基于PSO的信息熵数据融合非均匀分簇路由算法

针对无线传感器网络分簇算法中能量分布不均衡导致的"热区"和簇头负载过重问题,提出了一种基于PSO算法优化簇头选举的非均匀分簇算法。在候选簇头选举和竞争半径计算过程中综合考虑节点动态能量、节点密度和节点距基站距离,将网络进行非均匀分簇,并引入PSO算法进行最终簇头选举。根据节点能量、节点密度和距基站距离确定簇间单跳多跳结合的路由规则,选取代价函数小的节点作为下一跳节点。基于节点信息熵确定融合阈值,进行簇内数据融合剔除冗余数据。仿真结果表明,改进算法的数据传输量比EEUC算法和UCRA算法分别提高了20%和10%,提升了数据的融合效率,有效延长了网络生命周期,簇头能量消耗得到均衡,减少了网络能量消耗,网络的整体性能显著优于其他对比算法。     

A gossip based information fusion protocol for distributed frequent itemset mining

The computational complexity, huge memory space requirement, and time-consuming nature of frequent pattern mining process are the most important motivations for distribution and parallelization of this mining process. On the other hand, the emergence of distributed computational and operational environments, which causes the production and maintenance of data on different distributed data sources, makes the parallelization and distribution of the knowledge discovery process inevitable. In this paper, a gossip based distributed itemset mining (GDIM) algorithm is proposed to extract frequent itemsets, which are special types of frequent patterns, in a wireless sensor network environment. In this algorithm, local frequent itemsets of each sensor are extracted using a bit-wise horizontal approach (LHPM) from the nodes which are clustered using a leach-based protocol. Heads of clusters exploit a gossip based protocol in order to communicate each other to find the patterns which their global support is equal to or more than the specified support threshold. Experimental results show that the proposed algorithm outperforms the best existing gossip based algorithm in term of execution time.     

一种利用优先经验回放深度Q-Learning的频谱接入算法

针对认知无线传感器网络中频谱接入算法的频谱利用率不高、重要经验利用率不足、收敛速度慢等问题，提出了一种采用优先经验回放双深度Q-Learning的动态频谱接入算法。该算法的次用户对经验库进行抽样时，采用基于优先级抽样的方式，以打破样本相关性并充分利用重要的经验样本，并采用一种非排序批量删除方式删除经验库的无用经验样本，以降低能量开销。仿真结果表明，该算法与采用双深度Q-Learning的频谱接入算法相比提高了收敛速度；与传统随机频谱接入算法相比，其阻塞概率降低了6%~10%，吞吐量提高了18%~20%，提高了系统的性能。     

多传感器长途客车智能新风系统设计研究

本研究设计了一种结合二氧化碳传感器、PM2.5粉尘传感器、TVOC传感器来实现智能更新长途客车封闭空间内污浊空气的长途客车车载新风系统。本系统由各项传感器实时采集车厢内CO2、PM2.5、TVOC等数据上传至单片机,单片机判断各项数据是否超过设定阈值,若超过设定阈值,新风系统自动工作改变进、排风量;反之,新风系统不工作,实现智能控制。实验证明,本系统具有明显地改善长途客车内空气质量的效果,能够更好地保护人体健康。     

基于Aptina ASX340的倒车影像系统设计与实现

为了避免倒车过程中因视觉盲区可能出现的安全隐患问题[1][2],文章采用飞思卡尔单片机做主机,Aptina图像传感器ASX340[3]做从机,利用ASX340内部的OVERLAY位图叠加系统,设计了一种能够将倒车过程中实时的车辆尾部的影像,距离障碍物的安全距离,倒车轨迹路径等信息提供驾驶员倒车影像系统[9][10]。使用HYRes3_1、Imatest软件[4]测量了影像图像质量的主要因素。测量结果表明成像质量能够满足目前车厂的要求。     

我国细胞治疗产业发展现状研究

中国在细胞治疗领域的基础研究位列世界前列,一些龙头企业正在积极进行相关技术的研发,加紧全产业链的布局,我国的细胞治疗产业正处于成功商业化和临床受益的爆发点。介绍了我国细胞治疗的市场环境、政策环境、国内外最新研究进展和我国细胞治疗行业代表企业的发展情况,提出了未来我国发展细胞治疗产业的关键挑战,并对今后的发展方向进行了展望。     

基于物联网技术平台的O2O商业模式研究

在国家大力推进产业结构调整、信息化的压力下,物联网技术和O2O商业模式作为信息时代的新型生产力,在国内的普及率并不高。本文运用案例分析的方法通过研究大学生创业项目引入O2O商业模式的概念,并与物联网技术有机结合,把握两者之间优势互补的关系,提出两者创新应用,同时从商业模式构成要素的角度对该模式进行系统、深入分析,为中国现代农业信息化建设和结构调整提供了新思路。     

基于无线生理传感器与虚拟现实技术的复愈性环境注意力恢复作用研究

基于卡普兰复愈性环境理论，运用脑机接口设备、智能腕带传感器等无线生理指标检测技术，测试大学生被试人群在沉浸式虚拟现实(VR)情境下，对4种复愈性环境及城市环境在注意力恢复和压力减轻方面的反应及其作用强度，获得被试在不同环境中脑电、心率、肌电的客观生理数据。采用非参数统计方法R语言对数据进行分析。结果显示，多数被试在复愈性环境中较城市环境表现出更好的注意力恢复效果。其中具有逃离性和迷人性特征的自然环境，复愈效果最为明显；在虚拟现实环境中，观赏城市环境图片也具有一定的复愈效果。     

An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

Zika virus is a mosquito-borne disease that spreads very quickly in different parts of the world. In this article, we proposed a system to prevent and control the spread of Zika virus disease using integration of Fog computing, cloud computing, mobile phones and the Internet of things (IoT)-based sensor devices. Fog computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud-based systems. A fuzzy k-nearest neighbour is used to diagnose the possibly infected users, and Google map web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each Zika virus (ZikaV)-infected user, mosquito-dense sites and breeding sites on the Google map that help the government healthcare authorities to control such risk-prone areas effectively and efficiently. The proposed system is deployed on Amazon EC2 cloud to evaluate its performance and accuracy using data set for 2 million users. Our system provides high accuracy of 94.5% for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment.     

An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.