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9th EAI International conference on Cloud Computing, CloudComp 2019 and the 4th EAI International conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

9th EAI International Conference on Cloud Computing, CloudCo...

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9th EAI International conference on Cloud Computing, CloudComp 2019 and the 4th EAI International conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

ISBN: (纸本)9783030485122

The proceedings contain 55 papers. The special focus in this conference is on Cloud Computing. The topics include: Rendering of Three-Dimensional Cloud Based on Cloud Computing;distributed Stochastic Alternating Direction Method of Multipliers for Big Data Classification;personalized Recommendation Algorithm Considering Time Sensitivity;cloud-Based Master Data Platform for Smart Manufacturing Process;a Semi-supervised Classification Method for Hyperspectral images by Triple Classifiers with Data Editing and Deep Learning;A Survey of image Super Resolution Based on CNN;design and Development of an Intelligent Semantic Recommendation System for Websites;a Lightweight Neural Network Combining Dilated Convolution and Depthwise Separable Convolution;resource Allocation Algorithms of Vehicle Networks with Stackelberg Game;research on Coordination Control Theory of Greenhouse Cluster Based on Cloud Computing;a Multi-objective Computation Offloading Method in Multi-cloudlet Environment;anomalous Taxi Route Detection System Based on Cloud Services;collaborative Recommendation Method Based on Knowledge Graph for Cloud Services;efficient Multi-user Computation Scheduling Strategy Based on Clustering for Mobile-Edge Computing;Grazing Trajectory Statistics and Visualization Platform Based on Cloud GIS;Cloud-Based AGV Control System;a parallel Drone image Mosaic Method Based on Apache Spark;cycleSafe: Safe Route Planning for Urban Cyclists;prediction of Future Appearances via Convolutional Recurrent Neural Networks Based on image Time Series in Cloud Computing;video Knowledge Discovery Based on Convolutional Neural Network;time-Varying Water Quality Analysis with Semantical Mining Technology;a Survey of QoS Optimization and Energy Saving in Cloud, Edge and IoT;data-Driven Fast Real-Time Flood Forecasting Model for processing Concept Drift;intelligent System Security Event Description Method.

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DENOISING CONVOLUTIONAL AUTOENCODER BASED B-MODE ULTRASOUND TONGUE image FEATURE EXTRACTION 44

DENOISING CONVOLUTIONAL AUTOENCODER BASED B-MODE ULTRASOUND ...

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44th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Li, Bo Xu, Kele Feng, Dawei Mi, Haibo Wang, Huaimin Zhu, Jian Beijing Univ Posts & Telecommun Automat Dept Beijing 100876 Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Natl Univ Def Technol Changsha Hunan Peoples R China Univ Michigan Dept Linguist Ann Arbor MI 48109 USA

ISBN: (纸本)9781479981311

B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the recent success of unsupervised deep learning approach, we explore unsupervised convolutional network architecture for the feature extraction in the ultrasound tongue image, which can be helpful for the clinical linguist and phonetics. By quantitative comparison between different unsupervised feature extraction approaches, the denoising convolutional autoencoder (DCAE)-based method outperforms the other feature extraction methods on the reconstruction task and the 2010 silent speech interface challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the state-of-the-art value of 6.45% using Discrete cosine transform as the feature extractor. Our codes are available at https://***/DeePBluE666/Source-code1.

关键词： B-mode ultrasound tongue imaging unsupervised learning feature extraction convolutional autoencoder denoising convolutional autoencoder

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Unsupervised Moving Object Detection through Background Models for PTZ Camera

Unsupervised Moving Object Detection through Background Mode...

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International conference on Pattern Recognition

作者： Kimin Yun Hyungil Kim Kangmin Bae Jongyoul Park ETRI Rep. of Korea Seoul National University of Science & Technology

ISBN: (纸本)9781728188089;9781728188096

Moving object detection in a video plays an important role in many vision applications. Recently, moving object detection using appearance modeling based on a convolutional neural network has been actively developed. However, the CNN-based methods usually require the user's supervision of the first frame so that it becomes highly dependent on the training dataset. In contrast, the method of finding a foreground, which models a background occupying a large proportion in an image, can detect a moving object efficiently in an unsupervised manner. However, existing methods based on background modeling in a pan-tilt-zoom (PTZ) camera suffer many false positives or loss of moving objects due to the estimation error of camera motion. To overcome the aforementioned limitations, we propose a moving object detection method for a PTZ camera through two background models. In an unsupervised way, our method builds the two background models that have different roles: 1) a coarse background model for detecting large changes, and 2) a fine background model for detecting small changes. In more detail, the coarse background model builds a block-based Gaussian model, and the fine model builds a sample consensus model. Both models are adaptively updated according to the estimated camera motion in the video recorded by a PTZ camera. Then, each foreground result from two background models is incorporated to fill the moving object region. Through experiments, the proposed method achieves better performance than the state-of-the-art methods and operates in real-time without parallel processing. In addition, we showed the effectiveness of the proposed model through improved results of moving object detection through combination with the latest supervised method.

关键词： Training Adaptation models Estimation error Object detection Streaming media parallel processing Cameras

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GPU-accelerated QPSK Transceiver with FEC over a Flat-fading Channel

GPU-accelerated QPSK Transceiver with FEC over a Flat-fading...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Rehan Muzammil Mohd Wajid Aligarh Muslim University Aligarh India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

Rayleigh flat-fading path in wireless-channels leads to errors, and this makes the detection task very difficult. In such cases, forward error correction (FEC) is used to provide good performance. This paper gives the testing of a QPSK-transceiver using threshold detection and FEC in the form of (8, 4) block coding-decoding. The whole system was tested by transmitting a known digital image over a flat-fading channel, and detection was performed using the threshold detection process. Very recently, the advent of programmable graphics processing units (GPUs) as excessive parallel programming system has enabled high-performance computation. NVIDIA GTX 1050 Ti GPU has been used for implementing and testing transceiver in this work. The image is transmitted over a flatfading channel along with FEC, and the results are obtained in the form of Bit Error Rate (BER) versus signal-to-noise ratio (SNR) curve. All the baseband processing is performed in the NVIDIA GPU, and some of the computation is performed in the CPU. The purpose of this paper is to show that a lot of processing time can be saved using a highly parallel computing machine, the GPU, as compared to a sequentially programming device, the CPU. The speedup is indicated in the results.

关键词： Graphics processing units Phase shift keying Transceivers Central processing Unit Registers Task analysis Computer architecture

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A Study on Big Data processing Frameworks: Spark and Storm 3rd

A Study on Big Data Processing Frameworks: Spark and Storm

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3rd International conference on Smart Computing and Informatics (SCI)

作者： Deshai, N. Venkataramana, S. Sekhar, B. V. D. S. Srinivas, K. Varma, G. P. Saradhi JNTUK SRKR Engn Coll Dept Informat Technol Bhimavaram Andhra Pradesh India

ISBN: (纸本)9789813296909;9789813296893

Today's internet world impose a trade-off between Peta-byte to Exa-byte being created in digital computer world attributable enormous volume of unstructured datasets being generating from diverse social sites, IOT, Google, Twitter, Yahoo, monitoring surroundings through sensors, etc., is big data (BD). Because second to second doubles the datasets volume size but the shortage of smooth dynamic processing, analysis and scalability techniques. Because the recent high-speed decade we applied only extant methods and common tools about the gigabyte data process and perform computations on whole world huge data. Apache open free source Hadoop is the latest BD weapon can process zetta-byte dimensions of databases by its most developed and popular components as HDFS and map reduce (MR), to get done excellent storage features magnificent and reliable processing on zetta-byte of datasets. MR likes more famous software, popular framework for handling BD existing issues with full parallel, highly distributed, and most scalable manner. Despite, Hadoop, map and reduces tasks have more limitations like poor allocating custom resources, stream way processing, shortage of latency, the deficit of efficient performance, imperfection of optimization, the real-time trend of computations and diverse logical elucidation. We significant most modern progressive features computing procedures. This examination paper shows Apache fastest spark tool, world latest and fastest tool is apache storm has efficient frameworks to conquer those limitations.

关键词： Big data Hadoop Mapreduce Yarn Spark Storm

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360-DEGREE image COMPLETION BY TWO-STAGE CONDITIONAL GANS 26

360-DEGREE IMAGE COMPLETION BY TWO-STAGE CONDITIONAL GANS

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26th IEEE International conference on image processing (ICIP)

作者： Akimoto, Naofumi Kasai, Seito Hayashi, Masaki Aoki, Yoshimitsu Keio Univ Yokohama Kanagawa Japan

ISBN: (纸本)9781538662496

The latest generative adversarial networks (GANs) can generate realistic high resolution images. However, to the best of our knowledge, there are no GANs for generating 360-degree images. Therefore, this paper proposes the novel problem setting that by using a known area from the 360-degree image as an input, the remainder of the image can be completed with the GANs. To do so, we propose the approach of two-stage generation using network architecture with series-parallel dilated convolution layers. Moreover, we present how to rearrange images for data augmentation, simplify the problem, and make inputs for training the 2nd stage generator. Our experiments show that these methods generate the distortion seen in 360-degree images in the outlines of buildings and roads, and their boundaries are clearer than those of baseline methods. Furthermore, we discuss and clarify the difficulty of our proposed problem. Our work is the first step towards GANs predicting an unseen area within a 360-degree space.

关键词： Generative adversarial networks 360 degrees image completion extrapolation

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methods to enhance the automation of operational modal analysis 45

Methods to enhance the automation of operational modal analy...

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45th International conference on Vibroengineering

作者： Wiemann, Marcel Bonekemper, Lukas Kraemer, Peter Department of Mechanical Engineering University of Siegen Siegen Germany

The vibration-based damage detection and the monitoring of modal data are currently based on different Operational Modal Analysis (OMA) approaches. For the continuous monitoring of modal quantities, different techniques for automated feature extraction are known. Especially in recent years several research groups and companies have been working on the automatic interpretation of stability plots. Nevertheless, many questions regarding data pre-processing for OMA in time or frequency domain are still unanswered. The present paper deals with issues regarding effective pre-processing methods for OMA based on Covariance-Stochastic Subspace Identification. In this context, the orthogonality of matrices after model order reduction, etc. are referred. This includes, for example, a comparison between the classical calculation of the reduced-order matrices and a procedure that preserves the orthogonality of these matrices. A method known from the signal denoising and image processing is also successful used to extract and select the modes. The mode extraction method is validated with an innovative three-dimensional stability plot. This paper does not claim to solve all tasks of an automated OMA, but it contributes the calculation of clean, easy to interpret, stability plots, which should facilitate the automatic evaluation in the future. The effectiveness of the algorithms is demonstrated by means of simulated (3DOF-StateSpace) and measured data of a laboratory structure described in [1]. Afterwards the results and the future works on the topic are discussed. Copyright © 2020 Marcel Wiemann, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Structural health monitoring

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A new terrain classification algorithm based on convolutional neural network

A new terrain classification algorithm based on convolutiona...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Hui Yang Wang Jie Niu Qian School of Humanities Chang’an University Xi’an China School of Construction Machinery Chang’an University Xi’an China

ISBN: (数字)9781728196688

ISBN: (纸本)9781728196695

Aiming at the problems of existing terrain classification methods, this paper proposes a new terrain classification algorithm based on convolutional neural networks. First, the SegNet and SLIC superpixel segmentation methods are combined to solve the boundary blur problem in the terrain classification of the SegNet convolutional neural network. Then, through comparison experiments of irregular and complex terrain classification based on support vector machine, SegNet and SLIC-SegNet, the superiority of the new SLIC-SegNet terrain classification method is verified.

关键词： image segmentation Information science image recognition Convolutional neural networks conferences Classification algorithms

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parallel in-situ measurement of heliostat aim points in central receiver systems by image processing methods

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SOLAR ENERGY 2019年 180卷 648-663页

作者： Bern, Gregor Schoettl, Peter van Rooyen, De Wet Heimsath, Anna Nitz, Peter Fraunhofer Inst Solar Energy Syst ISE Heidenhofstr 2 D-79110 Freiburg Germany

Central Receiver Systems use thousands of heliostats distributed in a field to reflect the solar radiation to a receiver installed at a tower. The large distances between the heliostats and their aim point require a high precision of the heliostat control and drive system to efficiently concentrate the solar radiation and to allow for a safe and reliable plant operation. Precise calibration procedures are needed for the commissioning of open loop systems. Furthermore, the need for cost reduction in the heliostat field motivates for precise and fast calibration systems allowing for savings in the design of the individual heliostats. This work introduces a method for the measurement of true aim points of heliostats in operation. A proposed application is the plug-in system HelioControl, to be used for parallel in-situ calibration of multiple heliostats in operation. The approach aims at extracting the focal spot position of individual heliostats on the receiver domain using image analysis and signal modulation. A centralized remote vision system, installed far from the harsh conditions at the focal area, takes image sequences of the receiver domain. During the measurement, a signal is modulated on different heliostats which is then extracted for the retrieval of the true aim points. This paper describes the theoretical background of the methodology and demonstrates the functionality based on two simulated cases showing the practical advantages introduced with this approach.

关键词： Central receiver systems Heliostat tracking Closed-loop control Heliostat field calibration

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Collective Communication for the RISC-V xBGAS ISA Extension 19

Collective Communication for the RISC-V xBGAS ISA Extension

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48th International conference on parallel processing (ICPP)

作者： Williams, Brody Wang, Xi Leidel, John D. Chen, Yong Texas Tech Univ Lubbock TX 79409 USA Tact Comp Labs Muenster TX USA

ISBN: (纸本)9781450371964

parallel programming methodologies are fundamentally dissimilar to those of conventional programming, and software developers without the requisite skillset often find it difficult to adapt to these new methods. This is particularly true for parallel programming in a distributed address space, which is necessary for any meaningful degree of scalability. As such, an approach that combines a more intuitive interface together with excellent performance within the distributed address space model is desired. In this work, we present our initial API design and implementation as well as the underlying algorithms for a collective communication library built for the Extended Base Global Address Space (xBGAS) extension to the RISC-V microarchitecture. Our runtime library is designed to enact the Partitioned Global Address Space model (PGAS) in an attempt to alleviate the difficulty associated with traditional distributed address space programming while the underlying collective implementation is formulated to prevent the loss of, and even improve, performance over traditional solutions.

关键词： PGAS RISC-V Remote Memory Access parallel Programming Collectives

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