Web-enabled intelligent system for continuous sensor data processing and visualization
Proceedings - Web3D 2019: 24th International ACM Conference on 3D Web Technology, Page: 1-7
2019
- 3Citations
- 1Usage
- 16Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations3
- Citation Indexes3
- CrossRef1
- Usage1
- Abstract Views1
- Captures16
- Readers16
- 16
Conference Paper Description
A large number of sensors deployed in recent years in various setups and their data is readily available in dedicated databases or in the cloud. Of particular interest is real-time data processing and 3D visualization in web-based user interfaces that facilitate spatial information understanding and sharing, hence helping the decision making process for all the parties involved. In this research, we provide a prototype system for near realtime, continuous X3D-based visualization of processed sensor data for two significant applications: thermal monitoring for residential/ commercial buildings and nitrogen cycle monitoring in water beds for aquaponics systems. As sensors are sparsely placed, in each application, where they collect data for large periods (of up to one year), we employ a Finite Differences Method and a Neural Networks model to approximate data distribution in the entire volume.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85083953999&origin=inward; http://dx.doi.org/10.1145/3329714.3338127; https://dl.acm.org/doi/10.1145/3329714.3338127; https://digitalcommons.georgiasouthern.edu/compsci-facpubs/288; https://digitalcommons.georgiasouthern.edu/cgi/viewcontent.cgi?article=1299&context=compsci-facpubs; https://dx.doi.org/10.1145/3329714.3338127
Association for Computing Machinery (ACM)
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