Sandia National Laboratories

About: Sandia National Laboratories is a facility organization based out in Livermore, California, United States. It is known for research contribution in the topics: Laser & Thin film. The organization has 21501 authors who have published 46724 publications receiving 1484388 citations. The organization is also known as: SNL & Sandia National Labs.

Three-dimensional photonic crystal with a stop band from 1.35 to 1.95 microm.

Abstract: A combination of advanced silicon-processing techniques was used to create three-dimensional (3D) photonic crystals with a 180-nm minimum feature size. The resulting 3D crystal displayed a strong stop band at optical wavelengths from lambda=1.35 microm to lambda=1.95 microm . This is believed to be the smallest 3D crystal with a complete 3D photonic bandgap ever created.

Fiber‐Optic Network Observations of Earthquake Wavefields

Abstract: Author(s): Lindsey, NJ; Martin, ER; Dreger, DS; Freifeld, B; Cole, S; James, SR; Biondi, BL; Ajo-Franklin, JB | Abstract: Our understanding of subsurface processes suffers from a profound observation bias: seismometers are sparse and clustered on continents. A new seismic recording approach, distributed acoustic sensing (DAS), transforms telecommunication fiber-optic cables into sensor arrays enabling meter-scale recording over tens of kilometers of linear fiber length. We analyze cataloged earthquake observations from three DAS arrays with different horizontal geometries to demonstrate some possibilities using this technology. In Fairbanks, Alaska, we find that stacking ground motion records along 20nm of fiber yield a waveform that shows a high degree of correlation in amplitude and phase with a colocated inertial seismometer record at 0.8–1.6nHz. Using an L-shaped DAS array in Northern California, we record the nearly vertically incident arrival of an earthquake from The Geysers Geothermal Field and estimate its backazimuth and slowness via beamforming for different phases of the seismic wavefield. Lastly, we install a fiber in existing telecommunications conduits below Stanford University and show that little cable-to-soil coupling is required for teleseismic P and S phase arrival detection.

Decentralized control of cooperative robotic vehicles: theory and application

Abstract: Describes how decentralized control theory can be used to analyze the control of multiple cooperative robotic vehicles. Models of cooperation are discussed and related to the input/output reachability, structural observability, and controllability of the entire system. Whereas decentralized control research in the past has concentrated on using decentralized controllers to partition complex physically interconnected systems, this work uses decentralized methods to connect otherwise independent nontouching robotic vehicles so that they behave in a stable, coordinated fashion. A vector Liapunov method is used to prove stability of two examples: the controlled motion of multiple vehicles along a line and the controlled motion of multiple vehicles in formation. Also presented are three applications of this theory: controlling a formation, guarding a perimeter, and surrounding a facility.

Theory of one-dimensional hopping conductivity and diffusion

Abstract: Hopping motion is considered in a linear chain containing an arbitrary density of particles that are not allowed to hop to occupied sites. The general case of two inequivalent lattice sites $A$ and $B$ is treated. Transition-rate equations are solved by Monte Carlo simulation and, where possible, by analytic techniques. Only for the case of equivalent sites do the results here agree with those recently obtained by Huber from nonlinear differential equations for site occupancy which neglect certain correlations. The conductivity is mean-field-like for equivalent sites, but shows sizeable departure for inequivalent sites, having an activation energy increased over the mean-field value. Here "mean-field" behavior is one where the only effect of forbidden hops to occupied sites is to reduce effective transition rates by a factor $1\ensuremath{-}n$, where $n$ is the average occupation number of the site to which a jump occurs. The velocity correlation function is shown to consist of a $\ensuremath{\delta}$-function part which reproduces the mean-field conductivity and a function $\ensuremath{\beta}(t)$ which is negative for times $tg0$. This is qualitatively quite different from the picture given by Huber's equations. Motion of a single distinguishable particle shows an anomalous ${x}^{2}\ensuremath{\propto}{t}^{\frac{1}{2}}$ dependence of the mean square displacement upon time $t$, but the displacement $X$ of all the particles does obey a diffusion relation ${X}^{2}\ensuremath{\propto}t$. This difference is explained in terms of the number of particles which have to be pushed aside in order for a particle to move a distance $x$, and in terms of the ensuing density fluctuation. Differences between time dependences and attempt fequencies as measured by bulk conductivity and microscopic probes such as NMR are noted and discussed in light of data on the one-dimensional superionic conductor $\ensuremath{\beta}$ eucryptite (LiAlSi${\mathrm{O}}_{4}$). Reinterpretation of NMR relaxation data on some of the organic charge-transfer salts is also suggested.

Accounting for Poisson noise in the multivariate analysis of ToF‐SIMS spectrum images

Abstract: Recent years have seen the introduction of many surface characterization instruments and other spectral imaging systems that are capable of generating data in truly prodigious quantities. The challenge faced by the analyst, then, is to extract the essential chemical information from this overwhelming volume of spectral data. Multivariate statistical techniques such as principal component analysis (PCA) and other forms of factor analysis promise to be among the most important and powerful tools for accomplishing this task. In order to benefit fully from multivariate methods, the nature of the noise specific to each measurement technique must be taken into account. For spectroscopic techniques that rely upon counting particles (photons, electrons, etc.), the observed noise is typically dominated by ‘counting statistics’ and is Poisson in nature. This implies that the absolute uncertainty in any given data point is not constant, rather, it increases with the number of counts represented by that point. Performing PCA, for instance, directly on the raw data leads to less than satisfactory results in such cases. This paper will present a simple method for weighting the data to account for Poisson noise. Using a simple time-of-flight secondary ion mass spectrometry spectrum image as an example, it will be demonstrated that PCA, when applied to the weighted data, leads to results that are more interpretable, provide greater noise rejection and are more robust than standard PCA. The weighting presented here is also shown to be an optimal approach to scaling data as a pretreatment prior to multivariate statistical analysis. Published in 2004 by John Wiley & Sons, Ltd.