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IEEE Conference on Global Communications (GLOBECOM)

作者： Mattia Fabiani Asmaa Abdallah Abdulkadir Celik Ahmed M. Eltawil Department of Electrical Electronic and Information Engineering “Guglielmo Marconi” University of Bologna Bologna Italy Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal KSA

Cell-free massive MIMO (CF-mMIMO) is a transformative wireless network technology that surmounts conventional cellular network limitations concerning coverage, capacity, and interference management. Despite offering numerous benefits, CF-mMIMO also presents significant challenges, particularly in signal processing and power allocation. This paper introduces an unsupervised learning framework for downlink (DL) power allocation in CF-mMIMO networks, utilizing only large scaling fading coefficients instead of the hard-to-obtain exact user equipment (UE) locations or channel state information. We consider the sum spectral efficiency (sum-SE) optimization objective and investigate two distinct precoding schemes-maximum ratio (MR) and regularized zero-forcing (RZF)-for multi-antenna access points (APs). A custom loss function is formulated to maximize the sum-SE at each UE while accounting for pilot contamination and ensuring that power budget constraints are satisfied at each AP. The proposed unsupervised learning approach circumvents the arduous task of training data computations typically required in supervised learning methods, bypassing the use of conventional complex optimization methods and heuristic methodologies. The simulation results demonstrate that the proposed unsupervised learning approach outperforms existing methods in terms of SE, showcasing an improvement up to 20%. The proposed unsupervised neural network also approximates the optimal solutions generated by convex solvers while significantly reducing computational complexity.

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A Novel Discontinous Galerkin Method for the DC IR-Drop Analysis of Power Distribution Networks 4

A Novel Discontinous Galerkin Method for the DC IR-Drop Anal...

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4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021

作者： Yang, Anfa Tang, Min Mao, Junfa Li, Ping Bagci, Hakan Shanghai Jiao Tong University Dept. of Electronic Engineering Shanghai China King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Science and Engineering Thuwal Saudi Arabia

ISBN: (纸本)9781733509619

A Robin transmission condition (RTC) enhanced discontinuous Galerkin method (DGM) is proposed for direct current IR-Drop analysis of global PDN. Although DGM is one kind of domain decomposition method (DDM) naturally, it suffers much more number of unknowns compared with finite element method (FEM). To overcome this drawback, RTC is introduced to serve as a transmission condition apart from the numerical flux. As a result, the volume unknowns appeared in traditional DG method is elegantly transferred to the interface among neighboring subdomains. Thus, the DoFs are markedly reduced. To validate the proposed method, a representative numerical result is presented at the last of this paper. © 2021 Applied Computational Electromagnetics Society (ACES).

关键词： Domain decomposition methods

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A Numerical Method to Analyze Electromagnetic Scattering from Composite Plasmonic Structures Involving a Metal-Metal Interface

A Numerical Method to Analyze Electromagnetic Scattering fro...

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

作者： Uulu, Doolos Aibek Chen, Rui Chen, Liang Li, Ping Bagci, Hakan Division of Computer Electrical and Mathematical Science and Engineering Thuwal23955 Saudi Arabia Department of Mathematics and Natural Sciences Naryn722600 Kyrgyzstan Shanghai Jiao Tong University Key Lab. of Min. of Educ. of Des. and Electromagnetic Compatibility of High-Speed Electronic Systems Shanghai200240 China

ISBN: (纸本)9781665442282

A coupled system of volume integral and hydrodynamic equations is formulated to analyze electromagnetic scattering from composite plasmonic structures involving a metal-metal interface. This coupled system in unknowns electric flux and free electron velocity is discretized using Schaubert-Wilton-Glisson basis functions. Inserting these expansions into the coupled system of equations and applying Galerkin testing yield a matrix equation. Two-level iterative scheme is used to solve this matrix equation for the unknown expansion coefficients. To demonstrate the accuracy of the proposed method, numerical results are compared to those obtained using an extended Mie series approach that takes into account the hydrodynamic equation and the boundary conditions it requires. © 2023 IEEE.

关键词： Numerical methods

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Machine learning prediction of fundamental sewage sludge biochar properties based on sludge characteristics and pyrolysis conditions

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Chemosphere 2024年 369卷 143812页

作者： Deng, Yizhan Pu, Bing Tang, Xiang Liu, Xuran Tan, Xiaofei Yang, Qi Wang, Dongbo Fan, Changzheng Li, Xiaoming Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Fujian Provincial Key Laboratory of Soil Environmental Health and Regulation College of Resources and Environment Fujian Agriculture and Forestry University Fuzhou350002 China Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong SAR Kowloon China

Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS and its intricate interaction with pyrolysis conditions, presents notable challenges to its practical use. This research employed machine learning techniques to predict fundamental SSBC properties, including elemental content, proximate compositions, surface area, and yield, which are essential for assessing the applicability of SSBC. The models achieved robust predictive accuracy (test R2 = 0.82–0.95), except for surface area. Notably, analysis of the optimal models revealed SS characteristics had a significant impact on SSBC properties, particularly total and fixed carbon content (combined importance exceeding 80%). This underscores the needs of source analysis and preparation optimization in targeted SS recovery or SSBC applications. To facilitate this, a graphical user interface was developed for strategic analyzation of sludge sources and pyrolysis settings. © 2024 Elsevier Ltd

关键词： Adversarial machine learning

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Sensitivity of the distributed optical fiber sensor to measure soil movement for landslide monitoring application

Sensitivity of the distributed optical fiber sensor to measu...

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Student Conference on Research and Development (SCOReD)

作者： Abdulwahhab Essa Hamzah Tan Wui Kin Mohd Saiful Dzulkefly Zan Nor Shahidah Mohd Nazer Hisham Mohamad Yosuke Tanaka Mustafa Essa Hamzah Ahmad Ashrif A. Bakar Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Photonics Technology Laboratory Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia Department of Communications Engineering Iraq University College Basrah Iraq Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia (UKM) UKM Bangi Selangor Malaysia Civil & Environmental Engineering Department Universiti Teknologi PETRONAS seri Iskandar Perak Malaysia Division of Advanced Electrical and Electronic Engineering Institute of Engineering Tokyo University of Agriculture and Technology Japan

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Landslides are one of the major global geohazards, contributing to significant economic and social losses to private and public properties. In this paper, we deployed a Brillouin optical time-domain analysis (BOTDA) interrogator to measure soil movement and identify the soil movement location. The method was analyzed experimentally, detecting the soil movement location at around 935 m to 955 m of fiber. In addition, the system’s sensitivity was analyzed for soil movement within the displacement range of 0 mm to $\mathbf{4 0 \mathrm { mm }}$, with increments of 5 mm. The experimental result shows that the frequency shift of 1.66 MHz corresponds to 1 mm, and the tensile strain of $31.4 \mu \varepsilon$ corresponds to 1 mm. Moreover, the high reliability of measurement accuracy, demonstrated by an average of nine measurements with an error of 2.76 MHz, further supports the effectiveness of the system for detecting and monitoring soil movement in real-world applications.

关键词： Optical fibers Optical fiber sensors Accuracy Sensitivity Soil measurements Scattering Soil Optical variables measurement Motion measurement Monitoring

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LLM4WM: Adapting LLM for Wireless Multi-Tasking

arXiv

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arXiv 2025年

作者： Liu, Xuanyu Gao, Shijian Liu, Boxun Cheng, Xiang Yang, Liuqing State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Guangzhou511400 China Guangzhou511400 China Department of Electronic and Computer Engineering The Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

The wireless channel is fundamental to communication, encompassing numerous tasks collectively referred to as channel-associated tasks. These tasks can leverage joint learning based on channel characteristics to share representations and enhance system design. To capitalize on this advantage, LLM4WM is proposed—a large language model (LLM) multi-task fine-tuning framework specifically tailored for channel-associated tasks. This framework utilizes a Mixture of Experts with Low-Rank Adaptation (MoE-LoRA) approach for multi-task fine-tuning, enabling the transfer of the pre-trained LLM’s general knowledge to these tasks. Given the unique characteristics of wireless channel data, preprocessing modules, adapter modules, and multi-task output layers are designed to align the channel data with the LLM’s semantic feature space. Experiments on a channel-associated multi-task dataset demonstrate that LLM4WM outperforms existing methodologies in both full-sample and few-shot evaluations, owing to its robust multi-task joint modeling and transfer learning capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Dual Mode pMUT for Structural Health Monitoring of Piping Systems in Advanced Reactors

Dual Mode pMUT for Structural Health Monitoring of Piping Sy...

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IEEE Symposium (IUS) Ultrasonics

作者： Talha Masood Khan John Sabino Chenxi Xu Javier Obregon Joshua Wilis Adkins Matthew Daly Alexander Heifetz Derek Wiliam Kultgen Didem Ozevin Department of Civil Materials and Environmental Engineering University of Illinois Chicago Chicago IL USA Department of Electrical and Computer Engineering University of Illinois Chicago Chicago IL USA Nuclear Science and Engineering Division Argonne National Laboratory Lemont IL USA

The objective of this work is to develop a durable MEMS sensor for in situ monitoring of advanced reactor piping. Existing sensors for structural health monitoring (SHM) in nuclear reactor environments have limited temperature ranges and service lifetimes, owing to material constraints. Micromachined transducers using aluminum nitride (AlN) as a sensing element, and silicon carbide (SiC) as a substrate, have the potential to address this challenge. In this work, piezoelectric Micromachined Ultrasonic Transducers (pMUT) capable of delivering passive (acoustic emission) and active (guided wave) ultrasonic sensing for SHM in a high-temperature environment are studied. These sensors will be able to couple directly to the structures for real-time SHM of advanced reactor piping systems. A customized microfabrication process is used to produce dual-mode sensing operation at a small footprint of 5×5 mm 2 . This process involves sputtering a 1 µm thick AlN sandwiched between metal and handle layer. The frequency modes of the pMUT device are tuned to 140 kHz by varying the lateral dimensions of the sensor. Sensor locations on piping are determined by generating plane waves using finite element simulations and studying the wave dispersion. With higher sensitivity, our pMUT sensors are capable of detecting the smallest defect energy at high temperatures, while decreasing the number of sensors on piping.

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A Iterative Method to Analyze Misbehavior in Blockchain by Transaction Graph Matrix

A Iterative Method to Analyze Misbehavior in Blockchain by T...

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Information Communication and Software engineering (ICICSE), IEEE International Conference on

作者： Jian Liu Shaohua Zhang Lai Wei Ruoting Xiong Wei Ren China Electronic Product Reliability Environmental Testing Research Institute Guangzhou China China Construction Eighth Engineering Division Shanghai China Department of Computing Science University of East Anglia Norfolk UK School of Computer Science China University of Geosciences Wuhan China Anhui Engineering Research Center of Intelligent Perception and Elderly Care Chuzhou University Chuzhou China

ISBN: (数字)9798350351699

ISBN: (纸本)9798350351705

Blockchain provides an publicly accessed and accepted ledger without centralized control among open participators. The anonymity in payments and transactions may results in possible misbehaviors, e.g., money laundry. Fortunately, the transaction data is open and can be analyzed. In this paper, we propose The major challenges are the data volume is large and the global view of transactions in terms of hops and time sequences. In this paper, we address the transactions with multiple intermediators, and graph dynamics with time-spans. The analyzing models and computing algorithms are proposed. The extensive and formal analysis justifies the soundness of our proposed methods.

关键词： Analytical models Computational modeling Heuristic algorithms Data models Blockchains Iterative methods Software engineering

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Digital Smart Structure: engineering a Sensor-to-Cloud Structural Health Monitoring Pilot

Digital Smart Structure: Engineering a Sensor-to-Cloud Struc...

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IEEE International Workshop on Measurements and Networking Proceedings (M&N)

作者： Ivan Zyrianoff Mattia Forlesi Lorenzo Gigli Soroosh Kamali Nicola Testoni Canio Mennuti Giuseppe Augugliaro Luca De Marchi Alessandro Marzani Luciano Bononi Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy Technological Innovations Department INAIL Roma Italy Department of Civil Chemical Environmental and Materials Engineering University of Bologna Bologna Italy

ISBN: (数字)9798350370539

ISBN: (纸本)9798350370546

Real-time monitoring of civil infrastructures is of great importance to ensure their integrity and safety. Achieving these goals requires a strong synergy between multiple tools, disciplines, and approaches, which can be realized through a joint hardware-software co-design of the different components based on the Internet of Things (IoT) paradigm. Deploying IoT-based systems for bridge monitoring presents many challenges, including difficulties in deploying sensors in real-world testbeds, the lack of reliability of IoT devices, constant network disconnections, power outages, and challenges in rapid on-site maintenance. To address these challenges, we propose an IoT-SHM architecture that incorporates mechanisms to ensure robustness across system infrastructure, software components, data flow, and data quality. Our system was deployed on the Volto Santo bridge in Naples, where it successfully collected data for over six months, capturing more than 3000 acoustic emission events and more than 250 million accelerometer data points. Despite several interruptions due to environmental hazards, the system was able to automatically restart data collection. Additionally, we present the specifics of the testbed and a processing of the collected data based on operational modal analysis (OMA).

关键词： Accelerometers Data integrity Bridge circuits Acoustic emission Software Robustness Sensors

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Tracking cellular and subcellular dynamics with quantitative oblique back-illumination microscopy 10

Tracking cellular and subcellular dynamics with quantitative...

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Quantitative Phase Imaging X 2024

作者： Filan, Caroline E. Bharadwaj, Srinidhi Casteleiro Costa, Paloma Davarzani, Amin Cappabianca, Dan Tommasi, Anna Sarko, Lauren La Vonne Denne, Nina Mao, Leidong Saha, Krishanu Karumbaiah, Lohitash Robles, Francisco E. George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology 801 Ferst Dr. NW AtlantaGA United States Wallace H. Coulter Dept. of Biomedical Engineering Georgia Institute of Technology and Emory University 313 Ferst Dr. NW AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology 266 Ferst Drive AtlantaGA United States School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA United States Regenerative Bioscience Center University of Georgia AthensGA United States Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI United States Wisconsin Institute for Discovery University of Wisconsin-Madison MadisonWI United States Division of Neuroscience Biomedical and Health Sciences Institute University of Georgia AthensGA United States Edgar L. Rhodes Center for Animal and Dairy Science College of Agriculture and Environmental Science University of Georgia AthensGA United States

ISBN: (纸本)9781510669635

Quantitative oblique back-illumination microscopy (qOBM) is a label-free imaging technique that enables tomographic phase imaging of thick scattering samples with epi-illumination. Here, we propose the use of two forms of functional imaging with qOBM to study tissue and cell cultures. In doing so, we obtain the spatiotemporal and quantitative functional information associated with the phase values extrapolated from qOBM imaging. We have applied this process to study the efficacy of individual immune T cells to kill glioblastoma spheroid cultures in 3D spheroids. Data show that we can effectively distinguish between cell phenotypes and characterize the dynamic motion of these cells in 3D cultures. This work offers a distinct advantage in tracking 3D cellular dynamics in thick tissue as many function imaging modalities are limited to 2D samples. Further, this technology can be expanded to analyze a wide variety of cellular and subcellular dynamics non-invasively in thick tissue. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Tissue

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