Analytical Modeling for Translating Statistical Changes to Circuit Variability by Ultra-Deep Submicron Digital Circuit Design

This paper presents a physics-based compact gate delay model that includes all short-channel phenomena prevalent at the ultra-deep submicron technology node of 32 nm. To simplify calculations, the proposed model is connected to a compact α-power law-based (Sakurai-Newton) model. The model has been tested on a wide range of supply voltages. The model accurately predicts nominal delays and the delays under process variations. It has been shown that at lower technology nodes, the delay is more sensitive to threshold voltage variations, specifically at the sub-threshold operating region as compared with effective channel length variations above the threshold region.     

Adaptive Discretization for Computerized Tomography

Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.     

Delay compensated control of the Stefan problem and robustness to delay mismatch

This paper presents a control design for the one‐phase Stefan problem under actuator delay via a backstepping method. The Stefan problem represents a liquid‐solid phase change phenomenon which describes the time evolution of a material's temperature profile and the interface position. The actuator delay is modeled by a first‐order hyperbolic partial differential equation (PDE), resulting in a cascaded transport‐diffusion PDE system defined on a time‐varying spatial domain described by an ordinary differential equation (ODE). Two nonlinear backstepping transformations are utilized for the control design. The setpoint restriction is given to guarantee a physical constraint on the proposed controller for the melting process. This constraint ensures the exponential convergence of the moving interface to a setpoint and the exponential stability of the temperature equilibrium profile and the delayed controller in the norm. Furthermore, robustness analysis with respect to the delay mismatch between the plant and the controller is studied, which provides analogous results to the exact compensation by restricting the control gain.     

基于双层修正无迹卡尔曼的水下节点定位算法* .txt

摘要：针对水下无线传感网络中运动节点定位精度低的问题，提出了一种新的基于双层修正无迹卡尔曼的水下节点定位算法(DLMUKF)。该算法利用下层无迹卡尔曼滤波算法对节点状态进行预测，根据各信标节点的测距传播时延对预测的节点状态进行修正。运用上层无迹卡尔曼滤波算法对修正后的状态进行新的预测与修正。仿真实验中，DLMUKF算法的平均定位误差约为传统多边定位算法的15%，约为基于无迹卡尔曼滤波（UKF）定位算法的16%，受节点运动时间与速度的影响最小。通过实验证明DLMUKF算法能更充分利用实际距离值，可以有效减小运动节点的定位误差。 .txt     

融合环境因素的有限空间毒气扩散危害程度分析

为探究环境因素对毒气扩散危害程度的影响,通过对比有限空间内外环境差异,了解气体在有限空间内受力情况,并以传统高斯烟羽模型为基础,考虑扩散气体与有限空间边界碰撞产生的反射作用,建立了适用于有限空间气体扩散的改进高斯烟羽模型。深度剖析有限空间气体扩散环境,挖掘气体扩散主要关联因子,构建风速、地表粗糙度对有限空间气体扩散的影响函数;参照有限空间环境特征,分别设置3种不同风速与2种不同地表粗糙度,推求6种不同环境条件下气体扩散面积,阐明风速与地表粗糙度对有限空间气体扩散的影响程度。结果表明:在单一风速条件下,风速越高,地表粗糙度越低,洞室内H2S气体滞留时间越短,洞室内气体的扩散面积越小;风速越低,地表粗糙度越高,洞室内气体的扩散面积越大。     

Robust predictive extended state observer for a class of nonlinear systems with time-varying input delay

ABSTRACT

This paper deals with asymptotic stabilisation of a class of nonlinear input-delayed systems via dynamic output feedback in the presence of disturbances. The proposed strategy has the structure of an observer-based control law, in which the observer estimates and predicts both the plant state and the external disturbance. A nominal delay value is assumed to be known and stability conditions in terms of linear matrix inequalities are derived for fast-varying delay uncertainties. Asymptotic stability is achieved if the disturbance or the time delay is constant. The controller design problem is also addressed and a numerical example with an unstable system is provided to illustrate the usefulness of the proposed strategy.     

移动边缘计算环境下的动态资源分配策略

在通讯设备爆炸式增长的时代,移动边缘计算作为5G通讯技术的核心技术之一,对其进行合理的资源分配显得尤为重要。移动边缘计算的思想是把云计算中心下沉到基站部署(边缘云),使云计算中心更加靠近用户,以快速解决计算资源分配问题。但是,相对于大型的云计算中心,边缘云的计算资源有限,传统的虚拟机分配方式不足以灵活应对边缘云的计算资源分配问题。为解决此问题,提出一种根据用户综合需求变化的动态计算资源和频谱分配算法(DRFAA),采用"分治"策略,并将资源模拟成"流体"资源进行分配,以寻求较大的吞吐量和较低的传输时延。实验仿真结果显示,动态计算资源和频谱分配算法可以有效地降低用户与边缘云之间的传输时延,也可以提高边缘云的吞吐量。     

腔磁力系统中的诱导透明和快慢光传播

为了在腔磁力系统中实现可控的磁子诱导透明、磁力诱导透明以及快慢光传播,建立了一个混合腔磁力系统.该系统由一个含有YIG球的微波腔和在z方向对球施加一个均匀的偏置磁场组成,并用强泵浦场驱动磁子和弱探测场驱动微波腔.研究表明,通过调节腔与磁子之间的相互作用强度和微波腔与磁子的耗散比,可以增加磁子诱导透明(MIT)、磁力诱导透明(MMIT)的效果和提高快慢光传播的速度.该研究结果可为磁力诱导放大、量子光学操纵和量子信息存储以及灵敏光开关的研究提供参考.     

Determining robust reaction kinetics from limited data

Accurate chemical kinetics are essential for reactor design and operation. However, despite recent advances in “big data” approaches, availability of kinetic data is often limited in industrial practice. Herein, we present a comparative proof-of-concept study for kinetic parameter estimation from limited data. Cross-validation (CV) is implemented to nonlinear least-squares (LS) fitting and evaluated against Markov chain Monte Carlo (MCMC) and genetic algorithm (GA) routines using synthetic data generated from a simple model reaction. As expected, conventional LS is fastest but least accurate in predicting true kinetics. MCMC and GA are effective for larger data sets but tend to overfit to noise for limited data. LS-CV strongly outperforms these methods at much reduced computational cost, especially for significant noise. Our findings suggest that implementation of CV with conventional regression provides an efficient approach to kinetic parameter estimation with high accuracy, robustness against noise, and only minimal increase in complexity.     

Input-to-state learning of recurrent neural networks with delay and disturbance

This article deals with the issue of input-to-state stabilization for recurrent neural networks with delay and external disturbance. The goal is to design a suitable weight-learning law to make the considered network input-to-state stable with a predefined -gain. Based on the solution of linear matrix inequalities, two schemes for the desired learning law are presented via using decay-rate-dependent and decay-rate-independent Lyapunov functionals, respectively. It is shown that, in the absence of external disturbance, the proposed learning law also guarantees the exponential stability of the network. To illustrate the applicability of the present weight-learning law, two numerical examples with simulations are given.