double

scalar quantization of image and communication over erassure channel and error removal

雷达系统建模与仿真：回波波形、中放后波形、I/Q相干检波后波形、脉压后波形、MTI对消后波形、相参积累后波形、取模后波形、cfar后波形。(Modeling and simulation of radar systems: echo waveform, put after the waveform, the I/Q coherent the inspection Bo Houbo shaped pulse pressure after the waveform, the MTI elimination waveforms, coherent waveform modulo waveform after waveform cfar.)

Jpeg2000Gui的MATLAB程序实例，适合JPEG2000算法的初学者。(Jpeg2000Gui MATLAB procedures example, JPEG2000 algorithm suitable for beginners.)

一种最大Lyapunov指数的改进算法，以交通流混沌判别为例(Improved algorithm for maximum Lyapunov exponent, for example in order to determine the chaotic traffic flow)

introduction MIMO system

说明：  最速下降法是迭代法的一种,可以用于求解最小二乘问题(线性和非线性都可以)。在求解机器学习算法的模型参数，即无约束优化问题时，梯度下降（Gradient Descent）是最常采用的方法之一，另一种常用的方法是最小二乘法。在求解损失函数的最小值时，可以通过梯度下降法来一步步的迭代求解，得到最小化的损失函数和模型参数值。反过来，如果我们需要求解损失函数的最大值，这时就需要用梯度上升法来迭代了。在机器学习中，基于基本的梯度下降法发展了两种梯度下降方法，分别为随机梯度下降法和批量梯度下降法。(The steepest descent method is a kind of iterative method, which can be used to solve the least squares problem (both linear and nonlinear). In solving the model parameters of machine learning algorithm, that is, unconstrained optimization, gradient descent is one of the most commonly used methods, and the other is the least square method. When solving the minimum value of loss function, the gradient descent method can be used step by step to get the minimum value of loss function and model parameters. Conversely, if we need to solve the maximum value of the loss function, then we need to use the gradient rise method to iterate. In machine learning, two kinds of ladders are developed based on the basic gradient descent method)

optimization power flow

说明：  基于clarke模型的瑞利多径衰落产生器，由移动台速度确定的瑞利衰落随机过程(A Rayleigh fading simulator based on Clarke s Model. Creates a Rayleigh random process with PSD determined by the vehicle s speed)

ti is TV-L2 model for deblurring image

说明：  主要是matlab7.0在数字信号处理中的应用，仅供初学者看，本人是菜鸟级人物。(Mainly matlab7.0 in digital signal processing applications, only a beginner, I was the rookie-level characters.)