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.

Design and characterization of asymetrical super-lattice Si/4H-SiC pin photo diode array: a potential opto-sensor for future applications in bio-medical domain

Abstract: Photo-sensors are integral part of different bio-medical diagnostic equipment. Each type of bio-molecules possess unique spectral fingerprint in visible wavelength region of electro-magnetic spectrum. Now-a-days, the enhancement of quantum-efficiency and photo-responsivity of such bio-medical opto-sensors, for accurate identification of virus/anti-bodies in blood by optical means, is a big challenge to medical-device Engineers. The authors have addressed this issue in this research paper by proposing a novel structure of asymmetrical Si/4H-SiC super-lattice pin diode array for the development of high-sensitive visible light (300–800 nm) sensor on native substrate. The simulation experiment is carried out by developing a generalized large-signal quantum modified drift–diffusion simulator incorporating three different modes of carrier generation-recombination under light and dark conditions: avalanching, tunneling and photo-irradiation. The validity of the model has been established by comparing the simulation results with those of experimental observations. A good agreement between theory and experiment, under similar biasing conditions, establishes the validity of the developed model. The characteristics analysis depicts that the quantum efficiency of the designed single photo-sensor is ~ 65% within 400–700 nm wavelength region, whereas, the same enhances to nearly ~ 90% with a 3 × 3 photo-sensor array based on asymmetrical super-lattice single pin devices. In visible wavelength region, the simulated photo-sensors (both single and array) have demonstrated significant photo responsivity. The photo-responsivity values, at 500 nm wavelength of incident radiation, are observed to be 0.65 A/W for a single photo-diode and 0.85 A/W for a 3 × 3 combination of photo-diode array. This clearly establishes the potentiality of the asymmetrical super-lattice pin-array as a visible photo-sensor for future application in developing medical instruments. A comparative analysis of Si and Si/4H-SiC asymmetrical super-lattice photo-sensors establishes the superiority of the later as a high-sensitive visible light-sensor in terms of better photo-responsivity and quantum efficiency. To the best of authors’ knowledge, this is the first report on Si/4H-SiC super-lattice pin photo-sensor array in the visible range of optical irradiation. The experimental feasibility of the device and a proposed circuit for future bio-medical implementation are also incorporated in the present research-paper for further development.

Architecture of Cognitive Radio Networks

Abstract: Today’s wireless networks are characterized by fixed spectrum assignment policy. The spectral scarcity and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum, opportunistically. Cognitive Radio (CR) is that very paradigm for wireless communication, in which either a network or a wireless node reconfigures its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users. CR adapts itself to the newer environment on the basis of its intelligent sensing and captures the best available spectrum to meet user communication requirements. When the radio link features are extended to the network layer, the cognitive radios form the cognitive radio network. This book chapter is focused on cognitive radio network, architecture of the CR, and its relevance in the wireless and mobile Ad Hoc networks.

An improved flood warning system using WSN and Artificial Neural Network

Abstract: The operation of the surface water system for flood control is very important and crucial to minimizing the impacts of flood during the real-time flood events. The model considered here is assumed for a sophisticated flood warning system. Many works have been done with different parameters using Artificial Neural Network (ANN) but this paper considers six parameters related to flood effecting causes directly or indirectly. The input parameters are theoretically bounded and prediction simulated using ANN. The output results obtained are perfectly matched with the taken model and satisfactorily acceptable. The output gives early warning as well as flood situation for disaster management and preparedness to combat aftermath of flood.