Robust position estimation of a mobile vehicle
Energy Technology Data Exchange (ETDEWEB)
Conan, V. [CEA Centre d`Etudes de Saclay, 91 - Gif-sur-Yvette (France). Dept. des Procedes et Systemes Avances; Boulanger, P.; Elgazzar, S. [National Research Council of Canada, Ottawa, ON (Canada)
1994-12-31
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{sup 4} n{sup 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.
A three-dimensional robust ridge estimation positioning method for UWB in a complex environment
Li, Shuaixin; Li, Guangyun; Wang, Li; Zhou, Yanglin; Peng, Yifan; Fu, Jingyang
2017-12-01
The Ultra-Wide Bandwidth (UWB) signal has received much attention due to its penetrability and high positioning accuracy with the increasing demand for indoor positioning, in which Global Positioning System (GPS) signal is challenging. In practice, there are two main problems with indoor 3D positioning based on UWB. One is that the quality of Time Difference of Arrival (TDOA) measurements varies in different observation environments. Namely, the time delay generated by Non-Line-of-Sight (NLOS) causes an enormous deviation from the real distance and cannot be well distinguished from the measurement reducing the accuracy of positioning. The other problem is that the height estimates, which are calculated using the conventional least square method, are extremely unstable due to the limitation of the Base Station (BS) layout. To address these problems, this paper presents Robust Ridge Estimation (RRE) for UWB positioning. Firstly, NLOS errors are detected, and the weights of each measurement are automatically adjusted in accordance with their quality, which is represented by the residuals between the estimated measurements and real observations. Then, the ridge estimation algorithm is applied iteratively for position estimation based on a robust estimation framework, which updates the weight of the measurements at each iteration. This approach transforms unbiased estimation to biased estimation by adding constraints that minimize the weighted quadratic sum of some parameters. As a result, the impact of NLOS can be reduced. The experimental result shows an improvement of RMSE in positioning with 45.71% when compared with ridge estimation in an NLOS/Line-of-Sight (LOS) mixed environment and an increase of robustness to NLOS with 56.11%.
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.
Robust 3D Position Estimation in Wide and Unconstrained Indoor Environments.
Mossel, Annette
2015-12-14
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.
Directory of Open Access Journals (Sweden)
Gamantyo Hendrantoro
2011-12-01
Full Text Available The position of a sensor node at wireless sensor networks determines the received data sensing accuracy. By the knowledge of sensor positioning, the location of target sensed can be estimated. Localization techniques used to find out the position of sensor node by considering the distance of this sensor from the vicinity reference nodes. Centroid Algorithm is a robust, simple and low cost localization technique without dependence on hardware requirement. We propose Recursive Position Estimation Algorithm to obtain the more accurate node positioning on range-free localization technique. The simulation result shows that this algorithm has the ability on increasing position accuracy up to 50%. The trade off factor shows the smaller the number of reference nodes the higher the computational time required. The new method on the availability on sensor power controlled is proposed to optimize the estimated position.
Directory of Open Access Journals (Sweden)
Prima Kristalina
2013-09-01
Full Text Available The position of a sensor node at wireless sensor networks determines the received data sensing accuracy. By the knowledge of sensor positioning, the location of target sensed can be estimated. Localization techniques used to find out the position of sensor node by considering the distance of this sensor from the vicinity reference nodes. Centroid Algorithm is a robust, simple and low cost localization technique without dependence on hardware requirement. We propose Recursive Position Estimation Algorithm to obtain the more accurate node positioning on range-free localization technique. The simulation result shows that this algorithm has the ability on increasing position accuracy up to 50%. The trade off factor shows the smaller the number of reference nodes the higher the computational time required. The new method on the availability on sensor power controlled is proposed to optimize the estimated position.
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.
Robust Wave Resource Estimation
DEFF Research Database (Denmark)
Lavelle, John; Kofoed, Jens Peter
2013-01-01
An assessment of the wave energy resource at the location of the Danish Wave Energy test Centre (DanWEC) is presented in this paper. The Wave Energy Converter (WEC) test centre is located at Hanstholm in the of North West Denmark. Information about the long term wave statistics of the resource...... is necessary for WEC developers, both to optimise the WEC for the site, and to estimate its average yearly power production using a power matrix. The wave height and wave period sea states parameters are commonly characterized with a bivariate histogram. This paper presents bivariate histograms and kernel...... 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...
Robust estimation and hypothesis testing
Tiku, Moti L
2004-01-01
In statistical theory and practice, a certain distribution is usually assumed and then optimal solutions sought. Since deviations from an assumed distribution are very common, one cannot feel comfortable with assuming a particular distribution and believing it to be exactly correct. That brings the robustness issue in focus. In this book, we have given statistical procedures which are robust to plausible deviations from an assumed mode. The method of modified maximum likelihood estimation is used in formulating these procedures. The modified maximum likelihood estimators are explicit functions of sample observations and are easy to compute. They are asymptotically fully efficient and are as efficient as the maximum likelihood estimators for small sample sizes. The maximum likelihood estimators have computational problems and are, therefore, elusive. A broad range of topics are covered in this book. Solutions are given which are easy to implement and are efficient. The solutions are also robust to data anomali...
Robust Estimation of Cronbach's Alpha
Christmann, A.; Van Aelst, S.
2002-01-01
Cronbach’s alpha is a popular method to measure reliability, e.g. in quantifying the reliability of a score to summarize the information of several items in questionnaires. The alpha coefficient is known to be non-robust. We study the behavior of this coefficient in different settings to identify situations, which can easily occur in practice, but under which the Cronbach’s alpha coefficient is extremely sensitive to violations of the classical model assumptions. Furthermore, we construct a r...
Estimation robuste en population finie et infinie
Favre-Martinoz, Cyril
2015-01-01
The main topic of this thesis is the robust estimation in finite or infinite population. The thesis is divided in five chapters, an introduction and a conclusion. The chapter 2 is a literature review focus on several topics as: inference in finite population, small area estimation, robust estimation in finite and infinite population. In chapter 3, we deal with the winsorization, which is often used to treat the problem of influential values. This technique requires the determination of a cons...
Oda,Hirokuni; Xuan, Chuang; Yamamoto, Yuhji
2016-01-01
Pass-through superconducting rock magnetometers (SRM) offer rapid and high-precision remanence measurements for continuous samples that are essential for modern paleomagnetism studies. However, continuous SRM measurements are inevitably smoothed and distorted due to the convolution effect of SRM sensor response. Deconvolution is necessary to restore accurate magnetization from pass-through SRM data, and robust deconvolution requires reliable estimate of SRM sensor response as well as understa...
Minimum Distance and Robust Estimation.
1979-10-05
K(a)) - (L(b) - L(a))j -m<a maximal ’ interval probability distance. , , ,, i iv) Z ab(KL) a f (K(x) - L(x))2 dL(x) + b[7 (K(x) - L(x...support this proposition, showing :MD-8titors to be competitive with some of the better estimators thus far pro DD , O: ’\\1473 EDITIO O’ ROY 5 IS OBSOLETE SECURITY CLASSIFICATIONI OF TWIS PAGE (whmt Desl ’ntld) ~J
a comparative study of some robust ridge and liu estimators
African Journals Online (AJOL)
Dr A.B.Ahmed
, Ogbomoso, Oyo State, Nigeria. ... Liu Estimator, Robust Estimator, Robust Ridge Regression. Estimator, Robust Liu Estimator. 1.0. INTRODUCTION. Regression analysis is used to study the ..... in Probability and Mathematical Statistics.Wiley ...
Robust correlation coefficient based on Qn estimator
Zakaria, Nur Amira; Abdullah, Suhaida; Ahad, Nor Aishah
2017-11-01
This paper presents a new robust correlation coefficient called Qn correlation coefficient. This coefficient is developed as an alternative for classical correlation coefficient as the performance of classical correlation coefficient is nasty under contamination data. This study applied robust scale estimator called Qn because this estimator have high breakdown point. Simulation studies are carried out in determining the performances of the new robust correlation coefficient. Clean and contamination data are generated in assessing the performance of these coefficient. The performances of the Qn correlation coefficient is compared with classical correlation coefficient based on the value of coefficient, average bias and standard error. The outcome of the simulation studies shows that the performance of Qn correlation coefficient is superior compared to the classical and existing robust correlation coefficient.
Robust Solar Position Sensor for Tracking Systems
DEFF Research Database (Denmark)
Ritchie, Ewen; Argeseanu, Alin; Leban, Krisztina Monika
2009-01-01
The paper proposes a new solar position sensor used in tracking system control. The main advantages of the new solution are the robustness and the economical aspect. Positioning accuracy of the tracking system that uses the new sensor is better than 1°. The new sensor uses the ancient principle o...... (bright or dark). In addition, the proposed solar sensor significantly simplifies the operation of the tracking control device.......The paper proposes a new solar position sensor used in tracking system control. The main advantages of the new solution are the robustness and the economical aspect. Positioning accuracy of the tracking system that uses the new sensor is better than 1°. The new sensor uses the ancient principle...... of the solar clock. The sensitive elements are eight ordinary photo-resistors. It is important to note that all the sensors are not selected simultaneously. It is not necessary for sensor operating characteristics to be quasi-identical because the sensor principle is based on extreme operating duty measurement...
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.
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.
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
In many scenarios, a periodic signal of interest is often contaminated by different types of noise that may render many existing pitch estimation methods suboptimal, e.g., due to an incorrect white Gaussian noise assumption. In this paper, a method is established to estimate the pitch of such sig......In many scenarios, a periodic signal of interest is often contaminated by different types of noise that may render many existing pitch estimation methods suboptimal, e.g., due to an incorrect white Gaussian noise assumption. In this paper, a method is established to estimate the pitch...... against different noise situations. The simulation results confirm that the proposed MVDR method outperforms the state-of-the-art weighted least squares (WLS) pitch estimator in colored noise and has robust pitch estimates against missing harmonics in some time-frames....
Robust Global Motion Estimation with Matrix Completion
Directory of Open Access Journals (Sweden)
F. Arrigoni
2014-06-01
Full Text Available In this paper we address the problem of estimating the attitudes and positions of a set of cameras in an external coordinate system. Starting from a conventional global structure-from-motion pipeline, we present some substantial advances. In order to detect outlier relative rotations extracted from pairs of views, we improve a state-of-the-art algorithm based on cycle consistency, by introducing cycle bases. We estimate the angular attitudes of the cameras by proposing a novel gradient descent algorithm based on low-rank matrix completion, that naturally copes with the case of missing data. As for position recovery, we analyze an existing technique from a theoretical point of view, providing some insights on the conditions that guarantee solvability. We provide experimental results on both synthetic and real image sequences for which ground truth calibration is provided.
Robust weights of generalized M-estimator for panel data
Bakar, Nor Mazlina Abu; Midi, Habshah
2017-11-01
Ordinary Least Square estimation for panel data suffers biasness in the presence of high leverage points. Robust alternatives are proposed by incorporating new robust weights in Generalized M-estimator; determined by superior outlier detection methods. In this study, Diagnostic Robust Generalized Potential (DRGP) and Robust Diagnostic-F (RDF) are considered to form new weighting schemes for Robust Within GM-estimator. The performance of the newly proposed methods are called RWGM-DRGP and RWGM-RDF and investigated using real and simulated data sets. The ratios of root mean square error are evaluated and compared with the existing RWGM under robust centering procedures. The newly proposed estimators are found to be more efficient and resilient towards high leverage points due to the success of the new robust weights. The results are confirmed through reanalyzing numerical examples.
Introduction to Robust Estimation and Hypothesis Testing
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
Robust Estimation of Dimension Reduction Space
Cizek, P.; Härdle, W.K.
2005-01-01
Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions.We show that the recently proposed methods by Xia et al.(2002) can be made robust in such a way that preserves all advantages of the original
Second order statistics of bilinear forms of robust scatter estimators
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.
Robust Model-Free Multiclass Probability Estimation
Wu, Yichao; Zhang, Hao Helen; Liu, Yufeng
2010-01-01
Classical statistical approaches for multiclass probability estimation are typically based on regression techniques such as multiple logistic regression, or density estimation approaches such as linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). These methods often make certain assumptions on the form of probability functions or on the underlying distributions of subclasses. In this article, we develop a model-free procedure to estimate multiclass probabilities based on large-margin classifiers. In particular, the new estimation scheme is employed by solving a series of weighted large-margin classifiers and then systematically extracting the probability information from these multiple classification rules. A main advantage of the proposed probability estimation technique is that it does not impose any strong parametric assumption on the underlying distribution and can be applied for a wide range of large-margin classification methods. A general computational algorithm is developed for class probability estimation. Furthermore, we establish asymptotic consistency of the probability estimates. Both simulated and real data examples are presented to illustrate competitive performance of the new approach and compare it with several other existing methods. PMID:21113386
Estimation of joint position error.
Agostini, Valentina; Rosati, Samanta; Balestra, Gabriella; Trucco, Marco; Visconti, Lorenzo; Knaflitz, Marco
2017-07-01
Joint position error (JPE) is frequently used to assess proprioception in rehabilitation and sport science. During position-reposition tests the subject is asked to replicate a specific target angle (e.g. 30° of knee flexion) for a specific number of times. The aim of this study is to find an effective method to estimate JPE from the joint kinematic signal. Forty healthy subjects were tested to assess knee joint position sense. Three different methods of JPE estimation are described and compared using a hierarchical clustering approach. Overall, the 3 methods showed a high degree of similarity, ranging from 88% to 100%. We concluded that it is preferable to use the more user-independent method, in which the operator does not have to manually place "critical" markers.
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 ...
Scoredist: a simple and robust protein sequence distance estimator.
Sonnhammer, Erik L L; Hollich, Volker
2005-04-27
Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used. We propose a correction-based protein sequence estimator called Scoredist. It uses a logarithmic correction of observed divergence based on the alignment score according to the BLOSUM62 score matrix. We evaluated Scoredist and a number of optimal matrix methods using three evolutionary models for both training and testing Dayhoff, Jones-Taylor-Thornton, and Muller-Vingron, as well as Whelan and Goldman solely for testing. Test alignments with known distances between 0.01 and 2 substitutions per position (1-200 PAM) were simulated using ROSE. Scoredist proved as accurate as the optimal matrix methods, yet substantially more robust. When trained on one model but tested on another one, Scoredist was nearly always more accurate. The Jukes-Cantor and Kimura correction methods were also tested, but were substantially less accurate. The
Scoredist: A simple and robust protein sequence distance estimator
Directory of Open Access Journals (Sweden)
Hollich Volker
2005-04-01
Full Text Available Abstract Background Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used. Results We propose a correction-based protein sequence estimator called Scoredist. It uses a logarithmic correction of observed divergence based on the alignment score according to the BLOSUM62 score matrix. We evaluated Scoredist and a number of optimal matrix methods using three evolutionary models for both training and testing Dayhoff, Jones-Taylor-Thornton, and Müller-Vingron, as well as Whelan and Goldman solely for testing. Test alignments with known distances between 0.01 and 2 substitutions per position (1–200 PAM were simulated using ROSE. Scoredist proved as accurate as the optimal matrix methods, yet substantially more robust. When trained on one model but tested on another one, Scoredist was nearly always more accurate. The Jukes-Cantor and Kimura correction methods were also
Robust guaranteed-cost adaptive quantum phase estimation
Roy, Shibdas; Berry, Dominic W.; Petersen, Ian R.; Huntington, Elanor H.
2017-05-01
Quantum parameter estimation plays a key role in many fields like quantum computation, communication, and metrology. Optimal estimation allows one to achieve the most precise parameter estimates, but requires accurate knowledge of the model. Any inevitable uncertainty in the model parameters may heavily degrade the quality of the estimate. It is therefore desired to make the estimation process robust to such uncertainties. Robust estimation was previously studied for a varying phase, where the goal was to estimate the phase at some time in the past, using the measurement results from both before and after that time within a fixed time interval up to current time. Here, we consider a robust guaranteed-cost filter yielding robust estimates of a varying phase in real time, where the current phase is estimated using only past measurements. Our filter minimizes the largest (worst-case) variance in the allowable range of the uncertain model parameter(s) and this determines its guaranteed cost. It outperforms in the worst case the optimal Kalman filter designed for the model with no uncertainty, which corresponds to the center of the possible range of the uncertain parameter(s). Moreover, unlike the Kalman filter, our filter in the worst case always performs better than the best achievable variance for heterodyne measurements, which we consider as the tolerable threshold for our system. Furthermore, we consider effective quantum efficiency and effective noise power, and show that our filter provides the best results by these measures in the worst case.
Robust Parameter and Signal Estimation in Induction Motors
DEFF Research Database (Denmark)
Børsting, H.
This thesis deals with theories and methods for robust parameter and signal estimation in induction motors. The project originates in industrial interests concerning sensor-less control of electrical drives. During the work, some general problems concerning estimation of signals and parameters...
Doubly Robust Estimation of Optimal Dynamic Treatment Regimes
DEFF Research Database (Denmark)
Barrett, Jessica K; Henderson, Robin; Rosthøj, Susanne
2014-01-01
We compare methods for estimating optimal dynamic decision rules from observational data, with particular focus on estimating the regret functions defined by Murphy (in J. R. Stat. Soc., Ser. B, Stat. Methodol. 65:331-355, 2003). We formulate a doubly robust version of the regret-regression appro...
On robust parameter estimation in brain–computer interfacing
Samek, Wojciech; Nakajima, Shinichi; Kawanabe, Motoaki; Müller, Klaus-Robert
2017-12-01
Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain–computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.
Bias-Robust Estimates of Regression Based on Projections
Maronna, Ricardo A.; Yohai, Victor J
1993-01-01
A new class of bias-robust estimates of multiple regression is introduced. If $y$ and $x$ are two real random variables, let $T(y, x)$ be a univariate robust estimate of regression of $y$ on $x$ through the origin. The regression estimate $\\mathbf{T}(y, \\mathbf{x})$ of a random variable $y$ on a random vector $\\mathbf{x} = (x_1,\\cdots, x_p)'$ is defined as the vector $\\mathbf{t} \\in \\mathfrak{R}^p$ which minimizes $\\sup_{\\|\\mathbf{\\lambda}\\| = 1} \\mid T(y - \\mathbf{t'x, \\lambda' x}) \\mid s(\\m...
The EWMA control chart based on robust scale estimators
Nadia Saeed; Shahid Kamal
2016-01-01
The exponentially weighted moving average (EWMA) chart is very popular in statistical process control for detecting the small shifts in process mean and variance. This chart performs well under the assumption of normality but when data violate the assumption of normality, the robust approaches needed. We have developed the EWMA charts under different robust scale estimators available in literature and also compared the performance of these charts by calculating expected out-of-control points ...
On the robustness of two-stage estimators
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.
The EWMA control chart based on robust scale estimators
Directory of Open Access Journals (Sweden)
Nadia Saeed
2016-12-01
Full Text Available The exponentially weighted moving average (EWMA chart is very popular in statistical process control for detecting the small shifts in process mean and variance. This chart performs well under the assumption of normality but when data violate the assumption of normality, the robust approaches needed. We have developed the EWMA charts under different robust scale estimators available in literature and also compared the performance of these charts by calculating expected out-of-control points and expected widths under non-symmetric distributions (i.e. gamma and exponential. The simulation studies are being carried out for the purpose and results showed that amongst six robust estimators, the chart based on estimator Q_n relatively performed well for non-normal processes in terms of its shorter expected width and more number of expected out-of-control points which shows its sensitivity to detect the out of control signal.
Robust recursive impedance estimation for automotive lithium-ion batteries
Fridholm, Björn; Wik, Torsten; Nilsson, Magnus
2016-02-01
Recursive algorithms, such as recursive least squares (RLS) or Kalman filters, are commonly used in battery management systems to estimate the electrical impedance of the battery cell. However, these algorithms can in some cases run into problems with bias and even divergence of the estimates. This article illuminates problems that can arise in the online estimation using recursive methods, and lists modifications to handle these issues. An algorithm is also proposed that estimates the impedance by separating the problem in two parts; one estimating the ohmic resistance with an RLS approach, and another one where the dynamic effects are estimated using an adaptive Kalman filter (AKF) that is novel in the battery field. The algorithm produces robust estimates of ohmic resistance and time constant of the battery cell in closed loop with SoC estimation, as demonstrated by both in simulations and with experimental data from a lithium-ion battery cell.
Accurate and Robust Ego-Motion Estimation using Expectation Maximization
Dubbelman, G.; Mark, W. van der; Groen, F.C.A.
2008-01-01
A novel robust visual-odometry technique, called EM-SE(3) is presented and compared against using the random sample consensus (RANSAC) for ego-motion estimation. In this contribution, stereo-vision is used to generate a number of minimal-set motion hypothesis. By using EM-SE(3), which involves
Optimal and robust feedback controller estimation for a vibrating plate
Fraanje, P.R.; Verhaegen, M.; Doelman, N.J.; Berkhoff, A.
2004-01-01
This paper presents a method to estimate the H2 optimal and a robust feedback controller by means of Subspace Model Identification using the internal model control (IMC) approach. Using IMC an equivalent feed forward control problem is obtained, which is solved by the Causal Wiener filter for the H2
EnTracked: Energy-Efficient Robust Position Tracking for Mobile Devices
DEFF Research Database (Denmark)
Kjærgaard, Mikkel Baun; Jensen, Jakob Langdal; Godsk, Torben
2009-01-01
estimate and predict system conditions and mobility. Furthermore they provide evidence for that the system can lower the energy consumption considerably and remain robust when faced with changing system conditions. By validation in several real-world deployments we provide evidence that the real system...... conditions and mobility, schedules position updates to both minimize energy consumption and optimize robustness. The realized system tracks pedestrian targets equipped with GPS-enabled devices. The system is configurable to realize different trade-offs between energy consumption and robustness. We provide......An important feature of a modern mobile device is that it can position itself. Not only for use on the device but also for remote applications that require tracking of the device. To be useful, such position tracking has to be energy-efficient to avoid having a major impact on the battery life...
Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation
Directory of Open Access Journals (Sweden)
Maja Marasović
2017-01-01
Full Text Available Accurate estimation of essential enzyme kinetic parameters, such as Km and Vmax, is very important in modern biology. To this date, linearization of kinetic equations is still widely established practice for determining these parameters in chemical and enzyme catalysis. Although simplicity of linear optimization is alluring, these methods have certain pitfalls due to which they more often then not result in misleading estimation of enzyme parameters. In order to obtain more accurate predictions of parameter values, the use of nonlinear least-squares fitting techniques is recommended. However, when there are outliers present in the data, these techniques become unreliable. This paper proposes the use of a robust nonlinear regression estimator based on modified Tukey’s biweight function that can provide more resilient results in the presence of outliers and/or influential observations. Real and synthetic kinetic data have been used to test our approach. Monte Carlo simulations are performed to illustrate the efficacy and the robustness of the biweight estimator in comparison with the standard linearization methods and the ordinary least-squares nonlinear regression. We then apply this method to experimental data for the tyrosinase enzyme (EC 1.14.18.1 extracted from Solanum tuberosum, Agaricus bisporus, and Pleurotus ostreatus. The results on both artificial and experimental data clearly show that the proposed robust estimator can be successfully employed to determine accurate values of Km and Vmax.
Robust Estimation of Diffusion-Optimized Ensembles for Enhanced Sampling
DEFF Research Database (Denmark)
Tian, Pengfei; Jónsson, Sigurdur Æ.; Ferkinghoff-Borg, Jesper
2014-01-01
The multicanonical, or flat-histogram, method is a common technique to improve the sampling efficiency of molecular simulations. The idea is that free-energy barriers in a simulation can be removed by simulating from a distribution where all values of a reaction coordinate are equally likely...... accurate estimates of the diffusion coefficient. Here, we present a simple, yet robust solution to this problem. Compared to current state-of-the-art procedures, the new estimation method requires an order of magnitude fewer data to obtain reliable estimates, thus broadening the potential scope in which...
Adaptive and Robust Sliding Mode Position Control of IPMSM Drives
Directory of Open Access Journals (Sweden)
ZAKY, M.
2017-02-01
Full Text Available This paper proposes an adaptive and robust sliding mode control (SMC for the position control of Interior Permanent Magnet Synchronous Motor (IPMSM drives. A switching surface of SMC is designed using a Linear Quadratic Regulator (LQR technique to simultaneously control the tracking trajectory and load torque changes. The quadratic optimal control method is used to select the state feedback control gain that constitutes the system dynamic performance under uncertainties and disturbances. Feedback and switching gains are selected to satisfy both stability and fast convergence of the IPMSM. Matlab/Simulink is used to build the drive system. Experimental implementation of the IPMSM drive is carried out using DSP-DS1102 control board. The efficacy of the proposed position control method is validated using theoretical analysis and simulation and experimental results.
Constrained low-rank gamut completion for robust illumination estimation
Zhou, Jianshen; Yuan, Jiazheng; Liu, Hongzhe
2017-02-01
Illumination estimation is an important component of color constancy and automatic white balancing. According to recent survey and evaluation work, the supervised methods with a learning phase are competitive for illumination estimation. However, the robustness and performance of any supervised algorithm suffer from an incomplete gamut in training image sets because of limited reflectance surfaces in a scene. In order to address this problem, we present a constrained low-rank gamut completion algorithm, which can replenish gamut from limited surfaces in an image, for robust illumination estimation. In the proposed algorithm, we first discuss why the gamut completion is actually a low-rank matrix completion problem. Then a constrained low-rank matrix completion framework is proposed by adding illumination similarities among the training images as an additional constraint. An optimization algorithm is also given out by extending the augmented Lagrange multipliers. Finally, the completed gamut based on the proposed algorithm is fed into the support vector regression (SVR)-based illumination estimation method to evaluate the effect of gamut completion. The experimental results on both synthetic and real-world image sets show that the proposed gamut completion model not only can effectively improve the performance of the original SVR method but is also robust to the surface insufficiency in training samples.
A robust bayesian estimate of the concordance correlation coefficient.
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.
Using Robust Variance Estimation to Combine Multiple Regression Estimates with Meta-Analysis
Williams, Ryan
2013-01-01
The purpose of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. The proposed estimator obviates traditionally required information about the covariance structure of the dependent…
Robust combined position and formation control for marine surface craft
DEFF Research Database (Denmark)
Ihle, Ivar-Andre F.; Jouffroy, Jerome; Fossen, Thor I.
that affect each vessel and to disturbances that affect the inter-vessel relationships e.g. communication noise. Next, we prove ISS of a formation where at least one vessel is in closed loop with, a class of position control laws, in addition to the formation control law. This class encompass control laws......We consider the robustness properties of a formation control system for marine surface vessels. Intervessel constraint functions are stabilized to achieve the desired formation configuration. We show that the formation dynamics is Input-to-State Stable (ISS) to both environmental perturbations...... for point stabilization or path following. Hence, the designer can utilize previously developed controllers for single vessels in a formation control setting. A formation of three tugboats where one is in closed loop with a path following controller is simulated to verify the theoretical results....
Robust Extreme Learning Machine With its Application to Indoor Positioning.
Lu, Xiaoxuan; Zou, Han; Zhou, Hongming; Xie, Lihua; Huang, Guang-Bin
2016-01-01
The increasing demands of location-based services have spurred the rapid development of indoor positioning system and indoor localization system interchangeably (IPSs). However, the performance of IPSs suffers from noisy measurements. In this paper, two kinds of robust extreme learning machines (RELMs), corresponding to the close-to-mean constraint, and the small-residual constraint, have been proposed to address the issue of noisy measurements in IPSs. Based on whether the feature mapping in extreme learning machine is explicit, we respectively provide random-hidden-nodes and kernelized formulations of RELMs by second order cone programming. Furthermore, the computation of the covariance in feature space is discussed. Simulations and real-world indoor localization experiments are extensively carried out and the results demonstrate that the proposed algorithms can not only improve the accuracy and repeatability, but also reduce the deviation and worst case error of IPSs compared with other baseline algorithms.
Position Estimation Using Image Derivative
Mortari, Daniele; deDilectis, Francesco; Zanetti, Renato
2015-01-01
This paper describes an image processing algorithm to process Moon and/or Earth images. The theory presented is based on the fact that Moon hard edge points are characterized by the highest values of the image derivative. Outliers are eliminated by two sequential filters. Moon center and radius are then estimated by nonlinear least-squares using circular sigmoid functions. The proposed image processing has been applied and validated using real and synthetic Moon images.
Ikeda, Takeshi; Kawamoto, Mitsuru; Sashima, Akio; Suzuki, Keiji; Kurumatani, Koichi
In the field of the ubiquitous computing, positioning systems which can provide users' location information have paid attention as an important technical element which can be applied to various services, for example, indoor navigation services, evacuation services, market research services, guidance services, and so on. A lot of researchers have proposed various outdoor and indoor positioning systems. In this paper, we deal with indoor positioning systems. Many conventional indoor positioning systems use expensive infrastructures, because the propagated times of radio waves are used to measure users' positions with high accuracy. In this paper, we propose an indoor autonomous positioning system using radio signal strengths (RSSs) based on ISM band communications. In order to estimate users' positions, the proposed system utilizes a particle filter that is one of the Monte Carlo methods. Because the RSS information is used in the proposed system, the equipments configuring the system are not expensive compared with the conventional indoor positioning systems and it can be installed easily. Moreover, because the particle filter is used to estimate user's position, even if the RSS fluctuates due to, for example, multi-paths, the system can carry out position estimation robustly. We install the proposed system in one floor of a building and carry out some experiments in order to verify the validity of the proposed system. As a result, we confirmed that the average of the estimation errors of the proposed system was about 1.8 m, where the result is enough accuracy for achieving the services mentioned above.
Robust linear discriminant analysis with distance based estimators
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.
Robustness of Modal Parameter Estimation Methods Applied to Lightweight Structures
DEFF Research Database (Denmark)
Dickow, Kristoffer Ahrens; Kirkegaard, Poul Henning; Andersen, Lars Vabbersgaard
2013-01-01
. The ability to handle closely spaced modes and broad frequency ranges is investigated for a numerical model of a lightweight junction under dierent signal-to-noise ratios. The selection of both excitation points and response points are discussed. It is found that both the Rational Fraction Polynomial-Z method...... of nominally identical test subjects. However, the literature on modal testing of timber structures is rather limited and the applicability and robustness of dierent curve tting methods for modal analysis of such structures is not described in detail. The aim of this paper is to investigate the robustness...... of two parameter estimation methods built into the commercial modal testing software B&K Pulse Re ex Advanced Modal Analysis. The investigations are done by means of frequency response functions generated from a nite-element model and subjected to articial noise before being analyzed with Pulse Re ex...
Measurement Uncertainty Estimation of a Robust Photometer Circuit
Directory of Open Access Journals (Sweden)
Jesús de Vicente
2009-04-01
Full Text Available In this paper the uncertainty of a robust photometer circuit (RPC was estimated. Here, the RPC was considered as a measurement system, having input quantities that were inexactly known, and output quantities that consequently were also inexactly known. Input quantities represent information obtained from calibration certificates, specifications of manufacturers, and tabulated data. Output quantities describe the transfer function of the electrical part of the photodiode. Input quantities were the electronic components of the RPC, the parameters of the model of the photodiode and its sensitivity at 670 nm. The output quantities were the coefficients of both numerator and denominator of the closed-loop transfer function of the RPC. As an example, the gain and phase shift of the RPC versus frequency was evaluated from the transfer function, with their uncertainties and correlation coefficient. Results confirm the robustness of photodiode design.
Influence of binary mask estimation errors on robust speaker identification
DEFF Research Database (Denmark)
May, Tobias
2017-01-01
and unreliable feature components in the context of automatic speaker identification (SID). A systematic evaluation under ideal and non-ideal conditions demonstrated that the robustness to errors in the binary mask varied substantially across the different missing-data strategies. Moreover, full and bounded......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....... 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...
Preprocessing of gene expression data by optimally robust estimators
Directory of Open Access Journals (Sweden)
Deigner Hans-Peter
2010-11-01
Full Text Available Abstract Background The preprocessing of gene expression data obtained from several platforms routinely includes the aggregation of multiple raw signal intensities to one expression value. Examples are the computation of a single expression measure based on the perfect match (PM and mismatch (MM probes for the Affymetrix technology, the summarization of bead level values to bead summary values for the Illumina technology or the aggregation of replicated measurements in the case of other technologies including real-time quantitative polymerase chain reaction (RT-qPCR platforms. The summarization of technical replicates is also performed in other "-omics" disciplines like proteomics or metabolomics. Preprocessing methods like MAS 5.0, Illumina's default summarization method, RMA, or VSN show that the use of robust estimators is widely accepted in gene expression analysis. However, the selection of robust methods seems to be mainly driven by their high breakdown point and not by efficiency. Results We describe how optimally robust radius-minimax (rmx estimators, i.e. estimators that minimize an asymptotic maximum risk on shrinking neighborhoods about an ideal model, can be used for the aggregation of multiple raw signal intensities to one expression value for Affymetrix and Illumina data. With regard to the Affymetrix data, we have implemented an algorithm which is a variant of MAS 5.0. Using datasets from the literature and Monte-Carlo simulations we provide some reasoning for assuming approximate log-normal distributions of the raw signal intensities by means of the Kolmogorov distance, at least for the discussed datasets, and compare the results of our preprocessing algorithms with the results of Affymetrix's MAS 5.0 and Illumina's default method. The numerical results indicate that when using rmx estimators an accuracy improvement of about 10-20% is obtained compared to Affymetrix's MAS 5.0 and about 1-5% compared to Illumina's default method
An Analysis of the Indicator Saturation Estimator as a Robust Regression Estimator
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Bent
An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than observations, is analyzed with the purpose of finding an estimator that is robust to outliers and structural breaks. This estimator is an example of a one-step M-estimator based on Huber's skip functi....... The asymptotic theory is derived in the situation where there are no outliers or structural breaks using empirical process techniques. Stationary processes, trend stationary autoregressions and unit root processes are considered...
Robust Estimation of Evolutionary Distances with Information Theory.
Cao, Minh Duc; Allison, Lloyd; Dix, Trevor I; Bodén, Mikael
2016-05-01
Methods for measuring genetic distances in phylogenetics are known to be sensitive to the evolutionary model assumed. However, there is a lack of established methodology to accommodate the trade-off between incorporating sufficient biological reality and avoiding model overfitting. In addition, as traditional methods measure distances based on the observed number of substitutions, their tend to underestimate distances between diverged sequences due to backward and parallel substitutions. Various techniques were proposed to correct this, but they lack the robustness against sequences that are distantly related and of unequal base frequencies. In this article, we present a novel genetic distance estimate based on information theory that overcomes the above two hurdles. Instead of examining the observed number of substitutions, this method estimates genetic distances using Shannon's mutual information. This naturally provides an effective framework for balancing model complexity and goodness of fit. Our distance estimate is shown to be approximately linear to elapsed time and hence is less sensitive to the divergence of sequence data and compositional biased sequences. Using extensive simulation data, we show that our method 1) consistently reconstructs more accurate phylogeny topologies than existing methods, 2) is robust in extreme conditions such as diverged phylogenies, unequal base frequencies data, and heterogeneous mutation patterns, and 3) scales well with large phylogenies. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Contrasting treatment-specific survival using double-robust estimators.
Zhang, Min; Schaubel, Douglas E
2012-12-30
In settings where a randomized trial is infeasible, observational data are frequently used to compare treatment-specific survival. The average causal effect (ACE) can be used to make inference regarding treatment policies on patient populations, and a valid ACE estimator must account for imbalances with respect to treatment-specific covariate distributions. One method through which the ACE on survival can be estimated involves appropriately averaging over Cox-regression-based fitted survival functions. A second available method balances the treatment-specific covariate distributions through inverse probability of treatment weighting and then contrasts weighted nonparametric survival function estimators. Because both methods have their advantages and disadvantages, we propose methods that essentially combine both estimators. The proposed methods are double robust, in the sense that they are consistent if at least one of the two working regression models (i.e., logistic model for treatment and Cox model for death hazard) is correct. The proposed methods involve estimating the ACE with respect to restricted mean survival time, defined as the area under the survival curve up to some prespecified time point. We derive and evaluate asymptotic results through simulation. We apply the proposed methods to estimate the ACE of donation-after-cardiac-death kidney transplantation with the use of data obtained from multiple centers in the Netherlands. Copyright © 2012 John Wiley & Sons, Ltd.
Robust estimation of the correlation matrix of longitudinal data
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.
Robust Parallel Motion Estimation and Mapping with Stereo Cameras in Underground Infrastructure
Liu, Chun; Li, Zhengning; Zhou, Yuan
2016-06-01
Presently, we developed a novel robust motion estimation method for localization and mapping in underground infrastructure using a pre-calibrated rigid stereo camera rig. Localization and mapping in underground infrastructure is important to safety. Yet it's also nontrivial since most underground infrastructures have poor lighting condition and featureless structure. Overcoming these difficulties, we discovered that parallel system is more efficient than the EKF-based SLAM approach since parallel system divides motion estimation and 3D mapping tasks into separate threads, eliminating data-association problem which is quite an issue in SLAM. Moreover, the motion estimation thread takes the advantage of state-of-art robust visual odometry algorithm which is highly functional under low illumination and provides accurate pose information. We designed and built an unmanned vehicle and used the vehicle to collect a dataset in an underground garage. The parallel system was evaluated by the actual dataset. Motion estimation results indicated a relative position error of 0.3%, and 3D mapping results showed a mean position error of 13cm. Off-line process reduced position error to 2cm. Performance evaluation by actual dataset showed that our system is capable of robust motion estimation and accurate 3D mapping in poor illumination and featureless underground environment.
ROBUST PARALLEL MOTION ESTIMATION AND MAPPING WITH STEREO CAMERAS IN UNDERGROUND INFRASTRUCTURE
Directory of Open Access Journals (Sweden)
C. Liu
2016-06-01
Full Text Available Presently, we developed a novel robust motion estimation method for localization and mapping in underground infrastructure using a pre-calibrated rigid stereo camera rig. Localization and mapping in underground infrastructure is important to safety. Yet it’s also nontrivial since most underground infrastructures have poor lighting condition and featureless structure. Overcoming these difficulties, we discovered that parallel system is more efficient than the EKF-based SLAM approach since parallel system divides motion estimation and 3D mapping tasks into separate threads, eliminating data-association problem which is quite an issue in SLAM. Moreover, the motion estimation thread takes the advantage of state-of-art robust visual odometry algorithm which is highly functional under low illumination and provides accurate pose information. We designed and built an unmanned vehicle and used the vehicle to collect a dataset in an underground garage. The parallel system was evaluated by the actual dataset. Motion estimation results indicated a relative position error of 0.3%, and 3D mapping results showed a mean position error of 13cm. Off-line process reduced position error to 2cm. Performance evaluation by actual dataset showed that our system is capable of robust motion estimation and accurate 3D mapping in poor illumination and featureless underground environment.
Robust estimates of extinction time in the geological record
Bradshaw, C. J. A.; Cooper, A.; Turney, C. S. M.; Brook, B. W.
2012-02-01
The rate at which a once-abundant population declines in density prior to local or global extinction can strongly influence the precision of statistical estimates of extinction time. Here we report the development of a new, robust method of inference which accounts for these potential biases and uncertainties, and test it against known simulated data and dated Pleistocene fossil remains (mammoths, horses and Neanderthals). Our method is a Gaussian-resampled, inverse-weighted McInerny et al. (GRIWM) approach which weights observations inversely according to their temporal distance from the last observation of a species' confirmed occurrence, and for dates with associated radiometric errors, is able to sample individual dates from an underlying fossilization probability distribution. We show that this leads to less biased estimates of the 'true' extinction date. In general, our method provides a flexible tool for hypothesis testing, including inferring the probability that the extinctions of pairs or groups of species overlap, and for more robustly evaluating the relative likelihood of different extinction drivers such as climate perturbation and human exploitation.
A robust methodology for modal parameters estimation applied to SHM
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.
Robust regularized least-squares beamforming approach to signal estimation
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.
Bias and robustness of uncertainty components estimates in transient climate projections
Hingray, Benoit; Blanchet, Juliette; Jean-Philippe, Vidal
2016-04-01
is always positive. It can be especially high with STANOVA. In the most critical configurations, when the number of members available for each modeling chain is small (number of climate impact studies resulting in a likely large overestimation of the contribution of model uncertainty to total variance. For both approaches, the robustness of all uncertainty estimates is higher when more members are available, when internal variability is smaller and/or the response-to-uncertainty ratio is higher. QEANOVA estimates are much more robust than STANOVA ones: QEANOVA simulated confidence intervals are roughly 3 to 5 times smaller than STANOVA ones. Excepted for STANOVA when less than 3 members is available, the robustness is rather high for total uncertainty and moderate for internal variability estimates. For model uncertainty or response-to-uncertainty ratio estimates, the robustness is conversely low for QEANOVA to very low for STANOVA. In the most critical configurations (small number of member, large internal variability), large over- or underestimation of uncertainty components is very thus likely. To propose relevant uncertainty analyses and avoid misleading interpretations, estimates of uncertainty components should be therefore bias corrected and ideally come with estimates of their robustness. This work is part of the COMPLEX Project (European Collaborative Project FP7-ENV-2012 number: 308601; http://www.complex.ac.uk/). Hingray, B., Saïd, M., 2014. Partitioning internal variability and model uncertainty components in a multimodel multireplicate ensemble of climate projections. J.Climate. doi:10.1175/JCLI-D-13-00629.1 Hingray, B., Blanchet, J. (revision) Unbiased estimators for uncertainty components in transient climate projections. J. Climate Hingray, B., Blanchet, J., Vidal, J.P. (revision) Robustness of uncertainty components estimates in climate projections. J.Climate
Robust feature tracking for endoscopic pose estimation and structure recovery
Speidel, S.; Krappe, S.; Röhl, S.; Bodenstedt, S.; Müller-Stich, B.; Dillmann, R.
2013-03-01
Minimally invasive surgery is a highly complex medical discipline with several difficulties for the surgeon. To alleviate these difficulties, augmented reality can be used for intraoperative assistance. For visualization, the endoscope pose must be known which can be acquired with a SLAM (Simultaneous Localization and Mapping) approach using the endoscopic images. In this paper we focus on feature tracking for SLAM in minimally invasive surgery. Robust feature tracking and minimization of false correspondences is crucial for localizing the endoscope. As sensory input we use a stereo endoscope and evaluate different feature types in a developed SLAM framework. The accuracy of the endoscope pose estimation is validated with synthetic and ex vivo data. Furthermore we test the approach with in vivo image sequences from da Vinci interventions.
Most robust estimate of the Transient Climate Response yet?
Haustein, Karsten; Venema, Victor; Schurer, Andrew
2017-04-01
Estimates of the Transient Climate Response often lack a coherent hemispheric or otherwise spatio-temporal representation. In the light of recent work that highlights the importance of inhomogeneous forcing considerations (Shindell et al 2014; Marvel et al 2015) and tas/tos-related inaccuracies (Richardson et al. 2016), here we present results from a well-tested two-box response model that takes these effects carefully into account. All external forcing data are updated based on latest emission estimates as well as recent TSI and volcanic AOD estimates. So are observed GMST data which include data for the entire year of 2016. Hence we also provide one of the first TCR estimates taking the latest El Nino into account. We demonstrate that short-term climate variability is not going to change the TCR estimate beyond very minor fluctuations. The method is therefore shown to be robust within surprisingly small uncertainty estimates. Using PMIP3 and an extended ensemble of HadCM3 simulations (Euro500; Schurer et al. 2014) GCM simulations for the pre-industrial period, we test the fast and slow response time constants that are tailored for observational data (Ripdal 2012). We also test the hemispheric response as well as the response over land and ocean separately. The TCR/ECS ratio is taken from a selected sub-set of CMIP5 simulations. The selection criteria is the best spatiotemporal match over 4 different time periods between 1860 and 2010. We will argue that this procedure should also be standard procedure to estimate ECS from observations, rather than relying on OHC estimates only. Finally, the demonstrate that PMIP3-type simulations that are initialised at least a century before 1850 (as is the standard initialisation for CMIP5-type simulations) are to be preferred. Remaining long-term radiative imbalance due to strong volcanic eruptions (e.g. Gleckler et al. 2006) tend to make CMIP5-type simulations slightly more sensitive to forcing, which leads to detectable
Estimating nonrigid motion from inconsistent intensity with robust shape features
Energy Technology Data Exchange (ETDEWEB)
Liu, Wenyang [Department of Bioengineering, University of California, Los Angeles, California 90095 (United States); Ruan, Dan, E-mail: druan@mednet.ucla.edu [Department of Bioengineering, University of California, Los Angeles, California 90095 (United States); Department of Radiation Oncology, University of California, Los Angeles, California 90095 (United States); Department of Biomedical Physics, University of California, Los Angeles, California 90095 (United States)
2013-12-15
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
Robust estimation of scattering in pulsar timing analysis
Lentati, L.; Kerr, M.; Dai, S.; Shannon, R. M.; Hobbs, G.; Osłowski, S.
2017-06-01
We present a robust approach to incorporating models for the time-variable broadening of the pulse profile due to scattering in the ionized interstellar medium into profile-domain pulsar timing analysis. We use this approach to simultaneously estimate temporal variations in both the dispersion measure (DM) and scattering, together with a model for the pulse profile that includes smooth evolution as a function of frequency, and the pulsar's timing model. We show that fixing the scattering time-scales when forming time-of-arrival estimates, as has been suggested in the context of traditional pulsar timing analysis, can significantly underestimate the uncertainties in both DM and the arrival time of the pulse, leading to bias in the timing parameters. We apply our method using a new, publicly available, GPU-accelerated code, both to simulations and observations of the millisecond pulsar PSR J1643-1224. This pulsar is known to exhibit significant scattering variability compared to typical millisecond pulsars, and we find including low-frequency (pulsar timing is ideally suited.
Detecting Positioning Errors and Estimating Correct Positions by Moving Window.
Directory of Open Access Journals (Sweden)
Ha Yoon Song
Full Text Available In recent times, improvements in smart mobile devices have led to new functionalities related to their embedded positioning abilities. Many related applications that use positioning data have been introduced and are widely being used. However, the positioning data acquired by such devices are prone to erroneous values caused by environmental factors. In this research, a detection algorithm is implemented to detect erroneous data over a continuous positioning data set with several options. Our algorithm is based on a moving window for speed values derived by consecutive positioning data. Both the moving average of the speed and standard deviation in a moving window compose a moving significant interval at a given time, which is utilized to detect erroneous positioning data along with other parameters by checking the newly obtained speed value. In order to fulfill the designated operation, we need to examine the physical parameters and also determine the parameters for the moving windows. Along with the detection of erroneous speed data, estimations of correct positioning are presented. The proposed algorithm first estimates the speed, and then the correct positions. In addition, it removes the effect of errors on the moving window statistics in order to maintain accuracy. Experimental verifications based on our algorithm are presented in various ways. We hope that our approach can help other researchers with regard to positioning applications and human mobility research.
Robust Vision-Based Pose Estimation Algorithm for AN Uav with Known Gravity Vector
Kniaz, V. V.
2016-06-01
Accurate estimation of camera external orientation with respect to a known object is one of the central problems in photogrammetry and computer vision. In recent years this problem is gaining an increasing attention in the field of UAV autonomous flight. Such application requires a real-time performance and robustness of the external orientation estimation algorithm. The accuracy of the solution is strongly dependent on the number of reference points visible on the given image. The problem only has an analytical solution if 3 or more reference points are visible. However, in limited visibility conditions it is often needed to perform external orientation with only 2 visible reference points. In such case the solution could be found if the gravity vector direction in the camera coordinate system is known. A number of algorithms for external orientation estimation for the case of 2 known reference points and a gravity vector were developed to date. Most of these algorithms provide analytical solution in the form of polynomial equation that is subject to large errors in the case of complex reference points configurations. This paper is focused on the development of a new computationally effective and robust algorithm for external orientation based on positions of 2 known reference points and a gravity vector. The algorithm implementation for guidance of a Parrot AR.Drone 2.0 micro-UAV is discussed. The experimental evaluation of the algorithm proved its computational efficiency and robustness against errors in reference points positions and complex configurations.
Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems
Directory of Open Access Journals (Sweden)
Miao Lingjuan
2014-08-01
Full Text Available In micro-electro-mechanical system based inertial navigation system (MEMS-INS/global position system (GPS integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation (RE and fault detection (FD. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.
Directory of Open Access Journals (Sweden)
Zhanshan Wang
2014-01-01
Full Text Available The control of a high performance alternative current (AC motor drive under sensorless operation needs the accurate estimation of rotor position. In this paper, one method of accurately estimating rotor position by using both motor complex number model based position estimation and position estimation error suppression proportion integral (PI controller is proposed for the sensorless control of the surface permanent magnet synchronous motor (SPMSM. In order to guarantee the accuracy of rotor position estimation in the flux-weakening region, one scheme of identifying the permanent magnet flux of SPMSM by extended Kalman filter (EKF is also proposed, which formed the effective combination method to realize the sensorless control of SPMSM with high accuracy. The simulation results demonstrated the validity and feasibility of the proposed position/speed estimation system.
Pernet, Cyril R; Wilcox, Rand; Rousselet, Guillaume A
2012-01-01
Pearson's correlation measures the strength of the association between two variables. The technique is, however, restricted to linear associations and is overly sensitive to outliers. Indeed, a single outlier can result in a highly inaccurate summary of the data. Yet, it remains the most commonly used measure of association in psychology research. Here we describe a free Matlab((R)) based toolbox (http://sourceforge.net/projects/robustcorrtool/) that computes robust measures of association between two or more random variables: the percentage-bend correlation and skipped-correlations. After illustrating how to use the toolbox, we show that robust methods, where outliers are down weighted or removed and accounted for in significance testing, provide better estimates of the true association with accurate false positive control and without loss of power. The different correlation methods were tested with normal data and normal data contaminated with marginal or bivariate outliers. We report estimates of effect size, false positive rate and power, and advise on which technique to use depending on the data at hand.
Pernet, Cyril R.; Wilcox, Rand; Rousselet, Guillaume A.
2012-01-01
Pearson’s correlation measures the strength of the association between two variables. The technique is, however, restricted to linear associations and is overly sensitive to outliers. Indeed, a single outlier can result in a highly inaccurate summary of the data. Yet, it remains the most commonly used measure of association in psychology research. Here we describe a free Matlab(R) based toolbox (http://sourceforge.net/projects/robustcorrtool/) that computes robust measures of association between two or more random variables: the percentage-bend correlation and skipped-correlations. After illustrating how to use the toolbox, we show that robust methods, where outliers are down weighted or removed and accounted for in significance testing, provide better estimates of the true association with accurate false positive control and without loss of power. The different correlation methods were tested with normal data and normal data contaminated with marginal or bivariate outliers. We report estimates of effect size, false positive rate and power, and advise on which technique to use depending on the data at hand. PMID:23335907
Directory of Open Access Journals (Sweden)
Cyril R Pernet
2013-01-01
Full Text Available Pearson’s correlation measures the strength of the association between two variables. The technique is, however, restricted to linear associations and is overly sensitive to outliers. Indeed, a single outlier can result in a highly inaccurate summary of the data. Yet, it remains the most commonly used measure of association in psychology research. Here we describe a free Matlab(R based toolbox (http://sourceforge.net/projects/robustcorrtool/ that computes robust measures of association between two or more random variables: the percentage-bend correlation and skipped-correlations. After illustrating how to use the toolbox, we show that robust methods, where outliers are down weighted or removed and accounted for in significance testing, provide better estimates of the true association with accurate false positive control and without loss of power. The different correlation methods were tested with normal data and normal data contaminated with marginal or bivariate outliers. We report estimates of effect size, false positive rate and power, and advise on which technique to use depending on the data at hand.
Position estimation of transceivers in communication networks
Kent, Claudia A [Pleasanton, CA; Dowla, Farid [Castro Valley, CA
2008-06-03
This invention provides a system and method using wireless communication interfaces coupled with statistical processing of time-of-flight data to locate by position estimation unknown wireless receivers. Such an invention can be applied in sensor network applications, such as environmental monitoring of water in the soil or chemicals in the air where the position of the network nodes is deemed critical. Moreover, the present invention can be arranged to operate in areas where a Global Positioning System (GPS) is not available, such as inside buildings, caves, and tunnels.
multiangulation position estimation performance analysis using a ...
African Journals Online (AJOL)
HOD
classical technique but has AOA estimation resolution between 2° to 5° [5].The resolution determines how ... into the linearized equation which is then iteratively refined and the final source location that minimizes the ..... [1] Dardari, D., Falletti, E. and Luise, M. Satellite and terrestrial radio positioning techniques: a signal.
Chave, Alan D.
2017-08-01
The robust statistical model of a Gaussian core contaminated by outlying data in use since the 1980s, and which underlies modern estimation of the magnetotelluric (MT) response function, is re-examined from first principles. The residuals from robust estimators applied to MT data are shown to be systematically long-tailed compared to a distribution based on the Gaussian and hence inconsistent with the robust model. Instead, MT data are pervasively described by the stable distribution family for which the Gaussian is an end member, but whose remaining distributions have algebraic rather than exponential tails. The validity of the stable model is rigorously demonstrated using a permutation test. A maximum likelihood estimator (MLE), including the use of a remote reference, that exploits the stable nature of MT data is formulated, and its two-stage implementation, in which stable parameters are first fit to the residuals, and then the MT responses are solved for, with iteration between them, is described. The MLE is inherently robust, but differs from a conventional robust estimator because it is based on a statistical model derived from the data rather than being ad hoc. Finally, the covariance matrices obtained from MT data are pervasively improper as a result of weak non-stationarity, and the Cramér-Rao lower bound for the improper covariance matrix is derived, resulting in reliable second-order statistics for MT responses. The stable MLE was applied to an exemplar broadband data set from northwest Namibia. The stable MLE is shown to be consistent with the statistical model underlying linear regression and hence is unconditionally unbiased, in contrast to the robust model. The MLE is compared to conventional robust remote reference and two-stage estimators, establishing that the standard errors of the former are systematically smaller than for either of the latter, and that the standardized differences between them exhibit excursions that are both too frequent and
Directory of Open Access Journals (Sweden)
Wang Longbiao
2006-01-01
Full Text Available We propose robust distant speech recognition by combining multiple microphone-array processing with position-dependent cepstral mean normalization (CMN. In the recognition stage, the system estimates the speaker position and adopts compensation parameters estimated a priori corresponding to the estimated position. Then the system applies CMN to the speech (i.e., position-dependent CMN and performs speech recognition for each channel. The features obtained from the multiple channels are integrated with the following two types of processings. The first method is to use the maximum vote or the maximum summation likelihood of recognition results from multiple channels to obtain the final result, which is called multiple-decoder processing. The second method is to calculate the output probability of each input at frame level, and a single decoder using these output probabilities is used to perform speech recognition. This is called single-decoder processing, resulting in lower computational cost. We combine the delay-and-sum beamforming with multiple-decoder processing or single-decoder processing, which is termed multiple microphone-array processing. We conducted the experiments of our proposed method using a limited vocabulary (100 words distant isolated word recognition in a real environment. The proposed multiple microphone-array processing using multiple decoders with position-dependent CMN achieved a 3.2% improvement (50% relative error reduction rate over the delay-and-sum beamforming with conventional CMN (i.e., the conventional method. The multiple microphone-array processing using a single decoder needs about one-third the computational time of that using multiple decoders without degrading speech recognition performance.
Robust positioning control of pneumatic servo system with pressure control loop
Noritsugu, Toshiro; Takaiwa, Masahiro
1995-01-01
The goal of this paper is to attain a robust positioning control of a pneumatic driving system. A positioning control system positively focusing on the pressure control is investigated from the view that the pressure control is indispensable for improvement of control performances. A disturbance observer is employed to improve the pressure response and compensate the influence of friction force and parameter change. Consequently the improvements of robustness against payload and of positionin...
DEFF Research Database (Denmark)
Ni, Ronggang; Xu, Dianguo; Blaabjerg, Frede
2017-01-01
relationship with the magnetic field distortion. Position estimation errors caused by higher order harmonic inductances and voltage harmonics generated by the SVPWM are also discussed. Both simulations and experiments are carried out based on a commercial PMSM to verify the superiority of the proposed method......Rotor position estimated with high-frequency (HF) voltage injection methods can be distorted by voltage errors due to inverter nonlinearities, motor resistance, and rotational voltage drops, etc. This paper proposes an improved HF square-wave voltage injection algorithm, which is robust to voltage...... errors without any compensations meanwhile has less fluctuation in the position estimation error. The average position estimation error is investigated based on the analysis of phase harmonic inductances, and deduced in the form of the phase shift of the second-order harmonic inductances to derive its...
A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Jun Qi
2017-11-01
Full Text Available This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS. The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF module, which is only used for time synchronization between different nodes, with accuracy up to 1 μs. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM. The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach 0.61 mm and 0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of 10.2 mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS signal.
A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network.
Qi, Jun; Liu, Guo-Ping
2017-11-06
This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN) node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF) module, which is only used for time synchronization between different nodes, with accuracy up to 1 μ s. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF) for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM). The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach 0.61 mm and 0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of 10.2 mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS) signal.
Robust Cognitive-GN BER Estimator for Dynamic WDM Networks
DEFF Research Database (Denmark)
Borkowski, Robert; Caballero Jambrina, Antonio; Arlunno, Valeria
2014-01-01
We introduce and experimentally demonstrate a simple yet reliable and fast tool for estimating BER of lightpaths over uncompensated links. The model provides accurate estimates for capacity upgrade scenarios when modulation format order is increased.......We introduce and experimentally demonstrate a simple yet reliable and fast tool for estimating BER of lightpaths over uncompensated links. The model provides accurate estimates for capacity upgrade scenarios when modulation format order is increased....
A Robust Threshold for Iterative Channel Estimation in OFDM Systems
Directory of Open Access Journals (Sweden)
A. Kalaycioglu
2010-04-01
Full Text Available A novel threshold computation method for pilot symbol assisted iterative channel estimation in OFDM systems is considered. As the bits are transmitted in packets, the proposed technique is based on calculating a particular threshold for each data packet in order to select the reliable decoder output symbols to improve the channel estimation performance. Iteratively, additional pilot symbols are established according to the threshold and the channel is re-estimated with the new pilots inserted to the known channel estimation pilot set. The proposed threshold calculation method for selecting additional pilots performs better than non-iterative channel estimation, no threshold and fixed threshold techniques in poor HF channel simulations.
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Rizzuto, Enrico; Narasimhan, Harikrishna
2012-01-01
More frequent use of advanced types of structures with limited redundancy and serious consequences in case of failure combined with increased requirements to efficiency in design and execution followed by increased risk of human errors has made the need of requirements to robustness of structures...
Directory of Open Access Journals (Sweden)
Xiaoping Li
2013-01-01
Full Text Available We present an efficient algorithm based on the robust Chinese remainder theorem (CRT to perform single frequency determination from multiple undersampled waveforms. The optimal estimate of common remainder in robust CRT, which plays an important role in the final frequency estimation, is first discussed. To avoid the exhausted searching in the optimal estimation, we then provide an improved algorithm with the same performance but less computation. Besides, the sufficient and necessary condition of the robust estimation was proposed. Numerical examples are also provided to verify the effectiveness of the proposed algorithm and related conclusions.
On Improving the Energy Efficiency and Robustness of Position Tracking for Mobile Devices
DEFF Research Database (Denmark)
Kjærgaard, Mikkel Baun
2010-01-01
An important feature of a modern mobile device is that it can position itself and support remote position tracking. To be useful, such position tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. Furthermore, tracking has to robustly deliver...
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.
Position Estimation of Transceivers in Communication Networks
Energy Technology Data Exchange (ETDEWEB)
Dowla, F; Kent, C
2004-01-20
With rapid developments in wireless sensor networks, there is a growing need for transceiver position estimation independent of GPS, which may not be available in indoor networks. Our approach is to use range estimates from time-of-flight (TOF) measurements, a technique well suited to large bandwidth physical links, such as in ultra-wideband (UWB) systems. In our UWB systems, pulse duration less than 200 psecs can easily be resolved to less than a foot. Assuming an encoded UWB physical layer, we first test positioning accuracy using simulations. We are interested in sensitivity to range errors and the required number of ranging nodes, and we show that in a high-precision environment, such as UWB, the optimal number of transmitters is four. Four transmitters with {+-}20ft. range error can locate a receiver to within one or two feet. We then implement these algorithms on an 802.11 wireless network and demonstrate the ability to locate a network access point to approximately 20 feet.
Robust Speed and Parameter Estimation in Induction Motors
DEFF Research Database (Denmark)
Børsting, H.; Vadstrup, P.
1995-01-01
This paper presents a Model Reference Adaptive System (MRAS) for the estimation of the induction motor speed, based on measured terminal voltages and currents.......This paper presents a Model Reference Adaptive System (MRAS) for the estimation of the induction motor speed, based on measured terminal voltages and currents....
Robust Point Location Estimators for the EWMA Control Chart
Zwetsloot, I.M.; Schoonhoven, M.; Does, R.J.M.M.
2016-01-01
In practice, the EWMA control chart for process monitoring is based on parameters estimated from a retrospective data set representing the process characteristic under study. This data set may contain contaminated observations, which in turn can affect the estimates and hence the control chart’s
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...... method, which is an optimization based approach, is applied to the subsystems of the propulsion system. The optimization problem has been solved within two different tools and the results are compared with two other optimization based approaches. The turbo-pump system is used to illustrate the employed...
Regresi Robust Mm-estimator Untuk Penanganan Pencilan Pada Regresi Linier Berganda
Candraningtyas, Sherly; Safitri, Diah; Ispriyanti, Dwi
2013-01-01
The multiple linear regression model is used to study the relationship between a dependent variable and more than one independent variables. Estimation method which is the most frequently be used to analyze regression is Ordinary Least Squares (OLS). OLS for linear regression models is known to be very sensitive to outliers. Robust regression is an important method for analyzing data contaminated by outliers. This paper will discuss the robust regression MM-estimator. This estimation is a com...
Computationally Efficient and Noise Robust DOA and Pitch Estimation
DEFF Research Database (Denmark)
Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll
2016-01-01
signals are often contaminated by different types of noise, which challenges the assumption of white Gaussian noise in most state-of-the-art methods. We establish filtering methods based on noise statistics to apply to nonparametric spectral and spatial parameter estimates of the harmonics. We design...... a joint DOA and pitch estimator. In white Gaussian noise, we derive even more computationally efficient solutions which are designed using the narrowband power spectrum of the harmonics. Numerical results reveal the performance of the estimators in colored noise compared with the Cram\\'{e}r-Rao lower...
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 of survival estimates for radio-marked animals
Bunck, C.M.; Chen, C.-L.
1992-01-01
Telemetry techniques are often used to study the survival of birds and mammals; particularly whcn mark-recapture approaches are unsuitable. Both parametric and nonparametric methods to estimate survival have becn developed or modified from other applications. An implicit assumption in these approaches is that the probability of re-locating an animal with a functioning transmitter is one. A Monte Carlo study was conducted to determine the bias and variance of the Kaplan-Meier estimator and an estimator based also on the assumption of constant hazard and to eva!uate the performance of the two-sample tests associated with each. Modifications of each estimator which allow a re-Iocation probability of less than one are described and evaluated. Generallv the unmodified estimators were biased but had lower variance. At low sample sizes all estimators performed poorly. Under the null hypothesis, the distribution of all test statistics reasonably approximated the null distribution when survival was low but not when it was high. The power of the two-sample tests were similar.
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.
Robust state estimation for stochastic genetic regulatory networks
Liang, Jinling; Lam, James
2010-01-01
In this article, the state estimation problem is investigated for genetic regulatory networks (GRNs) with parameter uncertainties and stochastic disturbances. To account for the unavoidable modelling errors and parameter fluctuations, the network parameters are assumed to be time-varying but norm-bounded. Furthermore, scalar multiplicative white noises are introduced into both the translation process and the feedback regulation process in order to reflect the inherent intracellular and extracellular noise perturbations. The purpose of the addressed problem is to design a linear state estimator that can estimate the true concentration of the mRNA and the protein of the uncertain GRNs. By resorting to the Lyapunov-Krasovskii functional method combined with the linear matrix inequality (LMI) technique, sufficient conditions are first established for ensuring the stochastic stability of the dynamics of the estimation error, and the estimator gains are then designed in terms of the solutions to some LMIs that can be easily solved by using the standard numerical software. A three-node GRN is presented to show the effectiveness of the proposed design procedures.
On robust tail index estimation under random censorship | Sayah ...
African Journals Online (AJOL)
type distributions in the framework of randomly censored samples, based on the ideas of Kaplan-Meier integration using the huberized M-estimator of the tail index. We derive their asymptotic results. We illustrate the performance and the ...
Robust estimators based on generalization of trimmed mean
Czech Academy of Sciences Publication Activity Database
Adam, Lukáš; Bejda, P.
(2018) ISSN 0361-0918 Institutional support: RVO:67985556 Keywords : Breakdown point * Estimators * Geometric median * Location * Trimmed mean Subject RIV: BA - General Mathematics Impact factor: 0.457, year: 2016 http:// library .utia.cas.cz/separaty/2017/MTR/adam-0481224.pdf
Monocular camera and IMU integration for indoor position estimation.
Zhang, Yinlong; Tan, Jindong; Zeng, Ziming; Liang, Wei; Xia, Ye
2014-01-01
This paper presents a monocular camera (MC) and inertial measurement unit (IMU) integrated approach for indoor position estimation. Unlike the traditional estimation methods, we fix the monocular camera downward to the floor and collect successive frames where textures are orderly distributed and feature points robustly detected, rather than using forward oriented camera in sampling unknown and disordered scenes with pre-determined frame rate and auto-focus metric scale. Meanwhile, camera adopts the constant metric scale and adaptive frame rate determined by IMU data. Furthermore, the corresponding distinctive image feature point matching approaches are employed for visual localizing, i.e., optical flow for fast motion mode; Canny Edge Detector & Harris Feature Point Detector & Sift Descriptor for slow motion mode. For superfast motion and abrupt rotation where images from camera are blurred and unusable, the Extended Kalman Filter is exploited to estimate IMU outputs and to derive the corresponding trajectory. Experimental results validate that our proposed method is effective and accurate in indoor positioning. Since our system is computationally efficient and in compact size, it's well suited for visually impaired people indoor navigation and wheelchaired people indoor localization.
Robust Estimation and Moment Selection in Dynamic Fixed-effects Panel Data Models
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
A signal strength priority based position estimation for mobile platforms
Kalgikar, Bhargav; Akopian, David; Chen, Philip
2010-01-01
Global Positioning System (GPS) products help to navigate while driving, hiking, boating, and flying. GPS uses a combination of orbiting satellites to determine position coordinates. This works great in most outdoor areas, but the satellite signals are not strong enough to penetrate inside most indoor environments. As a result, a new strain of indoor positioning technologies that make use of 802.11 wireless LANs (WLAN) is beginning to appear on the market. In WLAN positioning the system either monitors propagation delays between wireless access points and wireless device users to apply trilateration techniques or it maintains the database of location-specific signal fingerprints which is used to identify the most likely match of incoming signal data with those preliminary surveyed and saved in the database. In this paper we investigate the issue of deploying WLAN positioning software on mobile platforms with typically limited computational resources. We suggest a novel received signal strength rank order based location estimation system to reduce computational loads with a robust performance. The proposed system performance is compared to conventional approaches.
White matter lesion segmentation using robust parameter estimation algorithms
Yang, Faguo; Zhu, Litao; Jiang, Tianzi
2003-05-01
White matter lesions are common brain abnormalities. In this paper, we introduce an automatic algorithm for segmentation of white matter lesions from brain MRI images. The intensities of each tissue is assumed to be Gaussian distributed, whose parameters (mean vector and covariance matrix) are estimated using a tissue distribution model. And then a measure is defined to indicate in how much content a voxel belongs to the lesions. Experimental results demonstrate that our algorithm works well.
Face Value: Towards Robust Estimates of Snow Leopard Densities.
Alexander, Justine S; Gopalaswamy, Arjun M; Shi, Kun; Riordan, Philip
2015-01-01
When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems. Understanding the population status of such species remains a major challenge as they occur in low densities and their ranges are wide. This paper describes the use of non-invasive data collection techniques combined with recent spatial capture-recapture methods to estimate the density of snow leopards Panthera uncia. It also investigates the influence of environmental and human activity indicators on their spatial distribution. A total of 60 camera traps were systematically set up during a three-month period over a 480 km2 study area in Qilianshan National Nature Reserve, Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trap-days, representing an average capture success of 2.62 captures/100 trap-days. We identified a total number of 20 unique individuals from photographs and estimated snow leopard density at 3.31 (SE = 1.01) individuals per 100 km2. Results of our simulation exercise indicate that our estimates from the Spatial Capture Recapture models were not optimal to respect to bias and precision (RMSEs for density parameters less or equal to 0.87). Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures. Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality.
Face Value: Towards Robust Estimates of Snow Leopard Densities.
Directory of Open Access Journals (Sweden)
Justine S Alexander
Full Text Available When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems. Understanding the population status of such species remains a major challenge as they occur in low densities and their ranges are wide. This paper describes the use of non-invasive data collection techniques combined with recent spatial capture-recapture methods to estimate the density of snow leopards Panthera uncia. It also investigates the influence of environmental and human activity indicators on their spatial distribution. A total of 60 camera traps were systematically set up during a three-month period over a 480 km2 study area in Qilianshan National Nature Reserve, Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trap-days, representing an average capture success of 2.62 captures/100 trap-days. We identified a total number of 20 unique individuals from photographs and estimated snow leopard density at 3.31 (SE = 1.01 individuals per 100 km2. Results of our simulation exercise indicate that our estimates from the Spatial Capture Recapture models were not optimal to respect to bias and precision (RMSEs for density parameters less or equal to 0.87. Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures. Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality.
Noise robust integration for blind and non-blind reverberation time estimation
Schüldt, Christian; Händel, Peter
2015-01-01
The estimation of the decay rate of a signal section is an integral component of both blind and non-blind reverberation time estimation methods. Several decay rate estimators have previously been proposed, based on, e.g., linear regression and maximum-likelihood estimation. Unfortunately, most approaches are sensitive to background noise, and/or are fairly demanding in terms of computational complexity. This paper presents a low complexity decay rate estimator, robust to stationary noise, for...
Simultaneous Robust Fault and State Estimation for Linear Discrete-Time Uncertain Systems
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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.
Robust Estimation of Temporal Resistivity Variations from Magnetotelluric Data
Cortés-Arroyo, O. J.; Romo, J. M.; Gomez-Trevino, E.
2016-12-01
In recent years there has been a worldwide increase of projects related to fluid injection, such as enhanced geothermal systems, CO2 sequestration and/or fracture monitoring studies. For the success of such projects, a full knowledge of the fluid penetration and propagation is needed, making monitoring techniques sensible to the fluid displacement an essential tool. The magnetotelluric (MT) method is a widely used geophysical technique in tectonic and reservoir exploration studies, where its sensitivity to changes in the electrical resistivity of rocks makes it a very promising tool for monitoring application. Several authors have already reported experiments using the MT method to monitor the effects produced by the injection of fluids in the subsurface. Most of them analyze the changes registered in the variables measured at the surface, particularly the apparent resistivity and the impedance phase. However, few studies have tried to estimate the changes in the ground resistivity from the observable data at the surface. The main difficulty in this kind of problems is that a full control of the geo-electric structure is needed a priori, and distortion effects in the measured data can lead to estimate false temporal or spatial variations. In this work we remove distortion in the data by applying a combination of the phase tensor and the quadratic equation, and propose a new technique to estimate ground resistivity variations by applying a 1D inversion scheme, based on a relationship between changes in resistivity at depth to observed changes of rotation invariant MT responses, applying the Marquardt-Levenberg regularization technique. We test the method using synthetic data and then apply it to real MT data sets collected before and after the 2010, Mw 7.2 earthquake in the Mexicali Valley, Mexico. We also apply it to data sets registered continuously by permanent electromagnetic monitoring stations. Keywords: Magnetotellurics, continuous monitoring, regularized
Fast-coding robust motion estimation model in a GPU
García, Carlos; Botella, Guillermo; de Sande, Francisco; Prieto-Matias, Manuel
2015-02-01
Nowadays vision systems are used with countless purposes. Moreover, the motion estimation is a discipline that allow to extract relevant information as pattern segmentation, 3D structure or tracking objects. However, the real-time requirements in most applications has limited its consolidation, considering the adoption of high performance systems to meet response times. With the emergence of so-called highly parallel devices known as accelerators this gap has narrowed. Two extreme endpoints in the spectrum of most common accelerators are Field Programmable Gate Array (FPGA) and Graphics Processing Systems (GPU), which usually offer higher performance rates than general propose processors. Moreover, the use of GPUs as accelerators involves the efficient exploitation of any parallelism in the target application. This task is not easy because performance rates are affected by many aspects that programmers should overcome. In this paper, we evaluate OpenACC standard, a programming model with directives which favors porting any code to a GPU in the context of motion estimation application. The results confirm that this programming paradigm is suitable for this image processing applications achieving a very satisfactory acceleration in convolution based problems as in the well-known Lucas & Kanade method.
Robust Homography Estimation Based on Nonlinear Least Squares Optimization
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Wei Mou
2014-01-01
Full Text Available The homography between image pairs is normally estimated by minimizing a suitable cost function given 2D keypoint correspondences. The correspondences are typically established using descriptor distance of keypoints. However, the correspondences are often incorrect due to ambiguous descriptors which can introduce errors into following homography computing step. There have been numerous attempts to filter out these erroneous correspondences, but it is unlikely to always achieve perfect matching. To deal with this problem, we propose a nonlinear least squares optimization approach to compute homography such that false matches have no or little effect on computed homography. Unlike normal homography computation algorithms, our method formulates not only the keypoints’ geometric relationship but also their descriptor similarity into cost function. Moreover, the cost function is parametrized in such a way that incorrect correspondences can be simultaneously identified while the homography is computed. Experiments show that the proposed approach can perform well even with the presence of a large number of outliers.
Graphical Evaluation of the Ridge-Type Robust Regression Estimators in Mixture Experiments
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Ali Erkoc
2014-01-01
Full Text Available 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.
Graphical evaluation of the ridge-type robust regression estimators in mixture experiments.
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.
Robust domain decomposition preconditioners for abstract symmetric positive definite bilinear forms
Efendiev, Yalchin
2012-02-22
An abstract framework for constructing stable decompositions of the spaces corresponding to general symmetric positive definite problems into "local" subspaces and a global "coarse" space is developed. Particular applications of this abstract framework include practically important problems in porous media applications such as: the scalar elliptic (pressure) equation and the stream function formulation of its mixed form, Stokes\\' and Brinkman\\'s equations. The constant in the corresponding abstract energy estimate is shown to be robust with respect to mesh parameters as well as the contrast, which is defined as the ratio of high and low values of the conductivity (or permeability). The derived stable decomposition allows to construct additive overlapping Schwarz iterative methods with condition numbers uniformly bounded with respect to the contrast and mesh parameters. The coarse spaces are obtained by patching together the eigenfunctions corresponding to the smallest eigenvalues of certain local problems. A detailed analysis of the abstract setting is provided. The proposed decomposition builds on a method of Galvis and Efendiev [Multiscale Model. Simul. 8 (2010) 1461-1483] developed for second order scalar elliptic problems with high contrast. Applications to the finite element discretizations of the second order elliptic problem in Galerkin and mixed formulation, the Stokes equations, and Brinkman\\'s problem are presented. A number of numerical experiments for these problems in two spatial dimensions are provided. © EDP Sciences, SMAI, 2012.
Jump robust two time scale covariance estimation and realized volatility budgets
Boudt, K.M.R.; Zhang, J.
2015-01-01
We estimate the daily integrated variance and covariance of stock returns using high-frequency data in the presence of jumps, market microstructure noise and non-synchronous trading. For this we propose jump robust two time scale (co)variance estimators and verify their reduced bias and mean square
DEFF Research Database (Denmark)
Lu, Xiaobing; Liu, Zhigang; Song, Yang
2017-01-01
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 Variance Estimation in Meta-Regression with Binary Dependent Effects
Tipton, Elizabeth
2013-01-01
Dependent effect size estimates are a common problem in meta-analysis. Recently, a robust variance estimation method was introduced that can be used whenever effect sizes in a meta-analysis are not independent. This problem arises, for example, when effect sizes are nested or when multiple measures are collected on the same individuals. In this…
On Improving the Energy Efficiency and Robustness of Position Tracking for Mobile Devices
DEFF Research Database (Denmark)
Kjærgaard, Mikkel Baun
An important feature of a modern mobile device is that it can position itself and support remote position tracking. To be useful, such position tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. Furthermore, tracking has to robustly deliver...... position updates when faced with changing conditions such as delays and changing positioning conditions. Previous work has established dynamic tracking systems, such as our EnTracked system, as a solution to address these issues. In this paper we propose a responsibility division for position tracking...... into sensor management strategies and position update protocols and combine the sensor management strategy of EnTracked with position update protocols, which enables the system to further reduce the power consumption with up to 268 mW extending the battery life with up to 36\\%. As our evaluation identify...
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......-squares (WLS) DOA estimator....
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 ...... of the estimator are investigated. The efficiency of our methodology is illustrated on a small simulation study and by a real dataset from the actuarial context. (C) 2014 Elsevier B.V. All rights reserved....
Convergence Aspects of Some Robust Estimators Based Upon Prefiltering of the Input-Output Data
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Rolf Henriksen
1990-10-01
Full Text Available By prefiltering the input/output data and employing certain decentralized estimation techniques, it is possible to improve the robustness of some estimators significantly. Earlier papers on these techniques have been focused on local convergence properties of certain bootstrap estimators based upon these techniques. This paper is devoted to (1 global convergence properties, and (2 convergence rates when the underlying system is stiff.
Robust State of Charge Estimation for Hybrid Electric Vehicles: Framework and Algorithms
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Yangsheng Xu
2010-09-01
Full Text Available State of Charge (SoC estimation is one of the most significant and difficult techniques to promote the commercialization of electric vehicles (EVs. Suffering from various interference in vehicle driving environment and model uncertainties due to the strong time-variant property and inconsistency of batteries, the existing typical SoC estimators such as coulomb counting and extended Kalman filter cannot perform their theoretically optimal efficacy in practical applications. Aiming at enhancing the robustness of SoC estimation and improving accuracy under the real driving conditions with noises and uncertainties, this paper proposes a framework consisting of (1 an adaptive-κ nonlinear diffusion filter to reduce the noise in current measurement, (2 a self-learning strategy to estimate and remove the zero-drift, (3 a coulomb counting algorithm to realize open-loop SoC estimation, (4 an H∞ filter to implement closed-loop robust estimation, and (5 a data fusion unite to achieve the final estimation by integrating the advantages of the two SoC estimators. The availability and efficacy of each component have been demonstrated based on comparative studiesin simulation with the conventional approaches respectively, under the testing conditions of noises with various signal-noise-ratios, varying zero-drifts, and different model errors. The overall framework has also been verified to rationally and efficiently combine these components and achieve robust estimation results in the presence of kinds of noises and uncertainties.
A consensus-based multi-agent approach for estimation in robust fault detection.
Jiang, Yulian; Liu, Jianchang; Wang, Shenquan
2014-09-01
This paper is devoted to distributed estimation in robust fault detection for sensor networks with networked-induced delays and packet dropouts by using a consensus-based multi-agent approach. Utilizing the information interaction and coordination among the neighboring networks based on multi-agent theory, we design novel and multiple agent-based robust fault detection filters (RFDFs) subject to only partial estimated and measured information. Asymptotically stable sufficient conditions for the innovative constructed filters are derived in the form of linear matrix inequality (LMI) and the threshold fit for each agent-based RFDF is determined. An illustrative example is given to demonstrate the effectiveness of the consensus-based multi-agent approach for the estimation in robust fault detection. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Robust Hotelling T2 control chart using reweighted minimum vector variance estimators
Ali, Hazlina; Yahaya, Sharipah Soaad Syed; Omar, Zurni
2014-12-01
Hotelling T2 control chart is employed to monitor the stability of a multivariate process in Phase I and II. Traditional Hotelling T2 control chart using classical estimators in Phase I, however, suffers from masking and swamping effects and thus jeorpadizes its performance. To alleviate this problem, robust location and scale estimators are recommended instead. In this paper, a new Hotelling T2 control chart based on highly robust and efficient estimators of location and scatter estimators, known as reweighted minimum vector variance estimators, is proposed. Numerical results show that the new chart is not only capable of detecting outliers but it can also control the alarm rates better than the existing charts.
Position Estimation Using the Image Derivative
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Daniele Mortari
2015-07-01
Full Text Available This article describes an image processing algorithm to identify the size and shape of a spherical reflecting celestial body prominently depicted in images taken from a spacecraft with an optical camera, with the purpose of estimating the relative distance between target and observer in magnitude and direction. The approach is based on the fact that in such images, the pixels belonging to the target’s hard edge have the highest values of the image derivative; therefore, they are easily recognizable when the image is processed with a gradient filter. Eventual extraneous points polluting the dataset (outliers are eliminated by two methods applied in sequence. The target center and radius are estimated by non-linear least squares using circular sigmoid functions. The proposed image processing has been applied to real and synthetic Moon images. An error analysis is also performed to determine the performance of the proposed method.
Shrinkage Estimators for Robust and Efficient Inference in Haplotype-Based Case-Control Studies
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.
Sediment grain size estimation using airborne remote sensing, field sampling, and robust statistic.
Castillo, Elena; Pereda, Raúl; Luis, Julio Manuel de; Medina, Raúl; Viguri, Javier
2011-10-01
Remote sensing has been used since the 1980s to study parameters in relation with coastal zones. It was not until the beginning of the twenty-first century that it started to acquire imagery with good temporal and spectral resolution. This has encouraged the development of reliable imagery acquisition systems that consider remote sensing as a water management tool. Nevertheless, the spatial resolution that it provides is not adapted to carry out coastal studies. This article introduces a new methodology for estimating the most fundamental physical property of intertidal sediment, the grain size, in coastal zones. The study combines hyperspectral information (CASI-2 flight), robust statistic, and simultaneous field work (chemical and radiometric sampling), performed over Santander Bay, Spain. Field data acquisition was used to build a spectral library in order to study different atmospheric correction algorithms for CASI-2 data and to develop algorithms to estimate grain size in an estuary. Two robust estimation techniques (MVE and MCD multivariate M-estimators of location and scale) were applied to CASI-2 imagery, and the results showed that robust adjustments give acceptable and meaningful algorithms. These adjustments have given the following R(2) estimated results: 0.93 in the case of sandy loam contribution, 0.94 for the silty loam, and 0.67 for clay loam. The robust statistic is a powerful tool for large dataset.
Robust parameter estimation for dynamical systems from outlier-corrupted data.
Maier, Corinna; Loos, Carolin; Hasenauer, Jan
2017-03-01
Dynamics of cellular processes are often studied using mechanistic mathematical models. These models possess unknown parameters which are generally estimated from experimental data assuming normally distributed measurement noise. Outlier corruption of datasets often cannot be avoided. These outliers may distort the parameter estimates, resulting in incorrect model predictions. Robust parameter estimation methods are required which provide reliable parameter estimates in the presence of outliers. In this manuscript, we propose and evaluate methods for estimating the parameters of ordinary differential equation models from outlier-corrupted data. As alternatives to the normal distribution as noise distribution, we consider the Laplace, the Huber, the Cauchy and the Student's t distribution. We assess accuracy, robustness and computational efficiency of estimators using these different distribution assumptions. To this end, we consider artificial data of a conversion process, as well as published experimental data for Epo-induced JAK/STAT signaling. We study how well the methods can compensate and discover artificially introduced outliers. Our evaluation reveals that using alternative distributions improves the robustness of parameter estimates. The MATLAB implementation of the likelihood functions using the distribution assumptions is available at Bioinformatics online. jan.hasenauer@helmholtz-muenchen.de. Supplementary material are available at Bioinformatics online.
Novel Position and Speed Estimator for PM Single Phase Brushless D.C. Motor Drives
DEFF Research Database (Denmark)
Lepure, Liviu I.; Andreescu, Gheorghe-Daniel; Iles, Doris
2010-01-01
A novel position and speed estimator for single phase permanent magnet brushless d.c. (PMBLDC) motor drives, based on flux integration and prior knowledge of ΨPM (θ) is proposed here and an adequate correction algorithm is adopted in order to increase the robustness to noise and to reduce...... the sensitivity to accuracy of flux linkage estimation. A speed and current close loop control is employed based on the Hall signal and the motor is controlled at different speeds in order to validate the proposed estimation algorithm with satisfying results. The position correction effect is analyzed...
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 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)...
Robust all-source positioning of UAVs based on belief propagation
Chen, Xi; Gao, Wenyun; Wang, Jiabo
2013-12-01
For unmanned air vehicles (UAVs) to survive hostile operational environments, it is always preferable to utilize all wireless positioning sources available to fuse a robust position. While belief propagation is a well-established method for all source data fusion, it is not an easy job to handle all the mathematics therein. In this work, a comprehensive mathematical framework for belief propagation-based all-source positioning of UAVs is developed, taking wireless sources including Global Navigation Satellite Systems (GNSS) space vehicles, peer UAVs, ground control stations, and signal of opportunities. Based on the mathematical framework, a positioning algorithm named Belief propagation-based Opportunistic Positioning of UAVs (BOPU) is proposed, with an unscented particle filter for Bayesian approximation. The robustness of the proposed BOPU is evaluated by a fictitious scenario that a group of formation flying UAVs encounter GNSS countermeasures en route. Four different configurations of measurements availability are simulated. The results show that the performance of BOPU varies only slightly with different measurements availability.
Energy Technology Data Exchange (ETDEWEB)
Mo, Se Hyun [Amotech, Seoul (Korea, Republic of); Jeon, Young Pil [Samsung Electronics Co., Ltd. Suwon (Korea, Republic of); Park, Jong Ho [Seonam Univ., Namwon (Korea, Republic of); Chong, Kil To [Chon-buk Nat' 1 Univ., Junju (Korea, Republic of)
2017-07-15
With the development of ICT technology, the indoor utilization of robots is increasing. Research on transportation, cleaning, guidance robots, etc., that can be used now or increase the scope of future use will be advanced. To facilitate the use of mobile robots in indoor spaces, the problem of self-location recognition is an important research area to be addressed. If an unexpected collision occurs during the motion of a mobile robot, the position of the mobile robot deviates from the initially planned navigation path. In this case, the mobile robot needs a robust controller that enables the mobile robot to accurately navigate toward the goal. This research tries to address the issues related to self-location of the mobile robot. A robust position recognition system was implemented; the system estimates the position of the mobile robot using a combination of encoder information of the mobile robot and the absolute space coordinate transformation information obtained from external video sources such as a large number of CCTVs installed in the room. Furthermore, vector field histogram method of the pass traveling algorithm of the mobile robot system was applied, and the results of the research were confirmed after conducting experiments.
Robust Solvers for Symmetric Positive Definite Operators and Weighted Poincaré Inequalities
Efendiev, Yalchin
2012-01-01
An abstract setting for robustly preconditioning symmetric positive definite (SPD) operators is presented. The term "robust" refers to the property of the condition numbers of the preconditioned systems being independent of mesh parameters and problem parameters. Important instances of such problem parameters are in particular (highly varying) coefficients. The method belongs to the class of additive Schwarz preconditioners. The paper gives an overview of the results obtained in a recent paper by the authors. It, furthermore, focuses on the importance of weighted Poincaré inequalities, whose notion is extended to general SPD operators, for the analysis of stable decompositions. To demonstrate the applicability of the abstract preconditioner the scalar elliptic equation and the stream function formulation of Brinkman\\'s equations in two spatial dimensions are considered. Several numerical examples are presented. © 2012 Springer-Verlag.
Robust H∞ Control of Neutral System with Time-Delay for Dynamic Positioning Ships
Directory of Open Access Journals (Sweden)
Dawei Zhao
2015-01-01
Full Text Available Due to the input time-delay existing in most thrust systems of the ships, the robust H∞ controller is designed for the ship dynamic positioning (DP system with time-delay. The input delay system is turned to a neutral time-delay system by a state-derivative control law. The less conservative result is derived for the neutral system with state-derivative feedback by the delay-decomposition approach and linear matrix inequality (LMI. Finally, the numerical simulations demonstrate the asymptotic stability and robustness of the controller and verify that the designed DP controller is effective in the varying environment disturbances of wind, waves, and ocean currents.
Application of unscented Kalman filter for robust pose estimation in image-guided surgery
Vaccarella, Alberto; De Momi, Elena; Valenti, Marta; Ferrigno, Giancarlo; Enquobahrie, Andinet
2012-02-01
Image-guided surgery (IGS) allows clinicians to view current, intra-operative scenes superimposed on preoperative images (typically MRI or CT scans). IGS systems use localization systems to track and visualize surgical tools overlaid on top of preoperative images of the patient during surgery. The most commonly used localization systems in the Operating Rooms (OR) are optical tracking systems (OTS) due to their ease of use and cost effectiveness. However, OTS' suffer from the major drawback of line-of-sight requirements. State space approaches based on different implementations of the Kalman filter have recently been investigated in order to compensate for short line-of-sight occlusion. However, the proposed parameterizations for the rigid body orientation suffer from singularities at certain values of rotation angles. The purpose of this work is to develop a quaternion-based Unscented Kalman Filter (UKF) for robust optical tracking of both position and orientation of surgical tools in order to compensate marker occlusion issues. This paper presents preliminary results towards a Kalman-based Sensor Management Engine (SME). The engine will filter and fuse multimodal tracking streams of data. This work was motivated by our experience working in robot-based applications for keyhole neurosurgery (ROBOCAST project). The algorithm was evaluated using real data from NDI Polaris tracker. The results show that our estimation technique is able to compensate for marker occlusion with a maximum error of 2.5° for orientation and 2.36 mm for position. The proposed approach will be useful in over-crowded state-of-the-art ORs where achieving continuous visibility of all tracked objects will be difficult.
Kwon, Ji-Wook; Kim, Jin Hyo; Seo, Jiwon
2015-01-01
This paper proposes a Multiple Leader Candidate (MLC) structure and a Competitive Position Allocation (CPA) algorithm which can be applicable for various applications including environmental sensing. Unlike previous formation structures such as virtual-leader and actual-leader structures with position allocation including a rigid allocation and an optimization based allocation, the formation employing the proposed MLC structure and CPA algorithm is robust against the fault (or disappearance) of the member robots and reduces the entire cost. In the MLC structure, a leader of the entire system is chosen among leader candidate robots. The CPA algorithm is the decentralized position allocation algorithm that assigns the robots to the vertex of the formation via the competition of the adjacent robots. The numerical simulations and experimental results are included to show the feasibility and the performance of the multiple robot system employing the proposed MLC structure and the CPA algorithm. PMID:25954956
Kwon, Ji-Wook; Kim, Jin Hyo; Seo, Jiwon
2015-05-06
This paper proposes a Multiple Leader Candidate (MLC) structure and a Competitive Position Allocation (CPA) algorithm which can be applicable for various applications including environmental sensing. Unlike previous formation structures such as virtual-leader and actual-leader structures with position allocation including a rigid allocation and an optimization based allocation, the formation employing the proposed MLC structure and CPA algorithm is robust against the fault (or disappearance) of the member robots and reduces the entire cost. In the MLC structure, a leader of the entire system is chosen among leader candidate robots. The CPA algorithm is the decentralized position allocation algorithm that assigns the robots to the vertex of the formation via the competition of the adjacent robots. The numerical simulations and experimental results are included to show the feasibility and the performance of the multiple robot system employing the proposed MLC structure and the CPA algorithm.
Robust estimation for homoscedastic regression in the secondary analysis of case-control data
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.
Nonlatching positive feedback enables robust bimodality by decoupling expression noise from the mean
Razooky, Brandon S.; Cao, Youfang; Hansen, Maike M. K.; Perelson, Alan S.; Simpson, Michael L.
2017-01-01
Fundamental to biological decision-making is the ability to generate bimodal expression patterns where 2 alternate expression states simultaneously exist. Here, we use a combination of single-cell analysis and mathematical modeling to examine the sources of bimodality in the transcriptional program controlling HIV’s fate decision between active replication and viral latency. We find that the HIV transactivator of transcription (Tat) protein manipulates the intrinsic toggling of HIV’s promoter, the long terminal repeat (LTR), to generate bimodal ON-OFF expression and that transcriptional positive feedback from Tat shifts and expands the regime of LTR bimodality. This result holds for both minimal synthetic viral circuits and full-length virus. Strikingly, computational analysis indicates that the Tat circuit’s noncooperative “nonlatching” feedback architecture is optimized to slow the promoter’s toggling and generate bimodality by stochastic extinction of Tat. In contrast to the standard Poisson model, theory and experiment show that nonlatching positive feedback substantially dampens the inverse noise-mean relationship to maintain stochastic bimodality despite increasing mean expression levels. Given the rapid evolution of HIV, the presence of a circuit optimized to robustly generate bimodal expression appears consistent with the hypothesis that HIV’s decision between active replication and latency provides a viral fitness advantage. More broadly, the results suggest that positive-feedback circuits may have evolved not only for signal amplification but also for robustly generating bimodality by decoupling expression fluctuations (noise) from mean expression levels. PMID:29045398
Ma, Yingdong; Lu, Junguo; Chen, Weidong
2014-03-01
This paper investigates the robust stability and stabilization of fractional order linear systems with positive real uncertainty. Firstly, sufficient conditions for the asymptotical stability of such uncertain fractional order systems are presented. Secondly, the existence conditions and design methods of the state feedback controller, static output feedback controller and observer-based controller for asymptotically stabilizing such uncertain fractional order systems are derived. The results are obtained in terms of linear matrix inequalities. Finally, some numerical examples are given to validate the proposed theoretical results. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Robust Adaptive Filter for Small Satellite Attitude Estimation Based on Magnetometer and Gyro
Directory of Open Access Journals (Sweden)
Zhankui Zeng
2014-01-01
Full Text Available Based on magnetometer and gyro measurement, a sequential scheme is proposed to determine the orbit and attitude of small satellite simultaneously. In order to reduce the impact of orbital errors on attitude estimation, a robust adaptive Kalman filter is developed. It uses a scale factor and an adaptive factor, which are constructed by Huber function and innovation sequence, respectively, to adjust the covariance matrix of system state and observational noise, change the weights of predicted and measured parameters, get suitable Kalman filter gain and approximate optimal filtering results. Numerical simulations are carried out and the proposed filter is approved to be robust for the noise disturbance and parameter uncertainty and can provide higher accuracy attitude estimation.
ROBUST PARALLEL MOTION ESTIMATION AND MAPPING WITH STEREO CAMERAS IN UNDERGROUND INFRASTRUCTURE
Liu, Chun; Li, Zhengning; Zhou, Yuan
2016-01-01
Presently, we developed a novel robust motion estimation method for localization and mapping in underground infrastructure using a pre-calibrated rigid stereo camera rig. Localization and mapping in underground infrastructure is important to safety. Yet it’s also nontrivial since most underground infrastructures have poor lighting condition and featureless structure. Overcoming these difficulties, we discovered that parallel system is more efficient than the EKF-based SLAM approach since para...
Robust and sparse correlation matrix estimation for the analysis of high-dimensional genomics data.
Serra, Angela; Coretto, Pietro; Fratello, Michele; Tagliaferri, Roberto
2017-10-12
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 analysed. 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 data sets. 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
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.
Zhu, Zhiliang; Meng, Zhiqiang; Cao, Tingting; Zhang, Zhengjiang; Dai, Yuxing
2017-06-01
State and parameter estimation (SPE) plays an important role in process monitoring, online optimization, and process control. The estimation of states and parameters is generally solved simultaneously in the SPE problem, where the parameters to be estimated are specified as augmented states. When state and/or measurement equations are highly nonlinear and the posterior probability of the state is non-Gaussian, particle filter (PF) is commonly used for SPE. However, when the parameters switch with the operating conditions, the change of parameters cannot be detected and tracked by the conventional SPE method. This paper proposes a PF-based robust SPE method for a nonlinear process system with variable parameters. The measurement test criterion based on observation error is introduced to indirectly identify whether the parameters are changed. Based on the result of identification, the variances of the particles are modified adaptively for the tracking of the changed parameters. Finally, reliable SPE can be derived through iterative particles. The proposed PF-based robust SPE method is applied to two nonlinear process systems. The results demonstrate the effectiveness and robustness of the proposed method.
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…
Robust phase-shift estimation method for statistical generalized phase-shifting digital holography.
Yoshikawa, Nobukazu; Shiratori, Takaaki; Kajihara, Kazuki
2014-06-16
We propose a robust phase-shift estimation method for statistical generalized phase-shifting digital holography using a slightly off-axis optical configuration. The phase randomness condition in the Fresnel diffraction field of an object can be sufficiently established by the linear phase factor of the oblique incident reference wave. Signed phase-shift values can be estimated with a statistical approach regardless of the statistical properties of the Fresnel diffraction field of the object. We present computer simulations and optical experiments to verify the proposed method.
Robust Foot Clearance Estimation Based on the Integration of Foot-Mounted IMU Acceleration Data.
Benoussaad, Mourad; Sijobert, Benoît; Mombaur, Katja; Coste, Christine Azevedo
2015-12-23
This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU) placed on the subject's foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15 % under the various walking conditions.
Robust subspace estimation using low-rank optimization theory and applications
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
Efficient and robust estimation for longitudinal mixed models for binary data
DEFF Research Database (Denmark)
Holst, René
2009-01-01
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......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...
Energy Technology Data Exchange (ETDEWEB)
Kim, Joo Yeon; Lee, Seung Hyun; Park, Tai Jin [Korean Association for Radiation Application, Seoul (Korea, Republic of)
2016-06-15
Any real application of Bayesian inference must acknowledge that both prior distribution and likelihood function have only been specified as more or less convenient approximations to whatever the analyzer's true belief might be. If the inferences from the Bayesian analysis are to be trusted, it is important to determine that they are robust to such variations of prior and likelihood as might also be consistent with the analyzer's stated beliefs. The robust Bayesian inference was applied to atmospheric dispersion assessment using Gaussian plume model. The scopes of contaminations were specified as the uncertainties of distribution type and parametric variability. The probabilistic distribution of model parameters was assumed to be contaminated as the symmetric unimodal and unimodal distributions. The distribution of the sector-averaged relative concentrations was then calculated by applying the contaminated priors to the model parameters. The sector-averaged concentrations for stability class were compared by applying the symmetric unimodal and unimodal priors, respectively, as the contaminated one based on the class of ε-contamination. Though ε was assumed as 10%, the medians reflecting the symmetric unimodal priors were nearly approximated within 10% compared with ones reflecting the plausible ones. However, the medians reflecting the unimodal priors were approximated within 20% for a few downwind distances compared with ones reflecting the plausible ones. The robustness has been answered by estimating how the results of the Bayesian inferences are robust to reasonable variations of the plausible priors. From these robust inferences, it is reasonable to apply the symmetric unimodal priors for analyzing the robustness of the Bayesian inferences.
Directory of Open Access Journals (Sweden)
Lisanne V van Dijk
Full Text Available To compare the clinical benefit of robust optimized Intensity Modulated Proton Therapy (minimax IMPT with current photon Intensity Modulated Radiation Therapy (IMRT and PTV-based IMPT for head and neck cancer (HNC patients. The clinical benefit is quantified in terms of both Normal Tissue Complication Probability (NTCP and target coverage in the case of setup and range errors.For 10 HNC patients, PTV-based IMRT (7 fields, minimax and PTV-based IMPT (2, 3, 4, 5 and 7 fields plans were tested on robustness. Robust optimized plans differed from PTV-based plans in that they target the CTV and penalize possible error scenarios, instead of using the static isotropic CTV-PTV margin. Perturbed dose distributions of all plans were acquired by simulating in total 8060 setup (±3.5 mm and range error (±3% combinations. NTCP models for xerostomia and dysphagia were used to predict the clinical benefit of IMPT versus IMRT.The robustness criterion was met in the IMRT and minimax IMPT plans in all error scenarios, but this was only the case in 1 of 40 PTV-based IMPT plans. Seven (out of 10 patients had relatively large NTCP reductions in minimax IMPT plans compared to IMRT. For these patients, xerostomia and dysphagia NTCP values were reduced by 17.0% (95% CI; 13.0-21.1 and 8.1% (95% CI; 4.9-11.2 on average with minimax IMPT. Increasing the number of fields did not contribute to plan robustness, but improved organ sparing.The estimated clinical benefit in terms of NTCP of robust optimized (minimax IMPT is greater than that of IMRT and PTV-based IMPT in HNC patients. Furthermore, the target coverage of minimax IMPT plans in the presence of errors was comparable to IMRT plans.
Yi Zhou; Hongqing Zhu; Xuan Tao
2017-07-01
Finite mixture model (FMM) has been widely used for unsupervised segmentation of magnetic resonance (MR) images in recent years. However, in real applications, the distribution of the observed data usually contains an unknown fraction of outliers, which would interfere with the estimation of the parameters of the mixture model. The statistical model-based technique which provides a theoretically well segmentation criterion in presence of outliers is the mixture modeling and the trimming approach. Therefore, in this paper, a robust estimation of asymmetric Student's-t mixture model (ASMM) using the trimmed likelihood estimator for MR image segmentation has been proposed. The proposed method is supposed to discard the outliers, and then to estimate the parameters of the ASMM with the remaining samples. The advantages of the proposed algorithm are that its robustness to dispose the disturbance of outliers and its flexibility to describe various shapes of data. Finally, expectation-maximization (EM) algorithm is adopted to maximize the log-likelihood and to obtain the estimation of the parameters. The experimental results show that the proposed method has a better performance on the segmentation of synthetic data and real MR images.
Fortunati, Stefano; Gini, Fulvio; Greco, Maria S.
2016-12-01
Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector.
Robust estimation of albedo for illumination-invariant matching and shape recovery.
Biswas, Soma; Aggarwal, Gaurav; Chellappa, Rama
2009-05-01
We present a nonstationary stochastic filtering framework for the task of albedo estimation from a single image. There are several approaches in the literature for albedo estimation, but few include the errors in estimates of surface normals and light source direction to improve the albedo estimate. The proposed approach effectively utilizes the error statistics of surface normals and illumination direction for robust estimation of albedo, for images illuminated by single and multiple light sources. The albedo estimate obtained is subsequently used to generate albedo-free normalized images for recovering the shape of an object. Traditional Shape-from-Shading (SFS) approaches often assume constant/piecewise constant albedo and known light source direction to recover the underlying shape. Using the estimated albedo, the general problem of estimating the shape of an object with varying albedo map and unknown illumination source is reduced to one that can be handled by traditional SFS approaches. Experimental results are provided to show the effectiveness of the approach and its application to illumination-invariant matching and shape recovery. The estimated albedo maps are compared with the ground truth. The maps are used as illumination-invariant signatures for the task of face recognition across illumination variations. The recognition results obtained compare well with the current state-of-the-art approaches. Impressive shape recovery results are obtained using images downloaded from the Web with little control over imaging conditions. The recovered shapes are also used to synthesize novel views under novel illumination conditions.
Shen, Yu; Zelen, Marvin
2005-10-01
In early-detection clinical trials, quantities such as the sensitivity of the screening modality and the preclinical duration of the disease are important to describe the natural history of the disease and its interaction with a screening program. Assume that the schedule of a screening program is periodic and that the sojourn time in the preclinical state has a piecewise density function. Modeling the preclinical sojourn time distribution as a piecewise density function results in robust estimation of the distribution function. Our aim is to estimate the piecewise density function and the examination sensitivity using both generalized least squares and maximum likelihood methods. We carried out extensive simulations to evaluate the performance of the methods of estimation. The different estimation methods provide complimentary tools to obtain the unknown parameters. The methods are applied to three breast cancer early-detection trials.
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.
Ishigaki, Tsukasa; Yamamoto, Yoshinobu; Nakamura, Yoshiyuki; Akamatsu, Motoyuki
Patients that have an health service by doctor have to wait long time at many hospitals. The long waiting time is the worst factor of patient's dissatisfaction for hospital service according to questionnaire for patients. The present paper describes an estimation method of the waiting time for each patient without an electronic medical chart system. The method applies a portable RFID system to data acquisition and robust estimation of probability distribution of the health service and test time by doctor for high-accurate waiting time estimation. We carried out an health service of data acquisition at a real hospital and verified the efficiency of the proposed method. The proposed system widely can be used as data acquisition system in various fields such as marketing service, entertainment or human behavior measurement.
Pimperl, Alexander F; Rodriguez, Hector P; Schmittdiel, Julie A; Shortell, Stephen M
2017-04-06
To identify positive deviant (PD) physician organizations of Accountable Care Organizations (ACOs) with robust performance management systems (PMSYS). Third National Survey of Physician Organizations (NSPO3, n = 1,398). Organizational and external factors from NSPO3 were analyzed. Linear regression estimated the association of internal and contextual factors on PMSYS. Two cutpoints (75th/90th percentiles) identified PDs with the largest residuals and highest PMSYS scores. A total of 65 and 41 PDs were identified using 75th and 90th percentiles cutpoints, respectively. The 90th percentile more strongly differentiated PDs from non-PDs. Having a high proportion of vulnerable patients appears to constrain PMSYS development. Our PD identification method increases the likelihood that PD organizations selected for in-depth inquiry are high-performing organizations that exceed expectations. © Health Research and Educational Trust.
Estimation of UAV Position with Use of Smoothing Algorithms
Directory of Open Access Journals (Sweden)
Kaniewski Piotr
2017-03-01
Full Text Available The paper presents methods of on-line and off-line estimation of UAV position on the basis of measurements from its integrated navigation system. The navigation system installed on board UAV contains an INS and a GNSS receiver. The UAV position, as well as its velocity and orientation are estimated with the use of smoothing algorithms. For off-line estimation, a fixed-interval smoothing algorithm has been applied. On-line estimation has been accomplished with the use of a fixed-lag smoothing algorithm. The paper includes chosen results of simulations demonstrating improvements of accuracy of UAV position estimation with the use of smoothing algorithms in comparison with the use of a Kalman filter.
Positional estimation techniques for an autonomous mobile robot
Nandhakumar, N.; Aggarwal, J. K.
1990-01-01
Techniques for positional estimation of a mobile robot navigation in an indoor environment are described. A comprehensive review of the various positional estimation techniques studied in the literature is first presented. The techniques are divided into four different types and each of them is discussed briefly. Two different kinds of environments are considered for positional estimation; mountainous natural terrain and an urban, man-made environment with polyhedral buildings. In both cases, the robot is assumed to be equipped with single visual camera that can be panned and tilted and also a 3-D description (world model) of the environment is given. Such a description could be obtained from a stereo pair of aerial images or from the architectural plans of the buildings. Techniques for positional estimation using the camera input and the world model are presented.
From toe to head: use of robust regression methods in stature estimation based on foot remains.
Pablos, Adrián; Gómez-Olivencia, Asier; García-Pérez, Alfonso; Martínez, Ignacio; Lorenzo, Carlos; Arsuaga, Juan Luis
2013-03-10
Stature estimation is a standard procedure in the fields of forensic and biological anthropology, bio-archaeology and paleoanthropology, in order to gain biological insights into the individuals/populations studied. The most accurate stature estimation method is based on anatomical reconstruction (i.e., the Fully method), followed by type I regression equations (e.g., ordinary least squares - OLS) based on long bones, preferably from the lower limb. In some cases, due to the fragmentary nature of the osseous material recovered, stature estimates have to rely on other elements, such as foot remains. In this study, we explore stature estimation based on different foot bones: the talus, calcaneus, and metatarsals 1-4 in Afro- and Euroamericans of both sexes. The approach undertaken in this study is novel for two reasons. First, individual estimates for each bone are provided, and tarsals and metatarsals are combined in order to obtain more accurate estimates. Second, robust statistical methods based on type I regression equations are used, namely least trimmed squares (LTS). Our results show that the best individual bones for estimating stature are the first and second metatarsal and both the talus and the calcaneus. The combination of a tarsal and a metatarsal bone slightly improves the accuracy of the stature estimate. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
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.
Kurugol, Sila; Freiman, Moti; Afacan, Onur; Domachevsky, Liran; Perez-Rossello, Jeannette M; Callahan, Michael J; Warfield, Simon K
2017-07-01
Quantitative body DW-MRI can detect abdominal abnormalities as well as monitor response-to-therapy for applications including cancer and inflammatory bowel disease with increased accuracy. Parameter estimates are obtained by fitting a forward model of DW-MRI signal decay to the observed data acquired with several b-values. The DW-MRI signal decay models typically used do not account for respiratory, cardiac and peristaltic motion, however, which may deteriorate the accuracy and robustness of parameter estimates. In this work, we introduce a new model of DW-MRI signal decay that explicitly accounts for motion. Specifically, we estimated motion-compensated model parameters by simultaneously solving image registration and model estimation (SIR-ME) problems utilizing the interdependence of acquired volumes along the diffusion-weighting dimension. To accomplish this, we applied the SIR-ME model to the in-vivo DW-MRI data sets of 26 Crohn's disease (CD) patients and achieved improved precision of the estimated parameters by reducing the coefficient of variation by 8%, 24% and 8% for slow diffusion (D), fast diffusion (D*) and fast diffusion fraction (f) parameters respectively, compared to parameters estimated with independent registration in normal-appearing bowel regions. Moreover, the parameters estimated with the SIR-ME model reduced the error rate in classifying normal and abnormal bowel loops to 12% for D and 10% for f parameter with a reduction in error rate by 13% and 11% for D and f parameters, respectively, compared to the error rate in classifying parameter estimates obtained with independent registration. The experiments in DW-MRI of liver in 20 subjects also showed that the SIR-ME model improved the precision of parameter estimation by reducing the coefficient of variation to 7% for D, 23% for D*, and 8% for the f parameter. Using the SIR-ME model, the coefficient of variation was reduced by 4%, 14% and 6% for D, D* and f parameters, respectively, compared
Accurate Position Estimation in Switched Reluctance Motor With Smooth Starting
Panda, Debiprasad; Ramarayanan, V
2000-01-01
This paper presents a novel method of position estimation for switched reluctance (SR) motors. The method is suitable from starting to full speed. It ensures smooth starting without initial hesitation. The method further proposes a better position estimating algorithm incorporating corrections for eddy current and mutual inductance effects. The algorithm is better suited in a digital control platform for its realisation. In the present work, a Texas Instruments made DSP (TMS320c50) is used fo...
Robust Non-Local TV-L1 Optical Flow Estimation with Occlusion Detection.
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.
Sea State Estimation Using Vessel Response in Dynamic Positioning
DEFF Research Database (Denmark)
H. Brodtkorb, Astrid; Nielsen, Ulrik Dam; J. Sørensen, Asgeir
2017-01-01
This paper presents a novel method for estimating the sea state parameters based on the heave, roll and pitch response of a vessel in dynamic positioning (DP), i.e., without forward speed. The algorithm finds the wave spectrum estimate from the response measurements by directly solving a set...
Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity
DEFF Research Database (Denmark)
Boudt, Kris; Laurent, Sébastien; Lunde, Asger
observations as possible. The estimator is guaranteed positive semidefinite. Monte Carlo simulations confirm good finite sample properties. In the application we forecast portfolio Value-at-Risk and sector risk exposures for a portfolio of 52 stocks. We find that forecasts obtained from dynamic models...... utilizing the proposed high-frequency estimator provide statistically and economically superior forecasts to models using daily returns....
Robust best linear estimation for regression analysis using surrogate and instrumental variables.
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.
Fast and Robust Real-Time Estimation of Respiratory Rate from Photoplethysmography
Directory of Open Access Journals (Sweden)
Hodam Kim
2016-09-01
Full Text Available Respiratory rate (RR is a useful vital sign that can not only provide auxiliary information on physiological changes within the human body, but also indicate early symptoms of various diseases. Recently, methods for the estimation of RR from photoplethysmography (PPG have attracted increased interest, because PPG can be readily recorded using wearable sensors such as smart watches and smart bands. In the present study, we propose a new method for the fast and robust real-time estimation of RR using an adaptive infinite impulse response (IIR notch filter, which has not yet been applied to the PPG-based estimation of RR. In our offline simulation study, the performance of the proposed method was compared to that of recently developed RR estimation methods called an adaptive lattice-type RR estimator and a Smart Fusion. The results of the simulation study show that the proposed method could not only estimate RR more quickly and more accurately than the conventional methods, but also is most suitable for online RR monitoring systems, as it does not use any overlapping moving windows that require increased computational costs. In order to demonstrate the practical applicability of the proposed method, an online RR estimation system was implemented.
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
Effective estimation of parameters in biocatalytic reaction kinetic expressions are very important when building process models to enable evaluation of process technology options and alternative biocatalysts. The kinetic models used to describe enzyme-catalyzed reactions generally include several...... 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....... The parameter estimation problem is decomposed into five hierarchical steps, where the solution of each of the steps becomes the input for the subsequent step to achieve the final model with the corresponding regressed parameters. The model is further used for validating its performance and determining...
Directory of Open Access Journals (Sweden)
Simon Doclo
2003-10-01
Full Text Available Two adaptive algorithms are presented for robust time delay estimation (TDE in acoustic environments with a large amount of background noise and reverberation. Recently, an adaptive eigenvalue decomposition (EVD algorithm has been developed for TDE in highly reverberant acoustic environments. In this paper, we extend the adaptive EVD algorithm to noisy and reverberant acoustic environments, by deriving an adaptive stochastic gradient algorithm for the generalized eigenvalue decomposition (GEVD or by prewhitening the noisy microphone signals. We have performed simulations using a localized and a diffuse noise source for several SNRs, showing that the time delays can be estimated more accurately using the adaptive GEVD algorithm than using the adaptive EVD algorithm. In addition, we have analyzed the sensitivity of the adaptive GEVD algorithm with respect to the accuracy of the noise correlation matrix estimate, showing that its performance may be quite sensitive, especially for low SNR scenarios.
A neural circuit for robust time-to-contact estimation based on primate MST.
Browning, N Andrew
2012-11-01
Time-to-contact (TTC) estimation is beneficial for visual navigation. It can be estimated from an image projection, either in a camera or on the retina, by looking at the rate of expansion of an object. When expansion rate (E) is properly defined, TTC = 1/E. Primate dorsal MST cells have receptive field structures suited to the estimation of expansion and TTC. However, the role of MST cells in TTC estimation has been discounted because of large receptive fields, the fact that neither they nor preceding brain areas appear to decompose the motion field to estimate divergence, and a lack of experimental data. This letter demonstrates mathematically that template models of dorsal MST cells can be constructed such that the output of the template match provides an accurate and robust estimate of TTC. The template match extracts the relevant components of the motion field and scales them such that the output of each component of the template match is an estimate of expansion. It then combines these component estimates to provide a mean estimate of expansion across the object. The output of model MST provides a direct measure of TTC. The ViSTARS model of primate visual navigation was updated to incorporate the modified templates. In ViSTARS and in primates, speed is represented as a population code in V1 and MT. A population code for speed complicates TTC estimation from a template match. Results presented in this letter demonstrate that the updated template model of MST accurately codes TTC across a population of model MST cells. We conclude that the updated template model of dorsal MST simultaneously and accurately codes TTC and heading regardless of receptive field size, object size, or motion representation. It is possible that a subpopulation of MST cells in primates represents expansion in this way.
Robust H(∞) positional control of 2-DOF robotic arm driven by electro-hydraulic servo system.
Guo, Qing; Yu, Tian; Jiang, Dan
2015-11-01
In this paper an H∞ positional feedback controller is developed to improve the robust performance under structural and parametric uncertainty disturbance in electro-hydraulic servo system (EHSS). The robust control model is described as the linear state-space equation by upper linear fractional transformation. According to the solution of H∞ sub-optimal control problem, the robust controller is designed and simplified to lower order linear model which is easily realized in EHSS. The simulation and experimental results can validate the robustness of this proposed method. The comparison result with PI control shows that the robust controller is suitable for this EHSS under the critical condition where the desired system bandwidth is higher and the external load of the hydraulic actuator is closed to its limited capability. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
A robust design mark-resight abundance estimator allowing heterogeneity in resighting probabilities
McClintock, B.T.; White, Gary C.; Burnham, K.P.
2006-01-01
This article introduces the beta-binomial estimator (BBE), a closed-population abundance mark-resight model combining the favorable qualities of maximum likelihood theory and the allowance of individual heterogeneity in sighting probability (p). The model may be parameterized for a robust sampling design consisting of multiple primary sampling occasions where closure need not be met between primary occasions. We applied the model to brown bear data from three study areas in Alaska and compared its performance to the joint hypergeometric estimator (JHE) and Bowden's estimator (BOWE). BBE estimates suggest heterogeneity levels were non-negligible and discourage the use of JHE for these data. Compared to JHE and BOWE, confidence intervals were considerably shorter for the AICc model-averaged BBE. To evaluate the properties of BBE relative to JHE and BOWE when sample sizes are small, simulations were performed with data from three primary occasions generated under both individual heterogeneity and temporal variation in p. All models remained consistent regardless of levels of variation in p. In terms of precision, the AICc model-averaged BBE showed advantages over JHE and BOWE when heterogeneity was present and mean sighting probabilities were similar between primary occasions. Based on the conditions examined, BBE is a reliable alternative to JHE or BOWE and provides a framework for further advances in mark-resight abundance estimation. ?? 2006 American Statistical Association and the International Biometric Society.
Directory of Open Access Journals (Sweden)
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.
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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.
Fixed-Order Robust H∞ Estimator Design for Side-Slip Angle of Vehicle
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Akın Delibaşı
2014-01-01
Full Text Available We present a novel linear observer with an extension dealing with polytopic uncertainties in a vehicle dynamic system to identify the side-slip angle. The performance optimization issue is addressed by the minimization of H∞ norm of the system considering the estimation error as an output and the steer angle as an input. Contrary to the standard robust optimal design approaches, we use a convex inner approximation technique to reduce the order of the observer and this enables us to derive suboptimal, fixed-order, and efficiently practicable estimators. Moreover, the numerical examples performed on two-track nonlinear model of the system are provided to illustrate the impacts of design parameters on the optimization results and the efficiency of the technique.
Robustness of Estimators of Long-Range Dependence and Self-Similarity under non-Gaussianity
Franzke, Christian L E; Watkins, Nicholas W; Gramacy, Robert B; Hughes, Cecilia
2011-01-01
Long-range dependence and non-Gaussianity are ubiquitous in many natural systems like ecosystems, biological systems and climate. However, it is not always appreciated that both phenomena usually occur together in natural systems and that the superposition of both phenomena constitute the self-similarity of a system. These features, which are common in complex systems, impact the attribution of trends and the occurrence and clustering of extremes. The risk assessment of systems with these properties will lead to different outcomes (e.g. return periods) than the more common assumption of independence of extremes. Two paradigmatic models are discussed which can simultaneously account for long-range dependence and non-Gaussianity: Autoregressive Fractional Integrated Moving Average (ARFIMA) and Linear Fractional Stable Motion (LFSM). Statistical properties of estimators for long-range dependence and self-similarity are critically assessed. It is found that the most popular estimators are not robust. In particula...
Robust Foot Clearance Estimation Based on the Integration of Foot-Mounted IMU Acceleration Data
Benoussaad, Mourad; Sijobert, Benoît; Mombaur, Katja; Azevedo Coste, Christine
2015-01-01
This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU) placed on the subject’s foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15% under the various walking conditions. PMID:26703622
Robust time delay estimation for speech signals using information theory: A comparison study
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Wen Fei
2011-01-01
Full Text Available Abstract Time delay estimation (TDE is a fundamental subsystem for a speaker localization and tracking system. Most of the traditional TDE methods are based on second-order statistics (SOS under Gaussian assumption for the source. This article resolves the TDE problem using two information-theoretic measures, joint entropy and mutual information (MI, which can be considered to indirectly include higher order statistics (HOS. The TDE solutions using the two measures are presented for both Gaussian and Laplacian models. We show that, for stationary signals, the two measures are equivalent for TDE. However, for non-stationary signals (e.g., noisy speech signals, maximizing MI gives more consistent estimate than minimizing joint entropy. Moreover, an existing idea of using modified MI to embed information about reverberation is generalized to the multiple microphones case. From the experimental results for speech signals, this scheme with Gaussian model shows the most robust performance in various noisy and reverberant environments.
Robust Foot Clearance Estimation Based on the Integration of Foot-Mounted IMU Acceleration Data
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Mourad Benoussaad
2015-12-01
Full Text Available This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU placed on the subject’s foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15 % under the various walking conditions.
Sensorless position estimator applied to nonlinear IPMC model
Bernat, Jakub; Kolota, Jakub
2016-11-01
This paper addresses the issue of estimating position for an ionic polymer metal composite (IPMC) known as electro active polymer (EAP). The key step is the construction of a sensorless mode considering only current feedback. This work takes into account nonlinearities caused by electrochemical effects in the material. Owing to the recent observer design technique, the authors obtained both Lyapunov function based estimation law as well as sliding mode observer. To accomplish the observer design, the IPMC model was identified through a series of experiments. The research comprises time domain measurements. The identification process was completed by means of geometric scaling of three test samples. In the proposed design, the estimated position accurately tracks the polymer position, which is illustrated by the experiments.
Robust 3D object localization and pose estimation for random bin picking with the 3DMaMa algorithm
Skotheim, Øystein; Thielemann, Jens T.; Berge, Asbjørn; Sommerfelt, Arne
2010-02-01
Enabling robots to automatically locate and pick up randomly placed and oriented objects from a bin is an important challenge in factory automation, replacing tedious and heavy manual labor. A system should be able to recognize and locate objects with a predefined shape and estimate the position with the precision necessary for a gripping robot to pick it up. We describe a system that consists of a structured light instrument for capturing 3D data and a robust approach for object location and pose estimation. The method does not depend on segmentation of range images, but instead searches through pairs of 2D manifolds to localize candidates for object match. This leads to an algorithm that is not very sensitive to scene complexity or the number of objects in the scene. Furthermore, the strategy for candidate search is easily reconfigurable to arbitrary objects. Experiments reported in this paper show the utility of the method on a general random bin picking problem, in this paper exemplified by localization of car parts with random position and orientation. Full pose estimation is done in less than 380 ms per image. We believe that the method is applicable for a wide range of industrial automation problems where precise localization of 3D objects in a scene is needed.
Positive semidefinite integrated covariance estimation, factorizations and asynchronicity
DEFF Research Database (Denmark)
Boudt, Kris; Laurent, Sébastien; Lunde, Asger
2017-01-01
observations as possible. The estimator is positive semidefinite by construction. We derive asymptotic results and confirm their good finite sample properties by means of a Monte Carlo simulation. In the application we forecast portfolio Value-at-Risk and sector risk exposures for a portfolio of 52 stocks. We...... find that the dynamic models utilizing the proposed high-frequency estimator provide statistically and economically superior forecasts....
Positive semidefinite integrated covariance estimation, factorizations and asynchronicity
DEFF Research Database (Denmark)
Sauri, Orimar; Lunde, Asger; Laurent, Sébastien
2017-01-01
An estimator of the ex-post covariation of log-prices under asynchronicity and microstructure noise is proposed. It uses the Cholesky factorization of the covariance matrix in order to exploit the heterogeneity in trading intensities to estimate the different parameters sequentially with as many...... observations as possible. The estimator is positive semidefinite by construction. We derive asymptotic results and confirm their good finite sample properties by means of a Monte Carlo simulation. In the application we forecast portfolio Value-at-Risk and sector risk exposures for a portfolio of 52 stocks. We...
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.
Accurate position estimation methods based on electrical impedance tomography measurements
Vergara, Samuel; Sbarbaro, Daniel; Johansen, T. A.
2017-08-01
Electrical impedance tomography (EIT) is a technology that estimates the electrical properties of a body or a cross section. Its main advantages are its non-invasiveness, low cost and operation free of radiation. The estimation of the conductivity field leads to low resolution images compared with other technologies, and high computational cost. However, in many applications the target information lies in a low intrinsic dimensionality of the conductivity field. The estimation of this low-dimensional information is addressed in this work. It proposes optimization-based and data-driven approaches for estimating this low-dimensional information. The accuracy of the results obtained with these approaches depends on modelling and experimental conditions. Optimization approaches are sensitive to model discretization, type of cost function and searching algorithms. Data-driven methods are sensitive to the assumed model structure and the data set used for parameter estimation. The system configuration and experimental conditions, such as number of electrodes and signal-to-noise ratio (SNR), also have an impact on the results. In order to illustrate the effects of all these factors, the position estimation of a circular anomaly is addressed. Optimization methods based on weighted error cost functions and derivate-free optimization algorithms provided the best results. Data-driven approaches based on linear models provided, in this case, good estimates, but the use of nonlinear models enhanced the estimation accuracy. The results obtained by optimization-based algorithms were less sensitive to experimental conditions, such as number of electrodes and SNR, than data-driven approaches. Position estimation mean squared errors for simulation and experimental conditions were more than twice for the optimization-based approaches compared with the data-driven ones. The experimental position estimation mean squared error of the data-driven models using a 16-electrode setup was less
Position Estimation and Local Mapping Using Omnidirectional Images and Global Appearance Descriptors
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Yerai Berenguer
2015-10-01
Full Text Available This work presents some methods to create local maps and to estimate the position of a mobile robot, using the global appearance of omnidirectional images. We use a robot that carries an omnidirectional vision system on it. Every omnidirectional image acquired by the robot is described only with one global appearance descriptor, based on the Radon transform. In the work presented in this paper, two different possibilities have been considered. In the first one, we assume the existence of a map previously built composed of omnidirectional images that have been captured from previously-known positions. The purpose in this case consists of estimating the nearest position of the map to the current position of the robot, making use of the visual information acquired by the robot from its current (unknown position. In the second one, we assume that we have a model of the environment composed of omnidirectional images, but with no information about the location of where the images were acquired. The purpose in this case consists of building a local map and estimating the position of the robot within this map. Both methods are tested with different databases (including virtual and real images taking into consideration the changes of the position of different objects in the environment, different lighting conditions and occlusions. The results show the effectiveness and the robustness of both methods.
Position Estimation and Local Mapping Using Omnidirectional Images and Global Appearance Descriptors
Berenguer, Yerai; Payá, Luis; Ballesta, Mónica; Reinoso, Oscar
2015-01-01
This work presents some methods to create local maps and to estimate the position of a mobile robot, using the global appearance of omnidirectional images. We use a robot that carries an omnidirectional vision system on it. Every omnidirectional image acquired by the robot is described only with one global appearance descriptor, based on the Radon transform. In the work presented in this paper, two different possibilities have been considered. In the first one, we assume the existence of a map previously built composed of omnidirectional images that have been captured from previously-known positions. The purpose in this case consists of estimating the nearest position of the map to the current position of the robot, making use of the visual information acquired by the robot from its current (unknown) position. In the second one, we assume that we have a model of the environment composed of omnidirectional images, but with no information about the location of where the images were acquired. The purpose in this case consists of building a local map and estimating the position of the robot within this map. Both methods are tested with different databases (including virtual and real images) taking into consideration the changes of the position of different objects in the environment, different lighting conditions and occlusions. The results show the effectiveness and the robustness of both methods. PMID:26501289
Stochastic magnetic measurement model for relative position and orientation estimation
Schepers, H. Martin; Veltink, Petrus H.
2010-01-01
This study presents a stochastic magnetic measurement model that can be used to estimate relative position and orientation. The model predicts the magnetic field generated by a single source coil at the location of the sensor. The model was used in a fusion filter that predicts the change of
Estimating the probability of positive crossmatch after negative virtual crossmatch
K.M. Glorie (Kristiaan)
2012-01-01
textabstractThis paper estimates the probability of virtual crossmatch failure in kidney exchange matching. In particu-lar, the probability of a positive crossmatch after a negative virtual crossmatch is related to the recipient’s PRA level. Using Dutch kidney exchange data, we find significant
Asman, Andrew J.; Landman, Bennett A.
2011-01-01
Segmentation and delineation of structures of interest in medical images is paramount to quantifying and characterizing structural, morphological, and functional correlations with clinically relevant conditions. The established gold standard for performing segmentation has been manual voxel-by-voxel labeling by a neuroanatomist expert. This process can be extremely time consuming, resource intensive and fraught with high inter-observer variability. Hence, studies involving characterizations of novel structures or appearances have been limited in scope (numbers of subjects), scale (extent of regions assessed), and statistical power. Statistical methods to fuse data sets from several different sources (e.g., multiple human observers) have been proposed to simultaneously estimate both rater performance and the ground truth labels. However, with empirical datasets, statistical fusion has been observed to result in visually inconsistent findings. So, despite the ease and elegance of a statistical approach, single observers and/or direct voting are often used in practice. Hence, rater performance is not systematically quantified and exploited during label estimation. To date, statistical fusion methods have relied on characterizations of rater performance that do not intrinsically include spatially varying models of rater performance. Herein, we present a novel, robust statistical label fusion algorithm to estimate and account for spatially varying performance. This algorithm, COnsensus Level, Labeler Accuracy and Truth Estimation (COLLATE), is based on the simple idea that some regions of an image are difficult to label (e.g., confusion regions: boundaries or low contrast areas) while other regions are intrinsically obvious (e.g., consensus regions: centers of large regions or high contrast edges). Unlike its predecessors, COLLATE estimates the consensus level of each voxel and estimates differing models of observer behavior in each region. We show that COLLATE provides
Kalman Filter-Based Hybrid Indoor Position Estimation Technique in Bluetooth Networks
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Fazli Subhan
2013-01-01
Full Text Available This paper presents an extended Kalman filter-based hybrid indoor position estimation technique which is based on integration of fingerprinting and trilateration approach. In this paper, Euclidian distance formula is used for the first time instead of radio propagation model to convert the received signal to distance estimates. This technique combines the features of fingerprinting and trilateration approach in a more simple and robust way. The proposed hybrid technique works in two stages. In the first stage, it uses an online phase of fingerprinting and calculates nearest neighbors (NN of the target node, while in the second stage it uses trilateration approach to estimate the coordinate without the use of radio propagation model. The distance between calculated NN and detective access points (AP is estimated using Euclidian distance formula. Thus, distance between NN and APs provides radii for trilateration approach. Therefore, the position estimation accuracy compared to the lateration approach is better. Kalman filter is used to further enhance the accuracy of the estimated position. Simulation and experimental results validate the performance of proposed hybrid technique and improve the accuracy up to 53.64% and 25.58% compared to lateration and fingerprinting approaches, respectively.
A less field-intensive robust design for estimating demographic parameters with Mark-resight data
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 Manhattan Frame Estimation From a Single RGB-D Image
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.
FracFit: A Robust Parameter Estimation Tool for Anomalous Transport Problems
Kelly, J. F.; Bolster, D.; Meerschaert, M. M.; Drummond, J. D.; Packman, A. I.
2016-12-01
Anomalous transport cannot be adequately described with classical Fickian advection-dispersion equations (ADE). Rather, fractional calculus models may be used, which capture non-Fickian behavior (e.g. skewness and power-law tails). FracFit is a robust parameter estimation tool based on space- and time-fractional models used to model anomalous transport. Currently, four fractional models are supported: 1) space fractional advection-dispersion equation (sFADE), 2) time-fractional dispersion equation with drift (TFDE), 3) fractional mobile-immobile equation (FMIE), and 4) tempered fractional mobile-immobile equation (TFMIE); additional models may be added in the future. Model solutions using pulse initial conditions and continuous injections are evaluated using stable distribution PDFs and CDFs or subordination integrals. Parameter estimates are extracted from measured breakthrough curves (BTCs) using a weighted nonlinear least squares (WNLS) algorithm. Optimal weights for BTCs for pulse initial conditions and continuous injections are presented, facilitating the estimation of power-law tails. Two sample applications are analyzed: 1) continuous injection laboratory experiments using natural organic matter and 2) pulse injection BTCs in the Selke river. Model parameters are compared across models and goodness-of- fit metrics are presented, assisting model evaluation. The sFADE and time-fractional models are compared using space-time duality (Baeumer et. al., 2009), which links the two paradigms.
Aviles, Angelica I.; Widlak, Thomas; Casals, Alicia; Nillesen, Maartje M.; Ammari, Habib
2017-06-01
Cardiac motion estimation is an important diagnostic tool for detecting heart diseases and it has been explored with modalities such as MRI and conventional ultrasound (US) sequences. US cardiac motion estimation still presents challenges because of complex motion patterns and the presence of noise. In this work, we propose a novel approach to estimate cardiac motion using ultrafast ultrasound data. Our solution is based on a variational formulation characterized by the L 2-regularized class. Displacement is represented by a lattice of b-splines and we ensure robustness, in the sense of eliminating outliers, by applying a maximum likelihood type estimator. While this is an important part of our solution, the main object of this work is to combine low-rank data representation with topology preservation. Low-rank data representation (achieved by finding the k-dominant singular values of a Casorati matrix arranged from the data sequence) speeds up the global solution and achieves noise reduction. On the other hand, topology preservation (achieved by monitoring the Jacobian determinant) allows one to radically rule out distortions while carefully controlling the size of allowed expansions and contractions. Our variational approach is carried out on a realistic dataset as well as on a simulated one. We demonstrate how our proposed variational solution deals with complex deformations through careful numerical experiments. The low-rank constraint speeds up the convergence of the optimization problem while topology preservation ensures a more accurate displacement. Beyond cardiac motion estimation, our approach is promising for the analysis of other organs that exhibit motion.
Robust time estimation reconciles views of the antiquity of placental mammals.
Directory of Open Access Journals (Sweden)
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
Lepine, Nicholas N; Tajima, Takuro; Ogasawara, Takayuki; Kasahara, Ryoichi; Koizumi, Hiroshi
2016-08-01
An adaptive Kalman filter-based fusion algorithm capable of estimating respiration rate for unobtrusive respiratory monitoring is proposed. Using both signal characteristics and a priori information, the Kalman filter is adaptively optimized to improve accuracy. Furthermore, the system is able to combine the respiration-related signals extracted from a textile ECG sensor and an accelerometer to create a single robust measurement. We measured derived respiratory rates and, when compared to a reference, found root-mean-square error of 2.11 breaths-per-minute (BrPM) while lying down, 2.30 BrPM while sitting, 5.97 BrPM while walking, and 5.98 BrPM while running. These results demonstrate that the proposed system is applicable to unobtrusive monitoring for various applications.
A Landmark Based Position Estimation for Pinpoint Landing on Mars
Cheng, Yang; Ansar, Adnan
2005-01-01
Real-time position estimation for a descent lander is a critical technological need for many of NASA's planned in situ missions including landing on a number of bodies at locations of greatest scientific interest and sample return. In particular, it enables the capability to land precisely and safely in a scientifically promising but hazardous site and is a key technology to be demonstrated by NASA in the next decade. The key challenge of pinpoint landing (PPL) is how to localize the lander by recognizing the landmarks (craters) in the landing area and match them positively to a preexisting landmark database while the spacecraft is descending. In this paper, a real-time landmark based position estimation technique for pinpoint landing is suggested. This system includes three crucial components: (1) real time landmark detection, (2) real-time landmark matching and (3) state (both position and velocity) estimation. We discuss the performance analysis of this system. Finally, we show that the suggested technology is able to deliver a spacecraft to less than 100 m from a pre-selected landing site on Mars.
Reliable estimation of virtual source position for SAFT imaging.
Chang, Chih-Hsiung; Chang, Young-Fo; Ma, Yushieh; Shung, K K
2013-02-01
The synthetic aperture focusing technique (SAFT), employing a scanned focused transducer as a virtual source, is commonly used to image flaws in immersion testing. The position of a virtual source is estimated from rays emitted from the rim of a focused transducer. However, it is often found that the virtual source position cannot be uniquely determined because of severe focal spot aberration at the focal zone. Based on an analysis of the energy radiated from the focused transducer and the refracted energy varied with the incident angle of ultrasound, we propose that paraxial rays emitted from the focused transducer are the best for estimating the position of a virtual source for incorporation into SAFT. This study results also shows that by using this simple virtual source position estimation for SAFT, the axial resolution and SNR of the reconstructed image can be greatly improved. This new approach minimizes the effect of such factors as refraction at high-velocity-contrast interfaces, distance of the transducer to the couplant-specimen interface, and the focal length of a focused transducer, which may cause focal spot aberration resulting in decreased sensitivity in SAFT imaging.
Adaptive algorithm for mobile user positioning based on environment estimation
Directory of Open Access Journals (Sweden)
Grujović Darko
2014-01-01
Full Text Available This paper analyzes the challenges to realize an infrastructure independent and a low-cost positioning method in cellular networks based on RSS (Received Signal Strength parameter, auxiliary timing parameter and environment estimation. The proposed algorithm has been evaluated using field measurements collected from GSM (Global System for Mobile Communications network, but it is technology independent and can be applied in UMTS (Universal Mobile Telecommunication Systems and LTE (Long-Term Evolution networks, also.
Approaches to relativistic positioning around Earth and error estimations
Puchades, Neus
2016-01-01
In the context of relativistic positioning, the coordinates of a given user may be calculated by using suitable information broadcast by a 4-tuple of satellites. Our 4-tuples belong to the Galileo constellation. Recently, we estimated the positioning errors due to uncertainties in the satellite world lines (U-errors). A distribution of U-errors was obtained, at various times, in a set of points covering a large region surrounding Earth. Here, the positioning errors associated to the simplifying assumption that photons move in Minkowski space-time (S-errors) are estimated and compared with the U-errors. Both errors have been calculated for the same points and times to make comparisons possible. For a certain realistic modeling of the world line uncertainties, the estimated S-errors have proved to be smaller than the U-errors, which shows that the approach based on the assumption that the Earth's gravitational field produces negligible effects on photons may be used in a large region surrounding Earth. The appl...
Li, Dongming; Sun, Changming; Yang, Jinhua; Liu, Huan; Peng, Jiaqi; Zhang, Lijuan
2017-04-06
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.
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.
A Robust Approach for Clock Offset Estimation in Wireless Sensor Networks
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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.
Robust and Accurate Multiple-Camera Pose Estimation toward Robotic Applications
Directory of Open Access Journals (Sweden)
Yong Liu
2014-09-01
Full Text Available Pose estimation methods in robotics applications frequently suffer from inaccuracy due to a lack of correspondence and real-time constraints, and instability from a wide range of viewpoints, etc. In this paper, we present a novel approach for estimating the poses of all the cameras in a multi-camera system in which each camera is placed rigidly using only a few coplanar points simultaneously. Instead of solving the orientation and translation for the multi-camera system from the overlapping point correspondences among all the cameras directly, we employ homography, which can map image points with 3D coplanar-referenced points. In our method, we first establish the corresponding relations between each camera by their Euclidean geometries and optimize the homographies of the cameras; then, we solve the orientation and translation for the optimal homographies. The results from simulations and real case experiments show that our approach is accurate and robust for implementation in robotics applications. Finally, a practical implementation in a ping-pong robot is described in order to confirm the validity of our approach.
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.
Position estimation in magnetic bearings using inductance measurements
Energy Technology Data Exchange (ETDEWEB)
Noh, M.D.; Maslen, E.H. [Univ. of Virginia, Charlottesville, VA (United States)
1995-12-31
A signal processing technique is presented by which the position of a rotor supported in magnetic bearings may be deduced from the bearing current waveform. The bearing currents are presumed to be developed with a bi-state switching amplifier which produces a substantial high frequency switching ripple. The amplitude of this ripple is a function of the power supply voltage, switching duty cycle, and bearing inductance. The inductance is predominantly a function of bearing air gap or, equivalently, rotor position while the duty cycle is primarily a function of developed bearing force. Ideally, the sensor signal processor should exactly extract the rotor position information while perfectly reflecting the bearing force information. When the bearing is a perfect inductor, these functional relationships are easily established and the gap dependence is monotonic. Since the voltage and duty cycle are both easily measured, the relationships can be inverted with a non-linear parameter estimator to extract the rotor position. One method of implementing this estimator is presented and its performance is evaluated by simulation. The method is demonstrated to produce a fairly wide bandwidth sensor with acceptably low feed-through of the bearing force.
Dama, James F; Rotskoff, Grant; Parrinello, Michele; Voth, Gregory A
2014-09-09
Well-tempered metadynamics has proven to be a practical and efficient adaptive enhanced sampling method for the computational study of biomolecular and materials systems. However, choosing its tunable parameter can be challenging and requires balancing a trade-off between fast escape from local metastable states and fast convergence of an overall free energy estimate. In this article, we present a new smoothly convergent variant of metadynamics, transition-tempered metadynamics, that removes that trade-off and is more robust to changes in its own single tunable parameter, resulting in substantial speed and accuracy improvements. The new method is specifically designed to study state-to-state transitions in which the states of greatest interest are known ahead of time, but transition mechanisms are not. The design is guided by a picture of adaptive enhanced sampling as a means to increase dynamical connectivity of a model's state space until percolation between all points of interest is reached, and it uses the degree of dynamical percolation to automatically tune the convergence rate. We apply the new method to Brownian dynamics on 48 random 1D surfaces, blocked alanine dipeptide in vacuo, and aqueous myoglobin, finding that transition-tempered metadynamics substantially and reproducibly improves upon well-tempered metadynamics in terms of first barrier crossing rate, convergence rate, and robustness to the choice of tuning parameter. Moreover, the trade-off between first barrier crossing rate and convergence rate is eliminated: the new method drives escape from an initial metastable state as fast as metadynamics without tempering, regardless of tuning.
Global positioning system watches for estimating energy expenditure.
Hongu, Nobuko; Orr, Barron J; Roe, Denise J; Reed, Rebecca G; Going, Scott B
2013-11-01
Global positioning system (GPS) watches have been introduced commercially, converting frequent measurements of time, location, speed (pace), and elevation into energy expenditure (EE) estimates. The purpose of this study was to compare EE estimates of 4 different GPS watches (Forerunner, Suunto, Polar, Adeo), at various walking speeds, with EE estimate from a triaxial accelerometer (RT3), which was used as a reference measure in this study. Sixteen healthy young adults completed the study. Participants wore 4 different GPS watches and an RT3 accelerometer and walked at 6-minute intervals on an outdoor track at 3 speeds (3, 5, and 7 km/hr). The statistical significance of differences in EE between the 3 watches was assessed using linear contrasts of the coefficients from the overall model. Reliability across trials for a given device was assessed using intraclass correlation coefficients as estimated in the mixed model. The GPS watches demonstrated lower reliability (intraclass correlation coefficient) across trials when compared with the RT3, particularly at the higher speed, 7 km/hr. Three GPS watches (Forerunner, Polar, and Suunto) significantly and consistently underestimated EE compared with the reference EE given by the RT3 accelerometer (average mean difference: Garmin, -50.5%; Polar, -41.7%; and Suunto, -41.7%; all p < 0.001). Results suggested that caution should be exercised when using commercial GPS watches to estimate EE in athletes during field-based testing and training.
Information-geometric measures as robust estimators of connection strengths and external inputs.
Tatsuno, Masami; Fellous, Jean-Marc; Amari, Shun-Ichi
2009-08-01
Information geometry has been suggested to provide a powerful tool for analyzing multineuronal spike trains. Among several advantages of this approach, a significant property is the close link between information-geometric measures and neural network architectures. Previous modeling studies established that the first- and second-order information-geometric measures corresponded to the number of external inputs and the connection strengths of the network, respectively. This relationship was, however, limited to a symmetrically connected network, and the number of neurons used in the parameter estimation of the log-linear model needed to be known. Recently, simulation studies of biophysical model neurons have suggested that information geometry can estimate the relative change of connection strengths and external inputs even with asymmetric connections. Inspired by these studies, we analytically investigated the link between the information-geometric measures and the neural network structure with asymmetrically connected networks of N neurons. We focused on the information-geometric measures of orders one and two, which can be derived from the two-neuron log-linear model, because unlike higher-order measures, they can be easily estimated experimentally. Considering the equilibrium state of a network of binary model neurons that obey stochastic dynamics, we analytically showed that the corrected first- and second-order information-geometric measures provided robust and consistent approximation of the external inputs and connection strengths, respectively. These results suggest that information-geometric measures provide useful insights into the neural network architecture and that they will contribute to the study of system-level neuroscience.
Zhu, S; Keeling, A; Hsung, T C; Yang, Y; Khambay, B
2018-02-01
This study determined the intra-rater and inter-rater reliability of re-orientating three-dimensional (3D) facial images into the estimated natural head position. Three-dimensional facial images of 15 pre-surgical class III orthognathic patients were obtained and automatically re-orientated into natural head position (RNHP) using a 3D stereophotogrammetry system and in-house software. Six clinicians were asked to estimate the NHP of these patients (ENHP); they re-estimated five randomly selected 3D images after a 2-week interval. The differences in yaw, roll, pitch, and chin position between RNHP and ENHP were measured. For intra-rater reliability, the intra-class correlation coefficient (ICC) values ranged from 0.55 to 0.77, representing moderate reliability for roll, yaw, pitch, and chin position, while for inter-rater reliability, the ICC values ranged from 0.38 to 0.58, indicating poor to moderate reliability. The median difference between ENHP and RNHP was small for roll and yaw, but larger for pitch. There was a tendency for the clinicians to estimate NHP with the chin tipped more posteriorly (6.3±5.2mm) compared to RNHP, reducing the severity of the skeletal deformity in the anterior-posterior direction. Copyright © 2017 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.
DEFF Research Database (Denmark)
Schmidt, Lasse; Andersen, Torben Ole; Pedersen, Henrik C.
2012-01-01
This paper presents a robust tracking control concept based on accurate feedforward compensation for hydraulic valve-cylinder drives. The proposed feedforward compensator is obtained utilizing a generalized description of the valve flow that takes into account any asymmetry of valves and/or cylin......This paper presents a robust tracking control concept based on accurate feedforward compensation for hydraulic valve-cylinder drives. The proposed feedforward compensator is obtained utilizing a generalized description of the valve flow that takes into account any asymmetry of valves and...
Estimation of Rotor Position in a 3-Phase SRM at Standstill and Low Speeds
Komatsuzaki, Akitomo; Bamba, Tatsunori; Miki, Ichiro
Switched reluctance motors (SRMs) are widely employed as industrial drives because they are inexpensive, simple, and sturdy, further, they deliver a robust and reliable performance. SRMs are controlled with a rotor position sensor attached to the motor shaft. Normally, encoders, resolvers, or Hall sensors are used as position sensors. The use of these sensors, however, increases the size and cost of the machine and degrades its performance. Therefore, to overcome these difficulties, several sensorless drive techniques have been reported. In this paper, a method for estimating the position of a rotor in an SRM; this method is based on calculation of the space vector of phase inductance at standstill and low speeds. The position at standstill is obtained simply without making use of the magnetic characteristics of the motor or any additional hardware. Assuming the inductance waveform to be a sine wave, the position of rotor at standstill is obtained from the phase inductance vectors of all phases. At low speeds, position estimation is carried out by applying a DC link voltage to the unenergized phases. The validity of the proposed method is experimentally verified.
Directory of Open Access Journals (Sweden)
Chao Peng
2016-01-01
Full Text Available This paper proposed a novel H∞ optimal inversion feedforward and robust feedback based two-freedom-of-freedom (2DOF control approach to address the positioning error caused by system uncertainties in high speed-precision positioning system. To minimize the H∞ norm of the positioning error in the presence of model uncertainty, a linear matrix inequality (LMI synthesis approach for optimal inversion feedforward controller design is presented. The specification of position resolution, control width, robustness, and output signal magnitude imposed on the entire 2DOF control system are taken as optimization objectives of feedback controller design. The robust feedback controller design approach integrates with feedforward controller systematically and is obtained via LMI optimization. The proposed approach was illustrated through a simulation example of nanopositioning control in atomic force microscope (AFM; the experiment results demonstrated that the proposed 2DOF control approach not only achieves the performance specification but also could improve the positioning control performance compared with H∞ mixed sensitivity feedback control and inversion-based 2DOF control.
Herrero-Medrano, J M; Mathur, P K; ten Napel, J; Rashidi, H; Alexandri, P; Knol, E F; Mulder, H A
2015-04-01
Robustness is an important issue in the pig production industry. Since pigs from international breeding organizations have to withstand a variety of environmental challenges, selection of pigs with the inherent ability to sustain their productivity in diverse environments may be an economically feasible approach in the livestock industry. The objective of this study was to estimate genetic parameters and breeding values across different levels of environmental challenge load. The challenge load (CL) was estimated as the reduction in reproductive performance during different weeks of a year using 925,711 farrowing records from farms distributed worldwide. A wide range of levels of challenge, from favorable to unfavorable environments, was observed among farms with high CL values being associated with confirmed situations of unfavorable environment. Genetic parameters and breeding values were estimated in high- and low-challenge environments using a bivariate analysis, as well as across increasing levels of challenge with a random regression model using Legendre polynomials. Although heritability estimates of number of pigs born alive were slightly higher in environments with extreme CL than in those with intermediate levels of CL, the heritabilities of number of piglet losses increased progressively as CL increased. Genetic correlations among environments with different levels of CL suggest that selection in environments with extremes of low or high CL would result in low response to selection. Therefore, selection programs of breeding organizations that are commonly conducted under favorable environments could have low response to selection in commercial farms that have unfavorable environmental conditions. Sows that had experienced high levels of challenge at least once during their productive life were ranked according to their EBV. The selection of pigs using EBV ignoring environmental challenges or on the basis of records from only favorable environments
Robust sound speed estimation for ultrasound-based hepatic steatosis assessment
Imbault, Marion; Faccinetto, Alex; Osmanski, Bruno-Félix; Tissier, Antoine; Deffieux, Thomas; Gennisson, Jean-Luc; Vilgrain, Valérie; Tanter, Mickaël
2017-05-01
Hepatic steatosis is a common condition, the prevalence of which is increasing along with non-alcoholic fatty liver disease (NAFLD). Currently, the most accurate noninvasive imaging method for diagnosing and quantifying hepatic steatosis is MRI, which estimates the proton-density fat fraction (PDFF) as a measure of fractional fat content. However, MRI suffers several limitations including cost, contra-indications and poor availability. Although conventional ultrasound is widely used by radiologists for hepatic steatosis assessment, it remains qualitative and operator dependent. Interestingly, the speed of sound within soft tissues is known to vary slightly from muscle (1.575 mm · µs-1) to fat (1.450 mm · µs-1). Building upon this fact, steatosis could affect liver sound speed when the fat content increases. The main objectives of this study are to propose a robust method for sound speed estimation (SSE) locally in the liver and to assess its accuracy for steatosis detection and staging. This technique was first validated on two phantoms and SSE was assessed with a precision of 0.006 and 0.003 mm · µs-1 respectively for the two phantoms. Then a preliminary clinical trial (N = 17 patients) was performed. SSE results was found to be highly correlated with MRI proton density fat fraction (R 2 = 0.69) and biopsy (AUROC = 0.952) results. This new method based on the assessment of spatio-temporal properties of the local speckle noise for SSE provides an efficient way to diagnose and stage hepatic steatosis.
Directory of Open Access Journals (Sweden)
Holschneider Matthias
2007-05-01
Full Text Available Abstract Background The size and magnitude of the metabolome, the ratio between individual metabolites and the response of metabolic networks is controlled by multiple cellular factors. A tight control over metabolite ratios will be reflected by a linear relationship of pairs of metabolite due to the flexibility of metabolic pathways. Hence, unbiased detection and validation of linear metabolic variance can be interpreted in terms of biological control. For robust analyses, criteria for rejecting or accepting linearities need to be developed despite technical measurement errors. The entirety of all pair wise linear metabolic relationships then yields insights into the network of cellular regulation. Results The Bayesian law was applied for detecting linearities that are validated by explaining the residues by the degree of technical measurement errors. Test statistics were developed and the algorithm was tested on simulated data using 3–150 samples and 0–100% technical error. Under the null hypothesis of the existence of a linear relationship, type I errors remained below 5% for data sets consisting of more than four samples, whereas the type II error rate quickly raised with increasing technical errors. Conversely, a filter was developed to balance the error rates in the opposite direction. A minimum of 20 biological replicates is recommended if technical errors remain below 20% relative standard deviation and if thresholds for false error rates are acceptable at less than 5%. The algorithm was proven to be robust against outliers, unlike Pearson's correlations. Conclusion The algorithm facilitates finding linear relationships in complex datasets, which is radically different from estimating linearity parameters from given linear relationships. Without filter, it provides high sensitivity and fair specificity. If the filter is activated, high specificity but only fair sensitivity is yielded. Total error rates are more favorable with
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.
Tipton, Elizabeth; Pustejovsky, James E.
2015-01-01
Meta-analyses often include studies that report multiple effect sizes based on a common pool of subjects or that report effect sizes from several samples that were treated with very similar research protocols. The inclusion of such studies introduces dependence among the effect size estimates. When the number of studies is large, robust variance…
A robust method for estimating gene expression states using Affymetrix microarray probe level data
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Satoh Kenichi
2010-04-01
Full Text Available Abstract Background Microarray technology is a high-throughput method for measuring the expression levels of thousand of genes simultaneously. The observed intensities combine a non-specific binding, which is a major disadvantage with microarray data. The Affymetrix GeneChip assigned a mismatch (MM probe with the intention of measuring non-specific binding, but various opinions exist regarding usefulness of MM measures. It should be noted that not all observed intensities are associated with expressed genes and many of those are associated with unexpressed genes, of which measured values express mere noise due to non-specific binding, cross-hybridization, or stray signals. The implicit assumption that all genes are expressed leads to poor performance of microarray data analyses. We assume two functional states of a gene - expressed or unexpressed - and propose a robust method to estimate gene expression states using an order relationship between PM and MM measures. Results An indicator 'probability of a gene being expressed' was obtained using the number of probe pairs within a probe set where the PM measure exceeds the MM measure. We examined the validity of the proposed indicator using Human Genome U95 data sets provided by Affymetrix. The usefulness of 'probability of a gene being expressed' is illustrated through an exploration of candidate genes involved in neuroblastoma prognosis. We identified the candidate genes for which expression states differed (un-expressed or expressed when compared between two outcomes. The validity of this result was subsequently confirmed by quantitative RT-PCR. Conclusion The proposed qualitative evaluation, 'probability of a gene being expressed', is a useful indicator for improving microarray data analysis. It is useful to reduce the number of false discoveries. Expression states - expressed or unexpressed - correspond to the most fundamental gene function 'On' and 'Off', which can lead to biologically
Directory of Open Access Journals (Sweden)
Yingsong Li
2016-10-01
Full Text Available A robust sparse least-mean mixture-norm (LMMN algorithm is proposed, and its performance is appraised in the context of estimating a broadband multi-path wireless channel. The proposed algorithm is implemented via integrating a correntropy-induced metric (CIM penalty into the conventional LMMN algorithm to modify the basic cost function, which is denoted as the CIM-based LMMN (CIM-LMMN algorithm. The proposed CIM-LMMN algorithm is derived in detail within the kernel framework. The updating equation of CIM-LMMN can provide a zero attractor to attract the non-dominant channel coefficients to zeros, and it also gives a tradeoff between the sparsity and the estimation misalignment. Moreover, the channel estimation behavior is investigated over a broadband sparse multi-path wireless channel, and the simulation results are compared with the least mean square/fourth (LMS/F, least mean square (LMS, least mean fourth (LMF and the recently-developed sparse channel estimation algorithms. The channel estimation performance obtained from the designated sparse channel estimation demonstrates that the CIM-LMMN algorithm outperforms the recently-developed sparse LMMN algorithms and the relevant sparse channel estimation algorithms. From the results, we can see that our CIM-LMMN algorithm is robust and is superior to these mentioned algorithms in terms of both the convergence speed rate and the channel estimation misalignment for estimating a sparse channel.
Directory of Open Access Journals (Sweden)
Nitish Katal
2016-01-01
Full Text Available Automation of the robust control system synthesis for uncertain systems is of great practical interest. In this paper, the loop shaping step for synthesizing quantitative feedback theory (QFT based controller for a two-phase permanent magnet stepper motor (PMSM has been automated using teaching learning-based optimization (TLBO algorithm. The QFT controller design problem has been posed as an optimization problem and TLBO algorithm has been used to minimize the proposed cost function. This facilitates designing low-order fixed-structure controller, eliminates the need of manual loop shaping step on the Nichols charts, and prevents the overdesign of the controller. A performance comparison of the designed controller has been made with the classical PID tuning method of Ziegler-Nichols and QFT controller tuned using other optimization algorithms. The simulation results show that the designed QFT controller using TLBO offers robust stability, disturbance rejection, and proper reference tracking over a range of PMSM’s parametric uncertainties as compared to the classical design techniques.
Robust Operation of Tendon-Driven Robot Fingers Using Force and Position-Based Control Laws
Abdallah, Muhammad E (Inventor); Platt, Jr., Robert J. (Inventor); Reiland, Matthew J (Inventor); Hargrave, Brian (Inventor); Diftler, Myron A (Inventor); Strawser, Philip A (Inventor); Ihrke, Chris A. (Inventor)
2013-01-01
A robotic system includes a tendon-driven finger and a control system. The system controls the finger via a force-based control law when a tension sensor is available, and via a position-based control law when a sensor is not available. Multiple tendons may each have a corresponding sensor. The system selectively injects a compliance value into the position-based control law when only some sensors are available. A control system includes a host machine and a non-transitory computer-readable medium having a control process, which is executed by the host machine to control the finger via the force- or position-based control law. A method for controlling the finger includes determining the availability of a tension sensor(s), and selectively controlling the finger, using the control system, via the force or position-based control law. The position control law allows the control system to resist disturbances while nominally maintaining the initial state of internal tendon tensions.
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.
Khandelwal, Siddhartha; Wickström, Nicholas
2018-01-01
Identifying Initial Contact events (ICE) is essential in gait analysis as they segment the walking pattern into gait cycles and facilitate the computation of other gait parameters. As such, numerous algorithms have been developed to identify ICE by placing the accelerometer at a specific body location. Simultaneously, many researchers have studied the effects of device positioning for participant or patient compliance, which is an important factor to consider especially for long-term studies in real-life settings. With the adoption of accelerometery for long-term gait analysis in daily living, current and future applications will require robust algorithms that can either autonomously adapt to changes in sensor positioning or can detect ICE from multiple sensors locations. This study presents a novel methodology that is capable of estimating ICE from accelerometers placed at different body locations. The proposed methodology, called DK-TiFA, is based on utilizing domain knowledge about the fundamental spectral relationships present between the movement of different body parts during gait to drive the time-frequency analysis of the acceleration signal. In order to assess the performance, DK-TiFA is benchmarked on four large publicly available gait databases, consisting of a total of 613 subjects and 7 unique body locations, namely, ankle, thigh, center waist, side waist, chest, upper arm and wrist. The DK-TiFA methodology is demonstrated to achieve high accuracy and robustness for estimating ICE from data consisting of different accelerometer specifications, varying gait speeds and different environments. Copyright © 2017 Elsevier B.V. All rights reserved.
Lai, Guanyu; Liu, Zhi; Zhang, Yun; Chen, C L Philip
2016-01-01
This paper presents a novel adaptive controller for controlling an autonomous helicopter with unknown inertial matrix to asymptotically track the desired trajectory. To identify the unknown inertial matrix included in the attitude dynamic model, this paper proposes a new structural identifier that differs from those previously proposed in that it additionally contains a neural networks (NNs) mechanism and a robust adaptive mechanism, respectively. Using the NNs to compensate the unknown aerodynamic forces online and the robust adaptive mechanism to cancel the combination of the overlarge NNs compensation error and the external disturbances, the new robust neural identifier exhibits a better identification performance in the complex flight environment. Moreover, an optimized algorithm is included in the NNs mechanism to alleviate the burdensome online computation. By the strict Lyapunov argument, the asymptotic convergence of the inertial matrix identification error, position tracking error, and attitude tracking error to arbitrarily small neighborhood of the origin is proved. The simulation and implementation results are provided to evaluate the performance of the proposed controller.
DEFF Research Database (Denmark)
Jensen, Anders Vestergaard; Barfod, Michael Bruhn; Leleur, Steen
2011-01-01
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...
Variance-Constrained Robust Estimation for Discrete-Time Systems with Communication Constraints
Directory of Open Access Journals (Sweden)
Baofeng Wang
2014-01-01
Full Text Available This paper is concerned with a new filtering problem in networked control systems (NCSs subject to limited communication capacity, which includes measurement quantization, random transmission delay, and packets loss. The measurements are first quantized via a logarithmic quantizer and then transmitted through a digital communication network with random delay and packet loss. The three communication constraints phenomena which can be seen as a class of uncertainties are formulated by a stochastic parameter uncertainty system. The purpose of the paper is to design a linear filter such that, for all the communication constraints, the error state of the filtering process is mean square bounded and the steady-state variance of the estimation error for each state is not more than the individual prescribed upper bound. It is shown that the desired filtering can effectively be solved if there are positive definite solutions to a couple of algebraic Riccati-like inequalities or linear matrix inequalities. Finally, an illustrative numerical example is presented to demonstrate the effectiveness and flexibility of the proposed design approach.
Venturelli, Ophelia S; El-Samad, Hana; Murray, Richard M
2012-11-27
Feedback loops are ubiquitous features of biological networks and can produce significant phenotypic heterogeneity, including a bimodal distribution of gene expression across an isogenic cell population. In this work, a combination of experiments and computational modeling was used to explore the roles of multiple feedback loops in the bimodal, switch-like response of the Saccharomyces cerevisiae galactose regulatory network. Here, we show that bistability underlies the observed bimodality, as opposed to stochastic effects, and that two unique positive feedback loops established by Gal1p and Gal3p, which both regulate network activity by molecular sequestration of Gal80p, induce this bimodality. Indeed, systematically scanning through different single and multiple feedback loop knockouts, we demonstrate that there is always a concentration regime that preserves the system's bimodality, except for the double deletion of GAL1 and the GAL3 feedback loop, which exhibits a graded response for all conditions tested. The constitutive production rates of Gal1p and Gal3p operate as bifurcation parameters because variations in these rates can also abolish the system's bimodal response. Our model indicates that this second loss of bistability ensues from the inactivation of the remaining feedback loop by the overexpressed regulatory component. More broadly, we show that the sequestration binding affinity is a critical parameter that can tune the range of conditions for bistability in a circuit with positive feedback established by molecular sequestration. In this system, two positive feedback loops can significantly enhance the region of bistability and the dynamic response time.
Friedel, M. J.; Daughney, C.
2016-12-01
The development of a successful surface-groundwater management strategy depends on the quality of data provided for analysis. This study evaluates the statistical robustness when using a modified self-organizing map (MSOM) technique to estimate missing values for three hypersurface models: synoptic groundwater-surface water hydrochemistry, time-series of groundwater-surface water hydrochemistry, and mixed-survey (combination of groundwater-surface water hydrochemistry and lithologies) hydrostratigraphic unit data. These models of increasing complexity are developed and validated based on observations from the Southland region of New Zealand. In each case, the estimation method is sufficiently robust to cope with groundwater-surface water hydrochemistry vagaries due to sample size and extreme data insufficiency, even when >80% of the data are missing. The estimation of surface water hydrochemistry time series values enabled the evaluation of seasonal variation, and the imputation of lithologies facilitated the evaluation of hydrostratigraphic controls on groundwater-surface water interaction. The robust statistical results for groundwater-surface water models of increasing data complexity provide justification to apply the MSOM technique in other regions of New Zealand and abroad.
Ryskin, Rachel A.; Sarah Brown-Schmidt
2014-01-01
Seven experiments use large sample sizes to robustly estimate the effect size of a previous finding that adults are more likely to commit egocentric errors in a false-belief task when the egocentric response is plausible in light of their prior knowledge. We estimate the true effect size to be less than half of that reported in the original findings. Even though we found effects in the same direction as the original, they were substantively smaller; the original study would have had less than...
Robust Tracking Control of Robot Manipulators Using Only Joint Position Measurements
Directory of Open Access Journals (Sweden)
Ancai Zhang
2013-01-01
Full Text Available This paper concerns the tracking control of a robot manipulator with unknown uncertainties and disturbances. It presents a new control method that uses only joint position measurements to design a tracking controller. The controller has two parts. One is based on a feedback linearization technique; it makes the nominal model of a manipulator asymptotically track a desired trajectory. The other is based on the idea of equivalent input disturbance (EID; it compensates for uncertainties and disturbances. Together they enable a robot manipulator to precisely track the desired trajectory. The new control algorithm is applied to a two-link robot manipulator, and simulation results demonstrate the validity of this method.
Belfield, Clive; Bailey, Thomas
2017-01-01
Recently, studies have adopted fixed effects modeling to identify the returns to college. This method has the advantage over ordinary least squares estimates in that unobservable, individual-level characteristics that may bias the estimated returns are differenced out. But the method requires extensive longitudinal data and involves complex…
Directory of Open Access Journals (Sweden)
Yuguan Hou
2016-01-01
Full Text Available Due to the fluctuation of the signal-to-noise ratio (SNR and the single snapshot case in the MIMO HF sky-wave radar system, the accuracy of the online estimation of the mutual coupling coefficients matrix of the uniform rectangle array (URA might be degraded by the classical approach, especially in the case of low SNR. In this paper, an Online Particle Mean-Shift Approach (OPMA is proposed, which is to get a relatively more effective estimation of the mutual coupling coefficients matrix with the low SNR. Firstly, the spatial smoothing technique combined with the MUSIC algorithm of URA is introduced for the DOA estimation of the multiple targets in the case of single snapshot which are taken as coherent sources. Then, based on the idea of the particle filter, the online particles with a moderate computational complexity are used to generate some different estimation results. Finally, the mean-shift algorithm is applied to get a more robust estimate of the equivalent mutual coupling coefficients matrix. The simulation results demonstrate the validity of the proposed approach in terms of the success probability, the statistics of bias, and the variance. The proposed approach is more robust and more accurate than the other two approaches.
Comparison of Classical and Robust Estimates of Threshold Auto-regression Parameters
Directory of Open Access Journals (Sweden)
V. B. Goryainov
2017-01-01
Full Text Available The study object is the first-order threshold auto-regression model with a single zero-located threshold. The model describes a stochastic temporal series with discrete time by means of a piecewise linear equation consisting of two linear classical first-order autoregressive equations. One of these equations is used to calculate a running value of the temporal series. A control variable that determines the choice between these two equations is the sign of the previous value of the same series.The first-order threshold autoregressive model with a single threshold depends on two real parameters that coincide with the coefficients of the piecewise linear threshold equation. These parameters are assumed to be unknown. The paper studies an estimate of the least squares, an estimate the least modules, and the M-estimates of these parameters. The aim of the paper is a comparative study of the accuracy of these estimates for the main probabilistic distributions of the updating process of the threshold autoregressive equation. These probability distributions were normal, contaminated normal, logistic, double-exponential distributions, a Student's distribution with different number of degrees of freedom, and a Cauchy distribution.As a measure of the accuracy of each estimate, was chosen its variance to measure the scattering of the estimate around the estimated parameter. An estimate with smaller variance made from the two estimates was considered to be the best. The variance was estimated by computer simulation. To estimate the smallest modules an iterative weighted least-squares method was used and the M-estimates were done by the method of a deformable polyhedron (the Nelder-Mead method. To calculate the least squares estimate, an explicit analytic expression was used.It turned out that the estimation of least squares is best only with the normal distribution of the updating process. For the logistic distribution and the Student's distribution with the
Khoshravesh, Mojtaba; Sefidkouhi, Mohammad Ali Gholami; Valipour, Mohammad
2017-07-01
The proper evaluation of evapotranspiration is essential in food security investigation, farm management, pollution detection, irrigation scheduling, nutrient flows, carbon balance as well as hydrologic modeling, especially in arid environments. To achieve sustainable development and to ensure water supply, especially in arid environments, irrigation experts need tools to estimate reference evapotranspiration on a large scale. In this study, the monthly reference evapotranspiration was estimated by three different regression models including the multivariate fractional polynomial (MFP), robust regression, and Bayesian regression in Ardestan, Esfahan, and Kashan. The results were compared with Food and Agriculture Organization (FAO)-Penman-Monteith (FAO-PM) to select the best model. The results show that at a monthly scale, all models provided a closer agreement with the calculated values for FAO-PM ( R 2 > 0.95 and RMSE < 12.07 mm month-1). However, the MFP model gives better estimates than the other two models for estimating reference evapotranspiration at all stations.
Locatelli, Isabella; Marazzi, Alfio
2013-06-30
We consider the problem of estimating the mean hospital cost of stays of a class of patients (e.g., a diagnosis-related group) as a function of patient characteristics. The statistical analysis is complicated by the asymmetry of the cost distribution, the possibility of censoring on the cost variable, and the occurrence of outliers. These problems have often been treated separately in the literature, and a method offering a joint solution to all of them is still missing. Indirect procedures have been proposed, combining an estimate of the duration distribution with an estimate of the conditional cost for a given duration. We propose a parametric version of this approach, allowing for asymmetry and censoring in the cost distribution and providing a mean cost estimator that is robust in the presence of extreme values. In addition, the new method takes covariate information into account. Copyright © 2012 John Wiley & Sons, Ltd.
Kondor, Dániel; Csabai, István; Dobos, László; Szüle, János; Barankai, Norbert; Hanyecz, Tamás; Sebők, Tamás; Kallus, Zsófia; Vattay, Gábor
2013-01-01
Principal component analysis (PCA) and related techniques have been successfully employed in natural language processing. Text mining applications in the age of the online social media (OSM) face new challenges due to properties specific to these use cases (e.g. spelling issues specific to texts posted by users, the presence of spammers and bots, service announcements, etc.). In this paper, we employ a Robust PCA technique to separate typical outliers and highly localized topics from the low-...
Senkel, Luise
2016-01-01
This edited book aims at presenting current research activities in the field of robust variable-structure systems. The scope equally comprises highlighting novel methodological aspects as well as presenting the use of variable-structure techniques in industrial applications including their efficient implementation on hardware for real-time control. The target audience primarily comprises research experts in the field of control theory and nonlinear dynamics but the book may also be beneficial for graduate students.
Energy Technology Data Exchange (ETDEWEB)
Orton, Matthew R [Cancer Research UK Clinical MR Research Group, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT (United Kingdom); Collins, David J [Cancer Research UK Clinical MR Research Group, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT (United Kingdom); Walker-Samuel, Simon [Cancer Research UK Clinical MR Research Group, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT (United Kingdom); D' Arcy, James A [Cancer Research UK Clinical MR Research Group, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT (United Kingdom); Hawkes, David J [Centre for Medical Image Computing, University College London, WC1E 6BT (United Kingdom); Atkinson, David [Centre for Medical Image Computing, University College London, WC1E 6BT (United Kingdom); Leach, Martin O [Cancer Research UK Clinical MR Research Group, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT (United Kingdom)
2007-05-07
When applying pharmacokinetic (PK) models to dynamic contrast enhanced MRI (DCE-MRI) data it is important to appropriately deal with the enhancement onset time, because errors in the onset time will affect the PK parameter estimates. This paper presents a Bayesian approach to the estimation of the PK parameters k{sub ep} and K{sup trans} that robustly treats the onset time. This approach involves the computation of an analytically intractable integral, so two approximate methods are developed. The first uses adaptive numerical quadrature, which produces results accurate to a given tolerance, and the other a simple approximation with a summation. These approaches are compared with each other, and with the standard least-squares (LS) approach. The results of a Monte Carlo experiment show that the LS approach produces biased estimates when k{sub ep} is large and K{sup trans} is small, whereas both the Bayesian methods are unbiased. The two Bayesian methods produce very similar estimates, but the simple summation method requires less than half the computation time of either the LS, or the quadrature approximation. The standard deviation of the LS estimates is shown to be larger than either of the Bayesian estimates, while uncertainty estimates based around a Hessian approximation are shown to be too small for all three methods. A more detailed method of assessing the uncertainty of the Bayesian approach is described, and the results show that this is a more accurate description of the estimation uncertainty.
DEFF Research Database (Denmark)
Chon, K H; Hoyer, D; Armoundas, A A
1999-01-01
error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate......In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...
Parente, Daniel J; Ray, J Christian J; Swint-Kruse, Liskin
2015-12-01
As proteins evolve, amino acid positions key to protein structure or function are subject to mutational constraints. These positions can be detected by analyzing sequence families for amino acid conservation or for coevolution between pairs of positions. Coevolutionary scores are usually rank-ordered and thresholded to reveal the top pairwise scores, but they also can be treated as weighted networks. Here, we used network analyses to bypass a major complication of coevolution studies: For a given sequence alignment, alternative algorithms usually identify different, top pairwise scores. We reconciled results from five commonly-used, mathematically divergent algorithms (ELSC, McBASC, OMES, SCA, and ZNMI), using the LacI/GalR and 1,6-bisphosphate aldolase protein families as models. Calculations used unthresholded coevolution scores from which column-specific properties such as sequence entropy and random noise were subtracted; "central" positions were identified by calculating various network centrality scores. When compared among algorithms, network centrality methods, particularly eigenvector centrality, showed markedly better agreement than comparisons of the top pairwise scores. Positions with large centrality scores occurred at key structural locations and/or were functionally sensitive to mutations. Further, the top central positions often differed from those with top pairwise coevolution scores: instead of a few strong scores, central positions often had multiple, moderate scores. We conclude that eigenvector centrality calculations reveal a robust evolutionary pattern of constraints-detectable by divergent algorithms--that occur at key protein locations. Finally, we discuss the fact that multiple patterns coexist in evolutionary data that, together, give rise to emergent protein functions. © 2015 Wiley Periodicals, Inc.
Parente, Daniel J.; Ray, J. Christian J.; Swint-Kruse, Liskin
2015-01-01
As proteins evolve, amino acid positions key to protein structure or function are subject to mutational constraints. These positions can be detected by analyzing sequence families for amino acid conservation or for co-evolution between pairs of positions. Co-evolutionary scores are usually rank-ordered and thresholded to reveal the top pairwise scores, but they also can be treated as weighted networks. Here, we used network analyses to bypass a major complication of co-evolution studies: For a given sequence alignment, alternative algorithms usually identify different, top pairwise scores. We reconciled results from five commonly-used, mathematically divergent algorithms (ELSC, McBASC, OMES, SCA, and ZNMI), using the LacI/GalR and 1,6-bisphosphate aldolase protein families as models. Calculations used unthresholded co-evolution scores from which column-specific properties such as sequence entropy and random noise were subtracted; “central” positions were identified by calculating various network centrality scores. When compared among algorithms, network centrality methods, particularly eigenvector centrality, showed markedly better agreement than comparisons of the top pairwise scores. Positions with large centrality scores occurred at key structural locations and/or were functionally sensitive to mutations. Further, the top central positions often differed from those with top pairwise co-evolution scores: Instead of a few strong scores, central positions often had multiple, moderate scores. We conclude that eigenvector centrality calculations reveal a robust evolutionary pattern of constraints – detectable by divergent algorithms – that occur at key protein locations. Finally, we discuss the fact that multiple patterns co-exist in evolutionary data that, together, give rise to emergent protein functions. PMID:26503808
Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis
Williams, Ryan T.
2012-01-01
Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…
Robust and Efficient Adaptive Estimation of Binary-Choice Regression Models
Cizek, P.
2007-01-01
The binary-choice regression models such as probit and logit are used to describe the effect of explanatory variables on a binary response vari- able. Typically estimated by the maximum likelihood method, estimates are very sensitive to deviations from a model, such as heteroscedastic- ity and data
Yuan, Ke-Hai; Bentler, Peter M.
2002-01-01
Examined the asymptotic distributions of three reliability coefficient estimates: (1) sample coefficient alpha; (2) reliability estimate of a composite score following factor analysis; and (3) maximal reliability of a linear combination of item scores after factor analysis. Findings show that normal theory based asymptotic distributions for these…
Uncertainties in the Item Parameter Estimates and Robust Automated Test Assembly
Veldkamp, Bernard P.; Matteucci, Mariagiulia; de Jong, Martijn G.
2013-01-01
Item response theory parameters have to be estimated, and because of the estimation process, they do have uncertainty in them. In most large-scale testing programs, the parameters are stored in item banks, and automated test assembly algorithms are applied to assemble operational test forms. These algorithms treat item parameters as fixed values,…
Badshah, Amir; Choudhry, Aadil Jaleel; Ullah, Shan
2017-03-01
Industries are moving towards automation in order to increase productivity and ensure quality. Variety of electronic and electromagnetic systems are being employed to assist human operator in fast and accurate quality inspection of products. Majority of these systems are equipped with cameras and rely on diverse image processing algorithms. Information is lost in 2D image, therefore acquiring accurate 3D data from 2D images is an open issue. FAST, SURF and SIFT are well-known spatial domain techniques for features extraction and henceforth image registration to find correspondence between images. The efficiency of these methods is measured in terms of the number of perfect matches found. A novel fast and robust technique for stereo-image processing is proposed. It is based on non-rigid registration using modified normalized phase correlation. The proposed method registers two images in hierarchical fashion using quad-tree structure. The registration process works through global to local level resulting in robust matches even in presence of blur and noise. The computed matches can further be utilized to determine disparity and depth for industrial product inspection. The same can be used in driver assistance systems. The preliminary tests on Middlebury dataset produced satisfactory results. The execution time for a 413 x 370 stereo-pair is 500ms approximately on a low cost DSP.
Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.
Li, Xuewen; Xie, Yunfeng; Li, Lianfa; Yang, Xunfeng; Wang, Ning; Wang, Jinfeng
2015-11-01
Prediction of antibiotic pollution and its consequences is difficult, due to the uncertainties and complexities associated with multiple related factors. This article employed domain knowledge and spatial data to construct a Bayesian network (BN) model to assess fluoroquinolone antibiotic (FQs) pollution in the soil of an intensive vegetable cultivation area. The results show: (1) The relationships between FQs pollution and contributory factors: Three factors (cultivation methods, crop rotations, and chicken manure types) were consistently identified as predictors in the topological structures of three FQs, indicating their importance in FQs pollution; deduced with domain knowledge, the cultivation methods are determined by the crop rotations, which require different nutrients (derived from the manure) according to different plant biomass. (2) The performance of BN model: The integrative robust Bayesian network model achieved the highest detection probability (pd) of high-risk and receiver operating characteristic (ROC) area, since it incorporates domain knowledge and model uncertainty. Our encouraging findings have implications for the use of BN as a robust approach to assessment of FQs pollution and for informing decisions on appropriate remedial measures.
Robust albedo estimation from a facial image with cast shadow under general unknown lighting.
Suh, Sungho; Lee, Minsik; Choi, Chong-Ho
2013-01-01
Albedo estimation from a facial image is crucial for various computer vision tasks, such as 3-D morphable-model fitting, shape recovery, and illumination-invariant face recognition, but the currently available methods do not give good estimation results. Most methods ignore the influence of cast shadows and require a statistical model to obtain facial albedo. This paper describes a method for albedo estimation that makes combined use of image intensity and facial depth information for an image with cast shadows and general unknown light. In order to estimate the albedo map of a face, we formulate the albedo estimation problem as a linear programming problem that minimizes intensity error under the assumption that the surface of the face has constant albedo. Since the solution thus obtained has significant errors in certain parts of the facial image, the albedo estimate needs to be compensated. We minimize the mean square error of albedo under the assumption that the surface normals, which are calculated from the facial depth information, are corrupted with noise. The proposed method is simple and the experimental results show that this method gives better estimates than other methods.
John Loomis; Armando Gonzalez-Caban; Joseph Champ
2011-01-01
Over the past four decades teh contingent valuation method (CVM) has become a technique frequently used by economists to estimate willingness-to-pay (WTP) for improvements in environmental quality and prot3tion of natural resources. The CVM was originall applied to estmate recreation use values (Davis, 1963; Hammack and Brown, 1974)and air quality (Brookshire et al....
Robust a posteriori error estimation for finite element approximation to H(curl) problem
Cai, Zhiqiang; Cao, Shuhao; Falgout, Rob
2016-09-01
In this paper, we introduce a novel a posteriori error estimator for the conforming finite element approximation to the H(curl) problem with inhomogeneous media and with the right-hand side only in L^2. The estimator is of the recovery type. Independent with the current approximation to the primary variable (the electric field), an auxiliary variable (the magnetizing field) is recovered in parallel by solving a similar H(curl) problem. An alternate way of recovery is presented as well by localizing the error flux. The estimator is then defined as the sum of the modified element residual and the residual of the constitutive equation defining the auxiliary variable. It is proved that the estimator is approximately equal to the true error in the energy norm without the quasi-monotonicity assumption. Finally, we present numerical results for two H(curl) interface problems.
A Robust Subpixel Motion Estimation Algorithm Using HOS in the Parametric Domain
Directory of Open Access Journals (Sweden)
E. M. Ismaili Aalaoui
2009-02-01
Full Text Available Motion estimation techniques are widely used in todays video processing systems. The most frequently used techniques are the optical flow method and phase correlation method. The vast majority of these algorithms consider noise-free data. Thus, in the case of the image sequences are severely corrupted by additive Gaussian (perhaps non-Gaussian noises of unknown covariance, the classical techniques will fail to work because they will also estimate the noise spatial correlation. In this paper, we have studied this topic from a viewpoint different from the above to explore the fundamental limits in image motion estimation. Our scheme is based on subpixel motion estimation algorithm using bispectrum in the parametric domain. The motion vector of a moving object is estimated by solving linear equations involving third-order hologram and the matrix containing Dirac delta function. Simulation results are presented and compared to the optical flow and phase correlation algorithms; this approach provides more reliable displacement estimates particularly for complex noisy image sequences. In our simulation, we used the database freely available on the web.
A Robust Subpixel Motion Estimation Algorithm Using HOS in the Parametric Domain
Directory of Open Access Journals (Sweden)
Ibn-Elhaj E
2009-01-01
Full Text Available Motion estimation techniques are widely used in todays video processing systems. The most frequently used techniques are the optical flow method and phase correlation method. The vast majority of these algorithms consider noise-free data. Thus, in the case of the image sequences are severely corrupted by additive Gaussian (perhaps non-Gaussian noises of unknown covariance, the classical techniques will fail to work because they will also estimate the noise spatial correlation. In this paper, we have studied this topic from a viewpoint different from the above to explore the fundamental limits in image motion estimation. Our scheme is based on subpixel motion estimation algorithm using bispectrum in the parametric domain. The motion vector of a moving object is estimated by solving linear equations involving third-order hologram and the matrix containing Dirac delta function. Simulation results are presented and compared to the optical flow and phase correlation algorithms; this approach provides more reliable displacement estimates particularly for complex noisy image sequences. In our simulation, we used the database freely available on the web.
Robust motion estimation on a low-power multi-core DSP
Igual, Francisco D.; Botella, Guillermo; García, Carlos; Prieto, Manuel; Tirado, Francisco
2013-12-01
This paper addresses the efficient implementation of a robust gradient-based optical flow model in a low-power platform based on a multi-core digital signal processor (DSP). The aim of this work was to carry out a feasibility study on the use of these devices in autonomous systems such as robot navigation, biomedical assistance, or tracking, with not only power restrictions but also real-time requirements. We consider the C6678 DSP from Texas Instruments (Dallas, TX, USA) as the target platform of our implementation. The interest of this research is particularly relevant in optical flow scope because this system can be considered as an alternative solution for mid-range video resolutions when a combination of in-processor parallelism with optimizations such as efficient memory-hierarchy exploitation and multi-processor parallelization are applied.
Vision-Based Position Estimation Utilizing an Extended Kalman Filter
2016-12-01
GPS jammers are available for as little as $50 [1]. Even malfunctioning equipment can lead to a drone losing its position and thus aborting the...the UAV loses GPS but must maintain a relative position to the target. The drone uses the algorithm to accomplish this task. The exemplary scenario...passive measurements without using GPS . The algorithm receives an image from the drone . It locates the target in the image and outputs an angle. The
Novel rotor position estimation technique for switched reluctance motor (SRM)
Moradi, Hassan; Afjei, Ebrahim
2011-09-01
This article presents a new and novel method which is designed to detect the rotor position at standstill and at low speeds in switched reluctance motor. Since the inductance parameter plays a significant role both in the steady state and in the dynamic characteristics of an electromagnetic device, the rotor position can be determined using inductance bridge systems to measure unknown inductance and resistance values. In this method we use motor winding in Maxwell-Wien Bridge, with the standard capacitor and the resistor in parallel with it adjusted to achieve balance in an aligned position when the maximum inductance occurs. The supply voltage, in conjunction with the drive transistor, produces short pulses for this AC bridge. The condition of the balanced bridge v 0 = 0 leads to the relation between the impedances of the bridge branches. The phase inductance varies with the rotor position. Therefore the motor goes into an unaligned position and the Maxwell-Wien Bridge goes into an unbalanced condition thus causing variation in the state of the bridge output. It then continues to sense the rotor position with the motor running by applying the same procedure, but only to the un-energised phases winding. The simulation and experimentally obtained results demonstrate the feasibility and practicability of this method.
Hamelmann, Paul; Vullings, Rik; Schmitt, Lars; Kolen, Alexander F; Mischi, Massimo; van Laar, Judith O E H; Bergmans, Jan W M
2017-09-21
Doppler ultrasound (US) is the most commonly applied method to measure the fetal heart rate (fHR). When the fetal heart is not properly located within the ultrasonic beam, fHR measurements often fail. As a consequence, clinical staff need to reposition the US transducer on the maternal abdomen, which can be a time consuming and tedious task. In this article, a method is presented to aid clinicians with the positioning of the US transducer to produce robust fHR measurements. A maximum likelihood estimation (MLE) algorithm is developed, which provides information on fetal heart location using the power of the Doppler signals received in the individual elements of a standard US transducer for fHR recordings. The performance of the algorithm is evaluated with simulations and in vitro experiments performed on a beating-heart setup. Both the experiments and the simulations show that the heart location can be accurately determined with an error of less than 7 mm within the measurement volume of the employed US transducer. The results show that the developed algorithm can be used to provide accurate feedback on fetal heart location for improved positioning of the US transducer, which may lead to improved measurements of the fHR.
Directory of Open Access Journals (Sweden)
Mizue Hisano
applying multiple assessment methods, to obtain robust estimates of population trends in species threatened by overfishing.
Simultaneous estimation of QTL effects and positions when using ...
Indian Academy of Sciences (India)
Keywords. backcross model; EM algorithm; genotyping errors; maximum likelihood estimation; QTL mapping. ... However, due to the constraint at the technical level, most of the genetic data that people used so far contain errors. In this paper, we considered the problem of QTL mapping based on biological data with ...
Robustness of Input features from Noisy Silhouettes in Human Pose Estimation
DEFF Research Database (Denmark)
Gong, Wenjuan; Fihl, Preben; Gonzàlez, Jordi
2014-01-01
. In this paper, we explore this problem. First, We compare performances of several image features widely used for human pose estimation and explore their performances against each other and select one with best performance. Second, iterative closest point algorithm is introduced for a new quantitative...... measurement of noisy inputs. The proposed measurement is able to automatically discard noise, like camouflage from the background or shadows. With the proposed measurement, we split inputs into different noise levels and assess their pose estimation accuracies. Furthermore, we explore performances...
DEFF Research Database (Denmark)
Lu, Xiaobing; Liu, Zhigang; Wang, Yanbo
2016-01-01
Active control of pantograph could be performed to decrease the fluctuation in pantograph-catenary contact force (PCCF) in high-speed railway. However, it is difficult to obtain the states of the pantograph when state feedback control is implemented. And the measurements may randomly miss due......, the RRSEM is introduced to estimate the pantograph states based on the dynamic model. The simulation results indicate that the proposed RRSEM is able to accurately estimate the states of the leading pantograph (LP) and the trailing pantograph (TP)....
The Joint Position-Amplitude Formulation for Hurricane State Estimation
Ravela, S.; Williams, J.; Emanuel, K.
2008-12-01
Classical formulations of data assimilation, whether sequential, ensemble-based or variational, are amplitude adjustment methods. Such approaches can perform poorly when forecast locations of weather systems are displaced from their observations. Compensating position errors by adjusting amplitudes can produce unacceptably 'distorted' states, adversely affecting analysis, verification and subsequent forecasts. There are many sources of position error. It is non-trivial to decompose position error into constituent sources and yet correcting position errors during assimilation can be essential for operationally predicting strong, localized weather events such as tropical cyclones. We will argue and show that if we assume a perfect world where forecast errors do not have position errors and have a Gaussian uncertainty, then in the real world, the bias or variance induced by position errors is the only reason for suboptimal performance of contemporary assimilation methods. Therefore, we propose a method that accounts for both position and amplitude errors using a variational approach. We show that the objective can be solved for position and amplitude decision variables using stochastic methods, thus corresponding with ensemble data assimilation. We then show that if an Euler-Lagrange approximation is made, can solve the objective nearly as well in two steps. This approach is entirely consistent with contemporary data assimilation practice. In the two-step approach, the first step is field alignment, where the current model state is aligned with observations by adjusting a continuous field of local displacements, subject to certain constraints. The second step is amplitude adjustment, where contemporary assimilation approaches are used. We will then demonstrate several choices of constraints on the displacement field, first starting with fluid-like viscous constraints and then proceeding to a multiscale wavelet representation that allows better balance in the
Mollah, Mohammad Manir Hossain; Jamal, Rahman; Mokhtar, Norfilza Mohd; Harun, Roslan; Mollah, Md Nurul Haque
2015-01-01
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-sample cases in
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
Korendijk, Elly J. H.; Moerbeek, Mirjam; Maas, Cora J. M.
2010-01-01
In the case of trials with nested data, the optimal allocation of units depends on the budget, the costs, and the intracluster correlation coefficient. In general, the intracluster correlation coefficient is unknown in advance and an initial guess has to be made based on published values or subject matter knowledge. This initial estimate is likely…
Multilevel Modeling in the Presence of Outliers: A Comparison of Robust Estimation Methods
Finch, Holmes
2017-01-01
Multilevel models (MLMs) have proven themselves to be very useful in social science research, as data from a variety of sources is sampled such that individuals at level-1 are nested within clusters such as schools, hospitals, counseling centers, and business entities at level-2. MLMs using restricted maximum likelihood estimation (REML) provide…
Robust observer-based fault estimation and accommodation of discrete-time piecewise linear systems
DEFF Research Database (Denmark)
Tabatabaeipour, Mojtaba; Bak, Thomas
2013-01-01
are formulated in terms of linear matrix inequalities (LMI) which can be solved efficiently. Also, performance of the estimator and the state feedback controller are minimized by solving convex optimization problems. The efficiency of the method is demonstrated by means of a numerical example....
A robust estimate of the number and characteristics of persons released from prison in Australia.
Avery, Alex; Kinner, Stuart A
2015-08-01
To estimate the number and characteristics of adults released from prison in Australia. We calculated ratios, stratified by age, sex and Indigenous status, by comparing the number of persons released from prison in New South Wales (NSW), with the number in NSW prisons on 30 June of the corresponding year. These stratified ratios were applied to Australia-wide prison data to estimate the number and characteristics of persons released annually. We estimated that in 2013, 38,576 persons were released from prison in Australia - 25.3% more than the daily prison population. Young people, Indigenous people and women were over-represented among those released. We estimated that 3.69 Indigenous women aged 18-24 were released annually for each equivalent person in prison; and 2.75 non-Indigenous women aged 18-24 were released annually for each equivalent person in prison. The annual 'flow' through Australia's prisons is well in excess of the daily number, but information on those moving through prison systems is not yet publicly available. The characteristics of those released from prison differ meaningfully from those of people in prison. Routine, national reporting of prison separations is critical to informing upscaling and targeting of Throughcare services for this profoundly vulnerable population. © 2015 Public Health Association of Australia.
Small-sample robust estimators of noncentrality-based and incremental model fit
Boomsma, Anne; Herzog, W.
2009-01-01
Traditional estimators of fit measures based on the noncentral chi-square distribution (root mean square error of approximation [RMSEA], Steiger's , etc.) tend to overreject acceptable models when the sample size is small. To handle this problem, it is proposed to employ Bartlett's (1950), Yuan's
A robust estimator for location in Phase I based on an EWMA chart
Zwetsloot, I.M.; Schoonhoven, M.; Does, R.J.M.M.
2014-01-01
In practice, a control chart for process monitoring (Phase II) is based on parameters estimated from data collected on the process characteristic under study (Phase I). The Phase I data could contain unacceptable data, which in turn could affect the monitoring. This article considers various
Estimating parameters of hidden Markov models based on marked individuals: use of robust design data
Kendall, William L.; White, Gary C.; Hines, James E.; Langtimm, Catherine A.; Yoshizaki, Jun
2012-01-01
Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last twenty years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We also provide user-friendly software to implement these models. This general framework could also be used by practitioners to consider constrained models of particular interest, or model the relationship between within-primary period parameters (e.g., state structure) and between-primary period parameters (e.g., state transition probabilities).
Estimating willingness to accept using paired comparison choice experiments: tests of robustness
David C. Kingsley; Thomas C. Brown
2013-01-01
Paired comparison (PC) choice experiments offer researchers and policy-makers an alternative nonmarket valuation method particularly apt when a ranking of the public's priorities across policy alternatives is paramount. Similar to contingent valuation, PC choice experiments estimate the total value associated with a specific environmental good or service. Similar...
Directory of Open Access Journals (Sweden)
Mark I Rowley
Full Text Available We present novel Bayesian methods for the analysis of exponential decay data that exploit the evidence carried by every detected decay event and enables robust extension to advanced processing. Our algorithms are presented in the context of fluorescence lifetime imaging microscopy (FLIM and particular attention has been paid to model the time-domain system (based on time-correlated single photon counting with unprecedented accuracy. We present estimates of decay parameters for mono- and bi-exponential systems, offering up to a factor of two improvement in accuracy compared to previous popular techniques. Results of the analysis of synthetic and experimental data are presented, and areas where the superior precision of our techniques can be exploited in Förster Resonance Energy Transfer (FRET experiments are described. Furthermore, we demonstrate two advanced processing methods: decay model selection to choose between differing models such as mono- and bi-exponential, and the simultaneous estimation of instrument and decay parameters.
Rowley, Mark I; Coolen, Anthonius C C; Vojnovic, Borivoj; Barber, Paul R
2016-01-01
We present novel Bayesian methods for the analysis of exponential decay data that exploit the evidence carried by every detected decay event and enables robust extension to advanced processing. Our algorithms are presented in the context of fluorescence lifetime imaging microscopy (FLIM) and particular attention has been paid to model the time-domain system (based on time-correlated single photon counting) with unprecedented accuracy. We present estimates of decay parameters for mono- and bi-exponential systems, offering up to a factor of two improvement in accuracy compared to previous popular techniques. Results of the analysis of synthetic and experimental data are presented, and areas where the superior precision of our techniques can be exploited in Förster Resonance Energy Transfer (FRET) experiments are described. Furthermore, we demonstrate two advanced processing methods: decay model selection to choose between differing models such as mono- and bi-exponential, and the simultaneous estimation of instrument and decay parameters.
Ryskin, Rachel A; Brown-Schmidt, Sarah
2014-01-01
Seven experiments use large sample sizes to robustly estimate the effect size of a previous finding that adults are more likely to commit egocentric errors in a false-belief task when the egocentric response is plausible in light of their prior knowledge. We estimate the true effect size to be less than half of that reported in the original findings. Even though we found effects in the same direction as the original, they were substantively smaller; the original study would have had less than 33% power to detect an effect of this magnitude. The influence of plausibility on the curse of knowledge in adults appears to be small enough that its impact on real-life perspective-taking may need to be reevaluated.
Directory of Open Access Journals (Sweden)
Rachel A Ryskin
Full Text Available Seven experiments use large sample sizes to robustly estimate the effect size of a previous finding that adults are more likely to commit egocentric errors in a false-belief task when the egocentric response is plausible in light of their prior knowledge. We estimate the true effect size to be less than half of that reported in the original findings. Even though we found effects in the same direction as the original, they were substantively smaller; the original study would have had less than 33% power to detect an effect of this magnitude. The influence of plausibility on the curse of knowledge in adults appears to be small enough that its impact on real-life perspective-taking may need to be reevaluated.
Influence of catheter position on estimated strain in intravascular elastography.
De Korte, C L; Cespedes, E I; Van Der Steen, A W
1999-01-01
In elastography, an erroneous strain estimate is obtained when the radial strain and the probing ultrasound beam are not properly aligned: the "strain projection artifact". In practice, an angle between the strain and the ultrasound beam will be present in most of the cases due to inhomogeneities or nonuniform compression. In this study, a theoretical function describing the strain projection artifact is derived as a function of the angle between the radial strain and the ultrasound beam. Two main factors for an angle between strain and ultrasound beam in intravascular elastographic experiments are eccentricity and tilt of the transducer. The theoretical functions describing these errors are corroborated with strain estimates from an experiment with a circular, homogeneous gel-based vessel phantom. Comparison between the theoretical functions and the experimental results reveals that the strain projection artifact is well described by the theoretical findings. As a result, the experimental data can be corrected for this artifact. The corrected elastograms reveal that correct strain estimates are obtained when the eccentricity of the intravascular catheter is less than 63%. An "off-the-wall" device may be required to advance intravascular elastography to in vivo implementation.
Bayesian Estimation of the Logistic Positive Exponent IRT Model
Bolfarine, Heleno; Bazan, Jorge Luis
2010-01-01
A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric…
Eigenvalue estimates of positive integral operators with analytic ...
Indian Academy of Sciences (India)
In this paper, we exhibit canonical positive definite integral kernels associated with simply connected domains. We give lower bounds for the eigenvalues of the sums of such kernels. Author Affiliations. Yüksel Soykan1. Department of Mathematics, Art and Science Faculty, Zonguldak Karaelmas University, 67100, ...
Range Estimation for Indoor Positioning via Drifting Clocks
DEFF Research Database (Denmark)
Bagdonas, Kazimieras; Schiøler, Henrik; Borre, Kai
2009-01-01
This paper presents results from the “Indoor Positioning” project conducted at Danish GPS Center (DGC), Aalborg University. We focus on creating theoretical background and experimental verification for a software based indoor positioning solution. We present a novel theory to improve the ranging...
Directory of Open Access Journals (Sweden)
S. Jitapunkul
2007-01-01
Full Text Available Recently, there has been a great deal of work developing super-resolution reconstruction (SRR algorithms. While many such algorithms have been proposed, the almost SRR estimations are based on L1 or L2 statistical norm estimation, therefore these SRR algorithms are usually very sensitive to their assumed noise model that limits their utility. The real noise models that corrupt the measure sequence are unknown; consequently, SRR algorithm using L1 or L2 norm may degrade the image sequence rather than enhance it. Therefore, the robust norm applicable to several noise and data models is desired in SRR algorithms. This paper first comprehensively reviews the SRR algorithms in this last decade and addresses their shortcomings, and latter proposes a novel robust SRR algorithm that can be applied on several noise models. The proposed SRR algorithm is based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. For removing outliers in the data, the Lorentzian error norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image. Moreover, Tikhonov regularization and Lorentzian-Tikhonov regularization are used to remove artifacts from the final answer and improve the rate of convergence. The experimental results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norms for several noise models such as noiseless, additive white Gaussian noise (AWGN, poisson noise, salt and pepper noise, and speckle noise.
Gelech, Jan; Bayly, Melanie; Desjardins, Michel
2017-04-10
Despite common experiences of identity damage, decline, and deterioration, many brain injury survivors succeed in reconstructing robust identities in the wake of injury. Yet, while this accomplishment greatly benefits survivors' quality of life, little is known about how positive identity work might be facilitated or enhanced in therapeutic institutions. Drawing on data from a women's self-help group, we argue that an egalitarian, reflective, strength-focused, and gender-segregated environment can provide female ABI (acquired brain injury) survivors with a fertile scene for identity enhancement and offer unique opportunities for collective identity development. Sociolinguistic interactional analysis revealed four types of positive identity work undertaken within the group: constructing competent selves; tempering the threat of loss and impairment; resisting infantilisation and delegitimisation; and asserting a collective gender identity. This identity work was facilitated by specific programme attributes and activities and contributed to the global project of decentring disability and destigmatising impairments and losses. We call for increased attention to identity issues in brain injury rehabilitation and argue that gender-segregated programming can provide a unique space for female survivors to construct empowering individual and collective identities after injury.
An empirical Bayes method for robust variance estimation in detecting DEGs using microarray data.
You, Na; Wang, Xueqin
2017-10-01
The microarray technology is widely used to identify the differentially expressed genes due to its high throughput capability. The number of replicated microarray chips in each group is usually not abundant. It is an efficient way to borrow information across different genes to improve the parameter estimation which suffers from the limited sample size. In this paper, we use a hierarchical model to describe the dispersion of gene expression profiles and model the variance through the gene expression level via a link function. A heuristic algorithm is proposed to estimate the hyper-parameters and link function. The differentially expressed genes are identified using a multiple testing procedure. Compared to SAM and LIMMA, our proposed method shows a significant superiority in term of detection power as the false discovery rate being controlled.
Robust Minimum Distance Estimation of the Four-Parameter Generalized Gamma Distribution.
1982-09-01
thesis students in the field of parameter estimation (2; 4; 7; 8; 10; 14). 3 I CHAPTER I FOUR-PARAMETER GENERALIZED GAMNA DISTRIBUTION Generalized Gamma...closed form (19: 352). Solutions can be found by iteration; and the iterative technique developed by Harter will be used to solve these equations for the...William, Richard L. Scheaffer, and Dennis D. Wackerly . Mathematical Statistics with Applications. 2nd ed. Boston: Duxbury Press, 1982. 13. Mihram, G. A
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Sayak Mukherjee
Full Text Available The inositol-phosphate messenger inositol(1,3,4,5tetrakisphosphate (IP4 is essential for thymocyte positive selection by regulating plasma-membrane association of the protein tyrosine kinase Itk downstream of the T cell receptor (TCR. IP4 can act as a soluble analog of the phosphoinositide 3-kinase (PI3K membrane lipid product phosphatidylinositol(3,4,5trisphosphate (PIP3. PIP3 recruits signaling proteins such as Itk to cellular membranes by binding to PH and other domains. In thymocytes, low-dose IP4 binding to the Itk PH domain surprisingly promoted and high-dose IP4 inhibited PIP3 binding of Itk PH domains. However, the mechanisms that underlie the regulation of membrane recruitment of Itk by IP4 and PIP3 remain unclear. The distinct Itk PH domain ability to oligomerize is consistent with a cooperative-allosteric mode of IP4 action. However, other possibilities cannot be ruled out due to difficulties in quantitatively measuring the interactions between Itk, IP4 and PIP3, and in generating non-oligomerizing Itk PH domain mutants. This has hindered a full mechanistic understanding of how IP4 controls Itk function. By combining experimentally measured kinetics of PLCγ1 phosphorylation by Itk with in silico modeling of multiple Itk signaling circuits and a maximum entropy (MaxEnt based computational approach, we show that those in silico models which are most robust against variations of protein and lipid expression levels and kinetic rates at the single cell level share a cooperative-allosteric mode of Itk regulation by IP4 involving oligomeric Itk PH domains at the plasma membrane. This identifies MaxEnt as an excellent tool for quantifying robustness for complex TCR signaling circuits and provides testable predictions to further elucidate a controversial mechanism of PIP3 signaling.
Noise estimation of beam position monitors at RHIC
Energy Technology Data Exchange (ETDEWEB)
Shen, X. [Indiana Univ., Bloomington, IN (United States); Bai, M. [Brookhaven National Lab. (BNL), Upton, NY (United States). Collider-Accelerator Dept.; Lee, S. Y. [Indiana Univ., Bloomington, IN (United States)
2014-02-10
Beam position monitors (BPM) are used to record the average orbits and transverse turn-by-turn displacements of the beam centroid motion. The Relativistic Hadron Ion Collider (RHIC) has 160 BPMs for each plane in each of the Blue and Yellow rings: 72 dual-plane BPMs in the insertion regions (IR) and 176 single-plane modules in the arcs. Each BPM is able to acquire 1024 or 4096 consecutive turn-by-turn beam positions. Inevitably, there are broadband noisy signals in the turn-by-turn data due to BPM electronics as well as other sources. A detailed study of the BPM noise performance is critical for reliable optics measurement and beam dynamics analysis based on turn-by-turn data.
Estimate Of Positive Ground Lightning Flashes In Ibadan, Nigeria ...
African Journals Online (AJOL)
The instrument used was lightning flash counter designed to isolate only positive lightning discharges at average radiation field change of 5.0 v/m and peak frequency response of 17.0kHz with 3dB attenuation and effective range of 60km. A total of 444 flashes were obtained with most of the flashes occurring at night times.
Robust Estimation of HDR in fMRI using H-infinity Filters
DEFF Research Database (Denmark)
Puthusserypady, Sadasivan; Jue, R.; Ratnarajah, T.
2010-01-01
. The H8 approach is used because it safeguards against the worst case disturbances and makes no assumptions on the (statistical) nature of the signals [B. Hassibi and T. Kailath, in Proc. ICASSP, 1995, vol. 2, pp. 949-952; T. Ratnarajah and S. Puthusserypady, in Proc. 8th IEEEWorkshopDSP, 1998, pp. 1483......-1487]. Performances of the proposed techniques are compared to the conventional t-test method as well as the well-known LMSs and recursive least squares algorithms. Extensive numerical simulations show that the proposed methods result in better HDR estimations and activation detections....
Ainsworth, Mark; Rankin, Richard
2008-12-01
We obtain a computable a posteriori error bound on the broken energy norm of the error in the Fortin-Soulie finite element approximation of a linear second order elliptic problem with variable permeability. This bound is shown to be efficient in the sense that it also provides a lower bound for the broken energy norm of the error up to a constant and higher order data oscillation terms. The estimator is completely free of unknown constants and provides a guaranteed numerical bound on the error.
Exploring super-gaussianity towards robust information-theoretical time delay estimation
DEFF Research Database (Denmark)
Petsatodis, Theodoros; Talantzis, Fotios; Boukis, Christos
2013-01-01
Time delay estimation (TDE) is a fundamental component of speaker localization and tracking algorithms. Most of the existing systems are based on the generalized cross-correlation method assuming gaussianity of the source. It has been shown that the distribution of speech, captured with far...... the effect upon TDE when modeling the source signal with different speech-based distributions. An information theoretical TDE method indirectly encapsulating higher order statistics (HOS) formed the basis of this work. The underlying assumption of Gaussian distributed source has been replaced...
Contrôle d'une machine asynchrone par estimation robuste de la vitesse
Roboam, X.; Hapiot, J. C.; de Fornel, B.; Andrieux, C.
1992-03-01
This paper describes the study and the implementation of a variable speed drive for induction motor with no mechanical sensor. Power is supplied by a current-controlled voltage-source inverter. The lack of information (only two line-current sensors) requires an accurate and reliable reconstitution of the main quantities not sensed (flux, speed, torque). The results obtained show the excellent quality of the estimated speed, whatever the operating condition and despite the perturbations applied to the system. This study finally leads to the construction of a prototype. Cet article décrit l'étude et la mise en oeuvre d'un variateur de vitesse pour machine asynchrone sans capteur mécanique et alimentée par un onduleur de tension cotrôlé en courant. Le manque d'information (2 capteurs de courants de ligne uniquement) nécessite la reconstitution fiable et précise des grandeurs fondamentales non mesurées (flux, vitesse, couple). Les résultats obtenus montrent notamment l'excellente qualité de l'estimation de vitesse, quel que soit le point de fonctionnement et en dépit des perturbations auxquelles le système est soumis. Cette étude donne lieu à la réalisation d'un variateur prototype.
Watté, Rodrigo; Aernouts, Ben; Van Beers, Robbe; Saeys, Wouter
2015-10-19
robust inverse estimation algorithm was validated on an independent set of intralipid® phantoms and its performance was also compared to that of a classical single-wavelength inverse estimation algorithm. While its performance in estimating µ(a) was comparable (R2 of 0.844 vs. 0.862), it resulted in a large improvement in the estimation of µ(s)' (R2 of 0.987 vs. 0.681). The change in performance is more apparent in the improvement of RMSE of µ(s)', which decreases from 10.36 cm(-1) to 2.10 cm(-1). The SRS profiles change more sensitively as a function of µ(a). As a result, there is a large range of µ(s)' and a small range of µa resulting in a good fit between measurement and simulation. The robust inverse estimator incorporates information over the different wavelengths, to increase the accuracy of µ(s)'estimations and robustify the estimation process.
Robust node estimation and topology discovery for large-scale networks
Alouini, Mohamed-Slim
2017-02-23
Various examples are provided for node estimation and topology discovery for networks. In one example, a method includes receiving a packet having an identifier from a first node; adding the identifier to another transmission packet based on a comparison between the first identifier and existing identifiers associated with the other packet; adjusting a transmit probability based on the comparison; and transmitting the other packet based on a comparison between the transmit probability and a probability distribution. In another example, a system includes a network device that can adds an identifier received in a packet to a list including existing identifiers and adjust a transmit probability based on a comparison between the identifiers; and transmit another packet based on a comparison between the transmit probability and a probability distribution. In another example, a method includes determining a quantity of sensor devices based on a plurality of identifiers received in a packet.
Robust Depth Estimation and Image Fusion Based on Optimal Area Selection
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Tae-Sun Choi
2013-09-01
Full Text Available Mostly, 3D cameras having depth sensing capabilities employ active depth estimation techniques, such as stereo, the triangulation method or time-of-flight. However, these methods are expensive. The cost can be reduced by applying optical passive methods, as they are inexpensive and efficient. In this paper, we suggest the use of one of the passive optical methods named shape from focus (SFF for 3D cameras. In the proposed scheme, first, an adaptive window is computed through an iterative process using a criterion. Then, the window is divided into four regions. In the next step, the best focused area among the four regions is selected based on variation in the data. The effectiveness of the proposed scheme is validated using image sequences of synthetic and real objects. Comparative analysis based on statistical metrics correlation, mean square error (MSE, universal image quality index (UIQI and structural similarity (SSIM shows the effectiveness of the proposed scheme.
Challenges in using compliant ligaments for position estimation within robotic joints.
Russell, Felix; Gao, Lei; Ellison, Peter; Vaidyanathan, Ravi
2017-07-01
The mechanical advantages of bio-inspired condylar robotic knee joints for use in prosthetics or rehabilitation has been argued extensively in literature. A common limitation of these designs is the difficulty of estimating joint angle and therefore accurately controlling the joint. Furthermore, the potential role of ligament-like structures in robotic knees is not very well established. In this work, we investigate the role of compliant stretch sensing ligaments and their integration into a condylar robotic knee. Simulations and experiments are executed out in order to establish whether measurement of stretch in these structures can be used to produce a new feedback controller for joint position. We report results from a computer model, as well as the design and construction of a robotic knee that show, for a chosen condyle shape, ligament stretch is a function of muscle force and joint velocity as well as joint angle. We have developed a genetic algorithm optimised controller incorporating ligament feedback that demonstrates improved performance for a desired joint angle in response to step inputs. The controller showed marginal improvement in response to a cyclic command signal and further investigation is required in order to use these measurements in robust control, nevertheless we believe these results demonstrate the that ligament-like structures have the potential to improve the performance of robotic knees for prosthetics and rehabilitation devices.
Directory of Open Access Journals (Sweden)
Adytia Darmawan
2016-12-01
Full Text Available Position estimation using WIMU (Wireless Inertial Measurement Unit is one of emerging technology in the field of indoor positioning systems. WIMU can detect movement and does not depend on GPS signals. The position is then estimated using a modified ZUPT (Zero Velocity Update method that was using Filter Magnitude Acceleration (FMA, Variance Magnitude Acceleration (VMA and Angular Rate (AR estimation. Performance of this method was justified on a six-legged robot navigation system. Experimental result shows that the combination of VMA-AR gives the best position estimation.
De Filippis, G.; Noël, J. P.; Kerschen, G.; Soria, L.; Stephan, C.
2017-09-01
The introduction of the frequency-domain nonlinear subspace identification (FNSI) method in 2013 constitutes one in a series of recent attempts toward developing a realistic, first-generation framework applicable to complex structures. If this method showed promising capabilities when applied to academic structures, it is still confronted with a number of limitations which needs to be addressed. In particular, the removal of nonphysical poles in the identified nonlinear models is a distinct challenge. In the present paper, it is proposed as a first contribution to operate directly on the identified state-space matrices to carry out spurious pole removal. A modal-space decomposition of the state and output matrices is examined to discriminate genuine from numerical poles, prior to estimating the extended input and feedthrough matrices. The final state-space model thus contains physical information only and naturally leads to nonlinear coefficients free of spurious variations. Besides spurious variations due to nonphysical poles, vibration modes lying outside the frequency band of interest may also produce drifts of the nonlinear coefficients. The second contribution of the paper is to include residual terms, accounting for the existence of these modes. The proposed improved FNSI methodology is validated numerically and experimentally using a full-scale structure, the Morane-Saulnier Paris aircraft.
Robust estimation of mammographic breast density: a patient-based approach
Heese, Harald S.; Erhard, Klaus; Gooßen, Andre; Bulow, Thomas
2012-02-01
Breast density has become an established risk indicator for developing breast cancer. Current clinical practice reflects this by grading mammograms patient-wise as entirely fat, scattered fibroglandular, heterogeneously dense, or extremely dense based on visual perception. Existing (semi-) automated methods work on a per-image basis and mimic clinical practice by calculating an area fraction of fibroglandular tissue (mammographic percent density). We suggest a method that follows clinical practice more strictly by segmenting the fibroglandular tissue portion directly from the joint data of all four available mammographic views (cranio-caudal and medio-lateral oblique, left and right), and by subsequently calculating a consistently patient-based mammographic percent density estimate. In particular, each mammographic view is first processed separately to determine a region of interest (ROI) for segmentation into fibroglandular and adipose tissue. ROI determination includes breast outline detection via edge-based methods, peripheral tissue suppression via geometric breast height modeling, and - for medio-lateral oblique views only - pectoral muscle outline detection based on optimizing a three-parameter analytic curve with respect to local appearance. Intensity harmonization based on separately acquired calibration data is performed with respect to compression height and tube voltage to facilitate joint segmentation of available mammographic views. A Gaussian mixture model (GMM) on the joint histogram data with a posteriori calibration guided plausibility correction is finally employed for tissue separation. The proposed method was tested on patient data from 82 subjects. Results show excellent correlation (r = 0.86) to radiologist's grading with deviations ranging between -28%, (q = 0.025) and +16%, (q = 0.975).
A Robust Nonlinear Observer for Real-Time Attitude Estimation Using Low-Cost MEMS Inertial Sensors
Guerrero-Castellanos, José Fermi; Madrigal-Sastre, Heberto; Durand, Sylvain; Torres, Lizeth; Muñoz-Hernández, German Ardul
2013-01-01
This paper deals with the attitude estimation of a rigid body equipped with angular velocity sensors and reference vector sensors. A quaternion-based nonlinear observer is proposed in order to fuse all information sources and to obtain an accurate estimation of the attitude. It is shown that the observer error dynamics can be separated into two passive subsystems connected in “feedback”. Then, this property is used to show that the error dynamics is input-to-state stable when the measurement disturbance is seen as an input and the error as the state. These results allow one to affirm that the observer is “robustly stable”. The proposed observer is evaluated in real-time with the design and implementation of an Attitude and Heading Reference System (AHRS) based on low-cost MEMS (Micro-Electro-Mechanical Systems) Inertial Measure Unit (IMU) and magnetic sensors and a 16-bit microcontroller. The resulting estimates are compared with a high precision motion system to demonstrate its performance. PMID:24201316
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.
A Robust Nonlinear Observer for Real-Time Attitude Estimation Using Low-Cost MEMS Inertial Sensors
Directory of Open Access Journals (Sweden)
Lizeth Torres
2013-11-01
Full Text Available This paper deals with the attitude estimation of a rigid body equipped with angular velocity sensors and reference vector sensors. A quaternion-based nonlinear observer is proposed in order to fuse all information sources and to obtain an accurate estimation of the attitude. It is shown that the observer error dynamics can be separated into two passive subsystems connected in “feedback”. Then, this property is used to show that the error dynamics is input-to-state stable when the measurement disturbance is seen as an input and the error as the state. These results allow one to affirm that the observer is “robustly stable”. The proposed observer is evaluated in real-time with the design and implementation of an Attitude and Heading Reference System (AHRS based on low-cost MEMS (Micro-Electro-Mechanical Systems Inertial Measure Unit (IMU and magnetic sensors and a 16-bit microcontroller. The resulting estimates are compared with a high precision motion system to demonstrate its performance.
Energy Technology Data Exchange (ETDEWEB)
Ren, Shangjie [Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin (China); Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States); Hara, Wendy; Wang, Lei; Buyyounouski, Mark K.; Le, Quynh-Thu; Xing, Lei [Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States); Li, Ruijiang, E-mail: rli2@stanford.edu [Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States)
2017-03-15
Purpose: To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain. Methods and Materials: We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed. Results: The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively). Conclusion: The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.
Estimation Robuste de la Matrice de Covariance en contexte Hétérogène Rang Faible
Breloy, Arnaud; Ginolhac, Guillaume; Pascal, Frédéric; Forster, Philippe
2015-01-01
National audience; Nous considérons le problème d'estimation de la matrice de covariance (CM) d'un bruit composé d'un fouillis hétérogène de rang faible plus un bruit blanc Gaussien (BBG). Le fouillis est modélisé comme un SIRV ayant a fort rapport "fouillis à bruit" (dénoté CNR). Nous proposons dans ce papier un algorithme générique permettant d'obtenir des estimateurs robustes de la CM adaptés au contexte considéré, i.e. ayant une structure Rang Faible. Les performances de cet algorithme so...
Faranda, Davide; Miralles, Sophie; Odier, Philippe; Pinton, Jean-Francois; Plihon, Nicolas; Daviaud, François; Dubrulle, Bérengère
2014-01-01
We apply a new threshold detection method based on the extreme value theory to the von K\\'arm\\'an sodium (VKS) experiment data. The VKS experiment is a successful attempt to get a dynamo magnetic field in a laboratory liquid-metal experiment. We first show that the dynamo threshold is associated to a change of the probability density function of the extreme values of the magnetic field. This method does not require the measurement of response functions from applied external perturbations, and thus provides a simple threshold estimate. We apply our method to different configurations in the VKS experiment showing that it yields a robust indication of the dynamo threshold as well as evidence of hysteretic behaviors. Moreover, for the experimental configurations in which a dynamo transition is not observed, the method provides a way to extrapolate an interval of possible threshold values.
Goñi, Joaquín; Sporns, Olaf; Cheng, Hu; Aznárez-Sanado, Maite; Wang, Yang; Josa, Santiago; Arrondo, Gonzalo; Mathews, Vincent P; Hummer, Tom A; Kronenberger, William G; Avena-Koenigsberger, Andrea; Saykin, Andrew J; Pastor, María A
2013-12-01
High-resolution isotropic three-dimensional reconstructions of human brain gray and white matter structures can be characterized to quantify aspects of their shape, volume and topological complexity. In particular, methods based on fractal analysis have been applied in neuroimaging studies to quantify the structural complexity of the brain in both healthy and impaired conditions. The usefulness of such measures for characterizing individual differences in brain structure critically depends on their within-subject reproducibility in order to allow the robust detection of between-subject differences. This study analyzes key analytic parameters of three fractal-based methods that rely on the box-counting algorithm with the aim to maximize within-subject reproducibility of the fractal characterizations of different brain objects, including the pial surface, the cortical ribbon volume, the white matter volume and the gray matter/white matter boundary. Two separate datasets originating from different imaging centers were analyzed, comprising 50 subjects with three and 24 subjects with four successive scanning sessions per subject, respectively. The reproducibility of fractal measures was statistically assessed by computing their intra-class correlations. Results reveal differences between different fractal estimators and allow the identification of several parameters that are critical for high reproducibility. Highest reproducibility with intra-class correlations in the range of 0.9-0.95 is achieved with the correlation dimension. Further analyses of the fractal dimensions of parcellated cortical and subcortical gray matter regions suggest robustly estimated and region-specific patterns of individual variability. These results are valuable for defining appropriate parameter configurations when studying changes in fractal descriptors of human brain structure, for instance in studies of neurological diseases that do not allow repeated measurements or for disease
Energy Technology Data Exchange (ETDEWEB)
Sasaki, Y. [Akita University, Akita (Japan). Faculty of Engineering and Resouce and Science; Nakamura, T.; Takahashi, Y. [Akita University, Akita (Japan). Faculty of System Science and Technology
2000-03-15
In order to eliminate the steady state tracking error, it is common to employ integral compensators in servo Systems for constant reference signals. The LQ optimization technique is extensively used for the stabilization, and the obtained feedback control system has been applied choose to an optimal servo system. However, if the dynamic response of an identical model is unequal to that of the real plant and there is disturbance to the plant, accurate control using the LQ optimization control method is difficult. In this paper, several robust control system designs for a reference model are introduced in the following and robust stability and transient behavior are considered. We propose such robust control systems based on an optimal control method that has a two-degree-of-freedom (2DOF) system. First design of the robust optimal servo system is by a 2DOF optimal control with disturbance observer. Second is designed by a 2DOF optimal control with H controller with consideration to the mixed sensitivity problem. The experiments are carried out under several conditions, and we discuss the difference of control performance by L{sub 2} norm. As a result, the 2DOF optimal control with disturbance observer has shown good control performance in the robust and stable positioning of the oil hydraulic cylinder. (author)
Nakano, Alberto Yoshihiro; Nakagawa, Seiichi; Yamamoto, Kazumasa
2009-12-01
A method which automatically provides the position and orientation of a directional acoustic source in an enclosed environment is proposed. In this method, different combinations of the estimated parameters from the received signals and the microphone positions of each array are used as inputs to the artificial neural network (ANN). The estimated parameters are composed of time delay estimates (TDEs), source position estimates, distance estimates, and energy features. The outputs of the ANN are the source orientation (one out of four possible orientations shifted by 90 degrees and either the best array which is defined as the nearest to the source) or the source position in two dimensional/three dimensional (2D/3D) space. This paper studies the position and orientation estimation performances of the ANN for different input/output combinations (and different numbers of hidden units). The best combination of parameters (TDEs and microphone positions) yields 21.8% reduction in the average position error compared to the following baselines and a correct orientation ratio greater than 99%. Position localization baselines consist of a time delay of arrival based method with an average position error of 34.1 cm and the steered response power with phase transform method with an average position error of 29.8 cm in 3D space.
Directory of Open Access Journals (Sweden)
Holly C Smith
Full Text Available As delphinid populations become increasingly exposed to human activities we rely on our capacity to produce accurate abundance estimates upon which to base management decisions. This study applied mark-recapture methods following the Robust Design to estimate abundance, demographic parameters, and temporary emigration rates of an Indo-Pacific bottlenose dolphin (Tursiops aduncus population off Bunbury, Western Australia. Boat-based photo-identification surveys were conducted year-round over three consecutive years along pre-determined transect lines to create a consistent sampling effort throughout the study period and area. The best fitting capture-recapture model showed a population with a seasonal Markovian temporary emigration with time varying survival and capture probabilities. Abundance estimates were seasonally dependent with consistently lower numbers obtained during winter and higher during summer and autumn across the three-year study period. Specifically, abundance estimates for all adults and juveniles (combined varied from a low of 63 (95% CI 59 to 73 in winter of 2007 to a high of 139 (95% CI 134 to148 in autumn of 2009. Temporary emigration rates (γ' for animals absent in the previous period ranged from 0.34 to 0.97 (mean = 0.54; ±SE 0.11 with a peak during spring. Temporary emigration rates for animals present during the previous period (γ'' were lower, ranging from 0.00 to 0.29, with a mean of 0.16 (± SE 0.04. This model yielded a mean apparent survival estimate for juveniles and adults (combined of 0.95 (± SE 0.02 and a capture probability from 0.07 to 0.51 with a mean of 0.30 (± SE 0.04. This study demonstrates the importance of incorporating temporary emigration to accurately estimate abundance of coastal delphinids. Temporary emigration rates were high in this study, despite the large area surveyed, indicating the challenges of sampling highly mobile animals which range over large spatial areas.
Eldeniz, Cihat; Fraum, Tyler; Salter, Amber; Chen, Yasheng; Gach, H Michael; Parikh, Parag J; Fowler, Kathryn J; An, Hongyu
2018-01-08
In this study, we present a fully automated and robust self-navigated approach to obtain 4-dimensional (4-D) motion-resolved images during free breathing. The proposed method, Consistently Acquired Projections for Tuned and Robust Estimation (CAPTURE), is a variant of the stack-of-stars gradient-echo sequence. A 1-D navigator was consistently acquired at a fixed azimuthal angle for all stacks of spokes to reduce nonphysiological signal contamination due to system imperfections. The resulting projections were then "tuned" using complex phase rotation to adapt to scan-to-scan variations, followed by the detection of the respiratory curve. Four-dimensional motion-corrected and uncorrected images were then reconstructed via respiratory and temporal binning, respectively.This Health Insurance Portability and Accountability Act-compliant study was performed with Institutional Review Board approval. A phantom experiment was performed using a custom-made deformable motion phantom with an adjustable frequency and amplitude. For in vivo experiments, 10 healthy participants and 12 liver tumor patients provided informed consent and were imaged with the CAPTURE sequence.Two radiologists, blinded to which images were motion-corrected and which were not, independently reviewed the images and scored the image quality using a 5-point Likert scale. In the respiratory motion phantom experiment, CAPTURE reversed the effects of motion blurring and restored edge sharpness from 36% to 78% of that observed in the images from the static scan.Despite large intra- and intersubject variability in respiration patterns, CAPTURE successfully detected the respiratory motion signal in all participants and significantly improved the image quality according to the subjective radiological assessments of 2 raters (P motion blurring were more clearly depicted on the CAPTURE images. CAPTURE provides a robust and fully automated solution for obtaining 4-D motion-resolved images in a free
Estimating relative foot positions for assessment of body balance in an ambulatory setting
van Meulen, Fokke; Limón Alonzo, D. P.; Schepers, H. Martin; Veltink, Petrus H.
2012-01-01
For the assessment of body balance, foot placement (i.e. relative foot position), is a crucial variable. In this study, the relative foot positions estimated using Xsens MVN Biomech were compared with position measurements of the optical reference system, Vicon. The results show a good
Laitinen, Elina; Lohan, Elena Simona
2016-05-20
The positioning based on Wireless Local Area Networks (WLAN) is one of the most promising technologies for indoor location-based services, generally using the information carried by Received Signal Strengths (RSS). One challenge, however, is the huge amount of data in the radiomap database due to the enormous number of hearable Access Points (AP) that could make the positioning system very complex. This paper concentrates on WLAN-based indoor location by comparing fingerprinting, path loss and weighted centroid based positioning approaches in terms of complexity and performance and studying the effects of grid size and AP reduction with several choices for appropriate selection criterion. All results are based on real field measurements in three multi-floor buildings. We validate our earlier findings concerning several different AP selection criteria and conclude that the best results are obtained with a maximum RSS-based criterion, which also proved to be the most consistent among the different investigated approaches. We show that the weighted centroid based low-complexity method is very sensitive to AP reduction, while the path loss-based method is also very robust to high percentage removals. Indeed, for fingerprinting, 50% of the APs can be removed safely with a properly chosen removal criterion without increasing the positioning error much.
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.
Inoue, Yukinori; Yamada, Koji; Morimoto, Shigeo; Sanada, Masayuki
This paper proposes a position sensorless drive system combined with on-line parameter identification for an interior permanent magnet synchronous motor. The accuracy of the position estimation can be improved by the proposed system, in which the motor parameters used for the position estimation are identified according to the operating conditions. First, the influence of the parameter error on the estimation position error is examined from the simulation and experimental results. Next, the characteristics of the sensorless drive system and the performance of parameter identification are shown. The experimental results show that the proposed system can achieve more accurate position estimation than the drive system without the parameter identification for all operating conditions.
Methods for robustness programming
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
Computers, coders, and voters: Comparing automated methods for estimating party positions
DEFF Research Database (Denmark)
Hjorth, F.; Klemmensen, R.; Hobolt, S.
2015-01-01
Assigning political actors positions in ideological space is a task of key importance to political scientists. In this paper we compare estimates obtained using the automated Wordscores and Wordfish techniques, along with estimates from voters and the Comparative Manifesto Project (CMP), against...... replicates the positions in the German manifestos only. The results demonstrate that automated methods can produce valid estimates of party positions, but also that the appropriateness of each method hinges on the quality of the textual data. Additional analyses suggest that Wordfish requires both longer...
Directory of Open Access Journals (Sweden)
Ilwoo eLyu
2015-06-01
Full Text Available We present a novel group-wise registration method for cortical correspondence for local cortical thickness analysis in human and non-human primate neuroimaging studies. The proposed method is based on our earlier template based registration that estimates a continuous, smooth deformation field via sulcal curve-constrained registration employing spherical harmonic decomposition of the deformation field. This pairwise registration though results in a well-known template selection bias, which we aim to overcome here via a group-wise approach. We propose the use of an unbiased ensemble entropy minimization following the use of the pairwise registration as an initialization. An individual deformation field is then iteratively updated onto the unbiased average. For the optimization, we use metrics specific for cortical correspondence though all of these are straightforwardly extendable to the generic setting: The first focused on optimizing the correspondence of automatically extracted sulcal landmarks and the second on that of sulcal depth property maps. We further propose a robust entropy metric and a hierarchical optimization by employing spherical harmonic basis orthogonality. We also provide the detailed methodological description of both our earlier work and the proposed method with a set of experiments on a population of human and non-human primate subjects. In the experiment, we have shown that our method achieves superior results on consistency through quantitative and visual comparisons as compared to the existing methods.
Position Estimation for Switched Reluctance Motor Based on the Single Threshold Angle
Zhang, Lei; Li, Pang; Yu, Yue
2017-05-01
This paper presents a position estimate model of switched reluctance motor based on the single threshold angle. In view of the relationship of between the inductance and rotor position, the position is estimated by comparing the real-time dynamic flux linkage with the threshold angle position flux linkage (7.5° threshold angle, 12/8SRM). The sensorless model is built by Maltab/Simulink, the simulation are implemented under the steady state and transient state different condition, and verified its validity and feasibility of the method..
Initial Rotor Position Estimation of Half-Wave Rectified Brushless Synchronous Motor
Abe, Takashi; Oyama, Jun; Higuchi, Tsuyoshi
This paper presents an initial rotor position estimation of Half-Wave Rectified Brushless Synchronous Motor. In the previous paper, we proposed this motor as AC servo motor, which is based on the half-wave rectified brushless excitation principle. The basic principle of this estimation technique utilizes the dependence of inductance on the rotor position. The bias frequency component of half-Wave rectified brushless excitation is used to estimate the rotor position error. The magnetic pole is discriminated by the switching condition of the diode inserted into the rotor field winding. This estimation technique is confirmed by simulation include inverter circuit, control program and motor model. Finally, the effectiveness of the proposed estimation technique has been verified by experiments.
Directory of Open Access Journals (Sweden)
Meherdad Jafarboland
2010-07-01
Full Text Available Permanent Magnet Synchronous Machines (PMSM are increasingly used because of their advantages over other machines, which include compactness, high efficiency, and well developed drives.. The substitution of the position sensors by advanced algorithms embedded in the controls hardware and software has been investigated for the last couple of decades. This Paper presents the modeling, analysis, design and experimental validation of a robust sensor less control method for PMSM based on Extended Kalman Filter. The position/speed sensor less control scheme along with the power electronic circuitry is modeled. The performance of the proposed control is assessed and verified for different types of dynamic and static torque loads.
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.
DEFF Research Database (Denmark)
Wang, Z.; Lu, K.; Ye, Y.
2011-01-01
According to saliency of permanent magnet synchronous motor (PMSM), the information of rotor position is implied in performance of stator inductances due to the magnetic saturation effect. Researches focused on the initial rotor position estimation of PMSM by injecting modulated pulse voltage...
Estimation in the positive stable shared frailty Cox proportional hazards model
DEFF Research Database (Denmark)
Martinussen, Torben; Pipper, Christian Bressen
2005-01-01
Shared frailty models are of interest when one has clustered survival data and when focus is on comparing the lifetimes within clusters and further on estimating the correlation between lifetimes from the same cluster. It is well known that the positive stable model should be preferred to the gamma...... model in situations where the correlated survival data show a decreasing association with time. In this paper, we devise a likelihood based estimation procedure for the positive stable shared frailty Cox model, which is expected to obtain high efficiency. The proposed estimator is provided with large...
Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.
Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu
2015-08-01
This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.
Van Uffelen, Lora J; Nosal, Eva-Marie; Howe, Bruce M; Carter, Glenn S; Worcester, Peter F; Dzieciuch, Matthew A; Heaney, Kevin D; Campbell, Richard L; Cross, Patrick S
2013-10-01
Four acoustic Seagliders were deployed in the Philippine Sea November 2010 to April 2011 in the vicinity of an acoustic tomography array. The gliders recorded over 2000 broadband transmissions at ranges up to 700 km from moored acoustic sources as they transited between mooring sites. The precision of glider positioning at the time of acoustic reception is important to resolve the fundamental ambiguity between position and sound speed. The Seagliders utilized GPS at the surface and a kinematic model below for positioning. The gliders were typically underwater for about 6.4 h, diving to depths of 1000 m and traveling on average 3.6 km during a dive. Measured acoustic arrival peaks were unambiguously associated with predicted ray arrivals. Statistics of travel-time offsets between received arrivals and acoustic predictions were used to estimate range uncertainty. Range (travel time) uncertainty between the source and the glider position from the kinematic model is estimated to be 639 m (426 ms) rms. Least-squares solutions for glider position estimated from acoustically derived ranges from 5 sources differed by 914 m rms from modeled positions, with estimated uncertainty of 106 m rms in horizontal position. Error analysis included 70 ms rms of uncertainty due to oceanic sound-speed variability.
The Impact of Estimating High-Resolution Tropospheric Gradients on Multi-GNSS Precise Positioning.
Zhou, Feng; Li, Xingxing; Li, Weiwei; Chen, Wen; Dong, Danan; Wickert, Jens; Schuh, Harald
2017-04-03
Benefits from the modernized US Global Positioning System (GPS), the revitalized Russian GLObal NAvigation Satellite System (GLONASS), and the newly-developed Chinese BeiDou Navigation Satellite System (BDS) and European Galileo, multi-constellation Global Navigation Satellite System (GNSS) has emerged as a powerful tool not only in positioning, navigation, and timing (PNT), but also in remote sensing of the atmosphere and ionosphere. Both precise positioning and the derivation of atmospheric parameters can benefit from multi-GNSS observations. In this contribution, extensive evaluations are conducted with multi-GNSS datasets collected from 134 globally-distributed ground stations of the International GNSS Service (IGS) Multi-GNSS Experiment (MGEX) network in July 2016. The datasets are processed in six different constellation combinations, i.e., GPS-, GLONASS-, BDS-only, GPS + GLONASS, GPS + BDS, and GPS + GLONASS + BDS + Galileo precise point positioning (PPP). Tropospheric gradients are estimated with eight different temporal resolutions, from 1 h to 24 h, to investigate the impact of estimating high-resolution gradients on position estimates. The standard deviation (STD) is used as an indicator of positioning repeatability. The results show that estimating tropospheric gradients with high temporal resolution can achieve better positioning performance than the traditional strategy in which tropospheric gradients are estimated on a daily basis. Moreover, the impact of estimating tropospheric gradients with different temporal resolutions at various elevation cutoff angles (from 3° to 20°) is investigated. It can be observed that with increasing elevation cutoff angles, the improvement in positioning repeatability is decreased.
Mahmoud Magdi S.
2001-01-01
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 &in...
Directory of Open Access Journals (Sweden)
Kewal Krishan
2015-12-01
Full Text Available Estimation of stature from handprints/palmprints recovered at the crime scene may help in the identification of the criminal/perpetrator. The present communication is an advisory on the recently published studies regarding stature estimation from different dimensions of handprints in various populations. We emphasize that at the crime scenes, the prints of the hands are usually found in a way that the fingers are apart from each other that may or may not be fully stretched or in any other working position of the hand; and rarely similar to the position described in studies as a non-stretched normal position with all the fingers joined with one another except for the thumb. The communication further stresses on the need for further studies on hand prints describing various positional variations pertaining to the practical forensic situations especially when the prints are taken in stretched/flexed/extended positions of the hand.
Directory of Open Access Journals (Sweden)
Yeun-Sub Byun
2015-11-01
Full Text Available The real-time recognition of absolute (or relative position and orientation on a network of roads is a core technology for fully automated or driving-assisted vehicles. This paper presents an empirical investigation of the design, implementation, and evaluation of a self-positioning system based on a magnetic marker reference sensing method for an autonomous vehicle. Specifically, the estimation accuracy of the magnetic sensing ruler (MSR in the up-to-date estimation of the actual position was successfully enhanced by compensating for time delays in signal processing when detecting the vertical magnetic field (VMF in an array of signals. In this study, the signal processing scheme was developed to minimize the effects of the distortion of measured signals when estimating the relative positional information based on magnetic signals obtained using the MSR. In other words, the center point in a 2D magnetic field contour plot corresponding to the actual position of magnetic markers was estimated by tracking the errors between pre-defined reference models and measured magnetic signals. The algorithm proposed in this study was validated by experimental measurements using a test vehicle on a pilot network of roads. From the results, the positioning error was found to be less than 0.04 m on average in an operational test.
Systematic error mitigation in multi-GNSS positioning based on semiparametric estimation
Yu, Wenkun; Ding, Xiaoli; Dai, Wujiao; Chen, Wu
2017-12-01
Joint use of observations from multiple global navigation satellite systems (GNSS) is advantageous in high-accuracy positioning. However, systematic errors in the observations can significantly impact on the positioning accuracy if such errors cannot be properly mitigated. The errors can distort least squares estimations and also affect the results of variance component estimation that is frequently used to determine the stochastic model when observations from multiple GNSS are used. We present an approach that is based on the concept of semiparametric estimation for mitigating the effects of the systematic errors. Experimental results based on both simulated and real GNSS datasets show that the approach is effective, especially when applied before carrying out variance component estimation.
Remediating Non-Positive Definite State Covariances for Collision Probability Estimation
Hall, Doyle T.; Hejduk, Matthew D.; Johnson, Lauren C.
2017-01-01
The NASA Conjunction Assessment Risk Analysis team estimates the probability of collision (Pc) for a set of Earth-orbiting satellites. The Pc estimation software processes satellite position+velocity states and their associated covariance matri-ces. On occasion, the software encounters non-positive definite (NPD) state co-variances, which can adversely affect or prevent the Pc estimation process. Inter-polation inaccuracies appear to account for the majority of such covariances, alt-hough other mechanisms contribute also. This paper investigates the origin of NPD state covariance matrices, three different methods for remediating these co-variances when and if necessary, and the associated effects on the Pc estimation process.
Phase-Inductance-Based Position Estimation Method for Interior Permanent Magnet Synchronous Motors
Directory of Open Access Journals (Sweden)
Xin Qiu
2017-12-01
Full Text Available This paper presents a phase-inductance-based position estimation method for interior permanent magnet synchronous motors (IPMSMs. According to the characteristics of phase induction of IPMSMs, the corresponding relationship of the rotor position and the phase inductance is obtained. In order to eliminate the effect of the zero-sequence component of phase inductance and reduce the rotor position estimation error, the phase inductance difference is employed. With the iterative computation of inductance vectors, the position plane is further subdivided, and the rotor position is extracted by comparing the amplitudes of inductance vectors. To decrease the consumption of computer resources and increase the practicability, a simplified implementation is also investigated. In this method, the rotor position information is achieved easily, with several basic math operations and logical comparisons of phase inductances, without any coordinate transformation or trigonometric function calculation. Based on this position estimation method, the field orientated control (FOC strategy is established, and the detailed implementation is also provided. A series of experiment results from a prototype demonstrate the correctness and feasibility of the proposed method.
Wang, Y; Zhu, W; Cheng, X; Li, D
2013-03-07
Continuous crystal based PET detectors have features of simple design, low cost, good energy resolution and high detection efficiency. Through single-end readout of scintillation light, direct three-dimensional (3D) position estimation could be another advantage that the continuous crystal detector would have. In this paper, we propose to use artificial neural networks to simultaneously estimate the plane coordinate and DOI coordinate of incident γ photons with detected scintillation light. Using our experimental setup with an '8 + 8' simplified signal readout scheme, the training data of perpendicular irradiation on the front surface and one side surface are obtained, and the plane (x, y) networks and DOI networks are trained and evaluated. The test results show that the artificial neural network for DOI estimation is as effective as for plane estimation. The performance of both estimators is presented by resolution and bias. Without bias correction, the resolution of the plane estimator is on average better than 2 mm and that of the DOI estimator is about 2 mm over the whole area of the detector. With bias correction, the resolution at the edge area for plane estimation or at the end of the block away from the readout PMT for DOI estimation becomes worse, as we expect. The comprehensive performance of the 3D positioning by a neural network is accessed by the experimental test data of oblique irradiations. To show the combined effect of the 3D positioning over the whole area of the detector, the 2D flood images of oblique irradiation are presented with and without bias correction.
Cortez, Celia Martins; Fragoso, Viviane Muniz S.; Silva, Dilson
2014-10-01
In this work, we used a mathematical model to study the interaction of risperidone with human and bovine serum albumins estimating the relative position of the primary binding site, based on the fluorescence quenching theory. Results have shown that the model was able to demonstrate that primary binding site for risperidone in HSA and BSA is very close to the position where is tryptophan 134 of BSA, possibly in domain 1B.
Directory of Open Access Journals (Sweden)
Yanjuan Geng
2017-01-01
Full Text Available Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC and multiposition classifier (MPC have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.. The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees.
Markov Jump Linear Systems-Based Position Estimation for Lower Limb Exoskeletons
Directory of Open Access Journals (Sweden)
Samuel L. Nogueira
2014-01-01
Full Text Available In this paper, we deal with Markov Jump Linear Systems-based filtering applied to robotic rehabilitation. The angular positions of an impedance-controlled exoskeleton, designed to help stroke and spinal cord injured patients during walking rehabilitation, are estimated. Standard position estimate approaches adopt Kalman filters (KF to improve the performance of inertial measurement units (IMUs based on individual link configurations. Consequently, for a multi-body system, like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank are not taken into account in other link position estimation (e.g., the foot. In this paper, we propose a collective modeling of all inertial sensors attached to the exoskeleton, combining them in a Markovian estimation model in order to get the best information from each sensor. In order to demonstrate the effectiveness of our approach, simulation results regarding a set of human footsteps, with four IMUs and three encoders attached to the lower limb exoskeleton, are presented. A comparative study between the Markovian estimation system and the standard one is performed considering a wide range of parametric uncertainties.
Position Sensorless Speed Estimation in Switched Reluctance Motor Drive with Direct Torque Control
Kucuk, Fuat; Goto, Hiroki; Guo, Hai-Jiao; Ichinokura, Osamu
Feedback signals of rotor speed and motor torque are essential in most of Switched Reluctance (SR) motor control applications. An SR motor has highly nonlinear characteristic that does not allow to be modeled by simple equations. In Direct Torque Control (DTC) drive, which enables easy control of torque ripple in the SR motor, position sensor is employed to obtain the feedback signals. Position sensor causes DTC drive not only less reliable but also more expensive. Estimation of feedback signals is required in order to eliminate position sensor. This paper concerns about sensorless speed estimation under the DTC condition and presents a simple method. Simple sensorless speed estimation is proposed based on inductance vector angle. The inductance vector angle is obtained by applying α-β transformation to the phase inductances. A relay triggers a speed calculation circuit according to its band limits and the inductance vector angle. Inside the circuit, triggering time is kept in a memory until the next triggering. Rotor pole pitch is divided by the time difference between two consecutive triggerings. Finally, the estimation circuit outputs the rotor speed. Sensorless speed estimation is simulated and verified experimentally to show its validity.
Training data representation in a neural based robot position estimation system
Energy Technology Data Exchange (ETDEWEB)
Taraglio, S. [ENEA, Centro Ricerche Casaccia, Rome (Italy). Dipt. Innovazione; Di Fonzo, F. [Rome Univ. `La Sapienza` (Italy). Dipt. Ingegneria; Burrascano, P. [Rome Univ. `La Sapienza` (Italy). Ist. di Elettronica
1997-03-01
The vision subsystem of an autonomous vehicle is studies. It is based on a multi layer perceptron that uses TV images to estimate the position of the vehicle. A comparative study of the effects of output data representation and input data processing is presented and discussed.
Estimating the spatial position of marine mammals based on digital camera recordings
Hoekendijk, Jeroen P. A.; de Vries, Jurre; van der Bolt, Krissy; Greinert, Jens; Brasseur, Sophie; Camphuysen, Kees C. J.; Aarts, Geert
Estimating the spatial position of organisms is essential to quantify interactions between the organism and the characteristics of its surroundings, for example, predator-prey interactions, habitat selection, and social associations. Because marine mammals spend most of their time under water and
Estimating the spatial position of marine mammals based on digital camera recordings
Hoekendijk, J.P.A.; Vries, de J.; Bolt, van der K.; Greinert, J.; Brasseur, S.M.J.M.; Camphuysen, C.J.; Aarts, G.M.
2015-01-01
Estimating the spatial position of organisms is essential to quantify interactions between the organism and the characteristics of its surroundings, for example, predator-prey interactions, habitat selection, and social associations. Because marine mammals spend most of their time under water and
Estimating the spatial position of marine mammals based on digital camera recordings
Hoekendijk, J.P.A.; de Vries, J.J.; van der Bolt, K.; Greinert, J.; Brasseur, S.; Camphuysen, K.C.J.; Aarts, G.
2015-01-01
Estimating the spatial position of organisms is essential to quantify interactions between the organism and the characteristics of its surroundings, for example,predator–prey interactions, habitat selection, and social associations. Because marine mammals spend most of their time under water and may
Fushiki, Tadayoshi
2009-07-01
The correlation matrix is a fundamental statistic that is used in many fields. For example, GroupLens, a collaborative filtering system, uses the correlation between users for predictive purposes. Since the correlation is a natural similarity measure between users, the correlation matrix may be used in the Gram matrix in kernel methods. However, the estimated correlation matrix sometimes has a serious defect: although the correlation matrix is originally positive semidefinite, the estimated one may not be positive semidefinite when not all ratings are observed. To obtain a positive semidefinite correlation matrix, the nearest correlation matrix problem has recently been studied in the fields of numerical analysis and optimization. However, statistical properties are not explicitly used in such studies. To obtain a positive semidefinite correlation matrix, we assume the approximate model. By using the model, an estimate is obtained as the optimal point of an optimization problem formulated with information on the variances of the estimated correlation coefficients. The problem is solved by a convex quadratic semidefinite program. A penalized likelihood approach is also examined. The MovieLens data set is used to test our approach.
CREDIT BUREAU BENCHMARKING AS A TOOL FOR ESTIMATION OF BANK'S POSITION AT THE MARKET
Directory of Open Access Journals (Sweden)
A. Kaminsky
2015-03-01
Full Text Available The article presents the conception of benchmarking on the market of consumer loans. The essence of such benchmarking is comparative analysis of bank’s activity parameters with market average values from bureau of credit histories. Such benchmarking using is considering as a tool for estimation of bank’s market position.
An extended set-value observer for position estimation using single range measurements
DEFF Research Database (Denmark)
Marcal, Jose; Jouffroy, Jerome; Fossen, Thor I.
of transponders. The knowledge of the bearing of the vehicle and the range measurements from a single location can provide a solution which is sensitive to the trajectory that the vehicle is following, since there is no complete constraint on the position estimate with a single beacon. In this paper...
A Novel Endoscope System for Position Detection and Depth Estimation of the Ureter.
Song, Enmin; Yu, Feng; Liu, Hong; Cheng, Ning; Li, Yunlong; Jin, Lianghai; Hung, Chih-Cheng
2016-12-01
Iatrogenic injury of ureter occurs occasionally in the clinical laparoscopic surgery. The ureter injury may cause the serious complications and kidney damage. To avoid such an injury, it is necessary to detect the ureter position in real-time. Currently, the endoscope cannot perform this type of function in detecting the ureter position in real-time. In order to have the real-time display of ureter position during the surgical operation, we propose a novel endoscope system which consists of a modified endoscope light and a new lumiontron tube with the LED light. The endoscope light is modified to detect the position of ureter by using our proposed dim target detection algorithm (DTDA). To make this new system functioning, two algorithmic approaches are proposed for the display of ureter position. The horizontal position of ureter is detected by the center line extraction method and the depth of ureter is estimated by the depth estimation method. Experimental results demonstrate that the proposed endoscope system can extract the position and depth information of ureter and exhibit superior performance in terms of accuracy and stabilization.
An Increase in Estimation Accuracy Position Determination of Inertial Measurement Units
Directory of Open Access Journals (Sweden)
Beran Ladislav
2016-01-01
Full Text Available This paper deals with an increase in measurement accuracy of the Inertial Measurement Units (IMU. In the Inertial Navigation Systems (INS a fusion of gyroscopes, accelerometers and in some cases magnetometers are typically used. The typical problem of cheap IMU is non-stationary offset and high level of noise. The next problem of IMU is a problem with a bumpy floor. For this case it is necessary to a have high quality chassis to eliminate additional noise. Also, it is possible to eliminate this noise by using some algorithm, but results are still poor. These properties lead to the inaccurate position estimation in the integration process. Even a small offset error leads to a big mistake in position determination and grows quickly with a time. This research is focused on the elimination of these poor properties and increase of accuracy of position estimation using Kalman Filtration.
Directory of Open Access Journals (Sweden)
Markku Renfors
2005-04-01
Full Text Available Line-of-sight signal delay estimation is a crucial element for any mobile positioning system. Estimating correctly the delay of the first arriving path is a challenging topic in severe propagation environments, such as closely spaced multipaths in multiuser scenario. Previous studies showed that there are many linear and nonlinear techniques able to solve closely spaced multipaths when the system is not bandlimited. However, using root raised cosine (RRC pulse shaping introduces additional errors in the delay estimation process compared to the case with rectangular pulse shaping due to the inherent bandwidth limitation. In this paper, we introduce a novel technique for asynchronous WCDMA multipath delay estimation based on deconvolution with a suitable pulse shape, followed by Teager-Kaiser operator. The deconvolution stage is employed to reduce the effect of the bandlimiting pulse shape.
A maximum likelihood approach to estimating articulator positions from speech acoustics
Energy Technology Data Exchange (ETDEWEB)
Hogden, J.
1996-09-23
This proposal presents an algorithm called maximum likelihood continuity mapping (MALCOM) which recovers the positions of the tongue, jaw, lips, and other speech articulators from measurements of the sound-pressure waveform of speech. MALCOM differs from other techniques for recovering articulator positions from speech in three critical respects: it does not require training on measured or modeled articulator positions, it does not rely on any particular model of sound propagation through the vocal tract, and it recovers a mapping from acoustics to articulator positions that is linearly, not topographically, related to the actual mapping from acoustics to articulation. The approach categorizes short-time windows of speech into a finite number of sound types, and assumes the probability of using any articulator position to produce a given sound type can be described by a parameterized probability density function. MALCOM then uses maximum likelihood estimation techniques to: (1) find the most likely smooth articulator path given a speech sample and a set of distribution functions (one distribution function for each sound type), and (2) change the parameters of the distribution functions to better account for the data. Using this technique improves the accuracy of articulator position estimates compared to continuity mapping -- the only other technique that learns the relationship between acoustics and articulation solely from acoustics. The technique has potential application to computer speech recognition, speech synthesis and coding, teaching the hearing impaired to speak, improving foreign language instruction, and teaching dyslexics to read. 34 refs., 7 figs.
Lima, José; Pereira, Ana I.; Costa, Paulo; Pinto, Andry; Costa, Pedro
2017-07-01
This paper describes an optimization procedure for a robot with 12 degrees of freedom avoiding the inverse kinematics problem, which is a hard task for this type of robot manipulator. This robot can be used to pick and place tasks in complex designs. Combining an accurate and fast direct kinematics model with optimization strategies, it is possible to achieve the joints angles for a desired end-effector position and orientation. The optimization methods stretched simulated annealing algorithm and genetic algorithm were used. The solutions found were validated using data originated by a real and by a simulated robot formed by 12 servomotors with a gripper.
Towards rapid uncertainty estimation in linear finite fault inversion with positivity constraints
Benavente, R. F.; Cummins, P. R.; Sambridge, M.; Dettmer, J.
2015-12-01
Rapid estimation of the slip distribution for large earthquakes can assist greatly during the early phases of emergency response. These estimates can be used for rapid impact assessment and tsunami early warning. While model parameter uncertainties can be crucial for meaningful interpretation of such slip models, they are often ignored. Since the finite fault problem can be posed as a linear inverse problem (via the multiple time window method), an analytic expression for the posterior covariance matrix can be obtained, in principle. However, positivity constraints are often employed in practice, which breaks the assumption of a Gaussian posterior probability density function (PDF). To our knowledge, two solutions to this issue exist in the literature: 1) Not using positivity constraints (may lead to exotic slip patterns) or 2) to use positivity constraints but apply Bayesian sampling for the posterior. The latter is computationally expensive and currently unsuitable for rapid inversion. In this work, we explore an alternative approach in which we realize positivity by imposing a prior such that the log of each subfault scalar moment are smoothly distributed on the fault surface. This results in each scalar moment to be intrinsically non-negative while the posterior PDF can still be approximated as Gaussian. While the inversion is not linear anymore, we show that the most probable solution can be found by iterative methods which are less computationally expensive than numerical sampling of the posterior. In addition, the posterior covariance matrix (which provides uncertainties) can be estimated from the most probable solution, using an analytic expression for the Hessian of the cost function. We study this approach for both synthetic and observed W-phase data and the results suggest that a first order estimation of the uncertainty in the slip model can be obtained, therefore aiding in the interpretation of the slip distribution estimate.
Estimating Gulf Stream Position with HF Radar off Cape Hatteras NC
Muglia, M.; Seim, H.; Haines, S.
2016-02-01
We present a method to measure the landward edge of the Gulf Stream, estimate the width of the cyclonic shear zone, and estimate the orientation of the Gulf Stream by identifying the maxima in a single radar's radial surface current shears and current speeds. Maxima are chosen from within areas of consistent radar measurements over the time period sampled. Four bearings are chosen, two where the Gulf Stream enters and two where it exits the radar coverage. The width of the cyclonic shear zone is measured as the distance between the maximum in the gradient of the radial current speed, and the maximum in the speed along a single bearing. The orientation of the current is estimated by comparing the location of these maxima between the four selected bearings. This method is applied to three separate 5MHz Seasonde radars that have coverage along the NC coast. Comparisons between collocated radar estimates and those made bi-daily of Gulf Stream position by the Naval Oceanographic Office will be presented. The radar hourly surface currents measurements are more frequent than satellite SST (sea surface temperature) observations and are not inhibited by cloud cover. Consistent long-term Gulf Stream position estimates are expected to provide valuable new insights about the oceanography offshore of Cape Hatteras, NC.
Impact of the Fano Factor on Position and Energy Estimation in Scintillation Detectors.
Bora, Vaibhav; Barrett, Harrison H; Jha, Abhinav K; Clarkson, Eric
2015-02-01
The Fano factor for an integer-valued random variable is defined as the ratio of its variance to its mean. Light from various scintillation crystals have been reported to have Fano factors from sub-Poisson (Fano factor factor > 1). For a given mean, a smaller Fano factor implies a smaller variance and thus less noise. We investigated if lower noise in the scintillation light will result in better spatial and energy resolutions. The impact of Fano factor on the estimation of position of interaction and energy deposited in simple gamma-camera geometries is estimated by two methods - calculating the Cramér-Rao bound and estimating the variance of a maximum likelihood estimator. The methods are consistent with each other and indicate that when estimating the position of interaction and energy deposited by a gamma-ray photon, the Fano factor of a scintillator does not affect the spatial resolution. A smaller Fano factor results in a better energy resolution.
Precise Point Positioning with Ionosphere Estimation and application of Regional Ionospheric Maps
Galera Monico, J. F.; Marques, H. A.; Rocha, G. D. D. C.
2015-12-01
The ionosphere is one of most difficult source of errors to be modelled in the GPS positioning, mainly when applying data collected by single frequency receivers. Considering Precise Point Positioning (PPP) with single frequency data the options available include, for example, the use of Klobuchar model or applying Global Ionosphere Maps (GIM). The GIM contains Vertical Electron Content (VTEC) values that are commonly estimated considering a global network with poor covering in certain regions. For this reason Regional Ionosphere Maps (RIM) have been developed considering local GNSS network, for instance, the La Plata Ionospheric Model (LPIM) developed inside the context of SIRGAS (Geocentric Reference System for Americas). The South American RIM are produced with data from nearly 50 GPS ground receivers and considering these maps are generated for each hour with spatial resolution of one degree it is expected to provide better accuracy in GPS positioning for such region. Another possibility to correct for ionosphere effects in the PPP is to apply the ionosphere estimation technique based on Kalman filter. In this case, the ionosphere can be treated as a stochastic process and a good initial guess is necessary what can be obtained from an ionospheric map. In this paper we present the methodology involved with ionosphere estimation by using Kalman filter and also the application of global and regional ionospheric maps in the PPP as first guess. The ionosphere estimation strategy was implemented in the house software called RT_PPP that is capable of accomplishing PPP either for single or dual frequency data. GPS data from Brazilian station near equatorial region were processed and results with regional maps were compared with those by using global maps. Improvements of the order 15% were observed. In case of ionosphere estimation, the estimated coordinates were compared with ionosphere free solution and after PPP convergence the results reached centimeter accuracy.
The effect of aborting ongoing movements on end point position estimation.
Itaguchi, Yoshihiro; Fukuzawa, Kazuyoshi
2013-11-01
The present study investigated the impact of motor commands to abort ongoing movement on position estimation. Participants carried out visually guided reaching movements on a horizontal plane with their eyes open. By setting a mirror above their arm, however, they could not see the arm, only the start and target points. They estimated the position of their fingertip based solely on proprioception after their reaching movement was stopped before reaching the target. The participants stopped reaching as soon as they heard an auditory cue or were mechanically prevented from moving any further by an obstacle in their path. These reaching movements were carried out at two different speeds (fast or slow). It was assumed that additional motor commands to abort ongoing movement were required and that their magnitude was high, low, and zero, in the auditory-fast condition, the auditory-slow condition, and both the obstacle conditions, respectively. There were two main results. (1) When the participants voluntarily stopped a fast movement in response to the auditory cue (the auditory-fast condition), they showed more underestimates than in the other three conditions. This underestimate effect was positively related to movement velocity. (2) An inverted-U-shaped bias pattern as a function of movement distance was observed consistently, except in the auditory-fast condition. These findings indicate that voluntarily stopping fast ongoing movement created a negative bias in the position estimate, supporting the idea that additional motor commands or efforts to abort planned movement are involved with the position estimation system. In addition, spatially probabilistic inference and signal-dependent noise may explain the underestimate effect of aborting ongoing movement.
Robust multiplatform RF emitter localization
Al Issa, Huthaifa; Ordóñez, Raúl
2012-06-01
In recent years, position based services has increase. Thus, recent developments in communications and RF technology have enabled system concept formulations and designs for low-cost radar systems using state-of-the-art software radio modules. This research is done to investigate a novel multi-platform RF emitter localization technique denoted as Position-Adaptive RF Direction Finding (PADF). The formulation is based on the investigation of iterative path-loss (i.e., Path Loss Exponent, or PLE) metrics estimates that are measured across multiple platforms in order to autonomously adapt (i.e. self-adjust) of the location of each distributed/cooperative platform. Experiments conducted at the Air-Force Research laboratory (AFRL) indicate that this position-adaptive approach exhibits potential for accurate emitter localization in challenging embedded multipath environments such as in urban environments. The focus of this paper is on the robustness of the distributed approach to RF-based location tracking. In order to localize the transmitter, we use the Received Signal Strength Indicator (RSSI) data to approximate distance from the transmitter to the revolving receivers. We provide an algorithm for on-line estimation of the Path Loss Exponent (PLE) that is used in modeling the distance based on Received Signal Strength (RSS) measurements. The emitter position estimation is calculated based on surrounding sensors RSS values using Least-Square Estimation (LSE). The PADF has been tested on a number of different configurations in the laboratory via the design and implementation of four IRIS wireless sensor nodes as receivers and one hidden sensor as a transmitter during the localization phase. The robustness of detecting the transmitters position is initiated by getting the RSSI data through experiments and then data manipulation in MATLAB will determine the robustness of each node and ultimately that of each configuration. The parameters that are used in the functions are
Position and Attitude Estimation from a Image Sequence of a Circle
佐藤, 真知子; Machiko, SATO; Tokyo Institute of Polytechnics Faculty of Engineering
1995-01-01
A method to estimate the position and attitude of a helicopter with respect to the landing site from a image sequence of a heliport is presented. The method use the circle of the heliport marking as the visual cue. The projection of the circle on the successive image taken by on board camera will change, therefore a Kalman filter can be build for the recursive estimation. The method needs to know just there is a circle ; The size of the circle is not necessary. The result of the experiment on...
2017-01-01
Along the past years, mobile robots have proliferated both in domestic and in industrial environments to solve some tasks such as cleaning, assistance, or material transportation. One of their advantages is the ability to operate in wide areas without the necessity of introducing changes into the existing infrastructure. Thanks to the sensors they may be equipped with and their processing systems, mobile robots constitute a versatile alternative to solve a wide range of applications. When designing the control system of a mobile robot so that it carries out a task autonomously in an unknown environment, it is expected to take decisions about its localization in the environment and about the trajectory that it has to follow in order to arrive to the target points. More concisely, the robot has to find a relatively good solution to two crucial problems: building a model of the environment, and estimating the position of the robot within this model. In this work, we propose a framework to solve these problems using only visual information. The mobile robot is equipped with a catadioptric vision sensor that provides omnidirectional images from the environment. First, the robot goes along the trajectories to include in the model and uses the visual information captured to build this model. After that, the robot is able to estimate its position and orientation with respect to the trajectory. Among the possible approaches to solve these problems, global appearance techniques are used in this work. They have emerged recently as a robust and efficient alternative compared to landmark extraction techniques. A global description method based on Radon Transform is used to design mapping and localization algorithms and a set of images captured by a mobile robot in a real environment, under realistic operation conditions, is used to test the performance of these algorithms. PMID:28464032
Payá, Luis; Reinoso, Oscar; Jiménez, Luis M; Juliá, Miguel
2017-01-01
Along the past years, mobile robots have proliferated both in domestic and in industrial environments to solve some tasks such as cleaning, assistance, or material transportation. One of their advantages is the ability to operate in wide areas without the necessity of introducing changes into the existing infrastructure. Thanks to the sensors they may be equipped with and their processing systems, mobile robots constitute a versatile alternative to solve a wide range of applications. When designing the control system of a mobile robot so that it carries out a task autonomously in an unknown environment, it is expected to take decisions about its localization in the environment and about the trajectory that it has to follow in order to arrive to the target points. More concisely, the robot has to find a relatively good solution to two crucial problems: building a model of the environment, and estimating the position of the robot within this model. In this work, we propose a framework to solve these problems using only visual information. The mobile robot is equipped with a catadioptric vision sensor that provides omnidirectional images from the environment. First, the robot goes along the trajectories to include in the model and uses the visual information captured to build this model. After that, the robot is able to estimate its position and orientation with respect to the trajectory. Among the possible approaches to solve these problems, global appearance techniques are used in this work. They have emerged recently as a robust and efficient alternative compared to landmark extraction techniques. A global description method based on Radon Transform is used to design mapping and localization algorithms and a set of images captured by a mobile robot in a real environment, under realistic operation conditions, is used to test the performance of these algorithms.
van der Heide, T.; van der Zee, E.; Donadi, S.; Eklöf, J.S.; Eriksson, B.K.; Olff, H.; Piersma, T.; van der Heide, Wopke
2011-01-01
Estimating the spatial position of birds in open habitats like intertidal mudflats is important for many studies, for example, detailed density estimates or linking predation pressure to resource availability. To date, several methods have been used to estimate the positions of birds, including
van der Heide, Tjisse; van der Zee, Els; Donadi, Serena; Eklöf, Johan S.; Eriksson, Britas Klemens; Olff, Han; Piersma, Theunis; van der Heide, Wopke
Estimating the spatial position of birds in open habitats like intertidal mudflats is important for many studies, for example, detailed density estimates or linking predation pressure to resource availability. To date, several methods have been used to estimate the positions of birds, including
Indoor positioning system using WLAN channel estimates as fingerprints for mobile devices
Schmidt, Erick; Akopian, David
2015-03-01
With the growing integration of location based services (LBS) such as GPS in mobile devices, indoor position systems (IPS) have become an important role for research. There are several IPS methods such as AOA, TOA, TDOA, which use trilateration for indoor location estimation but are generally based on line-of-sight. Other methods rely on classification such as fingerprinting which uses WLAN indoor signals. This paper re-examines the classical WLAN fingerprinting accuracy which uses received signal strength (RSS) measurements by introducing channel estimates for improvements in the classification of indoor locations. The purpose of this paper is to improve existing classification algorithms used in fingerprinting by introducing channel estimates when there are a low number of APs available. The channel impulse response, or in this case the channel estimation from the receiver, should characterize a complex indoor area which usually has multipath, thus providing a unique signature for each location which proves useful for better pattern recognition. In this experiment, channel estimates are extracted from a Software-Defined Radio (SDR) environment, thus exploiting the benefits of SDR from a NI-USRP model and LabVIEW software. Measurements are taken from a known building, and several scenarios with one and two access points (APs) are used in this experiment. Also, three granularities in distance between locations are analyzed. A Support Vector Machine (SVM) is used as the algorithm for pattern recognition of different locations based on the samples taken from RSS and channel estimation coefficients.
Galante, Joseph M.; Van Eepoel, John; D'Souza, Chris; Patrick, Bryan
2016-01-01
The Raven ISS Hosted Payload will feature several pose measurement sensors on a pan/tilt gimbal which will be used to autonomously track resupply vehicles as they approach and depart the International Space Station. This paper discusses the derivation of a Relative Navigation Filter (RNF) to fuse measurements from the different pose measurement sensors to produce relative position and attitude estimates. The RNF relies on relative translation and orientation kinematics and careful pose sensor modeling to eliminate dependence on orbital position information and associated orbital dynamics models. The filter state is augmented with sensor biases to provide a mechanism for the filter to estimate and mitigate the offset between the measurements from different pose sensors
An FEM-Based State Estimation Approach to Nonlinear Hybrid Positioning Systems
Directory of Open Access Journals (Sweden)
Yu-Xin Zhao
2013-01-01
Full Text Available For hybrid positioning systems (HPSs, the estimator design is a crucial and important problem. In this paper, a finite-element-method- (FEM- based state estimation approach is proposed to HPS. As the weak solution of hybrid stochastic differential model is denoted by the Kolmogorov's forward equation, this paper constructs its interpolating point through the classical fourth-order Runge-Kutta method. Then, it approaches the solution with biquadratic interpolation function to obtain a prior probability density function of the state. A posterior probability density function is gained through Bayesian formula finally. In theory, the proposed scheme has more advantages in the performance of complexity and convergence for low-dimensional systems. By taking an illustrative example, numerical experiment results show that the new state estimator is feasible and has good performance than PF and UKF.
Cooperative Anchor-Free Position Estimation for Hierarchical Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Fu-Kai Chan
2010-02-01
Full Text Available This paper proposes a distributed algorithm for establishing connectivity and location estimation in cluster-based wireless sensor networks. The algorithm exploits the information flow while coping with distributed signal processing and the requirements of network scalability. Once the estimation procedure and communication protocol are performed, sensor clusters can be merged to establish a single global coordinate system without GPS sensors using only distance information. In order to adjust the sensor positions, the refinement schemes and cooperative fusion approaches are applied to reduce the estimation error and improve the measurement accuracy. This paper outlines the technical foundations of the localization techniques and presents the tradeoffs in algorithm design. The feasibility of the proposed schemes is shown to be effective under certain assumptions and the analysis is supported by simulation and numerical studies.
Bias Estimations for Ill-posed Problem of Celestial Positioning Using the Sun and Precision Analysis
Directory of Open Access Journals (Sweden)
ZHAN Yinhu
2016-08-01
Full Text Available Lunar/Mars rovers own sun sensors for navigation, however, long-time tracking for the sun impacts on the real-time activity of navigation. Absolute positioning method by observing the sun with a super short tracking period such as 1 or 2 minutes is researched in this paper. Linear least squares model of altitude positioning method is deduced, and the ill-posed problem of celestial positioning using the sun is brought out for the first time. Singular value decomposition method is used to diagnose the ill-posed problem, and different bias estimations are employed and compared by simulative calculations. Results of the calculations indicate the superiority of bias estimations which can effectively improve initial values. However, bias estimations are greatly impacted by initial values, because the initial values converge at a line which passes by the real value and is vertical relative to the orientation of the sun. The research of this paper is of some value to application.
Estimation Methods of the Point Spread Function Axial Position: A Comparative Computational Study
Directory of Open Access Journals (Sweden)
Javier Eduardo Diaz Zamboni
2017-01-01
Full Text Available The precise knowledge of the point spread function is central for any imaging system characterization. In fluorescence microscopy, point spread function (PSF determination has become a common and obligatory task for each new experimental device, mainly due to its strong dependence on acquisition conditions. During the last decade, algorithms have been developed for the precise calculation of the PSF, which fit model parameters that describe image formation on the microscope to experimental data. In order to contribute to this subject, a comparative study of three parameter estimation methods is reported, namely: I-divergence minimization (MIDIV, maximum likelihood (ML and non-linear least square (LSQR. They were applied to the estimation of the point source position on the optical axis, using a physical model. Methods’ performance was evaluated under different conditions and noise levels using synthetic images and considering success percentage, iteration number, computation time, accuracy and precision. The main results showed that the axial position estimation requires a high SNR to achieve an acceptable success level and higher still to be close to the estimation error lower bound. ML achieved a higher success percentage at lower SNR compared to MIDIV and LSQR with an intrinsic noise source. Only the ML and MIDIV methods achieved the error lower bound, but only with data belonging to the optical axis and high SNR. Extrinsic noise sources worsened the success percentage, but no difference was found between noise sources for the same method for all methods studied.
The role of internal forward models and proprioception in hand position estimation.
Yavari, Fatemeh; Towhidkhah, Farzad; Ahmadi-Pajouh, Mohammad-Ali; Darainy, Mohammad
2015-09-01
Our ability to properly move and react in different situations is largely dependent on our perception of our limbs' position. At least three sources - vision, proprioception, and internal forward models (FMs) - seem to contribute to this perception. To the best of our knowledge, the effect of each source has not been studied individually. Specifically, role of FM has been ignored in some previous studies. We hypothesized that FM has a critical role in subjects' perception which needs to be considered in the relevant studies to obtain more reliable results. Therefore, we designed an experiment with the goal of investigating FM and proprioception role in subjects' perception of their hand's position. Three groups of subjects were recruited in the study. Based on the experiment design, it was supposed that subjects in different groups relied on proprioception, FM, and both of them for estimating their unseen hand's position. Comparing the results of three groups revealed significant difference between their estimation' errors. FM provided minimum estimation error, while proprioception had a bias error in the tested region. Integrating proprioception with FM decreased this error. Integration of two Gaussian functions, fitted to the error distribution of FM and proprioception groups, was simulated and created a mean error value almost similar to the experimental observation. These results suggest that FM role needs to be considered when studying the perceived position of the limbs. This can lead to gain better insights into the mechanisms underlying the perception of our limbs' position which might have potential clinical and rehabilitation applications, e.g., in the postural control of elderly which are at high risk of falls and injury because of deterioration of their perception with age.
Directory of Open Access Journals (Sweden)
Jewon Lee
2015-11-01
Full Text Available 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.
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.
High-precision position estimation in PET using artificial neural networks
Energy Technology Data Exchange (ETDEWEB)
Mateo, F. [Digital Systems Design Group (DSD), Instituto de las Tecnologias de la Informacion y de las Comunicaciones Avanzadas (ITACA), Universidad Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia (Spain)], E-mail: fermaji@upvnet.upv.es; Aliaga, R.J.; Ferrando, N.; Martinez, J.D.; Herrero, V.; Lerche, Ch.W.; Colom, R.J.; Monzo, J.M.; Sebastia, A.; Gadea, R. [Digital Systems Design Group (DSD), Instituto de las Tecnologias de la Informacion y de las Comunicaciones Avanzadas (ITACA), Universidad Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia (Spain)
2009-06-01
Traditionally, the most popular technique to predict the impact position of gamma photons on a PET detector has been Anger's logic. However, it introduces nonlinearities that compress the light distribution, reducing the useful field of view and the spatial resolution, especially at the edges of the scintillator crystal. In this work, we make use of neural networks to address a bias-corrected position estimation from real stimulus obtained from a 2D PET system setup. The preprocessing and data acquisition were performed by separate custom boards, especially designed for this application. The results show that neural networks yield a more uniform field of view while improving the systematic error and the spatial resolution. Therefore, they stand as a better performing and readily available alternative to classic positioning methods.
Li, Luyang; Liu, Yun-Hui; Jiang, Tianjiao; Wang, Kai; Fang, Mu
2018-02-01
Despite tremendous efforts made for years, trajectory tracking control (TC) of a nonholonomic mobile robot (NMR) without global positioning system remains an open problem. The major reason is the difficulty to localize the robot by using its onboard sensors only. In this paper, a newly designed adaptive trajectory TC method is proposed for the NMR without its position, orientation, and velocity measurements. The controller is designed on the basis of a novel algorithm to estimate position and velocity of the robot online from visual feedback of an omnidirectional camera. It is theoretically proved that the proposed algorithm yields the TC errors to asymptotically converge to zero. Real-world experiments are conducted on a wheeled NMR to validate the feasibility of the control system.
Point Light Source Position Estimation From RGB-D Images by Learning Surface Attributes.
Karaoglu, Sezer; Liu, Yang; Gevers, Theo; Smeulders, Arnold W M
2017-11-01
Light source position (LSP) estimation is a difficult yet an important problem in computer vision. A common approach for estimating the LSP assumes Lambert's law. However, in real-world scenes, Lambert's law does not hold for all different types of surfaces. Instead of assuming all that surfaces follow Lambert's law, our approach classifies image surface segments based on their photometric and geometric surface attributes (i.e. glossy, matte, curved, and so on) and assigns weights to image surface segments based on their suitability for LSP estimation. In addition, we propose the use of the estimated camera pose to globally constrain LSP for RGB-D video sequences. Experiments on Boom and a newly collected RGB-D video data sets show that the state-of-the-art methods are outperformed by the proposed method. The results demonstrate that weighting image surface segments based on their attributes outperform the state-of-the-art methods in which the image surface segments are considered to equally contribute. In particular, by using the proposed surface weighting, the angular error for LSP estimation is reduced from 12.6° to 8.2° and 24.6° to 4.8° for Boom and RGB-D video data sets, respectively. Moreover, using the camera pose to globally constrain LSP provides higher accuracy (4.8°) compared with using single frames (8.5°).
GPS satellite clock error estimation for real time PPP and the assessment of position quality
Galera Monico, J. F.; Marques, H. A.
2012-12-01
Real time PPP method requires the availability of real time precise orbits and corrections or errors of the satellites clocks. Currently, it is possible to use the predicted IGU ephemerides available by the IGS centers. However, the satellites clocks corrections available in the IGU do not present enough accuracy (3 ns or 0.9 m) to accomplish real time PPP with centimeter accuracy. Therefore, it is necessary to develop appropriate methodologies for estimating the satellite clock corrections in real time with better quality. The estimation of satellite clock corrections can be carried out based on a GNSS network of reference and performing the adjustment in a combined PPP mode. Thus, all systematic effects involved with the GNSS satellite signals must be modeled appropriately for each station of the network. Once the corrections of the satellite clocks are estimated in real time, they should be sent to the users, which will use them for application in the GNSS data processing from a particular station also in real time PPP mode. To achieve such aim, a system composed by two software's, one for estimating the satellite clock corrections based on data from a GNSS network and the other for the realization of the real time PPP was developed. The results were generated in real time and post-processed mode (simulating real time). The estimate of the satellites clocks corrections was generated based on the measurements of the pseudorange smoothed by carrier phase and also using the original pseudorange and carrier phase with ambiguities estimation for each satellite available at each station. The daily accuracy of the estimated satellite clock corrections reached the order of 0.15 ns (0,05 m) and the application in the GNSS positioning shows that is possible now to accomplish real time PPP in the kinematic mode with accuracy of the order of 10 to 20 cm.
Directory of Open Access Journals (Sweden)
O. Jakubov
2013-09-01
Full Text Available Common techniques for position-velocity-time estimation in satellite navigation, iterative least squares and the extended Kalman filter, involve matrix operations. The matrix inversion and inclusion of a matrix library pose requirements on a computational power and operating platform of the navigation processor. In this paper, we introduce a novel distributed algorithm suitable for implementation in simple parallel processing units each for a tracked satellite. Such a unit performs only scalar sum, subtraction, multiplication, and division. The algorithm can be efficiently implemented in hardware logic. Given the fast position-velocity-time estimator, frequent estimates can foster dynamic performance of a vector tracking receiver. The algorithm has been designed from a factor graph representing the extended Kalman filter by splitting vector nodes into scalar ones resulting in a cyclic graph with few iterations needed. Monte Carlo simulations have been conducted to investigate convergence and accuracy. Simulation case studies for a vector tracking architecture and experimental measurements with a real-time software receiver developed at CTU in Prague were conducted. The algorithm offers compromises in stability, accuracy, and complexity depending on the number of iterations. In scenarios with a large number of tracked satellites, it can outperform the traditional methods at low complexity.
Indoor positioning in wireless local area networks with online path-loss parameter estimation.
Bruno, Luigi; Addesso, Paolo; Restaino, Rocco
2014-01-01
Location based services are gathering an even wider interest also in indoor environments and urban canyons, where satellite systems like GPS are no longer accurate. A much addressed solution for estimating the user position exploits the received signal strengths (RSS) in wireless local area networks (WLANs), which are very common nowadays. However, the performances of RSS based location systems are still unsatisfactory for many applications, due to the difficult modeling of the propagation channel, whose features are affected by severe changes. In this paper we propose a localization algorithm which takes into account the nonstationarity of the working conditions by estimating and tracking the key parameters of RSS propagation. It is based on a Sequential Monte Carlo realization of the optimal Bayesian estimation scheme, whose functioning is improved by exploiting the Rao-Blackwellization rationale. Two key statistical models for RSS characterization are deeply analyzed, by presenting effective implementations of the proposed scheme and by assessing the positioning accuracy by extensive computer experiments. Many different working conditions are analyzed by simulated data and corroborated through the validation in a real world scenario.
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...
Directory of Open Access Journals (Sweden)
Breno Carvalho
2013-10-01
Full Text Available This paper purpose is to implement a computational program to estimate the states (complex nodal voltages of a power system and showing that the largest normalized residual (LNR test fails many times. The chosen solution method was the Weighted Least Squares (WLS. Once the states are estimated a gross error analysis is made with the purpose to detect and identify the measurements that may contain gross errors (GEs, which can interfere in the estimated states, leading the process to an erroneous state estimation. If a measure is identified as having error, it is discarded of the measurement set and the whole process is remade until all measures are within an acceptable error threshold. To validate the implemented software there have been done several computer simulations in the IEEE´s systems of 6 and 14 buses, where satisfactory results were obtained. Another purpose is to show that even a widespread method as the LNR test is subjected to serious conceptual flaws, probably due to a lack of mathematical foundation attendance in the methodology. The paper highlights the need for continuous improvement of the employed techniques and a critical view, on the part of the researchers, to see those types of failures.
Energy Technology Data Exchange (ETDEWEB)
Blais, AR; Dekaban, M; Lee, T-Y [Department of Medical Biophysics and Robarts Research Institute, Western University, Lawson Health Research Institute, London, ON (Canada)
2014-08-15
Quantitative analysis of dynamic positron emission tomography (PET) data usually involves minimizing a cost function with nonlinear regression, wherein the choice of starting parameter values and the presence of local minima affect the bias and variability of the estimated kinetic parameters. These nonlinear methods can also require lengthy computation time, making them unsuitable for use in clinical settings. Kinetic modeling of PET aims to estimate the rate parameter k{sub 3}, which is the binding affinity of the tracer to a biological process of interest and is highly susceptible to noise inherent in PET image acquisition. We have developed linearized kinetic models for kinetic analysis of dynamic contrast enhanced computed tomography (DCE-CT)/PET imaging, including a 2-compartment model for DCE-CT and a 3-compartment model for PET. Use of kinetic parameters estimated from DCE-CT can stabilize the kinetic analysis of dynamic PET data, allowing for more robust estimation of k{sub 3}. Furthermore, these linearized models are solved with a non-negative least squares algorithm and together they provide other advantages including: 1) only one possible solution and they do not require a choice of starting parameter values, 2) parameter estimates are comparable in accuracy to those from nonlinear models, 3) significantly reduced computational time. Our simulated data show that when blood volume and permeability are estimated with DCE-CT, the bias of k{sub 3} estimation with our linearized model is 1.97 ± 38.5% for 1,000 runs with a signal-to-noise ratio of 10. In summary, we have developed a computationally efficient technique for accurate estimation of k{sub 3} from noisy dynamic PET data.
Use of Tikhonov Regularization to Improve the Accuracy of Position Estimates in Inertial Navigation
Directory of Open Access Journals (Sweden)
Tuukka Nieminen
2011-01-01
Full Text Available Inertial navigation problems are often understood as initial value problems. However, there are many applications where boundary value problems naturally arise. In these situations, it has been shown that the finite element method can be efficiently used to compute accurate position and velocity estimates. We will propose that finite element method complemented with Tikhonov regularization—a basic tool for inverse problems—is a powerful combination for further accuracy improvements. The proposed method provides a straightforward way to exploit prior information of various types and is subject to rigorous optimality results. Use and accuracy of the proposed method are demonstrated with examples.
El-Melegy, Moumen T
2013-07-01
This paper addresses the problem of fitting a functional model to data corrupted with outliers using a multilayered feed-forward neural network. Although it is of high importance in practical applications, this problem has not received careful attention from the neural network research community. One recent approach to solving this problem is to use a neural network training algorithm based on the random sample consensus (RANSAC) framework. This paper proposes a new algorithm that offers two enhancements over the original RANSAC algorithm. The first one improves the algorithm accuracy and robustness by employing an M-estimator cost function to decide on the best estimated model from the randomly selected samples. The other one improves the time performance of the algorithm by utilizing a statistical pretest based on Wald's sequential probability ratio test. The proposed algorithm is successfully evaluated on synthetic and real data, contaminated with varying degrees of outliers, and compared with existing neural network training algorithms.
Che-Castaldo, Christian; Jenouvrier, Stephanie; Youngflesh, Casey; Shoemaker, Kevin T; Humphries, Grant; McDowall, Philip; Landrum, Laura; Holland, Marika M; Li, Yun; Ji, Rubao; Lynch, Heather J
2017-10-10
Colonially-breeding seabirds have long served as indicator species for the health of the oceans on which they depend. Abundance and breeding data are repeatedly collected at fixed study sites in the hopes that changes in abundance and productivity may be useful for adaptive management of marine resources, but their suitability for this purpose is often unknown. To address this, we fit a Bayesian population dynamics model that includes process and observation error to all known Adélie penguin abundance data (1982-2015) in the Antarctic, covering >95% of their population globally. We find that process error exceeds observation error in this system, and that continent-wide "year effects" strongly influence population growth rates. Our findings have important implications for the use of Adélie penguins in Southern Ocean feedback management, and suggest that aggregating abundance across space provides the fastest reliable signal of true population change for species whose dynamics are driven by stochastic processes.Adélie penguins are a key Antarctic indicator species, but data patchiness has challenged efforts to link population dynamics to key drivers. Che-Castaldo et al. resolve this issue using a pan-Antarctic Bayesian model to infer missing data, and show that spatial aggregation leads to more robust inference regarding dynamics.
Directory of Open Access Journals (Sweden)
Eusebio Eduardo Hernández Martinez
2013-01-01
Full Text Available In robotics, solving the direct kinematics problem (DKP for parallel robots is very often more difficult and time consuming than for their serial counterparts. The problem is stated as follows: given the joint variables, the Cartesian variables should be computed, namely the pose of the mobile platform. Most of the time, the DKP requires solving a non-linear system of equations. In addition, given that the system could be non-convex, Newton or Quasi-Newton (Dogleg based solvers get trapped on local minima. The capacity of such kinds of solvers to find an adequate solution strongly depends on the starting point. A well-known problem is the selection of such a starting point, which requires a priori information about the neighbouring region of the solution. In order to circumvent this issue, this article proposes an efficient method to select and to generate the starting point based on probabilistic learning. Experiments and discussion are presented to show the method performance. The method successfully avoids getting trapped on local minima without the need for human intervention, which increases its robustness when compared with a single Dogleg approach. This proposal can be extended to other structures, to any non-linear system of equations, and of course, to non-linear optimization problems.
Method to estimate position, motion and trajectory of a target with a single x-ray imager
DEFF Research Database (Denmark)
2010-01-01
component along at least one imager axis of the target using a spatial probability density. The present invention provides a probability-based method for accurate estimation of the mean position, motion magnitude, motion correlation, and trajectory of a tumor from CBCT projections. The applicability......The present invention provides a method for estimation of retrospective and real-time 3D target position by a single imager. The invention includes imaging a target on at least one 2D plane to determine 2D position and/or position components of the target, and resolving a position and/or position...
De Neve, Jan-Emmanuel; Oswald, Andrew J
2012-12-04
The question of whether there is a connection between income and psychological well-being is a long-studied issue across the social, psychological, and behavioral sciences. Much research has found that richer people tend to be happier. However, relatively little attention has been paid to whether happier individuals perform better financially in the first place. This possibility of reverse causality is arguably understudied. Using data from a large US representative panel, we show that adolescents and young adults who report higher life satisfaction or positive affect grow up to earn significantly higher levels of income later in life. We focus on earnings approximately one decade after the person's well-being is measured; we exploit the availability of sibling clusters to introduce family fixed effects; we account for the human capacity to imagine later socioeconomic outcomes and to anticipate the resulting feelings in current well-being. The study's results are robust to the inclusion of controls such as education, intelligence quotient, physical health, height, self-esteem, and later happiness. We consider how psychological well-being may influence income. Sobel-Goodman mediation tests reveal direct and indirect effects that carry the influence from happiness to income. Significant mediating pathways include a higher probability of obtaining a college degree, getting hired and promoted, having higher degrees of optimism and extraversion, and less neuroticism.
Estimation of The Scale Factor For Short Observing Session Duration In GNSS Positioning
Hasan Dogan, Ali; Erdogan, Bahattin
2017-04-01
In recent years, users prefer Global Navigation Satellite System (GNSS) technique rather than traditional techniques for geodetic applications. Accuracy of GNNS observations depends on several parameters such as surveying method, data processing strategy and software. GNSS observations are generally processed by using academic software or commercial software. Commercial software can provide solution up to 20-25 km baseline length. Moreover, academic software is preferred for scientific researches as monitoring of the movements of manmade structures or plate tectonic that are required high accurate point positioning. However, academic software gives optimistic results in terms of positioning accuracy. This situation causes wrong interpretations for important decision in deformation analysis. Therefore, the variance-covariance (VCV) matrices that are obtained from academic software should be scaled. In this study, the estimation of the scaling factor was carried out for short observing session duration in GNSS positioning. Baselines whose lengths ranging from 8 km to 268 km and session durations between 60 min and 180 min were processed using Bernese v5.2 with single baseline strategy. According to initial results, a significant dependence based on baseline lengths cannot be determined. Moreover, the results show that scaling factor changes depending on the session duration. Keywords: Relative Positioning, Short Observing Session Duration, Scale Factor, Bernese
Sullivan, Benjamin W.; Smith, William K.; Townsend, Alan R.; Nasto, Megan K.; Reed, Sasha C.; Chazdon, Robin L.; Cleveland, Cory C.
2014-01-01
Biological nitrogen fixation (BNF) is the largest natural source of exogenous nitrogen (N) to unmanaged ecosystems and also the primary baseline against which anthropogenic changes to the N cycle are measured. Rates of BNF in tropical rainforest are thought to be among the highest on Earth, but they are notoriously difficult to quantify and are based on little empirical data. We adapted a sampling strategy from community ecology to generate spatial estimates of symbiotic and free-living BNF in secondary and primary forest sites that span a typical range of tropical forest legume abundance. Although total BNF was higher in secondary than primary forest, overall rates were roughly five times lower than previous estimates for the tropical forest biome. We found strong correlations between symbiotic BNF and legume abundance, but we also show that spatially free-living BNF often exceeds symbiotic inputs. Our results suggest that BNF in tropical forest has been overestimated, and our data are consistent with a recent top-down estimate of global BNF that implied but did not measure low tropical BNF rates. Finally, comparing tropical BNF within the historical area of tropical rainforest with current anthropogenic N inputs indicates that humans have already at least doubled reactive N inputs to the tropical forest biome, a far greater change than previously thought. Because N inputs are increasing faster in the tropics than anywhere on Earth, both the proportion and the effects of human N enrichment are likely to grow in the future.
LENUS (Irish Health Repository)
Dooley, Ian
2012-02-01
PURPOSE: To evaluate the validity of a keratometry (K)-independent method of estimating effective lens position (ELP) before phacoemulsification cataract surgery. SETTING: Institute of Eye Surgery, Whitfield Clinic, Waterford, Ireland. DESIGN: Evaluation of diagnostic test or technology. METHODS: The anterior chamber diameter and corneal height in eyes scheduled for cataract surgery were measured with a rotating Scheimpflug camera. Corneal height and anterior chamber diameter were used to estimate the ELP in a K-independent method (using the SRK\\/T [ELP(rs)] and Holladay 1 [ELP(rh)] formulas). RESULTS: The mean ELP was calculated using the traditional (mean ELP(s) 5.59 mm +\\/- 0.52 mm [SD]; mean ELP(h) 5.63 +\\/- 0.42 mm) and K-independent (mean ELP(rs) 5.55 +\\/- 0.42 mm; mean ELP(rh) +\\/- SD 5.60 +\\/- 0.36 mm) methods. Agreement between ELP(s) and ELP(rs) and between ELP(h) and ELP(rh) were represented by Bland-Altman plots, with mean differences (+\\/- 1.96 SD) of 0.06 +\\/- 0.65 mm (range -0.59 to +0.71 mm; P=.08) in association with ELP(rs) and -0.04 +\\/- 0.39 mm (range -0.43 to +0.35 mm; P=.08) in association with ELP(rh). The mean absolute error for ELP(s) versus ELP(rs) estimation and for ELP(h) versus ELP(rh) estimation was 0.242 +\\/- 0.222 mm (range 0.001 to 1.272 mm) and 0.152 +\\/- 0.137 mm (range 0.001 to 0.814 mm), respectively. CONCLUSION: This study confirms that the K-independent ELP estimation method is comparable to traditional K-dependent methods and may be useful in post-refractive surgery patients.
Fang, Li; Xu, Yusheng; Yao, Wei; Stilla, Uwe
2016-11-01
For monitoring of glacier surface motion in pole and alpine areas, radar remote sensing is becoming a popular technology accounting for its specific advantages of being independent of weather conditions and sunlight. In this paper we propose a method for glacier surface motion monitoring using phase correlation (PC) based on point-like features (PLF). We carry out experiments using repeat-pass TerraSAR X-band (TSX) and Sentinel-1 C-band (S1C) intensity images of the Taku glacier in Juneau icefield located in southeast Alaska. The intensity imagery is first filtered by an improved adaptive refined Lee filter while the effect of topographic reliefs is removed via SRTM-X DEM. Then, a robust phase correlation algorithm based on singular value decomposition (SVD) and an improved random sample consensus (RANSAC) algorithm is applied to sequential PLF pairs generated by correlation using a 2D sinc function template. The approaches for glacier monitoring are validated by both simulated SAR data and real SAR data from two satellites. The results obtained from these three test datasets confirm the superiority of the proposed approach compared to standard correlation-like methods. By the use of the proposed adaptive refined Lee filter, we achieve a good balance between the suppression of noise and the preservation of local image textures. The presented phase correlation algorithm shows the accuracy of better than 0.25 pixels, when conducting matching tests using simulated SAR intensity images with strong noise. Quantitative 3D motions and velocities of the investigated Taku glacier during a repeat-pass period are obtained, which allows a comprehensive and reliable analysis for the investigation of large-scale glacier surface dynamics.
A Simple Interface for 3D Position Estimation of a Mobile Robot with Single Camera.
Chao, Chun-Tang; Chung, Ming-Hsuan; Chiou, Juing-Shian; Wang, Chi-Jo
2016-03-25
In recent years, there has been an increase in the number of mobile robots controlled by a smart phone or tablet. This paper proposes a visual control interface for a mobile robot with a single camera to easily control the robot actions and estimate the 3D position of a target. In this proposal, the mobile robot employed an Arduino Yun as the core processor and was remote-controlled by a tablet with an Android operating system. In addition, the robot was fitted with a three-axis robotic arm for grasping. Both the real-time control signal and video transmission are transmitted via Wi-Fi. We show that with a properly calibrated camera and the proposed prototype procedures, the users can click on a desired position or object on the touchscreen and estimate its 3D coordinates in the real world by simple analytic geometry instead of a complicated algorithm. The results of the measurement verification demonstrates that this approach has great potential for mobile robots.
Position estimation for fiducial marks based on high intensity retroreflective tape
Trushkina, Anna; Serikova, Mariya; Pantyushin, Anton
2016-04-01
3D position estimation of an object usually involve computer vision techniques, which require fiducial markers attached to the objects surface. Modern technology provides a high intensity retroreflective material in the form of a tape which is easy to mount to the object and can be used as a base for fiducial marks. But inevitable drawback of the tapes with the highest retroreflective intensity is the presence of technological pattern which affects spatial distribution of retroreflected light and deforms border of any print on tape's surface. In this work we compare various shapes of metrological pattern and examine Fourier descriptors based image processing to obtain estimation of accuracy of mark image position. To verify results we developed a setup consisting of a camera based on Sony ICX274 CCD, 25 mm lens, 800 nm LED lightning and high intensity microprismatic tape. The experiment showed that there is no significant difference between proposed mark shapes as well as between direct and indirect contrast when proposed image processing is used. The experiments confirmed that the image processing implemented without elimination of non-reflective netting pattern can only provide an accuracy of coordinates extraction close to 1 pix.
A Simple Interface for 3D Position Estimation of a Mobile Robot with Single Camera
Directory of Open Access Journals (Sweden)
Chun-Tang Chao
2016-03-01
Full Text Available In recent years, there has been an increase in the number of mobile robots controlled by a smart phone or tablet. This paper proposes a visual control interface for a mobile robot with a single camera to easily control the robot actions and estimate the 3D position of a target. In this proposal, the mobile robot employed an Arduino Yun as the core processor and was remote-controlled by a tablet with an Android operating system. In addition, the robot was fitted with a three-axis robotic arm for grasping. Both the real-time control signal and video transmission are transmitted via Wi-Fi. We show that with a properly calibrated camera and the proposed prototype procedures, the users can click on a desired position or object on the touchscreen and estimate its 3D coordinates in the real world by simple analytic geometry instead of a complicated algorithm. The results of the measurement verification demonstrates that this approach has great potential for mobile robots.
A New Sensorless MRAS Based on Active Power Calculations for Rotor Position Estimation of a DFIG
Directory of Open Access Journals (Sweden)
Gil Domingos Marques
2011-01-01
Full Text Available A sensorless method for the estimation of the rotor position of the wound-rotor induction machine is described in this paper. The method is based on the MRAS methodology and consists in the comparison of two models for the evaluation of the active power transferred across the air gap: the reference model and the adaptive model. The reference model obtains the power transferred across the air gap using directly available and measured stator variables. The adaptive model obtains the same quantity in function of electromotive forces and rotor currents that are measurable on the rotor position, which is under estimation. The method does not need any information about the stator or rotor flux and can be implemented in the rotor or in the stator reference frames with a hysteresis or with a PI controller. The stability analysis gives an unstable region on the rotor current dq plane. Simulation and experimental results show that the method is appropriate for the vector control of the doubly fed induction machine under the stability region.
Boada, Beatriz L.; Boada, Maria Jesus L.; Vargas-Melendez, Leandro; Diaz, Vicente
2018-01-01
Nowadays, one of the main objectives in road transport is to decrease the number of accident victims. Rollover accidents caused nearly 33% of all deaths from passenger vehicle crashes. Roll Stability Control (RSC) systems prevent vehicles from untripped rollover accidents. The lateral load transfer is the main parameter which is taken into account in the RSC systems. This parameter is related to the roll angle, which can be directly measured from a dual-antenna GPS. Nevertheless, this is a costly technique. For this reason, roll angle has to be estimated. In this paper, a novel observer based on H∞ filtering in combination with a neural network (NN) for the vehicle roll angle estimation is proposed. The design of this observer is based on four main criteria: to use a simplified vehicle model, to use signals of sensors which are installed onboard in current vehicles, to consider the inaccuracy in the system model and to attenuate the effect of the external disturbances. Experimental results show the effectiveness of the proposed observer.
Directory of Open Access Journals (Sweden)
Orlando N. Grillo
2011-03-01
Full Text Available Missing data is a common problem in paleontology. It makes it difficult to reconstruct extinct taxa accurately and restrains the inclusion of some taxa on comparative and biomechanical studies. Particularly, estimating the position of vertebrae on incomplete series is often non-empirical and does not allow precise estimation of missing parts. In this work we present a method for calculating the position of preserved middle sequences of caudal vertebrae in the saurischian dinosaur Staurikosaurus pricei, based on the length and height of preserved anterior and posterior caudal vertebral centra. Regression equations were used to estimate these dimensions for middle vertebrae and, consequently, to assess the position of the preserved middle sequences. It also allowed estimating these dimensions for non-preserved vertebrae. Results indicate that the preserved caudal vertebrae of Staurikosaurus may correspond to positions 1-3, 5, 7, 14-19/15-20, 24-25/25-26, and 29-47, and that at least 25 vertebrae had transverse processes. Total length of the tail was estimated in 134 cm and total body length was 220-225 cm.Dados lacunares são um problema comum na paleontologia. Eles dificultam a reconstrução acurada de táxons extintos e limitam a inclusão de alguns táxons em estudos comparativose biomecânicos. Particularmente, estimar a posição de vértebras em séries incompletas tem sido feito com base em métodos não empíricos que não permitem estimar corretamente as partes ausentes. Neste trabalho apresentamos uma metodologia que permite estimar a posição de sequências médias preservadas de vértebras caudais no dinossauro saurísquio Staurikosaurus pricei, com base no comprimento e altura dos centros das vértebras anteriores e posteriores preservadas. Equações de regressão foram usadas para estimar essas dimensões para as vértebras médias e, consequentemente, para posicionar as sequências médias preservadas e para estimar o tamanho das
Guarnieri, A.; Milan, N.; Pirotti, F.; Vettore, A.
2011-12-01
In the automotive sector, especially in these last decade, a growing number of investigations have taken into account electronic systems to check and correct the behavior of drivers, increasing road safety. The possibility to identify with high accuracy the vehicle position in a mapping reference frame for driving directions and best-route analysis is also another topic which attracts lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate time by time the position, orientation and velocity of the system. To this aim low cost GPS and MEMS (sensors can be used. In comparison to a four wheel vehicle, the dynamics of a two wheel vehicle (e.g. a scooter) feature a higher level of complexity. Indeed more degrees of freedom must be taken into account to describe the motion of the latter. For example a scooter can twist sideways, thus generating a roll angle. A slight pitch angle has to be considered as well, since wheel suspensions have a higher degree of motion with respect to four wheel vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a motorcycle ("Vespa" scooter), which can be used as alternative to the "classical" approach based on the integration of GPS and INS sensors. Position and orientation of the scooter are derived from MEMS data and images acquired by on-board digital camera. A Bayesian filter provides the means for integrating the data from MEMS-based orientation sensor and the GPS receiver.
Energy Technology Data Exchange (ETDEWEB)
Lee, Jung Uk [Samsung Electroics, Suwon (Korea, Republic of); Sun, Ju Young; Won, Mooncheol [Chungnam Nat' l Univ., Daejeon (Korea, Republic of)
2013-12-15
In this paper, we propose a real-time algorithm for estimating the relative position of a person with respect to a robot (camera) using a monocular camera. The algorithm detects the head and shoulder regions of a person using HOG (Histogram of Oriented Gradient) feature vectors and an SVM (Support Vector Machine) classifier. The size and location of the detected area are used for calculating the relative distance and angle between the person and the camera on a robot. To increase the speed of the algorithm, we use a GPU and NVIDIA's CUDA library; the resulting algorithm speed is ∼ 15 Hz. The accuracy of the algorithm is compared with the output of a SICK laser scanner.
Directory of Open Access Journals (Sweden)
Ramzi Alsaedi
2014-01-01
Full Text Available We give global estimates on some potential of functions in a bounded domain of the Euclidean space ${\\mathbb{R}}^n\\; (n\\geq 2$. These functions may be singular near the boundary and are globally comparable to a product of a power of the distance to the boundary by some particularly well behaved slowly varying function near zero. Next, we prove the existence and uniqueness of a positive solution for the integral equation $u=V(a u^{\\sigma}$ with $0\\leq \\sigma <1$, where V belongs to a class of kernels that contains in particular the potential kernel of the classical Laplacian $V=(-\\Delta^{-1}$ or the fractional laplacian $V=(-\\Delta^{\\alpha/2}$, $0<\\alpha<2$.
Directory of Open Access Journals (Sweden)
Waqas Majeed
Full Text Available Independent component analysis (ICA has been successfully utilized for analysis of functional MRI (fMRI data for task related as well as resting state studies. Although it holds the promise of becoming an unbiased data-driven analysis technique, a few choices have to be made prior to performing ICA, selection of a method for determining the number of independent components (nIC being one of them. Choice of nIC has been shown to influence the ICA maps, and various approaches (mostly relying on information theoretic criteria have been proposed and implemented in commonly used ICA analysis packages, such as MELODIC and GIFT. However, there has been no consensus on the optimal method for nIC selection, and many studies utilize arbitrarily chosen values for nIC. Accurate and reliable determination of true nIC is especially important in the setting where the signals of interest contribute only a small fraction of the total variance, i.e. very low contrast-to-noise ratio (CNR, and/or very focal response. In this study, we evaluate the performance of different model order selection criteria and demonstrate that the model order selected based upon bootstrap stability of principal components yields more reliable and accurate estimates of model order. We then demonstrate the utility of this fully data-driven approach to detect weak and focal stimulus-driven responses in real data. Finally, we compare the performance of different multi-run ICA approaches using pseudo-real 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)
Target Centroid Position Estimation of Phase-Path Volume Kalman Filtering
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Fengjun Hu
2016-01-01
Full Text Available For the problem of easily losing track target when obstacles appear in intelligent robot target tracking, this paper proposes a target tracking algorithm integrating reduced dimension optimal Kalman filtering algorithm based on phase-path volume integral with Camshift algorithm. After analyzing the defects of Camshift algorithm, compare the performance with the SIFT algorithm and Mean Shift algorithm, and Kalman filtering algorithm is used for fusion optimization aiming at the defects. Then aiming at the increasing amount of calculation in integrated algorithm, reduce dimension with the phase-path volume integral instead of the Gaussian integral in Kalman algorithm and reduce the number of sampling points in the filtering process without influencing the operational precision of the original algorithm. Finally set the target centroid position from the Camshift algorithm iteration as the observation value of the improved Kalman filtering algorithm to fix predictive value; thus to make optimal estimation of target centroid position and keep the target tracking so that the robot can understand the environmental scene and react in time correctly according to the changes. The experiments show that the improved algorithm proposed in this paper shows good performance in target tracking with obstructions and reduces the computational complexity of the algorithm through the dimension reduction.
Estimating False Positive Contamination in Crater Annotations from Citizen Science Data
Tar, P. D.; Bugiolacchi, R.; Thacker, N. A.; Gilmour, J. D.
2017-01-01
Web-based citizen science often involves the classification of image features by large numbers of minimally trained volunteers, such as the identification of lunar impact craters under the Moon Zoo project. Whilst such approaches facilitate the analysis of large image data sets, the inexperience of users and ambiguity in image content can lead to contamination from false positive identifications. We give an approach, using Linear Poisson Models and image template matching, that can quantify levels of false positive contamination in citizen science Moon Zoo crater annotations. Linear Poisson Models are a form of machine learning which supports predictive error modelling and goodness-of-fits, unlike most alternative machine learning methods. The proposed supervised learning system can reduce the variability in crater counts whilst providing predictive error assessments of estimated quantities of remaining true verses false annotations. In an area of research influenced by human subjectivity, the proposed method provides a level of objectivity through the utilisation of image evidence, guided by candidate crater identifications.
Lee, Taewoong; Lee, Hyounggun; Kim, Younghak; Lee, Wonho
2017-07-01
The performance of a Compton imager using a single three-dimensional position-sensitive LYSO scintillator detector was estimated using a Monte Carlo simulation. The Compton imager consisted of a single LYSO scintillator with a pixelized structure. The size of the scintillator and each pixel were 1.3 × 1.3 × 1.3 cm3 and 0.3 × 0.3 × 0.3 cm3, respectively. The order of γ-ray interactions was determined based on the deposited energies in each detector. After the determination of the interaction sequence, various types of reconstruction algorithms such as simple back-projection, filtered back-projection, and list-mode maximum-likelihood expectation maximization (LM-MLEM) were applied and compared with each other in terms of their angular resolution and signal-to-noise ratio (SNR) for several γ-ray energies. The LM-MLEM reconstruction algorithm exhibited the best performance for Compton imaging in maintaining high angular resolution and SNR. The two sources of 137Cs (662 keV) could be distinguishable if they were more than 17° apart. The reconstructed Compton images showed the precise position and distribution of various radiation isotopes, which demonstrated the feasibility of the monitoring of nuclear materials in homeland security and radioactive waste management applications.
Energy Technology Data Exchange (ETDEWEB)
Lee, Taewoong; Lee, Hyounggun; Kim, Younghak; Lee, Wonho [Korea University, Seoul (Korea, Republic of)
2017-07-15
The performance of a Compton imager using a single three-dimensional position-sensitive LYSO scintillator detector was estimated using a Monte Carlo simulation. The Compton imager consisted of a single LYSO scintillator with a pixelized structure. The size of the scintillator and each pixel were 1.3 × 1.3 × 1.3 cm{sup 3} and 0.3 × 0.3 × 0.3 cm{sup 3}, respectively. The order of γ-ray interactions was determined based on the deposited energies in each detector. After the determination of the interaction sequence, various types of reconstruction algorithms such as simple back-projection, filtered back-projection, and list-mode maximum-likelihood expectation maximization (LM-MLEM) were applied and compared with each other in terms of their angular resolution and signal-tonoise ratio (SNR) for several γ-ray energies. The LM-MLEM reconstruction algorithm exhibited the best performance for Compton imaging in maintaining high angular resolution and SNR. The two sources of {sup 137}Cs (662 keV) could be distinguishable if they were more than 17 ◦ apart. The reconstructed Compton images showed the precise position and distribution of various radiation isotopes, which demonstrated the feasibility of the monitoring of nuclear materials in homeland security and radioactive waste management applications.
Multi-User Identification-Based Eye-Tracking Algorithm Using Position Estimation
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Suk-Ju Kang
2016-12-01
Full Text Available This paper proposes a new multi-user eye-tracking algorithm using position estimation. Conventional eye-tracking algorithms are typically suitable only for a single user, and thereby cannot be used for a multi-user system. Even though they can be used to track the eyes of multiple users, their detection accuracy is low and they cannot identify multiple users individually. The proposed algorithm solves these problems and enhances the detection accuracy. Specifically, the proposed algorithm adopts a classifier to detect faces for the red, green, and blue (RGB and depth images. Then, it calculates features based on the histogram of the oriented gradient for the detected facial region to identify multiple users, and selects the template that best matches the users from a pre-determined face database. Finally, the proposed algorithm extracts the final eye positions based on anatomical proportions. Simulation results show that the proposed algorithm improved the average F1 score by up to 0.490, compared with benchmark algorithms.
Garcia-Martinez, P.; Montes, P.; Schuster, E.
2017-10-01
The feasibility of controlling the q-profile using closed-loop controllers designed from first-principles-driven control-oriented models has been demonstrated in tokamaks like DIII-D. These control-oriented models typically use the magnetic diffusion equation for the poloidal magnetic flux profile evolution, combined with simplified models for other plasma quantities such as the electron density, the electron temperature, and the noninductive current-drives. The magnetic diffusion equation is expressed in flux coordinates thus requiring several geometric profiles that depend on the underlying MHD equilibrium of the plasma. In this work, a self-consistent method to improve the estimation of the MHD equilibrium and the required geometric profiles is proposed. The method combines a two-dimensional linear model, that takes into account the geometry of the flux surfaces, with a one-dimensional non-linear model that incorporates the evolution of the magnetic profiles resulting in a robust and fast strategy for MHD equilibrium estimation. Supported by the US DOE under DE-SC0010537, DE-SC0010661, and the Fulbright-CONICET scholar program.
Directory of Open Access Journals (Sweden)
Wilmar Hernandez
2005-11-01
Full Text Available In the present paper, in order to estimate the response of both a wheel speedsensor and an accelerometer placed in a car under performance tests, robust and optimalmultivariable estimation techniques are used. In this case, the disturbances and noisescorrupting the relevant information coming from the sensorsÃ¢Â€Â™ outputs are so dangerous thattheir negative influence on the electrical systems impoverish the general performance of thecar. In short, the solution to this problem is a safety related problem that deserves our fullattention. Therefore, in order to diminish the negative effects of the disturbances and noiseson the carÃ¢Â€Â™s electrical and electromechanical systems, an optimum observer is used. Theexperimental results show a satisfactory improvement in the signal-to-noise ratio of therelevant signals and demonstrate the importance of the fusion of several intelligent sensordesign techniques when designing the intelligent sensors that todayÃ¢Â€Â™s cars need.
Liu, Jing; Sun, Yang; Qi, Jinyi; Marcu, Laura
2012-02-21
We report a novel method for estimating fluorescence impulse response function (fIRF) from noise-corrupted time-domain fluorescence measurements of biological tissue. This method is based on the use of high-order Laguerre basis functions and a constrained least-squares approach that addresses the problem of overfitting due to increased model complexity. The new method was extensively evaluated on fluorescence data from simulation, fluorescent standard dyes, ex vivo tissue samples of atherosclerotic plaques and in vivo oral carcinoma. Current results demonstrate that this method allows for rapid and accurate deconvolution of multiple channel fluorescence decays without adaptively adjusting the Laguerre scale parameter. The appropriate choice of the scale parameter is essential for accurate estimation of the fIRF. The method described here is anticipated to play an important role in the development of computational techniques for real-time analysis of time-resolved fluorescence data from biological tissues and to support the advancement of fluorescence lifetime instrumentation for biomedical diagnostics by providing a means for on-line robust analysis of fluorescence decay.
Mendez Astudillo, Jorge; Lau, Lawrence; Tang, Yu-Ting; Moore, Terry
2018-02-14
As Global Navigation Satellite System (GNSS) signals travel through the troposphere, a tropospheric delay occurs due to a change in the refractive index of the medium. The Precise Point Positioning (PPP) technique can achieve centimeter/millimeter positioning accuracy with only one GNSS receiver. The Zenith Tropospheric Delay (ZTD) is estimated alongside with the position unknowns in PPP. Estimated ZTD can be very useful for meteorological applications, an example is the estimation of water vapor content in the atmosphere from the estimated ZTD. PPP is implemented with different algorithms and models in online services and software packages. In this study, a performance assessment with analysis of ZTD estimates from three PPP online services and three software packages is presented. The main contribution of this paper is to show the accuracy of ZTD estimation achievable in PPP. The analysis also provides the GNSS users and researchers the insight of the processing algorithm dependence and impact on PPP ZTD estimation. Observation data of eight whole days from a total of nine International GNSS Service (IGS) tracking stations spread in the northern hemisphere, the equatorial region and the southern hemisphere is used in this analysis. The PPP ZTD estimates are compared with the ZTD obtained from the IGS tropospheric product of the same days. The estimates of two of the three online PPP services show good agreement (southern hemisphere stations. The results also show that the online PPP services perform better than the selected PPP software packages at all stations.
DEFF Research Database (Denmark)
Wu, Xuan; Huang, Shoudao; Liu, Xiao
2017-01-01
accurately, two opposite voltage pulses are injected on the estimated d-axis. In the proposed method, no filter is needed to draw the high frequency current signal for position estimation and no low pass filter is required in the current control loop for exacting the fundamental current component for field...... oriented control. The fast and reliable feature of this proposed method allows starting the motor with 100% load torque. The effectiveness of the proposed method is verified experimentally and the maximum position estimation error is around 6 electrical degrees....
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Hou Qingjiang
2012-08-01
Full Text Available Abstract Background To evaluate institutional nursing care performance in the context of national comparative statistics (benchmarks, approximately one in every three major healthcare institutions (over 1,800 hospitals across the United States, have joined the National Database for Nursing Quality Indicators® (NDNQI®. With over 18,000 hospital units contributing data for nearly 200 quantitative measures at present, a reliable and efficient input data screening for all quantitative measures for data quality control is critical to the integrity, validity, and on-time delivery of NDNQI reports. Methods With Monte Carlo simulation and quantitative NDNQI indicator examples, we compared two ad-hoc methods using robust scale estimators, Inter Quartile Range (IQR and Median Absolute Deviation from the Median (MAD, to the classic, theoretically-based Minimum Covariance Determinant (FAST-MCD approach, for initial univariate outlier detection. Results While the theoretically based FAST-MCD used in one dimension can be sensitive and is better suited for identifying groups of outliers because of its high breakdown point, the ad-hoc IQR and MAD approaches are fast, easy to implement, and could be more robust and efficient, depending on the distributional property of the underlying measure of interest. Conclusion With highly skewed distributions for most NDNQI indicators within a short data screen window, the FAST-MCD approach, when used in one dimensional raw data setting, could overestimate the false alarm rates for potential outliers than the IQR and MAD with the same pre-set of critical value, thus, overburden data quality control at both the data entry and administrative ends in our setting.
Numerical algorithm for rigid body position estimation using the quaternion approach
Zigic, Miodrag; Grahovac, Nenad
2017-11-01
This paper deals with rigid body attitude estimation on the basis of the data obtained from an inertial measurement unit mounted on the body. The aim of this work is to present the numerical algorithm, which can be easily applied to the wide class of problems concerning rigid body positioning, arising in aerospace and marine engineering, or in increasingly popular robotic systems and unmanned aerial vehicles. Following the considerations of kinematics of rigid bodies, the relations between accelerations of different points of the body are given. A rotation matrix is formed using the quaternion approach to avoid singularities. We present numerical procedures for determination of the absolute accelerations of the center of mass and of an arbitrary point of the body expressed in the inertial reference frame, as well as its attitude. An application of the algorithm to the example of a heavy symmetrical gyroscope is presented, where input data for the numerical procedure are obtained from the solution of differential equations of motion, instead of using sensor measurements.
Estimates of sexual partnership dynamics: extending negative and positive gaps to status lengths.
Castillo-Guajardo, D; García-Ramos, G
2010-08-01
The aims of this study are to generalise the concept of gap length between partners and to estimate the duration of four types of measures for heterosexual partnerships, called status lengths: (1) time spent as single before becoming monogamous (S-M, positive gap), (2) duration of concurrency before monogamy (C-M, negative gap), (3) duration of monogamy before concurrency (M-C) and (4) time spent in monogamy before becoming single (M-S). Medians and CIs were obtained using the US National Survey of Family Growth Cycle 6 conducted in 2002. A significant gender difference was found in the monogamous to single status length (medians 38 months for women, and 19.3 months for men). Other status lengths were similar between genders (S-M: 20 women, 18 men; M-C: 16 women, 13 men; and C-M: 5 for women and men). Respondents younger than the median age at first marriage showed shorter status lengths compared to older ones. Median status lengths were comparable between heterosexuals and bisexuals. Percentage of concurrency in 1 year was 3.3% for women and 3.8% for men. One of the new status lengths (M-C) qualitatively indicates the transmission risk to an upcoming concurrent partner. The set of four status lengths may be useful in the context of epidemiological models with partnership dynamics.
Jing, Nan; Li, Chuang; Chong, Yaqin
2017-01-20
An estimation method for indirectly observable parameters for a typical low dynamic vehicle (LDV) is presented. The estimation method utilizes apparent magnitude, azimuth angle, and elevation angle to estimate the position and velocity of a typical LDV, such as a high altitude balloon (HAB). In order to validate the accuracy of the estimated parameters gained from an unscented Kalman filter, two sets of experiments are carried out to obtain the nonresolved photometric and astrometric data. In the experiments, a HAB launch is planned; models of the HAB dynamics and kinematics and observation models are built to use as time update and measurement update functions, respectively. When the HAB is launched, a ground-based optoelectronic detector is used to capture the object images, which are processed using aperture photometry technology to obtain the time-varying apparent magnitude of the HAB. Two sets of actual and estimated parameters are given to clearly indicate the parameter differences. Two sets of errors between the actual and estimated parameters are also given to show how the estimated position and velocity differ with respect to the observation time. The similar distribution curve results from the two scenarios, which agree within 3σ, verify that nonresolved photometric and astrometric data can be used to estimate the indirectly observable state parameters (position and velocity) for a typical LDV. This technique can be applied to small and dim space objects in the future.
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Xuan Wu
2015-10-01
Full Text Available The accurate information of the initial rotor position is very critical for successful starting of the Surface-mounted Permanent Magnet Synchronous Motor (SPMSM. In order to solve the problems of low accuracy and unreliability in the conventional estimation strategy, in this paper, an improved initial rotor position estimation strategy without any position sensor for SPMSM at standstill is proposed based on rectangular pulse voltage injection. In the work, when the second series of pulse voltages were applied. By the ways of strengthening the effect of weakening or strengthening magnetic fields and increasing the difference between each current of the vector. The improved strategy enhanced reliability and raised the initial position estimation accuracy from 7.5° to 1.875°. The improved strategy does not need any additional hardware. Experimental results demonstrate the validity and usefulness of the improved strategy.
E. Waarts (Eric); M.A. Carree (Martin); B. Wierenga (Berend)
1991-01-01
textabstractThe authors build on the idea put forward by Shugan to infer product maps from scanning data. They demonstrate that the actual estimation procedure used by Shugan has several methodological problems and may yield unstable estimates. They propose an alternative estimation procedure,
Ionospheric Slant Total Electron Content Analysis Using Global Positioning System Based Estimation
Sparks, Lawrence C. (Inventor); Mannucci, Anthony J. (Inventor); Komjathy, Attila (Inventor)
2017-01-01
A method, system, apparatus, and computer program product provide the ability to analyze ionospheric slant total electron content (TEC) using global navigation satellite systems (GNSS)-based estimation. Slant TEC is estimated for a given set of raypath geometries by fitting historical GNSS data to a specified delay model. The accuracy of the specified delay model is estimated by computing delay estimate residuals and plotting a behavior of the delay estimate residuals. An ionospheric threat model is computed based on the specified delay model. Ionospheric grid delays (IGDs) and grid ionospheric vertical errors (GIVEs) are computed based on the ionospheric threat model.
Sidler, Dominik; Schwaninger, Arthur; Riniker, Sereina
2016-10-21
In molecular dynamics (MD) simulations, free-energy differences are often calculated using free energy perturbation or thermodynamic integration (TI) methods. However, both techniques are only suited to calculate free-energy differences between two end states. Enveloping distribution sampling (EDS) presents an attractive alternative that allows to calculate multiple free-energy differences in a single simulation. In EDS, a reference state is simulated which "envelopes" the end states. The challenge of this methodology is the determination of optimal reference-state parameters to ensure equal sampling of all end states. Currently, the automatic determination of the reference-state parameters for multiple end states is an unsolved issue that limits the application of the methodology. To resolve this, we have generalised the replica-exchange EDS (RE-EDS) approach, introduced by Lee et al. [J. Chem. Theory Comput. 10, 2738 (2014)] for constant-pH MD simulations. By exchanging configurations between replicas with different reference-state parameters, the complexity of the parameter-choice problem can be substantially reduced. A new robust scheme to estimate the reference-state parameters from a short initial RE-EDS simulation with default parameters was developed, which allowed the calculation of 36 free-energy differences between nine small-molecule inhibitors of phenylethanolamine N-methyltransferase from a single simulation. The resulting free-energy differences were in excellent agreement with values obtained previously by TI and two-state EDS simulations.
Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro
2017-09-01
To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.
1989-02-01
Squares Collocation . The method is an analogue of the Kolmogorov-Wiener predictor [ Moritz , 1980, p.801, and it is well established within the geodetic...acceleration measurements. The simulation software employed Least-Squares Collocation estimation technique for mean anomaly estimation. The preliminary...Preliminary simulation results using Least-Squares Collocation to estimate mean gravity anomalies are presented in this section. 2.1 Introduction In
time of arrival 3-d position estimation using minimum ads-b receiver ...
African Journals Online (AJOL)
HOD
super resolution technique [7, 8, 9] which are based on frequency domain deconvolution. The second stage involves ... representing a nonlinear equation between the TOA measurements and emitter position. To solve for the ..... Positioning and Navigation,” i Satellite and. Terrestrial Radio Positioning Techniques, pp. 75–.
The hepatitis C epidemic among HIV-positive MSM: incidence estimates from 1990 to 2007
van der Helm, Jannie J.; Prins, Maria; del Amo, Julia; Bucher, Heiner C.; Chêne, Geneviève; Dorrucci, Maria; Gill, John; Hamouda, Osamah; Sannes, Mette; Porter, Kholoud; Geskus, Ronald B.; Meyer, Laurence; Pillay, Deenan; Rosinska, Magda; Sabin, Caroline; Touloumi, Giota; Lodi, Sara; Coughlin, Kate; Walker, Sarah; Babiker, Abdel; de Luca, Andrea; Fisher, Martin; Muga, Roberto; Zangerle, Robert; Kelleher, Tony; Ramacciotti, Tim; Gelgor, Linda; Cooper, David; Smith, Don; Bruun Jørgensen, Louise; Nielsen, Claus; Pedersen, Court; Lutsar, Irja; Dabis, Francois; Thiebaut, Rodolphe; Masquelier, Bernard; Costagliola, Dominique; Guiguet, Marguerite; Vanhems, Philippe; Chaix, Marie-Laure; Ghosn, Jade; Boufassa, Faroudy; Kücherer, Claudia; Bartmeyer, Barbara; Pantazis, Nikos; Hatzakis, Angelos; Paraskevis, Dimitrios; Karafoulidou, Anastasia; Rezza, Giovanni; Balotta, Claudia; D'Arminio Monforte, Antonella; Cozzi-Lepri, Alessandro; Schuitemaker, Hanneke; Brubakk, Oddbjorn; Kran, Anne-Marte B.; Rosinska, Magdalena; Tor, Jordi; de Olalla, Patricia G.; Cayla, Joan; del Romero, Jorge; Pérez-Hoyos, Santiago; Rickenbach, Martin; Francioli, Patrick; Malyuta, Ruslan; Brettle, Ray; Murphy, Gary; Johnson, Anne; Phillips, Andrew; Delpech, Valerie; Jaffe, Harold; Morrison, Charles; Salata, Robert; Mugerwa, Roy; Chipato, Tsungai; Amornkul, Pauli
2011-01-01
Outbreaks of acute hepatitis C virus (HCV) infection among HIV-infected MSM have been described since 2000. However, phylogenetic analysis suggests that the spread of HCV started around 1996. We estimated the incidence of HCV in HIV-infected MSM with well estimated dates of HIV seroconversion from
Khider, Mohammed; Robertson, Patrick; Zampella, Francisco; Jiménez Ruiz, Antonio R.
2012-01-01
[EN] Method for estimating the position and orientation using an inertial measurement unit fixed to a moving pedestrian The method is for estimating the position and orientation using an inertial measurement unit fixed to a moving pedestrian, in particular to a leg, a knee, a foot, or an arm of a pedestrian, for detecting movement of the pedestrian within an observation area in particular not being covered by GNSS-signals as e.g. in buildings. The method comprises the following steps assuming...
Estimating 3D positions and velocities of projectiles from monocular views.
Ribnick, Evan; Atev, Stefan; Papanikolopoulos, Nikolaos P
2009-05-01
In this paper, we consider the problem of localizing a projectile in 3D based on its apparent motion in a stationary monocular view. A thorough theoretical analysis is developed, from which we establish the minimum conditions for the existence of a unique solution. The theoretical results obtained have important implications for applications involving projectile motion. A robust, nonlinear optimization-based formulation is proposed, and the use of a local optimization method is justified by detailed examination of the local convexity structure of the cost function. The potential of this approach is validated by experimental results.
Directory of Open Access Journals (Sweden)
Jorge Mendez Astudillo
2018-02-01
Full Text Available As Global Navigation Satellite System (GNSS signals travel through the troposphere, a tropospheric delay occurs due to a change in the refractive index of the medium. The Precise Point Positioning (PPP technique can achieve centimeter/millimeter positioning accuracy with only one GNSS receiver. The Zenith Tropospheric Delay (ZTD is estimated alongside with the position unknowns in PPP. Estimated ZTD can be very useful for meteorological applications, an example is the estimation of water vapor content in the atmosphere from the estimated ZTD. PPP is implemented with different algorithms and models in online services and software packages. In this study, a performance assessment with analysis of ZTD estimates from three PPP online services and three software packages is presented. The main contribution of this paper is to show the accuracy of ZTD estimation achievable in PPP. The analysis also provides the GNSS users and researchers the insight of the processing algorithm dependence and impact on PPP ZTD estimation. Observation data of eight whole days from a total of nine International GNSS Service (IGS tracking stations spread in the northern hemisphere, the equatorial region and the southern hemisphere is used in this analysis. The PPP ZTD estimates are compared with the ZTD obtained from the IGS tropospheric product of the same days. The estimates of two of the three online PPP services show good agreement (<1 cm with the IGS ZTD values at the northern and southern hemisphere stations. The results also show that the online PPP services perform better than the selected PPP software packages at all stations.
Dube, Timothy; Mutanga, Onisimo
2015-10-01
The successful launch of the 30-m Landsat-8 Operational Land Imager (OLI) pushbroom sensor offers a new primary data source necessary for aboveground biomass (AGB) estimation, especially in resource-limited environments. In this work, the strength and performance of Landsat-8 OLI image derived texture metrics (i.e. texture measures and texture ratios) in estimating plantation forest species AGB was investigated. It was hypothesized that the sensor's pushbroom design, coupled with the presence of refined spectral properties, enhanced radiometric resolution (i.e. from 8 bits to 12 bits) and improved signal-to-noise ratio have the potential to provide detailed spectral information necessary for significantly strengthening AGB estimation in medium-density forest canopies. The relationship between image texture metrics and measurements of forest attributes can be used to help characterize complex forests, and enhance fine vegetation biophysical properties, a difficult challenge when using spectral vegetation indices especially in closed canopies. This study examines the prospects of using Landsat-8 OLI sensor derived texture metrics for estimating AGB for three medium-density plantation forest species in KwaZulu Natal, South Africa. In order to achieve this objective, three unique data pre-processing techniques were tested (analysis I: Landsat-8 OLI raw spectral-bands vs. raw texture bands; analysis II: Landsat-8 OLI raw spectral-band ratios vs. texture band ratios and analysis III: Landsat-8 OLI derived vegetation indices vs. texture band ratios). The landsat-8 OLI derived texture parameters were examined for robustness in estimating AGB using linear regression, stepwise-multiple linear regression and stochastic gradient boosting regression models. The results of this study demonstrated that all texture parameters particularly band texture ratios calculated using a 3 × 3 window size, could enhance AGB estimation when compared to simple spectral reflectance, simple
Time-Dependent Noise in GPS Position Time Series By a Network Noise Estimator
Dmitrieva, K.; Segall, P.
2014-12-01
Some current estimates of GPS velocity uncertainties for continuous stations with more than a decade of data can be very low, noise, such as random walk. Traditional estimators, based on individual time series, are insensitive to low amplitude random walk, yet such noise significantly increases GPS velocity uncertainties. We develop a new approach to estimating noise in GPS time series, focusing on areas where the signal in the data is well characterized. We analyze data from the seismically inactive parts of central US. The data is decomposed into signal, plate rotation and Glacial Isostatic Adjustment (GIA), and various noise components. Our method processes multiple stations simultaneously with a Kalman Filter, and estimates average noise components for the network by maximum likelihood. Currently, we model white noise, flicker noise and random walk. Synthetic tests show that this approach correctly estimates the velocity uncertainty by determining a good estimate of random walk variance, even when it is too small to be correctly estimated by traditional approaches. We present preliminary results from a network of 15 GPS stations in the central USA. The data is in a North America fixed reference frame, we subtract seasonal components and GIA displacements used in the SNARF model. Hence, all data in this reference frame is treated as noise. We estimate random walk of 0.82 mm/yr0.5, flicker noise of 3.96 mm/yr0.25 and white noise of 1.05 mm. From these noise parameters the estimated velocity uncertainty is 0.29 mm/yr for 10 years of daily data. This uncertainty is significantly greater than estimated by the traditional methods, at 0.12 mm/yr. The estimated uncertainty is still less than the median residual velocity in the North America fixed reference frame, which could indicate that the true uncertainties are even larger. Additionally we estimated noise parameters and velocity uncertainties for the vertical component and for the data with common-mode signal
The Optimal Sampling Period of a Fingerprint Positioning Algorithm for Vehicle Speed Estimation
National Research Council Canada - National Science Library
Cheng, Ding-Yuan; Chen, Chi-Hua; Hsiang, Chia-Hung; Lo, Chi-Chun; Lin, Hui-Fei; Lin, Bon-Yeh
.... In this paper, two analytical models are proposed to analyze the optimal sampling period based on communication behaviors, traffic conditions, and two consecutive fingerprint positioning locations...
National Research Council Canada - National Science Library
CIPOV Vladimír; DOBOŠ Lubomír; PAPAJ Ján
2011-01-01
.... For outdoor-urban and especially for indoor localization, it is necessary to reach a precise distance estimation between the reference node and the node to be located which is one of the essential...
DEFF Research Database (Denmark)
Jakobsen, Jakob; Jensen, Anna B. O.; Nielsen, Allan Aasbjerg
2015-01-01
non-line-of-sight satellites. The signal reflections are implemented using the extended geometric path length of the signal path caused by reflections from the surrounding buildings. Based on real GPS satellite positions, simulated Galileo satellite positions, models of atmospheric effect...
DEFF Research Database (Denmark)
Mocroft, Amanda; Kirk, Ole; Reiss, Peter
2010-01-01
OBJECTIVES:: Chronic kidney disease (CKD) in HIV-positive persons might be caused by both HIV and traditional or non-HIV-related factors. Our objective was to investigate long-term exposure to specific antiretroviral drugs and CKD. DESIGN:: A cohort study including 6843 HIV-positive persons...
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....
Chen, Liang; Zhao, Qile; Hu, Zhigang; Jiang, Xinyuan; Geng, Changjiang; Ge, Maorong; Shi, Chuang
2018-01-01
Lots of ambiguities in un-differenced (UD) model lead to lower calculation efficiency, which isn't appropriate for the high-frequency real-time GNSS clock estimation, like 1 Hz. Mixed differenced model fusing UD pseudo-range and epoch-differenced (ED) phase observations has been introduced into real-time clock estimation. In this contribution, we extend the mixed differenced model for realizing multi-GNSS real-time clock high-frequency updating and a rigorous comparison and analysis on same conditions are performed to achieve the best real-time clock estimation performance taking the efficiency, accuracy, consistency and reliability into consideration. Based on the multi-GNSS real-time data streams provided by multi-GNSS Experiment (MGEX) and Wuhan University, GPS + BeiDou + Galileo global real-time augmentation positioning prototype system is designed and constructed, including real-time precise orbit determination, real-time precise clock estimation, real-time Precise Point Positioning (RT-PPP) and real-time Standard Point Positioning (RT-SPP). The statistical analysis of the 6 h-predicted real-time orbits shows that the root mean square (RMS) in radial direction is about 1-5 cm for GPS, Beidou MEO and Galileo satellites and about 10 cm for Beidou GEO and IGSO satellites. Using the mixed differenced estimation model, the prototype system can realize high-efficient real-time satellite absolute clock estimation with no constant clock-bias and can be used for high-frequency augmentation message updating (such as 1 Hz). The real-time augmentation message signal-in-space ranging error (SISRE), a comprehensive accuracy of orbit and clock and effecting the users' actual positioning performance, is introduced to evaluate and analyze the performance of GPS + BeiDou + Galileo global real-time augmentation positioning system. The statistical analysis of real-time augmentation message SISRE is about 4-7 cm for GPS, whlile 10 cm for Beidou IGSO/MEO, Galileo and about 30 cm
Directory of Open Access Journals (Sweden)
Klum Michael
2016-09-01
Full Text Available Unobtrusive medical instrumentation is a key in continuous patient monitoring. To increase compliance, multi-functional sensor concepts and measurement sites different from gold-standards are used. In this work, we aim to combine both approaches. We focus on minimally spaced electrode positions with high signal correlations to gold-standards. We present twofold experimental data from six and eleven healthy volunteers and provide chest positions with individual correlations up to 0.83 ± 0.06 for ECG and 0.73 ± 0.28 for the respiratory frequency. Using a performance index, we assess positions with correlations up to 0.77 ± 0.12 for ECG and 0.65 ± 0.35 for the respiratory frequency with 24 mm electrode distance.
Nakagawa, M.; Akano, K.; Kobayashi, T.; Sekiguchi, Y.
2017-09-01
Image-based virtual reality (VR) is a virtual space generated with panoramic images projected onto a primitive model. In imagebased VR, realistic VR scenes can be generated with lower rendering cost, and network data can be described as relationships among VR scenes. The camera network data are generated manually or by an automated procedure using camera position and rotation data. When panoramic images are acquired in indoor environments, network data should be generated without Global Navigation Satellite Systems (GNSS) positioning data. Thus, we focused on image-based VR generation using a panoramic camera in indoor environments. We propose a methodology to automate network data generation using panoramic images for an image-based VR space. We verified and evaluated our methodology through five experiments in indoor environments, including a corridor, elevator hall, room, and stairs. We confirmed that our methodology can automatically reconstruct network data using panoramic images for image-based VR in indoor environments without GNSS position data.
Blow-up Estimates of the Positive Solution of a Parabolic System
DEFF Research Database (Denmark)
Pedersen, Michael; Zhigui, Lin
1999-01-01
This paper establishes the blowup estimates for the systems: $u_t-\\Delta u=0,$ $v_t-\\Delta v=0$ in $B_R\\times (0,T)$, $B_R\\subset\\Bbb R^n$, with the nonlinear boundary conditions $\\frac{\\partial u}{\\partial \\eta}=u^{m_1}v^{n_1}$ and $\\frac{\\partial v}{\\partial \\eta}=u^{m_2}v^{n_2}$ on $S_R\\times ...
Blow-up Estimates of the Positive Solution of a Parabolic System
DEFF Research Database (Denmark)
Pedersen, Michael; Zhigui, Lin
2001-01-01
This paper establishes the blow-up estimates for the systems u(t) - Deltau = 0, v(t) - Deltav = 0 in B-R x (0, T), B-R subset of R-n, with the nonlinear boundary conditions partial derivativeu/partial derivativen = u(m1)v(n1) and partial derivativev/partial derivativen = u(m2)v(n2) on S-R x (0, T...
On Optimal Placement of Short Range Base Stations for Indoor Position Estimation
Directory of Open Access Journals (Sweden)
A. Bais
2014-10-01
Full Text Available The use of short-range wireless for object positioning has seen a growing interest in recent years. This interest is compounded by the inherent GPS limitations especially in indoor situations and in urban canyons. In order to achieve the highest performance of short-range positioning systems it is important to optimize the placement of Base-Stations (BSs in a given area. The problems of BSs placement to minimize error and to achieve multiple coverage of the area have been addressed separately in the literature. In this paper, we discuss that using short range BSs the two problems are interrelated and need to be solved jointly. We study the impact of different influential attributes of the positioning problem as we alter the layout of BSs in the area. We investigate different scenarios for short-range BSs placement that maximize coverage and minimize positioning error. Simulation results demonstrate that better performance could be achieved using layouts that tend to distribute the BSs uniformly.
Rotor Position Estimation for Switched Reluctance Wind Generator Using Extreme Learning Machine
DEFF Research Database (Denmark)
Wang, Chao; Liu, Xiao; Chen, Zhe
2014-01-01
Reluctance Wind Generator (SRWG) based on Extreme Learning Machine (ELM) which could build a nonlinear mapping between flux linkage-current and rotor position. The learning data are derived from magnetization curves of the SRWG which are obtained from Finite Element Analysis (FEA) of an SRG with 8/6 stator...
Wang, Yonggang; Li, Deng; Lu, Xiaoming; Cheng, Xinyi; Wang, Liwei
2014-10-01
Continuous crystal-based positron emission tomography (PET) detectors could be an ideal alternative for current high-resolution pixelated PET detectors if the issues of high performance γ interaction position estimation and its real-time implementation are solved. Unfortunately, existing position estimators are not very feasible for implementation on field-programmable gate array (FPGA). In this paper, we propose a new self-organizing map neural network-based nearest neighbor (SOM-NN) positioning scheme aiming not only at providing high performance, but also at being realistic for FPGA implementation. Benefitting from the SOM feature mapping mechanism, the large set of input reference events at each calibration position is approximated by a small set of prototypes, and the computation of the nearest neighbor searching for unknown events is largely reduced. Using our experimental data, the scheme was evaluated, optimized and compared with the smoothed k-NN method. The spatial resolutions of full-width-at-half-maximum (FWHM) of both methods averaged over the center axis of the detector were obtained as 1.87 ±0.17 mm and 1.92 ±0.09 mm, respectively. The test results show that the SOM-NN scheme has an equivalent positioning performance with the smoothed k-NN method, but the amount of computation is only about one-tenth of the smoothed k-NN method. In addition, the algorithm structure of the SOM-NN scheme is more feasible for implementation on FPGA. It has the potential to realize real-time position estimation on an FPGA with a high-event processing throughput.
Estimating Common Pedestrian Routes through Indoor Path Networks using Position Traces
DEFF Research Database (Denmark)
Prentow, Thor Siiger; Blunck, Henrik; Grønbæk, Kaj
2014-01-01
-density and positioningaccuracy, ii) a flat path hierarchy, and iii) providing cost-effective scalability. Through an evaluation based on data collected by staff members at a hospital covering more than 10 hectare over three floors, we show that the proposed methods detect routes that are representative of the commonly used......-scale deployable indoor Wi-Fi positioning systems, and with no prior information on building layout....
Cooperative Indoor Positioning by Exchange of Bluetooth Signals and State Estimates Between Users
Karlsson, Martin; Karlsson, Fredrik
2017-01-01
This paper presents a Bayesian indoor positioningsystem for smartphones based on the strengths of WiFi andBluetooth signals. A framework for improving the performanceof existing positioning methods with the help informationsharing between users is proposed and evaluated. Bluetoothsignals are sent between users, and the signal strengths containinformation about their relative distances, which is used toevaluate the probability distribution functions of their states.A particle filter is used fo...
Parwani, Ajit K.; Talukdar, Prabal; Subbarao, P. M. V.
2013-09-01
An inverse heat transfer problem is discussed to estimate simultaneously the unknown position and timewise varying strength of a heat source by utilizing differential evolution approach. A two dimensional enclosure with isothermal and black boundaries containing non-scattering, absorbing and emitting gray medium is considered. Both radiation and conduction heat transfer are included. No prior information is used for the functional form of timewise varying strength of heat source. The finite volume method is used to solve the radiative transfer equation and the energy equation. In this work, instead of measured data, some temperature data required in the solution of the inverse problem are taken from the solution of the direct problem. The effect of measurement errors on the accuracy of estimation is examined by introducing errors in the temperature data of the direct problem. The prediction of source strength and its position by the differential evolution (DE) algorithm is found to be quite reasonable.
In vivo estimates of the position of advanced bionics electrode arrays in the human cochlea.
Skinner, Margaret W; Holden, Timothy A; Whiting, Bruce R; Voie, Arne H; Brunsden, Barry; Neely, J Gail; Saxon, Eugene A; Hullar, Timothy E; Finley, Charles C
2007-04-01
A new technique for determining the position of each electrode in the cochlea is described and applied to spiral computed tomography data from 15 patients implanted with Advanced Bionics HiFocus I, Ij, or Helix arrays. ANALYZE imaging software was used to register 3-dimensional image volumes from patients' preoperative and postoperative scans and from a single body donor whose unimplanted ears were scanned clinically, with micro computed tomography and with orthogonal-plane fluorescence optical sectioning (OPFOS) microscopy. By use of this registration, we compared the atlas of OPFOS images of soft tissue within the body donor's cochlea with the bone and fluid/ tissue boundary available in patient scan data to choose the midmodiolar axis position and judge the electrode position in the scala tympani or scala vestibuli, including the distance to the medial and lateral scalar walls. The angular rotation 0 degrees start point is a line joining the midmodiolar axis and the middle of the cochlear canal entry from the vestibule. The group mean array insertion depth was 477 degrees (range, 286 degrees to 655 degrees). The word scores were negatively correlated (r = -0.59; p = .028) with the number of electrodes in the scala vestibuli. Although the individual variability in all measures was large, repeated patterns of suboptimal electrode placement were observed across subjects, underscoring the applicability of this technique.
Directory of Open Access Journals (Sweden)
M. Nakagawa
2017-09-01
Full Text Available Image-based virtual reality (VR is a virtual space generated with panoramic images projected onto a primitive model. In imagebased VR, realistic VR scenes can be generated with lower rendering cost, and network data can be described as relationships among VR scenes. The camera network data are generated manually or by an automated procedure using camera position and rotation data. When panoramic images are acquired in indoor environments, network data should be generated without Global Navigation Satellite Systems (GNSS positioning data. Thus, we focused on image-based VR generation using a panoramic camera in indoor environments. We propose a methodology to automate network data generation using panoramic images for an image-based VR space. We verified and evaluated our methodology through five experiments in indoor environments, including a corridor, elevator hall, room, and stairs. We confirmed that our methodology can automatically reconstruct network data using panoramic images for image-based VR in indoor environments without GNSS position data.
Directory of Open Access Journals (Sweden)
I.V. Savelyeva
2012-12-01
Full Text Available In developing the strategy for development of port container terminal in the competitive environment, one of the main problems is the method of estimating the competitive position of each port (separate terminals and ranking them on a number of key indicators that characterize the dynamics of their development. To solve this problem it is necessary to develop scientifically sound and practicable methodology to assess the effectiveness of the provisions of industrial and financial activity of a single terminal.
Czech Academy of Sciences Publication Activity Database
Pavelková, Lenka
2011-01-01
Roč. 47, č. 3 (2011), s. 370-384 ISSN 0023-5954 R&D Projects: GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : non-linear state space model * bounded uncertainty * missing measurements * state filtering * vehicle position estimation Subject RIV: BC - Control Systems Theory Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/AS/pavelkova-0360239.pdf
Estimation of physical development of young sportsmen from traditional and modern positions
Directory of Open Access Journals (Sweden)
Khor'yakov V.A.
2012-12-01
Full Text Available The problem of evaluation of anthropometric status of young sportsmen is examined with the use of method of indexes and modern pictures of somatic health of man. In research young boxers took part 10-11 (n=41, 12-13 (n=48 and 14-16 years (n=39. Contradiction and ambiguousness of estimations of physical development of children and teenagers is rotined by means of traditional indexes of Erismana, Quetelet, Pin'e, sthenic and development of thorax. It is marked that an estimation of physical development of children and teenagers with the use of standard deviation of selection is not productive, because in most cases distributing of the studied signs falls short of a normal law. A concept «norm» is recommended to replace a concept «norm» as an obligatory requirement of the state to the level of somatic health of children and teenagers of different regions of country. It is marked that it is expedient to examine physical development of individuals as a structural element of bodily condition the major components of which are indexes of power and capacity of mechanisms of energy supply.
Directory of Open Access Journals (Sweden)
Abdulmalik Shehu Yaro
2017-01-01
Full Text Available Multilateration estimates aircraft position using the Time Difference Of Arrival (TDOA with a lateration algorithm. The Position Estimation (PE accuracy of the lateration algorithm depends on several factors which are the TDOA estimation error, the lateration algorithm approach, the number of deployed GRSs and the selection of the GRS reference used for the PE process. Using the minimum number of GRSs for 3D emitter PE, a technique based on the condition number calculation is proposed to select the suitable GRS reference pair for improving the accuracy of the PE using the lateration algorithm. Validation of the proposed technique was performed with the GRSs in the square and triangular GRS configuration. For the selected emitter positions, the result shows that the proposed technique can be used to select the suitable GRS reference pair for the PE process. A unity condition number is achieved for GRS pair most suitable for the PE process. Monte Carlo simulation result, in comparison with the fixed GRS reference pair lateration algorithm, shows a reduction in PE error of at least 70% for both GRS in the square and triangular configuration.
Schoonhoven, M.; Does, R.J.M.M.
2013-01-01
This article studies alternative standard deviation estimators that serve as a basis to determine the control chart limits used for real-time process monitoring (phase II). Several existing (robust) estimation methods are considered. In addition, we propose a new estimation method based on a phase I
Soloviev, V.; Krivtsov, V.
2017-11-01
According to performed numerical simulation of the surface dielectric barrier discharge driven by positive polarity nanosecond voltage pulse the discharge in this case evolves as a streamer “flying” above the dielectric surface. The distance between the streamer and dielectric surface does not depend on dielectric barrier parameters and applied voltage value. The developed analytical model for surface streamer evolution confirms these results and explains the physics of this phenomenon. The electric field in front of a stationary streamer head is constant and defined only by ionization rate constant of the gas and its density.
Greenland halibut SCAA robustness tests
National Research Council Canada - National Science Library
Butterworth, D.S; Rademeyer, R.A
2010-01-01
.... It first summarises the Reference Case operating model and the six variants thereof to serve as robustness tests, and then provides values of key associated parameter values and lots of estimated...
Lerman, Gilad M; Levy, Uriel
2013-03-13
Great hopes rest on surface plasmon polaritons' (SPPs) potential to bring new functionalities and applications into various branches of optics. In this paper, we demonstrate a pin cushion structure capable of coupling light from free space into SPPs, split them based on the polarization content of the illuminating beam of light, and focus them into small spots. We also show that for a circularly or randomly polarized light, four focal spots will be generated at the center of each quarter circle comprising the pin cushion device. Furthermore, following the relation between the relative intensity of the obtained four focal spots and the relative position of the illuminating beam with respect to the structure, we propose and demonstrate the potential use of our structure as a miniaturized plasmonic version of the well-known four quadrant detector. Additional potential applications may vary from multichannel microscopy and multioptical traps to real time beam tracking systems.
Ultra-wideband radios for time-of-flight-ranging and network position estimation
Hertzog, Claudia A [Houston, TX; Dowla, Farid U [Castro Valley, CA; Dallum, Gregory E [Livermore, CA; Romero, Carlos E [Livermore, CA
2011-06-14
This invention provides a novel high-accuracy indoor ranging device that uses ultra-wideband (UWB) RF pulsing with low-power and low-cost electronics. A unique of the present invention is that it exploits multiple measurements in time and space for very accurate ranging. The wideband radio signals utilized herein are particularly suited to ranging in harsh RF environments because they allow signal reconstruction in spite of multipath propagation distortion. Furthermore, the ranging and positioning techniques discussed herein directly address many of the known technical challenges encountered in UWB localization regarding synchronization and sampling. In the method developed, noisy, corrupted signals can be recovered by repeating range measurements across a channel, and the distance measurements are combined from many locations surrounding the target in a way that minimizes the range biases associated to indirect flight paths and through-wall propagation delays.
Kassem Jebai, Al; Malrait, François; Martin, Philippe; Rouchon, Pierre
2016-03-01
Sensorless control of permanent-magnet synchronous motors at low velocity remains a challenging task. A now well-established method consists of injecting a high-frequency signal and using the rotor saliency, both geometric and magnetic-saturation induced. This paper proposes a clear and original analysis based on second-order averaging of how to recover the position information from signal injection; this analysis blends well with a general model of magnetic saturation. It also proposes a simple parametric model of the saturated motor, based on an energy function which simply encompasses saturation and cross-saturation effects. Experimental results on a surface-mounted motor and an interior magnet motor illustrate the relevance of the approach.
DEFF Research Database (Denmark)
Aanæs, Henrik; Fisker, Rune; Åström, Kalle
2002-01-01
Factorization algorithms for recovering structure and motion from an image stream have many advantages, but they usually require a set of well-tracked features. Such a set is in generally not available in practical applications. There is thus a need for making factorization algorithms deal...... effectively with errors in the tracked features. We propose a new and computationally efficient algorithm for applying an arbitrary error function in the factorization scheme. This algorithm enables the use of robust statistical techniques and arbitrary noise models for the individual features....... These techniques and models enable the factorization scheme to deal effectively with mismatched features, missing features, and noise on the individual features. The proposed approach further includes a new method for Euclidean reconstruction that significantly improves convergence of the factorization algorithms...
Miller, David A W; Nichols, James D; Gude, Justin A; Rich, Lindsey N; Podruzny, Kevin M; Hines, James E; Mitchell, Michael S
2013-01-01
Large-scale presence-absence monitoring programs have great promise for many conservation applications. Their value can be limited by potential incorrect inferences owing to observational errors, especially when data are collected by the public. To combat this, previous analytical methods have focused on addressing non-detection from public survey data. Misclassification errors have received less attention but are also likely to be a common component of public surveys, as well as many other data types. We derive estimators for dynamic occupancy parameters (extinction and colonization), focusing on the case where certainty can be assumed for a subset of detections. We demonstrate how to simultaneously account for non-detection (false negatives) and misclassification (false positives) when estimating occurrence parameters for gray wolves in northern Montana from 2007-2010. Our primary data source for the analysis was observations by deer and elk hunters, reported as part of the state's annual hunter survey. This data was supplemented with data from known locations of radio-collared wolves. We found that occupancy was relatively stable during the years of the study and wolves were largely restricted to the highest quality habitats in the study area. Transitions in the occupancy status of sites were rare, as occupied sites almost always remained occupied and unoccupied sites remained unoccupied. Failing to account for false positives led to over estimation of both the area inhabited by wolves and the frequency of turnover. The ability to properly account for both false negatives and false positives is an important step to improve inferences for conservation from large-scale public surveys. The approach we propose will improve our understanding of the status of wolf populations and is relevant to many other data types where false positives are a component of observations.
Directory of Open Access Journals (Sweden)
David A W Miller
Full Text Available Large-scale presence-absence monitoring programs have great promise for many conservation applications. Their value can be limited by potential incorrect inferences owing to observational errors, especially when data are collected by the public. To combat this, previous analytical methods have focused on addressing non-detection from public survey data. Misclassification errors have received less attention but are also likely to be a common component of public surveys, as well as many other data types. We derive estimators for dynamic occupancy parameters (extinction and colonization, focusing on the case where certainty can be assumed for a subset of detections. We demonstrate how to simultaneously account for non-detection (false negatives and misclassification (false positives when estimating occurrence parameters for gray wolves in northern Montana from 2007-2010. Our primary data source for the analysis was observations by deer and elk hunters, reported as part of the state's annual hunter survey. This data was supplemented with data from known locations of radio-collared wolves. We found that occupancy was relatively stable during the years of the study and wolves were largely restricted to the highest quality habitats in the study area. Transitions in the occupancy status of sites were rare, as occupied sites almost always remained occupied and unoccupied sites remained unoccupied. Failing to account for false positives led to over estimation of both the area inhabited by wolves and the frequency of turnover. The ability to properly account for both false negatives and false positives is an important step to improve inferences for conservation from large-scale public surveys. The approach we propose will improve our understanding of the status of wolf populations and is relevant to many other data types where false positives are a component of observations.
Directory of Open Access Journals (Sweden)
Andreas Tuerk
2017-05-01
Full Text Available Accuracy of transcript quantification with RNA-Seq is negatively affected by positional fragment bias. This article introduces Mix2 (rd. "mixquare", a transcript quantification method which uses a mixture of probability distributions to model and thereby neutralize the effects of positional fragment bias. The parameters of Mix2 are trained by Expectation Maximization resulting in simultaneous transcript abundance and bias estimates. We compare Mix2 to Cufflinks, RSEM, eXpress and PennSeq; state-of-the-art quantification methods implementing some form of bias correction. On four synthetic biases we show that the accuracy of Mix2 overall exceeds the accuracy of the other methods and that its bias estimates converge to the correct solution. We further evaluate Mix2 on real RNA-Seq data from the Microarray and Sequencing Quality Control (MAQC, SEQC Consortia. On MAQC data, Mix2 achieves improved correlation to qPCR measurements with a relative increase in R2 between 4% and 50%. Mix2 also yields repeatable concentration estimates across technical replicates with a relative increase in R2 between 8% and 47% and reduced standard deviation across the full concentration range. We further observe more accurate detection of differential expression with a relative increase in true positives between 74% and 378% for 5% false positives. In addition, Mix2 reveals 5 dominant biases in MAQC data deviating from the common assumption of a uniform fragment distribution. On SEQC data, Mix2 yields higher consistency between measured and predicted concentration ratios. A relative error of 20% or less is obtained for 51% of transcripts by Mix2, 40% of transcripts by Cufflinks and RSEM and 30% by eXpress. Titration order consistency is correct for 47% of transcripts for Mix2, 41% for Cufflinks and RSEM and 34% for eXpress. We, further, observe improved repeatability across laboratory sites with a relative increase in R2 between 8% and 44% and reduced standard deviation.
Estimating risk of C. difficile transmission from PCR positive but cytotoxin negative cases.
Directory of Open Access Journals (Sweden)
Mini Kamboj
Full Text Available The use of molecular methods to diagnose Clostridium difficile infection (CDI has improved diagnostic yield compared to conventional methods. However, PCR testing can detect colonization and has introduced several practical challenges pertaining to need for treatment and isolation of cases.For all new cases detected by real-time PCR, concurrent cytotoxin assay was performed and genetic characterization with MLVA (multi-locus variable number tandem repeat analysis was done to determine relatedness. We used PCR cycle threshold (Ct of detection as surrogate marker for bacterial burden in stool.Overall, 54 cases of CDI were detected during the study period. 42 were concurrently tested by CYT and characterized by MLVA .MLVA analysis revealed marked genetic diversity with no ongoing outbreaks; four cases were due to NAP1 strain. CYT -/PCR + cases had a higher median Ct value of detection compared to CYT+/PCR + cases (28.2 vs 22.5; p = 0.01. Among 25 strains that were genetically related, 9/11 isolates in this dominant cluster were positive by CYT compared to 4/14 in non-dominant clusters (p = 0.02.CYT-/PCR+ cases contribute to hospital based transmission. However, the risk of transmission of C. difficile from CYT +/PCR+ cases may be higher than those that are CYT-/PCR+.
Ng, J. A.; Booth, J.; Poulsen, P.; Kuncic, Z.; Keall, P. J.
2013-09-01
Kilovoltage intratreatment monitoring (KIM) is a novel real-time localization modality where the tumor position is continuously measured during intensity modulated radiation therapy (IMRT) or intensity modulated arc therapy (IMAT) by a kilovoltage (kV) x-ray imager. Adding kV imaging during therapy adds radiation dose. The additional effective dose is quantified for prostate radiotherapy and compared to dose from other localization modalities. The software PCXMC 2.0 was used to calculate the effective dose delivered to a phantom as a function of imager angle and field size for a Varian On-Board Imager. The average angular effective dose was calculated for a field size of 6 cm × 6 cm. The average angular effective dose was used in calculations for different treatment scenarios. Treatment scenarios considered were treatment type and fractionation. For all treatment scenarios, (i.e. conventionally fractionated and stereotactic body radiotherapy (SBRT), IMRT and IMAT), the total KIM dose at 1 Hz ranged from 2-10 mSv. This imaging dose is less than the Navotek radioactive implant dose (64 mSv) and a standard SBRT cone beam computed tomography pretreatment scan dose (22 mSv) over an entire treatment regime. KIM delivers an acceptably low effective dose for daily use as a real-time image-guidance method for prostate radiotherapy.
Moving-Target Position Estimation Using GPU-Based Particle Filter for IoT Sensing Applications
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Seongseop Kim
2017-11-01
Full Text Available A particle filter (PF has been introduced for effective position estimation of moving targets for non-Gaussian and nonlinear systems. The time difference of arrival (TDOA method using acoustic sensor array has normally been used to for estimation by concealing the location of a moving target, especially underwater. In this paper, we propose a GPU -based acceleration of target position estimation using a PF and propose an efficient system and software architecture. The proposed graphic processing unit (GPU-based algorithm has more advantages in applying PF signal processing to a target system, which consists of large-scale Internet of Things (IoT-driven sensors because of the parallelization which is scalable. For the TDOA measurement from the acoustic sensor array, we use the generalized cross correlation phase transform (GCC-PHAT method to obtain the correlation coefficient of the signal using Fast Fourier Transform (FFT, and we try to accelerate the calculations of GCC-PHAT based TDOA measurements using FFT with GPU compute unified device architecture (CUDA. The proposed approach utilizes a parallelization method in the target position estimation algorithm using GPU-based PF processing. In addition, it could efficiently estimate sudden movement change of the target using GPU-based parallel computing which also can be used for multiple target tracking. It also provides scalability in extending the detection algorithm according to the increase of the number of sensors. Therefore, the proposed architecture can be applied in IoT sensing applications with a large number of sensors. The target estimation algorithm was verified using MATLAB and implemented using GPU CUDA. We implemented the proposed signal processing acceleration system using target GPU to analyze in terms of execution time. The execution time of the algorithm is reduced by 55% from to the CPU standalone operation in target embedded board, NVIDIA Jetson TX1. Also, to apply large
Broeckx, B J G; Verhoeven, G; Coopman, F; Van Haeringen, W; Bosmans, T; Gielen, I; Henckens, S; Saunders, J H; van Bree, H; Van Ryssen, B; Verbeke, V; Van Steendam, K; Van Nieuwerburgh, F; Deforce, D
2014-09-01
Although the prevalence of canine hip dysplasia (HD) has been the subject of a number of published studies, estimates vary widely. This study evaluated several possible causes for these differences. Sixty Belgian, Dutch and German veterinarians were asked to submit all hip radiographs obtained for screening purposes (irrespective of HD status) over a 2-year period, resulting in a database of 583 dogs. Each set of radiographs was accompanied by information on the reason for screening (breeding soundness examination, clinical complaint, assistance dogs, or other reasons), and dog breed, date of birth and age. Dog positioning exerted an effect at multiple levels. The agreement among different observers regarding correct or incorrect positioning was limited and incorrect positioning itself reduced the inter-observer agreement for radiographic hip conformation. Dysplastic dogs were more commonly positioned incorrectly than non-dysplastic dogs. The clinical complaint population had a high prevalence of dysplastic dogs (>70%) compared with the breeding population (11%) and the assistance dogs (6%). There was a significantly lower prevalence of HD among cases referred by veterinarians who frequently submitted hip-extended radiographs for evaluation (P = 0.002) compared to those who refer less frequently. However, this was likely to be selection bias, as radiographs that were from dogs suspected to be dysplastic were not submitted by frequent senders. The prevalence of dysplastic dogs varied widely between breeds (16.7-71.4%). Dogs diagnosed with dysplasia were significantly older than dogs considered healthy (P = 0.001) and dogs classified as borderline dysplastic (P = 0.035). Inter-observer agreement for hip conformation was moderately low, resulting in >7% variation in prevalence estimates for dysplasia. Copyright © 2014 Elsevier Ltd. All rights reserved.
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
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R. Vijayapriya
2017-10-01
Full Text Available A stator flux oriented synchronous reference frame - phase locked loop (SRF-PLL is proposed for the precise computation of rotor speed and position of permanent magnet synchronous machine (PMSM. A direct method of rotational speed computation based on the stator electromotive force (EMF is initially formulated. Using the speed as a reference to the inverse Park and Clarke transformation blocks, the three-phase positive sequence stator flux is derived. A pre-stage low pass filter (LPF is implemented to cancel out the ripples in the d-q components of the stator flux introduced by the dynamic operating conditions of inverter non-linearities and grid disturbances. The estimated three-phase positive sequence stator flux is used to compute the rotor position by aligning the total stator flux along the direct axis through a PLL block. Provision of the frequency amendment and ripple cancellation outside the PLL block results in a fast-dynamic response with an enhanced frequency adaptable capability. To validate the effectiveness of the proposed method, the sensorless vector control of grid integrated PMSM based wind-driven generator (WG is analytically verified using the PSCAD/EMTDC simulation tool under various dynamic operating conditions such as wind speed variation and grid disturbances.
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Sabine Nagel
Full Text Available Human skin harbours multiple different stem cell populations. In contrast to the relatively well-characterized niches of epidermal and hair follicle stem cells, the localization and niches of stem cells in other human skin compartments are as yet insufficiently investigated. Previously, we had shown in a pilot study that human sweat gland stroma contains Nestin-positive stem cells. Isolated sweat gland stroma-derived stem cells (SGSCs proliferated in vitro and expressed Nestin in 80% of the cells. In this study, we were able to determine the precise localization of Nestin-positive cells in both eccrine and apocrine sweat glands of human axillary skin. We established a reproducible isolation procedure and characterized the spontaneous, long-lasting multipotent differentiation capacity of SGSCs. Thereby, a pronounced ectodermal differentiation was observed. Moreover, the secretion of prominent cytokines demonstrated the immunological potential of SGSCs. The comparison to human adult epidermal stem cells (EpiSCs and bone marrow stem cells (BMSCs revealed differences in protein expression and differentiation capacity. Furthermore, we found a coexpression of the stem cell markers Nestin and Iα6 within SGSCs and human sweat gland stroma. In conclusion the initial results of the pilot study were confirmed, indicating that human sweat glands are a new source of unique stem cells with multilineage differentiation potential, high proliferation capacity and remarkable self renewal. With regard to the easy accessibility of skin tissue biopsies, an autologous application of SGSCs in clinical therapies appears promising.
Yoshidome, Satoshi; Arimura, Hidetaka; Nakamura, Katsumasa; Shioyama, Yoshiyuki; Atsumi, Kazushige; Nakamura, Yasuhiko; Yoshikawa, Hideki; Nishikawa, Kei; Hirata, Hideki
2015-01-01
To investigate the feasibility of an automated framework for estimating the lung tumor locations for tumor-based patient positioning with megavolt-cone-beam computed tomography (MV-CBCT) during stereotactic body radiotherapy (SBRT). A lung screening phantom and ten lung cancer cases with solid lung tumors, who were treated with SBRT, were employed to this study. The locations of tumors in MV-CBCT images were estimated using a tumor-template matching technique between a tumor template and the MV-CBCT. Tumor templates were produced by cropping the gross tumor volume (GTV) regions, which were enhanced by a Sobel filter or a blob structure enhancement (BSE) filter. Reference tumor locations (grand truth) were determined based on a consensus between a radiation oncologist and a medical physicist. According to the results of the phantom study, the average Euclidean distances of the location errors in the original, Sobel-filtered, and BSE-filtered images were 2.0 ± 4.1 mm, 12.8 ± 9.4 mm, and 0.4 ± 0.5 mm, respectively. For clinical cases, these were 3.4 ± 7.1 mm, 7.2 ± 11.6 mm, and 1.6 ± 1.2 mm, respectively. The feasibility study suggests that our proposed framework based on the BSE filter may be a useful tool for tumor-based patient positioning in SBRT.
Muglia, M.; Seim, H.; Haines, S.; Taylor, P.
2016-12-01
Hourly time series of the landward edge of the Gulf Stream (GS), width of the cyclonic shear zone, and the orientation of the GS have been formed by first identifying the maxima in a single radar's radial surface current shears and current speeds. Maxima are chosen from within areas of consistent radar measurements over the time period sampled. Four bearings are selected for analysis, two where the GS enters and two where it exits the radar coverage. The width of the cyclonic shear zone is measured as the distance between the maximum in the gradient of the radial current speed, and the maximum in the speed along a single bearing. The orientation of the current is estimated by comparing the location of these maxima between the four selected bearings. This method is applied to two separate 5MHz Seasonde radars that consistently make GS measurements along the NC coast. The method benefits from recent application of radial metric quality controls on radial surface currents in the NC radar network that improves radial and total surface currents. The efficacy of the method is evaluated by comparing these estimates to those made using total surface currents from the radar network, satellite sea surface temperatures, and satellite altimetry. The radar hourly surface current measurements are more frequent than satellite observations and are not inhibited by cloud cover. Consistent long-term GS position estimates are expected to provide valuable new insights about the oceanography offshore of Cape Hatteras, NC.
Fan, Lei; Li, Min; Wang, Cheng; Shi, Chuang
2017-02-01
The differential code bias (DCB) of BeiDou satellite is an important topic to make better use of BeiDou system (BDS) for many practical applications. This paper proposes a new method to estimate the BDS satellite DCBs based on triple-frequency uncombined precise point positioning (UPPP). A general model of both triple-frequency UPPP and Geometry-Free linear combination of Phase-Smoothed Range (GFPSR) is presented, in which, the ionospheric observable and the combination of triple-frequency satellite and receiver DCBs (TF-SRDCBs) are derived. Then the satellite and receiver DCBs (SRDCBs) are estimated together with the ionospheric delay that is modeled at each individual station in a weighted least-squares estimator, and the satellite DCBs are determined by introducing the zero-mean condition of all available BDS satellites. To validate the new method, 90 day's real tracking GNSS data (from January to March in 2014) collected from 9 Multi-GNSS Experiment (MGEX) stations (equipped with Trimble NETR9 receiver) is used, and the BDS satellite DCB products from German Aerospace Center (DLR) are taken as reference values for comparison. Results show that the proposed method is able to precisely estimate BDS satellite DCBs: (1) the mean value of the day-to-day scattering for all available BDS satellites is about 0.24 ns, which is reduced in average by 23% when compared with the results derived by only GFPSR. Moreover, the mean value of the day-to-day scattering of IGSO satellites is lower than that of GEO and MEO satellites; (2) the mean value of RMS of the difference with respect to DLR DCB products is about 0.39 ns, which is improved by an average of 11% when compared with the results derived by only GFPSR. Besides, the RMS of IGSO and MEO satellites is at the same level which is better than that of GEO satellites.
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Kamya Moses R
2009-09-01
Full Text Available Abstract Background As malaria control efforts intensify, it is critical to monitor trends in disease burden and measure the impact of interventions. A key surveillance indicator is the incidence of malaria. Yet measurement of incidence is challenging. The slide positivity rate (SPR has been used as a surrogate measure of malaria incidence, but limited data exist on the relationship between SPR and the incidence of malaria. Methods A cohort of 690 children aged 1-10 years at enrollment were followed for all their health care needs over a four-year period in Kampala, Uganda. All children with fever underwent laboratory testing, allowing us to measure the incidence of malaria and non-malaria fevers. A formula was derived to estimate relative changes in the incidence of malaria (rΔIm based on changes in the SPR and the assumption that the incidence of non-malaria fevers was consistent over time. Observed and estimated values of rΔIm were compared over two, six, and 12 month time intervals after restricting the analysis to children contributing observation time between the ages of 4-10 years to control for aging of the cohort. Results Over the four-year observation period the incidence of malaria declined significantly from 0.93 episodes per person-year in 2005 to 0.39 episodes per person-year in 2008 (p Conclusion Changes in SPR provided a useful estimate of changes in the incidence of malaria in a well defined cohort; however, a gradual decline in the incidence of non-malaria fevers introduced some bias in these estimates.
Jensen, Trevor P; Bukirwa, Hasifa; Njama-Meya, Denise; Francis, Damon; Kamya, Moses R; Rosenthal, Philip J; Dorsey, Grant
2009-09-15
As malaria control efforts intensify, it is critical to monitor trends in disease burden and measure the impact of interventions. A key surveillance indicator is the incidence of malaria. Yet measurement of incidence is challenging. The slide positivity rate (SPR) has been used as a surrogate measure of malaria incidence, but limited data exist on the relationship between SPR and the incidence of malaria. A cohort of 690 children aged 1-10 years at enrollment were followed for all their health care needs over a four-year period in Kampala, Uganda. All children with fever underwent laboratory testing, allowing us to measure the incidence of malaria and non-malaria fevers. A formula was derived to estimate relative changes in the incidence of malaria (rDeltaIm) based on changes in the SPR and the assumption that the incidence of non-malaria fevers was consistent over time. Observed and estimated values of rDeltaIm were compared over two, six, and 12 month time intervals after restricting the analysis to children contributing observation time between the ages of 4-10 years to control for aging of the cohort. Over the four-year observation period the incidence of malaria declined significantly from 0.93 episodes per person-year in 2005 to 0.39 episodes per person-year in 2008 (p age was associated with a significantly greater incidence of malaria and the incidence of malaria was significantly higher during seasonal peaks occurring each January-February and May-June. Changes in SPR produced reasonably accurate estimates of rDeltaIm over all time intervals. The average absolute difference in observed and estimated values of rDeltaIm was lower for six-month intervals (0.13) than it was for two-month (0.21) or 12 month intervals (0.21). Changes in SPR provided a useful estimate of changes in the incidence of malaria in a well defined cohort; however, a gradual decline in the incidence of non-malaria fevers introduced some bias in these estimates.
National Research Council Canada - National Science Library
Byung-Keun Song; Jin-Hee An; Seung-Bok Choi
2017-01-01
.... A solution for estimating the compensative quantity of the impact of UAD on a control system and a set of solutions are then presented in order to avoid the singular cases of the fuzzy-based function...
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Marek Hicar
2004-01-01
Full Text Available The article is about a control design for complete structure of the crane: crab, bridge and crane uplift.The most important unknown parameters for simulations are burden weight and length of hanging rope. We will use robustcontrol for crab and bridge control to ensure adaptivity for burden weight and rope length. Robust control will be designed for current control of the crab and bridge, necessary is to know the range of unknown parameters. Whole robust will be splitto subintervals and after correct identification of unknown parameters the most suitable robust controllers will be chosen.The most important condition at the crab and bridge motion is avoiding from burden swinging in the final position. Crab and bridge drive is designed by asynchronous motor fed from frequency converter. We will use crane uplift with burden weightobserver in combination for uplift, crab and bridge drive with cooperation of their parameters: burden weight, rope length and crab and bridge position. Controllers are designed by state control method. We will use preferably a disturbance observerwhich will identify burden weight as a disturbance. The system will be working in both modes at empty hook as well asat maximum load: burden uplifting and dropping down.
Capdeville, H.; Lemoine, J. M.
2016-12-01
All the DORIS Analysis Centers observe a jump in the scale factor of their combined solution in 2012. The introduction of the HY-2A solution seems to cause the largest jump in the DORIS scale. However, some investigations show that the Jason-2 and Cryosat-2 solutions are also responsible of the DORIS scale jump. This contribution in the scale jump seems fully explained by a variation in the number of low elevation measurements included in the processing. We propose here to demonstrate the origin of this scale jump by several tests in particular by taking into account another data format (RINEX) and by processing DORIS data with different cutoff angles. We plan also to analyze the impact of the low elevation measurements on the height station position estimation and the Helmert parameters (scale factor and geocenter).
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Satoshi Yoshidome
2015-01-01
Full Text Available Objective. To investigate the feasibility of an automated framework for estimating the lung tumor locations for tumor-based patient positioning with megavolt-cone-beam computed tomography (MV-CBCT during stereotactic body radiotherapy (SBRT. Methods. A lung screening phantom and ten lung cancer cases with solid lung tumors, who were treated with SBRT, were employed to this study. The locations of tumors in MV-CBCT images were estimated using a tumor-template matching technique between a tumor template and the MV-CBCT. Tumor templates were produced by cropping the gross tumor volume (GTV regions, which were enhanced by a Sobel filter or a blob structure enhancement (BSE filter. Reference tumor locations (grand truth were determined based on a consensus between a radiation oncologist and a medical physicist. Results. According to the results of the phantom study, the average Euclidean distances of the location errors in the original, Sobel-filtered, and BSE-filtered images were 2.0 ± 4.1 mm, 12.8 ± 9.4 mm, and 0.4 ± 0.5 mm, respectively. For clinical cases, these were 3.4 ± 7.1 mm, 7.2 ± 11.6 mm, and 1.6 ± 1.2 mm, respectively. Conclusion. The feasibility study suggests that our proposed framework based on the BSE filter may be a useful tool for tumor-based patient positioning in SBRT.
Robust Helicopter Stabilization in the Face of Wind Disturbance
DEFF Research Database (Denmark)
A. Danapalasingam, Kumeresan; Leth, John-Josef; la Cour-Harbo, Anders
2010-01-01
When a helicopter is required to hover with minimum deviations from a desired position without measurements of an affecting persistent wind disturbance, a robustly stabilizing control action is vital. In this paper, the stabilization of the position and translational velocity of a nonlinear...... controller is then designed based on nonlinear adaptive output regulations and robust stabilization of a chain of integrators by a saturated feedback. Simulation results show the effectiveness of the control design in the stabilization of helicopter motion and the built-in robustness of the controller...... helicopter model affected by a wind disturbance is addressed. The wind disturbance is assumed to be a sum of a fixed number of sinusoids with unknown amplitudes, frequencies and phases. An estimate of the disturbance is introduced to be adapted using state measurements for control purposes. A nonlinear...
Robust and distributed hypothesis testing
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...
Speer, Thomas; Vanlaer, Pascal; Waltenberger, Wolfgang
2005-01-01
While linear least-square estimators are optimal when the model is linear and all random noise is Gaussian, they are very sensitive to outlying tracks. Non-linear vertex reconstruction algorithms offer a higher degree of robustness against such outliers Two of the algorithms presented, the Adaptive filter and the Trimmed Kalman filter are able to down-weight or discard these outlying tracks, while a third, the Gaussian-sum filter, offers a better treatment of non-Gaussian distributions of track parameter errors when these are modelled by Gaussian mixtures.
Energy Technology Data Exchange (ETDEWEB)
Désert, Jean-Michel; Brown, Timothy M. [CASA, Department of Astrophysical and Planetary Sciences, University of Colorado, 389-UCB, Boulder, CO 80309 (United States); Charbonneau, David; Torres, Guillermo; Fressin, François; Ballard, Sarah; Latham, David W. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Bryson, Stephen T.; Borucki, William J. [NASA Ames Research Center, Moffett Field, CA 94035 (United States); Knutson, Heather A. [Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125 (United States); Batalha, Natalie M. [San Jose State University, San Jose, CA 95192 (United States); Deming, Drake [Department of Astronomy, University of Maryland, College Park, MD 20742-2421 (United States); Ford, Eric B. [University of Florida, Gainesville, FL 32611 (United States); Fortney, Jonathan J. [Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064 (United States); Gilliland, Ronald L. [Center for Exoplanets and Habitable Worlds, The Pennsylvania State University, University Park, PA 16802 (United States); Seager, Sara, E-mail: desert@colorado.edu [Massachusetts Institute of Technology, Cambridge, MA 02159 (United States)
2015-05-01
NASA’s Kepler mission has provided several thousand transiting planet candidates during the 4 yr of its nominal mission, yet only a small subset of these candidates have been confirmed as true planets. Therefore, the most fundamental question about these candidates is the fraction of bona fide planets. Estimating the rate of false positives of the overall Kepler sample is necessary to derive the planet occurrence rate. We present the results from two large observational campaigns that were conducted with the Spitzer Space Telescope during the the Kepler mission. These observations are dedicated to estimating the false positive rate (FPR) among the Kepler candidates. We select a sub-sample of 51 candidates, spanning wide ranges in stellar, orbital, and planetary parameter space, and we observe their transits with Spitzer at 4.5 μm. We use these observations to measures the candidate’s transit depths and infrared magnitudes. An authentic planet produces an achromatic transit depth (neglecting the modest effect of limb darkening). Conversely a bandpass-dependent depth alerts us to the potential presence of a blending star that could be the source of the observed eclipse: a false positive scenario. For most of the candidates (85%), the transit depths measured with Kepler are consistent with the transit depths measured with Spitzer as expected for planetary objects, while we find that the most discrepant measurements are due to the presence of unresolved stars that dilute the photometry. The Spitzer constraints on their own yield FPRs between 5% and depending on the Kepler Objects of Interest. By considering the population of the Kepler field stars, and by combining follow-up observations (imaging) when available, we find that the overall FPR of our sample is low. The measured upper limit on the FPR of our sample is 8.8% at a confidence level of 3σ. This observational result, which uses the achromatic property of planetary transit signals that is not investigated
Goad, Clyde C.; Chadwell, C. David
1993-01-01
GEODYNII is a conventional batch least-squares differential corrector computer program with deterministic models of the physical environment. Conventional algorithms were used to process differenced phase and pseudorange data to determine eight-day Global Positioning system (GPS) orbits with several meter accuracy. However, random physical processes drive the errors whose magnitudes prevent improving the GPS orbit accuracy. To improve the orbit accuracy, these random processes should be modeled stochastically. The conventional batch least-squares algorithm cannot accommodate stochastic models, only a stochastic estimation algorithm is suitable, such as a sequential filter/smoother. Also, GEODYNII cannot currently model the correlation among data values. Differenced pseudorange, and especially differenced phase, are precise data types that can be used to improve the GPS orbit precision. To overcome these limitations and improve the accuracy of GPS orbits computed using GEODYNII, we proposed to develop a sequential stochastic filter/smoother processor by using GEODYNII as a type of trajectory preprocessor. Our proposed processor is now completed. It contains a correlated double difference range processing capability, first order Gauss Markov models for the solar radiation pressure scale coefficient and y-bias acceleration, and a random walk model for the tropospheric refraction correction. The development approach was to interface the standard GEODYNII output files (measurement partials and variationals) with software modules containing the stochastic estimator, the stochastic models, and a double differenced phase range processing routine. Thus, no modifications to the original GEODYNII software were required. A schematic of the development is shown. The observational data are edited in the preprocessor and the data are passed to GEODYNII as one of its standard data types. A reference orbit is determined using GEODYNII as a batch least-squares processor and the
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Nils Ternès
2017-05-01
Full Text Available Abstract Background Thanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized clinical trial. Despite the methodological framework for the development and validation of prediction models in a high-dimensional setting is getting more and more established, no clear guidance exists yet on how to estimate expected survival probabilities in a penalized model with biomarker-by-treatment interactions. Methods Based on a parsimonious biomarker selection in a penalized high-dimensional Cox model (lasso or adaptive lasso, we propose a unified framework to: estimate internally the predictive accuracy metrics of the developed model (using double cross-validation; estimate the individual survival probabilities at a given timepoint; construct confidence intervals thereof (analytical or bootstrap; and visualize them graphically (pointwise or smoothed with spline. We compared these strategies through a simulation study covering scenarios with or without biomarker effects. We applied the strategies to a large randomized phase III clinical trial that evaluated the effect of adding trastuzumab to chemotherapy in 1574 early breast cancer patients, for which the expression of 462 genes was measured. Results In our simulations, penalized regression models using the adaptive lasso estimated the survival probability of new patients with low bias and standard error; bootstrapped confidence intervals had empirical coverage probability close to the nominal level across very different scenarios. The double cross-validation performed on the training data set closely mimicked the predictive accuracy of the selected models in external validation data. We also propose a useful visual representation of the expected survival probabilities using splines. In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4
Kale, David; Stork, David G.
2009-02-01
The problems of estimating the position of an illuminant and the direction of illumination in realist paintings have been addressed using algorithms from computer vision. These algorithms fall into two general categories: In model-independent methods (cast-shadow analysis, occluding-contour analysis, ...), one does not need to know or assume the three-dimensional shapes of the objects in the scene. In model-dependent methods (shape-fromshading, full computer graphics synthesis, ...), one does need to know or assume the three-dimensional shapes. We explore the intermediate- or weak-model condition, where the three-dimensional object rendered is so simple one can very confidently assume its three-dimensional shape and, further, that this shape admits an analytic derivation of the appearance model. Specifically, we can assume that floors and walls are flat and that they are horizontal and vertical, respectively. We derived the maximum-likelihood estimator for the two-dimensional spatial location of a point source in an image as a function of the pattern of brightness (or grayscale value) over such a planar surface. We applied our methods to two paintings of the Baroque, paintings for which the question of the illuminant position is of interest to art historians: Georges de la Tour's Christ in the carpenter's studio (1645) and Caravaggio's The calling of St. Matthew (1599-1600). Our analyses show that a single point source (somewhat near to the depicted candle) is a slightly better explanation of the pattern of brightness on the floor in Christ than are two point sources, one in place of each of the figures. The luminance pattern on the rear wall in The calling implies the source is local, a few meters outside the picture frame-not the infinitely distant sun. Both results are consistent with previous rebuttals of the recent art historical claim that these paintings were executed by means of tracing optically projected images. Our method is the first application of such
Robust Portfolio Optimization Using Pseudodistances
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
Beretta, Elisa; De Momi, Elena; Camomilla, Valentina; Cereatti, Andrea; Cappozzo, Aurelio; Ferrigno, Giancarlo
2014-09-01
In computer-assisted knee surgery, the accuracy of the localization of the femur centre of rotation relative to the hip-bone (hip joint centre) is affected by the unavoidable and untracked pelvic movements because only the femoral pose is acquired during passive pivoting manoeuvres. We present a dual unscented Kalman filter algorithm that allows the estimation of the hip joint centre also using as input the position of a pelvic reference point that can be acquired with a skin marker placed on the hip, without increasing the invasiveness of the surgical procedure. A comparative assessment of the algorithm was carried out using data provided by in vitro experiments mimicking in vivo surgical conditions. Soft tissue artefacts were simulated and superimposed onto the position of a pelvic landmark. Femoral pivoting made of a sequence of star-like quasi-planar movements followed by a circumduction was performed. The dual unscented Kalman filter method proved to be less sensitive to pelvic displacements, which were shown to be larger during the manoeuvres in which the femur was more adducted. Comparable accuracy between all the analysed methods resulted for hip joint centre displacements smaller than 1 mm (error: 2.2 ± [0.2; 0.3] mm, median ± [inter-quartile range 25%; inter-quartile range 75%]) and between 1 and 6 mm (error: 4.8 ± [0.5; 0.8] mm) during planar movements. When the hip joint centre displacement exceeded 6 mm, the dual unscented Kalman filter proved to be more accurate than the other methods by 30% during multi-planar movements (error: 5.2 ± [1.2; 1] mm). © IMechE 2014.
Directory of Open Access Journals (Sweden)
Byung-Keun Song
2017-10-01
Full Text Available This paper presents a new fuzzy sliding mode controller (FSMC to improve control performances in the presence of uncertainties related to model errors and external disturbance (UAD. As a first step, an adaptive control law is designed using Lyapunov stability analysis. The control law can update control parameters of the FSMC with a disturbance estimator (DE in which the closed-loop stability and finite-time convergence of tracking error are guaranteed. A solution for estimating the compensative quantity of the impact of UAD on a control system and a set of solutions are then presented in order to avoid the singular cases of the fuzzy-based function approximation, increase convergence ability, and reduce the calculating cost. Subsequently, the effectiveness of the proposed controller is verified through the investigation of vibration control performances of a semi-active vehicle suspension system featuring a magnetorheological damper (MRD. It is shown that the proposed controller can provide better control ability of vibration control with lower consumed power compared with two existing fuzzy sliding mode controllers.
Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm
Directory of Open Access Journals (Sweden)
Fengkai Yang
2014-01-01
normal distribution, we developed the expectation maximization (EM algorithm to estimate the position of mean change-point. We investigated the performance of the algorithm through different simulations, finding that our methods is robust to the distributions of errors and is effective to estimate the position of mean change-point. Finally, we applied our method to the classical Holbert data and detected a change-point.
Crenshaw, H C; Ciampaglio, C N; McHenry, M
2000-03-01
Most biological motions are three-dimensional. This includes the trajectories of whole organisms and of their appendages. While recordings of three-dimensional trajectories are sometimes published, quantitative analysis of these trajectories is uncommon, primarily because there are no standard techniques or conventions in biology for the analysis of three-dimensional trajectories. This paper describes a new technique, finite helix fit (FHF), based on the geometry of three-dimensional curves, whereby a three-dimensional trajectory is completely described by its velocity, curvature and torsion. FHF estimates these parameters from discretely sampled points on a trajectory (i.e. from positional data such as x,y,z coordinates). Other measures of motion can be derived from these parameters, such as the translational and rotational (or angular) velocities of an organism. The performance of the algorithms is demonstrated using simulated trajectories and trajectories of freely swimming organisms (a flagellate, Chlamydomonas reinhardtii; a ciliate, Paramecium tetraurelia; spermatozoa of a sea urchin, Arbacia punctulata; larvae of an ascidian, Botrylloides sp.).
Karabatic, A.; Weber, R.
2009-04-01
Microwave signals of the GNSS satellites (GPS, GLONASS and in future GALILEO) are time delayed when passing the atmosphere. Based on this signal delay, e.g. the humidity distribution within the troposphere can be determined. It has already been shown that delivery of the Zenith Wet Delays derived from a network solution with hourly resolution and accuracy of 1mm PW is achievable. In the case of very large networks along with an increased number of observation and computational demands, an alternative processing technique has to be applied - Precise Point Positioning (PPP). In this presentation we investigate how the atmospheric precipitable water content derived from GNSS data can be assimilated within an operational Nowcasting system (INCA) and how PPP results compare to the network solution. It is to be expected that the accuracy of the PPP estimates decreases due to several effects (satellite clocks, biases, no ambiguity resolution), but independency from the reference station data will significantly shorten the latency of the results (few min), and provide the regional/national service to enhance the prognosis in the numerical forecast model. It has been proved that e.g. passing weather fronts can be analysed much better by introduced GNSS derived tropospheric wet delays because this data is influenced by changes in humidity in the free atmosphere, whereas the data at the meteorological ground stations reacts to these changes with a considerable time delay. This allows to forecast heavy rainfall causing potentially local floodings more reliable and to narrow down the affected region.
Holmgren, J.; Tulldahl, H. M.; Nordlöf, J.; Nyström, M.; Olofsson, K.; Rydell, J.; Willén, E.
2017-10-01
A system was developed for automatic estimations of tree positions and stem diameters. The sensor trajectory was first estimated using a positioning system that consists of a low precision inertial measurement unit supported by image matching with data from a stereo-camera. The initial estimation of the sensor trajectory was then calibrated by adjustments of the sensor pose using the laser scanner data. Special features suitable for forest environments were used to solve the correspondence and matching problems. Tree stem diameters were estimated for stem sections using laser data from individual scanner rotations and were then used for calibration of the sensor pose. A segmentation algorithm was used to associate stem sections to individual tree stems. The stem diameter estimates of all stem sections associated to the same tree stem were then combined for estimation of stem diameter at breast height (DBH). The system was validated on four 20 m radius circular plots and manual measured trees were automatically linked to trees detected in laser data. The DBH could be estimated with a RMSE of 19 mm (6 %) and a bias of 8 mm (3 %). The calibrated sensor trajectory and the combined use of circle fits from individual scanner rotations made it possible to obtain reliable DBH estimates also with a low precision positioning system.
Directory of Open Access Journals (Sweden)
J. Holmgren
2017-10-01
Full Text Available A system was developed for automatic estimations of tree positions and stem diameters. The sensor trajectory was first estimated using a positioning system that consists of a low precision inertial measurement unit supported by image matching with data from a stereo-camera. The initial estimation of the sensor trajectory was then calibrated by adjustments of the sensor pose using the laser scanner data. Special features suitable for forest environments were used to solve the correspondence and matching problems. Tree stem diameters were estimated for stem sections using laser data from individual scanner rotations and were then used for calibration of the sensor pose. A segmentation algorithm was used to associate stem sections to individual tree stems. The stem diameter estimates of all stem sections associated to the same tree stem were then combined for estimation of stem diameter at breast height (DBH. The system was validated on four 20 m radius circular plots and manual measured trees were automatically linked to trees detected in laser data. The DBH could be estimated with a RMSE of 19 mm (6 % and a bias of 8 mm (3 %. The calibrated sensor trajectory and the combined use of circle fits from individual scanner rotations made it possible to obtain reliable DBH estimates also with a low precision positioning system.
Robust Optimization of Database Queries
Indian Academy of Sciences (India)
JAYANT
2011-07-06
Jul 6, 2011 ... enterprise data b ki i t i t l. – banking, inventory, insurance, travel, … ○ Cornerstone of computer industry. – Uses more than 80 percent of computers worldwide .... g. (estimated) plan execution costs over the same relational selectivity space. July 2011. Robust Query Optimization (IASc Mid-year Meeting). 12 ...
Hsu, Li; Gorfine, Malka; Malone, Kathleen
2007-11-10
The shared frailty model is an extension of the Cox model to correlated failure times and, essentially, a random effects model for failure time outcomes. In this model, the latent frailty shared by individual members in a cluster acts multiplicatively as a factor on the hazard function and is typically modelled parametrically. One commonly used distribution is gamma, where both shape and scale parameters are set to be the same to allow for unique identification of baseline hazard function. It is popular because it is a conjugate prior, and the posterior distribution possesses the same form as gamma. In addition, the parameter can be interpreted as a time-independent cross-ratio function, a natural extension of odds ratio to failure time outcomes. In this paper, we study the effect of frailty distribution mis-specification on the marginal regression estimates and hazard functions under assumed gamma distribution with an application to family studies. The simulation results show that the biases are generally 10% and lower, even when the true frailty distribution deviates substantially from the assumed gamma distribution. This suggests that the gamma frailty model can be a practical choice in real data analyses if the regression parameters and marginal hazard function are of primary interest and individual cluster members are exchangeable with respect to their dependencies. Copyright 2007 John Wiley & Sons, Ltd.
Ma, Dinglong; Liu, Jing; Qi, Jinyi; Marcu, Laura
2017-02-21
In this response we underscore that the instrumentation described in the original publication (Liu et al 2012 Phys. Med. Biol. 57 843-65) was based on pulse-sampling technique, while the comment by Zhang et al is based on the assumption that a time-correlated single photon counting (TCSPC) instrumentation was used. Therefore the arguments made in the comment are not applicable to the noise model reported by Liu et al. As reported in the literature (Lakowicz 2006 Principles of Fluorescence Spectroscopy (New York: Springer)), while in the TCSPC the experimental noise can be estimated from Poisson statistics, such an assumption is not valid for pulse-sampling (transient recording) techniques. To further clarify this aspect, we present here a comprehensive noise model describing the signal and noise propagation of the pulse sampling time-resolved fluorescence detection. Experimental data recorded in various conditions are analyzed as a case study to demonstrate the noise model of our instrumental system. In addition, regarding the statement of correcting equation (3) in Liu et al (2012 Phys. Med. Biol. 57 843-65), the notation of discrete time Laguerre function in the original publication was clear and consistent with literature conventions (Marmarelis 1993 Ann. Biomed. Eng. 21 573-89, Westwick and Kearney 2003 Identification of Nonlinear Physiological Systems (Hoboken, NJ: Wiley)). Thus, it does not require revision.
Robust regression for large-scale neuroimaging studies.
Fritsch, Virgile; Da Mota, Benoit; Loth, Eva; Varoquaux, Gaël; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Brühl, Rüdiger; Butzek, Brigitte; Conrod, Patricia; Flor, Herta; Garavan, Hugh; Lemaitre, Hervé; Mann, Karl; Nees, Frauke; Paus, Tomas; Schad, Daniel J; Schümann, Gunter; Frouin, Vincent; Poline, Jean-Baptiste; Thirion, Bertrand
2015-05-01
Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts. While studies with small sample sizes can rarely be shown to deviate from standard hypotheses (such as the normality of the residuals) due to the poor sensitivity of normality tests with low degrees of freedom, large-scale studies (e.g. >100 subjects) exhibit more obvious deviations from these hypotheses and call for more refined models for statistical inference. Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts. First, we use an analytic test based on robust parameter estimates; based on simulations, this procedure is shown to provide an accurate statistical control without resorting to permutations. Second, we show that robust regression yields more detections than standard algorithms using as an example an imaging genetics study with 392 subjects. Third, we show that robust regression can avoid false positives in a large-scale analysis of brain-behavior relationships with over 1500 subjects. Finally we embed robust regression in the Randomized Parcellation Based Inference (RPBI) method and demonstrate that this combination further improves the sensitivity of tests carried out across the whole brain. Altogether, our results show that robust procedures provide important advantages in large-scale neuroimaging group studies. Copyright © 2015 Elsevier Inc. All rights reserved.