The E3 architecture: enabling future cellular networks with cognitive and self‐x capabilities

Future mobile networks are expected to be complex heterogeneous systems. On the one hand this will enable users to take advantage of a number of different access technologies. On the other hand it will seriously affect network management procedures since more extensive operations and decisions will have to be dealt with. To tackle these challenges a number of new dynamic mechanisms need to be designed. It is imperative that certain network management tasks have to be performed without human intervention to reduce the OPEX costs and achieve faster responses in different events. To achieve this goal, the introduction of self‐x functionalities, combined with cognitive mechanisms and the ability to reconfigure network entities and terminals, is required. Moreover, the introduction of a new pilot channel needs to be considered to assist the terminals in selecting the most suitable radio access technology according to their requirements. We present the functional architecture of an evolved network that was designed in the context of the EU‐funded IP project ‘E³: End‐to‐End Efficiency’. This architecture aims to enhance existing procedures usually performed in traditional operation and maintenance systems (e.g. spectrum management, network planning, configuration actions). We explain the rationale of our design and provide specific examples to illustrate the role of the different functional entities and their interfaces. A considerable part of this architecture has recently been approved as a feasibility study by the ETSI Committee Reconfigurable Radio System. Copyright © 2010 John Wiley & Sons, Ltd.     

GSM phase 2+ general packet radio service GPRS: Architecture, protocols, and air interface

Selective packet dropping policies have been used to reduce congestion and transmission of traffic that would inevitably be retransmitted. For data applications using best-effort services, packet dropping policies (PDPs) are congestion management mechanisms implemented at each intermediate node that decide, reactively or proactively, to drop packets to reduce congestion and free up precious buffer space. While the primary goal of PDPs is to avoid or combat congestion, the individual PDP designs can significantly affect application throughput, network utilization, performance fairness, and synchronization problems with multiple Transmission Control Protocol (TCP) connections. Scalability and simplicity are also important design issues. This article surveys the most important selective packet dropping policies that have been designed for best-effort traffic in ATM and IP networks, providing a comprehensive comparison between the different mechanisms.     

100%无焊接IGBT模块——专为汽车工业而设计

减少二氧化碳的排放和可持续性发展——这些是我们这个时代的流行语。为了不辜负这些今天和明天的流行语,需要先进的电控驱动技术涉足新的领域。其中一个最大且最具挑战性的领域是汽车产业.现有的功率模块无法满足该行业的严格要求,这就是为什么它们不能服务市场的原因。为了面对这项挑战,赛米控开发了SKiM（赛米控集成模块）,适用于汽车工业的IGBT模块。[编者按]     

A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing

A large effort is devoted to the research of new computing paradigms associated with innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS (complementary metal oxide semiconductor) association. Among various propositions, spiking neural network (SNN) seems a valid candidate. i) In terms of functions, SNN using relative spike timing for information coding are deemed to be the most effective at taking inspiration from the brain to allow fast and efficient processing of information for complex tasks in recognition or classification. ii) In terms of technology, SNN may be able to benefit the most from nanodevices because SNN architectures are intrinsically tolerant to defective devices and performance variability. Here, spike‐timing‐dependent plasticity (STDP), a basic and primordial learning function in the brain, is demonstrated with a new class of synapstor (synapse‐transistor), called nanoparticle organic memory field‐effect transistor (NOMFET). This learning function is obtained with a simple hybrid material made of the self‐assembly of gold nanoparticles and organic semiconductor thin films. Beyond mimicking biological synapses, it is also demonstrated how the shape of the applied spikes can tailor the STDP learning function. Moreover, the experiments and modeling show that this synapstor is a memristive device. Finally, these synapstors are successfully coupled with a CMOS platform emulating the pre‐ and postsynaptic neurons, and a behavioral macromodel is developed on usual device simulator.     

运用数字暗场和结构重构方法测定FePt纳米粒子的正二十面体结构

小粒子结构一直是人们关注的研究热点,上世纪70年代运用暗场像技术发现了具有多重孪晶结构的正二十面体和正十面体结构.近年来,人们已经能够合成直径只有几纳米、结构复杂的纳米粒子,传统的高分辨像和衍射等方法难以获得这些纳米粒子的结构.本文以直径约6nm的FePt粒子为研究对象,通过系列欠焦像波函数重构获得了该粒子亚埃分辨率高分辨相位像,并对该相位像进行了数字暗场像分析研究,通过结构重构并结合高分辨像模拟测定该粒子具有二十面体结构,为确定具有复杂结构的纳米粒子结构提供了一种新途径.     

Synthesis optimization and characterization of multiwalled carbon nanotubes

The unique properties of carbon nanotubes (CNTs) have suggested applications in a variety of fields. Multiwalled nanotubes were synthesized using chemical vapor deposition (CVD) procedures and subsequently characterized. Reaction parameters such as catalyst type and carrier gas flow rate were optimized to produce well-aligned multiwalled nanotubes with lengths between a few microns to several millimeters. Characterization was performed with scanning electron microscopy (SEM), transmission electron microscopy (TEM), x-ray diffraction (XRD), energy dispersive x-ray spectroscopy (EDS), thermo-gravimetric analysis, and Raman spectroscopy, focusing on composition and purity. Results show the synthesis of high-purity nanotubes of several millimeters in length from iron, nickel, cobalt, and titanium carbide catalysts with thermal stability to above 550°C.