dianliuxiebohanliang110

说明：  可根据所给参数绘制出头模型图形，并对其进行投影和反投影重建的算法，可正常运行，得到较为理想的结果。(Can be drawn according to the parameters of his early model of graphics, and its projection and back projection reconstruction algorithm, running, get more desirable results.)

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

in this program we estimate the random phase of the channel and then we will compensate it

包括64QAM,16QAM,8QAM,4QAM,高斯白噪声(involved 64QAM,16QAM,8QAM,4QAM,under gngauss channel)

The noises are one of the research topic in image processing. In digital images removal of gaussian noises using bilateral filter is performd in this file

统计学习编程题目，是在做作业的时候碰到的一些小例子，对统计学习课程的学习有帮助(Statistical learning programming problem, some small examples encountered at homework time, can be helpful to learn the statistical learning courses)

student s experiment majored in automation

关于卡尔曼滤波的最经典起步资料，介绍卡尔曼滤波协同算法，值得学习(A classical paper which introduce the Kalman filtering algorithm,especially for the Kalman-Consensus filter)

说明：  一个关于随机二进制M序列的一种产生的程序(M on a random binary sequence generated by a procedure)

BROADBAND_MATCH_AMPLIFIER_OBJECTIVE_FUNCTION Is the objective function