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Smaller DFTs from bigger DFTs

Aditya DuaAditya Dua January 22, 20198 comments
Introduction

Let's consider the following hypothetical situation: You have a sequence $x$ with $N/2$ points and a black box which can compute the DFT (Discrete Fourier Transform) of an $N$ point sequence. How will you use the black box to compute the $N/2$ point DFT of $x$? While the problem may appear to be a bit contrived, the answer(s) shed light on some basic yet insightful and useful properties of the DFT.

On a related note, the reverse problem of computing an $N$...


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Cedron DawgCedron Dawg March 14, 201812 comments

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Phase and Amplitude Calculation for a Pure Complex Tone in a DFT

Cedron DawgCedron Dawg January 6, 2018

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Markus NentwigMarkus Nentwig September 26, 20102 comments

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The DFT Output and Its Dimensions

Leonid OvanesyanLeonid Ovanesyan December 29, 20155 comments

The DFT gives N outputs for N samples, yet for real-valued signals most of those outputs are redundant. This post explains how conjugate symmetry organizes the output into a real DC bin, N/2-1 complex positive-frequency bins, a real Nyquist bin for even N, and then the conjugate mirror bins. A 64-point example illustrates which bins carry unique information and which can be discarded.


Python scipy.signal IIR Filter Design Cont.

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Rick LyonsRick Lyons September 14, 201613 comments

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Sam ShearmanSam Shearman March 28, 20183 comments

Precision is quantifiable, not guesswork. This post walks through practical, measurement-oriented statistics you can apply to static or dynamic signals to answer the question, "How precise is my measurement?" It focuses on using multiple samples, checking distribution assumptions, and constructing confidence intervals and levels so you can trade measurement time for a desired precision.


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David David October 27, 20101 comment

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