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arXiv 2024年

作者： Zhu, Yabin Wang, Qianwu Li, Chenglong Tang, Jin Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Public Safety and Emergency Management Anhui University of Science and Technology Hefei231131 China School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Center for Big Data and Population Health of IHM China

The complementary benefits from visible and thermal infrared data are widely utilized in various computer vision task, such as visual tracking, semantic segmentation and object detection, but rarely explored in Multiple Object Tracking (MOT). In this work, we contribute a large-scale Visible-Thermal video benchmark for MOT, called VT-MOT. VT-MOT has the following main advantages. 1) The data is large scale and high diversity. VT-MOT includes 582 video sequence pairs, 401k frame pairs from surveillance, drone, and handheld platforms. 2) The cross-modal alignment is highly accurate. We invite several professionals to perform both spatial and temporal alignment frame by frame. 3) The annotation is dense and high-quality. VT-MOT has 3.99 million annotation boxes annotated and double-checked by professionals, including heavy occlusion and object re-acquisition (object disappear and reappear) challenges. To provide a strong baseline, we design a simple yet effective tracking framework, which effectively fuses temporal information and complementary information of two modalities in a progressive manner, for robust visible-thermal MOT. A comprehensive experiment are conducted on VT-MOT and the results prove the superiority and effectiveness of the proposed method compared with state-of-the-art methods. From the evaluation results and analysis, we specify several potential future directions for visible-thermal MOT. The project is released in https://***/wqw123wqw/PFTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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RGBT Tracking via Progressive Fusion Transformer with Dynamically Guided Learning

arXiv

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arXiv 2023年

作者： Zhu, Yabin Li, Chenglong Wang, Xiao Tang, Jin Huang, Zhixiang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes School of Electronic and Information Engineering Anhui University Hefei230601 China 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 Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

Existing Transformer-based RGBT tracking methods either use cross-attention to fuse the two modalities, or use self-attention and cross-attention to model both modality-specific and modality-sharing information. However, the significant appearance gap between modalities limits the feature representation ability of certain modalities during the fusion process. To address this problem, we propose a novel Progressive Fusion Transformer called ProFormer, which progressively integrates single-modality information into the multimodal representation for robust RGBT tracking. In particular, ProFormer first uses a self-attention module to collaboratively extract the multimodal representation, and then uses two cross-attention modules to interact it with the features of the dual modalities respectively. In this way, the modality-specific information can well be activated in the multimodal representation. Finally, a feed-forward network is used to fuse two interacted multimodal representations for the further enhancement of the final multimodal representation. In addition, existing learning methods of RGBT trackers either fuse multimodal features into one for final classification, or exploit the relationship between unimodal branches and fused branch through a competitive learning strategy. However, they either ignore the learning of single-modality branches or result in one branch failing to be well optimized. To solve these problems, we propose a dynamically guided learning algorithm that adaptively uses well-performing branches to guide the learning of other branches, for enhancing the representation ability of each branch. Extensive experiments demonstrate that our proposed ProFormer sets a new state-of-the-art performance on RGBT210, RGBT234, LasHeR, and VTUAV datasets. In particular, the improvements of ProFormer on the LasHeR dataset are up to +6.2%/+ 4.1% in PR/SR compared with existing best RGBT tracker. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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A Fast Proportionate RLS Adaptive Equalization for Underwater Acoustic Communications

A Fast Proportionate RLS Adaptive Equalization for Underwate...

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OCEANS - Europe

作者： Zhen Qin Jun Tao Feng Tong Hongtao Zhang Fengzhong Qu Xiao Han Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing China State Key Lab. of Acoustics Institute of Acoustics Chinese Academy of Sciences Beijing China College of Ocean and Earth Sciences Xiamen University Xiamen China Hangzhou Appiled Acoustics Research Institute Hangzhou China Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Zhejiang University Zhoushan China Acoustic Science and Technology Laboratory Harbin Engineering University Harbin China

In this paper, a low-complexity recursive least squares (RLS) type sparse direct adaptive equalizer (DAE) is proposed for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. The underlying proportionate RLS (PRLS) adaptive filter algorithm is motivated by the idea of proportionate updating (PU) originated in the improved proportionate normalized least mean squares (IPNLMS) adaptive filter scheme. To overcome the high complexity of the PRLS that is quadratic in the filter size, a fast implementation is developed in a way similar to the design of the stable fast transversal filter (SFTF) as a low-complexity approximation of the standard RLS adaptive filter algorithm. The resulting fast version of the PRLS is named the proportionate SFTF (PSFTF). The PSFTF is then adopted to update coefficients of a linear equalizer (LE), which was tested by experimental data collected in at-sea UWA communication trials. Experimental results showed the PSFTFDAE achieves faster convergence and better performance than existing SFTF-DAE and RLS-DAE.

关键词： Complexity theory MIMO communication Standards Equalizers Channel estimation Underwater acoustics signal processing algorithms

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ECG Denoising Using Mutual Information Based Classification of IMFs and Interval Thresholding

ECG Denoising Using Mutual Information Based Classification ...

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International Conference on Telecommunications and signal processing

作者： Marjaneh Taghavi Mohammad B. Shamsollahi Lotfi Senhadji School of Engineering and Science Sharif University of Technology International Campus Kish Island Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Laboratoire Traitement du Signal et de l'Image (LTSI) Universitede Rennes 1

ISBN: (纸本)9781479984992

The Electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Therefore, the quality of information extracted from the ECG has a vital role. In real recordings, ECG is corrupted by artifacts such as prolonged repolarization, respiration, changes of electrode position, muscle contraction, and power line interface. In this paper, a denoising technique for ECG signals based on Empirical Mode Decomposition (EMD) is proposed. We use Ensemble Empirical Mode Decomposition (EEMD) to overcome the limitations of EMD. Moreover, to overcome the limitations of thresholding methods we use the combination of mutual information and two EMD based interval thresholding approaches. Our new method is evaluated on ECG signals available in MIT-BIH database. This method is compared with two EEMD based interval thresholding methods. The results show that our proposed method has a better signal to Noise Ratio improvement (SNRimp) and a lower Mean Square Error (MSE) than the other two methods.

关键词： Ensemble Empirical Mode Decomposition Denoising Mutual Information Instantaneous Half Period Interval Thresholding ECG

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Cross-Modality Coherent Registration of Whole Mouse Brains

Research Square

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Research Square 2021年

作者： Peng, Hanchuan Qu, Lei Li, Yuanyuan Xie, Peng Liu, Lijuan Wang, Yimin Wu, Jun Liu, Yu Wang, Tao Li, Longfei Guo, Kaixuan Wan, Wan Ouyang, Lei Xiong, Feng Zheng, Yefeng Xu, Fang Gong, Hui Dong, Hongwei Luo, Qingming Bi, Guo-Qiang Hawrylycz, Michael Zeng, Hongkui Allen Institute for Brain Science United States Key Laboratory of Intelligent Computation & Signal Processing Ministry of Education Anhui University China SEU-ALLEN Joint Center Institute for Brain Intelligence Southeast University Bangladesh Institute for Brain and Intelligence Southeast University Bangladesh Tencent Jarvis Lab China CAS Key Laboratory of Brain Connectome and Manipulation China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology China Center for Integrative Connectomics Department of Neurobiology David Geffen School of Medicine UCLA United States Hainan University China Hefei National Laboratory for Physical Sciences at the Microscale School of Life Sciences University of Science and Technology of China China

Recent whole brain mapping projects are collecting large-scale 3D images using powerful and informative modalities, such as STPT, fMOST, VISoR, or MRI. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modality image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulted from different sample preparation methods and imaging modalities. We introduced a cross-modality registration method, called mBrainAligner, which uses coherent landmark mapping as well as deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We also built a single cell resolution atlas using the fMOST modality, and used our method to generate whole brain map of 3D full single neuron morphology and neuron cell types. © 2021, CC BY.

关键词： Brain mapping

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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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Obtaining Higher-Order Derivatives of Low-Frequency signals Using Multirate signal processing

Obtaining Higher-Order Derivatives of Low-Frequency Signals ...

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IEEE International Instrumentation and Measurement technology Conference

作者： Li Tan Jean Jiang Liangmo Wang Signal Processing and Instrumentation Laboratory College of Engineering and Technology Purdue University North Central Westville Indiana 46391 School of Mechanical Engineering Nanjing University of Science and Technology Nanjing P. R. China

ISBN: (纸本)9781467346214

In this paper, a multi-rate signal processing technique is proposed for obtaining higher-order derivatives of digital signals with an improved signal-to-quantization noise ratio (SQNR). The technique consists of a classical oversampling analog-to-digital conversion (ADC) unit with a dithering process and multi-stage down sampling processing followed by a high performance digital differentiator cascaded using a decimator with a factor of two. In the cascaded multi-stage processing, each stage contains a simple half-band low-pass filter followed with decimation by two. The proposed technique uses an extremely small FIR filter size, hence requires much less memory and computational complexity in comparison with single-stage processing. Computer simulations and experimental data processing demonstrate the effectiveness of the proposed method.

关键词： Oversampling FIR digital differentiator higher-order derivative multi-rate signal processing

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Correction to: Multi-step inertial Krasnosel’skiǐ–Mann iteration with new inertial parameters arrays

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Journal of Fixed Point Theory and Applications 2021年第4期23卷 1-4页

作者： Dong, Qiao-Li Li, Xiao-Huan Cho, Yeol Je Rassias, Themistocles M. Tianjin Key Laboratory for Advanced Signal Processing and College of Science Civil Aviation University of China Tianjin People’s Republic of China School of Mathematics and Statistics Shandong University of Technology Shandong People’s Republic of China Department of Mathematics Education Gyeongsang National University Jinju Korea Center for General Education China Medical University Taichung Taiwan Department of Mathematics National Technical University of Athens Athens Greece

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signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks

arXiv

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arXiv 2022年

作者： Gao, Dawei Guo, Qinghua Liao, Guisheng Eldar, Yonina C. Li, Yonghui Yu, Yanguang Vucetic, Branka The Hangzhou Institute of Technology Xidian University Hangzhou311200 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW2522 Australia The Faculty of Math and CS Weizmann Institute of Science Rehovot7610001 Israel The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia

In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical applications. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to ‘model’ the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Granular theorem of quotient space in image segmentation

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Jisuanji Xuebao/Chinese Journal of Computers 2005年第10期28卷 1680-1685页

作者： Liu, Ren-Jin Huang, Xian-Wu Department of Computer Science and Technology West Anhui University Lu'an 237012 China Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei 230039 China School of Electronic and Information Engineering Soochow University Suzhou 215006 China

Based on the quotient space granular theorem, the image segmentation concept is analyzed and the image segmentation methods are studied, and then the quotient space granular theorem of image segmentation is demonstrated. The image segmentation problems are described with triple elements function of the quotient space model (X,f,Γ)⇔([X],[f],[Γ]), according to the quotient space granularity computing, the image segmentation theorem is presented. The methods of images segmentation based on hierarchical and synthesis and combinational technique are exploited and then the segmentation algorithm based granularity synthesis technique is proposed. In this technique, the features of directionality and roughness in texture images data set are firstly extracted respectively to form the different granularities of image, then the different granularity are synthesized according to the theorem of granularity synthesis, finally the texture images is segmented. The experimental results demonstrate that the algorithm is valid for the segmentation of complicated texture images.

关键词： Image segmentation

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