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Diffraction Separation and Imaging Using Multidirectional Wavefield Low-Rank Approximation

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Chuang Li Yibo Hou Shixuan Jia Zhaoqi Gao Feipeng Li Zhen Li Jinghuai Gao Rongrong Wang Zhiguo Huang Ling Qian School of Information and Communications Engineering and the National Engineering Research Center for Offshore Oil and Gas Exploration Xi’an Jiaotong University Xi’an Shaanxi China China Mobile (Suzhou) Software Technology Company Ltd. Suzhou China National Laboratory of Radar Signal Processing Xidian University Xi’an China

Low-rank approximation (LRA) is a powerful technique for seismic diffraction separation and imaging, providing higher-resolution images of subsurface discontinuities compared to traditional reflection imaging. However, in complex wavefields where reflections lack distinct low-rank characteristics, diffractions and reflections can overlap within the same eigenimages, making traditional LRA less effective for separation. To address this limitation, we propose a diffraction separation and imaging method based on multidirectional wavefield LRA (MDWLRA). The MDWLRA method employs multidirectional wavefield decomposition (MDWD) to divide complex wavefields into angular slices with similar dip angles. These slices are classified as either diffraction slices or reflection slices, with the latter containing mostly reflections and some residual diffractions. LRA is then applied to the reflection slices to separate the remaining diffractions from reflections. By reducing wavefield complexity using MDWD, reflections in the reflection slices exhibit clearer low-rank characteristics than those in the full wavefields, allowing for more effective separation using LRA. Numerical tests on synthetic data from the modified Sigsbee2A model and field data demonstrate that the MDWLRA method outperforms traditional methods, achieving more accurate separation with fewer leakages than traditional LRA while also improving diffraction fidelity compared to the curvelet transform-based method.

关键词： Diffraction Reflection Imaging Synthetic data Training Data models Computed tomography Accuracy Vectors Time-frequency analysis

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Data gravitation-based three-way sampling method for imbalanced data classification

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Information sciences 2025年 718卷

作者： Yuanting Yan Weiqing Wang Yingao Ma Yang Tian Peng Zhou Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui Province 230601 PR China School of Computer Science and Technology Anhui University Hefei Anhui Province 230601 PR China

Data resampling is widely used for addressing the class imbalance problem, with numerous methods developed over recent decades. Among these, the K-nearest neighbor (KNN) technique is widely adopted for leveraging local neighborhood information in rebalancing imbalanced data. However, KNN struggles to accurately characterize local data distribution, as samples with identical KNNs may also exhibit divergent local structures, which may further hamper imbalanced learning performance. We propose a hybrid sampling method called data gravitation-based three-way sampling (DGS) to improve classification performance on imbalanced data. DGS introduces the concept of data gravitation into KNN to evaluate the local importance of samples, and combines it with three-way decision theory to selectively oversample minority-class samples and remove risky majority-class samples. DGS introduces a cost-sensitive sampling mechanism and adaptive threshold estimation using a Gaussian-based model, reducing reliance on manually tuned parameters. Extensive experiments on 38 datasets and seven classifiers demonstrate the superiority of DGS over 18 resampling methods across three evaluation metrics. Our code is available at https://***/ytyancp/DGS .

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MWENet: Multi-Wavelet Enhanced Network for seasonal-trend analysis in long-term forecasting

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Neurocomputing 2025年

作者： Zhiwei Zhang Qin Jiwei Dezhi Sun Zihao Zhang Jing Ma XueFeng Feng Huiguo Zhang School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Research Institute of Measurement and Testing Urumqi China

Deep neural networks have recently been applied to long-term time series forecasting (LTSF) tasks, aiming to capture dynamic temporal features from historical data for more accurate predictions. However, time series data inevitably contain noise, influencing prediction accuracy. Recent denoising studies suggest that high-frequency components in the frequency domain are the source of noise, and these studies use filters to remove them. While these filters effectively suppress noise, they also unintentionally discard valuable high-frequency information. To tackle this problem, We propose MWENet , a Multi-Wavelet Enhanced Network for seasonal-trend modeling in long-term forecasting. Specifically, we introduce an Enhanced Spectral Denoising module that combines Fourier Transform and Discrete Wavelet Transform (DWT) for joint denoising. This module achieves effective denoising through adaptive wavelet decomposition with multiple wavelet bases. Additionally, this work presents an Enhanced LSTM module, which integrates exponential gating units into LSTM to capture complex patterns and enhance LTSF *** evaluations on seven real-world datasets for long-term forecasting tasks demonstrate that MWENet consistently achieves state-of-the-art performance, ranking first in 51 out of 70 evaluation cases and second in another 17. These results surpass a wide range of competitive baselines—including Transformer-based, MLP-based, RNN-based, and state-space models—highlighting the robustness and generalization capacity of our approach in capturing complex temporal dynamics over extended horizons.

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A Imaging Passive Localization Method for Wideband signal Based on SAR 6

A Imaging Passive Localization Method for Wideband Signal Ba...

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6th Asia-Pacific Conference on Synthetic Aperture radar, APSAR 2019

作者： Wang, Yuqi Sun, Guang-Cai Xiang, Jixiang Xing, Mengdao Guo, Liang Yang, Jun National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Physics and Optoelectronic Engineering Xidian University Xi'an China College of Geomatics Xi'An University of Science and Technology Xi'an China

ISBN: (纸本)9781728129129

A imaging passive localization method for wideband signal is proposed in this paper. By introducing synthetic aperture radar (SAR) imaging method, the location of the signal emitter is directly given in SAR image. The key of this method is to focus unknown wideband data in range and azimuth domain. To focus the data in range domain without signal parameter, a new pulse compress method is proposed by constructing reference signal from raw data. To focus the data in azimuth domain without knowing range, a range-searching azimuth focus method is proposed by constructing azimuth focus functions with different range. Simulation result validate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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Performance Analysis and Power Allocation of Joint Communication and Sensing Towards Future Communication Networks

arXiv

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arXiv 2022年

作者： Liu, Meng Yang, Minglei Li, Huifang Zeng, Kun Zhang, Zhaoming Cheng, Xiancheng Nallanathan, Arumugam Ng, Derrick Wing Kwan Wang, Guangjian The National Laboratory of Radar Signal Processing Xidian University Shaanxi Xi'An710071 China State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xi'An710071 China Huawei Technologies Co. Ltd Sichuan Chengdu611731 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The School of Electrical Engineering and Telecommunications University of New South Wales KensingtonNSW2052 Australia

To mitigate the radar and communication frequency overlapping caused by massive devices access, we propose a novel joint communication and sensing (JCS) system in this paper, where a micro base station (MiBS) can realize target sensing and cooperative communication simultaneously. Concretely, the MiBS, as the sensing equipment, can also serve as a full-duplex (FD) decode-and-forward (DF) relay to assist the end-to-end communication. To further improve the spectrum utilization, non-orthogonal multiple access (NOMA) is adopted such that the communication between the macro base station (MaBS) and the Internet-of-Things (IoT) devices. To facilitate the performance evaluation, the exact and asymptotic outage probabilities, ergodic rates, sensing probability of the system are characterized. Subsequently, two optimal power allocation (OPA) problems of maximizing the received signal-to-interference-plus-noise ratio of sensing signal and maximizing the sum rate for communication are designed that are solved by means of the Lagrangian method and function monotonicity. The simulation results demonstrate that: 1) the proposed JCS NOMA system can accomplish both communication enhancement and sensing function under the premise of the same power consumption as non-cooperative NOMA;2) the proposed OPA schemes manifest superiorities over a random power allocation scheme. Copyright © 2022, The Authors. All rights reserved.

关键词： Cooperative communication

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An UKF-based Extremum Seeking Control of Two-Stage Anaerobic Digestion Process

An UKF-based Extremum Seeking Control of Two-Stage Anaerobic...

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Automatics and Informatics (ICAI), International Conference

作者： Dandan Xu Haoping Wang Yang Tian Nicolai Christov Lyudmila Kabaivanova Ivan Simeonov School of Automation Nanjing University of Science and Technology Nanjing China Sino-French Laboratory of Automatic Control and Signal Processing (LaFCAS) Lille University France The Stephan Angeloff Institute of Microbology Bulgarian Academy of Sciences Sofia Bulgaria

In this paper, the Unscented Kalman Filter (UKF) is used in the Extremum Seeking Control (ESC) framework to deal with nonlinear system optimization problem. Compared with the classical ESC algorithm, the UKF-based ESC algorithm can reduce the amplitude of the disturbance signal and eliminate the steady-state oscillation. Applying the novel ESC algorithm to two-stage anaerobic digestion model, more accurate real-time gradient estimation and faster control convergence have been obtained in comparison with use of the classical ESC algorithm.

关键词： Simulation Process control Estimation Prediction algorithms Approximation algorithms Real-time systems Steady-state

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Design of GLR-Based Detectors for FDA-MIMO radar

Design of GLR-Based Detectors for FDA-MIMO radar

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IEEE Metrology for Aerospace

作者： Lan Lan Angela Marino Augusto Aubry Antonio De Maio Guisheng Liao Jingwei Xu National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Department of Electrical Engineering and Information Technology University of Naples "Federico II" Naples Italy

ISBN: (数字)9781728166360

ISBN: (纸本)9781728166377

This paper deals with the problem of adaptive detectors design for a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. At the design stage, adaptive detectors are devised according to the generalized likelihood ratio (GLR) criterion, where the target range is assumed unknown within the radar cell. In this respect, the maximum likelihood (ML) estimate of the target range under the H 1 hypothesis is approximated either assuming a discrete grid of the possible values or resorting to a Newton-based optimization procedure. Numerical results are provided to illustrate the effectiveness of the devised detectors.

关键词： Benchmark testing Interference Detectors radar detection Maximum likelihood estimation Object detection

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Experimental Demonstrations of ELC-type Microwave Optomechanical Metamaterial

Experimental Demonstrations of ELC-type Microwave Optomechan...

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UK/ China Emerging Technologies (UCET)

作者： Yuedan Zhou Yifeng Liu Xueming Wei Jian Li Yongjun Huang Guangjun Wen School of Information and Communication Engineering Sichuan Provincial Engineering Research Center of Communication Technology for Intelligent IoT University of Electronic Science and Technology of China Chengdu China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781665495769

In this paper, we experimentally demonstrate a new type of microwave optomechanical metamaterial based on an ELC-type resonator structure deposed on a flexible substrate. The operating mechanism for the proposed optomechanical metamaterial is firstly explained and then the basic numerical simulations are performed before the sample fabrication. In the end the experimental demonstration results shows that the ELC-type microwave optomechanical metamaterial can have ∼40 MHz resonance shift when the incident EM wave is increased from 20 dBm to 35 dBm.

关键词： Radio frequency Fabrication Force Electromagnetic forces Numerical simulation Microwave frequencies Metamaterials

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Design of adaptive detectors for FDA-MIMO radar

Design of adaptive detectors for FDA-MIMO radar

引用

Sensor Array and Multichannel signal processing Workshop

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Adaptive target detection in the presence of Gaussian interference considering a frequency diverse array multipleinput multiple-output (FDA-MIMO) radar is investigated. At the design stage, adaptive detectors are devised according to the generalized likelihood ratio test (GLRT) criterion, where the target range is assumed unknown within the radar cell. Hence, the maximum likelihood (ML) estimate of the target range under the H 1 hypothesis is approximated either assuming a discrete grid or resorting to a Newton-based optimization procedure. Numerical results are provided to illustrate the effectiveness of the devised detectors.

关键词： Maximum likelihood estimation radar detection Adaptive arrays Detectors radar Computer architecture Receivers

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Hand Hygiene Assessment via Joint Step Segmentation and Key Action Scorer

arXiv

引用

arXiv 2022年

作者： Li, Chenglong Zhu, Qiwen Liu, Tubiao Tang, Jin Su, Yu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Hefei Normal University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230061 China

Hand hygiene is a standard six-step hand-washing action proposed by the World Health Organization (WHO). However, there is no good way to supervise medical staff to do hand hygiene, which brings the potential risk of disease spread. Existing action assessment works usually make an overall quality prediction on an entire video. However, the internal structures of hand hygiene action are important in hand hygiene assessment. Therefore, we propose a novel fine-grained learning framework to perform step segmentation and key action scorer in a joint manner for accurate hand hygiene assessment. Existing temporal segmentation methods usually employ multi-stage convolutional network to improve the segmentation robustness, but easily lead to over-segmentation due to the lack of the long-range dependence. To address this issue, we design a multi-stage convolution-transformer network for step segmentation. Based on the observation that each hand-washing step involves several key actions which determine the hand-washing quality, we design a set of key action scorers to evaluate the quality of key actions in each step. In addition, there lacks a unified dataset in hand hygiene assessment. Therefore, under the supervision of medical staff, we contribute a video dataset that contains 300 video sequences with fine-grained annotations. Extensive experiments on the dataset suggest that our method well assesses hand hygiene videos and achieves outstanding performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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