Spatial Sets Research Articles

Near-optimal probabilistic search using spatial Fourier sparse set

Autonomous search is an essential research topic for rescue and other robotic applications. However, searching for targets efficiently is still an unsolved problem. To achieve this objective, a robot needs to simultaneously maximize environmental coverage, maximize probability of detection (PD) and minimize motion cost. The problems associated with these objectives are NP-hard. This research reformulates the three objective functions as a maximum cumulative PD problem with motion cost. Since the PD function depends on the environment, the robot needs to both learn the PD function and the cost-to-go (CTG) function. This research proposes a reinforcement learning algorithm to learn the PD and CTG functions simultaneously. Since the PD function is sparse in the Fourier domain under certain subgoal patterns, spatial Fourier sparse set is proposed to learn PD functions based on the compressed sensing technique. The learned PD and CTG functions can then be used to generate subgoals that achieve $$(1-1/e)$$ of the optimum due to the submodularity. Experiments conducted with this algorithm demonstrate that the robot can search for the target faster than prior learning approaches (e.g., PMAC and FSS) and the benchmark model (e.g., PD).

Combining measured sites, soilscapes map and soil sensing for mapping soil properties of a region

The limited availability of soil information has been recognized as a main limiting factor in digital soil mapping (DSM) studies. It is therefore important to optimize the joint use of the three sources of soil data that can be used as inputs of DSM models, namely spatial sets of measured sites, soil maps and soil sensing products.In this paper, we propose to combine these three inputs, through a cokriging with a categorical external drift (CKCED). This new interpolation technique was applied for mapping seven soil properties over a 24.6km2 area located in the vineyard plain of Languedoc (Southern France), using an hyperspectral imagery product as example of a soil sensing data. Cross-validation results of CKCED were compared with those of five spatial and non-spatial techniques using one of these inputs or a combination of two of them.The results obtained in the La Peyne Catchment showed i) the utility of soil map and hyperspectral imagery products as auxiliary data for improving soil property predictions ii) the greater added-value of the latter against the former in most situations and iii) the feasibility and the interest of CKCED in a limited number of soil properties and data configurations. Testing CKCED in case study with soil maps of better quality and soil sensing techniques covering more area and depths should be necessary to better evaluate the benefits of this new technique.

Highly Variable Genomic Landscape of Endogenous Retroviruses in the C57BL/6J Inbred Strain, Depending on Individual Mouse, Gender, Organ Type, and Organ Location.

Transposable repetitive elements, named the “TREome,” represent ~40% of the mouse genome. We postulate that the germ line genome undergoes temporal and spatial diversification into somatic genomes in conjunction with the TREome activity. C57BL/6J inbred mice were subjected to genomic landscape analyses using a TREome probe from murine leukemia virus-type endogenous retroviruses (MLV-ERVs). None shared the same MLV-ERV landscape within each comparison group: (1) sperm and 18 tissues from one mouse, (2) six brain compartments from two females, (3) spleen and thymus samples from four age groups, (4) three spatial tissue sets from two females, and (5) kidney and liver samples from three females and three males. Interestingly, males had more genomic MLV-ERV copies than females; moreover, only in the males, the kidneys had higher MLV-ERV copies than the livers. Perhaps, the mouse-, gender-, and tissue/cell-dependent MLV-ERV landscapes are linked to the individual-specific and dynamic phenotypes of the C57BL/6J inbred population.

Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering

The classification of hyperspectral images with a few labeled samples is a major challenge which is difficult to meet unless some spatial characteristics can be exploited. In this study, we proposed a novel spectral-spatial hyperspectral image classification method that exploited spatial autocorrelation of hyperspectral images. First, image segmentation is performed on the hyperspectral image to assign each pixel to a homogeneous region. Second, the visible and infrared bands of hyperspectral image are partitioned into multiple subsets of adjacent bands, and each subset is merged into one band. Recursive edge-preserving filtering is performed on each merged band which utilizes the spectral information of neighborhood pixels. Third, the resulting spectral and spatial feature band set is classified using the SVM classifier. Finally, bilateral filtering is performed to remove “salt-and-pepper” noise in the classification result. To preserve the spatial structure of hyperspectral image, edge-preserving filtering is applied independently before and after the classification process. Experimental results on different hyperspectral images prove that the proposed spectral-spatial classification approach is robust and offers more classification accuracy than state-of-the-art methods when the number of labeled samples is small.

Transmission principle and geometrical model of eccentric face gear

Noncircular gears acquire wide application in various filed. A novel spatial noncircular gear set comprised of a cylinder gear and an eccentric face gear is presented, which has advantages of light weight, high interchangeability and convenient installation. The transmission mechanism of the eccentric face gear is expounded by kinematical analysis, with the pitch curves and drive ratio deduced. Based on the spatial meshing theory of gears, the mathematical model of the tooth surface of the eccentric face gear is built. Defining the basic design parameters of the eccentric face gear, an kinematical simulation and tooth contact analysis of the eccentric face gear and a cylinder gear are made, which demonstrate the feasibility of the transmission and the correctness of the mathematical model of the eccentric face gear.

Multi-objective optimization design of a multi-layer honeycomb sandwich structure under blast loading

In order to improve the shielding performance of the underbody protective structure of military vehicles when subjected to explosive events, a multi-layer honeycomb sandwich structure is proposed. Full consideration of the computing response of the underbody protective structure under blast loading is a large-scale and strongly non-linear problem; a reasonably simplified finite element model is constructed in this paper. LS-DYNA software was employed to simulate blast loading by using the *LOAD_BLAST equation and to compute the dynamic responses of the vehicle; then, full-scale experiments were performed to validate the accuracy of the numerical simulation. The geometric dimensions and the shape parameters of the multi-layer honeycomb sandwich structure are selected as the design variables, thereby establishing a response surface and a mathematical optimization model by employing the design-of-experiments method. A Pareto spatial optimal set is obtained by applying a multi-objective genetic algorithm. Eventually, using the normal-boundary intersection algorithm an optimum design was obtained, which can apparently enhance the shielding performance of the underbody protective structure of military vehicles without increasing the mass.

Transmission Optimized Generalized Spatial Modulation Over Correlated Fading Channels

Generalized spatial modulation (GSM) has shown its promising performance as an alternative to large-scale multiple-input multiple-output technology for future wireless communications. A novel transmission optimized GSM (TOGSM) is proposed based on minimizing the average bit error probability (ABEP) over spatial correlated fading environments. First, a union upper bound and a high signal-to-noise ratio (SNR) approximation of ABEP for traditional GSM with multi-stream transmission is derived when assuming maximum likelihood detection at the receiver side. In contrast, the new analytical result relaxes basic constraints on the channel model and the symbol modulation type in use when measuring the ABEP of SM. Then, TOGSM is presented through seeking the solution of an optimization problem for particular channel realizations, where the best combination of spatial modulation set and symbol modulation set can be obtained at a given spectral efficiency. Finally, the accuracy of the ABEP upper bound and its high SNR approximation is verified by Monte Carlo simulations. In addition, the performance advantages of TOGSM over the other schemes, such as fixed-GSM, V-BLAST and generalized space shift keying, are comparatively illustrated via some numerical examples.

A Combined Spatial Fuzzy C-Means and Level Set Approach for Endocardium Segmentation in MRI Image Series

A Combined Spatial Fuzzy C-Means and Level Set Approach for Endocardium Segmentation in MRI Image Series

Constructing local information feature for spatial image steganalysis

Feature is a key part for steganalysis. In this paper we propose a spatial feature set for image steganalysis, named Local Information Feature (LIF), to increase the diversity of spatial steganalysis feature and improve its performance. It also provide a heuristic framework for designing steganalysis feature through 3 steps. It first collects local information from its local region consisting of adjacent pixels. Then according to certain rules, it maps each pixel to its corresponding local type by its local information. Finally, the feature set is formed by adaptive weighted statistical histograms of local types. We design two schemes for LIF, each of which can generate different feature sets using different methods of local information computing. Experimental results show that our feature is effective for detecting stego images embedded by adaptive steganography. We also discussed some possible method to extent the feature designing based on LIF.

Assessing spatial and temporal variability of acid-extractable organics in oil sands process-affected waters

The acid-extractable organic compounds (AEOs), including naphthenic acids (NAs), present within oil sands process-affected water (OSPW) receive great attention due to their known toxicity. While recent progress in advanced separation and analytical methodologies for AEOs has improved our understanding of the composition of these mixtures, little is known regarding any variability (i.e., spatial, temporal) inherent within, or between, tailings ponds. In this study, 5 samples were collected from the same location of one tailings pond over a 2-week period. In addition, 5 samples were collected simultaneously from different locations within a tailings pond from a different mine site, as well as its associated recycling pond. In both cases, the AEOs were analyzed using SFS, ESI-MS, HRMS, GC×GC-ToF/MS, and GC- & LC-QToF/MS (GC analyses following conversion to methyl esters). Principal component analysis of HRMS data was able to distinguish the ponds from each other, while data from GC×GC-ToF/MS, and LC- and GC-QToF/MS were used to differentiate samples from within the temporal and spatial sample sets, with the greater variability associated with the latter. Spatial differences could be attributed to pond dynamics, including differences in inputs of tailings and surface run-off. Application of novel chemometric data analyses of unknown compounds detected by LC- and GC-QToF/MS allowed further differentiation of samples both within and between data sets, providing an innovative approach for future fingerprinting studies.

CSPS: An Adaptive Pooling Method for Image Classification

This paper proposes an adaptive approach to learn class-specific pooling shapes (CSPS) for image classification. Prevalent methods for spatial pooling are often conducted on predefined grids of images, which is an ad-hoc method and, thus, lacks generalization power across different categories. In contrast, our CSPS is designed in a data-driven fashion by generating plenty of candidates and selecting the optimal subset for each class. Specifically, we establish an overcomplete spatial shape set that preserves as many geometric patterns as possible. Then, the class-specific subset is selected by training a linear classifier with structured sparsity constraints and color distribution cues. To address the high computational cost and the risk of overfitting due to the overcomplete scheme, the image representations for CSPS are first compressed according to dictionary sensitivity and shape importance. These representations are finally fed to SVMs for the classification task. We demonstrate that CSPS can learn compact yet discriminative geometric information for different classes that carries more semantic meaning than other methods. Experimental results on four datasets demonstrate the benefits of the proposed method compared with other pooling schemes and illustrate its effectiveness on both object and scene images.

IAEA Isotope-enabled coupled catchment–lake water balance model, IWBMIso: description and validation

ABSTRACTThe International Atomic Energy Agency (IAEA) Water Balance Model with Isotopes (IWBMIso) is a spatially distributed monthly water balance model that considers water fluxes and storages and their associated isotopic compositions. It is composed of a lake water balance model that is tightly coupled with a catchment water balance model. Measured isotope compositions of precipitation, rivers, lakes, and groundwater provide data that can be used to make an improved estimate of the magnitude of the fluxes among the model components. The model has been developed using the Object Modelling System (OMS). A variety of open source geographic information systems and web-based tools have been combined to provide user support for (1) basin delineation, characterization, and parameterization; (2) data pre-processing; (3) model calibration and application; and (4) visualization and analysis of model results. In regions where measured data are limited, the model can use freely available global data sets of climate, isotopic composition of precipitation, and soils and vegetation characteristics to create input data files and estimate spatially distributed model parameters. The OMS model engine and support functions, and the spatial and web-based tool set are integrated using the Colorado State University Environmental Risk Assessment and Management System (eRAMS) framework. The IWBMIso can be used to assess the spatial and temporal variability of annual and monthly water balance components for input to water planning and management.

Finding a needle by removing the haystack: A spatio-temporal normalization method for geophysical data

We introduce a normalization algorithm which highlights short-term, localized, non-periodic fluctuations in hyper-temporal satellite data by dividing each pixel by the mean value of its spatial neighbourhood set. In this way we suppress signal patterns that are common in the central and surrounding pixels, utilizing both spatial and temporal information at different scales. We test the method on two subsets of a hyper-temporal thermal infra-red (TIR) dataset. Both subsets are acquired from the SEVIRI instrument onboard the Meteosat-9 geostationary satellite; they cover areas with different spatiotemporal TIR variability. We impose artificial fluctuations on the original data and apply a window-based technique to retrieve them from the normalized time series. We show that localized short-term fluctuations as low as 2K, which were obscured by large-scale variable patterns, can be retrieved in the normalized time series. Sensitivity of retrieval is determined by the intrinsic variability of the normalized TIR signal and by the amount of missing values in the dataset. Finally, we compare our approach with widely used techniques of statistical and spectral analysis and we discuss the improvements introduced by our method.

Integrating geo web services for a user driven exploratory analysis

In data exploration, several online data sources may need to be dynamically aggregated or summarised over spatial region, time interval, or set of attributes. With respect to thematic data, web services are mainly used to present results leading to a supplier driven service model limiting the exploration of the data. In this paper we propose a user need driven service model based on geo web processing services. The aim of the framework is to provide a method for the scalable and interactive access to various geographic data sources on the web. The architecture combines a data query, processing technique and visualisation methodology to rapidly integrate and visually summarise properties of a dataset. We illustrate the environment on a health related use case that derives Age Standardised Rate – a dynamic index that needs integration of the existing interoperable web services of demographic data in conjunction with standalone non-spatial secure database servers used in health research. Although the example is specific to the health field, the architecture and the proposed approach are relevant and applicable to other fields that require integration and visualisation of geo datasets from various web services and thus, we believe is generic in its approach.

Control of the spatial Mandelbrot set generated in coupled map lattice

This paper broadens the research on planar Mandelbrot set to the spatial case by investigating the Mandelbrot set generated in coupled map lattice (CML M-set for short). The visualization and symmetry property of the 3D slice of CML M-set are investigated. Furthermore, a proper controller is designed for the 3D slice of CML M-set to achieve its symmetry change, position change and size change. Numerical simulations are included to verify the effectiveness of the control method.

Mixed Spectrum Analysis on fMRI Time-Series

Temporal autocorrelation present in functional magnetic resonance image (fMRI) data poses challenges to its analysis. The existing approaches handling autocorrelation in fMRI time-series often presume a specific model of autocorrelation such as an auto-regressive model. The main limitation here is that the correlation structure of voxels is generally unknown and varies in different brain regions because of different levels of neurogenic noises and pulsatile effects. Enforcing a universal model on all brain regions leads to bias and loss of efficiency in the analysis. In this paper, we propose the mixed spectrum analysis of the voxel time-series to separate the discrete component corresponding to input stimuli and the continuous component carrying temporal autocorrelation. A mixed spectral analysis technique based on M-spectral estimator is proposed, which effectively removes autocorrelation effects from voxel time-series and identify significant peaks of the spectrum. As the proposed method does not assume any prior model for the autocorrelation effect in voxel time-series, varying correlation structure among the brain regions does not affect its performance. We have modified the standard M-spectral method for an application on a spatial set of time-series by incorporating the contextual information related to the continuous spectrum of neighborhood voxels, thus reducing considerably the computation cost. Likelihood of the activation is predicted by comparing the amplitude of discrete component at stimulus frequency of voxels across the brain by using normal distribution and modeling spatial correlations among the likelihood with a conditional random field. We also demonstrate the application of the proposed method in detecting other desired frequencies.

The spatial rhetoric of Gustav Zeiller's popular anatomical museum.

This article focuses on the public experience of science by studying the exhibition practice of a small popular anatomy museum. The owner, Gustav Zeiller, a little-known German model maker and entrepreneur, opened his private collection in Dresden in 1888 with the aim of providing experts and laymen alike with a scientific education on bodily matters and health care. The spatial configuration of his museum environment turned the wax models into didactic instruments. Relying on the possible connexion between material culture studies and history of the emotions, this article highlights how Zeiller choreographed the encounter between the museum objects and its visitors. I argue that the spatial set up of his museum objects entailed rhetorical choices that did not simply address the social utility of his museum. Moreover, it fulfilled the aim of modifying the emotional disposition of his intended spectatorship. I hope to show that studying the emotional responses toward artefacts can offer a fruitful approach to examine the public experience of medicine.

Static Structure, Dynamic Form: An Analysis of Elliott Carter's Concerto for Orchestra

STATIC STRUCTURE, DYNAMIC FORM: AN ANALYSIS OF ELLIOTT CARTER’S CONCERTO FOR ORCHESTRA KLAAS COULEMBIER INTRODUCTION LLIOTT CARTER’S CONCERTO FOR ORCHESTRA is one of his most intriguing, complex, and fascinating compositions. It is a tour de force in the organization and arrangement of different musical materials, in the domains of both pitch and rhythm. Several authors have expressed their admiration for the composition, while acknowledging that it is very difficult to penetrate. In 1989, David Harvey opened his chapter on Carter’s Concerto for Orchestra with the following statement: The Concerto for Orchestra is Carter’s richest and most complex work to date in every respect. A complete account of its material, techniques, and their realisation in the textures of the work is beyond the scope of the present study; indeed, it may be doubted that such a project is at all feasible, given the size of the work, the density of the orchestral writing, the richness of the harmonic elaborations generated by Carter’s intervallic techniques of composition , and the limitations of analysis at the present time.1 E 98 Perspectives of New Music That this work is attractive to analysts is beyond question; the trepidation with which they have approached is, however, not only the result of its multi-layered musical surface, but also, no doubt, stems from the startling number of sketches Carter generated in producing it, conveying the impression that the construction of the composition may be even more puzzling than its form. Nevertheless, to gain a deeper insight into the workings of this music, the sketches appear to be as necessary as they are daunting. Jonathan Bernard concluded his 1983 article in Music Analysis with the remark that it would be impossible to make a fully comprehensive analysis of this composition. Dealing with the huge expanses of Carter’s scores . . . is still not easy. To retrace the steps of a composer who produces thousands of pages of sketches and works for thousands of hours in the course of writing a piece is likely to be a formidable undertaking, to say the least. . . . The prospect of reading, much less writing, a so-called “complete analysis” carried out according to the methods presented here is truly fearsome to contemplate. Eventually, perhaps, someone will have a bright idea that will make everything seem much simpler. Until then we can only have faith in the music, continue to analyse, and hope for the best.2 Rather than try to be that person with the bright idea, I want to contribute to the understanding of this composition by focusing on its overall temporal and dramatic organization. Therefore, in dealing with the more than 3000 pages of sketches that I studied during a weeklong stay at the Paul Sacher Foundation, I have kept my focus here exclusively on rhythmic sketches and temporal calculations. In that respect the following analysis complements existing literature in which pitch organization has often been the focal point. Despite the dependence on sketches, this analysis is not aimed at a mere reconstruction of the compositional process.3 By revealing the intricate relation between the rigid and static background structures and their more supple and subtle surface manifestations, this analysis tries to show which strategies and methods Carter applied to achieve such a compelling dramatic and dynamic musical discourse. THE CONCERTO FOR ORCHESTRA IN LITERATURE: AN OVERVIEW Apart from a chapter in David Harvey’s dissertation,4 there are no exhaustive, start-to-finish analyses of the Concerto for Orchestra. Static Structure, Dynamic Form 99 David Schiff gives a comprehensible overview of the composition in The Music of Elliott Carter.5 Jonathan Bernard has returned to the composition on several occasions, dealing with pitch structures in his article on “spatial sets,”6 or with the relationship between the literary source of the composition in “Poem as Non-Verbal Text.”7 Several publications of the Paul Sacher Foundation also include descriptions of the composition’s elaborate sketch resources available in their collection.8 Scholarly literature on Carter’s music in general has regained new energy in the last decade, particularly since the composer’s centennial celebration in 2008. Recent publications often consolidate important studies (such as Jonathan Bernard...

Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set

An accurate contour estimation plays a significant role in classification and estimation of shape, size, and position of thyroid nodule. This helps to reduce the number of false positives, improves the accurate detection and efficient diagnosis of thyroid nodules. This paper introduces an automated delineation method that integrates spatial information with neutrosophic clustering and level-sets for accurate and effective segmentation of thyroid nodules in ultrasound images. The proposed delineation method named as Spatial Neutrosophic Distance Regularized Level Set (SNDRLS) is based on Neutrosophic L-Means (NLM) clustering which incorporates spatial information for Level Set evolution. The SNDRLS takes rough estimation of region of interest (ROI) as input provided by Spatial NLM (SNLM) clustering for precise delineation of one or more nodules. The performance of the proposed method is compared with level set, NLM clustering, Active Contour Without Edges (ACWE), Fuzzy C-Means (FCM) clustering and Neutrosophic based Watershed segmentation methods using the same image dataset. To validate the SNDRLS method, the manual demarcations from three expert radiologists are employed as ground truth. The SNDRLS yields the closest boundaries to the ground truth compared to other methods as revealed by six assessment measures (true positive rate is 95.45±3.5%, false positive rate is 7.32±5.3% and overlap is 93.15±5. 2%, mean absolute distance is 1.8±1.4 pixels, Hausdorff distance is 0.7±0.4 pixels and Dice metric is 94.25±4.6%). The experimental results show that the SNDRLS is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. The proposed method achieves the automated nodule boundary even for low-contrast, blurred, and noisy thyroid ultrasound images without any human intervention. Additionally, the SNDRLS has the ability to determine the controlling parameters adaptively from SNLM clustering.

Coupled Damage-Plasticity Modelling of Ductile Failure in an Aluminium Alloy

The ductile failure of metallic alloys is characterized by the long plateau of the stress-strain response during plastic deformation. In aluminium alloys this complex process is principally mediated by crystal slip associated with dislocation nucleation, motion, interaction, and locking. This results in hardening, i.e. the increase in the flow stress and progressive exhaustion of ductility, eventually leading to damage. Therefore, in the advanced stages of deformation the strength increase at the material level competes with overall stiffness and strength decrease due to effective cross-section reduction by decohesion and voiding. Capturing the complex hierarchical failure of these materials requires developing sophisticated concurrent constitutive descriptions of both plastic deformation and damage at different stages of failure. In the present study the modelling of aluminium alloy failure is accomplished using a plasticity-based model with nonlinear hardening coupled with isotropic damage in a thermodynamically consistent framework. The model developed in this way is enhanced with nonlocal regularization to deal with material instabilities issues due to softening. Emphasis is placed on the correspondence between experimental measurements of the essential work of fracture and the non-essential work of fracture, and both local and spatial sets of model parameters. This approach is the key to assuring a constitutive response consistent with experimental observations, an issue usually overlooked in nonlocal constitutive modelling. Numerical examples are used to demonstrate the features of the new approach.