Call for Papers We cordially invite you to the 18th International Workshop on Multimedia and Signal Processing, Redžúr 2026, which will be organized at the Faculty of Electrical Engineering and Information Technology of the Slovak University of Technology in Bratislava. Important Deadlines Paper Submission: 16 April 2026 Notification of Acceptance: 30 April 2026 Conference Day: 14 May 2026 More detailed information in full → Call for Papers For any inquiries please contact us by email redzur@stuba.sk About the Conference Redžúr is an international workshop on multimedia information and communication technologies. Throughout its history, Redžúr has evolved from a narrow-focused workshop on signal processing into an international meetup of researchers in wide spread of fields covering signal and multimedia creation, processing and transmission across various media and underlying infrastructure. Redžúr brings together researchers and developers from both academia and industry to report on the latest scientific and theoretical advances, to discuss and debate major issues and to demonstrate state-of-the-art systems.
A Web Portal for Managing Educational Resources
This paper presents the design and implementation of a centralized web portal for study materials management that addresses the challenges of digital transformation in education. Traditional methods of distributing educational materials suffer from fragmentation, limited organization capabilities, and accessibility issues. Our solution offers a hierarchical folder structure, robust user role management, and specialized viewing capabilities for various file formats. Built on Spring Boot with a layered MVC architecture, the system supports multiple file formats (PDF, video, ZIP) and provides role-based access control. User testing confirms improved accessibility and organization of study materials compared to existing solutions. The system effectively balances administrative flexibility with user accessibility, creating an efficient and centralized platform for educational content management without the complexity of full learning management systems.
This paper presents a practical approach to integrating Large Language Models (LLMs) into the educational process through a custom web application. The solution leverages LangChain to enable semantic search over both structured and unstructured educational content. The system is designed to provide educators with tools for uploading and managing materials, while allowing learners to receive accurate, context-aware answers generated by LLMs. We describe the use of Retrieval-Augmented Generation (RAG) to improve response relevance and outline the implementation strategy, including the use of vector databases, prompt design, and user interface considerations.
Utilization of Large Language Models in the Educational Process
This paper addresses the potential of Large Language Models (LLMs) to support students in the educational process by providing personalized assistance, explanations, and examples. LLMs are used by many students, but they often lack up-to-date information or specific knowledge on the subjects they need. This paper looks at how using Retrieval-Augmented Generation (RAG) can help by linking language models to external sources of information so answers can be more accurate and relevant. The proposed solution is implemented using the LangChain framework, which simplifies integration with vector databases, embedding models, and custom prompt workflows. The system is designed as a modular architecture with specialized agents that respond to different types of student queries. This approach demonstrates how LLMs can be enhanced to better learning experience.
Application for Effective Memorization of Learning Material
In this paper, the development of a web-based application designed for effective memorization of learning material using flashcards is presented. The study addresses the problem of knowledge retention and memory decay, referencing the widely recognized Ebbinghaus forgetting curve. A comparative analysis of existing spaced repetition algorithms and applications was conducted, including manual spaced repetition systems, absolute, relative, memory-model-based, and neural network-based approaches. Based on the findings, a suitable algorithm was selected and implemented within the application to optimize the learning process. Additionally, the paper explores modern approaches to automatic flashcard generation from text using large language models (LLMs). The proposed solution integrates AI-based generation as an optional feature, allowing users to create personalized flashcards from arbitrary text content.
Design and Implementation of a Personalized AI Tutor for University Education Using RAG
This paper presents the design and implementation of an AI-based educational assistant aimed at supporting university students in their learning process. The system utilizes Retrieval- Augmented Generation (RAG) [1] to provide personalized guidance and encourage active learning through questions and exploration rather than supplying direct answers. Technical implementation focuses on a dynamic chat interface combining modern AI models [2], vector databases for retrieval, and advanced prompt engineering strategies. Preliminary observations based on simulated interactions suggest that the tutoring approach encourages deeper engagement and critical reasoning. This aligns with findings from prior research indicating that AI systems promoting active student reflection can lead to improved learning outcomes compared to direct-answer systems [6], [7]. Future improvements aim to include features such as knowledge gap identification, adaptation to the student's current understanding level, and curriculum-aware guidance.
This article explores the design and development of a modular web application – a student portal enhanced with an artificial intelligence (AI) assistant based on the Retrieval-Augmented Generation (RAG) approach. The system aims to provide students with a centralized platform for accessing study-related information and receiving contextual assistance through AI. The solution includes role management, ensuring that features are accessible according to role permissions (e.g., student, professor, administrator). The modular architecture ensures flexibility, scalability and the potential for future enhancements.
Maximizing a Nonlinear Function using Evolutionary Strategies and Parameter Tuning
Maximization and minimization of functions are problems that occur in most scientific fields. There are several methods for solving them and one such method is the evo- lutionary strategy algorithm, which is an algorithm inspired by the biological theory of evolution. The algorithm works with a set of candidate solutions, and in each iteration, these solutions are crossed and mutated to create new ones. The newly created solutions are then evaluated and the best solutions are moved to the next iteration. This process is repeated until the optimal solution is found or another terminating condition occurs. In this paper, we implemented such an algorithm and used it to maximize the chosen function. We also found the best combination of parameters for which the algorithm gives the best result.
Data Classification using the Random Forest Method
This paper focuses on the problem of data clas- sification using decision trees and Random Forests. The theoretical background of de- cision trees, entropy and information gain as the basic principles of classification is described. Sub- sequently, a custom decision tree-based classifier is implemented as well as an ensemble Random For- est model that improves robustness and accuracy through bagging. The task of the models was to predict the ticket price category based on various attributes. The results are evaluated using accu- racy and confusion matrices, with Random Forest demonstrating higher classification accuracy com- pared to the standalone decision tree.
Classification of Written Numbers using 2D Convolutional Neural Network
This article introduces a configurable system for building CNNs designed for grayscale image classification. The network architecture and training settings are defined via a JSON file, allowing full control over the layers and parameters without modifying the code. The system was tested on MNIST dataset using two CNN configurations to classify handwritten numbers. Both achieved satisfactory accuracy. The modular design makes it suitable for rapid prototyping and experimentation in computer vision applications.
Media Storage in the Context of Trusted Services
This paper addresses the challenges and solutions of electronic archiving within the context of trusted services as defined by European legislation, particularly the eIDAS regulation. The work outlines the theoretical background of digital preservation, including signature formats, data integrity protocols, and archival models defined by ETSI. A hybrid archiving service is proposed and implemented, integrating MinIO object storage and PostgreSQL metadata management. The modular system is designed for long-term accessibility, document integrity, and regulatory compliance. Test scenarios confirm the system’s effectiveness in handling preservation operations and data retrieval. The paper also presents real implementation details and evaluates the proposed architecture's strengths and limitations in comparison with existing solutions.
Web application for hybrid communication integration
In my thesis I will cover research and development process of creating web application with hybrid communication
Videoconferencing Systems in Virtual Environment
This research paper describes various challenges, that appear during development of the web videoconferencing systems in virtual environment, and what approaches could be used to cope with those problems. What are the possible ways of representing the virtual environment, how can the interaction and communication among users be constructed and what up-to-date technologies could be used in the development.
Advanced Videoconference Technologies for Hybrid Communications
This paper presents an innovative system for intelligent PTZ (pan-tilt-zoom) camera control integrated into a videoconferencing platform. The system leverages advanced face tracking algorithms, real-time communication protocols, and dynamic parameter adaptation to deliver a highly responsive and interactive solution. In addition to automated camera adjustment based on face detection, the system provides remote control capabilities that enable participants to steer the camera upon user consent. This modular approach supports hybrid communication environments and introduces virtual teleportation functionalities, allowing remote users to dynamically explore meeting spaces as if they were physically present.
Videoconferencing system in virtual environment
This work presents an immersive videoconferencing system based on WebSocket technology and Splat model rendering.The proposed solution enables real-time video communication integrated within a dynamic virtual environment with first-person navigation. Unlike traditional platforms, it offers high responsiveness, low hardware requirements, and greater accessibility, bridging the gap between conventional videoconferencing and full VR immersion.
This bachelor thesis focuses on the development of virtual meeting rooms an d collaborative educational spaces using the FrameVR platform. FrameVR enables web-based creation, navigation, and interaction in immersive 3D environments. Unlike static 2D platforms, FrameVR supports live communication, media presentation, and modeling capabilities in real-time. A key part of this project was evaluating the potential of integrating interactive 3D modeling directly within FrameVR, alongside leveraging Gaussian Splatting for advanced visualization. The result is a set of interactive virtual classrooms and meeting rooms where students can communicate, collaborate, and co-create content inside a shared virtual world.
Prototype of a Hybrid Communication System: Bridging VR and Video Conferencing
The Metaverse and immersive technologies related to it open up new possibilities for remote communication and collaboration, but often create a barrier between users in virtual reality (VR) and those using traditional video conferencing tools. This work presents the design and implementation of a prototype hybrid communication system that addresses this problem by creating a bidirectional visual bridge. The system, developed in the Unity engine, allows users in VR to see participants of a standard video call on virtual panels, and conversely, video call participants see the view from the VR scene. It utilizes a combination of the Mirror library for synchronizing the state of the multiplayer VR scene and the Agora RTC SDK for real-time audio and video transmission between VR and non-VR clients, including sending custom video and audio sources from Unity. The result is a prototype demonstrating the technical feasibility of the proposed approach towards more flexible hybrid communication.
This document presents the development of an advanced video conferencing application that integrates unique functionalities not commonly available in current mainstream systems such as Zoom or Google Meet. The application enables multi-camera support, dynamic camera manipulation, role-based access control, real-time chat features, and conference customization. Built with Jitsi and WebRTC protocols, the system ensures secure and scalable video communication. The backend and frontend are combined in a single repository, and the application is containerized using Docker for easy deployment.(Abstract)
Application for advanced video conferencing in 3D space
This paper presents a novel approach to video conferencing by introducing immersive 3D environments that extend communication beyond traditional 2D interfaces. In response to the limitations of traditional platforms, we propose a browser-based solution that combines spatial visualisation with real-time video streaming. The system uses Gaussian Splatting and Three.js to render realistic 3D environments, and integrates the Agora SDK for efficient video communication. Our architecture uses React.js for the frontend, Quarkus for the backend, and a MySQL database for user management. Through iterative prototyping and validation, we have demonstrated that this approach significantly improves user engagement and presence without requiring specialised hardware. The solution combines the accessibility of a web-based application with the immersive qualities of a 3D environment, addressing a critical gap in today's video conferencing technologies.
Evaluation of Camera Topologies on Output Quality in Gaussian Splatting
Digital life has become an everyday reality, and life without it is almost unimaginable. Digitalization has significantly advanced humanity, improving quality of life, extending lifespans through medical innovations, and offering entertainment such as watching films or playing video games. This paper examines the digitalization of physical objects and environments, using various photo-based algorithms and digital model creation software.
Examination of Minimal Camera Setups for Volumetric Video Capturing from a Time Perspective
This study examines the impact of camera count on training time and quality in volumetric video capture using 3D Gaussian Splatting. Two datasets were processed with varying camera numbers, testing two reduction strategies: removing cameras from the center or edges. Results show that 14 cameras offer an optimal balance, reducing training time by up to 32 hours per minute of video (30 fps) with minimal quality loss, while fewer cameras cause significant artifacts. Edge removal proved more efficient, minimizing computational errors.
This bachelor thesis includes the creation of an immersive tour and the creation of the most accurate copy of the school as a 3D model in which the user will be able to move freely, interact with the objects and especially to allow foreign students to visit the corridors of the faculty FEI STU in this way. To create the 3D model, I will use the technology "Gaussian splatting", which is designed to create 3D representations of objects and spaces from 2D data to 3D objects. Thanks to this technology we can create one or more 3D models from a number of images. I will use the program "Jawset, Postshot"[1] to process the data consisting of images of the school premises. I will also use the game development program "Unreal engine" in my research, which has direct compatibility with the 3D models created in "Jawset, Postshot"[2]. This compatibility will make the creation and thorough finalization of the project possible and much easier.
Angular Limits of Image Samples for Novel View Synthesis
Novel view synthesis (NVS) is a technology that aims to generate new views of a scene from a limited set of images, enabling rendering from unseen perspectives. This thesis explores the development of NVS and tests the capabilities of NVS to create a middle view of a human face with increasingly more difficult sets of images to create the view from. A novel method is proposed to improve visual quality and efficiency, addressing challenges in reconstruction and view consistency. The results demonstrate the potential of the approach for applications in virtual reality, telepresence, and immersive media, contributing to the development of realistic scene generation.
3D Scene Reconstruction Using Instant-NGP
Instant Neural Graphics Primitives (instant- ngp) technology enables the rapid reconstruction of 3D scenes from 2D images. This paper provides a prac- tical guide to the installation, configuration, and exe- cution of the instant-NGP tool, focusing on processing video inputs using the colmap2nerf.py script and the main instant-ngp.exe application. The primary contribution of this work is an experimental analysis of the impact of key data preparation parameters – video frame rate (-video_fps) and scene bounding box size (-aabb_scale) – on processing speed and the final vi- sual quality of the 3D reconstruction. The results offer recommendations for the optimal setting of these param- eters depending on the nature of the input video and the desired output.
Novel View Synthesis with limited number of cameras
This article focuses on synthesize new views from a limited number of cameras, typically 2 to 4, using advanced image fusion and perspective transformation techniques. Furthermore, this work studies current methods of view synthesis in order to identify their strengths and weaknesses in the context of a limited number of input cameras. The main goal was to propose an algorithm that will be optimized for efficient fusion of views from a small number of cameras, while emphasizing the quality of the resulting images, the speed of processing, and the scalability of the solution. The proposed algorithm was then compared with another existing method, that solves the mentioned problem. The conclusion consists of evaluating the results of each method and describing their strengths and weaknesses.