调用摄像头示例

用MATLAB实现人工势场算法作路径规划(Using MATLAB to Realize Artificial Potential Field Algorithms for Path Planning)

说明：  基于高频信号注入的三相永磁同步电机无传感器控制(Sensorless control of three phase permanent magnet synchronous motor based on high frequency signal injection)

获取硬件信息 获取硬件信息 -access to the hardware access to information hardware access to information hardware access to information hardware information

Heading control of AUV

说明：  % Type of different detectors, parameters for Detector.m ML = 1; % Joint ML Detector JMMSE = 2; % Joint MMSE Detector ZF = 3; % Joint Zero-Forcing Detector % Type of different antenna selection criteria methods MBER = 1; MMI = 2; LAZY = 3; MNP = 4; %Minimum Noise Power MMNP = 5; LAZY2 = 6; real_ch = 0; % real_ch = 0 refers to simulated channel in MATLAB, otherwise refers to real channel SNR_dB = 20;% transmit SNR in the simulated channel nr_frames = 20; % number of frames sent when simulation. detect_type = ZF; % Detector choice select_type = MBER; % Antenna selection choice disp(''); tx(nr_frames); disp('Press any key to continue'); pause RF(real_ch,SNR_dB); disp('Press any key to continue'); pause BER = rx(detect_type,select_type);

本文档是计算机组成原理的课堂讲义，准确的讲述了该课程的主要内容(This document is a computer organization class notes, accurately describes the main elements of the course)

Android手势识别实例 仅用于手机输入法，Himi提示：手势超过9个我做了删除所有手势的操作，为了界面整洁一些！输入法手势练习~(*^__^*)~ 嘻嘻！ 　　操作介绍：1.(画手势我设置必须画两笔划才行哦~)添加手势：先EditText中输入名称，然后在屏幕上画出手势！2.匹配手势：在EditText输入"himi",然后输入手势即可！ 　　注意保存的路径默认是/sdcard/gesture ,so~别忘记AndroidMainfest.xml加上读写权限！ 　　这里抱怨一下，咳咳、其实昨天就应该出这篇博文的，就是因为这里总是异常,今天仔细看了才发现不是没写权限,而是我虽然在AndroidMainfest.xml中写了权限，但是写错了位置..哭死！

协同区间偏最小二乘 siPLS算法是 N rgaard等对其提出 的 iPLS方法的改进, 其基本算法步骤如下:(1)对原始谱图进行 预处理;(2)在全谱范围内建立全局偏最小二乘模型, 即上节的 模型;(3)在整个光谱区间采用 iPLS建立多个等窗口宽度的子 区间, 假设为 n个;(4)在每个子区间上建立偏最小二乘法模型, 即可得到 n个局部模型;(5)以交叉验证时的均方根误差 RMSE 值为各模型的精度衡量标准, 比较全光谱模型和各局部模型的 精度;(6)组合精度最高的局部子区间为新的回归区间;再在新 的回归区间建立 siPLS模型(Cooperative interval partial least squares (siPLS) algorithm is proposed by N rgaard et al. The improvements of the iPLS method are as follows: (1) the original spectrogram is performed. Preprocessing; (2) establishing a global partial least squares model in the full spectrum, that is, the upper part of the model. Model; (3) use iPLS to establish multiple equal window widths in the whole spectral interval. The interval is assumed to be n; (4) a partial least squares model is established on each sub interval. N local models can be obtained; (5) the root mean square error of cross validation is RMSE The values are the accuracy criteria of each model, and the comparison between the full spectral model and the local models is made. (6) the local interval with the highest combined precision is a new regression interval; and then new. The establishment of siPLS model in the regression interval)

利用geatpy库是实现多目标优化， 基于改进NSGA-Ⅱ算法求解多目标优化问题的进化算法模板，传统NSGA-Ⅱ算法的帕累托最优解来只源于当代种群个体，这样难以高效地获取更多的帕累托最优解，同时难以把种群大小控制在合适的范围内，改进的NSGA2整体上沿用传统的NSGA-Ⅱ算法，不同的是，该算法通过维护一个全局帕累托最优集来实现帕累托前沿的搜索，故并不需要保证种群所有个体都是非支配的。(Using geatpy library to realize multi-objective optimization)

说明：  digital gates and coding