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Two-Dimensional Beam Scanning Broadband Circularly Polarized Transmitarray With Low Sidelobe Level

IEEE Transactions on Antennas and Propagation 2025年

作者： Yang, Xujun Wang, Chenglong Ji, Yuan Hu, Jun Wong, Hang Anhui Agricultural University School of Information and Artificial Intelligence Anhui China Shenzhen518000 China Hefei University of Technology School of Microelectronics Hefei230601 China Huaiyin Normal University Jiangsu Provincial Key Laboratory of Intelligent Electromagnetic Sensing and Communication Huaian China Southeast University State Key Laboratory of Millimeter Waves Nanjing210096 China City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Hong Kong

This paper presents a broadband two-dimensional (2-D) beam-scanning circularly polarized (CP) transmitarray with low sidelobe level (SLL) for millimeter-wave (mmW) applications. A polarization conversion unit-cell, capable of receiving linearly-polarized (LP) incidence and transmitting CP radiation, is first designed. Based on the geometric phase (GP) compensation strategy, a full 360° transmission phase with good CP radiation performance is achieved by rotating the transmitting element. Moreover, the quad-focal phase compensation scheme is implemented to construct the GP phase distribution required by 2-D beam-scanning to minimize the scanning loss of the transmitarray. To lower the SLL during large-angle beam scanning, the multi-objective particle swarm optimization (MOPSO) algorithm is further employed to refine the phase distribution across the transmitting layer. For validation, a fully functional prototype of the proposed transmitarray illuminated by a movable LP horn is fabricated and measured. The measured results show a wide overlapped 3-dB gain and axial ratio (AR) bandwidth of 26% with a peak gain of 21.5 dBic. Besides, the measured SLL remains below -11.4 dB within a scan range of ±34° in two orthogonal planes. These preferred features make it a promising candidate for various mmW applications such as satellite communication systems. © 1963-2012 IEEE.

关键词： Millimeter waves

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

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IEEE International Symposium on Biomedical Imaging

作者： Fan Yang S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment.

关键词： Training Measurement Adaptation models Three-dimensional displays Smoothing methods Head Kernel Biomedical imaging

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NOMA Assisted Semi-Grant-Free Transmission for UAV Networks: A Multi-User Scheduling Approach

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IEEE Transactions on communications 2025年

作者： Huang, Junlin Lu, Huabing Tang, Jie Zhao, Nan Shi, Zhaoyuan Wang, Xianbin Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Nanchang University School of Information Engineering Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications Nanchang330031 China South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Anqing Normal University School of Computer and Information Anqing246052 China Western University Department of Electrical and Computer Engineering LondonN6A 5B9 Canada

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission, which enables grant-based users to share their spectrum with grant-free (GF) users, becomes an effective solution to the challenge of massive connections. However, this improvement is limited as most of the existing schemes can only admit one GF user. In this paper, we propose a novel NOMA-assisted SGF transmission scheme to support the access of multiple GF users in unmanned aerial vehicle networks. Moreover, in order to further enhance the advantages of the multi-user SGF scheme, two SGF schemes with power matching are devised to further improve the performance by employing the benefits of distributed contention. For ease of performance evaluation, the theoretical expressions of achievable sum rate and average age of information of the three SGF schemes are derived by applying order statistics. We also investigate the high signal-to- noise approximation expressions of sum rate for these schemes to give more insight. Finally, simulation results are provided to demonstrate the performance improvement of three proposed SGF schemes and validate the correctness of the theoretical analysis expressions. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Subband Adaptive Filtering Based on the Squared Sine Error Criterion: Analysis and Applications

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 1949-1959页

作者： Zhikai Gong Yingsong Li Xinqi Huang Liping Li Zhixiang Huang Rodrigo. C. de Lamare Yingying Zhu College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Centre for Telecommunications Research (CETUC) Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Gávea Brazil School of Physics Engineering and Technology University of York York U.K.

In this work, we present a subband adaptive filtering (SAF) algorithm with good robustness for impulsive noise environments. The proposed squared sine normalized SAF (SS-NSAF) algorithm employs a squared sine function and is inspired by the idea of normalization. A statistical analysis of the proposed SS-NSAF algorithm is carried out under impulsive noise environments, which includes stability conditions for the convergence and analytical formulas to predict the mean-square deviation of the SS-NSAF algorithm. Numerical simulations for system-identification and echo cancellation applications are conducted to illustrate the performance of the SS-NSAF algorithm and competing techniques in impulsive noise environments.

关键词： Noise Prediction algorithms Signal processing algorithms Filtering algorithms Nonlinear filters Maximum likelihood detection Convergence Vectors Mathematical models information filters

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Rate-Splitting Multiple Access Enabled Green Probabilistic Semantic communication over Wireless Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green communications and Networking 2025年

作者： Xu, Ruopeng Yang, Zhaohui Mao, Yijie Huang, Chongwen Yang, Qianqian Xu, Lexi Xu, Wei Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering China Hangzhou310027 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Zhejiang Prov. Key Lab. of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China China United Network Communications Corporation Research Institute Beijing100048 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China

In this paper, we propose a multi-user green probabilistic semantic communication (PSC) system for semantic communication (SemCom) facilitated by probabilistic knowledge graphs (PKGs), which are knowledge graphs (KGs) integrated with probability to represent semantic information. In the considered model, semantic information of different users is represented by PKGs. On this basis, we design a semantic compression model for multi-user downlink task-oriented SemCom, utilizing the semantic compression ratio (SCR) as a parameter to connect the computation and communication processes of information transmission. Utilizing rate-splitting multiple access (RSMA) technology, the transmitted messages are splitted into shared and private ones. Considering the limited wireless resources and constraint in semantic communication, an optimization problem with the goal of minimizing system energy consumption comprehensively considering the computation and communication process is formulated. In order to address the optimization problem, we propose an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design with successive convex approximation (SCA) method, semantic compression ratio optimization with variable substitution method, and computation capacity allocation with obtaining closed-form optimal solution. Simulation results verify the effectiveness of the proposed algorithm. © 2017 IEEE.

关键词： Knowledge graph

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Toward Micro Lip Reading in the Wild: Dataset, Theory, and Practices

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 11794-11807页

作者： Wang, Ran Cheng, Yingsong Cao, Anqi Zhong, Shengtao Liao, Xiang He, Yanjiu Ye, Han Sun, Chutian Zhao, Qinlao Du, Min Xiao, Yang School of Journalism and Information Communication School of Future Technology Huazhong University of Science and Technology Wuhan430074 China School of Electronic Information Wuhan University Wuhan430072 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Journalism and Information Communication Huazhong University of Science and Technology Wuhan430074 China Donald Bren School of Information and Computer Sciences University of California IrvineCA92697 United States School of Software and Microelectronics Peking University Beijing100871 China National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China ByteDance Beijing100089 China

Micro lip reading, is characterized by tiny lip movements when someone is speaking. It is highly preferred in robotics and automation, such as a social robot for geriatric care, a patrol robot system for hospitals, and speech system for hearing impaired individuals, etc. However, it has not been well studied before. In this paper, we shed the light to this research topic. A labelled micro lip reading dataset (i.e. HUST-LMLR) of 399 video samples is first established. The samples are captured from the unconstrained movies. One key challenge for micro lip reading in the wild lies on extracting fine features of lip movements effectively and robustly. In this paper, we pay the research efforts to address this issue from two aspects: facial context attention and feature extraction. First, we propose a multi-task learning model for micro lip reading in the wild for the first time. It concerns micro lip reading and facial landmark detection jointly for capturing global face context with soft attention, to facilitate micro lip reading. Second, we propose that motion feature should be concerned and combined with appearance feature jointly to characterize tiny lip movements effectively. Finally, the experiments on HUST-LMLR demonstrate the challenges of our dataset and our proposed approach results in a remarkable improvement of the state-of-the-art by over 26% WER in HUST-LMLR. Furthermore, results in a well-known public dataset LRS2 also show the generalization and superiority of our approach. If accepted, we will publish our HUST-LMLR dataset and relative supporting matierials at https://***/Micro-lip-reading/.Note to Practitioners - This paper aims to construct a challenging public dataset and propose a novel vision-based approach for automatically micro lip reading under the unconstrained conditions. A labelled dataset HUST-LMLR was first established to reveal the "micro"characteristics, with 399 video samples captured from 40 unconstrained movies or docu

关键词： Social robots

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MS-Glance: Bio-Inspired Non-Semantic Context Vectors and Their Applications in Supervising Image Reconstruction

MS-Glance: Bio-Inspired Non-Semantic Context Vectors and The...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Ziqi Gao Wendi Yang Yujia Li Lei Xing S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China Department of Radiation Oncology Stanford University Stanford CA USA Key Laboratory of Precision and Intelligent Chemistry USTC CAS Hefei Anhui

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Non-semantic context information is crucial for visual recognition, as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However, while semantic information is increasingly incorporated into computer vision tasks such as image reconstruction, non-semantic information, such as global spatial structures, is often overlooked. To bridge the gap, we propose a biologically informed non-semantic context descriptor, MS-Glance, along with the Glance Index Measure for comparing two images. A Global Glance vector is formulated by randomly retrieving pixels based on a perception-driven rule from an image to form a vector representing non-semantic global context, while a local Glance vector is a flattened local image window, mimicking a zoom in observation. The Glance Index is defined as the inner product of two standardized sets of Glance vectors. We evaluate the effectiveness of incorporating Glance supervision in two reconstruction tasks: image fitting with implicit neural representation (INR) and undersampled MRI reconstruction. Extensive experimental results show that MS-Glance outperforms existing image restoration losses across both natural and medical images. The code is available at https://***/Z7Gao/MSGlance.

关键词： Computer vision Visualization Magnetic resonance imaging Fitting Vectors Image restoration Indexes Image reconstruction Biomedical imaging Visual perception

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Blockchain-aided Mobile Edge Intelligence With Sparse Rewarded Computing Resource Allocation

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IEEE Transactions on Vehicular technology 2025年

作者： Niu, Wei Shi, Long Wang, Zhe Yang, Weiwei Guo, Yongan Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523000 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing210003 China Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Nanjing210003 China

In the blockchain-aided mobile edge intelligence (B-MEI) network, base stations (BSs) each with an edge server not only execute tasks offloaded from users but also serve as blockchain nodes to ensure trustworthy data management of task execution in a decentralized manner. A challenging issue of B-MEI is that, both task execution and blockchain consensus demand significant computing resources. Insufficient resource allocation inevitably leads to resource wastage and even degrades system performance, especially in scenarios where task arrivals and wireless channels are dynamically changing. Driven by this issue, we first formulate the dynamic allocation of computing resources as a Markov Decision Process (MDP) to maximize the average transaction throughput of the B-MEI system. In particular, we find that, due to the time-consuming nature of blockchain consensus, the MDP rewards stemmed from the block generation is typically delayed and sparse. To address this issue, we propose a novel reinforcement learning algorithm, named Stable Hindsight Credit Assignment (SHCA), to effectively learn the stable credit assignment for sparse rewards across intermediate actions to optimize policy decisions. Simulation results demonstrate that the proposed algorithm improves the average transaction throughput by 78% compared with the baseline algorithm. © 1967-2012 IEEE.

关键词： Resource allocation

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A Simple Single-Fed Penta-Band Omnidirectional Antenna with Independent Band Control

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IEEE Transactions on Antennas and Propagation 2025年

作者： Zhou, Changfei Gao, Tianyu Zhang, Ruyue Yu, Dapeng Zhang, Wenyu Li, Yingsong Wong, Hang Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Hubei University of Automotive Technology School of Mathematics Physics and Optoelectronic Engineering Shiyan China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China City University of Hong Kong The State Key Laboratory of Terahertz and Millimeter Waves Hong Kong

A novel method of designing a penta-band omnidirectional low-profile antenna with independent band control is proposed. Single-band, dual-band, tri-band, quad-band, and penta-band antennas are investigated, respectively. The proposed penta-band antenna consists of a circular patch, three concentric metal pins, and two square metal plates. The evolutionary step of the penta-band antenna is achieved by adding metal rings and a small metal plate above the big ground. Each of the four metal rings is excited at TM01 mode resonance, while the 5th mode is generated by a small loop resonance formed by the small plate, shorting pin, stub, and the outer conductor of the cable. The penta-band antenna has a small volume of π × (0.109λ1.53GHz)2 × 0.053λ1.53GHz. The corresponding impedance bandwidths (IMBWs) of the five operating bands are 4.9%, 9.4%, 9.0%, 4.8%, 5.0%. Compared with other designs, the proposed antennas exhibit more flexible bands by using a simple structure. Meanwhile, the antenna has a stable omnidirectional radiation pattern across the five bands and a compact size. © 1963-2012 IEEE.

关键词： Monopole antennas

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Lightweight Multi-Stage Aggregation Transformer for robust medical image segmentation

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Medical Image Analysis 2025年 103卷

作者： Wang, Xiaoyan Zhu, Yating Cui, Ying Huang, Xiaojie Guo, Dongyan Mu, Pan Xia, Ming Bai, Cong Teng, Zhongzhao Chen, Shengyong School of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China Zhejiang Key Laboratory of Visual Information Intelligent Processing Hangzhou310023 China The Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou310009 China School of Life and Health Sciences Fujian Fuyao University of Science and Technology Fuzhou350000 China Nanjing Jingsan Medical Science and Technology Ltd. Jiangsu China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

Capturing rich multi-scale features is essential to address complex variations in medical image segmentation. Multiple hybrid networks have been developed to integrate the complementary benefits of convolutional neural networks (CNN) and Transformers. However, existing methods may suffer from either huge computational cost required by the complicated networks or unsatisfied performance of lighter networks. How to give full play to the advantages of both convolution and self-attention and design networks that are both effective and efficient still remains an unsolved problem. In this work, we propose a robust lightweight multi-stage hybrid architecture, named Multi-stage Aggregation Transformer version 2 (MA-TransformerV2), to extract multi-scale features with progressive aggregations for accurate segmentation of highly variable medical images at a low computational cost. Specifically, lightweight Trans blocks and lightweight CNN blocks are parallelly introduced into the dual-branch encoder module in each stage, and then a vector quantization block is incorporated at the bottleneck to discretizes the features and discard the redundance. This design not only enhances the representation capabilities and computational efficiency of the model, but also makes the model interpretable. Extensive experimental results on public datasets show that our method outperforms state-of-the-art methods, including CNN-based, Transformer-based, advanced hybrid CNN-Transformer-based models, and several lightweight models, in terms of both segmentation accuracy and model capacity. Code will be made publicly available at https://***/zjmiaprojects/MATransformerV2. © 2025 Elsevier B.V.

关键词： Convolutional neural networks

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