Issue 10 Volume 41 May 16, 2003
Issue 10 Volume 41 May 16, 2003
Issue 10 Volume 41 May 16, 2003
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<strong>2003</strong>0033022 Sandia National Labs., Albuquerque, NM<br />
Distributed Sensing & Cooperative Control for Plume Tracing<br />
Hurtado, John E.; Jan 2000; 4 pp.; In English<br />
Contract(s)/Grant(s): N00014-99-F-0086<br />
Report No.(s): AD-A4<strong>10</strong>645; No Copyright; Avail: CASI; A01, Hardcopy<br />
The objective of this proposed research was to apply distributed sensing and cooperative control algorithms developed<br />
by Sandia researchers (under separate DOE contracts) to the problem of chemical plume localization. The distributed sensing<br />
and cooperative control algorithms were tested against mathematically modeled simulated plumes and against real plume data.<br />
DTIC<br />
Plumes; Position (Location)<br />
<strong>2003</strong>0033043 Air Force Research Lab., Edwards AFB, CA, USA<br />
Development of SMART Layer Technology for Health Monitoring of Structures<br />
Kumar, Amrita; Beard, Shawn; June 05, 2001; 4 pp.; In English<br />
Contract(s)/Grant(s): F04611-00-C-0026; AF Proj. 3005<br />
Report No.(s): AD-A4<strong>10</strong>538; AFRL/PRS-ED-VG-2001-128; No Copyright; Avail: CASI; A01, Hardcopy<br />
No abstract available<br />
DTIC<br />
Systems Health Monitoring; Rocket Engines; Smart Structures; Technology Utilization<br />
<strong>2003</strong>0033051 ASRC Aerospace Corp., Greenbelt, MD, USA<br />
Jell-Molds and Cookie-cutters: Shrinkwrap Isn’t Just for Leftovers Anymore<br />
Randazzo, John; [<strong>2003</strong>]; <strong>10</strong> pp.; In English; Original contains black and white illustrations<br />
Contract(s)/Grant(s): NAS<strong>10</strong>-03006<br />
Report No.(s): KSC-<strong>2003</strong>-054; No Copyright; Avail: CASI; A02, Hardcopy<br />
So what is Shrinkwrap all about? For those of you who may not know about it, Shrinkwrap is a type of data structure<br />
that can manifest itself as a feature or model. It is cleverly covered up, almost hidden, and doesn’t get the press or widespread<br />
use of a solid or surface. The shrinkwrap feature is located under the data sharing submenu of the feature menu. The<br />
shrinkwrap feature, as described by PTC, is a collection of surfaces and datum features of a model that represents the exterior<br />
of the model . The advantages and applications of the shrinkwrap feature are in the creation of minimal memory guzzling<br />
representations of assemblies. These can be used to represent subassemblies in parent assemblies, and can handle control of<br />
dependency issues, geometry represented, and additional references through the use of the shrinkwrap feature options. The<br />
shrinkwrap model is an option available under the save as umbrella. Its function, as described by PTC, is to share data with<br />
internal and external design groups and improve performance in large assembly design . Some of the benefits of the shrinkwrap<br />
model include being able to represent complex assemblies with a single, lightweight part that protects design intent and<br />
parametric data, and the ability to improve performance of large assembly modeling in the area of less load time. The<br />
proper-scale models can be saved as IGES, STEP, and VRML (for fly-throughs).<br />
Derived from text<br />
Data Structures; Models; Computer Aided Design<br />
<strong>2003</strong>0033930 Pontifical Catholic Univ. of Puerto Rico, Ponce, Puerto Rico<br />
Computerized System to Aid Deaf Children in Speech Learning<br />
Riella, Rodrigo J.; Linarth, Andre G.; Lippman, Lourival, Jr.; Nohama, Percy; October 25, 2001; 5 pp.; In English; Original<br />
contains color illustrations<br />
Report No.(s): AD-A4<strong>10</strong>403; No Copyright; Avail: CASI; A01, Hardcopy<br />
This paper describes a voice analyzer, whose purpose is deaf children’s assistance in the process of speech learning. The<br />
processing of the user’s speech signal is performed in real time in order to get an instantaneous feedback of the result of speech<br />
training. The aim of this analyzer is not to find the distinction between spoken words, main objective of a speech recognizer<br />
but to calculate a level of correctness in the toggle of a specific word. Voice signal analysis was developed through a digital<br />
signal processor (DSP), applying spectral analysis processes, extraction of voice’s formants, adaptation of formants to the<br />
standard levels in domain of time and frequency and statistical matching of the acquired speech signal and the standard one,<br />
resulted from training. After calculating the correctness coefficient, the system goes off a visual feedback to the user in the<br />
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