SILC as a measure of trap generation and predictor ofTBD in ultrathin oxides

The theoretical basis of stress-induced leakage current (SILC) as a measure of bulk trap density within thin oxide films is explored. Contrary to popular belief, this measure is neither absolute, nor do most papers in the literature sufficiently specify the measurement conditions to make their comparison meaningful. We also explore the relationship between SILC generation rate and the time-to-breakdown, and show that only a very specific definition of SILC generation can capture the voltage dependence of the time-to-breakdown     

An energy‐efficient and balanced clustering approach for improving throughput of wireless sensor networks

Maximizing the lifespan of wireless sensor networks is currently drawing a lot of attention in the research community. In order to reduce energy consumption, sensor nodes that are far from the base station avoid sending data directly. As a result, several disjoint clusters are formed, and nodes within a cluster send their data through the cluster head to avoid long transmissions. However, several parameters related to transmission cost need to be considered when selecting a cluster head. While most of the existing research work considers energy and distance as the most stringent parameters to reduce energy consumption, these approaches fail to create a fair and balanced cluster. Consequently, unbalanced clusters are formed, resulting in the degradation of overall performance. In this research work, a cluster head selection algorithm is proposed that covers all parts of the sensing area in a balanced manner, saving a significant amount of energy. Furthermore, a capture effect–based intracluster communication mechanism is proposed that efficiently utilizes the time slot under various traffic conditions. A Näive Bayes classifier is used to adapt the window size dynamically according to the traffic pattern. Finally, a simulation model using OMNeT++ is developed to compare the proposed approach with the pioneer clustering approach, LEACH, and the contemporary LEACH‐MAC protocol in terms of performance. The results of the simulation indicate that the proposed approach improves the overall performance in terms of network lifetime, energy efficiency, and throughput.     

Social learning for adaptive delta management: Tidal River Management in the Bangladesh Delta

ABSTRACT

The article analyzes Tidal River Management in Bangladesh from a social learning perspective. Four cases were investigated using participatory assessment. Knowledge acquisition through transformations in the Tidal River Management process was explored as an intended learning outcome. The study finds that social learning occurred more prominently at the individual stakeholder level and less at the collective level. For Tidal River Management to be responsive and sustainable, especially in times of increased uncertainty and climate vulnerability, more attention needs to be paid to coordination and facilitation of multi-level learning that includes all stakeholders.     

Heteroassociative multiple-target tracking by fringe-adjusted joint transform correlation

A heteroassociative joint transform correlation (JTC) technique is proposed for recognizing and tracking multiple heteroassociative or dissimilar targets from gray-level image sequences by use of the concept of fringe-adjusted JTC and a multiple-target-detection algorithm. A fringe-adjusted JTC technique is used to ensure quantification of the similarities among several input images while it satisfies the equal-correlation-peak criterion. Tracking is accomplished by retrieval of the target motion information estimated from multiple consecutive image frames. An enhanced version of the fringe-adjusted filter is incorporated into the heteroassociative multiple-target-detection process to optimize the correlation performance. The feasibility of the proposed technique is tested by computer simulation with real infrared image data.     

Identification of bioactive compounds with GC–Q-TOF–MS in the extracts from Clinacanthus nutans using subcritical carbon dioxide extraction

Subcritical carbon dioxide Soxhlet extraction of biologically active compounds from Clincanthus nutans was investigated by full factorial design to identify and optimize the factors (particle size and co-solvent) affecting extract yield, antioxidant activity, total phenolic content, total flavonoid content, and α-glucosidase inhibitory activity. An average of 3.103% yield, 98.90% antioxidant activity, 49.40 mg/g (GAE) TPC, 43.76 mg/g (RE), and 88.58% AGI activity can be achieved using the optimum levels of independent variables. The GC-Q-TOF MS identification of optimized extract shown that different classes of phytoconstituents were successfully separated by CO₂-Soxhlet to produce potential antioxidant and α-glucosidase inhibitory activity.     

ATOMS: Air Traffic Operations and Management Simulator

In this paper, we introduce the air traffic operations and management simulator (ATOMS), which is an air traffic and airspace modeling and simulation system for the analysis of free-flight concepts. This paper describes the design, architecture, functionality, and applications of the ATOMS. It is an intent-based simulator that discretizes the airspace in equal-sized hyper-rectangular cells to maintain intent reference points. It can simulate end-to-end airspace operations and air navigation procedures for conventional air traffic, as well as for free flight. Atmospheric and wind data that are modeled in the ATOMS result in accurate trajectory predictions. The ATOMS uses a multiagent-based modeling paradigm for modular design and easy integration of various air traffic subsystems. A variety of advanced air traffic management (ATM) concepts that are envisioned in free flight are prototyped in the ATOMS, including airborne separation assurance (ASA), cockpit display of traffic information (CDTI), weather avoidance, and decision support systems (DSSs). Experimental results indicate that advanced ATM concepts make a sound case for free flight; however, there is a need to investigate and understand their complex interaction under nonnominal scenarios.     

Accurate substrate modelling of RF CMOS

The losses within the substrate of an RF IC can have significant effect on performance in a mixed signal application. In order to model substrate coupling accurately, it is represented by an RC network to account for both resistive and dielectric losses at high frequency (> 1 GHz). A small‐signal equivalent circuit model of an RF IC inclusive of substrate parasitic effect is analysed in terms of its y‐parameters and an extraction procedure for substrate parameters has been developed. By coupling the extracted substrate parameters along with extrinsic resistances associated with gate, source and drain, a standard BSIM3 model has been extended for RF applications. The new model exhibits a significant improvement in prediction of output reflection coefficient S₂₂ in the frequency range from 1 to 10 GHz in device mode of operation and for a low noise amplifier (LNA) at 2.4 GHz. Copyright © 2006 John Wiley & Sons, Ltd.     

Modeling resistive switching materials and devices across scales

Resistance switching devices based on electrochemical processes have attractive significant attention in the field of nanoelectronics due to the possibility of switching in nanosecond timescales, miniaturization to tens of nanometer and multi-bit storage. Their deceptively simple structures (metal-insulator-metal stack) hide a set of complex, coupled, processes that govern their operation, from electrochemical reactions at interfaces, diffusion and aggregation of ionic species, to electron and hole trapping and Joule heating. A combination of experiments and modeling efforts are contributing to a fundamental understanding of these devices, and progress towards a predictive understanding of their operation is opening the possibility for the rational optimization. In this paper we review recent progress in modeling resistive switching devices at multiple scales; we briefly describe simulation tools appropriate at each scale and the key insight that has been derived from them. Starting with ab initio electronic structure simulations that provide an understanding of the mechanisms of operation of valence change devices pointing to the importance of the aggregation of oxygen vacancies in resistance switching and how dopants affect performance. At slightly larger scales we describe reactive molecular dynamics simulations of the operation of electrochemical metallization cells. Here the dynamical simulations provide an atomic picture of the mechanisms behind the electrochemical formation and stabilization of conductive metallic filaments that provide a low-resistance path for electronic conduction. Kinetic Monte Carlo simulations are one step higher in the multiscale ladder and enable larger scale simulations and longer times, enabling, for example, the study of variability in switching speed and resistance. Finally, we discuss physics-based simulations that accurately capture subtleties of device behavior and that can be incorporated in circuit simulations.     

Strain and electric field-modulated indirect-to-direct band transition of monolayer GaInS2

Journal of Computational Electronics - The strain- and electric field-dependent electronic and optical properties of monolayer GaInS2 have been calculated using density functional...