dr hab. inż. Przemysław Głomb

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0000-0002-0215-4674

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profesor instytutu
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Publikacje
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2023

1. 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.
2. Ciemięga, A., K. Maresz, M. Romaszewski, P. Głomb, P. Krupska-Wolas, and K. Prusik, "Hyperspectral Imaging as a Facile and Non-Destructive Method for Size Analysis of Gold Nanoparticles Deposited on Porous Materials", Particle & Particle Systems Characterization, 2023.
3. Książek, K., W. Masarczyk, P. Głomb, M. Romaszewski, I. Stokłosa, P. Ścisło, P. Dębski, R. Pudlo, P. Gorczyca, and M. Piegza, HRV-ACC: a dataset with R-R intervals and accelerometer data for the diagnosis of psychotic disorders using a Polar H10 wearable sensor, 2023.
4. 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.
5. 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.
6. 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.
7. Książek, K., W. Masarczyk, P. Głomb, M. Romaszewski, I. Stokłosa, P. Ścisło, P. Dębski, R. Pudlo, K. Buza, P. Gorczyca, et al., "The analysis of heart rate variability and accelerometer mobility data in the assessment of symptom severity in psychotic disorder patients using a wearable Polar H10 sensor", medXriv, 08/2023.
8. Głomb, P., M. Cholewa, W. Koral, A. Madej, and M. Romaszewski, "Detection of emergent leaks using machine learning approaches", Water Supply, vol. 23, issue 6, 05/2023.

2022

9. 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.
10. 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.
11. 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.

2021

12. Romaszewski, M., P. Głomb, A. Sochan, and M. Cholewa, "A dataset for evaluating blood detection in hyperspectral images", Forensic Science International, vol. 320, 2021.

2020

13. Masarczyk, W., P. Głomb, B. Grabowski, and M. Ostaszewski, "Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling", Remote Sensing, vol. 12, issue 16, 2020.
14. Romaszewski, M., P. Głomb, A. Sochan, and M. Cholewa, A Dataset for Evaluating Blood Detection in Hyperspectral Images [Data set], 2020.
15. 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.
16. Głomb, P., and M. Romaszewski, "Anomaly detection in hyperspectral remote sensing images", Hyperspectral Remote Sensing: Theory & Applications: Elsevier, 2020.

2019

17. 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.
18. 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.
19. 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.

2018

20. 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.
21. 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.
22. Głomb, P., M. Romaszewski, M. Cholewa, and K. Domino, "Application of hyperspectral imaging and Machine Learning methods for the detection of gunshot residue patterns", Forensic Science International, vol. 290, 07/2018.

2017

23. 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.

2016

24. Głomb, P., and M. Cholewa, "Performance of Interest Point Descriptors on Hyperspectral Images", Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, 2016.
25. Romaszewski, M., P. Głomb, and M. Cholewa, "Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 121, pp. 60 - 76, 2016.

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Data aktualizacji: 10/06/2022 - 10:42; autor zmian: Łukasz Zimny (lzimny@iitis.pl)