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X-ray absorption spectroscopy (XAS) today represents a widespread and powerful technique, able to monitor complex systems under in situ and operando conditions, while external variables, such us sampling time, sample temperature or even beam position over the analysed sample, are varied. X-ray absorption spectroscopy is an element-selective but bulk-averaging technique. Each measured XAS spectrum can be seen as an average signal arising from all the absorber-containing species/configurations present in the sample under study. The acquired XAS data are thus represented by a spectroscopic mixture composed of superimposed spectral profiles associated to well-defined components, characterised by concentration values evolving in the course of the experiment. The decomposition of an experimental XAS dataset in a set of pure spectral and concentration values is a typical example of an inverse problem and it goes, usually, under the name of multivariate curve resolution (MCR). In the present work, we present an overview on the major techniques developed to realize the MCR decomposition together with a selection of related results, with an emphasis on applications in catalysis. Therein, we will highlight the great potential of these methods which are imposing as an essential tool for quantitative analysis of large XAS datasets as well as the directions for further development in synergy with the continuous instrumental progresses at synchrotron sources.

Fluvial-deltaic channel sands act as an excellent host for hydrocarbons in various parts of the globe. However, the prediction of channel sand reservoirs is a challenging job for geoscientists. The aim of the present study is to provide a workflow that can be used to delineate fluvial-deltaic sand reservoirs by applying the continuous wavelet transform (CWT) technique of spectral decomposition (SD) on 3D poststack seismic data from the Miano gas field in the Southern Indus Basin, Pakistan. Full spectrum attributes such as amplitude, root-mean-square (RMS) amplitude, coherence and sweetness help to map the fluvial-deltaic sands down to a certain depth, but not at the reservoir level. Nevertheless, the CWT and some conventional seismic attributes, along with the 3D visualization, can be helpful in demarcating the potential sands, thereby predicting the lithology, gas pay zones and the thicknesses of the productive zones. This workflow may serve to mark out the stratigraphic hydrocarbon reservoir compartments within fluvial-deltaic systems in the Southern Indus Basin, Pakistan.

For diseases that affect brain function, such as strokes, post-onset rehabilitation plays a critical role in the wellbeing of patients. MEG is a technique with high temporal and spatial resolution that measures brain functions non-invasively, and it is widely used for clinical applications. Without the ability to concurrently monitor patient brain activity in real-time, the most effective rehabilitation cannot occur. To address this problem, it is necessary to develop a neurofeedback system that can aid rehabilitation in real time; however, doing so requires an analysis method that is quick (less processing time means the patient can better connect the feedback to their mental state), encourages brain-injured patients towards task-necessary neural oscillations, and allows for the spatial location of those oscillation patterns to change over the course of the rehabilitation. As preliminary work to establish such an analysis method, we compared three decomposition methods for their speed and accuracy in detecting event-related synchronization (ERS) and desynchronization (ERD) in a healthy brain during a finger movement task. We investigated FastICA with 10 components, FastICA with 20 components, and spatio-spectral decomposition (SSD). The results showed that FastICA with 10 components was the most suitable for real-time monitoring due to its combination of accuracy and analysis time.

The use of multitaper estimates for spectral proper orthogonal decomposition (SPOD) is explored. Multitaper and multitaper-Welch estimators that use discrete prolate spheroidal sequences (DPSS) as orthogonal data windows are compared to the standard SPOD algorithm that exclusively relies on weighted overlapped segment averaging, or Welch’s method, to estimate the cross-spectral density matrix. Two sets of turbulent flow data, one experimental and the other numerical, are used to discuss the choice of resolution bandwidth and the bias-variance tradeoff. Multitaper-Welch estimators that combine both approaches by applying orthogonal tapers to overlapping segments allow for flexible control of resolution, variance, and bias. At additional computational cost but for the same data, multitaper-Welch estimators provide lower variance estimates at fixed frequency resolution or higher frequency resolution at similar variance compared to the standard algorithm.Graphic abstract

A double-star S[sub.q1,q2] is the graph consisting of the union of two stars, K[sub.1,q1] and K[sub.1,q2] , together with an edge joining their centers. The spectrum for S[sub.q1,q2] -designs, i.e., the set of all the n∈N such that an S[sub.q1,q2] -design of the order n exists, is well-known when q[sub.1] =q[sub.2] =2. In this article, S[sub.2,2] -designs satisfying additional properties are investigated. We determine the spectrum for S[sub.2,2] -designs that can be transformed into (K[sub.4] −e)-designs by a double squash (bi-squash) passing through middle designs whose blocks are copies of a bull (the graph consisting of a triangle and two pendant edges). Here, the use of the difference method enables obtaining cyclic decompositions and determining the spectrum for cyclic S[sub.2,2] -designs that can be purely bi-squashed into cyclic (K[sub.4] −e)-designs (the middle bull designs are also cyclic).

Multi-modal systems extract information about the environment using specialized sensors that are optimized based on the wavelength of the phenomenology and material interactions. To maximize the entropy, complementary systems operating in regions of non-overlapping wavelengths are optimal. VIS-IR (Visible-Infrared) systems have been at the forefront of multi-modal fusion research and are used extensively to represent information in all-day all-weather applications. Prior to image fusion, the image pairs have to be properly registered and mapped to a common resolution palette. However, due to differences in the device physics of image capture, information from VIS-IR sensors cannot be directly correlated, which is a major bottleneck for this area of research. In the absence of camera metadata, image registration is performed manually, which is not practical for large datasets. Most of the work published in this area assumes calibrated sensors and the availability of camera metadata providing registered image pairs, which limits the generalization capability of these systems. In this work, we propose a novel end-to-end pipeline termed DeepFusion for image registration and fusion. Firstly, we design a recursive crop and scale wavelet spectral decomposition (WSD) algorithm for automatically extracting the patch of visible data representing the thermal information. After data extraction, both the images are registered to a common resolution palette and forwarded to the DNN for image fusion. The fusion performance of the proposed pipeline is compared and quantified with state-of-the-art classical and DNN architectures for open-source and custom datasets demonstrating the efficacy of the pipeline. Furthermore, we also propose a novel keypoint-based metric for quantifying the quality of fused output.

Fluvial sands host excellent oil and gas reservoirs in various fields throughout the world. However, the lateral heterogeneity of reservoir properties within these reservoirs can be significant and determining the distribution of good reservoirs is a challenge. This study attempts to predict sand distribution within fluvial depositional systems by applying the Continuous Wavelet Transformation technique of spectral decomposition along with full spectrum seismic attributes, to a 3D seismic data set in the Pattani Basin, Gulf of Thailand. Full spectrum seismic attributes such as root mean square and coherency help to effectively map fluvial systems down to certain depth below which imaging is difficult in the intervals of interest in this study. However, continuous wavelet transform used in conjunction with other attributes by applying visualization techniques of transparency and RGB can be used at greater depths to extract from 3D seismic data useful information of fluvial depositional elements. This workflow may help to identify different reservoir compartments within the fluvial systems of the Gulf of Thailand.