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22nd International Conference on parallel and distributed Computing (Euro-Par)

作者： Papatriantafyllou, Angelos Sacharidis, Dimitris TU Wien Fac Informat Inst Informat Syst Res Grp Parallel Comp Vienna Austria TU Wien Fac Informat Inst Software Technol & Interact Syst E Commerce Grp Vienna Austria

ISBN: (纸本)9783319436593;9783319436586

The summed-area table ( SAT), also known as integral image, is a data structure extensively used in computer graphics and vision for fast image filtering. The parallelization of its construction has been thoroughly investigated and many algorithms have been proposed for GPUs. Generally speaking, state-of-the-art methods cannot efficiently solve this problem in multi-core and many-core (Xeon Phi) systems due to cache misses, strided and/or remote memory accesses. This work proposes three novel cache-aware parallel SAT algorithms, which generalize parallel block-based prefix-sums algorithms. In addition, we discuss 2D matrix partitioning policies which play an important role in the efficient operation of the cache subsystem. The combination of a SAT algorithm and a partition is manually tuned according to the matrix layout and the number of threads. Experimental evaluation of our algorithms on two NUMA systems and Intel's Xeon Phi, and for three datatypes (int, float, double) by utilizing all system cores, shows, in all experimental settings, better performance compared to the best known CPU and GPU approaches (up to 4.55x on NUMA and 2.8x on Xeon Phi).

关键词： Program processors

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Group importance sampling for particle filtering and MCMC

arXiv

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arXiv 2017年

作者： Martino, Luca Elvira, Víctor Camps-Valls, Gustau Universidad Carlos III De Madrid Spain Image Processing Laboratory Universitat De València Spain Villeneuve d'Ascq France

Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in different works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discuss the application of GIS into the Sequential Importance Resampling framework and show that Independent Multiple Try Metropolis schemes can be interpreted as a standard MetropolisHastings algorithm, following the GIS approach. We also introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS. The first one, named Group Metropolis Sampling method, produces a Markov chain of sets of weighted samples. All these sets are then employed for obtaining a unique global estimator. The second one is the distributed Particle Metropolis-Hastings technique, where different parallel particle filters are jointly used to drive an MCMC algorithm. Different resampled trajectories are compared and then tested with a proper acceptance probability. The novel schemes are tested in different numerical experiments such as learning the hyperparameters of Gaussian Processes, two localization problems in a wireless sensor network (with synthetic and real data) and the tracking of vegetation parameters given satellite observations, where they are compared with several benchmark Monte Carlo techniques. Three illustrative Matlab demos are also provided. Copyright © 2017, The Authors. All rights reserved.

关键词： Importance sampling

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A systematic review of structured sparse learning

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Frontiers of Information Technology & Electronic Engineering 2017年第4期18卷 445-463页

作者： Lin-bo QIAO Bo-feng ZHANG Jin-shu SU Xi-cheng LU College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

High-dimensional data arising from diverse scientific research fields and industrial development have led to increased interest in sparse learning due to model parsimony and computational advantage. With the assumption of sparsity, many computational problems can be handled efficiently in practice. Structured sparse learning encodes the structural information of the variables and has been quite successful in numerous research fields. With various types of structures discovered, sorts of structured regularizations have been proposed. These regularizations have greatly improved the efficacy of sparse learning algorithms through the use of specific structural information. In this article, we present a systematic review of structured sparse learning including ideas, formulations, algorithms, and applications. We present these algorithms in the unified framework of minimizing the sum of loss and penalty functions, summarize publicly accessible software implementations, and compare the computational complexity of typical optimization methods to solve structured sparse learning problems. In experiments, we present applications in unsupervised learning, for structured signal recovery and hierarchical image reconstruction, and in supervised learning in the context of a novel graph-guided logistic regression.

关键词： Sparse learning Structured sparse learning Structured regularization

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Universal steganography detector based on an artificial immune system for JPEG images 15

Universal steganography detector based on an artificial immu...

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Joint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on parallel and distributed processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016

作者： Pérez, José De Jesús Serrano Rosales, Moises Salinas Cruz-Cortés, Nareli Cybersecurity Lab Centro de Investigación en Computación Instituto Politécnico Nacional Mexico City Mexico

ISBN: (纸本)9781509032051

Steganography is a hiding information technique heavily used nowadays. Though initially it was used to establish hidden communication channels, modern steganography has been found useful to hide code inside multimedia objects, mostly images. Its goal is to infiltrate malware into organizations or personal devices. This kind of malware is called stegomalware. As countermeasure, modern steganalysis methods employing different Computational Intelligence techniques such as Support Vector Machine, Machine Learning, Fisher Linear Discriminant, and others have been utilized. In this work we present a new stegananalysis method based on an Artificial Immune System (AIS), to detect JPEG images modified with three well known steganographic tools: F5, Outguess, or Steghide. It is also proposed the usage of Haar Wavelets to extract a feature vector that best describes the analyzed image, this is due the Haar Wavelets fast calculation and information synthesis. Our experimentation results are competitive against techniques representative of the state of the art. © 2016 IEEE.

关键词： image analysis

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SinglA image super-resolution via classified sparse Representation 13

SinglA image super-resolution via classified sparse Represen...

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13th International Conference on Embedded Software and System, ICESS 2016

作者： Lai, Chao Li, Fangzhao Li, Bao Jin, Shiyao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781509037261

Single image super-resolution reconstruction is a challenging ill-posed inverse problem currently. In this paper, we propose a method based on image classification and sparse representation for single image super-resolution reconstruction. Various images belong to different image category, and each category contains different contents and structures respectively, especially the high-frequency feature. Therefore, we extract the features of the input low-resolution image, and classify it into the corresponding category. Then the high-resolution image is reconstructed by sparse representation with the dictionary trained from the corresponding database, which consists of high-resolution and low-resolution image patch pairs. The experimental results demonstrate that our method achieves better performance in visual effect and qualitative analysis, by comparison with some well-known methods. © 2016 IEEE.

关键词： image classification

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Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets 4

Enabling Factor Analysis on Thousand-Subject Neuroimaging Da...

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4th IEEE International Conference on Big Data (Big Data)

作者： Anderson, Michael J. Capota, Mihai Turek, Javier S. Zhu, Xia Willke, Theodore L. Wang, Yida Chen, Po-Hsuan Manning, Jeremy R. Ramadge, Peter J. Norman, Kenneth A. Intel Corp Parallel Comp Lab Hillsboro OR 97124 USA Princeton Univ Princeton NJ 08544 USA Dartmouth Coll Hanover NH 03755 USA

ISBN: (纸本)9781467390057

The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has led neuroscientists to consider factor analysis methods to extract and analyze the underlying brain activity. In this work, we consider two recent multi-subject factor analysis methods: the Shared Response Model and the Hierarchical Topographic Factor Analysis. We perform analytical, algorithmic, and code optimization to enable multi-node parallel implementations to scale. Single-node improvements result in 99x and 2062x speedups on the two methods, and enables the processing of larger datasets. Our distributed implementations show strong scaling of 3.3x and 5.5x respectively with 20 nodes on real datasets. We demonstrate weak scaling on a synthetic dataset with 1024 subjects, equivalent in size to the biggest fMRI dataset collected until now, on up to 1024 nodes and 32,768 cores.

关键词： functional Magnetic Resonance Imaging Multi-subject Analysis Scaling Factor Analysis

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A parallel microsimulation package for modelling cancer screening policies 12

A parallel microsimulation package for modelling cancer scre...

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12th IEEE International Conference on e-Science (e-Science)

作者： Karlsson, Andreas Olofsson, Niten Laure, Erwin Clements, Mark Karolinska Inst Dept Med Epidemiol & Biostat Stockholm Sweden SeRC Stockholm Sweden Karolinska Inst Dept Neurosci Stockholm Sweden Combitech AB Stockholm Sweden Royal Inst Technol Ctr High Performance Comp Stockholm Sweden

ISBN: (纸本)9781509042739

Microsimulation with stochastic life histories is an important tool in the development of public policies. In this article, we use microsimulation to evaluate policies for prostate cancer testing. We implemented the microsimulations as an R package, with pre- and post-processing in R and with the simulations written in C++. Calibrating a microsimulation model with a large population can be computationally expensive. To address this issue, we investigated four forms of parallelism: (i) shared memory parallelism using R;(ii) shared memory parallelism using OpenMP at the C++ level;(iii) distributed memory parallelism using R;and (iv) a hybrid shared/distributed memory parallelism using OpenMP at the C++ level and MPI at the R level. The close coupling between R and C++ offered advantages for ease of software dissemination and the use of high-level R parallelisation methods. However, this combination brought challenges when trying to use shared memory parallelism at the C++ level: the performance gained by hybrid OpenMP/MPI came at the cost of significant re-factoring of the existing code. As a case study, we implemented a prostate cancer model in the microsimulation package. We used this model to investigate whether prostate cancer testing with specific re-testing protocols would reduce harms and maintain any mortality benefit from prostate-specific antigen testing. We showed that four-yearly testing would have a comparable effectiveness and a marked decrease in costs compared with two-yearly testing and current testing. In summary, we developed a microsimulation package in R and assessed the cost-effectiveness of prostate cancer testing. We were able to scale up the microsimulations using a combination of R and C++, however care was required when using shared memory parallelism at the C++ level.

关键词： C++ languages Prostate cancer parallel processing Libraries Testing Computational modeling

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液晶光栅自干涉测量空间光调制器相位调制特性

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激光与光电子学进展 2017年第6期54卷 140-146页

作者：赵自新肖昭贤张航瑛赵宏西安交通大学机械工程学院机械制造系统国家重点实验室陕西西安710049

提出了一种测量平行液晶空间光调制器(SLM)相位调制特性的自干涉法。该方法实验简单,具有良好的稳健性。加载到SLM的灰度图由三部分组成,一部分是二元衍射光栅灰度图;另外两部分加载的灰度图均匀分布,下部分灰度值恒为0,上部分灰度值从... 详细信息

提出了一种测量平行液晶空间光调制器(SLM)相位调制特性的自干涉法。该方法实验简单,具有良好的稳健性。加载到SLM的灰度图由三部分组成,一部分是二元衍射光栅灰度图;另外两部分加载的灰度图均匀分布,下部分灰度值恒为0,上部分灰度值从0逐渐增加。随着上部分灰度的改变,经过相位调制的反射光束与光栅产生的+1级衍射光干涉生成一整幅错位条纹图,通过测量同一幅干涉条纹的周期和上下条纹之间的相对移动量来计算SLM的相位调制量大小,该方法消除了环境振动或空气湍流引起的条纹抖动对测量的影响,提高了测量精度。与其他方法相比,该方法获取SLM相位调制特性不需要复杂的光学装置,且具有优良的机械稳定性,反应快速更易实施。

关键词：测量液晶空间光调制器自干涉法相位调制特性图像后处理

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Adaptive Polar based Filtering Method for image Copy-Move Forgery Detection 15

Adaptive Polar based Filtering Method for Image Copy-Move Fo...

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15th IEEE Int Conf on Trust, Security and Privacy in Comp and Commun / 10th IEEE Int Conf on Big Data Science and Engineering / 14th IEEE Int Symposium on parallel and distributed Proc with Applicat (IEEE Trustcom/BigDataSE/ISPA)

作者： Bi, Xiuli Pun, Chi-Man Yuan, Xiao-Chen Univ Macau Dept Comp & Informat Sci Taipa Macau Sar Peoples R China

ISBN: (纸本)9781509032051

In this paper, an Adaptive Polar based Filtering Method is proposed for image copy-move forgery detection. In order to improve the performance of detection method, the post-processing of the matching results is worth being focused on. To filter out the redundant pixels from the initially matched pixels, two pixels sets-Symmetrical Matched Pixels set and Unsymmetrical Matched Pixels set, are extracted from the matched pixel pairs;furthermore, the polar distributions of the two sets are calculated respectively. Then, the filtering thresholds can be adaptively calculated according to the polar distribution, thus the redundant pixels can be filtered out accordingly. Finally, some morphological operations are applied to the remained pixels to generate the detected forged regions. Experimental results show that the proposed scheme can achieve much better detection results compared with the existing state-of-the-art copy-move forgery detection methods.

关键词： Polar Distribution Adaptive Polar based Filtering Method Copy-Move Forgery

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Bilateral histogram equalization with pre-processing for contrast enhancement

Bilateral histogram equalization with pre-processing for con...

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Feroz Mahmud Amil Md. Mostafijur Rahman Shanto Rahman Emon Kumar Dey Mohammad Shoyaib Institute of Information Technology University of Dhaka Institute of Information Technology University of Dhaka Bangladesh

Various methods have been proposed for enhancing the images. Some of those perform well in some specific application areas but most of the techniques suffer from artifacts due to over enhancement. To overcome this problem, we have introduced a new image enhancement technique namely Bilateral Histogram Equalization with Pre-processing (BHEP) which uses Harmonic mean to divide the histogram of the image. We have performed both qualitative and quantitative measurements for experiments and the results show that BHEP creates less artifacts in several standard images than the existing state-of-the-art image enhancement techniques.

关键词： Histograms Mathematical model image enhancement Harmonic analysis image color analysis Adaptive equalizers image edge detection

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