Analysis Of Spectral Features Research Articles

Classification and automatic transcription of primate calls.

This paper reports on an automated and openly available tool for automatic acoustic analysis and transcription of primate calls, which takes raw field recordings and outputs call labels time-aligned with the audio. The system's output predicts a majority of the start times of calls accurately within 200 milliseconds. The tools do not require any manual acoustic analysis or selection of spectral features by the researcher.

Tracking Bulk and Interfacial Diffusion Using Multiplex Coherent Anti-Stokes Raman Scattering Correlation Spectroscopy.

Multiplex coherent anti-Stokes Raman scattering correlation spectroscopy (CARS-CS) is shown as a label-free, chemically specific approach for monitoring the molecular mobility of particles in solution and at interfaces on the millisecond time scale. The CARS spectral range afforded by broadband excitation facilitates a quantitative measurement for the number of particles in the focal volume, whereas the autocorrelation of spectral data elucidates dynamic events, such as diffusion. The measured diffusion coefficients for polymer beads ranging from 100 nm to 1.1 μm in diameter are on the order of 10(-8)-10(-9) cm(2)/s, in good agreement with predicted Stokes-Einstein values. Diffusion at different interfaces shows particles are fastest in bulk medium, marginally slower at the liquid/glass interface, and 1.5-2 times slower rate at the air/liquid interface. Multivariate curve resolution analysis of distinct spectral features in multiplex CARS measurement distinguishes different composition lipid vesicles in a mixture diffusing through the focal volume. The observed diffusion is consistent with results obtained from single particle tracking experiments. This work demonstrates the utility of multiplex CARS correlation spectroscopy for monitoring particle diffusion from different chemical species across diverse interfaces.

Ubiquity of Exciton Localization in Cryogenic Carbon Nanotubes.

We present photoluminescence studies of individual semiconducting single-wall carbon nanotubes at room and cryogenic temperatures. From the analysis of spatial and spectral features of nanotube photoluminescence, we identify characteristic signatures of unintentional exciton localization. Moreover, we quantify the energy scale of exciton localization potentials as ranging from a few to a few tens of millielectronvolts and stemming from both environmental disorder and shallow covalent side-wall defects. Our results establish disorder-induced crossover from the diffusive to the localized regime of nanotube excitons at cryogenic temperatures as a ubiquitous phenomenon in micelle-encapsulated and as-grown carbon nanotubes.

Spectral signatures of the ionospheric Alfvén resonator to be observed by low‐Earth orbit satellite

AbstractInterference of an incident and reflected Alfvén pulses propagating inside the ionospheric Alfvén resonator (IAR) is studied on the basis of a simple one‐dimensional model. Particular emphasis has been placed on the analysis of spectral features of ultralow frequency (∼1–15 Hz) electric perturbations recently observed by Communications/Navigation Outage Forecasting System satellite. This “fingerprint” multiband spectral structure was observed when satellite descended in the terminator vicinity. Among factors affecting spectral structure the satellite position and distance from the IAR boundaries are most significant. It is concluded that the observed spectrograms exhibit modulation with “period” depending on propagation delay time of reflected Alfvén pulses in such a way that this effect can mask a spectral resonance structure resulted from excitation of IAR eigenmodes. The proposed interference effect is capable to produce a spectral pattern resembling a fingerprint which is compatible with the satellite observations.

Quantitative Estimation of Carbonate Rock Fraction in Karst Regions Using Field Spectra in 2.0–2.5 μm

Considering the important roles of carbonate rock fraction in karst rocky desertification areas and their potential for indicating damage to vegetation, improved knowledge is desired to assess the application of spectroscopy and remote sensing to characterizing and quantifying the biophysical constituents of karst landscapes. In this study, we examined the spectra of major surface constituents in karst areas for direct evidence of absorption features attributable to carbonate rock fraction. Using spectral feature analysis with continuum removal, we observed that there are overlapping spectral absorption in 2.149–2.398 μm by soils and non-photosynthetic vegetation. These overlapping features complicated the carbonate absorption feature near 2.340 μm in synthetic mixed spectra. To remove the overprint signal, two hyperspectral carbonate rock indices (HCRIs) were developed. Compared to the absorption features including depths, areas, and KRDSIs (karst rocky desertification synthesis indices), linear regression of HCRIs with carbonate rock fraction in linear synthetic mixtures resulted in higher correlations and lower errors. This study demonstrates that spectral variation of the surface constituents spectra in 2.270–2.398 μm region can indicate carbonate rock fraction and be used to quantify them. Still, additional research is needed to advance our understanding of the spectral influences from carbonate petrography relative to carbonate mineralogy, components and physical state of rock surface.

激光诱导有机物中氮元素的光谱特性分析

激光诱导有机物中氮元素的光谱特性分析

Ex vivo peripheral nerve detection of rats by spontaneous Raman spectroscopy.

Nerve-sparing surgery is increasingly being applied to avoid functional deficits of the limbs and organs following surgery. Peripheral nerves that should be preserved are, however, sometimes misidentified due to similarity of shape and color to non-nerve tissues. To avoid misidentification of peripheral nerves, development of an in situ nerve detection method is desired. In this study, we report the label-free detection of ex vivo peripheral nerves of Wistar rats by using Raman spectroscopy. We obtained Raman spectra of peripheral nerves (myelinated and unmyelinated nerves) and their adjacent tissues of Wistar rats without any treatment such as fixation and/or staining. For the identification of tissue species and further analysis of spectral features, we proposed a principal component regression-based discriminant analysis with representative Raman spectra of peripheral nerves and their adjacent tissues. Our prediction model selectively detected myelinated nerves and unmyelinated nerves of Wistar rats with respective sensitivities of 95.5% and 88.3% and specificities of 99.4% and 93.5%. Furthermore, important spectral features for the identification of tissue species were revealed by detailed analysis of principal components of representative Raman spectra of tissues. Our proposed approach may provide a unique and powerful tool for peripheral nerve detection for nerve-sparing surgery in the future.

NMR of 133Cs+ in stretched hydrogels: One-dimensional, z- and NOESY spectra, and probing the ion’s environment in erythrocytes

133Cs nuclear magnetic resonance (NMR) spectroscopy was conducted on 133Cs+ in gelatin hydrogels that were either relaxed or stretched. Stretching generated a septet from this spin-7/2 nucleus, and its nuclear magnetic relaxation was studied via z-spectra, and two-dimensional nuclear Overhauser (NOESY) spectroscopy. Various spectral features were well simulated by using Mathematica and the software package SpinDynamica. Spectra of CsCl in suspensions of human erythrocytes embedded in gelatin gel showed separation of the resonances from the cation inside and outside the cells. Upon stretching the sample, the extracellular 133Cs+ signal split into a septet, while the intracellular peak was unchanged, revealing different alignment/ordering properties of the environment inside and around the cells. Differential interference contrast light microscopy confirmed that the cells were stretched when the overall sample was elongated. Analysis of the various spectral features of 133Cs+ reported here opens up applications of this K+ congener for studies of cation-handling by metabolically-active cells and tissues in aligned states.

The early phases of the Type Iax supernova SN 2011ay

We present a detailed study of the early phases of the peculiar supernova 2011ay based on BVRI photometry obtained at Konkoly Observatory, Hungary, and optical spectra taken with the Hobby-Eberly Telescope at McDonald Observatory, Texas. The spectral analysis carried out with SYN++ and SYNAPPS confirms that SN 2011ay belongs to the recently defined class of SNe Iax, which is also supported by the properties of its light and color curves. The estimated photospheric temperature around maximum light, T_{phot} ~8,000 K, is lower than in most Type Ia SNe, which results in the appearance of strong Fe II features in the spectra of SN 2011ay, even during the early phases. We also show that strong blending with metal features (those of Ti II, Fe II, Co II) makes the direct analysis of the broad spectral features very difficult, and this may be true for all SNe Iax. We find two alternative spectrum models that both describe the observed spectra adequately, but their photospheric velocities differ by at least 3,000 km/s. The quasi-bolometric light curve of SN~2011ay has been assembled by integrating the UV-optical spectral energy distributions. Fitting a modified Arnett-model to L_{bol}(t), the moment of explosion and other physical parameters, i.e. the rise time to maximum, the ^{56}Ni mass and the total ejecta mass are estimated as t_{rise} ~14 +/-1 days, M_{Ni} ~0.22 +/- 0.01 M_{sol} and M_{ej} ~0.8 M_{sol}, respectively.

Analysis of Spectral Features of EEG signal in Brain Tumor Condition

Abstract The scalp electroencephalography (EEG) signal is an important clinical tool for the diagnosis of several brain disorders. The objective of the presented work is to analyze the feasibility of the spectral features extracted from the scalp EEG signals in detecting brain tumors. A set of 16 candidate features from frequency domain is considered. The significance on the mean values of these features between 100 brain tumor patients and 102 normal subjects is statistically evaluated. Nine of the candidate features significantly discriminate the brain tumor case from the normal one. The results encourage the use of (quantitative) scalp EEG for the diagnosis of brain tumors

Reflectance spectroscopy of kimberlites—in parts of Dharwar Craton, India

In the present study, an attempt was made to analyse the reflectance spectra of kimberlites to evaluate its potential as key in remote sensing based spatial mapping. The spectral profiles of kimberlite samples were collected within the visible-near infrared-shortwave infrared (VNIR-SWIR) electromagnetic domain. In this regard, we analysed the reflectance spectra of three kimberlite pipes (having variable mineralogy) of Narayanpet kimberlite field (NKF) based on the comparative analysis of spectral features of kimberlite samples with the spectral features of their dominant constituent minerals. The relative abundances of each of the constituent minerals were confirmed using semiquantitative mineralogical data from X-ray diffraction analysis. This was supplemented with petrographical data as reference. We found that the absorption features imprinted in the reflectance spectra of kimberlites were mineralogically sensitive. These spectral features were imprinted by spectral features of serpentine, olivine, and calcite depending on the relative dominance of these minerals in kimberlites. With regard to understand the spectral behaviour of weathered residue of kimberlite for targeting buried kimberlite, we also attempted a comparative analysis of spectral profiles of in-situ soil developed above the pipes with the spectra of respective kimberlites in NKF area. While comparing aforementioned spectra, it was observed that the spectral signatures of NKF kimberlites were broadly translated to the in-situ soil. Further, we compared the spectral profiles of selected NKF kimberlites with the spectra of three distinct kimberlite pipes of Wajrakarur kimberlite field (WKF) characterised with similar mineralogy with respect to the selected NKF pipes. Relative dominance of constituent minerals (i.e., serpentine, olivine, calcite, etc.) in these pipes was taken as reference to identify the mineralogical similarity of the pipes of both the field. It was observed that the spectral profiles of NKF and WKF kimberlites were highly correlated with regard to wavelength of diagnostic absorption features. Finally, we also made an attempt to understand the effect of spectral mixing, in spectral separation of kimberlites and associated granite-granodiorite gneiss (i.e., Dharwar Gneiss). It was seen that the spectral contrast of kimberlite and gneiss was dependent on the relative size of the pipe with respect to pixel or ground sampling diameter of spectral data acquisition. Study confirmed the diagnostic nature of reflectance spectra of pipes along with their mineralogical sensitiveness and spatial integrity. It also highlighted how spectral mixing can influence the spectral feature based remote detection of kimberlites.

Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm

Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic CH bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2=0.95, RMSE=1.0%, n=56), fresh leaf (r2=0.79, RMSE=2.1%, n=56), and canopy (r2=0.78, RMSE=1.0%, n=13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin).

Neutron Spectrometry for <formula formulatype="inline"><tex Notation="TeX">${\rm UF}_6$</tex></formula> Enrichment Verification in Storage Cylinders

Verification of declared ${\rm UF}_6$ enrichment and mass in storage cylinders is of great interest in nuclear material nonproliferation. Nondestructive assay (NDA) techniques are commonly used for safeguards inspections to ensure accountancy of declared nuclear materials. Common NDA techniques used include gamma-ray spectrometry and both passive and active neutron measurements. In the present study, neutron spectrometry was investigated for verification of ${\rm UF}_6$ enrichment in 30B storage cylinders based on an unattended and passive measurement approach. MCNP5 and Geant4 simulated neutron spectra, for selected ${\rm UF}_6$ enrichments and filling profiles, were used in the investigation. The simulated neutron spectra were analyzed using principal component analysis (PCA). The PCA technique is a well-established technique and has a wide area of application including feature analysis, outlier detection, and gamma-ray spectral analysis. Results obtained demonstrate that neutron spectrometry supported by spectral feature analysis has potential for assaying ${\rm UF}_6$ enrichment in storage cylinders. Results from the present study also showed that difficulties associated with the ${\rm UF}_6$ filling profile and observed in other unattended passive neutron measurements can possibly be overcome using the approach presented.

In Situ Probing of the Mechanisms of Coking Resistance on Catalyst-Modified Anodes for Solid Oxide Fuel Cells

Coking is a major cause of performance degradation of Ni-based anodes in solid oxide fuel cells (SOFCs) powered by carbon-containing fuels. While modification of Ni surfaces using a thin coating of BaO, BaZr0.9Y0.1O3–d (BZY), and BaZr0.1Ce0.7Y0.1Yb0.1O3–d (BZCYYb) was reported to alleviate the problem, the mechanism is yet to be understood. In this study, in situ Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) are used to probe the surface chemistry of BaO, BZY, and BZCYYb. Analyses of the time-resolved spectral features of C–C bonds, −OH groups, and −CO3 groups reveal the interactions between surface functional groups and gas species (hydrocarbon, water steam, and CO2). While the switching from −OH to −CO3 groups is irreversible on BaO surfaces, it becomes reversible on both BZY and BZCYYb surfaces. Although the −OH mediated carbon removal is observed on the surfaces of all three catalysts, the −CO3 is found effective for carbon removal only on the BZCYYb surface.

Unsupervised spectral-spatial feature selection-based camouflaged object detection using VNIR hyperspectral camera.

The detection of camouflaged objects is important for industrial inspection, medical diagnoses, and military applications. Conventional supervised learning methods for hyperspectral images can be a feasible solution. Such approaches, however, require a priori information of a camouflaged object and background. This letter proposes a fully autonomous feature selection and camouflaged object detection method based on the online analysis of spectral and spatial features. The statistical distance metric can generate candidate feature bands and further analysis of the entropy-based spatial grouping property can trim the useless feature bands. Camouflaged objects can be detected better with less computational complexity by optical spectral-spatial feature analysis.

Radial variations in the stellar initial mass function of early-type galaxies

The hypothesis of a universal initial mass function (IMF) -- motivated by observations in nearby stellar systems -- has been recently challenged by the discovery of a systematic variation of the IMF with the central velocity dispersion, {\sigma}, of early-type galaxies (ETGs), towards an excess of low-mass stars in high-{\sigma} galaxies. This trend has been derived so far from integrated spectra, and remains unexplained at present. To test whether such trend depends on the local properties within a galaxy, we have obtained new, extremely deep, spectroscopic data, for three nearby ETGs, two galaxies with high {\sigma} (~300 km/s), and one lower mass system, with {\sigma} ~ 100 km/s. From the analysis of IMF-sensitive spectral features, we find that the IMF depends significantly on galactocentric distance in the massive ETGs, with the enhanced fraction of low-mass stars f mostly confined to their central regions. In contrast, the low-{\sigma} galaxy does not show any significant radial gradient in the IMF, well described by a shallower distribution, relative to the innermost regions of massive galaxies, at all radii. Such a result indicates that the IMF should be regarded as a local (rather than global) property, and suggests a significant difference between the formation process of the core and the outer regions of massive ETGs.

Chemometric analysis of correlations between electronic absorption characteristics and structural and/or physicochemical parameters for ampholytic substances of biological and pharmaceutical relevance

Forty ampholytic compounds of biological and pharmaceutical relevance were subjected to chemometric analysis based on unsupervised and supervised learning algorithms. This enabled relations to be found between empirical spectral characteristics derived from electronic absorption data and structural and physicochemical parameters predicted by quantum chemistry methods or phenomenological relationships based on additivity rules. It was found that the energies of long wavelength absorption bands are correlated through multiparametric linear relationships with parameters reflecting the bulkiness features of the absorbing molecules as well as their nucleophilicity and electrophilicity. These dependences enable the quantitative analysis of spectral features of the compounds, as well as a comparison of their similarities and certain pharmaceutical and biological features. Three QSPR models to predict the energies of long-wavelength absorption in buffers with pH=2.5 and pH=7.0, as well as in methanol, were developed and validated in this study. These models can be further used to predict the long-wavelength absorption energies of untested substances (if they are structurally similar to the training compounds).

Spectral features of solar plasma flows

Research to the identification of plasma flows in the Solar wind by spectral characteristics of solar plasma flows in the range of magnetohydrodynamics is devoted. To do this, the wavelet skeleton pattern of Solar wind parameters recorded on Earth orbit by patrol spacecraft and then executed their neural network classification differentiated by bandwidths is carry out. This analysis of spectral features of Solar plasma flows in the form of magnetic clouds (MC), corotating interaction regions (CIR), shock waves (Shocks) and highspeed streams from coronal holes (HSS) was made. The proposed data processing and the original correlation-spectral method for processing information about the Solar wind flows for further classification as online monitoring of near space can be used. This approach will allow on early stages in the Solar wind flow detect geoeffective structure to predict global geomagnetic disturbances.

Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum removal

This paper explores the use of spectral feature analysis to detect plant stress in visible/near infrared wavelengths. A time series of close range leaf and canopy reflectance data of two plant species grown in hydrocarbon-contaminated soil was acquired with a portable spectrometer. The ProSpecTIR-VS airborne imaging spectrometer was used to obtain far range hyperspectral remote sensing data over the field experiment. Parameters describing the chlorophyll 680nm absorption feature (depth, width, and area) were derived using continuum removal applied to the spectra. A new index, the Plant Stress Detection Index (PSDI), was calculated using continuum-removed values near the chlorophyll feature centre (680nm) and on the green-edge (560 and 575nm). Chlorophyll feature’s depth, width and area, the PSDI and a narrow-band normalised difference vegetation index were evaluated for their ability to detect stressed plants. The objective was to analyse how the parameters/indices were affected by increasing degrees of plant stress and to examine their utility as plant stress indicators at the remote sensing level (e.g. airborne sensor). For leaf data, PSDI and the chlorophyll feature area revealed the highest percentage (67–70%) of stressed plants. The PSDI also proved to be the best constraint for detecting the stress in hydrocarbon-impacted plants with field canopy spectra and airborne imaging spectroscopy data. This was particularly true using thresholds based on the ASD canopy data and considering the combination of higher percentage of stressed plants detected (across the thresholds) and fewer false-positives.

Research and Simulation for Vegetation Health Index Detector Based on Remote Sensing

Vegetation health index detector obtained by remote sensing is analyzed. Remote sensing information of wide range vegetation is complex since there are a lot of plants with their own spectral characteristics in a certain area. After absorbing the light, some light sensitive characteristics will have some change, which lead to the change of the entire vegetation health spectral curve. In traditional method, vegetation health is shown by the spectral remote sensing characteristic curve of green plant. There will be some misjudgment if the spectral remote sensing characteristic curve is distorted. Reflectance spectral characteristics analysis algorithm with classification constraint for the plant health is discussed in this paper. Traditional detection method is optimized according to the data classification theory. With the support of the optimization, wide range vegetation spectral characteristics can be distinguished and the interference of wide range of strong light is avoided. The new method can decrease the vegetation health detection fallacious rate, and it is showed in the simulation result.