Name
Abstract | ||
|---|---|---|
Real-time and fine-grained rain information is crucial not only for climate research, weather prediction, water resources management, agricultural production, urban planning and natural disasters monitoring, but also for applications in our daily lives. However, because of the lack of rain detection systems and the high variable attribute of rain, both in time and space, the rain detection today is still not precise enough. In such context, we propose and implement Tefnut (Tefnut is the rain deity in Ancient Egyptian religion.), a novel system that exploits opportunistically crowdsourced in-vehicle audio clips from an alternative, nowadays omnipresent source, smartphones, to achieve precise detection of rain leveraging a supervised recognizer constructed from a series of refined features. We conduct extensive experiments, and evaluation results demonstrate that Tefnut can detect the rain with 96.0% true positive rate, when deciding with a one-second-long in-vehicle audio segment only. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-42836-9_2 | WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS, WASA 2016 |
Keywords | Field | DocType |
Rain detection, Supervised classification, Signal processing, Smartphone | Signal processing,Computer vision,Weather prediction,Computer science,Natural disaster,Exploit,Real-time computing,Urban planning,Artificial intelligence,Water resources,True positive rate,Distributed computing | Conference |
Volume | ISSN | Citations |
9798 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 18 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hansong Guo | 1 | 4 | 1.77 |
| He Huang | 2 | 829 | 65.14 |
| Jianxin Wang | 3 | 2163 | 283.94 |
| Tang Shaojie | 4 | 2224 | 157.73 |
| Zhenhua Zhao | 5 | 1 | 0.37 |
| Zehao Sun | 6 | 19 | 4.07 |
| Yu-E Sun | 7 | 67 | 9.60 |
| Liusheng Huang | 8 | 1082 | 123.52 |
| Hengchang Liu | 9 | 381 | 34.84 |