Zobrazeno 1 - 10
of 194
pro vyhledávání: '"P. Tachtatzis"'
Autor:
Czerkawski, Mikolaj, Stewart, Fraser, Ilioudis, Christos, Michie, Craig, Andonovic, Ivan, Atkinson, Robert, Coull, Maurice, Sandilands, Donald, Kerr, Gareth, Clemente, Carmine, Tachtatzis, Christos
The monitoring of diver health during emergency events is crucial to ensuring the safety of personnel. A non-invasive system continuously providing a measure of the respiration rate of individual divers is exceedingly beneficial in this context. The
Externí odkaz:
http://arxiv.org/abs/2404.09598
Autor:
Czerkawski, Mikolaj, Ilioudis, Christos, Clemente, Carmine, Michie, Craig, Andonovic, Ivan, Tachtatzis, Christos
The paper centres on an assessment of the modelling approaches for the processing of signals in CW and FMCW radar-based systems for the detection of vital signs. It is shown that the use of the widely adopted phase extraction method, which relies on
Externí odkaz:
http://arxiv.org/abs/2404.09590
Autor:
Czerkawski, Mikolaj, Ilioudis, Christos, Clemente, Carmine, Michie, Craig, Andonovic, Ivan, Tachtatzis, Christos
Deep learning techniques are subject to increasing adoption for a wide range of micro-Doppler applications, where predictions need to be made based on time-frequency signal representations. Most, if not all, of the reported applications focus on tran
Externí odkaz:
http://arxiv.org/abs/2404.15346
Autor:
Czerkawski, Mikolaj, Ilioudis, Christos, Clemente, Carmine, Michie, Craig, Andonovic, Ivan, Tachtatzis, Christos
The treatment of interfering motion contributions remains one of the key challenges in the domain of radar-based vital sign monitoring. Removal of the interference to extract the vital sign contributions is demanding due to overlapping Doppler bands,
Externí odkaz:
http://arxiv.org/abs/2404.08298
Convolutional neural networks have often been proposed for processing radar Micro-Doppler signatures, most commonly with the goal of classifying the signals. The majority of works tend to disregard phase information from the complex time-frequency re
Externí odkaz:
http://arxiv.org/abs/2404.08291
Publikováno v:
International Radar Symposium 2022
With the great capabilities of deep classifiers for radar data processing come the risks of learning dataset-specific features that do not generalize well. In this work, the robustness of two deep convolutional architectures, trained and tested on th
Externí odkaz:
http://arxiv.org/abs/2402.13651
Autor:
Sommer, Klaus-Dieter, Harris, Peter, Eichstädt, Sascha, Füssl, Roland, Dorst, Tanja, Schütze, Andreas, Heizmann, Michael, Schiering, Nadine, Maier, Andreas, Luo, Yuhui, Tachtatzis, Christos, Andonovic, Ivan, Gourlay, Gordon
Mathematical modelling is at the core of metrology as it transforms raw measured data into useful measurement results. A model captures the relationship between the measurand and all relevant quantities on which the measurand depends, and is used to
Externí odkaz:
http://arxiv.org/abs/2312.13744
The letter investigates the utility of text-to-image inpainting models for satellite image data. Two technical challenges of injecting structural guiding signals into the generative process as well as translating the inpainted RGB pixels to a wider s
Externí odkaz:
http://arxiv.org/abs/2311.03008
Publikováno v:
IGARSS 2023
This work explores capabilities of the pre-trained CLIP vision-language model to identify satellite images affected by clouds. Several approaches to using the model to perform cloud presence detection are proposed and evaluated, including a purely ze
Externí odkaz:
http://arxiv.org/abs/2308.00541
Autor:
Czerkawski, Mikolaj, Cardona, Javier, Atkinson, Robert, Michie, Craig, Andonovic, Ivan, Clemente, Carmine, Tachtatzis, Christos
Publikováno v:
NeurIPS 2021 workshop in pre-registration
A variety of compression methods based on encoding images as weights of a neural network have been recently proposed. Yet, the potential of similar approaches for video compression remains unexplored. In this work, we suggest a set of experiments for
Externí odkaz:
http://arxiv.org/abs/2112.01504