Journal of
Systemics, Cybernetics and Informatics
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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.

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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


Smart Cities: Challenges and Opportunities
Mohammad Ilyas
(pages: 1-6)

Bridging the Gap: Communicating to Increase the Visibility and Impact of Your Academic Work
Erin Ryan
(pages: 7-12)

Cross-Cultural Online Networking Based on Biomedical Engineering to Motivate Transdisciplinary Communication Skills
Shigehiro Hashimoto
(pages: 13-17)

Interdisciplinary Approaches to Learning Informatics
Masaaki Kunigami
(pages: 18-22)

The Impact of Artificial Intelligence and the Importance of Transdisciplinary Research
R. Cherinka, J. Prezzama, P. O'Leary
(pages: 23-28)

Emotional Communication as Complex Phenomenon in Musical Interpretation – Proposal for a Systemic Model That Promotes a Transdisciplinary Process of Self-Formation and Reflection Around Expressiveness as a Lived Experience
Fuensanta Fernández de Velazco, Eduardo Carpinteyro-Lara, Saúl Rodríguez-Luna
(pages: 29-33)

A Multi-Disciplinary Cybernetic Approach to Pedagogic Excellence
Russell Jay Hendel
(pages: 34-41)

The Ethics of Artificial Intelligence in the Era of Generative AI
Vassilka D. Kirova, Cyril S. Ku, Joseph R. Laracy, Thomas J. Marlowe
(pages: 42-50)

Trans-Disciplinary Communication: Context and Semantics
Maurício Vieira Kritz
(pages: 51-57)

A Brave New World: AI as a Nascent Regime?
Jasmin Cowin, Birgit Oberer, Cristo Leon
(pages: 58-66)

The Role of Art and Science – Relational Dynamics in Human Ecology
Giorgio Pizziolo, Rita Micarelli
(pages: 67-75)

Advancing Entrepreneurship Education: An Integrated Approach to Empowering Future Innovators
Birgit Oberer, Alptekin Erkollar
(pages: 76-81)

Harmonizing Horizons: The Symphony of Human-Machine Collaboration in the Age of AI
Birgit Oberer, Alptekin Erkollar
(pages: 82-86)

How Do Students Learn Artificial Intelligence in Interdisciplinary Field of Biomedical Engineering?
Shigehiro Hashimoto
(pages: 87-91)

What is ChatGPT and its Present and Future for Artificial Intelligence in Trans-Disciplinary Communications?
Richard Segall
(pages: 92-98)


 

Abstracts

 


ABSTRACT


Accelerating Image Based Scientific Applications using Commodity Video Graphics Adapters

Randy P. Broussard, Robert W. Ives


The processing power available in current video graphics cards is approaching super computer levels. State-of-the-art graphical processing units (GPU) boast of computational performance in the range of 1.0-1.1 trillion floating point operations per second (1.0-1.1 Teraflops). Making this processing power accessible to the scientific community would benefit many fields of research. This research takes a relatively computationally expensive image-based iris segmentation algorithm and hosts it on a GPU using the High Level Shader Language which is part of DirectX 9.0. The selected segmentation algorithm uses basic image processing techniques such as image inversion, value squaring, thresholding, dilation, erosion and a computationally intensive local kurtosis (fourth central moment) calculation. Strengths and limitations of the DirectX rendering pipeline are discussed. The primary source of the graphical processing power, the pixel or fragment shader, is discussed in detail. Impressive acceleration results were obtained. The iris segmentation algorithm was accelerated by a factor of 40 over the highly optimized C++ version hosted on the computer’s central processing unit. Some parts of the algorithm ran at speeds that were over 100 times faster than their C++ counterpart. GPU programming details and HLSL code samples are presented as part of the acceleration discussion.

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