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001-es BibID:BIBFORM128684
035-os BibID:(WoS)001454481100001 (Scopus)105000949044
Első szerző:Gao, Tianyu
Cím:Acceleration of Image Classification and Object Tracking by the Intel Neural Compute Stick 2 with Power Efficiency Evaluation on Raspberry Pi 4B / Tianyu Gao, Jozsef Suto
Dátum:2025
ISSN:1424-8220
Megjegyzések:This work investigates the efficiency and power consumption of using the Intel® (Santa Clara, CA, USA) Neural Compute Stick 2 (NCS2) on the Raspberry Pi 4B platform to accelerate image classification and object tracking. The motivation behind this study is to enable the real-time operation of complex neural networks in embedded systems, potentially reducing the cost of deep learning neural network deployment and expanding industrial applications. This study also supplements the OpenVINO? 2022.3.2 documentation by recording the application of the Raspberry Pi 4B combined with the NCS2inthelatest Europeansoftware repositories. Supported by OpenVINO?2022.3.2 and the Deep SORT algorithm, this study consists of two distinct tests: image recognition and real-time object tracking. A single model is used for image recognition, while two models are deployed for object tracking. These test cases evaluate the performance of the execution hardware by varying the different number of models in different application scenarios and evaluating the impact of NCS2 acceleration under various conditions. The results indicate that, for the specific models used in this experiment, the NCS2 increases image recognition performance by approximately 400% and real-time object tracking by around 1400% to 1200%. The results presented in this work indicate that the NCS2 can achieve more than 50 FPS (frames per second) in image recognition and more than 20 FPS in object tracking. The power efficiency obtained by using the NCS2 can vary from 200% to 400%. These findings highlight the significant performance gains NCS2 offers in constrained hardware environments.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Intel® Neural Compute Stick 2
Raspberry Pi
OpenVINO
object tracking
image recognition
power efficiency
Megjelenés:Sensors. - 25 : 6 (2025), p. 1-16. -
További szerzők:Sütő József (1990-) (programtervező informatikus)
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2.

001-es BibID:BIBFORM126074
035-os BibID:(WOS)001323342700001 (Scopus)85205239003
Első szerző:Pap, Iuliu Alexandru
Cím:eHealth Assistant AI Chatbot Using a Large Language Model to Provide Personalized Answers through Secure Decentralized Communication / Iuliu Alexandru Pap, Stefan Oniga
Dátum:2024
ISSN:1424-8220
Megjegyzések:In this paper, we present the implementation of an artificial intelligence health assistant designed to complement a previously built eHealth data acquisition system for helping both patients and medical staff. The assistant allows users to query medical information in a smarter, more natural way, respecting patient privacy and using secure communications through a chat style interface based on the Matrix decentralized open protocol. Assistant responses are constructed locally by an interchangeable large language model (LLM) that can form rich and complete answers like most humanmedical staff would. Restricted access to patient information and other related resources is provided to the LLM through various methods for it to be able to respond correctly based on specific patient data. The Matrix protocol allows deployments to be run in an open federation; hence, the system can be easily scaled.
Tárgyszavak:Műszaki tudományok Informatikai tudományok magyar nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
eHealth
mHealth
telehealth
telemedicine
remote patient monitoring
Internet of Things
artificial intelligence
large language model
Matrix open communication protocol
Megjelenés:Sensors. - 24 : 18 (2024), p. 1-16. -
További szerzők:Oniga István László (1960-) (villamosmérnök)
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3.

001-es BibID:BIBFORM122678
035-os BibID:(Scopus)85199753784 (WoS)001277272700001
Első szerző:Sütő József (programtervező informatikus)
Cím:Using Data Augmentation to Improve the Generalization Capability of an Object Detector on Remote-Sensed Insect Trap Images / Jozsef Suto
Dátum:2024
ISSN:1424-8220
Megjegyzések:Traditionally, monitoring insect populations involved the use of externally placed sticky paper traps, which were periodically inspected by a human operator. To automate this process, a specialized sensing device and an accurate model for detecting and counting insect pests are essential. Despite considerable progress in insect pest detector models, their practical application is hindered by the shortage of insect trap images. To attenuate the "lack of data" issue, the literature proposes data augmentation. However, our knowledge about data augmentation is still quite limited, especially in the field of insect pest detection. The aim of this experimental study was to investigate the effect of several widely used augmentation techniques and their combinations on remote-sensed trap images with the YOLOv5 (small) object detector model. This study was carried out systematically on two different datasets starting from the single geometric and photometric transformation toward their combinations. Our results show that the model`s mean average precision value (mAP50) could be increased from 0.844 to 0.992 and from 0.421 to 0.727 on the two datasets using the appropriate augmentation methods combination. In addition, this study also points out that the integration of photometric image transformations into the mosaic augmentation can be more efficient than the native combination of augmentation techniques because this approach further improved the model`s mAP50values to 0.999 and 0.756 on the two test sets, respectively.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
automated trap
insect counting
data augmentation
YOLOv5
Megjelenés:Sensors. - 24 : 14 (2024), p. 1-12. -
Pályázati támogatás:MAEO-2023-24 / 183910
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4.

001-es BibID:BIBFORM133856
Első szerző:Talbi, Djamila (mérnök informatikus)
Cím:Higher-Order Markov Model-Based Analysis of Reinforcement Learning in 6G Mobile Retrial Queueing Systems / Djamila Talbi, Zoltan Gal
Dátum:2025
ISSN:1424-8220
Megjegyzések:The dynamic behavior of the retrial queueing system following the incorporation of Deep Q-Network Reinforcement Learning in 6G mobile communication services is examined in this study. The proposed method lies in analyzing the DQN-RL agent's learning convergence by using the first- and second-order Markov chain method. By simulating the temporal evolution of reward sequences as Markov and second-order Markov chains, we can quantify convergence characteristics through mixing time analysis. To capture a wide operational landscape, a thorough simulation framework with 120 independent parameter combinations is created. The obtained results indicate that Markov chain analysis confirms 10 training episodes are more than sufficient for policy convergence, and in some cases, as few as 5 episodes allow the agent to enhance the mobile network performance while maintaining low energy consumption. To assess learning stability and system responsiveness, the mixing time of DQN RL rewards is calculated for every episode and configuration. A deeper understanding of the temporal dependencies in the reward process can be gained by incorporating higher-order Markov models. This paper concentrates on studying the learning convergence using an analysis of the Markov model's spectral gap properties as an indicator. The results provide a rigorous foundation for optimizing 6G queueing strategies under uncertainty by highlighting the sensitivity of DQN convergence to system parameters and retrial dynamics.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
retrial queueing system
deep Q-network reinforcement learning
B5G/6G
Markov chain
higher-order Markov chain
spectral gap
queueing theory
dynamic time warping
Megjelenés:Sensors. - 25 : 23 (2025), p. 1-21. -
További szerzők:Gál Zoltán (1966-) (informatikus, villamosmérnök)
Pályázati támogatás:TKP2021-NKTA-34
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5.

001-es BibID:BIBFORM108683
035-os BibID:(WoS)000941838200001 (Scopus)85148970625
Első szerző:Xie, Yu (Computer Science Engineering)
Cím:Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks / Yu Xie, Stefan Oniga
Dátum:2023
ISSN:1424-8220
Megjegyzések:In brain-computer interface (BCI) systems, motor imagery electroencephalography (MIEEG) signals are commonly used to detect participant intent. Many factors, including low signal-tonoise ratios and few high-quality samples, make MI classification difficult. In order for BCI systems to function, MI-EEG signals must be studied. In pattern recognition and other fields, deep learning approaches have recently been successfully applied. In contrast, few effective deep learning algorithms have been applied to BCI systems, especially MI-based systems. In this paper, we address these problems from two aspects based on the characteristics of EEG signals: first, we proposed a combined time-frequency domain data enhancement method. This method guarantees that the size of the training data is effectively increased while maintaining the intrinsic composition of the data. Second, our design consists of a parallel CNN that takes both raw EEG images and images transformed through continuous wavelet transform (CWT) as inputs. We conducted classification experiments on a public data set to verify the effectiveness of the algorithm. According to experimental results based on the BCI Competition IV Dataset2a, the average classification accuracy is 97.61%. A comparison of the proposed algorithm with other algorithms shows that it performs better in classification. The algorithm can be used to improve the classification performance of MI-based BCIs and BCI systems created for people with disabilities.
Tárgyszavak:Műszaki tudományok Villamosmérnöki tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
motor imagery (MI)
electroencephalogram (EEG)
data augmentation (DA)
convolutional neural network (CNN)
Megjelenés:Sensors. - 23 : 4 (2023), p. 1-16. -
További szerzők:Oniga István László (1960-) (villamosmérnök)
Internet cím:Szerző által megadott URL
DOI
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