Publications

This report presents research on public key, digital signature algorithms for cryptographic authentication in low-powered, low-computation environments. We assessed algorithms for suitability based on their signature size, and computation and storage requirements. We evaluated a variety of general purpose and special purpose computing platforms to address issues such as memory, voltage requirements, and special functionality for low-powered applications. In addition, we examined custom design platforms. We found that a custom design offers the most flexibility and can be optimized for specific algorithms. Furthermore, the entire platform can exist on a single Application Specific Integrated Circuit (ASIC) or can be integrated with commercially available components to produce the desired computing platform.

This paper describes a solution for the problem of authenticating the shapes of statistically variant gamma spectra while simultaneously concealing the shapes and magnitudes of the sensitive spectra. The shape of a spectrum is given by the relative magnitudes and positions of the individual spectral elements. Class-specific linear orthonormal transformations of the measured spectra are used to produce output that meet both the authentication and concealment requirements. For purposes of concealment, the n-dimensional gamma spectra are transformed into n-dimensional output spectra that are effectively indistinguishable from Gaussian white noise (independent of the class). In addition, the proposed transformations are such that statistical authentication metrics computed on the transformed spectra are identical to those computed on the original spectra.

Multivariate calibration techniques have been used in a wide variety of spectroscopic situations. In many of these situations, spectral variation can be partitioned into separate classes. For example, suppose that multiple spectra are obtained from each of a number of different objects wherein the level of the analyze of interest varies within each object over time. In such situations, the total spectral variation observed across all measurements has two distinct general sources of variation: intraobject and interobject. One might want to develop a global multivariate calibration model that predicts the analyze of interest accurately both within and across objects, including new objects not involved in developing the calibration model. However, this goal might be hard to realize if the interobject spectral variation is complex and difficult to model. If the intraobject spectral variation is consistent across objects, an effective alternative approach might be to develop a generic intraobject model that can be adapted to each object separately. This paper contains recommendations for experimental protocols and data analysis in such situations. The approach is illustrated with an example involving the noninvasive measurement of glucose using near-infrared reflectance spectroscopy. Extensions to calibration maintenance and calibration transfer are discussed.

A {ital q}-vector of responses, y, is related to a {ital p}-vector of explanatory variables, x, through a causal linear model. In analytical chemistry, y and x might represent the spectrum and associated set of constituent concentrations of a multicomponent sample which are related through Beer`s law. The model parameters are estimated during a calibration process in which both x and y are available for a number of observations (samples/specimens) which are collectively referred to as the calibration set. For new observations, the fitted calibration model is then used as the basis for predicting the unknown values of the new x`s (concentrations) form the associated new y`s (spectra) in the prediction set. This prediction procedure can be viewed as parameter estimation in an errors-in-variables (EIV) framework. In addition to providing a basis for simultaneous inference about the new x`s, consideration of the EIV framework yields a number of insights relating to the design and execution of calibration studies. A particularly interesting result is that predictions of the new x`s for individual samples can be improved by using seemingly unrelated information contained in the y`s from the other members of the prediction set. Furthermore, motivated by this EIV analysis, this result can be extended beyond the causal modeling context to a broader range of applications of multivariate calibration which involve the use of principal components regression.

EN-7, EN-8, and EN-9 are polyurethane systems that are used in numerous applications in the Department of Energy complex. These systems contain high levels of toluene diisocyanate (TDI). Currently, TDI is being treated as a suspect human carcinogen within the Department of Energy complex. This report documents the results of a material characterization study of three polyurethane systems that contain low levels of free (potentially airborne) TDI. The characterization has been accomplished by performing a set of statistically designed experiments. The purpose of these experiments is to explore the effects of formulation and cure schedule on various material properties. In general, the material properties (pot life, glass transition temperature, hardness, and tear strength) were relatively insensitive to variation in the cure schedule. On the other hand, variation in curative level had measurable effects on material properties for the polyurethane systems studied. Furthermore, the material properties of the three low-free-TDI polyurethane systems were found to be comparable or superior (for certain curative levels) to commonly-used polyurethane systems. Thus, these low-free-TDI systems appear to be viable candidates for applications where a polyurethane is needed.

Abstract not provided.

Outlier samples can have very detrimental effects on the performances of multivariate calibration methods, as these methods are generally not very robust. Often, the software implementations of these methods do not check for outliers. If outliers are not detected, invalid predictions may result. This paper illustrates some simple exploratory procedures for detecting outliers with examples from near-infrared and mid-infrared spectroscopy using partial least-squares regression as the calibration method. 8 refs., 9 figs., 1 tab.