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arXiv 2024年

作者： Lei, Sen Xiao, Xinyu Zhang, Tianlin Li, Heng-Chao Shi, Zhenwei Zhu, Qing School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China Company of Ant Group Hangzhou688688 China Luoyang Institute of Electro-optical Equipment AVIC Luoyang 471000 China Image Processing Center School of Astronautics The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Faculty of Geosciences and Engineering Southwest Jiaotong University Chengdu611756 China

Given a language expression, referring remote sensing image segmentation (RRSIS) aims to identify ground objects and assign pixel-wise labels within the imagery. The one of key challenges for this task is to capture discriminative multimodal features via text-image alignment. However, the existing RRSIS methods use one vanilla and coarse alignment, where the language expression is directly extracted to be fused with the visual features. In this paper, we argue that a "fine-grained image-text alignment" can improve the extraction of multimodal information. To this point, we propose a new referring remote sensing image segmentation method to fully exploit the visual and linguistic representations. Specifically, the original referring expression is regarded as context text, which is further decoupled into the ground object and spatial position texts. The proposed fine-grained image-text alignment module (FIAM) would simultaneously leverage the features of the input image and the corresponding texts, obtaining better discriminative multi-modal representation. Meanwhile, to handle the various scales of ground objects in remote sensing, we introduce a Text-aware Multi-scale Enhancement Module (TMEM) to adaptively perform cross-scale fusion and intersections. We evaluate the effectiveness of the proposed method on two public referring remote sensing datasets including RefSegRS and RRSIS-D, and our method obtains superior performance over several state-of-the-art methods. The code will be publicly available at https://***/Shaosifan/FIANet. Copyright © 2024, The Authors. All rights reserved.

关键词： image segmentation

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Inter-pulse carrier frequency agile design for cognitive radar

Inter-pulse carrier frequency agile design for cognitive rad...

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IET International Radar Conference (IRC 2023)

作者： Yin Li Tao Fan Xianxiang Yu Lan Lan Huai Ding Guolong Cui School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu People's Republic of China National Key Laboratory of Radar Signal Processing Xidian University Xi'an People's Republic of China The 29th Research Institute of China Electronics Technology Group Corporation Chengdu People's Republic of China

ISBN: (数字)9781837240982

This paper deals with the joint design of inter-pulse frequency and receive filter to reduce the Doppler sidelobe for cognitive Frequency Agile Radar (FAR) working in search and confirmation modes. At first, we introduce an inter-pulse coherent processing method that integrates phase compensation with Weighted Non-Uniform Discrete Fourier Transform (W-NUDFT). Then, a multi-parameter non-convex optimization model is established based on the issue of high Doppler sidelobes caused by inter-pulse non-uniform accumulation. Subsequently, we develop an alternating iterative optimization algorithm that combines Coordinate Descent with Alternating Direction Method of Multipliers (CD-ADMM) to tackle the resulting non-convex optimization problem. Finally, through numerical simulations, the effectiveness of the waveform via the application on weak targets detection accompanied by strong targets is demonstrated.

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H∞interval observer design for uncertain discrete-time linear switched systems with unknown inputs

H∞interval observer design for uncertain discrete-time line...

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2021 International Conference on Control, Automation and Diagnosis, ICCAD 2021

作者： Marouani, Ghassen Dinh, Thach Ngoc Sehli, Naima Raissi, Tarek Wang, Zhenhua Messaoud, Hassani University of Monastir National Engineering School of Monastir Automatic Research Laboratory Signal and Image Processing Monastir5019 Tunisia Conservatoire National Des Arts Et Métiers CEDRIC-Lab Paris75141 France University of Tunis National Engineering School of Tunis Conception and Control of Systems Laboratory Le Belvedere 1002 Tunisia Harbin Institute of Technology School of Astronautics Harbin150001 China

ISBN: (纸本)9781665449687

This paper deals with unknown input interval observers for discrete-time linear switched systems. A new structure of interval observer is used to overcome the design difficulty of coordinate transformation often used in such context. The interval observer gains are computed by solving Linear Matrix Inequalities (LMI) derived from multiple quadratic Lyapunov functions (MQLF) under average dwell time switching signals. © 2021 IEEE.

关键词： Linear matrix inequalities

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SAR Target Recognition Using Simulated Data by an Ensemble Multi-scale Deep Domain Adaptation Recognition Framework

SAR Target Recognition Using Simulated Data by an Ensemble M...

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IEEE International Conference on Radar

作者： Yuanshuang Sun Yinghua Wang Liping Hu Hongwei Liu National Laboratory of Radar Signal Processing Xidian University Xi'an China Science and Technology on Electromagnetic Scattering Laboratory Beijing Institute of Environmental Features Beijing China

ISBN: (纸本)9781665468893

Obtaining lots of synthetic aperture radar (SAR) data for automatic target recognition (ATR) can be unrealistic in many situations. Relatively speaking, electromagnetic simulated SAR data is easier to acquire. However, there is an apparent distribution gap between the electromagnetic simulated data and the real data. It is not suitable to use the simulated data for recognition directly, and thus the way to apply the simulated SAR data for recognition is worth considering. In this paper, we focus on a challenging recognition problem of training solely on simulated data and testing on real data. Firstly, a multi-scale feature extraction module is used for robust feature extraction, which aims to make the extracted feature more generalized for both simulated data and real data. Secondly, the idea of domain adaptation is considered to learn a representation trained to jointly optimize for classification and domain invariance. Thirdly, an ensembling technique is applied, and an ensemble multi-scale deep domain adaptation recognition framework is proposed. The Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set and an electromagnetic simulated SAR data set are used for verification, and experimental results illustrate the effectiveness of our methods.

关键词： Training Target recognition Benchmark testing Feature extraction Robustness Data mining Electromagnetics

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IoT Urban River Water Quality System Using Federated Learning via Knowledge Distillation

IoT Urban River Water Quality System Using Federated Learnin...

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IEEE International Conference on Communications (ICC)

作者： Amine Dahane Rabaie Benameur Manel Naloufi Sami Souihi Thiago Abreu Francoise S. Lucas Abdelhamid Mellouk Laboratory of Industrial Computing and Networks (RIIR) University of Oran 1 Oran Algeria Institute of Applied Science and Technology ISTA Oran Algeria Image Signal and Intelligent Systems (LiSSi) Laboratory TincNET Research Team Univ. Paris-Est Créteil France Laboratoire Eau Environnement et Systèmes Urbains (Leesu) Univ. Paris-Est Créteil École des Ponts ParisTech France

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In the past decades, the use of urban rivers for recreational and sporting activities has gained increasing interest. However, bathing in urban surface waters is not without health risks due to short-term pollution of fecal origin, which may have an important impact on the overall population health within a region where bathing in water streams is possible. Therefore, EU member states are required to lower the contamination risk of such areas through active water quality management, as defined by the Bathing Water directory (BWD, 2006/7/EC). This paper develops and evaluates a cost-effective IoT-based water quality monitoring system, based on low-cost water quality sensors coupled with machine-learning approaches. By monitoring spatiotemporal dynamics of several physical and chemical parameters correlated with bacterial indicators, managers can more easily decide if the water quality of a bathing site is enough for usage. To determine the water suitability at particular river sites, the system employs a convolutional neural network (CNN) deep learning classifier, integrating federated learning (FL) with knowledge distillation (FedKD) to streamline model architecture, reduce communication costs, and preserve data privacy. The system is tested on the Seine and the Marne rivers (Paris area, France) and results demonstrate that FedKD outperforms centralized knowledge distillation (KD) and FL algorithms such as FedAvg and UFedAVG. Using the current features, it achieves a satisfactory average accuracy of 90.74% at Marne station.

关键词： Federated learning Surface contamination Water quality Water pollution Rivers Spatiotemporal phenomena Convolutional neural networks

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Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video

Real-time Multi-person Eyeblink Detection in the Wild for Un...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenzheng Zeng Yang Xiao Sicheng Wei Jinfang Gan Xintao Zhang Zhiguo Cao Zhiwen Fang Joey Tianyi Zhou Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Biomedical Engineering Southern Medical University Guangzhou China Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou China Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore

Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from uncon-strainedfilms to reveal “in the wild“ characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counter-parts, our proposition runs in a one-stage spatio-temporal way with end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed.

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BrcaSeg:A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological images

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Genomics, Proteomics & Bioinformatics 2021年第6期19卷 1032-1042页

作者： Zixiao Lu Xiaohui Zhan Yi Wu Jun Cheng Wei Shao Dong Ni Zhi Han Jie Zhang Qianjin Feng Kun Huang Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical EngineeringSouthern Medical UniversityGuangzhou 510515China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical EngineeringHealth Science CenterShenzhen UniversityShenzhen 518060China Department of Medicine Indiana University School of MedicineIndianapolisIN 46202USA Department of Medical and Molecular Genetics Indiana University School of MedicineIndianapolisIN 46202USA Department of Biostatistics and Health Data Science Indiana University School of MedicineIndianapolisIN 46202USA Regenstrief Institute IndianapolisIN 46202USA

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and *** stroma from epithelial tissues is critically important for spatial characterization of the tumor ***,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological *** CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)*** achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art *** this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression *** subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue *** Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these *** all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid *** can be accessed at https://***/Serian1992/ImgBio.

关键词： Whole-slide tissue image Computational pathology Deep learning Integrative genomics Breast cancer

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Scalable Parallel Static Learning

Scalable Parallel Static Learning

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International Test Conference in Asia (ITC-Asia)

作者： Xiaoze Lin Liyang Lai Huawei Li The Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Electrical Engineering Shantou University Shantou Guangdong China Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (纸本)9781665413350

Static learning is a learning algorithm for finding additional implicit implications between gates in a netlist. In automatic test pattern generation (ATPG) the learned implications help recognize conflicts and redundancies early, and thus greatly improve the performance of ATPG. Though ATPG can further benefit from multiple runs of incremental or dynamic learning, it is only feasible when the learning process is fast enough. In the paper, we study speeding up static learning through parallelization on heterogeneous computing platform, which includes multi-core microprocessors (CPUs), and graphics processing units (GPUs). We discuss the advantages and limitations in each of these architectures. With their specific features in mind, we propose two different parallelization strategies that are tailored to multi-core CPUs and GPUs. Speedup and performance scalability of the two proposed parallel algorithms are analyzed. As far as we know, this is the first time that parallel static learning is studied in the literature.

关键词： Scalability Microprocessors Redundancy Graphics processing units Computer architecture Automatic test pattern generation Heterogeneous networks

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One to multiple mapping dual learning: Learning multiple sources from one mixed signal

arXiv

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arXiv 2021年

作者： Liu, Ting Wang, Wenwu Zhang, Xiaofei Yan, Jingwen Guo, Yina The Department of Electronics and Communication Engineering Taiyuan University of Science and Technology Taiyuan030024 China The Centre for Vision Speech and Signal Processing University of Surrey Guildford SurreyGU2 7XH United Kingdom The Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Technology

Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor. The existing methods for SCBSS mainly focus on separating two sources and have weak generalization performance. To address these problems, an algorithm is proposed in this paper to separate multiple sources from a mixture by designing a parallel dual generative adversarial Network (PDualGAN) that can build the relationship between a mixture and the corresponding multiple sources to realize one-to-multiple cross-domain mapping. This algorithm can be applied to any mixed model such as linear instantaneous mixed model and convolutional mixed model. Besides, one-to-multiple datasets are created which including the mixtures and corresponding sources for this study. The experiment was carried out on four different datasets and tested with signals mixed in different proportions. Experimental results show that the proposed algorithm can achieve high performance in peak signal-to-noise ratio (PSNR) and correlation, which outperforms state-of-the-art algorithms. © 2021, CC BY.

关键词： Mapping

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Enhanced Gaussian Process Regression for Diagnosing Wind Energy Conversion Systems

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IFAC-PapersOnLine 2022年第6期55卷 673-678页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

Fault detection and diagnosis techniques are increasingly important to ensure robust and resource efficient operation of Wind Energy Conversion (WEC) systems. In this context, this paper presents a Reduced Enhanced Gaussian Process Regression (REGPR)-based Random Forest (RF) technique (REGPR-RF) to identify and diagnose faults occurring in a nonlinear WEC systems. The proposed technique uses REGPR technique for features extraction and selection from raw sensor data. Then, these selected features are fed to RF classifier to reliably detect and classify faults. The use of REGPR to learn features avoid the dimension problems and improves the classification performance significantly with a small number of training data. The results obtained by REGPR-RF are compared to those obtained with other conventional classifiers (Support Vector Machines (SVM), Naive Bayes (NB), ...). The results show that the developed REGPR-RF technique achieve higher accuracy (99.99%) with small data sets.

关键词： Gaussian Process Regression (GPR) Noise-signal-Dependence Random Forest (RF) Feature Extraction Selection Fault Detection Diagnosis (FDD) Wind Energy Conversion (WEC) Systems

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