Spectral Variances Research Articles

Dynamic analysis of damping structures considering support stiffness

An analysis method for the random seismic response of viscoelastic damper structures with support is proposed. This method considers the influence of support stiffness and analyzes the dynamic characteristics associated with support stiffness. It also combines the advantages of the complex modal method (CMM) and the pseudo-excitation method (PEM), improving the complex integration operation in calculating the spectral moments and variances of random seismic responses. The support and the viscoelastic damper constitute the equivalent damper. The damper's constitutive relationship and structural dynamic equations are decoupled to calculate conveniently the frequency domain solution of the responses. Second, the quadratic decomposition of the power spectral density function (QD-PSDF) is applied to simplify the power spectrum and obtain the closed-form solution of spectral moments and variances. Finally, the structural dynamic characteristics and the influence of support stiffness on the vibration control effect of the equivalent damper are investigated based on the closed-form solution. The structural dynamic reliability is also discussed according to the first excursion failure criterion. The results demonstrate that the damper's control effect and structural reactions are reduced, while the dynamic reliability is also enhanced when increasing support stiffness. The performance of the structure and damper tends to stabilize when the support coefficient reaches a specific level.

Sea Surface Roughness Determination from Grazing Angle GPS Ocean Observations and Scatterometry Simulations

Measurements of grazing angle GNSS-R ocean reflections combined with meteorological troposphere data are used for retrieval of ocean wave heights and surface roughness parameters. The observational results are compared to multiphase screen simulations for the same atmosphere conditions. The retrieved data from observations and simulations give equal results within the error bounds of the methods. The obtained ocean mean wave-heights are almost proportional to the square of the wind speed when applying a first-order approximation model to the high-wave-number part of the measured GNSS-R power spectra. The spectral variances from the measurements link directly to the ocean surface roughness, which is also verified by the performed multiple phase-screen wave propagation simulations. Thus, grazing angle GNSS-R techniques are an efficient method for determining the ocean state and the conditions in the boundary layer of the troposphere.

Combining hyperspectral imaging techniques with deep learning to aid in early pathological diagnosis of melanoma

BackgroundCutaneous melanoma, an exceedingly aggressive form of skin cancer, holds the top rank in both malignancy and mortality among skin cancers. In early stages, distinguishing malignant melanomas from benign pigmented nevi pathologically becomes a significant challenge due to their indistinguishable traits. Traditional skin histological examination techniques, largely reliant on light microscopic imagery, offer constrained information and yield low-contrast results, underscoring the necessity for swift and effective early diagnostic methodologies. As a non-contact, non-ionizing, and label-free imaging tool, hyperspectral imaging offers potential in assisting pathologists with identification procedures sans contrast agents. MethodsThis investigation leverages hyperspectral cameras to ascertain the optical properties and to capture the spectral features of malignant melanoma and pigmented nevus tissues, intending to facilitate early pathological diagnostic applications. We further enhance the diagnostic process by integrating transfer learning with deep convolutional networks to classify melanomas and pigmented nevi in hyperspectral pathology images. The study encompasses pathological sections from 50 melanoma and 50 pigmented nevus patients. To accurately represent the spectral variances between different tissues, we employed reflectance calibration, highlighting that the most distinctive spectral differences emerged within the 500–675 nm band range. ResultsThe classification accuracy of pigmented tumors and pigmented nevi was 89% for one-dimensional sample data and 98% for two-dimensional sample data. ConclusionsOur findings have the potential to expedite pathological diagnoses, enhance diagnostic precision, and offer novel research perspectives in differentiating melanoma and nevus.

Wind vibration responses of structure with generalized Maxwell model viscoelastic dampers

A new method for analysis of wind vibration response of structures with viscoelastic dampers is presented. Most existing studies focused on the numerical solution, and in this paper, an analytical method based on quadratic orthogonalized power spectral density function (QO-PSDF) was applied to obtain the closed-form solution of the structural dynamic responses with generalized Maxwell model (GMM) viscoelastic damper under Davenport spectral wind excitation. Firstly, the equations in motion of GMM viscoelastic dampers were coupled with the structural dynamic equation, which was decoupled into a linear combination of first-order vibration equations through complex mode method (CMM). Secondly, the frequency response eigenvalue function (FREF) was quadratic orthogonalized, which was combined with power spectral density function (PSDF) of Davenport spectral to obtain a QO-PSDF of the structural dynamic responses, and the exact closed-form solutions of the spectral moments and variances of the dynamic response were successfully acquired using the QO-PSDF. Finally, a 16-storey reinforced concrete building structure equipped with GMM viscoelastic dampers is taken as an example to validate the accuracy and applicability of the closed-form solution through a series of structural responses, including absolute displacements, inter-storey displacements, absolute velocities, inter-storey velocities.

Structural dynamic responses of building structures with non-viscous dampers under Kanai–Tajimi spectrum excitation

Non-viscous dampers are commonly used in building structures for seismic energy dissipation. In this study, a concise analytical method based on the pseudo-excitation method (PEM) was proposed to obtain closed-form solutions of the structural responses of a non-viscous damper system subjected to the Kanai–Tajimi spectrum excitation. For this purpose, first, the ground motion equation of the non-viscous damper system was decoupled using the complex model method (CMM), and the solution expressions for the dynamic responses were unified using the PEM. Second, the power spectral density function (PSDF) of the Kanai–Tajimi spectrum was quadratically orthogonalised using the residue theorem, which was combined with a quadratically orthogonalised frequency response eigenvalue function (FREF) to obtain a quadratic orthogonalised PSDF (QO-PSDF) of the dynamic responses. Subsequently, a concise closed-form solution of spectral moments and variances of the dynamic responses were successfully obtained using the QO-PSDF. Finally, a comparative study was conducted with three test cases to compare the results of the direct frequency response method, traditional PEM, and proposed method, which proved the applicability of the QO-PSDF and the accuracy of the quadratically orthogonalised PEM (QO-PEM). Moreover, the calculation time required in the proposed method was found to be approximately th one-third at of the traditional PEM.

Live Calibration Yields Lively Colors

AbstractNovel display technologies, such as near‐eye and microLED displays, enable exciting new user experiences. However, they also impose big challenges for quality control, especially with substantial production tolerances and spectral variances of light sources.

Improving Urban Impervious Surfaces Mapping through Integrating Statistical Methods and Spectral Mixture Analysis

Impervious surfaces have been widely considered as the key indicator for evaluating urbanization and environmental quality. As one of the most widely applied methods, spectral mixture analysis (SMA) has been commonly used for mapping urban impervious surface fractions. When implementing SMA, the original multispectral remote-sensing reflectance images are served as the foundation and key to successful SMA. However, the limited spectral variances among different land covers from the original reflectance images make it challenging in information extraction and results in unsatisfactory mapping results. To address this issue, a new method has been proposed in this study to improve urban impervious surface mapping through integrating statistical methods and SMA. In particular, two traditional statistical methods, principal component analysis (PCA) and minimum noise fraction rotation (MNF) were applied to highlight the spectral variances among different land covers. Three endmember classes (impervious surface, soil, and vegetation) and corresponding spectra were identified and extracted from the vertices of the 2-D space plots generated by the first three components of each of the statistical analysis methods, PCA and MNF. A new dataset was generated by stacking the first three components of the PCA and MNF (in a total of six components), and a fully constrained linear SMA was implemented to map the fractional impervious surfaces. Results indicate that a promising performance has been achieved by the proposed new method with the systematic error (SE) of −3.45% and mean absolute error (MAE) of 11.52%. Comparative analysis results also show a much better performance achieved by the proposed statistical method-based SMA than the conventional SMA.

Assimilating Observations with Spatially Correlated Errors Using a Serial Ensemble Filter with a Multiscale Approach

AbstractThe serial ensemble square root filter (EnSRF) typically assumes observation errors to be uncorrelated when assimilating the observations one at a time. This assumption causes the filter solution to be suboptimal when the observation errors are spatially correlated. Using the Lorenz-96 model, this study evaluates the suboptimality due to mischaracterization of observation error spatial correlations. Neglecting spatial correlations in observation errors results in mismatches between the specified and true observation error variances in spectral space, which cannot be resolved by inflating the overall observation error variance. As a remedy, a multiscale observation (MSO) method is proposed to decompose the observations into multiple scale components and assimilate each component with separately adjusted spectral error variance. Experimental results using the Lorenz-96 model show that the serial EnSRF, with the help from the MSO method, can produce solutions that approach the solution from the EnSRF with correctly specified observation error correlations as the number of scale components increases. The MSO method is further tested in a two-layer quasigeostrophic (QG) model framework. In this case, the MSO method is combined with the multiscale localization (MSL) method to allow the use of different localization radii when updating the model state at different scales. The combined method (MSOL) improves the serial EnSRF performance when assimilating observations with spatially correlated errors. With adjusted observation error spectral variances and localization radii, the combined MSOL method provides the best solution in terms of analysis accuracy and filter consistency. Prospects and challenges are also discussed for the implementation of the MSO method for more complex models and observing networks.

Deep neural networks based binary classification for single channel speaker independent multi-talker speech separation

Speech separation is an important task of separating a target speech from the mixture signals. Speaker-independent multi-talker speech separation is a challenging task due to unpredictability of the target and interfering speech in the target-interference mixtures. Conventionally, speech separation is used as a signal processing problem, but recently it is formulated as a deep learning problem and discriminative patterns of the speech are learned from the training data. In this paper, we consider the ideal binary mask (IBM) as a supervised binary classification training-target by using fully connected deep neural networks (DNN) for single-channel speaker-independent multi-talker speech separation. The train DNNs is used to estimate IBM training-target. The mean square error (MSE) is used as an objective cost function. Standard backpropagation and Monte-Carlo dropout regularization approaches are used for better generalization and overfitting during training. The estimated training-target is applied to the mixtures to obtain the separated target speech. We have addressed the over-smoothing problem and performed equalization of spectral variances to match the estimated and clean speech features. Our experimental results in various evaluating conditions report that the proposed method outperformed the competing methods in terms of the Perceptual Evaluation of Speech Quality (PESQ), Segmental SNR (SNRSeg), Short-time objective intelligibility (STOI), normalized Frequency weighted SNRSeg (nFwSNRSeg) and HIT-FA rates.

NASA NeMO-Net's Convolutional Neural Network: Mapping Marine Habitats with Spectrally Heterogeneous Remote Sensing Imagery

Recent advances in machine learning and computer vision have enabled increased automation in benthic habitat mapping through airborne and satellite remote sensing. Here, we applied deep learning and neural network architectures in NASA NeMO-Net, a novel neural multimodal observation and training network for global habitat mapping of shallow benthic tropical marine systems. These ecosystems, particularly coral reefs, are undergoing rapid changes as a result of increasing ocean temperatures, acidification, and pollution, among other stressors. Remote sensing from air and space has been the primary method in which changes are assessed within these important, often remote, ecosystems at a global scale. However, such global datasets often suffer from large spectral variances due to the time of observation, atmospheric effects, water column properties, and heterogeneous instruments and calibrations. To address these challenges, we developed an object-based fully convolutional network (FCN) to improve upon the spatial-spectral classification problem inherent in multimodal datasets. We showed that with training upon augmented data in conjunction with classical methods, such as K-nearest neighbors, we were able to achieve better overall classification and segmentation results. This suggests FCNs are able to effectively identify the relative applicable spectral and spatial spaces within an image, whereas pixel-based classical methods excel at classification within those identified spaces. Our spectrally invariant results, based on minimally preprocessed WorldView-2 and Planet satellite imagery, show a total accuracy of approximately 85% and 80%, respectively, over nine classes when trained and tested upon a chain of Fijian islands imaged under highly variable day-to-day spectral inputs.

Frequency Domain Response of Jacket Platforms under Random Wave Loads

This article presents a procedure that simplifies an offshore jacket platform as a non-uniform cantilever beam subjected to an axial force. A Ritz method combined with a pseudo-excitation method is then used to analyze the responses of the jacket platform under random wave loads with the associated power spectral densities, variances and higher spectral moments. The theoretical basis and pertinent governing equations are derived. The proposed procedure not only eases the process of determining the pseudo wave loads, but also requires only the rudimentary structural details that are typically available at the preliminary design stage. Additionally, the merit of the proposed procedure is that the process does not require one to compute the normal modes, which saves time and is particularly convenient for the dynamic-response analysis of a complex structure (such as an offshore platform). An illustrative example based on a three-deck jacket platform is presented to demonstrate the procedure used to obtain the power spectral densities, variances and second spectral moments of jacket-top displacement and the bending moment of the jacket at the mud line. The results obtained are compared with those obtained using a Finite Element Mothed (FEM) model. Based on the findings of the study and good agreement shown in the comparison of results, it is concluded that the proposed method is effective, simple and convenient, and can be a useful tool for the preliminary design analysis of offshore platforms.

Relations between the Spectral Indices and Flux Densities of Eight Blazars

Relations between the flux densities (F) and spectral indices (α) can help us analyze the emission process. In this paper, we choose 8 blazars (0235+164, 0430+052, 1156+295, 3C345, 1308+326, 1413+135, 3C454.3, and 1749+096) from the University of Michigan Radio Observatory (UMRAO) database to study the relations between the spectral indices (α) and flux densities at 14.5GHz (F14.5). The main results are the following. (1) There are strong anticorrelations between α and F14.5, with the correlation coefficient r in the range from -0.33 to -0.87. (2) The α-F14.5 distributions show elliptic appearance, which have been fitted by elliptic curves. (3) For most of the sources, the time intervals of α-F14.5 elliptic circle (Tc) are consistent with the quasi-periodicities calculated by the averaged light curves and spectral variances (PαF¯).

Numerical predictions of wind-induced buffeting vibration for structures by a developed pseudo-excitation method

A numerical analysis method for wind-induced response of structures is presented which is based on the pseudo-excitation method to significantly reduce the computational complexity while preserving accuracy. Original pseudo-excitation method was developed suitable for adoption by combining an effective computational fluid dynamic method which can be used to replace wind tunnel tests when finding important aerodynamic parameters. Two problems investigated are gust responses of a composite wing and buffeting vibration responses of the Tsing Ma Bridge. Atmospheric turbulence effects are modeled by either k–ω shear stress transport or detached eddy simulation. The power spectral responses and variances of the wing are computed by employing the Dryden atmospheric turbulence spectrum and the computed values of the local stress standard deviation of the Tsing Ma Bridge are compared with experimental values. The simulation results demonstrate that the proposed method can provide highly efficient numerical analysis of two kinds of wind-induced responses of structures and hence has significant benefits for wind-induced vibration engineering.

Discriminating normal regions within cancerous hen ovarian tissue using multivariate hyperspectral image analysis.

Identification of subregions under different pathological conditions on cancerous tissue is of great significance for understanding cancer progression and metastasis. Infrared matrix-assisted laser desorption electrospray ionization mass spectrometry (IR-MALDESI-MS) can be potentially used for diagnostic purposes since it can monitor spatial distribution and abundance of metabolites and lipids in biological tissues. However, the large size and high dimensionality of hyperspectral data make analysis and interpretation challenging. To overcome these barriers, multivariate methods were applied to IR-MALDESI data for the first time, aiming at efficiently resolving mass spectral images, from which these results were then used to identify normal regions within cancerous tissue. Molecular profiles of healthy and cancerous hen ovary tissues were generated by IR-MALDESI-MS. Principal component analysis (PCA) combined with color-coding built a single tissue image which summarizes the high-dimensional data features. Pixels with similar color indicated similar composition. PCA results from healthy tissue were further used to test each pixel in cancerous tissue to determine if it is healthy. Multivariate curve resolution-alternating least squares (MCR-ALS) was used to obtain major spatial features existing in ovary tissues, and group molecules with the same distribution patterns simultaneously. PCA as the predominating dimensionality reduction approach captured over 90% spectral variances by the first three PCs. The PCA images show the cancerous tissue is more chemically heterogeneous than healthy tissue, where at least four regions with different m/z profiles can be differentiated. PCA modeling assigns top regions of cancerous tissue as healthy-like. MCR-ALS extracted three and four major compounds from healthy and cancerous tissue, respectively. Evaluating similarities of resolved spectra uncovered the chemical components that were distinct in some regions on cancerous tissue, serving as a supplementary way to differentiate healthy and cancerous regions. Two unsupervised chemometric methods including PCA and MCR-ALS were applied for resolving and visualizing IR-MALDESI-MS data acquired from hen ovary tissues, improving the interpretation of mass spectrometry imaging results. Then possible normal regions were differentiated from cancerous tissue sections. No prior knowledge is required using either chemometric method, so our approach is readily suitable for unstained tissue samples, which allows one to reveal the molecular events happening during disease progression.

Semantic Segmentation for High Spatial Resolution Remote Sensing Images Based on Convolution Neural Network and Pyramid Pooling Module

Semantic segmentation provides a practical way to segment remotely sensed images into multiple ground objects simultaneously, which can be potentially applied to multiple remote sensed related aspects. Current classification algorithms in remotely sensed images are mostly limited by different imaging conditions, the multiple ground objects are difficult to be separated from each other due to high intraclass spectral variances and interclass spectral similarities. In this study, we propose an end-to-end framework to semantically segment high-resolution aerial images without postprocessing to refine the segmentation results. The framework provides a pixel-wise segmentation result, comprising convolutional neural network structure and pyramid pooling module, which aims to extract feature maps at multiple scales. The proposed model is applied to the ISPRS Vaihingen benchmark dataset from the ISPRS 2D Semantic Labeling Challenge. Its segmentation results are compared with previous state-of-the-art method UZ_1, UPB and three other methods that segment images into objects of all the classes (including clutter/background) based on true orthophoto tiles, and achieve the highest overall accuracy of 87.8% over the published performances, to the best of our knowledge. The results validate the efficiency of the proposed model in segmenting multiple ground objects from remotely sensed images simultaneously.

Raman biophysical markers in skin cancer diagnosis.

Raman spectroscopy (RS) has demonstrated great potential for in vivo cancer screening; however, the biophysical changes that occur for specific diagnoses remain unclear. We recently developed an inverse biophysical skin cancer model to address this issue. Here, we presented the first demonstration of in vivo melanoma and nonmelanoma skin cancer (NMSC) detection based on this model. We fit the model to our previous clinical dataset and extracted the concentration of eight Raman active components in 100 lesions in 65 patients diagnosed with malignant melanoma (MM), dysplastic nevi (DN), basal cell carcinoma, squamous cell carcinoma, and actinic keratosis. We then used logistic regression and leave-one-lesion-out cross validation to determine the diagnostically relevant model components. Our results showed that the biophysical model captures the diagnostic power of the previously used statistical classification model while also providing the skin's biophysical composition. In addition, collagen and triolein were the most relevant biomarkers to represent the spectral variances between MM and DN, and between NMSC and normal tissue. Our work demonstrates the ability of RS to reveal the biophysical basis for accurate diagnosis of different skin cancers, which may eventually lead to a reduction in the number of unnecessary excisional skin biopsies performed.

Human and non-human bone identification using FTIR spectroscopy.

Human and non-human identification of unknown skeletal remains is of great importance in forensic and anthropologic contexts. However, the traditional morphological methods for bone species identification are subjective or time-consuming. Here, we utilized Fourier transform infrared (FTIR) spectroscopy and chemometric methods to determinate the spectral variances between human and non-human (i.e., pig, goat, and cow) bones. To simulate real forensic situations as much as possible, fresh, boiled, and decomposed bones were included in this study. Principal component analysis (PCA) results illustrated pig bones were more sensitive to the environmental and external factors than other species studied in this work. Thus, pig bone might not be a suitable proxy for human bone in the study of postmortem changes. More importantly, score plots of PCA results showed clear separation with a slight overlap between the human and non-human fresh bones, but it failed to distinguish the boiled and decomposed bones. Then, partial least squares discriminant analysis (PLS-DA) was employed, and both internal and external validations were conducted to assess its classification ability, which resulted in 99.72 and 99.53% accuracy, respectively. According to the loading plots of PCA and PLS-DA, the spectral diversity was mainly due to the inorganic portion (i.e., carbonates and phosphates), which can remain relatively stable under various conditions. As such, our results illustrate that FTIR spectroscopy could serve as a reliable tool to assist in bone species determination and also has great potential in real forensic cases with natural conditions.

Modeling astronomical adaptive optics performance with temporally filtered Wiener reconstruction of slope data.

We build on a long-standing tradition in astronomical adaptive optics (AO) of specifying performance metrics and error budgets using linear systems modeling in the spatial-frequency domain. Our goal is to provide a comprehensive tool for the calculation of error budgets in terms of residual temporally filtered phase power spectral densities and variances. In addition, the fast simulation of AO-corrected point spread functions (PSFs) provided by this method can be used as inputs for simulations of science observations with next-generation instruments and telescopes, in particular to predict post-coronagraphic contrast improvements for planet finder systems. We extend the previous results presented in Correia and Teixeira [J. Opt. Soc. Am. A31, 2763 (2014)JOAOD60740-323210.1364/JOSAA.31.002763] to the closed-loop case with predictive controllers and generalize the analytical modeling of Rigaut etal. [Proc. SPIE3353, 1038 (1998)PSISDG0277-786X10.1117/12.321649], Flicker [Technical Report (W. M. Keck Observatory, 2007)], and Jolissaint [J. Eur. Opt. Soc.5, 10055 (2010)1990-257310.2971/jeos.2010.10055]. We follow closely the developments of Ellerbroek [J. Opt. Soc. Am. A22, 310 (2005)JOAOD60740-323210.1364/JOSAA.22.000310] and propose the synthesis of a distributed Kalman filter to mitigate both aniso-servo-lag and aliasing errors while minimizing the overall residual variance. We discuss applications to (i)analytic AO-corrected PSF modeling in the spatial-frequency domain, (ii)post-coronagraphic contrast enhancement, (iii)filter optimization for real-time wavefront reconstruction, and (iv)PSF reconstruction from system telemetry. Under perfect knowledge of wind velocities, we show that ∼60 nm rms error reduction can be achieved with the distributed Kalman filter embodying antialiasing reconstructors on 10m class high-order AO systems, leading to contrast improvement factors of up to three orders of magnitude at few λ/D separations (∼1-5λ/D) for a 0 magnitude star and reaching close to one order of magnitude for a 12 magnitude star.

Audio Source Separation in Reverberant Environments Using $\beta$-Divergence-Based Nonnegative Factorization

In Gaussian model-based multichannel audio source separation, the likelihood of observed mixtures of source signals is parametrized by source spectral variances and by associated spatial covariance matrices. These parameters are estimated by maximizing the likelihood through an expectation-maximization algorithm and used to separate the signals by means of multichannel Wiener filtering. We propose to estimate these parameters by applying nonnegative factorization based on prior information on source variances. In the nonnegative factorization, spectral basis matrices can be defined as the prior information. The matrices can be either extracted or indirectly made available through a redundant library that is trained in advance. In a separate step, applying nonnegative tensor factorization, two algorithms are proposed in order to either extract or detect the basis matrices that best represent the power spectra of the source signals in the observed mixtures. The factorization is achieved by minimizing the $\beta$ -divergence through multiplicative update rules. The sparsity of factorization can be controlled by tuning the value of $\beta$ . Experiments show that sparsity, rather than the value assigned to $\beta$ in the training, is crucial in order to increase the separation performance. The proposed method was evaluated in several mixing conditions. It provides better separation quality with respect to other comparable algorithms.

Model-based estimation of late reverberant spectral variance using modified weighted prediction error method

In this paper, we propose a new approach to estimate the late reverberant spectral variance (LRSV) for speech dereverberation in the short-time Fourier transform (STFT) domain. Our approach uses a model-based scheme involving the estimation of a smoothing (shape) parameter and the reverberant-only component of speech. We propose to obtain the shape parameter by using estimates of the spectral variances of the direct-path and reverberant-only components of the speech, which in turn, can be calculated by smoothing coarse estimates of these two components. Furthermore, an accurate estimate of the reverberant-only component is obtained by means of a moving average scheme. In order to obtain the preliminary estimates of the direct-path and reverberant speech components, we employ a modified version of the weighted prediction error (WPE) method. In contrast to the original WPE method, the suggested modification is implemented for shorter processing blocks, each consisting of a number of STFT frames. This block-wise procedure allows for adaptation to moderate changes in environment and makes the proposed approach also suitable for time-varying acoustic scenarios. Performance evaluations with respect to previous LRSV estimation methods demonstrate the superiority of the proposed approach in both time-invariant and time-variantreverberant environments.