Gravimetry_processing

关于医学图像的基于形状的图像检索。预处理用小波去噪 ，特征选用不变矩。(image preprocessing)

利用MATLAB GUI设计实现图像处理的图形用户界面，利用MATLAB图像处理工具箱实现以下的图像处理功能： 1. 采用matlab Guide设计实现图像界面与按钮界面。 2. 连接摄像头，编写MATLAB初始化程序 3. 用摄像头采集一副图像，并能实现保存（使用控件按钮保存）。 4. 设计按钮实现对图像进行任意的亮度和对比度变化调整，显示和对比度变化前后的图像。 5. 设计按钮实现用户能够用鼠标选取图像感兴趣区域，显示和保存该选择区域。 (Designed and implemented using MATLAB GUI graphical user interface, image processing, using MATLAB image processing toolbox of image processing functions to achieve the following: 1. Designed to achieve an image using matlab Guide button interface with the interface. 2 Connect the camera to write MATLAB initialization procedure 3, with the camera capture an image and save to achieve (using the control button to save). 4 Design button on the image to achieve any change in the brightness and contrast adjustment, display, and contrast changes before and after images. 5 button design to achieve the user to select an image region of interest with the mouse, display and save the selected area.)

LTE下行链路解PBCH会用到CRC检验，本程序就是实现这一功能(LTE downlink solution PBCH will use the CRC test this program is to achieve this functionality)

A working setup that focuses on dimensional prediction of emotions from spontaneous conversational head gestures. It maps the amount and direction of head motion, and occurrences of head nods and shakes into arousal, expectation, intensity, power and valence level of the observed subject as there has been virtually no research bearing on this topic. Preliminary experiments show that it is possible to automatically predict emotions in terms of these five dimensions (arousal, expectation, intensity, power and valence) from conversational head gestures. Dimensional and continuous emotion prediction from spontaneous head gestures has been integrated in the SEMAINE project [

Simulink动态仿真中Stateflow建模存在问题(Simulink dynamic simulation modeling problems in the Stateflow)

Algorithm Given are P training pairs {X1,d1,X2,d2....Xp,dp}, where Xi is (n*1) di is (n*1) No of Categories=R. i=1,2,...P Yi= Augmented input pattern( obtained by appending 1 to the input vector) i=1,2,…P In the following, k denotes the training step and p denotes the step counter within the training cycle Step 1: c>0 , Emin is chosen, Step 2: Weights are initialized at w at small values, w is (n+1)*1. Counters and error are initialized. k=1,p=1,E=0 Step 3: The training cycle begins here. Input is presented and output computed: Y=Yp, d=dp Oi=f(wtY) for i=1,2,….R Step 4: Weights are updated: wi=wi+1/2c(di-oi)Y for i=1,2,…..R. Step 5: Cycle error is computed: E=1/2(di-oi)2+E for i=1,2,…..R. Step 6: If p<P then p=p+1,k=k+1, and go to Step 3: Otherwise go to Step 7. Step 7: The training cycle is completed. For E=0,terminate the training session. Outputs weights and k. If E>0,then E=0 ,p=1, and enter the new training cycle by going to step 3.

该文档一步步的描述如何将matlab源程序打包成可执行exe文件。(This document describes step by step how to make matlab source packaged into an executable exe file.)

细菌优化算法程序，通过动态的演示说明细菌优化算法的优化过程，从而反应了该算法的有效性。(Bacteria optimization process, through dynamic optimization algorithm demonstrates that the optimization process of bacteria, which reflects the effectiveness of the algorithm.)

matlab的实例，是关于模糊c均值的一个小程序(Matlab example of the fuzzy c-means a small program)

说明：  Plot of signal neural