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IEEE International Conference on Radar

作者： Jiawei Ren Hongjian Wang Department of Electrical Engineering CAS Key Laboratory of Microwave Remote Sensing National Space Science Center Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665468893

A novel ultra-wide-band polarization selective reflectarray by using true-time delay is presented in this paper. Tightly coupled elements and the method of true-time-delay are used to broaden the bandwidth of the reflectarray. The reflectarray has the advantages of large bandwidth, polarization selective, and reduced complexity and fabrication cost. A reflectarray with the elements arranged in a $41\times 41$ grid is designed and simulated operating from 6 to 32GHz. The measured results show that the reflectarray has stable radiation patterns from 6 to 32GHz, over 5:1 frequency range and has a low loss and high efficiency over 60 %, it also has the low cross polarization below −28dB across the entire band and has the function of polarization selectivity.

关键词： Antenna measurements Fabrication Radar antennas Reflector antennas Loss measurement Radar applications Frequency measurement

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A Dynamic Transformer Network for Vehicle Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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Comparison of stratospheric evolution during the major sudden stratospheric warming events in 2018 and 2019

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Earth and Planetary Physics 2020年第5期4卷 493-503页

作者： Zheng Ma Yun Gong ShaoDong Zhang JiaHui Luo QiHou Zhou ChunMing Huang KaiMing Huang School of Electronic Information Wuhan UniversityWuhan 430072China State Key Laboratory of Space Weather Chinese Academy of SciencesBeijing 100190China Key Laboratory of Geospace Environment and Geodesy Ministry of EducationWuhan 430079China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan 430079China Electrical and Computer Engineering Department Miami UniversityOxfordOhio 45056USA

Using Modern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2)data in the northern hemisphere at the 10 hPa level,we compared the stratospheric evolution of temperature and geopotential height during two major sudden stratosphere warming events(SSWs)that occurred in the Arctic winter of 2018 and *** the prewarming period,poleward temperature-enhanced regions were mainly located around 120°E with a displaced vortex and around 120°E and 60°W with splitting *** evolution of geopotential height indicated that these temperature-enhanced regions were both on the western side of high-latitude *** the postwarming period,the polar vortex turned from splitting to displacement in the 2018 SSW but from displacement to splitting in the 2019 *** transitions were observed over the Atlantic region,which may have been caused by anticyclones moving through the polar *** findings revealed that the evolution of the anticyclone is important during SSWs and is closely related to temperature-enhanced regions in the prewarming periods and to transitions of the polar vortices in postwarming periods.

关键词： sudden stratosphere warming transitions of the polar vortex anticyclone Atlantic region

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Transclaw U-net: Claw U-net with transformers for medical image segmentation

arXiv

引用

arXiv 2021年

作者： Yao, Chang Hu, Menghan Zhai, Guangtao Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Institute of Image Communication and Information Processing Shanghai Jiao Tong University Department of Electrical Computer and Biomedical Engineering Ryerson University

In recent years, computer-aided diagnosis has become an increasingly popular topic. Methods based on convolutional neural networks have achieved good performance in medical image segmentation and classification. Due to the limitations of the convolution operation, the long-term spatial features are often not accurately obtained. Hence, we propose a TransClaw U-Net network structure, which combines the convolution operation with the transformer operation in the encoding part. The convolution part is applied for extracting the shallow spatial features to facilitate the recovery of the image resolution after upsampling. The transformer part is used to encode the patches, and the self-attention mechanism is used to obtain global information between sequences. The decoding part retains the bottom upsampling structure for better detail segmentation performance. The experimental results on Synapse Multi-organ Segmentation Datasets show that the performance of TransClaw U-Net is better than other network structures. The ablation experiments also prove the generalization performance of TransClaw U-Net. © 2021, CC BY.

关键词： Convolution

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Characteristics of the quasi-16-day wave in the mesosphere and lower thermosphere region as revealed by meteor radar,Aura satellite,and MERRA2 reanalysis data from 2008 to 2017

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Earth and Planetary Physics 2020年第3期4卷 274-284页

作者： Yun Gong Zheng Ma Chun Li XieDong Lv ShaoDong Zhang QiHou Zhou ChunMing Huang KaiMing Huang You Yu GuoZhu Li School of Electronic Information Wuhan UniversityWuhan 430079China Key Laboratory of Geospace Environment and Geodesy Ministry of EducationWuhan 430079China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan 430079China Electrical and Computer Engineering Department Miami UniversityOxfordOhioUSA Key Laboratory of Earth and Planetary Physics Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China

This study presents an analysis of the quasi-16-day wave(Q16DW)at three stations in the middle latitudes by using a meteor radar chain in conjunction with Aura Microwave Limb Sounder temperature data and MERRA2(Modern-Era Retrospective Analysis for Research and Applications,Version 2)reanalysis data from 2008 to *** radar chain consists of three meteor radar stations located at Mohe(MH,53.5°N,122.3°E),Beijing(BJ,40.3°N,116.2°E),and Wuhan(WH,30.5°N,114.6°E).The Q16DW wave exhibits similar seasonal variation in the neutral wind and temperature,and the Q16DW amplitude is generally strong during winter and weak around *** Q16DW at BJ was found to have secondary enhancement around September in the zonal wind,which is rarely reported at similar *** latitudinal variations of the Q16DW in the neutral wind and temperature are quite *** Q16DW at BJ is the most prominent in both neutral wind components among the three stations and the Q16DW amplitudes at MH and WH are comparable,whereas the wave amplitude in temperature decreases with decreasing *** quasi-geostrophic refractive index squared at the three stations in the period from 2008 to 2017 was *** results indicate that the Q16DW in the mesosphere and lower thermosphere(MLT)at MH has a limited contribution from the lower *** March and October,the Q16DW in the troposphere at BJ can propagate upward into the MLT region,whereas at WH,the contribution to the Q16DW in the MLT region is largely from the mesosphere.

关键词： quasi-16-day waves seasonal variations meteor radar winds Aura MLS temperature generation mechanisms

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DeepMerge: Deep-learning-based Region-Merging for image Segmentation

arXiv

引用

arXiv 2023年

作者： Lv, Xianwei Persello, Claudio Li, Wangbin Huang, Xiao Ming, Dongping Stein, Alfred School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China Hebei Key Laboratory of Marine Perception Network and Data Processing Northeastern University Qinhuangdao066004 China Dept. of Earth Observation Science Faculty ITC University of Twente Enschede7500AE Netherlands State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China The Department of Geosciences University of Arkansas FayettevilleAR United States Beijing100083 China

image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery. Current methods struggle to effectively consider land objects with diverse shapes and sizes. Additionally, the determination of segmentation scale parameters frequently adheres to a static and empirical doctrine, posing limitations on the segmentation of large-scale remote sensing images and yielding algorithms with limited interpretability. To address the above challenges, we propose a deep-learning-based region merging method dubbed DeepMerge to handle the segmentation of complete objects in large VHR images by integrating deep learning and region adjacency graph (RAG). This is the first method to use deep learning to learn the similarity and merge similar adjacent super-pixels in RAG. We propose a modified binary tree sampling method to generate shift-scale data, serving as inputs for transformer-based deep learning networks, a shift-scale attention with 3-Dimension relative position embedding to learn features across scales, and an embedding to fuse learned features with hand-crafted features. DeepMerge can achieve high segmentation accuracy in a supervised manner from large-scale remotely sensed images and provides an interpretable optimal scale parameter, which is validated using a remote sensing image of 0.55 m resolution covering an area of 5,660 km2. The experimental results show that DeepMerge achieves the highest F value (0.9550) and the lowest total error TE (0.0895), correctly segmenting objects of different sizes and outperforming all competing segmentation methods. © 2023, CC BY.

关键词： image segmentation

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1D quasi-bound states in the continuum with large operation bandwidth in theω~k space for nonlinear optical applications

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Photonics Research 2022年第7期10卷 1575-1581页

作者： KAILI SUN HUI JIANG DMITRY ABYKOV VIEN VAN URIEL LEVY YANGJIAN CAI ZHANGHUA HAN Shandong Provincial Key Laboratory of Optics and Photonic Devices Center of Light Manipulation and ApplicationsSchool of Physics and ElectronicsShandong Normal UniversityJinan 250358China Image Processing Systems Institute—Branch of the Federal Scientific Research Centre“Crystallography and Photonics”of Russian Academy of Sciences Samara 443001Russia Department of Electrical and Computer Engineering University of AlbertaEdmontonAlberta T6G 2V4Canada Department of Applied Physics and Center for Nanoscience and NanotechnologyThe Hebrew University of JerusalemJerusalemIsrael

The phenomenon of bound state in the continuum(BIC)with an infinite quality factor and lifetime has emerged in recent years in photonics as a new tool to manipulate light-matter ***,most of the investigated structures only support BIC resonances at very few discrete points in theω~k *** when the BIC is switched to a quasi-BIC(QBIC)resonance through perturbation,its frequency will still be located within a narrow spectral band close to that of the original BIC,restricting their applications in many fields where random or multiple input frequencies beyond the narrow band are *** this work,we demonstrate that a new set of QBIC resonances can be supported by using a special binary grating consisting of two alternatingly aligned ridge arrays with the same period and zero-approaching ridge width difference on a slab *** QBIC resonances are distributed continuously over a broad band along a line in theω~k space and can thus be considered as 1 D *** the Q factors generally affected by the ridge difference,it is now possible to arbitrarily choose any frequencies on the dispersion line to achieve significantly enhanced light-matter interactions,facilitating many applications where multiple input wavelengths are required;e.g.,sum or difference frequency generations in nonlinear optics.

关键词： quasi nonlinear narrow

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Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar imagery

arXiv

引用

arXiv 2024年

作者： Sledge, Isaac J. Byrne, Dominic M. King, Jonathan L. Ostertag, Steven H. Woods, Denton L. Prater, James L. Kennedy, Jermaine L. Marston, Timothy M. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States The Naval Sea Systems Command United States The Applied Sensing and Processing Branch Naval Surface Warfare Center Panama CityFL United States The Littoral Acoustics and Target Physics Branch Naval Surface Warfare Center Panama CityFL United States The Division Head of the Sensing Sciences and Systems Division Naval Surface Warfare Center Panama CityFL United States The Applied Physics Laboratory University of Washington SeattleWA United States The Department of Electrical and Computer Engineering The Department of Biomedical Engineering University of Florida GainesvilleFL United States The University of Florida United States

We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery. The first part of our framework is trained in a supervised manner, on image-level labels, to uncover a set of semi-sparse, spatially-discriminative regions in each image. The classification uncertainty of each region is then evaluated. Those areas with the lowest uncertainties are then chosen to be weakly labeled segmentation seeds, at the pixel level, for the second part of the framework. Each of the seed extents are progressively resized according to an unsupervised, information-theoretic loss with structured-prediction regularizers. This reshaping process uses multi-scale, adaptively-weighted features to delineate class-specific transitions in local image content. Content-addressable memories are inserted at various parts of our framework so that it can leverage features from previously seen images to improve segmentation performance for related images. We evaluate our weakly-supervised framework using real-world CSAS imagery that contains over ten seafloor classes and ten target classes. We show that our framework performs comparably to nine fully-supervised deep networks. Our framework also outperforms eleven of the best weakly-supervised deep networks. We achieve state-of-the-art performance when pre-training on natural imagery. The average absolute performance gap to the next-best weakly-supervised network is well over ten percent for both natural imagery and sonar imagery. This gap is found to be statistically significant. © 2024, CC BY.

关键词： Semantic Segmentation

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High-Resolution Detection of Stratospheric Aerosols in CALIPSO Atmospheric Lidar Data Facilitated by the CALIOP-Density-Dimension Algorithm

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IEEE Transactions on Geoscience and remote sensing 2025年

作者： Herzfeld, Ute C. Trantow, Thomas Vaughan, Mark Palm, Stephen Opfer, Camden Rodriguez, Eric University of Colorado GeomathematicsRemote Sensing and Cryospheric Sciences Laboratory Department of Electrical Energy and Computer Engineering Department of Computer Science Colorado Boulder80309-0425 United States University of Colorado Geomathematics Remote Sensing and Cryospheric Sciences Laboratory Department of Electrical Energy and Computer Engineering BoulderCO80309-0425 United States Lidar Science Branch NASA Langley Research Center Science Directorate HamptonVA United States Mesoscale Atmosphere Processes Laboratory NASA Goddard Space Flight Center GreenbeltMD United States

The objective of this paper is to demonstrate a new capability to detect faint stratospheric aerosols in atmospheric lidar data from NASA’s Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Mission (12 June 2006 30 June 2023) at high resolution. Faint aerosols from wildfires, distant volcanic eruptions, and Asian and Saharan dust storms play important roles in the Earth-Atmosphere system, as they provide essential air quality and pollution, airline safety, climate radiative forcing, monitoring of fires and volcanic eruptions, environmental safety, and weather forecasting. Tenuous aerosols are recorded in the CALIOP atmospheric lidar data from the CALIPSO mission, but especially extremely faint stratospheric aerosol layers often escape detection and classification partly or entirely in the current CALIOP data analysis scheme. To solve this problem, we introduce a new algorithm for detection and height determination of atmospheric layers, the Density-Dimension Algorithm for CALIOP data analysis (CALIOP-DDA). The DDA is an auto-adaptive algorithm that builds on concepts from artificial intelligence and spatial statistics. Core steps are calculation of a density field and application of a threshold function for signal-noise separation. Stratospheric aerosol detection is aided by the tropopause split concept. The CALIOP-DDA facilitates detection of extremely faint stratospheric aerosols from various sources, including distant wildfires and volcanic eruptions, in night-time and day-time CALIOP data, even in presence of complex types of other cloud and aerosol layers across a large range of optical thicknesses, while retaining the full 334m along-track, 30m height resolution, without creating false positives. CALIOP-DDA results are evaluated by comparison to layer heights derived from airborne validation experiments, conducted using the Cloud Physics Lidar (CPL). In conclusion, the CALIOP-DDA holds promise as the algorithmic basis for a future improv

关键词： Air quality

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