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Defective Microstrip Structure Based Phase Shifter with Reduced Size for Phased Antenna Array Applications 12

Defective Microstrip Structure Based Phase Shifter with Redu...

12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Khan, Hajra Khan, Shahid Nasir, Jamal Ali Shah, Syed Ahson Dalarsson, Mariana Alibakhshikenari, Mohammad Comsats University Islamabad Department of Electrical and Computer Engineering Pakistan Hanyang University Applied Bioelectronics Laboratory Seoul Korea Republic of School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Stockholm Sweden Universidad Carlos Iii de Madrid Department of Signal Theory and Communications Spain

ISBN: (纸本)9798350358056

In this work, we propose a small-size and low-cost phase shifter based on defective microstrip structure (DMS) technique, with a modified reconfigurable unit cell (MRDMS) for WLAN applications at 5.2 GHz. The phase shifter is made up of an MRDMS unit, created by etching slots on a simple microstrip line with an outline of three bridges. To further increase the phase shift, a T-shaped slot is introduced in the central bridge and PIN diodes on the edges of the outer bridges are used. By switching the diodes on and off, the MRDMS unit cell is capable of operating in a variety of different modes to give a phase shift of 37.71° at 5.2 GHz. Additionally, MRDMS unit cells are cascaded for higher phase shift as required. Cascaded two- and four-cell phase shifters were designed and simulated in HFSS and a parametric study on various parameters was conducted to show the detailed operating properties of the proposed phase shifter. Compared to previously reported research, the present study shows that two cascaded phase shifters introduce 75.08° phase shifts and 151.75°, respectively. To the best of our knowledge, this is the maximum phase shift obtained by a phase shifter based on MRDMS. © 2023 IEEE.

关键词： defected microstrip structure phase shifter reconfigurable microwave system

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Kalantzopoulos, Charalampos Pezoulas, Vasileios C. Koutsouris, Dimitrios D. Matsopoulos, George K. Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. National Technical University of Athens School of Electrical and Computer Engineering Biomedical Engineering Laboratory AthensGR15773 Greece Forth Biomedical Research Institute Ioannina Greece University of Ioannina Ioannina Greece Forth Institute of Computer Science Heraklion Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete Heraklion Greece FORTH-IMBB Ioannina Greece University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department IoanninaGR 45110 Greece

ISBN: (纸本)9798350383386

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland's complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model's performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net's deficiency leading to the development of more specialized models in respect to these factors. © 2023 IEEE.

关键词： Urology

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SF_(6)decomposition and insulation condition monitoring of GIE:A review

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High Voltage 2021年第6期6卷 955-966页

作者： Fuping Zeng Haotian Li Hongtu Cheng Ju Tang Yilu Liu School of Electrical Engineering and Automation Wuhan UniversityWuhanChina Department of Electrical Engineering and Computer Science USA Oak Ridge National Laboratory KnoxvilleTennesseeUSA

The statistics of the Conference International des Grands Reseaux Electriques(CIGRE)indicate that the operational reliability of SF6 gas‐insulated equipment(GIE)is very high;however,the failure rate of the GIE in operation is much higher than that of the IEC standard,and the fault occurs frequently in the GIE at a high voltage *** reason is due to the complex and strong on‐site electromagnetic interference environment and fully enclosed structure of *** key method and technology for effective on‐line monitoring and fault diagnosis of GIE are still *** the partial strong electromagnetic energy and high temperature induced by early latent insulation faults in the equipment,SF6 gas insulation presents different degrees of *** decomposition products mainly include SO_(2)F_(2),SOF_(2),SO_(2),HF,and *** decomposition characteristics of SF6 are closely related to the property of insulation *** present,this area is attracting attention from the power industry and research *** study summarises the current research on SF_(6)decomposition component analysis(DCA).The content mainly includes the latest progress of SF_(6)decomposition characteristics and mechanism under fault conditions,and fault diagnosis methods based on decomposition components.

关键词： insulation fault diagnosis

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Long-term radiofrequency electromagnetic fields exposure attenuates cognitive dysfunction in 5×FAD mice by regulating microglial function

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Neural Regeneration Research 2023年第11期18卷 2497-2503页

作者： Yeonghoon Son Hye-Jin Park Ye Ji Jeong Hyung-Do Choi Nam Kim Hae-June Lee Division of Radiation Biomedical Research Korea Institute of Radiological&Medical SciencesSeoulKorea Department of EMF Research Team Radio and Broadcasting Technology LaboratoryElectronics and Telecommunications Research InstituteDaejonKorea School of Electrical and Computer Engineering Chungbuk National UniversityCheongjuKorea

We have previously found that long-term effects of exposure to radiofrequency electromagnetic fields in 5xFAD mice with severe late-stage Alzheimer’s disease reduced both amyloid-βdeposition and glial activation,including *** examine whether this therapeutic effect is due to the regulation of activated microglia,we analyzed mic roglial gene expression profiles and the existence of microglia in the brain in this study.5xFAD mice at the age of 1.5 months were assigned to sham-and radiofrequency electromagnetic fields-exposed groups and then animals were exposed to 1950 MHz radiofrequency electromagnetic fields at a specific absorption rate of 5 W/kg for 2 hours/day and 5 days/week for 6 *** conducted behavioral tests including the object recognition and Y-maze tests and molecular and histopathological analysis of amyloid precursor protein/a myloid-beta metabolism in brain *** confirmed that radiofrequency electromagnetic field exposure for 6 months ameliorated cognitive impairment and amyloid-β*** expression levels of Iba1(pan-microglial marker)and colony-stimulating factor 1 receptor(CSF1R;regulates microglial prolife ration)in the hippocampus in 5xFAD mice treated with radiofrequency electromagnetic fields were significantly reduced compared with those of the sham-exposed ***,we analyzed the expression levels of genes related to mic rogliosis and microglial function in the radiofrequency electromagnetic fields-exposed group compared to those of a CSF1R inhibitor(PLX3397)-treated *** radiofrequency electromagnetic fields and PLX3397 suppressed the levels of genes related to microgliosis(Csf1r,CD68,and Ccl6)and pro-inflammatory cytokine interleukin-1β.N otably,the expression levels of genes related to mic roglial function,including Trem2,Fcgr1α,Ctss,and Spi1,were decreased after long-term radiofrequency electromagnetic field exposure,which was also observed in response to microglial suppression by *** res

关键词： 5×FAD Alzheimer’s disease CSF1R long term exposure microglial function neuroinflammation radiofrequency electromagnetic fields therapeutic effect

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Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning 41

Towards Efficient Deep Spiking Neural Networks Construction ...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Yaxin Xu, Qi Shen, Jiangrong Xu, Hongming Chen, Long Pan, Gang School of Computer Science and Technology Dalian University of Technology Dalian China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China College of Computer Science and Technology Zhejiang University Hangzhou China Centre for Systems Engineering and Innovation Imperial College London London United Kingdom

The emergence of deep and large-scale spiking neural networks (SNNs) exhibiting high performance across diverse complex datasets has led to a need for compressing network models due to the presence of a significant number of redundant structural units, aiming to more effectively leverage their low-power consumption and biological interpretability advantages. Currently, most model compression techniques for SNNs are based on unstructured pruning of individual connections, which requires specific hardware support. Hence, we propose a structured pruning approach based on the activity levels of convolutional kernels named Spiking Channel Activity-based (SCA) network pruning framework. Inspired by synaptic plasticity mechanisms, our method dynamically adjusts the network's structure by pruning and regenerating convolutional kernels during training, enhancing the model's adaptation to the current target task. While maintaining model performance, this approach refines the network architecture, ultimately reducing computational load and accelerating the inference process. This indicates that structured dynamic sparse learning methods can better facilitate the application of deep SNNs in low-power and high-efficiency scenarios. Copyright 2024 by the author(s)

关键词： Convolutional neural networks

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Cybertwin-Assisted Mode Selection in Ultra-Dense LEO Integrated Satellite-Terrestrial Network

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Journal of Communications and Information Networks 2022年第4期7卷 360-374页

作者： Xin Zhang Bo Qian Xiaohan Qin Ting Ma Jiachen Chen Haibo Zhou Xuemin(Sherman)Shen School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China Laboratory Shenzhen 518000China Department of Electrical and Computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

Ultra-dense low earth orbit(LEO)integrated satellite-terrestrial network(ULISTN)has become an emerging paradigm to support massive access of Internet of things(IoT)in beyond fifth generation mobile networks(B5G).In ULISTN,there are two communication modes:cellular mode and satellite mode,where IoT users assessing terrestrial small base stations(TSBSs)and terrestrial-satellite terminals(TSTs)***,how to optimize the network performance and guarantee self-interests of the operator and IoT users in ULISTN is a challenging *** this paper,we propose a cybertwin-assisted joint mode selection and dynamic pricing(JMSDP)scheme for effective network management in ULISTN,where cybertwin serves as the intelligent *** JMSDP,the operator determines optimal access prices of TSBSs and TSTs,while each user selects the access mode according to access ***,the operator conducts the Stackelberg game aiming at maximizing average throughput depending on the mode selection results of IoT ***,IoT users as followers adopt the evolutionary game to choose an access mode based on the access prices provided by the *** results show that the proposed JMSDP can improve the average throughput and reduce the delay effectively,comparing with random access(RA)and maximum rate access.

关键词： ULISTN massive IoT cybertwin mode selection dynamic pricing game theory

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Frequency multiplexed photothermal correlation tomography for non-destructive evaluation of manufactured materials

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International Journal of Extreme Manufacturing 2025年第3期7卷 533-546页

作者： Pengfei Zhu Rongbang Wang Koneswaran Sivagurunathan Stefano Sfarra Fabrizio Sarasini Clemente Ibarra-Castanedo Xavier Maldague Hai Zhang Andreas Mandelis Department of Electrical and Computer Engineering Computer Vision and Systems Laboratory(CVSL)Laval UniversityQuebec G1V 0A6Canada Centre for Composite Materials and Structures(CCMS) Harbin Institute of TechnologyHarbin 150001People’s Republic of China Center for Advanced Diffusion-Wave and Photoacoustic Technologies(CADIPT)and Institute for Advanced Non-Destructive and Non-Invasive Diagnostic Technologies(IANDIT) Department of Mechanical and Industrial EngineeringUniversity of TorontoTorontoCanada Department of Industrial and Information Engineering and Economics(DIIIE) University of L’AquilaI-67100 L’Aquila(AQ)Italy Department of Chemical Engineering Materials Environment&UDR INSTM Sapienza University of RomeRome I-00184Italy

Infrared thermography has been widely applied in real industrial inspection of aerospace,energy management systems,engines,and electric ***,two-dimensional imaging modality limits its ***,a technique named frequency multiplexed photothermal correlation tomography(FM-PCT)was developed to enable non-destructive and contactless cross-sectional imaging for manufactured material evaluation and *** combining advantages of photothermal tomography and pulsed thermography,FM-PCT facilitates the generation of three-dimensional thermal images through temporal superposition(stacking)of two-dimensional images from sequential subsurface ***-PCT image processing involves pulsed excitation signals to which frequency delay and matched filtering techniques are *** features of FM-PCT are high-resolution three-dimensional tomographic imaging under low camera frame-rate conditions with self-correcting capability for diffusion(blurring)correction of subsurface images due to cross-correlation processing of individual frequencies in the Fourier decomposition spectrum of the excitation ***,FM-PCT extends truncated-correlation photothermal coherence tomography from chirp and pulsed signals to more general linear heating ***-in thermography and x-ray computed tomography validation demonstrate that 3D FM-PCT imaging accurately reveals subsurface discontinuities/defects in solids despite the diffusive nature of thermal-wave imaging.

关键词： photothermal coherence tomography nondestructive evaluation infrared thermography impact damage

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Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

Understanding the Impact of Unobservable Variables on the Pe...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Juarez Garcia, Ezequiel Stephens, Chad L. Napoli, Nicholas J. Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Crew Systems & Aviation Operations Branch and System-Wide Safety Project NASA Langley Research Center HamptonVA23681 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separ ability of class distributions. Such challenges result in unrepresentative training data, complex skewedor warped decision boundaries, and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions with in the feature space through partitioning. This enables the optimization of local models with inthe regions to overcome the impact of un observ ability and complex decision boundaries indifferent feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of un observability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobserv ability in predictive mo

关键词： Information fusion

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Long-Term Water Quality Prediction with Patch Savitsky-Golay Filtering and Transformer

Long-Term Water Quality Prediction with Patch Savitsky-Golay...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Lin, Yongze Qiao, Junfei Bi, Jing Yuan, Haitao Zhai, Jiahui Zhou, MengChu Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Information Science and Technology Beijing University of Technology Beijing100124 China College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark07102 United States

ISBN: (纸本)9781665410205

In many fields, time series prediction is gaining more and more attention, e.g., air pollution, geological hazards, and network traffic prediction. Water quality prediction is based on historical data to predict future water quality. However, it is difficult to learn a representation map from a time series that captures the trends and fluctuations to effectively remove noise from time series data and capture complex nonlinear relationships. To solve these problems, this work proposes a time series prediction model, called PSGT for short, which integrates Patch Savitsky-Golay filtering and Transformer. First, this work adopts a Patching method to embed sub-time series data and obtains the trends and semantic information of the time series. Second, it uses the Savitsky-Golay filtering to effectively remove the noise data in the patch and improve the prediction accuracy. Third, it uses a Transformer mechanism to address the nonlinear problem of water quality time series and improve long-term prediction capability. Two real-world datasets are utilized to evaluate the proposed PSGT, and experiments prove that PSGT performs better than other benchmark models by at least 6%. © 2024 IEEE.

关键词： Self-supervised learning

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