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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-python-wavelet-transform-i-fundamentals/
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**Coursera课程总结：Python小波变换（一）：基础** 本课程是“Python小波变换”系列课程的基础部分，旨在为您介绍小波变换（WT）这一强大的信号处理工具。与傅里叶变换（FT）相比，小波变换能够同时保留时间分辨率和频率分辨率，从而克服了FT在处理非平稳信号时的局限性。 **课程核心内容：** * **小波变换的原理和优势：** 了解小波变换如何将信号分解为基本小波函数，并通过分析小波系数来提取信号信息。 * **小波变换的应用领域：** 涵盖信号去噪、趋势分析与预测、突变点检测、数据压缩（如JPEG2000）、数据加密以及与机器学习结合以提升模型精度等。 * **Python小波变换环境搭建：** 指导您如何设置和配置Python环境，为后续的小波变换实践做好准备。 * **基础概念和核心技术（预告）：** 课程将初步介绍离散小波变换（DWT）、平稳小波变换（SWT）、多分辨分析（MRA）、小波包变换（WPT）以及最大重叠离散小波变换（MODWT）及其多分辨分析（MODWTMRA）。 **学习目标：** 完成本课程后，您将掌握小波变换的基础概念，了解不同类型的小波家族及其成员，以及小波和缩放函数的可视化方法。您将为后续系列课程中更深入和高级的小波变换技术打下坚实的基础。 **重要提示：** 本课程的免费预览部分是后续高级课程的先修内容。
Attention: Please read careful about the description, especially the last paragraph, before buying this course. The 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 signalstrend analysis and forecastingdetection of abrupt discontinuities, change, or abnormal behavior, etc. andcompression of large amounts of datathe new image compression standard called JPEG2000 is fully based on waveletsdata encryption, i.e. secure the dataCombine it with machine learning to improve the modelling accuracyTherefore, it would be great for your future development if you could learn this great tool. Practical Python Wavelet Transforms includes a series of courses, in which one can learn Wavelet Transforms using word-real cases. The topics of this course series includes the following topics: Part (I): Fundamentals Discrete Wavelet Transform (DWT)Stationary Wavelet Transform (SWT)Multiresolutiom Analysis (MRA)Wavelet Packet Transform (WPT) Maximum Overlap Discrete Wavelet Transform (MODWT)Multiresolutiom Analysis based on MODWT (MODWTMRA)This course is the fundamental part of this course series, in which you will learn 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. After this course, you will obtain the basic knowledge and skills for the advanced topics in the future courses of this series. However, only the free preview parts in this course are prerequisites for the advanced topics of this series.
