Validation of analytical capillary electrophoresis methods for use in a regulated environment

Since its introduction into the analytical laboratory, CE has had to prove that it was capable of generating results comparable to HPLC or GC techniques in the six areas (specificity, precision, accuracy, linearity, ruggedness, and range) typically required of validated methods intended for submission to governmental agencies. This paper will showcase the development and validation of two analytical methods: one for specific identification of HEPES (N-[2-hydroxyethyl]piperazine-N'-[2-ethane sulfonic acid]) and the other to quantitate millimolar concentrations of tris (tris[hydroxymethyl]aminomethane) in high electrolyte solutions. Utilizing neutral markers and internal standards, results for HEPES demonstrate that migration time reproducibility, expressed as %RSD of 2% or less on a variety of capillaries, is obtainable. Additionally, for quantitation of tris, the values obtained for accuracy (%relative mean bias), precision (%RSD), and linearity (r2) over multiple days and capillaries meet the rigorous standards we require of HPLC or GC methods.     

Neural architecture for robotic vision sensor that discounts uneven illumination in a manufacturing environment

Computer vision algorithms for inspection or pick-and-place operations often depend on spatially uniform illumination of a workplace. This necessitates expensive lighting fixtures. To discount effects of uneven illumination we designed and tested a neural network that can adaptively control light sensitivity at the photosensor level. Our neural network architecture consists of multiple layers with hexagonally arranged nodes. All nodes have partially overlapping receptive fields of different spatial frequencies. Feedforward connections are excitatory while feedback pathways subserve lateral inhibition. The outputs of these nodes are combined so as to maximize the signal-to-noise ratio while constantly resetting thresholds to maintain high sensitivity. Our connectionist architecture can account for many characteristics attributed to the lightness constancy phenomenon observed in biological systems. The results suggest that our module maintains high sensitivity over the whole domain of intensities without interfering with transmission of visual information embedded in spatial discontinuities of intensity.     

A sorter-based architecture for a parallel implementation of communication intensive algorithms

This paper deals with the parallel execution of algorithms with global and/or irregular data dependencies on a regularly and locally connected processor array. The associated communication problems are solved by the use of a two-dimensional sorting algorithm. The proposed architecture, which is based on a two-dimensional sorting network, offers a high degree of flexibility and allows an efficient mapping of many irregularly structured algorithms. In this architecture a one-dimensional processor array performs all required control and arithmetic operations, whereas the sorter solves complex data transfer problems. The storage capability of the sorting network is also used as memory for data elements. The algorithms for sparse matrix computations, fast Fourier transformation and for the convex hull problem, which are mapped onto this architecture, as well as the simulation of a shared-memory computer show that the utilization of the most complex components, the processors, is O(1).     

Failure probability and optimum microstructure of pressure vessel steels

This paper presents a method for assessing the probability of brittle fracture in steels used for nuclear power generating equipment. The method is based on a statistical analysis of microstructural parameters and on physical models of the initiation and propagation of cleavage microcracks. The least probability of brittle failure ascertained by this method has been utilized for determining the optimum ferrite grain size. This method represents a foundation for systematic control of the brittle fracture characteristics of engineering steels employed for the components of nuclear power stations.     

An empirical InSAR-optical fusion approach to mapping vegetation canopy height

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km² in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service-Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r = 0.88) was achieved for the prototype mapping zone.