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International Workshop on Antenna Technology: Small Antennas and Novel Metamaterials, iWAT

作者： Yinfeng Xia Yingsong Li Wei Xue Luyu Zhao Eugene Sinkevich Vladimir Mordachev College of Information and Communication Engineering Harbin Engineering University Harbin China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China EMC R&D Laboratory Belarusian State University of Informatics and Radioelectronics Minsk Belarus

ISBN: (数字)9798350314755

ISBN: (纸本)9798350314762

A lumped elements integrated miniaturization super-wideband antenna is proposed, which is fed by coplanar waveguide (CPW) that consists of feeding line, extended ground plane, three S-shaped-slot rectangle radiation patches and several integrated lumped elements. The three S-shaped-slot rectangle radiation patches are electromagnetically coupled by the balanced CPW feeding line. To enhance the bandwidth of the designed antenna, several lumped elements are integrated into the antenna. The designed antenna is modeled in CST. By co-simulation and optimizing using CST and ADS. A -10 dB fractional bandwidth of 169.5% can be achieved which ranges from 0.8 GHz-9.7 GHz. Moreover, the proposed antenna has good omnidirectional radiation patterns in the operating band.

关键词： Conferences Rectangular waveguides Bandwidth Antenna feeds Distance measurement Coplanar waveguides Planar waveguides

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Ptpfusion: A Progressive Infrared and Visible Image Fusion Network Based on Texture Preserving

SSRN

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SSRN 2024年

作者： Lu, Yixiang Zhang, Weijian Zhao, Dawei Qian, Yucheng Maksim, Davydau Gao, Qingwei The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China The Belarusian State University of Informatics and Radioelectronics Minsk Belarus State Grid Anhui Electric Power Research Institute China

Infrared and visible image fusion is to provide a more comprehensive image for downstream tasks by highlighting the main tar get and maintaining rich texture information. Image fusion methods based on deep learning suffer from insufficient multimodal information extraction and texture loss. In this paper, we propose a texture preserving progressive fusion network to extract comple mentary information from multimodal images (PTPFusion) to solve these issues. To reduce image texture loss, we design multiple consecutive texture-preserving blocks (TPB) to enhance fused texture. The TPB can enhance the features by using a parallel ar chitecture consisting with a residual block and a derivative operators. In addition, a novel cross-channel attention (CCA) fusion module is developed to obtain complementary information by modeling global feature interactions via cross-queries mechanism, followed by information fusion to highlight the feature of salient target. To avoid information loss, the extracted features at different stages are merged as the output of TPB. Finally, the fused image will be generated by the decoder. Extensive experiments on three datasets show that our proposed fusion algorithm is better than existing state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Extended Coprime Arrays with Sum-Difference Coarray for DOA Estimation 5

Extended Coprime Arrays with Sum-Difference Coarray for DOA ...

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Xin, Wei Xu, Lin Gao, Jincheng Li, Yingsong Huang, Zhixiang College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Wuhan Second Ship Design and Research Institute Wuhan China Zhaojin Mining Industry Co. Ltd. Zhaoyuan China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A modified coprime array (CA) with a name of extended CA (ECA) is presented and constructed to reduce over-lapping sensors between sum and difference coarray using conventional CA (CCA) to get a sum-difference coarray with high degrees- of-freedoms (DOFs). The devised ECA is to shift a sub array of CCA to reduce redundancy and then to flip part of shifted sub array around zero point that is regarded as symmetric center. The onstructed ECA expands the inter-element separations with lower mutual coupling. Numerical simulation are provided to verify superiority of the devised ECA. © 2022 IEEE.

关键词： Direction of arrival

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Look Before You Decide: Prompting Active Deduction of MLLMs for Assumptive Reasoning

arXiv

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arXiv 2024年

作者： Li, Yian Tian, Wentao Jiao, Yang Chen, Jingjing Zhao, Na Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Singapore University of Technology and Design Singapore

Recently, Multimodal Large Language Models (MLLMs) have achieved significant success across multiple disciplines due to their exceptional instruction-following capabilities and extensive world knowledge. However, whether these MLLMs possess human-like compositional reasoning abilities remains an open problem. To unveil their reasoning behaviors, we first curate a Multimodal Assumptive Reasoning Benchmark (MARS-Bench) in this paper. Interestingly, we find that most prevalent MLLMs can be easily fooled by the introduction of a presupposition into the question, whereas such presuppositions appear naive to human reasoning. Besides, we also propose a simple yet effective method, Active Deduction (AD), to encourage the model to actively perform composite deduction before reaching a final decision. Equipped with the proposed AD method, a MLLM demonstrates significant improvements in assumptive reasoning abilities without compromising its general-purpose question-answering performance. We also provide extensive evaluations of both open-source and private MLLMs on MARS-Bench, along with experimental analyses of the AD method. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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ADAPTIVE RETENTION & CORRECTION: TEST-TIME TRAINING FOR CONTINUAL LEARNING

arXiv

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arXiv 2024年

作者： Chen, Haoran Goldblum, Micah Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China New York University United States

Continual learning, also known as lifelong learning or incremental learning, refers to the process by which a model learns from a stream of incoming data over time. A common problem in continual learning is the classification layer’s bias towards the most recent task. Traditionally, methods have relied on incorporating data from past tasks during training to mitigate this problem. However, the recent shift in continual learning to memory-free environments has rendered these approaches infeasible. In this study, we propose a solution focused on the testing phase. We first introduce a simple Out-of-Task Detection method, OTD, designed to accurately identify samples from past tasks during testing. Using OTD, we then propose: (1) an Adaptive Retention mechanism for dynamically tuning the classifier layer on past task data;(2) an Adaptive Correction mechanism for revising predictions when the model classifies the data from previous tasks into classes of the current task. We name our approach Adaptive Retention & Correction (ARC). Although designed for memory-free environments, ARC also proves effective in memory-based settings. Extensive experiments show that our proposed method can be plugged in to virtually any existing continual learning approach without requiring any modifications to its training procedure. Specifically, when integrated with state-of-the-art approaches, ARC achieves an average performance increase of 2.7% and 2.6% on the CIFAR-100 and Imagenet-R datasets, respectively. Code is available at Github Link. © 2024, CC BY.

关键词： Contrastive Learning

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Design of Planar Inverted-F Antenna (PIFA) for 5G Communication Indoor Micro-base station 13

Design of Planar Inverted-F Antenna (PIFA) for 5G Communicat...

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13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Yang, Zhourui Wu, Bo Kong, Meng Zhang, Liang Li, Xianghui Wu, Xianliang Ministry Of Education Anhui University Key Laboratory Of Intelligent Computing Signal Processing Hefei230039 China Hefei Normal University Anhui Province Key Laboratory Of Simulation And Design For Electronic Information System Hefei230601 China

ISBN: (纸本)9781665434379

In this paper, a planar inverted F antenna is designed for indoor distributed micro-base station of 5G communication. The overall size of the antenna is 16 mm × 15.3 mm × 10 mm, and the antenna works in the 3.5 - 3.6 GHz, which band number named n78. The antenna has the advantages of miniaturization, easy machining and convenient installation. The simulation results of ANSYS HFSS 2020 shows that in the working bandwidth range, when the central frequency of the designed antenna is 3.55 GHz, the S11 is -23.6 dB, and the VSWR is 1.1, which meets the requirements of impedance matching. In terms of directivity, the antenna coverage also meets the requirements of 5G micro-base station. © 2021 IEEE.

关键词： Base stations

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Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision

Learning Open-Vocabulary Semantic Segmentation Models From N...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jilan Xu Junlin Hou Yuejie Zhang Rui Feng Yi Wang Yu Qiao Weidi Xie Shanghai Key Lab of Intelligent Information Processing School of Computer Science Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai AI Laboratory CMIC Shanghai Jiao Tong University

This paper considers the problem of open-vocabulary semantic segmentation (OVS), that aims to segment objects of arbitrary classes beyond a pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled imagetext pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slotattention based binding module, then aligns the group tokens to corresponding caption embeddings. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, encouraging the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on four benchmark datasets, PASCAL VOC, PASCAL Context, COCO Object, and ADE20K. OVSegmentor achieves superior results over state-of-the-art approaches on PASCAL VOC using only 3% data (4M vs 134M) for pre-training.

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Ptpfusion: A Progressive Infrared and Visible Image Fusion Network Based on Texture Preserving

SSRN

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SSRN 2024年

关键词： Image fusion

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Compact Broadband Absorptive Bandstop Filter Based on Microstrip 2

Compact Broadband Absorptive Bandstop Filter Based on Micros...

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2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Lu, Qi Wu, Xianliang Wang, Chao Key Lab of Intelligent Computing and Signal Processing Ministry of Education AnHui University AnHui University Heifei AnHui230000 China

A compact broadband absorptive band rejection filter (ABSF) based on microstrip line is proposed. The filter consists of a pair of coupled wires and is loaded with a resistor. The coupling line of the filter has an open stub. The transmission zero point can be adjusted by adjusting the width of the coupling line. The center frequency can be adjusted by changing the length of the intermediate coupling line. Absorption performance can be achieved through loaded resistors. In this way, the signal not input in the filter will be dissipated in the filter and will not be reflected. When F0=3.8 GHz, 26% of the 20 dB bandwidth covers 3.15 GHz-4.2 GHz, the rejection depth of the stopband is less than-40 dB, and the in-band absorption is less than-10 dB. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Notch filters

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Research on Data Augmentation for Garbage Classification with Target Detection Algorithm

Research on Data Augmentation for Garbage Classification wit...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Chen, Jiansong Zuo, Shikai Lin, Yun Tang, Kai Zou, Jianhang Feng, Naixing Cai, Yijun Xiamen University of Technology Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen361024 China Xiamen Wish Information Technology Co. Ltd Xiamen361006 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9781665452366

Garbage classification is an essential work in daily life. With the development of artificial intelligence (AI), we have begun to use object detection to achieve garbage classification. However, the stacking and occlusion of garbage often make the detection performance unsatisfactory. In this paper, YOLOv5 algorithm is used for garbage detection and classification. YOLOv5 has three detection heads, which can detect objects at multiple scales. At the same time, Mosaic and Coarse Dropout image augmentation techniques are used to process our private data set of garbage, which improves the diversity of images. In this way, the target classification accuracy of the detection model can reach 75.7%, which is significantly improved compared with 69.9% without data augmentation. This paper provides a new technical support for the scene classification of stacked garbage based on deep learning. © 2022 IEEE.

关键词： Classification (of information)

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