by Convergence Design Lab Admin | Nov 23, 2020 | CISE Research Infrastructure, Convergence Accelerator, Future of Work, Karthik Ramani, News
Professor Karthik Ramani was interviewed by Kenneth Wong of Digital Engineering 24/7 regarding the recent release of the Mechanical Component Benchmark dataset. You can listen to Professor Ramani’s interview on Digital Engineering’s web site:...
by Convergence Design Lab Admin | Nov 5, 2020 | Convergence Accelerator, Karthik Ramani, News
WEST LAFAYETTE – A Purdue University team has entered into a $5 million cooperative agreement with the National Science Foundation to create an augmented and virtual reality experience prototype called Skill-XR. The university says the technology can be used to...
by Karthik Ramani | Oct 28, 2020 | Karthik Ramani, News, Outreach
Karthik Ramani Donald W. Feddersen Distinguished Professor of Mechanical Engineering, Purdue University November 6, 2020 1PM – 2PM (CDT) Seminar will be presented via Zoom (https://auburn.zoom.us/j/4465550646) Augmenting Humans in Design Fabrication, Robotics and...
by Karthik Ramani | Oct 5, 2020 | Karthik Ramani, News, Outreach
Karthik Ramani Donald W. Feddersen Distinguished Professor of Mechanical Engineering, Purdue University Friday, October 16, 2020 – 1:30pm to 3:00pm Seminar will be presented via Zoom Augmenting Humans in Design Fabrication, Robotics, and Workflows Through...
by Sangpil Kim | Jul 3, 2020 | 2020, CISE Research Infrastructure, Convergence Accelerator, Future of Work, Hyunggun Chi, Karthik Ramani, Karthik Ramani, Publications, Recent Publications, Sangpil Kim
We introduce a large-scale annotated mechanical components benchmark for classification and retrieval tasks named Mechanical Components Benchmark (MCB): a large-scale dataset of 3D objects of mechanical components. The dataset enables data-driven feature learning for...
by Tianyi Wang | Jul 3, 2020 | 2020, Fengming He, Karthik Ramani, Karthik Ramani, Ke Huo, Publications, Recent Publications, Tianyi Wang, Xun Qian, Yuanzhi Cao
Recognition of human behavior plays an important role in context-aware applications. However, it is still a challenge for end-users to build personalized applications that accurately recognize their own activities. Therefore, we present CAPturAR, an in-situ...