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IEEE International Conference on Communications Workshops, ICC

作者： Omid Esrafilian Rajeev Gangula David Gesbert Communication Systems Department EURECOM Sophia Antipolis France Department of Electrical and Computer Engineering Northeastern University Boston MA USA

This paper considers the problem of ground users localization aided by an unmanned aerial vehicle (UAV) flying radio capable of collecting measurements from users. To do so, we exploit the phase information of the received radio signals from users at the UAV in different locations along with time-of-arrival (ToA) measurements. The location of the UAV is also not perfectly known, hence the problem at hand turns to a simultaneous user localization and tracking of the UAV location. The phase measurements are inherently more precise than ToA measurements while bringing ambiguity since it follows a periodic behavior. To solve this issue, we unwrap the phase measurements by tracking the phase while the UAV moves. Then a time-difference-of-arrival (TDoA)-based algorithm is proposed by treating each UAV location as a virtual antenna/anchor (yet with unknown locations) and exploiting the unwrapped phase measurements to precisely localize the users and jointly track the UAV trajectory. We employed a least-squares simultaneous localization and mapping (SLAM) framework to fuse TDoA data, estimated from phase, with other measurements such as ToA, an erroneous estimate of UAV location available from GPS, and the UAV velocity measured by an inertial measurement unit (IMU) onboard. The simulations verified the performance of the developed algorithm when compared to other benchmarks including scenarios with traditional terrestrial anchor-enabled localization systems.

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CPS, Sustainability and Efficiency in an Aquaponic Greenhouse

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2022年 20卷 210-215页

作者： Galvão, J. Santos, P. Aires, L. Neves, F. Department of Electrical Engineering/ESTG Polytechnic of Leiria Leiria2411-901 Portugal LSRE-LCM-Laboratory of Separation and Reaction Engineering Laboratory of Catalysis and Materials Escola Superior de Tecnologia e Gestão Instituto Politécnico de Leiria Leiria2411-901 Portugal ALiCE-Associate Laboratory in Chemical Engineering Faculty of Engineering University of Porto Rua Dr. Roberto Frias Porto4200-465 Portugal R&D Unit Institute for Systems Engineering and Computers/INESC Coimbra Portugal Department of Computer Science Polytechnic of Leiria Leiria2411-901 Portugal

Nowadays the humanity needs to produce food in greater quantity and quality, in order to satisfy citizens and markets, supported by advanced technologies such as cyber-physical system (CPS) or intelligent systems where software and electronic devices and receivers of physical variables are mechanisms that control the management of multiple actions inherent to plant growth. Electronic devices and receivers of physical variables are mechanisms that involve a network of IoT components and control multiple actions inherent to plant growth in agricultural greenhouses, which in short, are intensive food production systems. In this context, this paper presents a case study that uses a monitoring and management system that considers environmental parameters measured inside a greenhouse, allowing the manager to assess the working status of an aquaponic systems, and detect critical conditions that require quick decisions in real time. It is intended to develop technology that can be used in greenhouses, close to consumer centres or cities, to mitigate the impact of transport, lowering CO2 emissions resulting from the distribution channels between the producers and consumers. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Energy efficiency

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Intra-class patch swap for self-distillation

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Neurocomputing 2025年 645卷

作者： Choi, Hongjun Jeon, Eun Som Shukla, Ankita Turaga, Pavan Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Department of Computer Science and Engineering University of Nevada RenoNV89557 United States Geometric Media Lab School of Arts Media and Engineering and School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States

Knowledge distillation (KD) is a valuable technique for compressing large deep learning models into smaller, edge-suitable networks. However, conventional KD frameworks rely on pre-trained high-capacity teacher networks, which introduce significant challenges such as increased memory/storage requirements, additional training costs, and ambiguity in selecting an appropriate teacher for a given student model. Although a teacher-free distillation (self-distillation) has emerged as a promising alternative, many existing approaches still rely on architectural modifications or complex training procedures, which limit their generality and efficiency. To address these limitations, we propose a novel framework based on teacher-free distillation that operates using a single student network without any auxiliary components, architectural modifications, or additional learnable parameters. Our approach is built on a simple yet highly effective augmentation, called intra-class patch swap augmentation. This augmentation simulates a teacher–student dynamic within a single model by generating pairs of intra-class samples with varying confidence levels, and then applying instance-to-instance distillation to align their predictive distributions. Our method is conceptually simple, model-agnostic, and easy to implement, requiring only a single augmentation function. Extensive experiments across image classification, semantic segmentation, and object detection show that our method consistently outperforms both existing self-distillation baselines and conventional teacher-based KD approaches. These results suggest that the success of self-distillation could hinge on the design of the augmentation itself. Our codes are available at https://***/hchoi71/Intra-class-Patch-Swap. © 2025 Elsevier B.V.

关键词： Semantic Segmentation

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A Lightweight CNN Model for Tomato Crop Diseases on Heterogeneous Embedded System

A Lightweight CNN Model for Tomato Crop Diseases on Heteroge...

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International Conference on Modern Circuits and systems Technologies (MOCAST)

作者： Theodora Sanida Maria Vasiliki Sanida Argyrios Sideris Minas Dasygenis Department of Electrical & Computer Engineering University of Western Macedonia Kozani Greece Department of Digital Systems University of Piraeus Piraeus Greece

Tomato plants are vulnerable to several diseases, each of which may cause severe harm to the plant. These adverse conditions can significantly reduce the amount and quality of agricultural yields. In crop disease diagnostics, convolutional neural network (CNN) algorithms have recently materialised as the most critical area of research. Therefore, this work aims to develop a robust and lightweight CNN-based diagnostic tool for tomato leaf tissue diseases in a low-power heterogeneous embedded device. The research data consists of one category of healthy tomatoes obtained from PlantVillage and nine diseases that may affect them. We evaluated the effectiveness of the lightweight CNN diagnostic tool using conventional identification criteria, including accuracy, precision, area under the curve (AUC), f1-score, and recall, on 3,635 previously unseen test images. The outcomes are more than satisfactory, with 99.04% accuracy, 99.56% AUC, 99.00% f1-score, 98.78% precision, and 99.23% recall, respectively. The developed CNN-based diagnostic tool represents a significant advance in tomato leaf disease diagnosis, paving the way for future research to develop lightweight networks in low-power embedded devices for rapid and accurate diagnosis of plant diseases, benefiting farmers.

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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Smart Soft Sensor for Froth flotation quality monitoring using pre-trained feature extractor and Vision Transformer ViT 17

Smart Soft Sensor for Froth flotation quality monitoring usi...

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17th International Conference on Signal-Image Technology and Internet-Based systems, SITIS 2023

作者： Hasidi, Oussama Bendaouia, Ahmed Abdelwahed, El Hassan Abdellah, El Alaoui-Chrifi Moulay Boussetta, Abdelmalek Benzakour, Intissar Bourzeix, Francois Oubouhss, Kaotar Mohammed Vi Polytechnic University Lisi Laboratory Computer Science Departement Uca Analytics Lab Marrakech Morocco Cadi Ayyad University Lisi Laboratory Computer Science Departement Marrakech Morocco Reminex Managem Group R&d and Engineering Center Marrakech Morocco Mohammed Vi Polytechnic University Analytics Lab Rabat Morocco National School of Applied Science Uae Department of Electrical and Industrial Engineering Tangier Morocco

ISBN: (纸本)9798350370911

Smart sensor technologies have gained prominence in the field of machine vision-based applications. Within this context, the utilization of machine vision systems for monitoring and controlling flotation plants has become increasingly prevalent. This paper addresses the critical need for accurate froth image classification in the development of an online machine vision-based control system. This paper explores the recent advancements in machine learning, with a focus on deep learning techniques. Specifically, it introduces the application of a Vision Transformer (VIT) model as an alternative to the traditional Convolutional Neural Network (CNN) for classifying froth images collected from an industrial lead flotation circuit. The results of this research demonstrate the remarkable performance of the ViT model, achieving an impressive overall accuracy rate of 96% when combined with pre-trained feature extractor models. This achievement surpasses the capabilities of conventional artificial neural network (CNN) classifiers of the state of art in terms of both accuracy and computational efficiency. These findings underscore the promising potential of deep learning neural networks in froth image analysis, highlighting their significance in the advancement of smart sensor-based machine vision systems within the field of flotation plant control. © 2023 IEEE.

关键词： computer vision

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Accurate Model for Active Impedance of Slot-Phased Array Antennas Considering Mutual Coupling Effect

Accurate Model for Active Impedance of Slot-Phased Array Ant...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Mahmoud M. Mostafa Wael M. Abdel-Wahab Department of Electrical and Computer Engineering Centre for Intelligent Antenna and Radio Systems (CIARS) University of Waterloo Waterloo Canada C-COM Satellite Systems Inc. Ottawa Canada

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

In this paper, an accurate analytical model for predicting the active impedance variation with the scan angle of slot-phased array antennas (S-PAAs). is presented. The model is based on Floquet modes expansion of magnetic currents and its application to phased array slot antennas. The mutual coupling effect is taken into account by considering the variation of magnetic current with the scan angle. The simulation results calculated by proposed analytical model are presented and compared to those obtained by the numerical EM solver (HFSS). A very good agreement is observed.

关键词： Phased arrays Mutual coupling Analytical models Slot antennas Accuracy Simulation Conferences

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Model and Data Hybrid Reinforcement Learning for Optimal Voltage-Current Control of Hybrid Active Power Filter

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IEEE Transactions on Power Electronics 2025年

作者： Gong, Cheng Lam, Chi-Seng University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI 999078 China University of Macau Institute of Microelectronics 999078 China University of Alberta Department of Electrical and Computer Engineering EdmontonAB Canada University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology 999078 China

In this paper, a novel design for optimal voltage and current control (OVCC) is introduced, employing a hybrid model-data reinforcement learning (MDRL) approach for hybrid active power filters (HAPF). High steady-state accuracy and rapid dynamic response are achieved by the MDRL-based OVCC, which also maintains low sampling and computational demands. The limitations of dynamic response, inherent in traditional control methods that require significantly lower bandwidth for DC voltage control compared to AC current control, are effectively addressed. Initially, a circuit configuration and mathematical model for the HAPF are proposed. Subsequently, an MDRL-OVCC structure for optimal control is established. The training of the MDRL-OVCC is conducted using an online robust policy iteration method, and the safety of the system is ensured through a proposed robust stability analysis. Comprehensive simulation and experimental results are presented, demonstrating the superior performance of the MDRL-OVCC compared to existing state-of-the-art methods. © 1986-2012 IEEE.

关键词： Optimal control systems

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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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Real-Time Economic Dispatch Approach for Wholesale Energy Market with Multi-Transmission-Node der Aggregation

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IEEE Transactions on Power systems 2024年

作者： Shao, Zhentong Wang, Weilun Eldridge, Brent Somani, Abhishek Wu, Lei Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Pacific Northwest National Laboratory RichlandWA99354 United States

Enabling the integration of distributed energy resources (DERs) into the wholesale market, as prompted by the FERC Order 2222, introduces substantial operational complexities. To align with the current wholesale energy market practice, regional transmission organizations (RTOs) are pursuing the aggregation of transmission-node-level DERs (T-DERs), in which each T-DER is a projected representation of all DERs in the distribution network underneath the transmission node, such as a virtual power plant (VPP). This paper advances a realtime economic dispatch (RTED) framework to facilitate the aggregation of multiple T-DERs, optimizing their participation in energy market operations. We enhance the distribution factors (DFs) based modeling to capture the impact of individual TDERs across the transmission network. Specifically, an innovative DF updating strategy is introduced in our methodology, incorporating a tailored K-Nearest Neighbors (KNN) predictor and a discrete PI corrector, along with transmission signals that sufficiently guarantee transmission constraints. These enhancements optimize the economic efficiency of the market clearing process, allowing numerous T-DERs to be involved in current energy market practices. The proposed method is tested on modified 24-bus and 118-bus systems via a rolling RTED process with real load data. The test results show that the proposed framework demonstrates marked improvements in minimizing system operation costs and satisfying the transmission network constraints. © 1969-2012 IEEE.

关键词： Distributed energy

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