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.

Fuzzy min-max neural network for satellite infrared image clustering

Abstract: The process of estimation of precipitation from satellite images begins with the detection and identification of convective clouds. Clustering of the satellite infrared images is required in order to estimate the cloud cover area. In this paper a neuro-fuzzy technique in the form of unsupervised fuzzy minmax clustering neural (FMMCN) network has been implemented for clustering satellite infrared image. Each cluster is in the form of an n-dimensional hyperbox defined by minimum and maximum points and a fuzzy membership function. FMMCN suits this application area because it is completely unsupervised and hence, unlabeled data can be used with it. Also the number of clusters is not required to be mentioned at the beginning as it is calculated dynamically.

An Improved Energy Detector for Spectrum Sensing in Cognitive Radio System with Adaptive Noise Cancellation and Adaptive Threshold

Abstract: Over the years the usage of wireless communication systems has increased rapidly leading to scarcity of bandwidth. Hence the concept of utilizing the existing system to its fullest has become very important. Cognitive radio is a technique based on this concept which identifies the unutilized white spaces in the spectrum, and are allotted to the secondary user in a non-interfering manner. The energy detection technique does not work at low SNR. In this paper we attempted to improve the efficiency by performing adaptive noise cancellation and adaptive threshold in the energy detector.

Stabilization method with the relativistic configuration-interaction calculation applied to two-electron resonances

Abstract: We applied a relativistic configuration-interaction (CI) framework to the stabilization method as an approach for obtaining the autoionization resonance structure of heliumlike ions. In this method, the ion is confined within an impenetrable spherical cavity, the size of which determines the radial space available for electron wave functions and electron-electron interactions. By varying the size of the cavity, one can obtain the autoinization resonance position and width. The applicability of this method is tested on the resonances of He atom while comparing with benchmark data available in the literature. The present method is further applied on the determination of the resonance structure of heliumlike uranium ion, where a relativistic framework is mandatory. In the strong-confinement region, the present method can be useful to simulate the properties of an atom or ion under extreme pressure. An exemplary application of the present method to determine the structure of ions embedded in dense plasma environment is briefly discussed.

Random Valued Impulse Noise Removal Using Region Based Detection Approach

Abstract: Removal of random valued noisy pixel is extremely challenging when the noise density is above 50%. The existing filters are generally not capable of eliminating such noise when density is above 70%. In this paper a region wise density based detection algorithm for random valued impulse noise has been proposed. On the basis of the intensity values, the pixels of a particular window are sorted and then stored into four regions. The higher density based region is considered for stepwise detection of noisy pixels. As a result of this detection scheme a maximum of 75% of noisy pixels can be detected. For this purpose this paper proposes a unique noise removal algorithm. It was experimentally proved that the proposed algorithm not only performs exceptionally when it comes to visual qualitative judgment of standard images but also this filter combination outsmarts the existing algorithm in terms of MSE, PSNR and SSIM comparison even up to 70% noise density level.