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

作者： Guo, Chen. 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 inability to reconstruct the frequency and the pattern of the traditional monopole antenna, an ultra-wideband monopole antenna with frequency reconstruction and pattern reconstruction is proposed. The antenna consists of a monopole antenna and a metasurface with a distance H1 between them. Using HFSS simulation software to simulate, the size of the monopole antenna is 30mm×30mm×1.6mm, and the center frequency is 4.3 GHz. The simulation results show that the impedance bandwidth of the antenna when S11 is less than -10 dB is 3.21-13.64 GHz. In this design, the antenna widens the bandwidth while realizing the frequency reconstruction and pattern reconstruction, and basically does not affect other performances. The simulation results are basically consistent with the measured results, indicating that the antenna has good gain and radiation characteristics in the working frequency band, and can be widely used in ultra-wideband communication systems. © 2022 IEEE.

关键词： Monopole antennas

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A Fast Clustering Based Evolutionary Algorithm for Super-Large-Scale Sparse Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1048-1063页

作者： Ye Tian Yuandong Feng Xingyi Zhang Changyin Sun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China School of Computer Science and Technology Anhui UniversityHefei 230601China School of Artificial Intelligence Anhui UniversityHefei 230601China School of Automation Southeast UniversityNanjing 210096China

During the last three decades,evolutionary algorithms(EAs)have shown superiority in solving complex optimization problems,especially those with multiple objectives and non-differentiable ***,due to the stochastic search strategies,the performance of most EAs deteriorates drastically when handling a large number of decision *** tackle the curse of dimensionality,this work proposes an efficient EA for solving super-large-scale multi-objective optimization problems with sparse optimal *** proposed algorithm estimates the sparse distribution of optimal solutions by optimizing a binary vector for each solution,and provides a fast clustering method to highly reduce the dimensionality of the search *** importantly,all the operations related to the decision variables only contain several matrix calculations,which can be directly accelerated by *** existing EAs are capable of handling fewer than 10000 real variables,the proposed algorithm is verified to be effective in handling 1000000 real ***,since the proposed algorithm handles the large number of variables via accelerated matrix calculations,its runtime can be reduced to less than 10%of the runtime of existing EAs.

关键词： Evolutionary computation fast clustering sparse multi-objective optimization super-large-scale optimization

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A novel miniaturized l-band filter with great stopband characteristics using interdigitated coupled lines CRLH-TL structure

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Progress In Electromagnetics Research C 2021年 114卷 57-67页

作者： Wang, Peng Duan, Kaiyue Li, Minquan Zhang, Man Jin, Baokun Anqing Normal University Anqing246011 China Key Lab of Ministry of Education of Intelligent Computing & Signal Processing Anhui University Hefei230601 China

This paper proposes a novel bandpass filter for L-band based on CRLH TL, which is mainly formed by coupling a high-pass characteristic module with a low-pass characteristic module in a cascade. The high-pass module consists of an interdigitated coupled line and a grounding via, owning to its singular characteristics, which the miniaturization is realized. The low-pass module is composed of a C-type resonator with high-low impedance lines, which can realize great sideband attenuation characteristics. To further improve its out-of-band rejection characteristics, a complementary split-ring resonator (CSRR) defective ground structure with single-pole attenuation characteristics is loaded, and a transmission zero is introduced at 2.5f0 out-of-band. The test results are in great agreement with the simulation ones, and the dimensions are only 0.20λg * 0.22λg. Compared with other similar types, the filter proposed in this paper has miniaturization, great passband selection characteristics, stopband characteristics, and the advantage of low insertion loss. © 2021, Electromagnetics Academy. All rights reserved.

关键词： Bandpass filters

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Exploring Trial-and-Error in Deep Learning:Initial Application to Isotope Detection in Mass Spectrometry

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Big Data Mining and Analytics 2024年第4期7卷 1251-1261页

作者： Qihong Jiao Yuxiao Wang Yongshuai Wang Shiwei Sun Xuefeng Cui School of Computer Science and Technology Shandong UniversityQingdao 266237China Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China

Mass spectrometry plays a crucial role in biomedicine by detecting isotopes,contributing significantly to research,diagnostics,and therapy *** study introduces IsoFusion,a deep learning model designed to address isotope detection in raw mass *** than directly applying convolutional layers to all signal and noise peaks,IsoFusion employs a trial-and-error ***,it investigates all potential charge states(trials)and collects signal peaks around expected m/z values for each ***,convolutional layers extract features from each trial,which are fused to identify the correct ***,the reparameterization trick predicts isotope features based on this correct trial.A key advantage of IsoFusion is shared model parameters across all trials,enhancing feature learning for less common charge states using data from prevalent *** results show a significant accuracy improvement for charge state 5,reaching 99.42%,compared to DeepIso’s 43.36%.Moreover,IsoFusion achieves a 97.33%detection accuracy for isotopes,with 2.4%of detected isotopes previously unidentified by four commonly used methods.

关键词： Liquid Chromatography-Mass Spectrometry(LC-MS) isotope detection retention time prediction charge state prediction deep learning

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Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models 38

Lumen: Unleashing Versatile Vision-Centric Capabilities of L...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Jiao, Yang Chen, Shaoxiang Jie, Zequn Chen, Jingjing Ma, Lin 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 Meituan China

Large Multimodal Model (LMM) is a hot research topic in the computer vision area and has also demonstrated remarkable potential across multiple disciplinary fields. A recent trend is to further extend and enhance the perception capabilities of LMMs. The current methods follow the paradigm of adapting the visual task outputs to language-oriented formats. This adaptation leads to the convenient development of such LMMs with minimal modifications, however, it overlooks the inductive biases within diverse visual tasks and hinders the learning of perception capabilities. To address this issue, we propose a novel LMM architecture named Lumen, which decouples the learning of perception capabilities into task-agnostic and task-specific stages. Firstly, Lumen promotes fine-grained vision-language concept alignment, which is the fundamental capability for various visual tasks. Thus the output of the task-agnostic stage is a shared representation for all vision-centric tasks we address in this paper. Afterward, the task-specific decoding is carried out by flexibly routing the shared representation to lightweight task decoders with negligible training efforts. Comprehensive experimental results on a series of vision-centric and VQA benchmarks indicate that our Lumen model not only achieves or surpasses the performance of existing LMM-based approaches in a range of vision-centric tasks while maintaining general visual understanding and instruction following capabilities. © 2024 Neural information processing systems foundation. All rights reserved.

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Multi-scale Cascaded Large-Model for Whole-body ROI Segmentation

arXiv

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arXiv 2024年

作者： Hao, Rui Tan, Dayu Su, Yansen Zheng, Chunhou Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

Organs-at-risk segmentation is critical for ensuring the safety and precision of radiotherapy and surgical procedures. However, existing methods for organs-at-risk image segmentation often suffer from uncertainties and biases in target selection, as well as insufficient model validation experiments, limiting their generality and reliability in practical applications. To address these issues, we propose an innovative cascaded network architecture called the Multi-scale Cascaded Fusing Network (MCFNet), which effectively captures complex multi-scale and multi-resolution features. MCFNet includes a Sharp Extraction Backbone and a Flexible Connection Backbone, which respectively enhance feature extraction in the downsampling and skip-connection stages. This design not only improves segmentation accuracy but also ensures computational efficiency, enabling precise detail capture even in low-resolution images. We conduct experiments using the A6000 GPU on diverse datasets from 671 patients, including 36,131 image-mask pairs across 10 different datasets. MCFNet demonstrates strong robustness, performing consistently well across 10 datasets. Additionally, MCFNet exhibits excellent generalizability, maintaining high accuracy in different clinical scenarios. We also introduce an adaptive loss aggregation strategy to further optimize the model training process, improving both segmentation accuracy and efficiency. Through extensive validation, MCFNet demonstrates superior performance compared to existing methods, providing more reliable image-guided support. Our solution aims to significantly improve the precision and safety of radiotherapy and surgical procedures, advancing personalized treatment. The code has been made available on GitHub: https://***/Henry991115/MCFNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Radiotherapy

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Near-field Source DOA Estimation Algorithm Based on De-impact Function under Impulse Noise 7

Near-field Source DOA Estimation Algorithm Based on De-impac...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Xu, Xingchao Pang, Baohe Li, Yingsong Li, Liping Zou, Dingcheng Zlatanov, Nikola 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 Innopolis University Faculty of Computer and Engineering Sciences Innopolis Russia

ISBN: (纸本)9798350384437

In addressing the issue of localizing near-field source signals in the presence of impulse noise, this manuscript introduces a novel de-impact function designed for preprocessing received signals to alleviate the interference caused by impulse noise. Simulation experiments verify the effectiveness of the proposed de-impact function in effectively suppressing impulse noise. Furthermore, to augment estimation precision, a novel algorithm for near-field source localization is proposed. Building upon the framework of correlation entropy theory, a new correntropy covariance matrix is formulated to replace the original signal covariance matrix for signal processing. Through a succession of low-bias approximations between near-field and far-field scenarios, the algorithm converts the original 2-D search into 1-D operations. Additionally, the conventional spectral peak search is replaced by polynomial solving to further mitigate the computational complexity of the algorithm. © 2024 IEEE.

关键词： Impulse noise

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High Degree of Freedom Low Coupling Sparse MIMO Array 7

High Degree of Freedom Low Coupling Sparse MIMO Array

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Yao, Yichang Pang, Baohe Li, Yingsong Yu, Canping Xiao, Wudang Li, Liping Zlatanov, Nikola 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 Innopolis University Faculty of Computer and Engineering Sciences Innopolis Russia

ISBN: (纸本)9798350384437

To achieve high precision direction-of-arrival estimation of MIMO arrays in highly coupled environments, based on the difference and cooperative array principles, the uniform array fitted with 5 base layer array and the uniform array fitted with 6 base layer array were used as transmitting arrays, and with the addition of 1 base layer layer sparse array as receiving array, two sparse multi-in multiple-out arrays were designed for single-base radar, which have the characteristics of low coupling of transmitting array and high freedom of receiving array. © 2024 IEEE.

关键词： Degrees of freedom (mechanics)

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Low Coupling Sparse Array Based on Uniform Linear Array Fitting Method 7

Low Coupling Sparse Array Based on Uniform Linear Array Fitt...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Yao, Yichang Li, Yingsong Li, Liping Zlatanov, Nikola 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 Innopolis University Faculty of Computer and Engineering Sciences Innopolis Russia

ISBN: (纸本)9798350384437

In order to solve the problem that traditional sparse arrays only emphasize high degrees of freedom and ignore low coupling, uniform array fitting method can design arrays with low coupling. However, the existing array coupling based on uniform array fitting method still has room for decline, and the value of weight function is not the minimum, so the performance of array DOA estimation needs to be further optimized. Therefore, based on the uniform matrix fitting method, a uniform matrix fitting 6-base-layer array is designed. By extending the spacing of each subarray of the uniform subarray in the base layer, the number of small array element spacing pairs in the differential cooperative array is reduced, and the coupling between the elements of the whole sparse array is reduced as a whole. The performance of DOA estimation of the designed array is simulated and analyzed by using spatial smoothing multiple signal classification algorithm. © 2024 IEEE.

关键词： Multiple signal classification

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DisenGCD: a meta multigraph-assisted disentangled graph learning framework for cognitive diagnosis 24

DisenGCD: a meta multigraph-assisted disentangled graph lear...

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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Shangshang Yang Mingyang Chen Ziwen Wang Xiaoshan Yu Panpan Zhang Haiping Ma Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui P. R. China and Anhui Province Key Laboratory of Intelligent Computing and Applications Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui P. R. China and Department of Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui P. R. China

ISBN: (纸本)9798331314385

Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poorly, failing to provide high robustness against noise in students' interactions. Besides, lower-order exercise latent representations obtained in shallow layers are not well explored when learning the student representation. To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency graphs, respectively. Specifically, the latter two graphs are first disentangled from the interaction graph. Then, the student representation is learned from the interaction graph by a devised meta multigraph learning module; multiple learnable propagation paths in this module enable current student latent representation to access lower-order exercise latent representations, which can lead to more effective nad robust student representations learned; the exercise and concept representations are learned on the relation and dependency graphs by graph attention modules. Finally, a novel diagnostic function is devised to handle three disentangled representations for prediction. Experiments show better performance and robustness of DisenGCD than state-of-the-art CD methods and demonstrate the effectiveness of the disentangled learning framework and meta multigraph module. The source code is available at https://***/BIMK/intelligent-Education/tree/main/DisenGCD.

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