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Correction to: Singular value decomposition compressed ghost imaging

Applied Physics B 2022年第4期128卷 1-2页

作者： Zhang, Cheng Tang, Jun Zhou, Jiaxuan Wei, Sui Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Key Laboratory of Modern Imaging and Displaying Technology Anhui University Hefei China

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Exploiting Multi-Decision and Deep Refinement for Ultrasound Image Segmentation

Exploiting Multi-Decision and Deep Refinement for Ultrasound...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wenjing Liu Xuanya Li Kai Hu Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Baidu Inc Beijing China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China

In this paper, we propose a novel convolutional neural network (MDR-Net) for ultrasound image segmentation by exploiting multi-decision and deep refinement of the target. Our MDR-Net consists of two main parts, i.e., a multi-decision module (MDM) and a deep refinement module (DRM). Specifically, the MDM effectively addresses the issue of inconspicuous target regions in ultrasound images by combining multi-scale features and multi-receptive field self-attention to enhance the discriminative representation of features and diagnose feature points multiple times. In addition, to alleviate the problem of blurred boundaries and severe speckle noise, the DRM progressively fuses multi-scale features and makes the fused features interact with higher-level features to refine the target details step by step. Finally, we evaluate the proposed method on two publicly available datasets, namely BUSI and UDIAT. We achieve a Dice of 0.8265 and 0.8827 on the two datasets, which are at least 2% and 1.24% higher than other state-of-the-art ultrasound image segmentation methods.

关键词： Deep learning Image segmentation Visualization Ultrasonic imaging Fuses Neural networks Speckle

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A Communication Protocol Identification Method Based on Time-Frequency Domain Combination for Internet of Things

A Communication Protocol Identification Method Based on Time...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Zhiwei Zheng Hui Zhi Kui Tie Xianpeng Wang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University School of Electronic and Information Engineering Anhui University Hefei China Zhongdian Kesiyi Technology (Anhui) Co. Ltd Bengbu China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

At present, Internet of Things (IoT) testing instruments and meters are unable to identify the communication protocol of communication data when all characteristic parameters of communication data are unknown. Regarding this problem, this paper utilizes the K-Nearest Neighbors (KNN) model in machine learning, designs communication protocol identification methods and proposes a communication protocol identification method based on time-frequency domain combination (CPI- TFC). First, we generate time-domain waveforms and frequency-domain waveforms from sampling communication data sequences, and attempt only time-domain identification method and only frequency-domain identification method based on KNN model. Then, we compare the results, design a communication protocol identification method based on the combination of time-domain identification and frequency domain identification, and propose CPI-TFC. Experimental results show that CPI-TFC can achieve higher identification accuracy than only time-domain identification method and only frequency-domain identification method, so it can effectively identify the communication protocol for a IoT communication data. Moreover, CPI- TFC can provide prior information for subsequent configuring parameters in IoT testing instruments, improve the parameter configuration efficiency.

关键词： Meters Time-frequency analysis Protocols Accuracy Simulation Machine learning Nearest neighbor methods Internet of Things Time-domain analysis Testing

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Consensus Disturbance Rejection of Nonlinear Multi-Agent Systems Over Cooperation-Competition Networks

Consensus Disturbance Rejection of Nonlinear Multi-Agent Sys...

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2021 China Automation Congress, CAC 2021

作者： Wang, Qiang He, Wangli Tan, Dayu Zhong, Weimin Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781665426473

This study addresses the consensus problem for nonlinear multi-agent systems (MASs) under the exogenous disturbances over cooperation-competition networks. Firstly, for judging the disturbances produced by the exogenous environment, a disturbance observer-based approach is developed and incorporated into the controller. Secondly, two novel nonlinear control protocols depended on state feedback and output feedback control are constructed to guarantee consensus for MASs over the signed directed graph, respectively. Thirdly, theoretical guarantees on the effectiveness of the proposed controller in steering the system to a bipartite leader-following consensus with exogenous disturbances are derived. Finally, numerical simulations inspired by a real-world physical MAS are provided to illustrate the effectiveness of the proposed controllers. © 2021 IEEE

关键词： Feedback control

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Sparsity-aware Subband Adaptive Filtering in Impulsive Interferences

Sparsity-aware Subband Adaptive Filtering in Impulsive Inter...

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

作者： Zhikai Gong Yingsong Li Baohe Pang Xueyan Li Wudang Xiao Liping Li Nikola Zlatanov College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In this paper, we propose two new subband adaptive filtering (SAF) algorithms based on the cost function of the logistic distance metric cost function and use the proportionate proximal gradient algorithm to exploit the sparsity of the system fully. Compared with prior works, the proposed algorithms can handle many different situations better. Through computer simulations, we prove that the algorithm performs excellently in sparse systems disturbed by impulse noises. The algorithm can be used in areas like system identifications.

关键词： Measurement Adaptive systems Computer simulation Noise Filtering algorithms Cost function Information filters

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Characterization of Plotkin-optimal two-weight codes over finite chain rings and related applications

arXiv

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arXiv 2023年

作者： Li, Shitao Shi, Minjia Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China

Few-weight codes over finite chain rings are associated with combinatorial objects such as strongly regular graphs (SRGs), strongly walk-regular graphs (SWRGs) and finite geometries, and are also widely used in data storage systems and secret sharing schemes. The first objective of this paper is to characterize all possible parameters of Plotkin-optimal two-homogeneous weight regular projective codes over finite chain rings, as well as their weight distributions. We show the existence of codes with these parameters by constructing an infinite family of two-homogeneous weight codes. The parameters of their Gray images have the same weight distribution as that of the two-weight codes of type SU1 in the sense of Calderbank and Kantor (Bull Lond Math Soc 18: 97-122, 1986). Further, we also construct three-homogeneous weight regular projective codes over finite chain rings combined with some known results. Finally, we study applications of our constructed codes in secret sharing schemes and graph theory. In particular, infinite families of SRGs and SWRGs with non-trivial parameters are *** Codes 94B05, 05E30 Copyright © 2023, The Authors. All rights reserved.

关键词： Graph theory

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A CLASSIFICATION OF TWO-DISTANCE-TRANSITIVE CAYLEY GRAPHS OVER THE GENERALIZED QUATERNION GROUPS

arXiv

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arXiv 2025年

作者： Jin, Wei Li, Ping Shan Tan, Li School of Mathematics and Computational Science Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China School of Statistics and Data Science Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China School of Mathematics and Statistics Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China

A non-complete graph is 2-distance-transitive if, for i = 1, 2 and for any two vertex pairs (u1, v1) and (u2, v2) with the same distance i in the graph, there exists an element of the graph automorphism group that maps (u1, v1) to (u2, v2). This is a generalization concept of the classical well-known distance-transitive graphs. In this paper, we completely determine the family of 2-distance-transitive Cayley graphs over the generalized quaternion groups. Copyright © 2025, The Authors. All rights reserved.

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A miniaturized double-sided UWB-MIMO antenna with four-notched band

A miniaturized double-sided UWB-MIMO antenna with four-notch...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

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

A $\boldsymbol{40}\times\boldsymbol{40}\times \boldsymbol{1.6}\mathbf{mm}^{3}$ ultra-wideband MIMO antenna is proposed in this paper. The antenna realizes four-notched band characteristics by embedding different types of slits in the radiating element. The antenna has good UWB performance, with four-band rejection to avoid interference caused by four narrow frequency bands WiMAX(3.3-3.8GHz), WLAN(S.1-S.8GHz), INSAT(6.7-7.3GHz) and ITV 8GHz (8.13-8.38GHz). It achieves better than - 15dB of isolation, and impedance bandwidth from 3.06-15.5GHz.

关键词： Antenna measurements Bandwidth Interference Size measurement Ultra wideband antennas Impedance Antennas

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Variational Clustering and Denoising of Spatial Transcriptomics

Variational Clustering and Denoising of Spatial Transcriptom...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Cuiyuan Zhang, Fa Hu, Kai Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Beijing Institute of Technology School of Medical Technology Beijing China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou China

ISBN: (纸本)9798350337488

Spatial transcriptomics data provides a unique opportunity to investigate both gene expression and spatial structure in tissues at the same time. However, incorporating spatial information to accurately identify spatial domains is difficult due to factors such as high-dimensionality, sparsity, noise, and dropout events. To address these issues, we introduce vGraphST, a novel graph-based deep learning approach tailored for spatial transcriptomics data. Our method combines auto-encoder and contrastive learning techniques to process high-dimensional data and generate meaningful low-dimensional embeddings. Additionally, we use continuous distributions instead of discrete values in both the latent space and the denoised gene expression space. Specifically, Gaussian distributions are used to model the latent space, while zero-inflated Poisson distributions are used to model the denoised gene expression space. Experimental results demonstrate the effectiveness of vGraphST in accurately representing and analyzing spatial transcriptomics data. When compared to other methods using the DLPFC dataset, vGraphST achieves an average Adjusted Rand Index (ARI) of 0.58, demonstrating its superiority in segmenting spatial domains and recognizing biologically relevant spatiotemporal patterns. © 2023 IEEE.

关键词： clustering contrastive learning denoising spatial transcriptomics variational auto-encoder

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

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

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International Conference on Data Engineering

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

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

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.

关键词： Training Bridges Accuracy Reliability engineering Data engineering Graph neural networks Generators

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