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Exploring techniques that breakthrough the unknown space or material species is of considerable significance to military and civilian fields, and it is a challenging task without any prior information. Nowadays, the use of material-specific spectral information to detect unknowns has received increasing interest. However, affected by noise and interference, high-dimensional hyperspectral anomaly detection is difficult to meet the requirements of high detection accuracy and low false alarm rate. Besides, there is a problem of insufficient and unbalanced samples. To address these problems, we propose a novel hyperspectral anomaly detection framework based on spectral mapping and feature selection (SMFS) in an unsupervised manner. The SMFS introduces the essential properties of hyperspectral data into an unsupervised neural network to construct the nonlinear mapping relationship from high-dimensional spectral space to low-dimensional deep feature space. And it searches the optimal feature subset from the candidate feature space for standing out anomalies. Because of the compelling characterization of the encoder, we develop it specifically for spectral signatures to reveal the hidden data. Quantitative and qualitative experiments on real hyperspectral datasets indicate that the proposed method can provide the compact features overcoming the problems of noise, interference, redundancy and time-consuming caused by high-dimensionality and limited samples. And it has advantages over some state-of-the-art competitors concerning detecting anomalies of different scales. [ABSTRACT FROM AUTHOR]

Arctic environments provide a challenging ground for geological mapping and mineral exploration. Inaccessibility complicates ground surveys and the presence of ice, vegetation, and lichens hinders supportive remote sensing surveys. Spectral mixing of lichens and bare rock can shift the wavelength position of characteristic absorption features, thereby complicating the spectral mapping of minerals and lithologies. The extent to which diagnostic rock features are preserved despite the presence of lichens is of major concern in remotely sensed geological studies and estimates of the critical level of lichen coverage, below which spectral features of the mineral substrate can still be identified, are needed. We investigated how surficial lichen cover affects the characteristics of shortwave infrared (SWIR) mineral absorption features and the efficacy of automated absorption feature extraction. For this purpose, mixed spectra were synthetically generated from laboratory spectra of common rock forming SWIR absorbing minerals and lichens. Wavelength displacements of characteristic absorption features for each mixed spectrum were then analyzed as a function of percentage lichen cover. Distinctive trends were identified that can be used in future analysis: The strong spectral features of mica group minerals around 2200 and 2340–2350 nm maintain their integrity for up to 30% lichen cover, despite the related shift toward shorter wavelengths for higher percentage lichen cover. In contrast, very weak absorption bands around 2440 nm in (white) micas spectra are completely obscured for a lichen cover of ≥ 50%. Our observations suggest that the chlorite feature around 2250 nm is shifted toward longer wavelengths and the depth of this feature as well as the contrast between lichen and substrate spectra define the amount of lichen needed to mask it. Furthermore, lichens induce a spectral shift towards shorter wavelengths for the features around 2320 nm for the rocks containing amphibole, chlorite, carbonate and serpentine group minerals. In addition, no wavelength displacement is observed for chlorite, biotite and phlogopite features around 2380 nm in mixtures with lichens. By quantifying lichen cover effects on mineral absorption features, our study highlights the importance of being precautious in any interpretation in areas characterized by abundant lichen-covered outcrops. This can be of significant importance for mineral and deposit vectoring as the presence of abundant lichen coverage causing slightly shifted features for a given spectra can be erroneously identified as a path to a deposit. [ABSTRACT FROM AUTHOR]

Commercially available hyperspectral drill core scanning systems in the Visible and Near-Infrared (VNIR), Short-Wave Infrared (SWIR) and Long-Wave Infrared (LWIR) regions, along with a handheld spectrometer for the Middle-Wave Infrared (MWIR) region, were used to characterise a rank suite of coal samples (0.45% to 1.76% Rr) with different preparations (slabbed surface, outer core and compressed powder pellets). The samples used in this study came from Australian basins of Permian and Jurassic ages. The spectral characteristics of the coals measured using these scanning systems were compared to those obtained using well-established spectroscopy techniques that operate in the same region of the electromagnetic spectrum, such as Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy. The observed spectral features of the coals changed with increasing coalification. The features occurring at 1910 nm in the SWIR region and 2940 nm in the MWIR region tended to disappear as rank increased; these features arise from OH bonds related to structural water and hence correlate with the moisture content. The CH stretching fundamentals at 3280 nm correspond to the aromatic fraction of the coal, while the spectral region between 3380 nm and 3500 nm corresponds to the CH 2 and CH 3 stretching fundamentals occurring in the aliphatic fraction of the coal. The ratio of the area of the aromatic absorption feature to that of the aliphatic absorption features, herein called Rank Spectral Index, displayed a strong linear correlation with coal rank parameters, such as vitrinite reflectance. Other spectral features in the SWIR and MWIR regions also correlated with coal rank parameters. In addition to rank, trends in the magnitudes of the spectral features were influenced by maceral composition, particularly in low rank samples with abundant liptinite or inertinite group macerals. The variable spectral features related to composition were mapped onto the slabbed core surface, discriminating bright and dull bands and fusain lenses enabling the classification of coal lithotypes, regardless of rank. Among the less banded lithotypes, it was established that high liptinite content coals can be distinguished by their aliphatic spectral features in core samples. Overall, this study has shown that hyperspectral drill core scanners can be used to characterise coal and non-destructively classify rank and lithotype. • Non-destructive hyperspectral scanning systems were applied to coal characterisation. • Wavelengths covered 450 nm to 14,500 nm or visible near to thermal infrared. • Rank related features structural water, aliphatic and aromatic features were identified in the spectra. • A Rank Spectral Index was created from spectral features in the Middle-Wave Infrared. • High resolution spectral maps distinguished banding and coal lithotypes. [ABSTRACT FROM AUTHOR]

Three types of morphological features observed in different lunar crustal terrains were studied and mapped using hyperspectral Moon mineralogy mapper (M 3 ) data onboard Chandrayaan 1 mission in order to assess the utility of cascaded MGM-SAM spectral mixture modeling approach to characterize the surface materials, which may occur as mineral mixtures, at different topography of the lunar surface. Selected morphological features include: the impact melts in Orientale basin, sinuous rilles in Procellarum KREEP Terrane (PKT) and a rayed crater in Feldspathic Highland Terrane (FHT). Methodology involves extraction of spectrally pure pixels (endmembers) of the area using Pixel Purity Index (PPI), identification of mineralogy of the selected endmember spectrum using the Modified Gaussian Method (MGM) and mapping of mineralogically identified endmembers using the Spectral Angle Mapper (SAM) method. Mapping results demonstrate both the capabilities and the limitations of the MGM method of spectral deconvolution and the SAM method of spectral matching as effective tools for compositional characterizations of morphological features on the lunar surface. As a method of spectral deconvolution, MGM was able to identify and characterize both high- and low – Ca pyroxenes along with plagioclase feldspar. The Spectral Angle Mapper (SAM) was able to map identified mineral mixtures from MGM. [ABSTRACT FROM AUTHOR]

Remote sensing has been widely used to map geological structure, regolith materials and hydrothermal alteration minerals in different regions of the world, based largely on the spectral characteristics of clay/carbonate and iron oxide minerals. Given that features related to minerals may form very subtle patterns in Landsat imagery, finding suitable methods for enhancing the spectral information due to alteration minerals has been the subject of much research since the satellites were launched. Although multispectral sensors such as Landsat do not have enough spectral resolution to distinguish between specific minerals, effective processing of the data does yield useful images for regional exploration and targeting when combined with a good understanding of the associated landforms. ASTER and various airborne systems can now provide useful, high-resolution mineral information. However, the cost and time taken to acquire and process good resolution spectral data should now be balanced against the enhanced spectral discrimination possible using temporal merging, or data stacking, from the existing Landsat data archive dating back to 1972. One critical characteristic of the spectra related to surface mineral composition is its persistence over time. This study proposes a further enhancement to the data using temporal merging to create images that are both easier to interpret, and more reliable indicators of lithology and alteration. The new approach is to select a large number of scenes, typically separated by two or more years, and to merge the data for each band to give a cumulative spectral signature. The technique of stacking Landsat data to create a new dataset with much greater spectral depth is termed Landsat TM3, or Landsat TM Temporal Merge for Terrain Mapping. This emphasises not only the process used to create the dataset, but also the dominant use to which these data are ideally suited, namely mapping geological and mineral-related terrains. [ABSTRACT FROM AUTHOR]

There are two types of voice conversion methods: statistical and deep learning-based. Although statistical methods can train with limited data, they face challenges, including spectral oversmoothing and time-domain discontinuity. On the other hand, extensively researched deep learning-based methods rely primarily on massive amounts of data, which limits their practical applicability. Given that voice conversion is an engineering problem with limited training data, it is crucial to develop techniques that can produce satisfactory results in terms of quality and similarity in the absence of a large amount of data. This paper proposes a voice conversion model based on stochastic variational deep kernel learning (SVDKL), which works with limited training data. The model allows the use of both the deep neural network's expressive capability and the high flexibility of the Gaussian process, which is a Bayesian and non-parametric method. The model utilizes a cascade of a deep neural network and a conventional kernel as the covariance function, which enables it to estimate non-smooth and more complex functions. Furthermore, the model's sparse variational Gaussian process solves the scalability problem of exact inference and enables the learning of a global mapping function for the entire acoustic space. One of the most important aspects of the proposed scheme is that the model parameters are trained using marginal likelihood optimization, which takes into account both data fitting and model complexity. Considering model complexity reduces the training data by increasing the robustness to overfitting. To evaluate the proposed scheme, we examined the model's performance with as little as approximately 80 s of training data. The results indicated that our method obtains a higher mean opinion score, smaller spectral distortion, and better preference tests than the state-of-the-art limited data methods. [ABSTRACT FROM AUTHOR]

Abstract: We investigate simultaneous solutions of the matrix Sylvester equations , where and are k-tuples of commuting matrices of order and , respectively. We show that the matrix Sylvester equations have a unique solution X for every compatible k-tuple of matrices if and only if the joint spectra and are disjoint. We discuss the connection between the simultaneous solutions of Sylvester equations and related questions about idempotent matrices separating disjoint subsets of the joint spectrum, spectral mapping for the differences of commuting k-tuples, and a characterization of the joint spectrum via simultaneous solutions of systems of linear equations. [Copyright &y& Elsevier]

Abstract: Tear fluid is a complex aqueous solution containing proteins, metabolites, electrolytes and lipids. This study uses Raman spectroscopy to analyse the composition of human tear fluid from three healthy volunteers. Two different methods are used to obtain Raman spectra from the 3μL tear samples: (i) solution-phase Raman spectroscopy, and (ii) drop coating deposition Raman spectroscopy (DCDRS). Tear samples were either basal fluid, or yawn reflex secreted fluid. Calibration of the solution technique with standard protein solutions (5–15mgmL−1) showed that this method could predict the protein concentration (cross-validation) with an error of less than 1mgmL−1. The Raman signals from the tear fluid were very weak but signals due to protein and urea were clearly observable in all samples. The drop coating deposition technique was shown to produce very high signal-to-noise spectra for relatively short acquisition times, and small sample volumes. Raman point mapping combined with principal components analysis showed that the protein, urea, bicarbonate and lipid could all be detected in the tear samples and that the distribution of these components was inhomogeneous. Their position within the drying pattern was shown to depend on their relative solubilities. The results of this study suggest that solution Raman measurements may be calibrated to give the total tear protein concentration and DCDRS could be used to give a fingerprint of the tear protein (and lipid) composition. [Copyright &y& Elsevier]

Abstract: Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, we devise a novel algorithm to identify overlapping communities in complex networks by the combination of a new modularity function based on generalizing NG''s Q function, an approximation mapping of network nodes into Euclidean space and fuzzy c-means clustering. Experimental results indicate that the new algorithm is efficient at detecting both good clusterings and the appropriate number of clusters. [Copyright &y& Elsevier]

Abstract: We have built two algorithms especially designed for the mapping of the intra-cluster medium emissivity and temperature, as seen by the XMM-Newton satellite using the European Photon Imaging Camera (EPIC). In both algorithms the searched parameter is firstly estimated within square resolutions elements at different spatial resolutions, then its variations are coded using Haar wavelet coefficients. The de-noised map of the searched parameter is finally obtained from structures selected as significant in the scale-space, while non-significant contributions are lowered using a regularisation process. Gas emissivity and temperature maps obtained using these algorithms have evidenced perturbations, anisotropies, shocked and disturbed regions within the two merging clusters A1750 and A3921, and have confirmed that the different component of each cluster were interacting with each other (see also Belsole, E., J-L. Sauvageot, Pratt G.W., Bourdin, H. Merging clusters of galaxies observed with XMM-Newton. Adv. Space Res., 2005 (this conference) (astro-ph/0501377)). [Copyright &y& Elsevier]

Abstract: Spectral mapping is a factorial method of multivariate data analysis that was developed around 1975 in the pharmacological research laboratory of Janssen Pharmaceutica. It has been applied in diverse fields of biology and economics where tabulated data are obtained which are defined on ratio scales. The approach has provided a few instances in which useful information was obtained that would be hard to get from bivariate analysis or from “eyeballing” of the raw data. Basically, the approach consists of logarithmic transformation and double centering of the data, factorization of the resulting cross-products matrix and a biplot of the resulting factor scores and loadings. Weighting by the marginal row and column sums of the original data can be applied in order to reduce the leverage of less important row and column items. This paper describes how the approach has fared over the past 30 years from a personal perspective. [Copyright &y& Elsevier]

The growth inhibitory effect of 30 synthetic dyes on 22 bacteria (test organisms) belonging to various taxonomic groups was determined. The strength (potency) and selectivity of the biological effect were separated by the spectral mapping technique, reducing the dimensionality of the selectivity maps to two by the nonlinear mapping technique. The relationship between biological effect and physicochemical parameters of dyes was elucidated by stepwise regression analysis. It has been established that the strength of the effect of anthracene and trityl derivatives was higher than that of azobenzene dyes and significantly depended on the hydrophobicity of the compound. The selectivity of the effect also depended on hydrophobicity and on the nonpolar unsaturated surface area of the dyes. Gram negative and Gram positive bacteria differed in the strength and selectivity of their response to dyes indicating the marked impact of the taxonomical position on the response. Contrary to other multivariate mathematical statistical methods biological activity may be divided by SPM into potency and selectivity values, therefore, application of the technique in future QSAR studies is highly recommended. [Copyright &y& Elsevier]