What is Autocorrelation Time & Covariance?

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Autocorrelation time refers to the duration it takes for a signal to lose its memory, indicating how past values influence current ones. In signal processing, this concept is crucial for analyzing the dynamics of systems, such as tracking the movement of diffusing particles over time. Covariance, on the other hand, measures the relationship between two signals, simplifying to autocorrelation when both signals originate from the same source. It quantifies how much two variables deviate from their means together. Understanding these concepts is essential for applications in light scattering experiments and optical processing techniques.
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Hi everybody, please someone can explain me what is the autocorrelation time and how it is linked to coviarance?

thank you!
 
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In signal processing, the autocorrelation time can mean the time it takes for a signal to 'lose it's memory'. For example, plotting the position of a diffusing particle over time and autocorrelating the signal gives a short-time scale where the position is non-random as compared to previous positions, and a longer time scale where the current position is not correlated with earlier positions. Autocorrelation is used a lot in light scattering experiments as well- it gives a scale for the dynamics of the system. Also in optical processing techniques such pattern recognition.

Covariance is a more general way of discussing the coherence properties of *two* signals, but probably reduces to the autocorrelation when the signals are from the same source.
 
Covariance is the correlation of the deviation from the mean.

It is the same as the correlation if the mean(s) of the analyzed signal(s) is/are zero.
 
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