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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-python-wavelet-transforms-ii-1d-dwt/
课程评论：没有评论
**课程名称：** 实用Python小波变换 (II): 一维离散小波变换 **课程概述：** 本课程是“实用Python小波变换”系列课程的第二部分，专注于一维离散小波变换 (1D DWT)。小波变换 (WT) 作为一种先进的信号处理技术，能够有效弥补傅里叶变换在时间分辨率上的不足，实现信号在时域和频域的同步分析。 课程将深入介绍小波变换的基本概念，以及如何将输入信号分解为一系列基本波形（小波）并分析其系数。小波变换在处理平稳和非平稳信号方面具有广泛应用，包括但不限于： * 信号去噪 * 趋势分析与预测 * 检测突变、变化或异常行为 * 数据压缩（例如JPEG2000标准） * 数据加密 * 结合机器学习提升模型精度 **本课程的主要学习内容：** * **一维离散小波变换 (1D DWT)**：深入理解单层和多层一维离散小波变换的原理和过程。 * **辅助内容**： * 平稳小波变换 (SWT) * 多分辨率分析 (MRA) * 小波包变换 (WPT) * 最大重叠离散小波变换 (MODWT) * 基于MODWT的多分辨率分析 (MODWTMRA) **学习方式：** 课程将通过易于理解的图示、示例以及两个真实的案例研究来讲解。此外，还提供练习来巩固学习成果。 **课程成果：** 学完本课程后，您将能够： * 将一维时间序列信号分解为近似和细节系数。 * 对信号进行重构和部分重构。 * 实现数据的降噪处理。 * 利用精美的图表可视化处理结果。 **适用人群：** 任何希望掌握小波变换这一强大工具以提升数据分析和信号处理能力，并为未来职业发展奠定基础的学习者。
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 Part (II): 1D 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 second part of this course series. In this course, you will learn the concepts and processes of single-level and multi-level 1D Discrete Wavelet Transforms through simple easy understand diagrams and examples and two concrete world-real cases and exercises. After this course, you will be able to decompose a 1D time series signal into approximation and details coefficients, reconstruct and partial reconstruct the signal, make noise reduction from the data signal, and visualize the results using beautiful figures.
