scholarly journals A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

2015 ◽  
Vol 8 (4) ◽  
pp. 1259-1273 ◽  
Author(s):  
J. Ray ◽  
J. Lee ◽  
V. Yadav ◽  
S. Lefantzi ◽  
A. M. Michalak ◽  
...  
Keyword(s):  
Field Model ◽  
Computational Cost ◽  
Estimation Method ◽  
Sparse Reconstruction ◽  
Model Parameters ◽  
Reconstruction Method ◽  
Reconstruction Procedure ◽  
Reconstruction Methods ◽  
Atmospheric Inversion ◽  
Atmospheric Inversions

Abstract. Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO2 (ffCO2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO2 emissions and synthetic observations of ffCO2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.

scholarly journals A sparse reconstruction method for the estimation of multiresolution emission fields via atmospheric inversion

2014 ◽  
Vol 7 (4) ◽  
pp. 5623-5659 ◽  
Author(s):  
J. Ray ◽  
J. Lee ◽  
V. Yadav ◽  
S. Lefantzi ◽  
A. M. Michalak ◽  
...  
Keyword(s):  
Inverse Problems ◽  
Random Field ◽  
Matching Pursuit ◽  
Computational Cost ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Flow Measurements ◽  
Linear Inverse Problems ◽  
Reconstruction Scheme ◽  
Atmospheric Inversion

Abstract. We present a sparse reconstruction scheme that can also be used to ensure non-negativity when fitting wavelet-based random field models to limited observations in non-rectangular geometries. The method is relevant when multiresolution fields are estimated using linear inverse problems. Examples include the estimation of emission fields for many anthropogenic pollutants using atmospheric inversion or hydraulic conductivity in aquifers from flow measurements. The scheme is based on three new developments. Firstly, we extend an existing sparse reconstruction method, Stagewise Orthogonal Matching Pursuit (StOMP), to incorporate prior information on the target field. Secondly, we develop an iterative method that uses StOMP to impose non-negativity on the estimated field. Finally, we devise a method, based on compressive sensing, to limit the estimated field within an irregularly shaped domain. We demonstrate the method on the estimation of fossil-fuel CO2 (ffCO2) emissions in the lower 48 states of the US. The application uses a recently developed multiresolution random field model and synthetic observations of ffCO2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of two. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.

scholarly journals Framework for Reconstruction of Pseudo Quad Polarimetric Imagery from General Compact Polarimetry

2021 ◽  
Vol 13 (3) ◽  
pp. 530
Author(s):  
Junjun Yin ◽  
Jian Yang
Keyword(s):  
Linear Equations ◽  
Estimation Method ◽  
Data Sets ◽  
Reconstruction Procedure ◽  
Component Decomposition ◽  
Reconstruction Methods ◽  
Sar Imagery ◽  
Key Aspects ◽  
Non Linear Equations ◽  
Reconstruction Model

Pseudo quad polarimetric (quad-pol) image reconstruction from the hybrid dual-pol (or compact polarimetric (CP)) synthetic aperture radar (SAR) imagery is a category of important techniques for radar polarimetric applications. There are three key aspects concerned in the literature for the reconstruction methods, i.e., the scattering symmetric assumption, the reconstruction model, and the solving approach of the unknowns. Since CP measurements depend on the CP mode configurations, different reconstruction procedures were designed when the transmit wave varies, which means the reconstruction procedures were not unified. In this study, we propose a unified reconstruction framework for the general CP mode, which is applicable to the mode with an arbitrary transmitted ellipse wave. The unified reconstruction procedure is based on the formalized CP descriptors. The general CP symmetric scattering model-based three-component decomposition method is also employed to fit the reconstruction model parameter. Finally, a least squares (LS) estimation method, which was proposed for the linear π/4 CP data, is extended for the arbitrary CP mode to estimate the solution of the system of non-linear equations. Validation is carried out based on polarimetric data sets from both RADARSAT-2 (C-band) and ALOS-2/PALSAR (L-band), to compare the performances of reconstruction models, methods, and CP modes.

scholarly journals Modeling and Parameter Estimation of Hurricane Wind Fields with Asymmetry

2020 ◽  
Vol 59 (4) ◽  
pp. 687-705
Author(s):  
Derek Chang ◽  
Saurabh Amin ◽  
Kerry Emanuel
Keyword(s):  
Wind Field ◽  
Wind Shear ◽  
Field Model ◽  
Estimation Method ◽  
Translation Vector ◽  
Model Parameters ◽  
Wind Fields ◽  
Translation Speed ◽  
Wind Field Model ◽  
Hurricane Wind

AbstractThis article presents an azimuthally asymmetric gradient hurricane wind field model that can be coupled with hurricane-track models for engineering wind risk assessments. The model incorporates low-wavenumber asymmetries into the maximum wind intensity parameter of the Holland et al. wind field model. The amplitudes and phases of the asymmetries are parametric functions of the storm-translation speed and wind shear. Model parameters are estimated by solving a constrained, nonlinear least squares (CNLS) problem that minimizes the sum of squared residuals between wind field intensities of historical storms and model-estimated winds. There are statistically significant wavenumber-1 asymmetries in the wind field resulting from both storm translation and wind shear. Adding the translation vector to the wind field model with wavenumber-1 asymmetries further improves the model’s estimation performance. In addition, inclusion of the wavenumber-1 asymmetry resulting from translation results in a greater decrease in modeling error than does inclusion of the wavenumber-1 shear-induced asymmetry. Overall, the CNLS estimation method can handle the inherently nonlinear wind field model in a flexible manner; thus, it is well suited to capture the radial variability in the hurricane wind field’s asymmetry. The article concludes with brief remarks on how the CNLS-estimated model can be applied for simulating wind fields in a statistically generated ensemble.

Fast Sparse Reconstruction of Sound Field Via Bayesian Compressive Sensing

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Ding-Yu Hu ◽  
Xin-Yue Liu ◽  
Yue Xiao ◽  
Yu Fang
Keyword(s):  
Compressive Sensing ◽  
Sound Field ◽  
Sparse Reconstruction ◽  
Limited Attention ◽  
Reconstruction Method ◽  
Sound Sources ◽  
Computation Efficiency ◽  
Reconstruction Methods ◽  
Spatially Extended ◽  
Value Decomposition

To overcome the contradiction between the resolution and the measurement cost, various algorithms for reconstructing the sound field with sparse measurement have been developed. However, limited attention is paid to the computation efficiency. In this study, a fast sparse reconstruction method is proposed based on the Bayesian compressive sensing. First, the reconstruction problem is modeled by a sparse decomposition of the sound field via singular value decomposition. Then, the Bayesian compressive sensing is adapted to reconstruct the sound field with sparse measurement of sound pressure. Numerical results demonstrate that the proposed method is applicable to either the spatially sparse distributed sound sources or the spatially extended sound sources. And comparisons with other two sparse reconstruction methods show that the proposed one has the advantages in terms of reconstruction accuracy and computational efficiency. In addition, as it is developed in the framework of multitask compressive sensing, the method can use multiple snapshots to perform reconstruction, which greatly enhances the robustness to noise. The validity and the advantage of the proposed method are further proved by experimental results.

scholarly journals A New Statistical Reconstruction Method for the Computed Tomography Using an X-Ray Tube with Flying Focal Spot

2021 ◽  
Vol 11 (4) ◽  
pp. 271-286
Author(s):  
Robert Cierniak ◽  
Piotr Pluta ◽  
Marek Waligóra ◽  
Zdzisław Szymański ◽  
Konrad Grzanek ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Focal Spot ◽  
Reconstruction Method ◽  
Reconstruction Procedure ◽  
X Ray ◽  
Statistical Reconstruction ◽  
Reconstruction Methods ◽  
Ct Scanners ◽  
The Impact

Abstract This paper presents a new image reconstruction method for spiral cone- beam tomography scanners in which an X-ray tube with a flying focal spot is used. The method is based on principles related to the statistical model-based iterative reconstruction (MBIR) methodology. The proposed approach is a continuous-to-continuous data model approach, and the forward model is formulated as a shift-invariant system. This allows for avoiding a nutating reconstruction-based approach, e.g. the advanced single slice rebinning methodology (ASSR) that is usually applied in computed tomography (CT) scanners with X-ray tubes with a flying focal spot. In turn, the proposed approach allows for significantly accelerating the reconstruction processing and, generally, for greatly simplifying the entire reconstruction procedure. Additionally, it improves the quality of the reconstructed images in comparison to the traditional algorithms, as confirmed by extensive simulations. It is worth noting that the main purpose of introducing statistical reconstruction methods to medical CT scanners is the reduction of the impact of measurement noise on the quality of tomography images and, consequently, the dose reduction of X-ray radiation absorbed by a patient. A series of computer simulations followed by doctor’s assessments have been performed, which indicate how great a reduction of the absorbed dose can be achieved using the reconstruction approach presented here.

scholarly journals Reconstruction of High-Resolution Computed Tomography Image in Sinogram Space

2021 ◽  
Vol 15 ◽  
pp. 84-88
Author(s):  
Osama A. Omer
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Image Quality ◽  
Computational Cost ◽  
Super Resolution ◽  
Reconstruction Method ◽  
High Resolution Computed Tomography ◽  
Resolution Image ◽  
Reconstruction Methods

An important part of any computed tomography (CT) system is the reconstruction method, which transforms the measured data into images. Reconstruction methods for CT can be either analytical or iterative. The analytical methods can be exact, by exact projector inversion, or non-exact based on Back projection (BP). The BP methods are attractive because of thier simplicity and low computational cost. But they produce suboptimal images with respect to artifacts, resolution, and noise. This paper deals with improve of the image quality of BP by using super-resolution technique. Super-resolution can be beneficial in improving the image quality of many medical imaging systems without the need for significant hardware alternation. In this paper, we propose to reconstruct a high-resolution image from the measured signals in Sinogram space instead of reconstructing low-resolution images and then post-process these images to get higher resolution image.

A Shape Reconstruction Method for Two Phase Materials Using Resistance Tomography Data

2005 ◽  
Author(s):  
Manuchehr Soleimani
Keyword(s):  
Image Reconstruction ◽  
Computational Cost ◽  
Shape Reconstruction ◽  
Monotonicity Property ◽  
Inversion Method ◽  
Reconstruction Method ◽  
Two Phase ◽  
Online Application ◽  
Flow Monitoring ◽  
Reconstruction Methods

Electrical resistance tomography (ERT) has great potential to be used for multi-phase flow monitoring. The Image reconstruction in ERT is computationally costly, so the online monitoring is a difficult task. The linear reconstruction methods are currently used as fast methods. The image reconstruction is a nonlinear inverse problem and the linear methods are not sufficient in many cases. The application of a recently proposed non-iterative inversion method for two-phase materials in has been studied. The method works based on Monotonicity property of the resistance matrix in ERT and it requires modest computational cost. In this paper we explain the application of this inversion method. We demonstrate the capabilities and drawbacks of the method by using 2D test examples. A major contribution of this paper is to optimize the software program for the inversion (by doing most of the computations offline), so it can be used for online application.

scholarly journals Li-ion Battery Modeling and State of Charge Estimation Method Including the Hysteresis Effect

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1324 ◽  
Author(s):  
Antonucci ◽  
Artale ◽  
Brunaccini ◽  
Caravello ◽  
Cataliotti ◽  
...  
Keyword(s):  
Computational Cost ◽  
Estimation Method ◽  
Lithium Ion ◽  
State Of Charge ◽  
Experimental Comparison ◽  
Model Parameters ◽  
Hysteresis Phenomenon ◽  
Accuracy Measurement ◽  
Soc Estimation ◽  
Characterization Procedure

In this paper, a new approach to modeling the hysteresis phenomenon of the open circuit voltage (OCV) of lithium-ion batteries and estimating the battery state of charge (SoC) is presented. A characterization procedure is proposed to identify the battery model parameters, in particular, those related to the hysteresis phenomenon and the transition between charging and discharging conditions. A linearization method is used to obtain a suitable trade-off between the model accuracy and a low computational cost, in order to allow the implementation of SoC estimation on common hardware platforms. The proposed characterization procedure and the model effectiveness for SoC estimation are experimentally verified using a real grid-connected storage system. A mixed algorithm is adopted for SoC estimation, which takes into account both the traditional Coulomb counting method and the developed model. The experimental comparison with the traditional approach and the obtained results show the feasibility of the proposed approach for accurate SoC estimation, even in the presence of low-accuracy measurement transducers.

scholarly journals A Novel Phase Current Reconstruction Method for a Three-Level Neutral Point Clamped Inverter (NPCI) with a Neutral Shunt Resistor

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2616 ◽  
Author(s):  
Yungdeug Son ◽  
Jangmok Kim
Keyword(s):  
Estimation Method ◽  
Harmonic Distortion ◽  
Current Measurement ◽  
Neutral Point ◽  
Minimum Time ◽  
Reconstruction Method ◽  
Phase Current ◽  
Reconstruction Methods ◽  
Restoration Method ◽  
Current Reconstruction

This paper presents three phase current reconstruction methods for a three-level neutral point clamped inverter (NPCI) by measuring the voltage of a shunt resistor placed in the neutral point of the inverter. In order to accurately acquire the phase currents from the shunt resister, the dwell time of the active voltage vectors need to exceed the minimum time. On the other hand, if the time of active voltage is shorter than the minimum time, the current measurement becomes impossible. In this paper, unmeasurable regions for current are classified into three areas. Area 1 is a region in which both phase currents can be measure. Therefore, it is not necessary to restore the current. In Area 2, only one phase current can be measured. Thus, an estimation or restoration method is needed to measure another phase current. In this paper, the current estimation method using an electrical model of the motor is proposed. Area 3 is the region in which both phase currents can not be measured. In this case, it is necessary to move the voltage vector to the current measurable area by injecting the voltage. In this paper, Area 3 is divided into 36 sectors to inject optimal voltage. The proposed methods have the advantages of high current measurement accuracy and low THD (total harmonic distortion). The effectiveness of the proposed methods are verified through experimental results.

scholarly journals Fast data-driven learning of parallel MRI sampling patterns for large scale problems

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcelo V. W. Zibetti ◽  
Gabor T. Herman ◽  
Ravinder R. Regatte
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Quantitative Mri ◽  
Data Driven ◽  
Optimization Approach ◽  
Reconstruction Method ◽  
Parallel Mri ◽  
Reconstruction Methods ◽  
Scan Time ◽  
Reconstruction Quality

AbstractIn this study, a fast data-driven optimization approach, named bias-accelerated subset selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the purpose of reducing scan time in large-dimensional parallel MRI. BASS is applicable when Cartesian fully-sampled k-space measurements of specific anatomy are available for training and the reconstruction method for undersampled measurements is specified; such information is used to define the efficacy of any SP for recovering the values at the non-sampled k-space points. BASS produces a sequence of SPs with the aim of finding one of a specified size with (near) optimal efficacy. BASS was tested with five reconstruction methods for parallel MRI based on low-rankness and sparsity that allow a free choice of the SP. Three datasets were used for testing, two of high-resolution brain images ($$\text {T}_{2}$$ T 2 -weighted images and, respectively, $$\text {T}_{1\rho }$$ T 1 ρ -weighted images) and another of knee images for quantitative mapping of the cartilage. The proposed approach has low computational cost and fast convergence; in the tested cases it obtained SPs up to 50 times faster than the currently best greedy approach. Reconstruction quality increased by up to 45% over that provided by variable density and Poisson disk SPs, for the same scan time. Optionally, the scan time can be nearly halved without loss of reconstruction quality. Quantitative MRI and prospective accelerated MRI results show improvements. Compared with greedy approaches, BASS rapidly learns effective SPs for various reconstruction methods, using larger SPs and larger datasets; enabling better selection of sampling-reconstruction pairs for specific MRI problems.

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