• Toward ergonomic risk prediction via segmentation of indoor object manipulation actions using spatiotemporal convolutional networks 

  Parsa, Behnoosh; Samani, Ekta; Hendrix, Rose; Devine, Cameron; Singh, Shashi; Devasia, Santosh; Banerjee, Ashis (IEEE, 2019-07)

  Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications. We present ...
• Primary energy and exergy of desalination technologies in a power-water cogeneration scheme 

  Altmann, Thomas; Robert, Justin; Bouma, Andrew; Swaminathan, Jaichander; Lienhard, John H. (Elsevier, 2019-10)

  The primary energy consumption of a spectrum of desalination systems is assessed using operating information and technical bids for real plants configured with coproduction of electricity. The energy efficiency of desalination ...
• Coupled electrohydrodynamic transport through fractures 

  Ghosh, Uddipta; Le Borgne, T.; Meheust, Y. (2019-05)
• A perspective on using input reconstruction for command following 

  Kadam, Sujay D.; Chavan, Roshan A.; Rajiv, Abhijith; Palanthandalam-Madapusi, Harish J. (Springer, 2019-05)

  In this short note, we present a perspective on viewing tracking control problems as input reconstruction problems and thereby suggesting that input reconstruction methods are naturally suited for tracking problems. This ...
• A possible explanation of how high-frequency deep brain stimulation suppresses low-frequency tremors in Parkinson�s disease 

  Shah, Vrutangkumar V.; Goyal, Sachin; Palanthandalam-Madapusi, Harish J. (IEEE, 2017-08)

  Parkinson's disease (PD) is a neurodegenerative disorder of the central nervous system and one of its key symptoms is rest tremor. Deep brain stimulation (DBS) effectively suppresses rest tremor in Parkinson's disease. ...
• Estimating Al2O3�CO2 nanofluid viscosity: a molecular dynamics approach 

  Ahmed, Zeeshan; Bhargav, Atul; Mallajosyula, Sairam S (EDP Sciences, 2018-12)

  High-viscosity CO2�is of interest to the oil and gas industry in enhanced oil recovery and well-fracturing applications. Dispersing nanoparticles in CO2�is one way of achieving increased viscosity. However, parametric ...
• Dynamic thermal characterization of raised floor plenum data centers: Experiments and CFD 

  Fulpagare, Yogesh; Bhargav, Atul; Joshi, Yogendra (Elsevier, 2019-05)

  World over, power consumption by “server farms” or data centers is a growing concern. With time dependent job arrival rates at the servers, the power dissipation, and concomitantly the cooling requirements are dynamic. ...
• CFD Study of Ethanol Autothermal Reforming on Rh/CeO2 catalysts using Detailed Micro-kinetic Mechanism 

  Baruah, Renika; Bhargav, Atul (2019-03-27)
• Surface-immobilized antimicrobial peptide (AMP) 

  Majhi, Sasmita; Mishra, Abhijit (2019-03-01)
• Numerical modeling of diffusion-based peritectic solidification in iron carbon system and experimental validation 

  Das, Ipsita Madhu Mita; Kumar, Nishant; Paliwal, Manas (Springer, 2019-03)

  Continuous casting of high-strength steels is challenging owing to peritectic phase transformation during solidification. This transformation is reported to be either diffusion controlled or “massive” like. The experimental ...
• Active flow control of convectively unstable flows 

  Kant, Ravi; Narayanan, Vinod (2018-10-12)
• Atomistically informed melting models for aluminum nanocrystals 

  Mathur, Nilkumar; Mane, Tejas; Sundaram, Dilip (Elsevier, 2019-01)

  Atomistically informed analysis of melting is conducted to predict equilibrium melting points of aluminum nanocrystals in the size range 2–10 nm. Spherical and cubic nanocrystals and planar films are considered. Relevant ...
• Data center rack analysis using detached eddy simulations 

  Saiyad, Anashusen R.; Fulpagare, Yogesh; Bhargav, Atul (2018-09-07)
• Using satellite remote sensing to quantify the impact of groundwater on dryland ecosystems' resilience globally 

  Shah, Reepal (2018-12-10)
• Predicting ergonomic risks during indoor object manipulation using spatiotemporal convolutional networks 

  Parsa, Behnoosh; Samani, Ekta U.; Hendrix, Rose; Singh, Shashi M.; Devasia, Santosh; Banerjee, Ashis G. (Cornell University Library, 2019-02)

  Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications. We present ...
• Global stability analysis of spatially developing boundary layers 

  Bhoraniya, Ramesh (Indian Institute of Technology Gandhinagar, 2018)
• Biomechanical view of parkinsonian rest tremor mechanism and its implication to therapy and early diagnosis 

  Shah, Vrutangkumar V (Indian Institute of Technology Gandhinagar, 2018)
• Predictive model development and validation for raised floor plenum data centers 

  Fulpagare, Yogesh Shantaram (Indian Institute of Technology Gandhinagar, 2018)
• Modeling and simulation of combustion of isolated and packed bed of nickel coated aluminum particles 

  Korat, Chirag (Indian Institute of Technology Gandhinagar, 2017)
• Modeling and Simulation of combustion of magnesium water mixture 

  Sakhare, Aditya Anil (Indian Institute of Technology Gandhinagar, 2018)

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