Singular Domain Research Articles

Robust image watermarking technique using support vector regression for blind geometric distortion correction in lifting wavelet transform and singular value decomposition domain

Robust image watermarking technique using support vector regression for blind geometric distortion correction in lifting wavelet transform and singular value decomposition domain

No-reference quality assessment of DIBR-synthesized videos by measuring temporal flickering

Depth-image-based-rendering (DIBR) is the most popular view synthesis method. The rendering process in DIBR introduces artifacts to synthesized views. Temporal inconsistency of these artifacts causes flickering, which is one of the dominant distortions in DIBR-synthesized videos. We propose a no-reference quality index for DIBR-synthesized videos. A two-stage flickering region detection method is first proposed by calculating the gradient distance between the matching blocks in adjacent frames followed by a refinement operation. Subsequently, the distortion intensity of flickering regions is measured in singular value decomposition domain. The average flickering intensity is computed as the quality score. Experimental results show that our method outperforms the state-of-the-arts on the IRCCyN/IVC database and ranks the second on the SIAT database, which is only slightly worse than the best-performing one. Furthermore, the presented method is applied to improve the performances of existing metrics and benchmark DIBR algorithms, which achieves very promising results for both tasks.

Comparative inhibitory profile and distribution of bacterial PARPs, using Clostridioides difficile CD160 PARP as a model

Poly-ADP-ribose polymerases (PARPs) are involved in the regulation of important cellular processes, such as DNA repair, aging and apoptosis, among others. They have been considered as promising therapeutic targets, since human cancer cells carrying BRCA1 and BRCA2 mutations are highly sensitive to human PARP-1 inhibitors. Although extensive work has been carried out with the latter enzyme, little is known on bacterial PARPs, of which only one has been demonstrated to be active. To extend this limited knowledge, we demonstrate that the Gram-positive bacterium Clostridioides difficile CD160 PARP is a highly active enzyme with a high production yield. Its phylogenetic analysis also pointed to a singular domain organization in contrast to other clostridiales, which could be due to the long-term divergence of C. difficile CD160. Surprisingly, its PARP becomes the first enzyme to be characterized from this strain, which has a genotype never before described based on its sequenced genome. Finally, the inhibition study carried out after a high-throughput in silico screening and an in vitro testing with hPARP1 and bacterial PARPs identified a different inhibitory profile, a new highly inhibitory compound never before described for hPARP1, and a specificity of bacterial PARPs for a compound that mimics NAD+ (EB-47).

Noise suppression for microseismic data by non‐subsampled shearlet transform based on singular value decomposition

ABSTRACTThe existence of strong random noise in surface microseismic data may decrease the utility of these data. Non‐subsampled shearlet transform can effectively suppress noise by properly setting a threshold to the non‐subsampled shearlet transform coefficients. However, when the signal‐to‐noise ratio of data is low, the coefficients related to the noise are very close to the coefficients associated with signals in the non‐subsampled shearlet transform domain that the coefficients related to the noise will be retained and be treated as signals. Therefore, we need to minimise the overlapping coefficients before thresholding. In this paper, a singular value decomposition algorithm is introduced to the non‐subsampled shearlet transform coefficients, and low‐rank approximation reconstructs each non‐subsampled shearlet transform coefficient matrix in the singular value decomposition domain. The non‐subsampled shearlet transform coefficients of signals have bigger singular values than those of the random noise, which implies that the non‐subsampled shearlet transform coefficients can be well estimated by taking only a few largest singular values. Therefore, those properties of singular value decomposition may significantly help minimise overlapping of noise and signals coefficients in the non‐subsampled shearlet transform domain. Finally, the denoised microseismic data are obtained easily by giving a simple threshold to the reconstructed coefficient matrix. The performance of the proposed method is evaluated on both synthetic and field microseismic data. The experimental results illustrate that the proposed method can eliminate random noise and preserve signals of interest more effectively.

Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

Wind induced noise is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. In this paper, a new method to mitigate wind induced noise in microphone signals is developed. Instead of applying filtering techniques, wind induced noise is statistically separated from wanted signals in a singular spectral subspace. The paper is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling. The method includes two complementary stages, namely decomposition and reconstruction. The first stage decomposes mixed signals in eigen-subspaces, selects and groups the principal components according to their contributions to wind noise and wanted signals in the singular spectrum domain. The second stage reconstructs the signals in the time domain, resulting in the separation of wind noise and wanted signals. Results show that microphone wind noise is separable in the singular spectrum domain evidenced by the weighted correlation. The new method might be generalized to other outdoor sound acquisition applications.

Detail-Preserving Image Denoising via Adaptive Clustering and Progressive PCA Thresholding

This paper proposes a detail-preserving image denoising method via cluster-wise progressive principal component analysis (PCA) thresholding based on the Marchenko–Pastur (MP) law in random matrix theory. According to random matrix theory, an efficient and stable noise-level estimation method is also presented. Specifically, a global Gaussian noise level is estimated by interpreting the relationship between noise and eigenvalues of PCA for noisy patch matrices via the MP law in conjunction with the observation that vectors extracted from a noise-free image often lie in a low-dimensional subspace. Before noise removal, an adaptive clustering method is developed to automatically determine a suitable number of clusters segregating patches with different features (edges and textures). To denoise each cluster matrix, progressive PCA thresholding is performed. First, a hard thresholding of singular values in the singular value decomposition domain based on the MP law is applied to find a low-rank approximation to the cluster matrix. Second, the remaining noises of the low-rank matrix are further removed in the PCA transform domain using a special soft thresholding, i.e., the linear minimum mean-square-error technique with locally estimated parameters. The experiments show that the proposed method not only achieves state-of-the-art denoising performance in terms of quantitative indices, but also preserves visually important image details best.

On efficient abstract method for the study of Cauchy problem for a second order differential equation set on a singular cylindrical domain

On efficient abstract method for the study of Cauchy problem for a second order differential equation set on a singular cylindrical domain

Imperceptible watermarking for security of fundus images in tele-ophthalmology applications and computer-aided diagnosis of retina diseases

Background and objectivesThe authentication and integrity verification of medical images is a critical and growing issue for patients in e-health services. Accurate identification of medical images and patient verification is an essential requirement to prevent error in medical diagnosis. The proposed work presents an imperceptible watermarking system to address the security issue of medical fundus images for tele-ophthalmology applications and computer aided automated diagnosis of retinal diseases. MethodsIn the proposed work, patient identity is embedded in fundus image in singular value decomposition domain with adaptive quantization parameter to maintain perceptual transparency for variety of fundus images like healthy fundus or disease affected image. In the proposed method insertion of watermark in fundus image does not affect the automatic image processing diagnosis of retinal objects & pathologies which ensure uncompromised computer-based diagnosis associated with fundus image. Patient ID is correctly recovered from watermarked fundus image for integrity verification of fundus image at the diagnosis centre. ResultsThe proposed watermarking system is tested in a comprehensive database of fundus images and results are convincing. Experimentalresults indicate that proposed watermarking method is imperceptible and it does not affect computer vision based automated diagnosis of retinal diseases. ConclusionsCorrect recovery of patient ID from watermarked fundus image makes the proposed watermarking system applicable for authentication of fundus images for computer aided diagnosis and Tele-ophthalmology applications.

P.7.f.002 - Prospective, observational cohort study of JAKE™, an autism knowledge

The authentication and integrity verification of medical images is a critical and growing issue for patients in e-health services. Accurate identification of medical images and patient verification is an essential requirement to prevent error in medical diagnosis. The proposed work presents an imperceptible watermarking system to address the security issue of medical fundus images for tele-ophthalmology applications and computer aided automated diagnosis of retinal diseases.In the proposed work, patient identity is embedded in fundus image in singular value decomposition domain with adaptive quantization parameter to maintain perceptual transparency for variety of fundus images like healthy fundus or disease affected image. In the proposed method insertion of watermark in fundus image does not affect the automatic image processing diagnosis of retinal objects & pathologies which ensure uncompromised computer-based diagnosis associated with fundus image. Patient ID is correctly recovered from watermarked fundus image for integrity verification of fundus image at the diagnosis centre.The proposed watermarking system is tested in a comprehensive database of fundus images and results are convincing.results indicate that proposed watermarking method is imperceptible and it does not affect computer vision based automated diagnosis of retinal diseases.Correct recovery of patient ID from watermarked fundus image makes the proposed watermarking system applicable for authentication of fundus images for computer aided diagnosis and Tele-ophthalmology applications.

No-Reference and Robust Image Sharpness Evaluation Based on Multiscale Spatial and Spectral Features

The human visual system exhibits multiscale characteristic when perceiving visual scenes. The hierarchical structures of an image are contained in its scale space representation, in which the image can be portrayed by a series of increasingly smoothed images. Inspired by this, this paper presents a no-reference and robust image sharpness evaluation (RISE) method by learning multiscale features extracted in both the spatial and spectral domains. For an image, the scale space is first built. Then sharpness-aware features are extracted in gradient domain and singular value decomposition domain, respectively. In order to take into account the impact of viewing distance on image quality, the input image is also down-sampled by several times, and the DCT-domain entropies are calculated as quality features. Finally, all features are utilized to learn a support vector regression model for sharpness prediction. Extensive experiments are conducted on four synthetically and two real blurred image databases. The experimental results demonstrate that the proposed RISE metric is superior to the relevant state-of-the-art methods for evaluating both synthetic and real blurring. Furthermore, the proposed metric is robust, which means that it has very good generalization ability.

Optimized interface conditions in domain decomposition methods to solve reaction–diffusion problems with strong heterogeneity in the coefficients in a sectorial domain

The aim of this paper is to derive appropriate second order transmission boundary conditions near the corner used in domain decomposition methods to study the reaction–diffusion problems (“−∇.(ν(x)∇.)+η(x).”) with strong heterogeneity in the coefficients in a singular non-convex domain with Neumann and Dirichlet boundary condition. These transmission conditions will be tested and compared numerically with other approaches.

Topological derivative-based topology optimization of structures subject to design-dependent hydrostatic pressure loading

In this paper the topological derivative concept is applied in the context of compliance topology optimization of structures subject to design-dependent hydrostatic pressure loading under volume constraint. The topological derivative represents the first term of the asymptotic expansion of a given shape functional with respect to the small parameter which measures the size of singular domain perturbations, such as holes, inclusions, source-terms and cracks. In particular, the topological asymptotic expansion of the total potential energy associated with plane stress or plane strain linear elasticity, taking into account the nucleation of a circular inclusion with non-homogeneous transmission condition on its boundary, is rigorously developed. Physically, there is a hydrostatic pressure acting on the interface of the topological perturbation, allowing to naturally deal with loading-dependent structural topology optimization. The obtained result is used in a topology optimization algorithm based on the associated topological derivative together with a level-set domain representation method. Finally, some numerical examples are presented, showing the influence of the hydrostatic pressure on the topology of the structure.

Reduced order modeling strategies for computational multiscale fracture

The paper proposes some new computational strategies for affordably solving multiscale fracture problems through a FE2 approach. To take into account the mechanical effects induced by fracture at the microstructure level the Representative Volume Element (RVE), assumed constituted by an elastic matrix and inclusions, is endowed with a large set of cohesive softening bands providing a good representation of the possible microstructure crack paths. The RVE response is then homogenized in accordance with a model previously developed by the authors and upscaled to the macro-scale level as a continuum stress–strain constitutive equation, which is then used in a conventional framework of a finite element modeling of propagating fracture.For reduced order modeling (ROM) purposes, the RVE boundary value problem is first formulated in displacement fluctuations and used, via the Proper Orthogonal Decomposition (POD), to find a low-dimension space for solving the reduced problem. A domain separation strategy is proposed as a first technique for model order reduction: unconventionally, the low-dimension space is spanned by a basis in terms of fluctuating strains, as primitive kinematic variables, instead of the conventional formulation in terms of displacement fluctuations. The RVE spatial domain is then decomposed into a regular domain (made of the matrix and the inclusions) and a singular domain (constituted by cohesive bands), the required RVE boundary conditions are rephrased in terms of strains and imposed via Lagrange multipliers in the corresponding variational problem. Specific low-dimensional strain basis is then derived, independently for each domain, via the POD of the corresponding strain snapshots.Next step consists of developing a hyper-reduced model (HPROM). It is based on a second proposed technique, the Reduced Optimal Quadrature (ROQ) which, again unconventionally, is determined through optimization of the numerical integration of the primitive saddle-point problem arising from the RVE problem, rather than its derived variational equations, and substitutes the conventional Gauss quadrature. The ROQ utilizes a very reduced number of, optimally placed, sampling points, the corresponding weights and placements being evaluated through a greedy algorithm. The resulting low-dimensional and reduced-quadrature variational problem translates into very relevant savings on the computational cost and high computational speed-ups.Particular attention is additionally given to numerical tests and performance evaluations of the new hyper-reduced methodology, by “a-priori” and “a-posteriori” error assessments. Moreover, for the purposes of validation of the present techniques, a real structural problem exhibiting propagating fracture at two-scales is modeled on the basis of the strain injection-based multiscale approach previously developed by the authors. The performance of the proposed strategy, in terms of speed-up vs. error, is deeply analyzed and reported.

Fast analytical spectral filtering methods for magnetic resonance perfusion quantification.

The deconvolution in the perfusion weighted imaging (PWI) plays an important role in quantifying the MR perfusion parameters. The PWI application to stroke and brain tumor studies has become a standard clinical practice. The standard approach for this deconvolution is oscillatory-limited singular value decomposition (oSVD) and frequency domain deconvolution (FDD). The FDD is widely recognized as the fastest approach currently available for deconvolution of MR perfusion data. In this work, two fast deconvolution methods (namely analytical fourier filtering and analytical showalter spectral filtering) are proposed. Through systematic evaluation, the proposed methods are shown to be computationally efficient and quantitatively accurate compared to FDD and oSVD.

EMPIRICAL MODELLING OF ACTIVATED SLUDGE PROCESS VIA SYSTEM IDENTIFICATION

Activated sludge process is an important stage in Wastewater Treatment Plant (WWTP). In this study, model of the activated sludge process from Bunus Regional Sewage Treatment Plant Kuala Lumpur, Malaysia is developed. This paper focuses on modelling and model reduction of the WWTP system. The model with best fits of higher than 80% and the order of less than 10 is selected. For modelling purposes, data obtained is stimulated and modelled using System Identification technique which employ linear model ARX. For model reduction purposes, the high order model is reduced using model order reduction (MOR) of a combination of Singular Perturbation Approximation (SPA) and Frequency Domain Gramian based Model Reduction (FDIG) method. From the modelling results obtained, the ARX model with best fit of 85.11% is selected. Meanwhile, for the MOR FD-SPA technique, a 9th order model is selected with reduction error between frequencies 0.05 rad/s and 1.4 rad/s.

A posteriori error analysis for a fractional differential equation

ABSTRACTNumerical treatment for a fractional differential equation (FDE) is proposed and analysed. The solution of the FDE may be singular near certain domain boundaries, which leads to numerical difficulty. We apply the upwind finite difference method to the FDE. The stability properties and a posteriori error analysis for the discrete scheme are given. Then, a posteriori adapted mesh based on a posteriori error analysis is established by equidistributing arc-length monitor function. Numerical experiments illustrate that the upwind finite difference method on a posteriori adapted mesh is more accurate than the method on uniform mesh.

New trend in electron holography

Electron holography using a coherent electron wave is a promising technique for high-resolution visualization of electromagnetic fields in and around objects. The capability of electron holography has been enhanced by the development of new technologies and has thus become an even more powerful tool for exploring scientific frontiers. This review introduces these technologies including split-illumination electron holography and vector-field electron tomography. Split-illumination electron holography, which uses separated coherent waves, overcomes the limits imposed by the lateral coherence requirement for electron waves in electron holography. Areas that are difficult to observe using conventional electron holography are now observable. Exemplified applications include observing a singular magnetic domain wall in electrical steel sheets, local magnetizations at anti-phase boundaries, and electrostatic potentials in metal-oxide-semiconductor field-effect transistors. Vector-field electron tomography can be used to visualize magnetic vectors in three dimensions. Two components of the vectors are reconstructed using dual-axis tomography, and the remaining one is calculated using div B = 0. A high-voltage electron microscope can be used to achieve precise magnetic reconstruction. For example, magnetic vortices have been visualized using a 1 MV holography electron microscope.

Topological derivatives applied to fluid flow channel design optimization problems

Topology optimization methods application for viscous flow problems is currently an active area of research. A general approach to deal with shape and topology optimization design is based on the topological derivative. This relatively new concept represents the first term of the asymptotic expansion of a given shape functional with respect to the small parameter which measures the size of singular domain perturbations, such as holes and inclusions. In previous topological derivative-based formulations for viscous fluid flow problems, the topology is obtained by nucleating and removing holes in the fluid domain which creates numerical difficulties to deal with the boundary conditions for these holes. Thus, we propose a topological derivative formulation for fluid flow channel design based on the concept of traditional topology optimization formulations in which solid or fluid material is distributed at each point of the domain to optimize the cost function subjected to some constraints. By using this idea, the problem of dealing with the hole boundary conditions during the optimization process is solved because the asymptotic expansion is performed with respect to the nucleation of inclusions --- which mimic solid or fluid phases --- instead of inserting or removing holes in the fluid domain, which allows for working in a fixed computational domain. To evaluate the formulation, an optimization problem which consists in minimizing the energy dissipation in fluid flow channels is implemented. Results from considering Stokes and Navier-Stokes are presented and compared, as well as two- (2D) and three-dimensional (3D) designs. The topologies can be obtained in a few iterations with well defined boundaries.

Neoliberalism, Governmentality, and Ethnography: A rejoinder

Mitchell Dean and I agree there is an important discussion to be had about Foucault and the social sciences. My recent article sought to advance this debate. Dean has responded because he views my article as an attack on his work. This interpretation is mistaken. My article is not focused on Dean’s work though, as he points out, it does make reference to what he has called his “very simplified framework” for engaging in an analytics of governmentality.1 My article seeks to explain the logics that underpin recent efforts to use ethnographic methods (broadly defined) together with an analytics of governmentality. The arguments that motivate these “ethnographic” works have been advanced by various scholars for a number of years. However, the visibility of these works has been reduced by the fact that they have been scattered across various social science disciplines. My article also locates these works within broader debates about governmentality studies, including common criticisms. In his response, Dean pursues four key criticisms of my article, suggesting that it: (1) makes an untenable claim that Foucault was a realist sociologist “seeking to access the complexity of everyday life”2; (2) claims ethnography has “special access to the real in the form of ‘actual people’”3; (3) accuses the governmentality literature of a tendency towards “cookie-cutter” analysis, but fails to name an author or work that displays this weakness; and (4) discusses what Dean has referred to as his “simplified framework” in Governmentality: Power and Rule in Modern Society4 but fails to consider his other works. I will address each of these points in turn. First, Dean devotes three pages to advancing the claim that my article argues that Foucault was a realist who addressed the “sociological reality of the singular ontological domain of practice”5 and who sought “to access the complexity of everyday life,”6 with

Topological derivative-based topology optimization of structures subject to multiple load-cases

The topological derivative measures the sensitivity of a shape functional with respect to an infinitesimal singular domain perturbation, such as the insertion of holes, inclusions or source-terms. The topological derivative has been successfully applied in obtaining the optimal topology for a large class of physics and engineering problems. In this paper the topological derivative is applied in the context of topology optimization of structures subject to multiple load-cases. In particular, the structural compliance under plane stress or plane strain assumptions is minimized under volume constraint. For the sake of completeness, the topological asymptotic analysis of the total potential energy with respect to the nucleation of a small circular inclusion is developed in all details. Since we are dealing with multiple load-cases, a multi-objective optimization problem is proposed and the topological sensitivity is obtained as a sum of the topological derivatives associated with each load-case. The volume constraint is imposed through the Augmented Lagrangian Method. The obtained result is used to devise a topology optimization algorithm based on the topological derivative together with a level-set domain representation method. Finally, several finite element-based examples of structural optimization are presented.