The project is directed toward developing computational algorithms based on advanced digital signal processing techniques and artificial intelligence methods. The research will particularly focus on the computer-aided analysis of non-stationary signals using two-dimensional quadratic time-frequency distributions, where the improvement in time-frequency resolution and robustness to noise will be thoroughly studied. Moreover, adaptive algorithms for non-stationary […]
Laboratory for Information Processing and Pattern Recognition
Method for Automatic Estimation of Instantaneous Frequency and Group Delay in Time–Frequency Distributions with Application in EEG Seizure Signals Analysis
Instantaneous 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 […]
Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach
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 […]
Block-Adaptive Rényi Entropy-Based Denoising for Non-Stationary Signals
This paper approaches the problem of signal denoising in time-variable noise conditions. Non-stationary noise results in variable degradation of the signal’s useful information content over time. In order to maximize the correct recovery of the useful part of the signal, this paper proposes a denoising method that uses a criterion based on amplitude segmentation and […]
INNO2MARE – Strengthening the capacity for excellence of Slovenian and Croatian innovation ecosystems to support the digital and green transitions of maritime regions
European Digital Innovation Hub Adria Croatia
Application of Laser Systems for Detection and Ranging in the Modern Road Transportation and Maritime Sector
The development of light detection and ranging (lidar) technology began in the 1960s, following the invention of the laser, which represents the central component of this system, integrating laser scanning with an inertial measurement unit (IMU) and Global Positioning System (GPS). Lidar technology is spreading to many different areas of application, from those in autonomous […]
Entropy-Based Concentration and Instantaneous Frequency of TFDs from Cohen’s, Affine, and Reassigned Classes
This paper explores three groups of time–frequency distributions: the Cohen’s, affine, and reassigned classes of time–frequency representations (TFRs). This study provides detailed insight into the theory behind the selected TFRs belonging to these classes. Extensive numerical simulations were performed with examples that illustrate the behavior of the analyzed TFR classes in the joint time–frequency domain. […]
Assoc. prof. Jonatan Lerga received the Croatian Academy of Sciences and Arts award
Assoc. Prof. Dr. Sc. Jonatan Lerga received an annual award from the Croatian Academy of Sciences and Arts for the highest scientific achievements in 2021 in the field of technical sciences for the work “Advanced Digital Signal Processing and Machine Learning,” containing 28 scientific papers in renowned international journals (13 of which were published in […]
Dr. Sc. Nikola Lopac successfully defended his doctoral dissertation
Dr. Sc. Nikola Lopac successfully defended his doctoral dissertation “Detection of Gravitational-Wave Signals from Time-Frequency Distributions Using Deep Learning” at the University of Rijeka, Faculty of Engineering on the 11th of March 2022. The doctoral dissertation was defended under the supervision of Assoc. Prof. Dr. Sc. Jonatan Lerga.
