基于视频分析和多传感器融合的移动式监控系统

安防场景下,大多数传感器系统（视频、红外、烟雾传感）只能实现单一数据采集、简单处理.提出的移动式智能监控系统以小车为载体,通过对采集视频进行智能分析处理（包括基于背景差分的入侵检测算法和改进的TLD目标跟踪算法）,并辅助多传感器模块进行多信息融合协同监控,可以实现入侵检测、移动跟踪监测等功能.实验表明该系统能提供多种终端形式与用户进行友好交互,实现真正的智能安防.     

BodyCloud: A SaaS approach for community Body Sensor Networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behavior surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals.     

Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

Body Sensor Networks (BSNs) have emerged as a revolutionary technology in many application domains in health-care, fitness, smart cities, and many other compelling Internet of Things (IoT) applications. Most commercially available systems assume that a single device monitors a plethora of user information. In reality, BSN technology is transitioning to multi-device synchronous measurement environments; fusion of the data from multiple, potentially heterogeneous, sensor sources is therefore becoming a fundamental yet non-trivial task that directly impacts application performance. Nevertheless, only recently researchers have started developing technical solutions for effective fusion of BSN data. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on multi-sensor fusion in the area of BSN. This survey discusses clear motivations and advantages of multi-sensor data fusion and particularly focuses on physical activity recognition, aiming at providing a systematic categorization and common comparison framework of the literature, by identifying distinctive properties and parameters affecting data fusion design choices at different levels (data, feature, and decision). The survey also covers data fusion in the domains of emotion recognition and general-health and introduce relevant directions and challenges of future research on multi-sensor fusion in the BSN domain.     

基于N-P准则的水声信号检测系统信息融合

多基阵数据融合技术在水声信号处理中具有重要意义，本文给出了基于Neyman Pearson准则的多传感器分布式水声检测信息融合系统．研究了全局最优融合系统以及局部传感器的最优判决准则．在假定各传感器检测独立的情况下，对三传感器的情况进行了仿真．结果表明，检测系统的性能有明显提高．     

基于贝叶斯网络的体域网多模态健康数据融合方法

体域网作为无线传感器网络在生物医学领域的一个重要分支能够远程实时监测人体多项健康数据.针对基于体域网采集到的多模态健康数据融合与分析方法进行研究,设计了一套包括动态心电传感器、血压传感器和血氧饱和度传感器的体域网组网方式,提出了一种基于贝叶斯网络模型和推理算法的心肌缺血监测识别方法.通过对60例确诊心脏病患者施行单一模态动态心电监测和多模态健康数据监测对比实验,验证了所提出的多模态健康数据融合方法能够有效提高无症状性心肌缺血的检出率,为临床应用提供了一种新的辅助判别手段.     

A new multiple decisions fusion rule for targets detection in multiple sensors distributed detection systems with data fusion

Currently, multiple sensors distributed detection systems with data fusion are used extensively in both civilian and military applications. The optimality of most detection fusion rules implemented in these systems relies on the knowledge of probability distributions for all distributed sensors. The overall detection performance of the central processor is often worse than expected due to instabilities of the sensors probability density functions. This paper proposes a new multiple decisions fusion rule for targets detection in distributed multiple sensor systems with data fusion. Unlike the published studies, in which the overall decision is based on single binary decision from each individual sensor and requires the knowledge of the sensors probability distributions, the proposed fusion method derives the overall decision based on multiple decisions from each individual sensor assuming that the probability distributions are not known. Therefore, the proposed fusion rule is insensitive to instabilities of the sensors probability distributions. The proposed multiple decisions fusion rule is derived and its overall performance is evaluated. Comparisons with the performance of single sensor, optimum hard detection, optimum centralized detection, and a multiple thresholds decision fusion, are also provided. The results show that the proposed multiple decisions fusion rule has higher performance than the optimum hard detection and the multiple thresholds detection systems. Thus it reduces the loss in performance between the optimum centralized detection and the optimum hard detection systems. Extension of the proposed method to the case of target detection when some probability density functions are known and applications to binary communication systems are also addressed.     

Multi-agent systems for protecting critical infrastructures: A survey

Multi-agent systems have emerged as a very significant platform in provisioning distributed and collaborative services to critical applications. Such applications require ubiquitous agent presence in the environment for monitoring, collecting data, communication, and subsequent data analysis, where the sensitivity of the application's nature cannot be understated. Recent advances in the field of autonomous, ubiquitous, intelligent and distributed computing have led to corresponding developments in the use of collaborating multi-agents to protect critical infrastructures. Such systems have witnessed crucial demand for deployment in diverse application scenarios such as E-commerce, E-health, Network Intrusion Detection, Telematics and Transport Systems, Environmental Monitoring, as well as for distributed information processing in general. Critical infrastructures have longed for a distributed system in place for their uninterrupted and accurate operations. Multi-agents have provided one such approach towards addressing the issue of protecting such infrastructures through collaborative and distributed information processing. In this paper, a state-of-the-art on the use of multi-agent based systems for protecting five most common critical infrastructures, is presented.     

基于多传感器数据融合的智能火灾预警系统

根据当今火灾探测的现状和实现火灾早期探测的需求,将多传感器数据融合技术应用在火灾预警系统中。选择多个传感器对火灾过程的多参数进行监测,特别对火灾发生初期产生的异常信息进行全面监控,弥补了采用单一传感器的不足,扩展了时间上和空间上的观测范围。采用自适应加权融合估算法配合智能判别技术,增强了系统报警输出的灵敏度和可靠性,使系统达到了实现提前预警的目的。     

基于多传感器融合的四旋翼飞行器姿态监控

设计了一种基于多传感器融合的四旋翼飞行器姿态监控系统.在有限的单片机计算资源下,通过卡尔曼滤波,对陀螺仪、加速度计和磁强计的测量数据进行数据融合,解算出姿态信息.减小了因电机引起的振动、低成本微机电系统(MEMS)传感器误差较大等因素对姿态信息解算时造成的干扰.通过无线将姿态信息传输至上位机,以民航客机中的主飞行显示器(PFD)形式,对飞行参数进行直观的图形化显示,实现了对四旋翼飞行器的姿态进行监控.     

一种基于多传感器决策融合的新颖监控方法

提供一种基于多传感器决策融合的新颖监控模型和方法.该方法在传感器层实现分布式判断过程,即每个传感器产生一个局部判断结果后,送到全局决策融合中心,由融合中心做出最终判断结果,从而实现大型工控系统的监控目的.将该方法在水电站监控系统平台上进行具体的实验和测试,结果表明融合模型和方法是可信且可用的,其控制与决策方式是通用的.     

Collaborative Multimedia Presentations in Mobile Environments

Distributed multimedia documents systems, distributed video servers are examples of multimedia presentations involving collaboration among multiple information sources. In such applications, objects have to be retrieved from their sources and presented to users according to specified temporal relationships. Objects retrieval in these collaborative applications is influenced by their presentation times, durations, and network throughput available to their sources. Replication of objects amongst the set of collaborating systems gives a choice for object retrieval. Client going through a multimedia presentation can be in a mobile environment. Here, object retrievals from collaborating servers are carried out by base stations to which the client is attached. Mobile client then downloads objects from the base station.In this paper, we present a graph-search based algorithm for computing and negotiating throughput requirements of collaborating multimedia presentations with replicated objects in a mobile environment. This algorithm maximizes the number of cached objects (that have already been played) for handling operations such as reverse presentation.     

基于轻负载代理的协同分布式入侵检测系统

The LAFCDIDS(Lightweight Agent for Collaborative Distribution Intrusion Detection System)presented in this paper is a distributed intrusion detection system with the ability of collaborative detection in real time.The hierarchy architecture of agents and the ability of collaborative detection in real time are evident characteristics of the LAFCDIDS.Lightweight agent and agent sensitivity are LAFCDIDS‘‘s new concepts,which can reduce the overload of protected system,shorten the period of intrusion detection,and are suitable for monitoring the distributed collaborating attacks.     

基于多传感信息融合的轨道线形检测

多传感信息融合是实现轨道线形高精度检测的重要方法,而加速度计和陀螺仪是多传感信息融合中的关键传感器。为了解决加速度计和陀螺仪存在累积误差导致测量精度较低的问题,提出一种基于多传感信息融合的轨道线形检测方法。基于捷联惯性系统和双目视觉的测量原理,建立了双目视觉与惯性测量结合的多传感数据融合模型,并利用扩展卡尔曼滤波实现了双目视觉、加速度计和陀螺仪测量信息的融合,提高轨道线形检测精度。通过实验进行验证,结果表明：基于多传感信息融合方法的测量精度比惯性测量方法提高了近9倍,且测量所得坐标在三个方向上的最大位移绝对误差不超过0.536mm,可有效实现高精度轨道线形检测。     

一种基于多传感器决策融合的新颖监控方法

提供了一种基于多传感器决策融合的新颖监控模型和方法,该方法在传感器层实现分布式判断过程即每个传感器产生一个局部判断结果后,再送到全局决策融合中心,由融合中心作出最终判断结果从而实现大型工控系统的监测目的。这种方法在研制开发的吉林丰满水电站监控系统平台上进行了具体的实验和测试,结果表明融合模型和方法是可用的、可信的,其控制与决策方式是通用的,在其它工控领域可推广应用。     

数据融合技术在火灾自动探测中的应用研究

根据当今火灾探测的现状和实现火灾早期探测的需求,将多传感器数据融合技术应用在火灾预警系统中;选择多个传感器对火灾过程的多参数进行监测,特别对火灾发生初期产生的异常信息进行全面监控,弥补了采用单一传感器的不足,扩展了时间上和空间上的观测范围;采用自适应加权融合估算法,增强了系统报警输出的灵敏度和可靠性,达到了实现提前预警的目的.     

一种基于遗传算法的多站点协同计算卸载算法

边缘计算为资源受限的物联网IoT设备扩展计算资源、增强存储容量,可以改善IoT应用程序的执行性能。在IoT环境中,大多数应用都将以分布式架构的形式部署在各站点中,站点之间需要协作完成任务。为了解决物联网环境中多站点协同计算的代价优化问题,提出了一种基于遗传算法的多站点协同计算卸载算法GAMCCO。该算法将应用程序抽象为任务依赖关系图模型,分析各任务之间的依赖关系,将多站点协同计算卸载的问题建模为代价模型,并利用遗传算法寻找最小代价的卸载方案。实验与评估结果表明,所提出的GAMCCO算法可以有效减少IoT应用的时延,同时降低终端设备的能耗。     

基于信息融合技术的多传感器大气波导探测研究

结合我国大气波导探测技术发展的实际情况,引入了信息融合技术,介绍并分析了采取多传感器大气波导探测的可行性和必要性,提出了信息融合中的关键技术。结合GNSS探测大气波导和气象水文模型计算大气波导2种方法的特点,初步建立了多传感器大气波导探测系统的信息融合体系结构。     

不确定图上的kNN查询处理

在现实中的许多领域产生大量不确定的图结构的数据,例如分子化合物、蛋白质交互网络等.同时现实中有很多应用例如推荐系统中的推荐过滤、欺诈检测和社会网络的链接预测等,需要查询给定节点的k个最相似节点,针对这一问题,提出了用基于SimRank度量的方法来求解.由于图的动态演变和不确定性导致用现有的SimRank计算方法求k个最近邻的代价昂贵,因此提出一个有效算法,在保证一定准确性的前提下,通过引入路径阈值,算法只需考虑查询点的邻居区域无需考虑整个图从而达到明显的剪枝效果,该方法在确定图和不确定图上都可以适用.在此基础上为了进一步提高效率,算法在不确定图上引入采样技术.最后从理论、实验说明验证了算法的高效性和有效性.     

多传感器信息融合性能的测试与评估

讨论了信息融合系统性能评估的一般性理论和方法，以军事上的应用为背景建立了信息融合性能指标体系。通过软件测试的方法，对融合算法进行测试与评估。提出了一种用于多传感器信息融合性能测试与评估的系统结构，并以一个舰艇信息融合系统为例，讨论了评估其航迹相关性能时的测试步骤，给出了部分性能指标的计算方法。     

基于参数估计的多传感器数据融合算法研究

为提高多传感器数据融合精度,结合传感器网络的特点及应用实例,研究分批估计、自适应加权和方差估计算法在数据融合中的有效性、准确度和实时性,提出按测量方差的自适应加权数据融合算法,利用600个传感器所提供的实例数据,对几种算法进行仿真,并比较了几种算法的有效性及其融合精度的差异,其结果表明采用自适应加权算法可以有效提高融合精度,对考虑了环境噪声的多传感器数据采集系统较为适合.