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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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Implicit Temporal Modeling with Learnable Alignment for Video Recognition

Implicit Temporal Modeling with Learnable Alignment for Vide...

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International Conference on Computer Vision (ICCV)

作者： Shuyuan Tu Qi Dai Zuxuan Wu Zhi-Qi Cheng Han Hu Yu-Gang Jiang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing Microsoft Research Asia Carnegie Mellon University

Contrastive language-image pretraining (CLIP) has demonstrated remarkable success in various image tasks. However, how to extend CLIP with effective temporal modeling is still an open and crucial problem. Existing factorized or joint spatial-temporal modeling trades off between the efficiency and performance. While modeling temporal information within straight through tube is widely adopted in literature, we find that simple frame alignment already provides enough essence without temporal attention. To this end, in this paper, we proposed a novel Implicit Learnable Alignment (ILA) method, which minimizes the temporal modeling effort while achieving incredibly high performance. Specifically, for a frame pair, an interactive point is predicted in each frame, serving as a mutual information rich region. By enhancing the features around the interactive point, two frames are implicitly aligned. The aligned features are then pooled into a single token, which is leveraged in the subsequent spatial self-attention. Our method allows eliminating the costly or insufficient temporal self-attention in video. Extensive experiments on benchmarks demonstrate the superiority and generality of our module. Particularly, the proposed ILA achieves a top-1 accuracy of 88.7% on Kinetics-400 with much fewer FLOPs compared with Swin-L and ViViT-H. Code is released at https://***/Francis-Rings/ILA.

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Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens

arXiv

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arXiv 2024年

作者： Sun, Dengdi Pan, Yajie Lu, Andong Li, Chenglong Luo, Bin The Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China The Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

Many RGBT tracking researches primarily focus on modal fusion design, while overlooking the effective handling of target appearance changes. While some approaches have introduced historical frames or fuse and replace initial templates to incorporate temporal information, they have the risk of disrupting the original target appearance and accumulating errors over time. To alleviate these limitations, we propose a novel Transformer RGBT tracking approach, which mixes spatio-temporal multimodal tokens from the static multimodal templates and multimodal search regions in Transformer to handle target appearance changes, for robust RGBT tracking. We introduce independent dynamic template tokens to interact with the search region, embedding temporal information to address appearance changes, while also retaining the involvement of the initial static template tokens in the joint feature extraction process to ensure the preservation of the original reliable target appearance information that prevent deviations from the target appearance caused by traditional temporal updates. We also use attention mechanisms to enhance the target features of multimodal template tokens by incorporating supplementary modal cues, and make the multimodal search region tokens interact with multimodal dynamic template tokens via attention mechanisms, which facilitates the conveyance of multimodal-enhanced target change information. Our module is inserted into the transformer backbone network and inherits joint feature extraction, search-template matching, and cross-modal interaction. Extensive experiments on three RGBT benchmark datasets show that the proposed approach maintains competitive performance compared to other state-of-the-art tracking algorithms while running at 39.1 FPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Article reranking by memory-enhanced key sentence matching for detecting previously fact-checked claims 59

Article reranking by memory-enhanced key sentence matching f...

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language processing, ACL-IJCNLP 2021

作者： Sheng, Qiang Cao, Juan Zhang, Xueyao Li, Xirong Zhong, Lei Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

ISBN: (纸本)9781954085527

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works retrieve fact-checking articles (FC-articles) for detection and focus on reranking candidate articles in the typical two-stage retrieval framework. However, their performance may be limited as they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics;and (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. In this paper, we propose a novel reranker, MTM (Memoryenhanced Transformers for Matching), to rank FC-articles using key sentences selected using event (lexical and semantic) and pattern information. For event information, we propose to finetune the Transformer with regression of ROUGE. For pattern information, we generate pattern vectors as a memory bank to match with the parts containing patterns. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://***/ICTMCG/MTM. © 2021 Association for Computational Linguistics

关键词： Semantics

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Patch Is Not All You Need

arXiv

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arXiv 2023年

作者： Li, Changzhen Zhang, Jie Wei, Yang Ji, Zhilong Bai, Jinfeng Shan, Shiguang Key Lab of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology China University of Chinese Academy of Sciences China Hangzhou lnstitute for Advanced Study UCAS School of Intelligent Scienceand Technology China Tomorrow Advancing Life China

Vision Transformers have achieved great success in computer visions, delivering exceptional performance across various tasks. However, their inherent reliance on sequential input enforces the manual partitioning of images into patch sequences, which disrupts the image’s inherent structural and semantic continuity. To handle this, we propose a novel Pattern Transformer (Patternformer) to adaptively convert images to pattern sequences for Transformer input. Specifically, we employ the Convolutional Neural Network to extract various patterns from the input image, with each channel representing a unique pattern that is fed into the succeeding Transformer as a visual token. By enabling the network to optimize these patterns, each pattern concentrates on its local region of interest, thereby preserving its intrinsic structural and semantic information. Only employing the vanilla ResNet and Transformer, we have accomplished state-of-the-art performance on CIFAR-10 and CIFAR-100, and have achieved competitive results on ImageNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Improved Uniform Linear Array Fitting Scheme with Increased Lower Bound on Uniform Degrees of Freedom for DOA Estimation

TechRxiv

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TechRxiv 2022年

作者： Shi, Wanlu Li, Yingsong The College of Information and Communication Engineering Harbin Engineering University Harbin150001 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China

Recently, uniform linear array (ULA) fitting (UF) principle is proposed for sparse array (SA) design using pseudo-polynomial equations. Typically, it is verified that the designed SAs via UF enjoy a lower bound on uniform degrees of freedom (uDOF), which is approximately 0.5N2 with N sensors. Herein, an improved UF (IUF) scheme is proposed to significantly increase the lower bound on uDOF, which is realized by introducing two sub-ULAs, with name of adjoint transfer arrays (ATAs), on both sides of a transfer sub-ULA. The ATAs together with the transfer sub-ULA serve as a new layer to construct an adjoint transfer layer (ATL) that has improved aperture in comparison with traditional transfer layer to improve the lower bound on uDOF, which is derived in detail. Furthermore, two novel SAs are developed based on the ATL to verify the effectiveness of the proposed IUF scheme to increase the uDOF. Numerical simulations verify the superiority of devised SAs for direction-of-arrival (DOA) estimations. © 2022, CC BY.

关键词： Polynomials

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Compact Broadband Rectifier With Wide Input Power Range Based on Adaptive Input Power Distribution

TechRxiv

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TechRxiv 2022年

作者： Liu, Xin Li, Yingsong Zhang, Dawei College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China

This paper presents a novel compact rectifier array with both broadband and wide input power range characteristics. The rectifier array consists of two rectifier units operating at low-power and high-power levels, respectively, and an adaptive input power distribution network (AIPDN) enabling automatic distribution of radio frequency (RF) energy at different power levels. In addition, a matching inductance is introduced to counteract the capacitance of the diode. As a result, the high RF-DC power conversion efficiency (PCE) is achieved in both broadband and wide input power range. For demonstration, a prototype of the presented rectifier array is fabricated and measured. The measured results are in good accordance with the simulated results. With an input power range of 8 – 25 dBm and a bandwidth of 1.82 – 2.75 GHz (fractional bandwidth of 40.7%), the PCE is greater than 50%. At 2.4 GHz, the input power ranges from -5.5 to 27.5 dBm with PCE greater than 50%. Besides, a peak PCE of 72.1% is obtained at an input power of 25 dBm. This work can lay the foundation for the development of a new RF energy harvest systems with wide frequency band and dynamic input power. © 2022, CC BY.

关键词： Horn antennas

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Transient Convergence Analysis of Least Lncosh Algorithm

TechRxiv

引用

TechRxiv 2022年

作者： Sun, Zeyang Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China

Lncosh function has been used for constructing a cost function for devising an adaptive filter algorithm to provide a desired performance under non-Gaussian noise, naming as least Lncosh algorithm (LLA). However, its transient behavior analysis has not been analyzed and investigated. As an important characteristic for the LLA, the transient behavior affects its performance in real-world applications. Herein, we present the theoretical model for evaluating the mean weight behavior and the transient excess mean square error (EMSE) behavior of the LLA. In addition, the proposed models can also be used to predict the transient behavior of the adaptive filtering algorithms with error nonlinearities under non-Gaussian noises. The experiment results have verified the validity of the presented models. © 2022, CC BY.

关键词： Adaptive filters

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Design of 0.8-3.7 GHz Broadband asymmetric multi-node directional coupler

Design of 0.8-3.7 GHz Broadband asymmetric multi-node direct...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Ruikang Wang Bo Wu Meng Kong Jingyang Wang Yue Ma Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal University Hefei China

ISBN: (纸本)9781665482271

In this paper, a 15 dB asymmetric broadband directional coupler for 4G and 5G communication is designed. The physical size of the coupler is $13\ \text{mm}\times 60\ \text{mm}$ . The dielectric constant is 4.4 and the thickness of single layer plate is 2 mm. The couplers operate in the 0.8-3.7 GHz band. The coupler has the advantages of bandwidth width, convenient processing and installation. ANSYS HFSS 2019 simulation results shows that in the 0.8-3.7 GHz band, the return loss (S 11 ) is more than 16.4 dB, the insertion loss (S 21 ) is from 0.14 dB to 1.18 dB, the coupling degree (S 31 ) is about 15 dB, and the isolation degree (S 41 ) is more than 29 dB. The method of multi-section coupling line cascade is adopted, to achieve the requirements of bandwidth width. The directivity of the coupler is increased by loading the branches to meet the design requirements of the 15 dB coupler.

关键词： Couplings Microwave technology Power transmission lines 5G mobile communication Simulation Millimeter wave technology Broadband communication

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Huang, Xiaodi Zhou, Hong Zhu, Shanfeng Fudan University School of Computer Science Shanghai China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China Charles Sturt University School of Computing Mathematics and Engineering Nsw Australia Atypon Systems Llc United Kingdom Zhangjiang Fudan International Innovation Center Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Moe Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9798350386226

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available. © 2024 IEEE.

关键词： Semantics

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