Spectral Correlation Research Articles

Hyperspectral and multispectral images fusion based on pyramid swin transformer

Remote sensing image fusion aims to generate a high spatial resolution hyperspectral image (HR-HSI) by integrating a low spatial resolution hyperspectral image (LR-HSI) and a high spatial resolution multispectral image (HR-MSI). While Convolutional Neural Networks (CNNs) have been widely employed in addressing the HSI-MSI fusion problem, their limited receptive field poses challenges in capturing global relationships within the feature maps. On the other hand, the computational complexity of Transformers hinders their application, especially in dealing with high-dimensional data like hyperspectral images (HSIs). To overcome this challenge, we propose an HSI-MSI fusion method based on the Pyramid Swin Transformer (PSTF). The pyramid design of the PSTF effectively extracts multi-scale information from images. The Spatial-Spectral Crossed Attention (SSCA) module, comprising the Cross Spatial Attention (CSA) and the Spectral Feature Integration (SFI) modules. The CSA module employs a cross-shaped self-attention mechanism, providing greater modeling flexibility for different spatial scales and non-local structures compared to traditional convolutional layers. Meanwhile, the SFI module introduces a global memory block (MB) to select the most relevant low-rank spectral vectors, integrating global spectral information with local spatial–spectral correlation to better extract and preserve spectral information. Additionally, the Separate Feature Extraction (SFE) module enhances the network’s ability to represent image features by independently processing positive and negative parts of shallow features, thus capturing details and structures more effectively and preventing the vanishing gradient problem. Compared with the state-of-the-art (SOTA) methods, experimental results demonstrate the effectiveness of the PSTF method.

Phase analysis of biphoton joint spectra by interference between different SPDC sources

In spontaneous parametric down-conversion, the spectral correlations between the signal and the idler are expressed by the joint spectral amplitude (JSA) function. However, in the standard coincidence measurements, the phase information of the JSA is lost, and only the square of the absolute value of the JSA is recorded, thus preventing full characterization of the biphoton state. Here, we present an experimental technique to investigate the interference of biphoton joint spectral amplitudes, unlocking new avenues in quantum photonics research. Our method explores phase-dependent phenomena within entangled biphoton spectra. This is achieved by simultaneously pumping two structured nonlinear photonic crystals and observing their interference, which reveals previously inaccessible effects with direct intensity measurements. We demonstrate the versatility of our technique by analyzing two types of joint spectra: one exhibiting a two-lobe shape and the other a three-lobe shape. Additionally, we reconstruct the joint spectral amplitudes for both scenarios and observe good agreement with theoretical predictions. These results pave the way for developing advanced quantum communication and information processing technologies using biphoton spectra.

Hierarchical Biclustering of Mouse Pancreas Mass Spectrometry Imaging Data Using Recursive Rank-2 Non-negative Matrix Factorization.

One of the main challenges in mass spectrometry imaging data analysis remains the analysis of m/z-spectra displaying a low signal-to-noise ratio caused by their low abundance, sample preparation, matrix effects, fragmentation, and other artifacts. Additionally, we observe that molecules with a high abundance suppress those with lower intensities and misdirect classical tools for MSI data analysis, such as principal component analysis. As a result, the observed significance of a molecule may not always be directly related to its abundance. In this work, we present a recursive rank-2 non-negative matrix factorization (rr2-NMF) algorithm that automatically returns spectral and spatial visualization of colocalized molecules, both highly and lowly abundant. Using this hierarchical decomposition, our method finds spatial and spectral correlations on different levels of abundances. The quality of the analysis is evaluated on MALDI-TOF data of healthy mouse pancreatic tissue for the annotation of molecules of interest in the lower abundances. The results show interesting findings regarding the functioning and colocalization of certain molecules.

An optimal demodulation frequency band selection method based on spectral coherence fault energy factor for rolling bearing fault diagnosis

Identifying a frequency band with abundant fault information from the original vibration signal under the condition of complex background noise and the interference of other rotating parts is still problematic for envelope spectrum-based bearing diagnosis. To address this issue, a new indicator called spectral coherence fault energy factor (SCFE) to is proposed to select the optimal demodulation frequency band (ODFB) under complex noise interference for bearing fault diagnosis. Firstly, the fast spectral correlation (Fast-SC) is used to analyze the raw vibration signal and generate the Spectral Coherence (SCoh) image. Then, SCFE is employed to locate the initial center frequency from the whole carrier frequency, and the threshold function is used to optimize the center frequency and bandwidth adaptively to obtain ODFB. Finally, the envelope spectrum (ES) is used to identify bearing faults. The effectiveness of the developed method in identifying the fault related frequency band and bearing fault diagnosis is validated and compared using simulated signals with different interference noises. The results of experimental signals demonstrate that the developed indicator can efficiently evaluate bearing failure information, and the presented approach can accurately discriminate the failure-related frequency band and detect different bearing faults compared with the traditional methods.

REXPACO ASDI: joint unmixing and deconvolution of the circumstellar environment by angular and spectral differential imaging

ABSTRACT Angular and spectral differential imaging is an observational technique of choice to investigate the immediate vicinity of stars. By leveraging the relative angular motion and spectral scaling between on-axis and off-axis sources, post-processing techniques can separate residual star light from light emitted by surrounding objects such as circumstellar discs or point-like objects. This paper introduces a new algorithm that jointly unmixes these components and deconvolves disc images. The proposed algorithm is based on a statistical model of the residual star light, accounting for its spatial and spectral correlations. These correlations are crucial yet remain inadequately modelled by existing reconstruction algorithms. We employ dedicated shrinkage techniques to estimate the large number of parameters of our correlation model in a data-driven fashion. We show that the resulting separable model of the spatial and spectral covariances captures very accurately the star light, enabling its efficient suppression. We apply our method to data sets from the Very Large Telescope/Spectro-Polarimetry High-contrast Exoplanet REsearch instrument and compare its performance with standard algorithms (median subtraction, PCA, PACO). We demonstrate that considering the multiple correlations within the data significantly improves reconstruction quality, resulting in better preservation of both disc morphology and photometry. With its unique joint spectral modelling, the proposed algorithm can reconstruct discs with circular symmetry (e.g. rings, spirals) at intensities one million times fainter than the star, without needing additional reference data sets free from off-axis objects.

Uncertainty of evaluation of spectral mismatch correction factor

Abstract Addressing the effect of partially correlated components of spectral irradiance data on spectral integrals poses a substantial challenge. This study employs Monte Carlo methods to conduct an uncertainty analysis of spectral integrals, with a focus on the uncertainty of the spectral mismatch correction factor. The investigation encompasses the primary types of uncertainty contributors of spectral irradiance, based on carefully calibrated FEL lamp measurements. A novel approach is introduced to estimate the impacts of partial correlations among spectral irradiance values at different wavelengths. The findings reveal that uncertainty components arising from partial correlations significantly outweigh those associated with full spectral correlation or complete lack of correlation at different wavelengths. This insight advances our understanding of uncertainty analysis of spectral integrals and underscores the importance of accounting for partial correlations in accurate measurements.

Blind Deblurring Method for CASEarth Multispectral Images Based on Inter-Band Gradient Similarity Prior.

Multispectral remote sensing images contain abundant information about the distribution and reflectance of ground objects, playing a crucial role in target detection, environmental monitoring, and resource exploration. However, due to the complexity of the imaging process in multispectral remote sensing, image blur is inevitable, and the blur kernel is typically unknown. In recent years, many researchers have focused on blind image deblurring, but most of these methods are based on single-band images. When applied to CASEarth satellite multispectral images, the spectral correlation is unutilized. To address this limitation, this paper proposes a novel approach that leverages the characteristics of multispectral data more effectively. We introduce an inter-band gradient similarity prior and incorporate it into the patch-wise minimal pixel (PMP)-based deblurring model. This approach aims to utilize the spectral correlation across bands to improve deblurring performance. A solution algorithm is established by combining the half-quadratic splitting method with alternating minimization. Subjectively, the final experiments on CASEarth multispectral images demonstrate that the proposed method offers good visual effects while enhancing edge sharpness. Objectively, our method leads to an average improvement in point sharpness by a factor of 1.6, an increase in edge strength level by a factor of 1.17, and an enhancement in RMS contrast by a factor of 1.11.

An early fault warning and diagnosis model based on a two-step analysis strategy for wind turbine bearings

Early bearing fault diagnosis plays a crucial role in ensuring the reliability and safety of wind turbines. This process encounters two primary challenges, that is, weak fault characteristics and strong background noise. Consequently, they restrict the effectiveness of conventional diagnosis methods. To address these difficulties, a two-step early fault diagnosis model is proposed based on an anomaly monitoring index [Formula: see text] and improved successive variational mode decomposition-fast spectral correlation (ISVMD-FSC). Initially, [Formula: see text] enables early anomaly detection. Subsequently, the fault type is further identified using ISVMD-FSC. The ISVMD adaptive decomposition of early warning signals can effectively reduce the noise and isolate the fault impact characteristics. Then, the fault characteristic frequency is extracted using spectral coherence analysis to achieve the purpose of fault diagnosis. This model is tested and validated on simulated data, laboratory accelerated bearing life span data, and real high-speed bearing fault data of wind turbines. Notably, the comparative study shows that [Formula: see text] is capable of detecting the fault earlier than the spectral L2/L1 norm, the sum of the weighted normalized square envelope, index 8, and index 9. Additionally, the fault feature extraction effect of ISVMD-FSC is better than many more advanced time-frequency analysis methods. These results emphasize the model’s excellent performance in early bearing fault diagnosis and its good generalization ability.

Mapping models of V1 and V2 selectivity with local spectral reverse correlation

Mapping models of V1 and V2 selectivity with local spectral reverse correlation

Quantum chaos on edge

Recently, the physics of many-body quantum chaotic systems close to their ground states has come under intensified scrutiny. Such studies are motivated by the emergence of model systems exhibiting chaotic fluctuations throughout the entire spectrum [the Sachdev-Ye-Kitaev (SYK) model being a renowned representative] as well as by the physics of holographic principles, which likewise unfold close to ground states. Interpreting the edge of the spectrum as a quantum critical point, here we combine a wide range of analytical and numerical methods to the identification and comprehensive description of two different universality classes: the near edge physics of “sparse” and the near edge of “dense” chaotic systems. The distinction lies in the ratio between the number of a system's random parameters and its Hilbert space dimension, which is exponentially small or algebraically small in the sparse and dense case, respectively. Notable representatives of the two classes are generic chaotic many-body models (sparse) and invariant random matrix ensembles or chaotic gravitational systems (dense). While the two families share identical spectral correlations at energy scales comparable to the level spacing, the density of states and its fluctuations near the edge are different. Considering the SYK model as a representative of the sparse class, we apply a combination of field theory and exact diagonalization to a detailed discussion of its edge spectrum. Conversely, Jackiw-Teitelboim gravity is our reference model for the dense class, where an analysis of the gravitational path integral and random matrix theory reveal universal differences to the sparse class, whose implications for the construction of holographic principles we discuss. Published by the American Physical Society 2024

Emphasizing on the envelope spectra of hydraulic turbomachinery vibration response towards cavitation diagnosis

Cavitation attracts the interest of engineers who target the better understanding of its complex physics and the development of efficient detection tools. This research explores the effectiveness of vibration-based indicators in the prompt and effective diagnosis of cavitation initiating in the rotating flow fields of turbomachinery. The indicators are derived from the envelope spectra estimated with the use of Hilbert Transform, Spectral Kurtosis and Cyclic Spectral Correlation algorithms and data acquired from two semi-open impellers. By utilizing a transparent casing, the onset of cavitation can be observed, enabling the establishment of reliable criteria for evaluating the new indicators. Also, their applicability is assessed across a wide range of flow-rate and suction pressure conditions and in two different geometries. The results demonstrate the consistent ability of the indicators to exploit the high frequency carrier information related with the resonances excited from bubble implosions, to promptly and efficiently detect the phenomenon.

Correlating light fields through disordered media across multiple degrees of freedom

Speckle patterns are inherent features of coherent light propagation through complex media. As a result of interference, they are sensitive to multiple experimental parameters such as the configuration of disorder or the propagating wavelength. Recent developments in wavefront shaping have made it possible to control speckle pattern statistics and correlations, for example using the concept of the transmission matrix. In this article, we address the problem of correlating scattered fields across multiple degrees of freedom. We highlight the common points between the specific techniques already demonstrated, and we propose a general framework based on the singular value decomposition of a linear combination of multiple transmission matrices. Following analytical predictions, we experimentally illustrate the technique on spectral and temporal correlations, and we show that both the amplitude and the phase of the field correlations can be tuned. Our work opens up new perspectives in speckle correlation manipulation, with potential applications in coherent control. Published by the American Physical Society 2024

A lightweight deep learning architecture for automatic modulation classification of wireless internet of things

AbstractThe wireless Internet of Things (IoT) is widely used for data transmission in power systems. Wireless communication is an important part of the IoT. The existing modulation classification algorithms have low classification accuracy when facing strong electromagnetic interference, which causes decoding error link interruption and wastes wireless channel resources. Therefore, it is necessary to study signal modulation classification methods in a low signal‐to‐noise ratio (SNR) environment. In this paper, a lightweight Deep Neural Networks (DNNs) modulation classification method based on the Informer architecture classifier and two‐dimensional (2‐D) curves input of the spectral correlation function (SCF) is proposed, which uses in‐phase and quadrature (I/Q) signals to generate 2‐D cross‐section SCF curve first and then feeds the feature curve into the Informer network to classify the modulation method. This model can better learn the robustness characteristics in a long sequence. Through testing, the classification accuracy of the modulation signal is not much lower than that of the current good classification method when the SNR is 10 dB, and this method can still show higher accuracy when hardware resources are limited. It is a compact design of a modulation classification model and easy to deploy on low‐cost embedded platforms.

Observational Evidence of Particle Acceleration by Relativistic Magnetic Reconnection in Gamma-Ray Bursts

Gamma-ray bursts (GRBs) as the most energetic explosions in the modern universe have been studied for over half a century, but the physics of the particle acceleration and radiation responsible for their observed spectral behaviors are still not well understood. Based on the comprehensive analysis of the pulse properties in both bright GRB 160625B and GRB 160509A, for the first time, we identify evidence of particle acceleration by relativistic magnetic reconnection from the evolutionary behavior of the two spectral breaks E p and E cut. Meanwhile, the adiabatic cooling process of the emitting particles in the magnetic reconnection regions produces a relation between the spectral index and the flux. We also discuss the physics behind spectral energy correlations. Finally, we argue that the identification of an anticorrelation between E cut and L iso may open a new avenue for diagnostics of the physics of the particle acceleration and radiation in a variety of astrophysical sources.

Neural correlates of memory in a naturalistic spatiotemporal context.

We investigated memory encoding and retrieval during a quasinaturalistic spatial-episodic memory task in which subjects delivered items to landmarks in a desktop virtual environment and later recalled the delivered items. Transition probabilities and latencies revealed the spatial and temporal organization of memory. As subjects gained experience with the town, their improved spatial knowledge led to more efficient navigation and increased spatial organization during recall. Subjects who exhibited stronger spatial organization exhibited weaker temporal organization. Scalp-recorded electroencephalographic signals revealed spectral correlates of successful encoding and retrieval. Increased theta power (T+) and decreased alpha/beta power (A-) accompanied successful encoding, with the addition of increased gamma (G+) accompanying successful retrieval. Logistic regression classifiers trained on spectral features reliably predicted mnemonic success in held-out sessions. Univariate and multivariate electroencephalographic analyses revealed a similar spectral T+A-G+ of successful memory. These findings extend behavioral and neural signatures of successful encoding and retrieval to a naturalistic task in which learning occurs within a spatiotemporal context. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Fast computation of the spectral correlation via frequency-averaging

Spectral Correlation (SC) has become a critical analytical instrument for assessing the second-order cyclostationarity (CS) in signals, especially for mechanical vibration signals. Nonetheless, the computational demand for computing SC is considerable. Built upon the Smoothed Cyclic Periodogram (SCP), a consistent estimator named Fast Smoothed Cyclic Periodogram (FastSCP) is proposed to address this issue. FastSCP represents the computational process of SCP through the convolution of spectral components and incorporates the Overlap-Save algorithm for accelerating. Comparison results with other fast SC estimators, including Fast Spectral Correlation (FastSC) and Faster Spectral Correlation (FasterSC), indicate that the proposed method is competitive in terms of both computational cost and memory usage, particularly when dealing with wide cyclic frequency ranges.

Structural stability hypothesis of dual unitary quantum chaos

Having spectral correlations that, over small enough energy scales, are described by random matrix theory is regarded as the most general defining feature of quantum chaotic systems as it applies in the many-body setting and away from any semiclassical limit. Although this property is extremely difficult to prove analytically for generic many-body systems, a rigorous proof has been achieved for dual-unitary circuits—a special class of local quantum circuits that remain unitary upon swapping space and time. Here we consider the fate of this property when moving from dual-unitary to generic quantum circuits focusing on the , i.e., the Fourier transform of the two-point correlation. We begin with a numerical survey that, in agreement with previous studies, suggests that there exists a finite region in parameter space where dual-unitary physics is stable and spectral correlations are still described by random matrix theory, although up to a maximal quasienergy scale. To explain these findings, we develop a perturbative expansion: it recovers the random matrix theory predictions, provided the terms occurring in perturbation theory obey a relatively simple set of assumptions. We then provide numerical evidence and a heuristic analytical argument supporting these assumptions. Published by the American Physical Society 2024

An automated sample generation method by integrating phenology domain optical-SAR features in rice cropping pattern mapping

Accurate spatio-temporal information on rice cropping patterns is vital for predicting grain production, managing water resource and assessing greenhouse gas emissions. However, current automated mapping of rice cropping patterns at regional scale is heavily constrained by insufficient training samples and frequent cloudy weathers in major rice-producing areas. To tackle this challenge, we proposed a Phenology domain Optical-SAR feature inTegration method to Automatically generate single (SC-Rice) and double cropping Rice (DC-Rice) sample (POSTAR) for efficient and refined rice mapping. POSTAR includes three major steps: (1) generating a potential rice map using a phenology- and object-based classification method with optical data (Sentinel-2 MSI) to select candidate rice samples; (2) employing K-means to identify SC- and DC-Rice candidate samples according to unique SAR-based (Sentinel-1 SAR) phenological features; (3) implementing a two-step refinement strategy to filter high-confidence SC- and DC-Rice samples, maintaining a balance between intraclass phenological variance and sample purity. Test areas selected for validation include the Dongting Lake plain and Poyang Lake plain in South China, as well as Fujin county located in the Sanjiang plain of North China. POSTAR proved effective in producing reliable SC- and DC-Rice samples, achieving a high spectral correlation similarity (>0.85) and low dynamic time wrapping distance (<8.5) with field samples. Applying POSTAR-derived samples to random forest classifier yielded an overall accuracy of 89.6%, with F1 score of 0.899 for SC-Rice and 0.938 for DC-Rice in the Dongting Lake plain. Owing to the incorporation of knowledge-based optical and SAR phenological features, POSTAR exhibited strong spatial transferability, achieving an overall accuracy of 96.0% in the Poyang Lake plain and 97.8% in the Fujin county. These results demonstrated the effectiveness of the POSTAR method in accurately mapping rice cropping patterns without extensive field visits, providing valuable insights for crop monitoring in large, diverse, and cloudy regions through the integration of optical and SAR data.

Multilinear compressive learning and reconstruction of hyperspectral data with U-Net architecture for enhanced spectral imaging

Hyperspectral (HSI) data provide vast amounts of data which contain rich spatial and spectral information about each pixel of an image, such information is useful in detecting objects and identifying materials. However, the information provided by the HSI image is challenging to process. Hence, the compression of HSI makes it easier to utilize the data without the need for any heavy computation. Traditional compressive sensing techniques are adequate for lower dimensional signals, but they fail to preserve the essential spectral information provided by HSIs while performing compression. The proposed method aims to implement a Multilinear Compressive Learning (MCL) model for ideal compression of HSIs and a CNN based U-NET architecture is used for effective reconstruction of the compressed signal. The core of the algorithm involves two key blocks: (a) A multidimensional compressive sensing block for performing compression along every dimension, preserving spectral and spatial correlations. The employed sensing matrix is learned with respect to the given data for effective compression, (b) Reconstruction of the signal using a CNN based U-NET algorithm, which consists of encoders and decoders for effective reconstruction of the signal. Band selection is also performed in this technique to optimally select the most informative bands. The proposed model was implemented on the Indian Pines, Pavia University, and Salinas scenes datasets. The compression and reconstruction results were evaluated using performance metrics.

Measuring G protein activation by spectrally resolved imaging fluorescence fluctuation spectroscopy

The activation of heterotrimeric G proteins through G-protein-coupled receptors (GPCRs) is a ubiquitous signaling mechanism in eukaryotic biology. The three principal molecular components of this cascade are the GPCR, Gα subunit, and Gβγ subunit. Measurement of interactions between these components and their downstream effectors in live cells is paramount to understanding how cells fine-tune their physiology in response to many external stimuli. Multicolor fluorescence fluctuation spectroscopy (FFS) approaches allow the sensitive detection of heteromeric interactions by using spectrally distinct fluorophores to label biomolecules of interest. We considered three imaging FFS approaches to measuring molecular interactions from the signals produced by a spectrally resolved confocal microscopy: raster spectral image correlation spectroscopy (RSICS), spectral spatial cumulant analysis, and native resolution spatial cumulant analysis. We characterized these approaches using simulation and experiments on heteromers with known stoichiometries. We found that RSICS had the best sensitivity for measuring heteromeric interactions and employed it to measure G protein complexes. As measured by RSICS, interactions between the G protein subunits Gαi1 and Gβ1γ2 were sensitive to the stimulation of two GPCRs, the D2 dopamine receptor and the α-2A adrenergic receptor. Interactions between GPCRs and G proteins were not detectable above background, supporting a collisional model of GPCR/G protein interactions in contrast to a preassembly model where strong interactions would be present. These data are uniquely available by this FFS framework, which is appropriate for not only multiplexed measurements of G protein biology but any dynamic protein complexes in the cell.