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 […]
Laboratory for Information Processing and Pattern Recognition
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) […]
Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques
Various operations at sea, such as maintaining a constant ship position and direction, require a complex control system. Under such conditions, the ship needs an efficient positioning technique. Dynamic positioning (DP) systems provide such an application with a combination of the actuators mechanism, analyses of crucial ship variables, and environmental conditions. The natural forces of […]
Improved Power System State Estimator With Preprocessing Based on the Modified Intersection of Confidence Intervals
The paper proposes a power system state estimator upgraded by the improved relative intersection of confidence intervals (RICI) algorithm combined with the local polynomial approximation (LPA). In the proposed approach, a novel LPA-RICI denoising technique is utilized to preprocess the input measurements. The accuracy of the adaptive, data-driven LPA-RICI based state estimator was tested on […]
Adaptive Filtering and Analysis of EEG Signals in the Time-Frequency Domain Based on the Local Entropy
The brain dynamics in the electroencephalogram (EEG) data are often challenging to interpret, specially when the signal is a combination of desired brain dynamics and noise. Thus, in an EEG signal, anything other than the desired electrical activity, which is produced due to coordinated electrochemical process, can be considered as unwanted or noise. To make […]
Hyperspectral Image Analysis Using Machine Learning and Adaptive Data-Driven Filtering
As a non-contact and non-invasive technique, hyperspectral imaging enables the collection of highly informative spectral (at different wavelengths) and spatial data on the observed sample. The collected spectra reflect the chemical composition and spatial morphology of the sample. The technique is successfully applied in the food industry, for industrial classification, and in medicine for the […]
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Talk on the Elliptic Curve Cryptography (ECC), Luka Martinez, ASSECO SEE
Luka Martinez from the company ASSECO SEE held a lecture on the “Elliptic Curve Cryptography (ECC)” at the Faculty of Engineering on the 12th of December, 2019. The event took place at the Faculty of Engineering, University of Rijeka (www.riteh.uniri.hr).
