@techreport{TD:101533,
	att_abstract={{Location information is one of the key enablers to context-aware systems and applications for mobile devices. However, most existing location sensing techniques do not work or will be significantly slowed down without infrastructure support, which limits their applicability in several cases. In this paper, we propose a localization system that works for both indoor and outdoor environments in a completely offline manner. Our system leverages human users’ perception of nearby textual signs, without using GPS, Wi-Fi, cellular triangulation, or Internet connectivity. It enables several important use cases, such as offline localization on wearable devices. Based on real data collected from Google Street View and OpenStreetMap, we examine the feasibility of our approach. The preliminary result was encouraging. Our system was able to achieve higher than 90% accuracy with only 4 iterations even when the speech recognition accuracy is 70%, requiring very small storage space, and consuming 44% less instantaneous power compared to GPS.}},
	att_authors={bh1729, fq1800, mr047v},
	att_categories={},
	att_copyright={{ACM}},
	att_copyright_notice={{(c) ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2014 {{, 2015-02-12}}.
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101533},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101533_DS1_2014-11-17T03:10:19.931Z.pdf},
	author={Bo Han and Feng Qian and Moo-ryong Ra},
	institution={{The Sixteenth Workshop on Mobile Computing Systems and Applications (ACM HotMobile 2015)}},
	month={February},
	title={{Human Assisted Positioning Using Textual Signs}},
	year=2015,
}