TwoDOF_Mass_Spring

说明：  摘 要 盲图像恢复的主要困难是信息不足，而为了恢复图像和确定点扩散函数需要适当的先验知识。解决这个问题的法、 法以及正则化方法等。但是这些方法的计算量都太大，针对上述方法的不足，文章提出了一种恢复图方法有图像的新算法，它通过恢复残差的最小化和后验概率的最大化来估计参数和恢复图像。其中，巧妙地利用了最陡梯度法和 共轭梯度法的迭代求解。对由于运动造成的模糊图像，可以明显地改善图像的质量，实验结果证明，在对模糊操作没有严格限制的情况下，仍可得到较好的恢复图像。(： : 2$*/<$ I?E L691/6@ F9MM9G:0=@ R9=? S092F 91/DE 6EC=76/=972 9C 92C:MM9G9E2= 92M761/=972$I?9C G/00C M76 L67LE6 92G76L7 （ ） =972 7M/L69769 U27R0EFDE /S7:= =?E 91/DE /2F =?E L792=8CL6E/F M:2G=972 &[ $RE008U27R CL/GE8/F/L=9OE 6ED:0/69</= 1E=?7F /2F 3+ 1E=?7F M76 91/DE 6EC=76/=972 /6E E =E2FEF =7 /FF6ECC =?9C L67S0E1$] 2ER 1E=?7F 9C L67L7CEF =7 6EC= S0:6 91/DE :C92D+5 /2F 5E/C=V:/6EC 1E=?7F$W= :CE 192919<E/G7C= M:2G=972 /2F 1/ 919<E &[ M:2G=972 =7 6EC= =?E 91/DE /2F 9FE2=9M@ =?E L/6/1E=E6C$I?9C 1E=?7F :CE C=EELEC= FECGE2= /2F G72Q:D/=E D6/F9E2= 1E=?7FC =7 192919<E ， G7C= M:2G=972 /2F 1/ 919<E L7C=E69769 L67S/S909=@ =7 91L67OE =?E S0:6 91/DE X?9G? 9C /0C7 =7 91L67OE =?E 91/DE V: =@ 92 C71E FED6EE R9=? 27 C=69G= 0919=/=972$)

digital signal processing

IEEE802.15.4信道的Matlab仿真。主要描述其在各种信道环境，比如各种噪声和各种干扰存在的情况下，各种性能的仿真。(Matlab simulation of the IEEE802.15.4 channel. Describes the environment in a variety of channels, such as the presence of noise and all kinds of interference, the various properties of the simulation.)

用matlab对图像的7个不变矩特征进行提取，并进行归一化。亲测可用，适合初学者。(Matlab for image seven moment invariant features were extracted and normalized. Pro-test is available for beginners.)

匀速模型目标跟踪方法，并给出相应的滤波分析，能运行，欢迎下载(Uniform model for target tracking method of filtering the corresponding analysis, will be able to run, please download)

无人驾驶汽车模型预测控制的算例代码，非常实用。(The code of the model predictive control of the unmanned vehicle)

本实验目的是了解非参数估计—Parzen窗估计，对表格3中的数据进行Parzen窗估计和设计分类器.Parzen窗方法：假设区间Rn是一个d维的超立方体，hn表示超立方体的边长，体积为Vn=hnd。(Purpose of this study is to understand the non-parametric estimation-Parzen window estimate of the data in Table 3 Parzen window estimate and design classifiers. Parzen window method: Suppose a d-dimensional interval Rn is the hypercube, hn that side of the hypercube , volume Vn = hnd.)

Mahalanobis Distance Function for Matlab.

performance improvement with different codes

this function is based on evolutionary algorithm for finding the optimal solution for multiple objective i.e. pareto front for the objectives.