关于这些主题，我是新手。我研究了很多有关此问题的文章。有很多不同的技术。但是我很困惑，因为我不知道从哪里开始。
根据我的研究，第一件事是重要的；我必须对原始传感器数据进行预处理。有一些技术，fft是其中之一。 （但是我如何搜索以学习所有技术？我没有在同一页面中看到所有技术。）
然后我开始统计计算以进行处理。
我没有制定路线图。您可以帮助解决这些问题或建议书籍或其他内容吗？
答案 0 :(得分：0)
欢迎使用...以利用此站点，将鼠标悬停在问题的标签fft上方...然后单击View tag ...然后单击learn more .. 。然后，在阅读有关fft的信息页面后，点击Votes，以查看此处投票率最高的帖子……这些问题/答案将带您进入球场。
我强烈建议您掌握此处Discrete Fourier Transform - Simple Step by Step
中解释的详细信息傅立叶变换的交互式指南
https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/
对傅立叶变换和FFT的直观理解
https://www.youtube.com/watch?v=FjmwwDHT98c
直观的离散傅立叶变换教程
http://practicalcryptography.com/miscellaneous/machine-learning/intuitive-guide-discrete-fourier-transform/
How to get frequency from fft result?
我可以继续从笔记中提及金块，但我会把这本摘录自一本好书的书摘给你
http://www.dspguide.com/ch10/6.htm
The Discrete Time Fourier Transform (DTFT) is the member of the Fourier transform family that operates on aperiodic,
discrete signals. The best way to understand the DTFT is how it relates to the DFT. To start, imagine that you
acquire an N sample signal, and want to find its frequency spectrum. By using the DFT, the signal can be
decomposed into sine and cosine waves, with frequencies equally spaced between zero and one-half of the
sampling rate. As discussed in the last chapter, padding the time domain signal with zeros makes the period
of the time domain longer, as well as making the spacing between samples in the frequency domain narrower.
As N approaches infinity, the time domain becomes aperiodic, and the frequency domain becomes a continuous signal.
This is the DTFT, the Fourier transform that relates an aperiodic, discrete signal, with a periodic,
continuous frequency spectrum
答案 1 :(得分：0)
第一步是数据清理和特征提取。您需要以适用于机器学习算法的格式准备数据。我向您推荐我的论文"Generic Data Imputation and Feature Extraction for Signals from Multifunctional Printers"。这是关于从物联网信号准备数据以进一步应用机器学习算法。