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IEEE Conference on Wireless Communications and Networking

作者： Xuanlin Liu Mingzhe Chen Sihua Wang Walid Saad Changchuan Yin Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical Engineering Princeton University Princeton NJ USA Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

In this paper, the problem of trajectory design for energy harvesting unmanned aerial vehicles (UAVs) is studied. In the considered model, the UAV acts as a moving base station to serve the ground users, while collecting energy from the charging stations located at the center of a user group. Meanwhile, to serve ground users and harvest energy, the UAV must be examined and repaired regularly. In consequence, it is necessary to optimize the trajectory design of the UAV while jointly considering the maintenance costs, the number of users that are served by the UAV, and the energy consumption and harvesting. To capture the relationship among these factors, we first model the completion of service and the harvested energy as reward, and the energy consumption during the deployment as cost. Then, the deployment profitability is defined as the reward to the cost of the UAV trajectory. Based on this definition, the trajectory design problem is formulated as an optimization problem whose goal is to maximize the deployment profitability of the UAV. To solve this problem, a foraging algorithm is proposed to find the optimal trajectory so as to maximize the deployment profitability. The proposed algorithm can find the optimal trajectory for the UAV with a polynomial time complexity. Fundamental analysis shows that the proposed algorithm can achieve the maximal deployment profitability. Simulation results show that the proposed algorithm can effectively reduce the operation time and achieve up to 25.6% gain in terms of the deployment profitability compared to Q-learning algorithm.

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Left ventricle contouring in cardiac images based on deep reinforcement learning

TechRxiv

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TechRxiv 2021年

作者： Yin, Sixing Han, Yameng Pan, Judong Wang, Yining Li, Shufang Yu, F. Richard Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China University of California San Francisco San FranciscoCA94117 United States State Key Laboratory of Complex Severe and Rare Diseases Chinese Academy of Medical Sciences Peking Union Medical College Beijing100730 China The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Assessment of the left ventricle segmentation in cardiac magnetic resonance imaging (MRI) is of crucial importance for cardiac disease diagnosis. However, conventional manual segmentation is a tedious task that requires excessive human effort, which makes automated segmentation highly desirable in practice to facilitate the process of clinical diagnosis. In this paper, we propose a novel reinforcement-learning-based framework for left ventricle contouring, which mimics how a cardiologist outlines the left ventricle in a cardiac image. Since such a contour drawing process is simply moving a paintbrush along a specific trajectory, it is thus analogized to a path finding problem. Following the algorithm of proximal policy optimization (PPO), we train a policy network, which makes a stochastic decision on the agent’s movement according to its local observation such that the generated trajectory matches the true contour of the left ventricle as much as possible. Moreover, we design a deep learning model with a customized loss function to generate the agent’s landing spot (or coordinate of its initial position on a cardiac image). The experiment results show that the coordinate of the generated landing spot is sufficiently close to the true contour and the proposed reinforcement-learning-based approach outperforms the existing U-net model even with limited training set. © 2021, CC BY.

关键词： Reinforcement learning

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Federated learning for task and resource allocation in wireless high altitude balloon networks

arXiv

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arXiv 2020年

作者： Wang, Sihua Chen, Mingzhe Yin, Changchuan Saad, Walid Hong, Choong Seon Cui, Shuguang Vincent Poor, H. Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States Department of Computer Science and Engineering Kyung Hee University Korea Republic of Chinese University of Hong Kong Shenzhen518172 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

In this paper, the problem of minimizing energy and time consumption for task computation and transmission is studied in a mobile edge computing (MEC)-enabled balloon network. In the considered network, each user needs to process a computational task in each time instant, where high-altitude balloons (HABs), acting as flying wireless base stations, can use their powerful computational abilities to process the tasks offloaded from their associated users. Since the data size of each user's computational task varies over time, the HABs must dynamically adjust the user association, service sequence, and task partition scheme to meet the users' needs. This problem is posed as an optimization problem whose goal is to minimize the energy and time consumption for task computing and transmission by adjusting the user association, service sequence, and task allocation scheme. To solve this problem, a support vector machine (SVM)-based federated learning (FL) algorithm is proposed to determine the user association proactively. The proposed SVM-based FL method enables each HAB to cooperatively build an SVM model that can determine all user associations without any transmissions of either user historical associations or computational tasks to other HABs. Given the prediction of the optimal user association, the service sequence and task allocation of each user can be optimized so as to minimize the weighted sum of the energy and time consumption. Simulations with real data of city cellular traffic from the OMNILab at Shanghai Jiao Tong University show that the proposed algorithm can reduce the weighted sum of the energy and time consumption of all users by up to 16.1% compared to a conventional centralized method. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

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Material recognition for automated progress monitoring using deep learning methods

arXiv

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arXiv 2020年

作者： Mahami, Hadi Ghassemi, Navid Darbandy, Mohammad Tayarani Shoeibi, Afshin Hussain, Sadiq Nasirzadeh, Farnad Alizadehsani, Roohallah Nahavandi, Darius Khosravi, Abbas Nahavandi, Saeid Department of Construction Project Management Art University of Tehran Tehran*** Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran School of Architecture Islamic Azad University Taft Iran System Administrator Dibrugarh University Assam786004 India School of Architecture and Built Environment Deakin Univ. GeelongVIC3220 Australia Deakin University Locked Bag 20000 GeelongVIC3220 Australia

Automated progress monitoring can provide immediate awareness of project-specific problems in a timely and accurate way. This will pave the way to adopt appropriate mitigation strategies against identified project issues. An essential step in the automated progress monitoring systems is to detect and recognize the type of material accurately, Advanced progress monitoring methods should involve object/material recognition for extracting contextual information. Although there are a number of studies conducted to detect and recognize construction material types automatically, their prediction accuracy should be improved. This research proposes a new deep learning technique to detect the type of different construction materials accurately. The proposed deep learning method is trained using a large dataset of 1231 images taken from several construction sites. The achieved results reveal the high accuracy of the proposed material recognition method comparing to the previous studies. It is believed that the proposed method provides a robust tool for detecting material types and prevents error propagation in a progress monitoring system 1 Copyright © 2020, The Authors. All rights reserved.

关键词： Monitoring

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Monitoring system for Prediction and Detection of Epilepsy Seizure

Monitoring System for Prediction and Detection of Epilepsy S...

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International Conference on Advances in Computational Tools for engineering Applications, ACTEA

作者： Nashaat el Halabi Roy Abi Zeid Daou Roger Achkar Ali Hayek Josef Börcsök Department of Computer and Communications Engineering American University of Science and Technology Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon Department of Computer Architecture and System Programming University of Kassel Germany

Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptoms, varies from patient to another. Since the seizures are not predictable, and most of epileptic patients suffer from physical risky symptoms during the seizure, such patients are not able to perform daily work activities. The objective of this project is to design and implement a monitoring system for epileptic patients; the system should continuously check some vital signs, analyze the measurements, and decide whether the patient is nearly to have a seizure or not. Whenever a seizure is predicted, the system initiates an alarm. In addition, a notification should be sent to the health care responsible, as well as one preferred contact. By implementing the monitoring system, people who suffer from epilepsy will have more chance to work and live a normal life. Thus, this paper presents the concept of the overall system and shows results of the implemented systems: EEG, ECG and Fall Detection system. Results have shown that the fall detection accuracy reached 99.89% whereas the accuracy of the prediction using the ANN was about 97.34%.

关键词： Electroencephalography Epilepsy Feature extraction Tools Monitoring Artificial neural networks Support vector machines

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Analysis of Memory Capacity for Deep Echo State Networks

arXiv

引用

arXiv 2019年

作者： Liu, Xuanlin Chen, Mingzhe Yin, Changchuan Saad, Walid Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States

—In this paper, the echo state network (ESN) memory capacity, which represents the amount of input data an ESN can store, is analyzed for a new type of deep ESNs. In particular, two deep ESN architectures are studied. First, a parallel deep ESN is proposed in which multiple reservoirs are connected in parallel allowing them to average outputs of multiple ESNs, thus decreasing the prediction error. Then, a series architecture ESN is proposed in which ESN reservoirs are placed in cascade that the output of each ESN is the input of the next ESN in the series. This series ESN architecture can capture more features between the input sequence and the output sequence thus improving the overall prediction accuracy. Fundamental analysis shows that the memory capacity of parallel ESNs is equivalent to that of a traditional shallow ESN, while the memory capacity of series ESNs is smaller than that of a traditional shallow ESN. In terms of normalized root mean square error, simulation results show that the parallel deep ESN achieves 38.5% reduction compared to the traditional shallow ESN while the series deep ESN achieves 16.8% reduction. Copyright © 2019, The Authors. All rights reserved.

关键词： Network architecture

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Optimization Model for Designing Multiple Virtualized Campus Area Networks Coordinating with Wide Area Networks

Optimization Model for Designing Multiple Virtualized Campus...

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作者： Kurimoto, Takashi Urushidani, Shigeo Oki, Eiji Information System Architecture Science Research Division National Institute of Informatics Tokyo101-8430 Japan Department of Communication and Computer Engineering Graduate School of Informatics Kyoto University Kyoto606-8501 Japan

We propose an optimization model for designing multiple network functions virtualization (NFV)-based campus area networks (CANs). Organizations, such as universities and research institutions have their own campus information and communication technology equipment, but many would like to move this equipment to NFV and cloud data centers for improving reliability and resiliency. However, NFV-based CAN is not affordable for them, because costs are higher with a cloud. One solution is for multiple organizations to procure NFV and cloud data center resources together. By doing so, their individual costs of using these resources will be reduced. To make progress on this approach, there are planning issues to resolve when choosing optimal NFV and cloud data center locations. The proposed model minimizes the total network costs incurred by the organizations, including the wide area network cost and data synchronization costs for recovery from faults at data centers and the various subcampus network configurations of legacy CANs. The model is formulated and analyzed by using mixed integer linear programming. The effect of cost minimization is evaluated in a ladder network and an actual network, SINET5, and it is found that the costs can be reduced by up to 63%. The calculation times of this model under practical conditions are short and the model will be useful in practice. It is also shown that the cost of fault recovery can be suppressed. These results will encourage organizations to deploy NFV-based CANs. © 2004-2012 IEEE.

关键词： Network function virtualization

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Federated Deep Learning for Immersive Virtual Reality over Wireless Networks

Federated Deep Learning for Immersive Virtual Reality over W...

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IEEE Global Communications Conference

作者： Mingzhe Chen Omid Semiari Walid Saad Xuanlin Liu Changchuan Yin Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Department of Electrical Engineering Princeton University Princeton NJ USA The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Colorado Colorado Springs CO USA Bradley Department of Electrical and Computer Engineering Wireless@VT Virginia Tech Blacksburg VA USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIPs) that can detach the users from their virtual world. To measure the BIPs for wireless VR users, a novel model that jointly considers the VR applications, transmission delay, VR video quality, and users' awareness of the virtual environment is proposed. In the developed model, the base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to increase the data rate of VR users, thus, reducing the number of BIPs for each user. Therefore, the mobility and orientation of VR users must be considered when minimizing BIPs, since the body movements of a VR user may result in blockage of its wireless link. The BIP problem is formulated as an optimization problem which jointly considers the predictions of users' mobility patterns, orientations, and their BS association. To predict the orientation and mobility patterns of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses concept from federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' mobility patterns and orientations. Using these predictions, the user association policy that minimizes BIPs is derived. Simulation results demonstrate that the developed algorithm reduces the users' BIPs by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.

关键词： Wireless communication Wireless sensor networks Delays Uplink Downlink Prediction algorithms Machine learning

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Switching Transient of Multi-step 3-Phase Capacitor Bank in 66/11 kV Bhutan Silicon Metal Private Ltd

Switching Transient of Multi-step 3-Phase Capacitor Bank in ...

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International Electrical engineering Congress (iEECON)

作者： Chophel Thanapong Suwanasri Phanupong Fuangpian Cattreeya Suwanasri Warunee Srisongkram Electrical and Software System Engineering The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand

Capacitor banks are finding greater acceptability in application with the integration of many Ferro Silicon Industries in Bhutan; however, switching of capacitor banks is one of the most challenging operations because many inherent characteristics are accompanied with these events that needs to be studied carefully. This paper presents the switching transients of multi-step 3-phase back-to-back capacitor bank of 66/11kV Bhutan Silicon Metal Private Limited. Simulation were carried out to observe the magnitude of peak inrush current, frequency of transient oscillation and peak oscillating voltage during energizing/de-energizing of capacitor banks. The result shows that the magnitude is very high without current limiting reactor but with current limiting reactor the magnitude is reduced to a permissible limit thereby mechanical stresses to the nearby electrical equipment is reduced. Capacitor bank with grounded system is recommended because the transient recovery voltage and the rate of rise of recovery voltage across circuit breaker contact are less than the standard value of 2S kV and the limit of equipment capability 28 kV/75 μs = 0.37 kV/μs as per ANSI C37.06-2000.

关键词： Capacitors Circuit breakers Transient analysis Limiting Inductors Silicon Metals

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A Management Model for Low Powered Wireless Personal Area Networks

A Management Model for Low Powered Wireless Personal Area Ne...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Yekta Türk Ali Akman Turk Telekom Mobile Access System Architecture Department Istanbul Turkey Computer Engineering Department Maltepe University Istanbul Turkey

ISBN: (纸本)9781538647264

Most of the researchers are investigating Internet of Things (IoT) to obtain information about our environment to control and act on it with the analysis of collected big data. An important topic with IoT is to manage the vast number of devices. In this demo, we demonstrate a management model in order to detect faults, analyze network and improve performance and connectivity in low powered mesh networks. The model can take actions when necessary and visualize those actions and the state of different managed objects. For building the proposed model, we have modified a well-known network management protocol to use for management data access and have performed fault and performance analysis in terms of values of counters collected from the interfaces. Our demonstrations indicate that the usage of the proposed method can perform well in limited bandwidth networks. At the same time, some security and device firmware related features are also added to the management system.

关键词： Protocols Performance evaluation Data visualization Bandwidth Databases Data models Security

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