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现代生物医学进展 2012年第2期12卷 344-347页

作者：朱利华武剑辉栾强厚电子科技大学生命科学与技术学院四川成都610054 东莞电子科技大学电子信息工程研究院广东东莞523808

目的:本文研究了基于现场可编程门阵列(Field Programmable Gate Array,FPGA)超声成像系统中数字动态滤波器的实现方法和过程。方法:动态滤波器中FIR滤波器采用分布式算法(distributed Arithmetic,DA)实现结构,并在应用中对DA算法进行... 详细信息

目的:本文研究了基于现场可编程门阵列(Field Programmable Gate Array,FPGA)超声成像系统中数字动态滤波器的实现方法和过程。方法:动态滤波器中FIR滤波器采用分布式算法(distributed Arithmetic,DA)实现结构,并在应用中对DA算法进行了改进,包括数据并行处理结构的设计、对查找表(Look Up Table,LUT)输入字长N大小的控制和具有对称系数的FIR滤波器的采用。改进后的DA实现在FPGA资源占用和处理速度之间达到了平衡。同时,结合多级流水线结构,动态滤波器实现了数字超声信号并行处理。结果:采用常值滤波器(远场匹配参数)进行滤波后,超声回波图像远场分辨率达到了要求,但越靠近近场效果越差。相比之下,本文设计的基于FPGA超声信号动态数字滤波器达到了很好的滤波效果,使回声图像近场和远场都有最佳分辨率。结论:利用FPGA实现超声系统中动态滤波器是完全可行的,并且有助于提高系统的稳定性和可靠性,并大大减低系统成本。

关键词：超声信号动态滤波分布式算法 FIR滤波器 FPGA

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A new method for lossless image compression using recursive crack coding

A new method for lossless image compression using recursive ...

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1st International conference on Digital image processing and Pattern Recognition, DPPR 2011

作者： Meyyappan, T. Thamarai, S.M. Jeya Nachiaban, N.M. Department of Computer Science and Engineering Alagappa University Karaikudi - 630 003 India Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai-9 India

ISBN: (纸本)9783642240546

Popular entropy coding methods for lossless compression of images depend on probability models. They start by predicting the model of the data. The accuracy of this prediction determines the optimality of the compression. These methods are very slow because of visiting the data (pixels) in left to right order. parallel implementation of these methods is adopted by researchers to speed up the process. In this paper, the authors propose a new approach to image compression using crack coding. The novelty and better compression ratio of the method is due to its recursiveness in finding the variable-length entropy. The proposed method starts with the original image and develop crack codes in a recursive manner, marking the pixels visited earlier and expanding the entropy in four directions. The proposed method is experimented with sample bitmap images and results are encouraging. The method is implemented in uni-processor machine using C language source code. © 2011 Springer-Verlag.

关键词： Entropy

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Efficient large Pearson correlation matrix computing using hybrid MPI/CUDA

Efficient large Pearson correlation matrix computing using h...

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International Joint conference on Computer Science and Software Engineering

作者： Kijsipongse, Ekasit U-Ruekolan, Suriya Ngamphiw, Chumpol Tongsima, Sissades Large Scale Simulation Research Laboratory National Electronics and Computer Technology Center Pathumthani Thailand Biostatistics and Informatics Laboratory Genome Institute National Center for Genetic Engineering and Biotechnology Pathumthani Thailand

ISBN: (纸本)9781457706875

The calculation of pairwise correlation coefficient on a dataset, known as the correlation matrix, is often used in data analysis, signal processing, pattern recognition, image processing, and bioinformatics. With the state-of-the-art Graphic processing Units (GPUs) that consist of massive cores capable to do processing up to several Gflops, the calculation of correlation matrix can be accelerated several times over traditional CPUs. However, due to the rapid growth of the data in the digital era, the correlation matrix calculation becomes computing intensive which needs to be executed on multiple GPUs. As of now, GPUs are common components in data center at many institutions. Their GPU deployment tends towards a GPU cluster which each node is equipped with GPUs. In this paper, we propose a parallel computing based on the hybrid MPI/CUDA programming for fast and efficient Pearson correlation matrix calculation on GPU clusters. At coarse grain parallelism, the correlation matrix is partitioned into tiles which are distributed to execute concurrently on many GPUs using MPI. At fine grain level, the CUDA kernel function on each node performs massively parallel computing on a GPU. To balance load across all GPUs, we adopt the work pool model which there is a master node that manages tasks in the work pool and dynamically assign tasks to worker nodes. The result of the evaluation shows that the proposed work can ensure the load balance across different GPUs and thus gives better execution time than using a simple static data partitioning. © 2011 IEEE.

关键词： Graphics processing unit

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Biomedical image processing with GPGPU using CUDA

Biomedical image processing with GPGPU using CUDA

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作者： Firstauthor, A.R. Secondauthor, I.B. Thirdauthor, G.V. Fourthauthor, P.B. Fifthauthor, M.K. Buda University John von Neumann Faculty of Informatics Budapest Hungary Semmelweiss University 2nd Department of Internal Medicine Budapest Hungary MTA SZTAKI Laboratory of Parallel and Distributed Computing Budapest Hungary

ISBN: (纸本)9789532330670

The main aim of this work is to show, how the GPGPUs can be used to speed up certain image processing methods. The algorithm explained in this paper is used to detect nuclei on (HE hematoxilin eosin) stained colon tissue sample images, and includes a Gauss blurring, an RGB-HSV color space conversion, a fixed binarization, an ultimate erode procedure and a local maximum search. Since the images retrieved from the digital slides require significant storage space (up to few hundred megapixels), the usage of GPGPUs to speed up image processing operations is necessary in the interest of achieving reasonable processing time. The CUDA software development kit was used to develop algorithms to GPUs made by NVIDIA. This work focuses on how to achieve coalesced global memory access when working with three-channel RGB images, and how to use the on-die shared memory efficiently. The exact test algorithm also included a linear connected component labeling, which was running on the CPU, and with iterative optimization of the GPU code, we managed to achieve significant speed up in well defined test environment. © 2011 MIPRO.

关键词： Iterative methods

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Provisioning Spot Market Cloud Resources to Create Cost-Effective Virtual Clusters

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11th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Voorsluys, William Garg, Saurabh Kumar Buyya, Rajkumar Univ Melbourne Dept Comp Sci & Software Engn Cloud Comp & Distributed Syst CLOUDS Lab Melbourne Vic 3010 Australia

ISBN: (纸本)9783642246494

Infrastructure-as-a-Service providers are offering their unused resources in the form of variable-priced virtual machines (VMs), known as "spot instances", at prices significantly lower than their standard fixed-priced resources. To lease spot instances, users specify a maximum price they are willing to pay per hour and VMs will run only when the current price is lower than the user's bid. This paper proposes a resource allocation policy that addresses the problem of running deadline-constrained compute-intensive jobs on a pool of composed solely of spot instances, while exploiting variations in price and performance to run applications in a fast and economical way. Our policy relies on job runtime estimations to decide what are the best types of VMs to run each job and when jobs should run. Several estimation methods are evaluated and compared, using trace-based simulations, which take real price variation traces obtained from Amazon Web Services as input, as well as an application trace from the parallel Workload Archive. Results demonstrate the effectiveness of running computational jobs on spot instances, at a fraction (up to 60% lower) of the price that would normally cost on fixed priced resources.

关键词： spot market virtual clusters cloud computing

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Efficient image denoising by MRF approximation with uniform-sampled multi-spanning-tree

Efficient image denoising by MRF approximation with uniform-...

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International conference on image and Graphics

作者： Sun, Jun Li, Hongdong He, Xuming Australian National University National ICT Australia Canberra Australia Australian National University Canberra Australia National ICT Australia Canberra Australia

ISBN: (纸本)9780769545417

Traditionally, image processing based on Markov Random Field (MRF) is often addressed on a 4-connected grid graph defined on the image. This structure is not computationally efficient. In our work, we develop a multiple-trees structure to approximate the 4-connected grid. A set of spanning trees are generated by a new algorithm: re-weighted random walk (RWRW). This structure effectively covers the original grid and guarantees uniformly distributed occurrence of each edge. Exact maximum a posterior (MAP) inference is performed on each tree structure by dynamic programming and a median filter is chosen to merge the results together. As an important application, image denoising is used to validate our method. Experimentally, our algorithm provides better performance and higher computational efficiency than traditional methods (such as Loopy Belief Propagation) on a 4-connected MRF. © 2011 IEEE.

关键词： image denoising

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Energy based medical imaging segmentation methods by using Cellular Neural Networks

Energy based medical imaging segmentation methods by using C...

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15th WSEAS International conference on Systems, Part of the 15th WSEAS CSCC Multiconference

作者： Gacsádi, A. Ţepelea, L. Gavriluţ, I. Straciuc, O. Electronics and Telecommunications Department University of Oradea Universitatii Str. 1 Oradea 410087 Romania Anatomy Department University of Oradea Universitatii Str. 1 Oradea 410087 Romania

ISBN: (纸本)9781618040237

The paper presents energy based medical imaging segmentation methods by using Cellular Neural Networks (CNN). By implementing the proposed algorithm on FPGA (Field Programmable Gate Array) with an emulated digital CNN-UM (CNN-Universal Machine), due to complete parallel processing, computing-time reduction is achieved and there is a possibility to meet the requirements for medical image segmentation.

关键词： Cellular neural networks

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GPU based implementation of 3DTV system

GPU based implementation of 3DTV system

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International conference on image and Graphics

作者： Wang, Liang-Hao Zhang, Jing Yao, Shao-Jun Li, Dong-Xiao Zhang, Ming Institute of Information and Communication Engineering Zhejiang University Hangzhou 310027 China Zhejiang Provincial Key Laboratory of Information Network Technology Hangzhou 310027 China

ISBN: (纸本)9780769545417

This paper focuses on the near real-time implementation of end-to-end 3DTV System. It is specially designed for the generation of high-quality disparity map and depth-image-based rendering (DIBR) on the graphics processing unit (GPU) through CUDA (Compute Unified Device Architecture) API. We propose our novel methods including a kind of stereo matching with adaptive windows and an asymmetric edge adaptive filter (AEAF) for industrial application. These algorithms are structured in a way that exposes as much data parallelism as possible and the power of shared memory and data parallel programming in GPU is exploited. We evaluate our proposed methods and implementation based on the benchmark Middlebury and the experiment results show that our method is suitable for application on the trade-off among accuracy and execution speed. Running on an NVIDIA Quadro FX4800 graphics card, for each 480x375 stereo images with 60 disparity levels, the proposed system reaches about 146ms for stereo matching and reaches the speed of DIBR 5.7ms for rendering 1 view or 14ms for rendering 8 views. © 2011 IEEE.

关键词： Graphics processing unit

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Research on online failure prediction model and status pretreatment method for exascale system

Research on online failure prediction model and status pretr...

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3rd International conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC 2011

作者： Zhou, Hao Jiang, Yanhuang National Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9780769545578

The reliability issue of Exascale system is extremely serious. Traditional passive fault-tolerant methods, such as rollback-recovery, can not fully guarantee system reliability any more because of their large executing overhead and long recovering duration. Active fault tolerance is expected to become another important fault-tolerant approach for Exascale system. Focusing on system failure prediction, which is one key step of active fault tolerance, we construct online failure prediction model and research on the effective method of system status pretreatment. In order to improve the accuracy and real-time feature of current methods, the proposed Improved Adaptive Semantic Filter (IASF) method processes the latest system logs regularly, filtering useless information out of them according to their semantics. Adopting the main idea of Vector Space Model (VSM), IASF method creates Event Vector corresponding to each log record. By calculating the cosine of vectorial angle, it evaluates the semantics correlation between different log records, and then executes temporal and spatial redundant filter considering the burst feature of log records. IASF method is insensitive to the type of system log and does not introduce any expert system or domain knowledge. The experiment result shows that system can eliminate about 99.6% of useless log records after executing IASF method. © 2011 IEEE.

关键词： Fault tolerance

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An extended registration framework for the triple registration of IBZM SPECT, DATSCAN SPECT and MRI brain images to support the evaluation of brain dopamine receptor scintigraphies

An extended registration framework for the triple registrati...

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33rd Annual International conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Papp, Laszlo Zsoter, Norbert Bandi, Peter Barna, Sandor Luetzen, Ulf Mediso Med Imaging Syst Ltd Baross Str 91-95 Budapest Hungary Scanomed Ltd Debrecen Hungary Med Univ Kiel Dept Nucl Med Kiel Germany

ISBN: (纸本)9781424441228

An extended registration model is presented to register medical image triples acquired for brain dopamine receptor scintigraphies. The model operates with rigid and nonlinear transformations in parallel, where all transformation parameters are optimized by one optimization method. The concept of the transformation-sampling-similarity measurement minimizes the memory usage of a real implementation. A partial-fine sampling method is proposed to decrease the processing time of the registration. Real medical data was collected to compare our method with well-known prior ones. The first tests show that the model outperforms the classic registration methods in both speed and accuracy.

关键词： Biomedical imaging conferences DATSCAN SPECT brain images Dementia IBZM SPECT brain images MRI brain images Magnetic resonance imaging Optimization methods Single photon emission computed tomography biomedical MRI brain brain dopamine receptor scintigraph

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