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International Conference on Virtual Reality and Visualization (ICVRV)

作者： Shibiao Xu Jiguang Zhang Weilong Ding Xukun Shen G. Hemanth Kumar Xiaopeng Zhang National Laboratory of Pattern Recognition Chinese Academy of Sciences Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Department of Computer Science University of Mysore Mysore India Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9781467376747

Either any of the current global or non-local stereo matching algorithms cannot be good enough to show both matching accuracy and calculation efficiency during the matching processing, especially while there are less texture regions or the images are captured from real scene. Therefore, the goal of our research is to break current bottleneck of stereo matching in aspects of the precision and speed, then get a relatively perfect method compared with other current stereo matching algorithms. Based on this ambitious goal, we proposed hybrid tree guided patch matching algorithm to get a dense and accurate depth image in fast processing speed. We utilize (1) pixel-level and region-level minimum spanning tree to achieve an initial disparity value searching constraint by using hybrid tree cost aggregation, and then (2) apply a robust guided patch matching method to calculate the final accurate disparity of each pixel efficiently by using a cost aggregation restricted through the hybrid tree generated disparity value. In the experience, we demonstrate that our proposed algorithm can generate a high quality depth images and better efficiency compared with recent new stereo matching algorithms. In the Middlebury evaluation, our algorithm also got top ten ranking and better performance among the most of the global stereo matching algorithms in both accuracy and efficiency.

关键词： Estimation Complexity theory Pattern matching Robustness computer vision Virtual reality Hybrid power systems

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An Optimum Free-Table Routing Algorithms of Modified and Traditional Chordal Ring Networks of Degree Four

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材料科学与工程（中英文版） 2010年第10期4卷 78-89页

作者： Raja Noor Farah Azura bt Raja Maamor Shah Mohamed Othman Mohd. Hasan Selamat Laboratory of Computational Science and lnformatics lnstitute for Mathematical Research Universiti Putra Malaysia Serdang 43400 Selangor Malaysia Department of Communication Technology and Networks Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang 43400 Selangor Malaysia Department of Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang 43400 Selangor Malaysia

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Improved Particle Swarm Optimization Algorithm for EIT Image Reconstruction

Improved Particle Swarm Optimization Algorithm for EIT Image...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Oumayma Kahouli Mariem Hafsa Hiba Hellara Imed Eddine Bennour Najoua Essoukri Ben Amara Olfa Kanoun Department of Computer Science National Engineering School of Sousse University of Sousse Sousse Tunisia Laboratory of Advanced Technology and Intelligent Systems National Engineering School of Sousse Laboratory of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Center of Sfax University of Sfax Tunisia

Electrical Impedance Tomography (EIT) is a clinical imaging technique that gained a lot of attention because it is a non-invasive and radiation-free method. To obtain an inner image of the domain under study, a specific number of electrodes is attached to the surface of the object, then through a pair of electrodes, a low alternative current is injected. Potentials produced by this injection are measured with the remaining electrodes. Based on these boundary voltages measurements, the inner conductivity can be inferred, thus the image is reconstructed. The focus of this paper is to overcome the major weakness of the EIT image reconstruction technique, which is the low spatial resolution of the reconstructed images caused by the non-linearity of EIT inverse problem. This paper considers this problem, an optimization problem that is solved using the Particle Swarm Optimization (PSO) meta-heuristic. A variant of the standard PSO is proposed and validated, first on a circular mesh composed of 686 triangles then on a lung mesh composed of 2707 triangles.

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Pioneering Deterministic Scheduling and Network Structure Optimization for Time-Critical Computing Tasks in Industrial IoT

arXiv

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arXiv 2024年

作者： Hu, Yujiao Zhu, Yining Zhang, Huayu Pan, Yan Jia, Qingmin Xie, Renchao Yang, Gang Yu, F. Richard Future Network Research Center Purple Mountain Laboratories Nanjing211111 China The School of Computer Science Northwestern Polytechnical University Xi’an710029 China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China The State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Systems and Computer Engineering Carleton University Ottawa Canada

The Industrial Internet of Things (IIoT) has become a critical technology to accelerate the process of digital and intelligent transformation of industries. As the cooperative relationship between smart devices in IIoT becomes more complex, getting deterministic responses of IIoT periodic time-critical computing tasks becomes a crucial and nontrivial problem. However, few current works in cloud/edge/fog computing focus on this problem. This paper is a pioneer to explore the deterministic scheduling and network structural optimization problems for IIoT periodic time-critical computing tasks. We first formulate the two problems and derive theorems to help quickly identify computation and network resource sharing conflicts. Based on this, we propose a deterministic scheduling algorithm, IIoTBroker, which realizes deterministic response for each IIoT task by optimizing the fine-grained computation and network resources allocations, and a network optimization algorithm, IIoTDeployer, providing a cost-effective structural upgrade solution for existing IIoT networks. Our methods are illustrated to be cost-friendly, scalable, and deterministic response guaranteed with low computation cost from our simulation results. © 2024, CC BY-NC-ND.

关键词： Internet of things

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Infrastructure-free NLoS Obstacle Detection for Autonomous Cars

Infrastructure-free NLoS Obstacle Detection for Autonomous C...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Felix Naser Igor Gilitschenski Alexander Amini Christina Liao Guy Rosman Sertac Karaman Daniela Rus Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute for Technology Cambridge MA USA Toyota Research Institute (TRI) Cambridge MA USA Laboratory for Information and Decision Systems (LIDS) Massachusetts Institute for Technology Cambridge MA USA

Current perception systems mostly require direct line of sight to anticipate and ultimately prevent potential collisions at intersections with other road users. We present a fully integrated autonomous system capable of detecting shadows or weak illumination changes on the ground caused by a dynamic obstacle in NLoS scenarios. This additional virtual sensor “ShadowCam” extends the signal range utilized so far by computer-vision ADASs. We show that (1) our algorithm maintains the mean classification accuracy of around 70% even when it doesn't rely on infrastructure - such as AprilTags - as an image registration method. We validate (2) in real-world experiments that our autonomous car driving in night time conditions detects a hidden approaching car earlier with our virtual sensor than with the front facing 2-D LiDAR.

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STOPPAGE: Spatio-temporal data driven cloud-fog-edge computing framework for pandemic monitoring and management

arXiv

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arXiv 2021年

作者： Ghosh, Shreya Mukherjee, Anwesha Ghosh, Soumya K. Buyya, Rajkumar Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science Mahishadal Raj College West Bengal India Laboratory School of Computing and Information Systems University of Melbourne Australia

Several researches and evidence show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease) which has far reaching sequels in all aspects of human lives ranging from rapid mortality rates to economic and social disruption across the world. In the recent time, COVID-19 (Coronavirus Disease 2019) pandemic disrupted normal human lives, and motivated by the urgent need of combating COVID-19, researchers have put significant efforts in modelling and analysing the disease spread patterns for effective preventive measures (in addition to developing pharmaceutical solutions, like vaccine). In this regards, it is absolutely necessary to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data-sources to deliver insights in improving administrative policy and enhance the preparedness to combat the pandemic. Specifically, human mobility, travel history and other transport statistics have significant impacts on the spread of any infectious disease. In this direction, this paper proposes a spatio-temporal knowledge mining framework, named STOPPAGE to model the impact of human mobility and other contextual information over large geographic area in different temporal scales. The framework has two major modules: (i) Spatio-temporal data and computing infrastructure using fog/edge based architecture;and (ii) Spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. Typically, we develop a Pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hot-spot zones;and provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods. © 2021, CC BY.

关键词： Coronavirus

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CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing

arXiv

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arXiv 2024年

作者： Hu, Yujiao Jia, Qingmin Chen, Jinchao Yao, Yuan Pan, Yan Xie, Renchao Yu, F. Richard Future Network Research Center Purple Mountain Laboratories Nanjing211111 China The School of Computer Science Northwestern Polytechnical University Xi'An710029 China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China The State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Systems and Computer Engineering Carleton University Ottawa Canada

Multi-edge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multi-edge computing system particularly complicated. This paper first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Secondly, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS, is proposed. CoRaiS embeds the real-time states of multi-edge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that CoRaiS can make a high-quality scheduling decision in real time, and can be generalized to other multi-edge computing system, regardless of system scales. Characteristic validation also demonstrates that CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling. © 2024, CC BY-NC-ND.

关键词： Edge computing

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A novel method for predicting harmonic current injection from non-linear loads using neural networks

A novel method for predicting harmonic current injection fro...

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Annual Conference of Industrial Electronics Society

作者： J. Mazumdar R. Harley F. Lambert G.K. Venayagamoorthy School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MA USA

Generation of harmonics and the existence of waveform pollution in power system networks is one of the major problems facing the utilities. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a nonlinear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. A recurrent neural network trained with the backpropagation through time (BPTT) training algorithm is used to find a way of distinguishing between the load harmonics and supply harmonics, without disconnecting the load from the network. The biggest advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This technology could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument

关键词： Neural networks Power system harmonics Current measurement Pollution measurement Voltage Instruments Power generation Power system measurements Distortion measurement Power measurement

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Real-Time Implementation of a STATCOM on a Wind Farm Equipped with Doubly Fed Induction Generators

Real-Time Implementation of a STATCOM on a Wind Farm Equippe...

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： Wei Qiao Ganesh K. Venayagamoorthy Ronald G. Harley School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MO USA

Voltage stability is a key issue to achieve the uninterrupted operation of wind farms equipped with doubly fed induction generators (DFIGs) during grid faults. A static synchronous compensator (STATCOM) is applied to a power network which includes a DFIG driven by a wind turbine, for steady state voltage regulation and transient voltage stability support. The control schemes of the DFIG rotor-side converter, grid-side converter and the STATCOM are suitably designed and coordinated. The system is implemented in real-time on a Real-Time Digital Simulator (RTDSreg). Results show that the STATCOM improves the transient voltage stability and therefore helps the wind turbine generator system to remain in service during grid faults

关键词： Automatic voltage control Wind farms Induction generators Stability Wind turbines Real time systems Steady-state Voltage control Rotors Mesh generation

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Network coding for wireless applications

Network coding for wireless applications

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Unmanned/Unattended Sensors and Sensor Networks II

作者： Lun, Desmond S. Ahmed, Ebad Brothers, L. Reggie DeBardelaben, James A. Effros, Michelle Ho, Tracey Karger, David Koetter, Ralf Lee, Anna MacMullan, Samuel J. Médard, Muriel Zhao, Fang Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA 02139 United States DARPA ATO 3701 N. Fairfax Dr. Arlington VA 22203 United States Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd. Laurel MD 20769 United States Data Compression Laboratory California Institute of Technology Pasadena CA 91125 United States Department of Electrical Engineering California Institute of Technology Pasadena CA 91125 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA 02139 United States Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana IL 61801 United States Lorenz Research Associates Inc. 4443 Brookfield Corporate Dr. Chantilly VA 20151 United States

The advent of network coding promises to change many aspects of networking. Network coding moves away from the classical approach of networking, which treats networks as akin to physical transportation systems. We overview some of the main features of network coding that are most relevant to wireless networks. In particular, we discuss the fact that random distributed network coding is asymptotically optimal for wireless networks with and without packet erasures. These results are extremely general and allow packet loss correlation, such as may occur in fading wireless channels. The coded network lends itself, for multicast connections, to a cost optimization which not only outperforms traditional routing tree-based approaches, but also lends itself to a distributed implementation and to a dynamic implementation when changing conditions, such as mobility, arise. We illustrate the performance of such optimization methods for energy efficiency in wireless networks and propose some new directions for research in the area.

关键词： Signal encoding

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