Bulletin of Electrical Engineering and Informatics
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Feature separation of music across diverse dataset: a comparative perspective
Full text linkIn music, feature separation is the process of separating distinguishable auditory characteristics, such as pitch, timbre, rhythm, and harmonic content, from a complicated, mixed signal. Virtual reality (VR), gaming, music transcription, karaoke systems, audio restoration, music information retrieval (MIR), music education, and audio forensics, are just a few of the areas where the topic has attracted a lot of attention. Feature extraction is crucial in music separation as it identifies and isolates sound elements, improving accuracy, and reducing noise. It simplifies raw audio into meaningful data for efficient processing and effective model learning. Without it, clean separation of audio components is very difficult. In this research, extracting features from mixed audio sources enables clean and accurate isolation of musical elements, enhancing quality, supporting precise evaluations, and boosting neural network performance across varied datasets including DSD100, MUSDB, and MUSDB18-HQ, which collectively afford rich musical content for making evaluations and benchmarks. Evaluation metrics, such as F1-score, precision, and recall, are utilized to demonstrate the performance data of the extracted features. The MUSDB18-HQ dataset yielded an overall increase of 17.86% in the F1-score metrics with significant increases in drums (+25.05%) and vocals (+20.04%), showing that the dataset was highly effective for feature separation
Dispersion compensation in single and multi-channel DWDM using chirped apodized fiber Bragg gratings
Full text linkChromatic dispersion (CD) is a key limiting factor in long-haul optical fiber communication, particularly in multi-channel dense wavelength division multiplexing (DWDM) systems, where it introduces signal distortion and inter-symbol interference (ISI). This paper proposes a low-dispersion-offset compensation (LDOC) scheme employing Gaussian-apodized linear chirped fiber Bragg gratings (CFBGs) to enhance dispersion management in single and multi-channel DWDM optical fiber communication systems. Simulations were performed in OptiSystem 7.0 for 10 Gbps single-channel transmission over standard single-mode fiber (SSMF) spanning 110–210 km, and were extended to 4- and 8-channel DWDM systems with a 0.8 nm channel spacing. System performance was evaluated in terms of quality factor (Q-factor), bit error rate (BER), and eye height under varying fiber lengths, input powers, and chirp coefficients. The LDOC-enhanced CFBG achieved a Q-factor of 7.04 with a BER of 9.82×10â»Â¹Â³ for single-channel transmission at 180 km, 13.83 with a BER of 5.57×10â»â´Â¹ for a 4-channel system at 150 km, and 7.56 with a BER of 7.76×10â»Â¹Â¹ for an 8-channel system at 150 km. These results confirm significant improvements compared to conventional CFBGs, demonstrating that the proposed LDOC-based approach is a compact and effective solution for next-generation metro-core, long-haul, DWDM, and 5G/6G optical networks
A metamaterial inspired multi band antenna using complementary split ring resonator for wireless applications
Full text linkThis research introduces a new printed metamaterial antenna with triple and quad bands for wireless applications. The suggested antenna is constructed of FR4 material, with two slots created in the radiating element. In addition, a circular complementary split ring resonator (C-CSRR), is carved from the ground plane. HFSS simulation software is being put into use to design, model, and measure the suggested antenna parameters in a real-world environment. The measured results indicate that an antenna with C-CSRR behind the radiating patch resonates at three distinct frequencies, including 3.5 GHz, 7.5 GHz, and 8.2 GHz, and an antenna with C-CSRR and slots on the radiating patch resonates at four different frequencies, including 3.5 GHz, 7.5 GHz, 8.8 GHz, and 9.32 GHz. An antenna without complementary split ring resonator (CSRR), or a conventional antenna, resonates at 9.6 GHz. The metamaterial antenna results in a 65% diminution in antenna size in contrast to a regular microstrip antenna. The simulated outcome demonstrates that the suggested metamaterial antenna's peak gain is around 6 dB to 8 dB and it has a resonance frequency for C-band applications, including weather radar systems and 5G applications
Comparative performance analysis of LSTM, GRU, and bidirectional neural networks for political ideology classification
Full text linkPolitical ideology classification is crucial for understanding social polarization, monitoring democratic processes, and identifying bias on online platforms. This study compares the performance of long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional GRU (Bi-GRU) neural network models in classifying liberal and conservative political ideologies from social media text data. The Bi-GRU achieved the best results with 88.75% accuracy and 89.16% F1-score, highlighting its strength in contextual analysis. These findings suggest their applicability in areas such as election monitoring and the analysis of political discourse. This study contributes to the field of political text classification by offering a comparative analysis of deep learning architectures. The dataset utilized covers a wide range of issues, including social, political, economic, religious, and racial topics, demonstrating its comprehensive nature. Visualizations using WordCloud and uniform manifold approximation and projection (UMAP) reveal distinct ideological patterns, validating the dataset’s quality for training models. The findings underscore the importance of utilizing advanced bidirectional architectures for nuanced tasks, such as ideology classification, where contextual understanding is crucial. These insights open avenues for future research, such as the application of Bi-GRU in analyzing multilingual political ideologies or real-time sentiment tracking during election campaigns
Assessing external factors of the agro-industrial complex efficiency based on data
Full text linkModern agriculture faces the challenge of increasing production efficiency in the context of limited resources and variable climatic conditions. This article presents an approach to assessing the impact of various factors on agro-industrial indicators using machine learning methods. The primary focus is on the development and application of a hybrid analysis that includes techniques such as gradient boosting (GB), mutual information (MI), and recursive feature elimination (RFE). The study was conducted using data from agro-industrial enterprises in the North Kazakhstan region for the period 2020–2022, encompassing production, climatic, and economic indicators. It was found that crop area, average crop weight, and precipitation are the most significant factors, accounting for up to 93% of the correlation with yield increase. The use of the proposed methods made it possible to reduce forecast uncertainty by 28% and increase the accuracy of key indicator predictions by 15–20%. The results of the analysis, visualized as correlation matrices and feature significance maps, confirm the possibility of applying the proposed approach to optimize the management of agro-industrial production. The application of the developed methodology contributes to the development of strategies aimed at the sustainable development of the agro-industrial complex
New approach to measuring researcher expertise using cosine similarity algorithm and association rules
Full text linkThis study proposes a new method to assess researcher expertise using publication data. The quality of research publications is an important indicator in the ranking of universities that are undergoing diversification. Research publications have become an important indicator in the university ranking system and have a major impact on the reputation of universities as a lens for the study of expertise and prestige for human resources. Expertise is often difficult to verify objectively, as a result, many people claim to be experts or are considered experts without evidence and correct data. To ensure the expertise of researchers, it must be proven with valid data support through measurable and presentable expertise parameters. The model built uses the cosine similarity and association rule approaches. The publication variables attached to the researcher are formulated in the collaboration of the algorithm to assess the level of researcher expertise. Validation of important points of publications as parameters for measuring expertise has been identified as the main factor contributing to the measurement of researcher expertise and its impact on university reputation. The model built successfully validated researcher expertise up to 72% which is relevant to its support for university rankings up to 75%
New perspective in enhancing Papanicolaou-smear image using CLAHE and spider monkey optimization
Full text linkHigh-quality Papanicolaou (Pap) smear images are essential for reliable early detection of cervical cancer, yet low contrast and noise often hinder accurate interpretation. This study introduces spider monkey optimization (SMO)-contrast-limited adaptive histogram equalization (CLAHE), an optimized CLAHE framework guided by the SMO algorithm. A novel signal contrast (SC) objective function is proposed, combining perceptual enhancement contrast enhancement-based image quality (CEIQ) with fidelity preservation peak signal-to-noise ratio (PSNR) to adaptively tune CLAHE parameters. Experiments on the publicly available SIPaKMeD and Mendeley LBC datasets demonstrate that SMO-CLAHE consistently outperforms manual settings and flower pollination algorithm (FPA)-based optimization, and achieves performance comparable to pelican optimization algorithm (POA) across key quality metrics including entropy, structural similarity index (SSIM), PSNR, enhancement measure estimation (EME), root mean square contrast (RMSC), standard deviation (STD-DEV), and CEIQ. Furthermore, downstream evaluation using a MobileNetV3-S classifier shows that the enhanced images lead to improved cervical cancer classification performance. These results highlight SMO-CLAHE as a robust and clinically relevant preprocessing framework, offering a new perspective for Pap smear image enhancement and diagnostic support
The utilization of the Taguchi method on microring resonator design parameters to enhance the value of the quality factor
Full text linkThis study uses the Taguchi method to optimize the quality factor (Q-factor) of microring resonators (MRRs) for sensor applications. The MRRs are compact optical components widely used in biosensors and environmental monitoring due to their sensitivity to refractive index changes. The Q-factor, a key performance metric for MRRs, is significantly influenced by structural parameters such as ring radius (R), gap (g), waveguide width (W), and waveguide height (h). We employed a finite difference time domain (FDTD) simulation to model light propagation within the MRR and compute the corresponding Q-factor to identify the optimal combination of these parameters. An L9 orthogonal array (OA) is used in the Taguchi method to analyze each factor's influence with three levels systematically. The optimization resulted in a Q-factor of 6208.44, significantly higher than the baseline value, indicating a substantial improvement. Compared to previous works, this research highlights the advantages of combining FDTD-based electromagnetic modeling with statistical optimization, offering a structured yet efficient approach to MRR design. The proposed method enhances Q-factor performance and provides scalability for practical applications in biomedical and environmental sensing. These findings underscore the utility of Taguchi-based design in advancing the field of photonic sensor optimization
Enhanced security and performance through permutation-byte key cipher with reduced-round AES
Full text linkThis paper introduces the permutation-byte key cipher with reduced-round advanced encryption standard (PBKC-RRAES), a novel enhancement of the AES designed to significantly improve both security and performance. The proposed algorithm integrates key modifications; i) replacing the computationally intensive MixColumns function with an efficient bit permutation technique that achieves superior diffusion while reducing computational overhad by eliminating complex matrix multiplication operations. This substitution enhances security through improved bit-level scrambling patterns, while simultaneously accelerating processing speed through simpler bitwise operations; ii) the addition of AddRoundKey operations between cipher states, iii) enhanced byte substitution operations and round constant additions in the key schedule algorithm before key expansion, and iv) reducing rounds from 10 to 6. These innovations yield heightened sensitivity to plaintext changes, evidenced by a 54.214% avalanche effect, surpassing the standard 50% threshold. Performance evaluations reveal PBKC-RRAES operates 26.90% improvement in encryption time and a 22.73% improvement in decryption time than standard AES, alongside throughput enhancements of 39.48% in encryption and 31.27% in decryption compared to the original AES, critical improvements for bandwidth-constrained applications. These results demonstrate that PBKC-RRAES is a robust and effective alternative for cryptographic applications, particularly beneficial for real-time video streaming, secure cloud storage, mobile payment systems, and IoT device where both security and processing effectivity are paramount
Analyzing 5G performance: investigating altitude-induced variations
Full text linkSince the launch of fifth generation (5G) services in Thailand in 2020, there have been continuous improvements in 5G coverage. Currently, 5G coverage extends to most areas throughout the country. However, coverage issues persist not only in rural areas but also in high-rise buildings in urban areas. Consequently, a study was conducted within such buildings. This paper assesses the performance of 5G at different altitude test points. The chosen location for the field tests was a high-rise building within a crowded public hospital, which receives numerous patients every weekday, in the major urban area of Bangkok. Two smartphones from the same manufacturer, both supporting 5G technology and equipped with the Speedtest application, were employed as tools for this study. Tests were carried out on the third and twenty-fourth floors of the high-rise building for data collection. The primary finding of this study reveals that download speeds exhibited a significant decrease with increasing altitude of the test points, as evidenced by statistical analysis (p-values0.001). This implies an issue with altitude-induced variations, indicating a need for the improvement of indoor 5G coverage in high-rise buildings