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Parallel Fast Walsh Transform Algorithm and Its Implementation with CUDA on GPUs

. CUDA C Programming Guide. https://docs.nvidia.com/cuda/cuda-c-programming-guide/ 7. CUDA Homepage. http://www.nvidia.com/object/cudahomenew.html 8. Demouth, J. Kepler’s Shuffle: Tips and Tricks. – GPU Technology Conference, 2013. http://on-demand.gputechconf.com/gtc/2013/presentations/S3174-Kepler-Shuffle-Tips-Tricks.pdf 9. Good, I. J. The Interaction Algorithm and Practical Fourier Analysis. – Journal of the Royal Statistical Society, Vol. 20 , 1958, No 2, pp. 361-372. 10. Joux, A. Algorithmic Cryptanalysis. Chapman & Hall

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A Cloud Computing Service Architecture of a Parallel Algorithm Oriented to Scientific Computing with CUDA and Monte Carlo

References 1. Bohn, C. A. Joint Conference on Intelligent Systems 1999. (JCIS’98), 1998, 2, 64. 2. Bao, J., X. Feng, Yujiangu o. GPU Triggered Revolution in Computational Chemistry [J]. - Acta Phys.-Chim. Sin., Vol. 27, 2011, No 9, 2019-2026. 3. Chen, G., P. Li et. al. High Performance Computing Viaa GPU. - In: Information Science and Engineering (ICISE), 1st International Conference, 2009, 238-241. 4. CUDA: Santa Clara, CA (accessed April 13, 2011). http://www.nvidia.com/object/cuda

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Yang-Mills lattice on CUDA

, AnTonCom Infokommunik ációs Kft. 2011, ⇒194 [8] D. B. Kirk, W. W. Hwu, Programming Massively Parallel Processors: A Handson Approach, Morgan Kaufmann Publisher, Burlington, MA, 2012. ⇒185, 196, 197 [9] B. Müller, A. Trayanov, Deterministic chaos on non-Abelian lattice gauge theory, Phys. Rev. Letters 68, 23 (1992) 3387-3390. ⇒185 [10] I. Montvay, G. Münster, Quantum Fields on a Lattice, Cambridge University Press, Cambridge CB2 1RP, 1994. ⇒186 [11] J. Sanders, E. Kandrot, CUDA by Example: An Introduction to

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Chaotic behavior of the lattice Yang-Mills on CUDA

lattice, Int. Journ. of Modern Phys. C 5 (1994) 113–149. ⇒220 [4] M. Creutz, Quarks, Gluons and Lattices , Cambridge University Press, Cambridge CB2 1RP, 1983. ⇒219 [5] R. Forster, A. Fülöp, Yang-Mills lattice on CUDA, Acta Univ. Sapientiae, Informatica , 5 , 2 (2013) 184–211. ⇒217, 230 [6] A. Fülöp, T. S. Biró, Towards the equation of state of a classical SU(2) lattice gauge theory, Phys. Rev. C 64 (2001) 064902(5). arxiv.org . ⇒224 [7] C. Gong, Lyapunov spectra in SU(2) lattice gauge theory, Phys. Rev D 49 (1994) 2642–2645. ⇒222

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Parallel RANSAC for Point Cloud Registration

. Three-dimensional model-based object recognition and segmentation in cluttered scenes. Pattern Analysis and Machine Intelligence, IEEE Transactions on , 28(10):1584–1601, Oct 2006. [13] NVIDIA Corporation. NVIDIA CUDA C programming guide, 2015. Version 7.0. [14] Raguram R., Frahm J.-M., and Pollefeys M. A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus. In Forsyth D., Torr P., and Zisserman A., editors, Computer Vision - ECCV 2008 , volume 5303 of Lecture Notes in Computer Science , pages 500–513. Springer

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Cuda Based Fuzzy C-Means Acceleration for the Segmentation of Images with Fungus Grown in Foam Matrices

References [1] J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms Plenum Press, New York, 1981 [2] J. Bezdek, R. Ehrlich, W. Full, FCM: The fuzzy c-means clastering algorithm, Computer & Geosciences Vol. 10, No. 2-3, pp. 191-203, Pergamon Press Ltd, 1984 [3] M. Garland, J. Anderson, J. Hardwick, Parallel computing experiences with CUDA, IEEE Computer Society, 2008 [4] E.R. Gonzalez, R E. Woods, S.L. Eddins, Digital Image Processing Using MATLAB, Prentice Hall, Upper

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Benchmark of 6D SLAM (6D Simultaneous Localisation and Mapping) Algorithms with Robotic Mobile Mapping Systems

Construction Volume 47, pp. 78-91, 2014. [8] Besl P. J., McKay N. D., A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 (2), pp. 239-256, 1992. [9] Biber P., Straßer W., The normal distributions transform: A new approach to laser scan matching. In Proc. IROS, volume 3, pp. 2743-2748, 2003. [10] Borrmann D., Elseberg J., Lingemann K., Nüchter A., Hertzberg J., Globally Consistent 3D Mapping with Scan Matching. J. Robotics and Autonomous Sytems, 65(2), pp. 130-142, 2008. [11] CUDA – Compute

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Jet browser model accelerated by GPUs

. Lett., B269, 3-4 (1991) 432-438. ⇒ 173 [4] S. D. Ellis, D. E. Soper, Successive combination jet algorithm for hadron colli­sions, Phys. Rev. D 48, 7 (1993) 3160. ⇒173 [5] R. Forster, A. Fülöp, Yang-Mills lattice on CUDA, Acta Univ. Sapientiae, Inf, 5, 2 (2013) 184-211. ⇒172, 174, 175 [6] M. E. Peskin, D. V. Schroeder, Quantum Field Theory, Westview Press, 1995. ⇒ 172 [7] S. Salur, Full Jet Reconstruction in Heavy Ion Collisions, Nuclear Physics A 830. 1-4 (2009) 139c-146c. ⇒ 173 [8] T

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GPU-Accelerated Reconstruction of T2 Maps in Magnetic Resonance Imaging

Abstract

The main tissue parameters targeted by MR tomography include, among others, relaxation times T1 and T2. This paper focuses on the computation of the relaxation time T2 measured with the Spin Echo method, where the sensing coil of the tomograph provides a multi-echo signal. The maxima of these echoes must be interleaved with an exponential function, and the T2 relaxation can be determined directly from the exponential waveform. As this procedure needs to be repeated for each pixel of the scanned tissue, the processing of large images then becomes very intensive. For example, given the common resolution of 256×256 with 20 slices and five echoes at different times TE, it is necessary to reconstruct 1.3∙106 exponential functions. At present, such computation performed on a regular PC may last even several minutes. This paper introduces the results provided by accelerated computation based on parallelization and carried out with a graphics card. By using the simple method of linear regression, we obtain a processing time of less than 36 ms. Another effective option consists in the Levenberg-Marquardt algorithm, which enables us to reconstruct the same image in 96 ms. This period is at least 900 times shorter than that achievable with professional software. In this context, the paper also comprises an analysis of the results provided by the above-discussed techniques.

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Gnss Signal Processing in Gpu

References Hobiger T., Gotoh T., et al. GPU based real-time GPS software receiver,GPS SOLUTIONS (2010), pp. 208-216 Mistry P., Schaa D., et al. OpenCL University Kit, [Cited 20-04-2012], AMD 2011, Available at: http://developer.amd.com/zones/OpenCLZone/universities/Pages/default.aspx NVidia: CUDA C, Programming Guide, Version 4.1, 2011, Available at: http://www.nvidia.com Kai B. Dennis M. Akos, et al.A Software-Defined GPS and Galileo Receiver, Boston, Birkhauser 2007 Chao

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