How to teach a van to drive: an undergraduate perspective on the 2005 DARPA Grand Challenge

This paper describes how a team of undergraduate volunteers from California Institute of Technology (Caltech) developed a robotic vehicle that can navigate completely autonomously through the Mojave Desert. Called Alice, the vehicle was Caltech's entry to the 2005 DARPA Grand Challenge which aimed to generate the technology needed to build and program an unmanned ground vehicle through 130 miles of difficult terrain completely autonomously in under ten hours. Although Alice failed to win the competition, she did succeed in her original purpose of teaching a new generation of students about engineering, how to apply theory to the real world, how to debug and deal with shortcomings and schedules, and most importantly, how to work as a team on a complex problem

Insights and Lessons: Community Arts and College Arts - A Report to The Kresge Foundation

This report examines two pilot initiatives, Community Arts and College Arts, launched during the 2008 economic downturn. After the completion of the multiyear initiatives, the Kresge Foundation commissioned a report on the effort. The qualitative analysis offers lessons and insights on the theme of art-based civic dialogue and community revitalization

Climate change and disaster impact reduction

Based on papers presented at the 'UK - South Asia Young Scientists and Practitioners Seminar on Climate Change and Disaster Impact Reduction' held at Kathmandu, Nepal on 5-6 June, 2008

Space Archaeology: Survey and Implementation of Deep Learning Methods for Detecting Ancient Structures

Remote sensing instruments are changing the nature of archaeological work. No longer are archaeological discoveries done by field work alone. Light Detection and Ranging, or LiDAR, optical imagery and different types of satellite data are giving opportunities for archaeological discoveries in areas which might be inaccessible to archaeologists. Different types of machine learning and deep learning methods are also being applied to remote sensing data, which enables automatic searches to large scale areas for detection of archaeological remains. In this thesis faster R-CNN object detection deep learning frameworks were used to train models and apply these to three types of archaeological remains. LiDAR based Digital Terrain Models were used to identify burial mounds in Norway. Optical imagery was used to identify fortress structures in Central Asia. Synthetic Aperture Radar data, or SAR, was used to detect archaeological settlement mounds in Central Asia. The success and limitations of these models are presented

Space Archaeology: Survey and Implementation of Deep Learning Methods for Detecting Ancient Structures

Remote sensing instruments are changing the nature of archaeological work. No longer are archaeological discoveries done by field work alone. Light Detection and Ranging, or LiDAR, optical imagery and different types of satellite data are giving opportunities for archaeological discoveries in areas which might be inaccessible to archaeologists. Different types of machine learning and deep learning methods are also being applied to remote sensing data, which enables automatic searches to large scale areas for detection of archaeological remains. In this thesis faster R-CNN object detection deep learning frameworks were used to train models and apply these to three types of archaeological remains. LiDAR based Digital Terrain Models were used to identify burial mounds in Norway. Optical imagery was used to identify fortress structures in Central Asia. Synthetic Aperture Radar data, or SAR, was used to detect archaeological settlement mounds in Central Asia. The success and limitations of these models are presented