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Institution

Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Steganography & Support vector machine. The organization has 581 authors who have published 1045 publications receiving 8345 citations.


Papers
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Book ChapterDOI
01 Jan 2020
TL;DR: In this paper, the underlying physics of field-fluid interactions are discussed and a number of applications of ferrofluid-actuated electronics cooling, hydrostatic & hydrodynamic bearings, extreme-boundary lubrication, damping systems, biomimetic locomotion, and medical diagnostics are discussed.
Abstract: Engineered ‘smart' fluids, called ferrofluids, which permit static and dynamic control by external magnetic fields of low or moderate strengths present a novel and challenging domain for scientists and technologists. The introduction of a ‘controllable' or ‘tunable' external force into the momentum transport equations opens up new fields of physical phenomena. In situations where the external field influence becomes strong enough to compete with gravitational forces or acts as a sole external agency to drive flows in the hypo-gravity environment, a new class of hydrodynamic phenomena becomes accessible. Ferrofluids exhibit special rheological properties that make them suited for a number of technological and biomedical applications. This chapter outlines the underlying physics of field-fluid interactions on one hand; on the other, it cites novel techniques in ferrofluid-actuated electronics cooling, hydrostatic & hydrodynamic bearings, extreme-boundary lubrication, damping systems, biomimetic locomotion, and medical diagnostics (e.g. magnetic drug targeting & magnetic cell sorting).

1 citations

Journal ArticleDOI
TL;DR: This study finds that PQDM is not going to help in identifying two statistical mixtures at a remote location, and derives the bound on deletion probability from a no-signalling condition.
Abstract: In this study, we show that one cannot use non-local resources for probabilistic signalling even if one can delete a quantum state with the help of a probabilistic quantum deletion machine (PQDM). Here, we find that PQDM is not going to help us in identifying two statistical mixtures at a remote location. Also we derive the bound on deletion probability from a no-signalling condition.

1 citations

Journal ArticleDOI
TL;DR: A summative type growth model has been proposed to explain the growth behaviour of P. acidilactici in the presence of the dual substrate, i.e. glucose and inulin, and results clearly indicate that cell growth is enhanced with the increase in inulin concentration.
Abstract: The present investigation deals with the optimization of cell growth rate of the candidate probiotic Pediococcus acidilactici in the presence of the specific prebiotic inulin. Three independent variables viz. concentration of inulin, concentration of glucose and pH have been selected for optimization study using response surface methodology. Theoretical analysis indicates that the maximum cell growth rate occurs at pH 7, 20 g/dm(3) concentration of inulin and 20 g/dm(3) concentration of glucose. Validation of these values has been done through a set of programmed experiments. Studies on cell dynamics in the presence of different concentrations of inulin have also been carried out to identify any limitation on the initial inulin concentration. Results clearly indicate that cell growth is enhanced with the increase in inulin concentration. However, there is a critical value of the prebiotic concentration (20 g/dm(3) inulin) beyond which the cell growth is inhibited. A summative type growth model has been proposed to explain the growth behaviour of P. acidilactici in the presence of the dual substrate, i.e. glucose and inulin. While growth on glucose follows Monod model, Haldane-type substrate-inhibited growth model holds good for growth on inulin. Intrinsic kinetic parameters for all the model equations have been determined experimentally.

1 citations

Journal ArticleDOI
TL;DR: Mixed microbial cultures contained in the sludge of effluent treatment plant of a coke oven industry studied for its resorcinol biodegradation capacity showed that, after necessary acclimatisation, the culture was able to biodegrade up to a concentration of 500 mg/L in the substrate.
Abstract: Mixed microbial cultures contained in the sludge of effluent treatment plant of a coke oven industry have been studied for its resorcinol biodegradation capacity under aerobic environment. The result showed that, after necessary acclimatisation, the culture was able to biodegrade up to a concentration of 500 mg/L of resorcinol in the substrate. The specific growth rate of microorganisms was found to be increased steadily up to 300 mg/L of resorcinol as sole carbon source, and then the rate was observed to be descended, but substrate degradation rate increases rapidly up to 400 mg/L, but decreased at 500 mg/L. The biodegradation kinetics is fitted to different substrate inhibition models. Among all models, Luong model was best fitted for resorcinol (RMSE = 0.0104). The biodegradation constants estimated using theses models showed good potential of the mixed microbial culture for resorcinol biodegradation.

1 citations

Proceedings ArticleDOI
04 Jan 2020
TL;DR: A convolutional neural network-based approach to locate damage in a disaster image and to quantify the degree of the damage is proposed, which exhibits high accuracy in classifying earthquake-affected buildings and determining the severity of damage at a negligible loss.
Abstract: After any disaster, the Government rehabilitates the victims based on the severity of the damage caused to their properties. Since a huge number of rehabilitation claims flow in after the disaster, it takes up a lot of manual labor in inspecting and validating the claims along with deciding the amount of rehabilitation to be granted. Moreover, such manual inspection leads to a lack of transparency. In recent years, social media posts, text, and images have become a rich source of post-disaster situational information that may be useful in assessing damage at a low cost. Most of the existing research explores the use of social media text for extracting situational information useful for disaster response. The usage of social media images to assess disaster damage is limited. In this paper, we propose a convolutional neural network-based approach to locate damage in a disaster image and to quantify the degree of the damage. The proposed damage assessment system categorizes images of earthquake-affected buildings and decides the severity of the damage caused by the earthquake. Our proposed approach enables the use of social media images for post-disaster damage assessment and provides an inexpensive and feasible alternative to the more expensive GIS approach. Our approach exhibits high accuracy in classifying earthquake-affected buildings and determining the severity of damage at a negligible loss.

1 citations


Authors

Showing all 581 results

NameH-indexPapersCitations
Debnath Bhattacharyya395786867
Samiran Mitra381985108
Dipankar Chakravorty353695288
S. Saha Ray342173888
Tai-hoon Kim335264974
Anindya Sen291093472
Ujjal Debnath293353828
Anirban Mukhopadhyay291693200
Avijit Ghosh281212639
Mrinal K. Ghosh26642243
Biswanath Bhunia23751466
Jayati Datta23551520
Nabarun Bhattacharyya231361960
Pinaki Bhattacharya191141193
Dwaipayan Sen18711086
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20227
2021110
202087
201992
201883
2017103