Dr. Vedran Jurdana
Department of Automation and Electronics, University of Rijeka, Faculty of Engineering, Rijeka, Primorsko-Goranska Županija, Croatia
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Academic Career Length
9Years (2018-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications28 March 2024 in IEEE Access
Journal Article
TDTamas Dozsa
Tamas DozsaVedran Jurdana
Sandi Baressi ŠegotaJVJános Volk
János VolkJRJanos Rado
Janos RadoPKPéter Kovács
Péter Kovács +1
Smart tire technologies offer a novel sensing methodology for vehicle environment perception by providing direct measurements of tire dynamics parameters. This information can be utilized in advanced driver assistance systems as well as autonomous vehicle control to enhance vehicle performance and safety. Considering these criteria, we develop algorithms for categorizing road types based on tire sensor signals. Road differentiation is a complex task due to the non-linear and non-stationary nature of the measured tire signals. To address this challenge, we fuse time-frequency distributions with machine learning approaches. Maintaining the robustness of the predictions, we integrate our own measurement system into a Nissan Leaf test vehicle and collect data involving diverse environmental factors and operational conditions, mimicking real-world scenarios. We showed that by experiments our predictions strongly correlate with road quality, which can be utilized in automatic vehicle control, such as intelligent speed adaptation.
1 November 2021 in Digital Signal Processing
Journal Article
Journal Article
Compressive sensing (CS) of the signal ambiguity function (AF) and enforcing the sparsity constraint on the resulting signal time-frequency distribution (TFD) has been shown to be an efficient method for time-frequency signal processing. This paper proposes a method for adaptive CS-AF area selection, which extracts the magnitude-significant AF samples through a clustering approach using the density-based spatial clustering algorithm. Moreover, an appropriate criterion for the performance of the method is formalized, i.e., component concentration and preservation, as well as interference suppression, are measured utilizing the information obtained from the short-term and the narrow-band Rényi entropies, while component connectivity is evaluated using the number of regions with continuously-connected samples. The CS-AF area selection and reconstruction algorithm parameters are optimized using an automatic multi-objective meta-heuristic optimization method, minimizing the here-proposed combination of measures as objective functions. Consistent improvement in CS-AF area selection and TFD reconstruction performance has been achieved without requiring a priori knowledge of the input signal for multiple reconstruction algorithms. This was demonstrated for both noisy synthetic and real-life signals.
Journal Article
Vedran JurdanaMVMiroslav Vrankic
Miroslav Vrankic
Nikola LopacGJGuruprasad Madhale Jadav
Guruprasad Madhale JadavInstantaneous frequency (IF) is commonly used in the analysis of electroencephalogram (EEG) signals to detect oscillatory-type seizures. However, IF cannot be used to analyze seizures that appear as spikes. In this paper, we present a novel method for the automatic estimation of IF and group delay (GD) in order to detect seizures with both spike and oscillatory characteristics. Unlike previous methods that use IF alone, the proposed method utilizes information obtained from localized Rényi entropies (LREs) to generate a binary map that automatically identifies regions requiring a different estimation strategy. The method combines IF estimation algorithms for multicomponent signals with time and frequency support information to improve signal ridge estimation in the time–frequency distribution (TFD). Our experimental results indicate the superiority of the proposed combined IF and GD estimation approach over the IF estimation alone, without requiring any prior knowledge about the input signal. The LRE-based mean squared error and mean absolute error metrics showed improvements of up to 95.70% and 86.79%, respectively, for synthetic signals and up to 46.45% and 36.61% for real-life EEG seizure signals.
1 July 2020
Conference Paper
Observing a non-stationary signal with the time and frequency representation being mutually exclusive often does not provide enough information. Thus, the joint time-frequency distribution (TFD) is used as a convenient and powerful tool for analysis of such signals. Although TFD overcomes many signal representation limitations, it also introduces additional challenges. The removal of artefacts, also called the cross-terms, while maintaining a high concentration of the signal components (auto-terms) is the main problem of the time-frequency (TF) signal analysis. Among different approaches of solving this problem, in this paper we are investigating the advantages of the TFD sparsity, that is, the fact that the energy is accumulated around the instantaneous frequency law of the signal components. In this paper, we present a sparse TFD reconstruction algorithm based on the iterative shrinkage algorithm. The shrinkage is performed independently for each TFD time-and frequency-slice by taking advantage obtained from the short-term and the narrow-band Rényi entropy. Using the TFD concentration measure and reconstruction algorithm execution time, the obtained results have been compared to the state-of-the-art sparse reconstruction algorithms.
About Me
Dr. Vedran Jurdana received his B.Sc., M.Sc. and Ph.D.
degrees in electrical engineering from the Faculty of Engineering, University
of Rijeka, Rijeka, Croatia, in 2015, 2017 and 2023, respectively. From 2017 to
2023, he was a Research and Teaching Assistant with the Department of
Automation and Electronics, Faculty of Engineering, University of Rijeka, where
he has been a Postdoctoral Researcher, since 2023. His master’s thesis was conducted in collaboration with the Institute for Electrical Drives and Power Electronics at Johannes Kepler University Linz, Austria. He has actively participated in several scientific projects and collaborated with research groups at national and international academic institutions, including the Croatian Science Foundation. He has authored a number of scientific articles in international peer-reviewed journals indexed in the Current Contents (CC) database and scientific papers in conference proceedings. Additionally, he has served as a reviewer for several international journals and as a Guest Editor in his research field. He has expanded his research experience by spending research visits at prestigious institutions in Austria, Slovenia, Hungary, and Finland. He received cum laude honors for his M.Sc. degree. His research interests
include time-frequency signal analysis, statistical signal analysis and
processing, deep learning, assistive technology, image processing, compressive sensing and information theory.