(Georgia Institute of Technology, 2008)
Kim, Kihwan; Summet, Jay; Starner, Thad; Ashbrook, Daniel; Kapade, Mrunal; Essa, Irfan
Using off-the-shelf Global Positioning System (GPS)
units, we reconstruct buildings in 3D by exploiting the reduction
in signal to noise ratio (SNR) that occurs when
the buildings obstruct the line-of-sight between the moving
units and the orbiting satellites. We measure the size and
height of skyscrapers as well as automatically constructing
a density map representing the location of multiple buildings
in an urban landscape. If deployed on a large scale, via
a cellular service provider’s GPS-enabled mobile phones or
GPS-tracked delivery vehicles, the system could provide an
inexpensive means of continuously creating and updating
3D maps of urban environments.
(Georgia Institute of Technology, 2006)
Vadas, Kristin; Lyons, Kenton Michael; Ashbrook, Daniel; Yi, Ji Soo; Starner, Thad; Jacko, Julie A.
As mobile technology becomes a more integral part of our everyday lives, understanding the impact of different displays on perceived ease of use and overall performance is becoming increasingly important. In this paper, we evaluate three mobile displays: the MicroOptical SV-3, the Sony Librie, and the OQO Model 01. These displays each use different underlying technologies and offer unique features which could impact mobile use. The OQO is a hand-held device that utilizes a traditional transflective liquid crystal display (LCD). The MicroOptical SV-3 is a head-mounted display that uses a miniature LCD and offers hands free use. Finally, the Librie uses a novel, low power reflective electronic ink technology. We present a controlled 15-participant evaluation to assess the effectiveness of using these displays for reading while in motion.
(Georgia Institute of Technology, 2002)
Ashbrook, Daniel; Starner, Thad
Wearable computers have the potential to act as intelligent agents in everyday life and assist the user in a variety of tasks depending on the context. Location is the most common form of context used by these agents to determine the user's task. However, another potential use is the creation of a predictive model of the user's future movements. We present a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales. These locations are then incorporated into a Markov model that can be consulted for use with a variety of applications in both single-user and collaborative scenarios.