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Gaurav Sharma
Researcher at Shenzhen University
Publications - 1520
Citations - 40824
Gaurav Sharma is an academic researcher from Shenzhen University. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 82, co-authored 1244 publications receiving 31482 citations. Previous affiliations of Gaurav Sharma include Northeastern University & D. E. Shaw & Co..
Papers
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Journal ArticleDOI
Combined sorptional–photocatalytic remediation of dyes by polyaniline Zr(IV) selenotungstophosphate nanocomposite
Deepak Pathania,Gaurav Sharma,Amit Kumar,Mu. Naushad,Susheel Kalia,Anu Sharma,Zeid A. ALOthman +6 more
TL;DR: In this article, a polyaniline Zr(IV) selenotungstophosphate nanocomposite was used for degradation of methylene blue and malachite green.
Proceedings ArticleDOI
A hierarchical image authentication watermark with improved localization and security
TL;DR: This work proposes a method that thwarts the VQ attack while sustaining the superior localization properties of blockwise independent watermarking methods by dividing the image into blocks in a multi-level hierarchy and calculating block signatures in this hierarchy.
Proceedings ArticleDOI
Wafer level embedding technology for 3D wafer level embedded package
Aditya Kumar,Xia Dingwei,V. N. Sekhar,Sharon Pei Siang Lim,Chin Keng,Gaurav Sharma,V.S. Rao,Vaidyanathan Kripesh,John H. Lau,Dim-Lee Kwong +9 more
TL;DR: In this article, a 3D embedded micro wafer level package (EMWLP) was developed by using compression molding machine and low-cost granular epoxy molding compound (EMC).
Journal Article
Internal mammary artery and saphenous vein graft patency. Effects of aspirin.
Steve Goldman,Jack G. Copeland,T Moritz,William G. Henderson,K Zadina,T Ovitt,Karl B. Kern,Gulshan K. Sethi,Gaurav Sharma,Shukri F. Khuri +9 more
TL;DR: Both the IMA and vein grafts had excellent patency rates at 1 year after coronary artery bypass surgery, and aspirin did not alter this at1 year, and there were no differences between IMA or vein graft patency to the LAD.
Proceedings ArticleDOI
Unsupervised and Semi-Supervised Domain Adaptation for Action Recognition from Drones
TL;DR: This work presents a combination of video and instance-based adaptation methods, paired with either a classifier or an embedding-based framework to transfer the knowledge from source to target and shows that the proposed adaptation approach substantially improves the performance on these challenging and practical tasks.