Novel tiO2 nanocatalysts for wastewater purification: tapping energy from the sun.

Water treatment using TiO2 semiconductor as a durable heterogeneous photocatalyst has been the focus of environmentalists in recent years. Currently, we developed an inexpensive and highly efficient approach for synthesizing nitrogen-doped TiO2 with lower band-gap energy that can respond to visible light. Doping on the molecular scale led to an enhanced nitrogen concentration of up to 21.8%. Reflectance measurements showed the synthesized N-doped TiO2 nanoparticles are catalytically active with the absorbance that extends into the visible region up to 600 nm. The water purification potential of this new class of compound was evaluated by studying the photodegradation of Acid Orange 7 (AO7) and E. coli. Experiments were conducted to compare the photocatalytic activities of N-doped TiO2 nanocatalysts and commercially available Degussa P25 power under identical solar light exposure. N-doped TiO2 demonstrated superior photocatalytic activities in both chemical compound degradation and bactericidal reactions. The result of this study shows the potential of applying new generations of catalyst for wastewater purification and disinfection.     

Modeling wireless fading channels via stochastic differential equations: identification and estimation based on noisy measurements

This paper is concerned with modeling and identification of wireless channels using noisy measurements. The models employed are governed by stochastic differential equations (SDEs) in state space form, while the identification method is based on the expectation-maximization (EM) algorithm and Kalman filtering. The algorithm is tested against real channel measurements. The results presented include state space models for the channels, estimates of inphase and quadrature components, and estimates of the corresponding Doppler power spectral densities (DPSDs), from sample noisy measurements. Based on the available measurements, it is concluded that state space models of order two are sufficient for wireless flat fading channel characterization.     

One-to-Many and Many-to-One Optical Chaos Communications Using Semiconductor Lasers

A theoretical construction of one-to-many (OTM) and many-to-one (MTO) chaos synchronization communications using semiconductor lasers is presented. One center laser provides strong injections (strong link) to other side lasers, and the side lasers also provide weak injections (weak link) to the center one. Simulation results show that the side lasers synchronize with the center laser through injection-locking. In addition, messages transmitted via the strong links or the weak links experience strong chaos pass filtering, enabling us to realize OTM and MTO communications.     

基于MoCA标准的家庭网络研究

本文描述了一种基于MOCA标准的家庭网络,并对该网络及设备进行了基本性能的测试.本文首先介绍了家庭网络的发展现状,对目前的各种家庭网络标准进行了比较.在此基础上详细描述了基于MOCA标准的家庭网络,并对该网络的设计,实现、网络性能进行了详述.然后对该网络进行了测试并得出了相关的测试结论.最后指出该网络的不足及改进之处.     

基于m法线性有限元分析在桩基计算中的应用

针对目前各类有限元软件在工程设计中的应用已相当普遍,提出了通过有限元模型解决桩基的强度验算和水平位移的计算,不仅使桩的设计变得简单,而且还可在相当的精度内考虑桩基和上部结构的共同作用。     

农村通自来水工程若干技术问题初探

根据在农村通自来水工程实践中积累的经验,提出并较好地解决了农村供水工程的评价标准,设计供水时间的确定,输配水管网设计中的优化等问题,对建好、管好和用好农村自来水工程具有一定的帮助作用.     

Tri-Gate Bulk MOSFET Design for CMOS Scaling to the End of the Roadmap

A tri-gate bulk MOSFET design utilizing a low-aspect-ratio channel is proposed to provide an evolutionary pathway for CMOS scaling to the end of the roadmap. 3-D device simulations indicate that this design offers the advantages of a multi-gate FET (reduced variability in performance and improved scalability) together with the advantages of a conventional planar MOSFET (low substrate cost and capability for dynamic threshold-voltage control).     

Optimal and self‐tuning fusion Kalman filters for discrete‐time stochastic singular systems

Based on the optimal fusion estimation algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion Kalman filter weighted by scalars is presented for discrete‐time stochastic singular systems with multiple sensors and correlated noises. A cross‐covariance matrix of filtering errors between any two sensors is derived. When the noise statistical information is unknown, a distributed identification approach is presented based on correlation functions and the weighted average method. Further, a distributed self‐tuning fusion filter is given, which includes two stage fusions where the first‐stage fusion is used to identify the noise covariance and the second‐stage fusion is used to obtain the fusion state filter. A simulation verifies the effectiveness of the proposed algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

Evolving artificial neural network ensembles

Using a coordinated group of simple solvers to tackle a complex problem is not an entirely new idea. Its root could be traced back hundreds of years ago when ancient Chinese suggested a team approach to problem solving. For a long time, engineers have used the divide-and-conquer strategy to decompose a complex problem into simpler sub-problems and then solve them by a group of solvers. However, knowing the best way to divide a complex problem into simpler ones relies heavily on the available domain knowledge. It is often a manual process by an experienced engineer. There have been few automatic divide-and-conquer methods reported in the literature. Fortunately, evolutionary computation provides some of the interesting avenues to automatic divide-and-conquer methods. An in-depth study of such methods reveals that there is a deep underlying connection between evolutionary computation and ANN ensembles. Ideas in one area can be usefully transferred into another in producing effective algorithms. For example, using speciation to create and maintain diversity had inspired the development of negative correlation learning for ANN ensembles, and an in-depth study of diversity in ensembles. This paper will review some of the recent work in evolutionary approaches to designing ANN ensembles.