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arXiv 2022年

作者： Mo, Hanlin Li, Hua Zhao, Guoying The Center for Machine Vision and Signal Analysis University of Oulu OuluFI-90014 Finland The Key lab of Intelligent Information Processing The Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China The School of Information and Technology Northwest University Xi'An710069 China

With the development of data acquisition technology, large amounts of multi-channel data are collected and widely used in many fields. Most of them, such as RGB images and vector fields, can be expressed as different types of multi-channel functions. Feature extraction of multi-channel data for identifying interest patterns is a critical but challenging task. This paper focuses on constructing moment-based features of general multi-channel functions. Specifically, we define two transform models, rotation-affine transform and total rotation transform, to describe real deformations of multi-channel data. Then, we design a structural framework to generate Gaussian-Hermite moment invariants for these two transform models systematically. It is the first time that a unified framework has been proposed in the literature to construct orthogonal moment invariants of general multi-channel functions. Given a specific type of multi-channel data, we demonstrate how to utilize the new method to derive all possible invariants and eliminate dependences among them. We obtain independent sets of invariants with low orders and low degrees for RGB images, 2D vector fields and color volume data. Based on synthetic and real multi-channel data, we conduct extensive experiments to evaluate the stability and discriminability of these invariants and their robustness to noise. The results show that new moment invariants significantly outperform previous moment invariants of multi-channel data in RGB image classification and vortex detection in 2D vector fields. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

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Some bounds on the cardinality of the b-symbol weight spectrum of codes

arXiv

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arXiv 2024年

作者： Zhu, Hongwei Li, Shitao Shi, Minjia Xia, Shu-Tao Solé, Patrick Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China School of Mathematical Sciences Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China Centrale Marseille CNRS I2M Aix Marseille University Marseille13009 France State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

The size of the Hamming distance spectrum of a code has received great attention in recent research. The main objective of this paper is to extend these significant theories to the b-symbol distance spectrum. We examine this question for various types of codes, including unrestricted codes, additive codes, linear codes, and cyclic codes, successively. For the first three cases, we determine the maximum size of the b-symbol distance spectra of these codes smoothly. For the case of cyclic codes, we introduce three approaches to characterize the upper bound for the cardinality of the b-symbol weight spectrum of cyclic codes, namely the period distribution approach, the primitive idempotent approach, and the b-symbol weight formula approach. As two by-products of this paper, the maximum number of symplectic weights of linear codes is determined, and a basic inequality among the parameters [n, k, dH(C)]q of cyclic codes is provided. © 2024, CC0.

关键词： Hamming distance

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Energy Consumption and Time Delay optimization of MEC Based on MultiDimensional Game 5

Energy Consumption and Time Delay optimization of MEC Based ...

引用

5th IEEE International Conference on Cloud computing and Big Data Analytics, ICCCBDA 2020

作者： Wang, Hui Wang, Yi Lu, Xingjun Hu, Yanjun Anhui University Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Hefei230601 China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China

ISBN: (纸本)9781728160245

Mobile edge computing (MEC) reduces latency and power consumption of mobile devices by deploying servers on the network edge. Due to the limited computing resources of the MEC server, excessive offloading of the users in local cells will bring unexpected queuing delay and high energy consumption. To deal with these problems, this paper proposes a Multi-user Queue Offloading multi-dimensional game algorithm (MQO). By the algorithm, users could choose the most suitable strategy, in which the strategy space is two-dimensional to satisfy different needs of different users to a certain extent, including the offload strategy and power control of users. Simulation results show that MQO can improve the performance of both overhead and offloading number with the increase of users. © 2020 IEEE.

关键词： Energy utilization

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A Low SAR, Small size, High gain, Circularly polarized Antenna for Wearable Application 2

A Low SAR, Small size, High gain, Circularly polarized Anten...

引用

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

作者： Wang, Chao Zhang, Liang Cheng, Yi Li, XueMei Wu, Xianliang Key Lab of Ministry of Education of Intelligent Computing and Signal Processing Anhui University Hefei Anhui230601 China AnHui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei Anhui230601 China

A planar, small size, high gain, low specific absorption rate (SAR) and circularly-polarized (CP) wearable antenna is proposed in this paper. The antenna is fabricated on a substrate with a dielectric constant of 3.5 with a thickness of 0.5 mm and sized at 42×42 mm only. Both the simulation results show that the overlap bandwidth of impedance and axial ratio of the proposed antenna is 13.7% (5.15-5.91 GHz). To isolate the antenna against the influence of the human body, a artificial magnetic conductor (AMC) plane is added on the reverse side of the radiator. The AMC structure effectively isolates the human body from the radiation of the antenna and reduces the specific absorption rate (SAR) of it by more than 96.1% at 5.5 GHz, which improves the antenna gain and the peak gain reaches 5.09 dBi at 5.5 GHz. The wearable performance of the antenna was analyzed, and it is observed that it can sustain good performance even under human body loading. Beside, the antenna has a small size, which makes it ideal for wearable applications. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Wearable antennas

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Unified-modal Salient Object Detection via Adaptive Prompt Learning

arXiv

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arXiv 2023年

作者： Wang, Kunpeng Li, Chenglong Tu, Zhengzheng Liu, Zhengyi Luo, Bin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China The Institute of Physical Science and Information Technology Anhui University Hefei230601 China

Existing single-modal and multi-modal salient object detection (SOD) methods focus on designing specific architectures tailored for their respective tasks. However, developing completely different models for different tasks leads to labor and time consumption, as well as high computational and practical deployment costs. In this paper, we attempt to address both single-modal and multi-modal SOD in a unified framework called UniSOD, which fully exploits the overlapping prior knowledge between different tasks. Nevertheless, assigning appropriate strategies to modality variable inputs is challenging. To this end, UniSOD learns modality-aware prompts with task-specific hints through adaptive prompt learning, which are plugged into the proposed pre-trained baseline SOD model to handle corresponding tasks, while only requiring few learnable parameters compared to training the entire model. Each modality-aware prompt is generated from a switchable prompt generation block, which adaptively performs structural switching based on single-modal and multi-modal inputs without human intervention. Through end-to-end joint training, UniSOD achieves overall performance improvement on 14 benchmark datasets for RGB, RGB-D, and RGB-T SOD, which demonstrates that our method effectively and efficiently unifies single-modal and multi-modal SOD tasks. The code and results are available at https://***/Angknpng/UniSOD. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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A CPW-Fed Miniaturized Dual-Band Antenna for 5G Applications

A CPW-Fed Miniaturized Dual-Band Antenna for 5G Applications

引用

International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Xuemei Li Haoran Zhu Zhixiang Huang Key Lab of Intelligent Computing & Signal Processing Ministry of Education AnHui University AnHui University Heifei China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

This paper designs a CPW-fed single-fed single- layer dual-frequency antenna for 5G applications. The overall size of the antenna is only 23×30.5×0.254mm 3 . The antenna consists of an improved folded rectangular slot, a Y-shaped monopole radiator fed by a coplanar waveguide (CPW) with cross-notched slots, and a curved sub-ring slot for isolating frequency bands. The dual frequency characteristics are realized by deformed folded rectangular grooves and Y-shaped monopoles with split ring grooves and cross grooves. By adjusting the position and size of these structures separately, two working frequency bands can be realized. Reduce the size of the antenna by folding the rectangular ground slot to increase the surface current (6.92 λ 0 × 9.17 λ 0 × 0.08 λ 0 ). The simulation results show that the working bandwidth of the antenna is 2.82- 3.84Ghz (31.58%) and 4.48-5.53Ghz (25.68%) respectively. It not only covers 5G bands 3.4-3.8Ghz and 4.8-5.0Ghz, but also covers up to part of the working frequency band of WLAN and WiMAX. The antenna has a good omnidirectional radiation pattern. The antenna is small in size, easy to integrate, and is well-suited to 5G wireless communication equipment.

关键词： Slot antennas 5G mobile communication Dual band Bandwidth Numerical models Coplanar waveguides Antenna radiation patterns

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A Dynamic Transformer Network for Vehicle Detection

引用

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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A Broadband Circular Polarization Antenna Applied For Base-Station

A Broadband Circular Polarization Antenna Applied For Base-S...

引用

International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Xiangkui Hu Haoran Zhu Xianliang Wu Zhixiang Huang Bo Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education AnHui University AnHui University Heifei China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

This paper studies the broadband circularly polarized antenna used in micro base stations. The antenna consists of a pair of rectangular loops. The ring patch is used to form circular polarization, and a pair of open resonant ring parasitic elements are loaded between the two rectangular rings to increase the bandwidth. By introducing balun feed to increase bandwidth, good impedance matching can be obtained. Add a metal reflector at the bottom of the antenna to form directional radiation. The antenna was printed on the Rogers RO4003 substrate, and the results showed that the -10dB return loss bandwidth was 43.4% (1.86-2.89GHz), and the -3dB axial ratio bandwidth was 41% (1.9-3GHz). The gain of the antenna in the frequency band is greater than 9dBi.

关键词： Polarization Bandwidth Broadband communication Broadband antennas Loaded antennas Substrates Antenna radiation patterns

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Application of Maehly Approximation to em scattering from a Three-dimensional Rough Surface over a Frequency Band 5

Application of Maehly Approximation to em scattering from a ...

引用

5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019

作者： Wang, An Qi Zhang, Xue Huang, Zhi Xiang Anhui University School of Electronics and Information Engineering Key Laboratory of Intelligent Computing Signal Processing Hefei China

ISBN: (纸本)9781538671115

In this paper, the Maehly approximation, which is based on Chebyshev series, is proposed to fast simulate the EM scattering from a three-dimensional (3D) rough surface over a frequency band. Comparing with the exact results obtained by the method of moments (MoM), results of the Maehly approximation have achieved an excellent agreement both in co-polarization and cross-polarization. Moreover, the Maehly approximation is superior to the MoM in terms of the CPU time required. © 2019 IEEE.

关键词： Method of moments

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Data gravitation-based three-way sampling method for imbalanced data classification

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Information Sciences 2025年 718卷

作者： Yuanting Yan Weiqing Wang Yingao Ma Yang Tian Peng Zhou Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui Province 230601 PR China School of Computer Science and Technology Anhui University Hefei Anhui Province 230601 PR China

Data resampling is widely used for addressing the class imbalance problem, with numerous methods developed over recent decades. Among these, the K-nearest neighbor (KNN) technique is widely adopted for leveraging local neighborhood information in rebalancing imbalanced data. However, KNN struggles to accurately characterize local data distribution, as samples with identical KNNs may also exhibit divergent local structures, which may further hamper imbalanced learning performance. We propose a hybrid sampling method called data gravitation-based three-way sampling (DGS) to improve classification performance on imbalanced data. DGS introduces the concept of data gravitation into KNN to evaluate the local importance of samples, and combines it with three-way decision theory to selectively oversample minority-class samples and remove risky majority-class samples. DGS introduces a cost-sensitive sampling mechanism and adaptive threshold estimation using a Gaussian-based model, reducing reliance on manually tuned parameters. Extensive experiments on 38 datasets and seven classifiers demonstrate the superiority of DGS over 18 resampling methods across three evaluation metrics. Our code is available at https://***/ytyancp/DGS .

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