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Recent Submissions

EBMDP: Equal Balance Message Drop Policy for QoS Optimization in Delay Tolerant Networks
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Ahmad, Zahoor; Saeed, Khalid; Anwar, Muhammad Shahid; Frnda, Jaroslav; Fajeed, Muhammad Faran; Chromý, Erik; Khan, Samiullah; Kutlimuratov, Alpamis
elay-tolerant networks (DTNs) are ad hoc in nature. It is known for characteristics such as in- termittent connectivity and dynamic topology. How- ever, a unique characteristic of DTNs is that there is no guarantee of an end-to-end connection between sender and receiver. Therefore, nodes observe long de- lays in establishing the connection. Once a connec- tion is established between the nodes, the links connect- ing the nodes are fully utilized, and the buffer mem- ory overflows, resulting in congestion that significantly compromises the quality of services (QoS). To avoid congestion, researchers have developed different buffer management policies. This research presents an effi- cient buffer management policy, known as the Equal Balance Message Drop Policy (EBMDP), designed to improve QoS in DTN. The EBMDP discourages un- necessary message drop. EBMDP drops selected mes- sages from the overflowed node, and the selection of messages for the drop from the overflowed node is based on the conditions defined by the EBMDP. The results of EBMDP are better than the drop-oldest ap- proach (DOA) and size-aware drop (SAD) regarding delivery probability (DP), overhead ratio (OR), buffer time average (BTA), and dropped messages. The de- livery probability of EBMDP obtained by simulation is 0.1861, which is higher than the delivery probabilities of SAD and DOA, which are 0.1069 and 0.1114, re- spectively. Similarly, the overhead ratio of EBMDP is lower than that of SAD and DOA. The results show a significant improvement in the buffer time average, as the buffer time average of messages using EBMDP is greater than that of SAD and DOA. The results also show lower messages dropped (MD) for EBMDP than for MD of SAD and DOA.
Bio-Inspired Optimization and Machine Learning for Multi-Band Impedance Matching Networks
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Amuda, Abdulrasaq Olanrewaju; Karataev, Tologon; Oshiga, Omotayo; Osanaiye, Opeyemi; Stittu, Moshood; Obetta, James; Araoye, Timothy Oluwaseu
The intelligent design of multi-band impedance matching networks was investigated through the integration of bio-inspired optimization and ma- chine learning classifiers. The Hippopotamus Opti- mization Algorithm (HOA) was employed in conjunc- tion with Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest models to derive accurate and fabrication-ready design param- eters. The optimal configuration, defined by a width of 2.7936 mm, spacing of 0.6103 mm, and length of 1.0893 mm, produced a reflection coefficient (S11) of −29.1456 dB, indicating excellent impedance matching across the target frequency band. Among the classi- fiers, the SVM achieved the highest generalization ac- curacy of 96.76% and the lowest mean squared error of 0.3174, surpassing the performance of ANN and Ran- dom Forest. The developed framework reduces reliance on computationally intensive electromagnetic simula- tions, shortens design time, and maintains high predic- tive precision. These results confirm the effectiveness of combining evolutionary optimization with machine learning for the efficient and compact design of multi- band RF matching networks.
HemoGAT: Heterogeneous multimodal speech emotion recognition with cross-modal transformer and graph attention network
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Nguyen, Nhut Min; Nguyen, Thanh Trung; Nguyen, Tien-Dat; Dang, Duc Ngoc Minh
Multimodal speech emotion recognition (SER) is a promising field, yet effectively fusing diverse information streams remains challenging. Addressing this requires architectures capable of modeling structural relationships across modalities with fine-grained, feature- level interactions. This paper proposes HemoGAT, a novel heterogeneous multimodal SER architecture that integrates a dual-stream architecture with two core mod- ules: a heterogeneous multimodal graph attention net- work (HM-GAT) and a cross-modal transformer (CMT) to address this. The HM-GAT module captures complex structural and contextual dependencies using a hetero- geneous graph constructed from deep embeddings. The CMT module enables precise cross-modal feature fusion through bidirectional cross-attention. This design effec- tively captures both high-level relationships and immedi- ate cross-modal influences. HemoGAT achieves state-of- the-art (SOTA) performance on the IEMOCAP dataset and highly competitive results on the MELD dataset, demonstrating its superiority over existing methods. Extensive ablation studies were conducted to evaluate HemoGAT. We assessed the impact of the Top-K algo- rithm for heterogeneous graph construction and com- pared unimodal and multimodal fusion strategies. We also examined the contributions of the HM-GAT and CMT modules, analyzed the role of the graph attention network (GAT) in graph learning, and evaluated the effect of GAT layer depth on performance
Features Of Boundary Condition Formation For The Long Transmission Line Equation Using Equivalent Circuit Approaches
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Levoniuk, Vitaliy; Lysiak, Heorhii; Lysiak, Vladyslav
This paper presents a comparative analysis of the application of Γ- and Π-type equivalent circuits for modeling boundary conditions to the long transmis- sion line equation with distributed parameters. The main objective is to assess how the choice of substi- tution scheme for the first and last discrete segments of the line affects the accuracy of transient process sim- ulation and the symmetry of voltage and current dis- tribution along the line. Mathematical models of an intersystem overhead AC transmission line in a single- phase representation were developed, the wave equation was discretized using the method of lines, and the simu- lations were implemented in the Fortran programming environment. Two numerical experiments were con- ducted: the first using a direct Γ-type equivalent cir- cuit, and the second using a symmetric Π-type equiv- alent circuit. In each experiment, the line was ener- gized alternately from both ends, enabling the evalua- tion of model symmetry. The simulation results show that the Π-type scheme ensures complete symmetry of the electromagnetic quantities regardless of the direc- tion of power flow, in contrast to the Γ-type scheme, which introduces minor asymmetry. The obtained find- ings justify the choice of equivalent circuit depending on the required model accuracy and complexity, and may be useful in the development of modern methods for analyzing non-stationary regimes in high-voltage trans- mission networks.
Development of internet of things based flood monitoring system with real-time dashboard at flood monitoring project seelab kencana SDN. BHD. Shah Alam
(Vysoká škola báňská - Technická univerzita Ostrava, 2026) Bagaskara, Alvino Tanjung; Irawati, Indrarini Dyah; Mahzan, Muhammad Akmal Bin; Bakar, Muhamad Husaini Bin Abu
This research develops an Internet of Things (IoT)-based flood monitoring system equipped with a real-time web dashboard and artificial intelli- gence (AI)-based prediction features, as a solution to the frequent flooding in Shah Alam, Malaysia, due to high rainfall and a sub-optimal drainage system. The system uses AJ-SR04M sensors to measure water lev- els, MH-RD sensors to detect rainfall, and ESP32 and ESP32-CAM for data processing and image cap- ture. Data is sent wirelessly to a Django-based back- end server and displayed on a web dashboard. The backend also processes the data and runs a time-series forecasting-based machine learning model to predict conditions five minutes ahead, with the predicted re- sults displayed alongside the actual data. In addition, the system provides automatic notifications via Tele- gram when sensor values exceed a threshold. The test results show that the system is able to display envi- ronmental data accurately and responsively, provide real-time early warnings, and generate predictions that match historical trends. The system has successfully supported effective flood risk mitigation in vulnerable areas by providing accurate sensor data and AI-based predictions that match historical trends.