资源列表
Unitary--ESPRIT.matlab
- 面阵中二维角度估计Unitary-ESPRIT的matlab仿真程序，这是采用面阵的二维的oda估计，能有效估计空间角。-Two-dimensional planar array of Unitary-ESPRIT for angle estimation matlab simulation program, which is the use of two-dimensional planar array of oda estimates can estimate spatial angle.
jiyinjiance
- 基音检测： 对给定语料估计其基音周期。 要求用MATLAB或C语言实现有关基音检测算法，并给出检测结果与实验报告 实验语料：MaoYiSheng.raw 发音：“茅以升”；时长0.9s；采样率：8kHz；量化精度为16bits/sample；数据格式 LSB,MSB 分析帧长:20ms(160样点) 总帧数：45-Pitch Detection: For a given corpus to estimate the pitch period. Require MAT
Transfer-Matrix-Optical-Modeling
- Transfer Matrix Optical Modeling
BPSK
- 实现了BPSK信号的调制与解调，以及同步载波的提取-Achieve BPSK modulation and demodulation of the signal, and the extraction of carrier synchronization
Alamouti-ofdm
- 这个程序是一种简单的协作通信的，基于ofdm传输的Alamouti空间的空时码设计-This program is a simple collaborative communications, based on ofdm transmission of the Alamouti space time code design
APF
- 有源电力滤波器，可以使用。是一种应用滞环的有源电力滤波器，matlab simulink平台。-Active power filter can be used. Hysteresis is an application of Active Power Filter, matlab simulink platform.
DCtoACPWMconverter
- AC-DC-AC整流逆变电路，三相全桥逆变电路-AC-DC-AC inverter circuit rectifier, three-phase full-bridge inverter circuit. . . . . . .
SP
- 一种快速有效、性能可靠的信号重构算法是压缩感知理论的核心部分，对于 这部分内容，许多卓有成效的研究工作正在陆续展开。从压缩感知理论提出至今， 已经出现了多种稀疏信号的重构算法。重构算法主要可以归结为三大类：贪婪算 法，凸松弛算法和组合算法。这里主要是SP算法-A fast and efficient, reliable signal reconstruction algorithm is the core of compressed sensing theory, for this
AdapGA
- 自适应遗传算法 MATLAB编程-Adaptive genetic algorithm
Code
- 改程序包里包含中值、均值滤波、自适应中值滤波和一种改进的椒盐噪声滤波程序，前三种是给第四种作对比的，第四种方法对椒盐滤除效果很好。-Change the program package contains the value, mean filtering, adaptive median filter and an improved impulse noise filtering program is the first three compare to the fourth, the four
kalman
- 基于非线性方程，由状态方程与观测方程，并求其观测值。-Nonlinear equations, the state equation and observation equation, and find their observations.
New--GP-programming
- 一种新的改进的遗传编程，调用的子函数描述的很清晰，涵盖了经典遗传编程的所有步骤，方便大家的学习和参考-A new and improved genetic programming, call the subroutine described in very clear and covers all the steps of classical genetic programming, we facilitate learning and reference