Multi-resolution Sampling Research Articles

A Multi-resolution Approach to 3D Controlled-source Electromagnetic Modeling

We have introduced a multi-resolution sampling approach for 3D finite-difference controlled-source electromagnetic forward modeling. Inside this scheme, the discrete operators are constructed based on the original sampling points. At the same time, the algorithm reduces the number of discrete electromagnetic fields instead of the number of cells. To avoid the deterioration of the condition number of the coefficient matrix caused by multiplying by different levels of sparse operators, we introduce a novel technique to optimize the condition number of the coefficient matrix. The results show that the new approach improves computational efficiency without compromising the accuracy of the solution.

An efficient algebraic multi-resolution sampling approach to 3-D magnetotelluric modelling

SUMMARY Since electromagnetic (EM) fields diffuse more smoothly to greater depth, it physically makes sense to apply fine discretization to model structure at near surface with an increasingly coarser grid both in horizontal and vertical directions as the depth increases for the numerical solution of EM fields. For finite-difference magnetotelluric (MT) forward modelling on regular staggered grids, this can lead to difficulties in discretizing the curl–curl equation governing the EM diffusion in the earth at regions, where the grid resolution changes. In this paper, we propose an efficient algebraic multi-resolution sampling (MRS) method for MT forward modelling. In this method, we do not require the generation of physical subgrids and merely subsample the field components. The coefficient matrix for the subsampled components can be obtained by matrix multiplication based on the initial linear system of equations and field interpolation. To guarantee divergence-free current during the iterative solution process at low frequencies, which is considered crucial for the development of efficient iterative solvers, our forward modelling is based a regularization equation obtained by augmenting the curl–curl equation with an equivalent scaled grad–div operator for electrical fields (explicitly enforcing the divergence-free condition). The correctness of our algebraic MRS algorithm is examined based on a layered model. Its stability and efficiency is exploited by comparing our results with the forward modelling on unsampled staggered grids for the Dublin Test Model 1 (DTM1) and a complex model with realistic topography, indicating a time reduction of about 42–82 per cent.

A two‐stage multiresolution neural network for automatic diagnosis of hepatic echinococcosis from ultrasound images: A multicenter study

Hepatic echinococcosis is a parasitic disease. Ultrasound imaging is a crucially important tool for the diagnosis of this disease. Based on ultrasonic manifestations, hepatic echinococcosis can be classified into many subtypes. However, subtyping is nontrivial due to the challenges of complex sonographic textures and large intraclass and small interclass differences. The purpose of this study is to develop a computer-aided diagnosis system for hepatic echinococcosis based on ultrasound images. We collected a multicenter ultrasound dataset containing 9112 images from 5028 patients who were diagnosed with hepatic echinococcosis (the largest cohort to date) and developed a two-stage multiresolution neural network for the automatic diagnosis of hepatic echinococcosis into nine subtypes, as suggested by the WHO. Our method was based on YOLO3 with two additional strategies to improve its performance: coarse grouping and multiresolution sampling. Considering that some subtypes are inherently very similar and difficult to differentiate; in the first stage, we detected and classified lesions into four coarse groups instead of making a direct classification into nine classes. In the second stage, we performed fine-grained classification within each coarse group. Multiple views with different resolutions were sampled from the detected lesions and were input into Darknet53. The softmax outputs for the multiresolution views were averaged to generate the final output. Both the proposed coarse grouping and multiresolution sampling strategies proved to be effective and improved the classification performance by a large margin compared with the setting without using the two strategies. Using fivefold cross-validation, our method achieved 87.1%, 86.2%, and 86.5% in the average recall, precision and F1-score, respectively, and outperformed other state-of-the-art methods remarkably. The experimental results demonstrate the great promise of our method for classifying hepatic echinococcosis. Our method can be used as an effective tool to facilitate large-scale screening for hepatic echinococcosis in high-risk, resource-poor areas, thus contributing to the early diagnosis of this disease and resulting in more successful treatment.

GP3: A Sampling-based Analysis Framework for Gaussian Processes

Although machine learning is increasingly applied in control approaches, only few methods guarantee certifiable safety, which is necessary for real world applications. These approaches typically rely on well-understood learning algorithms, which allow formal theoretical analysis. Gaussian process regression is a prominent example among those methods, which attracts growing attention due to its strong Bayesian foundations. Even though many problems regarding the analysis of Gaussian processes have a similar structure, specific approaches are typically tailored for them individually, without strong focus on computational efficiency. Thereby, the practical applicability and performance of these approaches is limited. In order to overcome this issue, we propose a novel framework called GP3, general purpose computation on graphics processing units for Gaussian processes, which allows to solve many of the existing problems efficiently. By employing interval analysis, local Lipschitz constants are computed in order to extend properties verified on a grid to continuous state spaces. Since the computation is completely parallelizable, the computational benefits of GPU processing are exploited in combination with multi-resolution sampling in order to allow high resolution analysis.

General Analysis of Resolution Enhancement on Time-Synchronized Sampling and its Multi-Resolution Solution in 20GSPS Acquisition System

Multiple resolutions have become a new feature required in ultra-fast data acquisition systems (UF-DASs) for various high-end applications, especially for large-scale physical experiments and radar equipment. However, plenty of repetitive efforts are paid to build various UF-DASs with different resolutions for many similar purposes. In this paper, a system-level sampling solution featuring multi-resolution is explored for UF-DASs to make the trade-off between accuracy and speed. Firstly, a resolution enhancement mechanism based on a time-synchronized sampling technique is studied more generally with probabilistic and statistical theory, and its applicable premise is discussed quantificationally at the same time. Then, a hardware-based reconfigurable structure for the purpose of multi-resolution high-speed sampling is discussed with the combination of time-interleaved and time-synchronized sampling methods. Furthermore, to verify the proposed theory, a prototype with a maximal 20GSPS sampling rate and different resolutions of 8,9,10-bit is established. The experimental results show that three vertical resolutions of 8, 9 and 10-bit are achieved in the same prototype, and the effective-number-of-bit (ENOB) in the two higher resolution modes has been improved remarkably from a single low-resolution analog-to-digital converter (ADC). Therefore, the proposed theory and multi-resolution sampling solution are important guides to the resolution trade-off requirement in ultra-fast acquisition systems.

Automated-Sampling-Based Stability Verification and DOA Estimation for Nonlinear Systems

This paper develops a new sampling-based method for stability verification of piecewise continuous nonlinear systems via Lyapunov functions. Depending on the nonlinear system dynamics, the candidate Lyapunov function and the set of states of interest, verifying stability requires solving complex, possibly non-convex or infeasible optimization problems. To avoid such problems, the proposed approach firstly distributes the verification of Lyapunov's inequality on a finite sampling of a bounded set of states of interest. Secondly, it extends the validity of Lyapunov's inequality to an infinite, bounded set of states by automatically exploiting local continuity properties. A sampling-based method for estimating the domain of attraction (DOA) via level sets of a validated Lyapunov function is also presented. Efficient state-space exploration is achieved using multi-resolution sampling and hyper-rectangles as basic sampling blocks. The operations that need to be performed for each sampling point in the state-space can be carried out in parallel, which improves scalability. The proposed methodology is illustrated for various benchmark examples from the literature.

Multiresolution alignment for multiple unsynchronized audio sequences using sequential Monte Carlo samplers

It is increasingly more common that an occasion is recorded by multiple individuals with the proliferation of recording devices such as smart phones. When properly aligned, these recordings may provide several audio and visual perspectives to a scene which leads to several applications in restoring, remastering and remixing frameworks in various fields. In this work, we propose a multiresolution alignment algorithm for aligning multiple unsynchronized audio sequences using Sequential Monte Carlo samplers. We employ a model based approach and a score function analogous to similarity based methods. The optimum alignments are obtained in a course to fine structure with multiresolution sampling and a heuristic sequential search method. The proposed method is evaluated with a real-life dataset from Jiku Mobile Video Datasets. The simulation results suggest that our method is competitive with the baseline methods in terms of accuracy with suitable choice of parameters.

Adaptive multiresolution sampling in event-driven WSNs

In time driven applications we are often bound to make a keen compromise between the sampling resolution (or sampler architecture) and available resources. This is especially true in WSNs (Wireless Sensor NetworkS), although the complexity of event driven environments present us new opportunities for optimizations. In this paper we propose a multi resolution sampling protocol for event driven WSNs that can dynamically adapt to event signatures and can efficiently schedule resource intensive samplers. The system can monitor the environment by a resource optimized sampler which is only to detect events and not to store or process any samples. When scheduled the high resolution sampler is activated to sample the appropriate physical property of the phenomenon marked by the event, via resolution optimized hardware. Since it takes time to detect an event (triggered by the signal we already ought to sample) and to power up the high resolution, resource intensive sampler, we must start the sampling before the signal is detected. This calls for dynamic event forecasting, which is the main feature of the proposed system.

Image Fusion Base on K-Means Clustering and Contourlet Transform

The robustness of K-means clustering is poor in non-spherical distribution data, in order to improve the universal ability of clustering algorithms, the cross-entropy distance measure was used to replace the Euclidean distance measure . Contour let transform, not only has characteristics of multi-resolution, locality and critical sampling which wavelet has, but also has the characteristics of multiple decomposition directions and anisotropy which wavelets lack. So we combine the modified K-means clustering and Contour let transform to apply for image fusion. Experimental results show that this method is feasible.

Integrating heterogeneity across spatial scales: interactions between Atrina zelandica and benthic macrofauna

Techniques are needed to extend the generality of detailed experimental field studies. Multi-resolution sampling designs nest fine-scale sampling within information about broad-scale processes that may affect finer-scale data. These designs are very appropriate for studies seeking to detect the effects of processes operating over a variety of scales. We tested the utility of a novel, low- intensity/cost, multi-resolution sampling design in an investigation of the relationship between the suspension-feeding pinnid bivalve Atrina zelandica and surrounding benthic macrofauna. Interac- tions were modelled at different spatial scales, and a variety of covariates were included in the search for the best statistical and most sensible ecological model. We chose our sampling sites to encompass broad-scale gradients in A. zelandica density and size, depth, sediment characteristics and hydro- dynamics. We found these gradients helped explain the relationship between A. zelandica and asso- ciated benthic macrofauna. Our results suggest that the strength of effects of A. zelandica on benthic macrofauna was dependent on 2 aspects of spatial scale: grain (the area over which A. zelandica measurements were made) and lag (the distance between where the A. zelandica and the macro- fauna were sampled). By nesting fine-scale measures of benthic macrofaunal community composition within different spatial arrangements of individual A. zelandica, we were able to determine that the maximum distance between individual A. zelandica, rather than density estimates, better explained effects of A. zelandica on benthic macrofauna. This suggests the value of integrating broad-scale surveys with localised experiments so that organism interactions can be more generally interpreted on the basis of dynamics within landscapes rather than as density-to-density comparisons.

Fishing disturbance and marine biodiversity: the role of habitat structure in simple soft-sediment systems

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 223:277-286 (2001) - doi:10.3354/meps223277 Fishing disturbance and marine biodiversity: the role of habitat structure in simple soft-sediment systems Simon F. Thrush1,*, Judi E. Hewitt1, Greig A. Funnell1, Vonda J. Cummings1, Joanne Ellis1, Diane Schultz1, Drew Talley2, Alf Norkko1 1National Institute of Water and Atmospheric Research, PO Box 11-115, Hamilton, New Zealand 2Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093-0201, USA *E-mail: s.thrush@niwa.cri.nz ABSTRACT: Broad-scale anthropogenic disturbances that reduce the density of epifauna and homogenise surficial sediments can have important consequences for seafloor biodiversity. We investigated the habitat structure and macrofaunal diversity of relatively simple soft-sediment habitats over a number of spatial scales (cm to km) to identify the role of habitat structure in influencing macrobenthic diversity and to assess the validity of using habitat structure as a surrogate measure for biodiversity. We sampled 10 locations with differences in habitat structure using a sampling design that nested macrobenthic core samples within videoed transects of the seafloor. This allowed us to determine relationships between observable habitat structure and macrobenthic diversity at a number of spatial scales. We characterised elements of habitat structure based on direct counts of surficial sediment characteristics and the presence of other immobile features, many of which were biogenic in origin. We also used multivariate measures (the relative multivariate dispersion, the mean and range of the Bray-Curtis dissimilarity along the transects) to characterise habitat structure at the transect scale. We developed regression models based on measures of habitat structure that explained 74 to 86% of the variance in macrobenthic diversity. This result suggests that removal of habitat structure in relatively low-structure soft-sediment systems will significantly decrease their biodiversity, and consequently that of the wider marine ecosystem. Erratum KEY WORDS: Biodiversity · Soft-sediments · Habitat structure · Habitat heterogeneity · Habitat complexity · Habitat disturbance · Fishing impacts · Nested multi-resolution sampling · Kawau Bay · New Zealand Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 223. Online publication date: November 28, 2001 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2001 Inter-Research.

Multiresolution sampling applications

Multiresolution sampling applications