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Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM systems

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arXiv 2019年

作者： Wang, Bolei Jian, Mengnan Gao, Feifei Ye Li, Geoffrey Jin, Shi Lin, Hai State Key Lab of Intelligent Technologies and Systems Tsinghua University Department of Automation Tsinghua University Beijing China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States National Communications Research Laboratory Southeast University Nanjing210096 China Department of Electrical and Information Systems Graduate School of Engineering Osaka Prefecture University SakaiOsaka Japan

With the increasing scale of antenna arrays in wideband millimeter-wave (mmWave) communications, the physical propagation delays of electromagnetic waves traveling across the whole array will become large and comparable to the time-domain sample period, which is known as the spatial-wideband effect. In this case, different subcarriers in an orthogonal frequency division multiplexing (OFDM) system will "see" distinct angles of arrival (AoAs) for the same path. This effect is known as beam squint, resulting from the spatial-wideband effect, and makes the approaches based on the conventional multiple-input multiple-output (MIMO) model, such as channel estimation and precoding, inapplicable. After discussing the relationship between beam squint and the spatial-wideband effect, we propose a channel estimation scheme for frequency-division duplex (FDD) mmWave massive MIMO-OFDM systems with hybrid analog/digital precoding, which takes the beam squint effect into consideration. A super-resolution compressed sensing approach is developed to extract the frequency-insensitive parameters of each uplink channel path, i.e., the AoA and the time delay, and the frequency-sensitive parameter, i.e., the complex channel gain. With the help of the reciprocity of these frequency-insensitive parameters in FDD systems, the downlink channel estimation can be greatly simplified, where only limited pilots are needed to obtain downlink complex gains and reconstruct downlink channels. Furthermore, the uplink and downlink Copyright © 2019, The Authors. All rights reserved.

关键词： Channel estimation

Online Worker Selection Towards High Quality Map Collection for Autonomous Driving

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Online Worker Selection Towards High Quality Map Collection ...

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Xiaofeng Cao Yan Li Jiarong Han Peng Yang Feng Lyu Deke Guo Xuemin Shen Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha P.R. China School of Automation Beijing University of Posts and Telecommunications Beijing P.R. China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada University of Waterloo Waterloo ON Canada

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Vehicle-based crowdsourcing is expected to be an economic yet efficient solution to build and maintain an accurate, fine-grained, and up-to-date environment map (i.e., high-definition map) for autonomous vehicles, which is an essential building block for safe and intelligent autonomous driving. However, how to select crowdsourcing workers with performance maximization is prudent and quite challenging since vehicles are highly dynamic and have unpredictable routes. In this paper, we study the worker selection problem for crowdsourced on-route map collection where the trade- off between the real-time worker exploration and exploitation is the main focus. Specifically, by adopting the multi-armed bandit model, we formulate a cumulative platform utility maximization problem. To solve this problem, we propose an Online Worker Selection (OWS) scheme, to learn drivers' performance and make worker selection decisions in real time. Essentially, two key designs are integrated in OWS: 1) performance transfer. If a new driver joins the crowdsourcing, we will initialize the new driver's performance based on the knowledge transferred from the existing drivers' records; and 2) marginal utility. Particularly, we carefully incorporate the platform utility to embody the marginal effect, i.e., repeated coverage by multiple vehicles on a certain road will undermine the utility. Based on the real-world vehicular GPS trace, we conduct extensive trace- driven simulations, and results demonstrate that our scheme can effectively obtain high-quality environment map, with on average 40.5% crowdsourcing utility gain over other benchmark schemes.

关键词： Crowdsourcing Roads Trajectory Task analysis Autonomous vehicles Global Positioning System

Deep likelihood network for image restoration with multiple degradation levels

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arXiv 2019年

作者： Guo, Yiwen Lu, Ming Zuo, Wangmeng Zhang, Changshui Chen, Yurong Bytedance AI Lab School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Intel Labs China Beijing100190 China Department of Automation State Key Lab of Intelligence Technologies and Systems Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing100084 China

Convolutional neural networks have been proven very effective in a variety of image restoration tasks. Most state-of-the-art solutions, however, are trained using images with a single particular degradation level, and can deteriorate drastically when being applied to some other degradation settings. In this paper, we propose a novel method dubbed deep likelihood network (DL-Net), aiming at generalizing off-the-shelf image restoration networks to succeed over a spectrum of degradation settings while keeping their original learning objectives and core architectures. In particular, we slightly modify the original restoration networks by appending a simple yet effective recursive module, which is derived from a fidelity term for disentangling the effect of degradations. Extensive experimental results on image inpainting, interpolation and super-resolution demonstrate the effectiveness of our DL-Net. Copyright © 2019, The Authors. All rights reserved.

关键词： Image reconstruction

The Robust Exact Differentiator Toolbox: Improved Discrete-Time Realization 15

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The Robust Exact Differentiator Toolbox: Improved Discrete-T...

15th International Workshop on Variable Structure systems, VSS 2018

作者： Reichhartinger, M. Koch, S. Niederwieser, H. Spurgeon, S.K. Graz University of Technology Institute of Automation and Control Graz Austria Institute of Automation and Control Graz University of Technology Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Graz Austria Information and Computer Engineering Graz University of Technology Graz Austria University College London Department of Electronic Electrical Engineering London United Kingdom

ISBN: (纸本)9781538664391

This paper presents a new release of A Robust Exact Differentiator Toolbox for Matlab®/Simulink® proposed in [1]. This release features a new discrete-time realization of the continuous-time robust exact differentiator. The implemented discretization scheme is less sensitive to gain overestimation and does not suffer from the discretization chattering effect. Hence, the single tuning parameter of the new version of the implemented differentiator is more intuitive to tune. Furthermore, it shows superior estimation performance in the case of large sampling times in comparison to the previous release. This is confirmed by the presented results obtained by numerical simulations and a real world application. © 2018 IEEE.

关键词： Simulink

Multi-sensor state estimation over lossy channels using coded measurements

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arXiv 2019年

作者： Sui, Tianju Marelli, Damian Sun, Ximing Fu, Minyue School of Control Science and Engineering Dalian University of Technology Dalian China School of Automation Guangdong University of Technology Guangzhou China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Electrical Engineering and Computer Science University of Newcastle NSW2308 Australia

This paper focuses on a networked state estimation problem for a spatially large linear system with a distributed array of sensors, each of which offers partial state measurements, and the transmission is lossy. We propose a measurement coding scheme with two goals. Firstly, it permits adjusting the communication requirements by controlling the dimension of the vector transmitted by each sensor to the central estimator. Secondly, for a given communication requirement, the scheme is optimal, within the family of linear causal coders, in the sense that the weakest channel condition is required to guarantee the stability of the estimator. For this coding scheme, we derive the minimum mean-square error (MMSE) state estimator, and state a necessary and sufficient condition with a trivial gap, for its stability. We also derive a sufficient but easily verifiable stability condition, and quantify the advantage offered by the proposed coding scheme. Finally, simulations results are presented to confirm our claims. Copyright © 2019, The Authors. All rights reserved.

关键词： Mean square error

The Influence of Knowledge Management Capabilities on Hospital Services Performance

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The Influence of Knowledge Management Capabilities on Hospit...

International Conference on information Management and Technology (ICIMTech)

作者： Johan Yohannes Kurniawan Sudiana Diego Fernando Cabezas Tapia Information Systems Department Systems Bina Nusantara University Jakarta Indonesia Interdepartmental Center of Embedded Systems of Automation and Computer Sciences Peter the Great St. Petersburg Polytechnic University Russia

Currently healthcare is one of the main problems in Indonesia, particularly with the primary care provided by hospitals. Problems that often occur at hospitals in Indonesia was shown from the high malpractice rate in Indonesia, this problem made public confidence to the hospital was decline, this is indicated by the increasing number of Indonesian citizens who choose to seek the treatment at the hospital in neighboring countries. The suspected problems related to the knowledge management capability of the hospital. This study aims to reveal the role of knowledge management capabilities on the performance of hospital services and provide a prototype system that can improve the knowledge management capabilities of the hospital. The sample for this study are hospitals located in Jakarta. Where the respondent is the head of the hospital. The research method that will be used in this research is qualitative and quantitative method. This research generated a framework strategy to improve knowledge management capability at hospital by looking at factors influencing knowledge management capability of hospital in Indonesia, especially Jakarta.

关键词： Hospitals Medical diagnostic imaging Knowledge management Personnel Organizations information systems

A Fast Image Dehazing Algorithm for Highway Tunnel Based on Artificial Multi-exposure Image Fusion

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IOP Conference Series: Materials Science and Engineering 2020年第1期741卷

作者： Wenfeng Li Hongyan Wei Guanqiu Qi Hao Ding Ke Li China Merchants Chongqing Communications Technology Research & Design Institute CO. LTD College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 China Computer Information Systems Department Buffalo State College Buffalo NY 14222 USA

Single image dehazing has been a challenging problem due to its ill-posed nature. This paper proposed a fast image dehazing algorithm for highway tunnel based on artificial multi-exposure image fusion. The original blurred images are transformed into under-exposed images using a series of gamma-correction operations. Meanwhile, a multi-exposure image fusion algorithm based on two-scale decomposition is proposed. It exploits the guided filter to separate each multi-exposure image into base and detail layers. The fused base and detail layers are integrated into the fused images. Then, a linear saturation adjustment method is utilized to enhance the saturation in spatial domain. Through the above image processing steps, the final haze-free images can be obtained. In this paper, the superiority of the proposed image dehazing algorithm based on artificial multi-exposure image fusion is demonstrated through theoretical analysis and experimental verification. It can quickly improve the visibility of foggy images, and make the processed images colorful and clear.

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Large margin multi-modal multi-task feature extraction for image classification

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arXiv 2019年

作者： Luo, Yong Wen, Yonggang Tao, Dacheng Gui, Jie Xu, Chao School of Electronics Engineering and Computer Science Peking University Beijing China Division of Networks and Distributed Systems School of Computer Engineering Nanyang Technological University Singapore Centre for Quantum Computation & Intelligent Systems Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We therefore propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization and each sub-problem can be efficiently solved. Experiments on two challenging real-world image datasets demonstrate the effectiveness and superiority of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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Network Resilience

arXiv 2020年

作者： Liu, Xueming Li, Daqing Ma, Manqing Szymanski, Boleslaw K. Eugene Stanley, H. Gao, Jianxi Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China School of Reliability and Systems Engineering Beihang University Beijing100191 China College of Safety Science and Engineering Civil Aviation University of China Tianjin 300300 China Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Network Science and Technology Center Rensselaer Polytechnic Institute TroyNY12180 United States Center for Polymer Studies Department of Physics Boston University BostonMA02215 United States

Many systems on our planet shift abruptly and irreversibly from the desired state to an undesired state when forced across a "tipping point". Some examples are mass extinctions within ecosystems, cascading failures in infrastructure systems, and changes in human and animal social networks. The ability to avoid such regime shifts or to recover quickly from such a non-resilient state demonstrates a system's resilience;system resilience is a quality that enables a system to adjust its activities to retain its basic functionality when errors and failures occur. In the past 50 years, attention has been paid almost exclusively to low-dimensional systems;scholars have focused on the calibration of the resilience functions of such systems and the identification of indicators of early warning signals based on two to three connected components. In recent years, taking advantage of network theory and the availability of lavish real datasets, network scientists have begun to explore real-world complex networked multidimensional systems, as well as their resilience functions and early warning indicators. This report presents a comprehensive review of resilience functions and regime shifts in complex systems in domains such as ecology, biology, society, and infrastructure. The research approach includes empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also review the definitions of some ambiguous terms, including robustness, resilience, and stability. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks