仿人机械手设计与硬度感知研究

硬度作为物体的重要物理属性之一,在一定程度上影响了机械手对物体的智能抓取控制.为提高机械手自适应性、集成化和轻量化,本文设计并制作了基于3D打印技术的模块化多自由度仿人机械手.通过微型丝杠电机牵引钢丝绳运动,实现四指弯曲和拇指弯曲与侧摆.本文重点研究了仅基于电流传感器反馈的物体硬度感知策略,无需增加额外的外部传感器,弥补了大多数机械手难以直接区分物体软硬属性的缺陷.通过将该机械手与软体机械臂集成,实现了对不同硬度的软烂水果和新鲜水果的分类挑选.     

Hand Gesture Recognition Based on Improved FRNN

The trained Gaussian mixture model is used to make skincolour segmentation for the input image sequences. The hand gesture region is extracted, and the relative normalization images are obtained by interpolation operation. To solve the proem of hand gesture recognition, Fuzzy-Rough based nearest neighbour（RNN） algorithm is applied for classification. For avoiding the costly compute, an improved nearest neighbour classification algorithm based on fuzzy-rough set theory （FRNNC） is proposed. The algorithm employs the represented cluster points instead of the whole training samples, and takes the hand gesture data＇s fuzziness and the roughness into account, so the campute spending is decreased and the recognition rate is increased. The 30 gestures in Chinese sign language alphabet are used for approving the effectiveness of the proposed algorithm. The recognition rate is 94.96%, which is better than that of KNN （K nearest neighbor）and Fuzzy- KNN （Fuzzy K nearest neighbor）.     

Active Appearance Model Based Hand Gesture Recognition

This paper addresses the application of hand gesture recognition in monocular image sequences using Active Appearance Model （AAM）, For this work, the proposed algorithm is composed of constricting AAMs and fitting the models to the interest region. In training stage, according to the manual labeled feature points, the relative AAM is constructed and the corresponding average feature is obtained. In recognition stage, the interesting hand gesture region is firstly segmented by skin and movement cues. Secondly, the models are fitted to the image that includes the hand gesture, and the relative features are extracted. Thirdly, the classification is done by comparing the extracted features and average features. 30 different gestures of Chinese sign language are applied for testing the effectiveness of the method. The Experimental results are given indicating good performance of the algorithm.     

第三代移动通信系统研究

对第三代移动通信系统的历史、发展、特点及从2G到3G的演进进行了全面的分析,重点介绍了第三代移动通信系统的关键技术,同时也分析了困扰3G发展的问题.     

基于挥手行为的性别识别方法

提出利用挥手行为进行性别识别的方法.使用基于含时切平面的方法检测周期,用平均剪影表征一个周期序列的挥手行为,PCA降维后利用支持向量机进行分类.实验在60人(30男,30女)的数据库上进行.实验结果表明,用提出的算法从三种挥手行为(挥左手、挥右手、挥双手)中识别出性别的正确率达到89.83%或更高.实验还将人体分成5部分:手臂、头肩、腰、臀和腿,研究人体各组成部分对性别识别的贡献.93个对比实验结果表明,去掉手臂部分识别率下降最快;只通过手臂识别正确率达到86.44%或更高;使用两部分识别,手臂+臀部是最优组合;使用三部分识别,手臂+头肩+腰是最优组合.