Lecture Notes

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Lecture Notes

In Part 1 of this "Lecture Notes" article [1], we introduced a modern perspective on the standard tools for power system analysis-the Clarke and related transforms-through the lens of data analytics. We also indicated their limitations when dealing with unbalanced power system conditions.

Functions of complex variables arise frequently in the formulation of sig - nal processing problems. The basic calculus rules on differentiation and integration for functions of complex variables resemble, but are not identical to, the rules of their real variable counterparts. 

The sliding discrete Fourier transform (SDFT) is an efficient method for computing the N-point DFT of a given signal starting at a given sample from the N-point DFT of the same signal starting at the previous sample [1]. However, the SDFT does not allow the use of a window function, generally incorporated in the computation of the DFT to reduce spectral leakage, as it would break its sliding property.

After decades of advances in signal processing, this article goes back to square one, when the word signal was defined. Here we investigate if everything is all right with this stepping stone of defining a signal.

In many signal processing applications, filtering is accomplished through linear time-invariant (LTI) systems described by linear constant-coefficient differential and difference equations since they are conveniently implemented using either analog or digital hardware [1]. An LTI system can be completely characterized in the time domain by its impulse response or in the frequency domain by its frequency response, which is the Fourier transform of the system’s impulse response.

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