Adaptive Frequency Bin Interval in FFT via Dense Sampling Factor $α$
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910383990636544 |
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| author | Xu, Haichao |
| author_facet | Xu, Haichao |
| contents | 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 $α$. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16665 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Frequency Bin Interval in FFT via Dense Sampling Factor $α$ Xu, Haichao Data Structures and Algorithms Discrete Mathematics Signal Processing Computation 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 $α$. 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. |
| title | Adaptive Frequency Bin Interval in FFT via Dense Sampling Factor $α$ |
| topic | Data Structures and Algorithms Discrete Mathematics Signal Processing Computation |
| url | https://arxiv.org/abs/2403.16665 |