NASA Scientific and Technical Aerospace Reports
NASA Scientific and Technical Aerospace Reports
NASA Scientific and Technical Aerospace Reports
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
20040073797 Naval Research Lab., Washington, DC<br />
Isotope Ratio Spectrometry Data Processing Software: Multivariate Statistical Methods for Hydrocarbon Source<br />
Identification <strong>and</strong> Comparison<br />
Boyd, Thomas J.; Coffin, Richard B.; Apr. 29, 2004; 145 pp.; In English<br />
Report No.(s): AD-A422798; NRL/MR/6110--04-8774; No Copyright; Avail: CASI; A07, Hardcopy<br />
The IRMS Data Processing software package is designed to allow easy stable isotope data entry <strong>and</strong> multivariate data<br />
analysis. When comparing two of more hydrocarbon samples using compound-specific isotope ratio mass spectrometry, an<br />
analyst obtains multiple data variables for each sample. Multivariate statistics allows rigorous comparison(s) to determine if<br />
the samples are in fact different <strong>and</strong> if so, how closely related they are. This software uses three main types of data analyses:<br />
Multiple Analysis of Variance (MAN0VA), Principal Components Analysis (PCA), <strong>and</strong> Cluster Analysis. The layout is a<br />
st<strong>and</strong>ard Windows interface which should be usable to anyone familiar with modem operating system software.<br />
DTIC<br />
Data Processing; Hydrocarbons; Isotope Ratios; Isotopes; Multivariate Statistical Analysis; Spectrometers; Statistical<br />
Analysis<br />
20040073812 Auburn Univ., AL<br />
On Channel Estimation Using Superimposed Training <strong>and</strong> First-Order Statistics<br />
Tugnait, Jitendra K.; Luo, Weilin; Oct. 6, 2003; 4 pp.; In English<br />
Report No.(s): AD-A422839; ARO-41703.14-CL; No Copyright; Avail: CASI; A01, Hardcopy<br />
Channel estimation for single-input multiple- output (SIMO) time-invariant channels is considered using only the<br />
first-order statistics of the data, A periodic (nonr<strong>and</strong>om) training sequence is added (superimposed) at a low power to the<br />
information sequence at the transmitter before modulation <strong>and</strong> transmission, Recently superimposed training has been used<br />
for channel estimation assuming no mean-value uncertainty at the receiver <strong>and</strong> using periodically inserted pilot symbols, We<br />
propose a different method that allows more general training sequences <strong>and</strong> explicitly exploits the underlying cyclostationary<br />
nature of the periodic training sequences, We also allow mean-value uncertainty at the receiver, Illustrative computer<br />
simulation examples are presented,<br />
DTIC<br />
Education<br />
20040073828 Air Force Inst. of Tech., Wright-Patterson AFB, OH<br />
An Investigation of the Effects of Correlation, Autocorrelation, <strong>and</strong> Sample Size in Classifier Fusion<br />
Leap, Nathan J.; Mar. 2004; 119 pp.; In English; Original contains color illustrations<br />
Report No.(s): AD-A422884; AFIT/GOR/ENS/04-06; No Copyright; Avail: CASI; A06, Hardcopy<br />
This thesis extends the research found in Storm, Bauer, <strong>and</strong> Oxley, 2003. Data correlation effects <strong>and</strong> sample size effects<br />
on three classifier fusion techniques <strong>and</strong> one data fusion technique were investigated. Identification System Operating<br />
Characteristic Fusion (Haspert, 2000), the Receiver Operating Characteristic Within Fusion method (Oxley <strong>and</strong> Bauer, 2002),<br />
<strong>and</strong> a Probabilistic Neural Network were the three classifier fusion techniques; a Generalized Regression Neural Network was<br />
the data fusion technique. Correlation was injected into the data set both within a feature set (autocorrelation) <strong>and</strong> across<br />
feature sets for a variety of classification problems, <strong>and</strong> sample size was varied throughout. Total Probability of<br />
Misclassification (TPM) was calculated for some problems to show the effect of correlation on TPM. Feature selection was<br />
performed in some experiments to show the effects of selecting only certain features. Finally, experiments were designed <strong>and</strong><br />
analyzed using analysis of variance to identify what factors had the most significant impact on fusion algorithm performance.<br />
DTIC<br />
Autocorrelation; Classifications; Classifiers; Multisensor Fusion<br />
20040074143 Department of the Navy, Arlington, VA<br />
Sensitivity of Track Velocity Error to Data Registration <strong>and</strong> Dynamic Errors within a Distributed System<br />
Apr. 13, 2004; 43 pp.; In English<br />
Report No.(s): AD-A422473; TR-2004-001; No Copyright; Avail: CASI; A03, Hardcopy<br />
The success of accurately tracking an aerospace object is strongly dependent on how well the position, velocity, <strong>and</strong><br />
acceleration of the object is known. Underst<strong>and</strong>ing what contributes to errors in these quantities, <strong>and</strong> how, is important in<br />
predicting the performance of a tracking system or as in the case of a Single Integrated Air Picture (SIAP) a distributed<br />
tracking system. A previous JSSEO <strong>Technical</strong> Report investigated position estimate errors as a function of data registration<br />
226
Change language