ltc5

3D FDTD 计算程序 ToyFDTD1 is a stripped-down, minimalist, 3-dimensional FDTD code that is published under the GNU General Public License. It is the first in the ToyFDTD series of codes, and it illustrates in heavily commented C and Fortran the basic tasks in implementing a simple 3D FDTD simulation.(3D FDTD computational procedures ToyFDTD1 is a stripped-down, minimalist, 3-dimensional FDTD code that is published under the GNU General Public License. It is the first in the ToyFDTD series of codes, and it illustrates in heav)

ATMEL的指纹识别模块说明书(全英文).(ATMEL specification of the fingerprint identification module (all English).)

一种认知无线电网络中的新型主动频谱选择算法，这篇文章提出的算法比较新颖(A cognitive radio network in the new active spectrum selection algorithm, the proposed algorithm of this relatively new article)

这个是TE过程的源代码程序，通过这个源代码可以实现TE过程(The TE process is the source code program, source code can be achieved through this process of TE)

Matlab Hwk for Datamining / Machine Learning

实现火箭的垂直发射，并在重力作用下实现转弯。(Achieve vertical launch rocket and turn under the influence of gravity.)

文对静态图像的压缩方法作了较深入的研究，利用多分辨分析对图像进行分解，并利用小波的标量量化图像压缩编码算法对图像进行压缩，其间对原算法进行了研究和探讨；在上述理论工作的基础上，应用Matlab仿真实现了相应的算法。实验结果证明，基于小波的标量量化图像压缩编码算法是可行的和有效的。(A deep researeh on static image compression 15 presented in this paper. on the basis of theoretical work above，programming achieves corresponding algorithm. It’s proved that programming achieves corresponding algorithm image compression and image code.)

学习好matlab，必须要亲动手，进行实践，而且要从基本做起(for matlab study)

LINEAR REGRESSION SOURS CODE

相关向量机(RVM)的matlab源程序，包含快速算法，内含代码使用说明。 RVM采取是与支持向量机相同的函数形式稀疏概率模型，对未知函数进行预测或分类。 优点： (1) 不仅仅输出预测目标量的点估计值,还可以输出预测值的分布. (2) 使用更少数量的支持向量,从而显著减少输出目标量预测值的计算时间. (3) RVM不需要估计过多的参数. (4) RVM对是否满足Mercer 定理的核函数没有限制,适应性更好(Relevance Vector Machine (RVM) of the matlab source code, including fast algorithm that contains code for use. RVM support vector machine is taken the same functional form sparse probabilistic model to predict the unknown function or classification. Advantages: (1) is not only the amount of output predicted target point estimates, but also the distribution of predicted values ​ ​ can be output. (2) using a smaller number of support vectors, thereby significantly reducing the output target amount predicted value calculation time. (3) RVM does not require excessive parameter estimation. (4) RVM meets Mercer theorem on the kernel function is not limited, and better adaptability)