Indonesian Journal of Electrical Engineering and Computer Science
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AlGaN/GaN MSM UV photodetector without and with BGaN back-barrier layer comparison study by SILVACO-TCAD
Get PDFUsing DevEDIT and atlas under SILVCAO-TCAD, we were able to achieve high photodetector metal-semiconductor-metal (MSM) AlGaN/GaN/BGaN performance with high electronic mobility. Our device demonstrated a sensitivity of 286 (I illumination/I dark) at Vanode 20V with an illumination current of 26 mA, a photocurrent of 1.56e-7 A at a wavelength of 0.350 µm, and an appropriate efficiency value of 87% without BGaN, and we also studied the influence of the boron B0.03Ga0.97N back-barrier layer. As a result, we obtain a sensitivity of 293,4 at Vanode 20V with an illumination current of 27 mA, a photocurrent of 1,85e-7 A at a wavelength of 0.350 µm, and an appropriate efficiency value of 90%. Additionally, this type of photodetector has been effectively created to detect UV light in the 100–450 nm range, and it may find value in both medical and military settings. Astronomical, medical diagnostics, environmental sensing, remote sensing, thermal imaging, optical signal detection, night vision cameras, missiles, and target tracking
Exploring stock price portfolio clusters in foreign exchange markets
Get PDFThis study explores a novel portfolio management approach dividing the currency pairs into clusters of periodic returns. The primary purpose is to improve diversification and risk-return ratios with currencies. This research studied USD, Euro, and Chinese Yuan to collect historical data from April 2012 to March 2022. The present study makes use of K-means clustering to find clusters of assets with similar return patterns, which constitute diversified portfolios. Optimized portfolio vs. benchmark portfolio performance was also evaluated based on critical performance measures like cumulative return, Sharpe ratio, and volatility. The clustering approach was also tested through sensitivity analysis to check how market-specific it is. The results suggest that more clustered portfolios outperform traditional benchmarks and provide a better risk-adjusted return. The conclusion drawn here from the findings is that portfolio segmentation is a superior approach because of risk management in ever-changing volatile markets and identifying situations that link currency pairs. This is beneficial for those investors and portfolio managers looking to maximize their foreign exchange (FOREX) investments by allowing greater visibility into how the market is functioning, which can, in turn, improve decision-making processes. According to the study, portfolio clustering substantially enhances a portfolio's return for the foreign exchange market
Unveiling educational enrollment factors in Egypt via ensemble learning
Get PDFEducation plays a vital role in the development of a nation and significantly influences the direction of societies. Understanding the various factors that impact educational enrollment is essential for policymakers and resource allocation strategies. This paper explores the factors impacting educational enrollment in Egypt using predictive modeling and machine learning techniques. The study evaluates six machine learning algorithms and ensemble learning approaches to predict enrollment rates, considering computational efficiency, robustness, and parameter sensitivity. By analyzing socio-economic and demographic indicators from Egyptian educational data, the research examines the interplay of these factors. Results highlight the effectiveness of these methods in elucidating enrollment patterns, with ensemble learning showing promising performance and significant improvements compared to traditional machine learning algorithms. This study offers insights into Egypt's educational landscape that could inform policy formulation and resource allocation strategies
A hybrid DWT-DCT-SVD watermarking scheme using arnold transform
Get PDFIn telemedicine, medical images and electronic patient records (EPRs) are frequently transmitted and stored, making them vulnerable to tampering and theft. To ensure data security and copyright protection, this paper proposes a hybrid watermarking scheme based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD). The method uses a two-level DWT to decompose the image, applies DCT to selected sub-bands, and embeds two watermarks. The first is a logo used for ownership verification, and the second is an EPR encrypted with the Arnold transform for privacy protection. SVD is then used to enhance robustness. Experimental results show that the proposed scheme achieves better image quality and stronger resistance to common attacks compared with existing watermarking methods
FEM-based analysis of the relationship between track insulation conductivity and stray current in DC traction systems
Get PDFThis research investigates the influence of track insulation conductivity on stray current in direct current (DC) traction systems, which is a significant issue in railway operations due to its potential to cause electrochemical corrosion. Utilizing the finite element method (FEM), a simplified geometric model of a DC tram traction system was analyzed under varying conditions of track insulation conductivity. The study examined three levels of insulation conductivity, represented by fastener resistances of 1,000 Ω, 3,000 Ω, and 6,000 Ω, to understand their impact on stray current density. Results revealed that increased insulation resistance leads to reduced stray current density, demonstrating the critical role of track insulation in mitigating stray currents. The study further highlights that the depth of soil beneath the track also significantly affects stray current distribution. These findings provide insights into improving track design and maintenance for better protection against the negative effects of stray current in DC traction systems
Toward nuanced sentiment analysis through multi-sense emoji embedding
Get PDFThis research investigates the role of emojis in sentiment analysis using a more comprehensive multi-sense skip-gram approach. Emojis, which can convey facial expressions, body movements, and intonations often challenging to express in text, enhance digital communication by enriching the meaning of messages. Previous studies have shown that emojis improve sentiment analysis, yet most focused solely on their positive and negative connotations. This study broadens the scope by incorporating positive, negative, and neutral sentiment contexts. In the experiments, emojis were embedded in text and converted into vector representations for further analysis. The classification of sentiment texts was performed using a bidirectional long short-term memory (Bi-LSTM) method enhanced with an attention layer. The experiments resulted in accuracy of 0.83, recall of 0.83, precision of 0.82, and F1-score of 0.82. Statistical tests confirmed the significance of these findings, indicating that the approach improves the accuracy of sentiment analysis involving emojis. Overall, the study demonstrates that the integration of text and emojis leads to a more nuanced and precise understanding of sentiment in sentences, confirming the effectiveness of this method
Monocular vision-based visual control for SCARA-type robotic arms: a depth mapping approach
Get PDFThe accelerated growth of an increasingly automated industry requires the use of autonomous robotic systems. However, the use of these systems commonly requires an enormous amount of sensors. In this paper we evaluate the performance of a new system for visual control of a selective compliance assembly robot arm (SCARA) robotic arm using a monocular depth map that only requires one monocular camera. This system aims to be an efficient alternative to reduce the number of sensors in the robotic arm area while maintaining the effectiveness of traditional vision algorithms that use stereoscopic architectures of cameras. For this purpose, this system is compared with representative state-of the-art vision algorithms focused on the control of robotic arms. The results are statistically analyzed, indicating that the algorithm proposed in this research has competitive performance compared to state-of-the-art robotic arm visual control algorithms only using a single monocular camera
A deep learning-integrated proxy model for efficient cryptocurrency payments
Get PDFBlockchain technology allows decentralized cryptocurrencies to change digital finances by providing secure, pseudonymous transactions to users. Since blockchain ledgers operate in a public environment, users can face potential privacy risks due to the exposure of their transaction patterns. Conventional cryptocurrency systems use block generation for transaction confirmation, yet this process produces latency and impacts the real-time efficiency of transactions. This paper develops a proxy-assisted cryptocurrency payment system that employs blind signature principles to achieve better system privacy and enhanced speed. The core functionality of this proposed system aims to protect transaction secrecy as it speeds up confirmation processes. A proxy node handles transaction requests through blind signature protocols that guarantee data confidentiality as part of the methodology. The proposed system utilizes deep learning tools, which include recurrent neural networks (RNN), graph neural networks (GNN), and reinforcement learning (RL) to forecast confirmation results, identify scams, and control proxy functions dynamically. Research indicates that the introduced method substantially boosts privacy features, decreases transaction latencies, and enhances the security of all transactions by providing an encouraging roadmap for secure cryptocurrency systems that preserve privacy
End-to-end system for translating bahasa isyarat Indonesia sign language gestures into Indonesian text
Get PDFThis study addresses critical challenges in developing an end-to-end bahasa isyarat Indonesia (BISINDO) SLT by integrating advanced deep learning techniques to overcome complex background interference, transitional gesture recognition, and limitations in dataset availability. While existing SLT systems struggle with isolated word recognition and manual preprocessing, our work introduces three key innovations: (1) implementation of YOLOv8 for optimized object detection, achieving 88% mAP and reducing WER to 11.40%, outperforming YOLOv5/v7 in handling complex backgrounds; (2) automated removal of transitional gestures using Threshold conditional random fields (TCRF), which attained 95.68% accuracy, significantly improving upon MobileNetV2’s performance (WER: 6.89% vs. 93.53%); and (3) end-to-end BISINDO SLT by expansion of the BISINDO dataset to 435 word labels, enabling comprehensive sentencelevel translation. Experimental results demonstrate the system’s robustness, with 8.31% of WER, 84.13% of SAcc, and 87.08% of SacreBLEU after dataset expansion and redundancy elimination through grouping methods. The proposed framework operates without manual intervention, marking a substantial advancement toward real-world applicability
GESS-based technical loss estimation for sustainable power networks
Get PDFIn the pursuit of global environmental sustainability, minimizing technical losses (TL) in power distribution networks has become a key priority for utility providers. Despite numerous advancements, precise loss estimation remains a challenge due to dynamic network conditions, complex configurations, and varying parameters such as load patterns and system topology. This issue is critical, as reducing TL not only enhances distribution efficiency but also contributes to lowering greenhouse gas (GHG) emissions. This study aims to develop and demonstrate a robust method for estimating TL aligned with the global environmental sensing and sustainability (GESS) principles. The proposed approach integrates an advanced loss estimation sequence comprising peak power loss (PPL), load loss factor, and an energy flow model. It is applied to real case studies, enabling assessment of both feeder and transformer losses. Results highlight the impact of key parameters including transformer capacity factor, cable length, load factor (LF), and loss factor on overall losses. Furthermore, the method facilitates quantification of environmental and economic impacts, revealing that both carbon footprint and cost rates are highly sensitive to total energy losses. This work underscores the significance of accurate TL estimation in promoting environmentally and economically sustainable power distribution systems