两区四机系统算例

说明：  这是一个基于pocketsphinx的例子程序，已经在andtoid studio编译成功，并能识别简单的词汇。这是第7部分(This is an example program based on pocketsphinx, which has been successfully compiled in andtoid studio and can recognize simple words. This is the 7th part.)

这是一篇关于相机标定的文章。该方法的特点是使用LCD屏幕而不使用棋盘格标定板。(This is an article about camera calibration. The characteristic of this method is to use LCD screen instead of checkerboard calibration board.)

说明：  建立水声信道的射线模型在工程应用上有重要的意义。射线声学是把声波的 传播看作是一束无数条垂直于等相位面的射线的传播，每一条射线与等相位面相 垂直，称为声线。声线途经的距离代表波传播的路程，声线经历的时问为波传播 的时间。声线束所携带的能量即为波传播的声能量。与几何光学相似，射线声学 的描述方法给人直观的感觉，在一定的条件下，射线声学的数学运算也比较简捷， 由射线理论给出的声线图可以给声场以直观、形象的理解。 本模型可以准确的衡量声波在海洋中传播路径(Establishing ray model of underwater acoustic channel is of great significance in engineering application. X-ray acoustics refers to sound waves. Propagation is regarded as the propagation of a bunch of innumerable rays perpendicular to the isophase plane, each ray being in phase with the isophase plane. Vertical, called sound line. The distance through which the sound line Travels represents the distance of the wave propagation, and the time that the sound line experiences is the wave propagation. The time. The energy carried by the sound beam is the sound energy of wave propagation. Like geometric optics, ray acoustics The descriptive method gives people an intuitive feeling. Under certain conditions, the mathematical operation of ray acoustics is relatively simple. The sonogram given by ray theory can give an intuitive and visual understanding of the sound field.Accurate measurement of sound wave propagation in the ocean)

OFDM信道估计算法仿真,统计无线OFDM系统信道估计算法的误码率。-The channel estimation of OFDM system and error rate caculation.

VB6.0 餐饮就餐席位管理系统，功能包括开桌，换桌，结账，打印等模块，数据库为SQLSERVER，调试前请先配置好数据库，请注意：如你不是系统管理员，请不要私自修改系统设置，否则将导致不可用。

说明：  146849215+8182161261281(1456216126184581hrhrqhq)

说明：  立体匹配 计算机视觉 SSD+SAD+NCC算法(SSD+SAD+NCC algorithm)

浏览MODIS遥感数据的HDF格式数据的IDL程序-Browse MODIS remote sensing data of HDF format data IDL procedures

说明：  python编写，使用sklearn机器学习库里的svm分类器，以MNIST为数据集，手动输入数字分类，MNIST.py为训练代码，训练10000次准确率为93.93%，手动3.py为手动输入并分类的代码。(Written in Python, using the svm classifier in the sklearn machine learning library, using MNIST as the data set, manually input the numeric classification, MNIST.py is the training code, the accuracy of training 10000 times is 93.93%, manual 3.py is manually entered and classified Code.)

Basic Nibbles (NO MFC) good