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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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ϵ-π: A Nonparametric Model for Neural Power Spectra Decomposition

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IEEE Journal of Biomedical and Health Informatics 2024年第5期28卷 2624-2635页

作者： Hu, Shiang Zhang, Zhihao Zhang, Xiaochu Wu, Xiaopei Valdes-Sosa, Pedro A. Anhui University Anhui Provincial Key Lab of Multimodal Cognitive Computation Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China University of Science and Technology of China School of Humanities and Social Science Department of Psychology Hefei230026 China University of Electronic Science and Technology of China MOE Key Lab for Neuroinformation School of Life Science and Technology Chengdu611731 China

The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks;the sleep EEG revealed that produced more distinguishable AC exponents and improved the sleep state classification accuracy;the iEEG showed that approached the clinical findings in peak discovery. Overall, offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases. © 2013 IEEE.

关键词： Electroencephalography

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Enhanced Symplectic Geometric Mode Decomposition via K-means Clustering for LFM signal Denoising

Enhanced Symplectic Geometric Mode Decomposition via K-means...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Jincheng Ni Jin Zhao Xianliang Wu Ping Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education AnHui University Hefei China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

This article presents a Symplectic Geometric Mode Decomposition (SGMD) method incorporating K-means clustering, coupled with wavelet denoising, for mitigating noise in Linear Frequency Modulation (LFM) signals. This method substitutes the original component recombination approach in SGMD with the K-means clustering algorithm, thus rendering the signal grouping more logically coherent. In the experiment, we compare the method proposed in this article with other denoising methods. The experimental findings demonstrate substantial advantages of the proposed method in mitigating noise in LFM signals, thereby enhancing both signal-to-Noise Ratio (SNR) and signal quality. The research outcomes underscore the feasibility and substantial potential of this method in mitigating noise in signals. In subsequent studies, additional investigation into its applicability across diverse domains can be pursued alongside the refinement of clustering and denoising algorithms.

关键词： Electric potential Chirp modulation Noise reduction Refining Clustering algorithms Rendering (computer graphics) Information and communication technology

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Research on signal Denoising Method Based on Symplectic Geometric Mode Decomposition and Savitzky-Golay Filtering

Research on Signal Denoising Method Based on Symplectic Geom...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Xuelong Xu Jin Zhao Bo Wu Xianliang Wu Jincheng Ni Ping Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Anhui University Hefei China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

The paper proposes a noise reduction algorithm based on symplectic Geometric Mode Decomposition (SGMD) and Savitzky-Golay (SG)filtering to address the issue of noise interference during signal transmission. Firstly, the noisy signal is decomposed using symplectic geometric mode decomposition to obtain several symplectic geometric components. The component with less noise is then selected and superimposed based on its energy entropy. Finally, the reconstructed signal undergoes denoising through SG filtering. The proposed method is applied to conduct comparison experiments on noise reduction for linear frequency modulation signals and multicomponent periodic signals. The results demonstrate a significant noise reduction effect of the proposed method, which holds great significance for signal analysis and research on noise reduction.

关键词： Smoothing methods Noise reduction Noise Interference Information filters Robustness signal denoising

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Broadband Circularly Polarized Hybrid Metasurface Antenna for Wearable Devices

Broadband Circularly Polarized Hybrid Metasurface Antenna fo...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Zhiyi Liu Yufa Sun Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University HeFei China

A 38×38×4mm3 wideband circularly polarized (CP) antenna with Hybrid Metasurface (MTS) for wearable devices is proposed. The proposed antenna achieves CP radiation by cutting corners on a square microstrip patch. A hybrid MTS is placed above a square patch with cut corners, achieving broadband radiation performance. The working mechanism of hybrid MTS is analyzed by characteristic mode analysis (CMA). The simulated -10dB bandwidth is 40.1% (4.61-6.82 GHz) and the 3dB axial ratio bandwidth is 25.2% (5.16-6.55 GHz) with a peak gain of 8.1 dBi. In addition, the antenna has a front-to-back ratio of approximately 20 dB, which makes it suitable for wearable applications.

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A Wideband GCPW-SIW Fed Slots Antenna for Millimeter-Wave Applications

A Wideband GCPW-SIW Fed Slots Antenna for Millimeter-Wave Ap...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Penghao Run Yufa Sun Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

A wideband GCPW-SIW fed slots antenna in millimeter wave band is designed in this paper. The structure is composed of four inclined slots which are placed on the one side of the SIW with three shorting vias and fed by GCPW transmission line. The simulated results show that the operating frequency band of the proposed antenna is 24.15-27.59GHz (13.23%), the antenna has a stable gain with a maximum value of 9.67dBi and high radiation efficiency.

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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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Reversible Data Hiding for 3D Mesh Model Based on Block Modulus Encryption and Multi-MSB Prediction 10th

Reversible Data Hiding for 3D Mesh Model Based on Block Modu...

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10th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Fu, Zihao Gao, Yang Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819787456

Reversible data hiding in encrypted domain (RDH-ED) fortifies data security and privacy safeguards while upholding the original data’s integrity and accessibility. Current research on RDH-ED focuses on 2D images, while research on 3D mesh models is still immature. This paper introduces an RDH-ED method using block modulus encryption and multi-MSB prediction. Initially, the original mesh model is partitioned into non-overlapping subblocks of equal size, and then the vertices in each subblock are encrypted with the same key for additive modulus encryption, ensuring that the spatial correlation present in the original mesh subblocks remains preserved within the encrypted subblocks. Subsequently, the subblocks are disrupted one by one using the 3D Arnold Transform to enhance security. The vertices in each embeddable subblock are divided into a reference vertex and several embeddable vertices, where the multi-MSB prediction strategy is employed to allocate embedding room for each embeddable vertex. Finally, the secret information is embedded into the allocated room. Since the proposed method almost completely preserves the spatial correlation within each subblock, the achieved embedding rate surpasses that of all previous outstanding methods that rely on vacating room after encryption (VRAE). The experimental findings demonstrate that the proposed approach achieves an average embedding rate of 45.55 bits per vertex (bpv), surpassing the state-of-the-art method that achieves 25.63 bpv. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mesh generation

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A U-shape Compensation Structure for Differential-to-Common Mode Conversion Noise Suppression in 90°-Bended differential Line 13

A U-shape Compensation Structure for Differential-to-Common ...

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13th Asia-Pacific International Symposium on Electromagnetic Compatibility and Technical Exhibition, APEMC 2022

作者： He, Zhikang Zhu, Haoran Anhui Province Anhui University Information Materials and Intelligent Sensing Laboratory Hefei230039 China Anhui University Ministry of Education Key Lab of Intelligent Computing and Signal Processing Hefei230039 China

ISBN: (数字)9781665416719

ISBN: (纸本)9781665416719

a U-shaped structure, based on the equivalent lumped circuit of differential transmission line, is proposed to suppress the noise of differential-common-mode conversion. With the equivalent lumped circuit, the cause of mode conversion noise is analyzed. The U-shaped structure has reached the suppression level of 24.31 dB from DC to 10 GHz according to the simulated result. © 2022 IEEE.

关键词： Electric lines

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A Miniaturized Microwave Limiter by Using T-Type LC Network Topology with GaAs Technology 13

A Miniaturized Microwave Limiter by Using T-Type LC Network ...

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13th Asia-Pacific International Symposium on Electromagnetic Compatibility and Technical Exhibition, APEMC 2022

作者： Wang, Jun Zhu, Haoran Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230039 China Anhui University Ministry of Education Key Lab of Intelligent Computing & Signal Processing Hefei230039 China

ISBN: (数字)9781665416719

ISBN: (纸本)9781665416719

A miniaturized ultra-wideband microwave limiter with low insertion loss is presented in this paper. This limiter adopts a three-stage antiparallel diode structure. A T-type LC network topology consisting of two spiral inductors and a parallel grounded metal-insulator-metal (MIM) capacitor is utilized to replace the traditional λ/4 transmission line in series between the limiting circuits. Compared with the traditional topology, the proposed limiter has the advantages of miniaturization and ultra-wideband. The area of the presented microwave limiter chip is only 1.25×0.75mm2. The simulation results show that the limiter can work in 0.05-18GHz. Insertion loss is less than 0.65dB at 18GHz. The output power can be maintained below 15dBm with a maximum power input of 40dBm continuous wave. © 2022 IEEE.

关键词： Network topology

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