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Institution

Indian Institute of Technology Kharagpur

EducationKharagpur, 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.


Papers
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Journal ArticleDOI
TL;DR: In this article, the effect of chemical, electrical conductivity, and electromagnetic interference shielding effectiveness (EMI SE) on the thermal stability of CPE/CNF nanocomposites was investigated.
Abstract: Chlorinated polyethylene (CPE) filled with functionalized heat treated carbon nanofiber (CNF) nanocomposites were prepared using two different techniques like melt mixing and solution cum melt mixing. A better dispersion of CNFs in nanocomposite was achieved by solution cum melt mixing compared to only melt mixing process. The effect of mechanical, electrical conductivity (σ), and electromagnetic interference shielding effectiveness (EMI SE) were studied. Nanocomposites prepared by solution cum melt mixing process showed higher mechanical properties, electrical conductivity (σ), and electromagnetic interference shielding effectiveness (EMI SE) compared to that of melt mixing process. 1 wt% CNFs filled nanocomposites prepared by the solution followed by melt mixing showed 124% higher tensile strength whereas at the same percentage of CNFs, melt processed nanocomposite exhibited 58% higher in tensile strength compared to neat CPE. The EMI SE and σ of both types of nanocomposites were increased with increasing CNFs loading. At 10 wt% CNFs loading, solution cum melt processed nanocomposite showed EMI SE 24 dB; whereas at the same wt% of CNFs loading, melt processed nanocomposite showed 22 dB. Thermogravimetric analysis was carried to investigate the thermal stability of CPE/CNF nanocomposites.

124 citations

Proceedings ArticleDOI
15 Oct 2018
TL;DR: This paper proposes a new SSE protocol, called Hidden Cross-Tags (HXT), that removes 'Keyword Pair Result Pattern' (KPRP) leakage for conjunctive keyword search, and proposes a 'lightweight' HVE scheme that only uses efficient symmetric-key building blocks, and entirely avoids elliptic curve-based operations.
Abstract: The recently proposed Oblivious Cross-Tags (OXT) protocol (CRYPTO 2013) has broken new ground in designing efficient searchable symmetric encryption (SSE) protocol with support for conjunctive keyword search in a single-writer single-reader framework. While the OXT protocol offers high performance by adopting a number of specialised data-structures, it also trades-off security by leaking 'partial' database information to the server. Recent attacks have exploited similar partial information leakage to breach database confidentiality. Consequently, it is an open problem to design SSE protocols that plug such leakages while retaining similar efficiency. In this paper, we propose a new SSE protocol, called Hidden Cross-Tags (HXT), that removes 'Keyword Pair Result Pattern' (KPRP) leakage for conjunctive keyword search. We avoid this leakage by adopting two additional cryptographic primitives - Hidden Vector Encryption (HVE) and probabilistic (Bloom filter) indexing into the HXT protocol. We propose a 'lightweight' HVE scheme that only uses efficient symmetric-key building blocks, and entirely avoids elliptic curve-based operations. At the same time, it affords selective simulation-security against an unbounded number of secret-key queries. Adopting this efficient HVE scheme, the overall practical storage and computational overheads of HXT over OXT are relatively small (no more than 10% for two keywords query, and 21% for six keywords query), while providing a higher level of security.

124 citations

Journal ArticleDOI
TL;DR: Optimize machine learning algorithms have been applied to predict the accident outcomes such as injury, near miss, and property damage using occupational accident data and PSO-based SVM outperforms the other algorithms with the highest level of accuracy and robustness.

124 citations

Journal ArticleDOI
TL;DR: The nutritional quality oriented attributes in this study were competent with recognized prominent aromatic and non-aromatic rice accessions as an index of their nutritional worth and recommend to farmers and consumers which may be graded as export quality rice with good unique nutritional values in international market.

124 citations

Journal ArticleDOI
TL;DR: The synergistic effect of the graphene with the pseudocapacitive Mn MoO4 caused an increased cycle stability of 88% specific capacitance retention after 1000 consecutive charge discharge cycles at 8 A g(-1) constant current density, which was higher than the virgin MnMoO4.
Abstract: A unique and cost effective hydrothermal procedure has been carried out for the synthesis of hexahedron shaped α MnMoO4 and its hybrid composite with graphene using three different weight percentages of graphene. Characterization techniques, such as XRD, Raman and FTIR analysis, established the phase and formation of the composite. The electrochemical characterization of the pseudocapacitive MnMoO4 and the MnMoO4/graphene composites in 1 M Na2SO4 displayed highest specific capacitances of 234 F g−1 and 364 F g−1, respectively at a current density of 2 A g−1. Unlike many other pseudocapacitive electrode materials our prepared materials responded in a wide range of working potentials of (−)1 V to (+)1 V, which indeed resulted in a high energy density without substantial loss of power density. The highest energy densities of 130 Wh kg−1 and 202.2 Wh kg−1 were achieved, respectively for the MnMoO4 and the MnMoO4/graphene composite at a constant power delivery rate of 2000 W kg−1. The synergistic effect of the graphene with the pseudocapacitive MnMoO4 caused an increased cycle stability of 88% specific capacitance retention after 1000 consecutive charge discharge cycles at 8 A g−1 constant current density, which was higher than the virgin MnMoO4 with 84% specific capacitance retention.

124 citations


Authors

Showing all 17290 results

NameH-indexPapersCitations
Rajdeep Mohan Chatterjee11099051407
Vijay P. Singh106169955831
Arun Majumdar10245952464
Sanjay Gupta9990235039
Biswajeet Pradhan9873532900
Sandeep Kumar94156338652
Jürgen Eckert92136842119
Praveen Kumar88133935718
Tuan Vo-Dinh8669824690
Lawrence Carin8494931928
Anindya Dutta8224833619
Aniruddha B. Pandit8042722552
Krishnendu Chakrabarty7999627583
Ramesh Jain7855637037
Thomas Thundat7862222684
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023284
2022849
20213,142
20202,907
20192,779
20182,489