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35th Irish Systems and Signals Conference, ISSC 2024

作者： Wu, Yun McAllister, John School of Electrical Electronic Engineering and Computer Science Queen's University Belfast Belfast United Kingdom

ISBN: (纸本)9798350352986

K-Singular Value Decomposition (K-SVD) is one of the key techniques in many signal processing and machine learning applications, such as image denoising, principal component analysis and dictionary learning. It is challenge to implement real-time K-SVD on resource constrained embedded systems, due to its exponentially increased computational complexity for large scale image or data dimensions. Approximate computing, as a trending paradigm in saving size, weight, and power (SWaP) in computation, trades the accuracy with complexity, so as to enable energy efficiency in modern computing systems. In this work, an approximate K-SVD based Dictionary Learning is studied primarily with sub-divided data blocks using reduced precision. The scalability of checkerboard K-SVD performance and arithmetic approximation are quantified with reconstructed image quality and computational complexity. It enables the efficient implementation by showing up to 16x estimated speed up and 98% reduction in memory foot-print using checkerboard division, and averagely 37% memory foot-print savings with approximate computing. © 2024 IEEE.

关键词： Singular value decomposition

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Theoretical analysis and design of a dual-wavelength and selectable all-optical broadband QDs semiconductor optical amplifier (QDs-SOA) with inhomogeneous broadening

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Optical and Quantum Electronics 2025年第2期57卷 1-26页

作者： Eyvazi, Mahdiyeh Yadipour, Reza Rostami, Ali Rostami, Parisa Mirtagioglu, Hamid Faculty of Electrical and Computer Engineering University of Tabriz Tabriz*** Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz*** Iran SP-EPT Lab ASEPE Company Industrial Park of Advanced Technologies Tabriz Iran Faculty of Medicine Tabriz University of Medical Science Tabriz Iran Department of Statistics Faculty of Science and Literature University of Bitlis Eren Bitlis Turkey

This study delves into the significant role played by Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs) in meeting the ever-growing bandwidth demands. QD-SOAs offer a unique blend of cost-effectiveness, integration capabilities, wide bandwidth, rapid responsiveness, robust power output, stability, and spectral adaptability, driving notable advancements in optical communication systems. In this work, we introduce a novel two-wavelength amplifier structure based on Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs) that utilizes quantum dots of different sizes to achieve efficient amplification at specific mid-infrared wavelengths. This innovative approach, which incorporates quantum dots with varying sizes, enables enhanced performance by optimizing the amplification process for each specific wavelength. Furthermore, this work demonstrates the use of tailored optical pumping mechanisms that enhance the carrier recovery process and reduce carrier relaxation times in the active region. This novel optical pumping technique leads to a significant increase in the efficiency and speed of the amplifier, distinguishing this study from others in the field. Simulation results provide detailed insights into the complex interplay between carrier interactions and gain spectra, offering a comprehensive understanding of QD-SOA operational dynamics. The proposed Quantum Dot Semiconductor Optical Amplifier (QD-SOA) achieves significant advancements in optical communication, offering maximum amplification rates of 26.9 and 19.9 times for QD1 and QD2, respectively, and bandwidths of 12 THz and 15 THz. The study highlights the role of quantum dot size, homogeneous and inhomogeneous broadening, and optical pumping in enhancing gain and performance, demonstrating the potential of QD-SOAs for high-gain, wide-bandwidth applications in photonic systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semiconductor quantum dots

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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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Text-to-Metaverse: Integrating Advanced Text-to-PointCloud Techniques for Enhanced 3D Scene Generation 1

Text-to-Metaverse: Integrating Advanced Text-to-PointCloud T...

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1st IEEE Conference on Telepresence, Telepresence 2024

作者： Elhagry, Ahmed El Saddik, Abdulmotaleb Abu Dhabi United Arab Emirates University of Ottawa School of Electrical Engineering and Computer Science Canada

ISBN: (纸本)9798331528249

This paper presents a novel approach to generating metaverse environments directly from textual descriptions by integrating advanced text-to-pointcloud techniques into the text-to-metaverse pipeline. The proposed system replaces conventional text understanding and design script components with a more efficient and accurate pipeline, enhancing the overall generation process. Our methodology leverages natural language processing for entity and relation extraction, which are then converted into structured scene descriptions. These descriptions guide a generative shape engine to produce 3D objects and scenes, which are subsequently rendered into immersive metaverse environments. Experimental evaluations demonstrate significant improvements in both efficiency and quality of the generated environments compared to the baseline model. The integration of text-to-pointcloud techniques ensures a higher fidelity in object representation and scene coherence, addressing limitations in existing metaverse generation methods. This work paves the way for more interactive and dynamic virtual environments, offering substantial advancements for applications in gaming, virtual reality, and remote collaboration. © 2024 IEEE.

关键词： Virtual environments

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A Unified IoT-based Smart Home System: Integration of Security and Convenience Features for Consumer 3

A Unified IoT-based Smart Home System: Integration of Securi...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Oeishee, Moslema Hoque Ahammad Muyeed, Munyem Alvi, Mishkat Mehedi, Rejoan Shariar Emon Shaikat, M. Molla, Md Nayan Brac University Electrical and Electronic Engineering Department Dhaka Bangladesh United International University Computer Science and Engineering Department Dhaka Bangladesh Brac University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798331523893

Home automation is growing rapidly in the Fourth Industrial Revolution (4IR), providing users with unwavering convenience and enhanced security. This paper presents a comprehensive Internet of Things (IoT) smart home automation solution with a focus on energy management, user-friendliness, and security. Our system includes real-time integrated dashboards accessible via web and mobile interfaces, smart irrigation for indoor gardening, passkeys for door security, voice commands, fingerprint access, night security lighting, and morning alarms. The hardware consists of an Arduino MEGA microcontroller, various sensors (smoke, temperature, humidity, and soil), an ESP Wi-Fi module for connectivity, and additional components. The system demonstrated a satisfactory effectiveness level with an accuracy of 78% based on various tests. The voice assistant bot and accessible dashboard significantly improve user experience. Despite the complexity of home automation processes, increased affordability and accessibility are the aims of this paper. Future work will focus on enhancing real-time capabilities, integrated circuits, data security, and web-based functionalities to improve system performance, scalability, and user accessibility. © 2025 IEEE.

关键词： Smart homes

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ZEROTH-ORDER OPTIMIZATION WITH TRAJECTORY-INFORMED DERIVATIVE ESTIMATION 11

ZEROTH-ORDER OPTIMIZATION WITH TRAJECTORY-INFORMED DERIVATIV...

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11th International Conference on Learning Representations, ICLR 2023

作者： Shu, Yao Dai, Zhongxiang Sng, Weicong Verma, Arun Jaillet, Patrick Low, Bryan Kian Hsiang Dept.of Computer Science National University of Singapore Singapore Dept.of Electrical Engineering and Computer Science MIT United States

Zeroth-order (ZO) optimization, in which the derivative is unavailable, has recently succeeded in many important machine learning *** algorithms rely on finite difference (FD) methods for derivative estimation and gradient descent (GD)-based approaches for ***, these algorithms suffer from query inefficiency because many additional function queries are required for derivative estimation in their every GD update, which typically hinders their deployment in real-world applications where every function query is *** this end, we propose a trajectory-informed derivative estimation method which only employs the optimization trajectory (i.e., the history of function queries during optimization) and hence can eliminate the need for additional function queries to estimate a ***, based on our derivative estimation, we propose the technique of dynamic virtual updates, which allows us to reliably perform multiple steps of GD updates without reapplying derivative *** on these two contributions, we introduce the zeroth-order optimization with trajectory-informed derivative estimation (ZORD) algorithm for query-efficient ZO *** theoretically demonstrate that our trajectory-informed derivative estimation and our ZORD algorithm improve over existing approaches, which is then supported by our real-world experiments such as black-box adversarial attack, non-differentiable metric optimization, and derivative-free reinforcement learning. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Trajectories

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Design of a Standalone Hybrid Renewable Energy System - A Hospital in Lafayette, Indiana, USA 5

Design of a Standalone Hybrid Renewable Energy System - A Ho...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

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

ISBN: (纸本)9798350379884

This paper presents the design of a standalone hybrid renewable energy system (HRES) for a hospital in Lafayette, Indiana, USA, to enhance energy reliability and reduce costs. The proposed system integrates photovoltaic (PV) panels, wind turbines, batteries, and a backup diesel generator. Using Hybrid Optimization Model for Electric Renewables (HOMER) software, the system is optimized for cost-effectiveness and sustainability, while electrical Transient Analyzer Program (ETAP) software is employed to ensure a safe operation under varying conditions. Results verify that the developed HRES meets the hospital's energy needs, significantly reduces operational costs, and minimizes carbon emissions, offering a sustainable model for healthcare facilities. © 2024 IEEE.

关键词： Windmill

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Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models

Pruning then Reweighting: Towards Data-Efficient Training of...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yize Zhang, Yihua Liu, Sijia Lin, Xue Department of Electrical and Computer Engineering Northeastern University Boston United States Department of Computer Science and Engineering Michigan State University East Lansing United States

ISBN: (纸本)9798350368741

Despite the remarkable generation capabilities of Diffusion Models (DMs), conducting training and inference remains computationally expensive. Previous works have been devoted to accelerating diffusion sampling, but achieving data-efficient diffusion training has often been overlooked. In this work, we investigate efficient diffusion training from the perspective of dataset pruning. Inspired by the principles of data-efficient training for generative models such as generative adversarial networks (GANs), we first extend the data selection scheme used in GANs to DM training, where data features are encoded by a surrogate model, and a score criterion is then applied to select the coreset. To further improve the generation performance, we employ a class-wise reweighting approach, which derives class weights through distributionally robust optimization (DRO) over a pre-trained reference DM. For a pixel-wise DM (DDPM) on CIFAR-10, experiments demonstrate the superiority of our methodology over existing approaches and its effectiveness in image synthesis comparable to that of the original full-data model while achieving the speed-up between 2.34× and 8.32×. Additionally, our method could be generalized to latent DMs (LDMs), e.g., Masked Diffusion Transformer (MDT) and Stable Diffusion (SD), and achieves competitive generation capability on ImageNet. © 2025 IEEE.

关键词： data reweighting data-efficient training diffusion model

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RHC-Based Consensus of Multi-Agent Systems with Simultaneous Packet Dropout and Input Delay

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Journal of Systems science & Complexity 2022年第4期35卷 1262-1277页

作者： XU Juanjuan ZHANG Zhaorong School of Control Science and Engineering Shandong UniversityJinan250061China School of Electrical Engineering and Computer Science University of NewcastleNewcastleCallaghanNSW2308Australia

This paper is concerned with the multi-agent systems with both packet dropout and input delay.A novel receding horizon control(RHC)based consensus protocol is proposed by solving a distributed RHC based optimization *** novelty of the optimization problem lines in the involvement of the neighbours’predictor information in the cost *** on the derived RHC based consensus protocol,the necessary and sufficient condition for the mean-square consensus is *** addition,the authors give a specific sufficient condition to guarantee the mean-square consensus.

关键词： Consensus delay packet dropout predictor-like protocol receding horizon control

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Evaluating the Impact of Unrestricted GenAI Usage on Experiential-Based Learning 13

Evaluating the Impact of Unrestricted GenAI Usage on Experie...

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13th IEEE International Conference on engineering Education, ICEED 2024

作者： Browning, Jonathan W. Bustard, John Anderson, Neil Galway, Leo School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom

ISBN: (纸本)9798350367416

This work examines the usage and experience of students using generative AI (genAl) in a engineering entrepreneurship experiential-based learning course. It utilizes a project-based learning approach, where students are in teams of five. The course takes place over a full academic year, i.e., the fall and spring semesters. One of the aims of the course is to be as realistic as possible within an academic setting, with the students trying to create a new technology-based business, in the hopes that they will continue their ventures after the course concludes. Therefore, the teams were allowed to and encouraged to use any genAI they wish throughout the course to assist them. Outside of academia, it would be expected that a start-up founder would use genAl to speed up many aspects of starting their business. Therefore, we want to examine how the students use genAI without there being any constraints. As part of the summative assessment for the course each student peer assesses the members of their team. Included within that they also 'peer assess' genAI as though it were another team member, which is a critical reflection of their genAI usage. This work addresses how students use genAI, in experiential-based learning courses when they are allowed to use it any way possible to assist them. The study uses a quantitative and qualitative approach with student data from their 'peer assessment' (i.e., critical reflection) of their genAI usage from the academic year 2023/24. Within the peer assessment of the genAI team member the students answer Likert-scale statements to be rated on a five-point semantic differential scale. The results indicate a varied adoption and value of genAI across different project phases. While some students appreciated genAI for speeding up specific tasks, its contribution to creative processes like ideation was less impactful. © 2024 IEEE.

关键词： Students

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