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A family of smoothing algorithms based on the for electron and other spectroscopies Chebyshev filter

THIN SOLID FILMS 2006年第1-2期513卷 72-77页

作者： Lopez-Camacho, E. Garcia-Cortes, A. Palacio, C. Univ Autonoma Madrid Fac Ciencias Dept Fis Aplicada Madrid 28049 Spain Univ Autonoma Madrid Consejo Super Invest Cientificas Inst Ciencia Mat Madrid 28049 Spain

smoothing is a useful tool to improve the signal-to-noise ratio of spectroscopic data. This paper reports a new family of smoothing formulas, the Chebyshev filters, which are derived approaching the original data to a polynomial and using the mini-max principle, that is, keeping the maximum error down to a minimum, as fitting criterion. The properties of the filters are studied analyzing their associated transfer functions in the frequency domain. This leads us to the concept of spectral window and spectral window width as tool and parameter, respectively, to remove the high frequency noise components accompanying the experimental data. Also, simple criteria to choose the appropriate width of the spectral windows are put forward. The behaviour of the filters is easy to understand and the filters are fast and simple to use and to program. Finally the proposed smoothing algorithms have been tested using synthetic data as well as X-ray photoelectron spectra and optical absorption spectra. (c) 2006 Elsevier B.V. All rights reserved.

关键词： smoothing algorithms Chebyshev filters X-ray photoelectron spectroscopy optical absorption spectroscopy

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An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data

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REMOTE SENSING OF ENVIRONMENT 2016年 174卷 258-265页

作者： Shao, Yang Lunetta, Ross S. Wheeler, Brandon Iiames, John S. Campbell, James B. Virginia Tech Coll Nat Resources & Environm Dept Geog 115 Major Williams Hall Blacksburg VA 24061 USA US EPA Natl Exposure Res Lab 109 TW Alexander Dr Res Triangle Pk NC 27711 USA

In this study we compared the Savitzky-Golay, asymmetric Gaussian, double-logistic, Whittaker smoother, and discrete Fourier transformation smoothing algorithms (noise reduction) applied to Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data, to provide continuous phenology data used for land-cover (LC) classifications across the Laurentian Great Lakes Basin (GLB). MODIS 16-day 250 m NDVI imagery for the GLB was used in conjunction with National Land Cover Database (NLCD) from 2001, 2006 and 2011, and the Cropland Data Layers (CDL) from 2011 to 2014 to conduct classification evaluations. Inter-class separability was measured by Jeffries-Matusita (JM) distances between selected cover type pairs (both general classes and specific crops), and intra-class variability was measured by calculating simple Euclidean distance for samples within cover types. For the GLB, we found that the application of a smoothing algorithm significantly reduced image noise compared to the raw data. However, the Jeffries-Matusita (JM) measures for smoothed NDVI temporal profiles resulted in large inconsistencies. Of the five algorithms tested, only the Fourier transformation algorithm and Whittaker smoother improved inter-class separability for corn soybean class pair and significantly improved overall classification accuracy. When compared to the raw NDVI data as input, the overall classification accuracy from the Fourier transformation and Whittaker smoother improved performance by approximately 2-6% for some years. Conversely, the asymmetric Gaussian and double-logistic smoothing algorithms actually led to degradation of classification performance. (C) 2015 Elsevier Inc. All rights reserved.

关键词： MODIS-NDVI Multi-temporal analysis smoothing algorithms Validation

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Evaluating time-series smoothing algorithms for multi-temporal land cover classification

Evaluating time-series smoothing algorithms for multi-tempor...

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作者： Wheeler, Brandon Myles Virginia Tech | University

In this study we applied the asymmetric Gaussian, double-logistic, and Savitzky-Golay filters to MODIS time-series NDVI data to compare the capability of smoothing algorithms in noise reduction for improving land cover classification in the Great Lakes Basin, and providing groundwork to support cyanobacteria and cyanotoxin monitoring efforts. We used inter-class separability and intra-class variability, at varying levels of pixel homogeneity, to evaluate the effectiveness of three smoothing algorithms. Based on these initial tests, the algorithm which returned the best results was used to analyze how image stratification by ecoregion can affect filter performance. MODIS 16-day 250m NDVI imagery of the Great Lakes Basin from 2001-2013 were used in conjunction with National Land Cover Database (NLCD) 2006 and 2011 data, and Cropland Data Layers (CDL) from 2008 to 2013 to conduct these evaluations. Inter-class separability was measured by Jeffries-Matusita (JM) distances between selected land cover classes (both general classes and specific crops), and intra-class variability was measured by calculating simple Euclidean distance for samples within a land cover class. Within the study area, it was found that the application of a smoothing algorithm can significantly reduce image noise, improving both inter-class separability and intra-class variability when compared to the raw data. Of the three filters examined, the asymmetric Gaussian filter consistently returned the highest values of interclass separability, while all three filters performed very similarly for within-class variability. The ecoregion analysis based on the asymmetric Gaussian dataset indicated that the scale of study area can heavily impact within-class separability. The criteria we established have potential for furthering our understanding of the strengths and weaknesses of different smoothing algorithms, thereby improving pre-processing decisions for land cover classification using time-series data.

关键词： Remote sensing smoothing algorithms time-series NDVI MODIS Thesis

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Dual smoothing ionospheric gradient monitoring algorithm for dual-frequency BDS GBAS

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Chinese Journal of Aeronautics 2020年第12期33卷 3395-3404页

作者： Zhipeng WANG Yu YIN Dan SONG Kun FANG Qiang LI Xiao LI National Key Laboratory of CNS/ATM School of Electronic and Information EngineeringBeihang UniversityBeijing 100083China Beijing Hualong Tong Science and Technology Co LtdBeijing 100084China

In this study,a Dual smoothing Ionospheric Gradient Monitor Algorithm(DSIGMA)was developed for Code-Carrier Divergence(CCD)faults of dual-frequency Ground-Based Augmentation Systems(GBAS)based on the Bei Dou Navigation Satellite System(BDS).Divergence-Free(DF)combinations of the signals were used to form test statistics for a dualfrequency ***,the single-frequency DSIGMA was reviewed,which supports the GBAS approach service type D(GAST-D)for protection against the effect of large ionospheric *** single-frequency DSIGMA was used to create a novel input scheme for the dual-frequency DSIGMA by introducing DF *** steady states of the test statistics were also *** monitors were characterized using BDS measurement data,whereby standard deviations of 0.0432 and 0.0639 m for the proposed two test statistics were used to calculate the monitor *** extensive simulation was designed to assess the monitor performance by comparing the Probability of Missed Detection(PMD)according to the differential error with the range domain PMD limits under different fault *** results showed that the proposed algorithm has a higher integrity performance than the single-frequency *** minimum detectable divergence with the same missed probability is less than 50%that of GAST-D.

关键词： Approach Augmentation Integrity Ionosphere-monitoring Satellite navigation smoothing algorithms Steady state Threshold

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Extended smoothing joint data association for multi-target tracking in cluttered environments

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IET RADAR SONAR AND NAVIGATION 2020年第4期14卷 564-571页

作者： Memon, Sufyan Ali Kim, Myunggun Shin, Minho Daudpoto, Jawaid Pathan, Dur Muhammad Son, Hungsun MUET Dept Mechatron Engn Jamshoro Pakistan UNIST Mech Aerosp & Nucl Engn 50 Unist Gil Ulsan South Korea

In heavily cluttered environments, it is difficult to estimate the uncertain motion of an unknown number of targets with low detection probabilities. In particular, for tracking multiple targets, standard multi-target data association algorithms such as joint integrated probabilistic data association (JIPDA), face complexity and severely limited applicability due to a combinatorially increasing number of possible measurement-to-track associations. Smoothers refine the target estimates based on future scan information. However, in this complex surveillance scenario, existing smoothing algorithms often fail to track the true target trajectories. To overcome such difficulties, this study proposes a new smoothing joint measurement-to-track association algorithm called fixed-interval smoothing JIPDA for tracking extended target trajectories (FIsJIPDA). The algorithm employs two independent JIPDA filters: forward JIPDA (fJIPDA) and backward JIPDA (bJIPDA). fJIPDA tracks the target state forward in time and is computed after the smoothing is achieved. bJIPDA estimates the target state in the backward time sequence. The numerical simulation is performed in a heavily populated cluttered environment with low target-detection probabilities. The results show better target trajectory accuracy and false-track discrimination performance of FIsJIPDA compared with that of existing algorithms for tracking multiple extended targets.

关键词： filtering theory probability radar tracking clutter target tracking sensor fusion low target-detection probabilities target trajectory accuracy false-track discrimination performance tracking multiple extended targets smoothing joint data association multitarget tracking heavily cluttered environments uncertain motion low detection probabilities multiple targets standard multitarget data association algorithms face complexity combinatorially increasing number measurement-to-track associations future scan information complex surveillance scenario smoothing algorithms smoothing joint measurement-to-track association algorithm fixed-interval smoothing JIPDA extended target trajectories independent JIPDA filters target state heavily populated cluttered environment

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Comparison of smoothing Techniques and Recognition Methods for Online Kannada Character Recognition System

Comparison of Smoothing Techniques and Recognition Methods f...

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International Conference on Advances in Engineering and Technology Research (ICAETR),

作者： Shwetha, D. Ramya, S. Manipal Inst Technol Dept Elect & Commun Manipal Karnataka India

ISBN: (纸本)9781479963935

This paper aimed at working on Online Recognition of Handwritten Kannada Characters. The recognition was done for the Top, Middle and Bottom strokes of Kannada characters. Genius MousePen i608X was used to collect the handwritten character samples to build the database. Handwritten character samples were collected for each character from a particular target-group which includes people who are native to Kannada language and belong to different age groups. These samples were semi-automatically validated, pre-processed and features were extracted. Segmentation of characters was done to divide the strokes into top stroke, middle stroke and bottom stroke. These segmented strokes were individually processed. The pre-processing techniques used in the project include removal of duplicated points, smoothing, interpolating missing points, resampling of points and size normalization. smoothing techniques was compared for Gaussian and Moving Average smoothing. Dominant point, writing direction and the curvature features were also extracted. In addition to this, recognition was carried out by KNN and SVM pattern recognition methods and a second level of verification rules was incorporated, yielding a maximum recognition rate of 92.5% for KNN and 94.35% for SVM.

关键词： Handwritten Kannada Characters Character Recognition smoothing algorithms KNN SVM

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The normalized least-squares order-recursive lattice smoother

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SIGNAL PROCESSING 2002年第6期82卷 895-905页

作者： Kim, DK Park, P Pohang Univ Sci & Technol Div Elect & Comp Engn Nam Gu Pohang 790784 South Korea

This paper introduces a variance- and angle-normalized version of the least-squares order-recursive lattice (LSORL) smoother, say, the normalized LSORL smoother, via a geometric approach, The normalized LSORL smoother has excellent round-off noise properties and inherits all the other advantages of the normalized LSORL as well. Simulation results show that the normalized LSORL smoother outperforms the existing LSORL and QRD-based least-squares lattice smoother algorithms when finite-precision arithmetic is used. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： adaptive filters normalized LSORL smoothers smoothing algorithms least-squares lattice algorithms order-recursive structures geometric interpretation finite precision arithmetic

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Interior point multigrid methods for topology optimization

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2000年第3期19卷 214-224页

作者： Maar, B Schulz, V Heidelberg Univ Interdisciplinary Ctr Sci Comp IWR D-69120 Heidelberg Germany Weierstrass Inst Appl Anal & Stochast D-10117 Berlin Germany

In this paper, a new multigrid interior point approach to topology optimization problems in the context of the homogenization method is presented. The key observation is that nonlinear interior point methods lead to linear-quadratic subproblems with structures that can be favourably exploited within multigrid methods. Primal as well as primal-dual formulations are discussed. The multigrid approach is based on the transformed smoother paradigm. Numerical results for an example problem are presented.

关键词： topology optimization multigrid methods interior point methods smoothing algorithms checkerboard suppression

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A unified continuous optimization framework for center-based clustering methods

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JOURNAL OF MACHINE LEARNING RESEARCH 2007年第1期8卷 65-102页

作者： Teboulle, Marc Tel Aviv Univ Sch Math Sci IL-69978 Tel Aviv Israel

Center-based partitioning clustering algorithms rely on minimizing an appropriately formulated objective function, and different formulations suggest different possible algorithms. In this paper, we start with the standard nonconvex and nonsmooth formulation of the partitioning clustering problem. We demonstrate that within this elementary formulation, convex analysis tools and optimization theory provide a unifying language and framework to design, analyze and extend hard and soft center-based clustering algorithms, through a generic algorithm which retains the computational simplicity of the popular k-means scheme. We show that several well known and more recent center-based clustering algorithms, which have been derived either heuristically, or/and have emerged from intuitive analogies in physics, statistical techniques and information theoretic perspectives can be recovered as special cases of the proposed analysis and we streamline their relationships.

关键词： clustering k-means algorithm convex analysis support and asymptotic functions distance-like functions Bregman and Csiszar divergences nonlinear means nonsmooth optimization smoothing algorithms fixed point methods deterministic annealing expectation maximization information theory and entropy methods

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Nearest-neighbour modelling of reciprocal chains

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STOCHASTICS-AN INTERNATIONAL JOURNAL OF PROBABILITY AND STOCHASTIC PROCESSES 2008年第6期80卷 525-584页

作者： Carravetta, Francesco CNR Ist Anal Sistemi & Informat A Ruberti I-00185 Rome Italy

This paper focuses on the class of finite-state, discrete-index, reciprocal processes (reciprocal chains). Such a class of processes seems to be a suitable setup in many applications and, in particular, it appears well-suited for image-processing. While addressing this issue, the aim is 2-fold: theoretic and practical. As to the theoretic purpose, some new results are provided: first, a general stochastic realization result is provided for reciprocal chains endowed with a known, arbitrary, distribution. Such a model has the form of a fixed-degree, nearest-neighbour polynomial model. Next, the polynomial model is shown to be exactly linearizable, which means it is equivalent to a nearest-neighbour linear model in a different set of variables. The latter model turns out to be formally identical to the Levi-Frezza-Krener linear model of a Gaussian reciprocal process, although actually non-linear with respect to the chain's values. As far as the practical purpose is concerned, in order to yield an example of application an estimation issue is addressed: a suboptimal (polynomial-optimal) solution is derived for the smoothing problem of a reciprocal chain partially observed under non-Gaussian noise. To this purpose, two kinds of boundary conditions (Dirichlet and Cyclic), specifying the reciprocal chain on a finite interval, are considered, and in both cases the model is shown to be well-posed, in a 'wide-sense'. Under this view, some well-known representation results about Gaussian reciprocal processes extend, in a sense, to a 'non-Gaussian' case.

关键词： reciprocal processes Markov chains Markov fields smoothing algorithms stochastic realization nearest-neighbour models

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