66GUIS

SPSA for design of an attentional strategy design

根据直扩原理，首先有随机数发生器产生一系列二进制信息数据（+1，-1），每个信息比特重复Lc次，Lc对应每个信息比特所包含的伪码片数，包含每一比特Lc次重复的序列与另一个随机数发生器产生的PN序列c(n)相乘。然后在该序列上叠加高斯白噪声和形式为i(n)=Acosw0n余弦干扰下次信号，切余弦干扰信号的振幅满足条件A<Lc。在解调器中进行与PN序列的互相关运算，并且将组成各信息比特的Lc个样本进行求和。加法器的输出送到判决器，将信号与门限值0进行比较，确定传送的数据为+1还是-1，计数器用来记录判决器的错判数目。(According to the principle, first some straight expansion random number generator produces a series of binary data (+ 1-1), each information bit repetitive Lc times, Lc for each information bit contains PN code piece count, embraces each bit Lc times repeated sequence and another random number generator produces PN sequence c (n) multiplication. Then in the sequence superimposed on gaussian white noise and forms as I (n) = Acosw0n cosine interference next time signal, cut cosine interfering signal amplitude satisfy conditions A < Lc. In the modem with the cross-correlation operation PN sequence, and the information will comprise the Lc a sample bits peace. The output of the adder judgment device, will signal to compare with threshold 0 data transmitted, determine for+ 1 or-1, counter is used to record the number victimised judgment. )

主要以英文版的介绍ofdm系统的内容请大家参阅(ofdm)

MATLAB小波分析高级技术中的基于小波变换域的数字水印MATLAB源代码。(High MATLAB wavelet analysis technique based on the wavelet transform domain digital watermarking MATLAB source of generation code.)

画方波和方波的频谱的matlab程序！画方波和方波的频谱的matlab程序(Picture of the frequency spectrum of square wave and square wave of matlab)

说明：  TOOLS REQURIED FOR MATLAB

遗传算法工具包，matlab 7.0包含了这个工具箱，matlab7.0以前的版本没有这个工具箱，雷英杰编著《Matlab遗传算法工具箱及应用》对这个工具箱的使用进行了介绍(Genetic Algorithm Toolkit, matlab 7.0 contains the toolbox, matlab7.0 previous versions do not have this toolbox, LEI Ying-jie edited )

在matlab中计算图像中的欧拉数，这是一个很有创新的例子(In matlab to calculate the Euler number of images, this is a great example of innovation)

用于跳频序列分析的matlab程序以及信号处理工具函数(For frequency hopping sequence analysis and signal processing tools matlab program functions)

考虑L的三个不同值：L＝256（3个数据段），L＝128(7个数据段)和L=64（15个数据段）。各自的谱估计图如上图所示。可以明显的看到，加窗明显的减小了频谱上的假谱峰，但也更加进一步平滑了谱峰。所以，对于L＝64的情况，在ω＝0.8π的谱线可以很确定的辨认，但是那两个靠近的谱峰不容易区分。对于L＝128的情况，这种情况提供了在分离和检测间最好的均衡。当然，对于在L＝256时的情况，效果是更好的，能够从谱估计图上明显的分辨出三条谱线的存在以及它们幅度关系的强弱。 除了Welch法外，还可以采用对多个周期图求平均的功率谱估计方法的其他方法如Bartlett法等等，在功率谱估计上也能取得较好的结果。(consider three different values : L = 256 (3 data), L = 128 (7 data) and L = 64 (15 data). Their spectral estimation map as shown in Fig. It is clear to see that increase significantly decreasing window of the spectrum of false peaks, but much more to further smooth the peaks. Therefore, L = 64, in the case = 0.8 spectrum can determine the identification, but that the two peaks around is not easy to distinguish. L = 128 for the situation, which provided the separation and detection among the best balanced. Of course, L = 256 in the case, the effect is better, from the spectral estimation map clearly distinguish between the three lines of the existence and their relationship to the rate of strength. In addition to Welch law, but also can be used to map multiple cycle for the average power)