Rapid determination of three-dimensional convex shapes by dispersion processing using Java RMI
|International Journal of Computer Science and Engineering|
|© 2019 by SSRG - IJCSE Journal|
|Volume 6 Issue 11|
|Year of Publication : 2019|
|Authors : SatoshiKodama, ReiNakagawa, YukaOzeki|
SatoshiKodama, ReiNakagawa, YukaOzeki, "Rapid determination of three-dimensional convex shapes by dispersion processing using Java RMI" SSRG International Journal of Computer Science and Engineering 6.11 (2019): 18-27.
SatoshiKodama, ReiNakagawa, YukaOzeki,(2019). Rapid determination of three-dimensional convex shapes by dispersion processing using Java RMI. SSRG International Journal of Computer Science and Engineering 6(11),18-27.
To quickly determine the interior and exterior of a three-dimensional (3D) shape, one must apply shape recognition and contact determination
algorithms. However, in general, a 3D figure largely differs from a two-dimensional figure, and is described by a large dataset. Consequently, the
determination process is time intensive. To alleviate this problem, determination methods of 3D complex shapes are often based on solid angles, but this approach is inapplicable to many shapes unless the computer is equipped with a graphics processing unit. On the other hand, the use of embedded
personal computers such as 3D printers and portable 3D scanners is increasing in modern data processing, and environments free of special devices
are also required. In this paper, we show that high-speed processing of convex object can be achieved by parallel computing using a plurality of relatively
inexpensive Raspberry Pi3s.
 Lin Lu, Andrei Sharf, Haisen Zhao, Yuan Wei, Qingnan Fan, Xuelin Chen, Yann Savoye, Changhe Tu, Daniel Cohen-Or, Baoquan Chen, “Build-to-last: strength to weight 3D printed objects,” ACM Transactions on Graphics, DOI:10.1145/2601097.2601168, Vol. 33, No. 4, 2014.
 Munir Eragubi, “Slicing 3D CAD Model in STL Format and Laser Path Generation,” International Journal of Innovation, Management and Technology, Volume. 4, No. 4, 2013.
 Andrew Gleadall, Ian Ashcroft, JoelSegal, “VOLCO: A predictive model for 3D printed microarchitecture,” Additive Manufacturing, Volume 21, 2018.
 Abhijit Kundu, Yin Li, James M. Rehg, “3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and- Compare,” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3559-3568, 2018.
 S. Kodama, “Effectiveness of inside/outside determination in relation to 3D non-convex shapes using CUDA,” The Imaging Science Journal, DOI:10.1080/13682199.2018.1497251 Volume 66, Issue 7,2018.
 Qian-Yi Zhou, Jaesik Park, Vladlen Koltun, “Open3D: A Modern Library for 3D Data Processing,” arXiv:1801.09847, 2018.
 John Cheng, Max Grossman, Ty McKercher, Professional CUDA C Programming, Wrox Press Ltd., 9781118739327, 2014.
 Xun Gong, Rafael Ubal, David Kaeli, “Multi2Sim Kepler: A detailed architectural GPU simulator,” 2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), DOI: 10.1109/ISPASS.2017.7975298,pp. 269-278, 2017.
 Ilya V. Afanasyev, Vadim V. Voevodin, Vladimir V. Voevodin, Kazuhiko Komatsu, Hiroaki Kobayashi, “Analysis of Relationship Between SIMD-Processing Features Used in NVIDIA GPUs and NEC SX-Aurora TSUBASA Vector Processors,” Parallel Computing Technologies, pp. 125-139,
 Scalable clusters make HPC R&D easy as Raspberry Pi, https://www.bitscope.com/cluster/bitscope-cluster-modulepress- release-ZWLJ6PZ3.pdf.
 Dejan Vujičić, Dragana Mitrović, Siniša Ranđić, “Image Processing on Raspberry Pi Cluster,” International Journal of Electrical Engineering and Computing, Vol.2, No 2, 2018.
 Vincent A. Cicirello, “Design, Configuration, Implementation, and Performance of a Simple 32 Core Raspberry Pi Cluster," arXiv:1708.05264, 2017.
 Dana A. Jacobsen, Julien C. Thibault, Inanc Senocak, “An MPI-CUDA Implementation for Massively Parallel Incompressible Flow Computations on Multi-GPU Clusters,” AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, DOI:10.2514/6.2010-522, 2010.
 J. Christy Jackson, V. Vijayakumar, Md. Abdul Quadir, C.Bharathi, “Survey on Programming Models and
Environments for Cluster, Cloud, and Grid Computing that Defends Big Data,” Procedia Computer Science, Volume 50, pp 517-523, 2015.
 Paul Heckbert, “Graphics Gems IV, Morgan Kaufmann,” ISBN 978-0123361554, 1994.
 Kai Hormann, Alexander Agathos, “The point in polygon problem for arbitrary polygons,” Computational Geometry, Volume 20, Issue 3, pp. 131-144, 2001.
 Dan Sunday, “Inclusion of a Point in a Polygon,” http://geomalgorithms.com/a03-inclusion.html.
 Michael K. Reed, “Solid Model Acquisition from Range Imagery,” Columbia University, http://crlab.cs.columbia.edu/files/solid-model acquisitionfrom- range-imagery.pdf, 1998.
 Arsalan Malik, Benjamin Loriot, Youssef Bokhabrine, Patrick Gorria, Ralph Seulin, “A Simulation of Automatic 3D Acquisition and Post-processing Pipeline,” Image Processing: Machine Vision Applications II, DOI: 10.1117/12.806148, Vol. 7251, 2009.
 Satoshi Kodama, Yuka Ozeki, Rei Nakagawa, “Internal and External Analysis Considering the Layers of Threedimensional Shapes Using CUDA,” International Journal of Computer Trends and Technology, Volume 67, Issue 6, DOI: 0.14445/22312803/IJCTT-V67I6P101, 2019.
 Atsushi Nakayama, Daisuke Kawakatsu, Ken-Ichi Kobori, Toshirou Kutsuwa, “A checking method for a point inside a polyhedron in grasping an object of VR,” Information Processing Society of Japan, 48, pp. 297-298, 1994.
 Daisuke Kawakatsu, Atsushi Nakayama, Ken-Ichi Kobori, Toshirou Kutsuwa, “A Method of Selecting an Object in Virtual Reality,” IPSJ SIG on CGVI, 110, 66-4, pp. 25-32,1993.
 Adrian Kaehler, Gary Bradski, Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, O'Reilly Media, 978-1491937990, 2017.
 Samuel D. Jaffee, Laura Marie Leventhal, Jordan Ringenberg, G. Michael Poor, “Interactive 3D Objects, Projections, and Touchscreens,” Proceedings of the Technology, Mind, and Society, DOI: 10.1145/3183654.3183669, Article No. 18, 2018.
 Andrew Davison, “Pro Java 6 3D Game Development: Java 3D, JOGL, JInput and JOAL APIs,” Apress, 978- 1430211860, 2014.
 John Cheng, Max Grossman, Ty McKercher, “Professional CUDA C Programming,” Wrox Press Ltd., 9781118739327, 2014.
 David B. Kirk, Wen-mei W. Hwu, “Programming Massively Parallel Processors,” A Hands-on Approach 3rd Edition, Morgan Kaufmann, 9780128119860, 2016.
 Jason Sanders, Edward Kandrot, “CUDA by Example: An Introduction to General-Purpose GPU Programming,” Addison-Wesley Professional, 978-0131387683, 2010.
 John Nickolls, GPU parallel computing architecture and CUDA programming model, IEEE Hot Chips 19 Symposium, DOI: 10.1109/HOTCHIPS.2007.7482491, 2007.
 Badr Benmammar, “Concurrent, Real-Time and Distributed Programming in Java: Threads, RTSJ and RMI,” ISBN 978-1786302588, 2018.
 Paweł T. Wojciechowski, Konrad Siek, “Atomic RMI 2: distributed transactions for Java,” Proceedings of the 6th International Workshop on Programming Based on Actors, Agents, and Decentralized Control, DOI: 10.1145/3001886.3001893, pp 61-69, 2016.
 University of Notre dame, “Java RMI”,https://en.pptonline. org/37724.
 Yutaro Hara, Satoshi Kodama, “A proposal for revising AR marker with infrared light,” IEICE Tech. Rep., vol. 113, no. 299, pp. 53-56, 2013.
 Yuka Ozeki, Shinya Kameyama, Satoshi Kodama, Shigeo Akashi, A Proposal for the User Interface by Using Laser Devices Arranged in a Three Dimensional Space, The Institute of Electronics, Information and Communication Engineers and Information Processing Society of Japan, Volume 3, pp 385-388, 2016.
Parallel computing, Java RMI, 3D Modeling, Inside/outside determination