dr inż. Michał Romaszewski

Email: 

Orcid ID: 

0000-0002-8227-929X

Stanowisko: 

adiunkt
Zespół badawczy

Pokój: 

1

Telefon: 

+48 32 2317319 w.201

Zainteresowania: 

Przetwarzanie obrazów i szeregów czasowych, uczenie maszynowe, duże modele językowe.

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Publikacje
Biblio RSS

2024

1. Romaszewski, M., and P. Sekuła, "Poster: Explainable classification of multimodal time series using LLMs", 5th Polish Conference on Artificial Intelligence (PP-RAI 2024), Warsaw, Poland, 04/2024.
2. Kołodziej, K., M. Cholewa, M. Romaszewski, P. Głomb, W. Koral, and P. Sekuła, "Calibration of Water Demand Network Through a Machine Learned Digital Twin Model", 5th Polish Conference on Artificial Intelligence (PP-RAI 2024), 2024.
3. Kołodziej, K., M. Cholewa, P. Głomb, W. Koral, and M. Romaszewski, "Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials", arXiv preprint arXiv:2410.02772, 09/2024.
4. Cholewa, M., M. Romaszewski, P. Głomb, K. Kołodziej, M. Gorawski, J. Koral, W. Koral, A. Madej, and K. Musioł, "‘Just One More Sensor is Enough’ – Iterative Water Leak Localization with Physical Simulation and a Small Number of Pressure Sensors", IEEE Sensors Journal, 06/2024.
5. Romaszewski, M., P. Sekuła, P. Głomb, M. Cholewa, and K. Kołodziej, "Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models", arXiv preprint arXiv:2406.04926, 06/2024.
6. Książek, K., "Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data", Computers in Biology and Medicine, vol. 176, 05/2024.

2023

7. 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.
8. 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.
9. 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.
10. 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

11. 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.

2021

12. Pałka, F., W. Książek, P. Plawiak, M. Romaszewski, and K. Książek, "Hyperspectral Classification of Blood-Like Substances Using Machine Learning Methods Combined with Genetic Algorithms in Transductive and Inductive Scenarios", Sensors, vol. 21, 03/2021.
13. 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

14. Domino, M., M. Romaszewski, T. Jasiński, and M. Maśko, Comparison of surface thermal patterns of horses and donkeys in IRT images [Data set], 2020.
15. Romaszewski, M., P. Głomb, A. Sochan, and M. Cholewa, A Dataset for Evaluating Blood Detection in Hyperspectral Images [Data set], 2020.
16. Domino, M., M. Romaszewski, T. Jasiński, and M. Maśko, "Comparison of the Surface Thermal Patterns of Horses and Donkeys in Infrared Thermography Images", Animals, vol. 10, issue 12, pp. 2201, 11/2020.
17. 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.

2019

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.

2018

22. Romaszewski, M., A. Sochan, and K. Skabek, "Matrix and Tensor-Based Approximation of 3D Face Animations from Low-Cost Range Sensors", Computer and Information Sciences, Cham, Springer International Publishing, 2018.
23. 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.
24. 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.

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.

Strony

Historia zmian

Data aktualizacji: 05/07/2024 - 09:03; autor zmian: Michał Romaszewski (michal@iitis.pl)