This easily understanding online course will guide you to get your foot in the door of Wavelet Transform
Wavelet Transforms (WT) or wavelet analysis is probably the most recent solution to overcome the shortcomings of the Fourier Transform (FT). WT transforms a signal in period (or frequency) without losing time resolution. In the signal processing context, WT provides a method to decompose an input signal of interest into a set of elementary waveforms, i.e. “wavelets”., and then analyze the signal by examining the coefficients (or weights) of these wavelets.
Wavelets transform can be used for stationary and nonstationary signals, including but not limited to the following:
- Noise removal from the signals
- Trend analysis and forecasting
- Detection of abrupt discontinuities, change, or abnormal behavior, etc. and
- Compression of large amounts of data
- The new image compression standard called JPEG2000 is fully based on wavelets
- Data encryption, i.e. secure the data
- Combine it with machine learning to improve the modelling accuracy
Therefore, it would be great for your future development if you could learn this great tool. Practical Python Wavelet Transforms (I): Fundamentals is an easily understand online course, which is taught by Dr. Shouke Wei. This fundamental course provides you with the basic concepts concerning Wavelet transforms, wavelets families and their members, wavelet and scaling functions and their visualization, as well as setting up Python Wavelet Transform Environment. In this course, Dr. Wei will guide you to get your foot in the door of Wavelet Transform.
If you are interested in knowing details, please visit its page.
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