Spectral Approach Research Articles

Refraction-Aware Structure from Motion for Airborne Bathymetry

In this work, we introduce the first pipeline that combines a refraction-aware structure from motion (SfM) method with a deep learning model specifically designed for airborne bathymetry. We accurately estimate the 3D positions of the submerged points by integrating refraction geometry within the SfM optimization problem. This way, no refraction correction as post-processing is required. Experiments with simulated data that approach real-world capturing conditions demonstrate that SfM with refraction correction is extremely accurate, with submillimeter errors. We integrate our refraction-aware SfM within a deep learning framework that also takes into account radiometrical information, developing a combined spectral and geometry-based approach, with further improvements in accuracy and robustness to different seafloor types, both textured and textureless. We conducted experiments with real-world data at two locations in the southern Mediterranean Sea, with varying seafloor types, which demonstrate the benefits of refraction correction for the deep learning framework. We made our refraction-aware SfM open source, providing researchers in airborne bathymetry with a practical tool to apply SfM in shallow water areas.

An intelligent spectral identification approach for the simultaneous detection of endocrine-disrupting chemicals in aquatic environments.

With the rapid progression of industrialization, the application and release of endocrine disruptors (EDCs), including bisphenol A (BPA), octylphenol and nonylphenol have significantly increased, presenting substantial health hazards. Conventional analytical techniques, such as high-performance liquid chromatography and gas chromatography-mass spectrometry, are highly sophisticated but suffer from complex procedures and high costs. To overcome these limitations, this study introduces an innovative spectral methodology for the simultaneous detection of multiple aquatic multicomponent EDCs. By leveraging chemical machine vision, specifically with convolutional neural network (CNN) models, we employed a long-path holographic spectrometer for rapid, cost-effective identification of BPA, 4-tert-octylphenol, and 4-nonylphenol in aqueous samples. The CNN, refined with the ResNet-50 architecture, demonstrated superior predictive performance, achieving detection limits as low as 3.34, 3.71 and 4.36 μg/L, respectively. The sensitivity and quantification capability of our approach were confirmed through the analysis of spectral image Euclidean distances, while its universality and resistance properties were validated by assessments of environmental samples. This technology offers significantly advantages over conventional techniques in terms of efficiency and cost, offering a novel solution for EDC monitoring in aquatic environments. The implications of this research extend beyond improved detection speed and cost reduction, presenting new methodologies for analyzing complex chemical systems and contributing to environmental protection and public health.

Stability analysis study for the time-fractional Galilei invariant advection-diffusion model of distributive order using an efficient hybrid approach

Abstract In this manuscript, a new model of the time-fractional Galilei-invariant advection-diffusion model of distributed order is studied. An efficient hybrid numerical approach with high accuracy is used to estimate this equation. The finite difference numerical method is used to approximate the fractional operator in terms of the time variable and to approximate the integral term of distributed order, the Gaussian–Legendre integration is applied. To obtain a fully discrete numerical approach, we used a spectral element numerical approach, in which Legendre polynomials are used as the basis function. For the proposed numerical approach, the error and stability analysis are studied. For the efficiency of the numerical approach, some numerical examples are presented with graphs and tables.

Direct ion chromatographic method for speciation micro analysis of arsenic forms in industrial samples with “rich” matrix composition

The speciation analysis of arsenic has consistently been a subject of great interest. However, it remains challenging to analyze complex matrix samples that contain both arsenic and interfering components. In this case, it can be hard to choose the right combinations of different instrumental methods, or a separation method followed by detection, which is usually done using a spectral approach (hybrid methods). In the production control of copper electrorefining, the determination of the concentration of As (III) and As (V) helps to improve the quality of the cathode copper produced. This work investigated the possibility of directly determining both arsenic forms and total As in an electrolyte bath using ion chromatography (IC) with conductometric detection. The use of the ion chromatographic approach for the determination of As(V) in complex matrix samples such as copper electrolyte must take into account the presence of potential interferences from anions such as sulphates, sulfites, selenites, selenates, etc. The results revealed that the method is accurate and precise, with As(V) quantification limits of 15 µg.L-1 and detection limits of 5 µg.L-1. This method is suitable for assessing various types of arsenic in the production of electrolytic copper, with the aim of replacing the current technique that requires liquid-liquid extraction and ICP-OES detection. This led to the following improvements: Enhanced efficiency: The method eliminates the need for extensive and time-consuming sample preparation for the initial separation of arsenic forms. At the same time, the method's characteristics are comparable to those of ICP-OES with liquid-liquid extraction, which is often used in the speciation analysis of arsenic. The method is environmentally friendly as it avoids the use of organic and poisonous extractants. The method can simultaneously analyze other anions (PO43-, SO42-, F-, Cl-, etc.) with arsenates with appropriate calibration.

A spectral dominance approach to large random matrices: Part II

This paper is the second of a series devoted to the study of the dynamics of the spectrum of large random matrices. We precise and extend some results of the first part. We study general extensions of the partial differential equation arising to characterize the limit spectral measure of the Dyson Brownian motion. We provide a regularizing result for those generalizations. We also show that several results of part I extend to cases in which there is no spectral dominance property. We then provide several modeling extensions of such models as well as several identities for the Dyson Brownian motion.

Nonmonotone Spectral Analysis for Variational Inclusions

Gradient descent algorithms perform well in convex optimization but can get tied for finding local minima in non-convex optimization. A robust method that combines a spectral approach with nonmonotone line search strategy for solving variational inclusion problems is proposed. Spectral properties using eigenvalues information are used for accelerating the convergence. Nonmonotonic behaviour is exhibited to relax descent property and escape local minima. Nonmonotone spectral conditions leverage adaptive search directions and global convergence for the proposed spectral subgradient algorithm.

Scaling and Depth Variability of Source Parameters in Central and Southern Italy Using Regional Attenuation Models

ABSTRACT We study the scaling between seismic moment and corner frequency, and the spatial variability of the stress drop, in central and southern Italy. We analyze a data set generated by 28,943 earthquakes that occurred between 2005 and 2023 recorded by 1045 temporary or permanent stations. Considering the trade-off between source and propagation effects, we develop a spectral decomposition approach in which several attenuation models are derived for different subregions, and we perform an iterative decomposition to propagate the site amplification constraint to the whole data set. The use of multiple attenuation models has a significant impact on the source parameter estimation, resulting in stress-drop ratios on the order of a factor of 10 compared to values obtained without considering the attenuation differences. Once the attenuation variability is taken into account, the scaling between seismic moment and corner frequency shows different slopes for different subregions, with different degrees of self-similarity breakdown. Furthermore, we observe a clear trend in the slopes with depth for several subregions. Finally, the spatial variability of the stress-drop anomalies with respect to the average values expected for a given seismic moment shows a large lateral variability, which makes it difficult to detect increasing stress-drop trends with depth.

The link between gender inequality, financial development, and economic growth in Nigeria: A spectral Granger causality approach

AbstractThe male and female genders have crucial roles to play in economic advancement. Thus, the main reason for this research is to pinpoint the causal link between gender inequality (GPIN), financial development (FIDX), inflation (INFL), and economic growth (GDPC) in Nigeria from the year 1980–2020 utilizing the Spectral Granger and Pairwise Granger causality methods and “Residual Augmented Least Square (RALS) cointegration. The RALS result show confirms the stationarity properties of the variables. The Spectral Granger causality outcomes show that GPIN granger causes GDPC in the long, medium, and short–term. In addition, the FIDX granger causes GDPC in the long and short–term, while the medium–term impact is insignificant. For INFL, causality only occurs in the long–term. On the other hand, the Pairwise Granger causality results show that GPIN and FIDX granger cause GDPC, while INFL is insignificant. Therefore, if GPIN and FIDX can predict economic expansion, policymakers need to create policies that can reduce diverse forms of inequalities, promote financial development, and reduce inflation to the barest minimum.

“There is plenty of energy at the bottom”: A spectral conversion approach for upconversion-powered water-splitting PEC cell

An avant-garde step towards the use of the long infrared tail of the incident solar radiation with untapped and crucial applications in photocatalysis and energy harvesting schemes is presented: “there is plenty of energy at the bottom”. In detail, a pure photonic approach to enhance titanium dioxide (TiO2) photocatalytic activity is proposed, by using rare-earth doped luminescent glassy materials, capable of performing NIR-to-UV-VIS spectral conversion (up-conversion). Therefore we report infrared-driven boosting of green hydrogen production in a photo-electrochemical water-splitting cell using TiO2 electrodes, revisiting the original design of Fujishima and Honda fifty years later. This proof-of-concept comprises infrared-induced hydrogen and oxygen evolution via water-splitting and determines univocally the role of a solely photonic effect to split water. Thus, the conversion of the incident NIR radiation, before its interaction with the photocatalyst, emerges as a significant contribution to the state-of-the-art for an effective harvest of the near-infrared portions of sunlight.

Optimal Control Allocation for 2D Reaction‐Diffusion Equations With Multiple Locally Distributed Inputs

ABSTRACTIn this article, the problem of stabilization of a 2D unstable parabolic equation with multiple distributed inputs is addressed using a spectral decomposition approach. Furthermore the underlying redundancy of the actuation arrangement is exploited and actively used by introducing a suitable control allocation architecture. In particular, two optimal allocation policies have been considered: gradient descent and linear quadratic allocation. A simulation study supports and illustrates the theoretical findings.

Precision crop mapping: within plant canopy discrimination of crop and soil using multi-sensor hyperspectral imagery

Leveraging diverse optomechanical and imaging technologies for precision agriculture applications is gaining attention in emerging economies. The precise spatial detection of plant objects in farms is crucial for optimizing plant-level nutrition and managing pests and diseases. High-resolution remote sensors mounted on drones have been increasingly deployed for large-scale crop mapping and field variability characterization. While field-level crop identification and crop-soil discrimination have been studied extensively, within-plant canopy discrimination of crop and soil has not been explored in real agricultural farms. The objectives of this study are: (i) adoption and assessment of spectral unmixing for discriminating crop and soil at within-plant canopy level, and (ii) generation of benchmark terrestrial and drone-based hyperspectral datasets for plant or sub-plant level discrimination using various spectral mixture modelling approaches and sources of endmembers. We acquired hyperspectral imagery of vegetable crops using a frame-based sensor mounted on a drone flying at different heights. Further, several linear, non-linear, and sparse-based spectral unmixing methods were used to discriminate plant and soil based on spectral signatures (endmembers) extracted from different spectral libraries prepared using in situ or field, ground-based, and drone-based hyperspectral imagery. The results, validated against pixel-to-pixel ground truth data, indicate an overall crop-soil discrimination accuracy of 99–100%, subject to a combination of endmember source and flying height. The influences of different endmember sources, spatial resolution as indicated by flying height, and inversion algorithms on the quality of estimated abundances are assessed from a verifiable and functionally relevant perspective. The generated hyperspectral datasets and ground truth data can be used for developing and testing new methods for sub-canopy level soil-crop discrimination in various agricultural applications of remote sensing.

A novel tetra hybrid bio-nanofluid model with stenosed artery

Abstract For treating and diagnosing cardiovascular diseases, the field of biomedical engineering is significant because it develops new ways and techniques. Stenosis is the narrowing of an artery, and it leads reduction in the flow rate of blood. This study investigates the blood flow mechanism in an artery using a mathematical model of Carreau nanofluid with four distinct nanoparticles. Tetra nanofluid model produces significant advancement in the simulation of blood flow through the stenosed arteries. The model is capable of predicting the pressure drop and velocity distribution for diagnosing and treating stenosis. The spectral relaxation approach is used to present the model's efficiency and effectiveness, which makes it a suitable method for solving the governing equations of this study. The findings of this study have important implications for the development of new treatments and diagnostic techniques for stenosis and other cardiovascular diseases.

Unified spectral approach for quasinormal modes of Lee-Wick black holes

Unified spectral approach for quasinormal modes of Lee-Wick black holes

Advances in Remote Sensing and Machine Learning Methods for Invasive Plants Study: A Comprehensive Review

This paper provides a comprehensive review of advancements in the detection; evaluation; and management of invasive plant species (IPS) using diverse remote sensing (RS) techniques and machine learning (ML) methods. Analyzing the high-resolution datasets received from drones, satellites, and aerial photography enables the perfect cartography technique and analysis of the spread and various impacts of ecology on IPS. The majority of current research on hyperspectral imaging with unmanned aerial vehicle (UAV) enhanced by ML has significantly improved the accuracy and efficiency of identifying mapping IPS, and it also serves as a powerful instrument for ecological management. The integrative association is essential to manage the alien species better, as researchers from multiple other fields participate in modeling innovative methods and structures. Incorporating advanced technologies like light detection and ranging (LiDAR) and hyperspectral imaging shows potential for improving spatial and spectral analysis approaches and utilizing ML approaches such as a support vector machine (SVM), random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and deep convolutional neural network (DCNN) analysis for detecting complex IPS. The significant results indicate that ML methods, most importantly SVM and RF, are victorious in recognizing the alien species via analyzing RS data. This report emphasizes the importance of continuous research efforts to improve predictive models, fill gaps in our understanding of the connections between climate, urbanization and invasion dynamics, and expands conservation initiatives via utilizing RS techniques. This study also highlights the potential for RS data to refine management plans, enabling the implementation of more efficient strategies for controlling IPS and preserving ecosystems.

Deep spectral improvement for unsupervised image instance segmentation.

Recently, there has been growing interest in deep spectral methods for image localization and segmentation, influenced by traditional spectral segmentation approaches. These methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing the Laplacian of the affinity matrix to obtain eigensegments. However, instance segmentation has received less attention than other tasks within the context of deep spectral methods. This paper addresses that not all channels of the feature map extracted from a self-supervised backbone contain sufficient information for instance segmentation purposes. Some channels are noisy and hinder the accuracy of the task. To overcome this issue, this paper proposes two channel reduction modules, Noise Channel Reduction (NCR) and Deviation-based Channel Reduction (DCR). The NCR retains channels with lower entropy, as they are less likely to be noisy, while DCR prunes channels with low standard deviation, as they lack sufficient information for effective instance segmentation. Furthermore, the paper demonstrates that the dot product, commonly used in deep spectral methods, is not suitable for instance segmentation due to its sensitivity to feature map values, potentially leading to incorrect instance segments. A novel similarity metric called Bray-curtis over Chebyshev (BoC) is proposed to address this issue. This metric considers the distribution of features in addition to their values, providing a more robust similarity measure for instance segmentation. Quantitative and qualitative results on the Youtube-VIS 2019 and OVIS datasets highlight the improvements achieved by the proposed channel reduction methods and using BoC instead of the conventional dot product for creating the affinity matrix. These improvements regarding mean Intersection over Union (mIoU) and extracted instance segments are observed, demonstrating enhanced instance segmentation performance. The code is available on: https://github.com/farnooshar/SpecUnIIS.

A unified spectral approach for quasinormal modes of Morris–Thorne wormholes

Abstract In this paper, we undertake a comprehensive examination of quasinormal modes (QNMs) linked to Morris–Thorne, also known as Bronnikov–Ellis wormholes, delving into scalar, electromagnetic, and gravitational perturbations using the spectral method. Our research corrects inaccuracies previously reported in the literature and addresses areas where the Wentzel–Kramers–Brillouin (WKB) approximation proves inadequate. Moreover, we introduce and evaluate a novel spectral technique designed to consolidate recent advancements in formulating QNM boundary conditions at both the wormhole throat and space-like infinity. This innovative approach bridges critical gaps in existing methodologies and enhances the accuracy and applicability of QNM analysis in the study of wormhole physics.

(In)stability of de Sitter quasinormal mode spectra

We consider how the quasinormal spectrum for the conformal wave operator on the static patch of de Sitter changes in response to the addition of a small potential. Since the quasinormal modes and co-modes are explicitly known, we are able to give explicit formulae for the instantaneous rate of change of each frequency in terms of the perturbing potential. We verify these exact computations numerically using a novel technique extending the spectral hyperboloidal approach of Jaramillo et al. (2021). We propose a definition for a family of pseudospectra that we show capture the instability properties of the quasinormal frequencies.

A multi-layer constrained spectral k-embedded clustering methodology approach for intelligent partitioning of power grid to enhance resiliency in transmission networks

The intentional controlled islanding by intelligent partitioning of the power grid is considered as essential to protect the grid from cascading events, faults, and High Impact Low Probability (HILP) events. To enhance the resilience, stability, and security of the power grid, the proposed model in this paper intentionally divides the affected power network into islands. This paper presents an intelligent partitioning approach to create an islanding solution through multilayer graphs using spectral clustering. The controlled islanding algorithm uses a multi-criteria objective function that considers the correlation coefficients among the frequency of the buses and minimal disturbances in the real and reactive power. The proposed control technique is implemented in two phases. The first phase employs correlation coefficients between frequency of the buses and modularity clustering to identify clusters of coherent buses. During the second stage, all nodes are categorised into groups using Multi-level constrained Spectral Clustering (ML-CSC) to determine the solution for Intentional controlled Islanding that satisfies bus coherency with the minimum level of disruptions to real and reactive power flows across the boundaries. The proposed algorithm for resolving the generator coherency issue and an intelligent islanding solution is demonstrated by simulation experiments conducted on an IEEE 39-bus transmission test system developed in DIGSILENT Powerfactory version 2023. The MATLAB version R2023a is used to construct the ML-CSC control method. The results demonstrated that the proposed ML-CSC algorithm substantially impacts the functioning of the power system, enabling the formation of intelligent islanding during abnormal conditions. Also, the results clearly show that instead of using single-layer spectral clustering, the multi-layer spectral clustering yields a better intentional islanding solution with minimum power flow mismatch which enhances the transient stability of the islands.

On the low-latitude ionospheric irregularities under geomagnetically active and quiet conditions using NavIC observables: A spectral analysis approach

Ionospheric irregularities and associated scintillations under geomagnetically active/quiet conditions have detrimental effects on the reliability and performance of space- and ground-based navigation satellite systems, especially over the low-latitude region. The current work investigates the low-latitude ionospheric irregularities using the phase screen theory and the corresponding temporal Power Spectral Density (PSD) analysis to present an estimate of the outer irregularity scale sizes over these locations. The study uses simultaneous L5 signal C/No observations of NavIC (a set of GEO and GSO navigation satellite systems) near the northern crest of EIA (Indore: 22.52∘N, 75.92∘E, dip: 32.23∘N) and in between the crest and the dip equator (Hyderabad: 17.42∘N, 78.55∘E, dip: 21.69∘N). The study period (2017–2018) covers disturbed and quiet-time conditions in the declining phase of the solar cycle 24. The PSD analysis brings forward the presence of irregularities, of the order of a few hundred meters during weak-to-moderate and quiet-time conditions and up to a few km during the strong event, over both locations. The ROTI values validate the presence of such structures in the Indian region. Furthermore, only for the strong event, a time delay of scintillation occurrence over Indore, with values of 36 min and 50 min for NavIC satellites (PRNs) 5 and 6, respectively, from scintillation occurrence at Hyderabad is observed, suggesting a poleward evolution of irregularity structures. Further observations show a westward propagation of these structures on this day. This study brings forward the advantage of utilizing continuous data from the GEO and GSO satellite systems in understanding the evolution and propagation of the ionospheric irregularities over the low-latitude region.

A Resolution of the Variability Discrepancy Between Peak Ground Acceleration and Spectral Stress Drop

Abstract It has been recognized that the between-event variability of peak ground acceleration (PGA) is significantly smaller than the variability of the stress drop calculated from corner frequency (the spectral stress drop). Resolving this discrepancy is indispensable for improving seismic hazard assessment because the spectral stress drop is considered a fundamental parameter for predicting high-frequency ground motions. This study addresses this paradox for Mw 3.6–7.1 crustal earthquakes in Japan. Two factors are essential for resolving the problem: (1) calculating the spectral stress drop using a high-frequency-fitted corner frequency (called the stress parameter Δσfch) and (2) considering the magnitude dependence of Δσfch. To estimate Δσfch, the source spectra for crustal earthquakes in Japan are obtained using the two-stage spectral ratio method, which enables the estimation of double-corner-frequency spectra. This two-stage approach is more effective for accurately estimating Δσfch than the standard spectral ratio approach that assumes the single-corner frequency model. This study shows that Δσfch increases with increasing magnitude up to Mw∼5.5 and then becomes constant. The variability of Δσfch calculated by considering this magnitude dependence of Δσfch aligns with the between-event variability of PGA. Incorporating a double-corner-frequency model is crucial for predicting ground-motion variability and enhancing seismic hazard assessments.