zhongxingdian

CASCADING OF UPSAMPLER(L) & DOWN SAMPLER(M) : we can change the sampling rate of the signal by a rational factor L/M. M followed by L & L followed by M cannot be interchanged when L and M contain a common integer factor, i.e., M and L have a common integer factor greater than one

16PSK和16QAM仿真使用Matlab(16PSK and 16QAM simulation using Matlab)

关于扩展卡尔曼的matlab程序 m文件(a file of EKF about pmsm )

说明：  VMD是一种自适应、完全非递归的模态变分和信号处理的方法。(VMD is an adaptive, completely non-recursive method of modal variational and signal processing.)

Some algorithms of variable step size LMS adaptive filtering are studied．The VS—LMS algorithm is improved． Another new non-linear function between肛and e(／ t)is established．The theoretic analysis and computer simulation results show that this algorithm converges more quickly than the origina1．Furthermore，better antinoise property is exhibited under Low—SNR environment than the original one．(variable step size of a LMS daptive filtering are studied. The VS-LMS algorithm is improved. Another new non-linear function between anus and e (/ t) is established. The theoretic analysis and computer simulatio n results show that this algorithm converges mo 're quickly than the origina1. Furthermore, better antinoise property is exhibited under L ow- SNR environment than the original one.)

OFDM通信方式，包括QAM调制，IFFT以及加入循环码前缀(OFDM modulation)

实现目标物体的检测，包括人脸检测，快速移动物体的检测，以及PCA算法等(Realize the object detection by Matlab,include face detection and fast motion detection and so on.)

双目资料，很好的学习资料，帮助很大，谢谢大家(Binocular information, good learning materials, helpful, thank you)

it is a code to process the data of CWB autosite in TAIWAN

This paper presents a new approach to image segmentation using Pillar K-means algorithm. This segmentation method includes a new mechanism for grouping the elements of high resolution images in order to improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric. This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.