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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Fangning Shi Xiaojun Jing Yuan He Michel Kadoch Mohamed Cheriet Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China ETS University of Quebec Montreal Canada

ISBN: (数字)9781728157849

ISBN: (纸本)9781728157856

Automatic modulation classification technology is an indispensable step in cognitive radio, and its recognition accuracy is related to the orderly progress of subsequent communications. In this paper, we introduce the higher-order statistics into residual neural network for the precise classification of different modulation types. The classification technology can recognize 24 digital and analog modulation types under both synthetic simulated channel effects and over-the-air recordings. We also consider a rigorous baseline method using residual neural network and compare performance between two approaches under a wide range of signal-to-noise ratio. Experimental results show that our proposed method achieves an average accuracy of 96.4% and obtains better performance in correct classification probability than the baseline method, especially in lower signal-to-noise ratio.

关键词： Modulation Probability Feature extraction Classification algorithms Signal to noise ratio Higher order statistics Residual neural networks

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An Information Security Evaluation Model Supporting Measurement Model Adaptation

An Information Security Evaluation Model Supporting Measurem...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Jinxin Zuo Ziyv Guo Yueming Lu School of Cyberspace Security Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information and Communication Engineering Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

In view of the difficulty in determining reasonably evaluation indicator system in the information security certification and accreditation work, an information security evaluation framework supporting the adaptation of measurement models is proposed. And the mapping-based information security evaluation indicator construction rule and a measurement model library are established. The optimal measurement model is adapted according to head-to-tail consistency and standard deviation index. Then, the evaluation model relies on the information security index feedback algorithm based on probability iteration to adjust the evaluation indicator system for more reasonableness. This paper provides a model reference for information security certification.

关键词： Adaptation models Standards Indexes Libraries Computational modeling

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Prior-guided deep interference mitigation for FMCW radars

arXiv

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arXiv 2021年

作者： Wang, Jianping Li, Runlong He, Yuan Yang, Yang Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628CD Netherlands Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing100876 China School of Electrical and Information Engineering Tianjin University Tianjin300072 China

A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. In this paper, the interference mitigation problem is tackled as a regression problem. Considering the complex-valued nature of radar signals, the complex-valued convolutional neural network is utilized as an architecture for implementation, which is different from the conventional real-valued counterparts. Meanwhile, as the useful beat signals of FMCW radars and interferences exhibit different distributions in the time-frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals. Compared to the real-valued counterparts, the CV-FCN shows a better interference mitigation performance with a potential of half memory reduction in low Signal to Interference plus Noise Ratio (SINR) scenarios. Moreover, the CV-FCN trained using only simulated data can be directly utilized for interference mitigation in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster. Copyright © 2021, The Authors. All rights reserved.

关键词： Continuous wave radar

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Human Motion Recognition by Three-view Kinect Sensors in Virtual Basketball Training

Human Motion Recognition by Three-view Kinect Sensors in Vir...

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IEEE Region 10 International Conference TENCON

作者： Baoqi Yao Hui Gao Xin Su Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781728184555

ISBN: (纸本)9781728184562

In recent years, human action recognition has received a considerable amount of research attention because of it's potential in a variety of applications, such as video surveillance, human-computer interaction, and virtual reality (VR). However, many researches on human action recognition performed in single-camera or double-camera system, which achieve reduced performance due to vulnerability to partial occlusion and miss-recognition of back. Some works on human action recognition use multiple cameras but are too complex for practical application. In this paper, we propose a new human action recognition system using triple Kinect sensors for VR application. Particularly, we design a mark detection method to determine the front of user and fusion skeleton data in real time. Features are extracted from three-dimensional (3D) skeleton data sequences, and divided into five parts according to body parts. A classification model based on the part-aware long short-term memory networks is proposed to recognize human motion. Finally, we demonstrate the system with a virtual reality basketball application and the results of experiment validate the feasibility of the proposed system.

关键词： Skeleton Sensors Cameras Sensor systems Feature extraction Face recognition Shoulder

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SMP-Graph: Structure-Enhanced Unsupervised Semantic Graph Representation for Precise Medical Procedure Coding on EMRs

SMP-Graph: Structure-Enhanced Unsupervised Semantic Graph Re...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yue Gao Xiangling Fu Xien Liu Kaiyin Zhou Ji Wu School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China Tsinghua University Beijing China THiFLY Research Tsinghua University Beijing China State Key Laboratory of Cognitive Intelligence Hefei Anhui China Institute for Precision Medicine Tsinghua University Beijing China

ISBN: (纸本)9781665429825

Automatic ICD coding, as a fundamental task in the field of healthcare management, has been paid much attention by researchers. However, the current deep learning-based ICD coding research mostly focus on the introduction of external diagnostic description text or the imposition of rules, while ignoring the structured features of the coding text itself. Especially for short texts of medical procedure codes, it is much more important to mine the information value in the texts. In this paper, we propose a structure-enhanced unsupervised semantic graph representation for precise medical procedure coding (SMP-Graph). The SMP-Graph representation method constructs each medical procedure text with an inductive heterogeneous graph and particularly enhances the kernel knowledge by extracting the axis words and chapter title and allocating them with distinct node representations. Both the nodes and edges are generated by the unsupervised pretrained model and then interact information in a bidirectionally weighted graph structure. Therefore, the SMP-Graph really realizes the intra-integration of unsupervised contextualized information and graph-based global information from the medical procedure code. Experiments conducted on the Chinese ICD-9-CM-3 procedure text dataset we collected from EMRs demonstrate that the SMP-Graph is a better representation method that outperforms other representative methods for medical procedure coding. Characteristic analysis is also conducted to prove the interpretability and adaptability of the SMP-Graph on the medical procedure coding task.

关键词： Codes Conferences Semantics Medical services Encoding Regulation Task analysis

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A Framework Analysis of the 6G Vertical Ecosystem Based on Stakeholder Theory 24

A Framework Analysis of the 6G Vertical Ecosystem Based on S...

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Proceedings of the 2024 10th International Conference on Communication and Information Processing

作者： Fengshuai Du Jiao Zhang Xinyue Yan Tianjian Yang Haiyan Yan Jiayin Qi Enzo Bivona Suya Xiao Xing Shi Department of Political Sciences and International Relations (DEMS) University of Palermo Palermo Sicily Italy and Huangpu Research School of Guangzhou University Guangzhou University Guanghzou Guangdong China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Fudan University Shanghai China Beijing University of Posts and Telecommunications Beijing China Shanghai University of International Business and Economics Shanghai China Cyberspace Institute of Advanced Technology Guangzhou Guangdong China and Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China Department of Political Sciences and International Relations (DEMS) University of Palermo Palermo Sicily Italy International Business School Shandong Jiaotong University Weihai Shandong China

ISBN: (纸本)9798400717444

This research provides a framework analysis of the 6G vertical ecosystem's development through stakeholder theory, identifying three phases: introduction, development, and maturity, and discussing the evolution from a simple value chain to a complex ecosystem driven by industry demands and technology advancements, focusing on the challenges and drivers including technological innovation, economic viability, regulatory needs, environmental sustainability, security, and societal impact.

关键词： 6G

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Neural Network Based Target User Recognition Model for Network Community 6

Neural Network Based Target User Recognition Model for Netwo...

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6th Annual 2018 International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2018

作者： Wu, Xu Dai, Yulun Xie, Xiaqing Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Cyberspace Security BUPT China Beijing University of Posts and Telecommunications Library Beijing100876 China

For multi-dimensional feature of user data in network community, available methods mainly use Rank scoring algorithm or user classification algorithm for target user recognition. However, Rank method has a low performance, and the classification algorithm needs high construction cost. Therefore, this paper uses a target user recognition model integrating the user content analysis and the user behavior analysis to improve performance and speed of the target user recognition by a neural network content analysis model with a single-layer neural network and N-gram features for discovering automatically the user feature. The proposed method outperforms the current Rank scoring and classification methods in terms of performance, in which the F value reaches 0.89 and the accuracy reaches 0.91. Moreover, avoiding the cost of manual design dependent on specific tasks shortens the training time. Ten thousand data can be modeled in one minute. © Published under licence by IOP Publishing Ltd.

关键词： Network layers

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Machine learning empowered beam management for intelligent reflecting surface assisted mmwave networks

arXiv

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arXiv 2020年

作者： Jia, Chenglu Gao, Hui Chen, Na He, Yuan Key Lab of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing100876 China Nara Institute of Science and Technology Japan

Recently, intelligent reflecting surface (IRS) assisted mmWave networks are emerging, which bear the potential to address the blockage issue of the millimeter wave (mmWave) communication in a more cost-effective way. In particular, IRS is built by passive and programmable electromagnetic elements that can manipulate the mmWave propagation channel into a more favorable condition that is free of blockage via judicious joint BS-IRS transmission design. However, the coexistence of IRSs and mmWave BSs complicates the network architecture, and thus poses great challenges for efficient beam management (BM) that is one critical prerequisite for high performance mmWave networks. In this paper, we systematically evaluate the key issues and challenges of BM for IRS-assisted mmWave networks to bring insights into the future network design. Specifically, we carefully classify and discuss the extensibility and limitations of the existing BM of conventional mmWave towards the IRSassisted new paradigm. Moreover, we propose a novel machine learning empowered BM framework for IRS-assisted networks with representative showcases, which processes environmental and mobility awareness to achieve highly efficient BM with significantly reduced system overhead. Finally, some interesting future directions are also suggested to inspire further researches. Copyright © 2020, The Authors. All rights reserved.

关键词： Millimeter waves

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A Blind Spectrum Sensing Based on Low-Rank and Sparse Matrix Decomposition

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China Communications 2018年第8期15卷 118-125页

作者： Junsheng Mu Xiaojun Jing Hai Huang Ning Gao Laboratory of Biological Recognition and Information Application Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education

As a crucial component in Cognitive Radio(CR) networks, spectrum sensing has been attracting lots of attention. Some conventional methods for spectrum sensing are sensitive to uncertain signal and noise, its applicability is limited thereof. In this paper, a novel blind spectrum sensing method is proposed, where low-rank and sparse matrix decomposition is applied to the observation signal of a CR in the frequency domain. Then the ratio of the energy of the sparse part and the received signal in the time domain is considered as the criterion to decide whether the radio frequency band is idle by means of a comparison with a predefined threshold. The proposed method is independent of prior knowledge of signal and white noise, and has a better detection performance. Simulation experiments verify the performance of the proposed method in additive white Gaussian noise(AWGN), Rayleighand Rician channels.

关键词： spectrum sensing cognitive radio OFDM Rayleigh AWGN

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Comparison of three data mining methods in predicting 5-year survival of colorectal cancer patients

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The Journal of China Universities of Posts and Telecommunications 2018年第6期25卷 65-73页

作者： Luo Yan Sun Yawei Fu Qunchao Xue Tengfei Zhou Ping School of Software Engineering Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of Education

The prediction of colorectal cancer(CRC) survivability has always been a challenging research issue. Considering the importance of predicting CRC patients’ survival rates, we compared the performance of three data mining methods: decision trees(DTs), artificial neural networks(ANNs) and support vector machines(SVMs), for predicting 5-year survival of CRC patients to assist clinicians in making treatment decisions. The CRC dataset used to build the prediction model comes from the surveillance, epidemiology, and end results(SEER) program. The 5-fold cross-validation and random forest algorithm were respectively utilized for measuring the model predictive accuracy and the importance of features. Experimental results show that the predictive accuracy of ANNs(0.73) and SVMs(0.75) were higher than that of DTs, and they also have the best result in the area under the receiver operating characteristic(ROC) curve(area under curve(AUC)=0.82). This result may indicate high predictive power of ANNs and SVMs for predicting 5-year survival of CRC patients.

关键词： data mining 5-year survival CRC SEER

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