R
Ranji Vaidyanathan
Researcher at Oklahoma State University–Tulsa
Publications - 81
Citations - 1828
Ranji Vaidyanathan is an academic researcher from Oklahoma State University–Tulsa. The author has contributed to research in topics: Epoxy & Heat sink. The author has an hindex of 19, co-authored 77 publications receiving 1646 citations. Previous affiliations of Ranji Vaidyanathan include Indian Institute of Management Ahmedabad & Old Dominion University.
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Proceedings ArticleDOI
Measurements of Young's modulus, Poisson's ratio, and tensile strength of polysilicon
TL;DR: In this article, the results of 48 tests on five different sets of MUMPs specimens yield the following material properties: Young's modulus=169/spl plusmn/6.15 GPa.
Journal ArticleDOI
Process fault detection and diagnosis using neural networks—I. steady-state processes
TL;DR: In this article, an analysis of the learning, recall and generalization characteristics of neural networks for detecting and diagnosing process failures in steady state processes is presented, where the single fault assumption has been relaxed to include multiple causal origins of the symptoms.
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Single wall nanotube and vapor grown carbon fiber reinforced polymers processed by extrusion freeform fabrication
TL;DR: In this article, single wall carbon nanotubes (SWNTs) and vapor grown carbon fibers (VGCFs) were combined with poly(acrylonitrile-co-butadiene-costyrene) to create composite materials for use with Extrusion Freeform Fabrication (EFF).
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Behaviour of reinforced concrete rectangular columns strengthened using GFRP
TL;DR: In this paper, the effect of axially loaded rectangular columns that have been strengthened with glass fiber reinforced polymer (GFRP) wrap was investigated, where three aspect ratios (a/b, where a and b are, respectively, the longer and shorter sides of the cross-section) were studied.
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Trabecular scaffolds created using micro CT guided fused deposition modeling
TL;DR: The trabecular scaffolds matched bone samples in porosity; however, achieving physiologic connectivity density and trabECular separation will require further refining of scaffold processing.