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作者： Dassanayake, Janith Kavindu Gunasinghe, Dulaj Aruma Baduge, Gayan Amarasuriya School of Electrical Computer and Biomedical Engineering Southern Illinois University CarbondaleIL62901 United States

The active pilot contamination attacks in intelligent reflecting surface (IRS) aided massive multiple-input multiple-output systems are investigated. By proposing a statistical channel state information based IRS phase-shift optimization technique, an achievable secrecy rate is derived in the presence of practical impediments, including erroneously estimated composite channels via linear minimum mean square error estimation criterion, residual interference due to active pilot contamination, artificial noise (AN) generation, and spatially correlated fading at the base-station antennas and IRS elements. A transmit power allocation technique is also proposed. Two active pilot attack detectors are designed based on the Neyman-Pearson and generalized likelihood ratio test criteria. The performance of these detectors is investigated by deriving the probability of detection, probability of false alarm, and receiver operating characteristics. Our secrecy rate analysis reveals that the rate leaked into the eavesdroppers by active pilot contamination attacks can be considerably high. The proposed power allocation algorithm jointly assigns transmit powers for the legitimate signals and AN sequences for maximizing the minimum secrecy rate of the weakest legitimate user to ensure user-fairness. The proposed detectors of active pilot attacks may be useful in designing remedial techniques to mitigate detrimental effects of active eavesdropping. © 2024 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： MIMO systems

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Effects of Mismatch on IC-Equipped Programmable Metasurfaces with Multibeam Functionality

Effects of Mismatch on IC-Equipped Programmable Metasurfaces...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Kossifos, Kypros M. Georgiou, Julius Antoniades, Marco A. University of Cyprus Dept. of Electrical & Computer Engineering Nicosia Cyprus Toronto Metropolitan University Dept. of Electrical Computer & Biomedical Engineering ON Canada

ISBN: (纸本)9781665442282

Integrated circuit (IC) technology can be incorporated in programmable metasurfaces (PMSFs) and reduce their cost and power consumption while increasing their electromagnetic manipulation capabilities. Such improvements can succour the incorporation of PMSFs as a common telecommunication technology. In order for this to be realised, the reproducibility and reliability of IC-equipped PMSFs need to be addressed. A study is presented on effects of IC mismatch on the performance of a PMSF with multibeam functionality. A mismatch of 10% to 40% of the nominal IC response is used and the effects on the far field are presented. © 2023 IEEE.

关键词： Timing circuits

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Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types 46

Wavelet Analysis of Noninvasive EEG Signals Discriminates Co...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Rabiee, Ali Ghafoori, Sima Cetera, Anna Shahriari, Yalda Abiri, Reza University of Rhode Island Dept. of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350371499

This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications. © 2024 IEEE.

关键词： Wavelet analysis

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Preventing Actuator Saturation in Linear Quadratic Regulators 63

Preventing Actuator Saturation in Linear Quadratic Regulator...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Komaee, Arash Southern Illinois University School of Electrical Computer and Biomedical Engineering CarbondaleIL62901 United States

ISBN: (纸本)9798350316339

This paper presents a nonlinear state feedback law for regulation of linear systems with control constraints. Such constraints mathematically describe the physical limitations on the actuators driving these systems, usually known as actuator saturation. The proposed state feedback law resembles a linear quadratic regulator (LQR) with an adaptable gain, dynamically adjusted to prevent actuator saturation. This feedback law is computationally inexpensive for real-time implementation, and offers a viable alternative to the typically more complex control laws derived from a constrained LQR formulation. Simulation results are presented that demonstrate the effectiveness of the presented feedback law. © 2024 IEEE.

关键词： State feedback

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A Comparison of Selected Agent-Based Modelling Frameworks 1

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Gacek, Paulina Bocheńczyk, Dominika Krajewski, Maciej Kruczek, Bartosz Lazarz, Krzysztof Magnuszewski, Pawel Zięciak, Antoni Kamiński, Filip Wąs, Jaroslaw Department of Applied Computer Science Faculty of Electrical Engineering Automatics IT and Biomedical Engineering AGH University of Krakow Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031857034

ISBN: (纸本)9783031857027

In recent years, a variety of new frameworks streamlining the process of agent-based modeling has emerged. These frameworks serve different purposes and each offers a unique set of features. In this practical comparative study, we evaluate the performance of various ABM frameworks through a series of benchmark simulations. By comparing the distinct functionalities offered by these tools, we aim to assist in the selection of an appropriate ABM toolkit for developing system models. This review presents a concise overview of seven popular agent-based modeling tools aiming to inspire further exploration and investigation into this subject. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital elevation model

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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PWM-Based Hardware Authentication for Smart Inverters: Detecting Counterfeit Inverter through Transient Response Analysis

PWM-Based Hardware Authentication for Smart Inverters: Detec...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hingu, Chanakya Fu, Xingang Mishan, Ramkrishna Kim, Taesic University of Nevada Reno Department of Electrical and Biomedical Engineering Reno United States University of Missouri-Columbia Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350376067

Amidst the growing complexity of inverter-based power systems, the security of smart inverters is becoming increasingly critical, particularly for protecting power electronics components from counterfeit threats. Typical methods of PCB hardware authentication utilize a signal injection to the PCB through a hardware interface such as JTAG, which might be impractical in smart inverters. This paper presents a cost-effective hardware authentication method using Pulse Width Modulation (PWM) and transient response to detect counterfeit solar inverters. By conducting a detailed analysis and comparing the transient responses from both inverters, this work reveals significant differences that can serve as analog signatures for authentication. The effectiveness of this validation method is demonstrated through an extensive statistical analysis, providing a solid basis for implementing this strategy as a safeguard against counterfeit penetration in power systems. Additionally, this method shows a potential tool for industry practitioners and researchers to verify, detect, and distinguish between counterfeit and genuine printed circuit boards, enhancing the security posture of power systems infrastructure. © 2024 IEEE.

关键词： Transient analysis

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Machine Learning Approach for Detecting Potential Anomalous Cosmic Rays Particles Tracks in Earth-Scale Cosmic Ray Extremely Distributed Observatory 1

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Tyc, Jan Zub, Marcin Hachaj, Tomasz Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031857034

ISBN: (纸本)9783031857027

In this paper, we present a novel contribution to the problem of the detection of potential anomalous cosmic rays particle tracks acquired by Earth-scale complex distributed observatory infrastructure. Detection of such signals provides an opportunity to analyze the performance of the entire research infrastructure of a complex system (for example, potential failures) and a chance to record unknown physical phenomena that can be recorded simultaneously with space radiation observations. The observatory comprises a worldwide network of CMOS detectors cooperating in the citizen science paradigm. We propose, evaluate, and compare several data processing, feature extractions, and potential anomaly discovery pipelines based on machine learning approaches. Although, to date, there have already been published works performing detection and analysis of anomalies in the Cosmic Ray Extremely Distributed Observatory Project (CREDO) dataset, they applied single methods without comparing the results with other possible approaches to analyze potential anomalies. In this work, for the first time, we have used and discussed many anomalous discovery approaches not previously applied to this issue to perform analysis of this extensive dataset (nearly 600,000 images). For this reason, the results published here are of significant cognitive importance to the problem under consideration. We have published both our research’s source codes and dataset so our results can be reproduced. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cosmic rays

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Microwave Sensors: Novel Techniques, Topologies, and Manufacturing Technologies 16

Microwave Sensors: Novel Techniques, Topologies, and Manufac...

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16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023

作者： Delmonte, Nicolo Silvestri, Lorenzo Masoumabad, Mehdi Alipour Perregrini, Luca Bozzi, Maurizio University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia27100 Italy

ISBN: (纸本)9798350347029

This paper presents an overview of the recent advances we made in the field of microwave sensors. Sensors for different physical quantities are presented, ranging from structures for the retrieval of the electric and magnetic characteristics of materials, to devices for the determination of position and angular rotation. These sensors are based on various fabrication technologies, including planar structures, additive manufacturing, and hybrid solutions, which take advantage of the best features of different technologies. © 2023 IEEE.

关键词： 3D printing

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What Is Inside Agent Brain: Application of Learning from Examples Using Rough Sets (LEM2) Rule Induction Algorithm to Explain Actions of Vision-Based Machine Learning Agents Trained with Proximal Policy Optimization 11th

What Is Inside Agent Brain: Application of Learning from E...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Hachaj, Tomasz Wąs, Jaroslaw Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031927430

In this work, we propose a method to explain the performance of a neural network-based brain of a machine learning agent trained with a Proximal Policy Optimization (PPO) algorithm. By explaining, we mean to discover probable criteria on which the PPO-trained agent system is making certain decisions. An additional complication is that the agent retrieves information about the environment using a camera. Our proposed explanation method consists of Gradient-weighted Class Activation Mapping (GradCAM) and Learning from Examples using Rough Sets (LEM2) algorithm. We also show how using a threshold on rules support and Laplace, we can reduce the number of rules and measure how reducing the number of rules affects the accuracy of approximation of the explained model. To the best of our knowledge, this is the first work proposing a PPO-generated network explanation for visual-based agents using the rough sets-based method. Our method has achieved a high efficiency of explanation of neural network performance on the test set, reaching F1 score = 0.93 and an Average per-class accuracy of 0.94. In some cases, we have also reduced the number of rules to a few or a dozen while maintaining a high level of explainability efficiency. We publish all source codes so our experiments can be reproduced. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge engineering

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