Field Reconstruction-Based Non-Rendezvous Calibration for Low Cost Mobile Sensors-Reference-Cited by-同舟云学术

Field Reconstruction-Based Non-Rendezvous Calibration for Low Cost Mobile Sensors

Published:2023-10-08 Issue: Volume: Page:
ISSN:
Container-title:Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing
language:
Short-container-title:

Author:

Luo Ji¹^ORCID,Hu Yiling²^ORCID,Yu Chengzhao¹^ORCID,Hong Chaopeng³^ORCID,Zhang Xiao-Ping⁴^ORCID,Chen Xinlei⁵^ORCID

Affiliation:

1. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, China

2. Shenzhen Environmental Thinking Science and Technology (ETST) Company Ltd., China

3. Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Shenzhen International Graduate School, Tsinghua University, China

4. Shenzhen International Graduate School, Tsinghua University, China and RISC-V International Open Source Laboratory, China and Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Canada

5. Shenzhen International Graduate School, Tsinghua University, China and Pengcheng Laboratory, China

Funder

the National Key R&D program of China

Shenzhen 2022 Stabilization Support Program

Guangdong Innovative and Entrepreneurial Research Team Program

Tsinghua Shenzhen International Graduate School Cross-disciplinary Research and Innovation Fund Research Plan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3594739.3612908

Reference31 articles.

1. Martín Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , 2016. Tensorflow: a system for large-scale machine learning .. In Osdi, Vol. 16. Savannah, GA, USA , 265–283. Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, 2016. Tensorflow: a system for large-scale machine learning.. In Osdi, Vol. 16. Savannah, GA, USA, 265–283.

2. Long-term field Evaluation of Low-cost Particulate Matter Sensors in Nanjing

3. A Distributed Low-Cost Pollution Monitoring Platform

4. Tianfeng Chai and Roland R Draxler . 2014. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development 7, 3 ( 2014 ), 1247–1250. Tianfeng Chai and Roland R Draxler. 2014. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development 7, 3 (2014), 1247–1250.

5. DrunkWalk

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Physics-informed Neural ODE for Post-disaster Mobility Recovery;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. MobiAir: Unleashing Sensor Mobility for City-scale and Fine-grained Air-Quality Monitoring with AirBERT;Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services;2024-06-03

3. Enhancing Physical-Layer Key Generation Accuracy through Deep Learning-Based Hardware Calibration;Proceedings of the Workshop on Adaptive AIoT Systems;2024-06-03

4. TransformLoc: Transforming MAVs into Mobile Localization Infrastructures in Heterogeneous Swarms;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20

5. QUEST: Quality-informed Multi-agent Dispatching System for Optimal Mobile Crowdsensing;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20