Semantic scene segmentation with LIDAR and RGB image fusion

V okviru diplomske naloge je predstavljena metoda za semantično segmentacijo voznih scen. Moderne metode semantične segmentacije voznih scen lahko razdelimo na tri kategorije. Prva kategorija za zajem podatkov uporablja samo kamere, druga samo senzorje LiDAR, tretja pa združi podatke obeh senzorjev. V delu se osredotočamo na združevanje meritev LiDAR in barvnih slik s pomočjo mehanizma medpozornosti. Razvijemo metodo SWINCrossFusion, ki temelji na arhitekturi transformerja SWIN, za združevanje meritev pa predstavimo nov transformerski blok SWIN za izvajanje medpozornosti. Metoda izračuna poizvedbe nad podatki iz enega, ključe in vrednosti pa na podatkih drugega senzorja. Tako dobimo učinkovito in hitro združevanje lastnosti obeh senzorjev. Metodo evalviramo na podatkovni zbirki SemanticKITTI in primerjamo z referenčno metodo PMF. Razvita metoda je s 54 % mIoU za dva odstotka slabša od referenčne metode, vendar vhodne podatke procesira 40 % hitreje in porabi 1 GB grafičnega pomnilnika manj.This diploma thesis presents a method for semantic segmentation of driving scenes. Modern methods for semantic segmentation of driving scenes can be divided into three categories. The first category uses only cameras, the second uses only LiDAR sensors, and the third combines data from both sensors to capture data. In this paper, we focus on the fusion of LiDAR and RGB image data using cross-attention mechanism. We develop SWINCrossFusion, a method based on the SWIN transformer architecture, and introduce a new SWIN transformer block for sensor fusion using cross-attention. The method computes queries over data from one sensor, and keys and values over data from the other sensor. This results in an efficient and fast merging of the measurements of the two sensors. We evaluate the method on the SemanticKITTI dataset and compare it with the reference PMF method. The developed method is with 54 % mIoU two percent worse than the reference method, but processes the input data 40 % faster and consumes 1 GB less graphic memory

COMPUTERISED DATA ANALYSIS: ENSURING CONFIDENTIALITY OF PERSONAL INFORMATION IN DISTRIBUTED SYSTEMS THROUGH SECURE IDENTIFIERS

V okviru te raziskave smo preučili problem zaupnosti ob izmenjavi osebnih podatkov v posredniških sistemih. Posebno pozornost smo posvetili varnemu naslavljanju in hranjenju odjemalsko šifriranih vsebin. Osnovni rezultat tega diplomskega dela je zasnova arhitekture ogrodja, ki s pomočjo posebnih varnih identifikatorjev ponuja hranjenje šifriranih podatkov ob tajni izmenjavi informacij med obstoječimi sistemi. Uporaba identifikatorjev prav tako omogoča povsem anonimne načine delovanja, kar je v mnogih realnih transakcijah še posebej zaželeno. Ob koncu smo v okviru primerov uporabe iz zdravstva preverili še delovanje prototipskega sistema, kjer smo primerjali osnovne značilnosti z že obstoječimi rešitvami.Within this diploma we have investigated the architectural shortcomings in middleware systems caused by the strict requirements of personal data confidentiality. Special attention has been paid to data addressing and client-side encryption. The main result is an architectural design of a framework that uses special identifiers for secure interchange of encrypted personal data between existing systems. The use of identifiers also allows anonymous usage, which is very beneficial in a large number of real-life scenarios. Consequently, a prototype system has been tested in the area of healthcare - we have compared main functional aspects of the prototype with some existing PHR solutions

Development of tactile floor plan for the blind and the visually impaired by 3D printing technique

The aim of the research was to produce tactile floor plans for blind and visually impaired people for the use in the museum. For the production of tactile floor plans 3D printing technique was selected among three different techniques. 3D prints were made of white and colored ABS polymer materials. Development of different elements of tactile floor plans, as well as the problems and the solutions during 3D printing, are described in the paper

SVONAR: A New Quantitative Method for Studying Learner Satisfaction

We investigated students’ feelings and well-being during the lesson. We were working on a problem of how to adjust teaching in the secondary school and achieve that students will feel as good as possible and better follow the instruction. A hypothesis was set that, by measuring the desired length of lesson and factors which influence lesson length, we can measure and influence student satisfaction. Based on this hypothesis we investigated the factors which have influence on lesson length in a particular subject. Data were collected by means of a questionnaire. The subjects were students of II. gimnazija (secondary grammar school). The responses were analysed to obtain correlations between the factors (average grade in a subject, boredness and likableness of the teacher, length and level of boreness of lesson content, fear of the teacher) and lesson length. Results suggest that, by modifying these factors we could change students’ opinions, that is, increase their willingness to attend classes. The set hypothesis was confirmed and it was shown that by using simple diagrammatic models built on the basis of linear regression we can identify and study the influence of these factors on lesson length