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Highly isolated two-elements ultra-wideband mimo fractal antenna with multi band-notched characteristics

Progress In Electromagnetics Research C 2021年 116卷 13-24页

作者： Cai, Yong Cheng, Guangshang Ren, Xingang Wu, Jie Ren, Hao Song, Kaihong Huang, Zhixiang Wu, Xianliang Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei230039 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan237000 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei230039 China Key Laboratory of Electromagnetic Environmental Sensing Department of Education of Anhui Province Anhui University Hefei230039 China

This work presents a high isolation UWB-MIMO antenna with a bandwidth up to 8.6 GHz based on a Minkowski fractal structure. The proposed antenna is fed by microstrip and comprises two orthogonal monopole antennas, which delivers a decent isolation effect. Moreover, the ground is designed as two separated blocks with an I-shaped branch for improving the isolation degree between the units. The resultant isolation degree of this antenna is greater than 25 dB. Besides, the electromagnetic interference in the partial frequency band (such as Wi-Max band (3.45–4.45 GHz), WLAN band (5.1– 5.8 GHz), and X-band (7.25–7.75 GHz)) is further prevented through etching a split-ring resonator (SRR) and C-slot on the unit. The antenna reflection coefficient of the UWB-MIMO antenna at the notch is 3.5 dB, which indicates that the antenna has a conspicuousness anti-interference effect. Through the above judicious design, the proposed UWB-MIMO antenna possesses a relative bandwidth of 113% (up to 8.6 GHz). The envelope correlation coefficient between antenna units is less than 0.005, and the antenna radiation efficiency is up to 80%. The results indicate that the proposed MIMO antenna meets UWB applications. © 2021, Electromagnetics Academy. All rights reserved.

关键词： Monopole antennas

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NetPrompt: Neural Network Prompting Enhances Event Extraction in Large Language Models

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IEEE Transactions on Big Data 2025年

作者： Mu, Lin Cheng, Yide Shen, Jun Zhang, Yiwen Zhong, Hong School of Computer Science and Technology Anhui University Anhui Hefei230601 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education the Anhui Engineering Laboratory of IoT Security Technologies the School of Computer Science and Technology the Institute of Physical Science and Information Technology Anhui University Hefei230039 China

Event Extraction involves extracting event-related information such as event types and event arguments from context, which has long been tackled through well-designed neural networks or fine-tuned pre-trained language models. These approaches require substantial annotated data for tuning parameters and are resource-intensive. Recently, Prompting strategies with frozen parameters, such as Chain-of-Thought and Self-Consistency, have delivered success in NLP using LLMs by generating intermediate thought steps. However, they suffer from the challenge of error propagation and lack of interaction between different thoughts. In this paper, we propose Neural Network-based Prompting (NetPrompt), a novel network-structured prompting strategy for event extraction. The core idea behind NetPrompt is to imitate the excellent information integration capabilities of neural network structures. Specifically, we first decompose the event extraction problem into diverse intermediate subtasks, and each subtask is represented as a node in different layers of the network, the output of the nodes in the preceding layer is fed into the subsequent layer. Secondly, we propose pruning strategies to adapt the reasoning overhead to different problems. Finally, we have conducted extensive experiments on two widely used event extraction benchmarks to evaluate NetPrompt. The results demonstrated that NetPrompt significantly improved the event extraction performance compared to previous methods. ©2015 IEEE.

关键词： Neural network models

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radar Echo Modeling and Doppler Characteristic Fitting Method for UAVs Based on Multi-Scattering Point Model

Radar Echo Modeling and Doppler Characteristic Fitting Metho...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Li’Ang Xu Qi Jiang Rui Wang Jichuan Zhang Longxiang Jiao Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The radar echo characteristics of unmanned aerial vehicles provide important information for identification. The radar echoes of various types of UAVs are modeled by the multi-scattering point modeling method. A Doppler characteristic modeling method for unmanned aerial vehicles based on the mixture Gaussian algorithm is proposed, in which the slow-time dimension data are extracted from the Range- Doppler and fitted by the mixture Gaussian algorithm. The parameters of the Gaussian distribution components representing the unmanned aerial vehicle's Doppler spread characteristics are obtained using the algorithm. The simulated radar echoes of the multi-rotor unmanned aerial vehicle and the flapping-wing unmanned aerial vehicle are used for analysis. The performance of the fitting is acceptable and the way in which various parameters affect the Doppler characteristics is discussed and concluded.

关键词： Analytical models radar cross-sections Target recognition Fitting Gaussian distribution Autonomous aerial vehicles Data models Doppler radar Doppler effect Data mining

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QE-WNet: A Quadratic Encoding-decoding W-shape Network for Road Segmentation based from Optical Remote Sensing Imagery

QE-WNet: A Quadratic Encoding-decoding W-shape Network for R...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Shilong Fan Zhibao Su Yin Zhuang Jianlin Xie National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP) Beijing Institute of Technology China North Artificial Intelligence & Innovation Research Institute Beijing China China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP) Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With advancements in imaging technology, high-resolution remote sensing images are now readily available for applications such as urban planning, resource assessment, and land cover analysis. Roads are a crucial element of infrastructure, and extracting road information from high-resolution remote sensing images is vital for tasks like urban planning and mapping. However, existing road extraction techniques, which rely on traditional encoder-decoder architectures, often struggle to accurately delineate road boundaries and surfaces. As a result, these methods tend to produce subpar outcomes and are challenging to implement in practical systems. To address this issue, we introduce a novel quadratic encoding-decoding W-shaped network, referred to as QE-WNet. This approach recognizes the differing characteristics of road bodies and boundaries—where road boundaries are more stable in semantic representation and road bodies are less so. In the first stage of encoding and decoding, we focus on modeling road boundaries, while the second stage emphasizes road bodies, leveraging the more stable secondary encoding-decoding information. This separation enhances the accuracy of road extraction. We conduct comparative experiments using several established methods on the Deepglobe dataset, and the results demonstrate the effectiveness of our proposed method.

关键词： Image segmentation Roads Urban planning Semantics Predictive models Feature extraction Encoding Decoding Data mining Remote sensing

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Computing Flow Experience Based on Physiological signals: A Systematic Literature Review

Computing Flow Experience Based on Physiological Signals: A ...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Hong, Yichen Zhou, Chao Liu, Zelu Zifeng, Ni Liu, Juan Li, Xiangxian Yang, Chenglei Bian, Yulong Shandong University Weihai China Human Computer Interaction Technology and Intelligent Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China Shandong University School of Mechanical Electrical & Information Engineering Weihai China Shandong University Jinan China School of Software Shandong University Jinan China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application Shandong University Shandong Weihai China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application China

ISBN: (纸本)9798400713958

Flow experience is a key user experience in human-computer interaction (HCI). Compared with traditional subjective measurements, physiological signal-based methods (i.e. physiological computation of flow experience) can measure flow experience in a more objective and real-time way without interrupting the ongoing activity of the user. Despite scattered studies, a systematic review of research in this field is absent. Synthesizing key aspects of this field, including the conceptualization of flow experience, prevalent theoretical models, foundational research steps for experiment, computerized tasks for inducing flow, commonly used physiological signals, methods for analyzing flow state, and application in HCI. Finally, we discuss the potential of this field to propel HCI and user experience evaluation technologies, while projecting future research avenues. This paper is expected to help researchers grasp existing work in physiological signal-based flow computing and inspire subsequent explorations. © 2025 Copyright held by the owner/author(s).

关键词： Deep learning Flow experience Flow model Machine learning Physiological signals

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radar-Based Multi-Target Localization and Vital Sign Monitoring

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Journal of Computer and Communications 2024年第11期12卷 263-278页

作者： Yuping Shi Qinwei Li Hang Wu Ming Yu Tianjin Key Laboratory for Advanced Signal Processing School of Electronic Information and Automation Civil Aviation University of China Tianjin China Systems Engineering Institute Academy of Military Science People’s Liberation Army Tianjin China

The frequency-modulated continuous wave (FMCW) radar, known for its high range resolution, has garnered significant attention in the field of non-contact vital sign monitoring. However, accurately locating multiple targets and separating their vital sign signals remains a challenging research topic. This paper proposes a scene-differentiated method for multi-target localization and vital sign monitoring. The approach identifies the relative positions of multiple targets using Range FFT and determines the directions of targets via the multiple signal classification (MUSIC) algorithm. Phase signals within the range bins corresponding to the targets are separated using bandpass filtering. If multiple targets reside in the same range bin, the variational mode decomposition (VMD) algorithm is employed to decompose their breathing or heartbeat signals. Experimental results demonstrate that the proposed method accurately localizes targets. When multiple targets occupy the same range bin, the mean absolute error (MAE) for respiratory signals is 3 bpm, and the MAE for heartbeat signals is 5 bpm.

关键词： Frequency-Modulated Continuous Wave (FMCW) radar Multi-Target Multiple signal Classification (MUSIC) Variational Mode Decomposition (VMD)

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Cross-Domain Detection Transformer with Multi-view Adaptive Feature Alignment in Remote Sensing Imagery

Cross-Domain Detection Transformer with Multi-view Adaptive ...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Shu Wang Jianhong Han Ying Wang Xinyuan Hao Zhaoyi Luo Yupei Wang Liang Chen National Key Laboratory for Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China Shanghai Academy of Spaceflight Technology Shanghai China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Unsupervised Domain Adaptation (UDA) techniques are crucial for remote sensing object detection, designed to address performance degradation caused by the domain gap between training and test data. These methods leverage unlabeled target domain data, thus alleviating the high costs associated with data annotation. Recent developments in Detection Transformers (DETR) have simplified the detection pipeline and attracted significant research interest. Building on this architecture, we introduce an unsupervised domain adaptation detector for remote sensing object detection. Specifically, we introduce a multi-view adaptive feature alignment module that initially captures domain-specific features in complex backgrounds by leveraging a cross-attention mechanism. Subsequently, we employ contrastive learning to enforce the aggregation of domain-specific features from various perspectives, thereby improving the accuracy of feature alignment. Moreover, we demonstrate that integrating the self-training framework into DETR-based detectors can significantly mitigate the domain gap by further utilizing unlabeled data in the target domain. We validated the effectiveness and generalizability of our method across two remote sensing cross-domain detection scenarios using four public datasets.

关键词： Training Degradation Supervised learning Pipelines Object detection Detectors Feature extraction Transformers Sensors Remote sensing

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An Inverse Reinforcement Learning Method to Infer Reward Function of intelligent Jammer

An Inverse Reinforcement Learning Method to Infer Reward Fun...

引用

IEEE International Conference on radar

作者： Youlin Fan Bo Jiu Wenqiang Pu Kang Li Yu Zhang Hongwei Liu National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

This paper takes into account a frequency-agile radar system designated as “us” and an adversarial spot jammer designated as “enemy”, where the enemy jammer is modeled as an agent, and the radar’s strategy is characterized by its state transition probability in a Markov decision process (MDP). To comprehend the underlying attack motives of an intelligent jammer, a radar system must infer the utility function of the jammer. To accomplish this, the radar system must gather a dataset of actions taken by the jammer through observation of their transmitted signal. Next, a maximum entropy inverse reinforcement learning approach is utilized to recover the jammer’s reward function. After recovering the jammer’s reward function, the radar can infer the jammer’s attack intent, and develop appropriate anti-jamming strategies to effectively mitigate against the intelligent jammer. Simulation results demonstrate the efficacy of the proposed method.

关键词：

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Unequal Error Protection Rateless Codes for Spatial Audio Transmission 24

Unequal Error Protection Rateless Codes for Spatial Audio Tr...

引用

24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Yin, Junpeng Fu, Yusun Huang, Haobo Tang, Jinhui Shanghai Jiao Tong University Ningbo Artificial Intelligence Institute Ningbo China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Ministry of Education of China School of Electronic Information and Electrical Engineering The Key Laboratory of System Control and Information Processing Shanghai China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350363760

Spatial audio is an important component of the metaverse. With the rapid increase of the spatial resolution and the number of objects, the data volume of the spatial side information has grown larger. However, the low bit rate transmission of spatial side information in the object-based audio coding methods leads to the low-quality reconstruction of spatial side information with limited bandwidth. To address this issue, this paper constructs a cascade design of spatial audio coding and unequal error protection (UEP) rateless codes. Furthermore, the further protection of specific spatial side information is proposed for different scenarios, which effectively addresses the limitation of traditional spatial audio coding in side information protection. For the situation where the code length K of different importance levels is different, we propose an adaptive degree distribution (ADD), adding K to the selection probability as an influencing factor, which has better performance than other degree distribution for short code length. ADD is used to replace the multiple degree distribution strategy of the traditional unequal error protection scheme, and the block error rate (BER) is reduced nearly 10 times compared with the traditional scheme. © 2024 IEEE.

关键词： Audio signal processing

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HNet: A Loss Rate Robust ISAR Imaging Framework Based on Hypernetwork

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Li, Xiaoyong Liu, Lei Huang, Jiale Li, Lei Bai, Xueru Zhou, Feng Xidian University Hangzhou Institute of Technology Hangzhou311231 China Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi'an710071 China Xidian University National Key Lab of Radar Signal Processing Xi'an710071 China Xidian University School of Aerospace Science and Technology Xi'an710071 China

Deep learning has significantly advanced the field of sparse inverse synthetic aperture radar (ISAR) imaging. Nonetheless, current methodologies often face constraints due to their reliance on fixed loss rates and types, which can limit practical applicability and result in elevated computational demands both spatially and temporally, as well as diminished generalizability. In tackle to these challenges, this paper introduces a resilient ISAR imaging architecture grounded in hypernetwork technology capable of accommodating diverse loss rates. Initially, we devise a sparse signal reconstruction technique that functions irrespective of the loss rate or matrix dimensions. Subsequently, a methodology for producing arbitrary loss rates is outlined. Building upon this foundation, we present a hypernetwork framework that harnesses hypernetworks to derive defocus characteristics from range-Doppler (RD) imagery across varying loss rates, subsequently transforming these insights into ideal parameters for the sparse signal reconstruction process. By executing the iterative algorithm with these optimized parameters, we achieve loss rate robust ISAR imaging. Comprehensive evaluations conducted on point simulated data, electromagnetic simulated data and measured data confirm the superiority of our proposed approach in terms of reconstruction accuracy and generalization capabilities. This advancement facilitates reliable ISAR imaging across a spectrum of loss rates. 1965-2011 IEEE.

关键词： radar imaging

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