agv

在不同UWB信号采用不同的调制方式，OOK、PPM调制下的功率谱计算，并观察周期数对UWB信号频谱的影响，7.5制作(UWB signals in different ways using different modulation, OOK, PPM modulation of the power spectrum, and observe the number of cycles on the impact of UWB signal spectrum, 7.5 Production)

使用LLC拓扑的软开关谐振变换器的SIMULINK仿真模型和相关参数计算(SIMULINK simulation model and related parameters of soft switching resonant converter using the LLC topology. )

说明：  基于mATLAB的双峰谱线插值，用于谐波的检测分析(Bimodal line interpolation based on mATLAB for harmonic detection and analysis)

Computing an Exact Gaussian Scale-space 介绍了一种图像去模糊算法 保护论文和源代码 用c 编写(Computing an Exact Gaussian Scale-space introduce a debulrring algorithm, including a source C code)

This disk contains numerical methods software coded in MATLAB. The algorithms are described in the text NUMERICAL METHODS for Mathematics, Science, and Engineering.FORTRAN

EKF的经典程序，可运行不同模型的EKF,算法精简，准确(The classic procedure of the EKF, can run a different model of the EKF algorithm streamline accurate)

脑机接口支持向量机工具箱最新版本,在matlab下安装使用(brain-Support Vector Machine Interface Toolbox latest version of the Matlab installation)

如何从CSV、文本文件中读取记录波形 并返回波形数据 逐行读取文件内容 (How CSV, text file read and returns the waveform data recorded waveforms to read file contents line by line)

用来计算T检验的信度值，给定自由度和置信度计算其比如相关系数的信度值(this program if for student-test)

说明：  遗传算法是计算数学中用于解决最佳化的搜索算法，是进化算法的一种。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等。遗传算法通常实现方式为一种计算机模拟。对于一个最优化问题，一定数量的候选解（称为个体）的抽象表示（称为染色体）的种群向更好的解进化。传统上，解用二进制表示（即0和1的串），但也可以用其他表示方法。进化从完全随机个体的种群开始，之后一代一代发生。在每一代中，整个种群的适应度被评价，从当前种群中随机地选择多个个体（基于它们的适应度），通过自然选择和突变产生新的生命种群，该种群在算法的下一次迭代中成为当前种群。(Genetic algorithm (GA) is a search algorithm used to solve optimization in computational mathematics. It is one of evolutionary algorithms. Evolutionary algorithms were originally developed by borrowing phenomena in evolutionary biology, such as heredity, mutation, natural selection and hybridization. Genetic algorithms are usually implemented as a computer simulation. For an optimization problem, an abstract representation of a certain number of candidate solutions (called individuals) (called chromosomes) evolves towards a better solution. Traditionally, solutions are represented in binary (that is, strings of zeros and ones), but other representations are possible. Evolution begins with a population of completely random individuals, and it happens from generation to generation.)