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Sütő József

Sütő József
DE IK
Informatikai Rendszerek és Hálózatok Tanszék
Egyetemi docens 2024-

Teljes publikációs lista

Tudóstér társszerzők statisztika szűrhető publikációs lista
Feltöltött közlemény:
36
DEA-ban:
36
OA:
18
Publikációs időszak:
2012-2026
2026
1.
Kusper, G., Barócsi, Z., Csóka, P., Vajda, K., Sütő, J.: Digital-Twin-Oriented Virtual Training Environment for Agricultural Robot Navigation: A Vineyard Rover Case Study.
Sensors. 26 (12), 1-23, (cikkazonosító: 3766), 2026.
doi DEA
Folyóirat-mutatók:
Q1 Analytical Chemistry (2025)
Q2 Atomic and Molecular Physics, and Optics (2025)
Q2 Biochemistry (2025)
Q1 Electrical and Electronic Engineering (2025)
Q2 Information Systems (2025)
Q1 Instrumentation (2025)
Q2 Medicine (miscellaneous) (2025)
2.
Sütő, J.: Inertial sensor-based road condition evaluation in laboratory and real environments.
Sci Rep. [Epub ahead of print], 2026.
doi DEA
Folyóirat-mutatók:
Q1 Multidisciplinary (2025)
2025
3.
Gao, T., Sütő, J.: Acceleration of Image Classification and Object Tracking by the Intel Neural Compute Stick 2 with Power Efficiency Evaluation on Raspberry Pi 4B.
Sensors. 25 (6), 1-16, (cikkazonosító: 1794), 2025.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q1 Analytical Chemistry
Q2 Atomic and Molecular Physics, and Optics
Q2 Biochemistry
Q1 Electrical and Electronic Engineering
Q2 Information Systems
Q1 Instrumentation
Q2 Medicine (miscellaneous)
4.
Sütő, J., Kumar, R. S.: EEG-Based Music Stimuli Classification Using Artificial Neural Network and the OpenBCI CytonDaisy System.
Technologies. 13 (9), 1-12, (cikkazonosító: 426), 2025.
doi DEA WoS
Folyóirat-mutatók:
Q1 Computer Science (miscellaneous)
2024
5.
Sütő, J.: Improving the generalization capability of YOLOv5 on remote sensed insect trap images with data augmentation.
Multimed. Tools Appl. 83 (9), 27921-27934, 2024.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q2 Computer Networks and Communications
Q2 Hardware and Architecture
Q1 Media Technology
Q2 Software
6.
Sütő, J.: Using Data Augmentation to Improve the Generalization Capability of an Object Detector on Remote-Sensed Insect Trap Images.
Sensors. 24 (14), 1-12, (cikkazonosító: 4502), 2024.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q1 Analytical Chemistry
Q2 Atomic and Molecular Physics, and Optics
Q2 Biochemistry
Q2 Electrical and Electronic Engineering
Q2 Information Systems
Q1 Instrumentation
Q2 Medicine (miscellaneous)
2023
7.
Sütő, J.: Hardware and Software Support for Insect Pest Management.
Agriculture-Basel. 13 (9), 1-2, (cikkazonosító: 1818), 2023.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q1 Agronomy and Crop Science
Q2 Food Science
Q2 Plant Science
2022
8.
Sütő, J.: An Improved Image Enhancement Method for Traffic Sign Detection.
Electronics (Switzerland). 11 (6), 1-13, (cikkazonosító: 871), 2022.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q2 Computer Networks and Communications
Q2 Control and Systems Engineering
Q2 Electrical and Electronic Engineering
Q2 Hardware and Architecture
Q2 Signal Processing
9.
Sütő, J.: A Novel Plug-in Board for Remote Insect Monitoring.
Agriculture-Basel. 12 (11), 1-16, (cikkazonosító: 1897), 2022.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q2 Agronomy and Crop Science
Q2 Food Science
Q2 Plant Science
10.
Sütő, J.: Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review.
Agriculture-Basel. 12 (10), 1721-, 2022.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q2 Agronomy and Crop Science
Q2 Food Science
Q2 Plant Science
2021
11.
Sütő, J.: Embedded System-Based Sticky Paper Trap with Deep Learning-Based Insect-Counting Algorithm.
Electronics (Switzerland). 10 (15), 1-13, (cikkazonosító: 1754), 2021.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q2 Computer Networks and Communications
Q2 Control and Systems Engineering
Q2 Electrical and Electronic Engineering
Q2 Hardware and Architecture
Q2 Signal Processing
12.
Sütő, J.: Real-Time Lane Line Tracking Algorithm to Mini Vehicles.
Transp. Telecommun. 22 (4), 461-470, 2021.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q4 Computer Science Applications
Q3 Engineering (miscellaneous)
13.
Sütő, J.: The effect of hyperparameter search on artificial neural network in human activity recognition.
Open Comp. Sci. 11 (1), 411-422, 2021.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q2 Computer Science (miscellaneous)
2020
14.
Sütő, J., Oniga, I. L., Lung, C., Orha, I.: Comparison of offline and real-time human activity recognition results using machine learning techniques.
Neural Comput. Appl. 32 (20), 15673-15686, 2020.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q2 Artificial Intelligence
Q1 Software
15.
Sütő, J., Jámbor, Z.: Music Stimuli Recognition from EEG Signals with EMOTIV EPOC Headset.
In: Proceedings of the 11th International Conference on Applied Informatics (ICAI 2020). Ed.: by Gergely Kovásznai, István Fazekas, Tibor Tómács, CEUR, Eger, 309-317, 2020, (CEUR Worksh o p Proceedings, ISSN 1613-0073 ; 2650.)
DEA Scopus
16.
Sütő, J.: Plant leaf recognition with shallow and deep learning: a comprehensive study.
Intell. Data Anal. 24 (6), 1311-1328, 2020.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q4 Artificial Intelligence
Q3 Computer Vision and Pattern Recognition
Q4 Theoretical Computer Science
2019
17.
Sütő, J., Oniga, I. L.: Efficiency investigation from shallow to deep neural network techniques in human activity recognition.
Cogn. Syst. Res. 54, 37-49, 2019.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q3 Artificial Intelligence
Q3 Cognitive Neuroscience
Q3 Experimental and Cognitive Psychology
2018
18.
Sütő, J., Oniga, I. L.: Efficiency investigation of artificial neural networks in human activity recognition.
J. Ambient Intell. Humaniz. Comput. 9 (4), 1049-1060, 2018.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q2 Computer Science (miscellaneous)
19.
Sütő, J., Oniga, I. L., Pop Sitar, P.: Music stimuli recognition from electroencephalogram signal with machine learning.
In: 2018 7th International Conference on Computers Communications and Control (ICCCC) : proceedings ISBN: 9781538619346
doi DEA
20.
Sütő, J., Oniga, I. L.: Music stimuli recognition in electroencephalogram signal.
Elektron. Elektrotech. 24 (4), 68-71, 2018.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q3 Electrical and Electronic Engineering
2017
21.
Sütő, J., Oniga, I. L., Pop-Sitar, P.: Feature analysis to human activity recognition.
Int. J. Comput. Commun. Control. 12 (1), 116-130, 2017.
DEA WoS Scopus
Folyóirat-mutatók:
Q3 Computational Theory and Mathematics
Q2 Computer Networks and Communications
Q3 Computer Science Applications
22.
Sütő, J., Oniga, I. L., Lung, C., Orha, I.: Recognition rate difference between real-time and offline human activity recognition.
In: Internet of Things for the Global Community : Proceedings : July 10-12, 2017, Madeira-Portugal / Fernando Morgado-Dias, IEEE, Danvers, 13-18, 2017. ISBN: 9781538620649
doi DEA
2016
23.
Oniga, I. L., Sütő, J.: Activity recognition in adaptive assistive systems using artificial neural networks.
Elektron. Elektrotech. 22 (1), 68-72, 2016.
doi DEA WoS Scopus
Folyóirat-mutatók:
Q2 Electrical and Electronic Engineering
24.
Sütő, J., Oniga, I. L., Pop Sitar, P.: Comparison of wrapper and filter feature selection algorithms on human activity recognition.
In: 2016 6th International Conference on Computers Communications & Control (ICCCC) : This edition of conference is dedicated to the 110th anniversary of Grigore C. Moisil (1906-1973) Romanian Mathematician, Computer Pioneer Award of IEEE Computer Society (1996 - posthumously). Eds.: I. Dzitac, F.G. Filip, M.J. Manolescu, IEEE, Piscataway, 124-129, 2016. ISBN: 9781509017355
doi DEA Scopus
2015
25.
Sütő, J., Oniga, I. L.: A new relation between "Twiddle Factors" in the fast Fourier transformation.
Elektron. Elektrotech. 21 (4), 56-59, 2015.
doi DEA
Folyóirat-mutatók:
Q2 Electrical and Electronic Engineering
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