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2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023

作者： Samaddar, Poulami Gaddam, Sunil Rahman, Md Mirazur Dey, Shuvashis Roy, Sayan Mitra, Dipankar Arunachalam, Shivaram P. Microwave Engineering & Imaging Laboratory Division of Gastroenterology and Hepatology Department of Medicine Mayo Clinic RochesterMN United States North Dakota State University Department of Electrical and Computer Engineering FargoND United States University of Missouri-Kansas City Division of Energy Matter and Systems School of Science and Engineering Kansas CityMO United States University of Wisconsin-La Crosse Department of Computer Science & Computer Engineering La CrosseWI United States

ISBN: (纸本)9798350328264

In this work, we studied the response of an antipodal Vivaldi antenna in the presence of different types of mice liver models. The scope of the study was limited to two types of liver diseases, i.e., nonalcoholic steatohepatitis (NASH) and fibrosis. The complex dielectric properties of each liver type were measured using the open-ended coaxial probe dielectric spectroscopy method. A Vivaldi antenna for near-field sensing was designed and then simulated in CST Studio to determine the antenna characteristics. The antenna performance from the simulation matched the reflection coefficients (S11) measurements collected with the fabricated antenna in an anechoic chamber. This verified antenna was used for further measurements of different liver models placed near the antenna. The antenna response, S11 measurements, varied from one tissue type to another, demonstrating the potential of using the Vivaldi antenna as a near-field sensor to differentiate liver disease models from the healthy liver model. © 2023 IEEE.

关键词： Antipodal Vivaldi antenna complex permittivity dielectric property fibrosis liver disease microstrip antenna Nonalcoholic steatohepatitis

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Wavelet Convolutional Neural Network for Low-Resolution Brain MRI Images 22

Wavelet Convolutional Neural Network for Low-Resolution Brai...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Oh, Soo Min Li, Yifan Chong, Jo Woon Massachusetts Institute of Technology Wireless Information and Network Sciences Laboratory Cambridge 02139 MA United States Massachusetts Institute of Technology Laboratory for Information and Decision Systems Cambridge 02139 MA United States Texas A&m University Department of Statistics College Station 77843 TX United States Texas Tech University Department of Electrical and Computer Engineering Lubbock 79409 TX United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon 16419 South Korea

ISBN: (纸本)9798331520526

Low-resolution images inherently contain less information, making effective feature extraction more challenging and posing difficulties for training neural networks. However, if neural networks can be trained successfully on low-resolution images, this could significantly reduce memory storage requirements and computational costs. In this study, we address the limitations of low-resolution brain MRI images by enhancing the available information through wavelet transform techniques. Specifically, we leverage the high-frequency coefficients obtained from wavelet transforms and the Hurst exponent to improve feature representation and optimize model training for convolutional neural networks (CNN), which is referred to as the wavelet CNN (WCNN). We demonstrate that WCNN outperforms standard CNNs in multi-class classification tasks, distinguishing among four categories of brain MRI images such as normal, glioma, meningioma, and pituitary tumor images. This approach highlights the potential to achieve high classification accuracy even with low-resolution data, ultimately reducing the memory and computational resources required for data processing and model training. © 2025 IEEE.

关键词： brain MRI images wavelet convolutional neural networks Wavelet transform

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Dual Inverter Parallel Consequent Pole PM-Assisted Two-Layer Sub-Harmonic Synchronous Machine

Dual Inverter Parallel Consequent Pole PM-Assisted Two-Layer...

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IEEE Conference on Electromagnetic Field Computation

作者： S M Sajjad Hossain Rafin Qasim Ali Osama A. Mohammed Dept. of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami Florida USA Department of Electrical Engineering Sukkur IBA University Sukkur Pakistan

ISBN: (数字)9798350348958

ISBN: (纸本)9798350348965

A parallel consequent pole PM-assisted 2-layer subharmonic machine consisting of dual inverter topology is proposed here, which is designed to enhance torque generation and control compared to its predecessors. The stator of the sub-harmonic synchronous machine has two sets of 3-phase windings, and the dual inverter supplies current to them separately. Similarly, the rotor has two concentric windings, one for field excitation and the other for harmonic excitation. To validate the proposed model, an 8-pole, 48-slot, 2-inverter, 2-layer sub-harmonic machine is designed and simulated through 2-D finite element analysis. The results show a better torque and performance than the subharmonic and wound field synchronous machines.

关键词： Performance evaluation Torque Windings Stator windings Rotors Motors Harmonic analysis

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Scaling Distributed Collaboration in Mixed Reality

Scaling Distributed Collaboration in Mixed Reality

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2025 CHI Conference on Human Factors in computing systems, CHI EA 2025

作者： Genay, Adélaïde Syiem, Brandon Victor Wong, Emily Feuchtner, Tiare Knibbe, Jarrod Grønbæk, Jens Emil Sloth Velloso, Eduardo School of Computer Science University of Sydney Sydney NSW Australia School of Computing and Information Systems The University of Melbourne Melbourne VIC Australia HCI Group University of Konstanz Konstanz Germany Department of Computer Science Aarhus University Aarhus Denmark School of Electrical Engineering and Computer Science The University of Queensland St. Lucia QLD Australia

ISBN: (纸本)9798400713958

Distributed collaboration in Mixed Reality (MR) promises to revolutionise how people connect across different physical environments, offering experiences akin to face-to-face interactions. However, previous work has mostly focused on enabling this vision in overly simplified settings such as with only two users interacting in identical distributed environments. Scaling current systems to work with large groups and for common real-life scenarios is a persistent challenge that requires addressing multiple tensions. We identified six challenges: 1) supporting locally congruent actions from heterogeneous remote spaces, 2) communicating accurate user behaviours through virtual representation instead of physical bodies, 3) facilitating organic group interactions within limited physical space, 4) maintaining conversational dynamics even in asynchronous exchanges, 5) providing equal access to physical objects for all participants, and 6) enabling efficient task switching within a complex ecology of applications, devices, and accessibility needs. This workshop aims to gather researchers and practitioners to explore actionable strategies for resolving these challenges. Through a mix of presentations, hands-on activities, and group discussions, participants will generate new ideas and develop a research agenda to articulate the future of MR collaboration systems. The workshop outcomes will include a list of concrete next steps for the community to bring distributed MR collaboration at scale. © 2025 Copyright held by the owner/author(s).

关键词： Collaboration Distributed systems Human-computer Interaction Mixed Reality

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Keynote Talk: Qualitative model based diagnosis of complex systems using decomposition 22

Keynote Talk: Qualitative model based diagnosis of complex s...

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Proceedings of the 7th International Conference on Sustainable Information engineering and Technology

作者： Katalin M. Hangos Systems and Control Laboratory Institute for Computer Science and Control Hungary and Department of Electrical Engineering and Information Systems University of Pannonia Hungary

ISBN: (纸本)9781450397117

Decomposition offers the potential to reduce the complexity of model-based optimization, prediction, control and diagnosis by accounting for the structure and sparsity of the describing model. Motivated by this fact, a rich and powerful collection of decomposition methods are available for model based diagnosis of large-scale complex dynamic systems, too. At the same time, one usually does not have enough information about a large-scale complex dynamic system to construct its precise enough model, so a kind of qualitative dynamic model is often used for the diagnosis [1]. Two structural decomposition based qualitative diagnostic methods are presented in this lecture, together with their component-driven system decomposition ***, a model-based diagnostic method is described that is able to detect and isolate non-technical losses (illegal loads) in low voltage electrical grids of one transformer area [2]. As a preliminary off-line step of the diagnosis, a powerful electrical decomposition method is proposed, which breaks down the overall network to subsystems with one feeder layout enabling to make the necessary computations efficient. The diagnostic method is based on analyzing the differences between the measured and model-predicted voltages. The uncertainty in the model parameters together with the measurement uncertainties are also taken into account to make the approach applicable in real-world cases. The proposed method is able to detect and localize multiple illegal loads, and the amount of the illegal consumption can also be *** a second case study, a high level decomposition approach for process system fault diagnosis using event traces is given [3], [4]. Using a simple component graph model behind the process system and the measured trace applied for the diagnosis, the method can find the root cause(s) of propagating failures between separate components. The method can connect individually operating lower-level component-specific diag

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PD-Refiner: An Underlying Surface Inheritance Refiner with Adaptive Edge-Aware Supervision for Point Cloud Denoising 24

PD-Refiner: An Underlying Surface Inheritance Refiner with A...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Chengwei Zhang, Xueyi Yue, Xianghu Lao, Mingrui Jiang, Tao Wang, Jiawei Zhang, Fubo Chen, Longyong National Key Laboratory of Microwave Imaging Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China Department of Electrical and Computer and Engineering National University of Singapore Singapore Singapore National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9798400706868

Point clouds from real-world scenarios inevitably contain complex noise, significantly impairing the accuracy of downstream tasks. To tackle this challenge, cascading encoder-decoder architecture has become a conventional technical route to iterative denoise. However, circularly feeding the output of denoiser as its input again involves the re-extraction of underlying surface, leading to unstable denoising process and over-smoothed geometric details. To address these issues, we propose a novel denoising paradigm dubbed PD-Refiner that employs a single encoder to model the underlying surface. Then, we leverage several lightweight hierarchical Underlying Surface Inheritance Refiners (USIRs) to inherit and strengthen it, thereby avoiding the re-extraction from the intermediate point cloud. Furthermore, we design adaptive edge-aware supervision to improve the edge awareness of the USIRs, allowing for the adjustment of the denoising preferences from global structure to local details. The results demonstrate that our method not only achieves state-of-the-art performance in terms of denoising stability and efficacy, but also enhances edge clarity and point cloud uniformity. © 2024 ACM.

关键词： edge-aware supervision

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

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Journal of computer Science & Technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation 7

Multi-Layer Perceptron Regression Model for Material Charact...

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7th International Youth Conference on Radio Electronics, electrical and Power engineering, REEPE 2025

作者： Hanima Kannan, C.H. Gupta, Deepa Mathur, Parul Kurup, Dhanesh G. Chaudhari, Tilesh Sanjay Mathur, Priyanka Amrita School of Engineering Amrita Vishwa Vidyapeetham Rf and Wireless Systems Laboratory Department of Electronics and Communication Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Indian Institute of Technology Creative and Advanced Research Based on Nanomaterials Laboratory Department of Chemical Engineering Hyderabad India School of Engineering and Applied Science Bennett University U.P. Greater Noida India

ISBN: (纸本)9798331531836

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an R² of 99.991% and MSE of 6.65 × 10^-5, with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions. © 2025 IEEE.

关键词： Complex Permittivity Cross-Dataset Generation Material Characterization Multi-Layer Perceptron Open-Ended Coaxial Probe Stan-dard Reference Loads

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Enabling Quadruple-D Compensation Coil Integration for Efficient Power Transfer to Receiver in Wireless Power Transfer systems for EV Charging

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Erel, Mehmet Zahid Imtiaz, Tabish Elsanabary, Ahmed Mubin, Marizan Soon, Tey Kok Mekhilef, Saad Ankara Yildirim Beyazit University Department of Energy Systems Engineering Ankara Turkey Universiti Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Port Said University Faculty of Engineering Department of Electrical Engineering Port Said Egypt Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia Presidency University Department of Electrical and Electronics Engineering Karnataka Bengaluru India

Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil structures in wireless charging systems has addressed various operational challenges, enhanced power transfer, and provided a scalable solution for EVs. However, cross-coupling issues and space limitation problems arise when multiple coils are introduced to the WPT system. In this paper, a novel integrated compact coupler is proposed, including two naturally decoupled coils on the transmitter side, while both are coupled with the receiver coil to improve the power transfer capability of the system. ANSYS Maxwell is employed to simulate the proposed coupler and evaluate its performance under misalignment operations. The proposed wireless charging system inherently detects misalignment direction based on the variation of output voltage with respect to x-misalignment. As a result, the experimental prototype of the designed wireless WPT system presents a peak efficiency of 97.08% at 1.147-kW output power for an air gap of 120 mm under fully-aligned condition. Additionally, the designed compensation coil can transfer power of 243.76-W under fully-aligned condition and increases reliability in power transfer during misalignment operations. © 2013 IEEE.

关键词： Inductive power transmission

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Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural computing and Applications, (2025), 10.1007/s00521-025-10991-3)

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Neural computing and Applications 2025年 1-2页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘department of electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘department of electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and Faculty of Information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘department of computer Science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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