贝叶斯分类器实现汉字识别(程序+样本集)

【实例简介】

在日常的生活中，通信是人们用来传递信息的方式。随着数字系统的飞速发展，对数字系统的性能和调制解调技术要求也越来越高。同时，由于计算技术的发展，通信系统的仿真已日益普遍，已逐渐成为今天设计和分析通信系统的主要工具。 本次设计将使用MATLAB软件设计函数对2FSK调制解调技术进行仿真和研究。 本文在第一章中介绍了通信系统的组成、MATLAB的使用。

为解决小升阻比月球返回舱再人大气层时安全性低、机动能力差的问题，本文提出采用一种带配平翼的中等升阻比返回舱做为月球采样返回舱,并对动压约束下轨迹优化问题进行了分析。构建一阶状态变量约東最优控制问题模型。然后采用庞德里亚金极大值原理以总吸热量最小为优化目标,对动压约束下再入轨道的初始再入段进行了优化设计,给出了升力系数的最优表达式。在最优轨迹的推导过程中，将问题分为自由段，约束段和自由段，并分别限定交界处的边值，最后问题变成了一个分段两点边值问题。

1998年，Norden E. Huang（黄锷：中国台湾海洋学家）等人提出了经验模态分解方法，并引入了Hilbert谱的概念和Hilbert谱分析的方法，美国国家航空和宇航局（NASA）将这一方法命名为Hilbert-Huang Transform，简称HHT，即希尔伯特-黄变换。

陣列方相圖matlab代碼 一． 实验目的  1. 了解阵列天线的波束形成原理写出方向图函数  2. 运用 MATLAB 仿真阵列天线的方向图曲线  3. 变换各参量观察曲线变化并分析参量间的关系

论文《As-Projective-As-Possible Image Stitching with Moving DLT》中的拼接算法，对于视差图像拼接具有一定的鲁棒性，但是对特征点数量及其分布均匀性有较高的要求。 The success of commercial image stitching tools often leads to the impression that image stitching is a “solved problem”.The reality, however, is that many tools give unconvincing results when the input photos violate fairly restrictive imaging assumptions;the main two being that the photos correspond to views that differ purely by rotation, or that the imaged scene is effectively planar.Such assumptions underpin the usage of 2D projective transforms or homographies to align photos. In the hands of the casual user,such conditions are often violated, yielding misalignment artifacts or “ghosting” in the results. Accordingly, many existing imagestitching tools depend critically on post-processing routines to conceal ghosting. In this paper, we propose a novel estimationtechnique called Moving Direct Linear Transformation (Moving DLT) that is able to tweak or fine-tune the projective warp toaccommodate the deviations of the input data from the idealized conditions. This produces as-projective-as-possible image alignmentthat significantly reduces ghosting without compromising the geometric realism of perspective image stitching. Our technique thuslessens the dependency on potentially expensive postprocessing algorithms. In addition, we describe how multipleas-projective-as-possible warps can be simultaneously refined via bundle adjustment to accurately align multiple images for largepanorama creation.

区域负荷功率曲线（不含电动汽车）

基于极大选择原理的自适应Fourier分解算法，比Fourier级数收敛速度更快。

【实例简介】机器学习中的多示例包层次SVM分类算法 【核心代码】Bag_KI_SVM.m KI-SVM ├── Bag KI-SVM │   ├── Bag_KISVM_prediction.m │   ├── Bag_KI_SVM.m │   ├── Find_y.m │   ├── Find_y_linear.m │   ├── Max_Violated_y_set.m │   ├── Readme.htm │   ├── celltomatrix.m │   ├── genIndex.m │   └── normalization_gaussian.m ├── Instance KI-SVM │   ├── Find_y.m │   ├── Find_y_linear.m │   ├── Inst_KISVM_prediction.m │   ├── Inst_KI_SVM.m │   ├── Max_Violated_y_set.m │   ├── Readme.htm │   ├── celltomatrix.m │   ├── genIndex.m │   └── normalization_gaussian.m ├── experiments.m ├── experiments_KISVM_musk1.m ├── libsvm-mat-2.88-MI-svm │   ├── COPYRIGHT │   ├── Makefile │   ├── README │   ├── doc.txt │   ├── heart_scale.mat │   ├── make.asv │   ├── make.m │   ├── read_sparse.c │   ├── read_sparse.mexw32 │   ├── read_sparse.mexw64 │   ├── svm.cpp │   ├── svm.cpp.bak │   ├── svm.h │   ├── svm.h.bak │   ├── svm.obj │   ├── svm_model_matlab.c │   ├── svm_model_matlab.c.bak │   ├── svm_model_matlab.h │   ├── svm_model_matlab.obj │   ├── svmpredict.c │   ├── svmpredict.mexw32 │   ├── svmpredict.mexw64 │   ├── svmtrain.c │   ├── svmtrain.c.bak │   ├── svmtrain.mexw32 │   └── svmtrain.mexw64 └── normedmusk1.mat 3 directories, 47 files

多智能体编队一致性分析