scholarly journals Blind Deconvolution for Jump-Preserving Curve Estimation

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Xingfang Huang ◽  
Peihua Qiu
Keyword(s):  
Blind Deconvolution ◽  
Random Noise ◽  
Regression Function ◽  
Real Data ◽  
Sum Of Squares ◽  
Linear Kernel ◽  
Kernel Estimates ◽  
Curve Estimation ◽  
Local Linear ◽  
The Given

In many applications, observed signals are contaminated by both random noise and blur. This paper proposes a blind deconvolution procedure for estimating a regression function with possible jumps preserved, by removing both noise and blur when recovering the signals. Our procedure is based on three local linear kernel estimates of the regression function, constructed from observations in a left-side, a right-side, and a two-side neighborhood of a given point, respectively. The estimated function at the given point is then defined by one of the three estimates with the smallest weighted residual sum of squares. To better remove the noise and blur, this estimate can also be updated iteratively. Performance of this procedure is investigated by both simulation and real data examples, from which it can be seen that our procedure performs well in various cases.

scholarly journals A Class of Local Linear Estimators with Functional Data

2019 ◽  
pp. 379-391 ◽  
Author(s):  
Sara Leulmi ◽  
Fatiha Messaci
Keyword(s):  
Nonparametric Estimation ◽  
Metric Space ◽  
Functional Data ◽  
Regression Function ◽  
Real Data ◽  
Random Variable ◽  
Data Set ◽  
Response Variable ◽  
Local Linear ◽  
Linear Estimators

We introduce a local linear nonparametric estimation for the generalized regression function of a scalar response variable given a random variable taking values in a semi metric space. We establish a rate of uniform consistency for the proposed estimators. Then, based on a real data set we illustrate the performance of a particular studied estimator with respect to other known estimators

Local linear kernel estimation of the discontinuous regression function

2006 ◽  
Vol 21 (3-4) ◽  
pp. 557-569 ◽  
Author(s):  
I. R. Sánchez-Borrego ◽  
M. D. Martínez-Miranda ◽  
A. González-Carmona
Keyword(s):  
Kernel Estimation ◽  
Regression Function ◽  
Linear Kernel ◽  
Local Linear

scholarly journals Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Random Noise ◽  
Synthetic Data ◽  
Regression Function ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Great Opportunity ◽  
Atmospheric Compensation

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

Multicomponent microseismic data denoising by 3D shearlet transform

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. A45-A51 ◽  
Author(s):  
Chao Zhang ◽  
Mirko van der Baan
Keyword(s):  
Noise Suppression ◽  
Random Noise ◽  
Real Data ◽  
Fracture Initiation ◽  
Multidimensional Data ◽  
Data Sets ◽  
Denoising Method ◽  
Shearlet Transform ◽  
Microseismic Data ◽  
Significant Step

The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We have developed an effective multicomponent, multidimensional microseismic-data denoising method by conducting a simplified polarization analysis in the 3D shearlet transform domain. The 3D shearlet transform is very competitive in dealing with multidimensional data because it captures details of signals at different scales and orientations, which benefits signal and noise separation. We have developed a novel processing strategy based on a signal-detection operator that can effectively identify signal coefficients in the shearlet domain by taking the correlation and energy distribution of 3C microseismic signals into account. We perform tests on synthetic and real data sets and determine that the proposed method can effectively remove random noise and preserve weak signals.

Automatic blind deconvolution with Toeplitz-structured sparse total least squares

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. V345-V357 ◽  
Author(s):  
Nasser Kazemi
Keyword(s):  
Least Squares ◽  
Cross Validation ◽  
Blind Deconvolution ◽  
Real Data ◽  
Total Least Squares ◽  
Data Matrix ◽  
Seismic Survey ◽  
Structural Constraints ◽  
Generalized Cross Validation ◽  
Free Data

Given the noise-corrupted seismic recordings, blind deconvolution simultaneously solves for the reflectivity series and the wavelet. Blind deconvolution can be formulated as a fully perturbed linear regression model and solved by the total least-squares (TLS) algorithm. However, this algorithm performs poorly when the data matrix is a structured matrix and ill-conditioned. In blind deconvolution, the data matrix has a Toeplitz structure and is ill-conditioned. Accordingly, we develop a fully automatic single-channel blind-deconvolution algorithm to improve the performance of the TLS method. The proposed algorithm, called Toeplitz-structured sparse TLS, has no assumptions about the phase of the wavelet. However, it assumes that the reflectivity series is sparse. In addition, to reduce the model space and the number of unknowns, the algorithm benefits from the structural constraints on the data matrix. Our algorithm is an alternating minimization method and uses a generalized cross validation function to define the optimum regularization parameter automatically. Because the generalized cross validation function does not require any prior information about the noise level of the data, our approach is suitable for real-world applications. We validate the proposed technique using synthetic examples. In noise-free data, we achieve a near-optimal recovery of the wavelet and the reflectivity series. For noise-corrupted data with a moderate signal-to-noise ratio (S/N), we found that the algorithm successfully accounts for the noise in its model, resulting in a satisfactory performance. However, the results deteriorate as the S/N and the sparsity level of the data are decreased. We also successfully apply the algorithm to real data. The real-data examples come from 2D and 3D data sets of the Teapot Dome seismic survey.

scholarly journals Production trend identification and forecast for shop-floor business intelligence

ACTA IMEKO ◽  
2016 ◽  
Vol 5 (4) ◽  
pp. 49 ◽  
Author(s):  
Zsolt János Viharos ◽  
Jeno Csanaki ◽  
János Nacsa ◽  
Márton Edelényi ◽  
Csaba Péntek ◽  
...  
Keyword(s):  
Production Control ◽  
Real Data ◽  
Shop Floor ◽  
Tolerance Limits ◽  
Applied Model ◽  
Production Environments ◽  
Production Trend ◽  
Trend Identification ◽  
The Given ◽  
Lower Tolerance

<p>The paper introduces a methodology to define production trend classes and also the results to serve with trend prognosis in a given manufacturing situation. The prognosis is valid for one, selected production measure (e.g. a quality dimension of one product, like diameters, angles, surface roughness, pressure, basis position, etc.) but the applied model takes into account the past values of many other, related production data collected typically on the shop-floor, too. Consequently, it is useful in batch or (customized) mass production environments. The proposed solution is applicable to realize production control inside the tolerance limits to proactively avoid the production process going outside from the given upper and lower tolerance limits.</p><p>The solution was developed and validated on real data collected on the shop-floor; the paper also summarizes the validated application results of the proposed methodology.</p>

scholarly journals Time Coordination of Periodic Passenger Train Connections in Conditions of Single-Track Lines

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Filip Lorenz ◽  
Vit Janos ◽  
Dusan Teichmann ◽  
Michal Dorda
Keyword(s):  
Mathematical Model ◽  
Mathematical Programming ◽  
Real Data ◽  
Real Problem ◽  
Single Track ◽  
Railway Stations ◽  
Optimization Software ◽  
Proposed Model ◽  
The Individual ◽  
The Given

The article addresses creation of a mathematical model for a real problem regarding time coordination of periodic train connections operated on single-track lines. The individual train connections are dispatched with a predefined tact, and their arrivals at and departures to predefined railway stations (transfer nodes) need to be coordinated one another. In addition, because the train connections are operated on single-track lines, trains that pass each other in a predefined railway stations must be also coordinated. To optimize the process, mathematical programming methods are used. The presented article includes a mathematical model of the given task, and the proposed model is tested with real data. The calculation experiments were implemented using optimization software Xpress-IVE.

A Data-Driven Exploratory Approach for Level Curve Estimation With Autonomous Underwater Agents

2017 ◽  
Author(s):  
Hsien-Chung Lin ◽  
Eugen Solowjow ◽  
Masayoshi Tomizuka ◽  
Edwin Kreuzer
Keyword(s):  
Concentration Field ◽  
Real Data ◽  
Support Vector ◽  
Level Curve ◽  
Data Set ◽  
Curve Estimation ◽  
Numerical Studies ◽  
Exploratory Approach ◽  
Vector Machines ◽  
Myopic Strategy

This contribution presents a method to estimate environmental boundaries with mobile agents. The agents sample a concentration field of interest at their respective positions and infer a level curve of the unknown field. The presented method is based on support vector machines (SVMs), whereby the concentration level of interest serves as the decision boundary. The field itself does not have to be estimated in order to obtain the level curve which makes the method computationally very appealing. A myopic strategy is developed to pick locations that yield most informative concentration measurements. Cooperative operations of multiple agents are demonstrated by dividing the domain in Voronoi tessellations. Numerical studies demonstrate the feasibility of the method on a real data set of the California coastal area. The exploration strategy is benchmarked against random walk which it clearly outperforms.

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