Robust power spectral estimation for EEG data.

Science.gov (United States)

Melman, Tamar; Victor, Jonathan D

2016-08-01

Typical electroencephalogram (EEG) recordings often contain substantial artifact. These artifacts, often large and intermittent, can interfere with quantification of the EEG via its power spectrum. To reduce the impact of artifact, EEG records are typically cleaned by a preprocessing stage that removes individual segments or components of the recording. However, such preprocessing can introduce bias, discard available signal, and be labor-intensive. With this motivation, we present a method that uses robust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittent outliers on the spectral estimates. Using the multitaper method (Thomson, 1982) as a starting point, we replaced the final step of the standard power spectrum calculation with a quantile-based estimator, and the Jackknife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLAB modules, which extend the widely used Chronux toolbox. Using both simulated and human data, we show that in the presence of large intermittent outliers, the robust method produces improved estimates of the power spectrum, and that the Bayesian confidence intervals yield close-to-veridical coverage factors. The robust method, as compared to the standard method, is less affected by artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well as in the coverage factor. In the presence of large intermittent outliers, the robust method can reduce dependence on data preprocessing as compared to standard methods of spectral estimation. Copyright © 2016 Elsevier B.V. All rights reserved.

Robust Visual Tracking Using the Bidirectional Scale Estimation

Directory of Open Access Journals (Sweden)

An Zhiyong

2017-01-01

Full Text Available Object tracking with robust scale estimation is a challenging task in computer vision. This paper presents a novel tracking algorithm that learns the translation and scale filters with a complementary scheme. The translation filter is constructed using the ridge regression and multidimensional features. A robust scale filter is constructed by the bidirectional scale estimation, including the forward scale and backward scale. Firstly, we learn the scale filter using the forward tracking information. Then the forward scale and backward scale can be estimated using the respective scale filter. Secondly, a conservative strategy is adopted to compromise the forward and backward scales. Finally, the scale filter is updated based on the final scale estimation. It is effective to update scale filter since the stable scale estimation can improve the performance of scale filter. To reveal the effectiveness of our tracker, experiments are performed on 32 sequences with significant scale variation and on the benchmark dataset with 50 challenging videos. Our results show that the proposed tracker outperforms several state-of-the-art trackers in terms of robustness and accuracy.

QuantiFly: Robust Trainable Software for Automated Drosophila Egg Counting.

Directory of Open Access Journals (Sweden)

Dominic Waithe

Full Text Available We report the development and testing of software called QuantiFly: an automated tool to quantify Drosophila egg laying. Many laboratories count Drosophila eggs as a marker of fitness. The existing method requires laboratory researchers to count eggs manually while looking down a microscope. This technique is both time-consuming and tedious, especially when experiments require daily counts of hundreds of vials. The basis of the QuantiFly software is an algorithm which applies and improves upon an existing advanced pattern recognition and machine-learning routine. The accuracy of the baseline algorithm is additionally increased in this study through correction of bias observed in the algorithm output. The QuantiFly software, which includes the refined algorithm, has been designed to be immediately accessible to scientists through an intuitive and responsive user-friendly graphical interface. The software is also open-source, self-contained, has no dependencies and is easily installed (https://github.com/dwaithe/quantifly. Compared to manual egg counts made from digital images, QuantiFly achieved average accuracies of 94% and 85% for eggs laid on transparent (defined and opaque (yeast-based fly media. Thus, the software is capable of detecting experimental differences in most experimental situations. Significantly, the advanced feature recognition capabilities of the software proved to be robust to food surface artefacts like bubbles and crevices. The user experience involves image acquisition, algorithm training by labelling a subset of eggs in images of some of the vials, followed by a batch analysis mode in which new images are automatically assessed for egg numbers. Initial training typically requires approximately 10 minutes, while subsequent image evaluation by the software is performed in just a few seconds. Given the average time per vial for manual counting is approximately 40 seconds, our software introduces a timesaving advantage for

Robust motion estimation using connected operators

OpenAIRE

Salembier Clairon, Philippe Jean; Sanson, H

1997-01-01

This paper discusses the use of connected operators for robust motion estimation The proposed strategy involves a motion estimation step extracting the dominant motion and a ltering step relying on connected operators that remove objects that do not fol low the dominant motion. These two steps are iterated in order to obtain an accurate motion estimation and a precise de nition of the objects fol lowing this motion This strategy can be applied on the entire frame or on individual connected c...

Robust median estimator in logisitc regression

Czech Academy of Sciences Publication Activity Database

Hobza, T.; Pardo, L.; Vajda, Igor

2008-01-01

Roč. 138, č. 12 (2008), s. 3822-3840 ISSN 0378-3758 R&D Projects: GA MŠk 1M0572 Grant - others:Instituto Nacional de Estadistica (ES) MPO FI - IM3/136; GA MŠk(CZ) MTM 2006-06872 Institutional research plan: CEZ:AV0Z10750506 Keywords : Logistic regression * Median * Robustness * Consistency and asymptotic normality * Morgenthaler * Bianco and Yohai * Croux and Hasellbroeck Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.679, year: 2008 http://library.utia.cas.cz/separaty/2008/SI/vajda-robust%20median%20estimator%20in%20logistic%20regression.pdf

Second order statistics of bilinear forms of robust scatter estimators

KAUST Repository

Kammoun, Abla

2015-08-12

This paper lies in the lineage of recent works studying the asymptotic behaviour of robust-scatter estimators in the case where the number of observations and the dimension of the population covariance matrix grow at infinity with the same pace. In particular, we analyze the fluctuations of bilinear forms of the robust shrinkage estimator of covariance matrix. We show that this result can be leveraged in order to improve the design of robust detection methods. As an example, we provide an improved generalized likelihood ratio based detector which combines robustness to impulsive observations and optimality across the shrinkage parameter, the optimality being considered for the false alarm regulation.

On the robustness of two-stage estimators

KAUST Repository

Zhelonkin, Mikhail

2012-04-01

The aim of this note is to provide a general framework for the analysis of the robustness properties of a broad class of two-stage models. We derive the influence function, the change-of-variance function, and the asymptotic variance of a general two-stage M-estimator, and provide their interpretations. We illustrate our results in the case of the two-stage maximum likelihood estimator and the two-stage least squares estimator. © 2011.

Robust Pitch Estimation Using an Optimal Filter on Frequency Estimates

DEFF Research Database (Denmark)

Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll

2014-01-01

of such signals from unconstrained frequency estimates (UFEs). A minimum variance distortionless response (MVDR) method is proposed as an optimal solution to minimize the variance of UFEs considering the constraint of integer harmonics. The MVDR filter is designed based on noise statistics making it robust...

Robust Covariance Estimators Based on Information Divergences and Riemannian Manifold

Directory of Open Access Journals (Sweden)

Xiaoqiang Hua

2018-03-01

Full Text Available This paper proposes a class of covariance estimators based on information divergences in heterogeneous environments. In particular, the problem of covariance estimation is reformulated on the Riemannian manifold of Hermitian positive-definite (HPD matrices. The means associated with information divergences are derived and used as the estimators. Without resorting to the complete knowledge of the probability distribution of the sample data, the geometry of the Riemannian manifold of HPD matrices is considered in mean estimators. Moreover, the robustness of mean estimators is analyzed using the influence function. Simulation results indicate the robustness and superiority of an adaptive normalized matched filter with our proposed estimators compared with the existing alternatives.

Heteroscedasticity resistant robust covariance matrix estimator

Czech Academy of Sciences Publication Activity Database

Víšek, Jan Ámos

2010-01-01

Roč. 17, č. 27 (2010), s. 33-49 ISSN 1212-074X Grant - others:GA UK(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10750506 Keywords : Regression * Covariance matrix * Heteroscedasticity * Resistant Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2011/SI/visek-heteroscedasticity resistant robust covariance matrix estimator.pdf

Robust estimation of track parameters in wire chambers

International Nuclear Information System (INIS)

Bogdanova, N.B.; Bourilkov, D.T.

1988-01-01

The aim of this paper is to compare numerically the possibilities of the least square fit (LSF) and robust methods for modelled and real track data to determine the linear regression parameters of charged particles in wire chambers. It is shown, that Tukey robust estimate is superior to more standard (versions of LSF) methods. The efficiency of the method is illustrated by tables and figures for some important physical characteristics

Robust linear discriminant analysis with distance based estimators

Science.gov (United States)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Ali, Hazlina

2017-11-01

Linear discriminant analysis (LDA) is one of the supervised classification techniques concerning relationship between a categorical variable and a set of continuous variables. The main objective of LDA is to create a function to distinguish between populations and allocating future observations to previously defined populations. Under the assumptions of normality and homoscedasticity, the LDA yields optimal linear discriminant rule (LDR) between two or more groups. However, the optimality of LDA highly relies on the sample mean and pooled sample covariance matrix which are known to be sensitive to outliers. To alleviate these conflicts, a new robust LDA using distance based estimators known as minimum variance vector (MVV) has been proposed in this study. The MVV estimators were used to substitute the classical sample mean and classical sample covariance to form a robust linear discriminant rule (RLDR). Simulation and real data study were conducted to examine on the performance of the proposed RLDR measured in terms of misclassification error rates. The computational result showed that the proposed RLDR is better than the classical LDR and was comparable with the existing robust LDR.

Robust bearing estimation for 3-component stations

International Nuclear Information System (INIS)

CLAASSEN, JOHN P.

2000-01-01

A robust bearing estimation process for 3-component stations has been developed and explored. The method, called SEEC for Search, Estimate, Evaluate and Correct, intelligently exploits the inherent information in the arrival at every step of the process to achieve near-optimal results. In particular the approach uses a consistent framework to define the optimal time-frequency windows on which to make estimates, to make the bearing estimates themselves, to construct metrics helpful in choosing the better estimates or admitting that the bearing is immeasurable, and finally to apply bias corrections when calibration information is available to yield a single final estimate. The algorithm was applied to a small but challenging set of events in a seismically active region. It demonstrated remarkable utility by providing better estimates and insights than previously available. Various monitoring implications are noted from these findings

Simultaneous Robust Fault and State Estimation for Linear Discrete-Time Uncertain Systems

Directory of Open Access Journals (Sweden)

Feten Gannouni

2017-01-01

Full Text Available We consider the problem of robust simultaneous fault and state estimation for linear uncertain discrete-time systems with unknown faults which affect both the state and the observation matrices. Using transformation of the original system, a new robust proportional integral filter (RPIF having an error variance with an optimized guaranteed upper bound for any allowed uncertainty is proposed to improve robust estimation of unknown time-varying faults and to improve robustness against uncertainties. In this study, the minimization problem of the upper bound of the estimation error variance is formulated as a convex optimization problem subject to linear matrix inequalities (LMI for all admissible uncertainties. The proportional and the integral gains are optimally chosen by solving the convex optimization problem. Simulation results are given in order to illustrate the performance of the proposed filter, in particular to solve the problem of joint fault and state estimation.

Second order statistics of bilinear forms of robust scatter estimators

KAUST Repository

Kammoun, Abla; Couillet, Romain; Pascal, Fré dé ric

2015-01-01

. In particular, we analyze the fluctuations of bilinear forms of the robust shrinkage estimator of covariance matrix. We show that this result can be leveraged in order to improve the design of robust detection methods. As an example, we provide an improved

Robust Optical Richness Estimation with Reduced Scatter

Energy Technology Data Exchange (ETDEWEB)

Rykoff, E.S.; /LBL, Berkeley; Koester, B.P.; /Chicago U. /Chicago U., KICP; Rozo, E.; /Chicago U. /Chicago U., KICP; Annis, J.; /Fermilab; Evrard, A.E.; /Michigan U. /Michigan U., MCTP; Hansen, S.M.; /Lick Observ.; Hao, J.; /Fermilab; Johnston, D.E.; /Fermilab; McKay, T.A.; /Michigan U. /Michigan U., MCTP; Wechsler, R.H.; /KIPAC, Menlo Park /SLAC

2012-06-07

Reducing the scatter between cluster mass and optical richness is a key goal for cluster cosmology from photometric catalogs. We consider various modifications to the red-sequence matched filter richness estimator of Rozo et al. (2009b), and evaluate their impact on the scatter in X-ray luminosity at fixed richness. Most significantly, we find that deeper luminosity cuts can reduce the recovered scatter, finding that {sigma}{sub ln L{sub X}|{lambda}} = 0.63 {+-} 0.02 for clusters with M{sub 500c} {approx}> 1.6 x 10{sup 14} h{sub 70}{sup -1} M{sub {circle_dot}}. The corresponding scatter in mass at fixed richness is {sigma}{sub ln M|{lambda}} {approx} 0.2-0.3 depending on the richness, comparable to that for total X-ray luminosity. We find that including blue galaxies in the richness estimate increases the scatter, as does weighting galaxies by their optical luminosity. We further demonstrate that our richness estimator is very robust. Specifically, the filter employed when estimating richness can be calibrated directly from the data, without requiring a-priori calibrations of the red-sequence. We also demonstrate that the recovered richness is robust to up to 50% uncertainties in the galaxy background, as well as to the choice of photometric filter employed, so long as the filters span the 4000 {angstrom} break of red-sequence galaxies. Consequently, our richness estimator can be used to compare richness estimates of different clusters, even if they do not share the same photometric data. Appendix A includes 'easy-bake' instructions for implementing our optimal richness estimator, and we are releasing an implementation of the code that works with SDSS data, as well as an augmented maxBCG catalog with the {lambda} richness measured for each cluster.

Graphical evaluation of the ridge-type robust regression estimators in mixture experiments.

Science.gov (United States)

Erkoc, Ali; Emiroglu, Esra; Akay, Kadri Ulas

2014-01-01

In mixture experiments, estimation of the parameters is generally based on ordinary least squares (OLS). However, in the presence of multicollinearity and outliers, OLS can result in very poor estimates. In this case, effects due to the combined outlier-multicollinearity problem can be reduced to certain extent by using alternative approaches. One of these approaches is to use biased-robust regression techniques for the estimation of parameters. In this paper, we evaluate various ridge-type robust estimators in the cases where there are multicollinearity and outliers during the analysis of mixture experiments. Also, for selection of biasing parameter, we use fraction of design space plots for evaluating the effect of the ridge-type robust estimators with respect to the scaled mean squared error of prediction. The suggested graphical approach is illustrated on Hald cement data set.

Neuromorphic Configurable Architecture for Robust Motion Estimation

Directory of Open Access Journals (Sweden)

Guillermo Botella

2008-01-01

Full Text Available The robustness of the human visual system recovering motion estimation in almost any visual situation is enviable, performing enormous calculation tasks continuously, robustly, efficiently, and effortlessly. There is obviously a great deal we can learn from our own visual system. Currently, there are several optical flow algorithms, although none of them deals efficiently with noise, illumination changes, second-order motion, occlusions, and so on. The main contribution of this work is the efficient implementation of a biologically inspired motion algorithm that borrows nature templates as inspiration in the design of architectures and makes use of a specific model of human visual motion perception: Multichannel Gradient Model (McGM. This novel customizable architecture of a neuromorphic robust optical flow can be constructed with FPGA or ASIC device using properties of the cortical motion pathway, constituting a useful framework for building future complex bioinspired systems running in real time with high computational complexity. This work includes the resource usage and performance data, and the comparison with actual systems. This hardware has many application fields like object recognition, navigation, or tracking in difficult environments due to its bioinspired and robustness properties.

Integrating chronological uncertainties for annually laminated lake sediments using layer counting, independent chronologies and Bayesian age modelling (Lake Ohau, South Island, New Zealand)

Science.gov (United States)

Vandergoes, Marcus J.; Howarth, Jamie D.; Dunbar, Gavin B.; Turnbull, Jocelyn C.; Roop, Heidi A.; Levy, Richard H.; Li, Xun; Prior, Christine; Norris, Margaret; Keller, Liz D.; Baisden, W. Troy; Ditchburn, Robert; Fitzsimons, Sean J.; Bronk Ramsey, Christopher

2018-05-01

Annually resolved (varved) lake sequences are important palaeoenvironmental archives as they offer a direct incremental dating technique for high-frequency reconstruction of environmental and climate change. Despite the importance of these records, establishing a robust chronology and quantifying its precision and accuracy (estimations of error) remains an essential but challenging component of their development. We outline an approach for building reliable independent chronologies, testing the accuracy of layer counts and integrating all chronological uncertainties to provide quantitative age and error estimates for varved lake sequences. The approach incorporates (1) layer counts and estimates of counting precision; (2) radiometric and biostratigrapic dating techniques to derive independent chronology; and (3) the application of Bayesian age modelling to produce an integrated age model. This approach is applied to a case study of an annually resolved sediment record from Lake Ohau, New Zealand. The most robust age model provides an average error of 72 years across the whole depth range. This represents a fractional uncertainty of ∼5%, higher than the <3% quoted for most published varve records. However, the age model and reported uncertainty represent the best fit between layer counts and independent chronology and the uncertainties account for both layer counting precision and the chronological accuracy of the layer counts. This integrated approach provides a more representative estimate of age uncertainty and therefore represents a statistically more robust chronology.

A robust background regression based score estimation algorithm for hyperspectral anomaly detection

Science.gov (United States)

Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

2016-12-01

Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement

A comparative study of some robust ridge and liu estimators ...

African Journals Online (AJOL)

In multiple linear regression analysis, multicollinearity and outliers are two main problems. When multicollinearity exists, biased estimation techniques such as Ridge and Liu Estimators are preferable to Ordinary Least Square. On the other hand, when outliers exist in the data, robust estimators like M, MM, LTS and S ...

Image Analytical Approach for Needle-Shaped Crystal Counting and Length Estimation

DEFF Research Database (Denmark)

Wu, Jian X.; Kucheryavskiy, Sergey V.; Jensen, Linda G.

2015-01-01

Estimation of nucleation and crystal growth rates from microscopic information is of critical importance. This can be an especially challenging task if needle growth of crystals is observed. To address this challenge, an image analytical method for counting of needle-shaped crystals and estimating...

A robust bayesian estimate of the concordance correlation coefficient.

Science.gov (United States)

Feng, Dai; Baumgartner, Richard; Svetnik, Vladimir

2015-01-01

A need for assessment of agreement arises in many situations including statistical biomarker qualification or assay or method validation. Concordance correlation coefficient (CCC) is one of the most popular scaled indices reported in evaluation of agreement. Robust methods for CCC estimation currently present an important statistical challenge. Here, we propose a novel Bayesian method of robust estimation of CCC based on multivariate Student's t-distribution and compare it with its alternatives. Furthermore, we extend the method to practically relevant settings, enabling incorporation of confounding covariates and replications. The superiority of the new approach is demonstrated using simulation as well as real datasets from biomarker application in electroencephalography (EEG). This biomarker is relevant in neuroscience for development of treatments for insomnia.

Robust stability and ℋ ∞ -estimation for uncertain discrete systems with state-delay

Directory of Open Access Journals (Sweden)

Mahmoud Magdi S.

2001-01-01

Full Text Available In this paper, we investigate the problems of robust stability and ℋ ∞ -estimation for a class of linear discrete-time systems with time-varying norm-bounded parameter uncertainty and unknown state-delay. We provide complete results for robust stability with prescribed performance measure and establish a version of the discrete Bounded Real Lemma. Then, we design a linear estimator such that the estimation error dynamics is robustly stable with a guaranteed ℋ ∞ -performance irrespective of the parameteric uncertainties and unknown state delays. A numerical example is worked out to illustrate the developed theory.

Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies

KAUST Repository

Chen, Yi-Hau; Chatterjee, Nilanjan; Carroll, Raymond J.

2009-01-01

Case-control association studies often aim to investigate the role of genes and gene-environment interactions in terms of the underlying haplotypes (i.e., the combinations of alleles at multiple genetic loci along chromosomal regions). The goal of this article is to develop robust but efficient approaches to the estimation of disease odds-ratio parameters associated with haplotypes and haplotype-environment interactions. We consider "shrinkage" estimation techniques that can adaptively relax the model assumptions of Hardy-Weinberg-Equilibrium and gene-environment independence required by recently proposed efficient "retrospective" methods. Our proposal involves first development of a novel retrospective approach to the analysis of case-control data, one that is robust to the nature of the gene-environment distribution in the underlying population. Next, it involves shrinkage of the robust retrospective estimator toward a more precise, but model-dependent, retrospective estimator using novel empirical Bayes and penalized regression techniques. Methods for variance estimation are proposed based on asymptotic theories. Simulations and two data examples illustrate both the robustness and efficiency of the proposed methods.

Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies

KAUST Repository

Chen, Yi-Hau

2009-03-01

Case-control association studies often aim to investigate the role of genes and gene-environment interactions in terms of the underlying haplotypes (i.e., the combinations of alleles at multiple genetic loci along chromosomal regions). The goal of this article is to develop robust but efficient approaches to the estimation of disease odds-ratio parameters associated with haplotypes and haplotype-environment interactions. We consider "shrinkage" estimation techniques that can adaptively relax the model assumptions of Hardy-Weinberg-Equilibrium and gene-environment independence required by recently proposed efficient "retrospective" methods. Our proposal involves first development of a novel retrospective approach to the analysis of case-control data, one that is robust to the nature of the gene-environment distribution in the underlying population. Next, it involves shrinkage of the robust retrospective estimator toward a more precise, but model-dependent, retrospective estimator using novel empirical Bayes and penalized regression techniques. Methods for variance estimation are proposed based on asymptotic theories. Simulations and two data examples illustrate both the robustness and efficiency of the proposed methods.

Trainable estimators for indirect people counting : a comparative study

NARCIS (Netherlands)

Acampora, G.; Loia, V.; Percannella, G.; Vento, M.

2011-01-01

Estimating the number of people in a scene is a very relevant issue due to the possibility of using it in a large number of contexts where it is necessary to automatically monitor an area for security/safety reasons, for economic purposes, etc. The large number of people counting approaches

Body protein losses estimated by nitrogen balance and potassium-40 counting

International Nuclear Information System (INIS)

Belyea, R.L.; Babbitt, C.L.; Sedgwick, H.T.; Zinn, G.M.

1986-01-01

Body protein losses estimated from N balance were compared with those estimated by 40K counting. Six nonlactating dairy cows were fed an adequate N diet for 7 wk, a low N diet for 9 wk, and a replete N diet for 3 wk. The low N diet contained high cell wall grass hay plus ground corn, starch, and molasses. Soybean meal was added to the low N diet to increase N in the adequate N and replete N diets. Intake was measured daily. Digestibilities, N balance, and body composition (estimated by 40K counting) were determined during each dietary regimen. During low N treatment, hay dry matter intake declined 2 kg/d, and supplement increased about .5 kg/d. Dry matter digestibility was not altered by N treatment. Protein and acid detergent fiber digestibilities decreased from 40 and 36% during adequate N to 20 and 2%, respectively, during low N. Fecal and urinary N also declined when cows were fed the low N diet. By the end of repletion, total intake, fiber, and protein digestibilities as well as N partition were similar to or exceeded those during adequate N intake. Body protein (N) loss was estimated by N balance to be about 3 kg compared with 8 kg by 40K counting. Body fat losses (32 kg) were large because of low energy digestibility and intake. Seven kilograms of body fat were regained during repletion, but there was no change in body protein

Manifold-Based Visual Object Counting.

Science.gov (United States)

Wang, Yi; Zou, Yuexian; Wang, Wenwu

2018-07-01

Visual object counting (VOC) is an emerging area in computer vision which aims to estimate the number of objects of interest in a given image or video. Recently, object density based estimation method is shown to be promising for object counting as well as rough instance localization. However, the performance of this method tends to degrade when dealing with new objects and scenes. To address this limitation, we propose a manifold-based method for visual object counting (M-VOC), based on the manifold assumption that similar image patches share similar object densities. Firstly, the local geometry of a given image patch is represented linearly by its neighbors using a predefined patch training set, and the object density of this given image patch is reconstructed by preserving the local geometry using locally linear embedding. To improve the characterization of local geometry, additional constraints such as sparsity and non-negativity are also considered via regularization, nonlinear mapping, and kernel trick. Compared with the state-of-the-art VOC methods, our proposed M-VOC methods achieve competitive performance on seven benchmark datasets. Experiments verify that the proposed M-VOC methods have several favorable properties, such as robustness to the variation in the size of training dataset and image resolution, as often encountered in real-world VOC applications.

Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries

International Nuclear Information System (INIS)

Ng, Kong Soon; Moo, Chin-Sien; Chen, Yi-Ping; Hsieh, Yao-Ching

2009-01-01

The coulomb counting method is expedient for state-of-charge (SOC) estimation of lithium-ion batteries with high charging and discharging efficiencies. The charging and discharging characteristics are investigated and reveal that the coulomb counting method is convenient and accurate for estimating the SOC of lithium-ion batteries. A smart estimation method based on coulomb counting is proposed to improve the estimation accuracy. The corrections are made by considering the charging and operating efficiencies. Furthermore, the state-of-health (SOH) is evaluated by the maximum releasable capacity. Through the experiments that emulate practical operations, the SOC estimation method is verified to demonstrate the effectiveness and accuracy.

Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study.

Science.gov (United States)

Chen, Wansu; Shi, Jiaxiao; Qian, Lei; Azen, Stanley P

2014-06-26

To estimate relative risks or risk ratios for common binary outcomes, the most popular model-based methods are the robust (also known as modified) Poisson and the log-binomial regression. Of the two methods, it is believed that the log-binomial regression yields more efficient estimators because it is maximum likelihood based, while the robust Poisson model may be less affected by outliers. Evidence to support the robustness of robust Poisson models in comparison with log-binomial models is very limited. In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed. The findings indicate that for data coming from a population where the relationship between the outcome and the covariate was in a simple form (e.g. log-linear), the two models yielded comparable biases and mean square errors. However, if the true relationship contained a higher order term, the robust Poisson models consistently outperformed the log-binomial models even when the level of contamination is low. The robust Poisson models are more robust (or less sensitive) to outliers compared to the log-binomial models when estimating relative risks or risk ratios for common binary outcomes. Users should be aware of the limitations when choosing appropriate models to estimate relative risks or risk ratios.

Where can pixel counting area estimates meet user-defined accuracy requirements?

Science.gov (United States)

Waldner, François; Defourny, Pierre

2017-08-01

Pixel counting is probably the most popular way to estimate class areas from satellite-derived maps. It involves determining the number of pixels allocated to a specific thematic class and multiplying it by the pixel area. In the presence of asymmetric classification errors, the pixel counting estimator is biased. The overarching objective of this article is to define the applicability conditions of pixel counting so that the estimates are below a user-defined accuracy target. By reasoning in terms of landscape fragmentation and spatial resolution, the proposed framework decouples the resolution bias and the classifier bias from the overall classification bias. The consequence is that prior to any classification, part of the tolerated bias is already committed due to the choice of the spatial resolution of the imagery. How much classification bias is affordable depends on the joint interaction of spatial resolution and fragmentation. The method was implemented over South Africa for cropland mapping, demonstrating its operational applicability. Particular attention was paid to modeling a realistic sensor's spatial response by explicitly accounting for the effect of its point spread function. The diagnostic capabilities offered by this framework have multiple potential domains of application such as guiding users in their choice of imagery and providing guidelines for space agencies to elaborate the design specifications of future instruments.

Uranium mass and neutron multiplication factor estimates from time-correlation coincidence counts

Energy Technology Data Exchange (ETDEWEB)

Xie, Wenxiong [China Academy of Engineering Physics, Center for Strategic Studies, Beijing 100088 (China); Li, Jiansheng [China Academy of Engineering Physics, Institute of Nuclear Physics and Chemistry, Mianyang 621900 (China); Zhu, Jianyu [China Academy of Engineering Physics, Center for Strategic Studies, Beijing 100088 (China)

2015-10-11

Time-correlation coincidence counts of neutrons are an important means to measure attributes of nuclear material. The main deficiency in the analysis is that an attribute of an unknown component can only be assessed by comparing it with similar known components. There is a lack of a universal method of measurement suitable for the different attributes of the components. This paper presents a new method that uses universal relations to estimate the mass and neutron multiplication factor of any uranium component with known enrichment. Based on numerical simulations and analyses of 64 highly enriched uranium components with different thicknesses and average radii, the relations between mass, multiplication and coincidence spectral features have been obtained by linear regression analysis. To examine the validity of the method in estimating the mass of uranium components with different sizes, shapes, enrichment, and shielding, the features of time-correlation coincidence-count spectra for other objects with similar attributes are simulated. Most of the masses and multiplications for these objects could also be derived by the formulation. Experimental measurements of highly enriched uranium castings have also been used to verify the formulation. The results show that for a well-designed time-dependent coincidence-count measuring system of a uranium attribute, there are a set of relations dependent on the uranium enrichment by which the mass and multiplication of the measured uranium components of any shape and size can be estimated from the features of the source-detector coincidence-count spectrum.

Determinants of long-term growth : New results applying robust estimation and extreme bounds analysis

NARCIS (Netherlands)

Sturm, J.-E.; de Haan, J.

2005-01-01

Two important problems exist in cross-country growth studies: outliers and model uncertainty. Employing Sala-i-Martin's (1997a,b) data set, we first use robust estimation and analyze to what extent outliers influence OLS regressions. We then use both OLS and robust estimation techniques in applying

A burst-mode photon counting receiver with automatic channel estimation and bit rate detection

Science.gov (United States)

Rao, Hemonth G.; DeVoe, Catherine E.; Fletcher, Andrew S.; Gaschits, Igor D.; Hakimi, Farhad; Hamilton, Scott A.; Hardy, Nicholas D.; Ingwersen, John G.; Kaminsky, Richard D.; Moores, John D.; Scheinbart, Marvin S.; Yarnall, Timothy M.

2016-04-01

We demonstrate a multi-rate burst-mode photon-counting receiver for undersea communication at data rates up to 10.416 Mb/s over a 30-foot water channel. To the best of our knowledge, this is the first demonstration of burst-mode photon-counting communication. With added attenuation, the maximum link loss is 97.1 dB at λ=517 nm. In clear ocean water, this equates to link distances up to 148 meters. For λ=470 nm, the achievable link distance in clear ocean water is 450 meters. The receiver incorporates soft-decision forward error correction (FEC) based on a product code of an inner LDPC code and an outer BCH code. The FEC supports multiple code rates to achieve error-free performance. We have selected a burst-mode receiver architecture to provide robust performance with respect to unpredictable channel obstructions. The receiver is capable of on-the-fly data rate detection and adapts to changing levels of signal and background light. The receiver updates its phase alignment and channel estimates every 1.6 ms, allowing for rapid changes in water quality as well as motion between transmitter and receiver. We demonstrate on-the-fly rate detection, channel BER within 0.2 dB of theory across all data rates, and error-free performance within 1.82 dB of soft-decision capacity across all tested code rates. All signal processing is done in FPGAs and runs continuously in real time.

Generalized estimators of avian abundance from count survey data

Directory of Open Access Journals (Sweden)

Royle, J. A.

2004-01-01

Full Text Available I consider modeling avian abundance from spatially referenced bird count data collected according to common protocols such as capture-recapture, multiple observer, removal sampling and simple point counts. Small sample sizes and large numbers of parameters have motivated many analyses that disregard the spatial indexing of the data, and thus do not provide an adequate treatment of spatial structure. I describe a general framework for modeling spatially replicated data that regards local abundance as a random process, motivated by the view that the set of spatially referenced local populations (at the sample locations constitute a metapopulation. Under this view, attention can be focused on developing a model for the variation in local abundance independent of the sampling protocol being considered. The metapopulation model structure, when combined with the data generating model, define a simple hierarchical model that can be analyzed using conventional methods. The proposed modeling framework is completely general in the sense that broad classes of metapopulation models may be considered, site level covariates on detection and abundance may be considered, and estimates of abundance and related quantities may be obtained for sample locations, groups of locations, unsampled locations. Two brief examples are given, the first involving simple point counts, and the second based on temporary removal counts. Extension of these models to open systems is briefly discussed.

The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

Science.gov (United States)

Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

2014-09-01

In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

Estimation of single-year-of-age counts of live births, fetal losses, abortions, and pregnant women for counties of Texas.

Science.gov (United States)

Singh, Bismark; Meyers, Lauren Ancel

2017-05-08

We provide a methodology for estimating counts of single-year-of-age live-births, fetal-losses, abortions, and pregnant women from aggregated age-group counts. As a case study, we estimate counts for the 254 counties of Texas for the year 2010. We use interpolation to estimate counts of live-births, fetal-losses, and abortions by women of each single-year-of-age for all Texas counties. We then use these counts to estimate the numbers of pregnant women for each single-year-of-age, which were previously available only in aggregate. To support public health policy and planning, we provide single-year-of-age estimates of live-births, fetal-losses, abortions, and pregnant women for all Texas counties in the year 2010, as well as the estimation method source code.

Robust and bias-corrected estimation of the coefficient of tail dependence

DEFF Research Database (Denmark)

Dutang, C.; Goegebeur, Y.; Guillou, A.

2014-01-01

We introduce a robust and asymptotically unbiased estimator for the coefficient of tail dependence in multivariate extreme value statistics. The estimator is obtained by fitting a second order model to the data by means of the minimum density power divergence criterion. The asymptotic properties ...

Robust Estimation and Moment Selection in Dynamic Fixed-effects Panel Data Models

NARCIS (Netherlands)

Cizek, P.; Aquaro, M.

2015-01-01

This paper extends an existing outlier-robust estimator of linear dynamic panel data models with fixed effects, which is based on the median ratio of two consecutive pairs of first-differenced data. To improve its precision and robust properties, a general procedure based on many pairwise

The estimation of differential counting measurements of possitive quantities with relatively large statistical errors

International Nuclear Information System (INIS)

Vincent, C.H.

1982-01-01

Bayes' principle is applied to the differential counting measurement of a positive quantity in which the statistical errors are not necessarily small in relation to the true value of the quantity. The methods of estimation derived are found to give consistent results and to avoid the anomalous negative estimates sometimes obtained by conventional methods. One of the methods given provides a simple means of deriving the required estimates from conventionally presented results and appears to have wide potential applications. Both methods provide the actual posterior probability distribution of the quantity to be measured. A particularly important potential application is the correction of counts on low radioacitvity samples for background. (orig.)

Robust cylinder pressure estimation in heavy-duty diesel engines

NARCIS (Netherlands)

Kulah, S.; Forrai, A.; Rentmeester, F.; Donkers, T.; Willems, F.P.T.

2017-01-01

The robustness of a new single-cylinder pressure sensor concept is experimentally demonstrated on a six-cylinder heavy-duty diesel engine. Using a single-cylinder pressure sensor and a crank angle sensor, this single-cylinder pressure sensor concept estimates the in-cylinder pressure traces in the

Robust experiment design for estimating myocardial β adrenergic receptor concentration using PET

International Nuclear Information System (INIS)

Salinas, Cristian; Muzic, Raymond F. Jr.; Ernsberger, Paul; Saidel, Gerald M.

2007-01-01

Myocardial β adrenergic receptor (β-AR) concentration can substantially decrease in congestive heart failure and significantly increase in chronic volume overload, such as in severe aortic valve regurgitation. Positron emission tomography (PET) with an appropriate ligand-receptor model can be used for noninvasive estimation of myocardial β-AR concentration in vivo. An optimal design of the experiment protocol, however, is needed for sufficiently precise estimates of β-AR concentration in a heterogeneous population. Standard methods of optimal design do not account for a heterogeneous population with a wide range of β-AR concentrations and other physiological parameters and consequently are inadequate. To address this, we have developed a methodology to design a robust two-injection protocol that provides reliable estimates of myocardial β-AR concentration in normal and pathologic states. A two-injection protocol of the high affinity β-AR antagonist [ 18 F]-(S)-fluorocarazolol was designed based on a computer-generated (or synthetic) population incorporating a wide range of β-AR concentrations. Timing and dosage of the ligand injections were optimally designed with minimax criterion to provide the least bad β-AR estimates for the worst case in the synthetic population. This robust experiment design for PET was applied to experiments with pigs before and after β-AR upregulation by chemical sympathectomy. Estimates of β-AR concentration were found by minimizing the difference between the model-predicted and experimental PET data. With this robust protocol, estimates of β-AR concentration showed high precision in both normal and pathologic states. The increase in β-AR concentration after sympathectomy predicted noninvasively with PET is consistent with the increase shown by in vitro assays in pig myocardium. A robust experiment protocol was designed for PET that yields reliable estimates of β-AR concentration in a population with normal and pathologic

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance.

Science.gov (United States)

Zheng, Binqi; Fu, Pengcheng; Li, Baoqing; Yuan, Xiaobing

2018-03-07

The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.

Robust variance estimation with dependent effect sizes: practical considerations including a software tutorial in Stata and spss.

Science.gov (United States)

Tanner-Smith, Emily E; Tipton, Elizabeth

2014-03-01

Methodologists have recently proposed robust variance estimation as one way to handle dependent effect sizes in meta-analysis. Software macros for robust variance estimation in meta-analysis are currently available for Stata (StataCorp LP, College Station, TX, USA) and spss (IBM, Armonk, NY, USA), yet there is little guidance for authors regarding the practical application and implementation of those macros. This paper provides a brief tutorial on the implementation of the Stata and spss macros and discusses practical issues meta-analysts should consider when estimating meta-regression models with robust variance estimates. Two example databases are used in the tutorial to illustrate the use of meta-analysis with robust variance estimates. Copyright © 2013 John Wiley & Sons, Ltd.

Robust efficient estimation of heart rate pulse from video

Science.gov (United States)

Xu, Shuchang; Sun, Lingyun; Rohde, Gustavo Kunde

2014-01-01

We describe a simple but robust algorithm for estimating the heart rate pulse from video sequences containing human skin in real time. Based on a model of light interaction with human skin, we define the change of blood concentration due to arterial pulsation as a pixel quotient in log space, and successfully use the derived signal for computing the pulse heart rate. Various experiments with different cameras, different illumination condition, and different skin locations were conducted to demonstrate the effectiveness and robustness of the proposed algorithm. Examples computed with normal illumination show the algorithm is comparable with pulse oximeter devices both in accuracy and sensitivity. PMID:24761294

On the robust nonparametric regression estimation for a functional regressor

OpenAIRE

Azzedine , Nadjia; Laksaci , Ali; Ould-Saïd , Elias

2009-01-01

On the robust nonparametric regression estimation for a functional regressor correspondance: Corresponding author. (Ould-Said, Elias) (Azzedine, Nadjia) (Laksaci, Ali) (Ould-Said, Elias) Departement de Mathematiques--> , Univ. Djillali Liabes--> , BP 89--> , 22000 Sidi Bel Abbes--> - ALGERIA (Azzedine, Nadjia) Departement de Mathema...

Robust estimation of the correlation matrix of longitudinal data

KAUST Repository

Maadooliat, Mehdi

2011-09-23

We propose a double-robust procedure for modeling the correlation matrix of a longitudinal dataset. It is based on an alternative Cholesky decomposition of the form Σ=DLL⊤D where D is a diagonal matrix proportional to the square roots of the diagonal entries of Σ and L is a unit lower-triangular matrix determining solely the correlation matrix. The first robustness is with respect to model misspecification for the innovation variances in D, and the second is robustness to outliers in the data. The latter is handled using heavy-tailed multivariate t-distributions with unknown degrees of freedom. We develop a Fisher scoring algorithm for computing the maximum likelihood estimator of the parameters when the nonredundant and unconstrained entries of (L,D) are modeled parsimoniously using covariates. We compare our results with those based on the modified Cholesky decomposition of the form LD2L⊤ using simulations and a real dataset. © 2011 Springer Science+Business Media, LLC.

Estimation non-paramétrique robuste pour données fonctionnelles

OpenAIRE

Crambes , Christophe; Delsol , Laurent; Laksaci , Ali

2009-01-01

International audience; L'estimation robuste présente une approche alternative aux méthodes de régression classiques, par exemple lorsque les observations sont affectées par la présence de données aberrantes. Récemment, ces estimateurs robustes ont été considérés pour des modèles avec données fonctionnelles. Dans cet exposé, nous considérons un modèle de régression robuste avec une variable d'intérêt réelle et une variable explicative fonctionnelle. Nous définissons un estimateur non-paramétr...

Robustness of SOC Estimation Algorithms for EV Lithium-Ion Batteries against Modeling Errors and Measurement Noise

Directory of Open Access Journals (Sweden)

Xue Li

2015-01-01

Full Text Available State of charge (SOC is one of the most important parameters in battery management system (BMS. There are numerous algorithms for SOC estimation, mostly of model-based observer/filter types such as Kalman filters, closed-loop observers, and robust observers. Modeling errors and measurement noises have critical impact on accuracy of SOC estimation in these algorithms. This paper is a comparative study of robustness of SOC estimation algorithms against modeling errors and measurement noises. By using a typical battery platform for vehicle applications with sensor noise and battery aging characterization, three popular and representative SOC estimation methods (extended Kalman filter, PI-controlled observer, and H∞ observer are compared on such robustness. The simulation and experimental results demonstrate that deterioration of SOC estimation accuracy under modeling errors resulted from aging and larger measurement noise, which is quantitatively characterized. The findings of this paper provide useful information on the following aspects: (1 how SOC estimation accuracy depends on modeling reliability and voltage measurement accuracy; (2 pros and cons of typical SOC estimators in their robustness and reliability; (3 guidelines for requirements on battery system identification and sensor selections.

Robust estimation and moment selection in dynamic fixed-effects panel data models

NARCIS (Netherlands)

Cizek, Pavel; Aquaro, Michele

Considering linear dynamic panel data models with fixed effects, existing outlier–robust estimators based on the median ratio of two consecutive pairs of first-differenced data are extended to higher-order differencing. The estimation procedure is thus based on many pairwise differences and their

Tower counts

Science.gov (United States)

Woody, Carol Ann; Johnson, D.H.; Shrier, Brianna M.; O'Neal, Jennifer S.; Knutzen, John A.; Augerot, Xanthippe; O'Neal, Thomas A.; Pearsons, Todd N.

2007-01-01

Counting towers provide an accurate, low-cost, low-maintenance, low-technology, and easily mobilized escapement estimation program compared to other methods (e.g., weirs, hydroacoustics, mark-recapture, and aerial surveys) (Thompson 1962; Siebel 1967; Cousens et al. 1982; Symons and Waldichuk 1984; Anderson 2000; Alaska Department of Fish and Game 2003). Counting tower data has been found to be consistent with that of digital video counts (Edwards 2005). Counting towers do not interfere with natural fish migration patterns, nor are fish handled or stressed; however, their use is generally limited to clear rivers that meet specific site selection criteria. The data provided by counting tower sampling allow fishery managers to determine reproductive population size, estimate total return (escapement + catch) and its uncertainty, evaluate population productivity and trends, set harvest rates, determine spawning escapement goals, and forecast future returns (Alaska Department of Fish and Game 1974-2000 and 1975-2004). The number of spawning fish is determined by subtracting subsistence, sport-caught fish, and prespawn mortality from the total estimated escapement. The methods outlined in this protocol for tower counts can be used to provide reasonable estimates ( plus or minus 6%-10%) of reproductive salmon population size and run timing in clear rivers. 

Robust estimation of hydrological model parameters

Directory of Open Access Journals (Sweden)

A. Bárdossy

2008-11-01

Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

A Method for Counting Moving People in Video Surveillance Videos

Directory of Open Access Journals (Sweden)

Mario Vento

2010-01-01

Full Text Available People counting is an important problem in video surveillance applications. This problem has been faced either by trying to detect people in the scene and then counting them or by establishing a mapping between some scene feature and the number of people (avoiding the complex detection problem. This paper presents a novel method, following this second approach, that is based on the use of SURF features and of an ϵ-SVR regressor provide an estimate of this count. The algorithm takes specifically into account problems due to partial occlusions and to perspective. In the experimental evaluation, the proposed method has been compared with the algorithm by Albiol et al., winner of the PETS 2009 contest on people counting, using the same PETS 2009 database. The provided results confirm that the proposed method yields an improved accuracy, while retaining the robustness of Albiol's algorithm.

A Method for Counting Moving People in Video Surveillance Videos

Directory of Open Access Journals (Sweden)

Conte Donatello

2010-01-01

Full Text Available People counting is an important problem in video surveillance applications. This problem has been faced either by trying to detect people in the scene and then counting them or by establishing a mapping between some scene feature and the number of people (avoiding the complex detection problem. This paper presents a novel method, following this second approach, that is based on the use of SURF features and of an -SVR regressor provide an estimate of this count. The algorithm takes specifically into account problems due to partial occlusions and to perspective. In the experimental evaluation, the proposed method has been compared with the algorithm by Albiol et al., winner of the PETS 2009 contest on people counting, using the same PETS 2009 database. The provided results confirm that the proposed method yields an improved accuracy, while retaining the robustness of Albiol's algorithm.

A Method for Counting Moving People in Video Surveillance Videos

Science.gov (United States)

Conte, Donatello; Foggia, Pasquale; Percannella, Gennaro; Tufano, Francesco; Vento, Mario

2010-12-01

People counting is an important problem in video surveillance applications. This problem has been faced either by trying to detect people in the scene and then counting them or by establishing a mapping between some scene feature and the number of people (avoiding the complex detection problem). This paper presents a novel method, following this second approach, that is based on the use of SURF features and of an [InlineEquation not available: see fulltext.]-SVR regressor provide an estimate of this count. The algorithm takes specifically into account problems due to partial occlusions and to perspective. In the experimental evaluation, the proposed method has been compared with the algorithm by Albiol et al., winner of the PETS 2009 contest on people counting, using the same PETS 2009 database. The provided results confirm that the proposed method yields an improved accuracy, while retaining the robustness of Albiol's algorithm.

Enumerating the Hidden Homeless: Strategies to Estimate the Homeless Gone Missing From a Point-in-Time Count

Directory of Open Access Journals (Sweden)

Agans Robert P.

2014-06-01

Full Text Available To receive federal homeless funds, communities are required to produce statistically reliable, unduplicated counts or estimates of homeless persons in sheltered and unsheltered locations during a one-night period (within the last ten days of January called a point-in-time (PIT count. In Los Angeles, a general population telephone survey was implemented to estimate the number of unsheltered homeless adults who are hidden from view during the PIT count. Two estimation approaches were investigated: i the number of homeless persons identified as living on private property, which employed a conventional household weight for the estimated total (Horvitz-Thompson approach; and ii the number of homeless persons identified as living on a neighbor’s property, which employed an additional adjustment derived from the size of the neighborhood network to estimate the total (multiplicity-based approach. This article compares the results of these two methods and discusses the implications therein.

Partial-Interval Estimation of Count: Uncorrected and Poisson-Corrected Error Levels

Science.gov (United States)

Yoder, Paul J.; Ledford, Jennifer R.; Harbison, Amy L.; Tapp, Jon T.

2018-01-01

A simulation study that used 3,000 computer-generated event streams with known behavior rates, interval durations, and session durations was conducted to test whether the main and interaction effects of true rate and interval duration affect the error level of uncorrected and Poisson-transformed (i.e., "corrected") count as estimated by…

Experimental estimation of snare detectability for robust threat monitoring.

Science.gov (United States)

O'Kelly, Hannah J; Rowcliffe, J Marcus; Durant, Sarah; Milner-Gulland, E J

2018-02-01

Hunting with wire snares is rife within many tropical forest systems, and constitutes one of the severest threats to a wide range of vertebrate taxa. As for all threats, reliable monitoring of snaring levels is critical for assessing the relative effectiveness of management interventions. However, snares pose a particular challenge in terms of tracking spatial or temporal trends in their prevalence because they are extremely difficult to detect, and are typically spread across large, inaccessible areas. As with cryptic animal targets, any approach used to monitor snaring levels must address the issue of imperfect detection, but no standard method exists to do so. We carried out a field experiment in Keo Seima Wildlife Reserve in eastern Cambodia with the following objectives: (1) To estimate the detection probably of wire snares within a tropical forest context, and to investigate how detectability might be affected by habitat type, snare type, or observer. (2) To trial two sets of sampling protocols feasible to implement in a range of challenging field conditions. (3) To conduct a preliminary assessment of two potential analytical approaches to dealing with the resulting snare encounter data. We found that although different observers had no discernible effect on detection probability, detectability did vary between habitat type and snare type. We contend that simple repeated counts carried out at multiple sites and analyzed using binomial mixture models could represent a practical yet robust solution to the problem of monitoring snaring levels both inside and outside of protected areas. This experiment represents an important first step in developing improved methods of threat monitoring, and such methods are greatly needed in southeast Asia, as well as in as many other regions.

Urban birds in the Sonoran Desert: estimating population density from point counts

Directory of Open Access Journals (Sweden)

Karina Johnston López

2015-01-01

Full Text Available We conducted bird surveys in Hermosillo, Sonora using distance sampling to characterize detection functions at point-transects for native and non-native urban birds in a desert environment. From March to August 2013 we sampled 240 plots in the city and its surroundings; each plot was visited three times. Our purpose was to provide information for a rapid assessment of bird density in this region by using point counts. We identified 72 species, including six non-native species. Sixteen species had sufficient detections to accurately estimate the parameters of the detection functions. To illustrate the estimation of density from bird count data using our inferred detection functions, we estimated the density of the Eurasian Collared-Dove (Streptopelia decaocto under two different levels of urbanization: highly urbanized (90-100% of urban impact and moderately urbanized zones (39-50% of urban impact. Density of S. decaocto in the highly-urbanized and moderately-urbanized zones was 3.97±0.52 and 2.92±0.52 individuals/ha, respectively. By using our detection functions, avian ecologists can efficiently relocate time and effort that is regularly used for the estimation of detection distances, to increase the number of sites surveyed and to collect other relevant ecological information.

Robust Backlash Estimation for Industrial Drive-Train Systems—Theory and Validation

DEFF Research Database (Denmark)

Papageorgiou, Dimitrios; Blanke, Mogens; Niemann, Hans Henrik

2018-01-01

Backlash compensation is used in modern machinetool controls to ensure high-accuracy positioning. When wear of a machine causes deadzone width to increase, high-accuracy control may be maintained if the deadzone is accurately estimated. Deadzone estimation is also an important parameter to indica......-of-the-art Siemens equipment. The experiments validate the theory and show that expected performance and robustness to parameter uncertainties are both achieved....

Estimation of U content in coffee samples by fission-track counting

International Nuclear Information System (INIS)

Sharma, P.K.; Lal, N.; Nagpaul, K.K.

1985-01-01

Because coffee is consumed in large quantities by humans, the authors undertook the study of the uranium content of coffee as a continuation of earlier work to estimate the U content of foodstuffs. Since literature on this subject is scarce, they decided to use the well-established fission-track-counting technique to determine the U content of coffee

A Lossy Counting-Based State of Charge Estimation Method and Its Application to Electric Vehicles

Directory of Open Access Journals (Sweden)

Hong Zhang

2015-12-01

Full Text Available Estimating the residual capacity or state-of-charge (SoC of commercial batteries on-line without destroying them or interrupting the power supply, is quite a challenging task for electric vehicle (EV designers. Many Coulomb counting-based methods have been used to calculate the remaining capacity in EV batteries or other portable devices. The main disadvantages of these methods are the cumulative error and the time-varying Coulombic efficiency, which are greatly influenced by the operating state (SoC, temperature and current. To deal with this problem, we propose a lossy counting-based Coulomb counting method for estimating the available capacity or SoC. The initial capacity of the tested battery is obtained from the open circuit voltage (OCV. The charging/discharging efficiencies, used for compensating the Coulombic losses, are calculated by the lossy counting-based method. The measurement drift, resulting from the current sensor, is amended with the distorted Coulombic efficiency matrix. Simulations and experimental results show that the proposed method is both effective and convenient.

Robust estimation of the noise variance from background MR data

NARCIS (Netherlands)

Sijbers, J.; Den Dekker, A.J.; Poot, D.; Bos, R.; Verhoye, M.; Van Camp, N.; Van der Linden, A.

2006-01-01

In the literature, many methods are available for estimation of the variance of the noise in magnetic resonance (MR) images. A commonly used method, based on the maximum of the background mode of the histogram, is revisited and a new, robust, and easy to use method is presented based on maximum

Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering

Directory of Open Access Journals (Sweden)

Suleiman M. Sharkh

2012-04-01

Full Text Available A robust extended Kalman filter (EKF is proposed as a method for estimation of the state of charge (SOC of lithium-ion batteries used in hybrid electric vehicles (HEVs. An equivalent circuit model of the battery, including its electromotive force (EMF hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analyzed, and an optimized gain coefficient is determined to restrain battery terminal voltage from fluctuating. Experimental and simulation results are presented. SOC estimates using the standard EKF are compared with the proposed robust EKF algorithm to demonstrate the accuracy and precision of the latter for SOC estimation.

Robust position estimation of a mobile vehicle

International Nuclear Information System (INIS)

Conan, V.

1994-01-01

The ability to estimate the position of a mobile vehicle is a key task for navigation over large distances in complex indoor environments such as nuclear power plants. Schematics of the plants are available, but they are incomplete, as real settings contain many objects, such as pipes, cables or furniture, that mask part of the model. The position estimation method described in this paper matches 3-D data with a simple schematic of a plant. It is basically independent of odometer information and viewpoint, robust to noisy data and spurious points and largely insensitive to occlusions. The method is based on a hypothesis/verification paradigm and its complexity is polynomial; it runs in O(m 4 n 4 ), where m represents the number of model patches and n the number of scene patches. Heuristics are presented to speed up the algorithm. Results on real 3-D data show good behaviour even when the scene is very occluded. (authors). 16 refs., 3 figs., 1 tab

Variation in pre-treatment count lead time and its effect on baseline estimates of cage-level sea lice abundance.

Science.gov (United States)

Gautam, R; Boerlage, A S; Vanderstichel, R; Revie, C W; Hammell, K L

2016-11-01

Treatment efficacy studies typically use pre-treatment sea lice abundance as the baseline. However, the pre-treatment counting window often varies from the day of treatment to several days before treatment. We assessed the effect of lead time on baseline estimates, using historical data (2010-14) from a sea lice data management programme (Fish-iTrends). Data were aggregated at the cage level for three life stages: (i) chalimus, (ii) pre-adult and adult male and (iii) adult female. Sea lice counts were log-transformed, and mean counts by lead time relative to treatment day were computed and compared separately for each life stage, using linear mixed models. There were 1,658 observations (treatment events) from 56 sites in 5 Bay Management Areas. Our study showed that lead time had a significant effect on the estimated sea lice abundance, which was moderated by season. During the late summer and autumn periods, counting on the day of treatment gave significantly higher values than other days and would be a more appropriate baseline estimate, while during spring and early summer abundance estimates were comparable among counts within 5 days of treatment. A season-based lead time window may be most appropriate when estimating baseline sea lice levels. © 2016 John Wiley & Sons Ltd.

METRIC CHARACTERISTICS OF VARIOUS METHODS FOR NUMERICAL DENSITY ESTIMATION IN TRANSMISSION LIGHT MICROSCOPY – A COMPUTER SIMULATION

Directory of Open Access Journals (Sweden)

Miroslav Kališnik

2011-05-01

Full Text Available In the introduction the evolution of methods for numerical density estimation of particles is presented shortly. Three pairs of methods have been analysed and compared: (1 classical methods for particles counting in thin and thick sections, (2 original and modified differential counting methods and (3 physical and optical disector methods. Metric characteristics such as accuracy, efficiency, robustness, and feasibility of methods have been estimated and compared. Logical, geometrical and mathematical analysis as well as computer simulations have been applied. In computer simulations a model of randomly distributed equal spheres with maximal contrast against surroundings has been used. According to our computer simulation all methods give accurate results provided that the sample is representative and sufficiently large. However, there are differences in their efficiency, robustness and feasibility. Efficiency and robustness increase with increasing slice thickness in all three pairs of methods. Robustness is superior in both differential and both disector methods compared to both classical methods. Feasibility can be judged according to the additional equipment as well as to the histotechnical and counting procedures necessary for performing individual counting methods. However, it is evident that not all practical problems can efficiently be solved with models.

Robust subspace estimation using low-rank optimization theory and applications

CERN Document Server

Oreifej, Omar

2014-01-01

Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book,?the authors?discuss fundame

Methodology for estimation of 32P in bioassay samples by Cerenkov counting

International Nuclear Information System (INIS)

Wankhede, Sonal; Sawant, Pramilla D.; Yadav, R.K.B.; Rao, D.D.

2016-01-01

Radioactive phosphorus ( 32 P) as phosphate is used to effectively reduce bone pain in terminal cancer patients. Several hospitals in India carry out this palliative care procedure on a regular basis. Thus, production as well as synthesis of 32 P compounds has increased over the years to meet this requirement. Monitoring of radiation workers handling 32 P compounds is important for further strengthening of radiological protection program at processing facility. 32 P being a pure beta emitter (β max = 1.71 MeV, t 1/2 = 14.3 d), bioassay is the preferred individual monitoring technique. Method standardized at Bioassay Lab, Trombay, includes estimation of 32 P in urine by co-precipitation with ammonium phosphomolybdate (AMP) followed by gross beta counting. In the present study, feasibility of Cerenkov counting for detection of 32 P in bioassay samples was explored and the results obtained were compared with the gross beta counting technique

Robust estimation of autoregressive processes using a mixture-based filter-bank

Czech Academy of Sciences Publication Activity Database

Šmídl, V.; Anthony, Q.; Kárný, Miroslav; Guy, Tatiana Valentine

2005-01-01

Roč. 54, č. 4 (2005), s. 315-323 ISSN 0167-6911 R&D Projects: GA AV ČR IBS1075351; GA ČR GA102/03/0049; GA ČR GP102/03/P010; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian estimation * probabilistic mixtures * recursive estimation Subject RIV: BC - Control Systems Theory Impact factor: 1.239, year: 2005 http://library.utia.cas.cz/separaty/historie/karny-robust estimation of autoregressive processes using a mixture-based filter- bank .pdf

Estimated allele substitution effects underlying genomic evaluation models depend on the scaling of allele counts

NARCIS (Netherlands)

Bouwman, Aniek C.; Hayes, Ben J.; Calus, Mario P.L.

2017-01-01

Background: Genomic evaluation is used to predict direct genomic values (DGV) for selection candidates in breeding programs, but also to estimate allele substitution effects (ASE) of single nucleotide polymorphisms (SNPs). Scaling of allele counts influences the estimated ASE, because scaling of

Estimation of equivalent dose and its uncertainty in the OSL SAR protocol when count numbers do not follow a Poisson distribution

International Nuclear Information System (INIS)

Bluszcz, Andrzej; Adamiec, Grzegorz; Heer, Aleksandra J.

2015-01-01

The current work focuses on the estimation of equivalent dose and its uncertainty using the single aliquot regenerative protocol in optically stimulated luminescence measurements. The authors show that the count numbers recorded with the use of photomultiplier tubes are well described by negative binomial distributions, different ones for background counts and photon induced counts. This fact is then exploited in pseudo-random count number generation and simulations of D e determination assuming a saturating exponential growth. A least squares fitting procedure is applied using different types of weights to determine whether the obtained D e 's and their error estimates are unbiased and accurate. A weighting procedure is suggested that leads to almost unbiased D e estimates. It is also shown that the assumption of Poisson distribution in D e estimation may lead to severe underestimation of the D e error. - Highlights: • Detailed analysis of statistics of count numbers in luminescence readers. • Generation of realistically scattered pseudo-random numbers of counts in luminescence measurements. • A practical guide for stringent analysis of D e values and errors assessment.

Simple robust technique using time delay estimation for the control and synchronization of Lorenz systems

International Nuclear Information System (INIS)

Jin, Maolin; Chang, Pyung Hun

2009-01-01

This work presents two simple and robust techniques based on time delay estimation for the respective control and synchronization of chaos systems. First, one of these techniques is applied to the control of a chaotic Lorenz system with both matched and mismatched uncertainties. The nonlinearities in the Lorenz system is cancelled by time delay estimation and desired error dynamics is inserted. Second, the other technique is applied to the synchronization of the Lue system and the Lorenz system with uncertainties. The synchronization input consists of three elements that have transparent and clear meanings. Since time delay estimation enables a very effective and efficient cancellation of disturbances and nonlinearities, the techniques turn out to be simple and robust. Numerical simulation results show fast, accurate and robust performance of the proposed techniques, thereby demonstrating their effectiveness for the control and synchronization of Lorenz systems.

Power System Real-Time Monitoring by Using PMU-Based Robust State Estimation Method

DEFF Research Database (Denmark)

Zhao, Junbo; Zhang, Gexiang; Das, Kaushik

2016-01-01

Accurate real-time states provided by the state estimator are critical for power system reliable operation and control. This paper proposes a novel phasor measurement unit (PMU)-based robust state estimation method (PRSEM) to real-time monitor a power system under different operation conditions...... the system real-time states with good robustness and can address several kinds of BD.......-based bad data (BD) detection method, which can handle the smearing effect and critical measurement errors, is presented. We evaluate PRSEM by using IEEE benchmark test systems and a realistic utility system. The numerical results indicate that, in short computation time, PRSEM can effectively track...

Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

Science.gov (United States)

Lin, Huiming; Fu, Bo; Qin, Guoyou; Zhu, Zhongyi

2017-12-01

We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study. © 2017, The International Biometric Society.

Robust w-Estimators for Cryo-EM Class Means

Science.gov (United States)

Huang, Chenxi; Tagare, Hemant D.

2016-01-01

A critical step in cryogenic electron microscopy (cryo-EM) image analysis is to calculate the average of all images aligned to a projection direction. This average, called the “class mean”, improves the signal-to-noise ratio in single particle reconstruction (SPR). The averaging step is often compromised because of outlier images of ice, contaminants, and particle fragments. Outlier detection and rejection in the majority of current cryo-EM methods is done using cross-correlation with a manually determined threshold. Empirical assessment shows that the performance of these methods is very sensitive to the threshold. This paper proposes an alternative: a “w-estimator” of the average image, which is robust to outliers and which does not use a threshold. Various properties of the estimator, such as consistency and influence function are investigated. An extension of the estimator to images with different contrast transfer functions (CTFs) is also provided. Experiments with simulated and real cryo-EM images show that the proposed estimator performs quite well in the presence of outliers. PMID:26841397

ESTIMATION OF GRASPING TORQUE USING ROBUST REACTION TORQUE OBSERVER FOR ROBOTIC FORCEPS

OpenAIRE

塚本, 祐介

2015-01-01

Abstract— In this paper, the estimation of the grasping torque of robotic forceps without the use of a force/torque sensor is discussed. To estimate the grasping torque when the robotic forceps driven by a rotary motor with a reduction gear grasps an object, a novel robust reaction torque observer is proposed. In the case where a conventional reaction force/torque observer is applied, the estimated torque includes not only the grasping torque, namely the reaction torque, but also t...

Robust Variance Estimation with Dependent Effect Sizes: Practical Considerations Including a Software Tutorial in Stata and SPSS

Science.gov (United States)

Tanner-Smith, Emily E.; Tipton, Elizabeth

2014-01-01

Methodologists have recently proposed robust variance estimation as one way to handle dependent effect sizes in meta-analysis. Software macros for robust variance estimation in meta-analysis are currently available for Stata (StataCorp LP, College Station, TX, USA) and SPSS (IBM, Armonk, NY, USA), yet there is little guidance for authors regarding…

The effect of volume and quenching on estimation of counting efficiencies in liquid scintillation counting

International Nuclear Information System (INIS)

Knoche, H.W.; Parkhurst, A.M.; Tam, S.W.

1979-01-01

The effect of volume on the liquid scintillation counting performance of 14 C-samples has been investigated. A decrease in counting efficiency was observed for samples with volumes below about 6 ml and those above about 18 ml when unquenched samples were assayed. Two quench-correction methods, sample channels ratio and external standard channels ratio, and three different liquid scintillation counters, were used in an investigation to determine the magnitude of the error in predicting counting efficiencies when small volume samples (2 ml) with different levels of quenching were assayed. The 2 ml samples exhibited slightly greater standard deviations of the difference between predicted and determined counting efficiencies than did 15 ml samples. Nevertheless, the magnitude of the errors indicate that if the sample channels ratio method of quench correction is employed, 2 ml samples may be counted in conventional counting vials with little loss in counting precision. (author)

Efficient and robust estimation for longitudinal mixed models for binary data

DEFF Research Database (Denmark)

Holst, René

2009-01-01

This paper proposes a longitudinal mixed model for binary data. The model extends the classical Poisson trick, in which a binomial regression is fitted by switching to a Poisson framework. A recent estimating equations method for generalized linear longitudinal mixed models, called GEEP, is used...... as a vehicle for fitting the conditional Poisson regressions, given a latent process of serial correlated Tweedie variables. The regression parameters are estimated using a quasi-score method, whereas the dispersion and correlation parameters are estimated by use of bias-corrected Pearson-type estimating...... equations, using second moments only. Random effects are predicted by BLUPs. The method provides a computationally efficient and robust approach to the estimation of longitudinal clustered binary data and accommodates linear and non-linear models. A simulation study is used for validation and finally...

Global robust stability of delayed neural networks: Estimating upper limit of norm of delayed connection weight matrix

International Nuclear Information System (INIS)

Singh, Vimal

2007-01-01

The question of estimating the upper limit of -parallel B -parallel 2 , which is a key step in some recently reported global robust stability criteria for delayed neural networks, is revisited ( B denotes the delayed connection weight matrix). Recently, Cao, Huang, and Qu have given an estimate of the upper limit of -parallel B -parallel 2 . In the present paper, an alternative estimate of the upper limit of -parallel B -parallel 2 is highlighted. It is shown that the alternative estimate may yield some new global robust stability results

Robust and efficient parameter estimation in dynamic models of biological systems.

Science.gov (United States)

Gábor, Attila; Banga, Julio R

2015-10-29

Dynamic modelling provides a systematic framework to understand function in biological systems. Parameter estimation in nonlinear dynamic models remains a very challenging inverse problem due to its nonconvexity and ill-conditioning. Associated issues like overfitting and local solutions are usually not properly addressed in the systems biology literature despite their importance. Here we present a method for robust and efficient parameter estimation which uses two main strategies to surmount the aforementioned difficulties: (i) efficient global optimization to deal with nonconvexity, and (ii) proper regularization methods to handle ill-conditioning. In the case of regularization, we present a detailed critical comparison of methods and guidelines for properly tuning them. Further, we show how regularized estimations ensure the best trade-offs between bias and variance, reducing overfitting, and allowing the incorporation of prior knowledge in a systematic way. We illustrate the performance of the presented method with seven case studies of different nature and increasing complexity, considering several scenarios of data availability, measurement noise and prior knowledge. We show how our method ensures improved estimations with faster and more stable convergence. We also show how the calibrated models are more generalizable. Finally, we give a set of simple guidelines to apply this strategy to a wide variety of calibration problems. Here we provide a parameter estimation strategy which combines efficient global optimization with a regularization scheme. This method is able to calibrate dynamic models in an efficient and robust way, effectively fighting overfitting and allowing the incorporation of prior information.

Robust estimation of adaptive tensors of curvature by tensor voting.

Science.gov (United States)

Tong, Wai-Shun; Tang, Chi-Keung

2005-03-01

Although curvature estimation from a given mesh or regularly sampled point set is a well-studied problem, it is still challenging when the input consists of a cloud of unstructured points corrupted by misalignment error and outlier noise. Such input is ubiquitous in computer vision. In this paper, we propose a three-pass tensor voting algorithm to robustly estimate curvature tensors, from which accurate principal curvatures and directions can be calculated. Our quantitative estimation is an improvement over the previous two-pass algorithm, where only qualitative curvature estimation (sign of Gaussian curvature) is performed. To overcome misalignment errors, our improved method automatically corrects input point locations at subvoxel precision, which also rejects outliers that are uncorrectable. To adapt to different scales locally, we define the RadiusHit of a curvature tensor to quantify estimation accuracy and applicability. Our curvature estimation algorithm has been proven with detailed quantitative experiments, performing better in a variety of standard error metrics (percentage error in curvature magnitudes, absolute angle difference in curvature direction) in the presence of a large amount of misalignment noise.

Robust estimation of seismic coda shape

Science.gov (United States)

Nikkilä, Mikko; Polishchuk, Valentin; Krasnoshchekov, Dmitry

2014-04-01

We present a new method for estimation of seismic coda shape. It falls into the same class of methods as non-parametric shape reconstruction with the use of neural network techniques where data are split into a training and validation data sets. We particularly pursue the well-known problem of image reconstruction formulated in this case as shape isolation in the presence of a broadly defined noise. This combined approach is enabled by the intrinsic feature of seismogram which can be divided objectively into a pre-signal seismic noise with lack of the target shape, and the remainder that contains scattered waveforms compounding the coda shape. In short, we separately apply shape restoration procedure to pre-signal seismic noise and the event record, which provides successful delineation of the coda shape in the form of a smooth almost non-oscillating function of time. The new algorithm uses a recently developed generalization of classical computational-geometry tool of α-shape. The generalization essentially yields robust shape estimation by ignoring locally a number of points treated as extreme values, noise or non-relevant data. Our algorithm is conceptually simple and enables the desired or pre-determined level of shape detail, constrainable by an arbitrary data fit criteria. The proposed tool for coda shape delineation provides an alternative to moving averaging and/or other smoothing techniques frequently used for this purpose. The new algorithm is illustrated with an application to the problem of estimating the coda duration after a local event. The obtained relation coefficient between coda duration and epicentral distance is consistent with the earlier findings in the region of interest.

A Robust and Multi-Weighted Approach to Estimating Topographically Correlated Tropospheric Delays in Radar Interferograms

Directory of Open Access Journals (Sweden)

Bangyan Zhu

2016-07-01

Full Text Available Spatial and temporal variations in the vertical stratification of the troposphere introduce significant propagation delays in interferometric synthetic aperture radar (InSAR observations. Observations of small amplitude surface deformations and regional subsidence rates are plagued by tropospheric delays, and strongly correlated with topographic height variations. Phase-based tropospheric correction techniques assuming a linear relationship between interferometric phase and topography have been exploited and developed, with mixed success. Producing robust estimates of tropospheric phase delay however plays a critical role in increasing the accuracy of InSAR measurements. Meanwhile, few phase-based correction methods account for the spatially variable tropospheric delay over lager study regions. Here, we present a robust and multi-weighted approach to estimate the correlation between phase and topography that is relatively insensitive to confounding processes such as regional subsidence over larger regions as well as under varying tropospheric conditions. An expanded form of robust least squares is introduced to estimate the spatially variable correlation between phase and topography by splitting the interferograms into multiple blocks. Within each block, correlation is robustly estimated from the band-filtered phase and topography. Phase-elevation ratios are multiply- weighted and extrapolated to each persistent scatter (PS pixel. We applied the proposed method to Envisat ASAR images over the Southern California area, USA, and found that our method mitigated the atmospheric noise better than the conventional phase-based method. The corrected ground surface deformation agreed better with those measured from GPS.

Robust head pose estimation via supervised manifold learning.

Science.gov (United States)

Wang, Chao; Song, Xubo

2014-05-01

Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with the pose being the only variable, the face images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background clutter, facial expression, and illumination. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. The process has three stages: neighborhood construction, graph weight computation and projection learning. For the first two stages, we redefine inter-point distance for neighborhood construction as well as graph weight by constraining them with the pose angle information. For Stage 3, we present a supervised neighborhood-based linear feature transformation algorithm to keep the data points with similar pose angles close together but the data points with dissimilar pose angles far apart. The experimental results show that our method has higher estimation accuracy than the other state-of-art algorithms and is robust to identity and illumination variations. Copyright © 2014 Elsevier Ltd. All rights reserved.

Geomagnetic matching navigation algorithm based on robust estimation

Science.gov (United States)

Xie, Weinan; Huang, Liping; Qu, Zhenshen; Wang, Zhenhuan

2017-08-01

The outliers in the geomagnetic survey data seriously affect the precision of the geomagnetic matching navigation and badly disrupt its reliability. A novel algorithm which can eliminate the outliers influence is investigated in this paper. First, the weight function is designed and its principle of the robust estimation is introduced. By combining the relation equation between the matching trajectory and the reference trajectory with the Taylor series expansion for geomagnetic information, a mathematical expression of the longitude, latitude and heading errors is acquired. The robust target function is obtained by the weight function and the mathematical expression. Then the geomagnetic matching problem is converted to the solutions of nonlinear equations. Finally, Newton iteration is applied to implement the novel algorithm. Simulation results show that the matching error of the novel algorithm is decreased to 7.75% compared to the conventional mean square difference (MSD) algorithm, and is decreased to 18.39% to the conventional iterative contour matching algorithm when the outlier is 40nT. Meanwhile, the position error of the novel algorithm is 0.017° while the other two algorithms fail to match when the outlier is 400nT.

A new method for robust video watermarking resistant against key estimation attacks

Science.gov (United States)

Mitekin, Vitaly

2015-12-01

This paper presents a new method for high-capacity robust digital video watermarking and algorithms of embedding and extraction of watermark based on this method. Proposed method uses password-based two-dimensional pseudonoise arrays for watermark embedding, making brute-force attacks aimed at steganographic key retrieval mostly impractical. Proposed algorithm for 2-dimensional "noise-like" watermarking patterns generation also allows to significantly decrease watermark collision probability ( i.e. probability of correct watermark detection and extraction using incorrect steganographic key or password).. Experimental research provided in this work also shows that simple correlation-based watermark detection procedure can be used, providing watermark robustness against lossy compression and watermark estimation attacks. At the same time, without decreasing robustness of embedded watermark, average complexity of the brute-force key retrieval attack can be increased to 1014 watermark extraction attempts (compared to 104-106 for a known robust watermarking schemes). Experimental results also shows that for lowest embedding intensity watermark preserves it's robustness against lossy compression of host video and at the same time preserves higher video quality (PSNR up to 51dB) compared to known wavelet-based and DCT-based watermarking algorithms.

Estimator-based multiobjective robust control strategy for an active pantograph in high-speed railways

DEFF Research Database (Denmark)

Lu, Xiaobing; Liu, Zhigang; Song, Yang

2018-01-01

Active control of the pantograph is one of the promising measures for decreasing fluctuation in the contact force between the pantograph and the catenary. In this paper, an estimator-based multiobjective robust control strategy is proposed for an active pantograph, which consists of a state estim...

Robust estimation of partially linear models for longitudinal data with dropouts and measurement error.

Science.gov (United States)

Qin, Guoyou; Zhang, Jiajia; Zhu, Zhongyi; Fung, Wing

2016-12-20

Outliers, measurement error, and missing data are commonly seen in longitudinal data because of its data collection process. However, no method can address all three of these issues simultaneously. This paper focuses on the robust estimation of partially linear models for longitudinal data with dropouts and measurement error. A new robust estimating equation, simultaneously tackling outliers, measurement error, and missingness, is proposed. The asymptotic properties of the proposed estimator are established under some regularity conditions. The proposed method is easy to implement in practice by utilizing the existing standard generalized estimating equations algorithms. The comprehensive simulation studies show the strength of the proposed method in dealing with longitudinal data with all three features. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition study and confirms the effectiveness of the intervention in producing weight loss at month 9. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Robust state estimation for uncertain fuzzy bidirectional associative memory networks with time-varying delays

Science.gov (United States)

Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Arunkumar, A.

2013-09-01

This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov-Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results.

Robust state estimation for uncertain fuzzy bidirectional associative memory networks with time-varying delays

International Nuclear Information System (INIS)

Vadivel, P; Sakthivel, R; Mathiyalagan, K; Arunkumar, A

2013-01-01

This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov–Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results. (paper)

A robust methodology for kinetic model parameter estimation for biocatalytic reactions

DEFF Research Database (Denmark)

Al-Haque, Naweed; Andrade Santacoloma, Paloma de Gracia; Lima Afonso Neto, Watson

2012-01-01

lead to globally optimized parameter values. In this article, a robust methodology to estimate parameters for biocatalytic reaction kinetic expressions is proposed. The methodology determines the parameters in a systematic manner by exploiting the best features of several of the current approaches...... parameters, which are strongly correlated with each other. State-of-the-art methodologies such as nonlinear regression (using progress curves) or graphical analysis (using initial rate data, for example, the Lineweaver-Burke plot, Hanes plot or Dixon plot) often incorporate errors in the estimates and rarely...

A Robust WLS Power System State Estimation Method Integrating a Wide-Area Measurement System and SCADA Technology

Directory of Open Access Journals (Sweden)

Tao Jin

2015-04-01

Full Text Available With the development of modern society, the scale of the power system is rapidly increased accordingly, and the framework and mode of running of power systems are trending towards more complexity. It is nowadays much more important for the dispatchers to know exactly the state parameters of the power network through state estimation. This paper proposes a robust power system WLS state estimation method integrating a wide-area measurement system (WAMS and SCADA technology, incorporating phasor measurements and the results of the traditional state estimator in a post-processing estimator, which greatly reduces the scale of the non-linear estimation problem as well as the number of iterations and the processing time per iteration. This paper firstly analyzes the wide-area state estimation model in detail, then according to the issue that least squares does not account for bad data and outliers, the paper proposes a robust weighted least squares (WLS method that combines a robust estimation principle with least squares by equivalent weight. The performance assessment is discussed through setting up mathematical models of the distribution network. The effectiveness of the proposed method was proved to be accurate and reliable by simulations and experiments.

BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition

Directory of Open Access Journals (Sweden)

Abdullah Makkeh

2018-04-01

Full Text Available Makkeh, Theis, and Vicente found that Cone Programming model is the most robust to compute the Bertschinger et al. partial information decomposition (BROJA PID measure. We developed a production-quality robust software that computes the BROJA PID measure based on the Cone Programming model. In this paper, we prove the important property of strong duality for the Cone Program and prove an equivalence between the Cone Program and the original Convex problem. Then, we describe in detail our software, explain how to use it, and perform some experiments comparing it to other estimators. Finally, we show that the software can be extended to compute some quantities of a trivaraite PID measure.

ROBUST: an interactive FORTRAN-77 package for exploratory data analysis using parametric, ROBUST and nonparametric location and scale estimates, data transformations, normality tests, and outlier assessment

Science.gov (United States)

Rock, N. M. S.

ROBUST calculates 53 statistics, plus significance levels for 6 hypothesis tests, on each of up to 52 variables. These together allow the following properties of the data distribution for each variable to be examined in detail: (1) Location. Three means (arithmetic, geometric, harmonic) are calculated, together with the midrange and 19 high-performance robust L-, M-, and W-estimates of location (combined, adaptive, trimmed estimates, etc.) (2) Scale. The standard deviation is calculated along with the H-spread/2 (≈ semi-interquartile range), the mean and median absolute deviations from both mean and median, and a biweight scale estimator. The 23 location and 6 scale estimators programmed cover all possible degrees of robustness. (3) Normality: Distributions are tested against the null hypothesis that they are normal, using the 3rd (√ h1) and 4th ( b 2) moments, Geary's ratio (mean deviation/standard deviation), Filliben's probability plot correlation coefficient, and a more robust test based on the biweight scale estimator. These statistics collectively are sensitive to most usual departures from normality. (4) Presence of outliers. The maximum and minimum values are assessed individually or jointly using Grubbs' maximum Studentized residuals, Harvey's and Dixon's criteria, and the Studentized range. For a single input variable, outliers can be either winsorized or eliminated and all estimates recalculated iteratively as desired. The following data-transformations also can be applied: linear, log 10, generalized Box Cox power (including log, reciprocal, and square root), exponentiation, and standardization. For more than one variable, all results are tabulated in a single run of ROBUST. Further options are incorporated to assess ratios (of two variables) as well as discrete variables, and be concerned with missing data. Cumulative S-plots (for assessing normality graphically) also can be generated. The mutual consistency or inconsistency of all these measures

Robust Estimation for a CSTR Using a High Order Sliding Mode Observer and an Observer-Based Estimator

Directory of Open Access Journals (Sweden)

Esteban Jiménez-Rodríguez

2016-12-01

Full Text Available This paper presents an estimation structure for a continuous stirred-tank reactor, which is comprised of a sliding mode observer-based estimator coupled with a high-order sliding-mode observer. The whole scheme allows the robust estimation of the state and some parameters, specifically the concentration of the reactive mass, the heat of reaction and the global coefficient of heat transfer, by measuring the temperature inside the reactor and the temperature inside the jacket. In order to verify the results, the convergence proof of the proposed structure is done, and numerical simulations are presented with noiseless and noisy measurements, suggesting the applicability of the posed approach.

Radon counting statistics - a Monte Carlo investigation

International Nuclear Information System (INIS)

Scott, A.G.

1996-01-01

Radioactive decay is a Poisson process, and so the Coefficient of Variation (COV) of open-quotes nclose quotes counts of a single nuclide is usually estimated as 1/√n. This is only true if the count duration is much shorter than the half-life of the nuclide. At longer count durations, the COV is smaller than the Poisson estimate. Most radon measurement methods count the alpha decays of 222 Rn, plus the progeny 218 Po and 214 Po, and estimate the 222 Rn activity from the sum of the counts. At long count durations, the chain decay of these nuclides means that every 222 Rn decay must be followed by two other alpha decays. The total number of decays is open-quotes 3Nclose quotes, where N is the number of radon decays, and the true COV of the radon concentration estimate is 1/√(N), √3 larger than the Poisson total count estimate of 1/√3N. Most count periods are comparable to the half lives of the progeny, so the relationship between COV and count time is complex. A Monte-Carlo estimate of the ratio of true COV to Poisson estimate was carried out for a range of count periods from 1 min to 16 h and three common radon measurement methods: liquid scintillation, scintillation cell, and electrostatic precipitation of progeny. The Poisson approximation underestimates COV by less than 20% for count durations of less than 60 min

A random sampling approach for robust estimation of tissue-to-plasma ratio from extremely sparse data.

Science.gov (United States)

Chu, Hui-May; Ette, Ene I

2005-09-02

his study was performed to develop a new nonparametric approach for the estimation of robust tissue-to-plasma ratio from extremely sparsely sampled paired data (ie, one sample each from plasma and tissue per subject). Tissue-to-plasma ratio was estimated from paired/unpaired experimental data using independent time points approach, area under the curve (AUC) values calculated with the naïve data averaging approach, and AUC values calculated using sampling based approaches (eg, the pseudoprofile-based bootstrap [PpbB] approach and the random sampling approach [our proposed approach]). The random sampling approach involves the use of a 2-phase algorithm. The convergence of the sampling/resampling approaches was investigated, as well as the robustness of the estimates produced by different approaches. To evaluate the latter, new data sets were generated by introducing outlier(s) into the real data set. One to 2 concentration values were inflated by 10% to 40% from their original values to produce the outliers. Tissue-to-plasma ratios computed using the independent time points approach varied between 0 and 50 across time points. The ratio obtained from AUC values acquired using the naive data averaging approach was not associated with any measure of uncertainty or variability. Calculating the ratio without regard to pairing yielded poorer estimates. The random sampling and pseudoprofile-based bootstrap approaches yielded tissue-to-plasma ratios with uncertainty and variability. However, the random sampling approach, because of the 2-phase nature of its algorithm, yielded more robust estimates and required fewer replications. Therefore, a 2-phase random sampling approach is proposed for the robust estimation of tissue-to-plasma ratio from extremely sparsely sampled data.

Robust estimation for homoscedastic regression in the secondary analysis of case-control data

KAUST Repository

Wei, Jiawei; Carroll, Raymond J.; Mü ller, Ursula U.; Keilegom, Ingrid Van; Chatterjee, Nilanjan

2012-01-01

Primary analysis of case-control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case-control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

Robust estimation for homoscedastic regression in the secondary analysis of case-control data

KAUST Repository

Wei, Jiawei

2012-12-04

Primary analysis of case-control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case-control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

Detection of heart beats in multimodal data: a robust beat-to-beat interval estimation approach.

Science.gov (United States)

Antink, Christoph Hoog; Brüser, Christoph; Leonhardt, Steffen

2015-08-01

The heart rate and its variability play a vital role in the continuous monitoring of patients, especially in the critical care unit. They are commonly derived automatically from the electrocardiogram as the interval between consecutive heart beat. While their identification by QRS-complexes is straightforward under ideal conditions, the exact localization can be a challenging task if the signal is severely contaminated with noise and artifacts. At the same time, other signals directly related to cardiac activity are often available. In this multi-sensor scenario, methods of multimodal sensor-fusion allow the exploitation of redundancies to increase the accuracy and robustness of beat detection.In this paper, an algorithm for the robust detection of heart beats in multimodal data is presented. Classic peak-detection is augmented by robust multi-channel, multimodal interval estimation to eliminate false detections and insert missing beats. This approach yielded a score of 90.70 and was thus ranked third place in the PhysioNet/Computing in Cardiology Challenge 2014: Robust Detection of Heart Beats in Muthmodal Data follow-up analysis.In the future, the robust beat-to-beat interval estimator may directly be used for the automated processing of multimodal patient data for applications such as diagnosis support and intelligent alarming.

Robust regularized least-squares beamforming approach to signal estimation

KAUST Repository

Suliman, Mohamed Abdalla Elhag

2017-05-12

In this paper, we address the problem of robust adaptive beamforming of signals received by a linear array. The challenge associated with the beamforming problem is twofold. Firstly, the process requires the inversion of the usually ill-conditioned covariance matrix of the received signals. Secondly, the steering vector pertaining to the direction of arrival of the signal of interest is not known precisely. To tackle these two challenges, the standard capon beamformer is manipulated to a form where the beamformer output is obtained as a scaled version of the inner product of two vectors. The two vectors are linearly related to the steering vector and the received signal snapshot, respectively. The linear operator, in both cases, is the square root of the covariance matrix. A regularized least-squares (RLS) approach is proposed to estimate these two vectors and to provide robustness without exploiting prior information. Simulation results show that the RLS beamformer using the proposed regularization algorithm outperforms state-of-the-art beamforming algorithms, as well as another RLS beamformers using a standard regularization approaches.

mBEEF-vdW: Robust fitting of error estimation density functionals

DEFF Research Database (Denmark)

Lundgård, Keld Troen; Wellendorff, Jess; Voss, Johannes

2016-01-01

. The functional is fitted within the Bayesian error estimation functional (BEEF) framework [J. Wellendorff et al., Phys. Rev. B 85, 235149 (2012); J. Wellendorff et al., J. Chem. Phys. 140, 144107 (2014)]. We improve the previously used fitting procedures by introducing a robust MM-estimator based loss function...... catalysis, including datasets that were not used for its training. Overall, we find that mBEEF-vdW has a higher general accuracy than competing popular functionals, and it is one of the best performing functionals on chemisorption systems, surface energies, lattice constants, and dispersion. We also show...

TasselNet: counting maize tassels in the wild via local counts regression network.

Science.gov (United States)

Lu, Hao; Cao, Zhiguo; Xiao, Yang; Zhuang, Bohan; Shen, Chunhua

2017-01-01

Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting ( MTC ) dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed TasselNet is proposed. TasselNet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that TasselNet outperforms other state-of-the-art approaches by large margins and achieves the overall best counting

TasselNet: counting maize tassels in the wild via local counts regression network

Directory of Open Access Journals (Sweden)

Hao Lu

2017-11-01

Full Text Available Abstract Background Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. Results This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting (MTC dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed TasselNet is proposed. TasselNet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that TasselNet outperforms other state-of-the-art approaches by large

Robust Non-Local TV-L1 Optical Flow Estimation with Occlusion Detection.

Science.gov (United States)

Zhang, Congxuan; Chen, Zhen; Wang, Mingrun; Li, Ming; Jiang, Shaofeng

2017-06-05

In this paper, we propose a robust non-local TV-L1 optical flow method with occlusion detection to address the problem of weak robustness of optical flow estimation with motion occlusion. Firstly, a TV-L1 form for flow estimation is defined using a combination of the brightness constancy and gradient constancy assumptions in the data term and by varying the weight under the Charbonnier function in the smoothing term. Secondly, to handle the potential risk of the outlier in the flow field, a general non-local term is added in the TV-L1 optical flow model to engender the typical non-local TV-L1 form. Thirdly, an occlusion detection method based on triangulation is presented to detect the occlusion regions of the sequence. The proposed non-local TV-L1 optical flow model is performed in a linearizing iterative scheme using improved median filtering and a coarse-to-fine computing strategy. The results of the complex experiment indicate that the proposed method can overcome the significant influence of non-rigid motion, motion occlusion, and large displacement motion. Results of experiments comparing the proposed method and existing state-of-the-art methods by respectively using Middlebury and MPI Sintel database test sequences show that the proposed method has higher accuracy and better robustness.

Robust inference in the negative binomial regression model with an application to falls data.

Science.gov (United States)

Aeberhard, William H; Cantoni, Eva; Heritier, Stephane

2014-12-01

A popular way to model overdispersed count data, such as the number of falls reported during intervention studies, is by means of the negative binomial (NB) distribution. Classical estimating methods are well-known to be sensitive to model misspecifications, taking the form of patients falling much more than expected in such intervention studies where the NB regression model is used. We extend in this article two approaches for building robust M-estimators of the regression parameters in the class of generalized linear models to the NB distribution. The first approach achieves robustness in the response by applying a bounded function on the Pearson residuals arising in the maximum likelihood estimating equations, while the second approach achieves robustness by bounding the unscaled deviance components. For both approaches, we explore different choices for the bounding functions. Through a unified notation, we show how close these approaches may actually be as long as the bounding functions are chosen and tuned appropriately, and provide the asymptotic distributions of the resulting estimators. Moreover, we introduce a robust weighted maximum likelihood estimator for the overdispersion parameter, specific to the NB distribution. Simulations under various settings show that redescending bounding functions yield estimates with smaller biases under contamination while keeping high efficiency at the assumed model, and this for both approaches. We present an application to a recent randomized controlled trial measuring the effectiveness of an exercise program at reducing the number of falls among people suffering from Parkinsons disease to illustrate the diagnostic use of such robust procedures and their need for reliable inference. © 2014, The International Biometric Society.

Radiation counting statistics

Energy Technology Data Exchange (ETDEWEB)

Suh, M. Y.; Jee, K. Y.; Park, K. K.; Park, Y. J.; Kim, W. H

1999-08-01

This report is intended to describe the statistical methods necessary to design and conduct radiation counting experiments and evaluate the data from the experiment. The methods are described for the evaluation of the stability of a counting system and the estimation of the precision of counting data by application of probability distribution models. The methods for the determination of the uncertainty of the results calculated from the number of counts, as well as various statistical methods for the reduction of counting error are also described. (Author). 11 refs., 8 tabs., 8 figs.

Radiation counting statistics

Energy Technology Data Exchange (ETDEWEB)

Suh, M. Y.; Jee, K. Y.; Park, K. K. [Korea Atomic Energy Research Institute, Taejon (Korea)

1999-08-01

This report is intended to describe the statistical methods necessary to design and conduct radiation counting experiments and evaluate the data from the experiments. The methods are described for the evaluation of the stability of a counting system and the estimation of the precision of counting data by application of probability distribution models. The methods for the determination of the uncertainty of the results calculated from the number of counts, as well as various statistical methods for the reduction of counting error are also described. 11 refs., 6 figs., 8 tabs. (Author)

Radiation counting statistics

International Nuclear Information System (INIS)

Suh, M. Y.; Jee, K. Y.; Park, K. K.; Park, Y. J.; Kim, W. H.

1999-08-01

This report is intended to describe the statistical methods necessary to design and conduct radiation counting experiments and evaluate the data from the experiment. The methods are described for the evaluation of the stability of a counting system and the estimation of the precision of counting data by application of probability distribution models. The methods for the determination of the uncertainty of the results calculated from the number of counts, as well as various statistical methods for the reduction of counting error are also described. (Author). 11 refs., 8 tabs., 8 figs

WTA estimates using the method of paired comparison: tests of robustness

Science.gov (United States)

Patricia A. Champ; John B. Loomis

1998-01-01

The method of paired comparison is modified to allow choices between two alternative gains so as to estimate willingness to accept (WTA) without loss aversion. The robustness of WTA values for two public goods is tested with respect to sensitivity of theWTA measure to the context of the bundle of goods used in the paired comparison exercise and to the scope (scale) of...

Estimating temporary emigration and breeding proportions using capture-recapture data with Pollock's robust design

Science.gov (United States)

Kendall, W.L.; Nichols, J.D.; Hines, J.E.

1997-01-01

Statistical inference for capture-recapture studies of open animal populations typically relies on the assumption that all emigration from the studied population is permanent. However, there are many instances in which this assumption is unlikely to be met. We define two general models for the process of temporary emigration, completely random and Markovian. We then consider effects of these two types of temporary emigration on Jolly-Seber (Seber 1982) estimators and on estimators arising from the full-likelihood approach of Kendall et al. (1995) to robust design data. Capture-recapture data arising from Pollock's (1982) robust design provide the basis for obtaining unbiased estimates of demographic parameters in the presence of temporary emigration and for estimating the probability of temporary emigration. We present a likelihood-based approach to dealing with temporary emigration that permits estimation under different models of temporary emigration and yields tests for completely random and Markovian emigration. In addition, we use the relationship between capture probability estimates based on closed and open models under completely random temporary emigration to derive three ad hoc estimators for the probability of temporary emigration, two of which should be especially useful in situations where capture probabilities are heterogeneous among individual animals. Ad hoc and full-likelihood estimators are illustrated for small mammal capture-recapture data sets. We believe that these models and estimators will be useful for testing hypotheses about the process of temporary emigration, for estimating demographic parameters in the presence of temporary emigration, and for estimating probabilities of temporary emigration. These latter estimates are frequently of ecological interest as indicators of animal movement and, in some sampling situations, as direct estimates of breeding probabilities and proportions.

Rotated Walsh-Hadamard Spreading with Robust Channel Estimation for a Coded MC-CDMA System

Directory of Open Access Journals (Sweden)

Raulefs Ronald

2004-01-01

Full Text Available We investigate rotated Walsh-Hadamard spreading matrices for a broadband MC-CDMA system with robust channel estimation in the synchronous downlink. The similarities between rotated spreading and signal space diversity are outlined. In a multiuser MC-CDMA system, possible performance improvements are based on the chosen detector, the channel code, and its Hamming distance. By applying rotated spreading in comparison to a standard Walsh-Hadamard spreading code, a higher throughput can be achieved. As combining the channel code and the spreading code forms a concatenated code, the overall minimum Hamming distance of the concatenated code increases. This asymptotically results in an improvement of the bit error rate for high signal-to-noise ratio. Higher convolutional channel code rates are mostly generated by puncturing good low-rate channel codes. The overall Hamming distance decreases significantly for the punctured channel codes. Higher channel code rates are favorable for MC-CDMA, as MC-CDMA utilizes diversity more efficiently compared to pure OFDMA. The application of rotated spreading in an MC-CDMA system allows exploiting diversity even further. We demonstrate that the rotated spreading gain is still present for a robust pilot-aided channel estimator. In a well-designed system, rotated spreading extends the performance by using a maximum likelihood detector with robust channel estimation at the receiver by about 1 dB.

Methodology in robust and nonparametric statistics

CERN Document Server

Jurecková, Jana; Picek, Jan

2012-01-01

Introduction and SynopsisIntroductionSynopsisPreliminariesIntroductionInference in Linear ModelsRobustness ConceptsRobust and Minimax Estimation of LocationClippings from Probability and Asymptotic TheoryProblemsRobust Estimation of Location and RegressionIntroductionM-EstimatorsL-EstimatorsR-EstimatorsMinimum Distance and Pitman EstimatorsDifferentiable Statistical FunctionsProblemsAsymptotic Representations for L-Estimators

Counting statistics in low level radioactivity measurements fluctuating counting efficiency

International Nuclear Information System (INIS)

Pazdur, M.F.

1976-01-01

A divergence between the probability distribution of the number of nuclear disintegrations and the number of observed counts, caused by counting efficiency fluctuation, is discussed. The negative binominal distribution is proposed to describe the probability distribution of the number of counts, instead of Poisson distribution, which is assumed to hold for the number of nuclear disintegrations only. From actual measurements the r.m.s. amplitude of counting efficiency fluctuation is estimated. Some consequences of counting efficiency fluctuation are investigated and the corresponding formulae are derived: (1) for detection limit as a function of the number of partial measurements and the relative amplitude of counting efficiency fluctuation, and (2) for optimum allocation of the number of partial measurements between sample and background. (author)

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

Science.gov (United States)

Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of

Efficient estimation of the robustness region of biological models with oscillatory behavior.

Directory of Open Access Journals (Sweden)

Mochamad Apri

Full Text Available Robustness is an essential feature of biological systems, and any mathematical model that describes such a system should reflect this feature. Especially, persistence of oscillatory behavior is an important issue. A benchmark model for this phenomenon is the Laub-Loomis model, a nonlinear model for cAMP oscillations in Dictyostelium discoideum. This model captures the most important features of biomolecular networks oscillating at constant frequencies. Nevertheless, the robustness of its oscillatory behavior is not yet fully understood. Given a system that exhibits oscillating behavior for some set of parameters, the central question of robustness is how far the parameters may be changed, such that the qualitative behavior does not change. The determination of such a "robustness region" in parameter space is an intricate task. If the number of parameters is high, it may be also time consuming. In the literature, several methods are proposed that partially tackle this problem. For example, some methods only detect particular bifurcations, or only find a relatively small box-shaped estimate for an irregularly shaped robustness region. Here, we present an approach that is much more general, and is especially designed to be efficient for systems with a large number of parameters. As an illustration, we apply the method first to a well understood low-dimensional system, the Rosenzweig-MacArthur model. This is a predator-prey model featuring satiation of the predator. It has only two parameters and its bifurcation diagram is available in the literature. We find a good agreement with the existing knowledge about this model. When we apply the new method to the high dimensional Laub-Loomis model, we obtain a much larger robustness region than reported earlier in the literature. This clearly demonstrates the power of our method. From the results, we conclude that the biological system underlying is much more robust than was realized until now.

Data-Driven Robust RVFLNs Modeling of a Blast Furnace Iron-Making Process Using Cauchy Distribution Weighted M-Estimation

Energy Technology Data Exchange (ETDEWEB)

Zhou, Ping; Lv, Youbin; Wang, Hong; Chai, Tianyou

2017-09-01

Optimal operation of a practical blast furnace (BF) ironmaking process depends largely on a good measurement of molten iron quality (MIQ) indices. However, measuring the MIQ online is not feasible using the available techniques. In this paper, a novel data-driven robust modeling is proposed for online estimation of MIQ using improved random vector functional-link networks (RVFLNs). Since the output weights of traditional RVFLNs are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with outliers. This affects the modeling accuracy of RVFLNs. To solve this problem, a Cauchy distribution weighted M-estimation based robust RFVLNs is proposed. Since the weights of different outlier data are properly determined by the Cauchy distribution, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Moreover, given that the BF is a complex nonlinear system with numerous coupling variables, the data-driven canonical correlation analysis is employed to identify the most influential components from multitudinous factors that affect the MIQ indices to reduce the model dimension. Finally, experiments using industrial data and comparative studies have demonstrated that the obtained model produces a better modeling and estimating accuracy and stronger robustness than other modeling methods.

A robust methodology for modal parameters estimation applied to SHM

Science.gov (United States)

Cardoso, Rharã; Cury, Alexandre; Barbosa, Flávio

2017-10-01

The subject of structural health monitoring is drawing more and more attention over the last years. Many vibration-based techniques aiming at detecting small structural changes or even damage have been developed or enhanced through successive researches. Lately, several studies have focused on the use of raw dynamic data to assess information about structural condition. Despite this trend and much skepticism, many methods still rely on the use of modal parameters as fundamental data for damage detection. Therefore, it is of utmost importance that modal identification procedures are performed with a sufficient level of precision and automation. To fulfill these requirements, this paper presents a novel automated time-domain methodology to identify modal parameters based on a two-step clustering analysis. The first step consists in clustering modes estimates from parametric models of different orders, usually presented in stabilization diagrams. In an automated manner, the first clustering analysis indicates which estimates correspond to physical modes. To circumvent the detection of spurious modes or the loss of physical ones, a second clustering step is then performed. The second step consists in the data mining of information gathered from the first step. To attest the robustness and efficiency of the proposed methodology, numerically generated signals as well as experimental data obtained from a simply supported beam tested in laboratory and from a railway bridge are utilized. The results appeared to be more robust and accurate comparing to those obtained from methods based on one-step clustering analysis.

Comparison of Drive Counts and Mark-Resight As Methods of Population Size Estimation of Highly Dense Sika Deer (Cervus nippon Populations.

Directory of Open Access Journals (Sweden)

Kazutaka Takeshita

Full Text Available Assessing temporal changes in abundance indices is an important issue in the management of large herbivore populations. The drive counts method has been frequently used as a deer abundance index in mountainous regions. However, despite an inherent risk for observation errors in drive counts, which increase with deer density, evaluations of the utility of drive counts at a high deer density remain scarce. We compared the drive counts and mark-resight (MR methods in the evaluation of a highly dense sika deer population (MR estimates ranged between 11 and 53 individuals/km2 on Nakanoshima Island, Hokkaido, Japan, between 1999 and 2006. This deer population experienced two large reductions in density; approximately 200 animals in total were taken from the population through a large-scale population removal and a separate winter mass mortality event. Although the drive counts tracked temporal changes in deer abundance on the island, they overestimated the counts for all years in comparison to the MR method. Increased overestimation in drive count estimates after the winter mass mortality event may be due to a double count derived from increased deer movement and recovery of body condition secondary to the mitigation of density-dependent food limitations. Drive counts are unreliable because they are affected by unfavorable factors such as bad weather, and they are cost-prohibitive to repeat, which precludes the calculation of confidence intervals. Therefore, the use of drive counts to infer the deer abundance needs to be reconsidered.

Evaluation of the robustness of estimating five components from a skin spectral image

Science.gov (United States)

Akaho, Rina; Hirose, Misa; Tsumura, Norimichi

2018-04-01

We evaluated the robustness of a method used to estimate five components (i.e., melanin, oxy-hemoglobin, deoxy-hemoglobin, shading, and surface reflectance) from the spectral reflectance of skin at five wavelengths against noise and a change in epidermis thickness. We also estimated the five components from recorded images of age spots and circles under the eyes using the method. We found that noise in the image must be no more 0.1% to accurately estimate the five components and that the thickness of the epidermis affects the estimation. We acquired the distribution of major causes for age spots and circles under the eyes by applying the method to recorded spectral images.

Fast and Robust Nanocellulose Width Estimation Using Turbidimetry.

Science.gov (United States)

Shimizu, Michiko; Saito, Tsuguyuki; Nishiyama, Yoshiharu; Iwamoto, Shinichiro; Yano, Hiroyuki; Isogai, Akira; Endo, Takashi

2016-10-01

The dimensions of nanocelluloses are important factors in controlling their material properties. The present study reports a fast and robust method for estimating the widths of individual nanocellulose particles based on the turbidities of their water dispersions. Seven types of nanocellulose, including short and rigid cellulose nanocrystals and long and flexible cellulose nanofibers, are prepared via different processes. Their widths are calculated from the respective turbidity plots of their water dispersions, based on the theory of light scattering by thin and long particles. The turbidity-derived widths of the seven nanocelluloses range from 2 to 10 nm, and show good correlations with the thicknesses of nanocellulose particles spread on flat mica surfaces determined using atomic force microscopy. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An Adaptive Smoother for Counting Measurements

International Nuclear Information System (INIS)

Kondrasovs Vladimir; Coulon Romain; Normand Stephane

2013-06-01

Counting measurements associated with nuclear instruments are tricky to carry out due to the stochastic process of the radioactivity. Indeed events counting have to be processed and filtered in order to display a stable count rate value and to allow variations monitoring in the measured activity. Smoothers (as the moving average) are adjusted by a time constant defined as a compromise between stability and response time. A new approach has been developed and consists in improving the response time while maintaining count rate stability. It uses the combination of a smoother together with a detection filter. A memory of counting data is processed to calculate several count rate estimates using several integration times. These estimates are then sorted into the memory from short to long integration times. A measurement position, in terms of integration time, is then chosen into this memory after a detection test. An inhomogeneity into the Poisson counting process is detected by comparison between current position estimate and the other estimates contained into the memory in respect with the associated statistical variance calculated with homogeneous assumption. The measurement position (historical time) and the ability to forget an obsolete data or to keep in memory a useful data are managed using the detection test result. The proposed smoother is then an adaptive and a learning algorithm allowing an optimization of the response time while maintaining measurement counting stability and converging efficiently to the best counting estimate after an effective change in activity. This algorithm has also the specificity to be low recursive and thus easily embedded into DSP electronics based on FPGA or micro-controllers meeting 'real life' time requirements. (authors)

Estimating the robustness of composite CBA & MCA assessments by variation of criteria importance order

DEFF Research Database (Denmark)

Jensen, Anders Vestergaard; Barfod, Michael Bruhn; Leleur, Steen

is based on the fact that when using MCA as a decision-support tool, questions often arise about the weighting (or prioritising) of the included criteria. This part of the MCA is seen as the most subjective part and could give reasons for discussion among the decision makers or stakeholders. Furthermore......This paper discusses the concept of using rank variation concerning the stake-holder prioritising of importance criteria for exploring the sensitivity of criteria weights in multi-criteria analysis (MCA). Thereby the robustness of the MCA-based decision support can be tested. The analysis described......, the relative weights can make a large difference in the resulting assessment of alternatives [1]. Therefore it is highly relevant to introduce a procedure for estimating the importance of criteria weights. This paper proposes a methodology for estimating the robustness of weights used in additive utility...

Estimated average annual rate of change of CD4(+) T-cell counts in patients on combination antiretroviral therapy

DEFF Research Database (Denmark)

Mocroft, Amanda; Phillips, Andrew N; Ledergerber, Bruno

2010-01-01

BACKGROUND: Patients receiving combination antiretroviral therapy (cART) might continue treatment with a virologically failing regimen. We sought to identify annual change in CD4(+) T-cell count according to levels of viraemia in patients on cART. METHODS: A total of 111,371 CD4(+) T-cell counts...... and viral load measurements in 8,227 patients were analysed. Annual change in CD4(+) T-cell numbers was estimated using mixed models. RESULTS: After adjustment, the estimated average annual change in CD4(+) T-cell count significantly increased when viral load was cells/mm(3), 95......% confidence interval [CI] 26.6-34.3), was stable when viral load was 500-9,999 copies/ml (3.1 cells/mm(3), 95% CI -5.3-11.5) and decreased when viral load was >/=10,000 copies/ml (-14.8 cells/mm(3), 95% CI -4.5--25.1). Patients taking a boosted protease inhibitor (PI) regimen had more positive annual CD4(+) T-cell...

Robust Manhattan Frame Estimation From a Single RGB-D Image

KAUST Repository

Bernard Ghanem; Heilbron, Fabian Caba; Niebles, Juan Carlos; Thabet, Ali Kassem

2015-01-01

This paper proposes a new framework for estimating the Manhattan Frame (MF) of an indoor scene from a single RGB-D image. Our technique formulates this problem as the estimation of a rotation matrix that best aligns the normals of the captured scene to a canonical world axes. By introducing sparsity constraints, our method can simultaneously estimate the scene MF, the surfaces in the scene that are best aligned to one of three coordinate axes, and the outlier surfaces that do not align with any of the axes. To test our approach, we contribute a new set of annotations to determine ground truth MFs in each image of the popular NYUv2 dataset. We use this new benchmark to experimentally demonstrate that our method is more accurate, faster, more reliable and more robust than the methods used in the literature. We further motivate our technique by showing how it can be used to address the RGB-D SLAM problem in indoor scenes by incorporating it into and improving the performance of a popular RGB-D SLAM method.

Robust Manhattan Frame Estimation From a Single RGB-D Image

KAUST Repository

Bernard Ghanem

2015-06-02

This paper proposes a new framework for estimating the Manhattan Frame (MF) of an indoor scene from a single RGB-D image. Our technique formulates this problem as the estimation of a rotation matrix that best aligns the normals of the captured scene to a canonical world axes. By introducing sparsity constraints, our method can simultaneously estimate the scene MF, the surfaces in the scene that are best aligned to one of three coordinate axes, and the outlier surfaces that do not align with any of the axes. To test our approach, we contribute a new set of annotations to determine ground truth MFs in each image of the popular NYUv2 dataset. We use this new benchmark to experimentally demonstrate that our method is more accurate, faster, more reliable and more robust than the methods used in the literature. We further motivate our technique by showing how it can be used to address the RGB-D SLAM problem in indoor scenes by incorporating it into and improving the performance of a popular RGB-D SLAM method.

The Big Pumpkin Count.

Science.gov (United States)

Coplestone-Loomis, Lenny

1981-01-01

Pumpkin seeds are counted after students convert pumpkins to jack-o-lanterns. Among the activities involved, pupils learn to count by 10s, make estimates, and to construct a visual representation of 1,000. (MP)

Population estimates and geographical distributions of swans and geese in East Asia based on counts during the non-breeding season

DEFF Research Database (Denmark)

Jia, Qiang; Koyama, Kazuo; Choi, Chang-Yong

2016-01-01

For the first time, we estimated the population sizes of two swan species and four goose species from observations during the non-breeding period in East Asia. Based on combined counts from South Korea, Japan and China, we estimated the total abundance of these species as follows: 42,000–47,000 W......For the first time, we estimated the population sizes of two swan species and four goose species from observations during the non-breeding period in East Asia. Based on combined counts from South Korea, Japan and China, we estimated the total abundance of these species as follows: 42......,000–47,000 Whooper Swans Cygnus cygnus ; 99,000–141,000 Tundra Swans C. columbianus bewickii ; 56,000–98,000 Swan Geese Anser cygnoides ; 157,000–194,000 Bean Geese A. fabalis ; 231,000–283,000 Greater White-fronted Geese A. albifrons ; and 14,000–19,000 Lesser White-fronted Geese A. erythropus. While the count data...... from Korea and Japan provide a good reflection of numbers present, there remain gaps in the coverage in China, which particularly affect the precision of the estimates for Bean, Greater and Lesser White-fronted Geese as well as Tundra Swans. Lack of subspecies distinction of Bean Geese in China until...

Robust and sparse correlation matrix estimation for the analysis of high-dimensional genomics data.

Science.gov (United States)

Serra, Angela; Coretto, Pietro; Fratello, Michele; Tagliaferri, Roberto; Stegle, Oliver

2018-02-15

Microarray technology can be used to study the expression of thousands of genes across a number of different experimental conditions, usually hundreds. The underlying principle is that genes sharing similar expression patterns, across different samples, can be part of the same co-expression system, or they may share the same biological functions. Groups of genes are usually identified based on cluster analysis. Clustering methods rely on the similarity matrix between genes. A common choice to measure similarity is to compute the sample correlation matrix. Dimensionality reduction is another popular data analysis task which is also based on covariance/correlation matrix estimates. Unfortunately, covariance/correlation matrix estimation suffers from the intrinsic noise present in high-dimensional data. Sources of noise are: sampling variations, presents of outlying sample units, and the fact that in most cases the number of units is much larger than the number of genes. In this paper, we propose a robust correlation matrix estimator that is regularized based on adaptive thresholding. The resulting method jointly tames the effects of the high-dimensionality, and data contamination. Computations are easy to implement and do not require hand tunings. Both simulated and real data are analyzed. A Monte Carlo experiment shows that the proposed method is capable of remarkable performances. Our correlation metric is more robust to outliers compared with the existing alternatives in two gene expression datasets. It is also shown how the regularization allows to automatically detect and filter spurious correlations. The same regularization is also extended to other less robust correlation measures. Finally, we apply the ARACNE algorithm on the SyNTreN gene expression data. Sensitivity and specificity of the reconstructed network is compared with the gold standard. We show that ARACNE performs better when it takes the proposed correlation matrix estimator as input. The R

A robust standard deviation control chart

NARCIS (Netherlands)

Schoonhoven, M.; Does, R.J.M.M.

2012-01-01

This article studies the robustness of Phase I estimators for the standard deviation control chart. A Phase I estimator should be efficient in the absence of contaminations and resistant to disturbances. Most of the robust estimators proposed in the literature are robust against either diffuse

Robust Pose Estimation using the SwissRanger SR-3000 Camera

DEFF Research Database (Denmark)

Gudmundsson, Sigurjon Arni; Larsen, Rasmus; Ersbøll, Bjarne Kjær

2007-01-01

In this paper a robust method is presented to classify and estimate an objects pose from a real time range image and a low dimensional model. The model is made from a range image training set which is reduced dimensionally by a nonlinear manifold learning method named Local Linear Embedding (LLE)......). New range images are then projected to this model giving the low dimensional coordinates of the object pose in an efficient manner. The range images are acquired by a state of the art SwissRanger SR-3000 camera making the projection process work in real-time....

Robust best linear estimation for regression analysis using surrogate and instrumental variables.

Science.gov (United States)

Wang, C Y

2012-04-01

We investigate methods for regression analysis when covariates are measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies the classical measurement error model, but it may not have repeated measurements. In addition to the surrogate variables that are available among the subjects in the calibration sample, we assume that there is an instrumental variable (IV) that is available for all study subjects. An IV is correlated with the unobserved true exposure variable and hence can be useful in the estimation of the regression coefficients. We propose a robust best linear estimator that uses all the available data, which is the most efficient among a class of consistent estimators. The proposed estimator is shown to be consistent and asymptotically normal under very weak distributional assumptions. For Poisson or linear regression, the proposed estimator is consistent even if the measurement error from the surrogate or IV is heteroscedastic. Finite-sample performance of the proposed estimator is examined and compared with other estimators via intensive simulation studies. The proposed method and other methods are applied to a bladder cancer case-control study.

Estimating nonrigid motion from inconsistent intensity with robust shape features

International Nuclear Information System (INIS)

Liu, Wenyang; Ruan, Dan

2013-01-01

Purpose: To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence. Methods: Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, and regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs. Results: To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided

Robustifying Bayesian nonparametric mixtures for count data.

Science.gov (United States)

Canale, Antonio; Prünster, Igor

2017-03-01

Our motivating application stems from surveys of natural populations and is characterized by large spatial heterogeneity in the counts, which makes parametric approaches to modeling local animal abundance too restrictive. We adopt a Bayesian nonparametric approach based on mixture models and innovate with respect to popular Dirichlet process mixture of Poisson kernels by increasing the model flexibility at the level both of the kernel and the nonparametric mixing measure. This allows to derive accurate and robust estimates of the distribution of local animal abundance and of the corresponding clusters. The application and a simulation study for different scenarios yield also some general methodological implications. Adding flexibility solely at the level of the mixing measure does not improve inferences, since its impact is severely limited by the rigidity of the Poisson kernel with considerable consequences in terms of bias. However, once a kernel more flexible than the Poisson is chosen, inferences can be robustified by choosing a prior more general than the Dirichlet process. Therefore, to improve the performance of Bayesian nonparametric mixtures for count data one has to enrich the model simultaneously at both levels, the kernel and the mixing measure. © 2016, The International Biometric Society.

Human Age Estimation Method Robust to Camera Sensor and/or Face Movement

Directory of Open Access Journals (Sweden)

Dat Tien Nguyen

2015-08-01

Full Text Available Human age can be employed in many useful real-life applications, such as customer service systems, automatic vending machines, entertainment, etc. In order to obtain age information, image-based age estimation systems have been developed using information from the human face. However, limitations exist for current age estimation systems because of the various factors of camera motion and optical blurring, facial expressions, gender, etc. Motion blurring can usually be presented on face images by the movement of the camera sensor and/or the movement of the face during image acquisition. Therefore, the facial feature in captured images can be transformed according to the amount of motion, which causes performance degradation of age estimation systems. In this paper, the problem caused by motion blurring is addressed and its solution is proposed in order to make age estimation systems robust to the effects of motion blurring. Experiment results show that our method is more efficient for enhancing age estimation performance compared with systems that do not employ our method.

Logistic quantile regression provides improved estimates for bounded avian counts: a case study of California Spotted Owl fledgling production

Science.gov (United States)

Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...

Methods for robustness programming

NARCIS (Netherlands)

Olieman, N.J.

2008-01-01

Robustness of an object is defined as the probability that an object will have properties as required. Robustness Programming (RP) is a mathematical approach for Robustness estimation and Robustness optimisation. An example in the context of designing a food product, is finding the best composition

Estimating the Robustness of Composite CBA and MCDA Assessments by Variation of Criteria Importance Order

DEFF Research Database (Denmark)

Jensen, Anders Vestergaard; Barfod, Michael Bruhn; Leleur, Steen

2011-01-01

described is based on the fact that when using MCA as a decision-support tool, questions often arise about the weighting (or prioritising) of the included criteria. This part of the MCA is seen as the most subjective part and could give reasons for discussion among the decision makers or stakeholders......Abstract This paper discusses the concept of using rank variation concerning the stakeholder prioritising of importance criteria for exploring the sensitivity of criteria weights in multi-criteria analysis (MCA). Thereby the robustness of the MCA-based decision support can be tested. The analysis....... Furthermore, the relative weights can make a large difference in the resulting assessment of alternatives (Hobbs and Meier 2000). Therefore it is highly relevant to introduce a procedure for estimating the importance of criteria weights. This paper proposes a methodology for estimating the robustness...

Robust driver heartbeat estimation: A q-Hurst exponent based automatic sensor change with interactive multi-model EKF.

Science.gov (United States)

Vrazic, Sacha

2015-08-01

Preventing car accidents by monitoring the driver's physiological parameters is of high importance. However, existing measurement methods are not robust to driver's body movements. In this paper, a system that estimates the heartbeat from the seat embedded piezoelectric sensors, and that is robust to strong body movements is presented. Multifractal q-Hurst exponents are used within a classifier to predict the most probable best sensor signal to be used in an Interactive Multi-Model Extended Kalman Filter pulsation estimation procedure. The car vibration noise is reduced using an autoregressive exogenous model to predict the noise on sensors. The performance of the proposed system was evaluated on real driving data up to 100 km/h and with slaloms at high speed. It is shown that this method improves by 36.7% the pulsation estimation under strong body movement compared to static sensor pulsation estimation and appears to provide reliable pulsation variability information for top-level analysis of drowsiness or other conditions.

Age Estimation Robust to Optical and Motion Blurring by Deep Residual CNN

Directory of Open Access Journals (Sweden)

Jeon Seong Kang

2018-04-01

Full Text Available Recently, real-time human age estimation based on facial images has been applied in various areas. Underneath this phenomenon lies an awareness that age estimation plays an important role in applying big data to target marketing for age groups, product demand surveys, consumer trend analysis, etc. However, in a real-world environment, various optical and motion blurring effects can occur. Such effects usually cause a problem in fully capturing facial features such as wrinkles, which are essential to age estimation, thereby degrading accuracy. Most of the previous studies on age estimation were conducted for input images almost free from blurring effect. To overcome this limitation, we propose the use of a deep ResNet-152 convolutional neural network for age estimation, which is robust to various optical and motion blurring effects of visible light camera sensors. We performed experiments with various optical and motion blurred images created from the park aging mind laboratory (PAL and craniofacial longitudinal morphological face database (MORPH databases, which are publicly available. According to the results, the proposed method exhibited better age estimation performance than the previous methods.

Equivalence of truncated count mixture distributions and mixtures of truncated count distributions.

Science.gov (United States)

Böhning, Dankmar; Kuhnert, Ronny

2006-12-01

This article is about modeling count data with zero truncation. A parametric count density family is considered. The truncated mixture of densities from this family is different from the mixture of truncated densities from the same family. Whereas the former model is more natural to formulate and to interpret, the latter model is theoretically easier to treat. It is shown that for any mixing distribution leading to a truncated mixture, a (usually different) mixing distribution can be found so that the associated mixture of truncated densities equals the truncated mixture, and vice versa. This implies that the likelihood surfaces for both situations agree, and in this sense both models are equivalent. Zero-truncated count data models are used frequently in the capture-recapture setting to estimate population size, and it can be shown that the two Horvitz-Thompson estimators, associated with the two models, agree. In particular, it is possible to achieve strong results for mixtures of truncated Poisson densities, including reliable, global construction of the unique NPMLE (nonparametric maximum likelihood estimator) of the mixing distribution, implying a unique estimator for the population size. The benefit of these results lies in the fact that it is valid to work with the mixture of truncated count densities, which is less appealing for the practitioner but theoretically easier. Mixtures of truncated count densities form a convex linear model, for which a developed theory exists, including global maximum likelihood theory as well as algorithmic approaches. Once the problem has been solved in this class, it might readily be transformed back to the original problem by means of an explicitly given mapping. Applications of these ideas are given, particularly in the case of the truncated Poisson family.

Estimation of atomic interaction parameters by photon counting

DEFF Research Database (Denmark)

Kiilerich, Alexander Holm; Mølmer, Klaus

2014-01-01

Detection of radiation signals is at the heart of precision metrology and sensing. In this article we show how the fluctuations in photon counting signals can be exploited to optimally extract information about the physical parameters that govern the dynamics of the emitter. For a simple two......-level emitter subject to photon counting, we show that the Fisher information and the Cram\\'er- Rao sensitivity bound based on the full detection record can be evaluated from the waiting time distribution in the fluorescence signal which can, in turn, be calculated for both perfect and imperfect detectors...

A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera.

Science.gov (United States)

Ci, Wenyan; Huang, Yingping

2016-10-17

Visual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An objective function fitted with a set of feature points is created by establishing the mathematical relationship between optical flow, depth and camera ego-motion parameters through the camera's 3-dimensional motion and planar imaging model. Accordingly, the six motion parameters are computed by minimizing the objective function, using the iterative Levenberg-Marquard method. One of key points for visual odometry is that the feature points selected for the computation should contain inliers as much as possible. In this work, the feature points and their optical flows are initially detected by using the Kanade-Lucas-Tomasi (KLT) algorithm. A circle matching is followed to remove the outliers caused by the mismatching of the KLT algorithm. A space position constraint is imposed to filter out the moving points from the point set detected by the KLT algorithm. The Random Sample Consensus (RANSAC) algorithm is employed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining points are tracked to estimate the ego-motion parameters in the subsequent frames. The approach presented here is tested on real traffic videos and the results prove the robustness and precision of the method.

Deep 3 GHz number counts from a P(D) fluctuation analysis

Science.gov (United States)

Vernstrom, T.; Scott, Douglas; Wall, J. V.; Condon, J. J.; Cotton, W. D.; Fomalont, E. B.; Kellermann, K. I.; Miller, N.; Perley, R. A.

2014-05-01

Radio source counts constrain galaxy populations and evolution, as well as the global star formation history. However, there is considerable disagreement among the published 1.4-GHz source counts below 100 μJy. Here, we present a statistical method for estimating the μJy and even sub-μJy source count using new deep wide-band 3-GHz data in the Lockman Hole from the Karl G. Jansky Very Large Array. We analysed the confusion amplitude distribution P(D), which provides a fresh approach in the form of a more robust model, with a comprehensive error analysis. We tested this method on a large-scale simulation, incorporating clustering and finite source sizes. We discuss in detail our statistical methods for fitting using Markov chain Monte Carlo, handling correlations, and systematic errors from the use of wide-band radio interferometric data. We demonstrated that the source count can be constrained down to 50 nJy, a factor of 20 below the rms confusion. We found the differential source count near 10 μJy to have a slope of -1.7, decreasing to about -1.4 at fainter flux densities. At 3 GHz, the rms confusion in an 8-arcsec full width at half-maximum beam is ˜ 1.2 μJy beam-1, and a radio background temperature ˜14 mK. Our counts are broadly consistent with published evolutionary models. With these results, we were also able to constrain the peak of the Euclidean normalized differential source count of any possible new radio populations that would contribute to the cosmic radio background down to 50 nJy.

Histogram equalization with Bayesian estimation for noise robust speech recognition.

Science.gov (United States)

Suh, Youngjoo; Kim, Hoirin

2018-02-01

The histogram equalization approach is an efficient feature normalization technique for noise robust automatic speech recognition. However, it suffers from performance degradation when some fundamental conditions are not satisfied in the test environment. To remedy these limitations of the original histogram equalization methods, class-based histogram equalization approach has been proposed. Although this approach showed substantial performance improvement under noise environments, it still suffers from performance degradation due to the overfitting problem when test data are insufficient. To address this issue, the proposed histogram equalization technique employs the Bayesian estimation method in the test cumulative distribution function estimation. It was reported in a previous study conducted on the Aurora-4 task that the proposed approach provided substantial performance gains in speech recognition systems based on the acoustic modeling of the Gaussian mixture model-hidden Markov model. In this work, the proposed approach was examined in speech recognition systems with deep neural network-hidden Markov model (DNN-HMM), the current mainstream speech recognition approach where it also showed meaningful performance improvement over the conventional maximum likelihood estimation-based method. The fusion of the proposed features with the mel-frequency cepstral coefficients provided additional performance gains in DNN-HMM systems, which otherwise suffer from performance degradation in the clean test condition.

Counts of low-Redshift SDSS quasar candidates

International Nuclear Information System (INIS)

Zeljko Ivezic

2004-01-01

We analyze the counts of low-redshift quasar candidates selected using nine-epoch SDSS imaging data. The co-added catalogs are more than 1 mag deeper than single-epoch SDSS data, and allow the selection of low-redshift quasar candidates using UV-excess and also variability techniques. The counts of selected candidates are robustly determined down to g = 21.5. This is about 2 magnitudes deeper than the position of a change in the slope of the counts reported by Boyle (and others) (1990, 2000) for a sample selected by UV-excess, and questioned by Hawkins and Veron (1995), who utilized a variability-selected sample. Using SDSS data, we confirm a change in the slope of the counts for both UV-excess and variability selected samples, providing strong support for the Boyle (and others) results

Robust Portfolio Optimization Using Pseudodistances

Science.gov (United States)

2015-01-01

The presence of outliers in financial asset returns is a frequently occurring phenomenon which may lead to unreliable mean-variance optimized portfolios. This fact is due to the unbounded influence that outliers can have on the mean returns and covariance estimators that are inputs in the optimization procedure. In this paper we present robust estimators of mean and covariance matrix obtained by minimizing an empirical version of a pseudodistance between the assumed model and the true model underlying the data. We prove and discuss theoretical properties of these estimators, such as affine equivariance, B-robustness, asymptotic normality and asymptotic relative efficiency. These estimators can be easily used in place of the classical estimators, thereby providing robust optimized portfolios. A Monte Carlo simulation study and applications to real data show the advantages of the proposed approach. We study both in-sample and out-of-sample performance of the proposed robust portfolios comparing them with some other portfolios known in literature. PMID:26468948

Robust Portfolio Optimization Using Pseudodistances.

Science.gov (United States)

Toma, Aida; Leoni-Aubin, Samuela

2015-01-01

The presence of outliers in financial asset returns is a frequently occurring phenomenon which may lead to unreliable mean-variance optimized portfolios. This fact is due to the unbounded influence that outliers can have on the mean returns and covariance estimators that are inputs in the optimization procedure. In this paper we present robust estimators of mean and covariance matrix obtained by minimizing an empirical version of a pseudodistance between the assumed model and the true model underlying the data. We prove and discuss theoretical properties of these estimators, such as affine equivariance, B-robustness, asymptotic normality and asymptotic relative efficiency. These estimators can be easily used in place of the classical estimators, thereby providing robust optimized portfolios. A Monte Carlo simulation study and applications to real data show the advantages of the proposed approach. We study both in-sample and out-of-sample performance of the proposed robust portfolios comparing them with some other portfolios known in literature.

Binomial distribution of Poisson statistics and tracks overlapping probability to estimate total tracks count with low uncertainty

International Nuclear Information System (INIS)

Khayat, Omid; Afarideh, Hossein; Mohammadnia, Meisam

2015-01-01

In the solid state nuclear track detectors of chemically etched type, nuclear tracks with center-to-center neighborhood of distance shorter than two times the radius of tracks will emerge as overlapping tracks. Track overlapping in this type of detectors causes tracks count losses and it becomes rather severe in high track densities. Therefore, tracks counting in this condition should include a correction factor for count losses of different tracks overlapping orders since a number of overlapping tracks may be counted as one track. Another aspect of the problem is the cases where imaging the whole area of the detector and counting all tracks are not possible. In these conditions a statistical generalization method is desired to be applicable in counting a segmented area of the detector and the results can be generalized to the whole surface of the detector. Also there is a challenge in counting the tracks in densely overlapped tracks because not sufficient geometrical or contextual information are available. It this paper we present a statistical counting method which gives the user a relation between the tracks overlapping probabilities on a segmented area of the detector surface and the total number of tracks. To apply the proposed method one can estimate the total number of tracks on a solid state detector of arbitrary shape and dimensions by approximating the tracks averaged area, whole detector surface area and some orders of tracks overlapping probabilities. It will be shown that this method is applicable in high and ultra high density tracks images and the count loss error can be enervated using a statistical generalization approach. - Highlights: • A correction factor for count losses of different tracks overlapping orders. • For the cases imaging the whole area of the detector is not possible. • Presenting a statistical generalization method for segmented areas. • Giving a relation between the tracks overlapping probabilities and the total tracks

Robust Estimator for Non-Line-of-Sight Error Mitigation in Indoor Localization

Directory of Open Access Journals (Sweden)

Marco A

2006-01-01

Full Text Available Indoor localization systems are undoubtedly of interest in many application fields. Like outdoor systems, they suffer from non-line-of-sight (NLOS errors which hinder their robustness and accuracy. Though many ad hoc techniques have been developed to deal with this problem, unfortunately most of them are not applicable indoors due to the high variability of the environment (movement of furniture and of people, etc.. In this paper, we describe the use of robust regression techniques to detect and reject NLOS measures in a location estimation using multilateration. We show how the least-median-of-squares technique can be used to overcome the effects of NLOS errors, even in environments with little infrastructure, and validate its suitability by comparing it to other methods described in the bibliography. We obtained remarkable results when using it in a real indoor positioning system that works with Bluetooth and ultrasound (BLUPS, even when nearly half the measures suffered from NLOS or other coarse errors.

Robust Estimator for Non-Line-of-Sight Error Mitigation in Indoor Localization

Science.gov (United States)

Casas, R.; Marco, A.; Guerrero, J. J.; Falcó, J.

2006-12-01

Indoor localization systems are undoubtedly of interest in many application fields. Like outdoor systems, they suffer from non-line-of-sight (NLOS) errors which hinder their robustness and accuracy. Though many ad hoc techniques have been developed to deal with this problem, unfortunately most of them are not applicable indoors due to the high variability of the environment (movement of furniture and of people, etc.). In this paper, we describe the use of robust regression techniques to detect and reject NLOS measures in a location estimation using multilateration. We show how the least-median-of-squares technique can be used to overcome the effects of NLOS errors, even in environments with little infrastructure, and validate its suitability by comparing it to other methods described in the bibliography. We obtained remarkable results when using it in a real indoor positioning system that works with Bluetooth and ultrasound (BLUPS), even when nearly half the measures suffered from NLOS or other coarse errors.

Estimating the standard deviation for 222Rn scintillation counting - a note concerning the paper by Sarmiento et al

International Nuclear Information System (INIS)

Key, R.M.

1977-01-01

In a recent report Sarmiento et al.(1976) presented a method for estimating the statistical error associated with 222 Rn scintillation counting. Because of certain approximations, the method is less accurate than that of an earlier work by Lucas and Woodward (1964). The Sarmiento method and the Lucas method are compared, and the magnitude of errors incurred using the approximations are determined. For counting times greater than 300 minutes, the disadvantage of the slight inaccuracies of the Sarmiento method are outweighed by the advantage of easier calculation. (Auth.)

Introduction to Robust Estimation and Hypothesis Testing

CERN Document Server

Wilcox, Rand R

2012-01-01

This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background on the foundations of modern methods, placing the new methods in historical context. Author Rand Wilcox includes chapter exercises and many real-world examples that illustrate how various methods perform in different situations.Introduction to R

Maximum Likelihood Time-of-Arrival Estimation of Optical Pulses via Photon-Counting Photodetectors

Science.gov (United States)

Erkmen, Baris I.; Moision, Bruce E.

2010-01-01

Many optical imaging, ranging, and communications systems rely on the estimation of the arrival time of an optical pulse. Recently, such systems have been increasingly employing photon-counting photodetector technology, which changes the statistics of the observed photocurrent. This requires time-of-arrival estimators to be developed and their performances characterized. The statistics of the output of an ideal photodetector, which are well modeled as a Poisson point process, were considered. An analytical model was developed for the mean-square error of the maximum likelihood (ML) estimator, demonstrating two phenomena that cause deviations from the minimum achievable error at low signal power. An approximation was derived to the threshold at which the ML estimator essentially fails to provide better than a random guess of the pulse arrival time. Comparing the analytic model performance predictions to those obtained via simulations, it was verified that the model accurately predicts the ML performance over all regimes considered. There is little prior art that attempts to understand the fundamental limitations to time-of-arrival estimation from Poisson statistics. This work establishes both a simple mathematical description of the error behavior, and the associated physical processes that yield this behavior. Previous work on mean-square error characterization for ML estimators has predominantly focused on additive Gaussian noise. This work demonstrates that the discrete nature of the Poisson noise process leads to a distinctly different error behavior.

Modified generalized method of moments for a robust estimation of polytomous logistic model

Directory of Open Access Journals (Sweden)

Xiaoshan Wang

2014-07-01

Full Text Available The maximum likelihood estimation (MLE method, typically used for polytomous logistic regression, is prone to bias due to both misclassification in outcome and contamination in the design matrix. Hence, robust estimators are needed. In this study, we propose such a method for nominal response data with continuous covariates. A generalized method of weighted moments (GMWM approach is developed for dealing with contaminated polytomous response data. In this approach, distances are calculated based on individual sample moments. And Huber weights are applied to those observations with large distances. Mellow-type weights are also used to downplay leverage points. We describe theoretical properties of the proposed approach. Simulations suggest that the GMWM performs very well in correcting contamination-caused biases. An empirical application of the GMWM estimator on data from a survey demonstrates its usefulness.

A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera

Directory of Open Access Journals (Sweden)

Wenyan Ci

2016-10-01

Full Text Available Visual odometry estimates the ego-motion of an agent (e.g., vehicle and robot using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An objective function fitted with a set of feature points is created by establishing the mathematical relationship between optical flow, depth and camera ego-motion parameters through the camera’s 3-dimensional motion and planar imaging model. Accordingly, the six motion parameters are computed by minimizing the objective function, using the iterative Levenberg–Marquard method. One of key points for visual odometry is that the feature points selected for the computation should contain inliers as much as possible. In this work, the feature points and their optical flows are initially detected by using the Kanade–Lucas–Tomasi (KLT algorithm. A circle matching is followed to remove the outliers caused by the mismatching of the KLT algorithm. A space position constraint is imposed to filter out the moving points from the point set detected by the KLT algorithm. The Random Sample Consensus (RANSAC algorithm is employed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining points are tracked to estimate the ego-motion parameters in the subsequent frames. The approach presented here is tested on real traffic videos and the results prove the robustness and precision of the method.

Using observation-level random effects to model overdispersion in count data in ecology and evolution

Directory of Open Access Journals (Sweden)

Xavier A. Harrison

2014-10-01

Full Text Available Overdispersion is common in models of count data in ecology and evolutionary biology, and can occur due to missing covariates, non-independent (aggregated data, or an excess frequency of zeroes (zero-inflation. Accounting for overdispersion in such models is vital, as failing to do so can lead to biased parameter estimates, and false conclusions regarding hypotheses of interest. Observation-level random effects (OLRE, where each data point receives a unique level of a random effect that models the extra-Poisson variation present in the data, are commonly employed to cope with overdispersion in count data. However studies investigating the efficacy of observation-level random effects as a means to deal with overdispersion are scarce. Here I use simulations to show that in cases where overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when overdispersion is simply ignored. Conversely, OLRE fail to reduce bias in zero-inflated data, and in some cases increase bias at high levels of overdispersion. There was a positive relationship between the magnitude of overdispersion and the degree of bias in parameter estimates. Critically, the simulations reveal that failing to account for overdispersion in mixed models can erroneously inflate measures of explained variance (r2, which may lead to researchers overestimating the predictive power of variables of interest. This work suggests use of observation-level random effects provides a simple and robust means to account for overdispersion in count data, but also that their ability to minimise bias is not uniform across all types of overdispersion and must be applied judiciously.

Robust Optical Flow Estimation

Directory of Open Access Journals (Sweden)

Javier Sánchez Pérez

2013-10-01

Full Text Available n this work, we describe an implementation of the variational method proposed by Brox etal. in 2004, which yields accurate optical flows with low running times. It has several benefitswith respect to the method of Horn and Schunck: it is more robust to the presence of outliers,produces piecewise-smooth flow fields and can cope with constant brightness changes. Thismethod relies on the brightness and gradient constancy assumptions, using the information ofthe image intensities and the image gradients to find correspondences. It also generalizes theuse of continuous L1 functionals, which help mitigate the effect of outliers and create a TotalVariation (TV regularization. Additionally, it introduces a simple temporal regularizationscheme that enforces a continuous temporal coherence of the flow fields.

A Robust Approach for Clock Offset Estimation in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Kim Jang-Sub

2010-01-01

Full Text Available The maximum likelihood estimators (MLEs for the clock phase offset assuming a two-way message exchange mechanism between the nodes of a wireless sensor network were recently derived assuming Gaussian and exponential network delays. However, the MLE performs poorly in the presence of non-Gaussian or nonexponential network delay distributions. Currently, there is a need to develop clock synchronization algorithms that are robust to the distribution of network delays. This paper proposes a clock offset estimator based on the composite particle filter (CPF to cope with the possible asymmetries and non-Gaussianity of the network delay distributions. Also, a variant of the CPF approach based on the bootstrap sampling (BS is shown to exhibit good performance in the presence of reduced number of observations. Computer simulations illustrate that the basic CPF and its BS-based variant present superior performance than MLE under general random network delay distributions such as asymmetric Gaussian, exponential, Gamma, Weibull as well as various mixtures.

New horizontal global solar radiation estimation models for Turkey based on robust coplot supported genetic programming technique

International Nuclear Information System (INIS)

Demirhan, Haydar; Kayhan Atilgan, Yasemin

2015-01-01

Highlights: • Precise horizontal global solar radiation estimation models are proposed for Turkey. • Genetic programming technique is used to construct the models. • Robust coplot analysis is applied to reduce the impact of outlier observations. • Better estimation and prediction properties are observed for the models. - Abstract: Renewable energy sources have been attracting more and more attention of researchers due to the diminishing and harmful nature of fossil energy sources. Because of the importance of solar energy as a renewable energy source, an accurate determination of significant covariates and their relationships with the amount of global solar radiation reaching the Earth is a critical research problem. There are numerous meteorological and terrestrial covariates that can be used in the analysis of horizontal global solar radiation. Some of these covariates are highly correlated with each other. It is possible to find a large variety of linear or non-linear models to explain the amount of horizontal global solar radiation. However, models that explain the amount of global solar radiation with the smallest set of covariates should be obtained. In this study, use of the robust coplot technique to reduce the number of covariates before going forward with advanced modelling techniques is considered. After reducing the dimensionality of model space, yearly and monthly mean daily horizontal global solar radiation estimation models for Turkey are built by using the genetic programming technique. It is observed that application of robust coplot analysis is helpful for building precise models that explain the amount of global solar radiation with the minimum number of covariates without suffering from outlier observations and the multicollinearity problem. Consequently, over a dataset of Turkey, precise yearly and monthly mean daily global solar radiation estimation models are introduced using the model spaces obtained by robust coplot technique and

Robust Inference with Multi-way Clustering

OpenAIRE

A. Colin Cameron; Jonah B. Gelbach; Douglas L. Miller; Doug Miller

2009-01-01

In this paper we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit and GMM. This variance estimator enables cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our...

Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.

Science.gov (United States)

Sehgal, Muhammad Shoaib B; Gondal, Iqbal; Dooley, Laurence S

2005-05-15

Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algorithms have been proposed, more robust techniques need to be developed so that further analysis of biological data can be accurately undertaken. In this paper, an innovative missing value imputation algorithm called collateral missing value estimation (CMVE) is presented which uses multiple covariance-based imputation matrices for the final prediction of missing values. The matrices are computed and optimized using least square regression and linear programming methods. The new CMVE algorithm has been compared with existing estimation techniques including Bayesian principal component analysis imputation (BPCA), least square impute (LSImpute) and K-nearest neighbour (KNN). All these methods were rigorously tested to estimate missing values in three separate non-time series (ovarian cancer based) and one time series (yeast sporulation) dataset. Each method was quantitatively analyzed using the normalized root mean square (NRMS) error measure, covering a wide range of randomly introduced missing value probabilities from 0.01 to 0.2. Experiments were also undertaken on the yeast dataset, which comprised 1.7% actual missing values, to test the hypothesis that CMVE performed better not only for randomly occurring but also for a real distribution of missing values. The results confirmed that CMVE consistently demonstrated superior and robust estimation capability of missing values compared with other methods for both series types of data, for the same order of computational complexity. A concise theoretical framework has also been formulated to validate the improved performance of the CMVE

Robust small area estimation of poverty indicators using M-quantile approach (Case study: Sub-district level in Bogor district)

Science.gov (United States)

Girinoto, Sadik, Kusman; Indahwati

2017-03-01

The National Socio-Economic Survey samples are designed to produce estimates of parameters of planned domains (provinces and districts). The estimation of unplanned domains (sub-districts and villages) has its limitation to obtain reliable direct estimates. One of the possible solutions to overcome this problem is employing small area estimation techniques. The popular choice of small area estimation is based on linear mixed models. However, such models need strong distributional assumptions and do not easy allow for outlier-robust estimation. As an alternative approach for this purpose, M-quantile regression approach to small area estimation based on modeling specific M-quantile coefficients of conditional distribution of study variable given auxiliary covariates. It obtained outlier-robust estimation from influence function of M-estimator type and also no need strong distributional assumptions. In this paper, the aim of study is to estimate the poverty indicator at sub-district level in Bogor District-West Java using M-quantile models for small area estimation. Using data taken from National Socioeconomic Survey and Villages Potential Statistics, the results provide a detailed description of pattern of incidence and intensity of poverty within Bogor district. We also compare the results with direct estimates. The results showed the framework may be preferable when direct estimate having no incidence of poverty at all in the small area.

Forecasting exchange rates: a robust regression approach

OpenAIRE

Preminger, Arie; Franck, Raphael

2005-01-01

The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach, based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by outliers. A robust linear autoregressive (RAR) and a robust neural network (RNN) models are estimated to study the predictabil...

Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection bias

KAUST Repository

Ma, Yanyuan

2013-09-01

We propose semiparametric methods to estimate the center and shape of a symmetric population when a representative sample of the population is unavailable due to selection bias. We allow an arbitrary sample selection mechanism determined by the data collection procedure, and we do not impose any parametric form on the population distribution. Under this general framework, we construct a family of consistent estimators of the center that is robust to population model misspecification, and we identify the efficient member that reaches the minimum possible estimation variance. The asymptotic properties and finite sample performance of the estimation and inference procedures are illustrated through theoretical analysis and simulations. A data example is also provided to illustrate the usefulness of the methods in practice. © 2013 American Statistical Association.

Robust inference in sample selection models

KAUST Repository

Zhelonkin, Mikhail; Genton, Marc G.; Ronchetti, Elvezio

2015-01-01

The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman's two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.

Robust inference in sample selection models

KAUST Repository

Zhelonkin, Mikhail

2015-11-20

The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman\\'s two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.

Feasibility of using single photon counting X-ray for lung tumor position estimation based on 4D-CT.

Science.gov (United States)

Aschenbrenner, Katharina P; Guthier, Christian V; Lyatskaya, Yulia; Boda-Heggemann, Judit; Wenz, Frederik; Hesser, Jürgen W

2017-09-01

In stereotactic body radiation therapy of lung tumors, reliable position estimation of the tumor is necessary in order to minimize normal tissue complication rate. While kV X-ray imaging is frequently used, continuous application during radiotherapy sessions is often not possible due to concerns about the additional dose. Thus, ultra low-dose (ULD) kV X-ray imaging based on a single photon counting detector is suggested. This paper addresses the lower limit of photons to locate the tumor reliably with an accuracy in the range of state-of-the-art methods, i.e. a few millimeters. 18 patient cases with four dimensional CT (4D-CT), which serves as a-priori information, are included in the study. ULD cone beam projections are simulated from the 4D-CTs including Poisson noise. The projections from the breathing phases which correspond to different tumor positions are compared to the ULD projection by means of Poisson log-likelihood (PML) and correlation coefficient (CC), and template matching under these metrics. The results indicate that in full thorax imaging five photons per pixel suffice for a standard deviation in tumor positions of less than half a breathing phase. Around 50 photons per pixel are needed to achieve this accuracy with the field of view restricted to the tumor region. Compared to CC, PML tends to perform better for low photon counts and shifts in patient setup. Template matching only improves the position estimation in high photon counts. The quality of the reconstruction is independent of the projection angle. The accuracy of the proposed ULD single photon counting system is in the range of a few millimeters and therefore comparable to state-of-the-art tumor tracking methods. At the same time, a reduction in photons per pixel by three to four orders of magnitude relative to commercial systems with flatpanel detectors can be achieved. This enables continuous kV image-based position estimation during all fractions since the additional dose to the

Feasibility of using single photon counting X-ray for lung tumor position estimation based on 4D-CT

Energy Technology Data Exchange (ETDEWEB)

Aschenbrenner, Katharina P.; Hesser, Juergen W. [Heidelberg Univ., Mannheim (Germany). Dept. of Experimental Radiation Oncology; Heidelberg Univ. (Germany). IWR; Guthier, Christian V. [Heidelberg Univ., Mannheim (Germany). Dept. of Experimental Radiation Oncology; Lyatskaya, Yulia [Brigham and Women' s Center, Boston, MA (United States); Harvard Medical School, Boston, MA (United States); Boda-Heggemann, Judit; Wenz, Frederik [Heidelberg Univ., Mannheim (Germany). Dept. of Radiation Oncology

2017-10-01

In stereotactic body radiation therapy of lung tumors, reliable position estimation of the tumor is necessary in order to minimize normal tissue complication rate. While kV X-ray imaging is frequently used, continuous application during radiotherapy sessions is often not possible due to concerns about the additional dose. Thus, ultra low-dose (ULD) kV X-ray imaging based on a single photon counting detector is suggested. This paper addresses the lower limit of photons to locate the tumor reliably with an accuracy in the range of state-of-the-art methods, i.e. a few millimeters. 18 patient cases with four dimensional CT (4D-CT), which serves as a-priori information, are included in the study. ULD cone beam projections are simulated from the 4D-CTs including Poisson noise. The projections from the breathing phases which correspond to different tumor positions are compared to the ULD projection by means of Poisson log-likelihood (PML) and correlation coefficient (CC), and template matching under these metrics. The results indicate that in full thorax imaging five photons per pixel suffice for a standard deviation in tumor positions of less than half a breathing phase. Around 50 photons per pixel are needed to achieve this accuracy with the field of view restricted to the tumor region. Compared to CC, PML tends to perform better for low photon counts and shifts in patient setup. Template matching only improves the position estimation in high photon counts. The quality of the reconstruction is independent of the projection angle. The accuracy of the proposed ULD single photon counting system is in the range of a few millimeters and therefore comparable to state-of-the-art tumor tracking methods. At the same time, a reduction in photons per pixel by three to four orders of magnitude relative to commercial systems with flatpanel detectors can be achieved. This enables continuous kV image-based position estimation during all fractions since the additional dose to the

Feasibility of using single photon counting X-ray for lung tumor position estimation based on 4D-CT

International Nuclear Information System (INIS)

Aschenbrenner, Katharina P.; Hesser, Juergen W.; Boda-Heggemann, Judit; Wenz, Frederik

2017-01-01

In stereotactic body radiation therapy of lung tumors, reliable position estimation of the tumor is necessary in order to minimize normal tissue complication rate. While kV X-ray imaging is frequently used, continuous application during radiotherapy sessions is often not possible due to concerns about the additional dose. Thus, ultra low-dose (ULD) kV X-ray imaging based on a single photon counting detector is suggested. This paper addresses the lower limit of photons to locate the tumor reliably with an accuracy in the range of state-of-the-art methods, i.e. a few millimeters. 18 patient cases with four dimensional CT (4D-CT), which serves as a-priori information, are included in the study. ULD cone beam projections are simulated from the 4D-CTs including Poisson noise. The projections from the breathing phases which correspond to different tumor positions are compared to the ULD projection by means of Poisson log-likelihood (PML) and correlation coefficient (CC), and template matching under these metrics. The results indicate that in full thorax imaging five photons per pixel suffice for a standard deviation in tumor positions of less than half a breathing phase. Around 50 photons per pixel are needed to achieve this accuracy with the field of view restricted to the tumor region. Compared to CC, PML tends to perform better for low photon counts and shifts in patient setup. Template matching only improves the position estimation in high photon counts. The quality of the reconstruction is independent of the projection angle. The accuracy of the proposed ULD single photon counting system is in the range of a few millimeters and therefore comparable to state-of-the-art tumor tracking methods. At the same time, a reduction in photons per pixel by three to four orders of magnitude relative to commercial systems with flatpanel detectors can be achieved. This enables continuous kV image-based position estimation during all fractions since the additional dose to the

Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems

International Nuclear Information System (INIS)

Souza, Rose Mary G.P.; Moreira, Joao M.L.

2006-01-01

This work presents results of robustness verification of artificial neural network correlations that improve the real time prediction of the power peak factor for reactor protection systems. The input variables considered in the correlation are those available in the reactor protection systems, namely, the axial power differences obtained from measured ex-core detectors, and the position of control rods. The correlations, based on radial basis function (RBF) and multilayer perceptron (MLP) neural networks, estimate the power peak factor, without faulty signals, with average errors between 0.13%, 0.19% and 0.15%, and maximum relative error of 2.35%. The robustness verification was performed for three different neural network correlations. The results show that they are robust against signal degradation, producing results with faulty signals with a maximum error of 6.90%. The average error associated to faulty signals for the MLP network is about half of that of the RBF network, and the maximum error is about 1% smaller. These results demonstrate that MLP neural network correlation is more robust than the RBF neural network correlation. The results also show that the input variables present redundant information. The axial power difference signals compensate the faulty signal for the position of a given control rod, and improves the results by about 10%. The results show that the errors in the power peak factor estimation by these neural network correlations, even in faulty conditions, are smaller than the current PWR schemes which may have uncertainties as high as 8%. Considering the maximum relative error of 2.35%, these neural network correlations would allow decreasing the power peak factor safety margin by about 5%. Such a reduction could be used for operating the reactor with a higher power level or with more flexibility. The neural network correlation has to meet requirements of high integrity software that performs safety grade actions. It is shown that the

A method for estimating abundance of mobile populations using telemetry and counts of unmarked animals

Science.gov (United States)

Clement, Matthew; O'Keefe, Joy M; Walters, Brianne

2015-01-01

While numerous methods exist for estimating abundance when detection is imperfect, these methods may not be appropriate due to logistical difficulties or unrealistic assumptions. In particular, if highly mobile taxa are frequently absent from survey locations, methods that estimate a probability of detection conditional on presence will generate biased abundance estimates. Here, we propose a new estimator for estimating abundance of mobile populations using telemetry and counts of unmarked animals. The estimator assumes that the target population conforms to a fission-fusion grouping pattern, in which the population is divided into groups that frequently change in size and composition. If assumptions are met, it is not necessary to locate all groups in the population to estimate abundance. We derive an estimator, perform a simulation study, conduct a power analysis, and apply the method to field data. The simulation study confirmed that our estimator is asymptotically unbiased with low bias, narrow confidence intervals, and good coverage, given a modest survey effort. The power analysis provided initial guidance on survey effort. When applied to small data sets obtained by radio-tracking Indiana bats, abundance estimates were reasonable, although imprecise. The proposed method has the potential to improve abundance estimates for mobile species that have a fission-fusion social structure, such as Indiana bats, because it does not condition detection on presence at survey locations and because it avoids certain restrictive assumptions.

Influence of binary mask estimation errors on robust speaker identification

DEFF Research Database (Denmark)

May, Tobias

2017-01-01

Missing-data strategies have been developed to improve the noise-robustness of automatic speech recognition systems in adverse acoustic conditions. This is achieved by classifying time-frequency (T-F) units into reliable and unreliable components, as indicated by a so-called binary mask. Different...... approaches have been proposed to handle unreliable feature components, each with distinct advantages. The direct masking (DM) approach attenuates unreliable T-F units in the spectral domain, which allows the extraction of conventionally used mel-frequency cepstral coefficients (MFCCs). Instead of attenuating....... Since each of these approaches utilizes the knowledge about reliable and unreliable feature components in a different way, they will respond differently to estimation errors in the binary mask. The goal of this study was to identify the most effective strategy to exploit knowledge about reliable...

VIDEO DENOISING USING SWITCHING ADAPTIVE DECISION BASED ALGORITHM WITH ROBUST MOTION ESTIMATION TECHNIQUE

Directory of Open Access Journals (Sweden)

V. Jayaraj

2010-08-01

Full Text Available A Non-linear adaptive decision based algorithm with robust motion estimation technique is proposed for removal of impulse noise, Gaussian noise and mixed noise (impulse and Gaussian with edge and fine detail preservation in images and videos. The algorithm includes detection of corrupted pixels and the estimation of values for replacing the corrupted pixels. The main advantage of the proposed algorithm is that an appropriate filter is used for replacing the corrupted pixel based on the estimation of the noise variance present in the filtering window. This leads to reduced blurring and better fine detail preservation even at the high mixed noise density. It performs both spatial and temporal filtering for removal of the noises in the filter window of the videos. The Improved Cross Diamond Search Motion Estimation technique uses Least Median Square as a cost function, which shows improved performance than other motion estimation techniques with existing cost functions. The results show that the proposed algorithm outperforms the other algorithms in the visual point of view and in Peak Signal to Noise Ratio, Mean Square Error and Image Enhancement Factor.

Robust Self Tuning Controllers

DEFF Research Database (Denmark)

Poulsen, Niels Kjølstad

1985-01-01

The present thesis concerns robustness properties of adaptive controllers. It is addressed to methods for robustifying self tuning controllers with respect to abrupt changes in the plant parameters. In the thesis an algorithm for estimating abruptly changing parameters is presented. The estimator...... has several operation modes and a detector for controlling the mode. A special self tuning controller has been developed to regulate plant with changing time delay.......The present thesis concerns robustness properties of adaptive controllers. It is addressed to methods for robustifying self tuning controllers with respect to abrupt changes in the plant parameters. In the thesis an algorithm for estimating abruptly changing parameters is presented. The estimator...

Comparison of least-squares vs. maximum likelihood estimation for standard spectrum technique of β−γ coincidence spectrum analysis

International Nuclear Information System (INIS)

Lowrey, Justin D.; Biegalski, Steven R.F.

2012-01-01

The spectrum deconvolution analysis tool (SDAT) software code was written and tested at The University of Texas at Austin utilizing the standard spectrum technique to determine activity levels of Xe-131m, Xe-133m, Xe-133, and Xe-135 in β–γ coincidence spectra. SDAT was originally written to utilize the method of least-squares to calculate the activity of each radionuclide component in the spectrum. Recently, maximum likelihood estimation was also incorporated into the SDAT tool. This is a robust statistical technique to determine the parameters that maximize the Poisson distribution likelihood function of the sample data. In this case it is used to parameterize the activity level of each of the radioxenon components in the spectra. A new test dataset was constructed utilizing Xe-131m placed on a Xe-133 background to compare the robustness of the least-squares and maximum likelihood estimation methods for low counting statistics data. The Xe-131m spectra were collected independently from the Xe-133 spectra and added to generate the spectra in the test dataset. The true independent counts of Xe-131m and Xe-133 are known, as they were calculated before the spectra were added together. Spectra with both high and low counting statistics are analyzed. Studies are also performed by analyzing only the 30 keV X-ray region of the β–γ coincidence spectra. Results show that maximum likelihood estimation slightly outperforms least-squares for low counting statistics data.

The Kruskal Count

OpenAIRE

Lagarias, Jeffrey C.; Rains, Eric; Vanderbei, Robert J.

2001-01-01

The Kruskal Count is a card trick invented by Martin J. Kruskal in which a magician "guesses" a card selected by a subject according to a certain counting procedure. With high probability the magician can correctly "guess" the card. The success of the trick is based on a mathematical principle related to coupling methods for Markov chains. This paper analyzes in detail two simplified variants of the trick and estimates the probability of success. The model predictions are compared with simula...

Robust Trust in Expert Testimony

Directory of Open Access Journals (Sweden)

Christian Dahlman

2015-05-01

Full Text Available The standard of proof in criminal trials should require that the evidence presented by the prosecution is robust. This requirement of robustness says that it must be unlikely that additional information would change the probability that the defendant is guilty. Robustness is difficult for a judge to estimate, as it requires the judge to assess the possible effect of information that the he or she does not have. This article is concerned with expert witnesses and proposes a method for reviewing the robustness of expert testimony. According to the proposed method, the robustness of expert testimony is estimated with regard to competence, motivation, external strength, internal strength and relevance. The danger of trusting non-robust expert testimony is illustrated with an analysis of the Thomas Quick Case, a Swedish legal scandal where a patient at a mental institution was wrongfully convicted for eight murders.

Real-time yield estimation based on deep learning

Science.gov (United States)

Rahnemoonfar, Maryam; Sheppard, Clay

2017-05-01

Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.

The robust corrective action priority-an improved approach for selecting competing corrective actions in FMEA based on principle of robust design

Science.gov (United States)

Sutrisno, Agung; Gunawan, Indra; Vanany, Iwan

2017-11-01

In spite of being integral part in risk - based quality improvement effort, studies improving quality of selection of corrective action priority using FMEA technique are still limited in literature. If any, none is considering robustness and risk in selecting competing improvement initiatives. This study proposed a theoretical model to select risk - based competing corrective action by considering robustness and risk of competing corrective actions. We incorporated the principle of robust design in counting the preference score among corrective action candidates. Along with considering cost and benefit of competing corrective actions, we also incorporate the risk and robustness of corrective actions. An example is provided to represent the applicability of the proposed model.

Aspartic acid racemization rate in narwhal (Monodon monoceros) eye lens nuclei estimated by counting of growth layers in tusks

DEFF Research Database (Denmark)

Garde, Eva; Heide-Jørgensen, Mads Peter; Ditlevsen, Susanne

2012-01-01

Ages of marine mammals have traditionally been estimated by counting dentinal growth layers in teeth. However, this method is difficult to use on narwhals (Monodon monoceros) because of their special tooth structures. Alternative methods are therefore needed. The aspartic acid racemization (AAR......) technique has been used in age estimation studies of cetaceans, including narwhals. The purpose of this study was to estimate a species-specific racemization rate for narwhals by regressing aspartic acid D/L ratios in eye lens nuclei against growth layer groups in tusks (n=9). Two racemization rates were...

Robustness in NAA evaluated by the Youden and Steiner test

International Nuclear Information System (INIS)

Bedregal, P.; Torres, B.; Ubillus, M.; Mendoza, P.; Montoya, E.

2008-01-01

The chemistry laboratory at the Peruvian Institute of Nuclear Energy (IPEN) has carried out a validation method for the samples of siliceous composition. At least seven variables affecting the robustness of the results were initially identified, which may interact simultaneously or individually. Conventional evaluation hereof would imply a massive number of analyses and a far more effective approach for assessment of the robustness for these effects was found in the Youden-Steiner test, which provides the necessary information by only eight analyses for each sample type. Three reference materials were used for evaluating the effects of variations in sample mass, irradiation duration, standard mass, neutron flux, decay time, counting time and counting distance. (author)

Kendall-Theil Robust Line (KTRLine--version 1.0)-A Visual Basic Program for Calculating and Graphing Robust Nonparametric Estimates of Linear-Regression Coefficients Between Two Continuous Variables

Science.gov (United States)

Granato, Gregory E.

2006-01-01

The Kendall-Theil Robust Line software (KTRLine-version 1.0) is a Visual Basic program that may be used with the Microsoft Windows operating system to calculate parameters for robust, nonparametric estimates of linear-regression coefficients between two continuous variables. The KTRLine software was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, for use in stochastic data modeling with local, regional, and national hydrologic data sets to develop planning-level estimates of potential effects of highway runoff on the quality of receiving waters. The Kendall-Theil robust line was selected because this robust nonparametric method is resistant to the effects of outliers and nonnormality in residuals that commonly characterize hydrologic data sets. The slope of the line is calculated as the median of all possible pairwise slopes between points. The intercept is calculated so that the line will run through the median of input data. A single-line model or a multisegment model may be specified. The program was developed to provide regression equations with an error component for stochastic data generation because nonparametric multisegment regression tools are not available with the software that is commonly used to develop regression models. The Kendall-Theil robust line is a median line and, therefore, may underestimate total mass, volume, or loads unless the error component or a bias correction factor is incorporated into the estimate. Regression statistics such as the median error, the median absolute deviation, the prediction error sum of squares, the root mean square error, the confidence interval for the slope, and the bias correction factor for median estimates are calculated by use of nonparametric methods. These statistics, however, may be used to formulate estimates of mass, volume, or total loads. The program is used to read a two- or three-column tab-delimited input file with variable names in the first row and

Robust multivariate analysis

CERN Document Server

J Olive, David

2017-01-01

This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given.  The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory.   The robust techniques  are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis.  A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...

Estimation of immune cell densities in immune cell conglomerates: an approach for high-throughput quantification.

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Niels Halama

2009-11-01

Full Text Available Determining the correct number of positive immune cells in immunohistological sections of colorectal cancer and other tumor entities is emerging as an important clinical predictor and therapy selector for an individual patient. This task is usually obstructed by cell conglomerates of various sizes. We here show that at least in colorectal cancer the inclusion of immune cell conglomerates is indispensable for estimating reliable patient cell counts. Integrating virtual microscopy and image processing principally allows the high-throughput evaluation of complete tissue slides.For such large-scale systems we demonstrate a robust quantitative image processing algorithm for the reproducible quantification of cell conglomerates on CD3 positive T cells in colorectal cancer. While isolated cells (28 to 80 microm(2 are counted directly, the number of cells contained in a conglomerate is estimated by dividing the area of the conglomerate in thin tissues sections (< or =6 microm by the median area covered by an isolated T cell which we determined as 58 microm(2. We applied our algorithm to large numbers of CD3 positive T cell conglomerates and compared the results to cell counts obtained manually by two independent observers. While especially for high cell counts, the manual counting showed a deviation of up to 400 cells/mm(2 (41% variation, algorithm-determined T cell numbers generally lay in between the manually observed cell numbers but with perfect reproducibility.In summary, we recommend our approach as an objective and robust strategy for quantifying immune cell densities in immunohistological sections which can be directly implemented into automated full slide image processing systems.

Dynamic Output Feedback Robust Model Predictive Control via Zonotopic Set-Membership Estimation for Constrained Quasi-LPV Systems

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Xubin Ping

2015-01-01

Full Text Available For the quasi-linear parameter varying (quasi-LPV system with bounded disturbance, a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC is investigated. The estimation error set is represented by a zonotope and refreshed by the zonotopic set-membership estimation method. By properly refreshing the estimation error set online, the bounds of true state at the next sampling time can be obtained. Furthermore, the feasibility of the main optimization problem at the next sampling time can be determined at the current time. A numerical example is given to illustrate the effectiveness of the approach.

More recent robust methods for the estimation of mean and standard deviation of data

International Nuclear Information System (INIS)

Kanisch, G.

2003-01-01

Outliers in a data set result in biased values of mean and standard deviation. One way to improve the estimation of a mean is to apply tests to identify outliers and to exclude them from the calculations. Tests according to Grubbs or to Dixon, which are frequently used in practice, especially within laboratory intercomparisons, are not very efficient in identifying outliers. Since more than ten years now so-called robust methods are used more and more, which determine mean and standard deviation by iteration and down-weighting values far from the mean, thereby diminishing the impact of outliers. In 1989 the Analytical Methods Committee of the British Royal Chemical Society published such a robust method. Since 1993 the US Environmental Protection Agency published a more efficient and quite versatile method. Mean and standard deviation are calculated by iteration and application of a special weight function for down-weighting outlier candidates. In 2000, W. Cofino et al. published a very efficient robust method which works quite different from the others. It applies methods taken from the basics of quantum mechanics, such as ''wave functions'' associated with each laboratory mean value and matrix algebra (solving eigenvalue problems). In contrast to the other ones, this method includes the individual measurement uncertainties. (orig.)

On the robustness of two-stage estimators

KAUST Repository

Zhelonkin, Mikhail; Genton, Marc G.; Ronchetti, Elvezio

2012-01-01

The aim of this note is to provide a general framework for the analysis of the robustness properties of a broad class of two-stage models. We derive the influence function, the change-of-variance function, and the asymptotic variance of a general

Robust time estimation reconciles views of the antiquity of placental mammals.

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Yasuhiro Kitazoe

2007-04-01

Full Text Available Molecular studies have reported divergence times of modern placental orders long before the Cretaceous-Tertiary boundary and far older than paleontological data. However, this discrepancy may not be real, but rather appear because of the violation of implicit assumptions in the estimation procedures, such as non-gradual change of evolutionary rate and failure to correct for convergent evolution.New procedures for divergence-time estimation robust to abrupt changes in the rate of molecular evolution are described. We used a variant of the multidimensional vector space (MVS procedure to take account of possible convergent evolution. Numerical simulations of abrupt rate change and convergent evolution showed good performance of the new procedures in contrast to current methods. Application to complete mitochondrial genomes identified marked rate accelerations and decelerations, which are not obtained with current methods. The root of placental mammals is estimated to be approximately 18 million years more recent than when assuming a log Brownian motion model. Correcting the pairwise distances for convergent evolution using MVS lowers the age of the root about another 20 million years compared to using standard maximum likelihood tree branch lengths. These two procedures combined revise the root time of placental mammals from around 122 million years ago to close to 84 million years ago. As a result, the estimated distribution of molecular divergence times is broadly consistent with quantitative analysis of the North American fossil record and traditional morphological views.By including the dual effects of abrupt rate change and directly accounting for convergent evolution at the molecular level, these estimates provide congruence between the molecular results, paleontological analyses and morphological expectations. The programs developed here are provided along with sample data that reproduce the results of this study and are especially

TAO-robust backpropagation learning algorithm.

Science.gov (United States)

Pernía-Espinoza, Alpha V; Ordieres-Meré, Joaquín B; Martínez-de-Pisón, Francisco J; González-Marcos, Ana

2005-03-01

In several fields, as industrial modelling, multilayer feedforward neural networks are often used as universal function approximations. These supervised neural networks are commonly trained by a traditional backpropagation learning format, which minimises the mean squared error (mse) of the training data. However, in the presence of corrupted data (outliers) this training scheme may produce wrong models. We combine the benefits of the non-linear regression model tau-estimates [introduced by Tabatabai, M. A. Argyros, I. K. Robust Estimation and testing for general nonlinear regression models. Applied Mathematics and Computation. 58 (1993) 85-101] with the backpropagation algorithm to produce the TAO-robust learning algorithm, in order to deal with the problems of modelling with outliers. The cost function of this approach has a bounded influence function given by the weighted average of two psi functions, one corresponding to a very robust estimate and the other to a highly efficient estimate. The advantages of the proposed algorithm are studied with an example.

Identification and red blood cell automated counting from blood smear images using computer-aided system.

Science.gov (United States)

Acharya, Vasundhara; Kumar, Preetham

2018-03-01

Red blood cell count plays a vital role in identifying the overall health of the patient. Hospitals use the hemocytometer to count the blood cells. Conventional method of placing the smear under microscope and counting the cells manually lead to erroneous results, and medical laboratory technicians are put under stress. A computer-aided system will help to attain precise results in less amount of time. This research work proposes an image-processing technique for counting the number of red blood cells. It aims to examine and process the blood smear image, in order to support the counting of red blood cells and identify the number of normal and abnormal cells in the image automatically. K-medoids algorithm which is robust to external noise is used to extract the WBCs from the image. Granulometric analysis is used to separate the red blood cells from the white blood cells. The red blood cells obtained are counted using the labeling algorithm and circular Hough transform. The radius range for the circle-drawing algorithm is estimated by computing the distance of the pixels from the boundary which automates the entire algorithm. A comparison is done between the counts obtained using the labeling algorithm and circular Hough transform. Results of the work showed that circular Hough transform was more accurate in counting the red blood cells than the labeling algorithm as it was successful in identifying even the overlapping cells. The work also intends to compare the results of cell count done using the proposed methodology and manual approach. The work is designed to address all the drawbacks of the previous research work. The research work can be extended to extract various texture and shape features of abnormal cells identified so that diseases like anemia of inflammation and chronic disease can be detected at the earliest.

Logistic regression for dichotomized counts.

Science.gov (United States)

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

Robust Online State of Charge Estimation of Lithium-Ion Battery Pack Based on Error Sensitivity Analysis

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Ting Zhao

2015-01-01

Full Text Available Accurate and reliable state of charge (SOC estimation is a key enabling technique for large format lithium-ion battery pack due to its vital role in battery safety and effective management. This paper tries to make three contributions to existing literatures through robust algorithms. (1 Observer based SOC estimation error model is established, where the crucial parameters on SOC estimation accuracy are determined by quantitative analysis, being a basis for parameters update. (2 The estimation method for a battery pack in which the inconsistency of cells is taken into consideration is proposed, ensuring all batteries’ SOC ranging from 0 to 1, effectively avoiding the battery overcharged/overdischarged. Online estimation of the parameters is also presented in this paper. (3 The SOC estimation accuracy of the battery pack is verified using the hardware-in-loop simulation platform. The experimental results at various dynamic test conditions, temperatures, and initial SOC difference between two cells demonstrate the efficacy of the proposed method.

National South African HIV prevalence estimates robust despite substantial test non-participation

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Guy Harling

2017-07-01

Full Text Available Background. South African (SA national HIV seroprevalence estimates are of crucial policy relevance in the country, and for the worldwide HIV response. However, the most recent nationally representative HIV test survey in 2012 had 22% test non-participation, leaving the potential for substantial bias in current seroprevalence estimates, even after controlling for selection on observed factors. Objective. To re-estimate national HIV prevalence in SA, controlling for bias due to selection on both observed and unobserved factors in the 2012 SA National HIV Prevalence, Incidence and Behaviour Survey. Methods. We jointly estimated regression models for consent to test and HIV status in a Heckman-type bivariate probit framework. As selection variable, we used assigned interviewer identity, a variable known to predict consent but highly unlikely to be associated with interviewees’ HIV status. From these models, we estimated the HIV status of interviewed participants who did not test. Results. Of 26 710 interviewed participants who were invited to test for HIV, 21.3% of females and 24.3% of males declined. Interviewer identity was strongly correlated with consent to test for HIV; declining a test was weakly associated with HIV serostatus. Our HIV prevalence estimates were not significantly different from those using standard methods to control for bias due to selection on observed factors: 15.1% (95% confidence interval (CI 12.1 - 18.6 v. 14.5% (95% CI 12.8 - 16.3 for 15 - 49-year-old males; 23.3% (95% CI 21.7 - 25.8 v. 23.2% (95% CI 21.3 - 25.1 for 15 - 49-year-old females. Conclusion. The most recent SA HIV prevalence estimates are robust under the strongest available test for selection bias due to missing data. Our findings support the reliability of inferences drawn from such data.

MIDAS robust trend estimator for accurate GPS station velocities without step detection

Science.gov (United States)

Blewitt, Geoffrey; Kreemer, Corné; Hammond, William C.; Gazeaux, Julien

2016-03-01

Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil-Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj-xi)/(tj-ti) computed between all data pairs i > j. For normally distributed data, Theil-Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil-Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one-sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root-mean-square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences.

The use of comparative 137Cs body burden estimates from environmental data/models and whole body counting to evaluate diet models for the ingestion pathway

International Nuclear Information System (INIS)

Robison, W.L.; Sun, C.

1997-01-01

Rongelap and Utirik Atolls were contaminated on 1 March 1954, by a U.S. nuclear test at Bikini Atoll code named BRAVO. The people at both atolls were removed from their atolls in the first few days after the detonation and were returned to their atolls at different times. Detailed studies have been carried out over the years by Lawrence Livermore National Laboratory (LLNL) to determine the radiological conditions at the atolls and estimate the doses to the populations. The contribution of each exposure pathway and radionuclide have been evaluated. All dose assessments show that the major potential contribution to the estimated dose is 137 Cs uptake via the terrestrial food chain. Brookhaven National Laboratory (BNL) has carried out an extensive whole body counting program at both atolls over several years to directly measure the 137 Cs body burden. Here we compare the estimates of the body burdens from the LLNL environmental method with body burdens measured by the BNL whole body counting method. The combination of the results from both methods is used to evaluate proposed diet models to establish more realistic dose assessments. Very good agreement is achieved between the two methods with a diet model that includes both local and imported foods. Other diet models greatly overestimate the body burdens (i.e., dose) observed by whole body counting. The upper 95% confidence limit of interindividual variability around the population mean value based on the environmental method is similar to that calculated from direct measurement by whole body counting. Moreover, the uncertainty in the population mean value based on the environmental method is in very good agreement with the whole body counting data. This provides additional confidence in extrapolating the estimated doses calculated by the environmental method to other islands and atolls. 46 refs., 8 figs., 5 tabs

Planck 2013 results. XX. Cosmology from Sunyaev-Zeldovich cluster counts

CERN Document Server

Ade, P.A.R.; Armitage-Caplan, C.; Arnaud, M.; Ashdown, M.; Atrio-Barandela, F.; Aumont, J.; Baccigalupi, C.; Banday, A.J.; Barreiro, R.B.; Barrena, R.; Bartlett, J.G.; Battaner, E.; Battye, R.; Benabed, K.; Benoit, A.; Benoit-Levy, A.; Bernard, J.P.; Bersanelli, M.; Bielewicz, P.; Bikmaev, I.; Blanchard, A.; Bobin, J.; Bock, J.J.; Bohringer, H.; Bonaldi, A.; Bond, J.R.; Borrill, J.; Bouchet, F.R.; Bourdin, H.; Bridges, M.; Brown, M.L.; Bucher, M.; Burenin, R.; Burigana, C.; Butler, R.C.; Cardoso, J.F.; Carvalho, P.; Catalano, A.; Challinor, A.; Chamballu, A.; Chary, R.R.; Chiang, L.Y.; Chiang, H.C.; Chon, G.; Christensen, P.R.; Church, S.; Clements, D.L.; Colombi, S.; Colombo, L.P.L.; Couchot, F.; Coulais, A.; Crill, B.P.; Curto, A.; Cuttaia, F.; Da Silva, A.; Dahle, H.; Danese, L.; Davies, R.D.; Davis, R.J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Delouis, J.M.; Democles, J.; Desert, F.X.; Dickinson, C.; Diego, J.M.; Dolag, K.; Dole, H.; Donzelli, S.; Dore, O.; Douspis, M.; Dupac, X.; Efstathiou, G.; Ensslin, T.A.; Eriksen, H.K.; Finelli, F.; Flores-Cacho, I.; Forni, O.; Frailis, M.; Franceschi, E.; Fromenteau, S.; Galeotta, S.; Ganga, K.; Genova-Santos, R.T.; Giard, M.; Giardino, G.; Giraud-Heraud, Y.; Gonzalez-Nuevo, J.; Gorski, K.M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Hansen, F.K.; Hanson, D.; Harrison, D.; Henrot-Versille, S.; Hernandez-Monteagudo, C.; Herranz, D.; Hildebrandt, S.R.; Hivon, E.; Hobson, M.; Holmes, W.A.; Hornstrup, A.; Hovest, W.; Huffenberger, K.M.; Hurier, G.; Jaffe, T.R.; Jaffe, A.H.; Jones, W.C.; Juvela, M.; Keihanen, E.; Keskitalo, R.; Khamitov, I.; Kisner, T.S.; Kneissl, R.; Knoche, J.; Knox, L.; Kunz, M.; Kurki-Suonio, H.; Lagache, G.; Lahteenmaki, A.; Lamarre, J.M.; Lasenby, A.; Laureijs, R.J.; Lawrence, C.R.; Leahy, J.P.; Leonardi, R.; Leon-Tavares, J.; Lesgourgues, J.; Liddle, A.; Liguori, M.; Lilje, P.B.; Linden-Vornle, M.; Lopez-Caniego, M.; Lubin, P.M.; Macias-Perez, J.F.; Maffei, B.; Maino, D.; Mandolesi, N.; Marcos-Caballero, A.; Maris, M.; Marshall, D.J.; Martin, P.G.; Martinez-Gonzalez, E.; Masi, S.; Matarrese, S.; Matthai, F.; Mazzotta, P.; Meinhold, P.R.; Melchiorri, A.; Melin, J.B.; Mendes, L.; Mennella, A.; Migliaccio, M.; Mitra, S.; Miville-Deschenes, M.A.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Moss, A.; Munshi, D.; Naselsky, P.; Nati, F.; Natoli, P.; Netterfield, C.B.; Norgaard-Nielsen, H.U.; Noviello, F.; Novikov, D.; Novikov, I.; Osborne, S.; Oxborrow, C.A.; Paci, F.; Pagano, L.; Pajot, F.; Paoletti, D.; Partridge, B.; Pasian, F.; Patanchon, G.; Perdereau, O.; Perotto, L.; Perrotta, F.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Popa, L.; Poutanen, T.; Pratt, G.W.; Prezeau, G.; Prunet, S.; Puget, J.L.; Rachen, J.P.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Ricciardi, S.; Riller, T.; Ristorcelli, I.; Rocha, G.; Roman, M.; Rosset, C.; Roudier, G.; Rowan-Robinson, M.; Rubino-Martin, J.A.; Rusholme, B.; Sandri, M.; Santos, D.; Savini, G.; Scott, D.; Seiffert, M.D.; Shellard, E.P.S.; Spencer, L.D.; Starck, J.L.; Stolyarov, V.; Stompor, R.; Sudiwala, R.; Sunyaev, R.; Sureau, F.; Sutton, D.; Suur-Uski, A.S.; Sygnet, J.F.; Tauber, J.A.; Tavagnacco, D.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Tuovinen, J.; Turler, M.; Umana, G.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Vielva, P.; Villa, F.; Vittorio, N.; Wade, L.A.; Wandelt, B.D.; Weller, J.; White, M.; White, S.D.M.; Yvon, D.; Zacchei, A.; Zonca, A.

2014-01-01

We present constraints on cosmological parameters using number counts as a function of redshift for a sub-sample of 189 galaxy clusters from the Planck SZ (PSZ) catalogue. The PSZ is selected through the signature of the Sunyaev--Zeldovich (SZ) effect, and the sub-sample used here has a signal-to-noise threshold of seven, with each object confirmed as a cluster and all but one with a redshift estimate. We discuss the completeness of the sample and our construction of a likelihood analysis. Using a relation between mass $M$ and SZ signal $Y$ calibrated to X-ray measurements, we derive constraints on the power spectrum amplitude $\\sigma_8$ and matter density parameter $\\Omega_{\\mathrm{m}}$ in a flat $\\Lambda$CDM model. We test the robustness of our estimates and find that possible biases in the $Y$--$M$ relation and the halo mass function are larger than the statistical uncertainties from the cluster sample. Assuming the X-ray determined mass to be biased low relative to the true mass by between zero and 30%, m...

Accurate and robust phylogeny estimation based on profile distances: a study of the Chlorophyceae (Chlorophyta

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Rahmann Sven

2004-06-01

Full Text Available Abstract Background In phylogenetic analysis we face the problem that several subclade topologies are known or easily inferred and well supported by bootstrap analysis, but basal branching patterns cannot be unambiguously estimated by the usual methods (maximum parsimony (MP, neighbor-joining (NJ, or maximum likelihood (ML, nor are they well supported. We represent each subclade by a sequence profile and estimate evolutionary distances between profiles to obtain a matrix of distances between subclades. Results Our estimator of profile distances generalizes the maximum likelihood estimator of sequence distances. The basal branching pattern can be estimated by any distance-based method, such as neighbor-joining. Our method (profile neighbor-joining, PNJ then inherits the accuracy and robustness of profiles and the time efficiency of neighbor-joining. Conclusions Phylogenetic analysis of Chlorophyceae with traditional methods (MP, NJ, ML and MrBayes reveals seven well supported subclades, but the methods disagree on the basal branching pattern. The tree reconstructed by our method is better supported and can be confirmed by known morphological characters. Moreover the accuracy is significantly improved as shown by parametric bootstrap.

Robust estimation of event-related potentials via particle filter.

Science.gov (United States)

Fukami, Tadanori; Watanabe, Jun; Ishikawa, Fumito

2016-03-01

In clinical examinations and brain-computer interface (BCI) research, a short electroencephalogram (EEG) measurement time is ideal. The use of event-related potentials (ERPs) relies on both estimation accuracy and processing time. We tested a particle filter that uses a large number of particles to construct a probability distribution. We constructed a simple model for recording EEG comprising three components: ERPs approximated via a trend model, background waves constructed via an autoregressive model, and noise. We evaluated the performance of the particle filter based on mean squared error (MSE), P300 peak amplitude, and latency. We then compared our filter with the Kalman filter and a conventional simple averaging method. To confirm the efficacy of the filter, we used it to estimate ERP elicited by a P300 BCI speller. A 400-particle filter produced the best MSE. We found that the merit of the filter increased when the original waveform already had a low signal-to-noise ratio (SNR) (i.e., the power ratio between ERP and background EEG). We calculated the amount of averaging necessary after applying a particle filter that produced a result equivalent to that associated with conventional averaging, and determined that the particle filter yielded a maximum 42.8% reduction in measurement time. The particle filter performed better than both the Kalman filter and conventional averaging for a low SNR in terms of both MSE and P300 peak amplitude and latency. For EEG data produced by the P300 speller, we were able to use our filter to obtain ERP waveforms that were stable compared with averages produced by a conventional averaging method, irrespective of the amount of averaging. We confirmed that particle filters are efficacious in reducing the measurement time required during simulations with a low SNR. Additionally, particle filters can perform robust ERP estimation for EEG data produced via a P300 speller. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Low-priced, time-saving, reliable and stable LR-115 counting system

International Nuclear Information System (INIS)

Tchorz-Trzeciakiewicz, D.E.

2015-01-01

Nuclear alpha particles leave etches (tracks) when they hit the surface of a LR-115 detector. The density of these tracks is used to measure radon concentration. Counting these tracks by human sense is tedious and time-consuming procedure and may introduce counting error, whereas most available automatic and semiautomatic counting systems are expensive or complex. An uncomplicated, robust, reliable and stable counting system using freely available on the Internet software as Digimizer™ and PhotoScape was developed and proposed. The effectiveness of the proposed procedure was evaluated by comparing the amount of tracks counted by software with the amount of tracks counted manually for 223 detectors. The percentage error for each analysed detector was obtained as a difference between automatic and manual counts divided by manual count. For more than 97% of detectors, the percentage errors oscillated between −3% and 3%. - Highlights: • Semiautomatic, uncomplicated procedure was proposed to count the amount of alpha tracks. • Freely available software on the Internet used as alpha tracks counting system for LR-115. • LR-115 detectors used to measure radon concentration and radon exhalation rate

Road safety performance measures and AADT uncertainty from short-term counts.

Science.gov (United States)

Milligan, Craig; Montufar, Jeannette; Regehr, Jonathan; Ghanney, Bartholomew

2016-12-01

The objective of this paper is to enable better risk analysis of road safety performance measures by creating the first knowledge base on uncertainty surrounding annual average daily traffic (AADT) estimates when the estimates are derived by expanding short-term counts with the individual permanent counter method. Many road safety performance measures and performance models use AADT as an input. While there is an awareness that the input suffers from uncertainty, the uncertainty is not well known or accounted for. The paper samples data from a set of 69 permanent automatic traffic recorders in Manitoba, Canada, to simulate almost 2 million short-term counts over a five year period. These short-term counts are expanded to AADT estimates by transferring temporal information from a directly linked nearby permanent count control station, and the resulting AADT values are compared to a known reference AADT to compute errors. The impacts of five factors on AADT error are considered: length of short-term count, number of short-term counts, use of weekday versus weekend counts, distance from a count to its expansion control station, and the AADT at the count site. The mean absolute transfer error for expanded AADT estimates is 6.7%, and this value varied by traffic pattern group from 5% to 10.5%. Reference percentiles of the error distribution show that almost all errors are between -20% and +30%. Error decreases substantially by using a 48-h count instead of a 24-h count, and only slightly by using two counts instead of one. Weekday counts are superior to weekend counts, especially if the count is only 24h. Mean absolute transfer error increases with distance to control station (elasticity 0.121, p=0.001), and increases with AADT (elasticity 0.857, proad safety performance measures that use AADT as inputs. Analytical frameworks for such analysis exist but are infrequently used in road safety because the evidence base on AADT uncertainty is not well developed. Copyright �

Bike-Ped Portal : development of an online nonmotorized traffic count archive.

Science.gov (United States)

2017-05-01

Robust bicycle and pedestrian data on a national scale would serve numerous purposes. Access to a centralized nonmotorized traffic count : archive can open the door for innovation through research, design and planning; provide safety researchers with...

Clutch pressure estimation for a power-split hybrid transmission using nonlinear robust observer

Science.gov (United States)

Zhou, Bin; Zhang, Jianwu; Gao, Ji; Yu, Haisheng; Liu, Dong

2018-06-01

For a power-split hybrid transmission, using the brake clutch to realize the transition from electric drive mode to hybrid drive mode is an available strategy. Since the pressure information of the brake clutch is essential for the mode transition control, this research designs a nonlinear robust reduced-order observer to estimate the brake clutch pressure. Model uncertainties or disturbances are considered as additional inputs, thus the observer is designed in order that the error dynamics is input-to-state stable. The nonlinear characteristics of the system are expressed as the lookup tables in the observer. Moreover, the gain matrix of the observer is solved by two optimization procedures under the constraints of the linear matrix inequalities. The proposed observer is validated by offline simulation and online test, the results have shown that the observer achieves significant performance during the mode transition, as the estimation error is within a reasonable range, more importantly, it is asymptotically stable.

A Field Evaluation of the Time-of-Detection Method to Estimate Population Size and Density for Aural Avian Point Counts

Directory of Open Access Journals (Sweden)

Mathew W. Alldredge

2007-12-01

Full Text Available The time-of-detection method for aural avian point counts is a new method of estimating abundance, allowing for uncertain probability of detection. The method has been specifically designed to allow for variation in singing rates of birds. It involves dividing the time interval of the point count into several subintervals and recording the detection history of the subintervals when each bird sings. The method can be viewed as generating data equivalent to closed capture-recapture information. The method is different from the distance and multiple-observer methods in that it is not required that all the birds sing during the point count. As this method is new and there is some concern as to how well individual birds can be followed, we carried out a field test of the method using simulated known populations of singing birds, using a laptop computer to send signals to audio stations distributed around a point. The system mimics actual aural avian point counts, but also allows us to know the size and spatial distribution of the populations we are sampling. Fifty 8-min point counts (broken into four 2-min intervals using eight species of birds were simulated. Singing rate of an individual bird of a species was simulated following a Markovian process (singing bouts followed by periods of silence, which we felt was more realistic than a truly random process. The main emphasis of our paper is to compare results from species singing at (high and low homogenous rates per interval with those singing at (high and low heterogeneous rates. Population size was estimated accurately for the species simulated, with a high homogeneous probability of singing. Populations of simulated species with lower but homogeneous singing probabilities were somewhat underestimated. Populations of species simulated with heterogeneous singing probabilities were substantially underestimated. Underestimation was caused by both the very low detection probabilities of all distant

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles.

Science.gov (United States)

Liu, Huawei; Li, Baoqing; Yuan, Xiaobing; Zhou, Qianwei; Huang, Jingchang

2018-03-27

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method.

Robust Methods for Moderation Analysis with a Two-Level Regression Model.

Science.gov (United States)

Yang, Miao; Yuan, Ke-Hai

2016-01-01

Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

Estimating spatial and temporal components of variation in count data using negative binomial mixed models

Science.gov (United States)

Irwin, Brian J.; Wagner, Tyler; Bence, James R.; Kepler, Megan V.; Liu, Weihai; Hayes, Daniel B.

2013-01-01

Partitioning total variability into its component temporal and spatial sources is a powerful way to better understand time series and elucidate trends. The data available for such analyses of fish and other populations are usually nonnegative integer counts of the number of organisms, often dominated by many low values with few observations of relatively high abundance. These characteristics are not well approximated by the Gaussian distribution. We present a detailed description of a negative binomial mixed-model framework that can be used to model count data and quantify temporal and spatial variability. We applied these models to data from four fishery-independent surveys of Walleyes Sander vitreus across the Great Lakes basin. Specifically, we fitted models to gill-net catches from Wisconsin waters of Lake Superior; Oneida Lake, New York; Saginaw Bay in Lake Huron, Michigan; and Ohio waters of Lake Erie. These long-term monitoring surveys varied in overall sampling intensity, the total catch of Walleyes, and the proportion of zero catches. Parameter estimation included the negative binomial scaling parameter, and we quantified the random effects as the variations among gill-net sampling sites, the variations among sampled years, and site × year interactions. This framework (i.e., the application of a mixed model appropriate for count data in a variance-partitioning context) represents a flexible approach that has implications for monitoring programs (e.g., trend detection) and for examining the potential of individual variance components to serve as response metrics to large-scale anthropogenic perturbations or ecological changes.

Algorithm for counting large directed loops

Energy Technology Data Exchange (ETDEWEB)

Bianconi, Ginestra [Abdus Salam International Center for Theoretical Physics, Strada Costiera 11, 34014 Trieste (Italy); Gulbahce, Natali [Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, NM 87545 (United States)

2008-06-06

We derive a Belief-Propagation algorithm for counting large loops in a directed network. We evaluate the distribution of the number of small loops in a directed random network with given degree sequence. We apply the algorithm to a few characteristic directed networks of various network sizes and loop structures and compare the algorithm with exhaustive counting results when possible. The algorithm is adequate in estimating loop counts for large directed networks and can be used to compare the loop structure of directed networks and their randomized counterparts.

Robust canonical correlations: A comparative study

OpenAIRE

Branco, JA; Croux, Christophe; Filzmoser, P; Oliveira, MR

2005-01-01

Several approaches for robust canonical correlation analysis will be presented and discussed. A first method is based on the definition of canonical correlation analysis as looking for linear combinations of two sets of variables having maximal (robust) correlation. A second method is based on alternating robust regressions. These methods axe discussed in detail and compared with the more traditional approach to robust canonical correlation via covariance matrix estimates. A simulation study ...

Study on advancement of in vivo counting using mathematical simulation

Energy Technology Data Exchange (ETDEWEB)

Kinase, Sakae [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

2003-05-01

To obtain an assessment of the committed effective dose, individual monitoring for the estimation of intakes of radionuclides is required. For individual monitoring of exposure to intakes of radionuclides, direct measurement of radionuclides in the body - in vivo counting- is very useful. To advance in a precision in vivo counting which fulfills the requirements of ICRP 1990 recommendations, some problems, such as the investigation of uncertainties in estimates of body burdens by in vivo counting, and the selection of the way to improve the precision, have been studied. In the present study, a calibration technique for in vivo counting application using Monte Carlo simulation was developed. The advantage of the technique is that counting efficiency can be obtained for various shapes and sizes that are very difficult to change for phantoms. To validate the calibration technique, the response functions and counting efficiencies of a whole-body counter installed in JAERI were evaluated using the simulation and measurements. Consequently, the calculations are in good agreement with the measurements. The method for the determination of counting efficiency curves as a function of energy was developed using the present technique and a physiques correction equation was derived from the relationship between parameters of correction factor and counting efficiencies of the JAERI whole-body counter. The uncertainties in body burdens of {sup 137}Cs estimated with the JAERI whole-body counter were also investigated using the Monte Carlo simulation and measurements. It was found that the uncertainties of body burdens estimated with the whole-body counter are strongly dependent on various sources of uncertainty such as radioactivity distribution within the body and counting statistics. Furthermore, the evaluation method of the peak efficiencies of a Ge semi-conductor detector was developed by Monte Carlo simulation for optimum arrangement of Ge semi-conductor detectors for

Fast and robust estimation of spectro-temporal receptive fields using stochastic approximations.

Science.gov (United States)

Meyer, Arne F; Diepenbrock, Jan-Philipp; Ohl, Frank W; Anemüller, Jörn

2015-05-15

The receptive field (RF) represents the signal preferences of sensory neurons and is the primary analysis method for understanding sensory coding. While it is essential to estimate a neuron's RF, finding numerical solutions to increasingly complex RF models can become computationally intensive, in particular for high-dimensional stimuli or when many neurons are involved. Here we propose an optimization scheme based on stochastic approximations that facilitate this task. The basic idea is to derive solutions on a random subset rather than computing the full solution on the available data set. To test this, we applied different optimization schemes based on stochastic gradient descent (SGD) to both the generalized linear model (GLM) and a recently developed classification-based RF estimation approach. Using simulated and recorded responses, we demonstrate that RF parameter optimization based on state-of-the-art SGD algorithms produces robust estimates of the spectro-temporal receptive field (STRF). Results on recordings from the auditory midbrain demonstrate that stochastic approximations preserve both predictive power and tuning properties of STRFs. A correlation of 0.93 with the STRF derived from the full solution may be obtained in less than 10% of the full solution's estimation time. We also present an on-line algorithm that allows simultaneous monitoring of STRF properties of more than 30 neurons on a single computer. The proposed approach may not only prove helpful for large-scale recordings but also provides a more comprehensive characterization of neural tuning in experiments than standard tuning curves. Copyright © 2015 Elsevier B.V. All rights reserved.

The use of ATSR active fire counts for estimating relative patterns of biomass burning - A study from the boreal forest region

NARCIS (Netherlands)

Kasischke, Eric S.; Hewson, Jennifer H.; Stocks, Brian; van der Werf, Guido; Randerson, James T.

2003-01-01

Satellite fire products have the potential to construct inter-annual time series of fire activity, but estimating area burned requires considering biases introduced by orbiting geometry, fire behavior, and the presence of clouds and smoke. Here we evaluated the performance of fire counts from the

Data analysis in emission tomography using emission-count posteriors

International Nuclear Information System (INIS)

Sitek, Arkadiusz

2012-01-01

A novel approach to the analysis of emission tomography data using the posterior probability of the number of emissions per voxel (emission count) conditioned on acquired tomographic data is explored. The posterior is derived from the prior and the Poisson likelihood of the emission-count data by marginalizing voxel activities. Based on emission-count posteriors, examples of Bayesian analysis including estimation and classification tasks in emission tomography are provided. The application of the method to computer simulations of 2D tomography is demonstrated. In particular, the minimum-mean-square-error point estimator of the emission count is demonstrated. The process of finding this estimator can be considered as a tomographic image reconstruction technique since the estimates of the number of emissions per voxel divided by voxel sensitivities and acquisition time are the estimates of the voxel activities. As an example of a classification task, a hypothesis stating that some region of interest (ROI) emitted at least or at most r-times the number of events in some other ROI is tested. The ROIs are specified by the user. The analysis described in this work provides new quantitative statistical measures that can be used in decision making in diagnostic imaging using emission tomography. (paper)

Data analysis in emission tomography using emission-count posteriors

Science.gov (United States)

Sitek, Arkadiusz

2012-11-01

A novel approach to the analysis of emission tomography data using the posterior probability of the number of emissions per voxel (emission count) conditioned on acquired tomographic data is explored. The posterior is derived from the prior and the Poisson likelihood of the emission-count data by marginalizing voxel activities. Based on emission-count posteriors, examples of Bayesian analysis including estimation and classification tasks in emission tomography are provided. The application of the method to computer simulations of 2D tomography is demonstrated. In particular, the minimum-mean-square-error point estimator of the emission count is demonstrated. The process of finding this estimator can be considered as a tomographic image reconstruction technique since the estimates of the number of emissions per voxel divided by voxel sensitivities and acquisition time are the estimates of the voxel activities. As an example of a classification task, a hypothesis stating that some region of interest (ROI) emitted at least or at most r-times the number of events in some other ROI is tested. The ROIs are specified by the user. The analysis described in this work provides new quantitative statistical measures that can be used in decision making in diagnostic imaging using emission tomography.

Assessing the composition of fragmented agglutinated foraminiferal assemblages in ancient sediments: comparison of counting and area-based methods in Famennian samples (Late Devonian)

Science.gov (United States)

Girard, Catherine; Dufour, Anne-Béatrice; Charruault, Anne-Lise; Renaud, Sabrina

2018-01-01

Benthic foraminifera have been used as proxies for various paleoenvironmental variables such as food availability, carbon flux from surface waters, microhabitats, and indirectly water depth. Estimating assemblage composition based on morphotypes, as opposed to genus- or species-level identification, potentially loses important ecological information but opens the way to the study of ancient time periods. However, the ability to accurately constrain benthic foraminiferal assemblages has been questioned when the most abundant foraminifera are fragile agglutinated forms, particularly prone to fragmentation. Here we test an alternate method for accurately estimating the composition of fragmented assemblages. The cumulated area per morphotype method is assessed, i.e., the sum of the area of all tests or fragments of a given morphotype in a sample. The percentage of each morphotype is calculated as a portion of the total cumulated area. Percentages of different morphotypes based on counting and cumulated area methods are compared one by one and analyzed using principal component analyses, a co-inertia analysis, and Shannon diversity indices. Morphotype percentages are further compared to an estimate of water depth based on microfacies description. Percentages of the morphotypes are not related to water depth. In all cases, counting and cumulated area methods deliver highly similar results, suggesting that the less time-consuming traditional counting method may provide robust estimates of assemblages. The size of each morphotype may deliver paleobiological information, for instance regarding biomass, but should be considered carefully due to the pervasive issue of fragmentation.

Fast, accurate, and robust frequency offset estimation based on modified adaptive Kalman filter in coherent optical communication system

Science.gov (United States)

Yang, Yanfu; Xiang, Qian; Zhang, Qun; Zhou, Zhongqing; Jiang, Wen; He, Qianwen; Yao, Yong

2017-09-01

We propose a joint estimation scheme for fast, accurate, and robust frequency offset (FO) estimation along with phase estimation based on modified adaptive Kalman filter (MAKF). The scheme consists of three key modules: extend Kalman filter (EKF), lock detector, and FO cycle slip recovery. The EKF module estimates time-varying phase induced by both FO and laser phase noise. The lock detector module makes decision between acquisition mode and tracking mode and consequently sets the EKF tuning parameter in an adaptive manner. The third module can detect possible cycle slip in the case of large FO and make proper correction. Based on the simulation and experimental results, the proposed MAKF has shown excellent estimation performance featuring high accuracy, fast convergence, as well as the capability of cycle slip recovery.

Robust methods and asymptotic theory in nonlinear econometrics

CERN Document Server

Bierens, Herman J

1981-01-01

This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic normality, of parameter estimators of nonlinear regression models and nonlinear structural equations under various assumptions on the distribution of the data. The estimation methods involved are nonlinear least squares estimation (NLLSE), nonlinear robust M-estimation (NLRME) and non­ linear weighted robust M-estimation (NLWRME) for the regression case and nonlinear two-stage least squares estimation (NL2SLSE) and a new method called minimum information estimation (MIE) for the case of structural equations. The asymptotic properties of the NLLSE and the two robust M-estimation methods are derived from further elaborations of results of Jennrich. Special attention is payed to the comparison of the asymptotic efficiency of NLLSE and NLRME. It is shown that if the tails of the error distribution are fatter than those of the normal distribution NLRME is more efficient than NLLSE. The NLWRME method is appropriate ...

Robust bayesian analysis of an autoregressive model with ...

African Journals Online (AJOL)

In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...

Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting.

Science.gov (United States)

Linden, Ariel

2017-08-01

When a randomized controlled trial is not feasible, health researchers typically use observational data and rely on statistical methods to adjust for confounding when estimating treatment effects. These methods generally fall into 3 categories: (1) estimators based on a model for the outcome using conventional regression adjustment; (2) weighted estimators based on the propensity score (ie, a model for the treatment assignment); and (3) "doubly robust" (DR) estimators that model both the outcome and propensity score within the same framework. In this paper, we introduce a new DR estimator that utilizes marginal mean weighting through stratification (MMWS) as the basis for weighted adjustment. This estimator may prove more accurate than treatment effect estimators because MMWS has been shown to be more accurate than other models when the propensity score is misspecified. We therefore compare the performance of this new estimator to other commonly used treatment effects estimators. Monte Carlo simulation is used to compare the DR-MMWS estimator to regression adjustment, 2 weighted estimators based on the propensity score and 2 other DR methods. To assess performance under varied conditions, we vary the level of misspecification of the propensity score model as well as misspecify the outcome model. Overall, DR estimators generally outperform methods that model one or the other components (eg, propensity score or outcome). The DR-MMWS estimator outperforms all other estimators when both the propensity score and outcome models are misspecified and performs equally as well as other DR estimators when only the propensity score is misspecified. Health researchers should consider using DR-MMWS as the principal evaluation strategy in observational studies, as this estimator appears to outperform other estimators in its class. © 2017 John Wiley & Sons, Ltd.

Temporal aggregation of migration counts can improve accuracy and precision of trends

Directory of Open Access Journals (Sweden)

Tara L. Crewe

2016-12-01

Full Text Available Temporal replicate counts are often aggregated to improve model fit by reducing zero-inflation and count variability, and in the case of migration counts collected hourly throughout a migration, allows one to ignore nonindependence. However, aggregation can represent a loss of potentially useful information on the hourly or seasonal distribution of counts, which might impact our ability to estimate reliable trends. We simulated 20-year hourly raptor migration count datasets with known rate of change to test the effect of aggregating hourly counts to daily or annual totals on our ability to recover known trend. We simulated data for three types of species, to test whether results varied with species abundance or migration strategy: a commonly detected species, e.g., Northern Harrier, Circus cyaneus; a rarely detected species, e.g., Peregrine Falcon, Falco peregrinus; and a species typically counted in large aggregations with overdispersed counts, e.g., Broad-winged Hawk, Buteo platypterus. We compared accuracy and precision of estimated trends across species and count types (hourly/daily/annual using hierarchical models that assumed a Poisson, negative binomial (NB or zero-inflated negative binomial (ZINB count distribution. We found little benefit of modeling zero-inflation or of modeling the hourly distribution of migration counts. For the rare species, trends analyzed using daily totals and an NB or ZINB data distribution resulted in a higher probability of detecting an accurate and precise trend. In contrast, trends of the common and overdispersed species benefited from aggregation to annual totals, and for the overdispersed species in particular, trends estimating using annual totals were more precise, and resulted in lower probabilities of estimating a trend (1 in the wrong direction, or (2 with credible intervals that excluded the true trend, as compared with hourly and daily counts.

Implementation of a nonlinear filter for online nuclear counting

International Nuclear Information System (INIS)

Coulon, R.; Dumazert, J.; Kondrasovs, V.; Normand, S.

2016-01-01

Nuclear counting is a challenging task for nuclear instrumentation because of the stochastic nature of radioactivity. Event counting has to be processed and filtered to determine a stable count rate value and perform variation monitoring of the measured event. An innovative approach for nuclear counting is presented in this study, improving response time and maintaining count rate stability. Some nonlinear filters providing a local maximum likelihood estimation of the signal have been recently developed, which have been tested and compared with conventional linear filters. A nonlinear filter thus developed shows significant performance in terms of response time and measurement precision. The filter also presents the specificity of easy embedment into digital signal processor (DSP) electronics based on field-programmable gate arrays (FPGA) or microcontrollers, compatible with real-time requirements. © 2001 Elsevier Science. All rights reserved. - Highlights: • An efficient approach based on nonlinear filtering has been implemented. • The hypothesis test provides a local maximum likelihood estimation of the count rate. • The filter ensures an optimal compromise between precision and response time.

Depth-weighted robust multivariate regression with application to sparse data

KAUST Repository

Dutta, Subhajit; Genton, Marc G.

2017-01-01

A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant estimator, and has an influence function which is bounded in both the response as well as the predictor variable. To increase the efficiency of this estimator, a re-weighted estimator based on robust Mahalanobis distances of the residual vectors is proposed. In practice, the method is more stable than existing methods that are constructed using subsamples of the data. The resulting multivariate regression technique is computationally feasible, and turns out to perform better than several popular robust multivariate regression methods when applied to various simulated data as well as a real benchmark data set. When the data dimension is quite high compared to the sample size it is still possible to use meaningful notions of data depth along with the corresponding depth values to construct a robust estimator in a sparse setting.

Depth-weighted robust multivariate regression with application to sparse data

KAUST Repository

Dutta, Subhajit

2017-04-05

A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant estimator, and has an influence function which is bounded in both the response as well as the predictor variable. To increase the efficiency of this estimator, a re-weighted estimator based on robust Mahalanobis distances of the residual vectors is proposed. In practice, the method is more stable than existing methods that are constructed using subsamples of the data. The resulting multivariate regression technique is computationally feasible, and turns out to perform better than several popular robust multivariate regression methods when applied to various simulated data as well as a real benchmark data set. When the data dimension is quite high compared to the sample size it is still possible to use meaningful notions of data depth along with the corresponding depth values to construct a robust estimator in a sparse setting.

Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method

International Nuclear Information System (INIS)

Alavi, Seyed Arash; Ahmadian, Ali; Aliakbar-Golkar, Masoud

2015-01-01

Highlights: • Energy management is necessary in the active distribution network to reduce operation costs. • Uncertainty modeling is essential in energy management studies in active distribution networks. • Point estimate method is a suitable method for uncertainty modeling due to its lower computation time and acceptable accuracy. • In the absence of Probability Distribution Function (PDF) robust optimization has a good ability for uncertainty modeling. - Abstract: Uncertainty can be defined as the probability of difference between the forecasted value and the real value. As this probability is small, the operation cost of the power system will be less. This purpose necessitates modeling of system random variables (such as the output power of renewable resources and the load demand) with appropriate and practicable methods. In this paper, an adequate procedure is proposed in order to do an optimal energy management on a typical micro-grid with regard to the relevant uncertainties. The point estimate method is applied for modeling the wind power and solar power uncertainties, and robust optimization technique is utilized to model load demand uncertainty. Finally, a comparison is done between deterministic and probabilistic management in different scenarios and their results are analyzed and evaluated

Robust Wave Resource Estimation

DEFF Research Database (Denmark)

Lavelle, John; Kofoed, Jens Peter

2013-01-01

density estimates of the PDF as a function both of Hm0 and Tp, and Hm0 and T0;2, together with the mean wave power per unit crest length, Pw, as a function of Hm0 and T0;2. The wave elevation parameters, from which the wave parameters are calculated, are filtered to correct or remove spurious data....... An overview is given of the methods used to do this, and a method for identifying outliers of the wave elevation data, based on the joint distribution of wave elevations and accelerations, is presented. The limitations of using a JONSWAP spectrum to model the measured wave spectra as a function of Hm0 and T0......;2 or Hm0 and Tp for the Hanstholm site data are demonstrated. As an alternative, the non-parametric loess method, which does not rely on any assumptions about the shape of the wave elevation spectra, is used to accurately estimate Pw as a function of Hm0 and T0;2....

Fitting a distribution to microbial counts: Making sense of zeroes

DEFF Research Database (Denmark)

Ribeiro Duarte, Ana Sofia; Stockmarr, Anders; Nauta, Maarten

2015-01-01

The accurate estimation of true prevalence and concentration of microorganisms in foods is an important element of quantitative microbiological risk assessment (QMRA). This estimation is often based on microbial detection and enumeration data. Among such data are artificial zero counts, that orig......The accurate estimation of true prevalence and concentration of microorganisms in foods is an important element of quantitative microbiological risk assessment (QMRA). This estimation is often based on microbial detection and enumeration data. Among such data are artificial zero counts......, that originated by chance from contaminated food products. When these products are not differentiated from uncontaminated products that originate true zero counts, the estimates of true prevalence and concentration may be inaccurate. This inaccuracy is especially relevant in situations where highly pathogenic...... bacteria are involved and where growth can occur along the food pathway. Our aim was to develop a method that provides accurate estimates of concentration parameters and differentiates between artificial and true zeroes, thus also accurately estimating true prevalence. We first show the disadvantages...

Models to estimate lactation curves of milk yield and somatic cell count in dairy cows at the herd level for the use in simulations and predictive models

Directory of Open Access Journals (Sweden)

Kaare Græsbøll

2016-12-01

Full Text Available Typically, central milk recording data from dairy herds are recorded less than monthly. Over-fitting early in lactation periods is a challenge, which we explored in different ways by reducing the number of parameters needed to describe the milk yield and somatic cell count of individual cows. Furthermore, we investigated how the parameters of lactation models correlate between parities and from dam to offspring. The aim of the study was to provide simple and robust models for cow level milk yield and somatic cell count (SCC for fitting to sparse data to parameterise herd- and cow-specific simulation of dairy herds.Data from 610 Danish Holstein herds were used to determine parity traits in milk production regarding milk yield and SCC of individual cows. Parity was stratified in first, second and third and higher for milk, and first to sixth and higher for SCC. Fitting of herd level parameters allowed for cow level lactation curves with three, two or one-parameters per lactation. Correlations of milk yield and SCC were estimated between lactations and between dam and offspring.The shape of the lactation curves varied markedly between farms. The correlation between lactations for milk yield and SCC were 0.2-0.6 and significant on more than 95% of farms. The variation in the daily milk yield was observed to be a source of variation to the SCC, and the total SCC was less correlated with the milk production than somatic cells per ml. A positive correlation was found between relative levels of the total SCC and the milk yield.The variation of lactation and SCC curves between farms highlights the importance of a herd level approach. The one-parameter per cow model using a herd level curve allows for estimating the cow production level from first the recording in the parity, while a two-parameter model requires more recordings for a credible estimate, but may more precisely predict persistence, and given the independence of parameters, these can be

Robust Improvement in Estimation of a Covariance Matrix in an Elliptically Contoured Distribution Respect to Quadratic Loss Function

Directory of Open Access Journals (Sweden)

Z. Khodadadi

2008-03-01

Full Text Available Let S be matrix of residual sum of square in linear model Y = Aβ + e where matrix e is distributed as elliptically contoured with unknown scale matrix Σ. In present work, we consider the problem of estimating Σ with respect to squared loss function, L(Σˆ , Σ = tr(ΣΣˆ −1 −I 2 . It is shown that improvement of the estimators were obtained by James, Stein [7], Dey and Srivasan [1] under the normality assumption remains robust under an elliptically contoured distribution respect to squared loss function

Robust and distributed hypothesis testing

CERN Document Server

Gül, Gökhan

2017-01-01

This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here, the distance can be chosen from a much general model, which includes the KL-divergence as a very special case. This is then extended by various means. A minimax robust test that is robust against both outliers as well as modeling errors is presented. Minimax robustness properties of the given tests are also explicitly proven for fixed sample size and sequential probability ratio tests. The theory of robust detection is extended to robust estimation and the theory of robust distributed detection is extended to classes of distributions, which are not necessarily stochastically bounded. It is shown that the quantization functions for the decision rules can also be chosen as non-monotone. Finally, the boo...

Robustness Property of Robust-BD Wald-Type Test for Varying-Dimensional General Linear Models

Directory of Open Access Journals (Sweden)

Xiao Guo

2018-03-01

Full Text Available An important issue for robust inference is to examine the stability of the asymptotic level and power of the test statistic in the presence of contaminated data. Most existing results are derived in finite-dimensional settings with some particular choices of loss functions. This paper re-examines this issue by allowing for a diverging number of parameters combined with a broader array of robust error measures, called “robust- BD ”, for the class of “general linear models”. Under regularity conditions, we derive the influence function of the robust- BD parameter estimator and demonstrate that the robust- BD Wald-type test enjoys the robustness of validity and efficiency asymptotically. Specifically, the asymptotic level of the test is stable under a small amount of contamination of the null hypothesis, whereas the asymptotic power is large enough under a contaminated distribution in a neighborhood of the contiguous alternatives, thus lending supports to the utility of the proposed robust- BD Wald-type test.

A Robust Parametric Technique for Multipath Channel Estimation in the Uplink of a DS-CDMA System

Directory of Open Access Journals (Sweden)

2006-01-01

Full Text Available The problem of estimating the multipath channel parameters of a new user entering the uplink of an asynchronous direct sequence-code division multiple access (DS-CDMA system is addressed. The problem is described via a least squares (LS cost function with a rich structure. This cost function, which is nonlinear with respect to the time delays and linear with respect to the gains of the multipath channel, is proved to be approximately decoupled in terms of the path delays. Due to this structure, an iterative procedure of 1D searches is adequate for time delays estimation. The resulting method is computationally efficient, does not require any specific pilot signal, and performs well for a small number of training symbols. Simulation results show that the proposed technique offers a better estimation accuracy compared to existing related methods, and is robust to multiple access interference.

An Overview of the Adaptive Robust DFT

Directory of Open Access Journals (Sweden)

Djurović Igor

2010-01-01

Full Text Available Abstract This paper overviews basic principles and applications of the robust DFT (RDFT approach, which is used for robust processing of frequency-modulated (FM signals embedded in non-Gaussian heavy-tailed noise. In particular, we concentrate on the spectral analysis and filtering of signals corrupted by impulsive distortions using adaptive and nonadaptive robust estimators. Several adaptive estimators of location parameter are considered, and it is shown that their application is preferable with respect to non-adaptive counterparts. This fact is demonstrated by efficiency comparison of adaptive and nonadaptive RDFT methods for different noise environments.

Regression Models For Multivariate Count Data.

Science.gov (United States)

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Robust Parametric Fault Estimation in a Hopper System

DEFF Research Database (Denmark)

Soltani, Mohsen; Izadi-Zamanabadi, Roozbeh; Wisniewski, Rafal

2012-01-01

The ability of diagnosis of the possible faults is a necessity for satellite launch vehicles during their mission. In this paper, a structural analysis method is employed to divide the complex propulsion system into simpler subsystems for fault diagnosis filter design. A robust fault diagnosis me...

New robust statistical procedures for the polytomous logistic regression models.

Science.gov (United States)

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

The intensity detection of single-photon detectors based on photon counting probability density statistics

International Nuclear Information System (INIS)

Zhang Zijing; Song Jie; Zhao Yuan; Wu Long

2017-01-01

Single-photon detectors possess the ultra-high sensitivity, but they cannot directly respond to signal intensity. Conventional methods adopt sampling gates with fixed width and count the triggered number of sampling gates, which is capable of obtaining photon counting probability to estimate the echo signal intensity. In this paper, we not only count the number of triggered sampling gates, but also record the triggered time position of photon counting pulses. The photon counting probability density distribution is obtained through the statistics of a series of the triggered time positions. Then Minimum Variance Unbiased Estimation (MVUE) method is used to estimate the echo signal intensity. Compared with conventional methods, this method can improve the estimation accuracy of echo signal intensity due to the acquisition of more detected information. Finally, a proof-of-principle laboratory system is established. The estimation accuracy of echo signal intensity is discussed and a high accuracy intensity image is acquired under low-light level environments. (paper)

A Robust Controller Structure for Pico-Satellite Applications

DEFF Research Database (Denmark)

Kragelund, Martin Nygaard; Green, Martin; Kristensen, Mads

This paper describes the development of a robust controller structure for use in pico-satellite missions. The structure relies on unknown disturbance estimation and use of robust control theory to implement a system that is robust to both unmodeled disturbances and parameter uncertainties. As one...

Fish growth parameters are commonly estimated from the counts of ...

African Journals Online (AJOL)

spamer

>300 mm TL were tagged with external plastic dart tags, 89 mm long ... lithognathus were compared to predictions from growth models based on otolith ring counts. Galjoen ..... On the other hand, a new type of bio-compatible tag, which does ...

Estimation and robust control of microalgae culture for optimization of biological fixation of CO2

International Nuclear Information System (INIS)

Filali, R.

2012-01-01

This thesis deals with the optimization of carbon dioxide consumption by microalgae. Indeed, following several current environmental issues primarily related to large emissions of CO 2 , it is shown that microalgae represent a very promising solution for CO 2 mitigation. From this perspective, we are interested in the optimization strategy of CO 2 consumption through the development of a robust control law. The main aim is to ensure optimal operating conditions for a Chlorella vulgaris culture in an instrumented photo-bioreactor. The thesis is based on three major axes. The first one concerns growth modeling of the selected species based on a mathematical model reflecting the influence of light and total inorganic carbon concentration. For the control context, the second axis is related to biomass estimation from the real-time measurement of dissolved carbon dioxide. This step is necessary for the control part due to the lack of affordable real-time sensors for this kind of measurement. Three observers structures have been studied and compared: an extended Kalman filter, an asymptotic observer and an interval observer. The last axis deals with the implementation of a non-linear predictive control law coupled to the estimation strategy for the regulation of the cellular concentration around a value which maximizes the CO 2 consumption. Performance and robustness of this control law have been validated in simulation and experimentally on a laboratory-scale instrumented photo-bioreactor. This thesis represents a preliminary study for the optimization of CO 2 mitigation strategy by microalgae. (author)

Robust linear discriminant models to solve financial crisis in banking sectors

Science.gov (United States)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Idris, Faoziah; Ali, Hazlina; Omar, Zurni

2014-12-01

Linear discriminant analysis (LDA) is a widely-used technique in patterns classification via an equation which will minimize the probability of misclassifying cases into their respective categories. However, the performance of classical estimators in LDA highly depends on the assumptions of normality and homoscedasticity. Several robust estimators in LDA such as Minimum Covariance Determinant (MCD), S-estimators and Minimum Volume Ellipsoid (MVE) are addressed by many authors to alleviate the problem of non-robustness of the classical estimates. In this paper, we investigate on the financial crisis of the Malaysian banking institutions using robust LDA and classical LDA methods. Our objective is to distinguish the "distress" and "non-distress" banks in Malaysia by using the LDA models. Hit ratio is used to validate the accuracy predictive of LDA models. The performance of LDA is evaluated by estimating the misclassification rate via apparent error rate. The results and comparisons show that the robust estimators provide a better performance than the classical estimators for LDA.

Point Count Length and Detection of Forest Neotropical Migrant Birds

Science.gov (United States)

Deanna K. Dawson; David R. Smith; Chandler S. Robbins

1995-01-01

Comparisons of bird abundances among years or among habitats assume that the rates at which birds are detected and counted are constant within species. We use point count data collected in forests of the Mid-Atlantic states to estimate detection probabilities for Neotropical migrant bird species as a function of count length. For some species, significant differences...

A measurement technique for counting processes

International Nuclear Information System (INIS)

Cantoni, V.; Pavia Univ.; De Lotto, I.; Valenziano, F.

1980-01-01

A technique for the estimation of first and second order properties of a stationary counting process is presented here which uses standard instruments for analysis of a continuous stationary random signal. (orig.)

Development of robust flexible OLED encapsulations using simulated estimations and experimental validations

International Nuclear Information System (INIS)

Lee, Chang-Chun; Shih, Yan-Shin; Wu, Chih-Sheng; Tsai, Chia-Hao; Yeh, Shu-Tang; Peng, Yi-Hao; Chen, Kuang-Jung

2012-01-01

This work analyses the overall stress/strain characteristic of flexible encapsulations with organic light-emitting diode (OLED) devices. A robust methodology composed of a mechanical model of multi-thin film under bending loads and related stress simulations based on nonlinear finite element analysis (FEA) is proposed, and validated to be more reliable compared with related experimental data. With various geometrical combinations of cover plate, stacked thin films and plastic substrate, the position of the neutral axis (NA) plate, which is regarded as a key design parameter to minimize stress impact for the concerned OLED devices, is acquired using the present methodology. The results point out that both the thickness and mechanical properties of the cover plate help in determining the NA location. In addition, several concave and convex radii are applied to examine the reliable mechanical tolerance and to provide an insight into the estimated reliability of foldable OLED encapsulations. (paper)

Detection limits for radioanalytical counting techniques

International Nuclear Information System (INIS)

Hartwell, J.K.

1975-06-01

In low-level radioanalysis it is usually necessary to test the sample net counts against some ''Critical Level'' in order to determine if a given result indicates detection. This is an interpretive review of the work by Nicholson (1963), Currie (1968) and Gilbert (1974). Nicholson's evaluation of three different computational formulas for estimation of the ''Critical Level'' is discussed. The details of Nicholson's evaluation are presented along with a basic discussion of the testing procedures used. Recommendations are presented for calculation of confidence intervals, for reporting of analytical results, and for extension of the derived formula to more complex cases such as multiple background counts, multiple use of a single background count, and gamma spectrometric analysis

Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data—How Close Can We Get?

Directory of Open Access Journals (Sweden)

Peter Tiňo

2013-04-01

Full Text Available Studies of learning algorithms typically concentrate on situations where potentially ever growing training sample is available. Yet, there can be situations (e.g., detection of differentially expressed genes on unreplicated data or estimation of time delay in non-stationary gravitationally lensed photon streams where only extremely small samples can be used in order to perform an inference. On unreplicated data, the inference has to be performed on the smallest sample possible—sample of size 1. We study whether anything useful can be learnt in such extreme situations by concentrating on a Bayesian approach that can account for possible prior information on expected counts. We perform a detailed information theoretic study of such Bayesian estimation and quantify the effect of Bayesian averaging on its first two moments. Finally, to analyze potential benefits of the Bayesian approach, we also consider Maximum Likelihood (ML estimation as a baseline approach. We show both theoretically and empirically that the Bayesian model averaging can be potentially beneficial.

Comparative Analysis for Robust Penalized Spline Smoothing Methods

Directory of Open Access Journals (Sweden)

Bin Wang

2014-01-01

Full Text Available Smoothing noisy data is commonly encountered in engineering domain, and currently robust penalized regression spline models are perceived to be the most promising methods for coping with this issue, due to their flexibilities in capturing the nonlinear trends in the data and effectively alleviating the disturbance from the outliers. Against such a background, this paper conducts a thoroughly comparative analysis of two popular robust smoothing techniques, the M-type estimator and S-estimation for penalized regression splines, both of which are reelaborated starting from their origins, with their derivation process reformulated and the corresponding algorithms reorganized under a unified framework. Performances of these two estimators are thoroughly evaluated from the aspects of fitting accuracy, robustness, and execution time upon the MATLAB platform. Elaborately comparative experiments demonstrate that robust penalized spline smoothing methods possess the capability of resistance to the noise effect compared with the nonrobust penalized LS spline regression method. Furthermore, the M-estimator exerts stable performance only for the observations with moderate perturbation error, whereas the S-estimator behaves fairly well even for heavily contaminated observations, but consuming more execution time. These findings can be served as guidance to the selection of appropriate approach for smoothing the noisy data.

HgCdTe APD-based linear-mode photon counting components and ladar receivers

Science.gov (United States)

Jack, Michael; Wehner, Justin; Edwards, John; Chapman, George; Hall, Donald N. B.; Jacobson, Shane M.

2011-05-01

Linear mode photon counting (LMPC) provides significant advantages in comparison with Geiger Mode (GM) Photon Counting including absence of after-pulsing, nanosecond pulse to pulse temporal resolution and robust operation in the present of high density obscurants or variable reflectivity objects. For this reason Raytheon has developed and previously reported on unique linear mode photon counting components and modules based on combining advanced APDs and advanced high gain circuits. By using HgCdTe APDs we enable Poisson number preserving photon counting. A metric of photon counting technology is dark count rate and detection probability. In this paper we report on a performance breakthrough resulting from improvement in design, process and readout operation enabling >10x reduction in dark counts rate to ~10,000 cps and >104x reduction in surface dark current enabling long 10 ms integration times. Our analysis of key dark current contributors suggest that substantial further reduction in DCR to ~ 1/sec or less can be achieved by optimizing wavelength, operating voltage and temperature.

Rapid bioassay method for estimation of 90Sr in urine samples by liquid scintillation counting

International Nuclear Information System (INIS)

Wankhede, Sonal; Chaudhary, Seema; Sawant, Pramilla D.

2018-01-01

Radiostrontium (Sr) is a by-product of the nuclear fission of uranium and plutonium in nuclear reactors and is an important radionuclide in spent nuclear fuel and radioactive waste. Rapid bioassay methods are required for estimating Sr in urine following internal contamination. Decision regarding medical intervention, if any can be based upon the results of urinalysis. The present method used at Bioassay Laboratory, Trombay is by Solid Extraction Chromatography (SEC) technique. The Sr separated from urine sample is precipitated as SrCO 3 and analyzed gravimetrically. However, gravimetric procedure is time consuming and therefore, in the present study, feasibility of Liquid Scintillation Counting for direct detection of radiostrontium in effluent was explored. The results obtained in the present study were compared with those obtained using gravimetric method

The effectiveness of robust RMCD control chart as outliers’ detector

Science.gov (United States)

Darmanto; Astutik, Suci

2017-12-01

A well-known control chart to monitor a multivariate process is Hotelling’s T 2 which its parameters are estimated classically, very sensitive and also marred by masking and swamping of outliers data effect. To overcome these situation, robust estimators are strongly recommended. One of robust estimators is re-weighted minimum covariance determinant (RMCD) which has robust characteristics as same as MCD. In this paper, the effectiveness term is accuracy of the RMCD control chart in detecting outliers as real outliers. In other word, how effectively this control chart can identify and remove masking and swamping effects of outliers. We assessed the effectiveness the robust control chart based on simulation by considering different scenarios: n sample sizes, proportion of outliers, number of p quality characteristics. We found that in some scenarios, this RMCD robust control chart works effectively.

Evaluating call-count procedures for measuring local mourning dove populations

Science.gov (United States)

Armbruster, M.J.; Baskett, T.S.; Goforth, W.R.; Sadler, K.C.

1978-01-01

Seventy-nine mourning dove call-count runs were made on a 32-km route in Osage County, Missouri, May 1-August 31, 1971 and 1972. Circular study areas, each 61 ha, surrounding stop numbers 4 and 5, were delineated for intensive nest searches and population estimates. Tallies of cooing male doves along the entire call-count route were quite variable in repeated runs, fluctuating as much as 50 percent on consecutive days. There were no consistent relationships between numbers of cooing males tallied at stops 4 and 5 and the numbers of current nests or doves estimated to be present in the surrounding study areas. We doubt the suitability of call-count procedures to estimate precisely the densities of breeding pairs, nests or production of doves on small areas. Our findings do not dispute the usefulness of the national call-count survey as an index to relative densities of mourning doves during the breeding season over large portions of the United States, or as an index to annual population trends.

A less field-intensive robust design for estimating demographic parameters with Mark-resight data

Science.gov (United States)

McClintock, B.T.; White, Gary C.

2009-01-01

The robust design has become popular among animal ecologists as a means for estimating population abundance and related demographic parameters with mark-recapture data. However, two drawbacks of traditional mark-recapture are financial cost and repeated disturbance to animals. Mark-resight methodology may in many circumstances be a less expensive and less invasive alternative to mark-recapture, but the models developed to date for these data have overwhelmingly concentrated only on the estimation of abundance. Here we introduce a mark-resight model analogous to that used in mark-recapture for the simultaneous estimation of abundance, apparent survival, and transition probabilities between observable and unobservable states. The model may be implemented using standard statistical computing software, but it has also been incorporated into the freeware package Program MARK. We illustrate the use of our model with mainland New Zealand Robin (Petroica australis) data collected to ascertain whether this methodology may be a reliable alternative for monitoring endangered populations of a closely related species inhabiting the Chatham Islands. We found this method to be a viable alternative to traditional mark-recapture when cost or disturbance to species is of particular concern in long-term population monitoring programs. ?? 2009 by the Ecological Society of America.

Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter

International Nuclear Information System (INIS)

Li, Q; Mark, R G; Clifford, G D

2008-01-01

Physiological signals such as the electrocardiogram (ECG) and arterial blood pressure (ABP) in the intensive care unit (ICU) are often severely corrupted by noise, artifact and missing data, which lead to large errors in the estimation of the heart rate (HR) and ABP. A robust HR estimation method is described that compensates for these problems. The method is based upon the concept of fusing multiple signal quality indices (SQIs) and HR estimates derived from multiple electrocardiogram (ECG) leads and an invasive ABP waveform recorded from ICU patients. Physiological SQIs were obtained by analyzing the statistical characteristics of each waveform and their relationships to each other. HR estimates from the ECG and ABP are tracked with separate Kalman filters, using a modified update sequence based upon the individual SQIs. Data fusion of each HR estimate was then performed by weighting each estimate by the Kalman filters' SQI-modified innovations. This method was evaluated on over 6000 h of simultaneously acquired ECG and ABP from a 437 patient subset of ICU data by adding real ECG and realistic artificial ABP noise. The method provides an accurate HR estimate even in the presence of high levels of persistent noise and artifact, and during episodes of extreme bradycardia and tachycardia

Aspects of robust linear regression

NARCIS (Netherlands)

Davies, P.L.

1993-01-01

Section 1 of the paper contains a general discussion of robustness. In Section 2 the influence function of the Hampel-Rousseeuw least median of squares estimator is derived. Linearly invariant weak metrics are constructed in Section 3. It is shown in Section 4 that $S$-estimators satisfy an exact

Robust statistical methods with R

CERN Document Server

Jureckova, Jana

2005-01-01

Robust statistical methods were developed to supplement the classical procedures when the data violate classical assumptions. They are ideally suited to applied research across a broad spectrum of study, yet most books on the subject are narrowly focused, overly theoretical, or simply outdated. Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on practical application.The authors work from underlying mathematical tools to implementation, paying special attention to the computational aspects. They cover the whole range of robust methods, including differentiable statistical functions, distance of measures, influence functions, and asymptotic distributions, in a rigorous yet approachable manner. Highlighting hands-on problem solving, many examples and computational algorithms using the R software supplement the discussion. The book examines the characteristics of robustness, estimators of real parameter, large sample properties, and goodness-of-fit tests. It...

Temporal trends in sperm count

DEFF Research Database (Denmark)

Levine, Hagai; Jørgensen, Niels; Martino-Andrade, Anderson

2017-01-01

a predefined protocol 7518 abstracts were screened and 2510 full articles reporting primary data on SC were reviewed. A total of 244 estimates of SC and TSC from 185 studies of 42 935 men who provided semen samples in 1973-2011 were extracted for meta-regression analysis, as well as information on years.......006, respectively). WIDER IMPLICATIONS: This comprehensive meta-regression analysis reports a significant decline in sperm counts (as measured by SC and TSC) between 1973 and 2011, driven by a 50-60% decline among men unselected by fertility from North America, Europe, Australia and New Zealand. Because......BACKGROUND: Reported declines in sperm counts remain controversial today and recent trends are unknown. A definitive meta-analysis is critical given the predictive value of sperm count for fertility, morbidity and mortality. OBJECTIVE AND RATIONALE: To provide a systematic review and meta-regression...

Dynamic Output Feedback Robust MPC with Input Saturation Based on Zonotopic Set-Membership Estimation

Directory of Open Access Journals (Sweden)

Xubin Ping

2016-01-01

Full Text Available For quasi-linear parameter varying (quasi-LPV systems with bounded disturbance, a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC with the consideration of input saturation is investigated. The saturated dynamic output feedback controller is represented by a convex hull involving the actual dynamic output controller and an introduced auxiliary controller. By taking both the actual output feedback controller and the auxiliary controller with a parameter-dependent form, the main optimization problem can be formulated as convex optimization. The consideration of input saturation in the main optimization problem reduces the conservatism of dynamic output feedback controller design. The estimation error set and bounded disturbance are represented by zonotopes and refreshed by zonotopic set-membership estimation. Compared with the previous results, the proposed algorithm can not only guarantee the recursive feasibility of the optimization problem, but also improve the control performance at the cost of higher computational burden. A nonlinear continuous stirred tank reactor (CSTR example is given to illustrate the effectiveness of the approach.

Robust regularized least-squares beamforming approach to signal estimation

KAUST Repository

Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Al-Naffouri, Tareq Y.

2017-01-01

In this paper, we address the problem of robust adaptive beamforming of signals received by a linear array. The challenge associated with the beamforming problem is twofold. Firstly, the process requires the inversion of the usually ill

Enhancing interferometer phase estimation, sensing sensitivity, and resolution using robust entangled states

Science.gov (United States)

Smith, James F.

2017-11-01

With the goal of designing interferometers and interferometer sensors, e.g., LADARs with enhanced sensitivity, resolution, and phase estimation, states using quantum entanglement are discussed. These states include N00N states, plain M and M states (PMMSs), and linear combinations of M and M states (LCMMS). Closed form expressions for the optimal detection operators; visibility, a measure of the state's robustness to loss and noise; a resolution measure; and phase estimate error, are provided in closed form. The optimal resolution for the maximum visibility and minimum phase error are found. For the visibility, comparisons between PMMSs, LCMMS, and N00N states are provided. For the minimum phase error, comparisons between LCMMS, PMMSs, N00N states, separate photon states (SPSs), the shot noise limit (SNL), and the Heisenberg limit (HL) are provided. A representative collection of computational results illustrating the superiority of LCMMS when compared to PMMSs and N00N states is given. It is found that for a resolution 12 times the classical result LCMMS has visibility 11 times that of N00N states and 4 times that of PMMSs. For the same case, the minimum phase error for LCMMS is 10.7 times smaller than that of PMMS and 29.7 times smaller than that of N00N states.

Robust Forecasting of Non-Stationary Time Series

NARCIS (Netherlands)

Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

2010-01-01

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable

Robust DOA Estimation of Harmonic Signals Using Constrained Filters on Phase Estimates

DEFF Research Database (Denmark)

Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll

2014-01-01

In array signal processing, distances between receivers, e.g., microphones, cause time delays depending on the direction of arrival (DOA) of a signal source. We can then estimate the DOA from the time-difference of arrival (TDOA) estimates. However, many conventional DOA estimators based on TDOA...... estimates are not optimal in colored noise. In this paper, we estimate the DOA of a harmonic signal source from multi-channel phase estimates, which relate to narrowband TDOA estimates. More specifically, we design filters to apply on phase estimates to obtain a DOA estimate with minimum variance. Using...

Robust Wavelet Estimation to Eliminate Simultaneously the Effects of Boundary Problems, Outliers, and Correlated Noise

Directory of Open Access Journals (Sweden)

Alsaidi M. Altaher

2012-01-01

Full Text Available Classical wavelet thresholding methods suffer from boundary problems caused by the application of the wavelet transformations to a finite signal. As a result, large bias at the edges and artificial wiggles occur when the classical boundary assumptions are not satisfied. Although polynomial wavelet regression and local polynomial wavelet regression effectively reduce the risk of this problem, the estimates from these two methods can be easily affected by the presence of correlated noise and outliers, giving inaccurate estimates. This paper introduces two robust methods in which the effects of boundary problems, outliers, and correlated noise are simultaneously taken into account. The proposed methods combine thresholding estimator with either a local polynomial model or a polynomial model using the generalized least squares method instead of the ordinary one. A primary step that involves removing the outlying observations through a statistical function is considered as well. The practical performance of the proposed methods has been evaluated through simulation experiments and real data examples. The results are strong evidence that the proposed method is extremely effective in terms of correcting the boundary bias and eliminating the effects of outliers and correlated noise.

Robust seismicity forecasting based on Bayesian parameter estimation for epidemiological spatio-temporal aftershock clustering models.

Science.gov (United States)

Ebrahimian, Hossein; Jalayer, Fatemeh

2017-08-29

In the immediate aftermath of a strong earthquake and in the presence of an ongoing aftershock sequence, scientific advisories in terms of seismicity forecasts play quite a crucial role in emergency decision-making and risk mitigation. Epidemic Type Aftershock Sequence (ETAS) models are frequently used for forecasting the spatio-temporal evolution of seismicity in the short-term. We propose robust forecasting of seismicity based on ETAS model, by exploiting the link between Bayesian inference and Markov Chain Monte Carlo Simulation. The methodology considers the uncertainty not only in the model parameters, conditioned on the available catalogue of events occurred before the forecasting interval, but also the uncertainty in the sequence of events that are going to happen during the forecasting interval. We demonstrate the methodology by retrospective early forecasting of seismicity associated with the 2016 Amatrice seismic sequence activities in central Italy. We provide robust spatio-temporal short-term seismicity forecasts with various time intervals in the first few days elapsed after each of the three main events within the sequence, which can predict the seismicity within plus/minus two standard deviations from the mean estimate within the few hours elapsed after the main event.

Efficient and robust pupil size and blink estimation from near-field video sequences for human-machine interaction.

Science.gov (United States)

Chen, Siyuan; Epps, Julien

2014-12-01

Monitoring pupil and blink dynamics has applications in cognitive load measurement during human-machine interaction. However, accurate, efficient, and robust pupil size and blink estimation pose significant challenges to the efficacy of real-time applications due to the variability of eye images, hence to date, require manual intervention for fine tuning of parameters. In this paper, a novel self-tuning threshold method, which is applicable to any infrared-illuminated eye images without a tuning parameter, is proposed for segmenting the pupil from the background images recorded by a low cost webcam placed near the eye. A convex hull and a dual-ellipse fitting method are also proposed to select pupil boundary points and to detect the eyelid occlusion state. Experimental results on a realistic video dataset show that the measurement accuracy using the proposed methods is higher than that of widely used manually tuned parameter methods or fixed parameter methods. Importantly, it demonstrates convenience and robustness for an accurate and fast estimate of eye activity in the presence of variations due to different users, task types, load, and environments. Cognitive load measurement in human-machine interaction can benefit from this computationally efficient implementation without requiring a threshold calibration beforehand. Thus, one can envisage a mini IR camera embedded in a lightweight glasses frame, like Google Glass, for convenient applications of real-time adaptive aiding and task management in the future.

Validation parameters of instrumental method for determination of total bacterial count in milk

Directory of Open Access Journals (Sweden)

Nataša Mikulec

2004-10-01

Full Text Available The method of flow citometry as rapid, instrumental and routine microbiological method is used for determination of total bacterial count in milk. The results of flow citometry are expressed as individual bacterial cells count. Problems regarding the interpretation of the results of total bacterial count can be avoided by transformation of the results of flow citometry method onto the scale of reference method (HRN ISO 6610:2001.. The method of flow citometry, like any analitycal method, according to the HRN EN ISO/IEC 17025:2000 standard, requires validation and verification. This paper describes parameters of validation: accuracy, precision, specificity, range, robustness and measuring uncertainty for the method of flow citometry.

Quality control methods in accelerometer data processing: identifying extreme counts.

Directory of Open Access Journals (Sweden)

Carly Rich

Full Text Available Accelerometers are designed to measure plausible human activity, however extremely high count values (EHCV have been recorded in large-scale studies. Using population data, we develop methodological principles for establishing an EHCV threshold, propose a threshold to define EHCV in the ActiGraph GT1M, determine occurrences of EHCV in a large-scale study, identify device-specific error values, and investigate the influence of varying EHCV thresholds on daily vigorous PA (VPA.We estimated quantiles to analyse the distribution of all accelerometer positive count values obtained from 9005 seven-year old children participating in the UK Millennium Cohort Study. A threshold to identify EHCV was derived by differentiating the quantile function. Data were screened for device-specific error count values and EHCV, and a sensitivity analysis conducted to compare daily VPA estimates using three approaches to accounting for EHCV.Using our proposed threshold of ≥ 11,715 counts/minute to identify EHCV, we found that only 0.7% of all non-zero counts measured in MCS children were EHCV; in 99.7% of these children, EHCV comprised < 1% of total non-zero counts. Only 11 MCS children (0.12% of sample returned accelerometers that contained negative counts; out of 237 such values, 211 counts were equal to -32,768 in one child. The medians of daily minutes spent in VPA obtained without excluding EHCV, and when using a higher threshold (≥19,442 counts/minute were, respectively, 6.2% and 4.6% higher than when using our threshold (6.5 minutes; p<0.0001.Quality control processes should be undertaken during accelerometer fieldwork and prior to analysing data to identify monitors recording error values and EHCV. The proposed threshold will improve the validity of VPA estimates in children's studies using the ActiGraph GT1M by ensuring only plausible data are analysed. These methods can be applied to define appropriate EHCV thresholds for different accelerometer models.

TVR-DART: A More Robust Algorithm for Discrete Tomography From Limited Projection Data With Automated Gray Value Estimation.

Science.gov (United States)

Xiaodong Zhuge; Palenstijn, Willem Jan; Batenburg, Kees Joost

2016-01-01

In this paper, we present a novel iterative reconstruction algorithm for discrete tomography (DT) named total variation regularized discrete algebraic reconstruction technique (TVR-DART) with automated gray value estimation. This algorithm is more robust and automated than the original DART algorithm, and is aimed at imaging of objects consisting of only a few different material compositions, each corresponding to a different gray value in the reconstruction. By exploiting two types of prior knowledge of the scanned object simultaneously, TVR-DART solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer the current reconstruction toward a solution with the specified number of discrete gray values. The gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments from simulated data, experimental μCT, and electron tomography data sets show that TVR-DART is capable of providing more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection images and/or from a small angular range. Furthermore, the new algorithm requires less effort on parameter tuning compared with the original DART algorithm. With TVR-DART, we aim to provide the tomography society with an easy-to-use and robust algorithm for DT.

Regression analysis of mixed recurrent-event and panel-count data.

Science.gov (United States)

Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L

2014-07-01

In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20: , 1-42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Zero-inflated count models for longitudinal measurements with heterogeneous random effects.

Science.gov (United States)

Zhu, Huirong; Luo, Sheng; DeSantis, Stacia M

2017-08-01

Longitudinal zero-inflated count data arise frequently in substance use research when assessing the effects of behavioral and pharmacological interventions. Zero-inflated count models (e.g. zero-inflated Poisson or zero-inflated negative binomial) with random effects have been developed to analyze this type of data. In random effects zero-inflated count models, the random effects covariance matrix is typically assumed to be homogeneous (constant across subjects). However, in many situations this matrix may be heterogeneous (differ by measured covariates). In this paper, we extend zero-inflated count models to account for random effects heterogeneity by modeling their variance as a function of covariates. We show via simulation that ignoring intervention and covariate-specific heterogeneity can produce biased estimates of covariate and random effect estimates. Moreover, those biased estimates can be rectified by correctly modeling the random effects covariance structure. The methodological development is motivated by and applied to the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study, the largest clinical trial of alcohol dependence performed in United States with 1383 individuals.

Robust Control Charts for Time Series Data

NARCIS (Netherlands)

Croux, C.; Gelper, S.; Mahieu, K.

2010-01-01

This article presents a control chart for time series data, based on the one-step- ahead forecast errors of the Holt-Winters forecasting method. We use robust techniques to prevent that outliers affect the estimation of the control limits of the chart. Moreover, robustness is important to maintain

Unbiased tensor-based morphometry: improved robustness and sample size estimates for Alzheimer's disease clinical trials.

Science.gov (United States)

Hua, Xue; Hibar, Derrek P; Ching, Christopher R K; Boyle, Christina P; Rajagopalan, Priya; Gutman, Boris A; Leow, Alex D; Toga, Arthur W; Jack, Clifford R; Harvey, Danielle; Weiner, Michael W; Thompson, Paul M

2013-02-01

Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI. Copyright © 2012 Elsevier Inc. All rights reserved.

Sperm count as a surrogate endpoint for male fertility control.

Science.gov (United States)

Benda, Norbert; Gerlinger, Christoph

2007-11-30

When assessing the effectiveness of a hormonal method of fertility control in men, the classical approach used for the assessment of hormonal contraceptives in women, by estimating the pregnancy rate or using a life-table analysis for the time to pregnancy, is difficult to apply in a clinical development program. The main reasons are the dissociation of the treated unit, i.e. the man, and the observed unit, i.e. his female partner, the high variability in the frequency of male intercourse, the logistical cost and ethical concerns related to the monitoring of the trial. A reasonable surrogate endpoint of the definite endpoint time to pregnancy is sperm count. In addition to the avoidance of the mentioned problems, trials that compare different treatments are possible with reasonable sample sizes, and study duration can be shorter. However, current products do not suppress sperm production to 100 per cent in all men and the sperm count is only observed with measurement error. Complete azoospermia might not be necessary in order to achieve an acceptable failure rate compared with other forms of male fertility control. Therefore, the use of sperm count as a surrogate endpoint must rely on the results of a previous trial in which both the definitive- and surrogate-endpoint results were assessed. The paper discusses different estimation functions of the mean pregnancy rate (corresponding to the cumulative hazard) that are based on the results of sperm count trial and a previous trial in which both sperm count and time to pregnancy were assessed, as well as the underlying assumptions. Sample size estimations are given for pregnancy rate estimation with a given precision.

Aerial population estimates of wild horses (Equus caballus) in the adobe town and salt wells creek herd management areas using an integrated simultaneous double-count and sightability bias correction technique

Science.gov (United States)

Lubow, Bruce C.; Ransom, Jason I.

2007-01-01

An aerial survey technique combining simultaneous double-count and sightability bias correction methodologies was used to estimate the population of wild horses inhabiting Adobe Town and Salt Wells Creek Herd Management Areas, Wyoming. Based on 5 surveys over 4 years, we conclude that the technique produced estimates consistent with the known number of horses removed between surveys and an annual population growth rate of 16.2 percent per year. Therefore, evidence from this series of surveys supports the validity of this survey method. Our results also indicate that the ability of aerial observers to see horse groups is very strongly dependent on skill of the individual observer, size of the horse group, and vegetation cover. It is also more modestly dependent on the ruggedness of the terrain and the position of the sun relative to the observer. We further conclude that censuses, or uncorrected raw counts, are inadequate estimates of population size for this herd. Such uncorrected counts were all undercounts in our trials, and varied in magnitude from year to year and observer to observer. As of April 2007, we estimate that the population of the Adobe Town /Salt Wells Creek complex is 906 horses with a 95 percent confidence interval ranging from 857 to 981 horses.

Validation of a Cytotechnologist Manual Counting Service for the Ki67 Index in Neuroendocrine Tumors of the Pancreas and Gastrointestinal Tract.

Science.gov (United States)

Cottenden, Jennielee; Filter, Emily R; Cottreau, Jon; Moore, David; Bullock, Martin; Huang, Weei-Yuarn; Arnason, Thomas

2018-03-01

- Pathologists routinely assess Ki67 immunohistochemistry to grade gastrointestinal and pancreatic neuroendocrine tumors. Unfortunately, manual counts of the Ki67 index are very time consuming and eyeball estimation has been criticized as unreliable. Manual Ki67 counts performed by cytotechnologists could potentially save pathologist time and improve accuracy. - To assess the concordance between manual Ki67 index counts performed by cytotechnologists versus eyeball estimates and manual Ki67 counts by pathologists. - One Ki67 immunohistochemical stain was retrieved from each of 18 archived gastrointestinal or pancreatic neuroendocrine tumor resections. We compared pathologists' Ki67 eyeball estimates on glass slides and printed color images with manual counts performed by 3 cytotechnologists and gold standard manual Ki67 index counts by 3 pathologists. - Tumor grade agreement between pathologist image eyeball estimate and gold standard pathologist manual count was fair (κ = 0.31; 95% CI, 0.030-0.60). In 9 of 20 cases (45%), the mean pathologist eyeball estimate was 1 grade higher than the mean pathologist manual count. There was almost perfect agreement in classifying tumor grade between the mean cytotechnologist manual count and the mean pathologist manual count (κ = 0.910; 95% CI, 0.697-1.00). In 20 cases, there was only 1 grade disagreement between the 2 methods. Eyeball estimation by pathologists required less than 1 minute, whereas manual counts by pathologists required a mean of 17 minutes per case. - Eyeball estimation of the Ki67 index has a high rate of tumor grade misclassification compared with manual counting. Cytotechnologist manual counts are accurate and save pathologist time.

Fast sequential Monte Carlo methods for counting and optimization

CERN Document Server

Rubinstein, Reuven Y; Vaisman, Radislav

2013-01-01

A comprehensive account of the theory and application of Monte Carlo methods Based on years of research in efficient Monte Carlo methods for estimation of rare-event probabilities, counting problems, and combinatorial optimization, Fast Sequential Monte Carlo Methods for Counting and Optimization is a complete illustration of fast sequential Monte Carlo techniques. The book provides an accessible overview of current work in the field of Monte Carlo methods, specifically sequential Monte Carlo techniques, for solving abstract counting and optimization problems. Written by authorities in the

Poisson and negative binomial item count techniques for surveys with sensitive question.

Science.gov (United States)

Tian, Guo-Liang; Tang, Man-Lai; Wu, Qin; Liu, Yin

2017-04-01

Although the item count technique is useful in surveys with sensitive questions, privacy of those respondents who possess the sensitive characteristic of interest may not be well protected due to a defect in its original design. In this article, we propose two new survey designs (namely the Poisson item count technique and negative binomial item count technique) which replace several independent Bernoulli random variables required by the original item count technique with a single Poisson or negative binomial random variable, respectively. The proposed models not only provide closed form variance estimate and confidence interval within [0, 1] for the sensitive proportion, but also simplify the survey design of the original item count technique. Most importantly, the new designs do not leak respondents' privacy. Empirical results show that the proposed techniques perform satisfactorily in the sense that it yields accurate parameter estimate and confidence interval.

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

Science.gov (United States)

Meng, Deyuan; Tao, Guoliang; Zhu, Xiaocong

2013-09-01

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.

A Robust Approach to Risk Assessment Based on Species Sensitivity Distributions.

Science.gov (United States)

Monti, Gianna S; Filzmoser, Peter; Deutsch, Roland C

2018-05-03

The guidelines for setting environmental quality standards are increasingly based on probabilistic risk assessment due to a growing general awareness of the need for probabilistic procedures. One of the commonly used tools in probabilistic risk assessment is the species sensitivity distribution (SSD), which represents the proportion of species affected belonging to a biological assemblage as a function of exposure to a specific toxicant. Our focus is on the inverse use of the SSD curve with the aim of estimating the concentration, HCp, of a toxic compound that is hazardous to p% of the biological community under study. Toward this end, we propose the use of robust statistical methods in order to take into account the presence of outliers or apparent skew in the data, which may occur without any ecological basis. A robust approach exploits the full neighborhood of a parametric model, enabling the analyst to account for the typical real-world deviations from ideal models. We examine two classic HCp estimation approaches and consider robust versions of these estimators. In addition, we also use data transformations in conjunction with robust estimation methods in case of heteroscedasticity. Different scenarios using real data sets as well as simulated data are presented in order to illustrate and compare the proposed approaches. These scenarios illustrate that the use of robust estimation methods enhances HCp estimation. © 2018 Society for Risk Analysis.

Identification and quantitative grade estimation of Uranium mineralization based on gross-count gamma ray log at Lemajung sector West Kalimantan

International Nuclear Information System (INIS)

Adi Gunawan Muhammad

2014-01-01

Lemajung sector, is one of uranium potential sector in Kalan Area, West Kalimantan. Uranium mineralization is found in metasiltstone and schistose metapelite rock with general direction of mineralization east - west tilted ± 70° to the north parallel with schistocity pattern (S1). Drilling evaluation has been implemented in 2013 in Lemajung sector at R-05 (LEML-(S1). Drilling evaluation has been implemented in 2013 in Lemajung sector at R-05 (LEML-gamma ray. The purpose of this activity is to determine uranium mineralization grade with quantitatively methode in the rocks and also determine the geological conditions in sorounding of drilling area. The methodology involves determining the value of k-factor, geological mapping for the sorounding of drill hole, determination of the thickness and grade estimation of uranium mineralization with gross-count gamma ray. Quantitatively from grade estimation of uranium using gross-count gamma ray log can be known that the highest % eU_3O_8 in the hole R-05 (LEML-40) reaches 0.7493≈6354 ppm eU found at depth interval from 30.1 to 34.96 m. Uranium mineralization is present as fracture filling (vein) or tectonic breccia matrix filling in metasiltstone with thickness from 0.10 to 2.40 m associated with sulphide (pyrite) and characterized by high ratio of U/Th. (author)

A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns.

Directory of Open Access Journals (Sweden)

Mohammad Manir Hossain Mollah

Full Text Available Identifying genes that are differentially expressed (DE between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA, are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression.The proposed method relies on a β-weight function, which produces values between 0 and 1. The β-weight function with β = 0.2 is used as a measure of outlier detection. It assigns smaller weights (≥ 0 to outlying expressions and larger weights (≤ 1 to typical expressions. The distribution of the β-weights is used to calculate the cut-off point, which is compared to the observed β-weight of an expression to determine whether that gene expression is an outlier. This weight function plays a key role in unifying the robustness and efficiency of estimation in one-way ANOVA.Analyses of simulated gene expression profiles revealed that all eight methods (ANOVA, SAM, LIMMA, EBarrays, eLNN, KW, robust BetaEB and proposed perform almost identically for m = 2 conditions in the absence of outliers. However, the robust BetaEB method and the proposed method exhibited considerably better performance than the other six methods in the presence of outliers. In this case, the BetaEB method exhibited slightly better performance than the proposed method for the small-sample cases, but the the proposed method exhibited much better performance than the BetaEB method for both the small- and large

Fast radio burst event rate counts - I. Interpreting the observations

Science.gov (United States)

Macquart, J.-P.; Ekers, R. D.

2018-02-01

The fluence distribution of the fast radio burst (FRB) population (the `source count' distribution, N (>F) ∝Fα), is a crucial diagnostic of its distance distribution, and hence the progenitor evolutionary history. We critically reanalyse current estimates of the FRB source count distribution. We demonstrate that the Lorimer burst (FRB 010724) is subject to discovery bias, and should be excluded from all statistical studies of the population. We re-examine the evidence for flat, α > -1, source count estimates based on the ratio of single-beam to multiple-beam detections with the Parkes multibeam receiver, and show that current data imply only a very weak constraint of α ≲ -1.3. A maximum-likelihood analysis applied to the portion of the Parkes FRB population detected above the observational completeness fluence of 2 Jy ms yields α = -2.6_{-1.3}^{+0.7 }. Uncertainties in the location of each FRB within the Parkes beam render estimates of the Parkes event rate uncertain in both normalizing survey area and the estimated post-beam-corrected completeness fluence; this uncertainty needs to be accounted for when comparing the event rate against event rates measured at other telescopes.

Robust Diagnosis Method Based on Parameter Estimation for an Interturn Short-Circuit Fault in Multipole PMSM under High-Speed Operation.

Science.gov (United States)

Lee, Jewon; Moon, Seokbae; Jeong, Hyeyun; Kim, Sang Woo

2015-11-20

This paper proposes a diagnosis method for a multipole permanent magnet synchronous motor (PMSM) under an interturn short circuit fault. Previous works in this area have suffered from the uncertainties of the PMSM parameters, which can lead to misdiagnosis. The proposed method estimates the q-axis inductance (Lq) of the faulty PMSM to solve this problem. The proposed method also estimates the faulty phase and the value of G, which serves as an index of the severity of the fault. The q-axis current is used to estimate the faulty phase, the values of G and Lq. For this reason, two open-loop observers and an optimization method based on a particle-swarm are implemented. The q-axis current of a healthy PMSM is estimated by the open-loop observer with the parameters of a healthy PMSM. The Lq estimation significantly compensates for the estimation errors in high-speed operation. The experimental results demonstrate that the proposed method can estimate the faulty phase, G, and Lq besides exhibiting robustness against parameter uncertainties.

Weak Properties and Robustness of t-Hill Estimators

Czech Academy of Sciences Publication Activity Database

Jordanova, P.; Fabián, Zdeněk; Hermann, P.; Střelec, L.; Rivera, A.; Girard, S.; Torres, S.; Stehlík, M.

2016-01-01

Roč. 19, č. 4 (2016), s. 591-626 ISSN 1386-1999 Institutional support: RVO:67985807 Keywords : asymptotic properties of estimators * point estimation * t-Hill estimator * t-lgHill estimator Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.679, year: 2016

Robust K-Median and K-Means Clustering Algorithms for Incomplete Data

Directory of Open Access Journals (Sweden)

Jinhua Li

2016-01-01

Full Text Available Incomplete data with missing feature values are prevalent in clustering problems. Traditional clustering methods first estimate the missing values by imputation and then apply the classical clustering algorithms for complete data, such as K-median and K-means. However, in practice, it is often hard to obtain accurate estimation of the missing values, which deteriorates the performance of clustering. To enhance the robustness of clustering algorithms, this paper represents the missing values by interval data and introduces the concept of robust cluster objective function. A minimax robust optimization (RO formulation is presented to provide clustering results, which are insensitive to estimation errors. To solve the proposed RO problem, we propose robust K-median and K-means clustering algorithms with low time and space complexity. Comparisons and analysis of experimental results on both artificially generated and real-world incomplete data sets validate the robustness and effectiveness of the proposed algorithms.

Robust statistics and geochemical data analysis

International Nuclear Information System (INIS)

Di, Z.

1987-01-01

Advantages of robust procedures over ordinary least-squares procedures in geochemical data analysis is demonstrated using NURE data from the Hot Springs Quadrangle, South Dakota, USA. Robust principal components analysis with 5% multivariate trimming successfully guarded the analysis against perturbations by outliers and increased the number of interpretable factors. Regression with SINE estimates significantly increased the goodness-of-fit of the regression and improved the correspondence of delineated anomalies with known uranium prospects. Because of the ubiquitous existence of outliers in geochemical data, robust statistical procedures are suggested as routine procedures to replace ordinary least-squares procedures

Fast and accurate phylogenetic reconstruction from high-resolution whole-genome data and a novel robustness estimator.

Science.gov (United States)

Lin, Y; Rajan, V; Moret, B M E

2011-09-01

The rapid accumulation of whole-genome data has renewed interest in the study of genomic rearrangements. Comparative genomics, evolutionary biology, and cancer research all require models and algorithms to elucidate the mechanisms, history, and consequences of these rearrangements. However, even simple models lead to NP-hard problems, particularly in the area of phylogenetic analysis. Current approaches are limited to small collections of genomes and low-resolution data (typically a few hundred syntenic blocks). Moreover, whereas phylogenetic analyses from sequence data are deemed incomplete unless bootstrapping scores (a measure of confidence) are given for each tree edge, no equivalent to bootstrapping exists for rearrangement-based phylogenetic analysis. We describe a fast and accurate algorithm for rearrangement analysis that scales up, in both time and accuracy, to modern high-resolution genomic data. We also describe a novel approach to estimate the robustness of results-an equivalent to the bootstrapping analysis used in sequence-based phylogenetic reconstruction. We present the results of extensive testing on both simulated and real data showing that our algorithm returns very accurate results, while scaling linearly with the size of the genomes and cubically with their number. We also present extensive experimental results showing that our approach to robustness testing provides excellent estimates of confidence, which, moreover, can be tuned to trade off thresholds between false positives and false negatives. Together, these two novel approaches enable us to attack heretofore intractable problems, such as phylogenetic inference for high-resolution vertebrate genomes, as we demonstrate on a set of six vertebrate genomes with 8,380 syntenic blocks. A copy of the software is available on demand.

Robustness of serial clustering of extratropical cyclones to the choice of tracking method

Directory of Open Access Journals (Sweden)

Joaquim G. Pinto

2016-07-01

Full Text Available Cyclone clusters are a frequent synoptic feature in the Euro-Atlantic area. Recent studies have shown that serial clustering of cyclones generally occurs on both flanks and downstream regions of the North Atlantic storm track, while cyclones tend to occur more regulary on the western side of the North Atlantic basin near Newfoundland. This study explores the sensitivity of serial clustering to the choice of cyclone tracking method using cyclone track data from 15 methods derived from ERA-Interim data (1979–2010. Clustering is estimated by the dispersion (ratio of variance to mean of winter [December – February (DJF] cyclone passages near each grid point over the Euro-Atlantic area. The mean number of cyclone counts and their variance are compared between methods, revealing considerable differences, particularly for the latter. Results show that all different tracking methods qualitatively capture similar large-scale spatial patterns of underdispersion and overdispersion over the study region. The quantitative differences can primarily be attributed to the differences in the variance of cyclone counts between the methods. Nevertheless, overdispersion is statistically significant for almost all methods over parts of the eastern North Atlantic and Western Europe, and is therefore considered as a robust feature. The influence of the North Atlantic Oscillation (NAO on cyclone clustering displays a similar pattern for all tracking methods, with one maximum near Iceland and another between the Azores and Iberia. The differences in variance between methods are not related with different sensitivities to the NAO, which can account to over 50% of the clustering in some regions. We conclude that the general features of underdispersion and overdispersion of extratropical cyclones over the North Atlantic and Western Europe are robust to the choice of tracking method. The same is true for the influence of the NAO on cyclone dispersion.

Accuracy of Platelet Counting by Optical and Impedance Methods in Patients with Thrombocytopaenia and Microcytosis

Directory of Open Access Journals (Sweden)

Mohamed-Rachid Boulassel

2015-11-01

Full Text Available Objectives: Obtaining accurate platelet counts in microcytic blood samples is challenging, even with the most reliable automated haematology analysers. The CELL-DYN™ Sapphire (Abbott Laboratories, Chicago, Illinois, USA analyser uses both optical density and electronic impedance methods for platelet counting. This study aimed to evaluate the accuracy of optical density and electrical impedance methods in determining true platelet counts in thrombocytopaenic samples with microcytosis as defined by low mean corpuscular volume (MCV of red blood cells. Additionally, the impact of microcytosis on platelet count accuracy was evaluated. Methods: This study was carried out between February and December 2014 at the Haematology Laboratory of the Sultan Qaboos University Hospital in Muscat, Oman. Blood samples were collected and analysed from 189 patients with thrombocytopaenia and MCV values of <76 femtolitres. Platelet counts were tested using both optical and impedance methods. Stained peripheral blood films for each sample were then reviewed as a reference method to confirm platelet counts. Results: The platelet counts estimated by the impedance method were on average 30% higher than those estimated by the optical method (P <0.001. The estimated intraclass correlation coefficient was 0.52 (95% confidence interval: 0.41–0.62, indicating moderate reliability between the methods. The degree of agreement between methods ranged from -85.5 to 24.3 with an estimated bias of -30, suggesting that these methods generate different platelet results. Conclusion: The impedance method significantly overestimated platelet counts in microcytic and thrombocytopaenic blood samples. Further attention is therefore needed to improve the accuracy of platelet counts, particularly for patients with conditions associated with microcytosis.

Can cover data be used as a surrogate for seedling counts in regeneration stocking evaluations in northern hardwood forests?

Science.gov (United States)

Todd E. Ristau; Susan L. Stout

2014-01-01

Assessment of regeneration can be time-consuming and costly. Often, foresters look for ways to minimize the cost of doing inventories. One potential method to reduce time required on a plot is use of percent cover data rather than seedling count data to determine stocking. Robust linear regression analysis was used in this report to predict seedling count data from...

Combining counts and incidence data: an efficient approach for estimating the log-normal species abundance distribution and diversity indices.

Science.gov (United States)

Bellier, Edwige; Grøtan, Vidar; Engen, Steinar; Schartau, Ann Kristin; Diserud, Ola H; Finstad, Anders G

2012-10-01

Obtaining accurate estimates of diversity indices is difficult because the number of species encountered in a sample increases with sampling intensity. We introduce a novel method that requires that the presence of species in a sample to be assessed while the counts of the number of individuals per species are only required for just a small part of the sample. To account for species included as incidence data in the species abundance distribution, we modify the likelihood function of the classical Poisson log-normal distribution. Using simulated community assemblages, we contrast diversity estimates based on a community sample, a subsample randomly extracted from the community sample, and a mixture sample where incidence data are added to a subsample. We show that the mixture sampling approach provides more accurate estimates than the subsample and at little extra cost. Diversity indices estimated from a freshwater zooplankton community sampled using the mixture approach show the same pattern of results as the simulation study. Our method efficiently increases the accuracy of diversity estimates and comprehension of the left tail of the species abundance distribution. We show how to choose the scale of sample size needed for a compromise between information gained, accuracy of the estimates and cost expended when assessing biological diversity. The sample size estimates are obtained from key community characteristics, such as the expected number of species in the community, the expected number of individuals in a sample and the evenness of the community.

Atmospheric mold spore counts in relation to meteorological parameters

Science.gov (United States)

Katial, R. K.; Zhang, Yiming; Jones, Richard H.; Dyer, Philip D.

Fungal spore counts of Cladosporium, Alternaria, and Epicoccum were studied during 8 years in Denver, Colorado. Fungal spore counts were obtained daily during the pollinating season by a Rotorod sampler. Weather data were obtained from the National Climatic Data Center. Daily averages of temperature, relative humidity, daily precipitation, barometric pressure, and wind speed were studied. A time series analysis was performed on the data to mathematically model the spore counts in relation to weather parameters. Using SAS PROC ARIMA software, a regression analysis was performed, regressing the spore counts on the weather variables assuming an autoregressive moving average (ARMA) error structure. Cladosporium was found to be positively correlated (Pmodel was derived for Cladosporium spore counts using the annual seasonal cycle and significant weather variables. The model for Alternaria and Epicoccum incorporated the annual seasonal cycle. Fungal spore counts can be modeled by time series analysis and related to meteorological parameters controlling for seasonallity; this modeling can provide estimates of exposure to fungal aeroallergens.

Pixel-Cluster Counting Luminosity Measurement in ATLAS

CERN Document Server

McCormack, William Patrick; The ATLAS collaboration

2016-01-01

A precision measurement of the delivered luminosity is a key component of the ATLAS physics program at the Large Hadron Collider (LHC). A fundamental ingredient of the strategy to control the systematic uncertainties affecting the absolute luminosity has been to compare the measurements of several luminometers, most of which use more than one counting technique. The level of consistency across the various methods provides valuable cross-checks as well as an estimate of the detector-related systematic uncertainties. This poster describes the development of a luminosity algorithm based on pixel-cluster counting in the recently installed ATLAS inner b-layer (IBL), using data recorded during the 2015 pp run at the LHC. The noise and background contamination of the luminosity-associated cluster count is minimized by a multi-component fit to the measured cluster-size distribution in the forward pixel modules of the IBL. The linearity, long-term stability and statistical precision of the cluster-counting method are ...

Pixel-Cluster Counting Luminosity Measurement In ATLAS

CERN Document Server

AUTHOR|(SzGeCERN)782710; The ATLAS collaboration

2017-01-01

A precision measurement of the delivered luminosity is a key component of the ATLAS physics program at the Large Hadron Collider (LHC). A fundamental ingredient of the strategy to control the systematic uncertainties affecting the absolute luminosity has been to compare the measure- ments of several luminometers, most of which use more than one counting technique. The level of consistency across the various methods provides valuable cross-checks as well as an estimate of the detector-related systematic uncertainties. This poster describes the development of a luminosity algorithm based on pixel-cluster counting in the recently installed ATLAS inner b-layer (IBL), using data recorded during the 2015 pp run at the LHC. The noise and background contamination of the luminosity-associated cluster count is minimized by a multi-component fit to the measured cluster-size distribution in the forward pixel modules of the IBL. The linearity, long-term stability and statistical precision of the cluster- counting method a...

2015-2016 Palila abundance estimates

Science.gov (United States)

Camp, Richard J.; Brinck, Kevin W.; Banko, Paul C.

2016-01-01

The palila (Loxioides bailleui) population was surveyed annually during 1998−2016 on Mauna Kea Volcano to determine abundance, population trend, and spatial distribution. In the latest surveys, the 2015 population was estimated at 852−1,406 birds (point estimate: 1,116) and the 2016 population was estimated at 1,494−2,385 (point estimate: 1,934). Similar numbers of palila were detected during the first and subsequent counts within each year during 2012−2016; the proportion of the total annual detections in each count ranged from 46% to 56%; and there was no difference in the detection probability due to count sequence. Furthermore, conducting repeat counts improved the abundance estimates by reducing the width of the confidence intervals between 9% and 32% annually. This suggests that multiple counts do not affect bird or observer behavior and can be continued in the future to improve the precision of abundance estimates. Five palila were detected on supplemental survey stations in the Ka‘ohe restoration area, outside the core survey area but still within Palila Critical Habitat (one in 2015 and four in 2016), suggesting that palila are present in habitat that is recovering from cattle grazing on the southwest slope. The average rate of decline during 1998−2016 was 150 birds per year. Over the 18-year monitoring period, the estimated rate of change equated to a 58% decline in the population.

Robust stator resistance identification of an IM drive using model reference adaptive system

International Nuclear Information System (INIS)

Madadi Kojabadi, Hossein; Abarzadeh, Mostafa; Aghaei Farouji, Said

2013-01-01

Highlights: ► We estimate the stator resistance and rotor speed of the IM. ► We proposed a new quantity to estimate the speed and stator resistance of IM. ► The proposed algorithm is robust to rotor resistance variations. ► We estimate the IM speed and stator resistance simultaneously to avoid speed error. - Abstract: Model reference adaptive system (MRAS) based robust stator resistance estimator for sensorless induction motor (IM) drive is proposed. The MRAS is formed with a semi-active power quantity. The proposed identification method can be achieved with on-line tuning of the stator resistance with robustness against rotor resistance variations. Stable and efficient estimation of IM speed at low region will be guaranteed by simultaneous identification of IM speed and stator resistance. The stability of proposed stator resistance estimator is checked through Popov’s hyperstability theorem. Simulation and experimental results are given to highlight the feasibility, the simplicity, and the robustness of the proposed method.

Platelet Counts in Insoluble Platelet-Rich Fibrin Clots: A Direct Method for Accurate Determination

Directory of Open Access Journals (Sweden)

Yutaka Kitamura

2018-02-01

Full Text Available Platelet-rich fibrin (PRF clots have been used in regenerative dentistry most often, with the assumption that growth factor levels are concentrated in proportion to the platelet concentration. Platelet counts in PRF are generally determined indirectly by platelet counting in other liquid fractions. This study shows a method for direct estimation of platelet counts in PRF. To validate this method by determination of the recovery rate, whole-blood samples were obtained with an anticoagulant from healthy donors, and platelet-rich plasma (PRP fractions were clotted with CaCl2 by centrifugation and digested with tissue-plasminogen activator. Platelet counts were estimated before clotting and after digestion using an automatic hemocytometer. The method was then tested on PRF clots. The quality of platelets was examined by scanning electron microscopy and flow cytometry. In PRP-derived fibrin matrices, the recovery rate of platelets and white blood cells was 91.6 and 74.6%, respectively, after 24 h of digestion. In PRF clots associated with small and large red thrombi, platelet counts were 92.6 and 67.2% of the respective total platelet counts. These findings suggest that our direct method is sufficient for estimating the number of platelets trapped in an insoluble fibrin matrix and for determining that platelets are distributed in PRF clots and red thrombi roughly in proportion to their individual volumes. Therefore, we propose this direct digestion method for more accurate estimation of platelet counts in most types of platelet-enriched fibrin matrix.

Operation voltage of the counting system of nuclear traces in solids

International Nuclear Information System (INIS)

Garcia, M.L.; Quirino, L.L.; Mireles, F.; Davila, J.I.; Pinedo, J.L.; Lugo, J.F.; Vadillo, V.E.

2002-01-01

The semi-automatic counting system based on electric spark and used for traces reading in solid state detectors is evaluated for obtaining its counting voltage and also the breaking voltage of material. In the treatment of the solid state detectors it is continued the NTD methodology for concluding with the individual counting of the films, whose graphics offer the existing relationship among the applied voltage and the traces number. From each film a counting and breaking voltages are obtained. Finally, an average voltage of all them is estimated. (Author)

Bird biodiversity assessments in temperate forest: the value of point count versus acoustic monitoring protocols

Directory of Open Access Journals (Sweden)

Brian T. Klingbeil

2015-05-01

Full Text Available Effective monitoring programs for biodiversity are needed to assess trends in biodiversity and evaluate the consequences of management. This is particularly true for birds and faunas that occupy interior forest and other areas of low human population density, as these are frequently under-sampled compared to other habitats. For birds, Autonomous Recording Units (ARUs have been proposed as a supplement or alternative to point counts made by human observers to enhance monitoring efforts. We employed two strategies (i.e., simultaneous-collection and same-season to compare point count and ARU methods for quantifying species richness and composition of birds in temperate interior forests. The simultaneous-collection strategy compares surveys by ARUs and point counts, with methods matched in time, location, and survey duration such that the person and machine simultaneously collect data. The same-season strategy compares surveys from ARUs and point counts conducted at the same locations throughout the breeding season, but methods differ in the number, duration, and frequency of surveys. This second strategy more closely follows the ways in which monitoring programs are likely to be implemented. Site-specific estimates of richness (but not species composition differed between methods; however, the nature of the relationship was dependent on the assessment strategy. Estimates of richness from point counts were greater than estimates from ARUs in the simultaneous-collection strategy. Woodpeckers in particular, were less frequently identified from ARUs than point counts with this strategy. Conversely, estimates of richness were lower from point counts than ARUs in the same-season strategy. Moreover, in the same-season strategy, ARUs detected the occurrence of passerines at a higher frequency than did point counts. Differences between ARU and point count methods were only detected in site-level comparisons. Importantly, both methods provide similar

Effects of calibration methods on quantitative material decomposition in photon-counting spectral computed tomography using a maximum a posteriori estimator.

Science.gov (United States)

Curtis, Tyler E; Roeder, Ryan K

2017-10-01

Advances in photon-counting detectors have enabled quantitative material decomposition using multi-energy or spectral computed tomography (CT). Supervised methods for material decomposition utilize an estimated attenuation for each material of interest at each photon energy level, which must be calibrated based upon calculated or measured values for known compositions. Measurements using a calibration phantom can advantageously account for system-specific noise, but the effect of calibration methods on the material basis matrix and subsequent quantitative material decomposition has not been experimentally investigated. Therefore, the objective of this study was to investigate the influence of the range and number of contrast agent concentrations within a modular calibration phantom on the accuracy of quantitative material decomposition in the image domain. Gadolinium was chosen as a model contrast agent in imaging phantoms, which also contained bone tissue and water as negative controls. The maximum gadolinium concentration (30, 60, and 90 mM) and total number of concentrations (2, 4, and 7) were independently varied to systematically investigate effects of the material basis matrix and scaling factor calibration on the quantitative (root mean squared error, RMSE) and spatial (sensitivity and specificity) accuracy of material decomposition. Images of calibration and sample phantoms were acquired using a commercially available photon-counting spectral micro-CT system with five energy bins selected to normalize photon counts and leverage the contrast agent k-edge. Material decomposition of gadolinium, calcium, and water was performed for each calibration method using a maximum a posteriori estimator. Both the quantitative and spatial accuracy of material decomposition were most improved by using an increased maximum gadolinium concentration (range) in the basis matrix calibration; the effects of using a greater number of concentrations were relatively small in

Robust 3D Position Estimation in Wide and Unconstrained Indoor Environments

Directory of Open Access Journals (Sweden)

Annette Mossel

2015-12-01

Full Text Available In this paper, a system for 3D position estimation in wide, unconstrained indoor environments is presented that employs infrared optical outside-in tracking of rigid-body targets with a stereo camera rig. To overcome limitations of state-of-the-art optical tracking systems, a pipeline for robust target identification and 3D point reconstruction has been investigated that enables camera calibration and tracking in environments with poor illumination, static and moving ambient light sources, occlusions and harsh conditions, such as fog. For evaluation, the system has been successfully applied in three different wide and unconstrained indoor environments, (1 user tracking for virtual and augmented reality applications, (2 handheld target tracking for tunneling and (3 machine guidance for mining. The results of each use case are discussed to embed the presented approach into a larger technological and application context. The experimental results demonstrate the system’s capabilities to track targets up to 100 m. Comparing the proposed approach to prior art in optical tracking in terms of range coverage and accuracy, it significantly extends the available tracking range, while only requiring two cameras and providing a relative 3D point accuracy with sub-centimeter deviation up to 30 m and low-centimeter deviation up to 100 m.

The prognostic value of angiogenesis by Chalkley counting in a confirmatory study design on 836 breast cancer patients

DEFF Research Database (Denmark)

Hansen, S; Grabau, D A; Sørensen, Flemming Brandt

2000-01-01

This study addresses the prognostic value of estimating angiogenesis by Chalkley counting in breast cancer. A population-based group consisting of 836 patients with operated primary, unilateral invasive breast carcinomas was included from a predefined region and period of time. The median follow...... with counts or =7 compared with counts between 5-7. The study confirmed that estimation of angiogenesis by Chalkley counting had independent prognostic value in breast cancer patients. The Chalkley count could be useful to stratify node-negative patients...

Robust lyapunov controller for uncertain systems

KAUST Repository

Laleg-Kirati, Taous-Meriem

2017-02-23

Various examples of systems and methods are provided for Lyapunov control for uncertain systems. In one example, a system includes a process plant and a robust Lyapunov controller configured to control an input of the process plant. The robust Lyapunov controller includes an inner closed loop Lyapunov controller and an outer closed loop error stabilizer. In another example, a method includes monitoring a system output of a process plant; generating an estimated system control input based upon a defined output reference; generating a system control input using the estimated system control input and a compensation term; and adjusting the process plant based upon the system control input to force the system output to track the defined output reference. An inner closed loop Lyapunov controller can generate the estimated system control input and an outer closed loop error stabilizer can generate the system control input.

Optimizing the calculation of point source count-centroid in pixel size measurement

International Nuclear Information System (INIS)

Zhou Luyi; Kuang Anren; Su Xianyu

2004-01-01

Purpose: Pixel size is an important parameter of gamma camera and SPECT. A number of Methods are used for its accurate measurement. In the original count-centroid method, where the image of a point source(PS) is acquired and its count-centroid calculated to represent PS position in the image, background counts are inevitable. Thus the measured count-centroid (Xm) is an approximation of the true count-centroid (Xp) of the PS, i.e. Xm=Xp+(Xb-Xp)/(1+Rp/Rb), where Rp is the net counting rate of the PS, Xb the background count-centroid and Rb the background counting rate. To get accurate measurement, Rp must be very big, which is unpractical, resulting in the variation of measured pixel size. Rp-independent calculation of PS count-centroid is desired. Methods: The proposed method attempted to eliminate the effect of the term (Xb-Xp)/(1+Rp/Rb) by bringing Xb closer to Xp and by reducing Rb. In the acquired PS image, a circular ROI was generated to enclose the PS, the pixel with the maximum count being the center of the ROI. To choose the diameter (D) of the ROI, a Gaussian count distribution was assumed for the PS, accordingly, K=I-(0.5)D/R percent of the total PS counts was in the ROI, R being the full width at half maximum of the PS count distribution. D was set to be 6*R to enclose most (K=98.4%) of the PS counts. The count-centroid of the ROI was calculated to represent Xp. The proposed method was tested in measuring the pixel size of a well-tuned SPECT, whose pixel size was estimated to be 3.02 mm according to its mechanical and electronic setting (128*128 matrix, 387 mm UFOV, ZOOM=1). For comparison, the original method, which was use in the former versions of some commercial SPECT software, was also tested. 12 PSs were prepared and their image acquired and stored. The net counting rate of the PSs increased from 10cps to 1183cps. Results: Using the proposed method, the measured pixel size (in mm) varied only between 3.00 and 3.01( mean= 3.01±0.00) as Rp increased

Robust mislabel logistic regression without modeling mislabel probabilities.

Science.gov (United States)

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

An algorithm for robust non-linear analysis of radioimmunoassays and other bioassays

International Nuclear Information System (INIS)

Normolle, D.P.

1993-01-01

The four-parameter logistic function is an appropriate model for many types of bioassays that have continuous response variables, such as radioimmunoassays. By modelling the variance of replicates in an assay, one can modify the usual parameter estimation techniques (for example, Gauss-Newton or Marquardt-Levenberg) to produce parameter estimates for the standard curve that are robust against outlying observations. This article describes the computation of robust (M-) estimates for the parameters of the four-parameter logistic function. It describes techniques for modelling the variance structure of the replicates, modifications to the usual iterative algorithms for parameter estimation in non-linear models, and a formula for inverse confidence intervals. To demonstrate the algorithm, the article presents examples where the robustly estimated four-parameter logistic model is compared with the logit-log and four-parameter logistic models with least-squares estimates. (author)

An empirical likelihood ratio test robust to individual heterogeneity for differential expression analysis of RNA-seq.

Science.gov (United States)

Xu, Maoqi; Chen, Liang

2018-01-01

The individual sample heterogeneity is one of the biggest obstacles in biomarker identification for complex diseases such as cancers. Current statistical models to identify differentially expressed genes between disease and control groups often overlook the substantial human sample heterogeneity. Meanwhile, traditional nonparametric tests lose detailed data information and sacrifice the analysis power, although they are distribution free and robust to heterogeneity. Here, we propose an empirical likelihood ratio test with a mean-variance relationship constraint (ELTSeq) for the differential expression analysis of RNA sequencing (RNA-seq). As a distribution-free nonparametric model, ELTSeq handles individual heterogeneity by estimating an empirical probability for each observation without making any assumption about read-count distribution. It also incorporates a constraint for the read-count overdispersion, which is widely observed in RNA-seq data. ELTSeq demonstrates a significant improvement over existing methods such as edgeR, DESeq, t-tests, Wilcoxon tests and the classic empirical likelihood-ratio test when handling heterogeneous groups. It will significantly advance the transcriptomics studies of cancers and other complex disease. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Synchronization of a class of chaotic signals via robust observer design

Energy Technology Data Exchange (ETDEWEB)

Aguilar-Lopez, Ricardo [Departamento de Energia, Universidad Autonoma Metropolitana - Azcapotzalco, San Pablo 180, Reynosa-Tamaulipas, Azcapotzalco 02200, Mexico, D.F. (Mexico)], E-mail: raguilar@correo.azc.uam.mx; Martinez-Guerra, Rafael [Departamento de Energia, Universidad Autonoma Metropolitana - Azcapotzalco, San Pablo 180, Reynosa-Tamaulipas, Azcapotzalco 02200, Mexico, D.F. (Mexico); Departamento de Control Automatico, CINVESTAV IPN, Apartado Postal 14-740, Mexico, D.F. C.P. 07360 (Mexico)], E-mail: rguerra@ctrl.cinvestav.mx

2008-07-15

In this paper the signal synchronization of a class of chaotic systems based on robust observer design is tackled. The task is the synchronization of the signals generated by two Chen oscillators with different initial condition. The proposed observer is robust against model uncertainties and noisy output measurements. An alternative system representation is proposed to transform the measured disturbance onto system disturbance, which leads a more adequate observer structure. The proposed methodology contains an uncertainty estimator based on the predictive contribution to infer the unobservable uncertainties and corrective contribution to estimate the observable uncertainties; which provides robustness against noisy measurements and model uncertainties. Convergence analysis of the proposed estimation methodology is realized, analyzing the dynamic equation of the estimation error, where asymptotic convergence is shown. Numerical experiments illustrate the good performance of the proposed methodology.

Synchronization of a class of chaotic signals via robust observer design

International Nuclear Information System (INIS)

Aguilar-Lopez, Ricardo; Martinez-Guerra, Rafael

2008-01-01

In this paper the signal synchronization of a class of chaotic systems based on robust observer design is tackled. The task is the synchronization of the signals generated by two Chen oscillators with different initial condition. The proposed observer is robust against model uncertainties and noisy output measurements. An alternative system representation is proposed to transform the measured disturbance onto system disturbance, which leads a more adequate observer structure. The proposed methodology contains an uncertainty estimator based on the predictive contribution to infer the unobservable uncertainties and corrective contribution to estimate the observable uncertainties; which provides robustness against noisy measurements and model uncertainties. Convergence analysis of the proposed estimation methodology is realized, analyzing the dynamic equation of the estimation error, where asymptotic convergence is shown. Numerical experiments illustrate the good performance of the proposed methodology

Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer’s disease clinical trials

Science.gov (United States)

Hua, Xue; Hibar, Derrek P.; Ching, Christopher R.K.; Boyle, Christina P.; Rajagopalan, Priya; Gutman, Boris A.; Leow, Alex D.; Toga, Arthur W.; Jack, Clifford R.; Harvey, Danielle; Weiner, Michael W.; Thompson, Paul M.

2013-01-01

Various neuroimaging measures are being evaluated for tracking Alzheimer’s disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24 months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39 AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI. PMID:23153970

Automatic vehicle counting system for traffic monitoring

Science.gov (United States)

Crouzil, Alain; Khoudour, Louahdi; Valiere, Paul; Truong Cong, Dung Nghy

2016-09-01

The article is dedicated to the presentation of a vision-based system for road vehicle counting and classification. The system is able to achieve counting with a very good accuracy even in difficult scenarios linked to occlusions and/or presence of shadows. The principle of the system is to use already installed cameras in road networks without any additional calibration procedure. We propose a robust segmentation algorithm that detects foreground pixels corresponding to moving vehicles. First, the approach models each pixel of the background with an adaptive Gaussian distribution. This model is coupled with a motion detection procedure, which allows correctly location of moving vehicles in space and time. The nature of trials carried out, including peak periods and various vehicle types, leads to an increase of occlusions between cars and between cars and trucks. A specific method for severe occlusion detection, based on the notion of solidity, has been carried out and tested. Furthermore, the method developed in this work is capable of managing shadows with high resolution. The related algorithm has been tested and compared to a classical method. Experimental results based on four large datasets show that our method can count and classify vehicles in real time with a high level of performance (>98%) under different environmental situations, thus performing better than the conventional inductive loop detectors.

Fitting a distribution to miccrobial counts: making sense of zeros

DEFF Research Database (Denmark)

Ribeiro Duarte, Ana Sofia; Stockmarr, Anders; Nauta, Maarten

and standard deviation) and the prevalence of contaminated food units (one minus the proportion of “true zeros”) from a set of microbial counts. By running the model with in silico generated concentration and count data, we could evaluate the performance of this method in terms of estimation of the three......Non-detects or left-censored results are inherent to the traditional methods of microbial enumeration in foods. Typically, a low concentration of microorganisms in a food unit goes undetected in plate counts or most probable number (MPN) counts, and produces “artificial zeros”. However......, these “artificial zeros” are only a share of the total number of zero counts resulting from a sample, as their number adds up to the number of “true zeros” resulting from uncontaminated units. In the process of fitting a probability distribution to microbial counts, “artificial” and “true” zeros are usually...

A robust metric for screening outliers from analogue product manufacturing tests responses

NARCIS (Netherlands)

Krishnan, S.; Kerkhoff, H.G.

2011-01-01

Mahalanobis distance is one of the commonly used multivariate metrics for finely segregating defective devices from non-defective ones. An associated problem with this approach is the estimation of a robust mean and a covariance matrix. In the absence of such robust estimates, especially in the

A Robust Metric for Screening Outliers from Analogue Product Manufacturing Tests Responses

NARCIS (Netherlands)

Krishnan, Shaji; Krishnan, Shaji; Kerkhoff, Hans G.

2011-01-01

Mahalanobis distance is one of the commonly used multivariate metrics for finely segregating defective devices from non-defective ones. An associated problem with this approach is the estimation of a robust mean and a covariance matrix. In the absence of such robust estimates, especially in the

A comparison of point counts with a new acoustic sampling method ...

African Journals Online (AJOL)

We showed that the estimates of species richness, abundance and community composition based on point counts and post-hoc laboratory listening to acoustic samples are very similar, especially for a distance limited up to 50 m. Species that were frequently missed during both point counts and listening to acoustic samples ...

The role of robust optimization in single-leg airline revenue management

NARCIS (Netherlands)

Birbil, S.I.; Frenk, J.B.G.; Gromicho Dos Santos, J.A.; Zhang, S.

2009-01-01

In this paper, we introduce robust versions of the classical static and dynamic single-leg seat allocation models. These robust models take into account the inaccurate estimates of the underlying probability distributions. As observed by simulation experiments, it turns out that for these robust

A modern robust approach to remotely estimate chlorophyll in coastal and inland zones

Science.gov (United States)

Shanmugam, Palanisamy; He, Xianqiang; Singh, Rakesh Kumar; Varunan, Theenathayalan

2018-05-01

The chlorophyll concentration of a water body is an important proxy for representing the phytoplankton biomass. Its estimation from multi or hyper-spectral remote sensing data in natural waters is generally achieved by using (i) the waveband ratioing in two or more bands in the blue-green or (ii) by using a combination of the radiance peak position and magnitude in the red-near-infrared (NIR) spectrum. The blue-green ratio algorithms have been extensively used with satellite ocean color data to investigate chlorophyll distributions in open ocean and clear waters and the application of red-NIR algorithms is often restricted to turbid productive water bodies. These issues present the greatest obstacles to our ability to formulate a modern robust method suitable for quantitative assessments of the chlorophyll concentration in a diverse range of water types. The present study is focused to investigate the normalized water-leaving radiance spectra in the visible and NIR region and propose a robust algorithm (Generalized ABI, GABI algorithm) for chlorophyll concentration retrieval based on Algal Bloom index (ABI) which separates phytoplankton signals from other constituents in the water column. The GABI algorithm is validated using independent in-situ data from various regional to global waters and its performance is further evaluated by comparison with the blue-green waveband ratios and red-NIR algorithms. The results revealed that GABI yields significantly more accurate chlorophyll concentrations (with uncertainties less than 13.5%) and remains more stable in different waters types when compared with the blue-green waveband ratios and red-NIR algorithms. The performance of GABI is further demonstrated using HICO images from nearshore turbid productive waters and MERIS and MODIS-Aqua images from coastal and offshore waters of the Arabian Sea, Bay of Bengal and East China Sea.

Optimizing the calculation of point source count-centroid in pixel size measurement

International Nuclear Information System (INIS)

Zhou Luyi; Kuang Anren; Su Xianyu

2004-01-01

Pixel size is an important parameter of gamma camera and SPECT. A number of methods are used for its accurate measurement. In the original count-centroid method, where the image of a point source (PS) is acquired and its count-centroid calculated to represent PS position in the image, background counts are inevitable. Thus the measured count-centroid (X m ) is an approximation of the true count-centroid (X p ) of the PS, i.e. X m =X p + (X b -X p )/(1+R p /R b ), where Rp is the net counting rate of the PS, X b the background count-centroid and Rb the background counting. To get accurate measurement, R p must be very big, which is unpractical, resulting in the variation of measured pixel size. R p -independent calculation of PS count-centroid is desired. Methods: The proposed method attempted to eliminate the effect of the term (X b -X p )/(1 + R p /R b ) by bringing X b closer to X p and by reducing R b . In the acquired PS image, a circular ROI was generated to enclose the PS, the pixel with the maximum count being the center of the ROI. To choose the diameter (D) of the ROI, a Gaussian count distribution was assumed for the PS, accordingly, K=1-(0.5) D/R percent of the total PS counts was in the ROI, R being the full width at half maximum of the PS count distribution. D was set to be 6*R to enclose most (K=98.4%) of the PS counts. The count-centroid of the ROI was calculated to represent X p . The proposed method was tested in measuring the pixel size of a well-tuned SPECT, whose pixel size was estimated to be 3.02 mm according to its mechanical and electronic setting (128 x 128 matrix, 387 mm UFOV, ZOOM=1). For comparison, the original method, which was use in the former versions of some commercial SPECT software, was also tested. 12 PSs were prepared and their image acquired and stored. The net counting rate of the PSs increased from 10 cps to 1183 cps. Results: Using the proposed method, the measured pixel size (in mm) varied only between 3.00 and 3.01 (mean

An automated approach for annual layer counting in ice cores

Science.gov (United States)

Winstrup, M.; Svensson, A.; Rasmussen, S. O.; Winther, O.; Steig, E.; Axelrod, A.

2012-04-01

The temporal resolution of some ice cores is sufficient to preserve seasonal information in the ice core record. In such cases, annual layer counting represents one of the most accurate methods to produce a chronology for the core. Yet, manual layer counting is a tedious and sometimes ambiguous job. As reliable layer recognition becomes more difficult, a manual approach increasingly relies on human interpretation of the available data. Thus, much may be gained by an automated and therefore objective approach for annual layer identification in ice cores. We have developed a novel method for automated annual layer counting in ice cores, which relies on Bayesian statistics. It uses algorithms from the statistical framework of Hidden Markov Models (HMM), originally developed for use in machine speech recognition. The strength of this layer detection algorithm lies in the way it is able to imitate the manual procedures for annual layer counting, while being based on purely objective criteria for annual layer identification. With this methodology, it is possible to determine the most likely position of multiple layer boundaries in an entire section of ice core data at once. It provides a probabilistic uncertainty estimate of the resulting layer count, hence ensuring a proper treatment of ambiguous layer boundaries in the data. Furthermore multiple data series can be incorporated to be used at once, hence allowing for a full multi-parameter annual layer counting method similar to a manual approach. In this study, the automated layer counting algorithm has been applied to data from the NGRIP ice core, Greenland. The NGRIP ice core has very high temporal resolution with depth, and hence the potential to be dated by annual layer counting far back in time. In previous studies [Andersen et al., 2006; Svensson et al., 2008], manual layer counting has been carried out back to 60 kyr BP. A comparison between the counted annual layers based on the two approaches will be presented

Oil Palm Counting and Age Estimation from WorldView-3 Imagery and LiDAR Data Using an Integrated OBIA Height Model and Regression Analysis

Directory of Open Access Journals (Sweden)

Hossein Mojaddadi Rizeei

2018-01-01

Full Text Available The current study proposes a new method for oil palm age estimation and counting. A support vector machine algorithm (SVM of object-based image analysis (OBIA was implemented for oil palm counting. It was integrated with height model and multiregression methods to accurately estimate the age of trees based on their heights in five different plantation blocks. Multiregression and multi-kernel size models were examined over five different oil palm plantation blocks to achieve the most optimized model for age estimation. The sensitivity analysis was conducted on four SVM kernel types (sigmoid (SIG, linear (LN, radial basis function (RBF, and polynomial (PL with associated parameters (threshold values, gamma γ, and penalty factor (c to obtain the optimal OBIA classification approaches for each plantation block. Very high-resolution imageries of WorldView-3 (WV-3 and light detection and range (LiDAR were used for oil palm detection and age assessment. The results of oil palm detection had an overall accuracy of 98.27%, 99.48%, 99.28%, 99.49%, and 97.49% for blocks A, B, C, D, and E, respectively. Moreover, the accuracy of age estimation analysis showed 90.1% for 3-year-old, 87.9% for 4-year-old, 88.0% for 6-year-old, 87.6% for 8-year-old, 79.1% for 9-year-old, and 76.8% for 22-year-old trees. Overall, the study revealed that remote sensing techniques can be useful to monitor and detect oil palm plantation for sustainable agricultural management.

Evaluation of Deterministic and Stochastic Components of Traffic Counts

Directory of Open Access Journals (Sweden)

Ivan Bošnjak

2012-10-01

Full Text Available Traffic counts or statistical evidence of the traffic processare often a characteristic of time-series data. In this paper fundamentalproblem of estimating deterministic and stochasticcomponents of a traffic process are considered, in the context of"generalised traffic modelling". Different methods for identificationand/or elimination of the trend and seasonal componentsare applied for concrete traffic counts. Further investigationsand applications of ARIMA models, Hilbert space formulationsand state-space representations are suggested.

Calculating concentration of inhaled radiolabeled particles from external gamma counting: External counting efficiency and attenuation coefficient of thorax

International Nuclear Information System (INIS)

Langenback, E.G.; Foster, W.M.; Bergofsky, E.H.

1989-01-01

We determined the overall external counting efficiency of radiolabeled particles deposited in the sheep lung. This efficiency permits the noninvasive calculation of the number of particles and microcuries from gamma-scintillation lung images of the live sheep. Additionally, we have calculated the attenuation of gamma radiation (120 keV) by the posterior chest wall and the gamma-scintillation camera collection efficiency of radiation emitted from the lung. Four methods were employed in our experiments: (1) by light microscopic counting of discrete carbonized polystyrene particles with a count median diameter (CMD) of 2.85 microns and tagged with cobalt-57, we delineated a linear relationship between the number of particles and the emitted counts per minute (cpm) detected by well scintillation counting; (2) from this conversion relationship we determined the number of particles inhaled and deposited in the lungs by scintillation counting fragments of dissected lung at autopsy; (3) we defined a linear association between the number of particles or microcuries contained in the lung and the emitted radiation as cpm detected by a gamma scintillation camera in the live sheep prior to autopsy; and (4) we compared the emitted radiation from the lungs of the live sheep to that of whole excised lungs in order to calculate the attenuation coefficient (ac) of the chest wall. The mean external counting efficiency was 4.00 X 10(4) particles/cpm (5.1 X 10(-3) microCi/cpm), the camera collection efficiency was 1 cpm/10(4) disintegrations per minute (dpm), and the ac had a mean of 0.178/cm. The external counting efficiency remained relatively constant over a range of particles and microcuries, permitting a more general use of this ratio to estimate number of particles or microcuries depositing after inhalation in a large mammalian lung if a similarly collimated gamma camera system is used

Closed contour fractal dimension estimation by the Fourier transform

International Nuclear Information System (INIS)

Florindo, J.B.; Bruno, O.M.

2011-01-01

Highlights: → A novel fractal dimension concept, based on Fourier spectrum, is proposed. → Computationally simple. Computational time smaller than conventional fractal methods. → Results are closer to Hausdorff-Besicovitch than conventional methods. → The method is more accurate and robustness to geometric operations and noise addition. - Abstract: This work proposes a novel technique for the numerical calculus of the fractal dimension of fractal objects which can be represented as a closed contour. The proposed method maps the fractal contour onto a complex signal and calculates its fractal dimension using the Fourier transform. The Fourier power spectrum is obtained and an exponential relation is verified between the power and the frequency. From the parameter (exponent) of the relation, is obtained the fractal dimension. The method is compared to other classical fractal dimension estimation methods in the literature, e.g., Bouligand-Minkowski, box-counting and classical Fourier. The comparison is achieved by the calculus of the fractal dimension of fractal contours whose dimensions are well-known analytically. The results showed the high precision and robustness of the proposed technique.

How fast can quantum annealers count?

International Nuclear Information System (INIS)

Hen, Itay

2014-01-01

We outline an algorithm for the quantum counting problem using adiabatic quantum computation (AQC). We show that the mechanism of quantum-adiabatic evolution may be utilized toward estimating the number of solutions to a problem, and not only to find them. Using local adiabatic evolution, a process in which the adiabatic procedure is performed at a variable rate, the problem of counting the number of marked items in an unstructured database is solved quadratically faster than the corresponding classical algorithm. The above algorithm provides further evidence for the potentially powerful capabilities of AQC as a paradigm for more efficient problem solving on a quantum computer, and may be used as the basis for solving more sophisticated problems. (paper)

Tutorial on Using Regression Models with Count Outcomes Using R

Directory of Open Access Journals (Sweden)

A. Alexander Beaujean

2016-02-01

Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.

Influence Function and Robust Variant of Kernel Canonical Correlation Analysis

OpenAIRE

Alam, Md. Ashad; Fukumizu, Kenji; Wang, Yu-Ping

2017-01-01

Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). Both kernel CO and kernel CCO are sensitive to contaminated data, even when bounded positive definite kernels are used. To the best of our knowledge, there are few well-founded robust kernel methods for statistical unsupervised learning. In addition, while the influence function (IF) of an estimator can characterize its robustness, asymptotic ...

Robust Position Control of Electro-mechanical Systems

OpenAIRE

Rong Mei; Mou Chen

2013-01-01

In this work, the robust position control scheme is proposed for the electro-mechanical system using the disturbance observer and backstepping control method. To the external unknown load of the electro-mechanical system, the nonlinear disturbance observer is given to estimate the external unknown load. Combining the output of the developed nonlinear disturbance observer with backstepping technology, the robust position control scheme is proposed for the electro-mechanical system. The stabili...

The origin and reduction of spurious extrahepatic counts observed in 90Y non-TOF PET imaging post radioembolization

Science.gov (United States)

Walrand, Stephan; Hesse, Michel; Jamar, François; Lhommel, Renaud

2018-04-01

Our literature survey revealed a physical effect unknown to the nuclear medicine community, i.e. internal bremsstrahlung emission, and also the existence of long energy resolution tails in crystal scintillation. None of these effects has ever been modelled in PET Monte Carlo (MC) simulations. This study investigates whether these two effects could be at the origin of two unexplained observations in 90Y imaging by PET: the increasing tails in the radial profile of true coincidences, and the presence of spurious extrahepatic counts post radioembolization in non-TOF PET and their absence in TOF PET. These spurious extrahepatic counts hamper the microsphere delivery check in liver radioembolization. An acquisition of a 32P vial was performed on a GSO PET system. This is the ideal setup to study the impact of bremsstrahlung x-rays on the true coincidence rate when no positron emission and no crystal radioactivity are present. A MC simulation of the acquisition was performed using Gate-Geant4. MC simulations of non-TOF PET and TOF-PET imaging of a synthetic 90Y human liver radioembolization phantom were also performed. Internal bremsstrahlung and long energy resolution tails inclusion in MC simulations quantitatively predict the increasing tails in the radial profile. In addition, internal bremsstrahlung explains the discrepancy previously observed in bremsstrahlung SPECT between the measure of the 90Y bremsstrahlung spectrum and its simulation with Gate-Geant4. However the spurious extrahepatic counts in non-TOF PET mainly result from the failure of conventional random correction methods in such low count rate studies and poor robustness versus emission-transmission inconsistency. A novel proposed random correction method succeeds in cleaning the spurious extrahepatic counts in non-TOF PET. Two physical effects not considered up to now in nuclear medicine were identified to be at the origin of the unusual 90Y true coincidences radial profile. TOF reconstruction removing

Can Detectability Analysis Improve the Utility of Point Counts for Temperate Forest Raptors?

Science.gov (United States)

Temperate forest breeding raptors are poorly represented in typical point count surveys because these birds are cryptic and typically breed at low densities. In recent years, many new methods for estimating detectability during point counts have been developed, including distanc...

Protecting count queries in study design.

Science.gov (United States)

Vinterbo, Staal A; Sarwate, Anand D; Boxwala, Aziz A

2012-01-01

Today's clinical research institutions provide tools for researchers to query their data warehouses for counts of patients. To protect patient privacy, counts are perturbed before reporting; this compromises their utility for increased privacy. The goal of this study is to extend current query answer systems to guarantee a quantifiable level of privacy and allow users to tailor perturbations to maximize the usefulness according to their needs. A perturbation mechanism was designed in which users are given options with respect to scale and direction of the perturbation. The mechanism translates the true count, user preferences, and a privacy level within administrator-specified bounds into a probability distribution from which the perturbed count is drawn. Users can significantly impact the scale and direction of the count perturbation and can receive more accurate final cohort estimates. Strong and semantically meaningful differential privacy is guaranteed, providing for a unified privacy accounting system that can support role-based trust levels. This study provides an open source web-enabled tool to investigate visually and numerically the interaction between system parameters, including required privacy level and user preference settings. Quantifying privacy allows system administrators to provide users with a privacy budget and to monitor its expenditure, enabling users to control the inevitable loss of utility. While current measures of privacy are conservative, this system can take advantage of future advances in privacy measurement. The system provides new ways of trading off privacy and utility that are not provided in current study design systems.

Robustness of Distance-to-Default

DEFF Research Database (Denmark)

Jessen, Cathrine; Lando, David

2013-01-01

Distance-to-default is a remarkably robust measure for ranking firms according to their risk of default. The ranking seems to work despite the fact that the Merton model from which the measure is derived produces default probabilities that are far too small when applied to real data. We use...... simulations to investigate the robustness of the distance-to-default measure to different model specifications. Overall we find distance-to-default to be robust to a number of deviations from the simple Merton model that involve different asset value dynamics and different default triggering mechanisms....... A notable exception is a model with stochastic volatility of assets. In this case both the ranking of firms and the estimated default probabilities using distance-to-default perform significantly worse. We therefore propose a volatility adjustment of the distance-to-default measure, that significantly...

Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model

International Nuclear Information System (INIS)

Zheng, Linfeng; Zhang, Lei; Zhu, Jianguo; Wang, Guoxiu; Jiang, Jiuchun

2016-01-01

Highlights: • The numerical solution for an electrochemical model is presented. • Trinal PI observers are used to concurrently estimate SOC, capacity and resistance. • An iteration-approaching method is incorporated to enhance estimation performance. • The robustness against aging and temperature variations is experimentally verified. - Abstract: Lithium-ion batteries have been widely used as enabling energy storage in many industrial fields. Accurate modeling and state estimation play fundamental roles in ensuring safe, reliable and efficient operation of lithium-ion battery systems. A physics-based electrochemical model (EM) is highly desirable for its inherent ability to push batteries to operate at their physical limits. For state-of-charge (SOC) estimation, the continuous capacity fade and resistance deterioration are more prone to erroneous estimation results. In this paper, trinal proportional-integral (PI) observers with a reduced physics-based EM are proposed to simultaneously estimate SOC, capacity and resistance for lithium-ion batteries. Firstly, a numerical solution for the employed model is derived. PI observers are then developed to realize the co-estimation of battery SOC, capacity and resistance. The moving-window ampere-hour counting technique and the iteration-approaching method are also incorporated for the estimation accuracy improvement. The robustness of the proposed approach against erroneous initial values, different battery cell aging levels and ambient temperatures is systematically evaluated, and the experimental results verify the effectiveness of the proposed method.

Compton suppression gamma-counting: The effect of count rate

Science.gov (United States)

Millard, H.T.

1984-01-01

Past research has shown that anti-coincidence shielded Ge(Li) spectrometers enhanced the signal-to-background ratios for gamma-photopeaks, which are situated on high Compton backgrounds. Ordinarily, an anti- or non-coincidence spectrum (A) and a coincidence spectrum (C) are collected simultaneously with these systems. To be useful in neutron activation analysis (NAA), the fractions of the photopeak counts routed to the two spectra must be constant from sample to sample to variations must be corrected quantitatively. Most Compton suppression counting has been done at low count rate, but in NAA applications, count rates may be much higher. To operate over the wider dynamic range, the effect of count rate on the ratio of the photopeak counts in the two spectra (A/C) was studied. It was found that as the count rate increases, A/C decreases for gammas not coincident with other gammas from the same decay. For gammas coincident with other gammas, A/C increases to a maximum and then decreases. These results suggest that calibration curves are required to correct photopeak areas so quantitative data can be obtained at higher count rates. ?? 1984.

Estimating milk yield and value losses from increased somatic cell count on US dairy farms.

Science.gov (United States)

Hadrich, J C; Wolf, C A; Lombard, J; Dolak, T M

2018-04-01

Milk loss due to increased somatic cell counts (SCC) results in economic losses for dairy producers. This research uses 10 mo of consecutive dairy herd improvement data from 2013 and 2014 to estimate milk yield loss using SCC as a proxy for clinical and subclinical mastitis. A fixed effects regression was used to examine factors that affected milk yield while controlling for herd-level management. Breed, milking frequency, days in milk, seasonality, SCC, cumulative months with SCC greater than 100,000 cells/mL, lactation, and herd size were variables included in the regression analysis. The cumulative months with SCC above a threshold was included as a proxy for chronic mastitis. Milk yield loss increased as the number of test days with SCC ≥100,000 cells/mL increased. Results from the regression were used to estimate a monetary value of milk loss related to SCC as a function of cow and operation related explanatory variables for a representative dairy cow. The largest losses occurred from increased cumulative test days with a SCC ≥100,000 cells/mL, with daily losses of $1.20/cow per day in the first month to $2.06/cow per day in mo 10. Results demonstrate the importance of including the duration of months above a threshold SCC when estimating milk yield losses. Cows with chronic mastitis, measured by increased consecutive test days with SCC ≥100,000 cells/mL, resulted in higher milk losses than cows with a new infection. This provides farm managers with a method to evaluate the trade-off between treatment and culling decisions as it relates to mastitis control and early detection. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

EcoCount

Directory of Open Access Journals (Sweden)

Phillip P. Allen

2014-05-01

Full Text Available Techniques that analyze biological remains from sediment sequences for environmental reconstructions are well established and widely used. Yet, identifying, counting, and recording biological evidence such as pollen grains remain a highly skilled, demanding, and time-consuming task. Standard procedure requires the classification and recording of between 300 and 500 pollen grains from each representative sample. Recording the data from a pollen count requires significant effort and focused resources from the palynologist. However, when an adaptation to the recording procedure is utilized, efficiency and time economy improve. We describe EcoCount, which represents a development in environmental data recording procedure. EcoCount is a voice activated fully customizable digital count sheet that allows the investigator to continuously interact with a field of view during the data recording. Continuous viewing allows the palynologist the opportunity to remain engaged with the essential task, identification, for longer, making pollen counting more efficient and economical. EcoCount is a versatile software package that can be used to record a variety of environmental evidence and can be installed onto different computer platforms, making the adoption by users and laboratories simple and inexpensive. The user-friendly format of EcoCount allows any novice to be competent and functional in a very short time.

RBC count

Science.gov (United States)

... by kidney disease) RBC destruction ( hemolysis ) due to transfusion, blood vessel injury, or other cause Leukemia Malnutrition Bone ... slight risk any time the skin is broken) Alternative Names Erythrocyte count; Red blood cell count; Anemia - RBC count Images Blood test ...

Wavelet Filtering to Reduce Conservatism in Aeroservoelastic Robust Stability Margins

Science.gov (United States)

Brenner, Marty; Lind, Rick

1998-01-01

Wavelet analysis for filtering and system identification was used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins was reduced with parametric and nonparametric time-frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data was used to reduce the effects of external desirableness and unmodeled dynamics. Parametric estimates of modal stability were also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. F-18 high Alpha Research Vehicle aeroservoelastic flight test data demonstrated improved robust stability prediction by extension of the stability boundary beyond the flight regime.

Distance estimation experiment for aerial minke whale surveys

Directory of Open Access Journals (Sweden)

Lars Witting

2009-09-01

Full Text Available A comparative study between aerial cue–counting and digital photography surveys for minke whales conducted in Faxaflói Bay in September 2003 is used to check the perpendicular distances estimated by the cue-counting observers. The study involved 2 aircraft with the photo plane at 1,700 feet flying above the cue–counting plane at 750 feet. The observer–based distance estimates were calculated from head angles estimated by angle-boards and declination angles estimated by declinometers. These distances were checked against image–based estimates of the perpendicular distance to the same whale. The 2 independent distance estimates were obtained for 21 sightings of minke whale, and there was a good agreement between the 2 types of estimates. The relative absolute deviations between the 2 estimates were on average 23% (se: 6%, with the errors in the observer–based distance estimates resembling that of a log-normal distribution. The linear regression of the observer–based estimates (obs on the image–based estimates (img was Obs=1.1Img (R2=0.85 with an intercept fixed at zero. There was no evidence of a distance estimation bias that could generate a positive bias in the absolute abundance estimated by cue–counting.

Robust Forecasting of Non-Stationary Time Series

OpenAIRE

Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

2010-01-01

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estima...

Free parameter, figure of merit and ionization quench in liquid scintillation counting

International Nuclear Information System (INIS)

Carles, P. Grau; Malonda, A. Grau

2001-01-01

A statistical study of the detection process demonstrates that the free parameter is essential to compute the counting efficiency in both CIEMAT/NIST and TDCR methods. An analysis of the computed counting efficiencies shows the uselessness of old definition of the figure of merit. A new definition is required and we adopt the idea of taking quantities related with the output of the photomultiplier. In addition, we justify the application of the chemical quenching simulation with the electronic variation of the photomultiplier gain. Finally, we describe a new procedure to determine the figure of merit and the optimum ionization-quenching factor from the pulse spectrum of different radionuclides. The robustness of the new procedure is tested with three different sets of stopping power for low-energy electrons

HOW MANY HIPPOS (HOMHIP: ALGORITHM FOR AUTOMATIC COUNTS OF ANIMALS WITH INFRA-RED THERMAL IMAGERY FROM UAV

Directory of Open Access Journals (Sweden)

S. Lhoest

2015-08-01

Full Text Available The common hippopotamus (Hippopotamus amphibius L. is part of the animal species endangered because of multiple human pressures. Monitoring of species for conservation is then essential, and the development of census protocols has to be chased. UAV technology is considering as one of the new perspectives for wildlife survey. Indeed, this technique has many advantages but its main drawback is the generation of a huge amount of data to handle. This study aims at developing an algorithm for automatic count of hippos, by exploiting thermal infrared aerial images acquired from UAV. This attempt is the first known for automatic detection of this species. Images taken at several flight heights can be used as inputs of the algorithm, ranging from 38 to 155 meters above ground level. A Graphical User Interface has been created in order to facilitate the use of the application. Three categories of animals have been defined following their position in water. The mean error of automatic counts compared with manual delineations is +2.3% and shows that the estimation is unbiased. Those results show great perspectives for the use of the algorithm in populations monitoring after some technical improvements and the elaboration of statistically robust inventories protocols.

The prognostic value of angiogenesis by Chalkley counting in a confirmatory study design on 836 breast cancer patients

DEFF Research Database (Denmark)

Hansen, S; Grabau, D A; Sørensen, Flemming Brandt

2000-01-01

This study addresses the prognostic value of estimating angiogenesis by Chalkley counting in breast cancer. A population-based group consisting of 836 patients with operated primary, unilateral invasive breast carcinomas was included from a predefined region and period of time. The median follow...... categories using predefined Chalkley cutoff points at five and seven. There were significant correlations between high Chalkley counts and axillary lymph node metastasis, large tumor size, high histological malignancy grade, and histological type. A high Chalkley count showed lower probabilities...... or =7 compared with counts between 5-7. The study confirmed that estimation of angiogenesis by Chalkley counting had independent prognostic value in breast cancer patients. The Chalkley count could be useful to stratify node-negative patients for adjuvant...

Using Length of Stay to Control for Unobserved Heterogeneity When Estimating Treatment Effect on Hospital Costs with Observational Data: Issues of Reliability, Robustness, and Usefulness.

Science.gov (United States)

May, Peter; Garrido, Melissa M; Cassel, J Brian; Morrison, R Sean; Normand, Charles

2016-10-01

To evaluate the sensitivity of treatment effect estimates when length of stay (LOS) is used to control for unobserved heterogeneity when estimating treatment effect on cost of hospital admission with observational data. We used data from a prospective cohort study on the impact of palliative care consultation teams (PCCTs) on direct cost of hospital care. Adult patients with an advanced cancer diagnosis admitted to five large medical and cancer centers in the United States between 2007 and 2011 were eligible for this study. Costs were modeled using generalized linear models with a gamma distribution and a log link. We compared variability in estimates of PCCT impact on hospitalization costs when LOS was used as a covariate, as a sample parameter, and as an outcome denominator. We used propensity scores to account for patient characteristics associated with both PCCT use and total direct hospitalization costs. We analyzed data from hospital cost databases, medical records, and questionnaires. Our propensity score weighted sample included 969 patients who were discharged alive. In analyses of hospitalization costs, treatment effect estimates are highly sensitive to methods that control for LOS, complicating interpretation. Both the magnitude and significance of results varied widely with the method of controlling for LOS. When we incorporated intervention timing into our analyses, results were robust to LOS-controls. Treatment effect estimates using LOS-controls are not only suboptimal in terms of reliability (given concerns over endogeneity and bias) and usefulness (given the need to validate the cost-effectiveness of an intervention using overall resource use for a sample defined at baseline) but also in terms of robustness (results depend on the approach taken, and there is little evidence to guide this choice). To derive results that minimize endogeneity concerns and maximize external validity, investigators should match and analyze treatment and comparison arms

Counting Parasites: Using Shrimp to Teach Students about Estimation

Science.gov (United States)

Gunzburger, Lindsay; Curran, Mary Carla

2013-01-01

Estimation is an important skill that we rely on every day for simple tasks, such as providing food for a dinner party or arriving at an appointment on time. Despite its importance, most people have never been formally taught how to estimate. Estimation can also be a vital tool for scientific inquiry. We have created an activity designed to teach…

A robust nonlinear filter for image restoration.

Science.gov (United States)

Koivunen, V

1995-01-01

A class of nonlinear regression filters based on robust estimation theory is introduced. The goal of the filtering is to recover a high-quality image from degraded observations. Models for desired image structures and contaminating processes are employed, but deviations from strict assumptions are allowed since the assumptions on signal and noise are typically only approximately true. The robustness of filters is usually addressed only in a distributional sense, i.e., the actual error distribution deviates from the nominal one. In this paper, the robustness is considered in a broad sense since the outliers may also be due to inappropriate signal model, or there may be more than one statistical population present in the processing window, causing biased estimates. Two filtering algorithms minimizing a least trimmed squares criterion are provided. The design of the filters is simple since no scale parameters or context-dependent threshold values are required. Experimental results using both real and simulated data are presented. The filters effectively attenuate both impulsive and nonimpulsive noise while recovering the signal structure and preserving interesting details.

Antiretroviral treatment cohort analysis using time-updated CD4 counts: assessment of bias with different analytic methods.

Directory of Open Access Journals (Sweden)

Katharina Kranzer

Full Text Available Survival analysis using time-updated CD4+ counts during antiretroviral therapy is frequently employed to determine risk of clinical events. The time-point when the CD4+ count is assumed to change potentially biases effect estimates but methods used to estimate this are infrequently reported.This study examined the effect of three different estimation methods: assuming i a constant CD4+ count from date of measurement until the date of next measurement, ii a constant CD4+ count from the midpoint of the preceding interval until the midpoint of the subsequent interval and iii a linear interpolation between consecutive CD4+ measurements to provide additional midpoint measurements. Person-time, tuberculosis rates and hazard ratios by CD4+ stratum were compared using all available CD4+ counts (measurement frequency 1-3 months and 6 monthly measurements from a clinical cohort. Simulated data were used to compare the extent of bias introduced by these methods.The midpoint method gave the closest fit to person-time spent with low CD4+ counts and for hazard ratios for outcomes both in the clinical dataset and the simulated data.The midpoint method presents a simple option to reduce bias in time-updated CD4+ analysis, particularly at low CD4 cell counts and rapidly increasing counts after ART initiation.

Evaluation of cyanobacteria cell count detection derived from ...

Science.gov (United States)

Inland waters across the United States (US) are at potential risk for increased outbreaks of toxic cyanobacteria (Cyano) harmful algal bloom (HAB) events resulting from elevated water temperatures and extreme hydrologic events attributable to climate change and increased nutrient loadings associated with intensive agricultural practices. Current monitoring efforts are limited in scope due to resource limitations, analytical complexity, and data integration efforts. The goals of this study were to validate a new ocean color algorithm for satellite imagery that could potentially be used to monitor CyanoHAB events in near real-time to provide a compressive monitoring capability for freshwater lakes (>100 ha). The algorithm incorporated narrow spectral bands specific to the European Space Agency’s (ESA’s) MEdium Resolution Imaging Spectrometer (MERIS) instrument that were optimally oriented at phytoplankton pigment absorption features including phycocyanin at 620 nm. A validation of derived Cyano cell counts was performed using available in situ data assembled from existing monitoring programs across eight states in the eastern US over a 39-month period (2009–2012). Results indicated that MERIS provided robust estimates for Low (10,000–109,000 cells/mL) and Very High (>1,000,000 cells/mL) cell enumeration ranges (approximately 90% and 83%, respectively). However, the results for two intermediate ranges (110,000–299,000 and 300,000–1,000,000 cells/mL)

Performance in population models for count data, part II: a new SAEM algorithm

Science.gov (United States)

Savic, Radojka; Lavielle, Marc

2009-01-01

Analysis of count data from clinical trials using mixed effect analysis has recently become widely used. However, algorithms available for the parameter estimation, including LAPLACE and Gaussian quadrature (GQ), are associated with certain limitations, including bias in parameter estimates and the long analysis runtime. The stochastic approximation expectation maximization (SAEM) algorithm has proven to be a very efficient and powerful tool in the analysis of continuous data. The aim of this study was to implement and investigate the performance of a new SAEM algorithm for application to count data. A new SAEM algorithm was implemented in MATLAB for estimation of both, parameters and the Fisher information matrix. Stochastic Monte Carlo simulations followed by re-estimation were performed according to scenarios used in previous studies (part I) to investigate properties of alternative algorithms (1). A single scenario was used to explore six probability distribution models. For parameter estimation, the relative bias was less than 0.92% and 4.13 % for fixed and random effects, for all models studied including ones accounting for over- or under-dispersion. Empirical and estimated relative standard errors were similar, with distance between them being <1.7 % for all explored scenarios. The longest CPU time was 95s for parameter estimation and 56s for SE estimation. The SAEM algorithm was extended for analysis of count data. It provides accurate estimates of both, parameters and standard errors. The estimation is significantly faster compared to LAPLACE and GQ. The algorithm is implemented in Monolix 3.1, (beta-version available in July 2009). PMID:19680795

Nondestructive, stereological estimation of canopy surface area

DEFF Research Database (Denmark)

Wulfsohn, Dvora-Laio; Sciortino, Marco; Aaslyng, Jesper M.

2010-01-01

We describe a stereological procedure to estimate the total leaf surface area of a plant canopy in vivo, and address the problem of how to predict the variance of the corresponding estimator. The procedure involves three nested systematic uniform random sampling stages: (i) selection of plants from...... a canopy using the smooth fractionator, (ii) sampling of leaves from the selected plants using the fractionator, and (iii) area estimation of the sampled leaves using point counting. We apply this procedure to estimate the total area of a chrysanthemum (Chrysanthemum morifolium L.) canopy and evaluate both...... the time required and the precision of the estimator. Furthermore, we compare the precision of point counting for three different grid intensities with that of several standard leaf area measurement techniques. Results showed that the precision of the plant leaf area estimator based on point counting...

Robust structural optimization using Gauss-type quadrature formula

International Nuclear Information System (INIS)

Lee, Sang Hoon; Seo, Ki Seog; Chen, Shikui; Chen, Wei

2009-01-01

In robust design, the mean and variance of design performance are frequently used to measure the design performance and its robustness under uncertainties. In this paper, we present the Gauss-type quadrature formula as a rigorous method for mean and variance estimation involving arbitrary input distributions and further extend its use to robust design optimization. One dimensional Gauss-type quadrature formula are constructed from the input probability distributions and utilized in the construction of multidimensional quadrature formula such as the Tensor Product Quadrature (TPQ) formula and the Univariate Dimension Reduction (UDR) method. To improve the efficiency of using it for robust design optimization, a semi-analytic design sensitivity analysis with respect to the statistical moments is proposed. The proposed approach is applied to a simple bench mark problems and robust topology optimization of structures considering various types of uncertainty.

Robust structural optimization using Gauss-type quadrature formula

Energy Technology Data Exchange (ETDEWEB)

Lee, Sang Hoon; Seo, Ki Seog [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Chen, Shikui; Chen, Wei [Northwestern University, Illinois (United States)

2009-07-01

In robust design, the mean and variance of design performance are frequently used to measure the design performance and its robustness under uncertainties. In this paper, we present the Gauss-type quadrature formula as a rigorous method for mean and variance estimation involving arbitrary input distributions and further extend its use to robust design optimization. One dimensional Gauss-type quadrature formula are constructed from the input probability distributions and utilized in the construction of multidimensional quadrature formula such as the Tensor Product Quadrature (TPQ) formula and the Univariate Dimension Reduction (UDR) method. To improve the efficiency of using it for robust design optimization, a semi-analytic design sensitivity analysis with respect to the statistical moments is proposed. The proposed approach is applied to a simple bench mark problems and robust topology optimization of structures considering various types of uncertainty.

Robust Structural Optimization Using Gauss-type Quadrature Formula

Energy Technology Data Exchange (ETDEWEB)

Lee, Sang Hoon; Seo, Ki Seog; Chen, Shikui; Chen, Wei [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2009-08-15

In robust design, the mean and variance of design performance are frequently used to measure the design performance and its robustness under uncertainties. In this paper, we present the Gauss-type quadrature formula as a rigorous method for mean and variance estimation involving arbitrary input distributions and further extend its use to robust design optimization. One dimensional Gauss-type quadrature formula are constructed from the input probability distributions and utilized in the construction of multidimensional quadrature formula such as the tensor product quadrature (TPQ) formula and the univariate dimension reduction (UDR) method. To improve the efficiency of using it for robust design optimization, a semi-analytic design sensitivity analysis with respect to the statistical moments is proposed. The proposed approach is applied to a simple bench mark problems and robust topology optimization of structures considering various types of uncertainty.

Robust Structural Optimization Using Gauss-type Quadrature Formula

International Nuclear Information System (INIS)

Lee, Sang Hoon; Seo, Ki Seog; Chen, Shikui; Chen, Wei

2009-01-01

In robust design, the mean and variance of design performance are frequently used to measure the design performance and its robustness under uncertainties. In this paper, we present the Gauss-type quadrature formula as a rigorous method for mean and variance estimation involving arbitrary input distributions and further extend its use to robust design optimization. One dimensional Gauss-type quadrature formula are constructed from the input probability distributions and utilized in the construction of multidimensional quadrature formula such as the tensor product quadrature (TPQ) formula and the univariate dimension reduction (UDR) method. To improve the efficiency of using it for robust design optimization, a semi-analytic design sensitivity analysis with respect to the statistical moments is proposed. The proposed approach is applied to a simple bench mark problems and robust topology optimization of structures considering various types of uncertainty

How robust are the estimated effects of air pollution on health? Accounting for model uncertainty using Bayesian model averaging.

Science.gov (United States)

Pannullo, Francesca; Lee, Duncan; Waclawski, Eugene; Leyland, Alastair H

2016-08-01

The long-term impact of air pollution on human health can be estimated from small-area ecological studies in which the health outcome is regressed against air pollution concentrations and other covariates, such as socio-economic deprivation. Socio-economic deprivation is multi-factorial and difficult to measure, and includes aspects of income, education, and housing as well as others. However, these variables are potentially highly correlated, meaning one can either create an overall deprivation index, or use the individual characteristics, which can result in a variety of pollution-health effects. Other aspects of model choice may affect the pollution-health estimate, such as the estimation of pollution, and spatial autocorrelation model. Therefore, we propose a Bayesian model averaging approach to combine the results from multiple statistical models to produce a more robust representation of the overall pollution-health effect. We investigate the relationship between nitrogen dioxide concentrations and cardio-respiratory mortality in West Central Scotland between 2006 and 2012. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Direct reconstruction of parametric images for brain PET with event-by-event motion correction: evaluation in two tracers across count levels

Science.gov (United States)

Germino, Mary; Gallezot, Jean-Dominque; Yan, Jianhua; Carson, Richard E.

2017-07-01

Parametric images for dynamic positron emission tomography (PET) are typically generated by an indirect method, i.e. reconstructing a time series of emission images, then fitting a kinetic model to each voxel time activity curve. Alternatively, ‘direct reconstruction’, incorporates the kinetic model into the reconstruction algorithm itself, directly producing parametric images from projection data. Direct reconstruction has been shown to achieve parametric images with lower standard error than the indirect method. Here, we present direct reconstruction for brain PET using event-by-event motion correction of list-mode data, applied to two tracers. Event-by-event motion correction was implemented for direct reconstruction in the Parametric Motion-compensation OSEM List-mode Algorithm for Resolution-recovery reconstruction. The direct implementation was tested on simulated and human datasets with tracers [11C]AFM (serotonin transporter) and [11C]UCB-J (synaptic density), which follow the 1-tissue compartment model. Rigid head motion was tracked with the Vicra system. Parametric images of K 1 and distribution volume (V T  =  K 1/k 2) were compared to those generated by the indirect method by regional coefficient of variation (CoV). Performance across count levels was assessed using sub-sampled datasets. For simulated and real datasets at high counts, the two methods estimated K 1 and V T with comparable accuracy. At lower count levels, the direct method was substantially more robust to outliers than the indirect method. Compared to the indirect method, direct reconstruction reduced regional K 1 CoV by 35-48% (simulated dataset), 39-43% ([11C]AFM dataset) and 30-36% ([11C]UCB-J dataset) across count levels (averaged over regions at matched iteration); V T CoV was reduced by 51-58%, 54-60% and 30-46%, respectively. Motion correction played an important role in the dataset with larger motion: correction increased regional V T by 51% on average in the [11C

Modeling Systematic Change in Stopover Duration Does Not Improve Bias in Trends Estimated from Migration Counts.

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Tara L Crewe

Full Text Available The use of counts of unmarked migrating animals to monitor long term population trends assumes independence of daily counts and a constant rate of detection. However, migratory stopovers often last days or weeks, violating the assumption of count independence. Further, a systematic change in stopover duration will result in a change in the probability of detecting individuals once, but also in the probability of detecting individuals on more than one sampling occasion. We tested how variation in stopover duration influenced accuracy and precision of population trends by simulating migration count data with known constant rate of population change and by allowing daily probability of survival (an index of stopover duration to remain constant, or to vary randomly, cyclically, or increase linearly over time by various levels. Using simulated datasets with a systematic increase in stopover duration, we also tested whether any resulting bias in population trend could be reduced by modeling the underlying source of variation in detection, or by subsampling data to every three or five days to reduce the incidence of recounting. Mean bias in population trend did not differ significantly from zero when stopover duration remained constant or varied randomly over time, but bias and the detection of false trends increased significantly with a systematic increase in stopover duration. Importantly, an increase in stopover duration over time resulted in a compounding effect on counts due to the increased probability of detection and of recounting on subsequent sampling occasions. Under this scenario, bias in population trend could not be modeled using a covariate for stopover duration alone. Rather, to improve inference drawn about long term population change using counts of unmarked migrants, analyses must include a covariate for stopover duration, as well as incorporate sampling modifications (e.g., subsampling to reduce the probability that individuals will

Robust Distributed Kalman Filter for Wireless Sensor Networks with Uncertain Communication Channels

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Du Yong Kim

2012-01-01

Full Text Available We address a state estimation problem over a large-scale sensor network with uncertain communication channel. Consensus protocol is usually used to adapt a large-scale sensor network. However, when certain parts of communication channels are broken down, the accuracy performance is seriously degraded. Specifically, outliers in the channel or temporal disconnection are avoided via proposed method for the practical implementation of the distributed estimation over large-scale sensor networks. We handle this practical challenge by using adaptive channel status estimator and robust L1-norm Kalman filter in design of the processor of the individual sensor node. Then, they are incorporated into the consensus algorithm in order to achieve the robust distributed state estimation. The robust property of the proposed algorithm enables the sensor network to selectively weight sensors of normal conditions so that the filter can be practically useful.

A Robust Mass Estimator for Dark Matter Subhalo Perturbations in Strong Gravitational Lenses

Energy Technology Data Exchange (ETDEWEB)

Minor, Quinn E. [Department of Science, Borough of Manhattan Community College, City University of New York, New York, NY 10007 (United States); Kaplinghat, Manoj [Department of Physics and Astronomy, University of California, Irvine CA 92697 (United States); Li, Nan [Department of Astronomy and Astrophysics, The University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637 (United States)

2017-08-20

A few dark matter substructures have recently been detected in strong gravitational lenses through their perturbations of highly magnified images. We derive a characteristic scale for lensing perturbations and show that they are significantly larger than the perturber’s Einstein radius. We show that the perturber’s projected mass enclosed within this radius, scaled by the log-slope of the host galaxy’s density profile, can be robustly inferred even if the inferred density profile and tidal radius of the perturber are biased. We demonstrate the validity of our analytic derivation using several gravitational lens simulations where the tidal radii and the inner log-slopes of the density profile of the perturbing subhalo are allowed to vary. By modeling these simulated data, we find that our mass estimator, which we call the effective subhalo lensing mass, is accurate to within about 10% or smaller in each case, whereas the inferred total subhalo mass can potentially be biased by nearly an order of magnitude. We therefore recommend that the effective subhalo lensing mass be reported in future lensing reconstructions, as this will allow for a more accurate comparison with the results of dark matter simulations.

Advanced neural network-based computational schemes for robust fault diagnosis

CERN Document Server

Mrugalski, Marcin

2014-01-01

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

Robust total energy demand estimation with a hybrid Variable Neighborhood Search – Extreme Learning Machine algorithm

International Nuclear Information System (INIS)

Sánchez-Oro, J.; Duarte, A.; Salcedo-Sanz, S.

2016-01-01

Highlights: • The total energy demand in Spain is estimated with a Variable Neighborhood algorithm. • Socio-economic variables are used, and one year ahead prediction horizon is considered. • Improvement of the prediction with an Extreme Learning Machine network is considered. • Experiments are carried out in real data for the case of Spain. - Abstract: Energy demand prediction is an important problem whose solution is evaluated by policy makers in order to take key decisions affecting the economy of a country. A number of previous approaches to improve the quality of this estimation have been proposed in the last decade, the majority of them applying different machine learning techniques. In this paper, the performance of a robust hybrid approach, composed of a Variable Neighborhood Search algorithm and a new class of neural network called Extreme Learning Machine, is discussed. The Variable Neighborhood Search algorithm is focused on obtaining the most relevant features among the set of initial ones, by including an exponential prediction model. While previous approaches consider that the number of macroeconomic variables used for prediction is a parameter of the algorithm (i.e., it is fixed a priori), the proposed Variable Neighborhood Search method optimizes both: the number of variables and the best ones. After this first step of feature selection, an Extreme Learning Machine network is applied to obtain the final energy demand prediction. Experiments in a real case of energy demand estimation in Spain show the excellent performance of the proposed approach. In particular, the whole method obtains an estimation of the energy demand with an error lower than 2%, even when considering the crisis years, which are a real challenge.

ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA

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A. Nurunnabi

2017-05-01

Full Text Available This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD. Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i in the presence of noise and high percentage of outliers, (ii for incomplete as well as complete data, (iii for small and large number of points, and (iv for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63 meter (m; the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic poles, diameter at breast height estimation for trees, and building and bridge information modelling.

Photon counting and fluctuation of molecular movement

International Nuclear Information System (INIS)

Inohara, Koichi

1978-01-01

The direct measurement of the fluctuation of molecular motions, which provides with useful information on the molecular movement, was conducted by introducing photon counting method. The utilization of photon counting makes it possible to treat the molecular system consisting of a small number of molecules like a radioisotope in the detection of a small number of atoms, which are significant in biological systems. This method is based on counting the number of photons of the definite polarization emitted in a definite time interval from the fluorescent molecules excited by pulsed light, which are bound to the marked large molecules found in a definite spatial region. Using the probability of finding a number of molecules oriented in a definite direction in the definite spatial region, the probability of counting a number of photons in a definite time interval can be calculated. Thus the measurable count rate of photons can be related with the fluctuation of molecular movement. The measurement was carried out under the condition, in which the probability of the simultaneous arrival of more than two photons at a detector is less than 1/100. As the experimental results, the resolving power of photon-counting apparatus, the frequency distribution of the number of photons of some definite polarization counted for 1 nanosecond are shown. In the solution, the variance of the number of molecules of 500 on the average is 1200, which was estimated from the experimental data by assuming normal distribution. This departure from the Poisson distribution means that a certain correlation does exist in molecular movement. In solid solution, no significant deviation was observed. The correlation existing in molecular movement can be expressed in terms of the fluctuation of the number of molecules. (Nakai, Y.)

QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model.

Science.gov (United States)

Liu, Lian; Zhang, Shao-Wu; Huang, Yufei; Meng, Jia

2017-08-31

As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task. We present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m 1 A-Seq, Par-CLIP, RIP-Seq, etc.

Disseminated HIV-Associated Kaposi’s Sarcoma With High CD4 Cell Count And Low Viral Load

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Diana Pereira Anjos

2017-12-01

Full Text Available Kaposi’s sarcoma is considered an acquired immunodeficiency syndrome-defining illness and is caused by human herpesvirus 8. It has been associated with patients infected with human immunodeficiency virus (HIV who have CD4 T lymphocytes <200 cells/uL and high viral loads. We report a case of a 23-year old woman infected with HIV-1 and receiving antiretroviral treatment since diagnosis, with high CD4 cell count and low viral load that presented with disseminated Kaposi’s sarcoma. Clinicians should be aware of the occurrence of Kaposi’s sarcoma despite robust CD4 cell counts.

Low-dose effect of developmental bisphenol A exposure on sperm count and behaviour in rats

DEFF Research Database (Denmark)

Hass, Ulla; Christiansen, Sofie; Boberg, Julie

2016-01-01

/day. In the offspring, growth, sexual maturation, weights and histopathology of reproductive organs, oestrus cyclicity and sperm counts were assessed. Neurobehavioural development was investigated using a behavioural testing battery including tests for motor activity, sweet preference, anxiety and spatial learning....... Decreased sperm count was found at the lowest bisphenol A dose, that is 25 μg/kg/day, but not at the higher doses. Reproductive organ weight and histology were not affected and no behavioural effects were seen in male offspring. In the female offspring, exposure to 25 μg/kg bw/day bisphenol A dose resulted...... not significantly affected. In conclusion, the present study using a robust experimental study design, has shown that developmental exposure to 25 μg/kg bw/day bisphenol A can cause adverse effects on fertility (decreased sperm count), neurodevelopment (masculinization of spatial learning in females) and lead...

Estimating and comparing microbial diversity in the presence of sequencing errors

Science.gov (United States)

Chiu, Chun-Huo

2016-01-01

Estimating and comparing microbial diversity are statistically challenging due to limited sampling and possible sequencing errors for low-frequency counts, producing spurious singletons. The inflated singleton count seriously affects statistical analysis and inferences about microbial diversity. Previous statistical approaches to tackle the sequencing errors generally require different parametric assumptions about the sampling model or about the functional form of frequency counts. Different parametric assumptions may lead to drastically different diversity estimates. We focus on nonparametric methods which are universally valid for all parametric assumptions and can be used to compare diversity across communities. We develop here a nonparametric estimator of the true singleton count to replace the spurious singleton count in all methods/approaches. Our estimator of the true singleton count is in terms of the frequency counts of doubletons, tripletons and quadrupletons, provided these three frequency counts are reliable. To quantify microbial alpha diversity for an individual community, we adopt the measure of Hill numbers (effective number of taxa) under a nonparametric framework. Hill numbers, parameterized by an order q that determines the measures’ emphasis on rare or common species, include taxa richness (q = 0), Shannon diversity (q = 1, the exponential of Shannon entropy), and Simpson diversity (q = 2, the inverse of Simpson index). A diversity profile which depicts the Hill number as a function of order q conveys all information contained in a taxa abundance distribution. Based on the estimated singleton count and the original non-singleton frequency counts, two statistical approaches (non-asymptotic and asymptotic) are developed to compare microbial diversity for multiple communities. (1) A non-asymptotic approach refers to the comparison of estimated diversities of standardized samples with a common finite sample size or sample completeness. This

Estimating passenger numbers in trains using existing weighing capabilities

DEFF Research Database (Denmark)

Nielsen, Bo Friis; Frølich, Laura; Nielsen, Otto Anker

2013-01-01

trains to control braking. This technique makes passenger counting cheaper and ensures a complete sample. The paper compares numbers estimated by this technique with manual counts and counts from an infrared system in trains in urban Copenhagen. It shows that the weighing system provides more accurate......Knowing passenger numbers is important for the planning and operation of the urban rail systems. Manual and electronic counting systems (typically infrared or video) are expensive and therefore entail small sample sizes. They usually count boarding and alighting passengers, which means that errors...... in estimates of total numbers of passengers propagate along train runs. Counting errors in manual and electronic counting systems are typically flow-dependent, making uncertainty a function of volume. This paper presents a new counting technique that exploits the weighing systems installed in most modern...

Robustness Analysis of a Wide-Span Timber Structure with Ductile Behaviour

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Cizmar, D.

2010-01-01

This paper considers robustness evaluation of a wide span timber truss structure where the ductile behavior is taken into account. The robustness analysis is based on a structural reliability framework used on a simplified mechanical system modelling a timber truss system. A measure of ductile...... behaviour is introduced and for different values of this measure the robustness indices are estimated. The results indicate that the robustness of a timber truss system can be increased by taking the ductile behavior into....

Cell type specific DNA methylation in cord blood: A 450K-reference data set and cell count-based validation of estimated cell type composition.

Science.gov (United States)

Gervin, Kristina; Page, Christian Magnus; Aass, Hans Christian D; Jansen, Michelle A; Fjeldstad, Heidi Elisabeth; Andreassen, Bettina Kulle; Duijts, Liesbeth; van Meurs, Joyce B; van Zelm, Menno C; Jaddoe, Vincent W; Nordeng, Hedvig; Knudsen, Gunn Peggy; Magnus, Per; Nystad, Wenche; Staff, Anne Cathrine; Felix, Janine F; Lyle, Robert

2016-09-01

Epigenome-wide association studies of prenatal exposure to different environmental factors are becoming increasingly common. These studies are usually performed in umbilical cord blood. Since blood comprises multiple cell types with specific DNA methylation patterns, confounding caused by cellular heterogeneity is a major concern. This can be adjusted for using reference data consisting of DNA methylation signatures in cell types isolated from blood. However, the most commonly used reference data set is based on blood samples from adult males and is not representative of the cell type composition in neonatal cord blood. The aim of this study was to generate a reference data set from cord blood to enable correct adjustment of the cell type composition in samples collected at birth. The purity of the isolated cell types was very high for all samples (>97.1%), and clustering analyses showed distinct grouping of the cell types according to hematopoietic lineage. We explored whether this cord blood and the adult peripheral blood reference data sets impact the estimation of cell type composition in cord blood samples from an independent birth cohort (MoBa, n = 1092). This revealed significant differences for all cell types. Importantly, comparison of the cell type estimates against matched cell counts both in the cord blood reference samples (n = 11) and in another independent birth cohort (Generation R, n = 195), demonstrated moderate to high correlation of the data. This is the first cord blood reference data set with a comprehensive examination of the downstream application of the data through validation of estimated cell types against matched cell counts.

Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

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Dongming Li

2017-04-01

Full Text Available An adaptive optics (AO system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.

MOSS-5: A Fast Method of Approximating Counts of 5-Node Graphlets in Large Graphs

KAUST Repository

Wang, Pinghui

2017-09-26

Counting 3-, 4-, and 5-node graphlets in graphs is important for graph mining applications such as discovering abnormal/evolution patterns in social and biology networks. In addition, it is recently widely used for computing similarities between graphs and graph classification applications such as protein function prediction and malware detection. However, it is challenging to compute these metrics for a large graph or a large set of graphs due to the combinatorial nature of the problem. Despite recent efforts in counting triangles (a 3-node graphlet) and 4-node graphlets, little attention has been paid to characterizing 5-node graphlets. In this paper, we develop a computationally efficient sampling method to estimate 5-node graphlet counts. We not only provide fast sampling methods and unbiased estimators of graphlet counts, but also derive simple yet exact formulas for the variances of the estimators which is of great value in practice-the variances can be used to bound the estimates\\' errors and determine the smallest necessary sampling budget for a desired accuracy. We conduct experiments on a variety of real-world datasets, and the results show that our method is several orders of magnitude faster than the state-of-the-art methods with the same accuracy.

An evaluation of the Bayesian approach to fitting the N-mixture model for use with pseudo-replicated count data

Science.gov (United States)

Toribo, S.G.; Gray, B.R.; Liang, S.

2011-01-01

The N-mixture model proposed by Royle in 2004 may be used to approximate the abundance and detection probability of animal species in a given region. In 2006, Royle and Dorazio discussed the advantages of using a Bayesian approach in modelling animal abundance and occurrence using a hierarchical N-mixture model. N-mixture models assume replication on sampling sites, an assumption that may be violated when the site is not closed to changes in abundance during the survey period or when nominal replicates are defined spatially. In this paper, we studied the robustness of a Bayesian approach to fitting the N-mixture model for pseudo-replicated count data. Our simulation results showed that the Bayesian estimates for abundance and detection probability are slightly biased when the actual detection probability is small and are sensitive to the presence of extra variability within local sites.

Determining random counts in liquid scintillation counting

International Nuclear Information System (INIS)

Horrocks, D.L.

1979-01-01

During measurements involving coincidence counting techniques, errors can arise due to the detection of chance or random coincidences in the multiple detectors used. A method and the electronic circuits necessary are here described for eliminating this source of error in liquid scintillation detectors used in coincidence counting. (UK)

Maximum entropy deconvolution of low count nuclear medicine images

International Nuclear Information System (INIS)

McGrath, D.M.

1998-12-01

Maximum entropy is applied to the problem of deconvolving nuclear medicine images, with special consideration for very low count data. The physics of the formation of scintigraphic images is described, illustrating the phenomena which degrade planar estimates of the tracer distribution. Various techniques which are used to restore these images are reviewed, outlining the relative merits of each. The development and theoretical justification of maximum entropy as an image processing technique is discussed. Maximum entropy is then applied to the problem of planar deconvolution, highlighting the question of the choice of error parameters for low count data. A novel iterative version of the algorithm is suggested which allows the errors to be estimated from the predicted Poisson mean values. This method is shown to produce the exact results predicted by combining Poisson statistics and a Bayesian interpretation of the maximum entropy approach. A facility for total count preservation has also been incorporated, leading to improved quantification. In order to evaluate this iterative maximum entropy technique, two comparable methods, Wiener filtering and a novel Bayesian maximum likelihood expectation maximisation technique, were implemented. The comparison of results obtained indicated that this maximum entropy approach may produce equivalent or better measures of image quality than the compared methods, depending upon the accuracy of the system model used. The novel Bayesian maximum likelihood expectation maximisation technique was shown to be preferable over many existing maximum a posteriori methods due to its simplicity of implementation. A single parameter is required to define the Bayesian prior, which suppresses noise in the solution and may reduce the processing time substantially. Finally, maximum entropy deconvolution was applied as a pre-processing step in single photon emission computed tomography reconstruction of low count data. Higher contrast results were

A Modified LQG Algorithm (MLQG for Robust Control of Nonlinear Multivariable Systems

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Jens G. Balchen

1993-07-01

Full Text Available The original LQG algorithm is often characterized for its lack of robustness. This is because in the design of the estimator (Kalman filter the process disturbance is assumed to be white noise. If the estimator is to give good estimates, the Kalman gain is increased which means that the estimator fails to become robust. A solution to this problem is to replace the proportional Kalman gain matrix by a dynamic PI algorithm and the proportional LQ feedback gain matrix by a PI algorithm. A tuning method is developed which facilitates the tuning of a modified LQG control system (MLQG by only two tuning parameters.

Finite Algorithms for Robust Linear Regression

DEFF Research Database (Denmark)

Madsen, Kaj; Nielsen, Hans Bruun

1990-01-01

The Huber M-estimator for robust linear regression is analyzed. Newton type methods for solution of the problem are defined and analyzed, and finite convergence is proved. Numerical experiments with a large number of test problems demonstrate efficiency and indicate that this kind of approach may...

Application of PSD for low level alpha counting using liquid scintillation counting

International Nuclear Information System (INIS)

Krishnamachari, G.; Vaze, P.K.; Iyer, M.R.

1989-01-01

In the liquid scintillator the light produced by alpha particles decays differently than those produced by electrons. Pulse shape discrimination (PSD) methods are employed to estimate low levels of alpha emitting radionuclides by reducing the background due to either beta or gamma events. An attempt is being made to develop a liquid scintillation counting sytem using a simple PSD circuit to achieve a background of 0.01 counts/min. The PSD circuit is based on measuring zero cross over points to differentiate particle types. The input signal is first differentiated by a delay line and subsequently by a RC circuit. The width of the initial part of the doubly differentiated pulse is different for alpha and beta pulses. This width is converted to amplitude by a time-to-amplitude converter (TAC). The higher amplitude pulses from the TAC are due to alpha particles and they are separated by an integral discriminator. The output from the integral discriminator opens a linear gate to record the pulse height spectrum. The figure of merit of the PSD circuit and background in the alpha energy channel have been worked out using different scintillator types. (author). 4 figs

Risk, Robustness and Water Resources Planning Under Uncertainty

Science.gov (United States)

Borgomeo, Edoardo; Mortazavi-Naeini, Mohammad; Hall, Jim W.; Guillod, Benoit P.

2018-03-01

Risk-based water resources planning is based on the premise that water managers should invest up to the point where the marginal benefit of risk reduction equals the marginal cost of achieving that benefit. However, this cost-benefit approach may not guarantee robustness under uncertain future conditions, for instance under climatic changes. In this paper, we expand risk-based decision analysis to explore possible ways of enhancing robustness in engineered water resources systems under different risk attitudes. Risk is measured as the expected annual cost of water use restrictions, while robustness is interpreted in the decision-theoretic sense as the ability of a water resource system to maintain performance—expressed as a tolerable risk of water use restrictions—under a wide range of possible future conditions. Linking risk attitudes with robustness allows stakeholders to explicitly trade-off incremental increases in robustness with investment costs for a given level of risk. We illustrate the framework through a case study of London's water supply system using state-of-the -art regional climate simulations to inform the estimation of risk and robustness.

An automated approach for annual layer counting in ice cores

Directory of Open Access Journals (Sweden)

M. Winstrup

2012-11-01

Full Text Available A novel method for automated annual layer counting in seasonally-resolved paleoclimate records has been developed. It relies on algorithms from the statistical framework of hidden Markov models (HMMs, which originally was developed for use in machine speech recognition. The strength of the layer detection algorithm lies in the way it is able to imitate the manual procedures for annual layer counting, while being based on statistical criteria for annual layer identification. The most likely positions of multiple layer boundaries in a section of ice core data are determined simultaneously, and a probabilistic uncertainty estimate of the resulting layer count is provided, ensuring an objective treatment of ambiguous layers in the data. Furthermore, multiple data series can be incorporated and used simultaneously. In this study, the automated layer counting algorithm has been applied to two ice core records from Greenland: one displaying a distinct annual signal and one which is more challenging. The algorithm shows high skill in reproducing the results from manual layer counts, and the resulting timescale compares well to absolute-dated volcanic marker horizons where these exist.

Replication and robustness in developmental research.

Science.gov (United States)

Duncan, Greg J; Engel, Mimi; Claessens, Amy; Dowsett, Chantelle J

2014-11-01

Replications and robustness checks are key elements of the scientific method and a staple in many disciplines. However, leading journals in developmental psychology rarely include explicit replications of prior research conducted by different investigators, and few require authors to establish in their articles or online appendices that their key results are robust across estimation methods, data sets, and demographic subgroups. This article makes the case for prioritizing both explicit replications and, especially, within-study robustness checks in developmental psychology. It provides evidence on variation in effect sizes in developmental studies and documents strikingly different replication and robustness-checking practices in a sample of journals in developmental psychology and a sister behavioral science-applied economics. Our goal is not to show that any one behavioral science has a monopoly on best practices, but rather to show how journals from a related discipline address vital concerns of replication and generalizability shared by all social and behavioral sciences. We provide recommendations for promoting graduate training in replication and robustness-checking methods and for editorial policies that encourage these practices. Although some of our recommendations may shift the form and substance of developmental research articles, we argue that they would generate considerable scientific benefits for the field. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Near infrared spectroscopy to estimate the temperature reached on burned soils: strategies to develop robust models.

Science.gov (United States)

Guerrero, César; Pedrosa, Elisabete T.; Pérez-Bejarano, Andrea; Keizer, Jan Jacob

2014-05-01

The temperature reached on soils is an important parameter needed to describe the wildfire effects. However, the methods for measure the temperature reached on burned soils have been poorly developed. Recently, the use of the near-infrared (NIR) spectroscopy has been pointed as a valuable tool for this purpose. The NIR spectrum of a soil sample contains information of the organic matter (quantity and quality), clay (quantity and quality), minerals (such as carbonates and iron oxides) and water contents. Some of these components are modified by the heat, and each temperature causes a group of changes, leaving a typical fingerprint on the NIR spectrum. This technique needs the use of a model (or calibration) where the changes in the NIR spectra are related with the temperature reached. For the development of the model, several aliquots are heated at known temperatures, and used as standards in the calibration set. This model offers the possibility to make estimations of the temperature reached on a burned sample from its NIR spectrum. However, the estimation of the temperature reached using NIR spectroscopy is due to changes in several components, and cannot be attributed to changes in a unique soil component. Thus, we can estimate the temperature reached by the interaction between temperature and the thermo-sensible soil components. In addition, we cannot expect the uniform distribution of these components, even at small scale. Consequently, the proportion of these soil components can vary spatially across the site. This variation will be present in the samples used to construct the model and also in the samples affected by the wildfire. Therefore, the strategies followed to develop robust models should be focused to manage this expected variation. In this work we compared the prediction accuracy of models constructed with different approaches. These approaches were designed to provide insights about how to distribute the efforts needed for the development of robust

An evaluation of the utility and limitations of counting motor unit action potentials in the surface electromyogram

Science.gov (United States)

Zhou, Ping; Zev Rymer, William

2004-12-01

The number of motor unit action potentials (MUAPs) appearing in the surface electromyogram (EMG) signal is directly related to motor unit recruitment and firing rates and therefore offers potentially valuable information about the level of activation of the motoneuron pool. In this paper, based on morphological features of the surface MUAPs, we try to estimate the number of MUAPs present in the surface EMG by counting the negative peaks in the signal. Several signal processing procedures are applied to the surface EMG to facilitate this peak counting process. The MUAP number estimation performance by this approach is first illustrated using the surface EMG simulations. Then, by evaluating the peak counting results from the EMG records detected by a very selective surface electrode, at different contraction levels of the first dorsal interosseous (FDI) muscles, the utility and limitations of such direct peak counts for MUAP number estimation in surface EMG are further explored.

Robust extrapolation scheme for fast estimation of 3D Ising field partition functions: application to within subject fMRI data

Energy Technology Data Exchange (ETDEWEB)

Risser, L.; Vincent, T.; Ciuciu, Ph. [NeuroSpin CEA, F-91191 Gif sur Yvette (France); Risser, L.; Vincent, T. [Laboratoire de Neuroimagerie Assistee par Ordinateur (LNAO) CEA - DSV/I2BM/NEUROSPIN (France); Risser, L. [Institut de mecanique des fluides de Toulouse (IMFT), CNRS: UMR5502 - Universite Paul Sabatier - Toulouse III - Institut National Polytechnique de Toulouse - INPT (France); Idier, J. [Institut de Recherche en Communications et en Cybernetique de Nantes (IRCCyN) CNRS - UMR6597 - Universite de Nantes - ecole Centrale de Nantes - Ecole des Mines de Nantes - Ecole Polytechnique de l' Universite de Nantes (France)

2009-07-01

In this paper, we present a first numerical scheme to estimate Partition Functions (PF) of 3D Ising fields. Our strategy is applied to the context of the joint detection-estimation of brain activity from functional Magnetic Resonance Imaging (fMRI) data, where the goal is to automatically recover activated regions and estimate region-dependent, hemodynamic filters. For any region, a specific binary Markov random field may embody spatial correlation over the hidden states of the voxels by modeling whether they are activated or not. To make this spatial regularization fully adaptive, our approach is first based upon it, classical path-sampling method to approximate a small subset of reference PFs corresponding to pre-specified regions. Then, file proposed extrapolation method allows its to approximate the PFs associated with the Ising fields defined over the remaining brain regions. In comparison with preexisting approaches, our method is robust; to topological inhomogeneities in the definition of the reference regions. As a result, it strongly alleviates the computational burden and makes spatially adaptive regularization of whole brain fMRI datasets feasible. (authors)

Reducing Inventory System Costs by Using Robust Demand Estimators

OpenAIRE

Raymond A. Jacobs; Harvey M. Wagner

1989-01-01

Applications of inventory theory typically use historical data to estimate demand distribution parameters. Imprecise knowledge of the demand distribution adds to the usual replenishment costs associated with stochastic demands. Only limited research has been directed at the problem of choosing cost effective statistical procedures for estimating these parameters. Available theoretical findings on estimating the demand parameters for (s, S) inventory replenishment policies are limited by their...

Multivariable robust adaptive controller using reduced-order model

Directory of Open Access Journals (Sweden)

Wei Wang

1990-04-01

Full Text Available In this paper a multivariable robust adaptive controller is presented for a plant with bounded disturbances and unmodeled dynamics due to plant-model order mismatches. The robust stability of the closed-loop system is achieved by using the normalization technique and the least squares parameter estimation scheme with dead zones. The weighting polynomial matrices are incorporated into the control law, so that the open-loop unstable or/and nonminimum phase plants can be handled.

HOTELLING'S T2 CONTROL CHARTS BASED ON ROBUST ESTIMATORS

Directory of Open Access Journals (Sweden)

SERGIO YÁÑEZ

2010-01-01

Full Text Available Under the presence of multivariate outliers, in a Phase I analysis of historical set of data, the T 2 control chart based on the usual sample mean vector and sample variance covariance matrix performs poorly. Several alternative estimators have been proposed. Among them, estimators based on the minimum volume ellipsoid (MVE and the minimum covariance determinant (MCD are powerful in detecting a reasonable number of outliers. In this paper we propose a T 2 control chart using the biweight S estimators for the location and dispersion parameters when monitoring multivariate individual observations. Simulation studies show that this method outperforms the T 2 control chart based on MVE estimators for a small number of observations.

Robustness Metrics: Consolidating the multiple approaches to quantify Robustness

DEFF Research Database (Denmark)

Göhler, Simon Moritz; Eifler, Tobias; Howard, Thomas J.

2016-01-01

robustness metrics; 3) Functional expectancy and dispersion robustness metrics; and 4) Probability of conformance robustness metrics. The goal was to give a comprehensive overview of robustness metrics and guidance to scholars and practitioners to understand the different types of robustness metrics...

Errors associated with moose-hunter counts of occupied beaver Castor fiber lodges in Norway

OpenAIRE

Parker, Howard; Rosell, Frank; Gustavsen, Per Øyvind

2002-01-01

In Norway, Sweden and Finland moose Alces alces hunting teams are often employed to survey occupied beaver (Castor fiber and C. canadensis) lodges while hunting. Results may be used to estimate population density or trend, or for issuing harvest permits. Despite the method's increasing popularity, the errors involved have never been identified. In this study we 1) compare hunting-team counts of occupied lodges with total counts, 2) identify the sources of error between counts and 3) evaluate ...

Efficient Levenberg-Marquardt minimization of the maximum likelihood estimator for Poisson deviates

International Nuclear Information System (INIS)

Laurence, T.; Chromy, B.

2010-01-01

Histograms of counted events are Poisson distributed, but are typically fitted without justification using nonlinear least squares fitting. The more appropriate maximum likelihood estimator (MLE) for Poisson distributed data is seldom used. We extend the use of the Levenberg-Marquardt algorithm commonly used for nonlinear least squares minimization for use with the MLE for Poisson distributed data. In so doing, we remove any excuse for not using this more appropriate MLE. We demonstrate the use of the algorithm and the superior performance of the MLE using simulations and experiments in the context of fluorescence lifetime imaging. Scientists commonly form histograms of counted events from their data, and extract parameters by fitting to a specified model. Assuming that the probability of occurrence for each bin is small, event counts in the histogram bins will be distributed according to the Poisson distribution. We develop here an efficient algorithm for fitting event counting histograms using the maximum likelihood estimator (MLE) for Poisson distributed data, rather than the non-linear least squares measure. This algorithm is a simple extension of the common Levenberg-Marquardt (L-M) algorithm, is simple to implement, quick and robust. Fitting using a least squares measure is most common, but it is the maximum likelihood estimator only for Gaussian-distributed data. Non-linear least squares methods may be applied to event counting histograms in cases where the number of events is very large, so that the Poisson distribution is well approximated by a Gaussian. However, it is not easy to satisfy this criterion in practice - which requires a large number of events. It has been well-known for years that least squares procedures lead to biased results when applied to Poisson-distributed data; a recent paper providing extensive characterization of these biases in exponential fitting is given. The more appropriate measure based on the maximum likelihood estimator (MLE

Counting carbohydrates

Science.gov (United States)

Carb counting; Carbohydrate-controlled diet; Diabetic diet; Diabetes-counting carbohydrates ... Many foods contain carbohydrates (carbs), including: Fruit and fruit juice Cereal, bread, pasta, and rice Milk and milk products, soy milk Beans, legumes, ...

Repeatability of differential goat bulk milk culture and associations with somatic cell count, total bacterial count, and standard plate count

OpenAIRE

Koop, G.; Dik, N.; Nielen, M.; Lipman, L.J.A.

2010-01-01

The aims of this study were to assess how different bacterial groups in bulk milk are related to bulk milk somatic cell count (SCC), bulk milk total bacterial count (TBC), and bulk milk standard plate count (SPC) and to measure the repeatability of bulk milk culturing. On 53 Dutch dairy goat farms, 3 bulk milk samples were collected at intervals of 2 wk. The samples were cultured for SPC, coliform count, and staphylococcal count and for the presence of Staphylococcus aureus. Furthermore, SCC ...

Enhanced echolocation via robust statistics and super-resolution of sonar images

Science.gov (United States)

Kim, Kio

Echolocation is a process in which an animal uses acoustic signals to exchange information with environments. In a recent study, Neretti et al. have shown that the use of robust statistics can significantly improve the resiliency of echolocation against noise and enhance its accuracy by suppressing the development of sidelobes in the processing of an echo signal. In this research, the use of robust statistics is extended to problems in underwater explorations. The dissertation consists of two parts. Part I describes how robust statistics can enhance the identification of target objects, which in this case are cylindrical containers filled with four different liquids. Particularly, this work employs a variation of an existing robust estimator called an L-estimator, which was first suggested by Koenker and Bassett. As pointed out by Au et al.; a 'highlight interval' is an important feature, and it is closely related with many other important features that are known to be crucial for dolphin echolocation. A varied L-estimator described in this text is used to enhance the detection of highlight intervals, which eventually leads to a successful classification of echo signals. Part II extends the problem into 2 dimensions. Thanks to the advances in material and computer technology, various sonar imaging modalities are available on the market. By registering acoustic images from such video sequences, one can extract more information on the region of interest. Computer vision and image processing allowed application of robust statistics to the acoustic images produced by forward looking sonar systems, such as Dual-frequency Identification Sonar and ProViewer. The first use of robust statistics for sonar image enhancement in this text is in image registration. Random Sampling Consensus (RANSAC) is widely used for image registration. The registration algorithm using RANSAC is optimized for sonar image registration, and the performance is studied. The second use of robust

Robust bayesian inference of generalized Pareto distribution ...

African Journals Online (AJOL)

En utilisant une etude exhaustive de Monte Carlo, nous prouvons que, moyennant une fonction perte generalisee adequate, on peut construire un estimateur Bayesien robuste du modele. Key words: Bayesian estimation; Extreme value; Generalized Fisher information; Gener- alized Pareto distribution; Monte Carlo; ...

Comparison and assessment of aerial and ground estimates of waterbird colonies

Science.gov (United States)

Green, M.C.; Luent, M.C.; Michot, T.C.; Jeske, C.W.; Leberg, P.L.

2008-01-01

Aerial surveys are often used to quantify sizes of waterbird colonies; however, these surveys would benefit from a better understanding of associated biases. We compared estimates of breeding pairs of waterbirds, in colonies across southern Louisiana, USA, made from the ground, fixed-wing aircraft, and a helicopter. We used a marked-subsample method for ground-counting colonies to obtain estimates of error and visibility bias. We made comparisons over 2 sampling periods: 1) surveys conducted on the same colonies using all 3 methods during 3-11 May 2005 and 2) an expanded fixed-wing and ground-survey comparison conducted over 4 periods (May and Jun, 2004-2005). Estimates from fixed-wing aircraft were approximately 65% higher than those from ground counts for overall estimated number of breeding pairs and for both dark and white-plumaged species. The coefficient of determination between estimates based on ground and fixed-wing aircraft was ???0.40 for most species, and based on the assumption that estimates from the ground were closer to the true count, fixed-wing aerial surveys appeared to overestimate numbers of nesting birds of some species; this bias often increased with the size of the colony. Unlike estimates from fixed-wing aircraft, numbers of nesting pairs made from ground and helicopter surveys were very similar for all species we observed. Ground counts by one observer resulted in underestimated number of breeding pairs by 20% on average. The marked-subsample method provided an estimate of the number of missed nests as well as an estimate of precision. These estimates represent a major advantage of marked-subsample ground counts over aerial methods; however, ground counts are difficult in large or remote colonies. Helicopter surveys and ground counts provide less biased, more precise estimates of breeding pairs than do surveys made from fixed-wing aircraft. We recommend managers employ ground counts using double observers for surveying waterbird colonies

Symmetry-Free, p-Robust Equilibrated Error Indication for the hp-Version of the FEMin Nearly Incompressible Linear Elasticity

OpenAIRE

Dörsek, Philipp; Melenk, Jens M.

2017-01-01

We consider the extension of the p-robust equilibrated error estimator due to Braess, Pillwein and Schöberl to linear elasticity. We derive a formulation where the local mixed auxiliary problems do not require symmetry of the stresses. The resulting error estimator is p-robust, and the reliability estimate is also robust in the incompressible limit if quadratics are included in the approximation space. Extensions to other systems of linear second-order partial differential equations are discu...

Robust Estimation of Productivity Changes in Japanese Shinkin Banks

Directory of Open Access Journals (Sweden)

Jianzhong DAI

2014-05-01

Full Text Available This paper estimates productivity changes in Japanese shinkin banks during the fiscal years 2001 to 2008 using the Malmquist index as the measure of productivity change. Data envelopment analysis (DEA is used to estimate the index. We also apply a smoothed bootstrapping approach to set up confidence intervals for estimates and study their statistical characteristics. By analyzing estimated scores, we identify trends in productivity changes in Japanese shinkin banks during the study period and investigate the sources of these trends. We find that in the latter half of the study period, productivity has significantly declined, primarily because of deterioration in technical efficiency, but scale efficiency has been significantly improved. Grouping the total sample according to the levels of competition reveals more details of productivity changes in shinkin banks.

Double hard scattering without double counting

Energy Technology Data Exchange (ETDEWEB)

Diehl, Markus [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Gaunt, Jonathan R. [VU Univ. Amsterdam (Netherlands). NIKHEF Theory Group; Schoenwald, Kay [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)

2017-02-15

Double parton scattering in proton-proton collisions includes kinematic regions in which two partons inside a proton originate from the perturbative splitting of a single parton. This leads to a double counting problem between single and double hard scattering. We present a solution to this problem, which allows for the definition of double parton distributions as operator matrix elements in a proton, and which can be used at higher orders in perturbation theory. We show how the evaluation of double hard scattering in this framework can provide a rough estimate for the size of the higher-order contributions to single hard scattering that are affected by double counting. In a numeric study, we identify situations in which these higher-order contributions must be explicitly calculated and included if one wants to attain an accuracy at which double hard scattering becomes relevant, and other situations where such contributions may be neglected.

Double hard scattering without double counting

International Nuclear Information System (INIS)

Diehl, Markus; Gaunt, Jonathan R.

2017-02-01

Double parton scattering in proton-proton collisions includes kinematic regions in which two partons inside a proton originate from the perturbative splitting of a single parton. This leads to a double counting problem between single and double hard scattering. We present a solution to this problem, which allows for the definition of double parton distributions as operator matrix elements in a proton, and which can be used at higher orders in perturbation theory. We show how the evaluation of double hard scattering in this framework can provide a rough estimate for the size of the higher-order contributions to single hard scattering that are affected by double counting. In a numeric study, we identify situations in which these higher-order contributions must be explicitly calculated and included if one wants to attain an accuracy at which double hard scattering becomes relevant, and other situations where such contributions may be neglected.

Gradient descent for robust kernel-based regression

Science.gov (United States)

Guo, Zheng-Chu; Hu, Ting; Shi, Lei

2018-06-01

In this paper, we study the gradient descent algorithm generated by a robust loss function over a reproducing kernel Hilbert space (RKHS). The loss function is defined by a windowing function G and a scale parameter σ, which can include a wide range of commonly used robust losses for regression. There is still a gap between theoretical analysis and optimization process of empirical risk minimization based on loss: the estimator needs to be global optimal in the theoretical analysis while the optimization method can not ensure the global optimality of its solutions. In this paper, we aim to fill this gap by developing a novel theoretical analysis on the performance of estimators generated by the gradient descent algorithm. We demonstrate that with an appropriately chosen scale parameter σ, the gradient update with early stopping rules can approximate the regression function. Our elegant error analysis can lead to convergence in the standard L 2 norm and the strong RKHS norm, both of which are optimal in the mini-max sense. We show that the scale parameter σ plays an important role in providing robustness as well as fast convergence. The numerical experiments implemented on synthetic examples and real data set also support our theoretical results.

The Herschel Virgo Cluster Survey. XVII. SPIRE point-source catalogs and number counts

Science.gov (United States)

Pappalardo, Ciro; Bendo, George J.; Bianchi, Simone; Hunt, Leslie; Zibetti, Stefano; Corbelli, Edvige; di Serego Alighieri, Sperello; Grossi, Marco; Davies, Jonathan; Baes, Maarten; De Looze, Ilse; Fritz, Jacopo; Pohlen, Michael; Smith, Matthew W. L.; Verstappen, Joris; Boquien, Médéric; Boselli, Alessandro; Cortese, Luca; Hughes, Thomas; Viaene, Sebastien; Bizzocchi, Luca; Clemens, Marcel

2015-01-01

Aims: We present three independent catalogs of point-sources extracted from SPIRE images at 250, 350, and 500 μm, acquired with the Herschel Space Observatory as a part of the Herschel Virgo Cluster Survey (HeViCS). The catalogs have been cross-correlated to consistently extract the photometry at SPIRE wavelengths for each object. Methods: Sources have been detected using an iterative loop. The source positions are determined by estimating the likelihood to be a real source for each peak on the maps, according to the criterion defined in the sourceExtractorSussextractor task. The flux densities are estimated using the sourceExtractorTimeline, a timeline-based point source fitter that also determines the fitting procedure with the width of the Gaussian that best reproduces the source considered. Afterwards, each source is subtracted from the maps, removing a Gaussian function in every position with the full width half maximum equal to that estimated in sourceExtractorTimeline. This procedure improves the robustness of our algorithm in terms of source identification. We calculate the completeness and the flux accuracy by injecting artificial sources in the timeline and estimate the reliability of the catalog using a permutation method. Results: The HeViCS catalogs contain about 52 000, 42 200, and 18 700 sources selected at 250, 350, and 500 μm above 3σ and are ~75%, 62%, and 50% complete at flux densities of 20 mJy at 250, 350, 500 μm, respectively. We then measured source number counts at 250, 350, and 500 μm and compare them with previous data and semi-analytical models. We also cross-correlated the catalogs with the Sloan Digital Sky Survey to investigate the redshift distribution of the nearby sources. From this cross-correlation, we select ~2000 sources with reliable fluxes and a high signal-to-noise ratio, finding an average redshift z ~ 0.3 ± 0.22 and 0.25 (16-84 percentile). Conclusions: The number counts at 250, 350, and 500 μm show an increase in

Ovarian volume and antral follicle count assessed by MRI and transvaginal ultrasonography: a methodological study.

Science.gov (United States)

Leonhardt, Henrik; Gull, Berit; Stener-Victorin, Elisabet; Hellström, Mikael

2014-03-01

Ultrasonographic measurements of ovarian volume and antral follicle count are of clinical importance as diagnostic features of polycystic ovarian syndrome (PCOS), and as a parameter in estimation of ovarian follicular reserve in infertility care. To compare two-dimensional (2D)/three-dimensional (3D) transvaginal ultrasonography (TVUS) and magnetic resonance imaging (MRI) for estimation of ovarian volume and antral follicle count, and to assess reproducibility and inter-observer agreement of MRI measurements. Volumes of 172 ovaries in 99 women aged 21-37 years were calculated (length x width x height x 0.523) with conventional 2D TVUS and 2D MRI. Semi-automatic estimates of ovarian volumes were obtained by 3D MRI. Antral follicles were counted manually on 2D MRI and automatically by 3D TVUS (SonoAVC), and stratified according to follicle size. Mean ovarian volume assessed by 2D TVUS (13.1 ± 6.4 mL) was larger than assessed by 2D MRI (9.6 ± 4.1) and 3D MRI (11.4 ± 4.5) (P 0.77. 2D MRI reveals more antral follicles, especially of small size, than 3D TVUS. Ovarian volume estimation by MRI provides smaller volumes than by the reference standard 2D TVUS. Ovarian volume estimation by 3D MRI, allowing independence of non-ellipsoid ovarian shape measurement errors, provides volumes closer to 2D TVUS values than does 2D MRI. Reproducibility and inter-observer agreement of 2D MRI measurements of ovarian volume and total follicle count are good.