@article{MTMT:34551736,
	title = {Hologram Noise Model for Data Augmentation and Deep Learning},
	url = {https://m2.mtmt.hu/api/publication/34551736},
	author = {Terbe, Dániel and Orzó, László and Bicsák, Barbara and Zarándy, Ákos},
	doi = {10.3390/s24030948},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {24},
	unique-id = {34551736},
	year = {2024},
	eissn = {1424-8220}
}

@article{MTMT:34426868,
	title = {Neonatal Activity Monitoring by Camera-Based Multi-LSTM Network},
	url = {https://m2.mtmt.hu/api/publication/34426868},
	author = {Jánoki, Imre Gergely and Nagy, Ádám and Földesy, Péter and Zarándy, Ákos and Siket, Máté and Varga, Judit and Szabó, Miklós},
	doi = {10.3390/engproc2023055016},
	journal-iso = {ENGENG PROC},
	journal = {ENGINEERING PROCEEDINGS},
	volume = {55},
	unique-id = {34426868},
	year = {2023},
	eissn = {2673-4591},
	orcid-numbers = {Földesy, Péter/0000-0001-7495-0971; Szabó, Miklós/0000-0003-3317-5619}
}

@article{MTMT:34399999,
	title = {Stability focused evaluation and tuning of special ground vehicle tracking algorithms},
	url = {https://m2.mtmt.hu/api/publication/34399999},
	author = {Bauer, Péter and Nagy, Mihály and Kuna, Gergely István and Kisari, A and Simonyi, Ernő and Hiba, Antal and Drotar, I},
	doi = {10.1016/j.ifacol.2023.10.212},
	journal-iso = {IFACOL},
	journal = {IFAC PAPERSONLINE},
	volume = {56},
	unique-id = {34399999},
	issn = {2405-8971},
	year = {2023},
	eissn = {2405-8963},
	pages = {9282-9287},
	orcid-numbers = {Bauer, Péter/0000-0002-1925-2270}
}

@inproceedings{MTMT:34107680,
	title = {Indoor vehicle-in-the-loop simulation of unmanned micro aerial vehicle with artificial companion},
	url = {https://m2.mtmt.hu/api/publication/34107680},
	author = {Hiba, Antal and Körtvélyesi, Viktor and Kiskároly, Albert and Bhoite, O and David, P and Majdik, András},
	booktitle = {2023 International Conference on Unmanned Aircraft Systems (ICUAS)},
	doi = {10.1109/ICUAS57906.2023.10156429},
	unique-id = {34107680},
	year = {2023},
	pages = {137-143},
	orcid-numbers = {Majdik, András/0000-0003-1807-2865}
}

@article{MTMT:33854267,
	title = {Sample-in-the-Loop Laser Speckle Contrast Imaging Based on Optimization},
	url = {https://m2.mtmt.hu/api/publication/33854267},
	author = {Siket, Máté and Jánoki, Imre Gergely and Nagy, Ádám and Földesy, Péter},
	doi = {10.12700/APH.20.2.2023.20.2},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {20},
	unique-id = {33854267},
	issn = {1785-8860},
	year = {2023},
	eissn = {1785-8860},
	pages = {25-39},
	orcid-numbers = {Földesy, Péter/0000-0001-7495-0971}
}

@article{MTMT:33698328,
	title = {Encounter Risk Evaluation with a Forerunner UAV},
	url = {https://m2.mtmt.hu/api/publication/33698328},
	author = {Bauer, Péter and Hiba, Antal and Nagy, Mihály and Simonyi, Ernő and Kuna, Gergely István and Kisari, Ádám and Drotár, István and Zarándy, Ákos},
	doi = {10.3390/rs15061512},
	journal-iso = {REMOTE SENS-BASEL},
	journal = {REMOTE SENSING},
	volume = {15},
	unique-id = {33698328},
	abstract = {Forerunner UAV refers to an unmanned aerial vehicle equipped with a downward-looking camera flying in front of the advancing emergency ground vehicles (EGV) to notify the driver about the hidden dangers (e.g., other vehicles). A feasibility demonstration in an urban environment having a multicopter as the forerunner UAV and two cars as the emergency and dangerous ground vehicles was done in ZalaZONE Proving Ground, Hungary. After the description of system hardware and software components, test scenarios, object detection and tracking, the main contribution of the paper is the development and evaluation of encounter risk decision methods. First, the basic collision risk evaluation applied in the demonstration is summarized, then the detailed development of an improved method is presented. It starts with the comparison of different velocity and acceleration estimation methods. Then, vehicle motion prediction is conducted, considering estimated data and its uncertainty. The prediction time horizon is determined based on actual EGV speed and so braking time. If the predicted trajectories intersect, then the EGV driver is notified about the danger. Some special relations between EGV and the other vehicle are also handled. Tuning and comparison of basic and improved methods is done based on real data from the demonstration. The improved method can notify the driver longer, identify special relations between the vehicles and it is adaptive considering actual EGV speed and EGV braking characteristics; therefore, it is selected for future application.},
	year = {2023},
	eissn = {2072-4292},
	orcid-numbers = {Bauer, Péter/0000-0002-1925-2270}
}

@inproceedings{MTMT:33589068,
	title = {An application programming interface for the widely used academic version of the UVA/Padova Type 1 Diabetes Mellitus Metabolic Simulator},
	url = {https://m2.mtmt.hu/api/publication/33589068},
	author = {Siket, Máté and Tóth, R and Szász, László and Novák, Kamilla and Eigner, György and Kovács, Levente},
	booktitle = {IEEE 21st World Symposium on Applied Machine Intelligence and Informatics SAMI (2023) : Proceedings},
	doi = {10.1109/SAMI58000.2023.10044485},
	unique-id = {33589068},
	year = {2023},
	pages = {287-292},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@article{MTMT:33589603,
	title = {Fast High Resolution In-line Phase Retrieval of Sparse Off-Axis Holograms},
	url = {https://m2.mtmt.hu/api/publication/33589603},
	author = {Orzó, László and Kiss, Márton and Terbe, Dániel and Zarándy, Ákos},
	doi = {10.1364/DH.2022.Tu4A.6},
	journal-iso = {OPTICS INFOBASE CONF PAP},
	journal = {OPTICS INFOBASE CONFERENCE PAPERS},
	volume = {2022},
	unique-id = {33589603},
	issn = {2162-2701},
	year = {2022}
}

@article{MTMT:33210448,
	title = {Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals},
	url = {https://m2.mtmt.hu/api/publication/33210448},
	author = {Dénes-Fazakas, Lehel and Siket, Máté and Szilágyi, László and Kovács, Levente and Eigner, György},
	doi = {10.3390/s22218568},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {2022},
	unique-id = {33210448},
	year = {2022},
	eissn = {1424-8220},
	orcid-numbers = {Dénes-Fazakas, Lehel/0009-0003-9879-5664; Kovács, Levente/0000-0002-3188-0800}
}

@article{MTMT:33203271,
	title = {Classification of Holograms with 3D-CNN},
	url = {https://m2.mtmt.hu/api/publication/33203271},
	author = {Terbe, Dániel and Orzó, László and Zarándy, Ákos},
	doi = {10.3390/s22218366},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {22},
	unique-id = {33203271},
	abstract = {A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed in different depths by using standard techniques of digital holography. We claim that a 2D convolutional network (CNN) cannot be efficient in decoding this volumetric information spread across the whole image as it inherently operates on local spatial features. Therefore, we propose a method, where we extract the volumetric information of the hologram by mapping it to a volume—using a standard wavefield propagation algorithm—and then feed it to a 3D-CNN-based architecture. We apply this method to a challenging real-life classification problem and compare its performance with an equivalent 2D-CNN counterpart. Furthermore, we inspect the robustness of the methods to slightly defocused inputs and find that the 3D method is inherently more robust in such cases. Additionally, we introduce a hologram-specific augmentation technique, called hologram defocus augmentation, that improves the performance of both methods for slightly defocused inputs. The proposed 3D-model outperforms the standard 2D method in classification accuracy both for in-focus and defocused input samples. Our results confirm and support our fundamental hypothesis that a 2D-CNN-based architecture is limited in the extraction of volumetric information globally encoded in the reconstructed hologram image.},
	year = {2022},
	eissn = {1424-8220}
}