randint

Convert a color image between color representations. B = COLORSPACE(S,A) converts the color representation of image A where S is a string specifying the conversion. S tells the source and destination color spaces, S = dest<-src , or alternatively, S = src->dest . Supported color spaces are RGB R G B Red Green Blue (ITU-R BT.709 gamma-corrected) YPbPr Luma (ITU-R BT.601) + Chroma YCbCr / YCC Luma + Chroma ("digitized" version of Y PbPr) YUV NTSC PAL Y UV Luma + Chroma YIQ NTSC Y IQ Luma + Chroma YDbDr SECAM Y DbDr Luma + Chroma JPEGYCbCr JPEG-Y CbCr Luma + Chroma HSV / HSB Hue Saturation Value/Brightness HSL / HLS / HSI Hue Saturation Luminance/Intensity XYZ CIE XYZ Lab CIE L*a*b* (CIELAB) Luv CIE L*u*v* (CIELUV) Lch CIE L*ch (CIELCH)(Convert a color image between color representations. B = COLORSPACE (S, A) converts the color representation of image A where S is a string specifying the conversion. S tells the source and destination color spaces, S = ' dest <-src' , or alternatively, S = ' src-> dest' . Supported color spaces are ' RGB' R' G' B ' Red Green Blue (ITU-R BT.709 gamma-corrected)' YPbPr ' Luma (ITU-R BT.601 )+ Chroma ' YCbCr' /' YCC' Luma+ Chroma ( " digitized" version of Y' PbPr) ' YUV' NTSC PAL Y' UV Luma+ Chroma ' YIQ' NTSC Y' IQ Luma+ Chroma ' YDbDr' SECAM Y ' DbDr Luma+ Chroma ' JPEGYCbCr' JPEG-Y' CbCr Luma+ Chroma ' HSV' /' HSB' Hue Saturation Value/Brightness' HSL ' /' HLS ' /' HSI ' Hue Saturation Luminance/Intensity' XYZ ' CIE XYZ' Lab ' CIE L* a* b* (CIELAB)' Luv ' CIE L* u* v* (CIELUV)' Lch ' CIE L* ch (CIE)

光纤光栅包层不同时，纤芯基模折射率与波长之间的关系(the relationship between the refractive index of the core fundamental mode fiber grating wavelength between cladding)

audio classification using SVM for 5 classes

ga for peaks function

链路自适应的matlab程序，从BPSK到256QAM，码率1/2.2/3,3/4,5/6(link adaptation simulation in matlab)

simulation de moteur

signal transient crosstalk noise

三相并网逆变器电流滞环控制MATLAB仿真模型(Simulink model of thre-phase grid-connected with current hysteresi controller)

为了有效地应用遗传算法解决 鲁棒控制系统设计问题，将遗传算法与局部优化方法相结合，提出了基于降维扫描方法的自适应多目标遗传算法（DRSA-MOGA）。通过引入适应度函数标准化方法、基于最优Pareto解集搜索的降维扫描方法和适应度函数自适应调整方法，提高了算法的全局优化性能和局部搜索能力。仿真结果表明，DRSA-MOGA算法在不损失解的均匀度的情况下可以达到很高的逼近度(For effective application of genetic algorithms to solve robust control system design problems, genetic algorithms and local optimization method, based on reduced-order adaptive scanning method multi-objective genetic algorithm (DRSA-MOGA). Fitness function through the introduction of standardized methods, based on the Pareto optimal solution set of search methods and dimensionality reduction scan fitness function adaptive adjustment method, the algorithm improve the performance of global optimization and local search capabilities. The simulation results show that, DRSA-MOGA algorithm solution without loss of uniformity can be achieved under a high degree of approximation)

计算VaR的，多种方法，功能比较强大，可以学习后替换成自己需要的(VaR calculation, and a number of ways, more powerful function, can be replaced after learning their needs)