我对这些话题还是个新手。我研究了很多关于这个问题的文章。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,首先要做的是对原始传感器数据进行预处理。有一些技术,fft就是其中之一。(但是我怎样才能学习所有的技术呢?我没有在同一页中看到所有的技术。)
然后我开始统计计算进行处理。
我没有画出路线图。你能帮我解决这些问题,或者推荐书籍或其他什么吗?
发布于 2018-10-21 07:20:41
欢迎来到SO ..。要利用此站点,请将鼠标悬停在tag fft
顶部来回答您的问题……然后单击View tag
...然后点击learn more
。然后,在阅读完fft上的信息页面后,点击Votes
查看这里投票最高的帖子。这些问题/答案会让你进入正题。
我强烈建议你掌握这里解释的细节,Discrete Fourier Transform - Simple Step by Step
傅里叶变换交互式指南
https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/
对傅里叶变换和快速傅立叶变换有直观的理解
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?
我可以继续从我的笔记中提到掘金,但是我会把这本优秀的书的摘录留给你们。
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
发布于 2019-03-08 14:46:12
第一步是数据清理和特征提取。您需要准备适用于机器学习算法的数据格式。我向你推荐我的论文"Generic Data Imputation and Feature Extraction for Signals from Multifunctional Printers"。它是关于从IoT信号中为ML算法的进一步应用准备数据。
https://stackoverflow.com/questions/52871051
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