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Herriott-cavity-assisted all-optical atomic vector magnetometer

Physical Review A 2020年第5期101卷 053436-053436页

作者： B. Cai C.-P. Hao Z.-R. Qiu Q.-Q. Yu W. Xiao D. Sheng Hefei National Laboratory of Physical Sciences at the Microscale University of Science and Technology of China Hefei 230026 China Department of Precision Machinery and Precision Instrumentation Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes University of Science and Technology of China Hefei 230027 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science and Center for Quantum Information Technology Peking University Beijing 100871 China

We report an all-optical atomic vector magnetometer using dual Bell-Bloom optical pumping beams in a Rb vapor cell. This vector magnetometer consists of two orthogonal optical pumping beams, with amplitude modulations at Rb85 and Rb87 Larmor frequencies, respectively. We simultaneously detect atomic signals excited by these two pumping beams using a single probe beam in the third direction, and extract the field orientation information using the phase delays between the modulated atomic signals and the driving beams. By adding a Herriott cavity inside the vapor cell, we improve the magnetometer sensitivity. We study the performance of this vector magnetometer in a magnetic field ranging from 100 mG to 500 mG, and demonstrate a field angle sensitivity better than 10 μrad/Hz1/2 above 10 Hz.

关键词： Atomic & molecular processes in external fields Optical pumping Zeeman effect

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Competition: Low-Power Wireless Bus Baseline 19

Competition: Low-Power Wireless Bus Baseline

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Proceedings of the 2019 International Conference on Embedded Wireless Systems and networks

作者： Fabian Mager Romain Jacob Reto Da Forno Marco Zimmerling Networked Embedded Systems Lab TU Dresden Germany Computer Engineering and Networks Laboratory ETH Zurich Switzerland

No abstract available.

ISBN: (纸本)9780994988638

No abstract available.

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3D Multi-Drone-Cell Trajectory Design for Efficient IoT Data Collection

3D Multi-Drone-Cell Trajectory Design for Efficient IoT Data...

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2019 IEEE International Conference on Communications, ICC 2019

作者： Shi, Weisen Li, Junling Cheng, Nan Lyu, Feng Dai, Yanpeng Zhou, Haibo Shen, Xuemin Sherman Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada State Key Laboratory of Integrated Service Networks Xidian University Xian710071 China School of Electronic Science and Engineering Nanjing University Nanjing210093 China

ISBN: (纸本)9781538680889

Drone cell (DC) is an emerging technique to offer flexible and cost-effective wireless connections to collect Internet-of-things (IoT) data in uncovered areas of terrestrial networks. The flying trajectory of DC significantly impacts the data collection performance. However, designing the trajectory is a challenging issue due to the complicated 3D mobility of DC, unique DC-to-ground (D2G) channel features, limited DC-to-BS (D2B) backhaul link quality, etc. In this paper, we propose a 3D DC trajectory design for the DC-assisted IoT data collection where multiple DCs periodically fly over IoT devices and relay the IoT data to the base stations (BSs). The trajectory design is formulated as a mixed integer non-linear programming (MINLP) problem to minimize the average user-to-DC (U2D) pathloss, considering the state-of-the-art practical D2G channel model. We decouple the MINLP problem into multiple quasi-convex or integer linear programming (ILP) sub-problems, which optimizes the user association, user scheduling, horizontal trajectories and DC flying altitudes of DCs, respectively. Then, a 3D multi-DC trajectory design algorithm is developed to solve the MINLP problem, in which the sub-problems are optimized iteratively through the block coordinate descent (BCD) method. Compared with the static DC deployment, the proposed trajectory design can lower the average U2D pathloss by 10-15 dB, and reduce the standard deviation of U2D pathloss by 56%, which indicates the improvements in both link quality and user fairness. © 2019 IEEE.

关键词： Internet of things

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The fourier spectral characterization for the correlation-immune functions over Fp

arXiv

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arXiv 2019年

作者： Wang, Zilong Chai, Jinjin Gong, Guang State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xidian University Department of Electrical and Computer Engineering University of Waterloo

The correlation-immune functions serve as an important metric for measuring resistance of a cryptosystem against correlation attacks. Existing literature emphasize on matrices, orthogonal arrays and Walsh-Hadamard spectra to characterize the correlation-immune functions over Fp (p ≥ 2 is a prime). Recently, Wang and Gong investigated the Fourier spectral characterization over the complex field for correlation-immune Boolean functions. In this paper, the discrete Fourier transform (DFT) of non-binary functions was studied. It was shown that a function f over Fp is mth-order correlation-immune if and only if its Fourier spectrum vanishes at a specific location under any permutation of variables. Moreover, if f is a symmetric function, f is correlation-immune if and only if its Fourier spectrum vanishes at only one location. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectrum analyzers

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Interspecific competition shapes the structural stability of mutualistic networks

arXiv

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arXiv 2021年

作者： Wang, Xiangrong Peron, Thomas Dubbeldam, Johan L.A. Kéfi, Sonia Moreno, Yamir Institute of Future Networks Southern University of Science and Technology China Research Center of Networks and Communications Peng Cheng Laboratory China Institute of Mathematics and Computer Science University of São Paulo São Carlos SP13566-590 Brazil Faculty of Electrical Engineering Mathematics and Computer Science P.O. Box 5031 Delft2600 GA Netherlands ISEM CNRS Univ. Montpellier IRD EPHE Montpellier France Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States University of Zaragoza Zaragoza Spain Department of Theoretical Physics Faculty of Sciences University of Zaragoza Zaragoza Spain Institute for Scientific Interchange Foundation Turin Italy

Mutualistic networks have attracted increasing attention in the ecological literature in the last decades as they play a key role in the maintenance of biodiversity. Here, we develop an analytical framework to study the structural stability of these networks including both mutualistic and competitive interactions. Analytical and numerical analyses show that the structure of the competitive network fundamentally alters the necessary conditions for species coexistence in communities. Using 50 real mutualistic networks, we show that when the relative importance of shared partners is incorporated via weighted competition, the feasibility area in the parameter space is highly correlated with May's stability criteria and can be predicted by a functional relationship between the number of species, the network connectance and the average interaction strength in the community. Our work reopens a decade-long debate about the complexity-stability relationship in ecological communities, and highlights the role of the relative structures of different interaction types. © 2021, CC BY-NC-ND.

关键词： Biodiversity

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Color pornographic image detection based on color-saliency preserved mixture deformable part model

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Multimedia Tools and Applications 2018年第6期77卷 6629-6645页

作者： Tian, Chunna Zhang, Xiangnan Wei, Wei Gao, Xinbo State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi’an710071 China School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710129 China

To utilize the rich semantic information of sexual organs, we propose a new framework for pornographic image detection based on sexual organ detectors. Traditional sexual organ detectors are built on shape features. Since the color distribution of sexual organ in same pose is consistent, color is an important visual clue to represent sexual organs. We use color attribute to describe the local color of sexual organs and concatenate it with histogram of oriented gradients based shape feature to represent sexual organs. Based on the concatenated feature, we train sexual organ detectors by the color-saliency preserved mixture deformable part model (CPMDPM). We detect pornographic images sequentially with sexual organ detectors. In experiments, the optimal part number of the deformable part model is chosen experimentally. We evaluate the performance of each CPMDPM based sexual organ detector, which is superior over the shape feature based detector. The proposed pornographic detection method is superior over methods based on low level features of skin regions, bag of words model and color incorporated SIFT features etc. © 2017, Springer Science+Business Media New York.

关键词： Color

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Streaming Media Low-Delay Coding Optimization Based on WebRTC

Streaming Media Low-Delay Coding Optimization Based on WebRT...

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International Conference on Intelligent Computation Technology and Automation, ICICTA

作者： Qingxin Ren Xiaoming Wu Shuai Wang Xiangzhi Liu Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology (Shandong Academy of Sciences) Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan China Heze Branch Qilu University of Technology(Shandong Academy of Sciences) Biological Engineering Technology Innovation Center of Shandong Province Heze China

With the development of the information society, video-conferencing has evolved from hardware-based conferences to software, and now based on browsers, its cost is getting lower, and its convenience is getting better. But even today with highly developed information technology, the delay of video-conferencing still bothers developers and users. WebRTC video-conferencing has some delays in different level from terminal collection to play. H.264 is one of the mainstream coding algorithms adopted by WebRTC. Through analyzing and researching the characteristics and structure of H.264 bit stream, a coding algorithm based on slice is proposed, and a suitable bit rate control algorithm is selected to optimize the delay in the encoding and decoding stage. It has been verified by experiments that after adopting this optimized coding structure, the delay in the coding and decoding process is reduced compared with the standard H.264 coding and decoding algorithm.

关键词： Bit rate Streaming media Encoding Delays Decoding Video codecs Standards

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Distributed Machine Learning for Resilient Operation of Electric Systems

Distributed Machine Learning for Resilient Operation of Elec...

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International Conference on Smart Energy Systems and Technologies (SEST)

作者： M. Hadi Amini Ahmed Imteaj Javad Mohammadi School of Computing and Information Sciences Florida International University Miami FL USA Sustainability Optimization and Learning for InterDependent networks laboratory (solid lab) FIU Miami FL USA Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9781728147017

ISBN: (纸本)9781728147024

Power system resilience is crucial to ensure secure energy delivery to electricity consumers. Power system outages lead to economical and societal burdens for the society and industries. To mitigate the socio-economical impacts of a power outage, we need to develop efficient algorithms to ensure resilient operation of the power system. In this paper, we first explain the notion of data-driven resilience. Then, we present a pathway of leveraging edge intelligence to improve resilience. To this end, we propose a novel distributed machine learning paradigm. Our proposed structure relies on local Resilience Management Systems (RMS) that serve as intelligent decision-making entities in each area, e.g. an autonomous micro-grid or a smart home can act as RMS. The RMS agents, which are available in different areas, can share their local data (i.e., a microgrid's operational data) with their neighboring RMS to coordinate their decisions in a distributed fashion. This will provide two major advantages: 1) distributed intelligence replaces centralized decision-making leading to robust decision-making and enhanced resilience; 2) since local data are locally shared among all entities within an RMS, if one of the RMS agents fails to communicate with the rest of network, we still can maintain a feasible solution (which is not necessarily optimal). Finally, we presents different scenarios in the simulation results section that showcases the system performance for two buildings under various outage scenarios.

关键词： Resilience Power system reliability Decision making Optimization Load management Collaborative work

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LBS-based location privacy protection mechanism in augmented reality

LBS-based location privacy protection mechanism in augmented...

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Internet of Things and Intelligent Applications (ITIA), International Conference on

作者： Yang Yang Ruchuan Wang Nanjing City Vocational College Institute of Engineering and Information Nanjing Radio and TV University Nanjing China College of Computer Nanjing University of Posts and Telecommunications Nanjing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China

ISBN: (数字)9781728193014

ISBN: (纸本)9781728193021

With the development of augmented reality(AR) technology and location-based service (LBS) technology, combining AR with LBS will create a new way of life and socializing. In AR, users may consider the privacy and security of data. In LBS, the leakage of user location privacy is an important threat to LBS users. Therefore, it is very important for privacy management of positioning information and user location privacy to avoid loopholes and abuse. In this review, the concepts and principles of AR technology and LBS would be introduced. The existing privacy measurement and privacy protection framework would be analyzed and summarized. Also future research direction of location privacy protection would be discussed.

关键词： Privacy Mobile handsets Trajectory Entropy Uncertainty Time measurement Size measurement

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A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction

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Disease Markers 2022年第1期2022卷 7593750页

作者： Xu, He Zheng, Qunli Zhu, Jingshu Xie, Zuoling Cheng, Haitao Li, Peng Ji, Yimu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Province Nanjing 210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing 210023 China Nanjing Center of HPC China Nanjing 210023 China Jiangsu Research Engineering of HPC and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China Department of Endocrinology Zhongda Hospital Southeast University Nanjing 210009 China

The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpretability and medical expertise limits its clinical application. This paper combines knowledge representation learning and deep learning methods, and a disease prediction model is constructed. The model initially constructs the relationship graph between the physical indicator and the test value based on the normal range of human physical examination index. And the human physical examination index for testing value by knowledge representation learning model is encoded. Then, the patient physical examination data is represented as a vector and input into a deep learning model built with self-attention mechanism and convolutional neural network to implement disease prediction. The experimental results show that the model which is used in diabetes prediction yields an accuracy of 97.18% and the recall of 87.55%, which outperforms other machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost). Compared with the best performing random forest method, the recall is increased by 5.34%, respectively. Therefore, it can be concluded that the application of medical knowledge into deep learning through knowledge representation learning can be used in diabetes prediction for the purpose of early detection and assisting diagnosis. © 2022 He Xu et al.

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