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15 pages, 3863 KiB

Open AccessProceeding Paper

Fast Parallel Gaussian Filter Based on Partial Sums

by Atanaska Bosakova-Ardenska, Hristina Andreeva and Ivan Halvadzhiev

Eng. Proc. 2025, 104(1), 1; https://doi.org/10.3390/engproc2025104001 - 21 Aug 2025

Viewed by 114

Abstract

As a convolutional operation in a space domain, Gaussian filtering involves a large number of computational operations, a number that increases when the sizes of images and the kernel size also increase. Thus, finding methods to accelerate such computations is significant for overall time complexity enhancement, and the current paper proposes the use of partial sums to achieve this acceleration. The MPI (Message Passing Interface) library and the C programming language are used for the parallel program implementation of Gaussian filtering, based on a 1D kernel and 2D kernel working with and without the use of partial sums, and then a theoretical and practical evaluation of the effectiveness of the proposed implementations is made. The experimental results indicate a significant acceleration of the computational process when partial sums are used in both sequential and parallel processing. A PSNR (Peak Signal to Noise Ratio) metric is used to assess the quality of filtering for the proposed algorithms in comparison with the MATLAB implementation of Gaussian filtering, and time performance for the proposed algorithms is also evaluated. Full article

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10 pages, 1274 KiB

Open AccessProceeding Paper

An Embedded Control System for a 3D-Printed Robot for Training

by Zhelyazko Terziyski, Nikolay Komitov and Margarita Terziyska

Eng. Proc. 2025, 104(1), 2; https://doi.org/10.3390/engproc2025104002 - 21 Aug 2025

Viewed by 500

Abstract

This study explores the application of 3D printing as a strategic tool in engineering education and robotics development. An embedded control system for a 3D-printed MK2 manipulator is implemented, including an Arduino microcontroller, servo motors, an analog joystick interface, and an LCD, with software developed in Arduino IDE. The design uses PLA material and a modular architecture for flexibility and extensibility. The platform is applied in laboratory training to develop algorithmic thinking and engineering creativity, demonstrating the potential of 3D printing as an integrated educational and engineering tool. Full article

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752 KiB

Open AccessProceeding Paper

Usage of OLAP Cubes as a Data Model for DSS

by Nikolai Scerbakov, Alexander Schukin and Eugenia Rezedinova

Eng. Proc. 2025, 104(1), 4; https://doi.org/10.3390/engproc2025104004 (registering DOI) - 22 Aug 2025

Abstract

A decision support system (DSS) is a software application designed to determine suitable actions for specific organizational situations. Its main component is a data repository analyzed to produce decisions. This paper describes the data organization (Data Model) as a multi-dimensional OLAP cube with amendments for decision-making support. We present DSS functionality as building (slicing) hyper-cubes into decision sub-cubes. The system’s adjustment and evolution involve changing the granularity of these sub-cubes. We discuss the merging and splitting of hyper-cubes, arguing that this functionality is adequate for creating complex, real-time DSSs for various incidents, such as cybersecurity incidents. Full article

6 pages, 1627 KiB

Open AccessProceeding Paper

A Reinforcement Learning Solution for Queue Management in Public Utility Services

by Todor Dobrev, Miroslav Markov and Valentina Markova

Eng. Proc. 2025, 104(1), 6; https://doi.org/10.3390/engproc2025104006 - 22 Aug 2025

Viewed by 52

Abstract

This paper presents a reinforcement learning-based approach for optimizing queue management in public utility service environments. Using one year of real operational data from a utility office, a simulation model is developed to replicate daily service dynamics. A Q-learning agent is trained to allocate service types to counters dynamically, aiming to minimize client waiting time. The model treats the environment as a Markov Decision Process and uses an epsilon-greedy policy for learning optimal actions. Experimental results across multiple counter configurations demonstrate significant reductions in average waiting times, confirming the effectiveness and adaptability of the proposed method in dynamic service environments. Full article

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