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Radioengineering

Radioeng

Proceedings of Czech and Slovak Technical Universities

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June 2025, Volume 34, Number 2 [DOI: 10.13164/re.2025-2]

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B. Huang, Z. Wang, J. Chen, B. Zhou, Y. Zhu, Y. Liu [references] [full-text] [DOI: 10.13164/re.2025.0181] [Download Citations]
Research on Site Selection and Capacity Determination of Electric Vehicle Public Charging Stations by Integrating K-Means++ and Improved RODDPSO

To address the suboptimal spatial distribution and low comprehensive utilization of existing electric vehicle (EV) public charging infrastructure, this study proposes an innovative charging station placement and capacity determination methodology integrating K-Means++ clustering with an enhanced RODDPSO variant. Building upon conventional K-Means and RODDPSO frameworks, we develop an improved hybrid algorithm incorporating three critical advancements: 1) an adaptive mutation mechanism within the RODDPSO architecture to enhance global search capabilities and prevent premature convergence; 2) synergistic optimization of K-Means++ cluster centroids through the enhanced RODDPSO operator; and 3) a novel cluster validation metric based on real-world utilization patterns. The proposed methodology effectively resolves the inherent limitations of conventional K-Means approaches, particularly their sensitivity to initial centroid selection and tendency toward local optima. Empirical validation through a case study of Nanjing's charging infrastructure demonstrates the algorithm's superior performance: stations sited using the proposed hybrid method exhibit 63.8% greater spatial correlation with high-utilization zones (>15% operational utilization) compared to baseline K-Means implementations. The advancements provide both methodological contributions to spatial optimization algorithms and practical insights for urban EV infrastructure planning.

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Keywords: K-Means++, variation randomly occurring distributedly delayed particle swarm optimization, public charging station, siting and capacity determination

T. Sivaranjani, B. Sasikumar, G. Sugitha [references] [full-text] [DOI: 10.13164/re.2025.0195] [Download Citations]
Rectified Adam Optimizer and LSTM with Attention Mechanism for ECG-Based Multi-class Classification of Cardiac Arrhythmia

Cardiac Arrhythmia (CA) is one of the most prevalent cardiac conditions and prime reasons for sudden death. The current CA detection methods face challenges in noise removal, R-peak detection, and low-level feature selection, which can impact diagnostic accuracy and signal stability. The research aims to develop an effective framework for detecting and classifying CA using advanced signal processing, feature extraction, feature selection, and classification for reliable medical diagnosis. The input electrocardiogram (ECG) signals are processed using hybrid noise reduction techniques such as cascaded variable step size normalized least mean square and sparse low-rank filter. The complex and high-level features are extracted using higher-order spectral energy distributed image, wavelet transform, and R-wave peak to R-wave peak interval to enhance the representation of cardiac data. Recursive feature elimination is applied to select the most relevant diagnostic features and the Rectified Adam optimizer is used to fine-tune parameters to achieve better training stability. The model integrates long-term memory with an attention mechanism to enhance the classification performance of arrhythmia detection. Simulation results demonstrate that the proposed model achieves 99.40% accuracy, outperforming existing models and showing its efficiency in classifying CA for better diagnosis and early treatments.

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Keywords: Cardiac arrhythmia, electrocardiogram, sparse low-rank filter, recursive feature elimination, long short-term memory, rectified Adam optimizer, attention mechanism

G. Ma, C. Xu, Z. Xu, X. Song [references] [full-text] [DOI: 10.13164/re.2025.0206] [Download Citations]
An Improved Small Target Detection Algorithm Based on YOLOv8s

Due to challenges such as the small size of targets, complex backgrounds, limited feature extraction capa-bilities, and frequent false positives and false negatives, traditional detection algorithms often perform poorly in small object detection tasks. To address these challenges, this pa¬per proposes an enhanced small object detection algorithm, SOD-YOLO, based on YOLOv8s. First, the S_C2f_CAFM module is integrated into the feature extraction network, enabling the effective capture of fine-grained local features and broad contextual information, while simultaneously reducing model parameters and computational complexity. Second, in the feature fusion stage, the redesigned bidirectional feature pyramid network employs a spatial context awareness module to extract key features, adding a top-down path to optimize feature fusion and enhance discriminative information. In the Neck section, the D_C2f_MSPA module is introduced, which, while being lightweight, accurately models channel dependencies in feature maps, effectively reducing both false positives and false negatives for small objects. Finally, the inclusion of Normalized Wasserstein Distance (NWD) further improves detection accuracy and reduces the model’s sensitivity to small positional deviations in small objects. Experimental results on the DOTAv1.0, VisDrone2019, and TT100K datasets confirm that SOD-YOLO achieves excellent performance, demonstrating the effectiveness of the modifications made to the original YOLOv8 model.

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Keywords: YOLOv8, small object detection, attention mechanism, feature fusion, loss function

R. Bozovic, V. Orlic, G. Kekovic [references] [full-text] [DOI: 10.13164/re.2025.0233] [Download Citations]
Artificial Bias Induction in Fourth-Order Cumulants Based Automatic Modulation Classification Algorithm in AWGN and Multipath Propagation Channel

Automatic modulation classification (AMC) represents a wide used technique for modulation format recognition of signals considered to be a priori unknown. Due to the low algorithm and hardware complexity, AMC algorithms based on fourth-order cumulants are still very popular. Presence of bias in standard cumulants estimated values of real signals constellations has positive impact on classification score for distinguishing real from complex signals. Therefore, one new approach in AMC is proposed in this paper, with focus on manipulation with theoretical expected cumulant values of real signals constellations, assuming artificially introduced bias will improve AMC performance. Artificial bias induction is done through modifications of standard cumulants mathematical formula. Performance of modified and standard fourth-order cumulants based AMC algorithms were explored in context of real and complex signals constellations. This was done through Monte Carlo simulations in propagation conditions which included Additive White Gaussian Noise (AWGN) and multipath propagation channel with known and unknown impulse response. Evaluation was done through the probability of correct classifications. Presented numerical results confirmed superiority of algorithm based on artificial bias induction in classification of real and complex signals, in each considered propagation scenarios, especially in a radio environment with lower signal-to- noise ratio (SNR) values. The remarkable AMC performance enhancements are up to 25%.

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Keywords: AMC, AWGN, bias, Binary Phase Shift Keying (BPSK), channel impulse response, cumulants, multipath, Quadrature Amplitude Modulation (QAM)