Institution
Indian Institute of Technology Kharagpur
Education•Kharagpur, India•
About: Indian Institute of Technology Kharagpur is a education organization based out in Kharagpur, India. It is known for research contribution in the topics: Natural rubber & Dielectric. The organization has 16887 authors who have published 38658 publications receiving 714526 citations.
Topics: Natural rubber, Dielectric, Microstructure, Population, Heat transfer
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors reported calcined EMHS as an effective adsorbent for removal of PO43− from aqueous solutions and investigated the cumulative % removal rate in a batch adsorber with different initial adsorbate concentrations, adsorbant dose, pH of the solution and adsorption temperature.
122 citations
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TL;DR: In this paper, the drying characteristics of the coconut presscake were investigated under varying conditions of presscake thickness (2, 3 and 4mm) and vacuum chamber plate temperature (65, 70 and 75 °C) at 65 Âmm Hg absolute pressure.
122 citations
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TL;DR: In this article, the organic-inorganic hybrid nanocomposites comprising poly(iminohexamethyleneiminoadipoyl), better known as Polyamide-6,6 (abbreviated henceforth as PA66), and silica (SiO2) were synthesized through sol-gel technique at ambient temperature.
122 citations
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TL;DR: The present study investigated the feasibility of using Chlorella sorokiniana for CO2 sequestration from industrial flue gas and fatty acid composition in the total lipid was determined to evaluate its suitability for food, feed, and biofuel.
122 citations
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TL;DR: This paper proposes DeepFix, a fully convolutional neural network, which models the bottom–up mechanism of visual attention via saliency prediction via Saliency prediction, and evaluates the model on multiple challenging saliency data sets and shows that it achieves the state-of-the-art results.
Abstract: Understanding and predicting the human visual attentional mechanism is an active area of research in the fields of neuroscience and computer vision. In this work, we propose DeepFix, a first-of-its-kind fully convolutional neural network for accurate saliency prediction. Unlike classical works which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant which prevents them from modeling location dependent patterns (e.g. centre-bias). Our network overcomes this limitation by incorporating a novel Location Biased Convolutional layer. We evaluate our model on two challenging eye fixation datasets -- MIT300, CAT2000 and show that it outperforms other recent approaches by a significant margin.
122 citations
Authors
Showing all 17290 results
Name | H-index | Papers | Citations |
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Rajdeep Mohan Chatterjee | 110 | 990 | 51407 |
Vijay P. Singh | 106 | 1699 | 55831 |
Arun Majumdar | 102 | 459 | 52464 |
Sanjay Gupta | 99 | 902 | 35039 |
Biswajeet Pradhan | 98 | 735 | 32900 |
Sandeep Kumar | 94 | 1563 | 38652 |
Jürgen Eckert | 92 | 1368 | 42119 |
Praveen Kumar | 88 | 1339 | 35718 |
Tuan Vo-Dinh | 86 | 698 | 24690 |
Lawrence Carin | 84 | 949 | 31928 |
Anindya Dutta | 82 | 248 | 33619 |
Aniruddha B. Pandit | 80 | 427 | 22552 |
Krishnendu Chakrabarty | 79 | 996 | 27583 |
Ramesh Jain | 78 | 556 | 37037 |
Thomas Thundat | 78 | 622 | 22684 |