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8th International Conference on Computing and Pattern Recognition, ICCPR 2019

作者： Liu, Yuanqing Guo, Xiaojing Zhang, Haigang Yang, Jinfeng Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China Civil Aviation University of China Tianjin China Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9781450376570

Civil Aviation's Radiotelephony Communication (CARC) in hina involves Chinese and English. Due to the particular grammatical structure and pronunciation of CARC, the universal cross-lingual acoustic model isn't applicable. For the purpose of achieving the Chinese-English speech recognition of CARC, this paper proposes a cross-lingual acoustic model using a sharedhidden- layer Convolution Depth Neural Network (CDNN) with hidden Markov model. It introduces the Convolution Neural Network (CNN) to overcome the diversity of speech signals. In order to shorten the training and decoding time, the Low Frame Rate (LFR) is added in the feature extraction stage. The experimental results show that the acoustic model based on CDNN is better applied to the domain of CARC. The introduction of CNN can further improve recognition performance. Adding the LFR effectively reduces the training time and the word error rate. © 2019 Association for Computing Machinery.

关键词： Radio telephone

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High-Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High-Resolution Data and an Auxiliary Attention Temporal Convolutional Network

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2025年第2期2卷

作者： Jianghe Chen Pan Xiong Haochen Wu Xiaoran Zhang Xuemin Zhang Rongzi Chai Ting Zhang Kaixin Wang Chaoyu Wang Institute of Earthquake Forecasting China Earthquake Administration Beijing China Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan China School of Civil Engineering and Geomatics Shandong University of Technology Zibo China Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China

Accurate prediction of Total Electron Content (TEC) in the ionosphere is crucial for navigation, communication, and space weather forecasting. However, the Global Ionosphere Maps provided by the International GNSS Service have limitations in resolution and adaptability in the China region, making high-precision predictions difficult. This study constructs a high-precision regional TEC map with 1° × 1° spatial resolution, 2-hr temporal resolution, and coverage from 2019 to 2023, based on data from 270 Crustal Movement Observation Network of China (CMONOC) GNSS stations. At the data level, a non-integrated spherical harmonic model and Differential Code Bias correction method are employed to significantly reduce interpolation errors and improve model accuracy. At the algorithmic level, an Auxiliary Attention Temporal Convolutional Network (AuxATTCN) model is proposed, integrating an auxiliary attention mechanism with a Temporal Convolutional Network (TCN) to effectively capture long-term dependencies and dynamically incorporate external driving factors such as geomagnetic activity and solar radiation. Comparative analysis with multiple experiments under varying geomagnetic and solar conditions shows that the AuxATTCN model significantly outperforms traditional time-series methods (such as ARIMA, Prophet), mainstream deep learning models (including ConvLSTM, CONGRU, and TCN), and international ionospheric models (IRI2020, NeQuick2) in terms of overall error, seasonal and diurnal variations, and prediction accuracy during geomagnetic storms and solar activity peaks. The results demonstrate that the synergistic optimization of high-quality CMONOC data sets and innovative algorithms achieves exceptional spatiotemporal accuracy and robustness in TEC prediction for the China region, providing new insights and technical support for fields such as navigation, communication, and space weather forecasting. Developed a high-precision ionospheric Total Electron Content (TEC) predi

关键词： ionospheric TEC deep learning AuxATTCN CMONOC forecasting

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Ground clutter suppression method based on FNN for dual-polarisation weather radar

Ground clutter suppression method based on FNN for dual-pola...

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IET International Radar Conference 2018, IRC 2018

作者： Li, Hai Ren, Jiawei Han, Jungong Fan, Yi Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China School of Computing and Communications Lancaster University Lancaster United Kingdom

Conventional clutter suppression methods in the field of precipitation detection generally use fixed parameters, resulting in poor clutter suppression in complex weather conditions and regional environments. Here, a ground clutter suppression method for dual-polarisation weather radar based on fuzzy neural network (FNN) is proposed. In the method, the FNN is trained with the ground clutter data acquired by the dual-polarisation weather radar in the clear sky mode. The membership function parameters of ground clutter polarisation parameters are adaptively calculated. Then, the ground clutter in the radar echo under the precipitation mode is identified and suppressed by the trained FNN. Experimental results of measured data show that the proposed method can be adapted to the specific clutter environment so that the ground clutter can be effectively suppressed in complex weather conditions. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Complex networks

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Wind speed estimation of low-altitude wind shear based on combined space-time main channel adaptive processing

Wind speed estimation of low-altitude wind shear based on co...

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IET International Radar Conference 2018, IRC 2018

作者： Li, Hai Wang, Jie Guo, Qing H. Li, Yi J. Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong Australia

This study presents a low-altitude wind shear speed estimation method based on combined space-time main channel adaptive processing. The method first reduces the dimension of the radar echo data of the measured range unit by constructing a dimension-reduced joint space-time transform matrix, and then constructs the optimal weight vector of the dimension-reducing processor to adaptively filter the reduced-dimensional data. Furthermore, accurate estimation of the speed of the wind field is achieved in the presence of the amplitude-phase error. Simulation results demonstrate the effectiveness of the proposed method. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Vector spaces

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Some new iterative algorithms for finding fixed points of nonexpansive mappings

Journal of Applied and Numerical Optimization

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Journal of Applied and Numerical Optimization 2020年第2期2卷 143-154页

作者： Li, Xiao-Huan Dong, Qiao-Li Gibali, Aviv Tianjin Key Lab for Advanced Signal Processing College of Science Civil Aviation University of China Tianjin300300 China Department of Mathematics ORT Braude College Karmiel2161002 Israel Center for Mathematics and Scientific Computation University of Haifa Mt. Carmel Haifa3498838 Israel

In this paper, we introduce some new iterative algorithms for finding fixed points of nonexpansive mappings with the aid of projection and contraction methods. Weak and strong convergence theorems are established under mild conditions in Hilbert spaces. The numerical examples are presented to illustrate the advantage of our proposed algorithms. © 2020 Journal of Applied and Numerical Optimization.

关键词： Iterative methods

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Finger Multimodal Features Fusion and Recognition Based on CNN

Finger Multimodal Features Fusion and Recognition Based on C...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Li Wang Haigang Zhang Jingfeng Yang Tanjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China Shenzhen Polytechnic Shenzhen China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Finger-based multimodal fusion recognition has attracted increasing attention due to its high stability and security in practical application. Traditional fusion methods exist some challenges in scale inconsistency and universality. In additon, multimodal dimension standardization has not been well realized. In this paper, we propose a novel convolutional neural network(CNN) framework for finger multimodal fusion and recognition, which obtains the fusion features by network learning automatically. The fusion network makes full use of the complementary information among three modals of finger to make the fusion features more stable and effective. Firstly, the three parallel CNNs have been designed to extract unimodal features. Then, a size standardization method based on principal component analysis(PCA) is utilized for different unimodal features. Finally, the high-level unimodal features are integrated to learn fusion features with better representation capability. Extensive experiments on the finger multimodal dataset show that the proposed multimodal fusion network performs better than other state-of-the-arts.

关键词： Feature extraction Biometrics (access control) Standardization Convolution Authentication Principal component analysis Imaging

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AN IMPROVED FAST TIME-DOMAIN ALGORITHM FOR BISTATIC FORWARD-LOOKING SAR IMAGING

AN IMPROVED FAST TIME-DOMAIN ALGORITHM FOR BISTATIC FORWARD-...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Song Zhou Lei Yang Lifan Zhao Yuhao Wang Chaoqun Zhang School of Information Engineering Nanchang University China Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China School of Electrical and Electronic Engineering (EEE) Nanyang Technological University (NTU) Singapore

Time-domain algorithms have special advantages for bistatic forward-looking synthetic aperture radar (BFSAR) applications with complex geometric configuration. In this paper, a improved fast time-domain algorithm based on orthogonal elliptical polar (OEP) coordinate is proposed for BFSAR imaging, which has prominently reduced burden in computation. In addition, the non-systematic range cell migration (NsRCM) is also analyzed and corrected in the motion compensation (MOCO) process. Simulation experiments are presented and analyzed to validate the superiority of the proposed algorithm.

关键词： Geometry Time-domain analysis signal processing algorithms Two dimensional displays Synthetic aperture radar Radar polarimetry Radar imaging

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Active Lighting Recurrence by Parallel Lighting Analogy for Fine-Grained Change Detection

arXiv

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arXiv 2020年

作者： Zhang, Qian Feng, Wei Wan, Liang Tian, Fei-Peng Wang, Xiaowei Tan, Ping School of Computer Science and Technology College of Intelligence and Computing Tianjin University TianJin300350 China State Administration of Cultural Heritage China School of Computer Software College of Intelligence and Computing Tianjin University TianJin300350 China Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Dunhuang Research Academy Dunhuang Gansu China School of Computing Science Simon Fraser University Canada

—This paper studies a new problem, namely active lighting recurrence (ALR) that physically relocalizes a light source to reproduce the lighting condition from single reference image for a same scene, which may suffer from fine-grained changes during twice observations. ALR is of great importance for fine-grained visual inspection and change detection, because some phenomena or minute changes can only be clearly observed under particular lighting conditions. Therefore, effective ALR should be able to online navigate a light source toward the target pose, which is challenging due to the complexity and diversity of real-world lighting and imaging processes. To this end, we propose to use the simple parallel lighting as an analogy model and based on Lambertian law to compose an instant navigation ball for this purpose. We theoretically prove the feasibility, i.e., equivalence and convergence, of this ALR approach for realistic near point light source and small near surface light source. Besides, we also theoretically prove the invariance of our ALR approach to the ambiguity of normal and lighting decomposition. The effectiveness and superiority of the proposed approach have been verified by both extensive quantitative experiments and challenging real-world tasks on fine-grained change detection of cultural heritages. We also validate the generality of our approach to non-Lambertian scenes. Copyright © 2020, The Authors. All rights reserved.

关键词： Light sources

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A New Technique for Improving the Anti-jamming Performance of Vector Tracking Loop

A New Technique for Improving the Anti-jamming Performance o...

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第九届中国卫星导航学术年会

作者： Ruihua Zhang Renbiao Wu Qiongqiong Jia Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China

The GNSS is vulnerable so that it often faces the risk of jamming,and blanket jamming is one of the *** inversion algorithm is a commonly method used to solve this problem,however,the receiver will lose lock when GNSS satellites are close to RFI *** improve the receiver's anti-jamming performance in this harsh scene,a method of vector tracking loop(VTL) based on despread-respread(DR) algorithm is ***,this paper suppress oppressive ***,local reference signal is reconstructed according to predicted tracking loop ***,beamforming algorithm is *** analysis and simulation experiments verify that the VTL based on DR algorithm can reduce phase discriminator output errors,improve receiver's positioning performance,and has superior anti-jamming performance in harsh *** method proposed in this paper can be applied to arbitrary array without prior information of satellites' directions and is not sensitive to the error of *** to VTL based on EIG,VTL based on DR algorithm has lower complexity.

关键词： Vector tracking loop Anti-jamming Despread-respread algorithm Beamforming algorithm

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LEARNING DISCRIMINATIVE GEODESIC FLOW KERNEL FOR UNSUPERVISED DOMAIN ADAPTATION

LEARNING DISCRIMINATIVE GEODESIC FLOW KERNEL FOR UNSUPERVISE...

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IEEE International Conference on Multimedia and Expo

作者： Jianze Wei Jian Liang Ran He Jinfeng Yang CAUC Tianjin Key Lab for Advanced Signal Processing University of Chinese Academy of Sciences (UCAS)

Extracting the domain-invariant features provides an important intuition for unsupervised domain adaptation. Due to the unavailable target labels, it is difficult to guarantee that the learned domain-invariant features are good for target instances classification. In this paper, we extend the classic geodesic flow kernel method by leveraging the pseudo labels during the training process to learn a discriminative geodesic flow kernel for unsupervised domain adaptation. Specifically, the proposed method alternately discovers the pseudo target labels and builds the geodesic flow from a discriminative source subspace to another 'discriminative' target subspace. More specially, the pseudo target labels are inferred via the learned kernel based on an easy yet effective label propagation strategy. Hence, the proposed method not only holds the property of domain-invariance, but also maximizes the consistency between pseudo label structure and data structure. Experimental results illustrate that the proposed method out-performs the state-of-the-art unsupervised domain adaptation methods for object recognition and sentiment analysis.

关键词： Unsupervised domain adaptation Geodesic flow kernel label propagation

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