Decorrelation Approach Research Articles

Separation of the topographical and chemical components of the wettability of textured surfaces by femtosecond laser

Contrary to what is usually found in the literature, we show in this work that the contribution of ripples (LSFL LIPSS) to the increase in hydrophobicity of femtosecond laser-textured metal surfaces is negligible. Following the demonstration of wettability according to the Wenzel regime, a methodology for decorrelating the chemical and the topographical components of wettability is implemented. This makes it possible to quantify the respective responsibilities of the chemical and topographical contributions to the increase in contact angle. For femtosecond laser-textured 304L steel, it is shown that the topographical contribution made by ripples is around 6% of the increase in hydrophobicity. In contrast, the chemical contribution accounts for 94% of the increase in contact angle after texturing. The chemistry/topography decorrelation approach is validated by comparing wettability results on PDMS as test-materials.

Anisotropic Bayesian linearized stochastic seismic inversion with multi-parameter decoupling

AbstractThe shale-oil reservoir emerges as a significant unconventional energy source, commonly predicted by anisotropic seismic inversion. Considering the intricate nature of shale-oil reservoirs, it becomes imperative to consider uncertainties during anisotropic inversion. An effective approach to address this involves stochastic inversion, specifically the anisotropic Bayesian linearized inversion (ABLI), which characterizes statistical and spatial correlations of subsurface parameters through a crucial multivariate correlation matrix constructed through geostatistics. However, an inevitable challenge in stochastic inversion arises from interference during the calibration of statistical and spatial correlations of subsurface parameters. This challenge becomes particularly pronounced in anisotropic inversion, heightened by the multitude of involved model parameters. Existing decorrelation approaches primarily address statistical correlation, neglecting the impact of spatial correlation. To tackle this issue, a novel multi-parameter decoupling strategy is proposed, formulating decoupling-ABLI (D-ABLI). D-ABLI introduces an advanced decorrelation approach, and uses principal component analysis (PCA) to simultaneously eliminate impact of statistical and spatial correlations on ABLI. The decoupling enhances the inversion accuracy of model parameters in ABLI, particularly for density and anisotropic parameters. The theoretical underpinnings of the decoupling strategy are demonstrated to be reasonable, and the effectiveness of D-ABLI is proved through a theoretical data test and a field data test regarding shale-oil reservoirs. The D-ABLI results offer the capability to estimate fracture density accurately and unveil the distribution of shale oil.

Genome-wide multimediator analyses using the generalized Berk-Jones statistics with the composite test.

Mediation analysis is performed to evaluate the effects of a hypothetical causal mechanism that marks the progression from an exposure, through mediators, to an outcome. In the age of high-throughput technologies, it has become routine to assess numerous potential mechanisms at the genome or proteome scales. Alongside this, the necessity to address issues related to multiple testing has also arisen. In a sparse scenario where only a few genes or proteins are causally involved, conventional methods for assessing mediation effects lose statistical power because the composite null distribution behind this experiment cannot be attained. The power loss hence decreases the true mechanisms identified after multiple testing corrections. To fairly delineate a uniform distribution under the composite null, Huang (Genome-wide analyses of sparse mediation effects under composite null hypotheses. Ann Appl Stat 2019a;13:60-84; AoAS) proposed the composite test to provide adjusted P-values for single-mediator analyses. Our contribution is to extend the method to multimediator analyses, which are commonly encountered in genomic studies and also flexible to various biological interests. Using the generalized Berk-Jones statistics with the composite test, we proposed a multivariate approach that favors dense and diverse mediation effects, a decorrelation approach that favors sparse and consistent effects, and a hybrid approach that captures the edges of both approaches. Our analysis suite has been implemented as an R package MACtest. The utility is demonstrated by analyzing the lung adenocarcinoma datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium. We further investigate the genes and networks whose expression may be regulated by smoking-induced epigenetic aberrations. An R package MACtest is available on https://github.com/roqe/MACtest.

High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation

The capacity of the downlink channel is a major bottleneck for applications based on remote sensing hyperspectral imagery (HSI). Data compression is an essential tool to maximize the amount of HSI scenes that can be retrieved on the ground. At the same time, energy and hardware constraints of spaceborne devices impose limitations on the complexity of practical compression algorithms. To avoid any distortion in the analysis of the HSI data, only lossless compression is considered in this study. This work aims at finding the most advantageous compression–complexity trade-off within the state of the art in HSI compression. To do so, a novel comparison of the most competitive spectral decorrelation approaches combined with the best performing low-complexity compressors of the state is presented. Compression performance and execution time results are obtained for a set of 47 HSI scenes produced by 14 different sensors in real remote sensing missions. Assuming only a limited amount of energy is available, obtained data suggest that the FAPEC algorithm yields the best trade-off. When compared to the CCSDS 123.0-B-2 standard, FAPEC is 5.0 times faster and its compressed data rates are on average within 16% of the CCSDS standard. In scenarios where energy constraints can be relaxed, CCSDS 123.0-B-2 yields the best average compression results of all evaluated methods.

An adaptive decorrelation procedure for signal detection

In global testing, where a large number of pointwise test statistics are aggregated to simultaneously test for a collection of null hypotheses, the handling of dependence is a crucial issue. In various fields, more particularly in genetic epidemiology and functional data analysis, many testing methods for detecting an association signal between a response and explanatory variables have been proposed. Some aggregation procedures ignore dependence across pointwise test statistics whereas others introduce a model for decorrelation, with unclear conclusions on their relative performance. Indeed, the benefit that can be expected from decorrelation highly depends on the interplay between the structure of dependence across pointwise test statistics and the pattern of the association signal. Within a large class of test statistics covering a continuum of decorrelation approaches, an optimal procedure is introduced. This procedure is based on the maximization of an ad-hoc cumulant generating function-based distance between the null and nonnull distributions of a global test statistic, in order to adapt the aggregation of the pointwise statistics to the pattern of the association signal. A comparative study including simulations and applications to genetic association studies demonstrates that the ability of this test to detect a signal is more robust to the dependence structure than existing methods.

Decorrelation of Previously Communicated Information for an Interacting Multiple Model Filter

In a sensor network compensation of the correlated information caused by previous communication is of utmost interest for distributed estimation. In this article, we investigate an information decorrelation approach that can be applied when using interacting multiple model filters in the sensor nodes for a family of jump Markov linear systems. Implementation issues that might arise while applying the decorrelation approach are addressed in detail. The investigated approach is compared with alternatives on simple distributed single maneuvering target tracking examples.

Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors.

In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.

On the Comparisons of Decorrelation Approaches for Non-Gaussian Neutral Vector Variables.

As a typical non-Gaussian vector variable, a neutral vector variable contains nonnegative elements only, and its l1 -norm equals one. In addition, its neutral properties make it significantly different from the commonly studied vector variables (e.g., the Gaussian vector variables). Due to the aforementioned properties, the conventionally applied linear transformation approaches [e.g., principal component analysis (PCA) and independent component analysis (ICA)] are not suitable for neutral vector variables, as PCA cannot transform a neutral vector variable, which is highly negatively correlated, into a set of mutually independent scalar variables and ICA cannot preserve the bounded property after transformation. In recent work, we proposed an efficient nonlinear transformation approach, i.e., the parallel nonlinear transformation (PNT), for decorrelating neutral vector variables. In this article, we extensively compare PNT with PCA and ICA through both theoretical analysis and experimental evaluations. The results of our investigations demonstrate the superiority of PNT for decorrelating the neutral vector variables.

A Novel Strong Decorrelation Approach for Image Subband Coding Using Polynomial EVD Algorithms

Subband coding is a popular technique to achieve multichannel data compression and efficient data transmission in image and video communications. In this paper, we focus on designing a data-dependent image subband coder which divides the image into strongly (total) decorrelated and spectrally majorized subbands, which seeks to reduce the redundancy to achieve better compression and efficient transmission. To achieve strong decorrelation and spectral majorization between the subbands, we adopt a set of new iterative polynomial eigenvalue decomposition (PEVD) algorithms: sequential matrix diagonalization (SMD) and maximum element sequential matrix diagonalization (ME-SMD). Using this SMD-based PEVD approach, we design the data dependent subband coder (DDSC) for image subband coding. We compare the performance of the proposed SMD/ME-SMD algorithms with the existing DDSC methods like SBR2, SBR2C, K–L transform (KLT) coder and data independent subband coder (DISC)-based discrete wavelet transform (DWT) technique. To measure the performance, we use the parameters like coding gain, correlation coefficient, MSE (mean square error) and peak signal-to-noise ratio (PSNR). The presented simulation results for standard images in the absence of quantization show that the proposed SMD-based PEVD technique performs far better than the existing techniques.

Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images

ABSTRACTManaging transmission and storage of hyperspectral (HS) images can be extremely difficult. Thus, the dimensionality reduction of HS data becomes necessary. Among several dimensionality reduction techniques, transform-based have found to be effective for HS data. While spatial transformation techniques provide good compression rates, the choice of the spectral decorrelation approaches can have strong impact on the quality of the compressed image. Since HS images are highly correlated within each spectral band and in particular across neighboring bands, the choice of a decorrelation method allowing to retain as much information content as possible is desirable. From this point of view, several methods based on PCA and Wavelet have been presented in the literature. In this paper, we propose the use of NLPCA transform as a method to reduce the spectral dimensionality of HS data. NLPCA represents in a lower dimensional space the same information content with less features than PCA. In these terms, aim of this research is focused on the analysis of the results obtained through the spectral decorrelation phase rather than taking advantage of both spectral and spatial compression. Experimental results assessing the advantage of NLPCA with respect to standard PCA are presented on four different HS datasets.

Estimations of the Seismic Pressure Noise on Mars Determined from Large Eddy Simulations and Demonstration of Pressure Decorrelation Techniques for the Insight Mission

The atmospheric pressure fluctuations on Mars induce an elastic response in the ground that creates a ground tilt, detectable as a seismic signal on the InSight seismometer SEIS. The seismic pressure noise is modeled using Large Eddy Simulations (LES) of the wind and surface pressure at the InSight landing site and a Green’s function ground deformation approach that is subsequently validated via a detailed comparison with two other methods: a spectral approach, and an approach based on Sorrells’ theory (Sorrells, Geophys. J. Int. 26:71–82, 1971; Sorrells et al., Nat. Phys. Sci. 229:14–16, 1971). The horizontal accelerations as a result of the ground tilt due to the LES turbulence-induced pressure fluctuations are found to be typically $\sim 2 \mbox{--} 40~\mbox{nm}/\mbox{s}^{2}$ in amplitude, whereas the direct horizontal acceleration is two orders of magnitude smaller and is thus negligible in comparison. The vertical accelerations are found to be $\sim 0.1\mbox{--}6~\mbox{nm}/\mbox{s}^{2}$ in amplitude. These are expected to be worst-case estimates for the seismic noise as we use a half-space approximation; the presence at some (shallow) depth of a harder layer would significantly reduce quasi-static displacement and tilt effects. We show that under calm conditions, a single-pressure measurement is representative of the large-scale pressure field (to a distance of several kilometers), particularly in the prevailing wind direction. However, during windy conditions, small-scale turbulence results in a reduced correlation between the pressure signals, and the single-pressure measurement becomes less representative of the pressure field. The correlation between the seismic signal and the pressure signal is found to be higher for the windiest period because the seismic pressure noise reflects the atmospheric structure close to the seismometer. In the same way that we reduce the atmospheric seismic signal by making use of a pressure sensor that is part of the InSight Auxiliary Payload Sensor Suite, we also the use the synthetic noise data obtained from the LES pressure field to demonstrate a decorrelation strategy. We show that our decorrelation approach is efficient, resulting in a reduction by a factor of $\sim 5$ in the observed horizontal tilt noise (in the wind direction) and the vertical noise. This technique can, therefore, be used to remove the pressure signal from the seismic data obtained on Mars during the InSight mission.

Quantitative characterization of stream turbidity‐discharge behavior using event loop shape modeling and power law parameter decorrelation

AbstractTurbidity behavior in streams is a complex and dynamic function of both source material supply and event‐driven transport. While the primary controls on turbidity behavior across time and space are still not fully understood, recent increases in the availability of high temporal resolution, colocated stream turbidity, and discharge data provide an opportunity for more detailed analysis. Here we examine methods to quantitatively characterize event responses by modeling the shape of turbidity‐discharge hysteresis loops. A total of 1559 events from 20 gages in the Mid‐Atlantic region of the U.S. were modeled using both previously reported models and new models combining elements of existing models. The results suggest that a more general power law‐based model, utilizing both a discharge rate of change term and a “supply” term, allows characterization of a wide range of simple and complex events. Additionally, this study explores a decorrelation approach to address the strong correlation frequently observed between the power law model coefficient ( ) and exponent ( ), with the goal of exposing the underlying behavior of each parameter individually. An examination of seasonal parameter behavior suggests that this approach may facilitate greater physically based interpretation of the power law coefficient. The power law parameter decorrelation strategy and the loop models examined here provide a step toward the larger goal of understanding the physical controls on turbidity‐discharge hysteretic behavior.

Complex pedigrees in the sequencing era: to track transmissions or decorrelate?

Next-generation sequencing (NGS) studies are becoming commonplace, and the NGS field is continuing to develop rapidly. Analytic methods aimed at testing for the various roles that genetic susceptibility plays in disease are also rapidly being developed and optimized. Studies that incorporate large, complex pedigrees are of particular importance because they provide detailed information about inheritance patterns and can be analyzed in a variety of complementary ways. The nine contributions from our Genetic Analysis Workshop 18 working group on family-based tests of association for rare variants using simulated data examined analytic methods for testing genetic association using whole-genome sequencing data from 20 large pedigrees with 200 phenotype simulation replicates. What distinguishes the approaches explored is how the complexities of analyzing familial genetic data were handled. Here, we explore the methods that either harness inheritance patterns and transmission information or attempt to adjust for the correlation between family members in order to utilize computationally and conceptually simpler statistical testing procedures. Although directly comparing these two classes of approaches across contributions is difficult, we note that the two classes balance robustness to population stratification and computational complexity (the transmission-based approaches) with simplicity and increased power, assuming no population stratification or proper adjustment for it (decorrelation approaches).

Genetic prediction in the Genetic Analysis Workshop 18 sequencing data.

High-throughput sequencing data can be used to predict phenotypes from genotypes, and this corresponds to establishing a prognostic model. In extended pedigrees the relatedness of subjects provides additional information so that genetic values, fixed or random genetic components, and heritability can be estimated. At the Genetic Analysis Workshop 18, the working group on genetic prediction dealt with both establishing a prognostic model and, in one contribution, comparing standard logistic regression with robust logistic regression in a sample of unrelated affected or unaffected individuals. Results of both logistic regression approaches were similar. All other contributions to this group used extended family data, in general using the quantitative trait blood pressure. The individual contributions varied in several important aspects, such as the estimation of the kinship matrix and the estimation method. Contributors chose various approaches for model validation, including different versions of cross-validation or within-family validation. Within-family validation included model building in the upper generations and validation in later generations. The choice of the statistical model and the computational algorithm had substantial effects on computation time. If decorrelation approaches were applied, the computational burden was substantially reduced. Some software packages estimated negative eigenvalues, although eigenvalues of correlation matrices should be non-negative. Most statistical models and software packages have been developed for experimental crosses and planned breeding programs. With their specialized pedigree structures, they are not sufficiently flexible to accommodate the variability of human pedigrees in general, and improved implementations are required.

A Combined Approach for Channel Decorrelation in Stereo Acoustic Echo Cancellation Exploiting Time-Varying Frequency Shifting

Multichannel acoustic echo cancellers are used in teleconferencing systems in order to reduce undesired echoes and to introduce better performance in terms of sound localization in the presence of more than one participant. Already stereophonic systems obtain realistic performance, since listeners have spatial information that helps to identify the speaker position. Unfortunately, the correlation between the two channels prevents a correct identification of the echo paths and signal decorrelation is needed. In this letter, a decorrelation approach is proposed, combining the missing-fundamental theory with frequency shifting. Therefore, the proposed approach is based on the computation of an adaptive parameter related to the fundamental frequency that is also used for controlling the desired frequency shift value. Experiments confirm that high performance in terms of magnitude squared coherence reduction and convergence speed increase can be obtained, also comparing it with other approaches of the state of the art and outperforming previous results.

Low-complexity implementation of a real-time decorrelation algorithm for stereophonic acoustic echo cancellation

Multichannel acoustic echo cancellers are used in teleconferencing systems in order to reduce undesired echoes and to introduce better performance in terms of sound localization in the presence of more than one participant. Already, stereophonic systems obtain realistic performance, since listeners have spatial information that helps to identify the speaker position. Unfortunately, the correlation between the two channels prevents a correct identification of the echo paths and signal decorrelation is needed. In this paper, a low-complexity implementation of a real-time decorrelation approach is proposed based on the “missing-fundamental” phenomenon. This implies that the proposed approach is focused on the computation of one adaptive parameter in order to guarantee high performance in terms of convergence speed, audio quality, and computational complexity. Experiments confirm the effectiveness of the proposed approach, showing its feasibility in real scenarios.

A Mixed Decorrelation Approach for Stereo Acoustic Echo Cancellation Based on the Estimation of the Fundamental Frequency

In teleconferencing systems, undesired echoes due to coupling between the loudspeaker and the microphone can be reduced using acoustic echo cancellers (AECs). In order to introduce better performance in terms of sound localization, at least stereophonic AECs (SAECs) have to be employed. In this case, the correlation between the two channels prevents a correct identification of the echo paths and a signal decorrelation is needed. In this paper, a novel mixed approach to signal decorrelation is proposed based on the estimation of the fundamental frequency. The principle of the proposed structure is to use an approach based on the “missing-fundamental” phenomenon at low frequencies (below 500 Hz), a time-varying all-pass filter controlled by the fundamental frequency at medium and high frequencies, and a time-varying multi-notch filter for improving performance in the upper part of the spectrum. Experiments confirm the effectiveness of the proposed approach in terms of correct identification of the echo paths and stereo perception preservation.

Learning to estimate out-of-plane motion in ultrasound imagery of real tissue

In freehand 3D ultrasound (US), the relative positions and orientations of the 2D US images are usually obtained from a position tracking device, at the expense of clinical convenience. As an alternative or complement to this approach, transducer motion can be inferred from image content, using image registration techniques to recover in-plane motion and speckle decorrelation to recover out-of-plane motion. One difficulty with the speckle decorrelation approach is that for real tissues, the rate of speckle decorrelation is not only transducer dependent, but also medium dependent. This paper proposes a novel method for estimating the elevational correlation length of US signals in such media by learning its relationship to in-plane image statistics from a pool of synthetic US imagery generated from virtual phantoms of varied micro-structure. Learning takes place within a sparse Gaussian process regression framework. In experiments with synthetic US imagery and real imagery of animal tissue, the approach is shown to generalise well across transducer and medium changes, with performance better than a method based on speckle classification and comparable to our implementation of the heuristic state-of-the-art method. The proposed approach better lends itself to improvement through the creation of more realistic training sets.

Regularized Solution to Fast GPS Ambiguity Resolution

In rapid global positioning systems (GPS) positioning one of the key problems is to quickly determine the ambiguities of GPS carrier phase observables. Since carrier phase observations are generally collected only for a few minutes in the mode of rapid GPS positioning, the least squares floating solution of the ambiguities will be highly correlated and the decorrelation approach has often been used in order to reduce the search space of integer ambiguities. In this paper we propose a regularized algorithm as an alternative approach to decorrelation, and compute the regularization parameter by minimizing the trace of mean squared errors. Since regularization has been essential to solve inverse ill-posed problems and shown to be very significant in reducing the condition number of normal matrices, we will explore possible applications of regularization for improving the high correlation of the estimated float ambiguities. Numerical experiments with 50 epochs of single frequency observations show that the co...

Correlation-based approach to color image compression

Most coding techniques for color image compression employ a de-correlation approach—the RGB primaries are transformed into a de-correlated color space, such as YUV or YCbCr, then the de-correlated color components are encoded separately. Examples of this approach are the JPEG and JPEG2000 image compression standards. A different method, of a correlation-based approach (CBA), is presented in this paper. Instead of de-correlating the color primaries, we employ the existing inter-color correlation to approximate two of the components as a parametric function of the third one, called the base component. We then propose to encode the parameters of the approximation function and part of the approximation errors. We use the DCT (discrete cosine transform) block transform to enhance the algorithm's performance. Thus the approximation of two of the color components based on the third color is performed for each DCT subband separately. We use the rate-distortion theory of subband transform coders to optimize the algorithm's bits allocation for each subband and to find the optimal color components transform to be applied prior to coding. This pre-processing stage is similar to the use of the RGB to YUV transform in JPEG and may further enhance the algorithm's performance. We introduce and compare two versions of the new algorithm and show that by using a Laplacian probability model for the DCT coefficients as well as down-sampling the subordinate colors, the compression results are further improved. Simulation results are provided showing that the new CBA algorithms are superior to presently available algorithms based on the common de-correlation approach, such as JPEG.