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Computer Science > Data Structures and Algorithms
Title: Adaptive Frequency Bin Interval in FFT via Dense Sampling Factor $α$
(Submitted on 25 Mar 2024 (v1), last revised 26 Mar 2024 (this version, v2))
Abstract: The Fast Fourier Transform (FFT) is a fundamental tool for signal analysis, widely used across various fields. However, traditional FFT methods encounter challenges in adjusting the frequency bin interval, which may impede accurate spectral analysis. In this study, we propose a method for adjusting the frequency bin interval in FFT by introducing a parameter $\alpha$. We elucidate the underlying principles of the proposed method and discuss its potential applications across various contexts. Our findings suggest that the proposed method offers a promising approach to overcome the limitations of traditional FFT methods and enhance spectral analysis accuracy.
Submission history
From: Haichao Xu [view email][v1] Mon, 25 Mar 2024 12:00:57 GMT (277kb,D)
[v2] Tue, 26 Mar 2024 11:25:09 GMT (291kb,D)
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