Modeling the drain current and its equation parameters for lightly doped symmetrical double-gate MOSFETs

A 2D model for the potential distribution in silicon film is derived for a symmetrical double gate MOSFET in weak inversion. This 2D potential distribution model is used to analytically derive an expression for the subthreshold slope and threshold voltage. A drain current model for lightly doped symmetrical DG MOSFETs is then presented by considering weak and strong inversion regions including short channel effects, series source to drain resistance and channel length modulation parameters. These derived models are compared with the simulation results of the SILVACO (Atlas) tool for different channel lengths and silicon film thicknesses. Lastly, the effect of the fixed oxide charge on the drain current model has been studied through simulation. It is observed that the obtained analytical models of symmetrical double gate MOSFETs are in good agreement with the simulated results for a channel length to silicon film thickness ratio greater than or equal to 2.     

Minimization in variations of different parameters in different φ planes of a small-size concentric ring array antenna using firefly algorithm

The radiation pattern of a small-size concentric ring array antenna does not remain φ-symmetric, if the array is made with an appreciably lesser number of antenna elements. The sidelobe level, first null beamwidth (FNBW), half-power beamwidth (HPBW), and the null depths of the radiation pattern change significantly in different φ planes. An efficient pattern synthesis method has been developed in this work to reduce the variation of sidelobe level, FNBW, and HPBW of a small-size concentric ring array of isotropic antennas, for different φ cuts. It is done by finding out an optimum set of amplitude distribution of the array elements using firefly algorithm. The sidelobe level and the first null depth of the array are kept below a desired value for all the defined φ cuts. The directivity of the optimized array and the uniform array has been computed. The variation of all these parameters for different φ cuts has been compared to the same array with uniform excitation among the elements.     

Single‐Walled Carbon Nanotube Dispersions in Poly(ethylene oxide)

Dispersions of single‐walled carbon nanotubes (SWNTs) in poly(ethylene oxide) (PEO) assisted by a lithium‐based anionic surfactant demonstrate an electrical percolation of 0.03 wt.‐% and a geometrical percolation, inferred from melt rheometry, of 0.09 wt.‐%. Both the melting temperature and the extent of crystallinity of the PEO crystals decrease with increasing SWNT loading. Raman spectroscopy of the nanocomposites indicates a down‐shift of the SWNT G‐modes and suggests that the nanotubes are subjected to tensile stress transfer from the polymer at room temperature.     

Molecular Dynamics Simulation of MBE Growth of CdTe/ZnTe/Si

We simulate in three dimensions molecular beam epitaxial (MBE) growth of CdTe/ZnTe/Si using classical molecular dynamics. Atomic interactions are simulated with Stillinger–Weber potentials, whose parameters are obtained by fitting to experimental data or density function theory-calculated distortion energies of the component crystals. The effects of substrate temperature and atomic species flux ratios on epilayer morphology are investigated. The agreement between simulations and experiments suggests that this model has reasonable ability to predict the microstructures of CdTe/ZnTe/Si grown by MBE.     

Development of cardiac prescreening device for rural population using ultralow-power embedded system

The invention is inspired by the desire to understand the opportunities and expectations of developing economies in terms of healthcare. The designed system is a point-of-care (POC) device that can deliver heart-care services to the rural population and bridge the rural-urban divide in healthcare delivery. The product design incorporates several innovations including the effective use of adaptive and multiresolution signal-processing techniques for acquisition, denoising, segmentation, and characterization of the heart sounds (HS) and murmurs using an ultralow-power embedded Mixed Signal Processor. The device is able to provide indicative diagnosis of cardiac conditions and classify a subject into either normal, abnormal, ischemic, or valvular abnormalities category. Preliminary results demonstrated by the prototype confirm the applicability of the device as a prescreening tool that can be used by paramedics in rural outreach programs. Feedback from medical professionals also shows that such a device is helpful in early detection of common congenital heart diseases. This letter aims to determine a framework for utilization of automated HS analysis system for community healthcare and healthcare inclusion.     

Design and analysis of an improved smartcard‐based remote user password authentication scheme

With the fast development of the Internet and the telecommunication technologies, internet users are carrying out various electronic transactions over internet by means of the authentication protocols. To ensure efficient and robust online transaction, security of authentication protocol turns out to be a great concern nowadays. As a result, smartcard‐based password authentication and session key agreement scheme receives significant attention in recent years. In the literature, various authentication schemes have been proposed by the cryptographic research community. Recently, Li et al. analyze some security weaknesses of the authentication scheme of Chen et al. and propose an enhancement based on the discrete logarithm problem and computational Diffie–Hellman problem. This paper further cryptanalyzes the scheme of Li et al. and identifies various security loopholes and then constructs a modified authentication scheme as a remedy. The security and efficiency evaluations demonstrate that our scheme has more security features and low computation costs than the related schemes. Copyright © 2014 John Wiley & Sons, Ltd.     

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with small data is troublesome. Extensive data is necessary for training machine learning algorithms for effective prediction. Data generation is a method for expanding the amount of data that will be further accessible for training. In this work, we proposed a model that takes time-series customers’ electric kickboard demand data as input, pre-processes it, and generates synthetic data according to the original data distribution using generative adversarial networks (GAN). The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data. We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results. We modified The Wasserstein GAN-gradient penalty (GP) with the RMSprop optimizer and then employed Spectral Normalization (SN) to improve training stability and faster convergence. Finally, we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction. We used various evaluation criteria and visual representations to compare our proposed model’s performance. Synthetic data generated by our suggested GAN model is also evaluated. The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem, and it also converges faster than previous GAN models for synthetic data creation. The presented model’s performance is compared to existing ensemble and baseline models. The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error (MAPE) of 4.476 and increase prediction accuracy.     

A review on pectinase properties,application in juice clarification,and membranes as immobilization support

Pectic substances cause haziness and high viscosity of fruit juices. Pectinase enzymes are biological compounds that degrade pectic compounds. Nontoxicity and ecofriendly nature make pectinases excellent biocatalysts for juice clarification. However, the poor stability and nonreusability of pectinases trim down the effectiveness of the operation. The immobilization techniques have gained the attention of researchers as it augments the properties of the enzymes. Literature has reported the stability improvement of enzymes like lipase, laccase, hydrogen peroxidase, and cellulase upon immobilization on the membrane. However, only a few research articles divulge pectinase immobilization using a membrane. The catalysis-separation synergy of membrane-reactor has put indelible imprints in industrial applications. Immobilization of pectinase on the membrane can enhance its performance in juice processing. This review delineates the importance of physicochemical and kinematic properties of pectinases relating to the juice processing parameters. It also includes the influence of metal-ion cofactors on enzymes’ activity. Considering the support and catalytic-separation facets of the membrane, the prediction of the membrane as support for pectinase immobilization has also been carried out.     

Consumption of brown rice: a potential pathway for arsenic exposure in rural Bengal

This study assesses the arsenic (As) accumulation in different varieties of rice grain, that people in rural Bengal mostly prefer for daily consumption, to estimate the potential risk of dietary As exposure through rice intake. The rice samples have been classified according to their average length (L) and L to breadth (B) ratio into four categories, such as short-bold (SB), medium-slender (MS), long-slender (LS), and extra-long slender (ELS). The brown colored rice samples fall into the SB, MS, or LS categories; while all Indian Basmati (white colored) are classified as ELS. The study indicates that the average accumulation of As in rice grain increases with a decrease of grain size (ELS: 0.04; LS: 0.10; MS: 0.16; and SB: 0.33 mg kg(-1)), however people living in the rural villages mostly prefer brown colored SB type of rice because of its lower cost. For the participants consuming SB type of brown rice, the total daily intake of inorganic As (TDI-iAs) in 29% of the cases exceeds the previous WHO recommended provisional tolerable daily intake value (2.1 μg day(-1) kg(-1) BW), and in more than 90% of cases, the As content in the drinking water equivalent to the inorganic As intake from rice consumption (C(W,eqv)) exceeds the WHO drinking water guideline of 10 μg L(-1). This study further demonstrates that participants in age groups 18-30 and 51-65 yrs are the most vulnerable to the potential health threat of dietary As exposure compared to participants of age group 31-50 yrs, because of higher amounts of brown rice consumption patterns and lower BMI.