signal. It is commonly used to find the output of a linear time-invariant system given its input and impulse response. On the other hand, cross-correlation is a measure of similarity between two signals as a function of the displacement of one relative to the other.

Now, let's consider the two signals gl(t) and g2(t), which are delayed by tl and t2 seconds respectively. When convolving these two signals, the resulting signal will be delayed by the sum of the two delays, i.e. tl + t2. This is because convolution is a linear operation, and the delay in one signal does not affect the delay in the other signal.

However, when cross-correlating the two signals, the resulting signal will be delayed by the difference of the two delays, i.e. tl - t2. This is because cross-correlation involves shifting one signal relative to the other, and the delay in one signal will affect the alignment of the two signals.

To better understand this concept, let's consider an example. Suppose we have two signals, x(t) and y(t), with time delays of tl = 2 seconds and t2 = 3 seconds, respectively. When we convolve these two signals, the resulting signal will have a time delay of tl + t2 = 5 seconds. However, when we cross-correlate these two signals, the resulting signal will have a time delay of tl - t2 = -1 second. This means that the two signals will be aligned in the opposite direction, with y(t) being shifted 1 second earlier than x(t).

I hope this helps to clarify the concept of time delays in convolution and cross-correlation. Let me know if you have any further questions or if you need any additional explanation.
 

Related to Convolve & Crosscorrelate Delayed Signals Example

What is convolution and cross-correlation?

Convolution and cross-correlation are mathematical operations used to measure the similarity between two signals. Convolution measures the overlap between two signals as one signal is shifted over the other, while cross-correlation measures the correlation between two signals at various time lags.

How are convolution and cross-correlation used in signal processing?

Convolution and cross-correlation are commonly used in signal processing to analyze and compare signals. They are particularly useful in identifying patterns and detecting similarities between time series data.

What is the difference between convolution and cross-correlation?

The main difference between convolution and cross-correlation is the direction in which the signals are shifted. In convolution, one signal is flipped and shifted over the other, while in cross-correlation, the signals are shifted in the same direction.

How do you calculate convolution and cross-correlation?

Convolution and cross-correlation can be calculated using mathematical formulas or through digital signal processing techniques. The process involves multiplying the values of two signals at each time step, summing the results, and shifting one signal over the other to repeat the process.

What are some real-world applications of convolution and cross-correlation?

Convolution and cross-correlation are used in various fields, including audio and image processing, radar and sonar technology, and biomedical signal analysis. They are also commonly used in machine learning and artificial intelligence algorithms for pattern recognition and prediction.

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