1,724,671 research outputs found
Ashish Kumar 2.pmd
No full textABSTRACT Kinetic investigation in Ru(III) catalyzed oxidation of D(+)-galactose and lactose in an alkaline solution of potassium bromate in the presence of mercuric acetate as a scavenger for Br -ion has been carried out in the temperature range 30-45°C. The rate shows first order dependence with respect to the bromate and zeroth order with respect to the substrate (sugars). The reaction exhibits first order dependence on the catalyst ruthenium(III) and there is inverse order on the rate of reaction. Potassium chloride and acetic acid have a positive effect on the rate. Negligible effect of change in Hg(OAc) 2 , ionic strength of the medium and D 2 O. [RuCl 3 (H 2 O)OH] -1 and BrO 3 -are the most reactive species Ru(III) chloride and bromated, respectively. Galactonic acid and lactobionic acid have been identified as the main oxidation products of the reaction. Various activation parameters have bee calculated and recorded. On the basis of experimental findings, a suitable mechanism consistent with the observed kinetics was proposed and the rate law has been derived on the basis of obtained data
Energy-Aware Battery-Less Bluetooth Low Energy Device Prototype Powered By Ambient Light
No full textEnergy-Aware Battery-Less Bluetooth Low Energy Device Prototype Powered By Ambient Light
No full textBatteryless NB-IoT prototype for bidirectional communication powered by ambient light?
No full textThis research was funded by the Flemish FWO SBO, Belgium S001521N IoBaLeT (Sustainable Internet of batteryless Things) project
First records of the jumping spider genus Irura Peckham & Peckham 1901 (Araneae: Salticidae: Simaethina) from India
No full textKadam, Gautam, Tripathi, Rishikesh, Jangid, Ashish Kumar, Sudhikumar, Ambalaparambil Vasu, Hill, David E. (2021): First records of the jumping spider genus Irura Peckham & Peckham 1901 (Araneae: Salticidae: Simaethina) from India. Peckhamia 243 (1): 1-9, DOI: 10.5281/zenodo.636018
sj-docx-1-pie-10.1177_09544089221112070 - Supplemental material for Investigation of wear and friction characteristic of Al/(Si<sub>3</sub>N<sub>4)np</sub> nano composites under as-cast and heat-treated conditions
No full textSupplemental material, sj-docx-1-pie-10.1177_09544089221112070 for Investigation of wear and friction characteristic of Al/(Si3N4)np nano composites under as-cast and
heat-treated conditions by Ashish Kumar, Ravindra Singh Rana and Rajesh Purohit in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p
Batteryless Bluetooth Low Energy Prototype With Energy-Aware Bidirectional Communication Powered by Ambient Light
No full textThis work was supported in part by the imec Interdisciplinary Cooperative Research (ICON) Project BLUESS, in part by the University of Antwerp Industry Research Fund (IOF) Funded Project COMBAT (Time-Sensitive Computing on Batteryless IoT Devices), and in part by the Flemish Fund for Scientific Research (FWO) Strategic Basic Research (SBO) through the Sustainable Internet of Batteryless Things (IoBaLeT) Project S001521N. The associate editor coordinating the review of this article and approving it for publication was Prof. Kazuaki Sawada
Batteryless Bluetooth Low Energy Prototype With Energy-Aware Bidirectional Communication Powered by Ambient Light
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PacGAN: The power of two samples in generative adversarial networks
Full text linkGenerative adversarial networks (GANs) are innovative techniques for learning generative models of complex data distributions from samples. Despite remarkable recent improvements in generating realistic images, one of their major shortcomings is the fact that in practice, they tend to produce samples with little diversity, even when trained on diverse datasets. This phenomenon, known as mode collapse, has been the main focus of several recent advances in GANs. Yet there is little understanding of why mode collapse happens and why existing approaches are able to mitigate mode collapse. We propose a principled approach to handling mode collapse, which we call {\em packing}. The main idea is to modify the discriminator to make decisions based on multiple samples from the same class, either real or artificially generated. We borrow analysis tools from binary hypothesis testing---in particular the seminal result of Blackwell \cite{Bla53}---to prove a fundamental connection between packing and mode collapse. We show that packing naturally penalizes generators with mode collapse, thereby favoring generator distributions with less mode collapse during the training process. Numerical experiments on benchmark datasets suggests that packing provides significant improvements in practice as well.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo termsThe student, Ashish Kumar Khetan, accepted the attached license on 2018-04-25 at 10:58.The student, Ashish Kumar Khetan, submitted this Thesis for approval on 2018-04-25 at 11:06.This Thesis was approved for publication on 2018-04-25 at 13:42.DSpace SAF Submission Ingestion Package generated from Vireo submission #12456 on 2018-08-31 at 17:14:33Made available in DSpace on 2018-09-04T20:27:27Z (GMT). No. of bitstreams: 2
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Previous issue date: 2018-04-2
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