Signal_and_System_Time_Frequency_Analysis_and_MATLAB_Application

语谱图实验介绍 左上角的图形框（the spectrum of the number）表示：输入的数字的频谱图； 左下角的图形框（yuputu）表示：连续输入的号码的语谱图； 右上角的图形框显示输入的数字； 右面中间的16个号码代表按键盘； “hangup”：表示“挂断电话”，同时将左上角的图形框（the spectrum of the number）和左下角的图形框（yuputu）清空； “recall”：表示“重播”，同时将上一次输入的一串号码数值显示（yuputu）中，并将上次号码的最后一个号码的频谱图显示在（the spectrum of the number）中。

信号与系统的试验MATLAB源程序

MATLAB求根号代码：使用MATLAB模拟信号与系统中的校正装置。首先画出未校正的负反馈系统被控对象的根轨迹图，分析系统稳定性；设计一个满足指标的串联校正装置；给出校正装置的传递函数；给出幅频特性，奈奎斯特图。 1. 代码文件的介绍：模型文件统一写成了solution.m脚件。该脚件能解决以上需求描述，具体代码实现如solution脚件所示。 2. 运行代码方法：直接打开MATLAB软件运行solution.m脚件。运行脚件后，模型会自动输出所有结果。

雷达系统分析与设计，这本书是雷达的基本入门书籍，内含MATLAB源代码，使用起来比较方便。虽然有中文翻译，但存在很多错误，建议查看英文版！

Matlab时频工具箱下载链接-Time_Frequency.rar的下载需求，我提供一个链接，不知道其他人是否已经分享过，需要的可以顶一下哦。

The Discrete Time Signal Processing 2nd Edition focuses on the fundamental concepts of Discrete-Time Signal Processing and digital signal processing. This edition covers advanced topics like filter design, Fourier analysis, and sampling theory. Key algorithms for signal manipulation and transformations are explained in-depth, providing insights into both theoretical and practical aspects of signal processing.

Composite Dual-Frequency Buck Converter提出了一种创新的复合双频Buck变换器，对其工作原理和性能特点进行了深入分析。该复合双频Buck变换器的高频部分由三电平Buck单元组成，而低频部分则采用两电平Buck单元。相比三电平Buck变换器，复合双频Buck变换器具有效率更高的优势，且其“隔直”电容体积更小。与传统双频Buck变换器相比，该复合双频Buck变换器在高频开关电压应力方面更低，输出滤波电感与滤波电容更小，并具备良好的动态负载响应能力。最后，在理论分析的基础上进行了实验研究，以验证其性能。

This Chaos Time Series Toolbox includes a variety of MATLAB programs for analyzing chaotic time series. The toolbox features methods for calculating delay time, embedding dimension, and various prediction techniques. The provided code is fully functional and ready to run, ensuring an effective and reliable approach to chaotic data analysis.

1D Signal Two-Level Wavelet Decomposition Overview Two-Level Approximation Decomposition: The original signal is averaged every 4 values to capture the approximate components at this level. Two-Level Detail Decomposition: The difference between every 2 consecutive values in the original signal provides the detailed components at this level. One-Level Detail Decomposition: The difference between the odd and even-indexed values of the original signal is calculated to extract finer details. Signal Recovery: After the decomposition, the signal can be reconstructed by combining the approximations and details from each level. In MATLAB, you can implement these wavelet decompositions to analyze various signals effectively, applying discrete wavelet transform (DWT) functions for both decomposition and reconstruction steps.