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数字全息和计算程式 计算全息图的制作和再现过程主要分为以下几个步骤： 1抽样。得到物体或波面在离散样点上的值。 2.计算。计算物光波在全息平面上的光场分布。 3.编码。把全息平面上光波的复振幅分布编码成全息图的透过率变化。 4.成图。在计算机控制下，将全息图的透过率变化在成图设备上成图。如果成图设备分辨率不够，再经光学缩版得到实用的全息图。 5.再现。这—步骤在本质上与光学全息图的再现没有区别。 (Digital Holography and CGH for calculating the production and reproduction process is divided into the following steps: 1 sampling. Be objects, or wavefront-like point in the discrete value. 2. Calculated. Calculation of complex light plane in the holographic optical field distribution. 3. Coding. The hologram plane the complex amplitude distribution of light into a hologram coding changes in the transmittance. 4. As Fig. Under the control of the computer, the hologram transmittance change in the mapping device mapping. Mapping equipment, if not resolution, then reduced by the optical version of a hologram to be practical. 5. Reproduction. This is- these in essence a hologram and the optical reproduction is no different.)

说明：  GPS导航中抗差自适应Kalman滤波算法，Matlab源代码。(GPS navigation robust adaptive Kalman filtering algorithms, source code Matlab.)

说明：  关于小波变换的ppt，有用的可以下载看看(Ppt on Wavelet Transform)

使用带通滤波器对图像进行处理，滤除不必要的杂波(Using band-pass filter to do image processing, filtering unnecessary clutter)

小波变换 求模极大值 三次B样条小波变换 希望对大家有帮助 (Wavelet transform modulus maxima cubic B-spline wavelet transform and we hope to)

说明：  关于小波包分析的图像绘制，小波分析，三次分解，单支重构 故障信号(Image registration algorithms based on piecewise linear interpolators (spatiotemporal gradients or block matching) are analyzed to determine subpixel registration accuracy.)

小波变换对电能质量暂态信号的分析和运用，包含电压骤升、骤降等(Wavelet transform for power quality analysis and the use of transient signals, including voltage dips, swells, etc.)

二次小波变换的方法用于数字信号码元速率估计(wavelet transform for bitrate estimation of digital modulated signal)

雷达系统设计部分信号处理程序，包括部分目标和干扰模型、匹配滤波、波形设计、多普勒处理、门限检测及恒虚警率等方面信号处理的程序(Signal processing program for radar system design)

通过设计Visual C程序源码对简单易懂的一维信号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波与Haar小波变换，从而的到小波分解系数；再通过改变分解的到的各层高频系数数进行信号的小波重构达到消噪噪的目的。在这一程序源码实现的过程中能直观地理解信号小波分解重构的过程与在信号消噪中的重要作用，和在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。 可直接 (Design Visual C program source code on a simple one-dimensional signal with a Gaussian white noise Daubechies wavelet, Morlet wavelet and Haar wavelet transform, and thus to the wavelet coefficients and then change the decomposition to each storey wavelet reconstruction frequency coefficient of the number of signal to noise canceling noise. In the process of realization of this program source code can be intuitively understood the signal wavelet decomposition and reconstruction process and an important role in signal denoising, and the selection coefficient in the high frequency coefficients of the layers of the weight of processing noise cancellation signal impact. Can be directly)