Kołodziej K, Cholewa M, Głomb P, Koral W, Romaszewski M.  2024.  Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials. arXiv preprint arXiv:2410.02772.

Cholewa M, Romaszewski M, Głomb P, Kołodziej K, Gorawski M, Koral J, Koral W, Madej A, Musioł K.  2024.  ‘Just One More Sensor is Enough’ – Iterative Water Leak Localization with Physical Simulation and a Small Number of Pressure Sensors. IEEE Sensors Journal.

Romaszewski M, Sekuła P, Głomb P, Cholewa M, Kołodziej K.  2024.  Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models. arXiv preprint arXiv:2406.04926.

Głomb P, Cholewa M, Foszner P, Bularz J.  2023.  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.

Głomb P, Cholewa M, Koral W, Madej A, Romaszewski M.  2023.  Detection of emergent leaks using machine learning approaches. Water Supply. 23(6)

Książek K, Głomb P, Romaszewski M, Cholewa M, Grabowski B, Buza K.  2022.  Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation. 21st International Conference on Image Analysis and Processing. 13231

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

Książek K, Romaszewski M, Głomb P, Grabowski B, Cholewa M.  2020.  Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks. Sensors. 20(Recent Advances in Multi- and Hyperspectral Image Analysis)

Romaszewski M, Głomb P, Sochan A, Cholewa M.  2020.  A Dataset for Evaluating Blood Detection in Hyperspectral Images [Data set].

Głomb P, Domino K, Romaszewski M, Cholewa M.  2019.  Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images. PP-RAI'2019. :p.121.

Cholewa M, Głomb P, Romaszewski M.  2019.  A Spatial-Spectral Disagreement-Based Sample Selection With an Application to Hyperspectral Data Classification. IEEE Geoscience and Remote Sensing Letters. 16:467-471.

Romaszewski M, Głomb P, Cholewa M.  2018.  Adaptive, Hubness-Aware Nearest Neighbour Classifier with Application to Hyperspectral Data. Computer and Information Sciences.

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

Cholewa M, Gawron P, Głomb P, Kurzyk D.  2017.  Quantum hidden Markov models based on transition operation matrices. Quantum Information Processing. 16:101.

Cholewa M, Sochan A.  2016.  Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition.

Głomb P, Cholewa M.  2016.  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.

Gawron P, Cholewa M, Kara K.  2016.  Rewolucja stanu - fantastyczne wprowadzenie do informatyki kwantowej. :128.

Romaszewski M, Głomb P, Cholewa M.  2016.  Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach. ISPRS Journal of Photogrammetry and Remote Sensing. 121:60-76.

Cholewa M, Głomb P.  2016.  Two Stage SVM Classification for Hyperspectral Data. Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM.

Głomb P, Cholewa M.  2015.  Experimental Evaluation of Selected Approaches to Covariance Matrix Regularization. Artificial Intelligence and Soft Computing. 9120:391-401.

Cholewa M, Głomb P, Opozda S, Romaszewski M, Sochan A, Kosiedowski M., Kuśmierek E., Bęben A., Krawiec P., Stroiński A..  2013.  Aplikacje Sieci Świadomej Treści. Inżynieria Internetu przyszłości część 2.

Cholewa M.  2013.  Czołowe funkcje kuliste jako baza do aproksymacji sygnałów o ograniczonym paśmie.

Kosiedowski M., Kuśmierek E., Stroiński A., Cholewa M, Głomb P, Opozda S, Romaszewski M, Sochan A, Bęben A., Krawiec P..  2012.  Aplikacje sieci świadomej treści. Przegląd telekomunikacyjny. :1491–1502.

Cholewa M, Głomb P.  2012.  Estimation of the number of states for gesture recognition with Hidden Markov Models based on the number of critical points in time sequence. Pattern Recognition Letters.

Cholewa M, Głomb P.  2011.  Gesture data modeling and classification based on critical points approximation. Advances in Intelligent and Soft Computing, 2011, Volume 95, Computer Recognition Systems 4,. 95:365–373.