SIA OpenIR  > 装备制造技术研究室
基于嵌入式的仿生呼吸防护系统研究
其他题名Traditional target tracking algorithms usually take the windowed area as the tracking template, adopt the correlation matching or iterated algorithm to calculate the new target position in the following frames. One of the prerequites of this method is that windowed area contains enough texture to be distinguished from its surrounding area. If not, the tracking will be drifted or losted. As to the application that the target do not have the enough texture to make the windowed tracking(WT), so we propose the extensive computational tracking(ECT) algorithm. The difference between the WT and ECT is that, WT uses correlation matching by the information in the windowed area to calculate the target position, but the ECT effectively exploits the information within the whole image to compute the target location. The information in the tracked window usually means the gray information in the windowed local patch, but the ECT adopts the multiple image feature detector to extract the useful information within the image and fuses all the information under the target tracking framework to compute the target new location. The image feature detector is research focus of the computer image and vision related field, image feature receives extensive attention and rapid development in the multi-view, 3D reconstruction and target tracking areas. The image feature is the local patch whose gray distribution agrees with the detector’s hypothesis. In the target tracking oriented application, for the imaging conditions are changing by the tracking process, so the stability of the image feature if critically important in the tracking application. The features are matched in the adjacent frames, and then the geometric transformation matrix is calculated and the target position is computed in the recursive way. One-by-One recursive calcution is always leading to the divergence of the tracking error. Multiple detectors and information fusion will suppress the divergence and realize the stability of the ECT. With the detailed description of the special requirements and the feature based image information extraction method, this dissertation proposed the multi-feature based ECT. In the tracking application, the stability of the image feature is the critical problem. With the analysis of the image radial-blurry under the opposite-direction movement, we proposed the feature sifting method to get the high-repeatability feature set. The computation precision and time complexity of the holography computation algorithm is important in the recursive tracking framework. After the comparison of the several holography calculation methods by the time complexity and computation precision, the method with the best overall performance is adopted in the ECT. Finally, we propose the ECT framework which fuses the point feature-SIFT and the region feature-MSER. The complementary of the SIFT and MSER suppress the divergence of the tracking error and realize the stable and robust ECT.
郭丽丽1,2
导师房灵申
分类号TP332
关键词空气净化 呼吸检测 仿生呼吸模式 Μc/os-ii
索取号TP332/G94/2013
页数55页
学位专业控制工程
学位名称硕士
2013-05-28
学位授予单位中国科学院沈阳自动化研究所
学位授予地点沈阳
作者部门装备制造技术研究室
摘要论文以城市交通污染与个体呼吸防护为背景,针对现有呼吸防护用品中存在的科学问题和实际应用需求,基于人机仿生学、呼吸采集及预测技术、嵌入式实时操作系统应用开发等技术,开展了应用于呼吸防护的呼吸频率检测、仿生呼吸模式送风技术的研究。仿生学是近些年发展起来的一门学科,其任务是研究生物系统的特定能力及实现原理,运用数学、工程技术学等知识将其模式化,是生物学、工程技术学及数学之间的交叉学科。本文采用仿生学原理,使系统送风模式与人的呼吸模式相匹配,起到提高佩戴舒适度的功能。硬件设计方面,采用模块化设计思想,按功能将系统分为空气质量检测模块、空气净化模块、送风模块、控制模块等,文章介绍了各模块方法原理、实现功能及相互间的通信设置。针对每个功能模块,论文详细叙述了其工作原理及相应硬件配置及参数等。呼吸信号检测技术是本系统仿生呼吸模式的关键技术之一,文章通过比较现有呼吸信号检测技术各自优缺点,给出了适合本系统的呼吸信号检测方法,并进行了单元实验,实验效果显示该方法可实时准确的检测人的呼吸信号变化。控制器根据一定算法从检测到的呼吸信号提取去呼吸模式特征值。软件设计方面,将系统分为下位机软件设计和上位机软件设计两部分。下位机以性能可靠的嵌入式实时操作系统μC/OS-II作为软件运行平台,通过μC/OS-II操作系统向STC12C5A60S2单片机的移植,实现μC/OS-II对软硬件资源进行管理分配,同时也为将来进一步的软件功能扩展提供基础。上位机以VC6.0为开发环境,编写了虚拟示波器软件,用于系统单元实验调试及实验数据的实时显示与保存。最后,本文对所做的研究工作进行了总结,并对今后的工作进行了展望。
其他摘要This research aims to solve problems in respiratory protection devices and application requirements. With the background of individual respiratory protection and air purification, the thesis investigate relative techniques on breathing rate detection, breathing rate prediction and system integration, and these investigations relies on bionics, breath collection and prediction and embedded real-time operation system. Bionics is a new developing discipline in recent years. With its principles, the bionics serves for realizing certain biotical system, modelling by mathematics and engineering techniques. It is the interdisciplinary of biology, engineering and mathematics. This thesis adopts bionics to match air supplying and breathing with comfortable wearing of this equipment. As the hardware design, modular design is employed including air quality evaluation module, air purification module, air supplying module and control module. Modules are introduced with principles, functions and communication configurations. Breathing signal detection is the crucial technique in the biotical breathing mode of this system. This thesis compares with other breathing signal detection techniques and proposes the suitable method of the given system. Experiments are conducted, and results show that the developed method could precisely detect the breathing signal. Characteristic values of breathing signals are extracted by a certain controller following a specific algorithm, and the prediction technique is the basis of air supplying in biotical breathing mode. The software design is based on embedded real-time operation system UC/OS-II. This system could manage and allocate system resources, and multi-task scheduling mechanism guarantees efficiency and further system functions extension. At last, the obtained research results are summarized and future work is addressed.
语种中文
产权排序1
文献类型学位论文
条目标识符http://ir.sia.cn/handle/173321/10755
专题装备制造技术研究室
作者单位1.中国科学院沈阳自动化研究所
2.中国科学院大学
推荐引用方式
GB/T 7714
郭丽丽. 基于嵌入式的仿生呼吸防护系统研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2013.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
基于嵌入式的仿生呼吸防护系统研究.pdf(1756KB) 开放获取CC BY-NC-SA请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[郭丽丽]的文章
百度学术
百度学术中相似的文章
[郭丽丽]的文章
必应学术
必应学术中相似的文章
[郭丽丽]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。