Back close

Technology inputs in promoting indigenous food recipes of Irulas and Kurumbas tribes and empowering disadvantaged youth of Masinagudi and Ebbanad village of The Nilgiri District

Start Date: Saturday, Dec 01,2018

End Date: Tuesday, Dec 01,2020

School: School of Engineering, Coimbatore

Project Incharge:Dr. Senthil Kumar T.
Co-Project Incharge:Dr. Udhaya Kumar
Funded by:Ministry of Tribal Affairs
Technology inputs in promoting indigenous food recipes of Irulas and Kurumbas tribes and empowering disadvantaged youth of Masinagudi and Ebbanad village of The Nilgiri District

The project titled ”Technology inputs in promoting indigenous food recipes of Irulas and Kurumbas tribes and empowering disadvantaged youth of Masinagudi and Ebbanad village of The Nilgiri District”, is funded by Ministry of Tribal Affairs. The investigators of the project are Dr. T. Senthil KumarDr. S. Rajendra Kumar, Dr. Udhaya Kumar. The duration of the project is 1 year.

Project Grant / Assistance: 9,52,780

Related Projects

Impacts of recent El-Nino Southern Oscillation (ENSO) on the Water-Food-Energy Nexus in South Asia (Indian PI)
Impacts of recent El-Nino Southern Oscillation (ENSO) on the Water-Food-Energy Nexus in South Asia (Indian PI)
Indigenous Development of Functionally-graded Aluminium Metal Matrix Composites using Centrifugal Casting Method and to Investigate the Mechanical and Tribological Properties
Indigenous Development of Functionally-graded Aluminium Metal Matrix Composites using Centrifugal Casting Method and to Investigate the Mechanical and Tribological Properties
Capacity,bit error rate and performance revaluations of MIMO based communication Systems on minimized multipath environment
Capacity,bit error rate and performance revaluations of MIMO based communication Systems on minimized multipath environment
Small Molecular Reactive Fluorescent Probes for Detection of Cellular Unusual Entities and Bio-imaging
Small Molecular Reactive Fluorescent Probes for Detection of Cellular Unusual Entities and Bio-imaging
Parallelizing Low Vision Feature Extraction through GPUs
Parallelizing Low Vision Feature Extraction through GPUs
Admissions Apply Now