Queries on Signal Processing - Cross Correlation

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SUMMARY

The discussion focuses on the use of cross-correlation in signal processing for detecting machine failures, specifically addressing the role of the complex conjugate in the correlation equation. The equation presented is the integral form of cross-correlation, defined as (f∗g)(t) = ∫_{−∞}^{∞} f*(τ)g(t+τ)dτ. The complex conjugate is crucial as it ensures the inner product in a Hilbert space is correctly defined, impacting the alignment and contribution of signals in the integral. Understanding this concept is essential for applying cross-correlation effectively in practical scenarios.

PREREQUISITES
  • Basic understanding of signal processing concepts
  • Familiarity with complex numbers and their properties
  • Knowledge of Hilbert spaces and inner product definitions
  • Experience with mathematical integration techniques
NEXT STEPS
  • Study the mathematical foundations of cross-correlation in signal processing
  • Explore the role of complex conjugates in inner product spaces
  • Read "Signals and Systems" by Alan V. Oppenheim for practical applications
  • Investigate the implications of signal alignment on correlation outcomes
USEFUL FOR

This discussion is beneficial for students and professionals in signal processing, engineers working on machine failure detection, and anyone interested in the mathematical underpinnings of cross-correlation techniques.

darknumbers
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I'm new to the field of signal processing and currently reading a paper which it uses cross-correlation of two signals for detecting some machine failures. But I'm confused with the complex conjugate in the equations. Take equation from wikipedia as example:

[itex](f\star g)(t)\equiv \int_{-\infty }^{\infty }f^*(\tau)g(t+\tau)d\tau[/itex]

Currently, I understood that they have significant contribution to the integral when some of their signals are aligned. But how does the complex conjugate involve and affect the function? And any recommended book on this topic?

Any guidance will be much appreciated. And please correct me if I have made any fundamental misconceptions.
 
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It has to do with the inner product in a Hilbert space. This correlation, at [itex]t=0[/itex], could be a good definition of the inner product of [itex]f[/itex] to [itex]g[/itex]. as it is, it's the inner product of [itex]f[/itex] to [itex]g[/itex] slid apart by displacement [itex]t[/itex]. It's so that if a complex [itex]f()[/itex] is convolved with itself, when [itex]t=0[/itex] then it's the integral of [itex]|f|^2[/itex].
 

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