Gaussian Process regression model for dynamically calibrating a wireless low-cost particulate matter sensor network in Delhi
Publication
, Journal Article
Zheng, T; Bergin, MH; Sutaria, R; Tripathi, SN; Caldow, R; Carlson, DE
Published in: Atmos. Meas. Tech. Discuss.
2019
Duke Scholars
Published In
Atmos. Meas. Tech. Discuss.
Publication Date
2019
Start / End Page
1 / 28
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Zheng, T., Bergin, M. H., Sutaria, R., Tripathi, S. N., Caldow, R., & Carlson, D. E. (2019). Gaussian Process regression model for dynamically calibrating a wireless low-cost particulate matter sensor network in Delhi. Atmos. Meas. Tech. Discuss., 1–28.
Zheng, Tongshu, Michael H. Bergin, Ronak Sutaria, Sachchida N. Tripathi, Robert Caldow, and David E. Carlson. “Gaussian Process regression model for dynamically calibrating a wireless low-cost particulate matter sensor network in Delhi.” Atmos. Meas. Tech. Discuss., 2019, 1–28.
Zheng T, Bergin MH, Sutaria R, Tripathi SN, Caldow R, Carlson DE. Gaussian Process regression model for dynamically calibrating a wireless low-cost particulate matter sensor network in Delhi. Atmos Meas Tech Discuss. 2019;1–28.
Zheng, Tongshu, et al. “Gaussian Process regression model for dynamically calibrating a wireless low-cost particulate matter sensor network in Delhi.” Atmos. Meas. Tech. Discuss., 2019, pp. 1–28.
Zheng T, Bergin MH, Sutaria R, Tripathi SN, Caldow R, Carlson DE. Gaussian Process regression model for dynamically calibrating a wireless low-cost particulate matter sensor network in Delhi. Atmos Meas Tech Discuss. 2019;1–28.
Published In
Atmos. Meas. Tech. Discuss.
Publication Date
2019
Start / End Page
1 / 28