Radar-sensor-switch

这是一个对动力学模型进行仿真的代码，可以得出动力学仿真图像(dynamics simulation )

OFDM调制的的AWGN加瑞丽衰落信道,其它的我就不多说了(OFDM modulation of AWGN and ruili fading channel, the other I will not say)

说明：  图像重建Matlab程序，仅供参考学习，希望对您有用。大家一起学习进步。(Image reconstruction matlab program, only for reference learning, hope to be useful to you. Let's learn and improve together.)

FPGA SDI 输入，HDMI输出例程(FPGA SDI_IN,HDMI_OUT)

连连看源代码及素材，丰富图片资源，适合初学者，大家一起来交换素材吧(Lianliankan source code and material, rich images of resources, suitable for beginners, everyone will work together to exchange material bar)

this Class used to re-sampling image and convert at as you wont and as u need to use it in your program

说明：  非均匀有理B样条 第2版.pdf NURBS理论经典书籍(The nurbs book(second edition) NURBS Theory Classic Books)

说明：  my_noise文件是已知信号方差，加入信号噪声，可以加入高斯噪声，淑盐噪声,等其他噪声十分有用 其他两个函数时加入噪声信噪比SNR,但是有一点,这种方法并不准确,不能保证加入噪声信噪比为SNR,实际测试中可以看出,但是可以加入相同的信噪比的噪声(my_noise document is known to signal variance, by adding signal noise, Gaussian noise can be added, suk salt noise, noise, and other very useful function when the other two noise signal to noise ratio SNR, but there is one point, this method is not accurate , can not be guaranteed by adding the noise signal to noise ratio for the SNR, the actual test can be seen, but can join the same noise signal to noise ratio)

医学图像 CT/MR 图像的融合算法 使用梯度频率对两图像进行增强并融合，代码运行良好，适合初学者学习（注意无配准功能）(Medical image CT/MR image fusion algorithm uses gradient frequency to enhance and fuse two images, the code runs well, suitable for beginners to learn (pay attention to no registration function))

基于Fisher线性判别基础学习器的集成分类器专门用于数字媒体的隐写分析，目前使用高维特征空间。目前，由于其良好的检测精度和较小的计算成本，它可能是设计监督分类器用于数字图像隐写分析的最常用方法。(The ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble.)