Chapter01-Art

此程序是对不同传感器得到的图像进行融合，以便得到更好的图像(This program is an image of different sensors are fused to obtain better image)

这是一个综合性的仿真程序。包括：用matlab生成一个FIR滤波器，得到滤波器系数及阶数；再用C语言产生一个混合频率的数字信号，之后将生成的数据样点送入到DSP主程序进行数字滤波，DSP主程序算法用的是循环缓冲区算法，这一部分在CCS上实现。(This is a comprehensive simulation program. Comprising: generating a matlab FIR filter, to obtain the filter coefficients and order then mixed to produce a C-frequency digital signal, and then the resulting data samples into the main program to the DSP for digital filtering, DSP main algorithm using a circular buffer algorithm, this part of the CCS to achieve.)

产生一个RGB立方图：R(红)-G（绿），R（红）-B（蓝），G（绿）-B（蓝）(Color model. Use Matlab to create six (6) color images of size 512×512 in ppm format, each of which represents one of the six sides of the RGB color cube.)

基于adaboost的人脸检测算法的matlab实现过程，希望对需要的朋友有帮助(Face detection based on adaboost algorithm matlab process, hope to help to friends)

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 MATLAB or C language on the pitch detection algorithm, and gives experimental results with the experimental report corpus: MaoYiSheng.raw pronunciation: " Mao Yisheng" duration 0.9s sampling rate: 8kHz quantify the accuracy of 16bits/sample data format LSB, MSB of frame size: 20ms (160 samples) the total number of frames: 45)

用matlab产生自相关系数固定的序列,然后再观察其功率谱密度.(using Matlab correlation coefficient derived from the fixed sequence, and then observed its power spectral density.)

最新的用于叶片各理化参量反演的模型，MATLAB语言，用于遥感反演(PROSPECT MODEL)

可用于拉曼光谱或色谱数据背景扣除的自适应迭代惩罚最小二乘方法(adaptive iteratively reweighted Penalized Least Squares (airPLS) method for background subtraction of Raman spectroscopy or chromatography data )

unsupervised local discriminative analysis each column is a data

模型预测控制的一个小例子，希望对学习模型预测的同志有所帮助，也希望大家多发些例子共同学习(A small example of model predictive control, hope to learn the model predictions comrades help, I hope you learn together some examples of multiple)