Journal of
Systemics, Cybernetics and Informatics
HOME   |   CURRENT ISSUE   |   PAST ISSUES   |   RELATED PUBLICATIONS   |   SEARCH     CONTACT US
 



ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

Indexed by
DOAJ (Directory of Open Access Journals)Benefits of supplying DOAJ with metadata:
  • DOAJ's statistics show more than 900 000 page views and 300 000 unique visitors a month to DOAJ from all over the world.
  • Many aggregators, databases, libraries, publishers and search portals collect our free metadata and include it in their products. Examples are Scopus, Serial Solutions and EBSCO.
  • DOAJ is OAI compliant and once an article is in DOAJ, it is automatically harvestable.
  • DOAJ is OpenURL compliant and once an article is in DOAJ, it is automatically linkable.
  • Over 95% of the DOAJ Publisher community said that DOAJ is important for increasing their journal's visibility.
  • DOAJ is often cited as a source of quality, open access journals in research and scholarly publishing circles.
JSCI Supplies DOAJ with Meta Data
, Academic Journals Database, and Google Scholar


Listed in
Cabell Directory of Publishing Opportunities and in Ulrich’s Periodical Directory


Published by
The International Institute of Informatics and Cybernetics


Re-Published in
Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Transdisciplinary Communication as a Meta-Framework of Digital Education
Rusudan Makhachashvili, Ivan Semenist
(pages: 1-6)

Multidisciplinary Learning Using Online Networking in Biomedical Engineering
Shigehiro Hashimoto
(pages: 7-12)

Augmented Intelligence for Advancing Healthcare
Mohammad Ilyas
(pages: 13-19)

A Transdisciplinary Approach to Refereeal
Russell Jay Hendel
(pages: 20-25)

The Impact of Convictions on Interlocking Systems
Teresa Henkle Langness
(pages: 26-33)

Collaborative Convergence: Finding the Language for Trans-Disciplinary Communication to Occur
Cristo Leon, James Lipuma
(pages: 34-37)

Bridging the Gap Between the World of Education and the World of Business via Standards to Develop Competences of the Future at Universities
Paweł Poszytek
(pages: 38-42)

Multidisciplinary Learning for Multifaceted Thinking in Globalized Society
Shigehiro Hashimoto
(pages: 43-48)

From Spirituality to Technontology in Education
Florent Pasquier
(pages: 49-52)

Differentiated Learning and Digital Game Based Learning: The KIDEDU Project
Eleni Tsami
(pages: 53-57)

Emerging Role of Artificial Intelligence
Mohammad Ilyas
(pages: 58-65)

Practicing Transdisciplinarity and Trans-Domain Approaches in Education: Theory of and Communication in Values and Knowledge Education (VaKE)
Jean-Luc Patry
(pages: 66-71)

Reflexive Practice for Inter and Trans Disciplinary Research in the Third Millennium
Maria Grazia Albanesi
(pages: 72-76)


 

Abstracts

 


ABSTRACT


3D Polygon Mesh Compression with Multi Layer Feed Forward Neural Networks

Emmanouil Piperakis, Itsuo Kumazawa


In this paper, an experiment is conducted which proves that multi layer feed forward neural networks are capable of compressing 3D polygon meshes. Our compression method not only preserves the initial accuracy of the represented object but also enhances it. The neural network employed includes the vertex coordinates, the connectivity and normal information in one compact form, converting the discrete and surface polygon representation into an analytic, solid colloquial. Furthermore, the 3D object in its compressed neural form can be directly - without decompression - used for rendering. The neural compression - representation is viable to 3D transformations without the need of any anti-aliasing techniques - transformations do not disrupt the accuracy of the geometry. Our method does not su.er any scaling problem and was tested with objects of 300 to 107 polygons - such as the David of Michelangelo - achieving in all cases an order of O(b3) less bits for the representation than any other commonly known compression method. The simplicity of our algorithm and the established mathematical background of neural networks combined with their aptness for hardware implementation can establish this method as a good solution for polygon compression and if further investigated, a novel approach for 3D collision, animation and morphing.

Full Text