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5th ENRI International Workshop on ATM/CNS, EIWAC 2017

作者： Han, Ping Lin, Zeshan Lu, Xiaoguang Shi, Qingyan Zhang, Zhe Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9789811370854

This paper proposes an unsupervised algorithm for airport runway area detection based on super-pixel PolSAR image classification. First, the simple linear iterative clustering (SLIC) algorithm are used to obtain super-pixel image by segmenting the PauliRGB image in order to reduce computational complexity and save computing time. Then, VAT-DBE algorithm is used to estimate and obtain the number of clusters of the image automatically. Combing the polarization information, the super-pixel image is classified by the method of spectral clustering. After that, the suspected airport runway area is extracted according to the scattering characteristics of the runway and classification result. Finally, the airport runway area is detected by using structural and topological characteristics of the runways. The experimental results show that the proposed algorithm can detect the airport runway area effectively with a clear outline, complete structure, and low false alarm rate. It also needs less time and a priori information compared with other methods. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Iterative methods

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Simulation System of Aircraft Surveillance in Airport Terminal Area

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Journal of Physics: Conference Series 2022年第1期2228卷

作者： Xiaoyun Shen Haosong Zhai Shuo Zhang Peng Cui Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin 300300 China

Because ordinary users cannot quickly obtain the three-dimensional space position of aircraft in the airport terminal area through a two-dimensional display navigation map, the aircraft surveillance simulation system in the airport terminal area based on virtual reality technology is designed. Through analyzing the Automatic Dependent Surveillance-Broadcast (ADS-B) data, the aircraft motion model is established. The three-dimensional aircraft surveillance simulation system for the airport terminal area is realized by Unreal Engine 4 (UE4). Especially, the changes in flight track and flight attitude about aircraft in the three-dimensional scene are designed. The results show that the aircraft movements can be visualized smoothly and correctly based on ADS-B data. It is helpful for users to observe the aircraft of the airport terminal area and analyze the operation status of the plane.

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Vector Approximate Message Passing based Channel Estimation for OFDM Underwater Acoustic Communications

Vector Approximate Message Passing based Channel Estimation ...

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OCEANS

作者： Wenxuan Chen Jun Tao Hongli Cao Ming Jiang Yongjie Qiao 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 Pengcheng Lab Shenzhen China

ISBN: (纸本)9781665427883

Accurate channel estimation is critical to the performance of orthogonal frequency-division multiplexing (OFDM) underwater acoustic (UWA) communications. In this paper, we explore vector approximate message passing (VAMP) based channel estimation (CE) for OFDM UWA communications. The VAMP-CE is developed by employing a Bernoulli-Gaussian as the prior distribution of the channel impulse response. Performance of the VAMP-CE is evaluated through real data collected during a UWA communication experiment conducted at Xiamen in 2019. Experimental results show that VAMP-CE requires less pilots and achieves better performance compared with conventional channel estimation methods.

关键词： Performance evaluation OFDM Message passing Oceans Channel estimation Estimation Frequency division multiplexing

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Synergistic Interaction of Cu(Ii) with Caffeic Acid and Chlorogenic Acid in Α-Glucosidase Inhibition

SSRN

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SSRN 2023年

作者： Tang, Lihua Guan, Qinhao Xu, Man Zhang, Liangliang Zhang, Meng Institute of Chemical Industry of Forest Products CAF Key Lab. of Biomass Energy and Material Jiangsu Province Nanjing210042 China Co-Innovation Center of Efficient Processing Utilization of Forest Resources Nanjing Forestry University Nanjing210042 China Academy of Advanced Carbon Conversion Technology Huaqiao University Xiamen361021 China Jiangsu Key Laboratory for Bioresources of Saline Soils School of Wetlands Yancheng Teachers University Yancheng224002 China

Phenolic acids are widely present in foods and are beneficial to human health. However, the role of metal ion in influencing the binding of protein with phenolic acids that contain the same parent nucleus structure remains unclear. In this study, we investigated the inhibitory effect of caffeic acid (CA) and chlorogenic acid (CHA) on α-glucosidase and the biological effect of copper on this process. It was found that the esterification of CA with quinic acid could increase the fluorescence quenching, conformational change, and inhibitory effect of CHA on α-glucosidase . Copper ion reduced their fluorescence quenching and conformation-changing ability by binding to the neighboring phenolic hydroxyl group, but also increased their ability to alter secondary structure, inhibit α-glucosidase and in vitro anti-glycation. Overall, this study shows that the binding of copper ion to the phenolic hydroxyl group adjacent to CA and CHA synergistically inhibits the α-glucosidase. © 2023, The Authors. All rights reserved.

关键词： Fluorescence quenching

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Intelligent checking model of Chinese radiotelephony read-backs in civil aviation air traffic control

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Chinese Journal of Aeronautics 2018年第12期31卷 2280-2289页

作者： Guimin JIA Fangyuan CHENG Jinfeng YANG Dan LI Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China

Federal Aviation Administration(FAA) and NASA technical reports indicate that the misunderstanding in radiotelephony communications is a primary causal factor associated with operation errors, and a sizable proportion of operation errors lead to read-back errors. We introduce deep learning method to solve this problem and propose a new semantic checking model based on Long Short-Time Memory network(LSTM) for intelligent read-back error checking. A meanpooling layer is added to the traditional LSTM, so as to utilize the information obtained by all the hidden activation vectors, and also to improve the robustness of the semantic vector extracted by LSTM. A MultiLayer Perceptron(MLP) layer, which can maintain the information of different regions in the concatenated vectors obtained by the mean-pooling layer, is applied instead of traditional similarity function in the new model to express the semantic similarity of the read-back pairs quantitatively. The K-Nearest Neighbor(KNN) classifier is used to verify whether the read-back pairs are consistent in semantics according to the output of MLP layer. Extensive experiments are conducted and the results show that the proposed model is more effective and more robust than the traditional checking model to verify the semantic consistency of read-backs automatically.

关键词： Air traffic control Chinese radiotelephony read-backs LSTM Mean pooling MLP Semantic checking

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Flexible Priori Proximal Bayesian Learning for High-Resolution SAR Imagery

Flexible Priori Proximal Bayesian Learning for High-Resoluti...

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IEEE International Conference on Radar

作者： Xianhua Liao Lei Yang Yuchen Dou Xuan Li Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China Macalester College Saint Paul United States China Greatwall Technology Group Co. Ltd Shenzhen China

ISBN: (纸本)9781665468893

The conventional high-resolution synthetic aperture radar(SAR) imagery based on Bayesian learning encounter the problems of static and inflexible of prior. In this paper, a novel Bayesian learning algorithm based on flexible prior (FP-Bayes) is proposed for high-resolution SAR imagery, in which a new flexible prior is introduced to represent the rich features of target scene. However, the model becomes complicated due to the intended prior, which is hard to be solved by the classic Markov Chain Monte Carlo (MCMC) sampling algorithms. Therefore, the Hamiltonian Monte Carlo (HMC) is applied to this problem. Considering the potential function of the posterior distribution is likely to be non-differentiable, the proximal operator is introduced, and the proximal-HMC (P-HMC) is developed, which is capable of solving the sampling problem of this posterior. To this end, the high-resolution SAR images can be obtained. In the experimental part, the raw SAR data is utilized to verify the effectiveness and superiority of the proposed algorithm. The phase transition diagram (PTD) is also adopted to analyze the imaging performance quantitatively.

关键词： Monte Carlo methods Imaging Scattering Radar imaging Markov processes Apertures Radar polarimetry

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Resource Allocation for Twin Maintenance and Task processing in Vehicular Edge Computing Network

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IEEE Internet of Things Journal 2025年

作者： Xie, Yu Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Wang, Jiangzhou Letaief, Khaled B. Jiangnan University School of Internet of Things Engineering Wuxi 214122 China Nanchang University School of Information Engineering Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications Nanchang 330031 China Tsinghua University Department of Electronic Engineering State Key Laboratory of Space Network and Communications Beijing National Research Center for Information Science and Technology Beijing 100084 China Xidian University State Key Lab. of ISN School of Telecommunications Engineering Xi’an 710071 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai 200240 China Southeast University School of Information Science and Engineering Nanjing 211111 China Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Hong Kong

In the digital twin mobile edge network, the maintenance of the vehicle twin model and vehicular task processing in the server require the support of computing resources. In addition, they are performed simultaneously. Therefore, how to allocate resources for twin maintenance and task processing under limited server resources is crucial. However, current research tends to ignore the aspect of resource competition for twin maintenance. In this study, we analyze the delays of these two affected by resource allocation under a generic digital twin mobile edge network (DTMEN) to construct the optimization problem. For this problem, we transformed the problem using a Markov decision process. Meanwhile, we propose a multi-agent reinforcement learning (MADRL) based twin maintenance and task processing resource collab.rative scheduling (TMTPRCS) algorithm to solve the problem. Experiments show that our proposed approach is effective in terms of resource allocation compared to other alternative algorithms. © 2014 IEEE.

关键词： Resource allocation Twin maintenance Vehicular edge computing

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CNN-based Automatic Modulation Classification Over Underwater Acoustic Channels

CNN-based Automatic Modulation Classification Over Underwate...

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OCEANS

作者： Yuhua Xiao Yifeng Zhang Jun Tao Hongli Cao Yanjun Wu Yongjie Qiao School of Information Science and Engineering Southeast University Nanjing China Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education Southeast University Nanjing China State Key Lab. of Acoustics Institute of Acoustics Chinese Academy of Sciences Beijing China Pengcheng Lab Shenzhen China

ISBN: (纸本)9781665427883

Automatic modulation classification (AMC) aims to recognize modulation schemes from received communication signals. Such a task is especially challenging over underwater acoustic (UWA) channels due to their harsh conditions including long multipath, high Doppler effect, and so on. In recent years, deep learning for AMC has attracted increasing attentions for its powerful feature-extraction capability. In this paper, we explore the feasibility and performance of a convolutional neural network (CNN)-based AMC method over UWA channels. Three transmission modes: single-carrier (SC), orthogonal frequency-division multiplexing (OFDM), direct sequence spread spectrum (DSSS), are employed. The modulation schemes include four coherent modulations: BPSK, QPSK, 8PSK and 16QAM, and two non-coherent modulations: BFSK, QFSK. In total, fourteen classes of communication signals are considered for classification. It showed recognition of single-carrier coherent signal is more difficult than others and to improve the classification accuracy, two hard example mining mechanisms were adopted. Numerical simulations showed the proposed scheme achieves decent recognition performance.

关键词： Oceans OFDM Neural networks Modulation Spread spectrum communication Numerical simulation Convolutional neural networks

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Image restoration of finger-vein networks based on encoder-decoder model

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Optoelectronics Letters 2019年第6期15卷 463-467页

作者： GUO Xiao-jing LI Dan ZHANG Hai-gang YANG Jin-feng College of Electronic Information and Automation Civil Aviation University of ChinaTianjin 300300China Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of ChinaTianjin 300300China

Finger-vein recognition is widely applied on access control system due to the high user acceptance and convince. Improving the integrity of finger-vein is helpful for increasing the finger-vein recognition accuracy. During the process of finger-vein imaging, foreign objects may be attached on fingers, which directly affects the integrity of finger-vein images. In order to effectively extract finger-vein networks, the integrity of venous networks is still not ideal after preprocessing of finger vein images. In this paper, we propose a novel deep learning based image restoration method to improve the integrity of finger-vein networks. First, a region detecting method based on adaptive threshold is presented to locate the incomplete region. Next, an encoder-decoder model is used to restore the venous networks of the finger-vein images. Then we analyze the restoration results using several different methods. Experimental results show that the proposed method is effective to restore the venous networks of the finger-vein images.

关键词： decoder networks incomplete

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Refining BERT embeddings for document hashing via mutual information maximization

arXiv

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arXiv 2021年

作者： Ou, Zijing Su, Qinliang Yu, Jianxing Zhao, Ruihui Zheng, Yefeng Liu, Bang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Artificial Intelligence Sun Yat-sen University Guangdong China Tencent Jarvis Lab Rali & Mila Université de Montréal Guangdong Key Lab. of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

Existing unsupervised document hashing methods are mostly established on generative models. Due to the difficulties of capturing long dependency structures, these methods rarely model the raw documents directly, but instead to model the features extracted from them (e.g. bag-of-words (BOW), TFIDF). In this paper, we propose to learn hash codes from BERT embeddings after observing their tremendous successes on downstream tasks. As a first try, we modify existing generative hashing models to accommodate the BERT embeddings. However, little improvement is observed over the codes learned from the old BOW or TFIDF features. We attribute this to the reconstruction requirement in the generative hashing, which will enforce irrelevant information that is abundant in the BERT embeddings also compressed into the codes. To remedy this issue, a new unsupervised hashing paradigm is further proposed based on the mutual information (MI) maximization principle. Specifically, the method first constructs appropriate global and local codes from the documents and then seeks to maximize their mutual information. Experimental results on three benchmark datasets demonstrate that the proposed method is able to generate hash codes that outperform existing ones learned from BOW features by a substantial margin. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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