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Sophia Reyes

dblp:122/1075 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Wireless sensing and localization · 75% Physical-layer communications · 25%
Human-computer interaction and pervasive computing
1 paper
Accessibility and assistive technology · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization
indoor localization
0.212013
Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device · IEEE Trans. Mob. Comput. 2013
Wireless sensing and localization › tracking
indoor tracking
0.212013
Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device · IEEE Trans. Mob. Comput. 2013
Physical-layer communications › digital signal processing › filtering
kalman filtering
0.212013
Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device · IEEE Trans. Mob. Comput. 2013
Wireless sensing and localization › RF-based localization
RSS-based localization
0.212013
Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device · IEEE Trans. Mob. Comput. 2013
Accessibility and assistive technology › assistive navigation
navigation assistance for visually impaired
0.012013
Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device · IEEE Trans. Mob. Comput. 2013

Methods — techniques the papers use, named apart from their topics

map matching · 0.3kalman filter · 0.3compressive sensing · 0.3
YearPublicationVenuePosition
2013 Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device
abstract
An indoor tracking and navigation system based on measurements of received signal strength (RSS) in wireless local area network (WLAN) is proposed. In the system, the location determination problem is solved by first applying a proximity constraint to limit the distance between a coarse estimate of the current position and a previous estimate. Then, a Compressive Sensing-based (CS--based) positioning scheme, proposed in our previous work , , is applied to obtain a refined position estimate. The refined estimate is used with a map-adaptive Kalman filter, which assumes a linear motion between intersections on a map that describes the user's path, to obtain a more robust position estimate. Experimental results with the system that is implemented on a PDA with limited resources (HP iPAQ hx2750 PDA) show that the proposed tracking system outperforms the widely used traditional positioning and tracking systems. Meanwhile, the tracking system leads to 12.6 percent reduction in the mean position error compared to the CS-based stationary positioning system when three APs are used. A navigation module that is integrated with the tracking system provides users with instructions to guide them to predefined destinations. Thirty visually impaired subjects from the Canadian National Institute for the Blind (CNIB) were invited to further evaluate the performance of the navigation system. Testing results suggest that the proposed system can be used to guide visually impaired subjects to their desired destinations.
Wain Sy Anthea Au, Chen Feng 0001, Shahrokh Valaee, Sophia Reyes, Sameh Sorour, Samuel N. Markowitz, Deborah Gold, Keith Gordon, Moshe Eizenman
IEEE Trans. Mob. Comput.4