Quantitative Measure Of Error Research Articles

Impact of different land use data on WRF model short range forecasts during pre-monsoon and monsoon seasons in India

This study focuses to analyse the impact of land use changes on short range weather forecasts over Indian region. Weather Research and Forecasting (WRF) model simulation experiments are conducted by using land use data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Indian Space Research Organization (ISRO) satellites for pre-monsoon and monsoon season. MODIS 2001 land use is used in control (CNT) experiment and updated land use with recent urban class from MODIS (EXP1) and ISRO (EXP2) for the year 2019 is used to generate model lower boundary conditions in other two experiments. Quantitative error measures and skill score computed for rainfall forecast showed that model skill is better with the use of realistic recent land use data from MODIS and ISRO during pre-monsoon and monsoon period. Extreme Dependency Index score computed also revealed that model skill in predicting extreme rare rainfall events is improved with recent landuse data. Model simulated surface meteorological variables and profiles at lower levels also found to be improved with the inclusion of realistic land use class from MODIS and ISRO. Between the two experiments, the one which used ISRO based land use showed larger improvement particularly during the monsoon season.

Improving phase-based conductivity reconstruction by means of deep learning-based denoising of phase data for 3T MRI.

To denoise phase using a deep learning method for phase-based in vivo electrical conductivity reconstruction in a 3T MR system. For phase deep-learning denoising, a convolutional neural network (U-net) was chosen. Training was performed on data sets from 10 healthy volunteers. Input data were the real and imaginary components of single averaged spin-echo data (SNR = 45), which was used to approximate the phase. For label data, multiple signal-averaged spin-echo data (SNR = 128) were used. Testing was performed on in silico and in vivo data. Reconstructed conductivity maps were derived using phase-based conductivity reconstructions. Additionally, we investigated the usability of the network to various SNR levels, imaging contrasts, and anatomical sites (ie, T1 , T2 , and proton density-weighted brain images and proton density-weighted breast images. In addition, conductivity reconstructions from deep learning-based denoised data were compared with conventional image filters, which were used for data denoising in electrical properties tomography (ie, the Gaussian filtering and the Savitzky-Golay filtering). The proposed deep learning-based denoising approach showed improvement for phase for both in silico and in vivo experiments with reduced quantitative error measures compared with other methods. Subsequently, this resulted in an improvement of reconstructed conductivity maps from the denoised phase with deep learning. The results suggest that the proposed approach can be used as an alternative preprocessing method to denoise maps for phase-based conductivity reconstruction without relying on image filters or signal averaging.

Learning a variational network for reconstruction of accelerated MRI data.

To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4. Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med 79:3055-3071, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Investigation of synthetic aperture methods in ultrasound surface imaging using elementary surface types

Synthetic aperture imaging methods have been employed widely in recent research in non-destructive testing (NDT), but uptake has been more limited in medical ultrasound imaging. Typically offering superior focussing power over more traditional phased array methods, these techniques have been employed in NDT applications to locate and characterise small defects within large samples, but have rarely been used to image surfaces. A desire to ultimately employ ultrasonic surface imaging for bone surface geometry measurement prior to surgical intervention motivates this research, and results are presented for initial laboratory trials of a surface reconstruction technique based on global thresholding of ultrasonic 3D point cloud data. In this study, representative geometry artefacts were imaged in the laboratory using two synthetic aperture techniques; the Total Focusing Method (TFM) and the Synthetic Aperture Focusing Technique (SAFT) employing full and narrow synthetic apertures, respectively.Three high precision metallic samples of known geometries (cuboid, sphere and cylinder) which featured a range of elementary surface primitives were imaged using a 5MHz, 128 element 1D phased array employing both SAFT and TFM approaches. The array was manipulated around the samples using a precision robotic positioning system, allowing for repeatable ultrasound derived 3D surface point clouds to be created. A global thresholding technique was then developed that allowed the extraction of the surface profiles, and these were compared with the known geometry samples to provide a quantitative measure of error of 3D surface reconstruction. The mean errors achieved with optimised SAFT imaging for the cuboidal, spherical and cylindrical samples were 1.3mm, 2.9mm and 2.0mm respectively, while those for TFM imaging were 3.7mm, 3.0mm and 3.1mm, respectively. These results were contrary to expectations given the higher information content associated with the TFM images. However, it was established that the reduced error associated with the SAFT technique was associated with significant reductions in side lobe levels of approximately 24dB in comparison to TFM imaging, although this came at the expense of reduced resolution and coverage.

Intra-operative correction of brain-shift

Brain-shift is a major source of error in neuronavigation systems based on pre-operative images. In this paper, we present intra-operative correction of brain-shift using 3D ultrasound. The method is based on image registration of vessels extracted from pre-operative MRA and intra-operative power Doppler-based ultrasound and is fully integrated in the neuronavigation software. We have performed correction of brain-shift in the operating room during surgery and provided the surgeon with updated information. Here, we present data from seven clinical cases with qualitative and quantitative error measures. The registration algorithm is fast enough to provide the surgeon with updated information within minutes and accounts for large portions of the experienced shift. Correction of brain-shift can make pre-operative data like fMRI and DTI reliable for a longer period of time and increase the usefulness of the MR data as a supplement to intra-operative 3D ultrasound in terms of overview and interpretation.

FUSION OF MULTISPECTRAL AND PANCHROMATIC IMAGES BASED ON THE NONSUBSAMPLED CONTOURLET TRANSFORM

This paper proposes two different methods for fusing multispectral (MS) and panchromatic (PAN) satellite images using the nonsubsampled contourlet transform (NSCT). The NSCT decomposes the images into several directional subbands at flexible resolutions. The finer subbands of the MS image decomposition, which represent the high frequency details, are modified with the subbands from PAN image. The inverse transform gives a high resolution MS representation of the given low resolution MS image. The paper also proposes the use of the parameters mean structural similarity index measure (MSSIM) and edge stability mean square error (ESMSE) to measure the quality of the fused image, in addition to standard parameters like correlation coefficient, PSNR, RASE and ERGAS. Experiments show that the proposed methods outperform standard fusion techniques in terms of both visual quality and quantitative error measures. The fused images obtained using the proposed methods show good structural similarity and better edge stability as the contourlet transform very well extracts the oriented edge details from the high resolution PAN image.

Simulating anisotropic etching of silicon in any etchant: evolutionary algorithm for the calibration of the continuous cellular automaton

An evolutionary algorithm is presented for the automated calibration of the continuous cellular automaton for the simulation of isotropic and anisotropic wet chemical etching of silicon in as many as 31 widely different and technologically relevant etchants, including KOH, KOH+IPA, TMAH and TMAH+Triton, in various concentrations and temperatures. Based on state-of-the-art evolutionary operators, we implement a robust algorithm for the simultaneous optimization of roughly 150 microscopic removal rates based on the minimization of a cost function with four quantitative error measures, including (i) the error between simulated and experimental macroscopic etch rates for numerous surface orientations all over the unit sphere, (ii) the error due to underetching asymmetries and floor corrugation features observed in simulated silicon samples masked using a circular pattern, (iii) the error associated with departures from a step-flow-based hierarchy in the values of the microscopic removal rates, and (iv) the error associated with deviations from a step-flow-based clustering of the microscopic removal rates. For the first time, we present the calibration and successful simulation of two technologically relevant CMOS compatible etchants, namely TMAH and, especially, TMAH+Triton, providing several comparisons between simulated and experimental MEMS structures based on multi-step etching in these etchants.

A geometric mass‐preserving redistancing scheme for the level set function

AbstractIn this paper we describe and evaluate a geometric mass‐preserving redistancing procedure for the level set function on general structured grids. The proposed algorithm is adapted from a recent finite element‐based method and preserves the mass by means of a localized mass correction. A salient feature of the scheme is the absence of adjustable parameters. The algorithm is tested in two and three spatial dimensions and compared with the widely used partial differential equation (PDE)‐based redistancing method using structured Cartesian grids. Through the use of quantitative error measures of interest in level set methods, we show that the overall performance of the proposed geometric procedure is better than PDE‐based reinitialization schemes, since it is more robust with comparable accuracy. We also show that the algorithm is well‐suited for the highly stretched curvilinear grids used in CFD simulations. Copyright © 2010 John Wiley & Sons, Ltd.

Vector Map Data Compression with Wavelets

This paper was first presented at a Symposium on ‘Advanced Moving-Map Displays’ held on the 3rd and 4th of August 1999 by the US Naval Research Laboratory Detachment at the NASA Stennis Space Centre, Mississippi, and is reproduced in modified form with the kind permission of the NRL Commanding Officer, Captain Douglas H. Rau USN.Wavelets and wavelet transforms can be used for vector-map data compression. The choice of wavelet, the level of decomposition, the method of thresholding, the height of the threshold, relative CPU times and file sizes, and reconstructed map appearance were investigated using the Wavelet Toolbox of MATLAB. Quantitative error measures were obtained. For two test vector-map data sets consisting of longitude and latitude points, compressions of 35 to 50 percent (1·5[ratio ]1 to 2[ratio ]1) were obtained with root-mean-square errors less than 0·003 to 0·01° longitude/latitude for wavelet packet decompositions using selected wavelets.

Adaptive-neighborhood image deblurring

A new technique is presented for the restoration of images degraded by a linear, shift-invariant blurring point-spread function in the presence of additive white Gaussian noise. The algorithm uses overlapping variable-size, variable-shape adaptive neighborhoods (ANs) to define stationary regions in the input image and obtains a spectral estimate of the noise in each AN region. This estimate is then used to obtain a spectral estimate of the original undegraded AN region, which is inverse Fourier transformed to obtain the space-domain deblurred AN region. The regions are then combined to form the final restored image. Mathematical derivation and implementation of the adaptive-neighborhood deblurring (AND) filter is discussed, and experimental results are presented with an analysis of the performance of the AND filter as compared to the fixed-neighborhood sectioned deblurring (FNSD) Wiener and power spectrum equalization filters. It is shown that using the AND algorithm for image deblurring enables the identification of relatively stationary regions. This improves the restoration process and produces results that are superior to those obtained using the FNSD method both visually and in terms of quantitative error measures.

Application of wavelet compression to digitized radiographs.

Image data compression is an enabling technology for teleradiology and picture archive and communication systems. Compression decreases the time and cost of image transmission and the requirements for image storage. Wavelets, discovered in 1987, constitute a new compression technique that has been described in radiologic publications but, to our knowledge, no previous studies of its use have been reported. The purpose of this study was to demonstrate the application of wavelet-based compression technology to digitized radiographs. Twelve radiographs with abnormal findings were digitized, compressed, and decompressed by using a new wavelet-based lossy compression algorithm. Images were compressed at ratios from 10:1 to 60:1. Seven board-certified radiologists reviewed images on a two-headed, high-resolution (2K x 2K) diagnostic workstation. Paired original and compressed/decompressed images were presented in random order. Reviewers adjusted contrast and magnification to judge whether image degradation was present, and if so, whether it was of diagnostic significance. Quantitative error measures were tabulated. Reviewers found no clinically relevant degradation below a compression ratio of 30:1. Skeletal radiographs appeared more sensitive to compression than did chest or abdominal radiographs, but the trend was not statistically significant. Quantitative error measures increased gradually with compression ratio. On the basis of subjective assessment of image quality and the computational efficiency of the algorithm, wavelet-base techniques appear promising for the compression of digitized radiographs. The results of this initial experience can be used to design appropriate observer performance studies.

Numerical Uncertainty in Fluid Flow Calculations: Needs for Future Research

Computational fluid dynamics (CFD) has established itself as a viable technique for performing research and solving engineering problems, and when used correctly, can give accurate results for many fairly complex problems. This success has led to an ever increasing number of journal publications, many code developers, and surprisingly many users in the industry. Commercial CFD packages are often marketed by claiming that a particular code can solve almost every fluid flow problem, while many users, both in industry and academia, stand aloof from quantitative error measures, instead being dazzled by colorful computer generated output. This is mostly due to insufficient education in the scientific computing discipline which often leads (intentional or not) to misuse and wrong conclusions. Every year, hundreds of papers are published in conference proceedings, and journals, on the advancement and application of CFD techniques. Whenever something is spawned in such large quantities it is very easy to lose sense of quality control. To assert quality, papers often end with a conclusion such as “good agreement is found between experiments and predictions” to which the readers have become so immune that it no longer has meaning. Unfortunately, very little information is provided about the numerical uncertainty and the experimental data are often treated as if they are 100 percent accurate.

Estimation of Soil Parameters from Driven Rod Neasurements

This paper presents a methodology for the statistical estimation of soil parameters from usual measurements. In particular, driven rod measurements and the fall of potential method are employed to estimate the parameters of an equivalent two layer soil model. Statistical estimation techniques, coupled with an accurate computer model for grounding system analysis are employed. These techniques provide a) the best estimate of the earth model parameters in the least square sense and b) a measure of the quality of the estimates. Specifically, the methodology provides a quantitative measure of error in the estimated parameters expressed in terms of probability that error will not exceed a given value. The procedure has been applied using field test data and has been shown to provide a satisfactory soil model.