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The Design of Network Pedometer Based on Bluetooth 4.0 3

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The Design of Network Pedometer Based on Bluetooth 4.0

3rd International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2018

作者： Tang, Meiyu Gao, Tianlei Zhou, Shuwang Zhao, Kai Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China School of Electrical Engineering and Automation Qilu University of Technology Jinan250353 China

ISBN: (纸本)9781538642016

The pedometer combined with Bluetooth 4.0 can effectively implement the communication with the laptops, PAD, and mobile phones. it can more accurately calculate the number of pace through the method of real-time digital filter smoothing data curve, the design not only can enhance people's enthusiasm of taking exercise but also can effectively save communication resource. Because of the Bluetooth technology is in the 2.4 GHz band which is free spectrum [1], Bluetooth technology is bound to get more widely applications and more recognition, it can effectively put the data recorded by pedometer sent to the mobile phone and other modern terminal devices through Bluetooth technology, and then the data is uploaded to the cloud server to be saved through the terminal. This can increase people's interest in the pedometer, because the relevant data can be visible saved for a long time, it tracks more convenient for people to exercise and reach the effect of physical fitness. So it is convenient for people to track and compare the physical activity to reach the effect of physical fitness. © 2018 IEEE.

关键词： Cellular telephones

Intelligent metasurface imager and recognizer

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arXiv 2019年

作者： Li, Lianlin Shuang, Ya Ma, Qian Li, Haoyang Zhao, Hanting Wei, Menglin Liu, Che Hao, Chenglong Qiu, Cheng-Wei Cui, Tie Jun State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Peking University Beijing100871 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore

It is ever-increasingly demanded to remotely monitor people in daily life using radio-frequency probing signals. However, conventional systems can hardly be deployed in real-world settings since they typically require objects to either deliberately cooperate or carry a wireless active device or identification tag. To accomplish the complicated successive tasks using a single device in real time, we propose a smart metasurface imager and recognizer simultaneously, empowered by a network of artificial neural networks (ANNs) for adaptively controlling data flow. Here, three ANNs are employed in an integrated hierarchy: transforming measured microwave data into images of whole human body;classifying the specifically designated spots (hand and chest) within the whole image;and recognizing human hand signs instantly at Wi-Fi frequency of 2.4 GHz. Instantaneous in-situ imaging of full scene and adaptive recognition of hand signs and vital signs of multiple non-cooperative people have been experimentally demonstrated. We also show that the proposed intelligent metasurface system work well even when it is passively excited by stray Wi-Fi signals that ubiquitously exist in our daily lives. The reported strategy could open a new avenue for future smart cities, smart homes, human-device interactive interfaces, healthy monitoring, and safety screening free of visual privacy issues. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

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Frontiers of Information Technology & Electronic engineering 2017年第6期18卷 796-807页

作者： Zhen-hua YUAN Chen CHEN Xiang CHENG Guo-cheng LV Liu-qing YANG Ye JIN State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Department of Electrical & Computer Engineering Colorado State University

This article is focused on secure relay beamformer design with a correlated channel model in the relay-eavesdropper network. In this network, a single-antenna source-destination pair transmits secure information with the help of an amplify-and-forward (AF) relay equipped with multiple antennas, and the legitimate and eavesdropping channels are correlated. The relay cannot obtain the instantaneous channel state information (CSI) of the eavesdropper, and has only the knowledge of correlation information between the legitimate and eavesdropping channels. Depending on this information, we derive the conditional distribution of the eavesdropping channel. Two beamformers at the relay are studied for the approximate ergodic secrecy rate:(1) the generalized match-andforward (GMF) beamformer to maximize the legitimate channel rate, and (2) the general-rank beamformer (GRBF).In addition, one lower-bound-maximizing (LBM) beamformer at the relay is discussed for maximizing the lower bound of the ergodic secrecy rate. We find that the GMF beamformer is the optimal rank-one beamformer, that the GRBF is the iteratively optimal beamformer, and that the performance of the LBM beamformer for the ergodic secrecy rate gets close to that of the GRBF for the approximate secrecy rate. It can also be observed that when the relay has lower power or the channel gain of the second hop is low, the performance of the GMF beamformer surpasses that of the GRBF. Numerical results are presented to illustrate the beamformers' performance.

关键词： Physical layer security Relay beamforming Correlated channels Ergodic secrecy rate

Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges

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arXiv 2019年

作者： Hussain, Fatima Hassan, Syed Ali Hussain, Rasheed Hossain, Ekram Royal Bank of Canada Toronto Canada Islamabad Pakistan Networks and Blockchain Laboratory Institute of Information Security and Cyber-Physical System Innopolis University Innopolis Russia Department of Electrical and Computer Engineering University of Manitoba Winnipeg Canada

Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML- and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML- and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Resource allocation

Robust all polarization-maintaining femtosecond fiber laser with various phase bias

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Robust all polarization-maintaining femtosecond fiber laser ...

International Conference on Optical Communications and networks (ICOCN)

作者： Renlai Zhou Xuanyi Liu Guanyu Liu H. Y. Fu Qian Li School of Electronic and Computer Engineering Peking University Shenzhen China State Key Laboratory of Advanced Optical Communication System and Networks eking University Beijing China Tsinghua-Berkeley Shenzhen Institute (TBSI) Tsinghua University Shenzhen China

We demonstrate a compact all polarization- maintaining mode-locked femtosecond fiber laser with three different non-reciprocal phase shifters. The achieved laser directly delivers 926 fs, 0.56 nJ pulses at the repetit... 详细信息

关键词： Laser mode locking Optical fiber polarization Optical fiber communication Erbium-doped fiber lasers Optical fiber dispersion Power generation

Timing jitter of high repetition rate mode-locked fiber lasers

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Timing jitter of high repetition rate mode-locked fiber lase...

Conference on Lasers and Electro-Optics/Pacific Rim, CLEOPR 2018

作者： Wang, Yan Tian, Haochen Ma, Yuxuan Song, Youjian Zhang, Zhigang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing100871 China Ultrafast Laser Laboratory Key Laboratory of Opto-electronic Information Technical Science Ministry of Education School of Precision Instrument and Opto-electronics Engineering Tianjin University Tianjin300072 China

We characterized the timing jitter of two 880 MHz Yb-doped mode-locked fiber lasers using the optical cross-correlation technique. The lowest residual timing jitter is 3.5 fs rms (integrated from 5MHz to 100 kHz). ... 详细信息

ISBN: (纸本)9781943580453

关键词： Timing jitter

Deep learning for signal demodulation in physical layer wireless communications: Prototype platform, open dataset, and analytics

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arXiv 2019年

作者： Wang, Hongmei Wu, Zhenzhen Ma, Shuai Lu, Songtao Zhang, Han Ding, Guoru Li, Shiyin Information and Control Engineering China University of Mining and Technology Xuzhou221116 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an710071 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Department of Electrical and Computer Engineering University of California DavisCA95616 United States College of Communications Engineering Army Engineering University Nanjing210007 China

In this paper, we investigate deep learning (DL)- enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured dataset, two DL-based demodulators, called deep belief network (DBN)-support vector machine (SVM) demodulator and adaptive boosting (AdaBoost) based demodulator, are proposed. The proposed DBN-SVM based demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of DBN as a feature extractor and SVM as a feature classifier. In DBNSVM based demodulator, the received signals are normalized before being fed to the DBN network. Furthermore, an AdaBoost based demodulator is developed, which employs the k-Nearest Neighbor (KNN) as a weak classifier to form a strong combined classifier. Finally, experimental results indicate that the proposed DBN-SVM based demodulator and AdaBoost based demodulator are superior to the single classification method using DBN, SVM, and maximum likelihood (MLD) based demodulator. Copyright © 2019, The Authors. All rights reserved.

关键词： Demodulators

A Laser-Pumped Cs-4He Magnetometer for Metrology

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A Laser-Pumped Cs-4He Magnetometer for Metrology

2018 Conference on Precision Electromagnetic Measurements, CPEM 2018

作者： Jiqing, Fu He, Wang Xiang, Peng Wei, Zhang Magnetic Quality Laboratory of National Institute of Metrology Beijing100013 China Center for Quantum Information Technology Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Beijing100871 China

ISBN: (纸本)9781538609736

This paper describes a laser-pumped cesium-helium magnetometer (CHM) with high polarization efficiency. The magnetic-resonance signal amplitude has been improved by one order of magnitude comparing with the lamp-pumped scheme. The optimal parameters for increasing the signal-to-noise ratio of the magnetometer are obtained and discussed. © 2018 IEEE.

关键词： Optical pumping

One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories

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arXiv 2025年

作者： Hébert-Dufresne, Laurent Ahn, Yong-Yeol Allard, Antoine Crothers, Jessica W. Dodds, Peter Sheridan Galesic, Mirta Ghanbarnejad, Fakhteh Gravel, Dominique Hammond, Ross A. Lerman, Kristina Lovato, Juniper Openshaw, John J. Redner, S. Scarpino, Samuel V. St-Onge, Guillaume Tangherlini, Timothy R. Young, Jean-Gabriel Vermont Complex Systems Institute University of Vermont BurlingtonVT United States Translational Global Infectious Disease Research Center University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Département de physique de génie physique et d’optique Université Laval QuébecQC Canada Department of Pathology and Laboratory Medicine Robert Larner M.D. College of Medicine University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States Complexity Science Hub Vienna Austria School of Technology and Architecture SRH University of Applied Sciences Heidelberg Leipzig Germany Département de biologie Université de Sherbrooke SherbrookeQC Canada Brown School Washington University in St. Louis St. LouisMO United States The Brookings Institution WashingtonDC United States Information Sciences Institute University of Southern California United States Division of Infectious Diseases and Geographic Medicine Stanford University StanfordCA United States Institute for Experiential AI Northeastern University BostonMA United States Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University BostonMA United States The Roux Institute Northeastern University PortlandME United States Department of Scandinavian School of Information University of California Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions. Copyright © 2025, The Authors. All rights reserved.

关键词： Histology