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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Siqi Liu Shuangyue Liu Xizi Tang Mengqi Guo Yueming Lu Yaojun Qiao State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication EngineeringBeijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace SecurityBeijing University of Posts and Telecommunications Beijing China

A QPSK-assisted MIMO equalization is investigated to compensate bandwidth limitation for 800-Gb/s/λ DP-256QAM systems with only 25G-class optics. Compared with conventional MIMO equalization, the proposed equalizatio... 详细信息

ISBN: (数字)9781943580828

ISBN: (纸本)9781728176567

关键词： Bandwidth Optics Optical fiber communication Optical noise MIMO communication Signal to noise ratio Photonics

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QPSK Pilot-Aided Carrier Phase Recovery Algorithm for 800-Gb/s/λ DP-256QAM Transmission

QPSK Pilot-Aided Carrier Phase Recovery Algorithm for 800-Gb...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Han Cui Xuekai Xu Shuangyue Liu Yueming Lu Yaojun Qiao State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

A QPSK pilot-aided carrier phase recovery algorithm is investigated for 800-Gb/s/λ DP-256QAM transmission systems. Simulation results show that, compared with BPS, this proposed algorithm reduces by 99% computational... 详细信息

ISBN: (数字)9781943580828

ISBN: (纸本)9781728176567

关键词： Phase shift keying Simulation Optical fiber communication Optical noise Computational complexity Signal to noise ratio Photonics

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SWRR:The Link Scheduling Algorithm Based on Weighted Round-Robin

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Computers, Materials & Continua 2019年第6期59卷 965-982页

作者： Tianbo Lu Ru Yan Chao Li Lihua Yin Hao Chen Xieyu Zou School of Software Engineering Beijing University of Posts and TelecommunicationsBeijing100876China Key Laboratory of Trustworthy Distributed Computing and Service(Beijing University of Posts and Telecommunications) Ministry of EducationBeijing100876China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Department Computer Science and Engineering The University of Texas at ArlingtonArlington76091USA

With the rapid development of the Internet,people pay more and more attention to the protection of *** second-generation onion routing system Tor is the most commonly used among anonymous communication systems,which can be used to protect user privacy *** recent years,Tor’s congestion problem has become the focus of attention,and it can affect Tor’s performance even user ***,we investigate the causes of Tor network congestion and summarize some link scheduling algorithms proposed in recent *** we propose the link scheduling algorithm SWRR based on WRR(Weighted Round Robin).In this process,we design multiple weight functions and compare the performance of these weight functions under different congestion conditions,and the appropriate weight function is selected to be used in our algorithms based on the experiment ***,we also compare the performance of SWRR with other link scheduling algorithms under different congestion conditions by experiments,and verify the effectiveness of the algorithm SWRR.

关键词： Weighted Round-Robin Tor congestion control link scheduling anonymous communication

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Carrier Phase Recovery Exploiting Recursive Blind Phase Search Algorithm for Probabilistic Shaping 64QAM Systems

Carrier Phase Recovery Exploiting Recursive Blind Phase Sear...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Jin Hu Zhongliang Sun Xuekai xu Yueming Lu Yaojun Qiao State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

Conventional BPS algorithm fails on probabilistic shaping signals when using practical length of noise rejection filters. The recursive BPS algorithm is implemented and verified to achieve a gain of more than 1 dB.

ISBN: (数字)9781943580828

ISBN: (纸本)9781728176567

关键词： Optical filters Simulation Filtering algorithms Probabilistic logic Optical fiber communication Gain Photonics

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Sentence segmentation for classical Chinese based on LSTM with radical embedding

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The Journal of China Universities of Posts and Telecommunications 2019年第2期26卷 1-8页

作者： Han Xu Wang Hongsu Zhang Sanqian Fu Qunchao Liu Jun School of software Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT)Ministry of EducationBeijing 100876China The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Institute of Scientific and Technical Information of ChinaBeijing 100038China Insitute of Quantitative Social Science Harvard UniversityCambridgeMAUSA Department of statistics Harvard UniversityCambridgeMAUSA

A low-than character feature embedding called radical embedding is proposed,and applied on a long-short term memory(LSTM) model for sentence segmentation of pre-modern Chinese *** dataset includes over 150 classical Chinese books from 3 different dynasties and contains different literary ***-conditional random fields(LSTM-CRF) model is a state-of-the-art method for the sequence labeling *** model adds a component of radical embedding,which leads to improved *** results based on the aforementioned Chinese books demonstrate better accuracy than earlier methods on sentence segmentation,especial in Tang’s epitaph texts(achieving an F1-score of 81.34%).

关键词： LSTM radical embedding sentence segmentation

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A QPSK-Pilot Carrier Phase Recovery Algorithm with Improved MI for PS-64QAM Systems

A QPSK-Pilot Carrier Phase Recovery Algorithm with Improved ...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Zhongliang Sun Jin Hu Xuekai Xu Yueming Lu Yaojun Qiao 1State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

Filter-oriented carrier phase recovery algorithms with MI impairment are analyzed in PS systems. A QPSK-pilot scheme is investigated to compensate the penalty in a PS-64QAM system, achieving approximated 1 bit/symbol ... 详细信息

ISBN: (数字)9781943580828

ISBN: (纸本)9781728176567

关键词： Optical filters Asia Filtering algorithms Approximation algorithms Optical fiber communication Photonics

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CoStock: A DeepFM Model for Stock Market Prediction with Attentional Embeddings

CoStock: A DeepFM Model for Stock Market Prediction with Att...

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IEEE International Conference on Big Data

作者： Jieyun Huang Xi Zhang Binxing Fang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Forecasting the stock market trend is a vital component of financial systems. Traditional methods mainly rely on quantitative trading data to make predictions. With the increasing volume of Web information, researchers begin to extract effective indicators (e.g., the events and sentiments) from the Web to facilitate the prediction. It is beneficial to fuse the heterogeneous multi-sourced data to achieve superior performance. Existing solutions adopt simple data fusing methods with conventional machine learning models, which may fail to effectively model the complex interactions among the data. With the success of deep neural networks (DNN) in various fields, we propose a DNN-based model that considers the various interactions and complex correlations among the multi-sourced data into one unified framework. In such a framework, the low-and high-order feature interactions are modeled with the factorization machine and the deep neural network respectively, and stock correlations are incorporated with an attention-based feature embedding method. Evaluations on the stock data from the year 2015 to 2017 show that our model can outperform the state-of-the-art methods.

关键词： Machine-to-machine communications Big Data Conferences

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GraphConvLSTM: Spatiotemporal Learning for Activity Recognition with Wearable Sensors

GraphConvLSTM: Spatiotemporal Learning for Activity Recognit...

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IEEE Global Communications Conference

作者： Jindong Han Yuan He Juan Liu Qianqian Zhang Xiaojun Jing Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Wearable activity recognition is an important area for healthcare applications, especially for children and elderly people. Current research proves that deep learning techniques are capable of learning deep features from the sensor raw signal. However, this task faces two challenges. First, it is hard to capture the dynamic and non-linear interactions between different sensor modalities and time slots. Besides, the spatial relationship between sensors convey significant information for wearable activity recognition, thus how to model the sensor relationship is one of the problems that need to be solved. In this work, we propose GraphConvLSTM, a scalable framework that model spatial relationship, local interaction, and long-term temporal dependency simultaneously. We evaluate our model on two real-world activity recognition datasets, and it achieves 1.75%, 2.68% accuracy improvements over existing methods (accuracy: 90.83%, 93.08%) for wearable activity recognition task, which demonstrates the ability of the proposed approach to learn spatial, local and global temporal features.

关键词： Convolution Activity recognition Feature extraction Task analysis Biomedical monitoring Computer architecture Wearable sensors

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Pulmonary tuberculosis detection model of chest X-ray images using convolutional neural network

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The Journal of China Universities of Posts and Telecommunications 2018年第6期25卷 1-6页

作者： He Jin Wang Cong Chen Zhao School of Software Engineering Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of Education China Electronic Data Service CO. LTD

The primary screening for pulmonary tuberculosis mainly relies on X-ray imaging all over the world. In recent years, the incidence of pulmonary tuberculosis has rebounded. This paper proposes a convolutional neural networks(CNN) based model on the tuberculosis detection of chest X-ray images, which is used for the automatic screening of pulmonary tuberculosis. Compared with the conventional CNN, this model can be used to detect the details of images and the areas of the disease quickly and accurately. There is an improvement in the learning speed and accuracy rate of our method, so it can better complete the work of anomaly detection and it can provide more effective auxiliary decision information for the practitioners.

关键词： X-ray images pulmonary tuberculosis CNN automatic screening

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BiGRU Network for Human Activity Recognition in High Resolution Range Profile

BiGRU Network for Human Activity Recognition in High Resolut...

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IEEE International Conference on Radar

作者： Chao Li Yuan He Xinyu Li Xiaojun Jing Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728126609

ISBN: (纸本)9781728137858

The movements of the human body result in unique radar echo changes that can be exploited for classifying human activities. In this paper, we propose a bi-directional gated recurrent neural network (BiGRU) to classify human activities based on high resolution range profiles (HRRPs). The network is trained on a simulated dataset, which is generated by an ellipsoid-based human backscattering model using MOCAP dataset from Carnegie Mellon University. An accuracy of 97.5% on three activities is achieved with the BiGRU based classification approach.

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