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2021 International Conference on Signal processing and Communication Technology, SPCT 2021

作者： Wang, Liang Hou, Yifeng Wang, Kuikui Wei, Chunming Guangxi Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou Guangxi543003 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

Aiming at the problems of my country's current camellia fruit picking mainly relying on manual picking, large labor and low efficiency, the development and promotion of camellia fruit picking equipment and technology are discussed, and technical analysis is carried out for the design of camellia fruit picking robots. This article analyzes the characteristics and existing problems of camellia fruit picking, and analyzes the four major components of the picking robot: "end effector", "manipulator motion control system", "visual recognition positioning system"and "driving walking device". In summary, summarize the problems that have been solved, the results achieved, and the difficulties that still exist, so as to provide enlightenment for the subsequent exploration of the subject research of "camellia fruit picking robot". © 2022 SPIE.

关键词： Fruits

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Dual-notch Ultra-wideband Bandpass Filter Based On Folded Dual-mode Resonator

Dual-notch Ultra-wideband Bandpass Filter Based On Folded Du...

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

作者： Li, Qingqing Wu, Xianliang Huang, Zhixiang Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781665452366

Aiming at the problem that ultra-wideband systems are easily interfered by narrowband signals, In this paper, a novel ultra-wideband (UWB) bandpass filter with dual-notch characteristics. This filter is mainly composed of Composition of a multimode resonator (MMR)and a folded dual-mode step-impedance resonator. Paralleling T-shaped SIR stubs in the basic MMR structure realizes the introduction of transmission zeros within the passband. At the same time, a pair of high-impedance stubs are also loaded to extend the upper stopband of the filter, and a folded dual-mode stepped impedance resonator is coupled below the filter to generate dual-notch frequencies. Through the electromagnetic simulation software HFSS for simulation optimization, the simulation results show that the passband of the filter is 3.4211.21GHz and its notch frequencies are 8.8GHz and 9.5GHz respectively. Insertion loss below 0.5dB and stopband up to 20GHz (S21 © 2022 IEEE.

关键词： Electromagnetic simulation

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A preconditioned iteration method for solving saddle point problems

arXiv

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arXiv 2024年

作者： Zhang, Juan Luo, Yiyi Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University China

This paper introduces a preconditioned method designed to comprehensively address the saddle point system with the aim of improving convergence efficiency. In the preprocessor construction phase, a technical approach for solving the approximate inverse matrix of sparse matrices is presented. The effectiveness of the proposed method is demonstrated through numerical examples, emphasizing its efficacy in approximating the inverse matrix. Furthermore, the preprocessing technology includes a low-rank processing step, effectively reducing algorithmic complexity. Numerical experiments validate the effectiveness and feasibility of PSLR-GMRES in solving the saddle point system. © 2024, CC BY.

关键词： Iterative methods

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BFP-Net: Boundary Feature Pyramid for Medical Image Segmentation

BFP-Net: Boundary Feature Pyramid for Medical Image Segmenta...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Min, Shiquan Zhang, Xiang Wang, Shunfang Yunnan University School of Information Science and Engineering Kunming650504 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650504 China

ISBN: (纸本)9798350337488

In this paper, we propose a novel method, namely boundary feature pyramid network (BFP-Net), which can effectively segment targets with blurred boundaries. Specifically, we first propose a global feature fusion module (GFFM) at the top of BFP-Net to fuse feature maps within different scales. It can learn more global region localization of the target and help the learning of boundaries more effectively. Then, we propose a series of boundary enhancement modules (BEMs) at the decoder to effectively extract and integrate boundary information during the upsampling process, thereby enhancing the ability to capture fine details (such as the boundaries). Furthermore, we introduce a boundary-enhanced composite loss function to effectively segment both the regions and their boundaries within different scales. Finally, extensive experiments on two widely-used datasets demonstrate that BFP-Net is more effective in fusing contextual information and guiding feature map boundaries compared to previous competitive methods. © 2023 IEEE.

关键词： Boundary enhancement Feature pyramid Global feature fusion Medical images Segmentation

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SERGCN: Graph Convolutional Neural Networks with Skip-Edge Residual Connection for Node Classification Tasks 6

SERGCN: Graph Convolutional Neural Networks with Skip-Edge R...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Qin, Aqian He, Guoqing Wang, Huiwei College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Three Gorges University Key Laboratory of Intelligent Information Processing Chongqing404100 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798350353594

Graph Convolutional Networks (GCNs) have been widely used for node categorization, graph embedding, and link prediction, etc. Two main challenges regarding developing deep GCNs for node classifying are overfitting and oversmoothing. The main causes of oversmoothing are overfitting and training barriers, which is directly caused by gradient disappearance. To resolve this issue, we propose the Skip-Edge Residual Graph Con-volution Network (SERGCN), which extracts a certain proportion of edge information at each layer in the novel way and integrates the initial information and the information of the previous layer in the convolution process, so as to prevent and slow down the gradient disappearance and further oversmoothing. Based on extensive experiments and real-world data sets used in several benchmarks, our proposed solution incorporates the benefits of a deep architecture while equaling or even exceeding the accuracy of existing approaches. © 2024 IEEE.

关键词： Convolutional neural networks

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Video Frame Interpolation Based on Lightweight Convolutional Unit and Three-scale Encoder 23

Video Frame Interpolation Based on Lightweight Convolutional...

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15th International Conference on Digital Image processing, ICDIP 2023

作者： Liu, Weijing Yang, Xiaohui Feng, Zhiquan Xu, Tao Guo, Qingbei School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9798400708237

Video frame interpolation (VFI) achieves temporal super-resolution by synthesizing intermediate frame between two original adjacent frames. This paper proposes a lightweight VFI network based on lightweight convolutional unit and three-scale encoder. We first introduce a three-scale encoding-decoding structure with two-level attention cascades to represent the multi-scale motion information. Then, recurrent convolutional layer (RCL) and residual operation are adopted to design the recurrent residual convolutional unit (RRCU) to refine the three-scale structure. Finally, we propose a lightweight unit by combining the depth separable convolution and RRCU, and introduce the local lightweight idea to reduce the model parameters. Experimental results show that the proposed method achieves a better performance against the state-of-the-art methods with fewer parameters. © 2023 ACM.

关键词： Convolution

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Deep Learning-based AP Selection Method in Software-defined Networks 5

Deep Learning-based AP Selection Method in Software-defined ...

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

作者： Duan, Miaomiao Zhi, Hui Yang, Lixia Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China School of Electronic and Information Engineering Anhui University Hefei230601 China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

In the Software Defined Network (SDN), the terminal has been connected to the network in the process when an access point failure or damage, if the access point is not properly selected, it will lead to a decline in the quality of service applications. To address this problem, most traditional AP selection methods use access point selection based on received signal strength without take into account the channel and channel capacity of the access point access, while this paper uses Deep Neural Networks (DNN)-based access point (AP) selection method and considers the channel and channel capacity as part of the parameters. In the SDN controller, the received signal strength, throughput, number of connected devices, channel, and the channel capacity and access point performance quality are used as the input and label of the DNN, respectively. The analysis and simulation results show that the algorithm can select AP with better performance quality compared with the traditional AP selection method based on received signal strength, and the correct rate is significantly improved by 5% when compared with the AP selection method based on feedforward neural network. Meanwhile, the DNN-based access point selection method in SDN achieves flexible control of network traffic and balances the load of access points in the network. © 2022 IEEE.

关键词： Deep neural networks

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Comments and extensions on "State-equivalent form and minimum-order compensator design for rectangular descriptor systems"

arXiv

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arXiv 2025年

作者： Shi, Shuo Zhang, Juan Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China

This technical note presents a counterexample showing that the equivalence conditions proposed by Geng et al. (IEEE Trans. Automat. Control, 2024), which use a minimum-order compensator (MOC) to achieve desired designs, including generalized regularity or both generalized regularity and free of impulse, are sufficient but not necessary. Furthermore, revised equivalence conditions are introduced, along with an equivalence condition ensuring the closed-loop system remains generalized regular, impulse-free, and stable using MOC. Additionally, it is shown that output feedback can replace the MOC, achieving the same design without increasing dimensionality. These findings are validated through a circuit *** Codes 93B05, 93B52, 15A03 Copyright © 2025, The Authors. All rights reserved.

关键词： Feedback

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Graph Spatial-Spectral Deep Subspace Clustering for Hyperspectral Image Classification 2nd

Graph Spatial-Spectral Deep Subspace Clustering for Hyperspe...

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2nd International Conference on Image, Vision and intelligent Systems, ICIVIS 2022

作者： Han, Tianhao Dong, Jiwen Niu, Sijie Yu, Wenyue Gao, Xizhan School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789819909223

Deep subspace clustering (DSC) has become a research hotspot and achieved considerable success in unsupervised hyperspectral image (HSI) classification domain. However, previous researches seldom consider global spatial-spectral information, which leads to an unsatisfactory performance when processing HSI with background (can be seen as a type of noise). In this work, we propose a Graph Spatial-Spectral Deep Subspace Clustering (GS2DSC) method which can make full use of the spatial-spectral information of HSI data. Specifically, a graph attention auto-encoder is utilized to extract the graph-structed features of bands and the spectral information. Then, we employ a fusion strategy to fuse the band-level global spatial-spectral features and the patch-level local spatial-spectral features into joint features to enhance the spatial-spectral information. Our proposed method alleviates the bad influence of the background information and produce an informative affinity matrix. The experimental results on two classic HSI datasets show that the overall accuracy of our approach is improved by 5.31% and 3.05% respectively compared with other DSC-based methods, which demonstrates that the structure can enhance the spatial-spectral information and perform better classification results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Efficient Parameter Learning of Bayesian Network with Latent Variables from High-Dimensional Data 5

Efficient Parameter Learning of Bayesian Network with Latent...

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5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Wu, Xinran Chen, Xiang Yue, Kun Yunnan University School of Information Science and Engineering Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9798350332674

Bayesian network with latent variables (BNLV) plays an important role in the representation of dependence relations and inference of uncertain knowledge with unobserved variables. The variables with large cardinalities in high-dimensional data make it challenging to efficiently learn the large-scaled probability parameters as the conditional probability distributions (CPDs) of BNLV. In this paper, we first propose the multinomial parameter network to parameterize the CPDs w.r.t. latent variables. Then, we extend the M-step of the classic EM algorithm and give the efficient algorithm for parameter learning of BNLV. Experimental results show that our proposed method outperforms some state-of-the-art competitors. © 2023 IEEE.

关键词： Bayesian networks

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