dr. J.W. Kamminga (Jacob)

Engineering & Materials Science

# Accelerometers
# Acoustic Waves
# Aerial Photography
# Animals
# Classifiers
# Sensors
# Smartphones

Physics & Astronomy

# Aerial Photography

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Pervasive Systems (PS)

Hogeweg, L., Gangireddy, R., Brunink, D., Kalkman, V., Cornelissen, L. , & Kamminga, J. W. (2024). COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification. (pp. 1-28). ArXiv.org. https://doi.org/10.48550/arXiv.2403.06874

van Ommen Kloeke, E., Huijbers, C., Beentjes, K. , Kamminga, J. W., Bakker, P. A. J., & Kissling, W. D. (2022). ARISE: Building an infrastructure for species recognition and biodiversity monitoring in the Netherlands. Abstract from Biodiversity Information Standards, TDWG 2022, Sofia, Bulgaria. https://doi.org/10.3897/biss.6.93613

Spink, S. , Kamminga, J. W. , & Kamilaris, A. (2022). Improving the Annotation Efficiency for Animal Activity Recognition using Active Learning. In A. Spink, J. Barski, A.-M. Brouwer, G. Riedel, & A. Sil (Eds.), Measuring Behavior 2022: 12th International Conference on Methods and Techniques in Behavioral Research, and 6th Seminar on Behavioral Methods (Vol. 2, pp. 51-58) https://doi.org/10.6084/m9.figshare.20066849

Padubidri, C. , Kamilaris, A., Karatsiolis, S. , & Kamminga, J. (2021). Counting sea lions and elephants from aerial photography using deep learning with density maps. Animal Biotelemetry, 9(1), 1-10. Article 27. https://doi.org/10.1186/s40317-021-00247-x

Kamminga, J. W. (2020). Hiding in the Deep: Online Animal Activity Recognition using Motion Sensors and Machine Learning. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente. https://doi.org/10.3990/1.9789036550550

Kamminga, J. W. , Meratnia, N. , Le, D. V. , & Havinga, P. J. M. (2020). Towards Deep Unsupervised Representation Learning from Accelerometer Time Series for Animal Activity Recognition. Paper presented at 6th Workshop on Mining and Learning from Time Series, MiLeTS 2020.

Kamminga, J. W. , Meratnia, N. , & Havinga, P. J. M. (2019). Dataset: Horse Movement Data and Analysis of its Potential for Activity Recognition. 22-25. Paper presented at 2nd Workshop on Data Acquisition To Analysis, DATA 2019, New York, New York, United States. https://doi.org/10.1145/3359427.3361908

Kamminga, J. W., Jones, M., Seppi, K. , Meratnia, N. , & Havinga, P. J. M. (2019). Synchronization between Sensors and Cameras in Movement Data Labeling Frameworks. In DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis (pp. 37-39) https://doi.org/10.1145/3359427.3361920

Kamminga, J. W., Janßen, L. , Meratnia, N. , & Havinga, P. J. M. (2019). Horsing Around: A Dataset Comprising Horse Movement. Data, 4(4), Article 131. https://doi.org/10.3390/data4040131

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Naar mijn profiel

Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.