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Alexander Varshavsky
Researcher at AT&T Labs
Publications - 46
Citations - 5306
Alexander Varshavsky is an academic researcher from AT&T Labs. The author has contributed to research in topics: GSM & Cellular network. The author has an hindex of 33, co-authored 46 publications receiving 5057 citations. Previous affiliations of Alexander Varshavsky include AT&T & University of Toronto.
Papers
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Book ChapterDOI
Accurate GSM indoor localization
TL;DR: The first accurate GSM indoor localization system that achieves median accuracy of 5 meters in large multi-floor buildings is presented, and can accurately differentiate between floors in both wooden and steel-reinforced concrete structures.
Book ChapterDOI
Identifying important places in people's lives from cellular network data
Sibren Isaacman,Richard A. Becker,Ramón Cáceres,Stephen G. Kobourov,Margaret Martonosi,James Rowland,Alexander Varshavsky +6 more
TL;DR: This paper proposes new techniques based on clustering and regression for analyzing anonymized cellular network data to identify generally important locations, and to discern semantically meaningful locations such as home and work.
Proceedings ArticleDOI
Tapprints: your finger taps have fingerprints
TL;DR: The location of screen taps on modern smartphones and tablets can be identified from accelerometer and gyroscope readings, and TapPrints, a framework for inferring the location of taps on mobile device touch-screens using motion sensor data combined with machine learning analysis is presented.
Book ChapterDOI
Practical metropolitan-scale positioning for GSM phones
Mike Y. Chen,Timothy Sohn,Dmitri Chmelev,Dirk Haehnel,Jeffrey Hightower,Jeff Hughes,Anthony LaMarca,Fred Potter,Ian Smith,Alexander Varshavsky +9 more
TL;DR: This paper examines the positioning accuracy of a GSM beacon-based location system in a metropolitan environment and shows that a small 60-hour calibration drive is sufficient for enabling a metropolitan area similar to Seattle.
Book ChapterDOI
Mobility detection using everyday GSM traces
Timothy Sohn,Alexander Varshavsky,Anthony LaMarca,Mike Y. Chen,Tanzeem Choudhury,Ian Smith,Sunny Consolvo,Jeffrey Hightower,William G. Griswold,Eyal de Lara +9 more
TL;DR: This paper explores how coarse-grained GSM data from mobile phones can be used to recognize high-level properties of user mobility, and daily step count, and demonstrates that even without knowledge of observed cell tower locations, mobility modes that are useful for several application domains are recognized.