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In signal processing, most transform basis functions look like \( e^{(\text{something})} \) — such as \( e^{j\omega t} \), \( e^{-st} \), or \( e^{-j2\pi kt/N} \). This is not a coincidence. Exponentials have deep mathematical properties that make them ideal for representing and analyzing signals. 1. Exponentials Are Eigenfunctions of LTI Systems If you feed an LTI system a complex exponential: x(t) = e^{st} the output has the same shape: y(t) = H(s) e^{st} This makes exponentials extremely convenient because they diagonalize LTI systems and turn convolution into simple multiplication. 2. Sines and Cosines Are Special Cases of Complex Exponentials Euler’s identity: e^{j\omega t} = \cos(\omega t) + j\sin(\omega t) ...