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IEEE International Conference on radar

作者： Jiabao Ding Jiadong Wang Yachao Li Ziyue Sun National Laboratory of Radar Signal Processing Xidian University Xi'an China Academy of Advanced Interdisciplinary Researsh Xidian University Xi'an China School of Engineering and Computer Science University of Exeter Exeter UK

ISBN: (纸本)9781665468893

When imaging a maneuvering target with complex three-dimensional (3-D) non-uniform rotational motion, the traditional range Doppler (RD) algorithm for bistatic inverse synthetic aperture radar (Bi-ISAR) will fail due to the presence of phase errors. Moreover, due to the time-varying property of the imaging projection plane (IPP), the phase errors are two-dimensional (2-D) spatial variant. In order to deal with these problems, we propose a spatial variant phase compensation (SVPC) method based on parametric minimun entropy optimization to compensate for the 2-D spatial variant phase errors. The main contribution of the proposed method is to give an accurate expression of the 2-D spatial variant model and optimally compensate the 2-D spatial variant phase errors in the Bi-ISAR configuration. Simulation results verify the effectiveness of the proposed algorithm.

关键词： Solid modeling Atmospheric modeling Airborne radar Two dimensional displays Imaging radar imaging Entropy

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Dadm: Enhancing Eeg Artifact Removal by Fusing Artifact Representation into the Time-Frequency Domain

SSRN

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SSRN 2024年

作者： Li, Haoran Feng, Fan Kang, Jiarong Zhang, Jin Gong, Xiaoli Lu, Tingjuan Sun, Zhe Solé-Casals, Jordi College of Computer Science Nankai University Tianjin China Department of Biomedical Engineering School of Medicine Tsinghua University Beijing China Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China State Key Laboratory of High-end Server and Storage Technology China 903rd Hospital of PLA China Faculty of Medicine Faculty of Health Data Science Juntendo University Japan Data and Signal Processing Group University of Vic Central University of Catalonia Catalonia Vic08500 Spain

The electroencephalogram (EEG) provides essential data for analyzing brain activities. However, artifacts such as electrooculography (EOG) and electromyography (EMG) often interleave with the EEG signals, significantly affecting the quality of EEG signal analysis. The heterogeneous distribution of these artifacts in the time-frequency domain makes it challenging to remove multiple artifacts using a unified model. In this paper, we propose an Artifact-Aware EEG Denoising Model, referred to as DADM, to effectively remove various types of artifacts in a unified manner. We first obtain an artifact representation that indicates the type of artifact from a pre-trained artifact classification model. This artifact representation is then used as prior knowledge, which is fused into the denoising model in the frequency-time domain. This enables the model to become aware of the artifact type and precisely remove artifacts based on their type. Due to the heterogeneous distributions of artifacts in the frequency domain, we introduce a frequency enhancement module that can identify specific types of artifacts based on their representation and remove them using a hard attention mechanism. Additionally, we design a time-domain compensation module to enhance the denoising capability of DADM by compensating for potential losses of global information. Comprehensive experiments demonstrate that DADM significantly outperforms the Novel CNN in denoising EEG signals, showing a notable 7.77% improvement in correlation coefficient (CC) metrics. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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A Modified Information Embedding Scheme for the Integrated System of radar and Communication

A Modified Information Embedding Scheme for the Integrated S...

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2019 IEEE International Conference on signal, Information and Data processing, ICSIDP 2019

作者： Wu, Wenhua Cao, Yunhe Liu, Yutao National Laboratory of Radar Signal Processing Xidian University Xi'an China Science and Technology on Communication Networks Laboratory Shi jia zhuang China

ISBN: (纸本)9781728123455

This paper has proposed a modified information embedding scheme for the integrated system of radar and communication without influence on the mainlobe. Exploiting the waveform diversity of multiple-input multiple-output (MIMO) radar, the beamforming weight vectors are designed for constellation mapping to increase the data rate. Simulation results are presented to demonstrate the theoretical analysis. © 2019 IEEE.

关键词： Beamforming

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Event-triggered Model-free Prescribed-time Quantized Control for Second-order Nonlinear System with Uncertainties and External Disturbances

Event-triggered Model-free Prescribed-time Quantized Control...

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International Conference on Robotics and Automation Engineering (ICRAE)

作者： Dingxin He Haoping Wang Yang Tian Yizhou Tao Sino-French International Joint Laboratory of Automatic Control and Signal Processing (LaFCAS) School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665406987

This paper proposes an event-triggered model-free prescribed-time controller (ET-MFPTC) for a second-order nonlinear system with uncertainties, external disturbances and input quantization. The designed control strategy consists of time-delay estimation (TDE), prescribed-time controller (PTC) and event-triggered mechanism (ETM). The TDE is employed to estimate and compensate for the lumped uncertainties. The PTC is designed to achieve tracking error convergence within the preset time by using a scaling function of time. To decrease signal transmission, an effective ETM based on system output is constructed. Furthermore, the Zeno behavior is excluded by theoretical analysis. Finally, to verify the effectiveness and performance, the simulation results on quadrotor attitude have been realized.

关键词： Uncertainty Quantization (signal) Automation Attitude control Simulation Estimation Nonlinear systems

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From early biological models to CNNs: do they look where humans look?

From early biological models to CNNs: do they look where hum...

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International Conference on Pattern Recognition

作者： Marinella Iole Cadoni Andrea Lagorio Enrico Grosso Tan Jia Huei Chan Chee Seng Computer Vision Laboratory University of Sassari Sassari Italy Faculty of Computer Science and Information Technology Center of Image and Signal Processing University of Malaya

Early hierarchical computational visual models as well as recent deep neural networks have been inspired by the functioning of the primate visual cortex system. Although much effort has been made to dissect neural networks to visualize the features they learn at the individual units, the scope of the visualizations has been limited to a categorization of the features in terms of their semantic level. Considering the ability humans have to select high semantic level regions of a scene, the question whether neural networks can match this ability, and if similarity with humans attention is correlated with neural networks performance naturally arise. To address this question we propose a pipeline to select and compare sets of feature points that maximally activate individual networks units to human fixations. We extract features from a variety of neural networks, from early hierarchical models such as HMAX up to recent deep convolutional neural netwoks such as Densnet, to compare them to human fixations. Experiments over the ETD database show that human fixations correlate with CNNs features from deep layers significantly better than with random sets of points, while they do not with features extracted from the first layers of CNNs, nor with the HMAX features, which seem to have low semantic level compared with the features that respond to the automatically learned filters of CNNs. It also turns out that there is a correlation between CNN's human similarity and classification performance.

关键词： Visualization Correlation Databases Neural networks Semantics Pipelines Visual systems

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An External Denoising Framework for Magnetic Resonance Imaging: Leveraging Anatomical Similarities Across Subjects with Fast Searches

An External Denoising Framework for Magnetic Resonance Imagi...

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IEEE International Conference on signal and Image processing (ICSIP)

作者： Lifeng Mei Sixing Liu Chenhui Tang Jiaqia Cai Jingli Wang Yilong Liu Ed X. Wu Mengye Lyu College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China Guangdong-Hongkong-Macau Institute of CNS Regeneration Key Laboratory of CNS Regeneration (Ministry of Education) Jinan University Guangzhou China Department of Electrical and Electronic Engineering Laboratory of Biomedical Imaging and Signal Processing The University of Hong Kong Hong Kong China

External denoising, also known as reference-based denoising, utilizes information from clean reference images, yielding more robust results than internal denoising, especially in situations with high noise levels. The effectiveness of reference-based denoising relies on the similarity between reference and noisy images. In this paper, we introduce a novel external denoising framework for magnetic resonance imaging (MRI) that takes advantage of the inherent anatomical structure similarities across subjects. Our framework employs noise-resistant neural networks to extract deep features, which facilitate searches for high-quality reference images from a large external database. This versatile framework is compatible with all reference-based denoising algorithms, obviating the need for acquiring additional reference images from the subject under examination.

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A Symbol Rate Estimation Scheme for Non-Cooperative Communication

A Symbol Rate Estimation Scheme for Non-Cooperative Communic...

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International Conference on Computer and Communications (ICCC)

作者： Jian Lu Wenbo Xu Siye Wang Huiying Qi Yabin Li Key Laboratory of Universal Wireless Communications Beijing University of Posts and Telecommunications Beijing China National Key Laboratory of Science and Technology on Blind Signal Processing Shanghai China

ISBN: (纸本)9781665409513

In non-cooperative communication, symbol rate estimation is a prerequisite for signal demodulation. However, it is particularly difficult to estimate the symbol rate in the scenario with unknown modulation type and root cosine roll-off shaping. In this paper, an estimator without the requirement of communication parameters is proposed. First, the signal is recognized as constant envelope or non-constant envelope signal by square spectrum. Then, for the former, the symbol rate estimation is based on wavelet transform; for the latter, an envelope velocity strategy is adopted. In the proposed scheme, we elaborate some design details, including resampling factor, extraction of discrete spectral lines in the squared spectrum, selection of wavelet transform scale, and compensation of frequency deviation. We further propose a low-complexity implementation to meet the requirements for real-time capability and accuracy. Simulation results show that our proposed scheme is effective and accurate.

关键词： Phase shift keying Quadrature amplitude modulation Simulation Estimation Real-time systems Discrete wavelet transforms Complexity theory

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Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps

arXiv

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arXiv 2022年

作者： Zhang, Junhao Yi, Zheyuan Zhao, Yujiao Xiao, Linfang Hu, Jiahao Man, Christopher Lau, Vick Su, Shi Chen, Fei Leong, Alex T.L. Wu, Ed X. Laboratory of Biomedical Imaging and Signal Processing The University of Hong Kong Hong Kong Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China

Purpose: To develop a truly calibrationless reconstruction method that derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep learning. Methods: ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. To utilize subject-coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi-slice axial stack. The performance of the approach was evaluated using publicly available T1-weighed brain and cardiac data. Results: The proposed model robustly predicted multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k-space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps. Conclusion: A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly-undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from coil and protocol specific data. © 2022, CC BY.

关键词： Deep learning

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Short-Time Variational Mode Decomposition

arXiv

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arXiv 2025年

作者： Jia, Hao Cao, Pengfei Liang, Tong Caiafa, Cesar F. Sun, Zhe Kushihashi, Yasuhiro Grau, Antoni Bolea, Yolanda Duan, Feng Solé-Casals, Jordi School of Medicine Nankai University Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Universitat Politècnica de Catalunya Barcelona Spain Mechanical Systems Engineering Nippon Institute of Technology Saitama Japan Villa Elisa Argentina Computational Bioengineering Laboratory Faculty of Health Data Science Juntendo University Tokyo Japan Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain

Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the entire time-domain signal, they are suboptimal for non-stationary time series. We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of the VMD algorithm that incorporates the Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows, converting these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of the bandwidths of these modes across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring the extraction of modes with narrow bandwidths. STVMD is divided into dynamic and non-dynamic types, depending on whether the central frequencies vary with time. Our experiments show that non-dynamic STVMD is comparable to VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals, evidenced by reduced mode function errors and tracking of dynamic central frequencies. This effectiveness is validated by steady-state visual-evoked potentials in electroencephalogram signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Coherence-Based Distributed Document Representation Learning for Scientific Documents

arXiv

引用

arXiv 2022年

作者： Tan, Shicheng Zhao, Shu Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province 230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province 230601 China

Distributed document representation is one of the basic problems in natural language processing. Currently distributed document representation methods mainly consider the context information of words or sentences. These methods do not take into account the coherence of the document as a whole, e.g., a relation between the paper title and abstract, headline and description, or adjacent bodies in the document. The coherence shows whether a document is meaningful, both logically and syntactically, especially in scientific documents (papers or patents, etc.). In this paper, we propose a coupled text pair embedding (CTPE) model to learn the representation of scientific documents, which maintains the coherence of the document with coupled text pairs formed by segmenting the document. First, we divide the document into two parts (e.g., title and abstract, etc) which construct a coupled text pair. Then, we adopt negative sampling to construct uncoupled text pairs whose two parts are from different documents. Finally, we train the model to judge whether the text pair is coupled or uncoupled and use the obtained embedding of coupled text pairs as the embedding of documents. We perform experiments on three datasets for one information retrieval task and two recommendation tasks. The experimental results verify the effectiveness of the proposed CTPE model. Copyright © 2022, The Authors. All rights reserved.

关键词： Information retrieval

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