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10 pages, 5020 KB

Open AccessProceeding Paper

Noise Identification and Performance Index Optimization for QoS in Communication Systems by Artificial Intelligence

by Ivelina Balabanova and Georgi Georgiev

Eng. Proc. 2026, 154(1), 1; https://doi.org/10.3390/engproc2026154001 - 27 Aug 2026

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This paper proposes a methodology for diagnosing the network environment in terms of disturbances and performance metrics in communication infrastructures. An approach for identifying GWN and PRN based on Artificial Intelligence is integrated. Naïve Bayes classification in Gaussian and Kernel probability density functions [...] Read more.

This paper proposes a methodology for diagnosing the network environment in terms of disturbances and performance metrics in communication infrastructures. An approach for identifying GWN and PRN based on Artificial Intelligence is integrated. Naïve Bayes classification in Gaussian and Kernel probability density functions of datasets are created with confirmed verification using Resubstitution and Cross-Validation techniques. The high efficiency of the GRNNs created for GWN and PRN recognition has been established. An approach for finding an optimum in non-linear minimization with inequality constraints using Interior-point, SQP, Active-set and Genetic Algorithm techniques has been synthesized regarding an analytical model for SRT performance index prediction. Full article

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12 pages, 8557 KB

Open AccessProceeding Paper

Analysis of Z-Wave RSSI: Simulation-Based Modeling and Real-World Data

by Aydan Haka

Eng. Proc. 2026, 154(1), 2; https://doi.org/10.3390/engproc2026154002 - 27 Aug 2026

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Abstract

This paper investigates the behavior of the Received Signal Strength Indicator (RSSI) in Z-Wave networks through a comparison between simulation-generated and experimentally measured data. A prototype star-topology Z-Wave network was implemented and RSSI values were measured at distances from 1 m to 5 [...] Read more.

This paper investigates the behavior of the Received Signal Strength Indicator (RSSI) in Z-Wave networks through a comparison between simulation-generated and experimentally measured data. A prototype star-topology Z-Wave network was implemented and RSSI values were measured at distances from 1 m to 5 m with a gradually increasing number of active end devices in a laboratory environment. In parallel, a software simulation environment was used to reproduce the same topology and generate RSSI values under different propagation assumptions. In order to compare the results obtained from real and simulated environments, the average deviation between the range of measured values for RSSI was calculated. The experimental results show that the RSSI values from the simulation product follow the trend of deteriorating values with increasing distance between the central and end devices. The calculated average deviation between the range of RSSI values in real and simulated environments shows that the simulated results are closest to the real ones at an exponential coefficient n ≈ 2, and the calculated deviation is from 2 dBm to 2.3 dBm. The results obtained support the evaluation of the simulation model and show its potential for studying signal behavior and aiding the design of reliable Z-Wave-based Internet of Things environments. Full article

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11 pages, 5651 KB

Open AccessProceeding Paper

Design and Implementation of a 3D-Printed Robotic Arm Model with Five Degrees of Freedom Using an ESP32 Microcontroller for Control

by Nikolay Komitov, Yosif Munev, Margarita Terziyska, Mariyana Sestrimska, Veselin Mengov and Angel Nikolov

Eng. Proc. 2026, 154(1), 3; https://doi.org/10.3390/engproc2026154003 - 27 Aug 2026

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Abstract

The present work is aimed at developing and researching a robotic arm with five degrees of freedom, manufactured using 3D-printing technology and controlled by an ESP32 microcontroller. This technology is increasingly used in robotics, especially in the educational process, due to the possibilities [...] Read more.

The present work is aimed at developing and researching a robotic arm with five degrees of freedom, manufactured using 3D-printing technology and controlled by an ESP32 microcontroller. This technology is increasingly used in robotics, especially in the educational process, due to the possibilities for rapid prototyping, modification and restoration of individual components. In the development process, the mechanical, hardware and software parts of the system were implemented, and a basic kinematic analysis of the manipulator was performed. A control program was created, allowing the performance of “pick and place” tasks, as well as visualization and manual control through a developed application. The results obtained show that the developed system provides sufficient functionality and flexibility for use in robotics training, while at the same time allowing expansion and upgrading with additional functionalities. The main contribution of the work lies in the implementation of an accessible and adaptable robotic platform, suitable for educational and experimental purposes. Full article

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18 pages, 2911 KB

Open AccessProceeding Paper

Chaos-Based Secure Data Transmission in Intelligent IoT Sensor Networks

by Stanimir Yordanov, Hristina Stoycheva, Georgi Mihalev and Stefan Ivanov

Eng. Proc. 2026, 154(1), 4; https://doi.org/10.3390/engproc2026154004 - 28 Aug 2026

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Abstract

This paper presents a secure data transmission framework for intelligent IoT sensor networks, specifically designed for an electronic nose system used in food quality analysis. The proposed architecture integrates a heterogeneous sensor array with an ESP32-based processing unit and GSM/GPRS communication module. To [...] Read more.

This paper presents a secure data transmission framework for intelligent IoT sensor networks, specifically designed for an electronic nose system used in food quality analysis. The proposed architecture integrates a heterogeneous sensor array with an ESP32-based processing unit and GSM/GPRS communication module. To ensure data confidentiality over public mobile networks, a hybrid security scheme combining cryptographic encryption (ChaCha20) and chaotic modulation (Brusselator + DCSK) is introduced. The data packet is first encrypted using the ChaCha20 stream cipher, then converted into a bipolar bit stream. The chaotic system used to generate a chaotic signal is a nonlinear Brusselator model. The system was implemented on a LILYGO TTGO T-Call V1.4 (ESP32 + SIM800L) and tested for discrimination of olive oil and sunflower oil mixtures. Experimental results demonstrate 98.7% classification accuracy using a neural network, 96% first-try transmission success rate over GPRS with 2.3 s average packet transmission time, and 99% overall reliability under RSSI above −85 dBm. Full article

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16 pages, 13901 KB

Open AccessProceeding Paper

A Reproducible Comparative Framework for Secure QR Code Transmission Under Controlled Distortions

by Diyan Dinev and Gergana Spasova

Eng. Proc. 2026, 154(1), 5; https://doi.org/10.3390/engproc2026154005 - 27 Aug 2026

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Abstract

Secure QR-based data transfer requires a careful balance between confidentiality, payload size, decoding reliability, and resistance to visual degradation. This paper presents a reproducible comparative framework for studying secure QR code transmission under controlled distortions. The framework combines protected payload generation, batch experimentation, [...] Read more.

Secure QR-based data transfer requires a careful balance between confidentiality, payload size, decoding reliability, and resistance to visual degradation. This paper presents a reproducible comparative framework for studying secure QR code transmission under controlled distortions. The framework combines protected payload generation, batch experimentation, and comparative visual analysis to examine how payload mode, error-correction level, decoder backend, and image degradation influence transmission success, overhead, QR complexity, and processing time. The results highlight key trade-offs among robustness, efficiency, and symbol density, and provide practical guidance for selecting suitable secure QR transmission settings in distortion-prone environments. Unlike existing evaluation approaches that address security and robustness factors in isolation, the proposed framework offers the first unified experimental environment for their joint reproducible assessment, making it directly applicable to systematic research in secure QR transmission design. Full article

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13 pages, 2158 KB

Open AccessProceeding Paper

Adaptive Multi-Embedding Quantum Feature Fusion for Intrusion Detection Systems

by Raid Anis Kerkatou, Hacene Belhadef, Aicha Eutamene and Svetlana Stefanova

Eng. Proc. 2026, 154(1), 6; https://doi.org/10.3390/engproc2026154006 - 28 Aug 2026

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Abstract

Quantum Machine Learning (QML) has recently emerged as a promising approach for cybersecurity applications due to its ability to encode data into high-dimensional Hilbert spaces. However, most QML-based intrusion detection systems rely on a single quantum embedding strategy, limiting representation diversity. This paper [...] Read more.

Quantum Machine Learning (QML) has recently emerged as a promising approach for cybersecurity applications due to its ability to encode data into high-dimensional Hilbert spaces. However, most QML-based intrusion detection systems rely on a single quantum embedding strategy, limiting representation diversity. This paper proposes a Multi-Embedding Quantum Ensemble (ME-QE) framework that combines Angle Embedding and IQP Embedding using concatenation and weighted fusion strategies. The proposed approach is evaluated on the NSL-KDD dataset for binary intrusion detection using stratified 5-fold cross-validation. Experimental results show that the best fusion configuration achieves 94.75% accuracy, while the classical Random Forest baseline reaches 97.50%. The analysis further demonstrates that Angle Embedding contributes more effectively to classification performance than IQP Embedding in this context. In addition, the study highlights important scalability limitations of quantum kernel methods due to the computational cost of pairwise kernel evaluation. The proposed framework provides insights into hybrid quantum–classical intrusion detection and establishes a foundation for future scalable quantum cybersecurity architectures. Full article

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13 pages, 2186 KB

Open AccessProceeding Paper

A Machine Learning-Based Network Anomaly Detection System Prototype Using Isolation Forest

by Viktoria Ivanova and Delyan Genkov

Eng. Proc. 2026, 154(1), 7; https://doi.org/10.3390/engproc2026154007 - 28 Aug 2026

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Abstract

Due to the large amount of network traffic, manual network security management has become increasingly difficult. This paper proposes an open-source network anomaly detection system prototype that operates with the Security Onion 2.4 platform. The system relies on a custom Python engine that [...] Read more.

Due to the large amount of network traffic, manual network security management has become increasingly difficult. This paper proposes an open-source network anomaly detection system prototype that operates with the Security Onion 2.4 platform. The system relies on a custom Python engine that uses the machine learning (ML) algorithm Isolation Forest to detect anomalies in multiple Zeek datasets. The prototype was developed as a virtual machine (VM), which is hosted on a server running the software for virtualization VMware ESXi 6.0.0. For the experimental tests, live network telemetry from a university network was used. The results show that the system achieves a constant anomaly detection rate of 5.4% after a volumetric threshold of 5000 logs is reached within operating periods ranging from fifteen to thirty minutes. During testing, it was discovered that the detection logic has a minimal resource impact (less than 1 GB) on the running system beyond the defined baseline of average Random Access Memory (RAM) usage. This proposed solution is an open-source and zero-cost alternative to other paid network security solutions, demonstrating that abnormal behavior detection in network traffic can be effectively integrated on existing computer configurations without the need for expensive licenses. Full article

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12 pages, 8558 KB

Open AccessProceeding Paper

Hybrid TPP-BESS Grid-Forming Strategy for Maintaining Frequency Stability in the Case of Reduced System Inertia

by Dimitrina Koeva and Dimitar Slavov

Eng. Proc. 2026, 154(1), 8; https://doi.org/10.3390/engproc2026154008 - 28 Aug 2026

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Abstract

The transition toward decarbonized power systems has led to the large-scale integration of inverter-connected renewable energy sources (ICRES) and the decommissioning of traditional synchronous generators. This shift significantly reduces system inertia, increasing the rate of change of frequency (RoCoF) and vulnerability during power [...] Read more.

The transition toward decarbonized power systems has led to the large-scale integration of inverter-connected renewable energy sources (ICRES) and the decommissioning of traditional synchronous generators. This shift significantly reduces system inertia, increasing the rate of change of frequency (RoCoF) and vulnerability during power imbalances. This paper investigates a hybrid system combining a thermal power plant (TPP) with a battery energy storage system (BESS) in grid-forming mode to enhance stability. Using MATLAB/Simulink and technical parameters from an existing turbogenerator, three scenarios with 0%, 30% and 50% renewable penetration were simulated. Results show that as the inertia constant H_sys decreases from 2.32 s to a critical 1.43 s, the frequency during a 20% deficit falls below the 49 Hz under-frequency load shedding (UFLS) threshold. Implementing virtual inertia control allows the BESS to successfully limit RoCoF and maintain frequency above safety limits. These findings justify BESS grid-forming technology as a vital solution for grid stability under low-inertia conditions. Full article

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6 pages, 901 KB

Open AccessProceeding Paper

Dynamics of Airflow in the Separation Process and Their Effect on Quantitative Parameters

by Ismail Mehmedov, Darina Dobreva, Zoya Tsoneva and Momchil Tachev

Eng. Proc. 2026, 154(1), 9; https://doi.org/10.3390/engproc2026154009 - 28 Aug 2026

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Abstract

The study focuses on the influence of airflow on the separation of the three primary materials under investigation: sand, sawdust, and ash. These materials were selected to facilitate a comparative analysis between two distinct airflows. The primary objective of the research is to [...] Read more.

The study focuses on the influence of airflow on the separation of the three primary materials under investigation: sand, sawdust, and ash. These materials were selected to facilitate a comparative analysis between two distinct airflows. The primary objective of the research is to increase the separation yield while maintaining the original design of the 2A mechanical cyclone model. The model utilized in this study was designed using SolidWorks 2025 and fabricated via a Raise3D Pro2 3D printer (Raise3D Technologies, Inc. 2019, Nantong, China, Е2). The purpose of the experiment is to determine how variations in flow affect the separation of fine particulate matter under 3 μm. Full article

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14 pages, 1791 KB

Open AccessProceeding Paper

Intelligent River Flood Monitoring and Early Warning System Based on Sensor Communication Network

by Hristina Stoycheva, Stanimir Sadinov, Georgi Mihalev, Nikolay Manchev and Boyan Karapenev

Eng. Proc. 2026, 154(1), 10; https://doi.org/10.3390/engproc2026154010 - 28 Aug 2026

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Abstract

River floods are considered a serious threat to human life and infrastructure; therefore, effective monitoring and early warning solutions are required. In this paper, an intelligent system based on a distributed network of ESP32 sensor nodes is proposed for real-time monitoring of river [...] Read more.

River floods are considered a serious threat to human life and infrastructure; therefore, effective monitoring and early warning solutions are required. In this paper, an intelligent system based on a distributed network of ESP32 sensor nodes is proposed for real-time monitoring of river water levels. The sensor nodes are deployed at multiple locations along the river, where hydrological data are continuously collected and transmitted to a central unit. The acquired time-series data are analyzed by means of a nonlinear autoregressive model with exogenous inputs (NARX) and artificial intelligence techniques, so that complex hydrological dynamics can be captured and future water-level variations can be predicted. The proposed system enables the early detection of potential flood events and supports timely decision-making. The solution is implemented as a low-cost Wireless Sensor Network (WSN), which makes it suitable for deployment in remote and resource-constrained areas and contributes to improved flood risk management. The architectures of the applied systems are presented, and an approach for implementing a NARX-based model for river water-level prediction is proposed. Full article

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12 pages, 1147 KB

Open AccessProceeding Paper

Polar-Coded Anti-Jamming Technique over Impulsive Noise Channels

by Ana-Maria Grigoras Oanca, Mihaela Andrei and Daniela Tarniceriu

Eng. Proc. 2026, 154(1), 11; https://doi.org/10.3390/engproc2026154011 - 28 Aug 2026

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Abstract

This paper presents a polar-coded anti-jamming communication system designed to improve reliability and security in channels affected by Gaussian jamming and impulsive α-stable noise. The proposed configuration combines polar decoding with an Anti-Jamming (AJ) mechanism based on Processing Gain (PG). Several configurations, including [...] Read more.

This paper presents a polar-coded anti-jamming communication system designed to improve reliability and security in channels affected by Gaussian jamming and impulsive α-stable noise. The proposed configuration combines polar decoding with an Anti-Jamming (AJ) mechanism based on Processing Gain (PG). Several configurations, including standalone polar decoding, anti-jamming only, and their combination, are evaluated using Bit Error Rate (BER) versus Signal-to-Jamming Ratio (SJR). Simulation results show that the combined Successive Cancellation List (SCL) decoder with anti-jamming achieves the best performance, offering about a 3 dB gain over standalone SCL decoding. The study also demonstrates improved physical-layer security through enhanced secrecy capacity. Full article

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9 pages, 4143 KB

Open AccessProceeding Paper

Vibro-Acoustic Analysis of a Thin Aluminum Plate with a Clamped Central Hole Using FEM and Acoustic Radiation Models

by Theodora-Maria Maroulaki, Despoina Grigoriou, Yannis Orphanos, Nektarios A. Papadogiannis, Vasilis Dimitriou and Evaggelos Kaselouris

Eng. Proc. 2026, 154(1), 12; https://doi.org/10.3390/engproc2026154012 - 28 Aug 2026

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Abstract

This study investigates the vibro-acoustic behavior of a thin square aluminum plate with a central clamped hole, inspired by splash cymbal configurations. The structural dynamics are analyzed using finite element method (FEM) modal and frequency response function (FRF) simulations, while experimental measurements validate [...] Read more.

This study investigates the vibro-acoustic behavior of a thin square aluminum plate with a central clamped hole, inspired by splash cymbal configurations. The structural dynamics are analyzed using finite element method (FEM) modal and frequency response function (FRF) simulations, while experimental measurements validate the modal characteristics. A semi-analytical Rayleigh–Ritz model is also employed to estimate the natural frequencies and provide an additional reference for comparison. Excellent agreement is observed between the predicted and measured resonance frequencies and overall dynamic response. Acoustic radiation is evaluated through coupled structural–acoustic analyses using a Rayleigh integral formulation and an indirect variational boundary element method (BEM). The sound pressure radiated to a field point above the plate is compared with the FEM-derived FRF results. The results demonstrate a correlation between structural vibration and acoustic response, with variations in radiation efficiency influenced by the central clamp and higher-frequency modal behavior. The Rayleigh approach shows good agreement but exhibits numerical noise and overprediction of modal contributions. In contrast, the BEM provides smoother responses and more physically consistent results. These findings are relevant for noise control, vibration reduction, and sound synthesis applications in mechanical systems. Full article

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12 pages, 2375 KB

Open AccessProceeding Paper

Hierarchical Security Framework for Drone Control in Parcel Delivery

by Ivan Ivanov and Filip Tsvetanov

Eng. Proc. 2026, 154(1), 13; https://doi.org/10.3390/engproc2026154013 - 31 Aug 2026

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Abstract

Secure delivery of packages by drones requires reliable management of cryptographic keys, despite limited on-board resources, multiple users, and the risk of interception or manipulation. This paper proposes a hierarchical model of a Key Distribution Center that manages the generation, distribution, storage, control, [...] Read more.

Secure delivery of packages by drones requires reliable management of cryptographic keys, despite limited on-board resources, multiple users, and the risk of interception or manipulation. This paper proposes a hierarchical model of a Key Distribution Center that manages the generation, distribution, storage, control, and destruction of cryptographic keys. The model uses public-key mechanisms for secure transmission of session keys and lightweight symmetric encryption for communication between the drone and the recipient. A session key lifecycle is defined, including verification, encrypted storage, validity control, and secure deletion. The operational workflow for the secure delivery of packages is also described. The proposed approach improves confidentiality, scalability, and access control. Full article

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8 pages, 2352 KB

Open AccessProceeding Paper

An Automated Workflow for Processing and 3D Visualization of Multi-Component Seismic Signals Using IRIS Telemetry Data

by Muazzam Artikova and Dilshodbek Jamoliddinov

Eng. Proc. 2026, 154(1), 14; https://doi.org/10.3390/engproc2026154014 - 1 Sep 2026

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Abstract

This paper presents an automated computational workflow for the acquisition, instrument-response correction and three-dimensional visualization of multi-component seismic records obtained from the IRIS Federation of Digital Seismograph Networks (FDSNs) using the open-source ObsPy (v1.5.0) package. The workflow targets engineering applications and consists of [...] Read more.

This paper presents an automated computational workflow for the acquisition, instrument-response correction and three-dimensional visualization of multi-component seismic records obtained from the IRIS Federation of Digital Seismograph Networks (FDSNs) using the open-source ObsPy (v1.5.0) package. The workflow targets engineering applications and consists of four stages: (i) selection of three-component (3C) broadband stations, (ii) bandpass filtering and spectral deconvolution of the instrument response to obtain ground displacement in physical units, (iii) calculation of theoretical P- and S-wave arrival times with the Tau-P kinematic algorithm based on the IASP91 reference Earth velocity model, and (iv) construction of an interactive 3D particle motion visualization in which segments associated with the P-wave, S-wave and background are color-coded. The pipeline is demonstrated on three seismic events recorded in February 2023 by the broadband station KO.BNN, including the destructive Mw 7.8 Kahramanmaraş earthquake. The workflow yields the absolute three-dimensional displacement vector and produces interactive visualizations that are intended for use by structural engineers as a complement to traditional one-dimensional acceleration records. Full article

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9 pages, 1951 KB

Open AccessProceeding Paper

Artificial Neural Network Model for Predicting Transients in a Heating and Domestic Hot Water System with Data from ThingSpeak

by Mariyana Sestrimska and Nikolay Komitov

Eng. Proc. 2026, 154(1), 15; https://doi.org/10.3390/engproc2026154015 - 1 Sep 2026

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Abstract

Modeling an artificial neural network to predict the behavior of a heating and domestic hot water system in a residential building is the basis of this report. The heating system includes three types of energy sources: biofuel, solar, and electricity. During transitional seasons, [...] Read more.

Modeling an artificial neural network to predict the behavior of a heating and domestic hot water system in a residential building is the basis of this report. The heating system includes three types of energy sources: biofuel, solar, and electricity. During transitional seasons, such as spring and autumn, it is possible to use all three sources periodically or simultaneously, which leads to increased consumption and inefficiency in the system. In order to study the dynamics of the process and optimize the control, a monitoring system based on Raspberry Pi Pico W was developed. Data on the operating parameters of the boiler, tank, and radiator is transmitted in real-time to the ThingSpeak cloud platform, using the free access option that requires only registration. Due to the reinforced concrete structure of the building, the local Wi-Fi connection drops at times, so an additional router was added. Although individual values from the data are missing, they can be used to obtain graphs of the transient process. The data received in the platform does not arrive evenly due to the time required for transmission. The artificial neural network was created in MATLAB (v2025, MathWorks, Natick, MA, USA) and uses the raw data to predict changes in the processes. The missing fragments of the data have no impact, and a good match of the actual values with the predicted values was obtained for the tank and radiator, while for the boiler, the match was satisfactory. Full article

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10 pages, 1974 KB

Open AccessProceeding Paper

Influence of Grounding Impedance on the Magnitude of Atmospheric Overvoltages

by Krasimir Ivanov, Georgi Velev and Ivaylo Lazarov

Eng. Proc. 2026, 154(1), 16; https://doi.org/10.3390/engproc2026154016 - 1 Sep 2026

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Abstract

Lightning strikes with large current amplitudes and a steep current wave front in cases of multiple lightning strikes can cause significant overvoltages. The number of overvoltage impulses depends significantly on the lightning parameters, the overhead line parameters and the tower grounding impedance. While [...] Read more.

Lightning strikes with large current amplitudes and a steep current wave front in cases of multiple lightning strikes can cause significant overvoltages. The number of overvoltage impulses depends significantly on the lightning parameters, the overhead line parameters and the tower grounding impedance. While the probability for back flashovers from towers to phase conductors (power line trips) depends on lightning current amplitude, impulse steepness and tower grounding resistance, the magnitudes of overvoltages reaching the nearby substation, Ground Potential Rise (GPR) and step and touch voltage distribution profiles depend exclusively on substation grounding impedance. In order to study and take into account such a large number of parameters influencing the probability for overvoltages to occur, several models in the ATP simulation program (v. 7.6) have been implemented. This allows us to study the influence of tower and substation grounding impedances on the magnitude and the form of atmospheric overvoltages and their impact on the safety of personnel in the substation area in cases of direct lightning strikes on nearby power line towers or lightning protection cables. A differentiation has been made between grounding resistance and grounding impulse impedance as a prerequisite for more accurate modeling and more precise calculations. Full article

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10 pages, 1725 KB

Open AccessProceeding Paper

Emotion Analysis for the Protection of Industrial Enterprises and Institutions via the IoT

by Gökhan Uçkan and Engin Oğuzay

Eng. Proc. 2026, 154(1), 17; https://doi.org/10.3390/engproc2026154017 - 1 Sep 2026

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Abstract

Emotion analysis through facial recognition has significant potential for enhancing security within organizational and industrial contexts by enabling early risk detection, reducing human error, and supporting real-time decision-making. However, existing systems often demand high computational resources and advanced imaging equipment, limiting their adoption [...] Read more.

Emotion analysis through facial recognition has significant potential for enhancing security within organizational and industrial contexts by enabling early risk detection, reducing human error, and supporting real-time decision-making. However, existing systems often demand high computational resources and advanced imaging equipment, limiting their adoption in small and medium-sized facilities. This study presents a cost-effective and practical emotion analysis application based on facial recognition, designed for seamless integration into diverse environments and IoT platforms. The system dynamically adapts its operation to the emotional state of the authorized user, mitigating potential errors when psychological resilience is insufficient. A Convolutional Neural Network (CNN) model was developed for emotion analysis using Conv2D, MaxPooling2D, and Dropout layers. Additionaly, several face recognition algorithms were tested to ensure reliable identification under different conditions. The proposed emotion analysis model achieved 98% overall accuracy and an 89% weighted F1 score. The system was trained and tested on a dataset with 58,179 training images and 28,706 test images, reaching the highest validation accuracy of 98.71% at the 25th epoch. By using a face recognition library, the system provided both high accuracy and fast processing. The developed face recognition-based folder security system offers a more user-friendly and innovative alternative to traditional password-based security methods. Full article

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9 pages, 880 KB

Open AccessProceeding Paper

Influence of Deformation Measurement Time on the Rubber Element of a ‘SEGME’ Flexible Coupling

by Stefan Tenev, Aleksandrina Bankova, Anastas Yangyozov and Asparuh Atanasov

Eng. Proc. 2026, 154(1), 18; https://doi.org/10.3390/engproc2026154018 - 1 Sep 2026

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Abstract

Particular attention is devoted to the characteristics of elastic couplings as essential connecting elements within machine drive systems. The operational characteristics of such couplings are determined under both static and dynamic loading conditions, while also taking into consideration the nature of the working [...] Read more.

Particular attention is devoted to the characteristics of elastic couplings as essential connecting elements within machine drive systems. The operational characteristics of such couplings are determined under both static and dynamic loading conditions, while also taking into consideration the nature of the working environment and the specific type of driven machinery, such as crushing machines, mills, and related industrial equipment. The relationship between the transmitted torque load and the deformation of the coupling’s rubber working elements constitutes a significant factor contributing to the compensation of vibrations and shaft misalignments. Furthermore, the rubber elements are characterized by inherent damping properties and residual elastic deformation. The parameters governing damping behavior and deformation exhibit variations over time, which is of particular importance in deformation analysis and measurement. A series of experimental investigations was carried out to evaluate the influence of measurement time on the deformation state of the rubber element in a “SEGME”-type coupling. Four time intervals—5, 10, 20, and 30 s—are considered for recording the instantaneous deformation of the working element. Based on the obtained experimental data, mathematical relationships describing the energy dissipation and damping capacity of the rubber working elements are established as functions of the deformation measurement time. In addition, a coefficient characterizing the influence of deformation measurement duration was determined. Full article

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12 pages, 907 KB

Open AccessProceeding Paper

Development of a Neural Network for Early Detection and Prevention of Insider Threats in Civil Aviation

by Darena Mihaylova and Velizar Vassilev

Eng. Proc. 2026, 154(1), 19; https://doi.org/10.3390/engproc2026154019 - 1 Sep 2026

Viewed by 79

Abstract

This study aims to develop a neural network for the early detection and prevention of insider threats in civil aviation. The approach is based on 23 behavioral indicators represented as binary input features. A multilayer neural network with two hidden layers is designed [...] Read more.

This study aims to develop a neural network for the early detection and prevention of insider threats in civil aviation. The approach is based on 23 behavioral indicators represented as binary input features. A multilayer neural network with two hidden layers is designed to model nonlinear relationships between the indicators. Due to the lack of real data, a synthetic dataset is generated based on expert-defined rules. The model is trained using a supervised learning approach and evaluated using standard metrics. The results show stable training and good generalization capability, achieving 89.36% accuracy on the test dataset, confirming the applicability of the proposed approach. Full article

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10 pages, 1382 KB

Open AccessProceeding Paper

Robust UAV Detection and Classification in Noisy RF Environments via Multimodal CNN-Based IQ and Spectrogram Fusion

by Tarık Talan, Serkan Gökkaya, Adem Korkmaz and Ivan Beloev

Eng. Proc. 2026, 154(1), 20; https://doi.org/10.3390/engproc2026154020 - 2 Sep 2026

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Abstract

This study aims to autonomously detect and classify Unmanned Aerial Vehicles (UAVs), which are becoming increasingly widespread, based on their emitted radio frequency (RF) signals in highly noisy and interference-prone RF environments where traditional optical and radar-based methods prove insufficient. The dataset used [...] Read more.

This study aims to autonomously detect and classify Unmanned Aerial Vehicles (UAVs), which are becoming increasingly widespread, based on their emitted radio frequency (RF) signals in highly noisy and interference-prone RF environments where traditional optical and radar-based methods prove insufficient. The dataset used in this study is the “Noisy Drone RF Signal Classification” dataset, which consists of seven classes in total—six different drone models and one noise class—and is balanced across classes. As the methodological approach, a multimodal hybrid deep learning (DL) architecture was developed, integrating raw time-domain IQ signals (1D-CNN) with frequency-domain spectrogram representations (2D-CNN). To enhance model performance and generalization capability, techniques such as Batch Normalization, Dropout, Label Smoothing, and Gradient Clipping were incorporated into the architecture. The model was trained using the AdamW optimization algorithm alongside a cosine annealing learning rate scheduler. In order to efficiently process large-scale datasets, memory management optimizations including memory-mapped files (mmap) and lazy loading were implemented. Experimental results demonstrate that the proposed multimodal model achieves an accuracy of 87.42% and an F1-score of 0.87, indicating strong detection performance. SNR-based analyses reveal that the model achieves accuracy levels ranging between 64.52% and 82.40% under negative-SNR conditions (−20 to 0 dB), while exceeding 89% accuracy under positive-SNR conditions. The findings indicate that multimodal DL approaches can achieve high performance in UAV detection and classification even in noisy RF environments. Full article

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9 pages, 3442 KB

Open AccessProceeding Paper

Investigation of Stepper Motor Position Drift in a Part Feeding System

by Penko Mitev, Mariyan Milev, Atanasi Tashev, Yordan Stoyanov and Kiril Mitev

Eng. Proc. 2026, 154(1), 21; https://doi.org/10.3390/engproc2026154021 - 2 Sep 2026

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Abstract

This paper investigates the position drift in a stepper motor used within a part feeding system for the orientation of cylindrical components. The system employs a bowl feeder, orientation sensors, and an indexing mechanism with eight precise positions at 45° intervals. During commissioning, [...] Read more.

This paper investigates the position drift in a stepper motor used within a part feeding system for the orientation of cylindrical components. The system employs a bowl feeder, orientation sensors, and an indexing mechanism with eight precise positions at 45° intervals. During commissioning, a gradual deviation between the theoretical and actual angular positions of the stepper mechanism was observed, leading to operational interruptions. Experimental tests were conducted to analyze the source of the drift by comparing theoretical and encoder-based angular positions over a large number of repetitions. The results revealed a small but cumulative positioning error caused by the recursive generation of new setpoints from encoder feedback. To eliminate this effect, an improved control strategy was implemented, where each new setpoint is calculated from the previous theoretical value. This modification effectively eliminates the accumulation of measurement variations and prevents long-term drift. Experimental validation demonstrated a significant improvement in long-term positioning accuracy, reducing the total deviation to approximately 0.1–0.2° and enabling stable system operation over extended periods. The proposed solution is simple to implement and enhances the reliability and precision of automated part feeding systems. Full article

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10 pages, 1210 KB

Open AccessProceeding Paper

Reliability Analysis of Spacecraft Onboard Control Systems Based on Graph Models

by Aizhan Oshmanova, Valentina Grichshenko and Ivaylo Stoyanov

Eng. Proc. 2026, 154(1), 22; https://doi.org/10.3390/engproc2026154022 - 2 Sep 2026

Viewed by 93

Abstract

This paper presents a comparative reliability analysis of spacecraft onboard control systems based on their structural representation using directed graph models. A modified approach to reliability assessment is proposed, grounded in representing onboard control systems as directed functional graphs and applied to the [...] Read more.

This paper presents a comparative reliability analysis of spacecraft onboard control systems based on their structural representation using directed graph models. A modified approach to reliability assessment is proposed, grounded in representing onboard control systems as directed functional graphs and applied to the analysis of spacecraft architectures in terms of structural fault tolerance. The initial functional schemes are simplified by identifying key system elements and the relationships between them. Based on the resulting representations, graph models are constructed, reflecting the structure of control signal propagation. Structural reliability and fault tolerance assessment are performed using an analysis of graph topological characteristics, including connectivity, graph centrality metrics, the presence of alternative paths, and identification of critical nodes. For systems with a comparable number of elements, differences in structural connectivity significantly affect system resilience to failures. It is shown that a higher degree of connectivity and the presence of redundant control pathways enhance the fault tolerance of the onboard control system. The obtained results confirm the effectiveness of graph-based models for structural reliability and robustness analysis of complex technical systems and can be applied in the design of spacecraft onboard control systems. The proposed approach focuses on the structural and topological properties of the system architecture and does not include probabilistic failure modeling. Full article

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14 pages, 1157 KB

Open AccessProceeding Paper

Integration of Large Language Models in Layered Software Systems: A Clean Architecture and CQRS Case Study

by Antonina Ivanova, Georgi Kolev, Fatima Sapundzhi, Teodora Bakardjieva and Slavi Georgiev

Eng. Proc. 2026, 154(1), 23; https://doi.org/10.3390/engproc2026154023 - 2 Sep 2026

Viewed by 112

Abstract

Large Language Models (LLMs) are increasingly incorporated into software systems. Their non-deterministic behavior, external hosting, response latency, and operational cost create challenges for established design approaches such as Clean Architecture. This paper examines the integration of an LLM component into a layered software [...] Read more.

Large Language Models (LLMs) are increasingly incorporated into software systems. Their non-deterministic behavior, external hosting, response latency, and operational cost create challenges for established design approaches such as Clean Architecture. This paper examines the integration of an LLM component into a layered software system and compares three possible placements within Clean Architecture: Domain, Application, and Infrastructure. The evaluation considers dependency management, testability, separation of concerns, and implementation complexity. The study proposes an approach in which the LLM is implemented in the Infrastructure layer and accessed through an interface defined in the Application layer. This approach is combined with the Command and Query Responsibility Segregation pattern to isolate LLM interaction within dedicated query handlers. The proposed pattern is demonstrated through the implementation of Budget, a personal finance tracking system that uses GPT-4.1 to convert free-form natural language input into structured transaction records. The results show that placement in the infrastructure layer provides the clearest separation between business logic and external AI services and avoids the introduction of non-deterministic behavior into the core application logic. Full article

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10 pages, 681 KB

Open AccessProceeding Paper

Improving Energy Performance in Polyethylene Film Production: A Real Industrial Case Study with Subsystem-Level Analysis

by Penka Zlateva, Angel Terziev, Krastin Yordanov and Nevena Mileva

Eng. Proc. 2026, 154(1), 24; https://doi.org/10.3390/engproc2026154024 - 2 Sep 2026

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Abstract

This study presents a system-level and subsystem-level evaluation of energy performance in an industrial polyethylene film production system based on real operational data. The analysis focuses on specific energy consumption (SEC) as a key performance indicator, considering both total system behavior and the [...] Read more.

This study presents a system-level and subsystem-level evaluation of energy performance in an industrial polyethylene film production system based on real operational data. The analysis focuses on specific energy consumption (SEC) as a key performance indicator, considering both total system behavior and the contribution of energy-intensive subsystems, namely extrusion and converting processes. The initial system exhibits SEC values ranging from 1.267 to 1.688 kWh/kg, indicating relatively high energy intensity compared to established industrial benchmarks. Following technological modernization, SEC is reduced to 0.43 kWh/kg for extrusion and 0.09 kWh/kg for converting, corresponding to improvements exceeding 60%. The total annual energy saving potential is estimated at 906 MWh (39%), accompanied by a proportional reduction in CO₂ emissions. The results demonstrate that energy performance is strongly influenced by production load, material losses, and process stability. The novelty of the study lies in the subsystem-level quantification of energy performance using real industrial data and in the identification of the interaction between production efficiency, waste generation, and energy consumption. The findings provide a practical framework for energy optimization in polymer processing systems and allow comparison with European best practices. Full article

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8 pages, 3230 KB

Open AccessProceeding Paper

Architecture of Cross-Platform Distributed Hierarchical System for Real-Time Network Monitoring

by Veneta Aleksieva and Hristo Valchanov

Eng. Proc. 2026, 154(1), 25; https://doi.org/10.3390/engproc2026154025 - 2 Sep 2026

Viewed by 82

Abstract

In today’s digital infrastructure, organizations and enterprises increasingly rely on distributed and remote IT environments. These environments may include multiple network sites, remote branch offices, employee home networks, or servers hosted in geographically distant locations. Monitoring and managing these networks presents a critical [...] Read more.

In today’s digital infrastructure, organizations and enterprises increasingly rely on distributed and remote IT environments. These environments may include multiple network sites, remote branch offices, employee home networks, or servers hosted in geographically distant locations. Monitoring and managing these networks presents a critical challenge, especially when administrators are expected to maintain security, performance, and uptime across all nodes. This paper presents an architecture for a cross-platform, multi-tenant network monitoring and management system that enables centralized visibility and control over a distributed infrastructure. Full article

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10 pages, 955 KB

Open AccessProceeding Paper

Adaptive Estimation of Risk in Gas Distribution Networks with an Extended Kalman Filter–Monte Carlo Framework

by Antoaneta P. Ivanova-Bares

Eng. Proc. 2026, 154(1), 26; https://doi.org/10.3390/engproc2026154026 - 2 Sep 2026

Viewed by 78

Abstract

Static forecast models used by gas distribution operators in regulatory submissions provide point estimates but cannot quantify the probability that approved targets will be met. This paper proposes an Extended Kalman Filter–Monte Carlo (EKF–MC) framework that (i) jointly estimates the residential client state [...] Read more.

Static forecast models used by gas distribution operators in regulatory submissions provide point estimates but cannot quantify the probability that approved targets will be met. This paper proposes an Extended Kalman Filter–Monte Carlo (EKF–MC) framework that (i) jointly estimates the residential client state and the parameters of the logistic S-curve growth model in a sequential Bayesian setting, and (ii) propagates the posterior parameter uncertainty through 100,000 Monte Carlo draws to construct calibrated probability distributions for the 2026–2027 regulatory forecast horizon. Applied to five years of regulatory submission data (2021–2025) for a licensed gas distribution operator in Sofia Province, Bulgaria, the EKF refines the saturation ceiling to M = 1995 ± 30 and the growth rate to r = 0.561 ± 0.083. The Monte Carlo analysis yields 90% forecast intervals of [1930–2002] for 2026 and [1939–2015] for 2027. Both intervals lie entirely below the regulator-approved targets (2039 and 2150), demonstrating a structural over-forecasting tendency in the regulatory approval process. The framework provides operators with a computationally efficient, auditable tool for quantifying forecast uncertainty in rate-case submissions and capital investment planning. Full article

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12 pages, 13392 KB

Open AccessProceeding Paper

Power System Operating and Managing Under Rapidly Changing Amounts of Grid-Inverter-Connected RES

by Dimitrina Koeva, Ivaylo Ivanov, Dimitar Slavov, Georgi Bankov and Metodi Dimitrov

Eng. Proc. 2026, 154(1), 27; https://doi.org/10.3390/engproc2026154027 - 2 Sep 2026

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Abstract

The energy sector is considered one of the most critical sectors because disruptions to the power supply, problems with systems for monitoring operational parameters, and issues with power management processes have a direct impact on the economy, public order, and national security. On [...] Read more.

The energy sector is considered one of the most critical sectors because disruptions to the power supply, problems with systems for monitoring operational parameters, and issues with power management processes have a direct impact on the economy, public order, and national security. On the one hand, the technological maturity of energy facilities is changing, and the share of grid-inverter-connected renewable energy sources (GICRES) is expanding. In this article, the authors discuss the basic concepts and classifications of stability-related subsystems. The new trends and challenges facing the resilience of the electrical energy system (EES) in the context of dynamic processes arising from the deployment of renewable energy sources (RES) are analyzed. This paper aims to present a more comprehensive concept of the static and dynamic stability of the EES, considering the impact of grid-inverter-connected renewable energy sources (GICRES). Full article

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6 pages, 3691 KB

Open AccessProceeding Paper

The Effect of Nodes’ Transmission Distance on Routing and Network Lifetime in Wireless Sensor Networks

by Demet Büyükkaya, Sami Açık, Hamid Yılmaz, Selahattin Koşunalp and Mehmet Barış Tabakcıoğlu

Eng. Proc. 2026, 154(1), 28; https://doi.org/10.3390/engproc2026154028 - 3 Sep 2026

Viewed by 79

Abstract

In wireless sensor networks, sensor nodes (SNs) powered by small-capacity batteries or supercapacitors are used to monitor an environment. These nodes are typically deployed in areas with harsh environmental conditions, such as volcanic craters and forests. They rapidly deplete their energy by continuously [...] Read more.

In wireless sensor networks, sensor nodes (SNs) powered by small-capacity batteries or supercapacitors are used to monitor an environment. These nodes are typically deployed in areas with harsh environmental conditions, such as volcanic craters and forests. They rapidly deplete their energy by continuously consuming power during data generation, transmission, reception, and idle states. In this study, a Mixed Integer Linear Programming (MILP) model aimed at maximizing the network lifetime of sensor-node-based systems is briefly introduced, and the effects of varying the transmission distances of nodes on routing and network lifetime are discussed. The MILP model is implemented on a 10-node scenario (1 data-generating node, 1 data-collecting node, and 8 transceiver nodes), and its impact on routing and network lifetime is analyzed. For each scenario, 100 simulations were conducted and validated on real hardware. Full article

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13 pages, 808 KB

Open AccessProceeding Paper

Comparative Evaluation of AI-Assisted Systematic Literature Reviews: NotebookLM, ChatGPT-4o, and Gemini 1.5 Pro in Wearable Technology, Smart Textiles, and Sustainable Fashion Research

by Selma Bulut, Beyza Buzol Mülayim and Bozhana Stoycheva

Eng. Proc. 2026, 154(1), 29; https://doi.org/10.3390/engproc2026154029 - 3 Sep 2026

Viewed by 146

Abstract

This study compares three AI platforms, Google NotebookLM (Google Labs, May 2026 release/access), ChatGPT-4o, and Gemini 1.5 Pro, for systematic literature review (SLR) tasks in sustainable textile and fashion research. A corpus of 40 peer-reviewed articles (2019–2025) was queried using 12 standardized prompts [...] Read more.

This study compares three AI platforms, Google NotebookLM (Google Labs, May 2026 release/access), ChatGPT-4o, and Gemini 1.5 Pro, for systematic literature review (SLR) tasks in sustainable textile and fashion research. A corpus of 40 peer-reviewed articles (2019–2025) was queried using 12 standardized prompts across four dimensions: source traceability, thematic synthesis, research gap identification, and output utility. NotebookLM achieved the highest weighted score (8.63/10) due to its document-grounded, structurally constrained architecture, which produced no out-of-corpus citations under the conditions of this study. ChatGPT-4o (8.50/10) excelled in narrative synthesis; Gemini 1.5 Pro (7.78/10) produced superior structured tables. Five corpus-grounded and two inferred gaps, including XAI, Federated Learning, and Edge AI, were identified. A four-step hybrid AI workflow is proposed. Full article

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8 pages, 832 KB

Open AccessProceeding Paper

Scalable Edge-Based Multilingual Speech Processing for Secure Communication Environments

by Baurzhan Abzhanov, Yernazar Ishanov and Malike Kazhimanova

Eng. Proc. 2026, 154(1), 30; https://doi.org/10.3390/engproc2026154030 - 3 Sep 2026

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Abstract

This paper presents a scalable edge-based multilingual speech processing system designed for secure communication environments with limited or intermittent network access. The proposed architecture integrates speech enhancement, language identification, streaming automatic speech recognition, confidence-aware re-ranking, and secure local output delivery within a unified [...] Read more.

This paper presents a scalable edge-based multilingual speech processing system designed for secure communication environments with limited or intermittent network access. The proposed architecture integrates speech enhancement, language identification, streaming automatic speech recognition, confidence-aware re-ranking, and secure local output delivery within a unified on-premise framework. Experimental evaluation was conducted using a multilingual corpus containing clean speech, command phrases, and degraded radio-channel recordings in Kazakh, Russian, English, and Turkish. Results of the experiments indicate that practical deployment performance depends not only on recognition accuracy, but also on latency stability, thermal resilience, queue management, and robustness to mixed-language noisy speech. Full article

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7 pages, 1217 KB

Open AccessProceeding Paper

Investigation of the Impact of Resonant Breathing Frequency on Heart-Rate Rhythm Using an Arduino-Based PPG Sensor System with Real-Time Python Visualization

by Enis Mustafa, Serdzhan Murad, Aleksandar Kolev and Elena Tolstosheeva

Eng. Proc. 2026, 154(1), 31; https://doi.org/10.3390/engproc2026154031 - 3 Sep 2026

Viewed by 61

Abstract

Mental stress has become a significant health issue because it can disrupt autonomic balance and alter breathing patterns. Breathing techniques, especially when monitored by the HeartMath coherence device, are popular for promoting balanced heart–mind interaction. In this project, we developed an open-source Arduino-based [...] Read more.

Mental stress has become a significant health issue because it can disrupt autonomic balance and alter breathing patterns. Breathing techniques, especially when monitored by the HeartMath coherence device, are popular for promoting balanced heart–mind interaction. In this project, we developed an open-source Arduino-based photoplethysmography (PPG) system capable of recording heart pulse signals and displaying them on a specialized Python interface. The heart pulse amplitudes are displayed to allow observation of breathing modulation as an envelope of the PPG amplitude train. Simultaneously, the increase in heart rate during inhalation and the decrease during exhalation are displayed. The Python interface enables real-time monitoring of how 0.1 Hz rhythmic breathing exercises synchronize the heart-rate waveform with the PPG amplitude envelope. The proposed platform can serve as a basis for future studies on breathing techniques to foster a harmonious state, enhancing productivity, creativity, and inner peace. Full article

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16 pages, 3669 KB

Open AccessProceeding Paper

Communication Failures in UAV-Based Wildfire Monitoring: Causes, Cascading Effects, and Resilience Strategies

by Filip Tsvetanov and Ivan Ivanov

Eng. Proc. 2026, 154(1), 32; https://doi.org/10.3390/engproc2026154032 - 3 Sep 2026

Viewed by 99

Abstract

Unmanned aerial vehicles (UAVs) are increasingly used for wildfire and disaster monitoring, enabling rapid data collection in hazardous, inaccessible areas. Secure and reliable communication is a major challenge in wildfire monitoring, as heat, smoke, terrain obstacles, and electromagnetic interference can degrade or interrupt [...] Read more.

Unmanned aerial vehicles (UAVs) are increasingly used for wildfire and disaster monitoring, enabling rapid data collection in hazardous, inaccessible areas. Secure and reliable communication is a major challenge in wildfire monitoring, as heat, smoke, terrain obstacles, and electromagnetic interference can degrade or interrupt data transmission. This paper analyzes communication failures in drone-based wildfire-monitoring systems, examining their causes, evolution, and operational implications. A multi-layered analytical framework integrating physical, technical, network, and security aspects is proposed. The study highlights cascading failure processes and supports the design of more resilient UAV communication architectures for dynamic wildfire environments. Full article

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9 pages, 2310 KB

Open AccessProceeding Paper

Dual-Band Graphene-Based Patch Antenna for Terahertz Imaging and Future 6G Communication Systems

by Rakesh N. Tiwari, M. Jyoshna, Prabhakar Singh, Pradeep Kumar, B. Harshitha and K. Iswarya

Eng. Proc. 2026, 154(1), 33; https://doi.org/10.3390/engproc2026154033 - 3 Sep 2026

Viewed by 159

Abstract

This paper presents the design and analysis of a graphene-based patch antenna for terahertz (THz) communication applications. The proposed antenna has a compact footprint of 60 μm × 60 μm and employs an inset-fed modified graphene patch to achieve dual-band operation. The antenna [...] Read more.

This paper presents the design and analysis of a graphene-based patch antenna for terahertz (THz) communication applications. The proposed antenna has a compact footprint of 60 μm × 60 μm and employs an inset-fed modified graphene patch to achieve dual-band operation. The antenna exhibits resonances at 0.58 THz and 1.01 THz, with |S₁₁| below −30 dB, indicating excellent impedance matching. The tunability of graphene is investigated by varying the chemical potential and relaxation time, and the antenna performance is optimized accordingly. It is observed that higher values of chemical potential and relaxation time enhance the surface conductivity of graphene, resulting in improved impedance matching and radiation efficiency. A detailed parametric study is also carried out by varying the dimensions of the slot etched on the patch. The results demonstrate that slot dimensions significantly influence both impedance matching and resonance frequency tuning. The antenna achieves a gain > 2.53 dBi and total efficiency exceeding 60% across both operating bands. The radiation patterns in both E- and H-planes are nearly omnidirectional at 0.58 THz and 1.01 THz, making the design suitable for near-field THz applications. The electromagnetic performance of the proposed graphene patch antenna is analyzed using CST Microwave Studio. Full article

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13 pages, 14146 KB

Open AccessProceeding Paper

Off-the-Shelf Piezoelectric Vibration Sensor Studied as Low-Energy Piezo Harvester

by Aleksandar Mandadzhiev, Ivaylo Belovski and Kaloyan Ivanov

Eng. Proc. 2026, 154(1), 34; https://doi.org/10.3390/engproc2026154034 - 3 Sep 2026

Viewed by 145

Abstract

With the growing power demand of small electronic devices worldwide, piezoelectric energy harvesting has become a promising solution for low-power energy generation. The primary objective of this study is to experimentally assess the energy harvesting capabilities of a commercially available piezoelectric sensor. The [...] Read more.

With the growing power demand of small electronic devices worldwide, piezoelectric energy harvesting has become a promising solution for low-power energy generation. The primary objective of this study is to experimentally assess the energy harvesting capabilities of a commercially available piezoelectric sensor. The output performance of the piezoelectric harvester is characterized, with particular focus on the generated output voltage and output power, under different resistive loads and various operating conditions, including acceleration amplitude and frequency of mechanical excitation. Full article

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14 pages, 5184 KB

Open AccessProceeding Paper

Application of Artificial Neural Networks for Signal Recovery After Chaotic Switching-Based Encryption

by Hristina Stoycheva and Georgi Mihalev

Eng. Proc. 2026, 154(1), 35; https://doi.org/10.3390/engproc2026154035 - 3 Sep 2026

Viewed by 147

Abstract

The problem of recovering an information sequence in the signal decoding process under the application of a chaotic switching scheme via synchronization is examined in the article. A classical chaotic switching scheme based on the bifurcation parameter of the Lorenz system is used, [...] Read more.

The problem of recovering an information sequence in the signal decoding process under the application of a chaotic switching scheme via synchronization is examined in the article. A classical chaotic switching scheme based on the bifurcation parameter of the Lorenz system is used, in which the two logical levels are encoded through fundamentally different system dynamics. Decoding is performed by means of chaotic synchronization, whereby the encoding system with one value of the bifurcation parameter, located in the transmitter, is synchronized with another system located in the receiver. Through the use of a pretrained nonlinear autoregressive artificial neural network (NARX), dynamic nonlinear signal reconstruction through input–output alignment (DNRS-IOA) is implemented for the recovery of the information sequence. Experiments are performed in the MATLAB environment, and graphical and numerical results are presented, showing low error levels (MSE = 0.002) and high correlation levels (R-value = 0.9998) between the output of the neural network and the reference signal. Full article

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3270 KB

Open AccessProceeding Paper

Design of Modular Mold for Injection Molding of Small-Sized Nonmetal Parts

by Anna-Maria Lazarova, Stiliyan Nikolov and Reneta Dimitrova

Eng. Proc. 2026, 154(1), 36; https://doi.org/10.3390/engproc2026154036 - 3 Sep 2026

Abstract

The article analyzes the design of molds for the production of nonmetallic parts by injection molding. Based on the analysis on an existing mold, a mold with a modular design was developed in environment of the SolidWorks Version 2022 CAD system. The developed [...] Read more.

The article analyzes the design of molds for the production of nonmetallic parts by injection molding. Based on the analysis on an existing mold, a mold with a modular design was developed in environment of the SolidWorks Version 2022 CAD system. The developed design of the matrix serves for the production of small-sized nonmetallic parts, with the possibility of readjustment when changing the parts being produced. The main steps necessary for readjusting the developed mold when changing the manufactured parts are defined. The proposed modular mold design was manufactured and tested during injection molding of two parts, “Handle” and “Fork”. The results obtained during the testing show the operability of the proposed design. The proposed modular mold design reduces the time and costs required to transition production from one part to another. Full article

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13 pages, 4688 KB

Open AccessProceeding Paper

Engineering a Two-Player Card Game in Godot 4 and GDScript: Architecture, Shader Design, and Artificial Intelligence Decision Making

by Ufuk Celik, Adem Korkmaz and Georgi Krastev

Eng. Proc. 2026, 154(1), 37; https://doi.org/10.3390/engproc2026154037 - 3 Sep 2026

Viewed by 103

Abstract

This paper presents a software engineering case study on designing a reusable architecture for interactive 2D card games using Godot 4 and GDScript. Rather than focusing only on a specific game implementation, the study investigates generalizable engineering patterns for managing interactive objects, visual [...] Read more.

This paper presents a software engineering case study on designing a reusable architecture for interactive 2D card games using Godot 4 and GDScript. Rather than focusing only on a specific game implementation, the study investigates generalizable engineering patterns for managing interactive objects, visual feedback, user interaction, and decision-making systems. A unified Item/Pool architecture is introduced to represent cards and game containers through reusable abstractions, reducing duplicated logic among different scene types. The proposed design separates input handling, object management, and rule enforcement through a signal-driven architecture. A canvas-item shader pipeline demonstrates how perspective tilt and dynamic shadow effects can be achieved in a 2D environment without requiring a 3D rendering pipeline. Furthermore, a transform-aware drop detection method based on affine inverse transformation is presented to overcome interaction errors caused by rotated interfaces. For opponent behavior, a deterministic rule-based artificial intelligence model is developed using weighted suit evaluation, legal-card filtering, and context-dependent card selection strategies. The results show that Godot 4’s scene instancing, typed GDScript, shader materials, and signal system provide suitable mechanisms for building maintainable interactive applications. The discussed solutions and engineering lessons can be applied to a broader class of UI-driven 2D games beyond card games. Full article

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10 pages, 984 KB

Open AccessProceeding Paper

Coordinated Control of a Dual-Motor Mobile Robot for Stable Motion on Uneven Surfaces

by Alina Fazylova, Kuanysh Alipbayev, Kenzhebek Myrzabekov, Fariza Oraz and Bagdat Sabyruly

Eng. Proc. 2026, 154(1), 38; https://doi.org/10.3390/engproc2026154038 - 3 Sep 2026

Viewed by 67

Abstract

Stable motion of differential-drive mobile robots on uneven surfaces remains a challenging control problem because local variations in rolling resistance and wheel–terrain interaction generate asymmetric traction forces, trajectory deviation, and heading instability. This study investigates a dual-motor mobile robot in which the left [...] Read more.

Stable motion of differential-drive mobile robots on uneven surfaces remains a challenging control problem because local variations in rolling resistance and wheel–terrain interaction generate asymmetric traction forces, trajectory deviation, and heading instability. This study investigates a dual-motor mobile robot in which the left and right driving wheels are actuated independently and coordinated through a coupled control structure that simultaneously regulates linear speed and suppresses yaw motion. A control-oriented nonlinear dynamic model is developed by combining the longitudinal and yaw dynamics of the platform with first-order actuator models and resistance terms that represent uneven-terrain effects through side-dependent rolling losses. Based on this model, a coordinated control law is formulated to redistribute the control effort between the two drives in response to both speed and angular-velocity errors. Numerical experiments are carried out for nominal motion, localized asymmetric resistance, prolonged uneven-surface excitation, robustness-boundary analysis, and actuator-dynamics sensitivity. The results show that the proposed strategy improves directional stability under uneven-surface disturbances, limits the degradation of forward motion, and enlarges the admissible operating region compared with decoupled drive action. The study demonstrates that coordinated inter-wheel control provides a physically interpretable and computationally efficient solution for enhancing the stability of wheeled mobile robots operating on irregular terrain. Full article

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12 pages, 23334 KB

Open AccessProceeding Paper

Intelligent Logic Design Strategies for Distributed IoT Environments with Robotic Support

by Peter Tsonkov, Fatima Sapundzhi, Slavi Georgiev and Ivan Georgiev

Eng. Proc. 2026, 154(1), 39; https://doi.org/10.3390/engproc2026154039 - 3 Sep 2026

Viewed by 97

Abstract

IoT devices and robotic technologies can support sensor-based feedback, contextual monitoring, and adaptive control in distributed cyber–physical environments. This article compares three logic design strategies for distributed IoT systems with robotic support: embedded firmware logic, external workflow-based logic, and framework-assisted firmware design. A [...] Read more.

IoT devices and robotic technologies can support sensor-based feedback, contextual monitoring, and adaptive control in distributed cyber–physical environments. This article compares three logic design strategies for distributed IoT systems with robotic support: embedded firmware logic, external workflow-based logic, and framework-assisted firmware design. A hybrid architecture is proposed to combine low-latency local control with flexible external orchestration, monitoring, and robotic feedback. To complement the conceptual comparison, a compact validation scenario is introduced, based on sensor reading, local validation, MQTT communication, Node-RED workflow processing, and actuator command execution. The indicative results show that embedded edge logic achieves the shortest response time, with an average value of 1.08 ms, while the hybrid workflow-based path introduces an additional delay, reaching 8.08 ms, but provides greater flexibility, scalability, and multi-device coordination. The findings indicate that hybrid architectures are suitable for inclusive IoT environments when immediate local reactions are combined with configurable external logic and reliable monitoring. Full article

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9 pages, 2381 KB

Open AccessProceeding Paper

On the Possibility of Energy Saving Using a Variable-Frequency Drive–Induction Motor System in a Pneumatic Conveying Device

by Duisenova Sholpan, Zhunussova Aiza, Amirkulov Bekzat, Demessova Saule and Yerzhigitov Yerkin

Eng. Proc. 2026, 154(1), 40; https://doi.org/10.3390/engproc2026154040 - 3 Sep 2026

Viewed by 61

Abstract

The article examines a “variable-frequency drive–induction motor” system of a pneumatic conveying device from the perspective of improving energy efficiency and enhancing the dynamic characteristics of the electric drive. A structural diagram of the frequency-controlled induction electric drive is presented, transfer functions of [...] Read more.

The article examines a “variable-frequency drive–induction motor” system of a pneumatic conveying device from the perspective of improving energy efficiency and enhancing the dynamic characteristics of the electric drive. A structural diagram of the frequency-controlled induction electric drive is presented, transfer functions of the system elements are derived, and its stability is analyzed. The study of the electric drive dynamics is carried out based on mathematical modeling in the MATLAB environment using both symbolic and numerical methods. It is established that the voltage stabilization system of the frequency converter ensures a stable operating mode of the induction motor for various values of the controller parameters. The work also includes the synthesis of control system parameters aimed at minimizing energy consumption. An objective function of energy costs is obtained, and optimal parameters of the frequency converter voltage controller are determined. Numerical modeling demonstrated a reduction in power consumption from 4 kW to 2.89 kW, confirming the effectiveness of the proposed approach. The results of the study indicate that the use of a frequency-controlled electric drive in pneumatic conveying systems makes it possible to increase energy efficiency, improve transient processes, and ensure stable system operation under varying loads. Full article

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14 pages, 13157 KB

Open AccessProceeding Paper

Comparative Analysis of V/F and Vector Control of Induction Motors Using AI-Based Optimization Methods

by Plamen Stanchev, Nikolay Hinov and Reni Kabakchieva

Eng. Proc. 2026, 154(1), 41; https://doi.org/10.3390/engproc2026154041 - 3 Sep 2026

Viewed by 68

Abstract

This paper presents a comparative analysis of scalar V/F and field-oriented control (FOC) control strategies for induction motor drives, including classical and AI-enhanced implementations. AI-based optimization is used to improve dynamic performance, energy efficiency, and robustness under various load conditions. The evaluation combines [...] Read more.

This paper presents a comparative analysis of scalar V/F and field-oriented control (FOC) control strategies for induction motor drives, including classical and AI-enhanced implementations. AI-based optimization is used to improve dynamic performance, energy efficiency, and robustness under various load conditions. The evaluation combines time-domain simulations with multi-objective Pareto analysis, taking into account speed-tracking accuracy, current voltage, control effort, and energy consumption. The results show that AI-enhanced controllers achieve superior trade-offs compared to conventional approaches. AI-enhanced V/F control also shows significant improvements over classical scalar control, offering a low-complexity alternative. The study demonstrates that AI effectively complements classical control structures, enabling efficient and reliable operation of induction motor drives. Full article

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10 pages, 1512 KB

Open AccessProceeding Paper

A CSMF- and LLM-Based Hybrid Approach for Charging Station Recommendations in Electrical Vehicle Networks

by Yusuf Eymen Sezen, Muhammad Jamil, Adnan Kavak, Sema Bayraktar and Günay Aslan

Eng. Proc. 2026, 154(1), 42; https://doi.org/10.3390/engproc2026154042 - 2 Sep 2026

Viewed by 77

Abstract

This study introduces a hybrid framework that combines Multi-Agent Reinforcement Learning (MARL) based on Mean Field Theory (CSMF) with Large Language Models (LLMs) to provide optimized and explainable charging station recommendations for EV users in Turkey. By integrating real-time data from 11,702 stations [...] Read more.

This study introduces a hybrid framework that combines Multi-Agent Reinforcement Learning (MARL) based on Mean Field Theory (CSMF) with Large Language Models (LLMs) to provide optimized and explainable charging station recommendations for EV users in Turkey. By integrating real-time data from 11,702 stations and 30,941 sockets via the Energy Market Regulatory Authority of Türkiye (EMRA) API, the system captures dynamic information on station availability, socket types, charging power, and pricing, enabling context-aware, user-centric guidance. The proposed architecture follows a Centralized Training and Decentralized Execution (CTDE) framework, in which multiple agents make local decisions while a centralized critic evaluates global states to improve policy learning and reduce congestion. CSMF reduces computational complexity and lowers the average waiting time by 71.8%, while the LLM component translates technical optimization outcomes into human-readable explanations, enhancing trust and transparency. A React Native mobile application demonstrates end-to-end usability, allowing users to filter stations by distance, cost, socket type, and green energy usage, with real-time availability updates. Experimental results show that the hybrid CSMF + LLM approach achieves a 100% Available Facility Rate (AFR) while maintaining low response time (180 ms), outperforming conventional nearest-station, random, and basic MARL methods. By combining technical efficiency with explainable recommendations and real-world deployment, this study presents a scalable, practical, and trustworthy solution for EV charging infrastructure management, offering both academic contributions and a ready-to-use application for drivers. Full article

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9 pages, 7346 KB

Open AccessProceeding Paper

Autonomous Parking Simulation Using Reinforcement Learning

by Mirkan Zyuhtyu and Georgi Krastev

Eng. Proc. 2026, 154(1), 43; https://doi.org/10.3390/engproc2026154043 - 4 Sep 2026

Viewed by 89

Abstract

This paper presents the development of a simulation game that models traffic interactions with a focus on autonomous parking using machine learning techniques. The simulator is built in the Unity environment and employs the Unity ML-Agents toolkit to train a virtual vehicle capable [...] Read more.

This paper presents the development of a simulation game that models traffic interactions with a focus on autonomous parking using machine learning techniques. The simulator is built in the Unity environment and employs the Unity ML-Agents toolkit to train a virtual vehicle capable of performing parking maneuvers autonomously. The training process is based on reinforcement learning, where the agent learns through interaction with the environment using virtual sensors that detect distances and surrounding objects. A custom reward system guides the learning process by encouraging safe, accurate, and efficient parking while penalizing collisions and incorrect maneuvers. The developed simulator demonstrates the potential of interactive environments for research and education in autonomous driving and artificial intelligence. Full article

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8 pages, 1017 KB

Open AccessProceeding Paper

Output Impedance Automatic Matching Devices Designed for High-RF Transmitter

by Miroslav Tomov, Michail Malamatoudis, Stanimir Sadinov, Plamen Tihov and Gancho Tanev

Eng. Proc. 2026, 154(1), 44; https://doi.org/10.3390/engproc2026154044 - 4 Sep 2026

Viewed by 70

Abstract

Among the critically important conditions for optimal mobile device power source efficiency, ensuring reliable and long-term operation, is the matching of the output impedance of the radio transmitter with that of the antenna used. A step forward in the development of technology for [...] Read more.

Among the critically important conditions for optimal mobile device power source efficiency, ensuring reliable and long-term operation, is the matching of the output impedance of the radio transmitter with that of the antenna used. A step forward in the development of technology for the implementation of high-quality radio frequency radiation with minimal energy losses in telecommunications equipment could be devices that automatically match the output impedance of radio transmitters designed for high frequencies such as VHF and UHF. What has been achieved to date and implemented in the serial production of telecommunications equipment and in the majority of explorations is relatively optimized impedance matching between the output of the final amplifier stage and the transmitting antenna for a relatively wider frequency bandwidth. This leads to a significant deterioration in the transmission efficiency of some frequencies of the digital signal spectrum and a decrease in the data transfer rate. This publication presents the development and simulation of suitable elements for the implementation of automated impedance matching circuits. Full article

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Open AccessProceeding Paper

Triple-Band 1 × 4 Millimeter-Wave MIMO Antenna for 5G/6G Communications

by Rakesh N. Tiwari, M. S. Vasundhara, Prabhakar Singh, Pradeep Kumar, Galeti Vandana and Battala Sai Sivani

Eng. Proc. 2026, 154(1), 45; https://doi.org/10.3390/engproc2026154045 - 4 Sep 2026

Abstract

The design and analysis of a 1 × 4 mm-wave MIMO antenna in a lateral configuration are presented. The antenna utilizes a square slotted patch structure and demonstrates triple-band operation at 28/38/43 GHz. The first band offers antenna bandwidth from 27.79 to 28.35 [...] Read more.

The design and analysis of a 1 × 4 mm-wave MIMO antenna in a lateral configuration are presented. The antenna utilizes a square slotted patch structure and demonstrates triple-band operation at 28/38/43 GHz. The first band offers antenna bandwidth from 27.79 to 28.35 GHz (0.56 GHz), while the second band spans 37.68 to 38.43 GHz (0.75 GHz) and the third band covers 42.56–43.55 GHz (0.99 GHz), respectively. The antenna is fabricated on Rogers RO3003 with material permittivity of 3.0, a loss tangent of 0.001, and a substrate height of 0.25 mm. For both bands, the antenna demonstrates the isolation > 20 dB. It also provides peak gain values of 6.54 dBi at 28 GHz, 7.10 dBi at 38 GHz, and 6.11 dBi at 43 GHz with an average radiation efficiency of 82%. Key diversity metrices including ECC, MEG, and CCL were evaluated. The ECC remains below 0.022, and the CCL of the antenna is below 0.32 bits/s/Hz, which is favorable for MIMO systems. These results indicate that the reported MIMO antenna is suitable for future 6G communication systems. Full article

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9 pages, 3801 KB

Open AccessProceeding Paper

Study of the Electrochemical Properties of Ceramic Composite Membranes for Electricity Generation

by Ivaylo Belovski, Blagovesta Midyurova, Kaloyan Ivanov, Aleksandar Mandadzhiev and Todor Mihalev

Eng. Proc. 2026, 154(1), 46; https://doi.org/10.3390/engproc2026154046 - 7 Sep 2026

Abstract

This study investigates the electrochemical properties of ceramic composite membranes for electricity generation. The system utilizes seawater or other saline solutions as a natural electrolyte in conjunction with copper and zinc electrodes, addressing the pressing need for carbon-emission-free energy sources. A significant challenge [...] Read more.

This study investigates the electrochemical properties of ceramic composite membranes for electricity generation. The system utilizes seawater or other saline solutions as a natural electrolyte in conjunction with copper and zinc electrodes, addressing the pressing need for carbon-emission-free energy sources. A significant challenge lies in identifying clays whose components can be integrated into the composition of low- to medium-power electromotive force generators. Their combination with zeolite, known for its electronegative potential, enables the synthesis of solid-state ceramic membranes. These membranes can subsequently be transformed into galvanic cells following the addition of metal electrodes and an electrolyte. The research focuses on emission-free electricity generation from natural sources, an increasingly relevant scientific objective. Naturally occurring raw materials offer an economically viable and sustainable solution, demonstrating favorable properties for integration as core components in electrochemical cells that utilize saltwater. Full article

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9 pages, 12202 KB

Open AccessProceeding Paper

Development of a Control System for an Electric Fuel Pump for an Ultra-Light Launch Vehicle

by Kenzhebek Myrzabekov, Kuanysh Alipbayev, Akylbek Bapyshev, Teodor Iliev and Alina Fazylova

Eng. Proc. 2026, 154(1), 48; https://doi.org/10.3390/engproc2026154048 - 3 Sep 2026

Abstract

This article discusses the development of a control system for an electric propellant pump for a light-lift launch vehicle. The relevance of this work stems from the need to create compact and energy-efficient propellant supply systems capable of ensuring stable operation within the [...] Read more.

This article discusses the development of a control system for an electric propellant pump for a light-lift launch vehicle. The relevance of this work stems from the need to create compact and energy-efficient propellant supply systems capable of ensuring stable operation within the weight, size, and energy resource constraints of onboard equipment. The research object is presented as a single electromechanical-hydraulic system comprising an electric drive, a pump unit, a hydraulic circuit, and a digital control system. A mathematical model of the electric pump unit is developed, its numerical parameterization is performed, and a cascade control structure with a feedforward channel is proposed. A series of computational experiments are conducted, including an analysis of transient processes during pressure target changes, a study of the system’s response to hydraulic disturbances, and a robustness assessment under parameter uncertainty. It is established that the proposed control system provides higher response speed, a smaller integral control error, and better resistance to external disturbances compared to a classical cascade PI structure. The obtained results confirm the potential of the proposed approach for electric pump units for fuel supply of ultra-light launch vehicles. Full article

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14 pages, 3418 KB

Open AccessProceeding Paper

Impact of RES in Contingency Analysis of an Island Power System

by Ioannis Mozakis and Emmanuel Karapidakis

Eng. Proc. 2026, 154(1), 49; https://doi.org/10.3390/engproc2026154049 - 7 Sep 2026

Abstract

Autonomous island power systems face inherent vulnerability to severe contingency events, yet current N-1 frameworks and thermal-only criteria systematically underestimate operational risk in grids with high renewable penetration. This paper presents an integrated 24 h N-2 contingency assessment explicitly combining transmission line outages [...] Read more.

Autonomous island power systems face inherent vulnerability to severe contingency events, yet current N-1 frameworks and thermal-only criteria systematically underestimate operational risk in grids with high renewable penetration. This paper presents an integrated 24 h N-2 contingency assessment explicitly combining transmission line outages with generator failures in a 6-bus island system, using PowerWorld Simulator (GOS Education 21) to evaluate 24,192 contingency cases across four load–RES operating scenarios. Results reveal a critical dual-channel risk structure. Thermal violations and network islanding represent mechanistically distinct failure modes that do not co-occur predictably. Critically, relying solely on thermal criteria misses adequacy impacts equivalent to 638 MWh/day in worst-case conditions. The time-dependent nature of vulnerability underscores the inadequacy of static snapshot analyses. These findings establish that N-2 contingency frameworks for autonomous grids must treat thermal security and islanding outcomes as co-equal metrics, fundamentally challenging conventional deterministic planning approaches. Full article

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11 pages, 1217 KB

Open AccessProceeding Paper

Multi-Objective Optimization of Heavy-Duty V-Arm Suspension Connection Problems Using ANN-Assisted Hybrid Metaheuristic Algorithms

by Cengiz Mert Türkmen, Fevzi Doğaner, Caner Baybaş and Hatice Akavioğlu

Eng. Proc. 2026, 154(1), 50; https://doi.org/10.3390/engproc2026154050 - 7 Sep 2026

Abstract

The goal of this project is to identify ways to improve the performance of the bushing–flange–circlip connection on the V-arm suspension components of heavy-duty commercial vehicles. Failures related to circlip ejection and flange loosening identified from customer relationship management (CRM) data motivated the [...] Read more.

The goal of this project is to identify ways to improve the performance of the bushing–flange–circlip connection on the V-arm suspension components of heavy-duty commercial vehicles. Failures related to circlip ejection and flange loosening identified from customer relationship management (CRM) data motivated the development of this computational optimization framework. This study involved generating a parametric dataset, using Latin Hypercube Sampling (LHS) with synthetic data, for the development of the artificial neural networks (ANNs). The networks use the surrogate model to optimize solutions through a combination of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. The ANN provided an overall test set of R² = 0.968 across the three objective functions. The hybrid optimization method produced 11 surrogate-predicted Pareto-optimal candidate designs, simultaneously minimizing the micro-displacement and stiffness loss while maximizing the fatigue life. The present results are based on analytically derived synthetic training data. Simcenter 3D Version 2506 Finite Element Analysis (FEA) integration and prototype validation constitute the planned next phase. The methods described here can be configured for other components of the suspension and are expected to be compatible with similar applications depending on future enhancements. Full article

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8 pages, 796 KB

Open AccessProceeding Paper

Real-Time Campus Occupancy Analysis and Indoor Localization System Using Existing Wi-Fi Infrastructure

by Kadir Kesgin, Selahattin Kosunalp and Desislava Atanasova

Eng. Proc. 2026, 154(1), 51; https://doi.org/10.3390/engproc2026154051 - 7 Sep 2026

Abstract

Large university campuses need timely, privacy-conscious information about how indoor spaces are used in order to improve space management, energy efficiency, and operational responsiveness. Yet many indoor positioning solutions still depend on additional hardware such as Bluetooth Low Energy beacons, ultra-wideband anchors, or [...] Read more.

Large university campuses need timely, privacy-conscious information about how indoor spaces are used in order to improve space management, energy efficiency, and operational responsiveness. Yet many indoor positioning solutions still depend on additional hardware such as Bluetooth Low Energy beacons, ultra-wideband anchors, or camera-based sensing, which increases deployment cost and maintenance complexity. This paper presents a lightweight campus occupancy analysis and indoor localization framework that reuses an existing Cisco Wireless LAN Controller (WLC) infrastructure as a sensing layer. The system retrieves received signal strength indicator (RSSI) observations from access points over secure SSH sessions, converts these observations into approximate distance estimates through a calibrated log-distance path loss model, and computes user positions using weighted non-linear least-squares multilateration (Mlat). In addition to point localization, the framework generates occupancy heatmaps, cumulative reliability curves, and access-point-density sensitivity analyses based on a 50 m × 50 m evaluation scenario with 500 randomized samples. To support privacy-preserving deployment, the service layer exposes only zone-level occupancy information and omits personally identifiable network identifiers. The results indicate that Wi-Fi-based localization can provide cost-effective and scalable occupancy intelligence with sufficient accuracy for campus-wide density monitoring and smart building applications.

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3148 KB

Open AccessProceeding Paper

Kinematic Design of a Hybrid 2-DoF Ankle Mechanism

by Sayat Akhmejanov, Zhanar Bigaliyeva, Abu Alim Ayazbay, Aidos Sultan, Yerkebulan Nurgizat, Arman Uzbekbayev, Kassymbek Ozhikenov, Gani Sergazin and Nursultan Zhetenbayev

Eng. Proc. 2026, 154(1), 52; https://doi.org/10.3390/engproc2026154052 - 7 Sep 2026

Abstract

This paper presents the kinematic design and experimental validation of a two-degree-of-freedom ankle mechanism based on stepper motor actuation and ball-screw transmission. The proposed system employs a hybrid architecture, combining actively controlled motion in the sagittal plane with passively compliant motion in the [...] Read more.

This paper presents the kinematic design and experimental validation of a two-degree-of-freedom ankle mechanism based on stepper motor actuation and ball-screw transmission. The proposed system employs a hybrid architecture, combining actively controlled motion in the sagittal plane with passively compliant motion in the frontal plane. Experimental evaluation under no-load laboratory conditions demonstrated a strong linear relationship between motor steps and joint angle within an operating range of approximately ±22°, with coefficients of determination exceeding 0.97. The results confirm the predictability and repeatability of the kinematic transformation while revealing minor hysteresis effects associated with mechanical transmission. The proposed mechanism is intended as a validation platform for studying motion transformation in multi-DoF (degree-of-freedom) ankle systems, providing a basis for future work on load analysis, torque modeling, and closed-loop control. Full article

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7 pages, 343 KB

Open AccessProceeding Paper

Impact of the Primary Zone Excess Air Ratio in Gas Turbine Engine Combustors on Pollutant Emissions

by Abay Dostiyarov, Iliya Iliev, Yerdaulet Baigozha, Madina Kumargazina, Nurasyl Tolembay, Hristo Beloev and Ivan Beloev

Eng. Proc. 2026, 154(1), 53; https://doi.org/10.3390/engproc2026154053 - 7 Sep 2026

Abstract

The transition to a low-carbon energy paradigm requires reducing nitrogen oxide NO_x and carbon monoxide CO emissions to 5–9 ppm. This study investigates the impact of the primary zone excess air ratio α and mixing quality on pollutant yields, addressing the “seesaw” [...] Read more.

The transition to a low-carbon energy paradigm requires reducing nitrogen oxide NO_x and carbon monoxide CO emissions to 5–9 ppm. This study investigates the impact of the primary zone excess air ratio α and mixing quality on pollutant yields, addressing the “seesaw” trade-off mechanism between NO_x and products of incomplete combustion. Analysis of Lean Premixed and Micromix technologies demonstrates that achieving NO_x levels below 5 ppm requires local α fluctuations to remain within a root-mean-square deviation of 3–4%. An original burner design with an intelligent emission control system is presented, enabling dynamic adjustment of local αin to maintain combustion within a narrow stability window. Experimental results confirm that minimum toxicity, with NO_x concentrations below 20 ppm, is achieved at α_in = 1.7–1.8. The implementation of this technology ensures stable operation across transient and part-load regimes while mitigating thermal NO_x formation and thermoacoustic instabilities. Full article

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10 pages, 1232 KB

Open AccessProceeding Paper

Comparative Analysis of Computer Vision Algorithms for Robotic Manipulator Grasping

by Lazzat Kurmangalieva, Assem Kabdoldina, Beibit Shingissov, Nurgul Smailova, Nursultan Kuldeev, Baglan Bekbossynova and Perizat Rakhmetova

Eng. Proc. 2026, 154(1), 54; https://doi.org/10.3390/engproc2026154054 - 7 Sep 2026

Abstract

Vision-based perception is essential for robotic manipulator grasping because the accuracy of object localization directly affects grasp planning and motion execution. This paper presents a comparative analysis of computer vision algorithms for object detection and localization in a structured robotic pick-and-place scenario. Classical [...] Read more.

Vision-based perception is essential for robotic manipulator grasping because the accuracy of object localization directly affects grasp planning and motion execution. This paper presents a comparative analysis of computer vision algorithms for object detection and localization in a structured robotic pick-and-place scenario. Classical edge detection methods, including Canny, Sobel, Prewitt, and Roberts, were applied to the same RGB image of a manipulator workspace containing box-shaped objects. The algorithms were evaluated using detection accuracy, F1-score, centroid localization error, contour stability, and processing time. The results showed that Canny provided the most reliable performance, detecting all 12 target objects with the highest localization stability. The study confirms that classical edge detection can support lightweight vision-based robotic grasping. Full article

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15 pages, 6925 KB

Open AccessProceeding Paper

An Interactive Web Platform for Analyzing the Impact of Hyperparameters in Text Classification Models

by Daniela Petrova

Eng. Proc. 2026, 154(1), 55; https://doi.org/10.3390/engproc2026154055 - 7 Sep 2026

Abstract

This paper presents a web-based platform for analyzing the impact of hyperparameters on machine learning models for text classification. The system integrates a PHP-based web interface with Python-based machine learning modules, enabling users to experiment with different algorithms and parameter configurations in an [...] Read more.

This paper presents a web-based platform for analyzing the impact of hyperparameters on machine learning models for text classification. The system integrates a PHP-based web interface with Python-based machine learning modules, enabling users to experiment with different algorithms and parameter configurations in an interactive environment. The platform supports multiple classification models and allows dynamic adjustment of parameters such as n-gram range, regularization strength, and feature extraction techniques. Experimental results demonstrate that hyperparameter tuning has a significant influence on classification performance. The proposed system provides an intuitive and practical tool for both educational purposes and applied research in natural language processing. Full article

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8066 KB

Open AccessProceeding Paper

BrTxNet: A Hybrid EfficientNetB0–VGG16 Model with Learnable Feature Fusion for Brain Tumor MRI Classification

by Sarah Al-Azzawi and Ghaida A. Al-Suhail

Eng. Proc. 2026, 154(1), 56; https://doi.org/10.3390/engproc2026154056 - 7 Sep 2026

Abstract

Brain tumor classification from magnetic resonance imaging is an important task in computer-aided diagnosis because tumor appearance varies substantially across cases. This study proposes a hybrid deep learning model—namely, BrTxNet—for binary brain tumor classification based on EfficientNetB0 and VGG16. Single-backbone and hybrid configurations [...] Read more.

Brain tumor classification from magnetic resonance imaging is an important task in computer-aided diagnosis because tumor appearance varies substantially across cases. This study proposes a hybrid deep learning model—namely, BrTxNet—for binary brain tumor classification based on EfficientNetB0 and VGG16. Single-backbone and hybrid configurations were evaluated using a group-aware balanced dataset. Two hybrid fusion strategies were examined: direct concatenation and feature-level learnable fusion. The proposed model projects and adaptively weights features from both branches before classification. Experimental results showed that the learnable fusion model achieved the best performance, reaching 98.79% accuracy and 99.93% ROC-AUC with strong sensitivity and specificity. Furthermore, Grad-CAM and t-SNE analyses support the interpretability and discriminative power of the learned fused representations. Full article

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3105 KB

Open AccessProceeding Paper

Efficiency Assessment of Grid-Connected PVPPs and BESS in Bulgaria

by Dimitrina Koeva, Georgi Bankov and Metodi Dimitrov

Eng. Proc. 2026, 154(1), 57; https://doi.org/10.3390/engproc2026154057 - 2 Sep 2026

Abstract

This study aims to demonstrate that, in the context of the energy transition and with solar generation playing a dominant role in the energy mix, key technical parameters of energy infrastructure should be re-evaluated and taken into account, as changes in these parameters [...] Read more.

This study aims to demonstrate that, in the context of the energy transition and with solar generation playing a dominant role in the energy mix, key technical parameters of energy infrastructure should be re-evaluated and taken into account, as changes in these parameters affect operational processes and energy-loss calculation mechanisms. Data from newly installed battery energy storage systems (BESSs) connected to the power grid and photovoltaic power plants (PVPPs) in Bulgaria are used to establish a portfolio-level baseline for installed capacity, storage duration, geographic distribution, and preliminary economic indicators. By analyzing annual consumption and generation for 2024, 2025, and the first three months of 2026, the seasonal and cyclical nature of these variables and their dynamics of change are observed. The monthly data distribution allows for an in-depth study of load dynamics and the identification of key factors. The analysis identifies three typical seasonal cycles—winter, summer, and transitional—each of them differing in terms of specific consumption and generation profiles; the operation of PVPPs, BESS, and power transformers exhibits specific characteristics. The results show how changes in power quality indicators, specific technical parameters, and operational values directly or indirectly affect losses during power generation and distribution, and consequently also impact operating, maintenance, and repair costs. Full article

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11 pages, 2043 KB

Open AccessProceeding Paper

The Influence of the Sampling Parameters’ Temperature and Top-P on the Quality of Automated Translation with OpenAI GPT Models

by Hristo Hristov, Vanya Ivanova, Ivan Ivanov, Kristina Yasenova and Georgi Dimov

Eng. Proc. 2026, 154(1), 58; https://doi.org/10.3390/engproc2026154058 - 8 Sep 2026

Abstract

This article presents the development of a web application for automated translation of TXT, DOCX, and PDF documents, built with Next.js/React and integrated with OpenAI LLM models. The study investigates the influence of the sampling parameters temperature and Top-P (nucleus sampling) on translation [...] Read more.

This article presents the development of a web application for automated translation of TXT, DOCX, and PDF documents, built with Next.js/React and integrated with OpenAI LLM models. The study investigates the influence of the sampling parameters temperature and Top-P (nucleus sampling) on translation quality between Bulgarian and English. Translation experiments were conducted on a bilingual corpus of popular-science and biographical texts, analysing effects on verb tense, lexical choice, word order, and output stability. Proper tuning of these parameters allows control over style and lexical richness without compromising reliability, and the trends remain relevant in newer model versions. Full article

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10 pages, 693 KB

Open AccessProceeding Paper

Real-Time Camera-Based Monitoring for Product Detection and Recognition

by Tsvetislava Lavchieva and Delyan Genkov

Eng. Proc. 2026, 154(1), 59; https://doi.org/10.3390/engproc2026154059 - 8 Sep 2026

Abstract

This paper presents a prototype system for real-time camera-based monitoring of product presence and change events. The proposed approach combines object detection, class-based counting, and lightweight event logic to determine whether products appear, disappear, or change in quantity over time. The system is [...] Read more.

This paper presents a prototype system for real-time camera-based monitoring of product presence and change events. The proposed approach combines object detection, class-based counting, and lightweight event logic to determine whether products appear, disappear, or change in quantity over time. The system is intended for live camera input, while the current implementation has also been tested in a controlled manner using prerecorded video sequences. A modular software structure is used to support future extensions, including experiments with additional detection models and product-specific training data. The current results show that the developed prototype can serve as a practical basis for intelligent visual monitoring in product observation scenarios. Full article

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879 KB

Open AccessProceeding Paper

Development of an Automated Quality Inspection System with Inline Measurement

by Penko Mitev and Kiril Mitev

Eng. Proc. 2026, 154(1), 60; https://doi.org/10.3390/engproc2026154060 - 7 Sep 2026

Abstract

This paper presents the design and implementation of an automated quality inspection system based on rotary indexing and inline dimensional measurement. The proposed system integrates a rotary table for precise part positioning with a contact displacement sensor for high-resolution measurement, enabling real-time evaluation [...] Read more.

This paper presents the design and implementation of an automated quality inspection system based on rotary indexing and inline dimensional measurement. The proposed system integrates a rotary table for precise part positioning with a contact displacement sensor for high-resolution measurement, enabling real-time evaluation of critical geometric parameters during the production cycle. A PLC-based control architecture ensures synchronized operation between mechanical motion and measurement acquisition, utilizing industrial communication over PROFINET for reliable data exchange. A dedicated decision algorithm is implemented to classify parts as acceptable or defective based on predefined tolerance thresholds, allowing immediate feedback and process control. Experimental validation demonstrates high repeatability and measurement stability under continuous operation, confirming the suitability of the system for industrial applications requiring fast and reliable quality control. The results highlight the effectiveness of combining rotary indexing mechanisms with inline measurement techniques for improving production efficiency and reducing inspection time. Full article

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10 pages, 3866 KB

Open AccessProceeding Paper

Improving Image Segmentation Accuracy Using Dynamic Performance Analysis of the GrabCut Algorithm

by Marwah Kamil Hussein, Noor Mohammed Almoosawi and Haider M. Al-Mashhadi

Eng. Proc. 2026, 154(1), 61; https://doi.org/10.3390/engproc2026154061 - 8 Sep 2026

Abstract

In this research, a computer vision model was developed that enables object detection, shape identification, and orientation prediction using image segmentation. The GrabCut algorithm was used to segment images. GrabCut was initialized in two ways: first, by initializing the input image with a [...] Read more.

In this research, a computer vision model was developed that enables object detection, shape identification, and orientation prediction using image segmentation. The GrabCut algorithm was used to segment images. GrabCut was initialized in two ways: first, by initializing the input image with a bounding box (a box surrounding the object’s position in the image), or second, by initializing the input image with a suggested approximation mask (a mask resembling the segmentation). Three steps are performed iteratively: first, the color distribution of the foreground and background is estimated using a mixed Gaussian model (GMM). Second, a random Markov field is generated to manipulate the pixels (foreground/background). Finally, a histogram optimization algorithm is applied to achieve the desired final segmentation. Although the iterative steps may require time and effort to work within the algorithm, GrabCut consistently delivers high-quality results in segmenting the input image and producing an output image that closely resembles the original. Full article

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8 pages, 1315 KB

Open AccessProceeding Paper

Building a Topology and Statistics Module for Software-Defined Network with OpenDaylight Controller

by Delyan Genkov and Miroslav Slavov

Eng. Proc. 2026, 154(1), 62; https://doi.org/10.3390/engproc2026154062 - 8 Sep 2026

Abstract

Software-defined networks are widely deployed in modern networking world. They allow centralized monitoring and control of all network devices through a software application—the controller. Many controllers exist; some of them are open source, and others are commercial. Currently, most networking vendors propose their [...] Read more.

Software-defined networks are widely deployed in modern networking world. They allow centralized monitoring and control of all network devices through a software application—the controller. Many controllers exist; some of them are open source, and others are commercial. Currently, most networking vendors propose their own solutions, using different controllers, but they are often incompatible with each other. There is no universal solution that can interact with most of the available vendors and platforms for software-defined networking. This article presents a topology-learning, visualization, and statistics module, designed to interact with one of the most popular controllers—OpenDaylight. Full article

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9 pages, 2499 KB

Open AccessProceeding Paper

Development of a 3D LED Cube System for Visual Information Presentation

by Gergana Spasova, Iliyan Boychev and Ayshe Shaban

Eng. Proc. 2026, 154(1), 63; https://doi.org/10.3390/engproc2026154063 (registering DOI) - 8 Sep 2026

Abstract

The main goal of this work is to develop an algorithm for visual presentation of information through light in three-dimensional space. The problem of controlling a large number of digital outputs at the same time using a very small number of control signals [...] Read more.

The main goal of this work is to develop an algorithm for visual presentation of information through light in three-dimensional space. The problem of controlling a large number of digital outputs at the same time using a very small number of control signals is considered. A single-chip microcontroller Arduino Atmega 328 is used as a processor in a control unit (CU). The control unit is implemented based on shift registers. A physical structure of LEDs is also implemented as an executive device (LED matrix). Remote control is done using a Bluetooth module, and software (for controlling the Arduino chip) is added to control the LED matrix. The matrix consists of a total of 125 LEDs, forming a structure of 5 × 5 × 5 LEDs representing a three-dimensional “screen” through which information is presented. The software environment for developing applications specifically for Arduino is used for the software, namely Arduino IDE. The proposed implementation achieves a low-cost 3D display architecture controlled by a voice-controlled volumetric interface. Based on the shift registers used, effective control over the entire 3D visualization system is achieved. Full article

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