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2009 7th Asian Control Conference, ASCC 2009

作者： Zhengtao, Zhang De, Xu Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9788995605691

A high-speed stereovision system based on smart cameras is presented to track table tennis ball. A distributed parallel processing architecture is developed to improve the real-time performance of the system. A set of novel target recognition and tracking algorithm is also proposed to distinguish the ball from the changing background with less computation and good robustness. It takes about 6ms to capture an image and extract the ball. The trajectory of the ball is well measured in real-time. The experimental results are provided to verify the effectiveness of the proposed methods. ©2009 ACA.

关键词： Target tracking

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Non-rigid Registration for Large Sets of Microscopic images on Graphics Processors

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JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL image AND VIDEO TECHNOLOGY 2009年第1-3期55卷 229-250页

作者： Ruiz, Antonio Ujaldon, Manuel Cooper, Lee Huang, Kun Ohio State Univ Biomed Informat Dept Columbus OH 43210 USA Univ Malaga Comp Architecture Dept E-29071 Malaga Spain

Microscopic imaging is an important tool for characterizing tissue morphology and pathology. 3D reconstruction and visualization of large sample tissue structure requires registration of large sets of high-resolution images. However, the scale of this problem presents a challenge for automatic registration methods. In this paper we present a novel method for efficient automatic registration using graphics processing units (GPUs) and parallel programming. Comparing a C++ CPU implementation with Compute Unified Device Architecture (CUDA) libraries and pthreads running on GPU we achieve a speed-up factor of up to 4.11x with a single GPU and 6.68x with a GPU pair. We present execution times for a benchmark composed of two sets of large-scale images: mouse placenta (16K x16K pixels) and breast cancer tumors (23K x62K pixels). It takes more than 12 hours for the genetic case in C++ to register a typical sample composed of 500 consecutive slides, which was reduced to less than 2 hours using two GPUs, in addition to a very promising scalability for extending those gains easily on a large number of GPUs in a distributed system.

关键词： Microscopic imaging image registration and segmentation Pattern analysis Feature detection Graphics processors High-performance computing

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Multiscale Data Reduction with Flexible Saliency Criterion for Biological image Analysis

Multiscale Data Reduction with Flexible Saliency Criterion f...

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Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Bosl, William J. Harvard Mit Div Hlth Sci & Technol Childrens Hosp Boston Informat Program Boston MA 02115 USA

ISBN: (纸本)9781424432950

Analysis of biomedical images requires attention to image features that represent a small fraction of the total image size. A rapid method for eliminating unnecessary detail, analogous to pre-attentive processing in biological vision, allows computational resources to be applied where most needed for higher-level analysis. In this report we describe a method for bottom up merging of pixels into larger units based on flexible saliency criteria using a method similar to structured adaptive grid methods used for solving differential equations on physical domains. While creating a multiscale quadtree representation of the image, a saliency test is applied to prune the tree to eliminate unneeded details, resulting in an image with adaptive resolution. This method may be used as a first step for image segmentation and analysis and is inherently parallel, enabling implementation on programmable hardware or distributed memory clusters.

关键词： image segmentation

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A Scalable parallel Approach for Peptide Identification from Large-scale Mass Spectrometry Data

A Scalable Parallel Approach for Peptide Identification from...

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38th International Conference on parallel processing

作者： Kulkarni, Gaurav Kalyanaraman, Ananth Cannon, William R. Baxter, Douglas Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Pacific Northwest Natl Lab Richland WA USA

ISBN: (纸本)9781424449231

Identifying peptides, which are short polymeric chains of amino acid residues in a protein sequence, is of fundamental importance in systems biology research. The most popular approach to identify peptides is through database search. In this approach, an experimental spectrum ("query") generated from fragments of a target peptide using mass spectrometry is computationally compared with a database of already known protein sequences. The goal is to detect database peptides that are most likely to have generated the target peptide. The exponential growth rates and overwhelming sizes of biomolecular databases make this an ideal application to benefit from parallel computing. However, the present generation of software tools is not expected to scale to the magnitudes and complexities of data that will be generated in the next few years. This is because they are all either serial algorithms or parallel strategies that have been designed over inherently serial methods, thereby requiring high space- and time-requirements. In this paper, we present an efficient parallel approach for peptide identification through database search. Three key factors distinguish our approach from that of existing solutions: i) (space) Given p processors and a database with N residues, we provide the first space-optimal algorithm (O(N/p)) under distributed memory machine model;ii) (time) Our algorithm uses a combination of parallel techniques such as one-sided communication and masking of communication with computation to ensure that the overhead introduced due to parallelism is minimal;and iii) (quality) The run-time savings achieved using parallel processing has allowed us to incorporate highly accurate statistical models that have previously been demonstrated to ensure high quality prediction albeit on smaller scale data. We present the design and evaluation of two different algorithms to implement our approach. Experimental results using 2.65 million microbial proteins show linear scaling up

关键词： parallel peptide identification mass spectrometry

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Evolution of query optimization methods

Evolution of query optimization methods

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作者： Hameurlain, Abdelkader Morvan, Franck Institut de Recherche en Informatique de Toulouse IRIT Paul Sabatier University 118 Route de Narbonne Toulouse Cedex 31062 France

ISBN: (纸本)3642037216

Query optimization is the most critical phase in query processing. In this paper, we try to describe synthetically the evolution of query optimization methods from uniprocessor relational database systems to data Grid systems through parallel, distributed and data integration systems. We point out a set of parameters to characterize and compare query optimization methods, mainly: (i) size of the search space, (ii) type of method (static or dynamic), (iii) modification types of execution plans (re-optimization or re-scheduling), (iv) level of modification (intra-operator and/or inter-operator), (v) type of event (estimation errors, delay, user preferences), and (vi) nature of decision-making (centralized or decentralized control). The major contributions of this paper are: (i) understanding the mechanisms of query optimization methods with respect to the considered environments and their constraints (e.g. parallelism, distribution, heterogeneity, large scale, dynamicity of nodes) (ii) pointing out their main characteristics which allow comparing them, and (iii) the reasons for which proposed methods become very sophisticated. © 2009 Springer.

关键词： Query processing

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Massively parallel electrical conductivity imaging of the subsurface: applications to hydrocarbon exploration

Massively parallel electrical conductivity imaging of the su...

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5th Annual Conference of Scientific Discovery through Advanced Computing (SciDAC 2009)

作者： Newman, Gregory A. Commer, Michael Univ Calif Berkeley Lawrence Berkeley Lab Berkeley CA 94720 USA

Three-dimensional (3D) geophysical imaging is now receiving considerable attention for electrical conductivity mapping of potential offshore oil and gas reservoirs. The imaging technology employs controlled source electromagnetic (CSEM) and magnetotelluric (MT) fields and treats geological media exhibiting transverse anisotropy. Moreover when combined with established seismic methods, direct imaging of reservoir fluids is possible. Because of the size of the 3D conductivity imaging problem, strategies are required exploiting computational parallelism and optimal meshing. The algorithm thus developed has been shown to scale to tens of thousands of processors. In one imaging experiment, 32,768 tasks/processors on the IBM Watson Research Blue Gene/L supercomputer were successfully utilized. Over a 24 hour period we were able to image a large scale field data set that previously required over four months of processing time on distributed clusters based on Intel or AMD processors utilizing 1024 tasks on an InfiniBand fabric. Electrical conductivity imaging using massively parallel computational resources produces results that cannot be obtained otherwise and are consistent with timeframes required for practical exploration problems.

关键词： Supercomputers

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Agility of wireless sensor networks for earthquake monitoring of bridges

Agility of wireless sensor networks for earthquake monitorin...

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6th International Conference on Networked Sensing Systems, INSS2009

作者： Cheng, Liang Pakzad, Shamim N. Department of Computer Science and Engineering Lehigh University Bethlehem PA 18015 United States Department of Civil and Environmental Engineering Lehigh University Bethlehem PA 18015 United States

ISBN: (纸本)9781424463145

Applying wireless sensor networks (WSNs) for bridge monitoring has received considerable interests over the past few years. In this paper two WSN-agility problems are identified based on the deployment experiences of WSNs in earthquake monitoring of bridges: (i) timely capturing the earthquake response of bridges and (ii) quasi-realtime processing of the measured data for bridge modal identification in monitoring the features of the structure. This paper also presents solutions for these problems: a pulse-based media access control scheme for the first problem and a distributed approach to modal identification by spectral methods for the second problem to address the combined agility issues. Results show the effectiveness of the proposed approaches. ©2009 IEEE.

关键词： parallel algorithms

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Real Time Low Level parallel image processing For Active Vision Systems

Real Time Low Level Parallel Image Processing For Active Vis...

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IEEE International Advance Computing Conference, IACC

作者： Ashish Tanwer Rattandeep Singh Parminder Singh Reel Electronics and Communication Engineering Department Thapar University Patiala Patiala India

This paper presents an overview of low level parallel image processing algorithms and their implementation for active vision systems. Authors have demonstrated novel low level image processing algorithms for point operators, local operators, dithering, smoothing, edge detection, morphological operators, image segmentation and image compression. The algorithms have been prepared & described as pseudo codes. These algorithms have been simulated using parallel Computing Toolboxtrade (PCT) of MATLAB. The PCT provides parallel constructs in the MATLAB language, such as parallel for loops, distributed arrays and message passing & enables rapid prototyping of parallel code through an interactive parallel MATLAB session.

关键词： Real time systems image processing Machine vision MATLAB Smoothing methods image edge detection image segmentation image coding Computational modeling parallel processing

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An FPGA-based face recognition using combined 5/3 DWT with PCA methods

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通讯和计算机（中英文版） 2009年第10期6卷 1-7,22页

作者： Dake Chen HAN Jiu-qiang School of Electronics and Information Engineering Xi ＇an Jiaotong University Xi ＇an 710049 China

This paper describes a new scheme for feature extraction from facial images on FPGA. The proposed method is comprised of two stages. The first stage uses the 5/3 DWT to decompose the original face image into LL, LH, HL, and HH wavelet coefficient to reduce the size of the image. In the second stage, PCA is employed to extract the face features from the wavelet coefficients. Here we use the power iteration algorithm to find the eigenvector of the covariance matrix. We present an efficient hardware architecture using combination of parallel processing module and serial processing module. This method can take the benefits of parallel usage advantage of FPGAs and can save hardware resources. Complete hardware implemented on a Stratix ii FPGA. The experimental results show that the system works with high processing rate and only 21% of the logic resources an FPGA are consumed by face recognition logic. Thus it is very suitable for the low cost implementation of the face recognition system.

关键词： FPGA 主成分分析方法人脸识别小波变换面部特征提取面部识别系统 Stratix 小波系数

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A Scalable parallel Approach for Peptide Identification from Large-Scale Mass Spectrometry Data

A Scalable Parallel Approach for Peptide Identification from...

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International Conference on parallel processing Workshops (ICPPW)

作者： Gaurav Kulkarni Ananth Kalyanaraman William R. Cannon Douglas Baxter School of Electrical Engineering and Computer Science Washington State University Pullman WA USA Pacific Northwest National Laboratory Richland WA USA

Identifying peptides, which are short polymeric chains of amino acid residues in a protein sequence, is of fundamental importance in systems biology research. The most popular approach to identify peptides is through database search. In this approach, an experimental spectrum ("query'') generated from fragments of a target peptide using mass spectrometry is computationally compared with a database of already known protein sequences. The goal is to detect database peptides that are most likely to have generated the target peptide. The exponential growth rates and overwhelming sizes of biomolecular databases make this an ideal application to benefit from parallel computing. However, the present generation of software tools is not expected to scale to the magnitudes and complexities of data that will be generated in the next few years. This is because they are all either serial algorithms or parallel strategies that have been designed over inherently serial methods, thereby requiring high space- and time- requirements. In this paper, we present an efficient parallel approach for peptide identification through database search. Three key factors distinguish our approach from that of existing solutions: (i) (space) Given p processors and a database with N residues, we provide the first space-optimal algorithm (O(N/p)) under distributed memory machine model; (ii) (time) Our algorithm uses a combination of parallel techniques such as one-sided communication and masking of communication with computation to ensure that the overhead introduced due to parallelism is minimal; and (iii) (quality) The run-time savings achieved using parallel processing has allowed us to incorporate highly accurate statistical models that have previously been demonstrated to ensure high quality prediction albeit on smaller scale data. We present the design and evaluation of two different algorithms to implement our approach. Experimental results using 2.65 million microbial proteins show linear sca

关键词： Peptides Large-scale systems Mass spectroscopy Databases parallel processing Proteins Algorithm design and analysis Predictive models Polymers Amino acids

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