Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


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Re-Published in
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(A Community of about 10.000.000 Academics)


Editorial Advisory Board's Chair
William Lesso

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org

 

Editorial Advisory Board

Journal's Reviewers
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology


A Proposed Model for Tracking the University Interdisciplinary Projects
Rafael Melgarejo, Paulina Cadena
(pages: 1-2)

Enhancing Teaching, Adaptability and Presentation Skills through Improvisational Theater
Thomas J. Marlowe
(pages: 3-7)

Interprofessional Collaborative Practice to Improve Patient Outcomes: A Pilot Study
Jennifer Styron, Catherine Dearman, Sheila Whitworth, Henrietta Brown
(pages: 8-13)

Using a Common Pedagogy across Multiple Disciplines to Improve Student Learning
Ronald A. Styron, Jr., Jennifer L. Styron
(pages: 14-16)

Enhancing Writing through Strengthened Executive Function
Russell Jay Hendel
(pages: 17-22)

Using Interdisciplinary and Active Research to Encourage Higher Resolution Research and Prototyping in Design
Adream Blair-Early, Frankie Flood
(pages: 23-27)

Integration of Education: Using Social Media Networks to Engage Students
Risa Blair, Tina M. Serafini
(pages: 28-31)

Management, Resources and Reproductive Biology
Bernard Wallner, Martin Fieder
(pages: 32-33)

Effectiveness and Utility of a Case-Based Model for Delivering Engineering Ethics Professional Development Units
Heidi Ann Hahn
(pages: 34-39)

Informing via Research: Methods, Challenges and Success when Using a Multi-Disciplinary Team and Reverse Engineering Analysis Processes to Answer a 200 Year Old Question
Melinda H. Connor, Sallyanne Payton
(pages: 40-47)


 

Abstracts

 


ABSTRACT


Aggregation of Composition States for Markov Estimation in Level 2 Fusion

Stephen Stubberud, Kathleen Kramer


In sensor fusion, the use of composition information can help define and understand relationships between targets. This process, part of the Situational Assessment problem, also referred to as Level 2 fusion, can be quite complex when using standard classification approaches such as the Bayesian taxonomy. Determination of the number and type of elements that comprise a group can vary from report to report based on the type of sensors, the environment, and the behavior of the group. Estimation of group composition that can take these factors into account has been developed using a Markov chain approach. If the number of potential target classes is significant and the various standard group compositions are numerous, the computational complexity becomes unmanageable. This effort investigates a useful and computationally attainable Level 2 composition state estimate based upon the use of state aggregation.

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