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Space-borne hyperspectral imagery data are known for their high spectral resolution in a number of narrow wavelength intervals, which makes these data useful for mineral mapping. However, the available free-of-charge hyperspectral scenes cover only narrow and scattered geographic areas. In contrast, multispectral imagery scenes have a nearly complete spatial coverage and wider wavelength intervals. The low spectral resolution of the multispectral data, however, limits their efficiency in the mineral mapping of small geological massifs or hydrothermal alteration halos. The present contribution presents a new transformation tool (SAM-HIT) to simulate the hyperspectral sensor responses in unscanned areas based on partially overlapping hyperspectral and multispectral scenes. Simulation or prediction of the pseudohyperspectral data is here accomplished by using the simulated annealing linear optimization algorithm, which allows the lowest possible mismatch between the original and predicted data. The high visual and numerical correlation of the resultant data confirms the reliability of the newly adopted transformation. Further, the application of the SAM-HIT to a well-exposed part of the Egyptian basement complex with available hyperspectral data showed high concordance and nearly identical band signatures, opening a new outlook for mineral exploration in vast areas by a nearly automated cost-free means. [ABSTRACT FROM AUTHOR]

In this article, we have processed Landsat-8 spectral bands, ground gravity, and magnetic data for delineating the mafic cumulates within the anorthositic-layered complex in Sittampundi, Tamilnadu, India. In this regard, surface anomaly map of mafic cumulates was derived by processing spectral bands of Operational Land Imager (OLI) sensor using matched filtering method. Residual gravity, magnetic anomaly maps are also derived. Spatial position of geophysical anomalies corroborate well with the spectral anomalies. A promising zone of chromitite occurrences is identified at the eastern fringe of the study area based on the concurrence of spectral and geophysical anomalies. Spatial profiles of geophysical and spectral anomalies around this promising zone are similar to the respective spatial profiles observed around the known chromite bearing mafic cumulates. Depth continuity of the mafic cumulates is confirmed by deriving of gravity and magnetic anomaly map at different depth using upward continuation filters to confirm concealed deposit. [ABSTRACT FROM AUTHOR]

We searched for an optimized protocol for mapping observations from a point spectrometer onto a shape model composed of triangular facets, in the context of NASA's asteroid sample return mission, OSIRIS‐REx (Origins, Spectral Interpretation, Resource Identification, and Security‐Regolith Explorer). Our study was conducted before the spacecraft arrived at the mission target asteroid (101955) Bennu, and we used observational sequence plans of the OSIRIS‐REx Visible and InfraRed Spectrometer (OVIRS). We explored six methods of mapping data to shape model facets, using three spatial resolutions. We attempted to boost map fidelity by increasing the observational coverage of the surface. We find that increasing shape model resolution improves mapping quality. However, once the shape model's mean facet edge length is smaller than two‐fifths of the diameter of the instrument's field of view (FOV), the increase in quality tapers off. The six mapping methods can be broken into two categories: facets that (1) select or (2) combine (average) data from observations. The quality differences between similar averaging methods (clipped average, weighted average, etc.) are insignificant. Selecting the nearest observation to a facet best preserves an enclosed outcrop's shape and signal, but averaging spots are more conservative against errors in photometric modeling. A completely enclosed outcrop border expands into the surrounding region by 0.8–1.5 radii of the instrument's FOV. Regions smaller than the instrument's FOV are present in resulting maps; however, their signal strength is reduced as a function of their size relative to the instrument FOV. Key Points: Spectral map quality is controlled by shape model facet size, boresight spot size, and method of assigning spot data to facetsShape models with facet edges smaller than 2/5 the boresight spot diameter cease to improve map fidelityData selection algorithms perform better than data combination methods for mapping distinct outcrops [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of parallel voice conversion (VC) with neural network (NN)-based statistical models for spectral mapping and waveform generation. The NN-based architectures for spectral mapping include deep NN (DNN), deep mixture density network (DMDN), and recurrent NN (RNN) models. WaveNet (WN) vocoder is employed as a high-quality NN-based waveform generation. In VC, though, owing to the oversmoothed characteristics of estimated speech parameters, quality degradation still occurs. To address this problem, we utilize post-conversion for the converted features based on direct waveform modifferential and global variance postfilter. To preserve the consistency with the post-conversion, we further propose a spectrum differential loss for the spectral modeling. The experimental results demonstrate that: (1) the RNN-based spectral modeling achieves higher accuracy with a faster convergence rate and better generalization compared to the DNN-/DMDN-based models; (2) the RNN-based spectral modeling is also capable of producing less oversmoothed spectral trajectory; (3) the use of proposed spectrum differential loss improves the performance in the same-gender conversions; and (4) the proposed post-conversion on converted features for the WN vocoder in VC yields the best performance in both naturalness and speaker similarity compared to the conventional use of WN vocoder. [ABSTRACT FROM AUTHOR]

The emergence of functional magnetic resonance imaging (fMRI) provides a good opportunity for brain functional parcellation. However, the high dimension and low signal-to-noise ratio of fMRI data brings difficulties to the existing parcellation methods. To address the issue, this paper presents a novel brain functional parcellation method based on artificial bee colony clustering (ABCC) algorithm with self-adaptive crossover and stepwise search (called CSABCC). In CSABCC, the preprocessed fMRI data is first mapped into a low-dimensional space by spectral mapping to reduce its dimension and each food source position is encoded as a clustering solution composed of cluster centers. Then, CSABCC utilizes an improved artificial bee colony search procedure with some robustness advantage to seek better food sources, where a self-adaptive crossover is employed to enhance information exchange between individuals and onlooker bees adopt a stepwise search to improve its search capability. Finally, a functional parcellation result is obtained by mapping cluster labels onto the corresponding voxels. The experiments on simulated fMRI data show that CSABCC can generate the parcellation closest to the real result, and these results on real insula fMRI data also demonstrate that CSABCC has better search capability and can produce parcellation structures with stronger functional consistency and regional continuity compared to some other typical algorithms. Moreover, the correctness of the parcellation results is also validated by functional connectivity fingerprints of the corresponding subregions. [ABSTRACT FROM AUTHOR]

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Abstract: A concise insight into the outputs provided by the latest prototype of visible‐near infrared (Vis‐NIR) multispectral scanner (National Research Council‐National Institute of Optics, CNR‐INO, Italy) is presented. The analytical data acquired on an oil painting Madonna of the Rabbit by É. Manet are described. In this work, the Vis‐NIR was complemented with X‐ray fluorescence (XRF) mapping for the chemical and spatial characterization of several pigments. The spatially registered Vis‐NIR data facilitated their processing by spectral correlation mapping (SCM) and artificial neural network (ANN) algorithm, respectively, for pigment mapping and improved visibility of pentimenti and of underdrawing style. The data provided several key elements for the comparison with a homonymous original work by Titian studied within the ARCHive LABoratory (ARCHLAB) transnational access project. [ABSTRACT FROM AUTHOR]

Aims: Asymptomatic nocturnal long ventricular pauses are usually detected accidentally and it has been suggested that they may lead to sudden death. Identification of predisposing factors could prevent cardiovascular events.Methods and Results: We report the case of a patient with frequent asymptomatic nocturnal ventricular pauses of 3-11 s, characteristic of a vagally mediated atrioventricular (AV) block. Echocardiography, treadmill test, thyroid function test levels, and polysomnogram were normal. In an attempt to reduce the risk, it was decided that an atrial vagal denervation induced by radiofrequency (RF) ablation (cardioneuroablation) could be useful. Spectral mapping was used to localize endocardial vagal innervation in the right and left aspects of the inter-atrial septum, responsible for the sinus node and AV node modulation, and RF pulses were applied in those sites only. After finishing the procedure, significant changes were observed in the heart rate (66-90 b.p.m.), atrial-His interval (115-74 ms), Wenckebach cycle length (820-570 ms), and sinus node recovery time (1100-760 ms). Follow-up Holter recording demonstrated that the number of ventricular pauses had reduced from 438 to 0. Heart rate and time domain characteristics were compatible with vagal denervation.Conclusion: Ablation of the endocardial vagal innervation sites seems to be safe and efficient in reducing the frequency and the length of the ventricular pauses. It was possible by identifying certain spectral components of the atrial electrogram, resulting in a conservative approach. [ABSTRACT FROM AUTHOR]

We attempt to extract a homological structure of two kinds of graphs by the Grover walk. The first one consists of a cycle and two semi-infinite lines, and the second one is assembled by a periodic embedding of the cycles in $$\mathbb {Z}$$ . We show that both of them have essentially the same eigenvalues induced by the existence of cycles in the infinite graphs. The eigenspace of the homological structure appears as so called localization in the Grover walks, in which the walk is partially trapped by the homological structure. On the other hand, the difference of the absolutely continuous part of spectrum between them provides different behaviors. We characterize the behaviors by the density functions in the weak convergence theorem: The first one is the delta measure at the bottom, while the second one is expressed by two kinds of continuous functions, which have different finite supports $$(-1/\sqrt{10},1/\sqrt{10})$$ and $$(-2/7,2/7)$$ , respectively. [ABSTRACT FROM AUTHOR]

The high beam current and subangstrom resolution of aberration-corrected scanning transmission electron microscopes has enabled electron energy loss spectroscopy (EELS) mapping with atomic resolution. These spectral maps are often dose limited and spatially oversampled, leading to low counts/channel and are thus highly sensitive to errors in background estimation. However, by taking advantage of redundancy in the dataset map, one can improve background estimation and increase chemical sensitivity. We consider two such approaches—linear combination of power laws and local background averaging—that reduce background error and improve signal extraction. Principal component analysis (PCA) can also be used to analyze spectrum images, but the poor peak-to-background ratio in EELS can lead to serious artifacts if raw EELS data are PCA filtered. We identify common artifacts and discuss alternative approaches. These algorithms are implemented within the Cornell Spectrum Imager, an open source software package for spectroscopic analysis. [ABSTRACT FROM AUTHOR]

In this paper, we use artificial neural networks (ANNs) for voice conversion and exploit the mapping abilities of an ANN model to perform mapping of spectral features of a source speaker to that of a target speaker. A comparative study of voice conversion using an ANN model and the state-of-the-art Gaussian mixture model (GMM) is conducted. The results of voice conversion, evaluated using subjective and objective measures, confirm that an ANN-based VC system performs as good as that of a GMM-based VC system, and the quality of the transformed speech is intelligible and possesses the characteristics of a target speaker. In this paper, we also address the issue of dependency of voice conversion techniques on parallel data between the source and the target speakers. While there have been efforts to use nonparallel data and speaker adaptation techniques, it is important to investigate techniques which capture speaker-specific characteristics of a target speaker, and avoid any need for source speaker's data either for training or for adaptation. In this paper, we propose a voice conversion approach using an ANN model to capture speaker-specific characteristics of a target speaker and demonstrate that such a voice conversion approach can perform monolingual as well as cross-lingual voice conversion of an arbitrary source speaker. [ABSTRACT FROM AUTHOR]

We consider two fundamental properties in the analysis of two-way tables of positive data: the principle of distributional equivalence, one of the cornerstones of correspondence analysis of contingency tables, and the principle of subcompositional coherence, which forms the basis of compositional data analysis. For an analysis to be subcompositionally coherent, it suffices to analyze the ratios of the data values. A common approach to dimension reduction in compositional data analysis is to perform principal component analysis on the logarithms of ratios, but this method does not obey the principle of distributional equivalence. We show that by introducing weights for the rows and columns, the method achieves this desirable property and can be applied to a wider class of methods. This weighted log-ratio analysis is theoretically equivalent to “spectral mapping”, a multivariate method developed almost 30 years ago for displaying ratio-scale data from biological activity spectra. The close relationship between spectral mapping and correspondence analysis is also explained, as well as their connection with association modeling. The weighted log-ratio methodology is used here to visualize frequency data in linguistics and chemical compositional data in archeology. [ABSTRACT FROM AUTHOR]

Background By studying the spectrum of atrial potentials by fast Fourier transform (FFT) we have found two types of atrial muscle: the compact (CM) and the fibrillar (FM) myocardium. The former presents normal in-phase conduction inferring a great number of cellular connections, long-lasting refractoriness and leftward FFT-shift. The latter shows anisotropic out-of-phase conduction, fewer cellular connections, short refractoriness and a segmented right-FFT-shift. The compact is the normal predominant muscle and the fibrillar is different and may be neural input, vein insertion, interatrial (1A) septum, left atrial (LA) roof, etc. or pathological tissue, being so by loss of cellular connections this is a possible mechanism for conversion of compact into fibrillar-like myocardium. During atrial fibrillation (AF), clusters of FM (AF nests) present higher frequencies than any surrounding tissue. Purpose The purpose was to describe a new method for paroxysmal AF RF-ablation targeting AF nests. Method Forty patients, six control and 34 having idiopathic drug-refractory paroxysmal or persistent AF were studied and treated. Two catheters were placed in the LA by transseptal approach. RF (30–40 J/60–70 °C) was applied to all sites outside the pulmonary veins (PV) presenting right-FFT-shift (AF nests). Results Numerous AF nests were found in 34/34 AF patients and only in 1/6 controls (only in this case it was possible to induce AF despite an absence of AF history). The main FM sites were: LA roof, LA septum, close to the insertion of the superior PV, near the insertion of the inferior PV, LA posterior wall, RA near the superior vena cava insertion, RA lateral and anterior wall and the right IA septum. Ablation of all AF nests near PV insertions resulted in 35 PV isolations. After 9.9 ± 5 months only two AF patients presented relapse of a different AF form (coarse AF) which was very well controlled with medication previously ineffective. The AF was more frequent as the ratio FM/CM increased. Conclusions The RF-ablation of AF nests decreasing the fibrillar/compact myocardium ratio eliminated 94% of the paroxysmal AF in patients in the FU of 9.9 ± 5 months. The AF nests may be easily identified by spectral analysis and seem to be the real AF substrate. Paroxysmal AF may be cured or controlled by applying RF in several places outside the PV and, thereby, avoiding PV stenosis. [ABSTRACT FROM PUBLISHER]

Fusion of the high-spatial-resolution hyperspectral (HHS) image using low-spatial- resolution hyperspectral (LHS) and high-spatial-resolution multispectral (HMS) image is usually formulated as a spatial super-resolution problem of LHS image with the help of an HMS image, and that may result in the loss of detailed structural information. Facing the above problem, the fusion of HMS with LHS image is formulated as a nonlinear spectral mapping from an HMS to HHS image with the help of an LHS image, and a novel cluster-based fusion method using multi-branch BP neural networks (named CF-BPNNs) is proposed, to ensure a more reasonable spectral mapping for each cluster. In the training stage, considering the intrinsic characteristics that the spectra are more similar within each cluster than that between clusters and so do the corresponding spectral mapping, an unsupervised clustering is used to divide the spectra of the down-sampled HMS image (marked as LMS) into several clusters according to spectral correlation. Then, the spectrum-pairs from the clustered LMS image and the corresponding LHS image are used to train multi-branch BP neural networks (BPNNs), to establish the nonlinear spectral mapping for each cluster. In the fusion stage, a supervised clustering is used to group the spectra of HMS image into the clusters determined during the training stage, and the final HHS image is reconstructed from the clustered HMS image using the trained multi-branch BPNNs accordingly. Comparison results with the related state-of-the-art methods demonstrate that our proposed method achieves a better fusion quality both in spatial and spectral domains. [ABSTRACT FROM AUTHOR]