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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Wang, Lihua Li, Yingsong Huang, Zhixiang Loh, Tian Hong Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China National Physical Laboratory Electromagnetic and Eletrochemical Technologies Department Middlesex United Kingdom

ISBN: (纸本)9798350336740

A simple broadband slotted circularly-polarized (CP) antenna (SBS-CP) is presented, which mainly consists of a ground structure with a modified slot and an asymmetric feeding structure. The two structures can give different CP characteristics in low and high frequencies. By etching rectangular slots in the ground structure, the coupling effect between feeding and ground structure will be improved and the bandwidth will be broadened. By modeling and optimizing the antenna, the proposed antenna impedance bandwidth (IBW) and axial ratio bandwidth (ARBW) are 113% (2.08~7.46 GHz) and 97% (2.5~7.24 GHz), respectively. © 2023 IEEE.

关键词： Slot antennas

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An Active Landing Recovery Method for Quadrotor UAV: Localization, Tracking and Buffering Landing

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IFAC-PapersOnLine 2023年第2期56卷 3366-3372页

作者： Yongkang Xu Zhihua Chen Shoukun Wang Junzheng Wang National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing CO 100081 P.R.China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology School of Information Engineering Nanchang Hangkong University Nanchang CO 330063 P.R. China

This paper proposes a principle of fully autonomous ground mobile landing recovery of Unmanned Aerial Vehicles (UAV) for the problems of relatively fixed landing point, passive recovery, poor flexibility, and environmental adaptability, which mainly includes localization, landing point tracking, and buffering landing for quadrotor UAV. Firstly, aiming at the problem that it is difficult to accurately obtain the position of a UAV in dynamic mobile landing recovery, a target location method based on Asynchronous Multisensor information Fusion(AMIF) and servo turntable focus tracking is proposed. Secondly, to achieve fast and high-precision tracking of UAVs, a tracking control strategy of an independently driven landing recovery system and a Stewart six-degree of freedom platform is proposed. Then, to solve the problems of large impact force and center of gravity instability in the landing process of UAV, a stationarity control algorithm based on model prediction and a compliance control algorithm based on adaptive variable impedance are designed to achieve active compliance control while adjusting the position and attitude of the receiving surface in real-time. Finally, a quadrotor unmanned landing and recovery experimental platform is built to verify the feasibility of the ground mobile landing and recovery strategy proposed in this paper and the effectiveness of the control algorithm.

关键词： Quadrotor UAV Mobile Autonomous recovery Target localization Falling point tracking Buffering landing

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EVE: Towards End-to-End Video Subtitle Extraction with Vision-Language Models

arXiv

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arXiv 2025年

作者： Yu, Haiyang Lu, Jinghui Wang, Yanjie Li, Yang Wang, Han Huang, Can Li, Bin Shanghai Key Laboratory of Intelligent Information Processing China School of Computer Science Fudan University China ByteDance Inc China

The advent of Large Vision-Language Models (LVLMs) has advanced the video-based tasks, such as video captioning and video understanding. Some previous research indicates that taking texts in videos as input can further improve the performance of video understanding. As a type of indispensable information in short videos or movies, subtitles can assist LVLMs to better understand videos. Most existing methods for video subtitle extraction are based on a multi-stage framework, handling each frame independently. They can hardly exploit the temporal information of videos. Although some LVLMs exhibit the robust OCR capability, predicting accurate timestamps for subtitle texts is still challenging. In this paper, we propose an End-to-end Video Subtitle Extraction method, called EVE, which consists of three modules: a vision encoder, an adapter module, and a large language model. To effectively compress the visual tokens from the vision encoder, we propose a novel adapter InterleavedVT to interleave two modalities. It contains a visual compressor and a textual region compressor. The proposed InterleavedVT exploits both the merits of average pooling and Q-Former in token compression. Taking the temporal information of videos into account, we introduce a sliding-window mechanism in the textual region compressor. To benchmark the video subtitle extraction task, we propose a large dataset ViSa including 2.5M videos. Extensive experiments on ViSa demonstrate that the proposed EVE can outperform existing open-sourced tools and LVLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 214-222页

作者： Shuping Liu Yantuan Xian Huafeng Li Zhengtao Yu School of Information Engineering and Automation and Key Laboratory of Intelligent Information Processing Kunming University of Science and Technology

Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection *** order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.

关键词： Dictionary learning Laplacian sparse regularization morphological component analysis(MCA) sparse representation text detection

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Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing

Resource Allocation Strategy for Wireless Powered Communicat...

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IEEE Conference on Vehicular Technology (VTC)

作者： Xinyu Zhang Yisheng Zhao Hongyi You Kaige Jian Li Liang Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Aiming at the problem of limited energy stored in unmanned aerial vehicle (UAV), a resource allocation strategy for UAV-assisted edge computing in wireless powered communication networks is investigated in this paper. By deploying a laser beam director on the ground, sufficient energy can be provided for the UAV in a short period of time. Then, multiple ground terminals obtain energy from this UAV by radio frequency energy harvesting method and offload their computational tasks to the UAV with edge server. The resource allocation problem is modeled as an optimization problem. The optimization objective is to minimize the total energy consumption of the UAV subject to the constraints of energy and data causality, computational resources, and transmitting power. The suboptimal solution is obtained by introducing an imperialist competitive algorithm. Simulation results show that the imperialist competitive algorithm consumes less energy compared with the particle swarm optimization algorithm and the equal upload time allocation method.

关键词： Wireless communication Energy consumption Simulation Autonomous aerial vehicles Trajectory Resource management Servers

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Software and Hardware Synergy for Accelerated Plant Disease Identification

SSRN

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SSRN 2024年

作者： Wen, Hongxing Chen, Ling College of Electronic Information and Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China

Plant diseases are one of the main causes of reduced crop yields. Therefore, it is necessary to adopt timely and effective identification methods and take corresponding measures. Some physical and biological detection methods have been proposed by related researchers, but these methods require specialised techniques and expensive detection costs, and the number of technicians is very limited, so the identification of plant diseases is always not timely and effective. In order to achieve real-time identification of plant diseases in some remote areas where networks and circuits are not well connected. We adopted a hardware and software co-acceleration approach to implement the design. First, we designed a lightweight convolutional neural network (CNN) and trained this network using a knowledge distillation approach. Then, we quantified the model parameters into int8 type using a method of model quantization to further compress the model size. After compression, the model size of the network is 0.035 MB and the recognition accuracy is 94.06% on the test set of the experiment. In order to deploy the proposed network on resource constrained FPGA devices, we used time-division multiplexing and feature map segmentation to deploy the network. Finally, our design is implemented on ZYNQ7020 with an inference speed of 35.73ms/frame and a power consumption of 1.97 W. The experimental results show that our design has the advantages of consuming less FPGA resources, low power consumption, high speed, and portability, and can be used for disease recognition in multiple plant classes. © 2024, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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STROKE LESION SEGMENTATION BASED ON MULTI-RESOLUTION MULTI-DIMENSIONAL AND TWO-STAGE ENSEMBLE LEARNING

STROKE LESION SEGMENTATION BASED ON MULTI-RESOLUTION MULTI-D...

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第十六届国际体视学与图像分析学术会议

作者： Li KANG Shunbin LI Jianjun HUANG Jin CHEN College of Electronics and Information Engineering Shenzhen University China and the Guangdong Key Laboratory of Intelligent Information Processing

Stroke is an acute cerebrovascular disease with a extremely high rate of disability and *** early diagnosis is key to developing a treatment plan,early diagnosis of stroke remains difficult due to the challenge of accurately segmenting the *** this paper,an adaptive segmentation model based on the U-Net framework is proposed for the lesion segmentation problem in ***,the model consists of three u-net frameworks that capture contextual features for 2 D and 3 D slices,respectively,and the outputs of different paths are integrated to obtain 3 D segmentation results through a two-stage voting ***,multiresolution constraints and dimensional information complementary strategies are introduced to alleviate the data heterogeneity problem and thus improve the accuracy of model *** experiments were conducted on the ISLES2015-SPES,ISLES2015-SISS and ISLES2017 datasets and submitted on the MICCAI Ischemic Stroke Lesion Segmentation(ISLES) platform to compare with other segmentation models,and the results show that the proposed model outperms the other models with promissing performance.

关键词： stroke multi-dimension multi-resolution segmentation

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Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System

Hybrid MI and RIS-Assisted Acoustic Communication for Channe...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zhiyi Hu Yisheng Zhao Peng Liu Chaohua Song Tengteng Li Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Aiming at the problem of low data rate in autonomous underwater vehicle (AUV)-based underwater acoustic communication system, a hybrid magnetic induction (MI) and reconfigurable intelligent surface (RIS)-assisted communication strategy is proposed to maximize system channel capacity. Specifically, an AUV first collects data from all the seafloor nodes by adopting the MI communication technology. Then, the AUV forwards the data to another AUV carried with a RIS by using the underwater acoustic communication method. With the help of the RIS, a strong reflective path between the first AUV and a surface base station (BS) on the sea is formed. The surface BS could receive the data at a relatively high data rate. In order to maximize the system channel capacity, the acoustic incidence angle, the distance between the first AUV and the RIS, the distance between the RIS and the surface BS, acoustic signal frequency, and transmitting power are jointly optimized. The formulated optimization problem is solved by employing a butterfly optimization algorithm (BOA) and an improved butterfly optimization algorithm (IBOA), respectively. Simulation results show that the IBOA can increase the system channel capacity more effectively than the basic BOA.

关键词： Sea surface Vehicular and wireless technologies Channel capacity Simulation Sea floor Reconfigurable intelligent surfaces Data communication

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Decentralized Kernel Ridge Regression Based on Data-Dependent Random Feature

arXiv

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arXiv 2024年

作者： Yang, Ruikai He, Fan He, Mingzhen Yang, Jie Huang, Xiaolin The Institute of Image Processing and Pattern Recognition The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China The STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven LeuvenB-3001 Belgium

Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR). Currently, the consistency is guaranteed by imposing constraints on coefficients of features, necessitating that the random features on different nodes are identical. However, in many applications, data on different nodes varies significantly on the number or distribution, which calls for adaptive and data-dependent methods that generate different RFs. To tackle the essential difficulty, we propose a new decentralized KRR algorithm that pursues consensus on decision functions, which allows great flexibility and well adapts data on nodes. The convergence is rigorously given and the effectiveness is numerically verified: by capturing the characteristics of the data on each node, while maintaining the same communication costs as other methods, we achieved an average regression accuracy improvement of 25.5% across six real-world data sets. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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Multiple degraded image restoration via degradation history estimation

Multiple degraded image restoration via degradation history ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Minhua Liu Yuanman Li Rongqin Liang Jiaxiang You Xia Li Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University China

Image restoration is a fundamental task in low-level computer vision. Most existing algorithms assume that the input image has a single known degradation type. In reality, images usually contain multiple degradations, making the restoration challenging. Though recent works restore the multiple degraded images, they assume that the degradation history is known. Obviously, such an ideal assumption often does not hold in real applications. This work proposes a novel restoration framework for multiple degraded images via degradation history estimation. Specifically, we first develop a sequential model to estimate the degradation history, including both the degradation operation chain and the corresponding parameters. By resorting to designed self-attention and cross-attention mechanisms, our method can effectively model the correlation of the input image, degradation operation chain, and parameters. Then, we apply our estimation framework for the multiple degraded image restoration, without requiring the degradation history. Experiment results demonstrate much better performance than existing approaches.

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