CDBMGCIG: Design of a Cross-Domain Bioinspired Model for identification of Gait Components via Iterated GANs

This Gait identification assists in recognition of human body components from temporal image sequences. Such components consist of connected-body entities including head, upper body, lower body regions. Existing Gait recognition models use deep learning methods including variants of Convolutional Neural Networks (CNNs), Q-Learning, etc. But these methods are either highly complex, or do not perform well under complex background conditions. Moreover, most of these models are validated on a specific environmental condition, and cannot be scaled for general-purpose deployments. To overcome these issues, this text proposes design of a novel cross-domain bioinspired model for identification of gait components via Iterated Generative Adversarial Networks (IGANs). The proposed model initially extracts multidomain pixel-level feature sets from different images. These include frequency components via Fourier analysis, entropy components via Cosine analysis, spatial components via Gabor analysis, and window-based components via Wavelet &Convolutional analysis. These feature sets are processed via a Grey Wolf Optimization (GWO) Model, which assists in identification of high-density & highly variant features for different gait components. These features are classified via an iterated GAN, which comprises of Generator & Discriminator ssModels that assist in evaluating connected body components. These operations generate component-level scores that assist in identification of gait from complex background images. Due to which, the proposed model was observed to achieve 9.5% higher accuracy, 3.4% higher precision, and 2.9% higher recall than existing gait identification methods. The model also uses iterative learning, due to which its accuracy is incrementally improved w.r.t. number of evaluated image sets

Thermoluminescence of ï§-irradiated SrAl2O4:Dy

Thermoluminescence (TL) properties of ï§-irradiated Strontium Aluminate doped with Dy (10% molar concentration) was studied. The phosphor was synthesized by combustion method using urea as a reducer at initiating temperature of 600oC.  Dy doped SrAl2O4 is a good dosimeter having linear response upto 2360 Gy of ï§-dose. The kinetic parameter have been calculated using Chen’s glow curve method

<span style="font-size:10.0pt;font-family: "Times New Roman";mso-fareast-font-family:"Times New Roman";mso-bidi-font-family: Mangal;mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language: HI" lang="EN-US">Synthesis of SrAl₂O₄:Eu phosphor by combustion method and its possible applications for mechanoluminescence dosimetry</span>

851-854Eu doped strontium aluminate phosphor (SrAl2O4:Eu) was synthesized by combustion method using urea as a reducer at initiating temperature of 600°C. The mechanoluminescence (ML) of SrAl2O4:Eu and their suitability for radiation dosimetry have been investigated. Eu doped SrAl2O4 is a good dosimeter having linear response up to about 1180 Gy of -radiation dose. Two peaks have been found in time versus ML intensity curve. The peak of ML intensity versus time curve increases with increasing dose of <span style="font-family:Symbol; mso-ascii-font-family:" times="" new="" roman";mso-hansi-font-family:"times="" roman";="" mso-char-type:symbol;mso-symbol-font-family:symbol"="" lang="EN-GB">-radiation. The total ML intensity (area below the curve) also increases with -dose. </span

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Not AvailableThis Newsletter highlights salient research achievements, training programs conducted, workshops organized and other significant activities performed at the Institute during October-December 2017.Not Availabl

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Not AvailableNot AvailableCAF Permanent Committee Secretariat, Winninpeg, Canad

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-