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29th International Conference on Information Technology, IT 2025

作者： Forcan, Miodrag Dobrić, Goran Abed, Amir Žarković, Mileta Ikić, Marko Forcan, Jovana Barragán-Villarejo, Manuel Maza-Ortega, José María De Paula Garcia-López, Francisco University of East Sarajevo Faculty of Electrical Engineering East Sarajevo71123 Bosnia and Herzegovina University of Belgrade School of Electrical Engineering Belgrade11120 Serbia University of East Sarajevo Faculty of Philosophy Department of Computer Science and Systems East Sarajevo71420 Bosnia and Herzegovina University of Sevilla Department of Electrical Engineering Seville41004 Spain

ISBN: (纸本)9798331517649

This paper provides an overview of microgrid control strategies, examining differences between centralized and decentralized approaches, and focusing on classical hierarchical control schemes as well as the structure of three-layer microgrid control systems. It also explores Hardware-in-the-Loop modeling, a crucial approach for microgrid simulation, with particular emphasis on devices supporting microgrid modeling. The paper primarily focuses on the design and implementation requirements of an experimental setup for real-time testing of microgrid control algorithms. The setup incorporates Hardware-in-the-Loop device, a power amplifier, a power converter, a battery energy storage system, and a digital controller. It is specifically designed to handle both power and information (control) signals, facilitating a comprehensive approach to evaluating and validating microgrid control strategies in real-world conditions. A detailed analysis of the setup elements and their interactions is also provided. © 2025 IEEE.

关键词： Hardware-in-the-loop simulation

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Study on Reconfigurable Repeater-Based RSMA Systems

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IEEE Wireless Communications Letters 2025年第5期14卷 1371-1375页

作者： Vu, Thai-Hoc Tu, Ngo Hoang Bao, Vo Nguyen Quoc University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of Seoul National University of Science and Technology Department of Electrical and Information Engineering Seoul01811 Korea Republic of Van Lang University Faculty of Information Technology School of Technology Ho Chi Minh City70000 Viet Nam

The key idea of using active reconfigurable repeaters (ARRs) is to enhance wireless communications by amplifying incoming signals with minimal signal processing complexity and design overhead, while leveraging rate-splitting multiple-access (RSMA) for efficient interference management and simplified multiple-access operations. However, signal attenuation in wireless propagation over long distances leads to poor performance quality for RSMA users. This raises the concern: Can RSMA systems exploit ARRs to improve user performance and fairness? This work concentrates on a typical two-user scenario, develops information-theoretic analytical frameworks for outage probability and ergodic rate, and constructs efficient optimization frameworks to ensure user fairness. © 2012 IEEE.

关键词： Resource allocation

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Rethinking Transferable Adversarial Attacks With Double Adversarial Neuron Attribution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 354-364页

作者： Zhu, Zhiyu Jin, Zhibo Wang, Xinyi Zhang, Jiayu Chen, Huaming Choo, Kim-Kwang Raymond University of Sydney School of Electrical and Computer Engineering DarlingtonNSW2008 Australia University of Malaya Faculty of Computer Science and Information Technology KualaLumpur50603 Malaysia SuZhou Yierqi Jiangsu Suzhou215000 China The University of Texas at San Antonio San AntonioTX78249 United States

Transferable adversarial attacks are a threat to deep neural networks, in particular, for black-box scenarios where access to model information is limited. One can, for example, exploit the intermediate layer neurons to generate transferable adversarial samples. However, current works show limitations in providing feature-level attack mechanisms across multiple victim models. In light of the attribution methods, in this article, we investigate the attribution similarity across different models. We leverage the similarity to incorporate different attribution properties to enhance the sample transferability, for the first time, formulating a novel neuron attribution-based transferable attack termed DANAA++. Specifically, we utilize a range of adversarial attack methods to generate different baseline points through adversarial training. The attribution results are thus obtained along both linear and nonlinear integration paths. In our experiments, the baseline points and integration paths significantly help improve the transferability of adversarial samples. Our approach provides novel insights for building effective attribution-based feature-level adversarial attacks. https://***/LMBTough/DANAAPP. © 2020 IEEE.

关键词： Iterative methods

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Enhancing Microgrid Energy Security Through Optimal Power Sharing in Multi-Modal Energy Storage Systems 4

Enhancing Microgrid Energy Security Through Optimal Power Sh...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Anik, Shaikh Tawhidul Islam Mithil, Md Salman Khan Ahmad, Shameem Hazari, Md. Rifat Islam, Mahmoodul Ahmed, Tofael Parvez, Md. Shahariar Hassan, Mahamudul American International University-Bangladesh Faculty of Engineering Department of Electrical and Electronic Engineering Dhaka1229 Bangladesh Department of Electrical Engineering and Computer Science Rapid CitySD57701 United States Chittagong University of Engineering & Technology Department of Electrical and Electronic Engineering Chattogram4349 Bangladesh American International University-Bangladesh Faculty of Engineering Department of Industrial and Production Engineering Dhaka1229 Bangladesh

ISBN: (纸本)9798331530938

Microgrids (MGs), which combine distributed energy resources (DERs) with energy storage systems (ESS), offer improved energy security, reliability and sustainability by utilizing renewable energy sources for subsequent use. However, single ESS options often face challenges such as short life cycles, low power density etc. More than one ESS can resolve the mentioned issues though it increases complexity and maintaining stability becomes an issue. This paper proposes an effective energy management strategy between two ESS namely battery and supercapacitor to increase energy security in MG. The proposed strategy is designed in such a way that one of the two ESS stays connected all the time to the DC bus to ensure uninterruptable power supply to loads. Due to the use of DC bus, the system complexity decreases as there is no need to control frequency. Two cases were considered to verify the performance of the proposed energy management strategy in Matlab/Simulink. In the first case, during the unavailability of supercapacitor, the battery supplies 50 W to the load in addition to PV whereas supercapacitor supplies 136 W in the absence of battery along with PV. Furthermore, it was shown that the absence of PV has no negative effect in terms of power flow by ESS to meet load demand. © 2025 IEEE.

关键词： Energy security Energy Storage Systems Microgrid Power sharing Supercapacitor

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Ontology-Driven Semantic Web Framework with Sentiment Analysis for Risk Assessment and Diagnosis of Diabetes Mellitus 4

Ontology-Driven Semantic Web Framework with Sentiment Analys...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Velmurugan, R. Kumar, R. Rajan, M. Sundar Sivaraj, V. Karnataka Bengaluru India Arba Minch Institute of Technology Arba Minch University Faculty of Electrical and Computer Engineering Ethiopia Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798331523923

This paper uses semantic web and ontology techniques to predict the risk analysis of patients with diabetes mellitus. The data is collected from patients through personal interaction and by accessing their previous medical history. However, during personal interactions, medical staff often fail to gather all the necessary details for an accurate diagnosis, and patients may lose their previous medical records, which hinders doctors' ability to gain a full understanding of the problem. To address this issue, ontology techniques are employed, and additional details are collected from patients, such as their food habits, smoking history, alcohol consumption, sexual activity, and cardiovascular health. Using the collected data, ontology and semantic web technologies are applied to predict the risk analysis of diabetic patients. The results are visualized in a graph, providing a more comprehensive and accurate assessment of the patients' health risks. © 2025 IEEE.

关键词： Electronic health record

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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Approximate Message Passing for Spatial Modulation with Structured Sparsity

Approximate Message Passing for Spatial Modulation with Stru...

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International ITG Conference on Source and Channel Coding (SCC)

作者： Daniel Franz Volker Kühn Faculty of Computer Science and Electrical Engineering University of Rostock

ISBN: (数字)9798331522896

ISBN: (纸本)9798331522902

Multiple antennas at transmitter and receiver have significantly improved the performance of wireless communications systems. Traditionally, space-time coding, beamforming, or spatial multiplexing are applied to achieve diversity, SNR, and multiplexing gains, respectively. A different concept is pursued with Generalized Spatial Modulation (GSM). It conveys information through the selection of active antennas from a set of available antennas in addition to the transmission of conventional modulation symbols. This results in sparse transmit vectors, and detection algorithms known from compressed sensing can be applied. In this paper, Vector Approximate Message Passing (VAMP) is considered for the detection of GSM signals in massive Multiple-Input Multiple-Output (MIMO) systems. Its performance is improved by introducing structured redundancy in space or in time. In order to exploit this redundancy, the VAMP algorithm has to be extended by an additional graph layer. The resulting performance is evaluated by analyzing the error rates and the throughput obtained from Monte Carlo simulations.

关键词： GSM Correlation Message passing Redundancy Transmitting antennas Modulation Throughput Vectors MIMO Encoding

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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Impact of defects on the spectral characteristics of PureB avalanche-mode light emitting diodes 29

Impact of defects on the spectral characteristics of PureB a...

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Light-Emitting Devices, Materials, and Applications XXIX 2025

作者： Jelavić, Eva Nanver, Lis K. Knežević, Tihomir Ruder Boskovic Institute Division of Materials Physics Laboratory for Semiconductors Zagreb Croatia University of Twente Faculty of Electrical Engineering Mathematics & Computer Science Enschede Netherlands

ISBN: (纸本)9781510685208

Single photon avalanche diodes (SPADs) fabricated in PureB silicon technology offer exceptional versatility, functioning both as light emitting diodes and detectors sensitive down to a single photon. In PureB technology, nanometer-thin layers of boron are deposited on the silicon surface, creating defect-free ultra-shallow junctions. In this paper, PureB diodes with a junction depth of a few tens of nanometers are studied as avalanche-mode light-emitting diodes (AMLEDs) with the advantage that optical absorption was minimal. They were systematically analyzed to investigate the impact of defects on their optical and electrical characteristics. Bulk defects were unintentionally introduced by contamination during the processing of the n+ buried layer. These defects cause the increase in dark currents and the formation of light-emitting spots, observable in light emission spectra taken at reverse biases below the breakdown voltage. An increase in relative intensity for wavelengths from 450 nm to 550 nm was observed in spectral characteristics of light emitting spots compared to the light emission of the active-region breakdown. Optical and electrical characterization was conducted on diodes with different geometry, implantation doses in the multiplication region, and distance to an implicit guard ring to examine the impact of these parameters on the light emission spectrum. Low-temperature electrical characteristics and light emission patterns of the devices were obtained by measurements at cryogenic temperatures. © 2025 SPIE.

关键词： Particle beams

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Array Design for Angle of Arrival Estimation Using the Worst-Case Two-Target Cramér-Rao Bound

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2025年 6卷 453-467页

作者： Kokke, Costas A. Coutino, Mario Heusdens, Richard Leus, Geert Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands Acoustics and Sonar Department Netherlands Organisation for Applied Scientific Research The Hague2509 JG Netherlands Netherlands Defence Academy Faculty of Military Sciences Den Helder1781 AC Netherlands

Sparse array design is used to help reduce computational, hardware, and power requirements compared to uniform arrays while maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown target directions. To handle the unknown target directions when optimizing the Cramér-Rao bound, we propose to use the worst-case Cramér-Rao bound of two uncorrelated equal power sources with arbitrary angles. This new worst-case two-target Cramér-Rao bound metric has some resemblance to the peak sidelobe level metric which is commonly used in unknown multi-target scenarios. We cast the sensor selection problem for 3-D arrays using the worst-case two-target Cramér-Rao bound as a convex semi-definite program and obtain the binary selection by randomized rounding. We illustrate the proposed method through numerical examples, comparing it to solutions obtained by minimizing the single-target Cramér-Rao bound, minimizing the Cramér-Rao bound for known target angles, the concentric rectangular array and the boundary array. We show that our method selects a combination of edge and center elements, which contrasts with solutions obtained by minimizing the single-target Cramér-Rao bound. The proposed selections also exhibit lower peak sidelobe levels without the need for sidelobe level constraints. © 2020 IEEE.

关键词： Cramer-Rao bounds

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