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International Conference on Systems and Informatics (ICSAI)

作者： Yang Liu Yan Zhou Yuliang Liu Optoelectronics System Laboratory Institute of Semiconductors Chinese Academy of Science Beijing China

ISBN: (纸本)9781479954599

Besides the regular biological or biomedical imaging applications, time-gated fluorescence lifetime imaging microscopy (FLIM) could be used to image living cells or rapid biochemistry reactions by its advantage of wide field and obvious parallel acquisition speed. As the basis of system detection mode, a good fitting algorithm could satisfy the lifetime precision and acquisition demand at the same time. The common used fluorescence lifetime fitting methods include rapid fluorescence lifetime determination(RLD), linear least square method (LLS)and iterative nonlinear least square (NLLS)method. In this study, we compared these three methods and analyzed the lifetime results. It demonstrated the RLD algorithm could achieve a fast processing speed and a good contrast of lifetime map, however, it shows the results of the rapid fitted lifetime map exists some distributed noise caused by insufficient time-resolved fluorescence intensity images. To remove these noise spots and preserve the lifetime map precision, we chose a four-order partial differential equations (PDEs), which achieved a trade-off between noise-removing and lifetime map feature. In this paper, we proposed a rapid fluorescence lifetime acquisition method combining rapid lifetime determination and four-order PDEs algorithm based on time-gated FLIM, which could satisfy the high speed detection and lifetime precision demand of living cells or rapid biochemical reactions.

关键词： Fluorescence Noise Fitting Logic gates Biomedical imaging Least squares methods

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Using Clusters in Undergraduate Research: distributed Animation Rendering, Photo processing, and image Transcoding

Using Clusters in Undergraduate Research: Distributed Animat...

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15th IEEE International Conference on Cluster Computing (CLUSTER)

作者： Bui, Peter Boettcher, Travis Jaeger, Nicholas Westphal, Jeffrey Univ Wisconsin Dept Comp Sci Eau Claire WI 54702 USA

ISBN: (纸本)9781479908981

With distributed and parallel computing becoming increasingly important in both industrial and scientific endeavors, it is imperative that students are introduced to the challenges and methods of high performance and high throughput computing. Because these topics are often absent in standard undergraduate computer science curriculums, it is necessary to encourage and support independent research and study involving distributed and parallel computing. In this paper, we present three undergraduate research projects that utilize distributed computing clusters: animation rendering, photo processing, and image transcoding. We describe the challenges faced in each project, examine the solutions developed by the students, and then evaluate the performance and behavior of each system. At the end, we reflect on our experience using distributed clusters in undergraduate research and offer six general guidelines for mentoring and pursing distributed computing research projects with undergraduates. Overall, these projects effectively promote skills in high performance and high throughput computing while enhancing the undergraduate educational experience.

关键词： computer animation computer science education image coding parallel processing rendering (computer graphics) research and development

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distributed and hardware accelerated computing for clinical medical imaging using proton computed tomography (pCT)

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JOURNAL OF parallel AND distributed COMPUTING 2013年第12期73卷 1605-1612页

作者： Karonis, Nicholas T. Duffin, Kirk L. Ordonez, Caesar E. Erdelyi, Bela Uram, Thomas D. Olson, Eric C. Coutrakon, George Papka, Michael E. No Illinois Univ Dept Comp Sci De Kalb IL 60115 USA No Illinois Univ Dept Phys De Kalb IL 60115 USA Argonne Natl Lab Argonne Leadership Comp Facil Argonne IL 60439 USA Argonne Natl Lab Math & Comp Sci Div Argonne IL 60439 USA Argonne Natl Lab Div Phys Argonne IL 60439 USA Univ Chicago Computat Inst Chicago IL 60637 USA

Proton computed tomography (pCT) is an imaging modality that has been in development to support targeted dose delivery in proton therapy. It aims to accurately map the distribution of relative stopping power. Because protons traverse material media in non-linear paths, pCT requires individual proton processing. image reconstruction then becomes a time-consuming process. Clinical-use scenarios that require images from billions of protons in less than ten or fifteen minutes have motivated us to use distributed and hardware-accelerated computing methods to achieve fast image reconstruction. Combined use of MPI and GPUs demonstrates that clinically viable image reconstruction is possible. On a 60-node CPU/GPU computer cluster, we achieved efficient strong and weak scaling when reconstructing images from two billion histories in under seven minutes. This represents a significant improvement over the previous state-of-the-art in pCT, which took almost seventy minutes to reconstruct an image from 131 million histories on a single-CPU, single-GPU computer. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Computed tomography Proton computed tomography image reconstruction Iterative reconstruction Relative stopping power RSP MLP GPU PCT CUDA MPI Scaling

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Rapid processing of remote sensing images based on cloud computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE 2013年第8期29卷 1963-1968页

作者： Wang, Pengyao Wang, Jianqin Chen, Ying Ni, Guangyuan China Agr Univ Beijing 100083 Peoples R China Beijing SINOPERM Technol Co Ltd Beijing 100083 Peoples R China

The rapid processing of remote sensing (RS) images is essential in many large-scale real-time monitoring, such as meteorological monitoring and natural disaster warning. However, the computation cost of RS is often expensive, traditional RS processing methods cannot satisfy the time requirement of dynamic monitoring. Fortunately, cloud computing not only provides an effective service for data management, but also offers a convenient way to execute RS computing, It is necessary to integrate the rapid RS processing services in a unified cloud computing architecture. The architecture can provide users with integrated rapid RS image processing service through effective huge data management and distributed parallel processing. This paper explores rapid processing methods and strategies for RS images based on cloud computing. In order to compare with other computing paradigms, we choose the maximum likelihood classification (MLC) as our experimental algorithm and Mahalanobis distance clustering (MDC) as our verifying algorithm to execute comparing. In these experiments, we compare the computation cost of RS processing in three computing paradigms (stand-alone, MPI, and MapReduce). From the intensive experimental results, we find that the RS processing based on cloud computing performs best from the aspects of programming convenience, data management and computational efficiency simultaneously, especially when processing huge amount of data. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Remote sensing image processing Cloud computing Timeliness

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Correntropy induced metric based graph regularized non-negative matrix factorization

Correntropy induced metric based graph regularized non-negat...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Bin Mao Naiyang Guan Dacheng Tao Xuhui Huang Zhigang Luo College of Science National University of Defense Technology Changsha China College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha China Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia

Non-negative matrix factorization (NMF) is an efficient dimension reduction method and plays an important role in many pattern recognition and computer vision tasks. However, conventional NMF methods are not robust since the objective functions are sensitive to outliers and do not consider the geometric structure in datasets. In this paper, we proposed a correntropy graph regularized NMF (CGNMF) to overcome the aforementioned problems. CGNMF maximizes the correntropy between data matrix and its reconstruction to filter out the noises of large magnitudes, and expects the coefficients to preserve the intrinsic geometric structure of data. We also proposed a modified version of our CGNMF which construct the adjacent graph by using sparse representation to enhance its reliability. Experimental results on popular image datasets confirm the effectiveness of CGNMF.

关键词： Robustness Vectors Clustering algorithms Mutual information Measurement Computer integrated manufacturing

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A Study of the Behavior of Synchronization methods in Commonly Used Languages and Systems

A Study of the Behavior of Synchronization Methods in Common...

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IEEE 27th International parallel and distributed processing Symposium (IPDPS)

作者： Cederman, Daniel Chatterjee, Bapi Nhan Nguyen Nikolakopoulos, Yiannis Papatriantafilou, Marina Tsigas, Philippas Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9780769549712

Synchronization is a central issue in concurrency and plays an important role in the behavior and performance of modern programmes. Programming languages and hardware designers are trying to provide synchronization constructs and primitives that can handle concurrency and synchronization issues efficiently. Programmers have to find a way to select the most appropriate constructs and primitives in order to gain the desired behavior and performance under concurrency. Several parameters and factors affect the choice, through complex interactions among (i) the language and the language constructs that it supports, (ii) the system architecture, (iii) possible run-time environments, virtual machine options and memory management support and (iv) applications. We present a systematic study of synchronization strategies, focusing on concurrent data structures. We have chosen concurrent data structures with different number of contention spots. We consider both coarse-grain and fine-grain locking strategies, as well as lock-free methods. We have investigated synchronization-aware implementations in C++, C# (. NET and Mono) and Java. Considering the machine architectures, we have studied the behavior of the implementations on both Intel's Nehalem and AMD's Bulldozer. The properties that we study are throughput and fairness under different workloads and multiprogramming execution environments. For NUMA architectures fairness is becoming as important as the typically considered throughput property. To the best of our knowledge this is the first systematic and comprehensive study of synchronization-aware implementations. This paper takes steps towards capturing a number of guiding principles and concerns for the selection of the programming environment and synchronization methods in connection to the application and the system characteristics.

关键词： C++ language Java concurrency control data structures memory architecture programming environments programming languages storage management synchronisation virtual machines

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Computing resultants on Graphics processing Units: Towards GPU-accelerated computer algebra

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JOURNAL OF parallel AND distributed COMPUTING 2013年第11期73卷 1494-1505页

作者： Emeliyanenko, Pavel Max Planck Inst Informat D-66123 Saarbrucken Germany

In this article we report on our experience in computing resultants of bivariate polynomials on Graphics processing Units (GPU). Following the outline of Collins' modular approach [6], our algorithm starts by mapping the input polynomials to a finite field for sufficiently many primes m. Next, the GPU algorithm evaluates the polynomials at a number of fixed points x is an element of Z(m), and computes a set of univariate resultants for each modular image. Afterwards, the resultant is reconstructed using polynomial interpolation and Chinese remaindering. The GPU returns resultant coefficients in the form of Mixed Radix (MR) digits. Finally, large integer coefficients are recovered from the MR representation on the CPU. All computations performed by the algorithm (except for, partly, Chinese remaindering) are outsourced to the graphics processor thereby minimizing the amount of work to be done on the host machine. The main theoretical contribution of this work is the modification of Collins' modular algorithm using the methods of matrix algebra to make an efficient realization on the GPU feasible. According to the benchmarks, our algorithm outperforms a CPU-based resultant algorithm from 64-bit Maple 14 by a factor of 100. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Symbolic algorithms Resultants Modular computations GPU CUDA parallel computing

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A Laplace transform method for the image in-painting

A Laplace transform method for the image in-painting

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12th International Symposium on distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Kokulan, N. Lai, C. -H. Univ Greenwich Sch Comp & Math Sci London SE18 6PF England

ISBN: (纸本)9780769550602

There are many image processing techniques based on partial differential equations that perform well, but they consume much computational time. It's vital that rapid and efficient ways of solving these equations are developed. Use of the Laplace transforms permits solution to the time dependent problems in a parallel environment. The solution procedure requires numerical computation of an inverse Laplace transform of which the Stehfest method was examined in the tests. We investigated the performance and efficiency of using the Laplace transform technique for the solution of a mathematical model related to image in-painting and compared the results with temporal integration.

关键词： CDD in-painting temporal parallel methods time dependent problems

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Document image Segmentation and Compression using Artificial Neural Networks and Evolutionary methods Document image Segmentation and Compression using Artificial Neural Networks and Evolutionary methods

Document Image Segmentation and Compression using Artificial...

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作者： V. AYSHA Cochin University of Science and Technology

学位级别：博士

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of iv fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work.

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Overlapping community detection via link partition of asymmetric weighted graph

Overlapping community detection via link partition of asymme...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Wenju Zhang Naiyang Guan Xuhui Huang Zhigang Luo Jianwu Li Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R. China 410073 National University of Defense Technology Changsha Hunan CN Beijing Institute of Technology Beijing Beijing CN

Link partition clusters edges of a complex network to discover its overlapping communities. Due to Its effectiveness, link partition has attracted much attentions from the network science community. However, since link partition assigns each edge of a network to unique community, it cannot detect the disjoint communities. To overcome this deficiency, this paper proposes a link partition on asymmetric weighted graph (LPAWG) method for detecting overlapping communities. Particularly, LPAWG divides each edge into two parts to distinguish the roles of connected nodes. This strategy biases edges to a specific node and helps assigning each node to its affiliated community. Since LPAWG introduces more edges than those in the original network, it cannot efficiently detect communities from some networks with relative large amount of edges. We therefore aggregate the line graph of LPAWG to shrink its scale. Experimental results of community detection on both synthetic datasets and the realworld networks show the effectiveness of LPAWG comparing with the representative methods.

关键词： Communities image edge detection Partitioning algorithms Benchmark testing Acceleration Educational institutions Complex networks

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