The real-life signals captured by different measurement systems (such as modern maritime transport characterized by challenging and varying operating conditions) are often subject to various types of noise and other external factors in the data collection and transmission processes. Therefore, the filtering algorithms are required to reduce the noise level in measured signals, thus enabling […]
Laboratory for Information Processing and Pattern Recognition
RANSAC-Based Signal Denoising Using Compressive Sensing
In this paper, we present an approach to the reconstruction of signals exhibiting sparsity in a transformation domain, having some heavily disturbed samples. This sparsity-driven signal recovery exploits a carefully suited random sampling consensus (RANSAC) methodology for the selection of a subset of inlier samples. To this aim, two fundamental properties are used: A signal […]
Rule-Based EEG Classifier Utilizing Local Entropy of Time–Frequency Distributions
Electroencephalogram (EEG) signals are known to contain signatures of stimuli that induce brain activities. However, detecting these signatures to classify captured EEG waveforms is one of the most challenging tasks of EEG analysis. This paper proposes a novel time–frequency-based method for EEG analysis and characterization implemented in a computer-aided decision-support system that can be used […]
Talk on conference “Exploring Digital Legal Landscapes”
Jonatan Lerga held a talk at the conference “Exploring Digital Legal Landscapes” on 11th of December, 2020 on “Labor Market Trends Leading to Establishing the Center for AI and Cybersecurity in Rijeka”.
Gravitational-Wave Burst Signals Denoising Based on the Adaptive Modification of Intersection of Confidence Intervals Rule
Gravitational-wave data (discovered first in 2015 by the Advanced LIGO interferometers and awarded by the Nobel prize in 2017) is characterized by non-Gaussian and non- stationary noise. An ever- increasing amount of the acquired data requires the development of efficient denoising algorithms that will enable the detection of gravitational- wave events embedded in low signal-to-noise-ratio […]
Estimating River Discharges in Highly Stratified Estuaries
Estuaries are transitional areas between river and marine environments. Understanding the estuarine dynamics is important for various water management issues, such as predicting floods and droughts, assessing impacts of sea level rise, planning freshwater intake for irrigation, and managing sediment transport. One of the key requirements for understanding and predicting the estuarine dynamics is to […]
Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification
Scene classification relying on images is essential in many systems and applications related to remote sensing. The scientific interest in scene classification from remotely collected images is increasing, and many datasets and algorithms are being developed. The introduction of convolutional neural networks (CNN) and other deep learning techniques contributed to vast improvements in the accuracy […]
Comparison of Entropy and Dictionary Based Text Compression in English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian
The rapid growth in the amount of data in the digital world leads to the need for data compression, and so forth, reducing the number of bits needed to represent a text file, an image, audio, or video content. Compressing data saves storage capacity and speeds up data transmission. In this paper, we focus on […]
National Competence Centres in the Framework of EuroHPC (EUROCC)
H2020-JTI-EuroHPC-2019-2
A Novel Approach to Extracting Useful Information From Noisy TFDs Using 2D Local Entropy Measures
The paper proposes a novel approach for extraction of useful information and blind source separation of signal components from noisy data in the time-frequency domain. The method is based on the local Rényi entropy calculated inside adaptive, data-driven 2D regions, the sizes of which are calculated utilizing the improved, relative intersection of confidence intervals (RICI) […]
