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小波去噪,利用小波系数在相邻尺度上的相关性关系,针对小波系数估计中硬阈值方法和软阈值方法的缺点, 通过对双重量收缩函数得到的阈值乘以一个合适系数进行修定的折衷方法,提出了一种新的小波域局部自适应去噪算法.实验结果表明,该方法既可以去除噪声,又可以较好地保留图像的高频细节特征.(Wavelet denoising using the wavelet coefficients in adjacent scales of the correlation relationship between the estimation of wavelet coefficients for the hard thresholding and soft thresholding shortcomings, through the contraction of the double weight of the threshold function has been multiplied by a suitable coefficient revised compromise, proposed a new wavelet-domain partially adaptive denoising algorithm. The experimental results show that the method can remove noise, also can be used to retain the details of the characteristics of high-frequency images.)

debussy的教材,对IC设计前端设计人员有很大的帮助.(debussy teaching materials, the design of the front-end IC designers a great help.)

OSCaR是一个锥形束CT（容积CT）重建算法，为AAPM开发的一个开源matlab gui代码，其投影数据需要到官方网站下载。也可以使用varian或者医科达的cone beam ct 投影进行重建。( OSCaR is a code about the reconstruction of cone beam CT， it is GUI，and very friendly。)

a menu that is created for a segmentation program(a simple menu)

matlab程序实现下面的功能：过程的输入驱动采用M序列，即u(k)是一个M序列。过程传递函数G(s)中K=120，T1=8.3Sec，T2=6.2Sec。过程的脉冲响应理论值g(k)满足。根据过程的输入输出数据u(k)和z(k)，利用相关分析算法估计出过程的脉冲响应值g2(k)，并且有。(Process input is driven by the M sequence, i.e., u (k) is an M sequence. Process transfer function G (s) K = 120, T1 = 8.3Sec T2 = 6.2Sec. The impulse response of the process of the theoretical value of g (k) meet. According to the process input and output data u (k) and z (k), the estimated impulse response of a process value g2 (k), and the correlation analysis algorithm.)

基于SIR粒子滤波器的故障诊断方法，参数有误报率和漏报率(SIR particle filter based on the fault diagnosis methods, parameters and reporting the rate of false alarm rate)

基于时间序列法的风电预测matlab程序，可根据已知数据预测未来的风电功率。(Method based on time series prediction matlab program for wind power, according to known data to predict the future wind power.)

巴克码和线性调频混合雷达信号仿真(Barker codes and linear FM radar mixed signal simulation)

说明：  本目录主要包括：文档和两个源代码。 其中一个源代码是识别程序的，另一个是一个矩阵类库的，书里面也使用过，所以一并附上。 因为已经保存了训练好的网络权值，所以第一次使用识别程序时，可以直接运行\\Release目录下的可执行文件，然后对图片目录中的测试图片进行读入、识别。 当然读者也可以自行用训练样本训练网络，不过要特别注意训练样本的选择，否则可能造成识别率很低。训练样本选择的原则是，尽可能的有代表性，在训练时间不至于太长的情况下，训练样本数目尽量多。 ( This directory includes: documentation and two source code. One of the source code is to identify the procedure, the other is a matrix class library, the book which is also used, so be attached. Because it has saved the trained network weights, so the first use of identification procedures, you can directly run \ \ Release directory of the executable files, and then tested on the images directory to read pictures, identification. Of course, readers are free to use the training samples to train the network, but pay special attention to training samples, otherwise it may cause very low recognition rate. Training sample selection principle is that as far as possible, representative, in the long training time Buzhi Yu s case, the number of training samples as much as possible. )

BPX神经网络在matlab开发环境中的实现实例，有利于初学者学习与掌握(BPX neural network in the matlab environment development to achieve the example, be helpful for beginners to learn and master)