Centar za istraživanje požara otvorenog prostora Split

Požari otvorenog prostora, a posebno požari raslinja, su nekontrolirana gorenja koji uzrokuju značajnu gospodarsku štetu i razorno djeluju na okoliš. Rano otkrivanje požara, te brza i odgovarajuća intervencija, od vitalne su važnosti za minimiziranje požarne štete. Požarna sezona 2003. g. bila je jedna od najtežih, posebice u Splitsko-dalmatinskoj županiji. Potaknuti velikim štetama uzrokovanim ovim požarima u jesen 2003.g. na Fakultetu elektrotehnike, strojarstva i brodogradnje (FESB) Sveučilišta u Splitu pokrenut je projekt čiji je cilj bio poboljšati prevenciju i zaštitu od požara raslinja primijenim naprednih postupaka informacijsko-komunikacijske tehnologije (ICT) i umjetne inteligencije. Splitsko-dalmatinska županija prepoznala je značaj projekta, pa je dogovorena izrada studije o integralnom modelu zaštite od šumskih požara koja je i dovršena 2004.g. Paralelno s radom na studiji, pokrenuta su i istraživanja na inovativnim sustavima za ranu detekciju požara, modeliranju širenja požara i procjeni požarne opasnosti. Na temelju ostvarenih rezultata, 2010.g. i službeno je osnovan Centar za istraživanje požara otvorenog prostora (CIPOP) u suradnji FESB-a i Splitsko-dalmatinske županije. Tema ovog rada su djelatnosti i rezultati istraživača koji su djelovali i djeluju u okviru Centra

Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images

Optic disc and optic cup are one of the most recognized retinal landmarks, and there are numerous methods for their automatic detection. Segmented optic disc and optic cup are useful in providing the contextual information about the retinal image that can aid in the detection of other retinal features, but it is also useful in the automatic detection and monitoring of glaucoma. This paper proposes a deep learning based approach for the automatic optic disc and optic cup semantic segmentation, but also the new model for possible glaucoma detection. The proposed method was trained on DRIVE and DIARETDB1 image datasets and evaluated on MESSIDOR dataset, where it achieved the average accuracy of 97.3% of optic disc and 88.1% of optic cup. Detection rate of glaucoma diesis is 96.75

A Stereo Approach to Wildfire Smoke Detection: The Improvement of the Existing Methods by Adding a New Dimension

In this paper, we present a novel approach to visual smoke detection based on stereo vision. General smoke detection is usually performed by analyzing the images from remote cameras using various computer vision techniques. The literature on smoke detection shows a variety of approaches, and the focus of this paper is the improvement of the general smoke detection process by introducing stereo vision. Two cameras are used to estimate the distance and size of the detected phenomena based on stereo triangulation. Using this information, the minimum size and overall dynamics of the detected regions are further examined to ensure the elimination of false alarms induced by various phenomena (such as the movement of objects located at short distances from the camera). Such false alarms could easily be detected by the proposed stereo system, allowing the increase of the sensitivity and overall performance of the detection. We analyzed the requirements of such system in terms of precision and robustness to possible error sources, especially when dealing with detection of smoke at various distances from the camera. For evaluation, three existing smoke detection methods were tested and the results were compared to their newly implemented stereo versions. The results demonstrated better overall performance, especially a decrease in false alarm rates for all tested methods

Performance Comparison of the Cogent Confabulation Classifier with Other Commonly Used Supervised Machine Learning Algorithms for Bathing Water Quality Assessment

The purpose of this study was to implement a reliable model for bathing water quality prediction using the Cogent Confabulation classifier and to compare it with other well-known classifiers. This study is a continuation of a previously published work and focuses on the areas of Kaštela Bay and the Brač Channel, located in the Republic of Croatia. The Cogent Confabulation classifier is a thorough and simple method for data classification based on the cogency measure for observed classes. To implement the model, we used data sets constructed of remote sensing data (band values) and in situ measurements presenting ground-truth bathing water quality. Satellite data was retrieved from the Sentinel-3 OLCI satellite and it was atmospherically corrected based on the characteristics and specifications of band wavelengths. The results showed that the Random Forest, K-Nearest Neighbour, and Decision Tree classifiers outperformed the Cogent Confabulation classifier. However, results showed that the Cogent Confabulation classifier achieved better results compared to classifiers based on Bayesian theory. Additionally, a qualitative analysis of the four best classifiers was conducted using spatial maps created in the QGIS tool

Centar za istraživanje požara otvorenog prostora Split

Požari otvorenog prostora, a posebno požari raslinja, su nekontrolirana gorenja koji uzrokuju značajnu gospodarsku štetu i razorno djeluju na okoliš. Rano otkrivanje požara, te brza i odgovarajuća intervencija, od vitalne su važnosti za minimiziranje požarne štete. Požarna sezona 2003. g. bila je jedna od najtežih, posebice u Splitsko-dalmatinskoj županiji. Potaknuti velikim štetama uzrokovanim ovim požarima u jesen 2003.g. na Fakultetu elektrotehnike, strojarstva i brodogradnje (FESB) Sveučilišta u Splitu pokrenut je projekt čiji je cilj bio poboljšati prevenciju i zaštitu od požara raslinja primijenim naprednih postupaka informacijsko-komunikacijske tehnologije (ICT) i umjetne inteligencije. Splitsko-dalmatinska županija prepoznala je značaj projekta, pa je dogovorena izrada studije o integralnom modelu zaštite od šumskih požara koja je i dovršena 2004.g. Paralelno s radom na studiji, pokrenuta su i istraživanja na inovativnim sustavima za ranu detekciju požara, modeliranju širenja požara i procjeni požarne opasnosti. Na temelju ostvarenih rezultata, 2010.g. i službeno je osnovan Centar za istraživanje požara otvorenog prostora (CIPOP) u suradnji FESB-a i Splitsko-dalmatinske županije. Tema ovog rada su djelatnosti i rezultati istraživača koji su djelovali i djeluju u okviru Centra

Entropy-Based Approach in Selection Exact String-Matching Algorithms

The string-matching paradigm is applied in every computer science and science branch in general. The existence of a plethora of string-matching algorithms makes it hard to choose the best one for any particular case. Expressing, measuring, and testing algorithm efficiency is a challenging task with many potential pitfalls. Algorithm efficiency can be measured based on the usage of different resources. In software engineering, algorithmic productivity is a property of an algorithm execution identified with the computational resources the algorithm consumes. Resource usage in algorithm execution could be determined, and for maximum efficiency, the goal is to minimize resource usage. Guided by the fact that standard measures of algorithm efficiency, such as execution time, directly depend on the number of executed actions. Without touching the problematics of computer power consumption or memory, which also depends on the algorithm type and the techniques used in algorithm development, we have developed a methodology which enables the researchers to choose an efficient algorithm for a specific domain. String searching algorithms efficiency is usually observed independently from the domain texts being searched. This research paper aims to present the idea that algorithm efficiency depends on the properties of searched string and properties of the texts being searched, accompanied by the theoretical analysis of the proposed approach. In the proposed methodology, algorithm efficiency is expressed through character comparison count metrics. The character comparison count metrics is a formal quantitative measure independent of algorithm implementation subtleties and computer platform differences. The model is developed for a particular problem domain by using appropriate domain data (patterns and texts) and provides for a specific domain the ranking of algorithms according to the patterns’ entropy. The proposed approach is limited to on-line exact string-matching problems based on information entropy for a search pattern. Meticulous empirical testing depicts the methodology implementation and purports soundness of the methodology