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用matlab实现MP算法进行信号重构，有详细的注释(MP algorithm using matlab to achieve signal reconstruction, detailed notes)

产生一个长为1000的二进制随机序列，“0”的概率为0.8，”1”的概率为0.2； 对上述数据进行归零AMI编码，脉冲宽度为符号宽度的50 ，波形采样率为符号率的8倍，画出前20个符号对应的波形（同时给出前20位信源序列） 改用HDB3码，画出前20个符号对应的波形 改用密勒码，画出前20个符号对应的波形 分别对上述1000个符号的波形进行功率谱估计，画出功率谱 改变信源“0”的概率，观察AMI码的功率谱变化情况(A length of 1000 random binary sequence, and " 0" with a probability of 0.8, " 1" with a probability of 0.2 the AMI code above zero, the pulse width of the symbol width of 50 , the waveform sampling rate Symbol rate 8 times, draw the top 20 of the waveform corresponding to the symbol (the first 20 and gives the source sequence) switch HDB3 code, the first 20 to draw a waveform corresponding to the symbol to switch Miller code, the first 20 symbols correspond to draw The waveforms of the symbol of the waveform 1000 power spectrum estimation, shown to change the source power spectrum " 0" , the probability of observation of the power spectrum of AMI code changes)

利用拉普拉斯变换的思想，通过对数据进行差分，近似得到拉普拉斯变换后的结果，然后检查正负交换的位置，则此为极值点位置，利用此思想得到极值点的位置。(Using the ideas of Laplace transform of the data by the difference approximation get results Laplace transform, and then check the position of the positive and negative exchange, this is the extreme position, take advantage of this thinking of getting extreme points location.)

说明：  matlab计算的小程序,适合matlab爱好者(matlab calculate the small program for matlab lovers)

matlab图象处理程序,第五章图象处理程序处理程序.(Matlab image processing procedures, the fifth chapter image processing procedures.)

图像融合算法实现 MATLAB版图像融合算法实现 MATLAB版图像融合算法实现 MATLAB版(MATLAB version of Image Fusion Algorithm)

说明：  一个有关核PCA的程序 主要用于模式识别和正交分解中(A nuclear PCA process is mainly used for pattern recognition and orthogonal decomposition)

The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima–media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale–space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen–intima

此例可采用BP神经网络算法用于设备故障类型的模式识别程序。 本次为变压器故障（均为低压测故障）类型的识别。(This example can be used BP neural network algorithm for pattern recognition procedures for equipment fault type. The identification of the type of transformer fault (both low pressure fault detection).)

课程设计中首先采用Ising model的思想建立一个二维的模型，然后利用重要性抽样和Monte Carlo方法及其思想模拟铁磁-顺磁相变过程。计算了顺磁物质的能量平均值Ev、热容Cv、磁化强度M及磁化率X的值，进而研究Ev、Cv、M、X与温度T的变化关系并绘制成Ev-T图、Cv-T图、M-T图、X-T图，得出顺磁物质的内能随着温度的升高先增大而后趋于稳定值；热容Cv、磁化率X随着温度的升高先增大后减小；磁化强度M在转变温度Tc处迅速减小为零，找出铁磁相变的转变温度Tc大约为2.35(First of all, curriculum design idea of the Ising model used to establish a two-dimensional model, the importance of sampling and then use Monte Carlo simulation methods and their idea of ferromagnetic- paramagnetic phase transition process. Paramagnetic material calculated average energy Ev, heat capacity Cv, magnetization M and susceptibility X value, and then study Ev, Cv, M, X and temperature changes in the relationship between T and plotted into Ev-T map, Cv- T diagram, MT map, XT maps drawn paramagnetic material can be increased as the temperature increased and then stabilized value heat capacity Cv, magnetic susceptibility X as the temperature increases after the first minus small magnetization M in the transition temperature Tc decreases rapidly to zero Department to identify ferromagnetic phase transition around the transition temperature Tc for 2.35)