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Outage Constrained Robust Transmission Design for IRS-aided Secure communications with Direct Communication Links

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arXiv 2020年

作者： Hong, Sheng Pan, Cunhua Zhou, Gui Ren, Hong Wang, Kezhi Information Engineering School Nanchang University Nanchang330031 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Erlangen91054 Germany Department of Computer and Information Sciences Northumbria University United Kingdom

This paper considers the outage constrained robust transmission design for an intelligent reflecting surface (IRS) aided secure communication with direct communication links. We assume a scenario of multiple-input-single-output wiretap channels where a legitimate receiver (Bob) served by a base station (BS) is overheard by multiple eavesdroppers (Eves), meanwhile the artificial noise (AN) is incorporated to confuse the Eves. In particular, we aim to jointly optimize the transmit beamforming, the AN spatial distribution at the BS, and the passive beamforming at the IRS, where the Eve-related channels are only partially available due to the channel estimation errors and their concealment. Two scenarios with partial channel state information (CSI) error of only cascaded BS-IRS-Eve channel and full CSI errors of both cascaded BS-IRS-Eve channel and direct BS-Eve channel are investigated under the statistical CSI error model. A robust power minimization problem constrained by the minimum data rate requirement of Bob and the outage probability of maximum data rate limitation of Eves is investigated. To solve it, the Bernstein-type inequality and alternating optimization strategy are exploited, and artful mathematical manipulations are performed to facilitate the optimization on the phase shifts of the IRS. Simulation results confirm the performance advantages of the proposed algorithm. © 2020, CC BY.

关键词： Channel state information

A Route Recommendation Method Based on Personal Preferences by Monte-Carlo Tree Search

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A Route Recommendation Method Based on Personal Preferences ...

IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Yuta Ishizaki Toshinori Takayama Nozomu Togawa Department of Computer Science and Communications Engineering Waseda University Zenrin DataCom Co. LTD.

In this paper, we propose a route recommendation method, called P-UCT method, considering individual user's preferences utilizing Monte-Carlo tree search. In the proposed method, we firstly extract route features based on the route recommendation history of every user and construct a route evaluator based on Support Vector Machine (SVM). After that, the method generates a random route from a start point to an end point by Monte-Carlo tree search. The route evaluator determines how well every generated route matches the user's preferences. By repeating the evaluation, the method obtains the route, which must be closest to the user's preferences. Experimental results demonstrate that the proposed method outperforms the existing method from the viewpoint of the average evaluation scores.

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A NOVEL ONLINE ACTION DETECTION FRAMEWORK FROM UNTRIMMED VIDEO STREAMS

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arXiv 2020年

作者： Yoon, Da-Hye Cho, Nam-Gyu Lee, Seong-Whan Department of Computer and Radio Communications Engineering Korea University Anam-dong Seongbuk-ku Seoul02841 Kazakhstan Department of Brain and Cognitive Engineering Korea University Anam-dong Seongbuk-ku Seoul02841 Kazakhstan Department of Artificial Intelligence Korea University Anam-dong Seongbuk-ku Seoul02841 Kazakhstan

Online temporal action localization from an untrimmed video stream is a challenging problem in computer vision. It is challenging because of i) in an untrimmed video stream, more than one action instance may appear, including background scenes, and ii) in online settings, only past and current information is available. Therefore, temporal priors, such as the average action duration of training data, which have been exploited by previous action detection methods, are not suitable for this task because of the high intra-class variation in human actions. We propose a novel online action detection framework that considers actions as a set of temporally ordered subclasses and leverages a future frame generation network to cope with the limited information issue associated with the problem outlined above. Additionally, we augment our data by varying the lengths of videos to allow the proposed method to learn about the high intra-class variation in human actions. We evaluate our method using two benchmark datasets, THUMOS’14 and ActivityNet, for an online temporal action localization scenario and demonstrate that the performance is comparable to state-of-the-art methods that have been proposed for offline settings. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

Performance and robustness assessment of Hactive anti-roll bar control system by using a software environment 9th

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Performance and robustness assessment of Hactive anti-roll b...

9th IFAC Symposium on Advances in Automotive Control, AAC 2019

作者： Vu, Van Tan Gaspar, Peter Department of Automotive Mechanical Engineering University of Transport and Communications Hanoi Viet Nam Systems and Control Laboratory Institute for Computer Science and Control Hungarian Academy of Sciences Kende u. 13-17 BudapestH-1111 Hungary

The active anti-roll bar system has been proven to be one of the most effective solutions to improve roll stability of heavy vehicles. In a previous work, the authors proposed an Hcontroller for this system. The Genetic Algorithms method was used to handle the vehicle roll stability and the energy consumption of the actuators via the Pareto optimality. This paper aims to assess the overall effectiveness of the proposed controller with nonlinear heavy vehicle models, which are already set up in the TruckSim® software. The controller is then evaluated in hard conditions to show the high performance and robust with the nonlinearity effects, such as the load distribution between the two axles, the side wind gusts and the abrupt steering. To conduct testing of the Hactive anti-roll bar control system, we propose a co-simulation structure between TruckSim® and Simulink®: the nonlinear vehicle model is determined from TruckSim®, based on using the block S-function of Simulink. Meanwhile, the controller and the actuators are built directly in the Matlab/Simulink ® environment. The validation results are made through two different types of heavy vehicles: a tour bus and a truck, using a selection of different velocities and scenarios. The results show that by using the Hactive anti-roll bar control system, in comparison to the passive anti roll bar system, roll stability is improved to minimise the risk of vehicle rollover. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Controllers

The Design of an H ∞/ LPV Active Braking Control to Improve Vehicle Roll Stability

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IFAC-PapersOnLine 2019年第17期52卷 54-59页

作者： Van Tan Vu Olivier Sename Luc Dugard Peter Gaspar Department of Automotive Mechanical Engineering University of Transport and Communications Hanoi Vietnam Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary

The active braking control system is an active safety system designed to prevent accidents and to stabilize dynamic manoeuvers of a vehicle by generating an artificial yaw moment using differential braking forces. In this paper, the yaw-roll model of a single unit heavy vehicle is used for studying the active braking system by using the longitudinal braking force at each wheel. The grid-based LPV approach is used to synthesize the H ∞ /LPV controller by considering the parameter dependant weighting function for the lateral acceleration. The braking monitor designs are proposed to allow the active braking system to react when the normalized load transfer at the rear axle reaches the criteria of rollover ±1. The simulation results indicate that the active braking system satisfies the adaptation of vehicle rollover in an emergency situation, with low braking forces and improved handling performance of the vehicle.

关键词： Vehicle dynamics Active braking system Rollover H ∞ control LPV system

Detection of Anomaly State Caused by Unexpected Accident using Data of Smart Card for Public Transportation

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Detection of Anomaly State Caused by Unexpected Accident usi...

IEEE International Conference on Big Data

作者： Sakura Yamaki Shou-de Lin Wataru Kameyama Department of Computer Science and Communications Engineering Waseda University Tokyo Japan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Faculty of Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

The railway is an indispensable means of transportation for people living in urban areas in Japan. However, unexpected accidents or disasters disturb the train operation. People usually check the operation status of trains on the official websites or Twitter of each railway company. However, it is still unclear whether such information is provided in realtime, when it is updated and which station is severely affected. Therefore, we tackle a real-world application of transportation big data using 8 months' data collected by smart cards for public transportation in Keikyu Line operating in Tokyo and Kanagawa Prefectures. We propose a method to detect the anomaly state by using the number of train users every 10 minutes in major 9 stations in Keikyu Line. In the method, outlier detections by interquartile range, interval estimation and Hotelling's theory are utilized to detect anomaly points. As the results, our proposal detects anomaly state better than the official announcement by Twitter on some points in terms of realtimeness, update frequency and geographic detail.

关键词： Big Data Conferences

Information bottleneck decoding of rate-compatible 5G-LDPC codes

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arXiv 2019年

作者： Stark, Maximilian Bauch, Gerhard Wang, Linfang Wesel, Richard D. Hamburg University of Technology Institute of Communications University of California Los Angeles Department of Electrical and Computer Engineering

The new 5G communications standard increases data rates and supports low-latency communication that places constraints on the computational complexity of channel decoders. 5G low-density parity-check (LDPC) codes have the so-called protograph-based raptor-like (PBRL) structure which offers inherent rate-compatibility and excellent performance. Practical LDPC decoder implementations use message-passing decoding with finite precision, which becomes coarse as complexity is more severely constrained. Performance degrades as the precision becomes more coarse. Recently, the information bottleneck (IB) method was used to design mutual-information-maximizing lookup tables that replace conventional finite-precision node computations. Additionally, the IB approach exchanges messages represented by integers with very small bit width. This paper extends the IB principle to the flexible class of PBRL LDPC codes as standardized in 5G. The extensions includes puncturing and rate-compatible IB decoder design. As an example of the new approach, a 4-bit information bottleneck decoder is evaluated for PBRL LDPC codes over a typical range of rates. Bit error rate simulations show that the proposed scheme outperforms offset min-sum decoding algorithms and operates within 0.2 dB of double-precision sum-product belief propagation decoding. Copyright © 2019, The Authors. All rights reserved.

关键词： 5G mobile communication systems

Energy-efficient activation and uplink transmission for cellular IoT

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arXiv 2019年

作者： Liu, Chun-Hung Shen, Yu-Han Lee, Chia-Han Department of Electrical and Computer Engineering Mississippi State University Mississippi State39762 United States Institute of Communications Engineering Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu30010 Taiwan

Consider a large-scale cellular network in which base stations (BSs) serve massive Internet-of-Things (IoT) devices. Since IoT devices are powered by a capacity-limited battery, how to prolong their working lifetime is a paramount problem for the success of cellular IoT systems. This paper proposes how to use BSs to manage the active and dormant operating modes of the IoT devices via downlink signaling in an energy-efficient fashion and how the IoT devices perform energyefficient uplink power control to improve their uplink coverage. We first investigate the fundamental statistical properties of an activation signaling process induced by BSs that would like to activate the devices in their cells, which helps to derive the neat expressions of the true, false and total activation probabilities that reveal joint downlink power control and BS coordination is an effective means to significantly improve the activation performance. We then propose an energy-efficient uplink power control for IoT devices which is shown to save power and ameliorate the uplink coverage probability at the same time. We also propose an energy-efficient downlink power control and BS coordination scheme, which is shown to remarkably improve the activation and uplink coverage performances at the same time. Copyright © 2019, The Authors. All rights reserved.

关键词： Wireless networks

Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII

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Database 2023年第2023期2023卷 baad005-baad005页

作者： Leaman, Robert Islamaj, Rezarta Adams, Virginia Alliheedi, Mohammed A. Almeida, João Rafael Antunes, Rui Bevan, Robert Chang, Yung-Chun Erdengasileng, Arslan Hodgskiss, Matthew Ida, Ryuki Kim, Hyunjae Li, Keqiao Mercer, Robert E. Mertová, Lukrécia Mobasher, Ghadeer Shin, Hoo-Chang Sung, Mujeen Tsujimura, Tomoki Yeh, Wen-Chao Lu, Zhiyong National Center for Biotechnology Information (NCBI) National Library of Medicine National Institutes of Health 8600 Rockville Pike Bethesda 20894 MD United States Nvidia 2788 San Tomas Expressway Santa Clara 95051 CA United States Department of Computer Science Al Baha University 4781 King Fahd Rd Al Aqiq 65779 Saudi Arabia Department of Electronics Telecommunications and Informatics (DETI) Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Department of Information and Communications Technologies University of A Coruña Camiño Do Lagar de Castro A Coruña 15008 Spain Informatics Department Medicines Discovery Catapult Alderley Park Block 35 Mereside Macclesfield SK10 4ZF United Kingdom Graduate Institute of Data Science Taipei Medical University No. 172-1 Section 2 Keelung Rd Da’an District Taipei City 106 Taiwan Department of Statistics Florida State University 117 N. Woodward Ave Tallahassee 32306 FL United States Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata Tempaku-ku Nagoya Aichi 468-8511 Japan Department of Computer Science and Engineering Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 South Korea Department of Computer Science The University of Western Ontario Middlesex College Room 355 London N6A 5B7 ON Canada Scientific Databases and Visualization Group Heidelberg Institute for Theoretical Studies (HITS GGmbH) Schloss-Wolfsbrunnenweg 35 Heidelberg 69118 Germany Institute of Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg 69120 Germany Institute of Information Systems and Applications National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu 30013 Taiwan

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and - as highlighted during the coronavirus disease 2019 pandemic - their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substanti

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