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Dithen: A Computation-as-a-Service Cloud Platform for Large-Scale Multimedia processing

IEEE TRANSACTIONS ON CLOUD COMPUTING 2019年第2期7卷 509-523页

作者： Doyle, Joseph Giotsas, Vasileios Anam, Mohammad Ashraful Andreopoulos, Yiannis Dithen 843 Finchley Rd London NW11 8NA England Univ East London Univ Way London E16 2RD England UCL Elect & Elect Engn Dept Roberts Bldg Torrington Pl London WC1E 7JE England Univ Calif San Diego Ctr Appl Internet Data Anal CAIDA UCSD-SDSC9500 Gilman Dr La Jolla CA 92093 USA

We present Dithen, a novel computation-as-a-service (CaaS) cloud platform specifically tailored to the parallel execution of large-scale multimedia tasks. Dithen handles the upload/download of both multimedia data and executable items, the assignment of compute units to multimedia workloads, and the reactive control of the available compute units to minimize the cloud infrastructure cost under deadline-abiding execution. Dithen combines three key properties: (i) the reactive assignment of individual multimedia tasks to available computing units according to availability and predetermined time-to-completion constraints;(ii) optimal resource estimation based on Kalman-filter estimates;(iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of units servicing workloads. The deployment of Dithen over Amazon EC2 spot instances is shown to be capable of processing more than 80,000 video transcoding, face detection and image processing tasks (equivalent to the processing of more than 116 GB of compressed data) for less than $1 in billing cost from EC2. Moreover, the proposed AIMD-based control mechanism, in conjunction with the Kalman estimates, is shown to provide for more than 27 percent reduction in EC2 spot instance cost against methods based on reactive resource estimation. Finally, Dithen is shown to offer a 38 to 500 percent reduction of the billing cost against the current state-of-the-art in CaaS platforms on Amazon EC2 (Amazon Lambda and Amazon Autoscale). A baseline version of Dithen is currently available at *** under the "AutoScale" option.

关键词： Computation-as-a-service big data multimedia computing cloud computing Amazon EC2 spot instances

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Common Information Enhanced Reconstruction for Accelerated High resolution Multi-shot Diffusion Imaging

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MAGNETIC RESONANCE IMAGING 2019年 62卷 28-37页

作者： Wu, Yuhsuan Ma, Xiaodong Huang, Feng Guo, Hua Tsinghua Univ Ctr Biomed Imaging Res Sch Med Dept Biomed Engn Beijing Peoples R China Univ Hong Kong Lab Biomed Imaging & Signal Proc Hong Kong Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Neusoft Med Syst Shanghai Shanghai Peoples R China

Purpose: Multi-shot technique can effectively achieve high-resolution diffusion weighted images, but the acquisition time of multi-shot technique is prolonged, especially for multiple direction diffusion encoding. Thus, increasing acquisition efficiency is highly desirable for high-resolution diffusion tensor imaging (DTI). In this study, based on the assumption that different diffusion directions share the common information, image ratio constrained reconstruction (IRCR) combined with iterative self-consistent parallel imaging reconstruction (SPIRiT) is proposed to improve data sampling efficiency and image reconstruction fidelity for high-resolution DTI. Theory and methods: The proposed reconstruction framework is named Common Information Enhanced Reconstruction (CIER). Inter-image correlation among different direction diffusion-weighted images is used through common information, which is an isotropic component and structure, for improving the performance of reconstruction. The framework consists of three steps. (i) Pre-processing: three intermediate multi-shot images, low-resolution composite image, high-resolution composite image and low-resolution diffusion weighted image, are generated based on the SPIRiT method. (ii) IRCR: the initial high resolution diffusion weighted image is calculated from the images in step (i) based on that the ratio map between high-resolution images is approximated by the ratio map between the corresponding low-resolution images. (iii) Final SPIRiT reconstruction: the final image is generated with the image from IRCR as initialization by considering data consistency only in the SPIRIT calculation. A specific implementation based on multishot variable density spiral (VDS) DTI is used to demonstrate the method. Results: The proposed CIER method was compared with the traditional reconstruction methods, conjugate gradient SENSE (CG-SENSE), L1-regularized SPIRIT (Li-SPIRiT), and anisotropic-sparsity SPIRiT (AS-SPIRiT) in brain DTI at accel

关键词： image ratio constrained reconstruction Common information SPIRiT High-resolution DTI Variable density spiral (VDS)

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distributed parallel Super-Block-Based Star Detection and Centroid Calculation

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IEEE SENSORS JOURNAL 2018年第19期18卷 8096-8107页

作者： Wang, Zhen Jiang, Jie Zhang, Guangjun Beijing Univ Aeronaut & Astronaut Sch Instrumentat Sci & Optoelect Engn Minist Educ Key Lab Precis Optomechatron Technol Beijing 100191 Peoples R China

Due to the inadequacies of the speed of star detection and centroid calculation (SDCC), the applications of high-resolution and high-frame-rate image sensors are greatly limited in star trackers, restricting the performance of the star trackers. To resolve this problem, a novel distributed parallel super-block-based SDCC method is proposed in this paper. In contrast to the previous SDCC methods, the proposed method divides the star images into several sub-images and processes these sub-images in parallel. Then, by using a super-block-based method and a distributed shared memory-based method to process the stars in sub-images (sub-stars) and in adjacent sub-images (boundary stars), respectively, the position information of all the stars are detected during the parallel scan of the sub-images. The proposed method exhibits a speed that is up to 4 x M times faster than the previous SDCC methods in processing the star images that are divided into M sub-images. Experimental results demonstrate that the proposed method is correct and effective. It is applicable to the use of most high-performance image sensors and resolves the performance limitation of star trackers to achieve better attitude accuracy and attitude update rate.

关键词： distributed parallel star detection centroid location star tracker distributed shared memory image sensors application

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Forgery Detection For High-Resolution Digital images Using FCM And PBFOAAlgorithm

Forgery Detection For High-Resolution Digital Images Using F...

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International Conference on parallel distributed and Grid Computing (PDGC)

作者： Satinder Jeet Kaur Nidhi Bhatla Sachdeva Engineering College for Girls Gharuan(Mohali) India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

image forgery detection is the area of research in the field of biometric and forensics. Digital pictures are the resource of data. In the present world of technology, image processing software tools have developed to generate and modify digital images from one location to another. With the current technology, it is simple to establish image forgery by addition and subtraction of the components from the pictures that lead to image interfering. Copy-move image forgery is created by copying and pasting the element in a similar image. Hence, copy-move forgery has become an area of research in the image forensic unit. Various methods have been implemented to detect digital image forgery. Some issues still required to resolve like time complexity, fake, and blurred image. In existing research, the block and feature-based approach used to remove a forged area from the image using SIFT and RANSAC algorithm. The forgery dataset of the 80 pictures collected to achieve accuracy of up to 95%. In the research work, the PBFOA method has been implemented to optimize and extract the features using the component analysis method. FCM is used for image segmentation in the input image. PBFOA is based on an optimization process to select valuable features based on the calculation of the fitness function. In this method, two steps are used to re-verify the instance, features (i) Slower and faster condition. BFOA steps are described in detail in this research paper. Initial steps, Spread the feature set in the whole system. In the rapid condition selected and to eliminate the valuable features one at a time, then reproduction phase is implemented with the help of the fitness function to recover the feature values and detect the forgery information in the uploaded image. The simulation setup using MATLAB 2016a version and improve the accuracy rate and image quality parameter. Performance analysis depends on the proposed metrics FAR, FRR, ACC, Precision, Recall, and compared with the exist

关键词： Forgery Feature extraction Digital images Databases image recognition Handheld computers Grid computing

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PSO-PS:Parameter Synchronization with Particle Swarm Optimization for distributed Training of Deep Neural Networks

PSO-PS:Parameter Synchronization with Particle Swarm Optimiz...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qing Ye Yuxuan Han Yanan Sun Jiancheng Lv College of Computer Science Sichuan University Chengdu China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Parameter updating is an important stage in parallelism-based distributed deep learning. Synchronous methods are widely used in distributed training the Deep Neural Networks (DNNs). To reduce the communication and synchronization overhead of synchronous methods, decreasing the synchronization frequency (e.g., every n mini-batches) is a straightforward approach. However, it often suffers from poor convergence. In this paper, we propose a new algorithm of integrating Particle Swarm Optimization (PSO) into the distributed training process of DNNs to automatically compute new parameters. In the proposed algorithm, a computing work is encoded by a particle, the weights of DNNs and the training loss are modeled by the particle attributes. At each synchronization stage, the weights are updated by PSO from the sub weights gathered from all workers, instead of averaging the weights or the gradients. To verify the performance of the proposed algorithm, the experiments are performed on two commonly used image classification benchmarks: MNIST and CIFAR10, and compared with the peer competitors at multiple different synchronization configurations. The experimental results demonstrate the competitiveness of the proposed algorithm.

关键词： Training Synchronization parallel processing Neural networks Computational modeling Mathematical model Data models

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CNN-based Monocular Decentralized SLAM on embedded FPGA

CNN-based Monocular Decentralized SLAM on embedded FPGA

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IEEE International Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Jincheng Yu Feng Gao Jianfei Cao Chao Yu Zhaoliang Zhang Zhengfeng Huang Yu Wang Huazhong Yang Electronic Engineering Department Tsinghua University Beijing China School of Microelectronics Hefei University of Technology Hefei China

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Decentralized visual simultaneous localization and mapping (DSLAM) can share locations and environmental information between robots, which is an essential task for many multi-robot applications. The visual odometry (VO) is a basic component to estimate the 6-DoF absolute pose for robot applications. Decentralized place recognition (DPR) is a fundamental element to produce candidate place matches for sharing information among different robots. The goal of this paper is to build a CNN-based real-time DSLAM system on embedded FPGA platforms. Because of the high precision requirement of VO, the existing quantization methods can not be directly applied. We improve the fixed-point fine-tune method for the CNN-based monocular VO, which enables VO can be deployed on the fixed-point FPGA accelerator. We also explore the influence of the DPR frequency on the DSLAM results, and find out a proper DPR frequency to balance the accuracy and speed. A cross-component pipeline scheduling method is proposed to improve DPR frequency and further improve the final accuracy of DSLAM under the same hardware resource constraints.

关键词： Robots Field programmable gate arrays Cameras image reconstruction Quantization (signal) Hardware Feature extraction

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Cloud-based difference algorithm using big GPR data for roadbed damage detection

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第23期32卷 e5545-e5545页

作者： Xu, Xianlei Gao, Wenru Zhang, Di Li, Taotao Qiao, Xu State Key Lab Water Resources Protect & Utilizat Beijing Peoples R China China Univ Min & Technol Beijing State Key Lab Coal Resources & Safe Min Beijing 100083 Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing Peoples R China

Ground-penetrating radar (GPR) technology is being widely used for urban roadbed damage detection. In this paper, we present the development of a cloud-based difference detection method using big GPR data for roadbed damage detection. Unlike many processing methods relying on standalone servers, the proposed method can achieve high accuracy and processing efficiency by distributing the computation over a server cluster. The method includes a difference detection algorithm for GPR image enhancement and damage extraction, the Hadoop distributed File System, and MapReduce distributed parallel computing framework for big GPR data parallel processing and interpretation. Moreover, to automatize roadbed damage classification, a criterion with seven grades is established based on the wave group shape, amplitude, phase, and attenuation characteristics. We validate the proposed method through a detection experiment that was conducted on the Fourth Ring Road in Beijing, China. The detection revealed 187 damage from the efficient processing of big GPR data, thus suggesting the ability of the proposed method to provide timely support for road safety in cities.

关键词： cloud computing difference detection GPR parallel computation roadbed damage detection

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An improved MCB localization algorithm based on received signal strength indicator 20

An improved MCB localization algorithm based on received sig...

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20th International Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Qiao, Yan Chunyue, Zhou Baitong, Zhong Hui, Tian School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Laboratory of Communication Engineering Beijing Jiaotong University Beijing China Institute of Image Communication and Network Engineering Uniview Hangzhou Zhejiang Province China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)9781728126166

An improved Monte Carlo Localization Boxed (MCB) localization Algorithm based on received signal strength indicator (RSSI) for mobile wireless sensor networks node localization is proposed. Aiming at the characteristics of instantaneity, mobility and complexity of mobile node location, this algorithm combines RSSI ranging model with MCL algorithm which has high positioning accuracy, good performance and wide application. It establishes anchor node sampling box by using the actual distance of signal propagation obtained, and effectively reduces the sampling range. The simulation results show that this algorithm improves the sampling efficiency, shortens the sampling time, and increases the positioning accuracy compared to the MCL algorithm. It is a low-cost, low-power and low-complexity localization algorithm without additional hardware and communication overhead. © 2019 IEEE.

关键词： Monte Carlo methods

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Challenges of Ship Focusing with Long Coherence processing Interval 39

Challenges of Ship Focusing with Long Coherence Processing I...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Liu, WenKang Xing, MengDao Sun, Guang-Cai Xidian Univ Xian 710071 Peoples R China

ISBN: (纸本)9781538691540

Ship oscillatory motions with long coherence processing interval (CPI) are complex and hard to accurately estimate. The traditional ship imaging methods usually avoid long-CPI focusing by dealing with a short observation time interval, which may make it hard to obtain a high-resolution and high-SNR image. In this paper, the mechanisms and challenges of long CPI imaging of oscillatory targets are investigated. The properties of wavenumber domain support (WDS) and point spreading function (PSF) of oscillatory targets are analyzed. It's found that the WDS spreads as a three-dimensional (3D) thin and curved sweep surface, with time-variant energy density, non-parallel boundaries and a complex structure. The PSF of an oscillatory target has a 3D resolution, but also multiple side-lobes with high level. We inspect the relationships between the properties of the WDS and the non-ideal PSF. Moreover, it's found that scatters distributed on a 3D oscillatory target cannot be focused uniformly on a predefined imaging plane (IP). The projection relationship of the target focusing positions on the slant-range plane (SRP) and the IP are also derived. The simulation results can well validate the proposed method.

关键词： ship image 3D oscillatory motion long coherence processing interval (long-CPI) imaging

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Deep Sketch-Based Modeling: Tips and Tricks

Deep Sketch-Based Modeling: Tips and Tricks

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International Conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Yue Zhong Yulia Gryaditskaya Honggang Zhang Yi-Zhe Song SketchX CVSSP University of Surrey Beijing University of Posts and Telecommunications Abstract iFlyTek-Surrey Joint Research Centre on Artificial Intelligence

ISBN: (数字)9781728181288

ISBN: (纸本)9781728181295

Deep image-based modeling received lots of attention in recent years, yet the parallel problem of sketch-based modeling has only been briefly studied, often as a potential application. In this work, for the first time, we identify the main differences between sketch and image inputs: (i) style variance, (ii) imprecise perspective, and (iii) sparsity. We discuss why each of these differences can pose a challenge, and even make a certain class of image-based methods inapplicable. We study alternative solutions to address each of the difference. By doing so, we drive out a few important insights: (i) sparsity commonly results in an incorrect prediction of foreground versus background, (ii) diversity of human styles, if not taken into account, can lead to very poor generalization properties, and finally (iii) unless a dedicated sketching interface is used, one can not expect sketches to match a perspective of a fixed viewpoint. Finally, we compare a set of representative deep single-image modeling solutions and show how their performance can be improved to tackle sketch input by taking into consideration the identified critical differences.

关键词： Shape Three-dimensional displays image reconstruction Solid modeling Rendering (computer graphics) Surface reconstruction Strain

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