Narula Institute of Technology

About: Narula Institute of Technology is a based out in . It is known for research contribution in the topics: Quantum dot cellular automaton & Cognitive radio. The organization has 288 authors who have published 490 publications receiving 2258 citations. The organization is also known as: NiT.

Enhancement of optical absorption in Plasmonic thin film solar cell

Abstract: Recently, plasmonics gives very much interest and closely involves in the main domains of nanophotonics that can control of optical fields at the nanoscale level. Its remarkable property is to concentrate and enhance the electromagnetic field on the nanometres scale especially in solar cell. In plasmonic field, Nobel metals used as nanoparticle where density of electron gas which oscillates at surface Plasmon frequency at the same time also improves absorption by scattering. So the use of plasmonic in solar cell gives better opportunity to enhance efficiency by absorption as the optical spectrum loss are main part of total loss in solar cell. So we investigate the induction of surface Plasmon theory for enhancement of extinction in terms of absorption and scattering. In this paper, we also studied finite-difference time domain based proposed model and find various plasmonic field component of Eigen value and characterized optical improvement in plasmonic thin film solar cell.

The movement of sensors within cluster in WSN is Elliptic in nature

Abstract: A wireless sensor network (WSN) has to maintain a desirable sensing area of coverage and periodically report sensed data to the administrative center (i.e., base station, router etc.). Then the coverage area of the movement of sensors and lifetime are two important problems in a WSN due to constraint of limited battery energy. Sensors are usually deployed randomly in coverage area of interest and therefore the sensor tracking become one of the biggest challenge in Wireless Sensor Networks (WSNs). Many works done to track the sensor nodes in WSNs and all previous theoretical analyses on the coverage area and lifetime are primarily focused on the random uniform distribution of sensors. Here is depicting a real analysis in WSNs, In this paper, we try to find out the coverage area of sensors and best of our knowledge we first provide a mathematical and analytical framework for the coverage area of the deployment sensor nodes in a WSN that follows a multivariable normal distribution or Gaussian distribution. The multivariate normal distribution is often used to describe correlated real-valued random variables each of which clusters around a mean value which gives the monitored region. We determine the randomly deployed sensor nodes, follows an Elliptic area in wireless sensor network.

Electrical Characteristics of MESFET Using GaAs, InP and GaN as Substrates

Abstract: The theoretical analysis of electrical properties of MESFETs has been carried out with different materials of narrow as well as wide energy bandgap III–V binary compound semiconductors. The gate-to-source capacitance (C gs) and transconductance (g m ) have been studied as a function of gate-to-source voltage (V gs). It is revealed that wide bandgap semiconductor is best suited at high frequency because it has lower capacitance. The transconductance also depends on temperature and decreases with temperature. This is because of the increasing scattering phenomenon within the channel. The wide bandgap semiconductors offer higher transconductance which enables them to be used as high-speed switch. The aim of this work is to improve the understanding of MESFET device performances based on different III–V binary compound materials and also to find out the most suitable material combination for MESFET.

Convective development at Kolkata (22.53° N, 88.33° E), India during pre-monsoon season using linear discriminant analysis technique

Abstract: In the present work, statistical indices are formed using the most effective different combinations of 22 and 20 thermodynamic and dynamic parameters for the convective development at Kolkata (22.53° N, 88.33° E), India during pre-monsoon season (Mar-May) utilizing the data of 12 years (1985-1996). A multivariate statistical technique, namely Linear discriminant analysis (LDA) has been utilized to 22 primarily selected parameters derived from the radiosonde observations of 0000 UTC for next 12 hours yields respectively 59.57% and 58.70% correct prediction for convective development (CD) and fair-weather (FW) in next three years (1997 to 1999). A similar analysis for radiosonde observations of 1200 UTC for next 12 hours yields 63.79% and 50% for CD and FW respectively. Another similar LDA analysis with the above data period utilizing 20 parameters [excluding Miller’s (1972) & George’s (1960) from the earlier set] built from the radiosonde observations of 0000 UTC for next 12 hours observation yield 63.83% & 56.21% correct prediction for CD and FW respectively in the next 3 years. The corresponding figures for 1200 UTC for next 12 hours are 54.41 % & 67.34% respectively. With a view to understand the effect of the parameters, namely convective available potential energy (CAPE) and Convective inhibition (CIN), a similar LDA analysis has been applied to 17 parameter set (including the ratio of CAPE and CIN and excluding (θes -θe) at each level constructed form the above; θes and θe being saturated equivalent potential temperature and equivalent potential temperature respectively). The radiosonde observations of 0000 UTC for next 12 hours yield 68.29% and 54.43% correct prediction for CD and FW respectively in the next 3 years. Next, 1200 UTC radiosonde observations for next 12 hours yield 77.08% and 57.44% correct prediction for CD and FW respectively. For all the 22, 20 and 17 set of parameters (both for 0000 UTC and 1200 UTC observations for next 12 hours), the efficient skill scores, namely, True skill score (TSS), Heidke skill score (HSS), Critical success index (CSI) are computed. The 17 parameter combination is most efficient and may be utilized in an effective manner for prediction purpose. The investigations reveal that both for 0000 UTC and 1200 UTC observations for next 12 hours, correct prediction improves with the inclusion of the parameter CAPE/CIN (17 parameter combination), especially for 1200 UTC observations. Also, afternoon predictions are more effective than morning predictions for 22 parameter, 20 parameter and 17 parameter cases.