Improved_DL_code

利用MATLAB对扩频信号进行检测和参数估计，结果表明本方法具有在低信躁比下检测的优势(The use of MATLAB for spread spectrum signal detection and parameter estimation results show that this method is in the low than under the letter impatient edge detection)

Mie散射算法的实现，用于Mie散射研究和激光探测 (Mie scattering algorithm, for Mie scattering and laser detection)

说明：  双发多收的8PSK仿真，统计MIMO的误码率(8PSK overcharged double simulation, statistical MIMO bit error rate)

画出直方图，并将该直方图均衡化，以达到图像画面均匀。(Draw the histogram and the histogram equalization in order to achieve uniformity of the image screen.)

　多输入多输出正交分频多工模拟器。 　　 　　正交分频多工:OFDM模拟器(外层函数) 　　 　　 　　create_channel:产生一个瑞利衰落信道复用,解的天线配置、正交分频多工结构、power-delay档案。 　　 　　 　　svd_decompose_channel:既然全网络知识是假设,在并行传输奇异值模式。这个函数分解这些频道模式。 　　 　　 　　BitLoad:适用bit-loading算法来实现预期的钻头和能量 (MIMO OFDM Simulator: OFDM.m: OFDM Simulator (outer function) create_channel.m: Generates a Rayleigh fading frequency-selective channel, parametrized by the antenna configuration, the OFDM configuration, and the power-delay profile. svd_decompose_channel.m: Since full channel knowledge is assumed, transmission is across parallel singular value modes. This function decomposes the channel into these modes. BitLoad.m: Apply the bit-loading algorithm to achieve the desired bit and energy allocation for the current channel instance. ComputeSNR.m: Given the subcarrier gains, this simple function generates the SNR values of each channel (each singular value on each tone is a separate channel). chow_algo.m: Apply Chow s algorithm to generate a particular bit and energy allocation. EnergyTableInit.m: Given the SNR values, form a table of energy increments for each channel. campello_algo.m: Apply Campello s algorithm to converge to the optimal bit and energy allocatio)

适当选择辅助变量，使之满足相应条件，参数估计值就可以是无偏一致。估计辅助变量法的计算量与最小二乘法相当，但辨识效果却比最小二乘法好的多。尤其当噪声是有色的，而噪声的模型结构又不好确定时，增广最小二乘法和广义最小二乘法一般都不好直接应用，因为他们需要选用特定的模型结构，而辅助变量法不需要确定噪声的模型结构，因此辅助变量法就显得更为灵活，但辅助变量法不能同时获得噪声模型的参数估计。(Choose appropriate secondary variables, meet the relevant conditions and parameter estimate can be unbiased consistent. Estimated auxiliary variable method calculation and least square method is quite, but the identification effect is much better than the least square method. Especially when the noise is colored, and noise model structure and not sure, augmented the least squares and the generalized least squares method is generally not used directly, because they need to choose specific model structure, and auxiliary variable method does not need to make sure that noise model structure, so the auxiliary variable method is more agile, but not at the same time auxiliary variable method for noise model parameter estimation.)

Edge detection result should be enhanced using linear method like Median filter to remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the centre of the pupil by counting the number of black pixels (zero value) of each column and row. Then get each row and column that has the maximum number of these black pixels. Then determine the center by simple calculation according to the image coordinate to set it correct on the image, consequently we can determine the radius of the pupil. Thus we can find the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms of circle and ellipse.

Steganography for images

此文件为matlab实例源文件，可以直接用在实例中，可加以修改，应用广泛。(matlab)

说明：  Matlab实现多线性主成份分析(MPCA)(Matlab multi linear principal component analysis (MPCA))