检索结果-内蒙古大学图书馆

Enhanced Clutter Suppression and GMTIm Algorithm with Modified DKP and NCS for Single-Channel Spaceborne-Maneuvering BFSAR

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Yuzhou Song, Xuan Li, Yachao Wang, Yitao Guo, Yanhong Ye, Pei Wang, Xuanqi Shi, Guangming Xidian University National Key Laboratory of Radar Signal Processing Xi’an710071 China Northwestern Polytechnical University School of Astronautics Xi’an710072 China DalianNaval Academy Operation Software and Simulation Institute Dalian116018 China Dalian University of Technology Institute for Advanced Intelligence Dalian116024 China Xidian University School of Physics Xi’an710071 China Xidian University School of Artificial Intelligence Xi’an710071 China Peng Cheng Laboratory Shenzhen518055 China

Single-channel spaceborne-maneuvering bistatic forward-looking synthetic aperture radar (SS-BFSAR) enables the maneuvering platform to achieve high-resolution forward-looking imaging without deploying additional antennas or transmitting the radar signal. Nevertheless, achieving the suppression of spatial variant clutter with only the single-channel configuration remains a critical challenge for the ground moving target imaging (GMTIm) mission of SS-BFSAR. This study proposes an enhanced clutter suppression and GMTIm algorithm for SS-BFSAR. First, the proposed modified deramp-keystone processing (DKP) completely decouples the echo signal in range and azimuth while avoiding the signal-to-clutter ratio (SCR) degradation caused by the azimuth spectrum aliasing. Subsequently, two pre-focusing approaches are developed with nonlinear chirp scaling (NCS), i.e., global NCS and block NCS, to achieve deep focusing of spatial variant clutter while introducing differences of Doppler frequency position (DFP) between the clutter and GMT. These approaches preserve the clutter consistency of the pre-focus results, thereby ensuring that the GMT signal exhibits high SCR following the image domain cancellation. Finally, a matched filter and the proposed monostatic-equivalent model are used to refocus the GMT and estimate its velocity. The proposed algorithm can simultaneously obtain the well-focused ground scene image and the GMTIm result without DFP drift caused by the target’s motion. Comparative experiments using simulation and real data demonstrate the effectiveness and superiority of the proposed algorithm. © 1980-2012 IEEE.

关键词： Echo suppression

来源：评论

学校读者我要写书评

暂无评论

Deep learning detection of anomalous patterns from bus trajectories for traffic insight analysis

引用

Knowledge-Based Systems 2021年 217卷

作者： Zhang, Xiaocai Zheng, Yi Zhao, Zhixun Liu, Yuansheng Blumenstein, Michael Li, Jinyan Advanced Analytics Institute Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia National Key Laboratory of Science and Technology on Blind Signal Processing ChengduSichuan610041 China College of Information Science and Engineering Hunan University ChangshaHunan410082 China Australian Artificial Intelligence Institute & Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia

Existing data-driven methods for traffic anomaly detection are modeled on taxi trajectory datasets. The concern is that the data may contain much inaccuracy about the actual traffic situations, because taxi drivers often choose optimal routes to evade from the congestions caused by traffic anomalies. We use bus trajectory data in this work. Bus trajectories can capture real traffic conditions in the road networks without drivers’ preference, which are more objective and appropriate for accurately detecting anomalous patterns for a broad range of insight analyses on traffics. We proposed a deep learning-based feature visualization method to map 3-dimensional features into a red–green–blue (RGB) color space. A color trajectory (CT) is then derived by encoding a trajectory with the RGB colors. With the spatial and temporal properties extracted from the CT, spatio-temporal outliers are detected by a novel offline detection method. We then conduct GIS map fusion to obtain insights for better understanding the traffic anomaly locations, and more importantly the influences on the road affected by the corresponding anomalies. Extended from the offline detection, an online detection method is developed for real-time detection of anomalous patterns. Our proposed methods were tested on 3 real-world bus trajectory datasets to demonstrate the performance of high accuracies, high detection rates and relatively low false alarm rates. © 2021

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Meta auxiliary learning for facial action unit detection

arXiv

引用

arXiv 2021年

作者： Li, Yong Shan, Shiguang Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Cas Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Cas Center for Excellence in Brain Science and Intelligence Technology

Despite the success of deep neural networks on facial action unit (AU) detection, better performance depends on a large number of training images with accurate AU annotations. However, labeling AU is time-consuming, expensive, and error-prone. Considering AU detection and facial expression recognition (FER) are two highly correlated tasks, and facial expression (FE) is relatively easy to annotate, we consider learning AU detection and FER in a multi-task manner. However, the performance of the AU detection task cannot be always enhanced due to the negative transfer in the multi-task scenario. To alleviate this issue, we propose a Meta Auxiliary Learning method (MAL) that automatically selects highly related FE samples by learning adaptative weights for the training FE samples in a meta learning manner. The learned sample weights alleviate the negative transfer from two aspects: 1) balance the loss of each task automatically, and 2) suppress the weights of FE samples that have large uncertainties. Experimental results on several popular AU datasets demonstrate MAL consistently improves the AU detection performance compared with the state-of-the-art multi-task and auxiliary learning methods. MAL automatically estimates adaptive weights for the auxiliary FE samples according to their semantic relevance with the primary AU detection task. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Graph jigsaw learning for cartoon face recognition

arXiv

引用

arXiv 2021年

作者： Li, Yong Lao, Lingjie Cui, Zhen Shan, Shiguang Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognize cartoon faces is to precisely perceive their sparse and critical shape patterns. However, it is quite difficult to learn a shape-oriented representation for cartoon face recognition with convolutional neural networks (CNNs). To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner. Solving the puzzles requires the model to spot the shape patterns of the cartoon faces as the texture information is quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by randomly shuffling the intermediate convolutional feature maps in the spatial dimension and exploiting the GCN to reason and recover the correct layout of the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw avoids training the classification model with the deconstructed images that would introduce noisy patterns and are harmful for the final classification. Specially, GraphJigsaw can be incorporated at various stages in a top-down manner within the classification model, which facilitates propagating the learned shape patterns gradually. GraphJigsaw does not rely on any extra manual annotation during the training process and incorporates no extra computation burden at inference time. Both quantitative and qualitative experimental results have verified the feasibility of our proposed GraphJigsaw, which consistently outperforms other face recognition or jigsaw-based methods on two popular cartoon face datasets with considerable improvements. © 2021, CC BY-NC-SA.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Learning fair face representation with progressive cross transformer

arXiv

引用

arXiv 2021年

作者： Li, Yong Sun, Yufei Cui, Zhen Shan, Shiguang Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Face recognition (FR) has made extraordinary progress owing to the advancement of deep convolutional neural networks. However, demographic bias among different racial cohorts still challenges the practical face recognition system. The race factor has been proven to be a dilemma for fair FR (FFR) as the subject-related specific attributes induce the classification bias whilst carrying some useful cues for FR. To mitigate racial bias and meantime preserve robust FR, we abstract face identityrelated representation as a signal denoising problem and propose a progressive cross transformer (PCT) method for fair face recognition. Originating from the signal decomposition theory, we attempt to decouple face representation into i) identity-related components and ii) noisy/identity-unrelated components induced by race. As an extension of signal subspace decomposition, we formulate face decoupling as a generalized functional expression model to cross-predict face identity and race information. The face expression model is further concretized by designing dual cross-transformers to distill identity-related components and suppress racial noises. In order to refine face representation, we take a progressive face decoupling way to learn identity/racespecific transformations, so that identity-unrelated components induced by race could be better disentangled. We evaluate the proposed PCT on the public fair face recognition benchmarks (BFW, RFW) and verify that PCT is capable of mitigating bias in face recognition while achieving state-of-the-art FR performance. Besides, visualization results also show that the attention maps in PCT can well reveal the race-related/biased facial regions. © 2021, CC BY-NC-SA.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

An Image Denoising Method Based on Deep Residual GAN 4

An Image Denoising Method Based on Deep Residual GAN

引用

2020 4th International Workshop on Advanced Algorithms and Control Engineering, IWAACE 2020

作者： Wang, Ziyuan Wang, Lidan Duan, Shukai Li, Yunfei Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China Schoolof Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificiall Intelligence Southwest University Chongqing400715 China Braininspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

As people come into contact with image data more often, high quality and clear images attract more attention. Many methods have been proposed to deal with image noise problem including deep learning (DL). However most of them is lack of capability when customers want more perceptual details of the image without information loss. In this paper, a deep residual network based on generative adversarial (GAN) network was proposed to complete the image denoising mission. Firstly, a generative-adversarial network structure based on residual blocks was designed. Secondly, a refined loss function was given to train the GAN network. The well designed loss function can help the generated image to be very close to the clear counterpart (ground truth) while enhancing more details in colours and brightness. Finally, extensive experiments show that our network is not only convincing for images denoising, but also effective for other image process tasks, such as image defogging, medical CT denoising etc., presenting impressive and competitive effects. © Published under licence by IOP Publishing Ltd.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

Harmonized-Multinational qEEG norms (HarMNqEEG)

引用

NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230601 China Cuban Center for Neurocience La Habana Cuba University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia The Cuban center aging longevity and healt

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

来源：评论

学校读者我要写书评

暂无评论

Development of Economic Model Predictive Control for Permanent Magnet Synchronous Motor

Development of Economic Model Predictive Control for Permane...

引用

Sustainable Power and Energy Conference (iSPEC)

作者： Peijie Chen Tianhong Pan School of Electrical and Information Engineering Jiangsu University Zhenjiang China Key Laboratory of Intelligent Computing and Signal Processing of Minister of Education School of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (数字)9781728191645

ISBN: (纸本)9781728191652

Permanent magnet synchronous motor (PMSM) has gradually become the main driving motor for electric vehicles (EVs). Reducing the electrical loss of PMSM can effectively improve the cruising distance of EVs after a single charge. In order to improve the system operation efficiency, the economic factors of the system are considered, and a drive system for PMSM using the economic model predictive control (EMPC) has been proposed in this paper. Firstly, the mathematical model of PMSM is described, and the electrical loss of the system is investigated. Then, economic performance indicators are embedded into the cost function, and the optimal control law is obtained by solving the optimization problem with constraints. Case studies demonstrate that the proposed EMPC not only improves the dynamic response of the motor, but also reduces the loss of the system. In this way, the system operation efficiency is improved and the purpose of energy saving is achieved.

关键词： Economics Systems operation Optimal control Permanent magnet motors Synchronous motors Cost function Predictive control

来源：评论

学校读者我要写书评

暂无评论

An Audio-Visual Whisper Database in Chinese 4

An Audio-Visual Whisper Database in Chinese

引用

2019 4th International Conference on Intelligent computing and signal processing, ICSP 2019

作者： Zhou, Jian Hu, Yuting Lian, Hailun Pang, Cong Wang, Huabin Tao, Liang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei AnHui230039 China School of Computer Science and Technology Anhui University Hefei AnHui230601 China

Converting whisper to normal vocalized speech has been a hot research topic in speech signal processing area. A complete and large scale whisper database is a major basis for this task. In this paper, we propose a multimodal whisper database in Chinese mandarin. A total of 103 syllables and 100 sentences were carefully selected. 5 male and 5 female participants pronounced the syllables and sentences in whisper and normal styles respectively, result in 4096 parallel speech utterances and 263, 849 frames of voicing face and lip image sequences. The beginning and ending sample point of each syllable were labeled both for speech signal and voicing face video. The lip region of interest were also extracted and provided in the proposed database. Experiments in various speech conversion tasks in different speech database show the effectiveness of the proposed multimodal whisper speech database. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Database systems

来源：评论

学校读者我要写书评

暂无评论

Pneumatic soft bionic actuators inspired by elephant trunk for multidisciplinary robotics applications

引用

Physica Scripta 2025年第7期100卷 075013-075013页

作者： Xiaohui Guo Deli Kong Zihan Lin Xianghui Li Yupeng Shao Zhaobin Li Yinuo Chen Xinyu Wu Weiqiang Hong Tianxu Zhang Xiaowen Zhu Congguang He Junjie Qiu Xiangchen Zhao Qi Hong Yunong Zhao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Integrated Circuits Anhui University Hefei 230601 People’s Republic of China State Key Laboratory of High-Performance Precision Manufacturing Dalian University of Technology Dalian 116024 People’s Republic of China Key Laboratory for Micro/Nano Technology and System of Liaoning Province Dalian University of Technology Dalian 116024 People’s Republic of China School of Physics University of Science and Technology of China Hefei 230000 People’s Republic of China

Soft robots in technology makes rapid development in the field of robotics, so that robots are widely used in all aspects of life. This paper proposed a bionic elephant trunk structure of pneumatic actuator (SPA). To solve the insufficient tip force and bending angle of SPA, mathematical model and Finite Element Analysis (FEA) are used to determine the optimal parameters of the SPA. Based on optimized SPAs, robots for multidisciplinary applications are developed. These applications include soft gripper with high gripping force of up to 480 N, a quadruped soft robot capable of adapting complex amphibious environments, and a robot designed to assist with sign language communication. These demonstrate the versatility of soft robots in addressing challenges across various domains, such as healthcare, human-machine interaction, and environmental exploration.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：