资源列表
nvergence
- NOD随机变量阵列加权乘积和的完全收敛性NOD array of random variables and the weighted product of the complete convergence-NOD array of random variables and the weighted product of the complete convergence
amrandom
- This a random source code for delphi. Examples ramdom functions as follows: The following Random Number Generators: - Normal (Gaussian) - Gamma - Chi-squared - Exponential - Weibull - Beta - t - Mult
Design-and-Analysis-of-GPS-SINS-Integrated-System
- A very good paper describing the detail of SINS and GPS integration
mean-power-frequency
- 将信号（本程序原本用于处理肌电信号）进行放大、滤波等处理后，在用本程序求取该段信号的平均功率频率（内附可用于测试的肌电信号数据）。-The signal (the Program for processing original EMG) amplification, filtering and other processing, using the procedures for obtaining the average power of the frequency of the signal
ant-colony-algorithm
- 详细介绍了蚁群算法，并提供了相关案例分析。是学习蚁群算法的好资料。-Ant colony algorithm was introduced in detail, and provides the related case analysis. Is the study of ant colony algorithm is a good information.
MatlabBayesFisherClassifierDesign
- 在matlab中bayes和Fisher分类器设计的小程序代码，实现垃圾邮件和正常邮件的分离，比较不同维度，不同样本，两种分类器正确率的优劣，适合初学者学习-In matlab the bayes and Fisher classifier design a small program code, junk mail and regular mail to achieve the separation of comparing different dimensions, different sa
expressionj-src-0.7
- ExpressionJ 是一个用来解析简单的算术表达式的 Java 类库。-ExpressionJ is a Java library allowing to interpret simple numeric expressions, which may be used in all applications which have to combine numeric values, but do not want to use full-blown scr ipting languages.
PathFind
- 迷宫自动寻路，核心代码都带了，初学人工智能的可以借鉴-Maze automatic pathfinding, the core code are brought, beginners can learn of artificial intelligence
libsvm
- 通过对样本训练得到模型，然后对待测样本用于分类预测。(The model is trained by sample training, and then the measured samples are used for classification prediction)
drn
- dilated network用于图像的语义分割。(an example to dilated netqork)
slotted-aloha-master
- Slotted aloha implementation in language of c, c++
Kalman Filter Introduction Chinese
- 卡曼滤波的介绍性文章，内有说明实例，简单易懂(Kalman Filter Introduction)