25 citations · 81 across the 17 of their papers we have counts for
18 papers
Agentic Multimodal Models for Environmental Hyperspectral Unmixing
Michał Cholewa, Luca Ciampi, Nicola Messina +2
Hyperspectral unmixing is a key task in remote sensing that aims to decompose mixed pixels in hyperspectral images into their constituent material signatures, or endmembers, and th…
Toward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types
Michał Romaszewski, Kamil Drejer, Katarzyna Kołodziej +7
Sentinel-2 imagery offers open access, global coverage, and frequent revisit times, making it attractive for practical building mapping at scale; however, its native 10m resolution…
Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing
Aygün Varol, Katarzyna Kołodziej, Łukasz Sobczak +5
Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however, cloud deployment introduces la…
From Articles to Canopies: Knowledge-Driven Pseudo-Labelling for Tree Species Classification using LLM Experts
Michał Romaszewski, Dominik Kopeć, Michał Cholewa +6
Hyperspectral tree species classification is challenging due to limited and imbalanced class labels, spectral mixing (overlapping light signatures from multiple species), and ecolo…
Quantum-aware Transformer model for state classification
Przemysław Sekuła, Michał Romaszewski, Przemysław Głomb +2
Entanglement is a fundamental feature of quantum mechanics, playing a crucial role in quantum information processing. However, classifying entangled states, particularly in the mix…
Efficient Numerical Calibration of Water Delivery Network Using Short-Burst Hydrant Trials
Katarzyna Kołodziej, Michał Cholewa, Przemysław Głomb +2
Calibration is a critical process for reducing uncertainty in Water Distribution Network Hydraulic Models (WDN HM). However, features of certain WDNs, such as oversized pipelines,…
`Just One More Sensor is Enough' -- Iterative Water Leak Localization with Physical Simulation and a Small Number of Pressure Sensors
Michał Cholewa, Michał Romaszewski, Przemysław Głomb +6
In this article, we propose an approach to leak localisation in a complex water delivery grid with the use of data from physical simulation (e.g. EPANET software). This task is usu…
Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models
Michał Romaszewski, Przemysław Sekuła, Przemysław Głomb +2
Large Language Models (LLMs) have shown exceptional performance in text processing. Notably, LLMs can synthesize information from large datasets and explain their decisions similar…
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks
Bartosz Grabowski, Przemysław Głomb, Wojciech Masarczyk +4
Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-ai…
Data structure > labels? Unsupervised heuristics for SVM hyperparameter estimation
Michał Cholewa, Michał Romaszewski, Przemysław Głomb
Classification is one of the main areas of pattern recognition research, and within it, Support Vector Machine (SVM) is one of the most popular methods outside of field of deep lea…
Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation
Kamil Książek, Przemysław Głomb, Michał Romaszewski +3
Neural networks, in particular autoencoders, are one of the most promising solutions for unmixing hyperspectral data, i.e. reconstructing the spectra of observed substances (endmem…
A Dataset for Evaluating Blood Detection in Hyperspectral Images
Michał Romaszewski, Przemysław Głomb, Arkadiusz Sochan +1
The sensitivity of imaging spectroscopy to haemoglobin derivatives makes it a promising tool for detecting blood. However, due to complexity and high dimensionality of hyperspectra…
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling
Wojciech Masarczyk, Przemysław Głomb, Bartosz Grabowski +1
Hyperspectral imaging is a rich source of data, allowing for multitude of effective applications. However, such imaging remains challenging because of large data dimension and, typ…
Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images
Przemysław Głomb, Krzysztof Domino, Michał Romaszewski +1
In the small target detection problem a pattern to be located is on the order of magnitude less numerous than other patterns present in the dataset. This applies both to the case o…
Quantum Hidden Markov Models based on Transition Operation Matrices
Michał Cholewa, Piotr Gawron, Przemysław Głomb +1
In this work, we extend the idea of Quantum Markov chains [S. Gudder. Quantum Markov chains. J. Math. Phys., 49(7), 2008] in order to propose Quantum Hidden Markov Models (QHMMs).…
Deciding of HMM parameters based on number of critical points for gesture recognition from motion capture data
Michał Cholewa, Przemysław Głomb
This paper presents a method of choosing number of states of a HMM based on number of critical points of the motion capture data. The choice of Hidden Markov Models(HMM) parameters…
Natural hand gestures for human identification in a Human-Computer Interface
Michał Romaszewski, Przemysław Głomb, Piotr Gawron
The goal of this work is the identification of humans based on motion data in the form of natural hand gestures. In this paper, the identification problem is formulated as classifi…
Eigengestures for natural human computer interface
Piotr Gawron, Przemysław Głomb, Jarosław Adam Miszczak +1
We present the application of Principal Component Analysis for data acquired during the design of a natural gesture interface. We investigate the concept of an eigengesture for mot…