delete-vars-from-eval

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此为matlab详尽学习资料第十章“高级数值计算”(This is a detailed matlab learning materials Chapter 10, " Advanced numerical computing" )

tempales very important very important very important

通信系统中随机过程的Matlab全部代码(Communication System random process all the Matlab code)

标准压缩感知（CS）理论决定了可靠的信号恢复是可能给M= O（KLOG（N / K））的测量。我们证明了它可以通过利用超越简单的稀疏性和可压缩性由包括价值观和信号系数的位置之间的依赖关系更加逼真信号模型大大降低Mwithout牺牲的鲁棒性。(The standard compressive sensing (CS) theory dictates that robust signal recovery is  possible from  M=O(Klog(N/K))  measurements. We demonstrate that it is possible to substantially  decrease  Mwithout sacrificing robustness by leveraging more  realistic signal models that go beyond simple sparsity and  compressibility by including dependencies between values and  locations of the signal coefficients.   )

研 究了双目视觉测量系统的结构参数优化问题.对双目视觉测量的有效视场约束条件进行了数学 描述，建立了双目视觉坐标测量系统的数学模型.在考虑像点提取误差的条件下，利用误差理论建立了测量 误差模型，并基于待测量的视场范围要求，定义了测量系统结构参数的优化指标。最后，通过枚举法进行了 优化求解.(Studied binocular vision measurement system to optimize the structural parameters of the problem. For the effective measurement of binocular vision bound by the terms of field of view of mathematical description of the establishment of binocular vision coordinate measuring system mathematical model. In considering the error as the point of extraction conditions the use of error theory the establishment of measurement error model and measurement based on the field of view to be the scope of the requirements of the definition of the measurement system parameters to optimize the structure of indicators. Finally, the optimized enumeration method for solving.)

模拟长周期光纤光栅（LPG）的损耗波的光谱特性(The loss of analog spectrum LPG )

Matlab Apriori Assosication Rule mining

齿轮传动参数测定和公法线公差计算 可以用来优化齿轮的传动系统(Gear parameters measured and calculated tangent tolerance can be used to optimize the transmission gear)

fit_rayleigh_pdf - Non Linear Least Squares fit of the Rayleigh distribution. given the samples of the histogram of the samples, finds the distribution parameter that fits the histogram samples.fits data to the probability of the form: p(r)=r*exp(-r^2/(2*s))/s with parameter: s format:result = fit_rayleigh_pdf( x,y,W,hAx ) input: y - vector, samples of the histogram to be fitted x - vector, position of the samples of the histogram (i.e. y = f(x,a)) W - matrix or scalar, a square weighting matrix of the size NxN where N = length(y), or 0 to indicate no weighting is needed. hAx - handle of an axis, on which the fitted distribution is plotted output: result - structure with the fields s - fitted parameter VAR - variance of the estimation type- weighted LS or not weighted LS iter- number of iteration for the solution