TY - JOUR AU - Terbe, Dániel AU - Orzó, László AU - Bicsák, Barbara AU - Zarándy, Ákos TI - Hologram Noise Model for Data Augmentation and Deep Learning JF - SENSORS J2 - SENSORS-BASEL VL - 24 PY - 2024 IS - 3 PG - 14 SN - 1424-8220 DO - 10.3390/s24030948 UR - https://m2.mtmt.hu/api/publication/34551736 ID - 34551736 LA - English DB - MTMT ER - TY - JOUR AU - Jánoki, Imre Gergely AU - Nagy, Ádám AU - Földesy, Péter AU - Zarándy, Ákos AU - Siket, Máté AU - Varga, Judit AU - Szabó, Miklós TI - Neonatal Activity Monitoring by Camera-Based Multi-LSTM Network JF - ENGINEERING PROCEEDINGS J2 - ENGENG PROC VL - 55 PY - 2023 IS - 1 PG - 7 SN - 2673-4591 DO - 10.3390/engproc2023055016 UR - https://m2.mtmt.hu/api/publication/34426868 ID - 34426868 LA - English DB - MTMT ER - TY - JOUR AU - Bauer, Péter AU - Hiba, Antal AU - Nagy, Mihály AU - Simonyi, Ernő AU - Kuna, Gergely István AU - Kisari, Ádám AU - Drotár, István AU - Zarándy, Ákos TI - Encounter Risk Evaluation with a Forerunner UAV JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 15 PY - 2023 IS - 6 PG - 45 SN - 2072-4292 DO - 10.3390/rs15061512 UR - https://m2.mtmt.hu/api/publication/33698328 ID - 33698328 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Orzó, László AU - Kiss, Márton AU - Terbe, Dániel AU - Zarándy, Ákos TI - Fast High Resolution In-line Phase Retrieval of Sparse Off-Axis Holograms JF - OPTICS INFOBASE CONFERENCE PAPERS J2 - OPTICS INFOBASE CONF PAP VL - 2022 PY - 2022 PG - 2 SN - 2162-2701 DO - 10.1364/DH.2022.Tu4A.6 UR - https://m2.mtmt.hu/api/publication/33589603 ID - 33589603 N1 - Conference code: 183142 Export Date: 24 January 2023 Correspondence Address: Orzó, L.; Institute for Computer Science and Control, Kende street. 13-17, Hungary; email: orzo@sztaki.hu LA - English DB - MTMT ER - TY - JOUR AU - Terbe, Dániel AU - Orzó, László AU - Zarándy, Ákos TI - Classification of Holograms with 3D-CNN JF - SENSORS J2 - SENSORS-BASEL VL - 22 PY - 2022 IS - 21 PG - 9 SN - 1424-8220 DO - 10.3390/s22218366 UR - https://m2.mtmt.hu/api/publication/33203271 ID - 33203271 N1 - Export Date: 10 February 2023 Correspondence Address: Zarándy, Á.; Institute for Computer Science and ControlHungary; email: zarandy@sztaki.hu AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Eigner, György AU - Siket, Máté AU - Czakó, Bence Géza AU - Drexler, Dániel András AU - Rudas, Imre AU - Zarándy, Ákos AU - Kovács, Levente TI - Model Predictive Tumour Volume Control using Nonlinear Optimization JF - STUDIES IN SYSTEMS DECISION AND CONTROL J2 - STUD SYST DECISION CONTROL VL - 415 PY - 2022 SP - 235 EP - 250 PG - 16 SN - 2198-4182 DO - 10.1007/978-3-031-00978-5_10 UR - https://m2.mtmt.hu/api/publication/33079908 ID - 33079908 LA - English DB - MTMT ER - TY - JOUR AU - Földesy, Péter AU - Jánoki, Imre Gergely AU - Nagy, Ádám AU - Siket, Máté AU - Zarándy, Ákos TI - Temperature measurement with photodiodes: Application to laser diode temperature monitoring JF - SENSORS AND ACTUATORS A-PHYSICAL J2 - SENSOR ACTUAT A PHYS VL - 337 PY - 2022 PG - 7 SN - 0924-4247 DO - 10.1016/j.sna.2022.113441 UR - https://m2.mtmt.hu/api/publication/32736182 ID - 32736182 N1 - Institute for Computer Science and Control, Budapest, 1111, Hungary Faculty of Information and Bionics, Pázmány Péter Catholic University, Budapest, 1083, Hungary Physiological Controls Research Center, Óbuda University, Budapest, 1034, Hungary Export Date: 29 June 2022 CODEN: SAAPE Correspondence Address: Földesy, P.; Institute for Computer Science and ControlHungary; email: foldesy.peter@sztaki.hu LA - English DB - MTMT ER - TY - JOUR AU - Terbe, Dániel AU - Orzó, László AU - Zarándy, Ákos TI - Deep-learning-based bright-field image generation from a single hologram using an unpaired dataset JF - OPTICS LETTERS J2 - OPT LETT VL - 46 PY - 2021 IS - 22 SP - 5567 EP - 5570 PG - 4 SN - 0146-9592 DO - 10.1364/OL.440900 UR - https://m2.mtmt.hu/api/publication/32478142 ID - 32478142 LA - English DB - MTMT ER - TY - JOUR AU - Nagy, Ádám AU - Földesy, Péter AU - Jánoki, Imre Gergely AU - Terbe, Dániel AU - Siket, Máté AU - Szabó, Miklós AU - Varga, J AU - Zarándy, Ákos TI - Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU JF - APPLIED SCIENCES-BASEL J2 - APPL SCI-BASEL VL - 11 PY - 2021 IS - 16 PG - 24 SN - 2076-3417 DO - 10.3390/app11167215 UR - https://m2.mtmt.hu/api/publication/32124307 ID - 32124307 LA - English DB - MTMT ER - TY - JOUR AU - Földesy, Péter AU - Zarándy, Ákos AU - Szabó, Miklós TI - Reference free incremental deep learning model applied for camera-based respiration monitoring JF - IEEE SENSORS JOURNAL J2 - IEEE SENS J VL - 21 PY - 2021 IS - 2 SP - 2346 EP - 2352 PG - 7 SN - 1530-437X DO - 10.1109/JSEN.2020.3021337 UR - https://m2.mtmt.hu/api/publication/31545511 ID - 31545511 LA - English DB - MTMT ER -