Near-Field and Far-Field Analyses of Alternating Impedance Electromagnetic Bandgap (AI-EBG) Structure for Mixed-Signal Applications

This paper presents near-field (NF) and far-field (FF) analysis of alternating impedance electromagnetic bandgap (AI-EBG) structure in packages and boards. Three test vehicles have been designed and fabricated for NF and FF measurements. Simulation results using a full-wave solver (SONNET) have been compared with measurement results. This paper investigates the radiation due to return current on different reference planes. The analysis results from simulations and measurements provide important guidelines for design of the AI-EBG structure based power distribution network for noise isolation and suppression in mixed-signal systems     

Evaluation and selection of hedging policies using stochastic reservoir simulation

A hedging policy is characterized by three parameters, namely, starting water availability (SWA), ending water availability (EWA) and hedging factor (HF). The effects of these three parameters on the reservoir performance indicators have been evaluated and discussed for a southwest monsoon-dependent within-year reservoir system in southern India. For the performance evaluation, synthetically generated periodic inflow sequences from a periodic autoregressive model have been used. Quite a number of the 1800 hedging policies considered for the reservoir system, yield a better overall performance compared to the standard operating policy (SOP). Reliability, Resilience and vulnerability are found to increase with SWA for a specified EWA. On the other hand, all these performance indicators are found to decrease with EWA for a specified SWA. Hence, it is desirable to start the hedging at reasonably high SWA. All performance indicators remain practically constant at higher ranges of EWA for a given SWA. If hedging is started when there is enough water in storage, reliability, resilience and average deficit increase with degree of hedging, whereas vulnerability decreases significantly up to a hedging factor of 0.3. An interactive computer program has been developed for the selection of compromising hedging policies, and its usefulness has been discussed.     

The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm

Current demonstrations of brain-machine interfaces (BMIs) have shown the potential for controlling neuroprostheses under pure motion control. For interaction with objects, however, pure motion control lacks the information required for versatile manipulation. This paper investigates the idea of applying impedance control in a BMI system. An extraction algorithm incorporating a musculoskeletal arm model was developed for this purpose. The new algorithm, called the muscle activation method (MAM), was tested on cortical recordings from a behaving monkey. The MAM was found to predict motion parameters with as much accuracy as a linear filter. Furthermore, it successfully predicted limb interactions with novel force fields, which is a new and significant capability lacking in other algorithms.     

Classification of Amniotic Fluid Level Using Bi-LSTM with Homomorphic filter and Contrast Enhancement Techniques

Wireless Personal Communications - Ultrasound scan plays a predominant role in assisting the health care provider for monitoring pregnancy. This methods is employed for providing insights about...     

Improved surface laplacian estimates of cortical potential using realistic models of head geometry

Surface Laplacian of scalp EEG can be used to estimate the potential distribution on the cortical surface as an alternative to invasive approaches. However, the accuracy of surface Laplacian estimation depends critically on the geometric shape of the head model. This paper presents a new method for computing the surface Laplacian of scalp potential directly on realistic scalp surfaces in the form of a triangular mesh reconstructed from MRI scans. Unlike previous methods, this algorithm does not resort to any surface fitting proxy and can improve the surface Laplacian estimation of cortical potential patterns by as much as 34% on realistically shaped head models. Simulations and experimental data are presented to demonstrate the advantage of the proposed method over the conventional spherical approximation and the utility of a more accurate surface Laplacian method for estimating cortical potentials from scalp electrodes.     

Top‐Down Fabrication of High Quality III–V Nanostructures by Monolayer Controlled Sculpting and Simultaneous Passivation

In the fabrication of III–V semiconductor nanostructures for electronic and optoelectronic devices, techniques that are capable of removing material with monolayer precision are as important as material growth to achieve best device performances. A robust chemical treatment is demonstrated using sulfur (S)‐oleylamine (OA) solution, which etches layer by layer in an inverse epitaxial fashion and simultaneously passivates the surface. The application of this process to push the limits of top‐down nanofabrication is demonstrated by the realization of InP‐based high optical quality nanowire arrays, with aspect ratios more than 50, and nanostructures with new topologies. The findings are relevant for other III–V semiconductors and have potential applications in III–V device technologies.     

Carbon Nanotube‐Encapsulated Noble Metal Nanoparticle Hybrid as a Cathode Material for Li‐Oxygen Batteries

Although Li‐oxygen batteries offer extremely high theoretical specific energy, their practical application still faces critical challenges. One of the main obstacles is the high charge overpotential caused by sluggish kinetics of charge transfer that is closely related to the morphology of discharge products and their distribution on the cathode. Here, a series of noble metal nanoparticles (Pd, Pt, Ru and Au) are encapsulated inside end‐opened carbon nanotubes (CNTs) by wet impregnation followed by thermal annealing. The resultant cathode materials exhibit a dramatic reduction of charge overpotentials compared to their counterparts with nanoparticles supported on CNT surface. Notably, the charge overpotential can be as low as 0.3 V when CNT‐encapsulated Pd nanoparticles are used on the cathode. The cathode also shows good stability during discharge–charge cycling. Density functional theory (DFT) calculations reveal that encapsulation of “guest” noble metal nanoparticles in “host” CNTs is able to strengthen the electron density on CNT surfaces, and to avoid the regional enrichment of electron density caused by the direct exposure of nanoparticles on CNT surface. These unique properties ensure the uniform coverage of Li₂O₂ nanocrystals on CNT surfaces instead of localized distribution of Li₂O₂ aggregation, thus providing efficient charge transfer for the decomposition of Li₂O₂.     

Microwave binstability in a planar ferrite-piezoelectric resonator

Bistability in a planar resonator containing an iron-yttrium garnet ferrite film and a lead zirconate titanate (PZT) plate is studied. The characteristics of the bistable resonator are measured in a frequency band of 0.8–16 GHz at an excitation power level ranging from 1 to 20 mW. Magnetic and electric tuning of the resonator frequency and switching of the resonator between stable states with the use of weak magnetic (∼0.5 Oe) and electric (0.75 kV/cm) fields are demonstrated. It is shown that, as the excitation power increases, the resonator’s switching time decreases from ∼1.7 to ∼0.3 μs.     

IPS-MAC: an informative preamble sampling MAC protocol for wireless sensor networks

Radio transceivers are the main source of energy consumption in wireless sensor networks (WSNs) where the source of energy supply is non-rechargeable battery. Several MAC protocols have been proposed in order to efficiently conserve energy in the link layer via duty-cycling. Low power listening (LPL) methods have been shown to outperform other schemes in lightly loaded situations which are common in environment monitoring applications. Nonetheless, as the network becomes dense, in LPL protocols such as BMAC a large number of nodes stay awake for each transmission, resulting in high levels of energy consumption. This paper introduces the informative preamble sampling (IPS) protocol in which a transmitter implicitly embeds information about its intended receiver via the power at which the preamble is transmitted. This results in far fewer nodes staying awake for each preamble. Upon hearing the preamble, a receiver executes a decision-making algorithm to decide whether to stay awake. If the decision-making algorithm is too lax, then more nodes stay awake following the preamble. On the other hand if the algorithm is too strict, it is likely that the intended receiver misses the preamble. In this paper we derive the optimal operating points for the IPS protocol. We show analytically that the IPS protocol can achieve a gain in energy by at least a factor of 2 over BMAC. We also conduct extensive simulations to show that IPS can achieve significant energy gains compared to BMAC.     

A comprehensive analysis of factors influencing the injury severity of large-truck crashes

Given the importance of trucking to the economic well being of a country and the safety concerns posed by the trucks, a study of large-truck crashes is critical. This paper contributes by undertaking an extensive analysis of the empirical factors affecting injury severity of large-truck crashes. Data from a recent, nationally representative sample of large-truck crashes are examined to determine the factors affecting the overall injury severity of these crashes. The explanatory factors include the characteristics of the crash, vehicle(s), and the driver(s). The injury severity was modeled using two measures. Several similarities and some differences were observed across the two models which underscore the need for improved accuracy in the assessment of injury severity of crashes. The estimated models capture the marginal effects of a variety of explanatory factors simultaneously. In particular, the models indicate the impacts of several driver behavior variables on the severity of the crashes, after controlling for a variety of other factors. For example, driver distraction (truck drivers), alcohol use (car drivers), and emotional factors (car drivers) are found to be associated with higher severity crashes. A further interesting finding is the strong statistical significance of several dummy variables that indicate missing data – these reflect how the nature of the crash itself could affect the completeness of the data. Future efforts should seek to collect such data more comprehensively so that the true effects of these aspects on the crash severity can be determined.