Institution
National Institute of Technology, Silchar
Education•Silchar, Assam, India•
About: National Institute of Technology, Silchar is a education organization based out in Silchar, Assam, India. It is known for research contribution in the topics: Control theory & Electric power system. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.
Papers published on a yearly basis
Papers
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TL;DR: In this article, a 2D model for surface potential, electric field and drain current for a nanoscale silicon tunnel field effect transistors (TFET) with a δp+Si 1−xGex layer at source-channel tunnel junction was developed.
Abstract: This article develops a 2-D model for surface potential, electric field and drain current for a nanoscale silicon tunnel field effect transistors (TFET) with a δp+Si1−xGex layer at source–channel tunnel junction. Mathematical formulation based on the TFET physics has been carried out throughout the text taking into consideration the various parameters involving the mole-fraction-dependent Si1−xGex layer. Both lateral and vertical electric fields have been modelled. A comparison is conducted between the modelled and the simulated values for three cases: polysilicon gate with silicon dioxide as gate dielectric, aluminium gate with alumina as gate dielectric and aluminium gate with hafnium oxide as gate dielectric. The model is found to be valid for all the three cases.
18 citations
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TL;DR: In this paper, a review of the state of the art of investigation conducted on local scour at bridge pier in cohesive bed material is presented, and the effects of parameters influencing local scouring around bridge pier are discussed.
Abstract: Bridge failure due to local scour has stimulated the enthusiasm of many researchers to study the causes of scouring and to predict the ultimate scour depth at bridge foundation. A brief review of the state of artwork of investigation conducted on local scour at bridge pier in cohesive bed material is presented. Scour process and mechanism at bridge pier in cohesive and noncohesive soil are presented. The effects of parameters influencing local scour around bridge pier is discussed. Empirical equations for predicting ultimate scour depth at bridge pier embedded in cohesive soil are outlined. Comparisons of the equations are made considering two examples: one under laboratory condition and another under field condition.
18 citations
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TL;DR: It can be commented that out of the above observations Tregear Model at Level 3 can be used for establishing the electrical equivalent of human skin due to its simplicity, however, fractional ordered CPE models provide a good approximation.
Abstract: Transdermal drug delivery is a non-invasive method of drug administration. However, to achieve this, the drug has to pass through the complicated structure of the skin. The complex structure of skin can be modelled by an electrical equivalent circuit to calculate its impedance. In this work, the transfer function of three electrical models of the human skin (Montague, Tregear and Lykken Model) based on physiological stratification are analysed. Sensitivity analysis of these models is carried out to consider the extent to which changes in system parameters (different types of R and C as described by different models) affect the behaviour of the model. Techniques like normal of derivative and Hausdorff Distance is also used to study and understand the different curves. Comparison is also made with CPE based model. As Montague Model is the most widely used model, Tregear and Lykken Model are compared with it. It can be commented that out of the above observations Tregear Model at Level 3 can be used for establishing the electrical equivalent of human skin due to its simplicity. However, fractional ordered CPE models provide a good approximation. Future prospect lies in developing a model that characterize both biological properties and physiological stratification.
18 citations
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18 citations
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TL;DR: FCM clustering has been incorporated with fuzzy- support vector machine (FSVM) classifier for classification of noisy and non-noisy pixels in removal of impulse noise from color images and proposed FSVM based fuzzy adaptive filter provides better performance than some of the established state-of-art filters.
Abstract: Impulse noise is an “On-Off” noise that corrupts an image drastically. Classification of noisy and non-noisy pixels should be performed more accurately so as to restore the corrupted image with less blurring effect and more image details. In this paper, fuzzy c-means (FCM) clustering has been incorporated with fuzzy- support vector machine (FSVM) classifier for classification of noisy and non-noisy pixels in removal of impulse noise from color images. Here, feature vector comprises of newly introduced local binary pattern (LBP) with previously used feature vector prediction error, median value, absolute difference between median and pixel under operation. In this work, features have been extracted from the image corrupted with 10%, 50 and 90% impulse noise respectively and FCM clustering has been used for reduction of size of the feature vector set before processing through FSVM during training procedure. If the pixel is depicted as noisy in testing phase, fuzzy decision based adaptive vector median filtering is performed in accordance with available non-corrupted pixels within the processing window centring the noisy pixel under operation. It has been observed that proposed FSVM based fuzzy adaptive filter provides better performance than some of the established state-of-art filters in terms of PSNR, MSE, SSIM and FSIMC. It is seen that performance is increased by ~4 dB than baseline filters such as modified histogram fuzzy color filter (MHFC) and multiclass SVM based adaptive filter (MSVMAF).
18 citations
Authors
Showing all 2010 results
Name | H-index | Papers | Citations |
---|---|---|---|
Abdullah Gani | 59 | 279 | 15355 |
Subhransu Ranjan Samantaray | 39 | 167 | 4880 |
Subhasish Dey | 39 | 220 | 4755 |
Bithin Datta | 37 | 158 | 3932 |
Arindam Ghosh | 33 | 248 | 6091 |
Raghavan Murugan | 33 | 126 | 3838 |
Md. Ahmaruzzaman | 32 | 113 | 6590 |
Deepak Puthal | 31 | 149 | 3213 |
Sivaji Bandyopadhyay | 31 | 310 | 4436 |
Ibrar Yaqoob | 30 | 77 | 7858 |
Lalit Chandra Saikia | 29 | 121 | 3154 |
Krishnamurthy Muralidhar | 28 | 218 | 2972 |
Sudip Dey | 28 | 155 | 1956 |
Krishna Murari Pandey | 27 | 262 | 2455 |
Shailendra Jain | 27 | 128 | 3907 |