Spatial Localization Properties Research Articles

On the Support of Compressed Modes

Compressed modes are solutions of the Laplace equation with a potential and a subgradient term. The subgradient term comes from addition of an $L^1$ penalty in the corresponding variational principle. This paper presents an analysis of compressed modes, finding the minimizer of the variational principle, showing the spatial localization property of compressed modes, and establishing an upper bound on the volume of their support.

A Container-Based Elastic Cloud Architecture for Pseudo Real-Time Exploitation of Wide Area Motion Imagery (WAMI) Stream

Real-time information fusion based on WAMI (Wide-Area Motion Imagery), FMV (Full Motion Video), and text data is highly desired for many mission critical emergency or military applications. However, due to the huge data rate, it is still infeasible to process streaming WAMI in a real-time manner and achieve the goal of online, uninterrupted target tracking. In this paper, a pseudo-real-time Dynamic Data Driven Applications System (DDDAS) WAMI data stream processing scheme is proposed. Taking advantage of the temporal and spatial locality properties, a divide-and-conquer strategy is adopted to overcome the challenge resulting from the large amount of dynamic data. In the Pseudo Real-time Exploitation of Sub-Area (PRESA) framework, each WAMI frame is divided into multiple sub-areas and specified sub-areas are assigned to the virtual machines in a container-based cloud computing architecture, which allows dynamic resource provisioning to meet the performance requirements. A prototype has been implemented and the experimental results validate the effectiveness of our approach.

A Nonlocal Means for Speckle Reduction of SAR Image With Multiscale-Fusion-Based Steerable Kernel Function

For the robustness of a patch-based metric, the nonlocal means method is widely applied for speckle reduction of synthetic aperture radar (SAR) images, where the similarity computed by the patch-based metric is used as weight, and weighted averaging is used to obtain the true value. However, not knowing the local spatial property, a fixed kernel (e.g., Gaussian kernel or uniform kernel) is always used to compute the weight. This is not good for the preservation of geometrical features (e.g., edges, lines, and points). In this letter, considering the characteristics of SAR imagery, a multiscale-fusion-based steerable kernel function was formed to explore the local spatial property of SAR images. In addition, by combining the kernel function with a ratio-based similarity metric designed with the distribution of the speckle's ratio, a new patch-based metric was formed and used with the nonlocal scheme for speckle reduction. In the experiments, by comparing with two state-of-the-art methods, a reasonable performance was obtained by our method, in terms of speckle reduction and detail preservation.

GPU-accelerated SPH fluids surface reconstruction using two-level spatial uniform grids

An efficient two-level spatial uniform grid structure-based high-quality surface reconstruction method with Marching Cubes (MC) for smoothed particle hydrodynamics (SPH) fluids was presented in this paper. Compared with the traditional way that dividing the simulation domain with uniform grid directly, an enhanced narrow-band approach using the parallel cuckoo hashing method was taken to index the coarse-level surface vertices, hence decrease the memory consumption. Moreover, a two-level spatial uniform grid structure was employed with a scheme of arranging the fine surface vertices, which could preserve the spatial locality property to facilitate the coalesced memory access on the GPU. Our algorithm was designed for parallel architectures, based on which a parallel version of the optimized surface reconstruction was performed on the CUDA platform. In the experiment of comparison to traditional approaches, the results indicated that our surface reconstruction method was more efficient at the same level of quality of the reconstructed surfaces.

Near-field photocurrent nanoscopy on bare and encapsulated graphene.

Optoelectronic devices utilizing graphene have demonstrated unique capabilities and performances beyond state-of-the-art technologies. However, requirements in terms of device quality and uniformity are demanding. A major roadblock towards high-performance devices are nanoscale variations of the graphene device properties, impacting their macroscopic behaviour. Here we present and apply non-invasive optoelectronic nanoscopy to measure the optical and electronic properties of graphene devices locally. This is achieved by combining scanning near-field infrared nanoscopy with electrical read-out, allowing infrared photocurrent mapping at length scales of tens of nanometres. Using this technique, we study the impact of edges and grain boundaries on the spatial carrier density profiles and local thermoelectric properties. Moreover, we show that the technique can readily be applied to encapsulated graphene devices. We observe charge build-up near the edges and demonstrate a solution to this issue.

Surface phonon dispersion on hydrogen-terminated Si(110)-(1 × 1) surfaces studied by first-principles calculations.

We studied the lattice constants, surface-phonon dispersion curves, spectral densities, and displacement vectors of the hydrogen-terminated Si(110)-(1 × 1) [H:Si(110)-(1 × 1)] surface using the first-principles calculations within the framework of density functional theory (DFT). The symmetry of the H:Si(110)-(1 × 1) surface belongs to the two-dimensional space group p2mg, which has two highly symmetric and orthogonal directions, ΓX¯ and ΓX(')¯, with the glide planes along the ΓX¯ direction. Because glide symmetry separates the even and odd surface phonon modes, we mapped the even surface modes in the first surface Brillouin zone (SBZ) and the odd surface modes in the second SBZ using the spectral densities and displacement vectors. The surface phonon modes were analyzed with respect to their physical origin, spatial localization properties, polarization, and the charge density of their electronic states. Our calculated surface phonon modes were in good agreement with recent high-resolution electron-energy-loss spectroscopy data in the first and second SBZs of the ΓX¯ direction. In the SBZ of the ΓX(')¯ direction, our calculated surface phonon modes agree well with the data in the energy region below 65 meV but are not satisfactorily compatible with those in the stretching and bending modes. In addition, we discuss the microscopic nature of the surface phonon dispersion of the H:Si(110)-(1 × 1) surface using the phonon eigen modes.

Image super-resolution employing a spatial adaptive prior model

Super-resolution (SR) methods based on total variation (TV) prior model is a very popular method because of its ability of edge preservation. However, as TV prior model favors a piecewise constant solution, some pseudoedges in the smooth regions which are also called block effect may be produced, especially at high noise levels. In order to overcome such shortcoming, an adaptive SR model based on a new edge indicator is proposed. In our proposed model, a robust trilateral structure tensor by simultaneously considering the spatial similarity, gray similarity and gradient similarity is first constructed to examine the image local pattern, and then we develop a new edge indicator based on the eigenvalues of the trilateral structure tensor to identify the local spatial property of each pixel. Finally, an adaptive prior model controlled by the new edge indicator is proposed, in which the prior model at edges is approximate to the L1 norm in order to preserve edges, while the prior model in smooth regions and noises is approximate to the L2 norm in order to remove the noises. Experimental results on both simulated and real image sequences, our proposed method can better preserve edges and reduce the block effects in smooth regions when compared with the state-of-the-art methods.

Revealing the impact of 17 mutations of human FMO3 protein associated with trimethylaminuria on its local spatial properties: a bioinformatic approach

Background Trimethylaminuria (TMAU) is a rare metabolic disorder manifesting in enormous excretion of trimethylamine (TMA) with urea, sweat and breath that leads to unpleasant body odour similar to rotting fish. TMAU has a strong genetic basis: 18 mutations (associated with 17 amino acid substitutions or chain truncation) of flavincontaining monooxygenase 3 (FMO3) are now recognized as a causative factor of TMAU. Surprisingly, only few of them are related with active site structure, while the molecular basis of other mutations impact on the protein structure is unknown. Moreover, there are no FMO3 models solved experimentally. So, the aim of study was to reveal the effects of 17 known mutations on human FMO3 structure by means of structural bioinformatics techniques.

Rare flavor processes in Maximally Natural Supersymmetry

We study CP-conserving rare flavor violating processes in the recently proposed theory of Maximally Natural Supersymmetry (MNSUSY). MNSUSY is an unusual supersymmetric (SUSY) extension of the Standard Model (SM) which, remarkably, is un-tuned at present LHC limits. It employs Scherk-Schwarz breaking of SUSY by boundary conditions upon compactifying an underlying 5-dimensional (5D) theory down to 4D, and is not well-described by softly-broken $\mathcal{N}=1$ SUSY, with much different phenomenology than the Minimal Supersymmetric Standard Model (MSSM) and its variants. The usual CP-conserving SUSY-flavor problem is automatically solved in MNSUSY due to a residual almost exact $U(1)_R$ symmetry, naturally heavy and highly degenerate 1st- and 2nd-generation sfermions, and heavy gauginos and Higgsinos. Depending on the exact implementation of MNSUSY there exist important new sources of flavor violation involving gauge boson Kaluza-Klein (KK) excitations. The spatial localization properties of the matter multiplets, in particular the brane localization of the 3rd generation states, imply KK-parity is broken and {\it tree-level} contributions to flavor changing neutral currents are present in general. Nevertheless, we show that simple variants of the basic MNSUSY model are safe from present flavor constraints arising from kaon and $B$-meson oscillations, the rare decays $B_{s,d} \to \mu^+ \mu^-$, $\mu \to {\bar e}ee$ and $\mu$-$e$ conversion in nuclei. We also briefly discuss some special features of the radiative decays $\mu \to e \gamma$ and ${\bar B}\to X_s \gamma$. Future experiments, especially those concerned with lepton flavor violation, should see deviations from SM predictions unless one of the MNSUSY variants with enhanced flavor symmetries is realized.

FACE REPRESENTATION WITH GRADIENT ORIENTATIONS AND EULER MAPPING: APPLICATION TO FACE RECOGNITION

This paper proposes a simple, yet very powerful local face representation, called the Gradient Orientations and Euler Mapping (GOEM). GOEM consists of two stages: gradient orientations and Euler mapping. In the first stage, we calculate gradient orientations of a central pixel and get the corresponding orientation representations by performing convolution operator. These representation results display spatial locality and orientation properties. To encompass different spatial localities and orientations, we concatenate all these representation results and derive a concatenated orientation feature vector. In the second stage, we define an explicit Euler mapping which maps the space of the concatenated orientation into a complex space. For a mapping image, we find that the imaginary part and the real part characterize the high frequency and the low frequency components, respectively. To encompass different frequencies, we concatenate the imaginary part and the real part and derive a concatenated mapping feature vector. For a given image, we use the two stages to construct a GOEM image and derive an augmented feature vector which resides in a space of very high dimensionality. In order to derive low-dimensional feature vector, we present a class of GOEM-based kernel subspace learning methods for face recognition. These methods, which are robust to changes in occlusion and illumination, apply the kernel subspace learning model with explicit Euler mapping to an augmented feature vector derived from the GOEM representation of face images. Experimental results show that our methods significantly outperform popular methods and achieve state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition.

Generalized Morse wavelets for the phase evaluation of projected fringe pattern

Generalized Morse wavelets are proposed to evaluate the phase information from projected fringe pattern with the spatial carrier frequency in the x direction. The height profile of the object is determined through the phase change distribution by using the phase of the continuous wavelet transform. The choice of an appropriate mother wavelet is an important step for the calculation of phase. As a mother wavelet, zero order generalized Morse wavelet is chosen because of the flexible spatial and frequency localization property, and it is exactly analytic. Experimental results for the Morlet and Paul wavelets are compared with the results of generalized Morse wavelets analysis.

Content-based adaptive image denoising using spatial information

The maximum a posteriori (MAP) model is widely used in image processing fields, such as denoising, deblurring, segmentation, reconstruction, and others. However, the existing methods usually employ a fixed prior item and regularization parameter for the whole image and ignore the local spatial adaptive properties. Though the non-local total variation model has shown great promise because of exploiting the correlation in the image, the computation cost and memory load are the issues. In this paper, a content-based local spatial adaptive denoising algorithm is proposed. To realize the local spatial adaptive process of the prior model and regularization parameter, first the degraded image is divided into several same-sized blocks and the Tchebichef moment is used to analyze the local spatial properties of each block. Different property prior items and regularization parameters are then applied adaptively to different properties’ blocks. To reduce the computational load in denoising process, the split Bregman iteration algorithm is employed to optimize the non-local total variation model and accelerate the speed of the image denoising. Finally, a set of experiments and performance evaluation using recent image quality assessment index are provided to assess the effectiveness of the proposed method.

A Robust Face Representation Method for Face Recognition

In this paper, a robust face representation method based on multiple gradient orientations for face recognition is proposed. We introduce multiple gradient orientations and compute multiple orientation images which display different spatial locality and orientation properties. Each orientation image is normalized using the “z-score” method, and all normalized vectors are concatenated into an augmented feature vector. The dimensionality of the augmented feature vector is reduced by linear discriminant analysis to yield a low-dimensional feature vector. Experimental results show that our method achieves state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition.

Evidence for sodium metasilicate receptors on the human osteoblast cell surface; spatial localization and binding properties

We report details of the interaction of sodium metasilicate with osteoblast cellular membranes using Fluoresceinphosphatidylethanolamine (FPE) as a fluorescent indicator of membrane interactions. Fluorescence imaging studies of the FPE-based indicator system revealed areas of localized binding that would be consistent with the presence of a structure with 'receptor-like' properties. From these results, it seems unlikely that silica binds 'non-specifically' to the osteoblast surface. Moreover, the receptors are localized into membrane domains. Such regions of the cell membrane could well be structures such as 'rafts' or other such localized domains within the membrane. The binding profile of silica with the osteoblast cell surface takes place with all the characteristics of a receptor-mediated process best represented by a cooperativity (sigmoidal) binding model with a Hill coefficient of 3.6.

Artifacts in regional gravity representations with spherical radial basis functions

With the increasing number of high-resolution gravity observations, which became available in the recent years, global Earth gravity models can be regionally refined. While global gravity models are usually represented in spherical harmonic basis functions with global support, a very promising option to model the regional refinements is the use of spherical radial basis functions with quasi-compact support. These functions are not necessarily orthogonal on a sphere, and usually, more functions are used in regional modelling than minimally needed from a global point of view. This makes the modelling more difficult. Furthermore, no techniques or choices of radial basis functions and other parameters in the regional modelling approach are established so far, as it is the case for global gravity modelling in spherical harmonics. In this article, a closed-loop simulation is used to investigate the mathematical modelling accuracy of different radial basis functions, which are compared to each other. Furthermore, artificial effects, which occur in the modelling results with very low levels of noise on the observations, are investigated. The whole study is performed on synthetic observations of a residual gravitational potential signal for the Himalaya area with different levels of noise. Spherical radial basis functions are a compromise between spatial and frequency localization, which are mutually exclusive. We show that spatial localization properties are even more important than frequency localization in the regional case, even though a band-limited signal is modeled.

The Construction of Smooth Parseval Frames of Shearlets

The shearlet representation has gained increasing recognition in recent years as a frame- work for the efficient representation of multidimensional data. This representation consists of a countable collection of functions defined at various locations, scales and orientations, where the orientations are obtained through the use of shearing matrices. While shearing matrices offer the advantage of preserving the integer lattice and being more appropriate than rotations for digital implementations, the drawback is that the action of the shearing matrices is restricted to cone- shaped regions in the frequency domain. Hence, in the standard construction, a Parseval frame of shearlets is obtained by combining different systems of cone-based shearlets which are projected onto certain subspaces of L 2 (R D ) with the consequence that the elements of the shearlet system corresponding to the boundary of the cone regions lose their good spatial localization property. In this paper, we present a new construction yielding smooth Parseval frame of shearlets for L 2 (R D ). Specifically, all elements of the shearlet systems obtained from this construction are compactly supported and C ∞ in the frequency domain, hence ensuring that the system has also excellent

Heterogeneous filtering of ensemble‐based background‐error variances

AbstractBackground‐error variances estimated from a finite size ensemble of data assimilations are affected by sampling noise, which degrades the accuracy of the variance estimates. Previous work highlighted the close link between the spatial structures of background error and the associated sampling noise, and demonstrated the ability of local spatial averaging to remove this sampling noise.Existing filtering techniques commonly assume a homogeneous smoothing of the estimated variances. However, this assumption can be inadequate to represent error structures of varying scales, e.g. small‐scale errors associated with localized severe weather events. To answer this problem, this article introduces and examines a heterogeneous filter based on the knowledge of the local spatial properties of the sampling noise. The filtering is realized with a diffusion process, and the diffusion coefficient is parametrized according to the local correlation length‐scale of the sampling noise. This enables the diffusion coefficient to vary spatially in such a way as to encourage smoothing in regions where the background error is large scale in preference to regions where the error is small scale.A simulated 1D framework is considered to test the proposed approach. It is shown that the filtering using a spatially varying diffusion coefficient is able to preserve high‐frequency variance structures, while this information tends to be smoothed with homogeneous filtering. The benefits of applying heterogeneous filtering are particularly pronounced with small ensemble sizes and in the vicinity of localized variance maxima. Copyright © 2012 Royal Meteorological Society

3D localized 2D ultrafast J-resolved magnetic resonance spectroscopy: In vitro study on a 7T imaging system

2D Magnetic Resonance Spectroscopy (MRS) is a well known tool for the analysis of complicated and overlapped MR spectra and was therefore originally used for structural analysis. It also presents a potential for biomedical applications as shown by an increasing number of works related to localized in vivo experiments. However, 2D MRS suffers from long acquisition times due to the necessary collection of numerous increments in the indirect dimension (t1). This paper presents the first 3D localized 2D ultrafast J-resolved MRS sequence, developed on a small animal imaging system, allowing the acquisition of a 3D localized 2D J-resolved MRS spectrum in a single scan. Sequence parameters were optimized regarding Signal-to-Noise ratio and spectral resolution. Sensitivity and spatial localization properties were characterized and discussed. An automatic post-processing method allowing the reduction of artifacts inherent to ultrafast excitation is also presented. This sequence offers an efficient signal localization and shows a great potential for in vivo dynamic spectroscopy.

Nonparametric tests of structure for high angular resolution diffusion imaging in Q-space

High angular resolution diffusion imaging data is the observed characteristic function for the local diffusion of water molecules in tissue. This data is used to infer structural information in brain imaging. Nonparametric scalar measures are proposed to summarize such data, and to locally characterize spatial features of the diffusion probability density function (PDF), relying on the geometry of the characteristic function. Summary statistics are defined so that their distributions are, to first-order, both independent of nuisance parameters and also analytically tractable. The dominant direction of the diffusion at a spatial location (voxel) is determined, and a new set of axes are introduced in Fourier space. Variation quantified in these axes determines the local spatial properties of the diffusion density. Nonparametric hypothesis tests for determining whether the diffusion is unimodal, isotropic or multi-modal are proposed. More subtle characteristics of white-matter microstructure, such as the degree of anisotropy of the PDF and symmetry compared with a variety of asymmetric PDF alternatives, may be ascertained directly in the Fourier domain without parametric assumptions on the form of the diffusion PDF. We simulate a set of diffusion processes and characterize their local properties using the newly introduced summaries. We show how complex white-matter structures across multiple voxels exhibit clear ellipsoidal and asymmetric structure in simulation, and assess the performance of the statistics in clinically-acquired magnetic resonance imaging data.

Multiframe image super-resolution adapted with local spatial information

Super-resolution image reconstruction, which has been a hot research topic in recent years, is a process to reconstruct high-resolution images from shifted, low-resolution, degraded observations. Among the available reconstruction frameworks, the maximum a posteriori (MAP) model is widely used. However, existing methods usually employ a fixed prior item and regularization parameter for the entire HR image, ignoring local spatially adaptive properties, and the large computation load caused by the solution of the large-scale ill-posed problem is another issue to be noted. In this paper, a block-based local spatially adaptive reconstruction algorithm is proposed. To reduce the large computation load and realize the local spatially adaptive process of the prior model and regularization parameter, first the target image is divided into several same-sized blocks and the structure tensor is used to analyze the local spatial properties of each block. Different property prior items and regularization parameters are then applied adaptively to different properties' blocks. Experimental results show that the proposed method achieves better performance than methods with a fixed prior item and regularization parameter.