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All Outputs (10)

Devising a Responsible Framework for Air Quality Sensor Placement (2024)
Presentation / Conference Contribution
Westcarr, J., Gunturi, V. M. V., Cabaneros, S. M., Raja, R., Thakker, D., & Porter, A. (2024, July). Devising a Responsible Framework for Air Quality Sensor Placement. Presented at 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS), London, United Kingdom

A major challenge faced when developing smart, sustainable urban environments is the reduction of air pollutants that adversely impact citizens' health. The UK has implemented strategies such as clean air zones (CAZs) coupled with the use of sensor t... Read More about Devising a Responsible Framework for Air Quality Sensor Placement.

NEAT Activity Detection using Smartwatch (2024)
Journal Article
Dewan, A., Gunturi, V., & Naik, V. (2024). NEAT Activity Detection using Smartwatch. International Journal of Ad Hoc and Ubiquitous Computing, 45(1), 36-51. https://doi.org/10.1504/IJAHUC.2024.136141

This paper presents a system for distinguishing non-exercise activity thermogenesis (NEAT) and non-NEAT activities at home. NEAT includes energy expended on activities apart from sleep, eating, or traditional exercise. Our study focuses on specific N... Read More about NEAT Activity Detection using Smartwatch.

NEAT Activity Detection using Smartwatch at Low Sampling Frequency (2021)
Presentation / Conference Contribution
Dewan, A., Gunturi, V. M., Naik, V., & Dutta, K. K. (2021, October). NEAT Activity Detection using Smartwatch at Low Sampling Frequency. Presented at 2021 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People, and Smart City Innovations, SmartWorld/ScalCom/UIC/ATC/IoP/SCI 2021, Atlanta, GA, USA

Our paper aims to build a classification model to discern the typical NEAT (Non-Exercise Activity Thermogenesis) activities done in a home setting. The concept of NEAT is broadly defined as the energy spent in everything which is not sleeping, eating... Read More about NEAT Activity Detection using Smartwatch at Low Sampling Frequency.

A Navigation System for Safe Routing (2021)
Presentation / Conference Contribution
Kaur, R., Goyal, V., Gunturi, V. M., Saini, A., Sanadhya, K., Gupta, R., & Ratra, S. (2021, June). A Navigation System for Safe Routing. Presented at International Conference on Mobile Data Management, Toronto, ON, Canada

Globally, women are cautious when planning their routine travel routes. In a recent survey on street harassment, 82% of international respondents reported taking a different route to their destination than the conventional route due to fear of harass... Read More about A Navigation System for Safe Routing.

Finding the most navigable path in road networks (2021)
Journal Article
Kaur, R., Goyal, V., & Gunturi, V. M. (2021). Finding the most navigable path in road networks. GeoInformatica, 25(1), 207-240. https://doi.org/10.1007/s10707-020-00428-5

Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and “navigability score” values; (b) a source and a destinatio... Read More about Finding the most navigable path in road networks.

Discovering non-compliant window co-occurrence patterns (2017)
Journal Article
Ali, R. Y., Gunturi, V. M., Kotz, A. J., Eftelioglu, E., Shekhar, S., & Northrop, W. F. (2017). Discovering non-compliant window co-occurrence patterns. GeoInformatica, 21(4), 829-866. https://doi.org/10.1007/s10707-016-0289-3

Given a set of trajectories annotated with measurements of physical variables, the problem of Non-compliant Window Co-occurrence (NWC) pattern discovery aims to determine temporal signatures in the explanatory variables which are highly associated wi... Read More about Discovering non-compliant window co-occurrence patterns.

Scalable computational techniques for centrality metrics on temporally detailed social network (2016)
Journal Article
Gunturi, V. M., Shekhar, S., Joseph, K., & Carley, K. M. (2017). Scalable computational techniques for centrality metrics on temporally detailed social network. Machine Learning, 106(8), 1133-1169. https://doi.org/10.1007/s10994-016-5583-7

Increasing proliferation of mobile and online social networking platforms have given us unprecedented opportunity to observe and study social interactions at a fine temporal scale. A collection of all such social interactions among a group of individ... Read More about Scalable computational techniques for centrality metrics on temporally detailed social network.

Spatiotemporal data mining: A computational perspective (2015)
Journal Article
Shekhar, S., Jiang, Z., Ali, R. Y., Eftelioglu, E., Tang, X., Gunturi, V. M., & Zhou, X. (2015). Spatiotemporal data mining: A computational perspective. ISPRS International Journal of Geo-Information, 4(4), 2306-2338. https://doi.org/10.3390/ijgi4042306

Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previo... Read More about Spatiotemporal data mining: A computational perspective.

A Critical-Time-Point Approach to All-Departure-Time Lagrangian Shortest Paths (2015)
Journal Article
Gunturi, V. M., Shekhar, S., & Yang, K. (2015). A Critical-Time-Point Approach to All-Departure-Time Lagrangian Shortest Paths. IEEE Transactions on Knowledge and Data Engineering, 27(10), 2591-2603. https://doi.org/10.1109/TKDE.2015.2426701

Given a spatiooral network, a source, a destination, and a desired departure time interval, the All-departure-time Lagrangian Shortest Paths (ALSP) problem determines a set which includes the shortest path for every departure time in the given interv... Read More about A Critical-Time-Point Approach to All-Departure-Time Lagrangian Shortest Paths.