检索结果-内蒙古大学图书馆

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： T. Jagdhuber A. Fluhrer D. Chaparro C. Dubois F. M. Hellwig B. Bayat C. Montzka M. J. Baur M. Ramati A. Kübert M. M. Mueller K. Schellenberg M. Boehm F. Jonard S. Steele-Dunne M. Piles D. Entekhabi German Aerospace Center (DLR) Microwaves and Radar Institute (HR) Wessling Germany Institute of Geography University of Augsburg Augsburg Germany Department for Earth Observation Friedrich Schiller University Jena Jena Germany Forschungszentrum Jülich Institute of Bio-and Geosciences: Agrosphere (IBG-3) Jülich Germany Department of Geography Cambridge University Cambridge UK Institute for Atmospheric and Earth System Research (INAR) University of Helsinki Helsinki Finland German Aerospace Center (DLR) Institute of Data Science Jena Germany Department of Biogeochemical Processes Max-Planck Institute for Biogeochemistry Jena Germany University of Liège Earth Observation and Ecosystem Modelling Laboratory SPHERES Unit Liège Belgium TU Delft Faculty of Civil Engineering and Geosciences Delft The Netherlands University of Valencia Image Processing Laboratory Valencia Spain Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge USA

Tracking seasonal dynamics of evapotranspiration (ET) across global biomes and along seasonal time periods using remote sensing is vital for monitoring ecosystem health and indicating early signals of drought. In this study, we assess the potential of adding weather and illumination-independent signals from active and passive microwave remote sensing (SAR backscatter & vegetation optical depth, VOD) to the established set of ET products, like from optical/thermal remote sensing (MODIS, SEVIRI) and reanalysis (ERA-5 land, GLDAS) *** study covers a four-year period (2017-2020), including dry (2018 & 2019) and wet (2017) years. The study was conducted over eight ICOS sites across Europe. These sites are predominantly forested with a low biomass dynamic over the observation *** find that the ET products from in situ Eddy Covariance (EC), MODIS, and GLDAS deviate relatively minor along the seasons (< 1 [mm/day]), but differ between years. Here, the years (2017-2020) indicate a slightly different ET rate between in situ measurements (EC) and derived products (MODIS & GLDAS), which is currently being investigated. The microwave-based indicators (backscatter & VOD) are proxies by their nature and serve as first-order indicators of relative dynamics allowing the identification of seasonal patterns of ET as well as their spatio-temporal anomalies along both dry and wet years.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reliable Multi-Modal Object Re-Identification via Modality-Aware Graph Reasoning

arXiv

引用

arXiv 2025年

作者： Wan, Xixi Zheng, Aihua Wang, Zi Jiang, Bo Tang, Jin Ma, Jixin 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 School of Biomedical Engineering Anhui Medical University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom

Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) tasks. However, existing approaches often overlook the quality variations in local features and fail to fully leverage the complementary information across modalities, particularly in the case of low-quality features. In this paper, we propose to address this issue by leveraging a novel graph reasoning model, termed the Modality-aware Graph Reasoning Network (MGRNet). Specifically, we first construct modality-aware graphs to enhance the extraction of fine-grained local details by effectively capturing and modeling the relationships between patches. Subsequently, the selective graph nodes swap operation is employed to alleviate the adverse effects of low-quality local features by considering both local and global information, enhancing the representation of discriminative information. Finally, the swapped modality-aware graphs are fed into the local-aware graph reasoning module, which propagates multimodal information to yield a reliable feature representation. Another advantage of the proposed graph reasoning approach is its ability to reconstruct missing modal information by exploiting inherent structural relationships, thereby minimizing disparities between different modalities. Experimental results on four benchmarks (RGBNT201, Market1501-MM, RGBNT100, MSVR310) indicate that the proposed method achieves state-of-the-art performance in multi-modal object ReID. The code for our method will be available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Modal analysis

来源：评论

学校读者我要写书评

暂无评论

Multi-Level Context Ultra-Aggregation for Stereo Matching

Multi-Level Context Ultra-Aggregation for Stereo Matching

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Guang-Yu Nie Ming-Ming Cheng Yun Liu Zhengfa Liang Deng-Ping Fan Yue Liu Yongtian Wang Beijing Institute of Technology TKLNDST CS Nankai University National Key Laboratory of Science and Technology on Blind Signal Processing

ISBN: (纸本)9781728132945

Exploiting multi-level context information to cost volume can improve the performance of learning-based stereo matching methods. In recent years, 3-D Convolution Neural Networks (3-D CNNs) show the advantages in regularizing cost volume but are limited by unary features learning in matching cost computation. However, existing methods only use features from plain convolution layers or a simple aggregation of multi-level features to calculate cost volume, which is insufficient because stereo matching requires discriminative features to identify corresponding pixels in rectified stereo image pairs. In this paper, we propose a unary features descriptor using multi-level context ultra-aggregation (MCUA), which encapsulates all convolutional features into a more discriminative representation by intra-and inter-level features combination. Specifically, a child module that takes low-resolution images as input captures larger context information;the larger context information from each layer is densely connected to the main branch of the network. MCUA makes good usage of multi-level features with richer context and performs the image-to-image prediction holistically. We introduce our MCUA scheme for cost volume calculation and test it on PSM-Net. We also evaluate our method on Scene Flow and KITTI 2012/2015 stereo datasets. Experimental results show that our method outperforms state-of-the-art methods by a notable margin and effectively improves the accuracy of stereo matching.

关键词： branch main Volume Costs Unary multilayers stereo imaging Convolution

来源：评论

学校读者我要写书评

暂无评论

Minutiae Triangle Graphs: A New Fingerprint Representation with Invariance Properties

Minutiae Triangle Graphs: A New Fingerprint Representation w...

引用

International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Akmal-Jahan Mohamed-Abdul-Cader Watcharapong Chaidee Jasmine Banks Vinod Chandran Vision and Signal Processing Lab School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia Signal and Image Processing Laboratory Kasetsart University Bangkok Thailand

A new algorithm for matching finger or palm prints is presented for use where the full hand is considered as a biometric and only parts may be available in images for comparison. The algorithm uses an extended version of the minutiae-based approach treating the pattern as a graph of minutiae-like points. The procedure to identify minutiae-like points uses Gabor filtering, edge detection and thinning and following line patterns. A set of such points is subjected to Delaunay triangulation yielding a starting set of base-triangles for matching. There can be multiple matches of such triangles between the template and test - as similar triangles with a tolerance in the angles. Graphs are then grown to 5 and more nodes as long as a match can be found, until the maximum size matching graph is obtained. If the test matches a significant part of the template, the maximum order of graph matched will be high. The matching process is robust to transformations such as rotation, translation and scale changes. It can be applied to any part of the hand provided minutiae-like points are identifiable prior to the matching steps. The algorithm is tested using 158 fingerprint images from FVC 2002 DB1. 100 genuine and 5048 impostor scores are generated from 46 templates and 112 testing images. It had an EER of about 6%. It proves the principle behind the methodology and demonstrates that the method can be effective with degraded fingerprint images and is robust to similarity transformations present in the data. It can be applied for forensic fingerprint matching from the palm or parts other than the fingertips. By using multiple parts and multiple templates, the accuracy of the method will be improved with fusion in future versions of the algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Novel deep hybrid model for electricity price prediction based on dual decomposition

引用

Applied Energy 2025年 395卷

作者： Ghimire, Sujan Nguyen-Huy, Thong Deo, Ravinesh C. Casillas-Pérez, David Masrur Ahmed, A.A. Salcedo-Sanz, Sancho Artificial Intelligence Applications Laboratory School of Mathematics Physics and Computing University of Southern Queensland SpringfieldQLD4300 Australia Centre for Applied Climate Sciences University of Southern Queensland ToowoombaQLD4350 Australia Faculty of Information Technology Thanh Do University Kim Chung Hoai Duc Ha Noi100000 Viet Nam Department of Signal Processing and Communications Universidad Rey Juan Carlos Madrid Fuenlabrada28942 Spain Science Economics and Insights Division Department of Climate Change Energy the Environment and Water NSW Government SydneyNSW2141 Australia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Madrid 28805 Spain

Electricity price (EP) forecasting is vital for effective market operation, strategic planning, and risk management in deregulated energy systems. However, the inherent volatility and complexity of electricity prices, shaped by demand supply dynamics, weather variability, and regulatory interventions, pose substantial challenges to accurate prediction. This study introduces a novel hybrid framework designed to improve forecasting accuracy by leveraging both signal decomposition and deep learning techniques. Specifically, the method integrates Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) for noise reduction and feature extraction, followed by a Multi Resolution Convolution (MRC) layer and a Bidirectional Long Short Term Memory (BiLSTM) network to capture multiscale temporal patterns in electricity price data. The model is applied to half hourly electricity price data from South Australia spanning January 2018 to December 2022. Its performance is benchmarked against a suite of traditional and hybrid models using a comprehensive set of twelve evaluation metrics. The results reveal that the proposed hybrid model consistently outperforms all baselines across seasons and forecast horizons. Notably, during the spring period, it achieved a Normalized Root Mean Square Error of ≈4.87%, a Mean Absolute Percentage Error of ≈12.09%, and a Global Performance Index of ≈3.22. These improvements demonstrate the model's ability to effectively handle the non-linear and nonstationary nature of EP. Overall, the findings underscore the potential of combining advanced decomposition methods with deep learning architectures to deliver more accurate and robust EP forecasts, thereby offering valuable support for decision making in complex and evolving energy markets. © 2025 The Author(s)

关键词： Risk perception

来源：评论

学校读者我要写书评

暂无评论

Infrared small-dim target detection with transformer under complex backgrounds

arXiv

引用

arXiv 2021年

作者： Liu, Fangcen Gao, Chenqiang Chen, Fang Meng, Deyu Zuo, Wangmeng Gao, Xinbo The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications Chongqing400065 China The Viterbi School of Engineering University of Southern California California90089 United States School of Mathematics and Statistics Xi’an Jiaotong University Shanxi Xi’an710049 China Macau Institute of Systems Engineering Macau University of Science and Technology Taipa999078 China School of Computer Science and Technology Harbin Institute of Techonlogy Heilongjiang Harbin47822 China

The infrared small-dim target detection is one of the key techniques in the infrared search and tracking system. Since the local regions similar to infrared small-dim targets spread over the whole background, exploring the interaction information amongst image features in large-range dependencies to mine the difference between the target and background is crucial for robust detection. However, existing deep learning-based methods are limited by the locality of convolutional neural networks, which impairs the ability to capture large-range dependencies. Additionally, the small-dim appearance of the infrared target makes the detection model highly possible to miss detection. To this end, we propose a robust and general infrared small-dim target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range. Moreover, we design a feature enhancement module to learn discriminative features of small-dim targets to avoid miss detection. After that, to avoid the loss of the target information, we adopt a decoder with the U-Net-like skip connection operation to contain more information of small-dim targets. Finally, we get the detection result by a segmentation head. Extensive experiments on two public datasets show the obvious superiority of the proposed method over state-of-the-art methods and the proposed method has stronger cross-scene generalization and anti-noise performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of an Anomaly-Detection Algorithm for Keystroke-Typing Based Insider Detection

引用

Tsinghua science and technology 2018年第5期23卷 513-525页

作者： Liang He Zhixiang Li Chao Shen National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu 610041 China Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an 710004 China

Keystroke dynamics is the process to identify or authenticate individuals based on their typing rhythm behaviors. Several classifications have been proposed to verify a user＇s legitimacy, and the performances of these classifications should be confirmed to identify the most promising research direction. However, classification research contains several experiments with different conditions such as datasets and methodologies. This study aims to benchmark the algorithms to the same dataset and features to equally measure all performances. Using a dataset that contains the typing rhythm of 51 subjects, we implement and evaluate 15 classifiers measured by Fl-measure, which is the harmonic mean of a false-negative identification rate and false-positive identification rate. We also develop a methodology to process the typing data. By considering a case in which the model will reject the outsider, we tested the algorithms on an open set. Additionally, we tested different parameters in random forest and k nearest neighbors classifications to achieve better results and explore the cause of their high performance. We also tested the dataset on one-class classification and explained the results of the experiment. The top-performing classifier achieves an Fl-measure rate of 92% while using the normalized typing data of 50 subjects to train and the remaining data to test. The results, along with the normalization methodology, constitute a benchmark for comparing the classifiers and measuring the performance of keystroke dynamics for insider detection.

关键词： keystroke dynamics insider identification Fl-measure normalization one-class classification

来源：评论

学校读者我要写书评

暂无评论

A New Approach for Optimization Selection of Spaceborne SAR Beam Position Parameters

A New Approach for Optimization Selection of Spaceborne SAR ...

引用

Asian and Pacific Conference on Synthetic Aperture radar (APSAR)

作者： Mingang Du Guang-Cai Sun Yandong Cao Mengdao Xing Liang Guo Jun Yang 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: (数字)9781728129129

ISBN: (纸本)9781728129136

In this paper, a new optimized selection method of beam position parameters for spaceborne synthetic aperture radar(SAR) is proposed. Aiming at the difficulty in selecting pulse repeatition frequency (PRF) and observation band width in spaceborne SAR beam position design, this paper deduces the iterative relationship between adjacent beam positions in detail from the spaceborne SAR beam geometry, and proposes an automatic optimization and selection method for beam position parameters. The proposed method meets the principles of PRF restriction and maximum observation band width. The PRF and observation band width of one beam position can be optimized and selected at the same time, and the next beam position parameters can be automatically iteratively selected according to the current selection result. Simulation results validate the effectiveness of the proposed method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Improved Particle Filter Based Track-Before-Detect Method for Underwater Target Bearing Tracking

An Improved Particle Filter Based Track-Before-Detect Method...

引用

OCEANS - Europe

作者： Shenglong Jin Haining Huang Yu Li Yufei Ren Yujie Wang Rongxing Zhong Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of Sciences Beijing China

Track-before-detect (TBD) methods have been shown to greatly abate measurement-to-track association (MTA) challenges which could cost plenty of operator workload in many detection systems. In the field of underwater acoustic signal processing, the low signal-to-noise ratio, random missing measurements, multiple interference scenarios, and merging-splitting contacts in measurement space are challenging for common target tracking algorithms. As a result, particle filter (PF) based track-before-detect methods that compute posterior density distribution directly using beamformer output instead of bearing measurements are effective in these cases. However, the general PF always suffers from the particle impoverishment problem which can lead to the misleading state estimation results due to system process error. To cope with above problems, an improved particle filter based track-before-detect method is proposed in this paper. The proposed PF-TBD method adopts crossover and mutation operators from genetic algorithm to evolve particles with small weight. And the quasi-Monte Carlo method is applied into resampling procedure of particle filter. The effectiveness of the method is verified by using experimental data obtained at sea, which can continuously and accurately track target bearings in the case of temporary signal disappearance and multi-target intersection. The estimated result is close to the real value even when the motion model suffers mismatch.

关键词： Target tracking Sea measurements radar tracking Atmospheric measurements Particle measurements Trajectory

来源：评论

学校读者我要写书评

暂无评论

Facial feature embedded CycleGAN for VIS-NIR translation

arXiv

引用

arXiv 2019年

作者： Wang, Huijiao Wang, Li Yang, Xulei Yu, Lei Zhang, Haijian Signal Processing Laboratory School of Electronic Information Wuhan University China Agency for Science Technology and Research Singapore

VIS-NIR face recognition remains a challenging task due to the distinction between spectral components of two modalities and insufficient paired training data. Inspired by the CycleGAN, this paper presents a method aiming to translate VIS face images into fake NIR images whose distributions are intended to approximate those of true NIR images, which is achieved by proposing a new facial feature embedded CycleGAN. Firstly, to learn the particular feature of NIR domain while preserving common facial representation between VIS and NIR domains, we employ a general facial feature extractor (FFE) to replace the encoder in the original generator of CycleGAN. For implementing the facial feature extractor, herein the MobileFaceNet is pretrained on a VIS face database, and is able to extract effective features. Secondly, the domain-invariant feature learning is enhanced by considering a new pixel consistency loss. Lastly, we establish a new WHU VIS-NIR database which varies in face rotation and expressions to enrich the training data. Experimental results on the Oulu-CASIA NIR-VIS database and the WHU VIS-NIR database show that the proposed FFE-based CycleGAN (FFE-CycleGAN) outperforms state-of-the-art VISNIR face recognition methods and achieves 96.5% accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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：