Effect and mechanism of on-chip electrostatic discharge protection circuit under fast rising time electromagnetic pulse

The electrostatic discharge(ESD)protection circuit widely exists in the input and output ports of CMOS digital circuits,and fast rising time electromagnetic pulse(FREMP)coupled into the device not only interacts with the CMOS circuit,but also acts on the protection circuit.This paper establishes a model of on-chip CMOS electrostatic discharge protection circuit and selects square pulse as the FREMP signals.Based on multiple physical parameter models,it depicts the distribution of the lattice temperature,current density,and electric field intensity inside the device.At the same time,this paper explores the changes of the internal devices in the circuit under the injection of fast rising time electromagnetic pulse and describes the relationship between the damage amplitude threshold and the pulse width.The results show that the ESD protection circuit has potential damage risk,and the injection of FREMP leads to irreversible heat loss inside the circuit.In addition,pulse signals with different attributes will change the damage threshold of the circuit.These results provide an important reference for further evaluation of the influence of electromagnetic environment on the chip,which is helpful to carry out the reliability enhancement research of ESD protection circuit.

无线供电MEC系统的计算能效最大化策略

为了解决无线供电移动边缘计算(MEC)系统的计算能效优化问题,提出一种基于非正交多址接入的无线供电MEC系统的资源分配策略。该策略将非线性能量收集模型应用到移动设备上,通过联合优化MEC服务器和移动设备的计算频率、执行时间、基站发射功率、设备发射功率、卸载时间和能量收集时间,比较充分地利用移动设备和MEC服务器的可用计算资源,提高设备的吞吐量和计算位数,进而最大限度地提升系统计算能效。将该联合优化问题转化为非凸分式规划问题,设计一种基于Dinkelbach的迭代算法来获得最优的资源分配方案。仿真实验表明:该资源分配策略所获得的系统计算能效更高,具有更好的性能增益。

MET receptor tyrosine kinase promotes the generation of functional synapses in adult cortical circuits

Loss of synapse and functional connectivity in brain circuits is associated with aging and neurodegeneration,however,few molecular mechanisms are known to intrinsically promote synaptogenesis or enhance synapse function.We have previously shown that MET receptor tyrosine kinase in the developing cortical circuits promotes dendritic growth and dendritic spine morphogenesis.To investigate whether enhancing MET in adult cortex has synapse regenerating potential,we created a knockin mouse line,in which the human MET gene expression and signaling can be turned on in adult(10–12 months)cortical neurons through doxycycline-containing chow.We found that similar to the developing brain,turning on MET signaling in the adult cortex activates small GTPases and increases spine density in prefrontal projection neurons.These findings are further corroborated by increased synaptic activity and transient generation of immature silent synapses.Prolonged MET signaling resulted in an increasedα-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid/N-methyl-Daspartate(AMPA/NMDA)receptor current ratio,indicative of enhanced synaptic function and connectivity.Our data reveal that enhancing MET signaling could be an interventional approach to promote synaptogenesis and preserve functional connectivity in the adult brain.These findings may have implications for regenerative therapy in aging and neurodegeneration conditions.

无人机辅助MEC系统中基于最优SIC顺序的能耗优化方案

在基于上行非正交多址接入(NOMA)的无人机(UAV)辅助移动边缘计算(MEC)系统中,NOMA的连续干扰消除(SIC)顺序已成为限制上行任务卸载链路传输性能的瓶颈,为降低系统能耗,对SIC顺序进行了讨论,提出了联合信道增益与任务时延约束的最优SIC顺序。在满足设备给定任务时延、设备最大发射功率约束以及UAV轨迹的约束下,基于最优SIC顺序提出了最小化系统能耗的问题。由于该问题是个复杂的非凸问题,采取交替优化的方法求解该优化问题,以实现功率分配和UAV轨迹的优化;利用匹配理论,提出了低复杂度算法来得到不同时隙的最优设备分组。仿真结果表明,与其他SIC顺序相比,最优SIC顺序能够在相同的任务时延约束下实现更小的系统能耗;所提的低复杂度设备分组算法能够得到最优设备分组。

5G+MEC承载车联网业务传输性能测试与验证

车联网产业正在快速发展,车路协同应用会在交通安全、效率和便利性等方面对日常交通出行产生积极的作用。5G作为LTE-V2X的重要补充,用于连续覆盖补盲、业务融合使能、兼容多种车载终端,在未来较长时间内,5G与LTEV2X将共同承载车联网业务。鉴于此,文中进行了5G承载车联网业务的性能测试与验证,在测试中模拟车联网应用的业务模式,通过5G网络传输并在MEC进行结果统计。重点关注单向时延、抖动、丢包率等网络指标,探索5G承载车联网业务的最佳模式。

MSs-MEC中基于DRL的服务缓存和任务迁移联合优化算法

多服务移动边缘计算(multiple-services mobile edge computing,MSs-MEC)能根据需求自适应调整服务缓存决策,使得部署在用户侧的边缘服务器能够灵活处理不同服务类型的任务。但在实际应用中,特定类型任务的成功迁移依赖于服务环境的提前安装。此外,同时进行任务迁移和服务缓存可能会因时间冲突而导致计算延时。因此,针对上述相关问题,首先将任务迁移和服务缓存决策进行解耦,针对深度强化学习(deep reinforcement learning,DRL)在具有高维的混合决策空间的性能提升不明显的缺点(例如资源分配时利用率不高),将DRL与Transformer结合,通过在历史数据中学习,输出当前时隙的任务迁移决策和下一时隙的任务决策,保证任务到达边缘服务器时能立即执行。其次,为了提高资源分配问题中的资源利用率,将问题分解为连续资源分配问题和离散的任务迁移与服务缓存问题,利用凸优化技术求解资源分配最优决策。广泛的数值结果表明,与其他基线算法相比,提出的算法能有效地减少任务的平均完成时延,同时在资源利用率和稳定性方面也有优异的表现。

Mechanism of hybrid-magnetic-circuit multi-couple motor

Discusses the interval between laminations in a permanent magnet inductor motor which makes the air gap magnetic field produced by the permanent magnet very uneven in the axial direction, and limits the performance of a motor. Proposes a hybrid magnetic circuit multi couple motor to compensate for the uneven air gap magnetic field, thereby improving the performance of a motor.

Method of Mechanical Fault Intelligent Diagnosis Based on Vibration Signal of High Voltage Circuit Breaker

Based on vibration signal of high voltage circuit breaker,a new method of intelligent fault diagnosis that wavelet packet extracts energy entropy which are used as characteristic vector of the support vector machine(SVM)to construct classifier for fault diagnosis is presented.The acceleration sensors are applied to collecting the vibration data of different states of high voltage circuit breakers based on self-made experimental platform in this method.The wavelet packet are fully applied to analyze the vibration signal and decompose vibration signal into three layers,and wavelet packet energy entropy of each frequency band are as the characteristic vector of circuit breaker failure mode.Then the intelligent diagnosis network is established on the basis of the support vector machine theory.It is verified that the method has a better capability of classification and a higher accuracy compared with the traditional neural network diagnosis method through distinguishing the three fault modes which are tripping device stuck,the vacuum arcing chamber fixed bolt looseness and too much friction force of the transmission mechanism of circuit breaker in this paper.

基于SDN的车联网多MEC动态负载均衡算法

车载自组织网络(VANET)承载的数据规模呈现爆炸性增长趋势。针对车联网中在线卸载场景下,多边缘服务器(MEC)负载不均衡导致车辆卸载成功率严重下降问题,提出一种基于软件定义网络(SDN)的车联网多MEC动态负载均衡算法DFPC。该算法结合排队论中先到先服务和有优先权的服务两种方式,SDN控制器通过一定的等待时延定时收集当前批任务,利用改进的K-means聚类算法快速对多维任务分类,优先入队紧急度相对高的任务;再利用SDN控制器定时收集的MEC上下文信息,实现卸载任务在多个MEC之间分配的动态反馈调节,解决了多MEC之间动态负载不均衡问题,充分利用MEC的计算资源,最终提升了整体车辆卸载成功率。为了验证DFPC算法在真实动态场景下的有效性,设计一种多MEC接入的在线卸载框架MOLF,通过低成本硬件部署模式完成在线卸载场景下负载均衡性能测试。实验结果表明,相比基准方案,DFPC算法平均卸载成功率提升了28%,平均负载方差降低了73%。

多用户MIMO-MEC网络中基于APSO的任务卸载研究

在移动边缘计算(Mobile Edge Computing,MEC)系统中引入多输入多输出(Multiple Input Multiple Output,MIMO)技术与数据压缩技术,能够降低数据冗余度和提高数据传输速率,从而降低任务的执行时延与能耗。针对具备数据压缩功能的多用户MIMO-MEC网络,研究了多用户任务卸载问题。通过联合优化任务卸载比例、数据压缩比例、发送功率、计算频率和信道带宽,来最小化系统总时延。在能耗、功率和带宽等约束条件下,将任务卸载归纳为一个非凸优化问题。由于能耗约束较为复杂,构造罚函数将其归并,得到一个相对简单的等价问题。将所有优化变量视为一个粒子,基于自适应粒子群优化(Adaptive Particle Swarm Optimization,APSO)框架提出多用户的任务卸载方法。由于粒子更新时可能违反约束条件,提出的方法对粒子越界的情形进行了特别处理。该方法能自适应地调整惯性权重来提高寻优能力和收敛性,通过不断迭代最终获得最优或者次优解。仿真实验评估了所提卸载方法的性能,分析了用户数、任务计算强度等参数对系统性能的影响。结果表明,提出的方法优于本地计算、传统粒子群优化(Particle Swarm Optimization,PSO)算法等对比方案,能够有效降低系统的任务执行时延。

面向车联网的MEC跨域协同技术研究

车联网跨域协同技术是多接入边缘计算(Multi-Access Edge Computing, MEC)和蜂窝车联网(Cellular Vehicle to Everything, C-V2X)融合场景中的重点研究内容,涉及到安全类、效率类、协作类、视频类、信息服务类跨域协同交互场景,每类场景都涉及MEC跨域流程及跨域过程中的上下文规范。目前国际上面向车联网的MEC跨域协同技术处于起步发展阶段,ETSI、5GAA等组织尚未制定比较完善的国际标准。以当前主流车联网边缘计算系统架构为基础,着重开展各类车联网场景应用层基于MEC的跨域需求、交互迁移流程及上下文规范等研究。

Recycling process assessment of mechanical recycling of printed circuit board

A comprehensive assessment index system was established. The mechanical recycling process of printed circuit board was evaluated according to the comprehensive evaluation index system using the fuzzy analytic hierarchy process. A process assessment software system of mechanical recycling was established to evaluate different recycling technologies. And the software system was developed in the environment of VB6.0 and Access2000.

基于MEC模型的Z世代“特种兵式旅游”消费价值诉求探讨

Z世代“特种兵式旅游”成为当前颇具市场潜力的热点消费领域。本文基于MEC模型的分析技术,综合运用软式、硬式阶梯方法,针对“特种兵式旅游”消费者的价值动机及其形成机理展开探索。研究表明:Z世代“特种兵式旅游”价值动机为属性、结果、价值3个阶层,属性是基础动因,结果是中介诱因,价值是根本原因,享乐、成就感、自我定向、刺激是其价值诉求的终极目标;价值诉求的形成机制存在5条关键路径,最重要的是MEC1(对世界的向往和好奇-兴奋-享乐);景区景点是目的地的核心吸引物,亦是Z世代累积炫耀资本并达成社会需求满足的重要属性项,交通设施在其消费行为中起到重要辅助支撑作用,并刺激其产生消费安全感知价值。

IRS辅助供能的多用户MEC系统性能研究

在物联网(IoT)场景中,边缘服务器可有效缓解IoT设备计算能力有限的问题,如何保证IoT设备的能量供应成为研究热点之一.无线能量传输(WPT)与移动边缘计算(MEC)的组合是一种解决方案,但混合接入点(HAP)与IoT设备之间不能保证总是存在直连链路.为了处理这个问题,考虑利用智能反射表面(IRS)辅助无线能量传输与任务卸载.在多用户MEC系统下,当IoT设备采用二进制卸载时,联合能量传输时间优化、卸载决策以及调度顺序以求得最小系统处理时延问题是一个混合整数非凸问题.考虑HAP与IoT设备之间的信道差异,采用一种两层交替迭代的算法,并利用MEC的计算能力最小化系统处理时延.实验结果表明,所用方法收敛快,能有效缩短系统处理时延,在IRS的辅助下能明显提升系统性能.

Coupled Electromagnetic Circuits and Their Connection to Quantum Mechanical Resonance Interactions and Biorhythms

The existence of specific biorhythms and the role of geomagnetic and/or solar magnetic activities are well-established by appropriate correlations in chronobiology. From a physical viewpoint, there are two different accesses to biorhythms to set up connections to molecular processes: quantum mechanical perturbation theoretical methods and their resonance dominators to characterize specific interactions between constituents. These methods permit the treatment of molecular processes by circuits with characteristic resonances and “beat-frequencies”, which result from primarily fast physical processes. As examples, the tunneling processes between DNA base pairs (H bonds), the ATP decomposition and the irradiation of tumor cells are accounted for.

电力物联网中基于SCMA-MEC的最优功率分配与卸载决策

在电力物联网(Power Internet of Things, PIoT)中,移动终端设备接入会产生大量的异构数据,给通信信道及中央存储计算系统带来巨大压力,对系统的稳定性和能源消耗带来了挑战。为应对这一挑战,提出了基于稀疏码多址接入(Sparse Code Multiple Access, SCMA)技术的移动边缘计算(Mobile Edge Computing, MEC)的异构网络优化方案。为了最小化系统总开销,考虑了在资源受限的情况下设备的能源消耗和计算资源分配,提出了基于最优功率分配的联合随机码本分配的算法,实现移动终端设备能耗最小化。通过构建约束多目标优化问题的数学模型,设计了一种基于帕累托前沿的多目标鲸鱼优化算法(Pareto Frontier-Multi Objective Whale Optimization Algorithm, PF-MOWOA)用于任务卸载。仿真结果表明,在满足能耗和时延的约束条件下,所提算法相较于其他算法具有更低的系统总能耗,且联合任务卸载优化算法可以进一步降低系统的总开销。

Carbon nanotube integrated circuit technology:purification,assembly and integration

As the manufacturing process of silicon-based integrated circuits(ICs)approaches its physical limit,the quantum effect of silicon-based field-effect transistors(FETs)has become increasingly evident.And the burgeoning carbon-based semiconductor technology has become one of the most disruptive technologies in the post-Moore era.As one-dimensional nanomaterials,carbon nanotubes(CNTs)are far superior to silicon at the same technology nodes of FETs because of their excellent electrical transport and scaling properties,rendering them the most competitive material in the next-generation ICs technology.However,certain challenges impede the industrialization of CNTs,particularly in terms of material preparation,which significantly hinders the development of CNT-based ICs.Focusing on CNT-based ICs technology,this review summarizes its main technical status,development trends,existing challenges,and future development directions.

Control of microwave signals using bichromatic electromechanically induced transparency in multimode circuit electromechanical systems

We theoretically investigate the tunable delay and advancement of microwave signals based on bichromatic electromechanically induced transparency in a three-mode circuit electromechanical system, where two nanomechanical resonators with closely spaced frequencies are independently coupled to a common microwave cavity. In the presence of a strong microwave pump field, we obtain two transparency windows accompanied by steep phase dispersion in the transmitted microwave probe field. The width of the transparency window and the group delay of the probe field can be controlled effectively by the power of the pump field. It is shown that the maximum group delay of 0.12 ms and the advancement of0.27 ms can be obtained in the current experiments.

Neuromorphic circuits based on memristors: endowing robots with a human-like brain

Robots are widely used,providing significant convenience in daily life and production.With the rapid development of artificial intelligence and neuromorphic computing in recent years,the realization of more intelligent robots through a pro-found intersection of neuroscience and robotics has received much attention.Neuromorphic circuits based on memristors used to construct hardware neural networks have proved to be a promising solution of shattering traditional control limita-tions in the field of robot control,showcasing characteristics that enhance robot intelligence,speed,and energy efficiency.Start-ing with introducing the working mechanism of memristors and peripheral circuit design,this review gives a comprehensive analysis on the biomimetic information processing and biomimetic driving operations achieved through the utilization of neuro-morphic circuits in brain-like control.Four hardware neural network approaches,including digital-analog hybrid circuit design,novel device structure design,multi-regulation mechanism,and crossbar array,are summarized,which can well simulate the motor decision-making mechanism,multi-information integration and parallel control of brain at the hardware level.It will be definitely conductive to promote the application of memristor-based neuromorphic circuits in areas such as intelligent robotics,artificial intelligence,and neural computing.Finally,a conclusion and future prospects are discussed.

基于改进人工蜂鸟算法的MEC任务卸载策略

面对信息化网络环境中大量时延敏感型和计算密集型任务的计算需求,移动边缘计算(MEC)及其计算卸载技术提供了一种行之有效的解决方案。针对资源受限移动边缘系统的任务卸载策略,设计一种成本最优化算法。首先,结合系统的基本数据构建多用户多服务器网络场景,并根据时延、能耗等待优化指标建立一种包含惩罚项的最小化成本优化模型;然后,提出一种改进人工蜂鸟算法(IAHA),通过对原算法的寻优方式与算法结构进行适应性地调整和优化,并引入一种紧急避险策略,实现系统模型与算法映射的高度契合以及对模型问题快速精确求解,进而得到系统的最优卸载策略;最后,应用策略进行部署以降低系统的成本支出和提升用户的服务体验。仿真实验结果表明,所提改进算法能够有效降低系统成本,并且在针对高维复杂模型求解时具有更突出的收敛性能和寻优精度,在特定实验条件下,所提改进算法相较于部分经典的元启发式算法和典型的新型群智能算法,系统成本减少20.79%~65.39%,所提任务卸载算法相对于本地计算策略的平均系统成本能够降低66.98%。