DTV.3

各种数据拟合的程序，对初学者很有用。可以做各种图(A variety of data fitting procedure, useful for beginners.)

simulation signal ofdm

Algortihm_difuso_for_scilab_example_with_turbine_amd power_wind

垂直分层编码ZF检测算法程序代码问题，此程序是在调制方式为QPSK或4QAM下，未编码的V-BLAST系统 计算噪声功率时,信号的功率要用总的接收信号的功率.每根接收天线上的噪声功率是相同的.同时总的信号能量归一化,每根天线上发送的信号能量是总的发 送能量的1/Nt,其中Nt是发送天线数, 改变发送天线Nt和接收天线数目Mt,可以比较QPSK和4QAM时4发4收,4发6收,8发8收,8发12收等多发多收的情况 (Vertical stratification coding ZF detection algorithm program code, the process is in a way for QPSK or 4 QAM, not coding V-BLAST system Calculating noise power, the power of the signal with general received signal power. Each root receiving antenna on the noise power are the same. And general signal energy normalization, each blade on the signal energy send antenna is overall hair Send energy 1/Nt, of which is transmitting antenna Nt number, change the transmitting antenna Nt and receiving antenna number Mt, can compare QPSK QAM and 4 when the hair 4 4, 4 hair and accept, eight hair 8 accept, eight hair 12 accept etc multiple collect more of the situation )

Matrix inverse by Gauss-Jordan elimination

TigerSharc TS201 32-bit floating point IIR filter routine. (TigerSharc TS-201 32-bit floating point II R filter routine.)

matlab R2012a源代码（正太分解法计算均值和方差、图方法正太检验、M稳健回归）。((calculated the mean and variance diagram method, inspection, teenage boy M robust regression teenage boy decomposition method). .)

matlab实现试验模态频域参数识别：导纳圆法和最小二乘法（复模态—频响函数实虚部） (matlab achieve experimental modal parameter identification in frequency domain: admittance circle method and the least squares method (complex modal- FRF real and imaginary parts))

说明：  利用时域有限差分（FDTD）仿真二维（2D）横电波（TE）的传播，吸收边界条件为完全匹配层（PML）。(The use of finite difference time domain (FDTD) simulation of two-dimensional (2D) transverse electric wave (TE) the spread of absorbing boundary conditions for the perfectly matched layer (PML).)

Input: X, training data, dim*num Y, training data labels, num*class para.alpha, para.beta, regularization parameters para.rd, reduced dimensionality Web Image Annotation via Subspace-Sparsity Collaborated Feature Selection. Zhigang Ma, Feiping Nie, Yi Yang, Jasper Uijlings, and Nicu Sebe. IEEE Transactions on Multimedia (TMM) 14(4):1021-1030, 2012.