Multiresolution Method Research Articles

An Adaptive Approach for Voltage Sag Automatic Segmentation

Voltage sag characterization is essential for extracting information about a sag event’s origin and how sag events impact sensitive equipment. In response to such needs, more characteristics are required, such as the phase-angle jump, point-on-wave, unbalance, and sag type. However, the absence of an effective automatic segmentation method is a barrier to obtaining these characteristics. In this paper, an automatic segmentation method is proposed to improve this situation. Firstly, an extended voltage sag characterization method is described, in which segmentation plays an important role. Then, a multi-resolution singular value decomposition method is introduced to detect the boundaries of each segment. Further, the unsolved problem of how to set a threshold adaptively for different waveforms is addressed, in which the sag depth, the mean square error, and the entropy of the sag waveform are considered. Simulation data and field measurements are utilized to validate the effectiveness and reliability of the proposed method. The results show that the accuracies of both boundary detection and segmentation obtained using the proposed method are higher than those obtained using existing methods. In general, the proposed method can be implemented into a power quality monitoring system as a preprocess to support related research activities.

Multi-resolution MPS for incompressible fluid-elastic structure interactions in ocean engineering

The paper presents a multi-resolution MPS (Moving Particle Semi-implicit)-based FSI (Fluid-Structure Interaction) solver for efficient and accurate simulations of incompressible fluid flows interacting with elastic structures. The fluid model is founded on the projection-based MPS solution of continuity and Navier-Stokes equations. The structure model is set based on MPS-based discretization of linear and angular momenta corresponding to an isotropic elastic solid. Fluid-structure coupling is conducted in a mathematically-physically consistent manner along with implementation of a multi-resolution scheme comprising of common radius of influence, revised weight function, revised number density and potential number density concepts to enhance i) consistency of particle-based discretizations, ii) imposition of boundary conditions and iii) volume conservation at fluid-structure interface. A set of previously developed enhanced schemes are also adopted for the fluid model. The robustness and efficiency of proposed Enhanced Multi-resolution MPS-MPS FSI solver are investigated through a set of ocean engineering-related benchmark tests. To the best knowledge of authors, this study presents the first multi-resolution particle method for FSI corresponding to incompressible fluid and elastic structures.

New multi-resolution and multi-scale electromagnetic detection methods for urban underground spaces

The “transparent” detections of urban underground spaces pose difficult challenges in the field of geophysics. Due to the complexities of urban underground environments, the existing detection methods are hard to reach the high capabilities of radiation sources and distinguish small-scale abnormal bodies at the same time. The “transparent” detections of urban underground spaces must achieve multi-resolution and multi-scale detection abilities. The previously applied electromagnetic detection methods have displayed limitations in achieving high radiation capability and rich harmonic component detections. Therefore, this paper focused on three key scientific issues: the design of a high-performance excitation source; a multi-resolution detection theory; and a multi-scale information extraction technique. First, in order to increase the radiation intensity, improve the directional radiation capability, and realize powerful and directional radiation, a new type of high-performance transient electromagnetic source was designed. Then, complex waveforms would be transmitted in order to enrich the frequency component of the excitation signals. The complex excitations were carried out using pulse scanning technology for the purpose of obtaining increased abundant underground information. The multi-resolution detections of the underground targets were realized in combination with a superposition algorithm. Finally, based on a related superposition principle, multi-aperture synthetic aperture technology was examined. By setting the sizes of the apertures, the vertical and lateral resolutions were adjusted, and the multi-scale information extractions of the urban underground spaces were realized. In this study, a large number of numerical model tests and simulation calculations were carried out. The results confirmed that, from the point of view of a detection mechanism, a high performance radiant source combined with a multi-resolution method and multi-scale signal extraction technology could successfully obtain correct and effective clear detection results in both transverse and longitudinal directions. The research results of this study will significantly promote the development of urban underground space detection technology, which will potentially be of major importance in making the underground spaces up to 200 m ‘transparent’ in urban areas.

Parallel multi-core and multi-processor methods on point-value multiresolution algorithms for hyperbolic conservation laws

The underlying sequential behavior of the multiresolution (MR) method has been exploited for parallel computing by introducing a concept of multiresolution forest structures (MFS) along with two new load-balancing algorithms. Another easy-to-implement multithreading approach has also been introduced for the multicore architectures. Tests were conducted using an Euler solver based on a fifth-order shock capturing WENO scheme and a third-order Runge–Kutta algorithm. The methods have been rigorously analyzed in terms of speedup ratio and parallel efficiency to bring forth their benefits as well as limitations. The performance yielded through these methods indicates that the MFS is a new headway for the MR method in parallel computing that has a potential to harness better scalability.

Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect

Affected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were developed, one for the base flow effect and one for daily variation of dam seepage elements. In the first model, to avoid the influence of the time lag effect on the evaluation of seepage variation with the time effect component of seepage elements, the base values of the seepage element and the reservoir water level were extracted using the wavelet multi-resolution analysis method, and the time effect component was separated by the established base flow effect monitoring model. For the development of the daily variation monitoring model for dam seepage elements, all the previous factors, of which the measured time series prior to the dam seepage element monitoring time may have certain influence on the monitored results, were considered. Those factors that were positively correlated with the analyzed seepage element were initially considered to be the support vector machine (SVM) model input factors, and then the SVM kernel function-based sensitivity analysis was performed to optimize the input factor set and establish the optimized daily variation SVM model. The efficiency and rationality of the two models were verified by case studies of the water level of two piezometric tubes buried under the slope of a concrete gravity dam. Sensitivity analysis of the optimized SVM model shows that the influences of the daily variation of the upstream reservoir water level and rainfall on the daily variation of piezometric tube water level are processes subject to normal distribution.

A new multi-resolution based method for estimating local surface roughness from point clouds

From some empirical and theoretical research on the digital elevation model (DEM) accuracy obtained for different source data densities, it can be observed that when the same degree of data reduction is applied to a whole area, the rate of change in the DEM error is statistically greater in local areas where the surface is rougher. Based on this observation, it is possible to characterize surface roughness or complexity from the differences between two digital elevation models (DEMs) built using point clouds that represent the same terrain surface but are of different spatial resolutions (or data spacings). Following this logic, a new approach for estimating surface roughness is proposed in this article. Numerical experiments are used to test the effectiveness of the approach. The study datasets considered in this article consist of four elevation point clouds obtained from terrestrial laser scanning (TLS) and airborne light detection and ranging (LiDAR). These types of topographical data are now used widely in Earth science and related disciplines. The method proposed was found to be an effective means of quantifying local terrain surface roughness.

Feature Extraction Method for DC Charging Signal of Electric Vehicle

The high-power DC charging of electric vehicle charging stations leads to a large amount of ripple and unsteady waves in the power grid, which seriously affects power quality. The existing algorithms cannot accurately identify the signal characteristics of the fundamental wave, ripple, and the unsteady wave, and it is urgent to develop a comprehensive signal feature extraction method. Aiming at the time-varying characteristics of fundamental and non-steady-state waves in DC charging signals, the layered parameters of wavelet transform are modified, a multi-resolution method is established, and the fundamental and unsteady wave signals are identified and separated. Based on the amplitude-frequency and phase-frequency characteristics of DC charging signal, Fourier transform (FFT) is used to identify the amplitude and frequency of each sub-ripple, and a signal feature recognition method based on Fourier/wavelet transform is proposed to extract the signal characteristics of the DC charging process. The experimental results show that the signal feature recognition method based on Fourier/wavelet transform can identify the DC charging signal characteristics of electric vehicle with high accuracy, and can separate the unsteady state signal from the steady state signal.

An efficient automatic segmentation of spinal cord in MRI images using interactive random walker (RW) with artificial bee colony (ABC) algorithm

Spinal cord Magnetic Resonance Images (MRI) have a remarkable role to play in the learning of neurological diseases such as Multiple Sclerosis (MS) affecting the Central Nervous System (CNS), in which spinal cord atrophy can help in the measurement of disease advancement and the changes in shape. Spinal cord segmentation plays a significant part in analyzing the neurological disease. In this paper here, an approach on the basis of the automatic spinal cord segmentation is proposed. This automatic technique presented performs the segmentation of the spinal cord with the help of MRI datasets. This new segmentation follows the interactive Random-Walk solvers (RW) along with Artificial Bee Colony (ABC) optimization algorithm in order to be an entirely automatic flow pipeline. The initialization of the automatic segmentation pipeline is then done with a reliable voxel-wise classification employing features similar to Haar and supervised machine learning technique i.e. Probabilistic Boosting Tree (PBT) along with Support Vector Machine (SVM) so named as PBTSVM. Thereafter, the extraction of the refinement topology of the spinal cord is then done from the temporary segmentation and it is fine-tuned for the further next random-walk solver with ABC. The refined topology results in the spinal cord’s boundary conditions from the MRI that permits the following random-walk solver with ABC for improving the segmentation result. The experimental outcomes of the novel segmentation approach depending on the MRI images indicate that the system proposed PBT-SVM algorithm provides better accuracy when compared to the other existing Active Contour Model, Multi-Resolution Propagation algorithms. Experimentation results of the proposed PBT-SVM algorithm produces higher accuracy results of 93% which is 2.5 and 3.233% higher when compared to Active Contour Model and Multi-Resolution Propagation methods respectively.

Impacts of connected vehicles in a complex, congested urban freeway setting using multi-resolution modeling methods

Determining the effect automated and connected vehicles could have on traffic flow would – ideally – require testing the vehicles themselves in a real-world environment. In the absence of large-scale, real-world testing, researchers used traffic modeling software to develop and test a vehicle mimicking the behaviors of several automated and connected vehicle (CV) applications in a congested and complex urban network. The algorithm behind the CV ran a suite of mobility-focused applications, inspired by cooperative adaptive cruise control (CACC), speed harmonization, and queue warning applications. The CV was first tested on a small sample network, consistent with approaches obtained from a review of the literature. The research team then sought to understand the potential effects of CV technology on congestion and mobility in a DTATexas context by modeling the traffic impacts of CVs at varying market penetrations on a twelve-mile section of I-35 in Austin, running from south of Riverside Dr. to Parmer Ln at 2035 population levels. Researchers used a multi-resolution modeling (MRM) methodology, which incorporates macroscopic, mesoscopic, and microscopic models.

Multi-scale multi-level marine spatial planning: A novel methodological approach applied in South Africa.

This study proposes and discusses a multi-scale spatial planning method implemented simultaneously at local and national level to prioritize ecosystem management actions across landscapes and seascapes. Mismatches in scale between the occurrence of biodiversity patterns and ecological processes, and the size and nature of the human footprint, and the different levels and scope of governance, are a significant challenge in conservation planning. These scale mismatches are further confounded by data resolution disparities across and amongst the different scales. To address this challenge, we developed a multi-resolution scale-linked marine spatial planning method. We tested this approach in the development of a Conservation Plan for a significant portion of South Africa’s exclusive economic zone, adjacent to the east coast province of KwaZulu-Natal (the SeaPlan project). The study’s dataset integrated the geographic distribution of 390 biodiversity elements (species, habitats, and oceanographic processes) and 38 human activities. A multi-resolution system of planning unit layers (PUL), with individual PUs ranging in resolution from 0.2 to 10 km, was designed to arrange and analyse these data. Spatial priorities for conservation were selected incrementally at different scales, contributing conservation targets from the fine-, medium- and large-scale analyses, and from the coast to the offshore. Compared to a basic single-resolution scale-unlinked plan, our multi-resolution scale-linked method selects 6% less conservation area to achieve the same targets. Compared to a multi-resolution scale-unlinked plan, our method requires only an additional 5% area. Overall, this method reflects the multi-scale nature of marine social-ecological systems more realistically, is relatively simple and replicable, and serves to better connect fine-scale and large-scale spatial management policies. We discuss the impacts of this study on protected area expansion planning processes in South Africa. This study showcases a methodological advance that has the potential to impact marine spatial planning practices and policies.

An Efficient Pan Sharpening via Texture Based Dictionary Learning and Sparse Representation

Remote sensing image pan sharpening has attracted researchers’ interest, since spatial resolution of multispectral (MS) image can be enhanced by injecting spatial details of a panchromatic image to MS image. In this paper, a novel sparse representation based pan sharpening method is proposed to overcome the disadvantages of traditional methods such as color distortion and blurring effect. This learning based method utilizes a compact single dictionary generated from texture information of high-resolution MS images in order to provide more effective and robust pan sharpening. Two data sets acquired from IKONOS and Quickbird satellites are used to evaluate the performance and robustness of the proposed algorithm. The proposed method is compared with nine well-known component substitution and multiresolution analysis methods and a state-of-art method using several quality measurement indices with references. The experimental results demonstrate that the proposed algorithm is competitive or superior to other conventional methods in terms of visual and quantitative analysis, as it preserves spectral information and provides high quality spatial details in the final product image.

A Leaf Modeling and Multi-Scale Remeshing Method for Visual Computation via Hierarchical Parametric Vein and Margin Representation.

This paper introduces a novel hierarchical structured representation for leaf modeling and proposes a corresponding multi-resolution remeshing method for large-scale visual computation. Leaf modeling is a very difficult and challenging problem due to the wide variations in the shape and structures among different species of plants. Firstly, we introduce a Hierarchical Parametric Veins and Margin (HPVM) representation approach, which describes the leaf biological structures and exact geometry via interpolation of parametric curves from the extracted vein features from non-manifold data. Secondly, a parametric surface model is constructed using HPVM with geometric and structured constraints. Finally, for a given size, we adapt a multi-step discrete point resampling strategy and a CDT-based (Constrained Delaunay Triangulation) meshing method to generate a mesh model. Our representation consists of three coupled data structures, a core hierarchical parametric data structure of veins and margin for the leaf skeleton, the corresponding parametric surface model, and a set of unstructured triangular meshes with user-specified density for the leaf membrane. Numerical experiments show that our method can obtain high quality meshes from the scanned non-manifold mesh data with well-preserved biological structures and geometry. This novel approach is suitable for effective leaf simulation, rendering, texture mapping, and simulation of light distribution in crop canopies.

Haar wavelets multi-resolution collocation analysis of unsteady inverse heat problems

ABSTRACT In this paper, two different Haar wavelet collocation multi-resolution procedures are proposed for linear partial differential equations (PDEs) with an unknown space-dependent heat source and an unknown solution. An appropriate transformation is used to convert a non-homogeneous PDE into a homogeneous form. Two techniques based on multi-resolution Haar wavelets collocation methods are proposed for numerical evaluation of the unknown space-dependent heat source. In homogeneous form, first-order finite-difference approximation is used to discretize the time derivative and finite Haar wavelets series is used for approximation of the space derivatives. Unlike other numerical methods, the proposed methods have well-conditioned Haar coefficient matrices and need not be supplemented by any regularization technique. Several numerical experiments are carried out to validate accuracy, simple applicability and well-conditioned behaviour of the Haar system coefficient matrices of the proposed algorithms.

Polynomial Radon-Polynomial Fourier Transform for Near Space Hypersonic Maneuvering Target Detection

For coherent integration detection of near space hypersonic maneuvering weak target via modern radar, a novel radar signal processing approach called polynomial Radon-polynomial Fourier transform (PRPFT) is proposed as a tool to compensate across range unit range walk and Doppler migration simultaneously caused by super-high speed and strong maneuvering. First, the target's observation values in the range-slow time plane are extracted by a kind of polynomial Radon transform according to the preset motion parameters of the searching space. Then, the observation values are matched and integrated by polynomial Fourier transform, and the hypersonic maneuvering target with arbitrary parameterized motion can be detected in the PRPFT domain. To reduce the computational complexity, a multiresolution method is proposed to search multidimensional parameter space. Finally, its application is illustrated to analyze and compare the performance between the proposed approach and the existing approach. It is shown that moving target detection and Radon-Fourier transform are actually special cases of PRPFT. The proposed method can compensate the range walk and the Doppler migration simultaneously, the detection performance of hypersonic maneuvering weak target may be significantly improved via PRPFT in the low signal-noise ratio background. The proposed algorithm can be used in the form of the second-order polynomial model, and it can also be extended to higher order form.

Multi-resolution simulation of focused ultrasound propagation through ovine skull from a single-element transducer

Transcranial focused ultrasound (tFUS) is emerging as a non-invasive brain stimulation modality. Complicated interactions between acoustic pressure waves and osseous tissue introduce many challenges in the accurate targeting of an acoustic focus through the cranium. Image-guidance accompanied by a numerical simulation is desired to predict the intracranial acoustic propagation through the skull; however, such simulations typically demand heavy computation, which warrants an expedited processing method to provide on-site feedback for the user in guiding the acoustic focus to a particular brain region. In this paper, we present a multi-resolution simulation method based on the finite-difference time-domain formulation to model the transcranial propagation of acoustic waves from a single-element transducer (250 kHz). The multi-resolution approach improved computational efficiency by providing the flexibility in adjusting the spatial resolution. The simulation was also accelerated by utilizing parallelized computation through the graphic processing unit. To evaluate the accuracy of the method, we measured the actual acoustic fields through ex vivo sheep skulls with different sonication incident angles. The measured acoustic fields were compared to the simulation results in terms of focal location, dimensions, and pressure levels. The computational efficiency of the presented method was also assessed by comparing simulation speeds at various combinations of resolution grid settings. The multi-resolution grids consisting of 0.5 and 1.0 mm resolutions gave acceptable accuracy (under 3 mm in terms of focal position and dimension, less than 5% difference in peak pressure ratio) with a speed compatible with semi real-time user feedback (within 30 s). The proposed multi-resolution approach may serve as a novel tool for simulation-based guidance for tFUS applications.

A Multiscale Approach for Geologically and Flow Consistent Modeling

Subsurface geological models are usually constructed on high-resolution grids in a way that various complexities and heterogeneities are depicted properly. Such models, however, cannot be used directly in the current flow simulators, as they are tied with high computational cost. Thus, using upscaling, by which one can produce flow consistent models that can alleviate the computational burden of flow simulators, is inevitable. Although the upscaling methods are able to reproduce the flow responses, they might not retain the initial geological assumptions. The reservoir models are initially constructed on uniform and high-resolution grids and then, if necessary, are upscaled to be used for flow simulations. A subsurface modeling approach that not only preserves the geological heterogeneity but also provides models that can be used, straight or with a small level of upscaling, in the flow simulators is desirable. In this paper, a new multiresolution method based on (1) the importance of conditioning well data and (2) being geologically and flow consistent is presented. This method discretizes the initial model into several regions based on the available data. Then, the initial assumed geological model is converted into, for example, various high- and low-resolution models. Next, the high-resolution model is used for regions with high-quality data (e.g., well locations), while the low-resolution model is used for the remaining areas. Finally, the patterns of these areas are interlocked, which result in a multiresolution geologically and flow consistent subsurface model. The accuracy of this method is demonstrated using two-phase flow simulation on four complex subsurface systems. The results indicate that the same flow responses, in a much less time, are reproduced using the multiscale models. The speed-up factor gained using the proposed method is also several orders of magnitude.

Interior tomography in microscopic CT with image reconstruction constrained by full field of view scan at low spatial resolution

In high resolution (microscopic) CT applications, the scan field of view should cover the entire specimen or sample to allow complete data acquisition and image reconstruction. However, truncation may occur in projection data and results in artifacts in reconstructed images. In this study, we propose a low resolution image constrained reconstruction algorithm (LRICR) for interior tomography in microscopic CT at high resolution. In general, the multi-resolution acquisition based methods can be employed to solve the data truncation problem if the project data acquired at low resolution are utilized to fill up the truncated projection data acquired at high resolution. However, most existing methods place quite strict restrictions on the data acquisition geometry, which greatly limits their utility in practice. In the proposed LRICR algorithm, full and partial data acquisition (scan) at low and high resolutions, respectively, are carried out. Using the image reconstructed from sparse projection data acquired at low resolution as the prior, a microscopic image at high resolution is reconstructed from the truncated projection data acquired at high resolution. Two synthesized digital phantoms, a raw bamboo culm and a specimen of mouse femur, were utilized to evaluate and verify performance of the proposed LRICR algorithm. Compared with the conventional TV minimization based algorithm and the multi-resolution scout-reconstruction algorithm, the proposed LRICR algorithm shows significant improvement in reduction of the artifacts caused by data truncation, providing a practical solution for high quality and reliable interior tomography in microscopic CT applications. The proposed LRICR algorithm outperforms the multi-resolution scout-reconstruction method and the TV minimization based reconstruction for interior tomography in microscopic CT.

A conservative interface-interaction method for compressible multi-material flows

In this paper we develop a conservative interface-interaction method dedicated to simulating multiple compressible fluids with sharp interfaces. Numerical models for finite-volume cells cut by more than two material-interface are proposed. First, we simplify the interface interaction inside such a cell to avoid the need for explicit interface reconstruction and very complex flux calculation. Second, conservation is strictly preserved by an efficient conservation correction procedure for the cut cell. To improve robustness, a multi-material scale separation model is developed to remove consistently non-resolved interface scales. In addition, a multi-resolution method and a local time-stepping scheme are incorporated into the proposed multi-material method to speed up high-resolution simulations. Various numerical test cases, including the multi-material shock tube problem, inertial confinement fusion implosion, triple-point shock interaction and shock interaction with multi-material bubbles, show that the method is suitable for a wide range of complex compressible multi-material flows.

Implementing Efficient Audio and Image Watermarking Using Multi-resolution Dual Wavelet and Fireflies Approach in Wireless Network

Watermarking is the process of embedding the particular information into the audio signal for managing the ownership copyrights through wireless network. During the watermarking process, the audio signal consumes high energy, conflicting problem of robustness and imperceptibility, signal to noise ratio, bit error rate and normalized correlation. To overcome these issues present in the audio watermarking process, novel wavelet decomposition and evolutionary algorithm is utilized. Initially the input information or message has been split into two and the spillted message is watermarked using the audio and the image in wireless network. The first half of the message is watermarked with the help of the image and the next half of the image is watermarked by audio. Initially the watermarked image is transferred into YIQ image, from the transferred image the scrambled image is generated with the help of the Hidden Markov tree counter let wavelet transform method for generating the watermarking image. Then the next part of information is watermarked by audio signal which is decomposed into various sub bands using the Multi-resolution complex dual tree wavelet method. From the decomposed audio signal, the message authentication code based watermarks have been embedded in the lower frequency coefficients. Then the embedded process is performed by using the Dead zone quantization process which is optimized with the help of the Fireflies algorithm for enhancing the quality of the watermarking process. Finally both watermarking process is embedded for improving the security to the information with efficient manner through wireless network. In addition the efficient watermarking process through wireless network reduces the various attacks while extracting the water marker. Thus the optimized wavelet and quantization process improves the image and audio watermarking through wireless network and efficiency of the proposed system is evaluated using the experimental results in terms of the Signal to noise ratio, normalized correlation and bit error rate.

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Single Input Multiple Output Technology.

As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. In this paper, we propose a three-dimensional (3D) TCAI architecture based on single input multiple output (SIMO) technology, which can reduce the coding and sampling times sharply. The coded aperture applied in the proposed TCAI architecture loads either purposive or random phase modulation factor. In the transmitting process, the purposive phase modulation factor drives the terahertz beam to scan the divided 3D imaging cells. In the receiving process, the random phase modulation factor is adopted to modulate the terahertz wave to be spatiotemporally independent for high resolution. Considering human-scale targets, images of each 3D imaging cell are reconstructed one by one to decompose the global computational complexity, and then are synthesized together to obtain the complete high-resolution image. As for each imaging cell, the multi-resolution imaging method helps to reduce the computational burden on a large-scale reference-signal matrix. The experimental results demonstrate that the proposed architecture can achieve high-resolution imaging with much less time for 3D targets and has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc.