PSO

生产信息与生产运营相关的热用户信息是各级决策者进行生产决策的两个关键因素。生产信息的及时、准确地获取和分析，能够及时掌握生产运营状况，在保证用户满意度的情况下，进行合理决策，来控制生产运营成本，提高公司整体效益信息处理是一个完整的概念。 (Production of information and manufacturing operations related to the heat user information is the decision-making at all levels of decision-makers to produce two key factors. Production information in a timely and accurate acquisition and analysis, and timely information on production and operation status, to ensure customer satisfaction in the circumstances, the rational decision-making, to control the production and operation costs and improve the overall efficiency of information processing company is a complete concept.)

包括了卡尔曼滤波、小波分解和Allan方差分析的matlab程序，运行时需要自己建立一个数据文件，载入数据后即可运行。(including the Kalman filter, wavelet decomposition and Allan variance analysis of Matlab procedures, Runtime creating a need for data files, data can be included in the operation.)

生成拍波信号：由自己控制采样频率，拍波频率，拍波个数，拍波周期数和拍波之间的时间间隔和拍波幅值，能生成符合上述要求的任意拍波。(Generate beat-wave signal: control by its own sampling frequency, beat-wave frequency, the number of beat-wave, beat-wave cycles and the time interval between the beat-wave amplitude and beat, meet the above requirements can generate any beat-wave.)

本压缩文件中，含有3个图像增强的Matlab程序，包括用小波方法及非小波实现图像的增强(The compressed file, containing three of the Matlab image enhancement procedures, including the use of wavelet methods and non-wavelet image enhancement)

3rd order Sigma delta ADC simulink 模型(3rd order Sigma delta ADC simulink model)

KALMAN滤波的基本算法，很有用的，适合初学者学习。(KALMAN filtering the basic algorithm, useful for beginners to learn.)

带有三个频率循环的UWB天线的时域分析程序(UWB antenna for time domain analysis)

说明：  CFD计算中的RANS湍流模型中，入口处的K和e的初始化(Initialization of K and e at the entrance of RANS turbulence model in CFD calculation)

计算“高位平面图像”的伪Zernike矩（低 6阶），并作为特征。(Computing " high dimensional images," the pseudo-Zernike moments (low order 6), and as a feature.)

Descriptor for simple shapes (e.g letters). Computes the descriptor twice in order to describe horizontally and vertically with the same accuracy. Input: image (2-D, binary or gray) depth of recursion (default 7), plotFlag(if on - plot Illustration) Output: vec - descriptor: Division locations, values around zero. Of length 2*(2^depth - 2) (levels: the depth of the division locations.)