Spectral identification of networks using sparse measurements

We propose a new method to recover global information about a network of interconnected dynamical systems based on observations made at a small number (possibly one) of its nodes. In contrast to classical identification of full graph topology, we focus on the identification of the spectral graph-theoretic properties of the network, a framework that we call spectral network identification. The main theoretical results connect the spectral properties of the network to the spectral properties of the dynamics, which are well-defined in the context of the so-called Koopman operator and can be extracted from data through the Dynamic Mode Decomposition algorithm. These results are obtained for networks of diffusively-coupled units that admit a stable equilibrium state. For large networks, a statistical approach is considered, which focuses on spectral moments of the network and is well-suited to the case of heterogeneous populations. Our framework provides efficient numerical methods to infer global information on the network from sparse local measurements at a few nodes. Numerical simulations show for instance the possibility of detecting the mean number of connections or the addition of a new vertex using measurements made at one single node, that need not be representative of the other nodes' properties.Comment: 3

The Use of AIS Data for Identifying and Mapping Calcareous Soils in Western Nebraska

The identification of calcareous soils, through unique spectral responses of the vegetation to the chemical nature of calcareous soils, can improve the accuracy of delineating the boundaries of soil mapping units over conventional field techniques. The objective of this experiment is to evaluate the use of the Airborne Imaging Spectrometer (AIS) in the identification and delineation of calcareous soils in the western Sandhills of Nebraska. Based upon statistical differences found in separating the spectral curves below 1.3 microns, calcareous and non-calcareous soils may be identified by differences in species of vegetation. Additional work is needed to identify biogeochemical differences between the two soils

Forest Species Identification with High Spectral Resolution Data

Data collected over the Sleeping Bear Sand Dunes Test Site and the Saginaw Forest Test Site (Michigan) with the JPL Airborne Imaging Spectrometer and the Collins' Airborne Spectroradiometer are being used for forest species identification. The linear discriminant function has provided higher identification accuracies than have principal components analyses. Highest identification accuracies are obtained in the 450 to 520 nm spectral region. Spectral bands near 1,300, 1,685 and 2,220 nm appear to be important, also

Charge identification for spectral lines in nitrogen

Ion charge identification for spectral lines in nitrogen by beam foil light source techniqu

Spectral emission measurement of igneous rocks using a spectroradiometer

Spectroradiometer is used for either close or remote identification of rocks not heated to high temperatures. Instrument yields reproducible data spectra with excellent signal-to-noise ratios and readily identifiable spectral details, including differences in subclasses

Single-Spin Spectrum-Analyzer for a Strongly Coupled Environment

A qubit can be used as a sensitive spectrum analyzer of its environment. Here we show how the problem of spectral analysis of noise induced by a strongly coupled environment can be solved for discrete spectra. Our analytical model shows non-linear signal dependence on noise power, as well as possible frequency mixing, both are inherent to quantum evolution. This model enabled us to use a single trapped ion as a sensitive probe for strong, non-Gaussian, discrete magnetic field noise. To overcome ambiguities arising from the non-linear character of strong noise, we develop a three step noise characterization scheme: peak identification, magnitude identification and fine-tuning. Finally, we compare experimentally equidistant versus Uhrig pulse schemes for spectral analysis. The method is readily available to any quantum probe which can be coherently manipulated

A robust spectral method for finding lumpings and meta stable states of non-reversible Markov chains

A spectral method for identifying lumping in large Markov chains is presented. Identification of meta stable states is treated as a special case. The method is based on spectral analysis of a self-adjoint matrix that is a function of the original transition matrix. It is demonstrated that the technique is more robust than existing methods when applied to noisy non-reversible Markov chains.Comment: 10 pages, 7 figure

Automatic classification system of Raman spectra applied to pigments analysis

Raman spectroscopy is one of the few non-destructive techniques capable of identifying pigments in art works. Raman spectra contain powerful information that can be used to identify unknown compounds and their chemical structures. However, the analysis of spectral data comes with some difficulties, and therefore the spectral interpretation is not straightforward. Sometimes, there are very little differences in the spectral data concerning to specific identification objectives, for instance, in polymorphic discrimination or in the discrimination of natural and synthetic forms of certain pigments. Moreover, this discrimination is often performed manually so that the process can be repetitive, subjective and particularly time-consuming. The result is an increasing motivation to automate the identification process involved in the classification of pigments in paint. In this paper, we propose a system to automatically classify the spectral data into specific and well-known classes, i.e. reference classes. The proposal is based on a combination of chemometric techniques, which provides a powerful way to achieve spectral separability so that it is possible to discriminate between very similar spectra in an automatic way. In this regard, a decision-making algorithm was specifically developed to select the corresponding reference class with no user input, which was successfully validated using simulated spectra. The implemented methodology was used to classify Raman spectra of pigments commonly present in artist's paints in experimental cases, providing reliable and consistent results. Therefore, the presented system can play a good auxiliary role in the analysts' endpoint classification.Peer ReviewedPostprint (author's final draft