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

作者： Taraji, Mahboubeh Antoniades, Marco A. Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoON Canada

ISBN: (纸本)9781665442282

This paper presents a 2-by-2 horn slot antenna array at X-band. Metamaterial concepts are applied to the antenna design using printed ridge gap waveguide (PRGW) technology as a magnetic ground for the antenna array, which is excited by a 1-to-4 power divider. Each of the array power-divider branches excites a slot, while maintaining the impedance bandwidth of the original unloaded power divider. Directive horn antennas are used to control sidelobe levels and increase the gain. Simulation results demonstrate an impedance bandwidth of 23% and a gain of 12.7 dBi at 10 GHz, which is a significant improvement compared to an impedance bandwidth of less than 14% and a gain of 9.7 dBi for the antenna array without the horns. © 2023 IEEE.

关键词： Antenna arrays

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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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computer-free computational imaging: optical computing for seeing through random media

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Light(Science & Applications) 2022年第7期11卷 4-5页

作者： Yunzhe Li Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA02215USA Department of Biomedical Engineering Boston UniversityBostonMA02215USA

Diffractive Deep Neural Network enables computer-free,all-optical“computational imaging”for seeing through unknown random diffusers at the speed of light.

关键词： random free enable

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A 3.97 μW 11.2b 500 kS/s Hybrid SAR ADC via Time-Mode Signal Processing 66

A 3.97 μW 11.2b 500 kS/s Hybrid SAR ADC via Time-Mode Signa...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Perczak, Ian Yuan, Fei Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoON Canada

ISBN: (纸本)9798350302103

This paper presents a 12b hybrid ADC consisting of a 8b successive approximation register (SAR) analog-to-digital converter (ADC) with top-plate sampling, a variable-slope voltage-to-time converter with built-in absolute-value function, and a 3-stage cyclic gated Vernier TDC. The proposed ADC features the ability to accommodate a large input without sacrificing linearity, low power consumption, and technology compatibility. A number of techniques are proposed to improve performance and power efficiency of the ADC. These techniques include a sub-threshold delay-locked loop with pulsed control signals for clock generation, clock kickback reduction to minimize kickback and mismatch induced sensitivity loss of the comparator of the SAR ADC, a gated cyclic Vernier TDC to minimize the resolution loss caused by the improper injection of time inputs and a virtually unlimited dynamic range, a self-resetting arbiter with zero metastability window to improve the resolution of Vernier TDC, and a significantly simplified synchronous SAR for a better power/area efficiency. The ADC is designed in a TSMC 130 nm 1.2 V CMOS technology with a reduced supply voltage of 0.8 V and analyzed using Spectre with BSIM3.3 device models. Simulation results show that at 500 kS/s, the ADC offers a SNDR of 69.04 dB and ENOB of 11.17 while consuming 3.70 μW, yielding a Walden FOM of 3.21 fJ/conv. © 2023 IEEE.

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Decentralized Federated Deep Learning Image Recognition Models 4

Decentralized Federated Deep Learning Image Recognition Mode...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Kugan, Sharun Islam, Md Quyyum Ul Kashef, Rasha Toronto Metropolitan University Department of Electrical Computer Biomedical Engineering Toronto Canada

ISBN: (纸本)9798350348248

In the era of IoT, numerous frameworks and cutting-edge models have been introduced to enhance user experience and privacy and reduce the risk of data breaches. Over time, IoT device usage has grown tremendously, and a flood of data has been sent to servers for processing. Federated learning has been deployed for efficient decentralization while preserving privacy. Federated learning has been applied in various IoT-related applications such as image classification, object segmentation, object detection, and sensor analytics. Existing centralized image recognition models fall short of providing accurate image classification with acceptable processing time for real-time deployment while preserving privacy. In this paper, we designed two decentralized deep learning models using federated learning, the CNN-TFF and the VGG16-TFF. With around 250 training iterations, we achieved a high accuracy rate of up to 90% with a decrease in the loss value for the CIFAR-100 dataset using the VGG16-TFF model while maintaining data privacy using federated learning. © 2023 IEEE.

关键词： Image classification

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A Remedial Action Scheme Against False Data Injection Cyberattacks Targeting ULTC Transformers in Smart Distribution Systems

A Remedial Action Scheme Against False Data Injection Cybera...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Naderi, Ehsan Asrari, Arash Fajri, Poria College of Engineering and Computer Science Arkansas State University Department of Electrical Engineering JonesboroAR United States Purdue University Northwest Department of Electrical and Computer Engineering HammondIN United States University of Nevada Electrical and Biomedical Engineering Department RenoNV United States

ISBN: (纸本)9798350352290

This paper proposes an on-line remedial action scheme (OLRAS) in order to mitigate the voltage violations caused by false data injection attacks (FDIAs) targeting under load tap changing (ULTC) transformers in smart distribution systems. The FDIA framework contains two different phases. In the attack phase, distribution system operator (DSO), being in attacker's shoe, considers cyberattack scenarios through compromising the results of volt-var optimization problem in a radial distribution grid modified with distributed energy resources (DERs) such as photovoltaic (PV) units and wind turbines (WTs). The outcome of the attack phase will be the compromised voltage profile of the distribution grid showing different rates of voltage violations. In the reaction phase, the DSO rapidly identifies a customized distribution feeder reconfiguration (CDFR) in order to update the flows of active and reactive power throughout the targeted distribution system and recover the voltage profile. The objective functions of the proposed CDFR are defined to minimize the impacts of such cyberattacks targeting ULTCs within distribution grids. This will empower DSOs to react to severe cyberattacks, bypassing the detection stage, and address the voltage violations in a timely manner. The effectiveness of the proposed OLRAS is validated on an IEEE test system. © 2024 IEEE.

关键词： Distribution transformers

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques 9

Sensitive Content Detection in Social Networks Using Deep Le...

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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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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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Increasing the RES Hosting Capacity of the Cyprus Distribution System

Increasing the RES Hosting Capacity of the Cyprus Distributi...

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CIRED 2024 Vienna Workshop

作者： Therapontos, Phivos Konstantinou, Rafail Charalambous, Charalambos A. Aristidou, Petros Electricity Authority of Cyprus Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Dept. of Electrical & Computer Engineering & Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9781837242672

The Renewable Energy Sources (RES) penetration in the power system of Cyprus has dramatically increased over the last years. As a result, the system is already facing significant challenges limiting the RES hosting capacity of the Distribution Network (DN). The major limitation factors are network congestion and voltage security. In this paper, alternative solutions for increasing the RES hosting capacity in DNs are reviewed, and a methodology is introduced to evaluate their effectiveness. More specifically, different inverter settings and an advanced centralised voltage control from power transformers are used to mitigate voltage-related issues, while network reinforcements and upgrading the operating voltage are considered for further increase in hosting capacity. The solutions and evaluation methodology are tested using a real MV network of the Cyprus distribution system and the strategic plan of the Cyprus Distribution System Operator (DSO) for maximizing RES hosting capacity is outlined. © The Institution of engineering & Technology 2024.

关键词： Distribution transformers

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Vpit: real-time embedded single object 3D tracking using voxel pseudo images

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Neural Computing and Applications 2024年第32期36卷 20341-20354页

作者： Oleksiienko, Illia Nousi, Paraskevi Passalis, Nikolaos Tefas, Anastasios Iosifidis, Alexandros Department of Electrical and Computer Engineering Aarhus University Finlandsgade Aarhus8200 Denmark Department of Informatics Aristotle University of Thessaloniki University Campus Thessaloniki54124 Greece Department of Chemical Engineering Aristotle University of Thessaloniki University Campus Thessaloniki54124 Greece

In this paper, we propose a novel voxel-based 3D single object tracking (3D SOT) method called Voxel Pseudo Image Tracking (VPIT). VPIT is the first method that uses voxel pseudo images for 3D SOT. The input point cloud is structured by pillar-based voxelization, and the resulting pseudo image is used as an input to a 2D-like Siamese SOT method. The pseudo image is created in the Bird’s-eye View (BEV) coordinates;and therefore, the objects in it have constant size. Thus, only the object rotation can change in the new coordinate system and not the object scale. For this reason, we replace multi-scale search with a multi-rotation search, where differently rotated search regions are compared against a single target representation to predict both position and rotation of the object. Experiments on KITTI [1] Tracking dataset show that VPIT is the fastest 3D SOT method and maintains competitive Success and Precision values. Application of a SOT method in a real-world scenario meets with limitations such as lower computational capabilities of embedded devices and a latency-unforgiving environment, where the method is forced to skip certain data frames if the inference speed is not high enough. We implement a real-time evaluation protocol and show that other methods lose most of their performance on embedded devices;while, VPIT maintains its ability to track the object. © The Author(s) 2024.

关键词： 3D tracking Embedded deep learning (DL) Pillars Pseudo images Real-time neural networks Single object tracking Voxels

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