get_peak

matlab的通信原理实验指导，对需要查东西的人很有帮助(matlab communications guidance principle experiment, the things people need to check very helpful)

卡尔曼的程序，里面有个基础的卡尔曼程序还有一个图像跟踪的卡尔曼程序。(Kalman s procedure, which has a basic program and a picture of Kalman Kalman tracking procedures.)

Maple Bai 2: Tinh toan trong maple

this file is about OPC algorithm

matlab 使用神经网络工具箱进行识别的例子，使用了bezdekIris数据作为实例，代码中包含数据，需用到matlab的神经网络工具箱。(matlab neural network toolbox identification using examples, the use of bezdekIris data as examples, the code contains test data, it required matlab neural network toolbox to run.)

无线传感器网络QoS路由Speed算法，matlab程序(Wireless sensor networks, QoS routing Speed ​ ​ algorithm. M )

是说明一个程序根据其数据n的规模大小 所使用的大致时间和空间(Is a diagram illustrating the substantially time and space of a program to be used according to the size of its data n)

一个三维的有限元程序代码， 程序里有详细的注释，包括网格的剖分等 参考文献，输入文档。(A three-dimensional finite element program code, program, there are detailed notes, including grid mesh and other references, the input document.)

We propose a novel fluorescence emission difference microscopy (FED) system based on focusing cy- lindrical vector beams. In conventional FED, a Gaussian beam and a 0–2 π vortex phase plate are used to generate solid and hollow spots. We focus radially polarized and azimuthally polarized cylindrical vector beams to obtain an expanded solid spot and a shrunken hollow spot, taking advantage of the optical properties of cylindrical vector beams to improve the conventional FED performance. Our novel method enhances FED performance

GMM-GMR is a set of Matlab functions to train a Gaussian Mixture Model (GMM) and retrieve generalized data through Gaussian Mixture Regression (GMR). It allows to encode efficiently any dataset in Gaussian Mixture Model (GMM) through the use of an Expectation-Maximization (EM) iterative learning algorithms. By using this model, Gaussian Mixture Regression (GMR) can then be used to retrieve partial output data by specifying the desired inputs. It then acts as a generalization process that computes conditional probability with respect to partially observed data.