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. Cholewa, W. Koral, A. Madej, and M. Romaszewski, "Detection of emergent leaks using machine learning approaches", Water Supply, vol. 23, issue 6, 05/2023.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.

2022

7. 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.
8. 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.
9. 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

10. 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

11. Głomb, P., and M. Romaszewski, "Anomaly detection in hyperspectral remote sensing images", Hyperspectral Remote Sensing: Theory & Applications: Elsevier, 2020.
12. Romaszewski, M., P. Głomb, A. Sochan, and M. Cholewa, A Dataset for Evaluating Blood Detection in Hyperspectral Images [Data set], 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. 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.

2019

15. 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.
16. 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.
17. 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

18. 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.
19. 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.
20. 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

21. 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

22. 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.
23. 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.
24. Cholewa, M., and P. Głomb, "Two Stage SVM Classification for Hyperspectral Data", Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, 2016.
25. Romaszewski, M., and P. Głomb, "Parameter Estimation for HOSVD-based Approximation of Temporally Coherent Mesh Sequences", Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2016.

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Historia zmian

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