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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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The Dual-3DCRU Model Based on Hemisphere Asymmetry for EEG Emotion Recognition

The Dual-3DCRU Model Based on Hemisphere Asymmetry for EEG E...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Hao Dong Jian Zhou Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In this paper, we propose an innovative multi scale model called Dual-3DCRU, designed to extract more discriminative feature from both the left and right hemispheres. Byleveraging spatial relationships of electrode locations and the distinct frequency bands present in EEG signals from brain two hemispheres regions, we reconstructed comprehensive three-dimensional frequency space fusion feature representation. The Dual-3DCRU utilizes two different multi scale 3D -CNN to effectively capture both frequency and spatial information embedded in the 3D feature representations. Furthermore, we integrate a Gated Recursive Unit (GRU) to model the temporal dynamics across continuous time periods and extract critical temporal information. Ultimately, the Dual-3DGRU model delivers a holistic feature representation that incorporates time, frequency, and spatial information seamlessly. Our experiments showcase that the Dual-3DCRU model attains exceptional performance on DEAP dataset. Specifically, the accuracy in the Valence dimensionis 94.35%, F1-score is 93.24%, accuracy in the Arousal dimensionis 95.04%, and F1-score is 94.17%.

关键词： Seminars Solid modeling Time-frequency analysis Three-dimensional displays Accuracy Logic gates Brain modeling

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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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A Broadband Circularly Polarized Filtering Patch Antenna with Stacked Structure 12

A Broadband Circularly Polarized Filtering Patch Antenna wit...

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12th IEEE International Conference on Computer Science and Network Technology, ICCSNT 2024

作者： Zhang, Jinping Kholodnyak, Dmitry Huang, Zhixiang Li, Yingsong Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Saint Petersburg Electrotechnical University Department of Microwave Electronics Russia Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350350791

A double-layer stacked patch filtering antenna with broadband circular polarization performance is proposed in this paper. The main patch consists of a corner-Truncated square patch with two shorting pins. The designed two shorting pins are located in the main patch and the upper stacked patch structure are introduced and designed, which can gain two radiation nulls for realizing the filtering effect of the proposed antenna. The circularly polarized (CP) operation is realized by corner-Truncated square patch. In addition, to increase the axial ratio (AR) bandwidth, a rectangular slot is carved on the ground plane. The simulated results indicate that the researched antenna can provide the impedance bandwidth of 20.58% (2.68-3.29 GHz) and AR bandwidth of 9.5% (2.80-3.08 GHz). Moreover, The average realized gain of the simulation is 6.48 dBic over bandwidth. © 2024 IEEE.

关键词： Slot antennas

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SES: Bridging the Gap between Explainability and Prediction of Graph Neural Networks 40

SES: Bridging the Gap between Explainability and Prediction ...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Huang, Zhenhua Li, Kunhao Wang, Shaojie Jia, Zhaohong Zhu, Wentao Mehrotra, Sharad Anhui University Hefei China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Amazon Research Seattle United States University of California Irvine Irvine United States

ISBN: (纸本)9798350317152

Despite the Graph Neural Networks' (GNNs) pro-ficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters typically provide post-hoc explanations disjointed from GNNs' predictions, resulting in misrepresentations. Self-explainable GNNs offer built-in explanations during the training process. However, they cannot exploit the explanatory outcomes to augment prediction performance, and they fail to provide high-quality explanations of node features and require additional processes to generate explainable subgraphs, which is costly. To address the aforementioned limitations, we propose a self-explained and self-supervised graph neural network (SES) to bridge the gap between explainability and prediction. SES comprises two processes: explainable training and enhanced predictive learning. During explainable training, SES employs a global mask generator co-trained with a graph encoder and directly produces crucial structure and feature masks, reducing time consumption and providing node feature and subgraph explanations. In the enhanced predictive learning phase, mask-based positive-negative pairs are constructed utilizing the ex-planations to compute a triplet loss and enhance the node representations by contrastive learning. Extensive experiments demonstrate the superiority of SES on multiple datasets and tasks. SES outperforms baselines on real-world node classification datasets by notable margins of up to 2.59% and achieves state-of-the-art (SOTA) performance in explanation tasks on synthetic datasets with improvements of up to 3.0%. Moreover, SES delivers more coherent explanations on real-world datasets, has a fourfold increase in Fidelity+ score for explanation quality, and demonstrates faster training and expla-nation generating times. To our knowledge, SES is a pioneering GNN to achieve SOTA performance on both explanation and prediction tasks. © 2024 IEEE.

关键词： Classification (of information)

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Optimized Multi-kernel Dictionary Learning and Its Application in Complex Industrial Processes Monitoring 41

Optimized Multi-kernel Dictionary Learning and Its Applicati...

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第41届中国控制会议

作者： Kai Zhong Zhengping Ding Donghui Pan Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information TechnologyAnhui University School of Mathematical Science Anhui University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The data in the chemical process is often complex in structure,and the coupling and correlation between variables are ***,it is difficult for conventional monitoring models to learn the essential characteristics of process data,which greatly affects the performance of the *** at the above problems,this paper proposes an optimized multi-kernel dictionary learning method,which is suitable for the industrial processes with strong nonlinearity and complex data ***,the synthetic kernel fused by multiple kernel functions is used to map the process data to a new high-dimensional feature space to mine the multivariate distribution characteristics of the ***,the grid search method is used to optimize the model parameters in the kernel function for better model ***,the effectiveness and practicability of the proposed method in fault detection are verified on TE benchmark platform.

关键词： Dictionary learning Multi-kernel function Chemical process monitoring

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Analysis of asymptotically tight approximation SER for cooperative NOMA systems

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High Technology Letters 2021年第3期27卷 249-255页

作者： Xu Lei Cai Jing Yao Yijing Zhu Dingcheng Fang Hongyu Li Xiaohui Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui UniversityHefei 230039P.R.China

The closed-form formula derivation of the power domain cooperative non-orthogonal multiple access(NOMA)system is of great significance for further improving the performance of the ***,the system performance formulas of the channel capacity and the paired bit error rate pairwise error probability(PEP)are too complicated,which have increased the difficulty in system performance ***,based on the amplify forward(AF)relay cooperative NOMA model,the signal interference noise ratio(SINR)formulas of the two user nodes are *** the assumption of that,the symbol error rate(SER)of each user is fair,the simplification condition of moment generating function(MGF)with the harmonic mean form is *** with the SER calculation formula of MGF,the system SER asymptotically tight approximation formula with simple structure is derived at high signal-to-noise ratio(SNR).The Monte Carlo simulation results show that,the formula can accurately describe the SER performance of the power domain cooperative NOMA system with the non-ideal successive interference cancellation(SIC)system when SNR is *** the condition of certain total power,the optimal power allocation factor is solved in order to minimize the total system SER.

关键词： non-orthogonal multiple access(NOMA) amplify forward(AF) symbol error rate(SER) moment generating function(MGF)

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A Novel Construction of Two-Dimensional Z-Complementary Array Code Sets Based on Extended Boolean Functions

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7821-7834页

作者： Tian, Liying Song, Xiaoshi Li, Yubo Zhang, Haijun Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Two-dimensional (2-D) array sets with good 2-D correlation properties have received considerable attention in wireless communication systems. This paper focuses on 2-D Z-complementary array code sets (ZCACSs), which have ideal 2-D correlation properties within a rectangular zone around the origin. By developing a theory linking 2-D extended Boolean functions with 2-D arrays, we establish an efficient construction of 2-D ZCACSs. In contrast to previous work, the proposed construction is capable of producing 2-D ZCACSs with new flexible parameters. Constructions of three-dimensional (3-D) and higher-dimensional ZCACSs are further investigated. We remark that 2-D and higher-dimensional complete complementary codes (CCCs) can also be generated from the proposed constructions. © 1967-2012 IEEE.

关键词： Codes (symbols)

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