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一个给予matlab编写的2维TM波的FDTD程序(Matlab prepared to give a two-dimensional TM wave FDTD program)

sobel filter for image processing

基于LMS自适应时延估计算法,可以进行时间延迟的估计。(Delay estimation algorithm based on LMS adaptive research based on the fractional lower order time delay estimation algorithm, including: covariation-based time delay estimation, based on the fractional lower order covariance delay estimation method, as well as the minimum norm when the average P delay estimation method.)

共振解调的源代码，用于对滚动轴承的振动信号处理，并通过fft变换，进行故障诊断(Resonance demodulation source code for rolling bearing vibration signal processing and through the fft transform, fault diagnosis)

硕士学位论文捷联惯性系统初始对准研究 惯导系统的初始对准是影响系统使用性能的关键技术之一，对准的精度与速度直接关系到惯性统的精度与启动特性。对卡尔曼滤波及其在初始对准中的应用进行了研究。首先介绍了卡尔曼滤波理论的应用背景，然后推导了离散卡尔曼滤波方程，并对连续系统的状态方程进行离散化提出了适用于舰载捷联惯性系统的动基座对准的2通道10个状态变量和2通道12个状态变量的系统动态模型，并建了相应的速度匹配和位置匹配量测模型。 (master's degree thesis SINS initial alignment study INS initial alignment is affecting system performance one of the key technologies, the alignment precision and speed is directly related to the precision inertial reunification with the startup characteristics. The Kalman filter and the initial alignment of the study. First introduced the theory of Kalman filtering background, and then reasoned a discrete Kalman filter equations, also for the state of discrete equations apply to the carrier SINS System Moving Base Alignment of two channels 10 state variables and two 12-channel system state variables dynamic model and built a corresponding speed matching and position matching measurement model.)

MVDR是空域DOA的一种方法。可以对信号进行加强，对干扰进行抑制，同时去除高斯白噪声。最终得到方向。(MVDR is a method of DOA airspace. The signal can be enhanced interference suppression while removing Gaussian white noise.)

本程序是基于方向优先权的机器人最优路径寻找算法，也可用于求解迷宫的最短路径。(This procedure is based on the priority direction of the robot to find the optimal path algorithm can also be used to solve the shortest path to the maze.)

无线电空间谱估计测向仿真MALAB源程序(Spatial spectrum estimation to the radio simulation MALAB source)

Prueba1 FletcherReeves Hernández Santiago José Maestría en Ciencias de la Computación Septiembre / 2011 1. Comenzar con un punto arbitrario 2. Calcular Gradiente de Fi 3. Si el Gradiente Fi es igual a 0(converge), termina 4. Si el Gradiente Fi es !=0 continuar 5. Encontrar dirección de búsqueda Si= -GradienteFi= - Gradiente F(Xi) 6. Determinar la Longitud Optima del incremento lamda(i) en dirección Si X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi) 7. Hacer i=2 8. Obtener Gradiente Fi 9. Calcular Si= -GradFi + ( [abs(GradFi)^2]/[abs(GradF(i-1))^2] )*S(i-1) 10. Determinar la Longitud Optima del incremento lamda(i) en dirección Si X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi) 7. Verificar Optimalidad de X(i+1) Si es optimo, detener Si no es optimo hacer i=i+1 e ir al paso 8

基于matlab的平行束滤波反投影重建算法(Based on parallel beam filtered backprojection reconstruction algorithm matlab)