资源列表
NMROAHX
- Simple image program, you can adjust RGB color-Simple image program, you can adjust the RGB color
ise
- spiht是当今流行的基于小波的图像压缩编码,本算法是用vc实现-Spiht is the popular image compression coding based on wavelet, this algorithm is to use vc to achieve
OUR-SOFTWARE
- RAW file conversion to .mat file. using a series of x-ray projections.
xill
- The generating algorithm of perspective projection-The generating algorithm of The perspective of The projection
EX_contour
- 采用网格序列法对二维网格的等值线进行绘制，编码环境为VC- U91C7 u7528 u7R1 u7R1 u7E1 u7E1 U4E3AVC
wj618
- 自己编的5种调制信号，进行逐步线性回归，雅克比迭代求解线性方程组课设。- Own five modulation signal, Stepwise linear regression, Jacobi iteration for solving linear equations class-based.
hai_cb13
- 基于matlab GUI界面设计,二维声子晶体FDTD方法计算禁带宽度的例子,包含收发两个客户端程序。- Based on matlab GUI interface design, Dimensional phononic crystals FDTD method calculation examples band gap, Transceiver contains two client programs.
fitting-model
- 要对单变量正态分布以及分类分布两种概率分布 模型，分别采用最大似然（ML），最大后验（MAP）以及贝叶斯估计（Bayes）的 方法进行概率密度估计。 -In this paper, the maximum likelihood (ML), maximum a posteriori (MAP) and Bayesian estimation (Bayes) methods are used to estimate the probability density of two kinds of pr
music
- 基于MATLAB的微动目标的信息提取，一人的呼吸心跳为例。-Based on MATLAB s information extraction of micro- moving target, one person s breathing heartbeat as an example.
bayes
- 贝叶斯分类器，通过某对象的先验概率，利用贝叶斯公式计算出其后验概率，即该对象属于某一类的概率，选择具有最大后验概率的类作为该对象所属的类。-Bias classifier, by a priori probability of an object, using the Bias formula to calculate the posterior probability, the probability that the object belongs to a certain category,
ywftu
- 单径或多径瑞利衰落信道仿真，算法优化非常好，几乎没有循环，关于非线性离散系统辨识。- Single path or multipath Rayleigh fading channel simulation, Algorithm optimization is very good, almost no circulation, Nonlinear discrete system identification.
knn-kdtree
- kd树，分割k维数据空间的数据结构。主要应用于多维空间关键数据的搜索（如：范围搜索和最近邻搜索）。K-D树是二进制空间分割树的特殊的情况。-KD tree, the data structure of K dimensional data space. It is mainly used in the search of key data in multidimensional space (such as range search and nearest neighbor search). K