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COMMERCE BUSINESS DAILY ISSUE OF MARCH 16,1998 PSA#2052AFOSR/PK, 110 Duncan Avenue, Room B115, Bolling AFB, DC 20332-8050 A -- INNOVATIVE COMPUTATIONAL MATHEMATICS FOR PHYSICAL APPLICATIONS
SOL AFOSR BAA 98-4 DUE 093098 POC Dr. Arje Nachman (AFOSR), (202)
767-4939; Dr. Anna Tsao (DARPA), (703) 696-2287; Dr. Dennis Healy
(DARPA), (703) 696-0143 WEB: Doing Business with AFOSR,
http://www.afosr.af.mil. E-MAIL: None, arje.nachman@afosr.af.mil.
AFOSR, on behalf of DARPA's Applied and Computational Mathematics
Program, is seeking projects demonstrating significant mathematical
innovation and high DoD payoff potential in the following technical
areas: 1) Physics-based modeling and signal processing applied to the
end-to-end design and optimization of DoD sensor systems; 2) Modeling,
data analysis, or scalable, high order numerical methods for
electromagnetic, sensing, chemical, and biological applications; and 3)
Optimized Portable Application Libraries. Proposers are strongly
encouraged to discuss prospective proposals with the AFOSR and DARPA
technical POCs in advance of proposal abstracts or full proposals to
get an indication of potential interest. 1. Physics-based modeling and
signal processing applied to the end-to-end design and optimization of
DoD sensor systems. 1. The emphasis of this topic is to develop and
apply innovative mathematical methods for achieving substantial and
demonstrable improvements over currently available state-of-the-art
capability in DoD sensor/processing systems. Candidate systems of
interest include, but are not limited to, imaging radar and sonar
including synthetic aperture radar (SAR) and synthetic aperture sonar
(SAS), foliage and ground penetrating radar, laser detection and
ranging (LADAR), infrared sensors, acoustic sensors, hyperspectral
systems and adaptive array systems. Some examples of challenging
applications include automatic target
recognition/detection/identification (ATR/D/I), imaging in difficult
clutter and noise environments such as that produced by landscape
features and speckle, forward battlefield awareness, nondestructive
evaluation, and unexploded ordinance/mine detection and recognition.
Methods developed should have potential to significantly advance both
theory and application in the areas proposed. 2. Modeling, data
analysis, or scalable, high order numerical methods for
electromagnetic, sensing, chemical, and biological applications. A
major emphasis of this topic is on new mathematically innovative
modeling and algorithmic approaches to electromagnetic, sensing,
biological and chemical problems of interest to the DoD. In addition,
innovative data representations are sought to allow effective
approaches to managing the statistical "curse of dimensionality"
associated with high dimensional modeling, data analysis and mining.
Applications of interest include antenna design, tracking, materials
processing, mine detection, and biological warfare defense. 3.
Optimized Portable Application Libraries. This topic is aimed at the
development of mathematical formulations to enable automatic
compilation of scalable, high performance software libraries of key
numerical kernels for sensor applications. In addition, algorithmic
approaches should facilitate systematic and effective machine-specific
tailoring of computational constructs such as data structures and
communication protocols on platforms having deep memory hierarchies and
multiprocessor architectures. One of the persistent problems in the
current computational environment is insufficiently effective error
control. The error in computational analyses is dependent not only on
the quality of the algorithms for performing individual steps in a
simulation, but on the ability to guarantee that the outputs at each
step satisfy the initial requirements of subsequent steps. Approaches
should include computational and mathematical models and theory for
analyzing and predicting both performance and error properties for
mathematically equivalent algorithmic formulations. Historically, it
has been difficult to realize algorithms that simultaneously guarantee
low computational complexity, low memory usage, accuracy, high
efficiency, and portability across diverse computer architectures.
Algorithmic design is further complicated by the disparity in
mathematical, computational, and application considerations that often
work at cross purposes when addressed in a stovepipe fashion. Finally,
the need to re-design and implement algorithms to run efficiently on
each new platform is extremely costly. The emphasis of OPAL is to
surmount these difficulties with coordinated formulations and toolkits
that would ultimately include automatic or semi-automatic code
generation programs based on the research. Platforms of interest
include high performance uniprocessors, tightly coupled multiprocessor
systems, and adaptive computing platforms. The viability of the
approach should be demonstrated through application to problems of real
interest to the DoD. Note 26. (0071) Loren Data Corp. http://www.ld.com (SYN# 0014 19980316\A-0014.SOL)
A - Research and Development Index Page
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