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A rapid fluorescence lifetime image acquistion method based on time-gated fluorescence lifetime imaging microscopy

A rapid fluorescence lifetime image acquistion method based ...

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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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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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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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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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Soft-Constrained Nonnegative Matrix Factorization via Normalization

Soft-Constrained Nonnegative Matrix Factorization via Normal...

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International Joint conference on Neural Networks

作者： Long Lan Naiyang Guan Xiang Zhang Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Semi-supervised clustering aims at boosting the clustering performance on unlabeled samples by using labels from a few labeled samples. Constrained NMF (CNMF) is one of the most significant semi-supervised clustering methods, and it factorizes the whole dataset by NMF and constrains those labeled samples from the same class to have identical encodings. In this paper, we propose a novel soft-constrained NMF (SCNMF) method by softening the hard constraint in CNMF. Particularly, SCNMF factorizes the whole dataset into two lower-dimensional factor matrices by using multiplicative update rule (MUR). To utilize the labels of labeled samples, SCNMF iteratively normalizes both factor matrices after updating them with MURs to make encodings of labeled samples close to their label vectors. It is therefore reasonable to believe that encodings of unlabeled samples are also close to their corresponding label vectors. Such strategy significantly boosts the clustering performance even when the labeled samples are rather limited, e.g., each class owns only a single labeled sample. Since the normalization procedure never increases the computational complexity of MUR, SCNMF is quite efficient and effective in practices. Experimental results on face image datasets illustrate both efficiency and effectiveness of SCNMF compared with both NMF and CNMF.

关键词： Matrix decomposition Encoding Vectors Clustering methods Clustering algorithms Time complexity Face

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Box-constrained Projective Nonnegative Matrix Factorization via Augmented Lagrangian Method

Box-constrained Projective Nonnegative Matrix Factorization ...

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International Joint conference on Neural Networks

作者： Xiang Zhang Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Projective non-negative matrix factorization (PNMF) projects a set of examples onto a subspace spanned by a non-negative basis whose transpose is regarded as the projection matrix. Since PNMF learns a natural parts-based representation, it has been successfully used in text mining and pattern recognition. However, it is non-trivial to analyze the convergence of the optimization algorithms for PNMF because its objective function is non-convex. In this paper, we propose a Box-constrained PNMF (BPNMF) method to overcome this deficiency of PNMF. In particular, BPNMF introduces an auxiliary variable, i.e., the coefficients of examples, and incorporates the following two types of constraints: 1) each entry of the basis is non-negative and upper-bounded, i.e., box-constrained, and 2) the coefficients equal to the projected points of the examples. The first box constraint makes the basis to be bound and the second equality constraint keeps its equivalence to PNMF. Similar to PNMF, BPNMF is difficult because the objective function is non-convex. To solve BPNMF, we developed an efficient algorithm in the frame of augmented Lagrangian multiplier (ALM) method and proved that the ALM-based algorithm converges to local minima. Experimental results on two face image datasets demonstrate the effectiveness of BPNMF compared with the representative methods.

关键词： Accuracy Linear programming Training Convergence Optimization Face Educational institutions

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distributed computing method based on mathematical morphology

引用

2nd International conference on Green Communications and Networks, GCN 2012

作者： Wang, Yanbo Modern Education Technique Center Changchun University of Chinese Medicine Changchun 130117 China

ISBN: (纸本)9783642354694

The mathematical basis of mathematical morphology is set theory, which is widely used in the field of image processing, and distributed computing methods will require significant computing resources case that is broken down into small pieces, it has a plurality of computer parallel collaborative, and finally gives the results. Based on the heterogeneous network workstation as research platform, the mapping hyperspectral image processing technology is studied as a case that discusses the mathematical morphology and distributed computing method in the platform applications surveying and mapping hyperspectral image processing heterogeneous. Hyperspectral imaging is a new remote sensing technology. Its effective response time is the key of space earth science and planetary exploration. In response to this problem, the distributed calculation method is realized based on mathematical morphology to establish performance evaluation indicators. Through comparative analysis, the method is verified that using the morphological algorithm of distributed algorithm has a time advantage, which will contribute to the high spectrum image to select the appropriate distributed computing technology and to adopt a particular application strategy. © 2013 Springer-Verlag.

关键词： distributed computer systems

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distributed and parallel Subarray Beamforming for Near-Field 3D Sonar Imaging 3

Distributed and Parallel Subarray Beamforming for Near-Field...

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3rd International conference on Computer Science and Network Technology (ICCSNT)

作者： Han, Yeqiang Fu, Jiabao Zhou, Fan Tian, Xiang Chen, Yaowu Zhejiang Univ Inst Adv Digital Technol & Instrumentat Hangzhou Zhejiang Peoples R China Zhejiang Prov Key Lab Network Multimedia Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781479905614

The computational load is prohibitive in 3D sonar systems for real-time image generation. In the literature, an efficient frequency beamforming method based on the distributed and parallel subarray (DPS) has been introduced for planar arrays working in the far field. This paper extends the DPS beamforming algorithm to process the signals acquired from a scene in the near field. The alogrithm divides the full populated transducer array into two stage distributed subarrays. It performs parallel beamforming on the first-stage subarrays and applies the approximation for time delays in the near-field beam patterns of the second-stage subarray. The computational efficiency is significantly improved as compared with the traditional methods, while achieving the similar resolutions. In addition, a multi-FPGA signal processor is proposed for implementing the underwater 3-D imaging.

关键词： underwater 3D imaging sonar sensor array system sonar signal processing distributed and parallel subarray beamforming

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Efficient Hardware Calculation of Running Statistics

Efficient Hardware Calculation of Running Statistics

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28th International conference on image and Vision Computing New Zealand (IVCNZ)

作者： Bailey, Donald G. Klaiber, Michael J. Massey Univ Sch Engn & Adv Technol Palmerston North New Zealand Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany

ISBN: (纸本)9781479908837;9781479908820

Calculation of mean, variance and standard deviation are often required for segmentation or feature extraction. In image processing, often an integer approximation is adequate. Conventional methods require division and square root operations, which are expensive to realize in hardware in terms of both the amount of required resources and latency. A new class of iterative algorithms is developed based on integer arithmetic. An implementation of the algorithms as a hardware architecture for a Field-Programmable Gate Array (FPGA) is compared with architectures using the conventional approach, which shows a significantly reduced latency while using less hardware resources.

关键词： Computer hardware hardware resource Field programmable gate arrays hardware architecture Iterative algorithms Feature extraction ARCHITECTURE Integer Latency integer arithmetic segmentation

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DECOMPOSITION BY PARTIAL LINEARIZATION IN MULTIUSER SYSTEMS

DECOMPOSITION BY PARTIAL LINEARIZATION IN MULTIUSER SYSTEMS

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IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Scutari, G. Facchinei, F. Song, P. Palomar, D. P. Pang, J. -S. SUNY Buffalo Dpt Elect Eng Buffalo NY 14260 USA Univ Roma La Sapienza Dept Comp Control & Management Eng Rome Italy Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Hong Kong Peoples R China Univ Illinois Dept Ind & Enterprise Syst Eng Urbana IL 61801 USA

ISBN: (纸本)9781479903566

We propose a decomposition framework for the distributed optimization of general noncoiwex stun-utility functions arising in the design of wireless multi-user interfering systems. Our main contributions are: i) the development of the first provably convergent Jacobi bestresponse algorithm, where all users simultaneously solve a suitably convexified version of the original sum-utility optimization problem;ii) the derivation of a general dynamic pricing mechanism that provides a unified view of existing pricing schemes that are based, instead, on heuristics;and iii) a framework that can be easily particularized to well-known applications, giving rise to practical algorithms that outperform all existing ad-hoc methods proposed for very specific problems. Our framework contains as special cases wellknown gradient algorithms for nonconvex sum-utility problems, and many block-coordinate descents schemes for convex functions.

关键词： Nonconvex social problems parallel & distributed optimization successive convex approximation.

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