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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]

Natural product screening programs have uncovered molecules from diverse natural sources with various biological activities and unique structures. However, much is yet underexplored and additional information is hidden in these exceptional collections. We applied untargeted mass spectrometry approaches to capture the chemical space and dispersal patterns of metabolites from an in-house library of marine cyanobacterial and algal collections. Remarkably, 86% of the metabolomics signals detected were not found in other available datasets of similar nature, supporting the hypothesis that marine cyanobacteria and algae possess distinctive metabolomes. The data were plotted onto a world map representing eight major sampling sites, and revealed potential geographic locations with high chemical diversity. We demonstrate the use of these inventories as a tool to explore the diversity and distribution of natural products. Finally, we utilized this tool to guide the isolation of a new cyclic lipopeptide, yuvalamide A, from a marine cyanobacterium.

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.

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]

Abstract 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.

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]

In this paper, we present a novel framework for a voice conversion (VC) system based on a cyclic recurrent neural network (CycleRNN) and a finely tuned WaveNet vocoder. Even though WaveNet is capable of producing natural speech waveforms when fed with natural speech features, it still suffers from speech quality degradation when fed with oversmoothed features, such as spectral parameters estimated from a statistical model. One way to address this problem is to introduce oversmoothed features while developing a WaveNet model. However, in a VC framework, providing oversmoothed spectral features of a target speaker for WaveNet modeling is not straightforward owing to the difference in the time-sequence alignment from that of a source speaker. To overcome this problem, we propose the use of a cyclic spectral conversion network, i.e., CycleRNN, capable of performing a conversion flow, i.e., source-to-target, and a cyclic flow, i.e., to generate self-predicted target spectra. The CycleRNN spectral model is trained using both conversion and weighted cyclic losses. To finely tune WaveNet, a pretrained multispeaker WaveNet model is optimized using the self-predicted features of the corresponding target speaker of a speaker conversion pair. The experimental results demonstrate that 1) the proposed CycleRNN-based spectral model for WaveNet fine-tuning significantly improves the naturalness of the converted speech waveforms, giving an overall mean opinion score of 3.50; and 2) the proposed model yields the highest speaker conversion accuracy, giving an overall speaker similarity score of 78.33%, which is a significant improvement compared with conventional WaveNet fine-tuning using natural target features.

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]

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]

Aiming at the issue of symmetric flips in the process of 3 D shape registration, this paper developed an efficient shape registration algorithm based on intrinsic symmetric feature detection. Firstly, it constructed intrinsic symmetric point pairs of the model by heat kernel signature( HKS) and geometric constraints. Secondly, based on the spectral embedding space analysis, it extracted the intrinsic symmetric plane of the model and effectively identified the symmetrical properties of the model according to the model surface normal vector, to get intrinsic symmetry point pair. Therefore it presented the consistent spectral symmetry structure of the model. Finally, combining the coherent point drift( CPD) method, this paper implemented the shape registration of non-rigid model based on spectral symmetry. The experimental results show that the matching method is efficient and robust to the non-rigid deformable shape matching. Moreover, the structural features in same category models are also effectively identified. [ABSTRACT FROM AUTHOR]

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]

Natural product screening programs have uncovered molecules from diverse natural sources with various biological activities and unique structures. However, much is yet underexplored and additional information is hidden in these exceptional collections. We applied untargeted mass spectrometry approaches to capture the chemical space and dispersal patterns of metabolites from an in-house library of marine cyanobacterial and algal collections. Remarkably, 86% of the metabolomics signals detected were not found in other available datasets of similar nature, supporting the hypothesis that marine cyanobacteria and algae possess distinctive metabolomes. The data were plotted onto a world map representing eight major sampling sites, and revealed potential geographic locations with high chemical diversity. We demonstrate the use of these inventories as a tool to explore the diversity and distribution of natural products. Finally, we utilized this tool to guide the isolation of a new cyclic lipopeptide, yuvalamide A, from a marine cyanobacterium.

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.

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]

Hyperspectral imaging (HSI) techniques using a newly-developed frame camera were applied to determine internal properties of mango fruits including firmness, total soluble solids (TSS) and titratable acidity (TA). Prediction models were developed using spectral data from relative surface reflectance of 160 fruits in the visible and near infrared (vis/NIR) region of 450–998 nm analysed by PLS regression. For data reduction, MLR analysis showed 16 significant factors for firmness, 17 for TA, and 20 for TSS. The results of MLR did not substantially affect the prediction performance as compared to PLS. An original approach with combined chemometric and HSI data analyses was applied using R programming. Significant correlations were found between HSI data and firmness (R 2 = 0.81 and RMSE = 2.83 N) followed by TA (R 2 = 0.81 and RMSE = 0.24%) and TSS (R 2 = 0.5 and RMSE = 2.0%). Prediction maps of physicochemical qualities were achieved by applying the prediction models to each pixel of HSI to visualise their spatial distribution. The variation of firmness, TSS, and TA within the fruit indicated fruit ripening started from shoulder toward to tip part. From these results, HSI can be used as a non-destructive technique for determining the quality of fruits which could potentially enhance grading capabilities in the industrial handling and processing of mango. [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]

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]

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]

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]