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Activity recognition has applications in a variety of human-in-the-loop settings such as smart home health monitoring, green building energy and occupancy management, intelligent transportation, and participatory sensing. While fine-grained activity recognition systems and approaches help enable a multitude of novel applications, discovering them with non-intrusive ambient sensor systems pose challenging design, as well as data processing, mining, and activity recognition issues. In this paper, we develop a low-cost heterogeneous Radar based Activity Monitoring (RAM) system for recognizing fine-grained activities. We exploit the feasibility of using an array of heterogeneous micro-doppler radars to recognize low-level activities. We prototype a short-range and a long-range radar system and evaluate the feasibility of using the system for fine-grained activity recognition. In our evaluation, using real data traces, we show that our system can detect fine-grained user activities with 92.84% accuracy.
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A Flash Flood Categorization System Using Scene-Text Recognition
Detecting flash floods in real-time and taking rapid actions are of utmost importance to save human lives, loss of infrastructures, and personal properties in a smart city. In this paper, we develop a low-cost low-power cyber-physical System prototype using a Raspberry Pi camera to detect the rising water level. We deployed the system in the real word and collected data in different environmental conditions (early morning in the presence of fog, sunny afternoon, late afternoon with sunsetting). We employ image processing and text recognition techniques to detect the rising water level and articulate several challenges in deploying such a system in the real environment. We envision this prototype design will pave the way for mass deployment of the flash flood detection system with minimal human intervention.
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- Award ID(s):
- 1640625
- PAR ID:
- 10073165
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Smart Computing (SMARTCOMP)
- Page Range / eLocation ID:
- 147 to 154
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SMARTCOMP.2018.00085
