University of Tennessee Research Foundation

US Government As Represented by the Secretary of the Army

Lawrence Livermore National Security, LLC

Bae Systems Land & Armaments L.p.

Other

Mark S Rader

Tse-Wei Wang

John Douglas Birdwell

David J Icove

John R Flaherty

J Douglas Birdwell

Dale V Stansberry

Roger Horn

Sally P Horn

Lars F Voss

Luis M Hernandez

Xue Ning

Ning Xue

Adam M Conway

Larry C Wadsworth

Igor Alexeff

Peter P Tsai

Caitlin Anne Chapin

Carl G Sapp

In one general embodiment, a structure includes a first diode, comprising: a first layer having a first type of dopant, and a second layer above the first layer, the second layer having a second type of dopant that is opposite to the first type of dopant. A second diode is formed directly on the first diode. The second diode comprises a first layer having a third type of dopant and a second layer above the first layer of the second diode, the second layer of the second diode having a fourth type of dopant that is opposite to the third type of dopant. In another general embodiment, a process includes a repeated sequence of growing a first layer having a first type of electrically active dopant and growing a second layer having a second type of electrically active dopant that is opposite to the first type of dopant.

Monolithic growth of epitaxial silicon devices via co-doping

Devices, methods and techniques are disclosed for providing a multi-layer diode without voids between layers. In one example aspect, a multi-stack diode includes at least two Drift Step Recovery Diodes (DSRDs). Each DSRD comprises a first layer having a first type of dopant, a second layer forming a region with at least ten times lower concentration of dopants compared to the adjacent layers, and a third layer having a second type of dopant that is opposite to the first type of dopant. The first layer of a second DSRD is positioned on top of the third layer of first DSRD. The first layer of the second DSRD and the third layer of the first DSRD are degenerate to form a tunneling diode at an interface of the first DSRD and second DSRD, the tunneling diode demonstrating a linear current-voltage characteristic.

Tunnel drift step recovery diode

Method and apparatus for predicting properties of a target object comprise application of a search manager for analyzing parameters of a plurality of databases for a plurality of objects, the databases comprising an electrical, electromagnetic, acoustic and thermal spectral database (ESD), a micro-body assemblage database (MAD) and a database of image data whereby the databases store data objects containing identifying features, source information and information on site properties and context including time and frequency varying data. The method comprises application of multivariate statistical analysis and principal component analysis in combination with content-based image retrieval for providing two-dimensional attributes of three dimensional objects, for example, via preferential image segmentation using a tree of shapes and to predict further properties of objects by means of k-means clustering and related methods. By way of example, an evidence tree display showing a target object linked by a pathway to a predicted property comprises a similarity value, a speculation value and a model-based rank.

Method and apparatus for predicting object properties and events using similarity-based information retrieval and modeling

Method and apparatus for determining a metric for use in predicting properties of an unknown specimen belonging to a group of reference specimen electrical devices comprises application of a network analyzer for collecting impedance spectra for the reference specimens and determining centroids and thresholds for the group of reference specimens so that an unknown specimen may be confidently classified as a member of the reference group using the metric. If a trait is stored with the reference group of electrical device specimens, then, the trait may be predictably associated with the unknown specimen along with any traits identified with the unknown specimen associated with the reference group.

Method and apparatus for classifying known specimens and media using spectral properties and identifying unknown specimens and media

Method and apparatus for predicting properties of a target object comprise application of a search manager for analyzing parameters of a plurality of databases for a plurality of objects, the databases comprising an electrical, electromagnetic, acoustic spectral database (ESD), a micro-body assemblage database (MAD) and a database of image data whereby the databases store data objects containing identifying features, source information and information on site properties and context including time and frequency varying data. The method comprises application of multivariate statistical analysis and principal component analysis in combination with content-based image retrieval for providing two-dimensional attributes of three dimensional objects, for example, via preferential image segmentation using a tree of shapes and to predict further properties of objects by means of k-means clustering and related methods. By way of example, a fire event and residual objects may be located and qualified such that, for example, properties of the residual objects may be qualified, for example, via black body radiation and micro-body databases including charcoal assemblages.

Method and apparatus for predicting properties of a target object, in particular, one of an origin and a source, comprise application of a search manager for analyzing parameters of a plurality of databases for a plurality of objects, the databases comprising an electrical, electromagnetic, acoustic spectral database (ESD), a micro-body assemblage database (MAD) and a database of image data whereby the databases store data objects containing identifying features, source information and information on site properties and context including time and frequency varying data. The method comprises application of multivariate statistical analysis and principal component analysis in combination with content-based image retrieval for providing two-dimensional attributes of three dimensional objects, for example, via preferential image segmentation using a tree of shapes and to predict further properties of objects by means of k-means clustering and related methods. By way of example, a fire event and residual objects may be located and qualified such that, for example, properties of the residual objects may be qualified, for example, via black body radiation and micro-body databases including charcoal assemblages.

Least Square Deconvolution (LSD) uses quantitative allele peak data, for example, allele peak area, allele peak height and optical density, derived/obtained from a sample containing the DNA of more than one contributor to resolve the best-fit genotype profile of each contributor. The resolution is based on finding the least square fit of the mass ratio coefficients at each locus to come closest to the quantitative allele peak data. Consistent top-ranked mass ratio combinations from each locus can be pooled to form at least one composite DNA profile at a subset of the available loci. The top-ranked DNA profiles can be used to check against the profile of a suspect or be used to search for a matching profile in a DNA database.

Least-square deconvolution (LSD): a method to resolve DNA mixtures

Least Square Deconvolution (LSD) uses quantitative allele peak data derived obtained from a sample containing the DNA of more than one contributor to resolve the best-fit genotype profile of each contributor. The resolution is based on finding the least square fit of the mass ratio coefficients at each locus to come closest to the quantitative allele peak data. Consistent top-ranked mass ratio combinations from each locus can be pooled to form at least one composite DNA profile at a subset of the available loci. The top-ranked DNA profiles can be used to check against the profile of a suspect or be used to search for a matching profile in a DNA database.

Least Square Deconvolution (LSD) uses quantitative allele peak area derived from a sample containing the DNA of more than one contributor to resolve the best-fit genotype profile of each contributor. The resolution is based on finding the least square fit of the mass ratio coefficients at each locus to come closest to the quantitative allele peak data. Consistent top-ranked mass ratio combinations from each locus can be pooled to form at least one composite DNA profile at a subset of the available loci. The top-ranked DNA profiles can be used to check against the profile of a suspect or be used to search for a matching profile in a DNA database.

Least-Square Deconvolution (LSD): a method to resolve DNA mixtures

A pair of electrodes are biased to produce an electrostatic discharge while placing a thin non electron absorbing gas layer near one or both of the electrodes which are used to develop the electric field. As a result, atmospheric oxygen, water vapor and other contaminants are removed from the regions surrounding the electrodes, allowing ionization to occur more easily. This is accomplished with an electrode structure including a hollow tube for receiving a gas, a plurality of apertures extending through the hollow tube for delivering the gas to the surface of the hollow tube, and the application of a biasing voltage for producing an electrostatic discharge in the presence of the gas. The tube can take the form of a longitudinally extending structure, or a hollow shell, depending upon the configuration of the electrostatic charging apparatus with which the electrode structure is used.

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Huntsville, AL, United States of America

Mark S Rader