The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Automated synchronization of driving data using vibration and steering events
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Several attempts have been made to address the time synchronization issue, such as SyncWISE.
“Several attempts have been made to address this issue, such as SyncWISE [288,289].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [289]
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Citations
Cites
The generalized correlation method for estimation of time delay
Knapp, C., & Carter, G. (1976) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1976.1162830 · Cited by 4,433
Two-Frame Motion Estimation Based on Polynomial Expansion
Farnebäck, G. (2003) · Lecture notes in computer science · doi:10.1007/3-540-45103-x_50 · Cited by 3,010
Fine-grained network time synchronization using reference broadcasts
Elson, J., Girod, L., & Estrin, D. L. (2002) · ACM SIGOPS Operating Systems Review · doi:10.1145/844128.844143 · Cited by 2,408
Time synchronization in sensor networks: a survey
Sivrikaya, F., & Yener, B. (2004) · IEEE Network · doi:10.1109/mnet.2004.1316761 · Cited by 859
Fast Normalized Cross-Correlation
Lewis, J. (2010) · Cited by 740
Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94)
(1994) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.1994.407417 · Cited by 551
The use of optical flow for road navigation
Giachetti, A., Campani, M., & Torre, V. (1998) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.660838 · Cited by 243
Clock Synchronization in Wireless Sensor Networks: An Overview
Rhee, I.-K., Lee, J., Kim, J.-S., et al. (2009) · Sensors · doi:10.3390/s90100056 · Cited by 203
Reliability Meets Big Data: Opportunities and Challenges
Meeker, W. Q., & Hong, Y. (2013) · Quality Engineering · doi:10.1080/08982112.2014.846119 · Cited by 200
Reimer, B. (2014) · Public Policy & Aging Report · doi:10.1093/ppar/prt006 · Cited by 109
Self-citationOvertaking vehicle detection using implicit optical flow
Batavia, P. H., Pomerleau, D. E., & Thorpe, C. (2002) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.1997.660564 · Cited by 90
Time on task and blink effects on saccade duration
MCGREGOR, D. K., & Stern, J. A. (1996) · Ergonomics · doi:10.1080/00140139608964487 · Cited by 68
Antin, J. F., Lee, S., Hankey, J. M., et al. (2011) · National Academies Press eBooks · doi:10.17226/14494 · Cited by 62
A passive solution to the sensor synchronization problem
Olson, E. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5650579 · Cited by 62
Nonlinear ego-motion estimation from optical flow for online control of a quadrotor UAV
Grabe, V., Bülthoff, H., Scaramuzza, D., & Giordano, P. R. (2015) · The International Journal of Robotics Research · doi:10.1177/0278364915578646 · Cited by 54
Plötz, T., Chen, C., Hammerla, N., & Abowd, G. D. (2012) · International Symposium on Wearable Computers · doi:10.1109/iswc.2012.15 · Cited by 51
Clock Synchronization in Wireless Sensor Networks
Aissaoua, H., Bounceur, A., Aliouat, M., et al. (2016) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 15
Interval-Based Time Synchronisation of Sensor Data in a Mobile Robot
Zaman, M., & Illingworth, J. (2005) · doi:10.1109/issnip.2004.1417505 · Cited by 8
Semi-Automated Annotation of Discrete States in Large Video DatasetsPreprint
Fridman, L., & Reimer, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.01035 · Cited by 6
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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625
“A number of attempts have been made to address this issue, such as [93] and the Window Induced Shift Estimation method (SyncWISE) [334].”
Method
Metadata
- Authors
- Fridman, L., Brown, D. E., Angell, W., Abdić, I., Reimer, B., & Noh, H. Y.
- Affiliations
- Massachusetts Institute of Technology (US)
- Carnegie Mellon University (US)
- Corresponding author
- Fridman, Lex
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 75
- Pages
- 9-15
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W1881965799
- Cited by
- 34
- References
- 20
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 3 · 2022: 8 · 2021: 3 · 2020: 6 · 2019: 2 · 2018: 2 · 2017: 1 · 2016: 3
- FWCI
- 2.09
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Pattern Recognition Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Time Synchronization Technologies
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Keywords
- sensor synchronization, vehicle driving data, cross correlation function, vehicle vibration, accelerometer, video cameras, optical flow, driver assistance systems, driver behavior, time delay estimation
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