基于直方图的水墨画艺术风格研究

基于内容的照片或自然图像分析和检索已经得到广泛的研究,但多数都基于颜色特征,不能直接用于水墨画艺术作品的分析。对图像进行预处理,提出基于灰度直方图描述水墨画笔道力度分布信息及构图风格,并利用神经网络学习水墨画整体技法风格特征,依据此特征进行不同艺术家间的分类。实验结果表明,通过对整体风格特征的提取,直方图模型能够有效表征画家不同的艺术风格,实现了水墨画的自动分类。     

一种低功耗的无线传感器网络MAC协议

在无线传感器网络中,为了避免不必要的能耗而达到延长网络寿命的目的,提出一种低功耗的无线传感器网络MAC协议。此协议在低功率侦听(LPL)基础上进行修改,通过前一次轮询的结果来推测这一次的流量状况,根据流量状况自适应调整侦听睡眠周期结构中相应的占空比,从而降低能耗。通过编程仿真并结合实验,对此模型进行验证,证明了此算法能降低能耗。     

一种无线传感器网络链式传输分簇路由协议

由于周围环境对无线传感器网络(WSNs)的影响,在布设到特殊环境下时会产生信号的衰减与损耗,导致通信不畅。针对此问题,提出一种以LEACH路由协议为基础适应特殊环境(长直空间)的新型路由算法。本算法采用链式传输,即从内部逐一将信号传输给距空间最外端且距基站位置较近的簇头,克服了内部节点死亡过快的问题。同时簇内采用链式传输并且改进簇头阈值与成簇半径,减小了能量消耗,提高了稳定性,克服了LEACH算法的不足。     

一种集中式水下无线传感器网跨层调度协议

分析了水下无线传感器网络（WSNs）中单链路发射功率和距离与频率的关系,提出一种跨层的集中式调度方法,AP（access point）在网络中通过广播信标的形式收集估计各节点的时延和距离信息,在MAC层考虑水下链路延迟特点,对各链路进行调度减少冲突;节点在物理层通过自适应地改变节点发射功率和发射频率,减少能耗.仿真结果表明：所提方案在节省系统能量消耗的同时,能够减少碰撞,提高系统吞吐量.     

基于无线传感器网络的小粒种咖啡园滴灌自动控制系统

为实现小粒种咖啡种植园的精量灌溉,达到节水增收的目的,基于无线传感器网络（WSNs）设计了一种滴灌自动控制系统,主要由传感器节点、簇头、控制节点和汇聚节点组成.测试了节点间的有效通信距离为50m,并在此基础上,采用静态簇和节点定位布置的方法进行了灌区划分和节点部署,每个灌区为边长50m的正方形,内设1个簇头、1个控制节点和4个传感器节点.采用定时通信与时间同步技术、休眠技术相结合的方法实现节点数据的无线传输,融合土壤含水率、光强和空气温湿度等信息,采用分区轮灌策略实现滴灌的自动控制.实验结果表明：系统实现了预期设计目标,能应用于小粒咖啡园的精量灌溉管理.     

气象传感器网络系统架构与体系结构研究

传感器网络将在气象观测领域发挥越来越重要的作用。针对地面气象观测的现实需求,提出了气象传感器网络的概念,构建了气象传感器网络的应用场景,完成了其需求分析与系统架构设计,深入分析了气象传感器网络可采用的通信手段。在陆上传感器网络体系结构的基础上,结合气象传感器网络的特点,设计了气象传感器网络的体系结构,将其结构划分为物理层、数据链路层和网络层。对该体系结构各层的功能进行了深入分析,最后对气象传感器网络的应用前景进行了展望。     

Synthesis of Biodiesel from Soybean Oil using Heterogeneous KF/ZnO Catalyst

Biodiesel was produced by transesterification of soybean oil with methanol using ZnO loaded with KF as a solid base catalyst. It was found that the catalyst with 15 wt.% KF loading and calcined at 873 K showed the optimum activity. XRD, IR and Hammett indicator method were employed for the catalyst characterization. The results showed the activity of the catalysts was correlated with their basicity. The influence of various reaction variables on the conversion was also discussed.     

Recent advances in the rheooptical characterization of silicon-containing networks

Recent advances in the various theories of strain-birefringenece of polymer networks are presented. The classical theories as well as the more advanced models are outlined together with some recent theoretical results on poly (dimethylsiloxane). Reference to work on other Si-containing polymers is made. The problems of intermolecular correlations occurring in polymer networks and their effect on the observed optical properties are discussed with reference to the optical-configuration parameter. Conclusions report areas of current research.This review was presented at the Second International Topical Workshop, Advances in Silicon-Based Polymer Science.     

Scaling laws and polystyrene networks: a quasi-elastic light scattering study

Randomly crosslinked networks with a range of crosslink densities were prepared by γ-irradiation. Quasielastic light scattering measurements were made on the gels swollen to equilibrium in cyclohexane at 308, 318 and 333 K. The longitudinal osmotic modulus M_os was obtained from the intensity of the dynamically scattered light, and the cooperative diffusion coefficient D_c calculated from the relaxation rate of the autocorrelation function. The concentration dependence of these parameters at the theta temperature was found to be consistent with the scaling predictions, but at higher temperatures where excluded volume conditions prevail, deviations from scaling behaviour were observed. These discrepancies are probably caused by defects in the network structure.     

Stochastically resilient extended Kalman filtering for discrete-time nonlinear systems with sensor failures

Missing sensor data is a common problem, which severely influences the overall performance of modern data-intensive control and computing applications. In order to address this important issue, a novel resilient extended Kalman filter is proposed for discrete-time nonlinear stochastic systems with sensor failures and random observer gain perturbations. The failure mechanisms of multiple sensors are assumed to be independent of each other with different failure rates. The locally unbiased robust minimum mean square filter is designed for state estimation under these conditions. The performance of the proposed estimation method is verified by means of numerical Monte Carlo simulation of two different nonlinear stochastic systems, involving a sinusoidal system and a Lorenz oscillator system.