Location and calculation-free node-scheduling schemes in large wireless sensor networks

In wireless sensor networks that consist of a large number of low-power, short-lived, unreliable sensors, one of the main design challenges is to obtain long system lifetime without sacrificing system original performance (sensing coverage and sensing reliability). To solve this problem, one of the potential approaches is to identify redundant nodes at the sensing interface and then assign them an off-duty operation mode that has lower energy consumption than the normal on-duty mode. In our previous work [J. Wireless Commun. Mobile Comput. 3 (2003) 271; Processing of ACM Wireless Sensor Network and Application Workshop 2002, September 2002], we proposed a node-scheduling scheme, which can provide a 100% sensing coverage preservation capability. This, however, requires each node to be aware of its own and its neighbors’ location information. Also, in that scheme, each node has to do accurate geometrical calculation to determine whether to take an off-duty status. In this paper, we propose and study several alternative node-scheduling schemes, which cannot completely preserve the original system coverage, but are nonetheless light-weighted and flexible compared with the previous one. Our simulation results compare these schemes with the previous one and demonstrate their effectiveness.     

Consolidation of Long-Term Memory: Evidence and Alternatives.

Memory loss in retrograde amnesia has long been held to be larger for recent periods than for remote periods, a pattern usually referred to as the Ribot gradient. One explanation for this gradient is consolidation of long-term memories. Several computational models of such a process have shown how consolidation can explain characteristics of amnesia, but they have not elucidated how consolidation must be envisaged. Here findings are reviewed that shed light on how consolidation may be implemented in the brain. Moreover, consolidation is contrasted with alternative theories of the Ribot gradient. Consolidation theory, multiple trace theory, and semantization can all handle some findings well but not others. Conclusive evidence for or against consolidation thus remains to be found. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

无线网状网技术与应用

无线网状网(WMNs)由网状路由器节点和客户机节点组成,其中的网状路由器节点组成了无线网状网的网络骨干,其移动性很小。他们一起为无线网状网和其他常规无线网络的客户机节点提供网络的无线接入。WMNs技术结合了中心式控制的蜂窝网与分布式控制的无线自组织网的优点,可有效克服这两种技术的缺陷并显著提高无线网络的性能,已经成为下一代无线通信网络的研究热点之一。WMNs可为无线个域网、局域网、校园网、城域网的一系列应用提供高速无线宽带接入服务。虽然目前WMNs技术发展很快,但其协议栈各层仍存在许多有待研究的课题。首先简要介绍了无线网状网的结构与特点;随后重点分析了其主要的几个应用领域;最后探讨了WMNs各协议层的研究现状与关键技术,并分析了该技术存在的问题及未来的研究方向。     

The Role of Network Topologies in the Optical Core of IP-over-WDM Networks with Static Wavelength Routing

In this paper, we present a performance analysis of network topologies for the optical core of IP-over-WDM networks with static wavelength routing. The performance analysis is focused on regular degree four topologies, and, for comparison purposes, degree three topologies are also considered. It is shown that the increase of the nodal degree from three (degree three topology with smallest diameter) to four (degree four topology with smallest diameter) improves the network performance if a larger number of wavelengths per link is available. However, the influence of wavelength interchange on the nodal degree gain is small. The performance of regular degree four topologies with smallest diameter is also compared with the performance of mesh–torus topologies (which are also degree four topologies), and it is shown that the blocking probability of degree four topologies with smallest diameter is about two orders of magnitude lower than the blocking probability of mesh–torus topologies. It is also presented a performance comparison of WDM-based networks with nodal degrees ranging from two to five and it is shown that the increase of the nodal degree from two to three leads to high nodal degree gains, while de increase of the nodal degree from four to five leads to low nodal degree gains. These results show that degree three and degree four topologies are very attractive for use in the optical core of IP-over-WDM networks.     

The Amygdala Modulates Hippocampus-Dependent Context Memory Formation and Stores Cue-Shock Associations.

Preexposing rats to the context facilitates subsequent contextual fear conditioning. This effect depends on the hippocampus (J. W. Rudy, R. M. Barrientos, & R. C. O'Reilly, 2002). The authors report that inactivating the basolateral region of the amygdala (BLA) by injecting muscimol, a GABAA agonist, before or after preexposure reduced this effect. In contrast, bilateral injections of anisomycin, a protein synthesis inhibitor, into BLA did not impair the consolidation of the context memory. However, when injected after fear conditioning, anisomycin impaired consolidation of both contextual and auditory-cue fear conditioning. Results are consistent with 2 ideas about the amygdala's contribution to memory: (a) It modulates memory formation in other regions of the brain, and (b) it is a storage site for cue-shock associations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

弹性分组环技术

弹性分组环技术是一种新兴的城域网技术。作者主要讨论了弹性分组环技术的特点和优势,并且简要介绍了弹性分组环技术目前在国内城域网建设中的一些应用。弹性分组环技术集以太网的经济性、IP的智能化、光纤环网的丰富带宽和可靠性于一体,是目前宽带IP城域网运营商的最佳组网方案。     

Nanoweb Formation: 2D Self‐Assembly of Semiconductor Gallium Oxide Nanowires/Nanotubes

This paper reports a method to produce networks of crystalline gallium oxide comprised of one‐dimensional (1D) nanostructures. Because of the unique arrangement of wires, these crystalline networks are termed as ‘nanowebs’. Nanowebs are of great technological interest since they contain wire densities of the order of 10⁹ cm^–2. A possible mechanism for the fast self‐assembly of crystalline metal oxide nanowires involves multiple nucleation and coalescence via oxidation–reduction reactions at the molecular level. The preferential growth of nanowires parallel to the substrate enabled them to coalesce into regular polygonal networks. The individual segments of the polygonal network consist of both nanowires and nanotubules of β‐gallium oxide. Individual wire properties contribute to a nanoweb’s overall capacity and the implications for devices based on nanowebs are expected to be enormous.     

Positively Thermo‐Sensitive Monodisperse Core–Shell Microspheres

In this paper, we report on a novel family of monodisperse thermo‐sensitive core–shell hydrogel microspheres that is featured with high monodispersity and positively thermo‐responsive volume phase transition characteristics with tunable swelling kinetics, i.e., the particle swelling is induced by an increase rather than a decrease in temperature. The microspheres were fabricated in a three‐step process. In the first step, monodisperse poly(acrylamide‐co‐styrene) seeds were prepared by emulsifier‐free emulsion polymerization. In the second step, poly(acrylamide) or poly[acrylamide‐co‐(butyl methacrylate)] shells were fabricated on the microsphere seeds by free radical polymerization. In the third step, the core–shell microspheres with poly‐ (acrylamide)/poly(acrylic acid) based interpenetrating polymer network (IPN) shells were finished by a method of sequential IPN synthesis. The proposed monodisperse core–shell microspheres provide a new mode of the phase transition behavior for thermo‐sensitive “smart” or “intelligent” monodisperse micro‐actuators that is highly attractive for targeting drug delivery systems, chemical separations, sensors, and so on.     

基于约束优化的联想记忆模型学习算法

本文提出了一种对称互连神经元网络的学习策略,利用全局约束优化方法确定连接权。优化过程采用了梯度下降技术。这种学习算法可以保证训练样本成为系统的稳定吸引子,并且具有优化意义上的最大吸引域。本文讨论了网络的存储容量,训练样本的渐近稳定性和吸引域大小。计算机实验结果说明了学习算法的优越性。     

Experiments of Fast Learning with High Order Boltzmann Machines

This work reports the results obtained with the application of High Order Boltzmann Machines without hidden units to construct classifiers for some problems that represent different learning paradigms. The Boltzmann Machine weight updating algorithm remains the same even when some of the units can take values in a discrete set or in a continuous interval. The absence of hidden units and the restriction to classification problems allows for the estimation of the connection statistics, without the computational cost involved in the application of simulated annealing. In this setting, the learning process can be sped up several orders of magnitude with no appreciable loss of quality of the results obtained.