Institute Professor
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2. Głomb, P., M. Romaszewski, M. Cholewa, W. Koral, A. Madej, M. Skrabski, and K. Kołodziej, "Machine Learning for Water Leak Detection and Localization in the WaterPrime Project", Wojciechowski A.(Ed.), Lipiński P.(Ed.)., Progress in Polish Artificial Intelligence Research 4, Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.: Wydawnictwo Politechniki Łódzkiej, 2023.
5. Głomb, P., M. Cholewa, P. Foszner, and J. Bularz, "Continual learning of a time series model using a mixture of HMMs with application to the IoT fuel sensor verification", Proceedings of the 18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023, Warsaw, Poland, September 17-20, 2023, 2023.
6. Buza, K., K. Książek, W. Masarczyk, P. Głomb, P. Gorczyca, and M. Piegza, "A Simple and Effective Classifier for the Detection of Psychotic Disorders based on Heart Rate Variability Time Series", Information Technologies – Applications and Theory 2023, vol. 3498, Tatranské Matliare, Knižnicné a edicné centrum, Fakulta matematiky, fyziky a informatiky, Univerzita Komenského, Mlynská dolina, Bratislava, 09/2023.
7. Gardas, B., P. Głomb, P. Sadowski, Z. Puchała, K. Jałowiecki, Ł. Pawela, O. Faucoz, P-M. Brunet, P. Gawron, M. Van Waveren, et al., "Hyper-Spectral Image Classification Using Adiabatic Quantum Computation", IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023.
8. Strzoda, A., K. Grochla, P. Głomb, and A. Madej, "Link failure prediction in LoRa networks", International Wireless Communications and Mobile Computing Conference, IWCMC, Marrakesh, Morocco, IEEE, 07/2023.


11. Grochla, K., A. Strzoda, R. Marjasz, P. Głomb, K. Książek, and Z. Łaskarzewski, "Energy-Aware Algorithm for Assignment of Relays in LPWAN", Transactions on Sensor Networks, vol. 18, issue 4, 11/2022.
12. Grabowski, B., P. Głomb, K. Książek, and K. Buza, " Improving Autoencoders Performance for Hyperspectral Unmixing Using Clustering", Asian Conference on Intelligent Information and Database Systems, vol. 1716, Ho Chi Minh City, Vietnam, Springer, 11/2022.
13. Książek, K., P. Głomb, M. Romaszewski, M. Cholewa, B. Grabowski, and K. Buza, "Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation", 21st International Conference on Image Analysis and Processing, vol. 13231, Lecce, Italy, Springer, Cham, 05/2022.



16. Książek, K., M. Romaszewski, P. Głomb, B. Grabowski, and M. Cholewa, "Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks", Sensors, vol. 20, issue Recent Advances in Multi- and Hyperspectral Image Analysis, 11/2020.
18. Głomb, P., and M. Romaszewski, "Anomaly detection in hyperspectral remote sensing images", Hyperspectral Remote Sensing: Theory & Applications: Elsevier, 2020.


19. Cholewa, M., P. Głomb, and M. Romaszewski, "A Spatial-Spectral Disagreement-Based Sample Selection With an Application to Hyperspectral Data Classification", IEEE Geoscience and Remote Sensing Letters, vol. 16, pp. 467-471, March, 2019.
20. Grabowski, B., P. Głomb, M. Romaszewski, and M. Ostaszewski, "Unsupervised deep learning approach to hyperspectral anomaly detection", PP-RAI'2019, Wrocław, Poland, Wroclaw University of Science and Technology, 2019.
21. Głomb, P., K. Domino, M. Romaszewski, and M. Cholewa, "Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images", PP-RAI'2019, Wrocław, Poland, Wroclaw University of Science and Technology, pp. p. 121, 2019.


22. Romaszewski, M., P. Głomb, and M. Cholewa, "Adaptive, Hubness-Aware Nearest Neighbour Classifier with Application to Hyperspectral Data", Computer and Information Sciences: Springer International Publishing, 2018.
23. Grabowski, B., W. Masarczyk, P. Głomb, and A. Mendys, "Automatic pigment identification from hyperspectral data", Journal of Cultural Heritage, vol. 31, pp. 1 - 12, 2018.


25. Cholewa, M., P. Gawron, P. Głomb, and D. Kurzyk, "Quantum hidden Markov models based on transition operation matrices", Quantum Information Processing, vol. 16, pp. 101, 2017.


Historia zmian

Data aktualizacji: 10/06/2022 - 10:42; autor zmian: Łukasz Zimny (