Perspectives on Nonlinear Filtering
Law, Kody
2015-01-07
The solution to the problem of nonlinear filtering may be given either as an estimate of the signal (and ideally some measure of concentration), or as a full posterior distribution. Similarly, one may evaluate the fidelity of the filter either by its ability to track the signal or its proximity to the posterior filtering distribution. Hence, the field enjoys a lively symbiosis between probability and control theory, and there are plenty of applications which benefit from algorithmic advances, from signal processing, to econometrics, to large-scale ocean, atmosphere, and climate modeling. This talk will survey some recent theoretical results involving accurate signal tracking with noise-free (degenerate) dynamics in high-dimensions (infinite, in principle, but say d between 103 and 108 , depending on the size of your application and your computer), and high-fidelity approximations of the filtering distribution in low dimensions (say d between 1 and several 10s).
Nonlinear projective filtering in a data stream
Schreiber, T; Schreiber, Thomas; Richter, Marcus
1998-01-01
We introduce a modified algorithm to perform nonlinear filtering of a time series by locally linear phase space projections. Unlike previous implementations, the algorithm can be used not only for a posteriori processing but includes the possibility to perform real time filtering in a data stream. The data base that represents the phase space structure generated by the data is updated dynamically. This also allows filtering of non-stationary signals and dynamic parameter adjustment. We discuss exemplary applications, including the real time extraction of the fetal electrocardiogram from abdominal recordings.
Nonlinear filtering for LIDAR signal processing
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D. G. Lainiotis
1996-01-01
Full Text Available LIDAR (Laser Integrated Radar is an engineering problem of great practical importance in environmental monitoring sciences. Signal processing for LIDAR applications involves highly nonlinear models and consequently nonlinear filtering. Optimal nonlinear filters, however, are practically unrealizable. In this paper, the Lainiotis's multi-model partitioning methodology and the related approximate but effective nonlinear filtering algorithms are reviewed and applied to LIDAR signal processing. Extensive simulation and performance evaluation of the multi-model partitioning approach and its application to LIDAR signal processing shows that the nonlinear partitioning methods are very effective and significantly superior to the nonlinear extended Kalman filter (EKF, which has been the standard nonlinear filter in past engineering applications.
Euclidean Quantum Mechanics and Universal Nonlinear Filtering
Directory of Open Access Journals (Sweden)
Bhashyam Balaji
2009-02-01
Full Text Available An important problem in applied science is the continuous nonlinear filtering problem, i.e., the estimation of a Langevin state that is observed indirectly. In this paper, it is shown that Euclidean quantum mechanics is closely related to the continuous nonlinear filtering problem. The key is the configuration space Feynman path integral representation of the fundamental solution of a Fokker-Planck type of equation termed the Yau Equation of continuous-continuous filtering. A corollary is the equivalence between nonlinear filtering problem and a time-varying Schr¨odinger equation.
Significance-aware filtering for nonlinear acoustic echo cancellation
Hofmann, Christian; Huemmer, Christian; Guenther, Michael; Kellermann, Walter
2016-12-01
This article summarizes and extends the recently proposed concept of Significance-Aware (SA) filtering for nonlinear acoustic echo cancellation. The core idea of SA filtering is to decompose the estimation of the nonlinear echo path into beneficially interacting subsystems, each of which can be adapted with high computational efficiency. The previously proposed SA Hammerstein Group Models (SA-HGMs) decompose the nonlinear acoustic echo path into a direct-path part, modeled by a Hammerstein Group Model (HGM) and a complementary part, modeled by a very efficient Hammerstein model. In this article, we furthermore propose a novel Equalization-based SA (ESA) structure, where the echo path is equalized by a linear filter to allow for an estimation of the loudspeaker nonlinearities by very small and efficient models. Additionally, we provide a novel in-depth analysis of the computational complexity of the previously proposed SA and the novel ESA filters and compare both SA filtering approaches to each other, to adaptive HGMs, and to linear filters, where fast partitioned-block frequency-domain realizations of the competing filter structures are considered. Finally, the echo reduction performance of the proposed SA filtering approaches is verified using real recordings from a commercially available smartphone. Beyond the scope of previous publications on SA-HGMs, the ability of the SA filters to generalize for double-talk situations is explicitly considered as well. The low complexity as well as the good echo reduction performance of both SA filters illustrate the potential of SA filtering in practice.
Nonlinear Attitude Filtering: A Comparison Study
Zamani, M.; Trumpf, J.; Mahony, R.
2015-01-01
This paper contains a concise comparison of a number of nonlinear attitude filtering methods that have attracted attention in the robotics and aviation literature. With the help of previously published surveys and comparison studies, the vast literature on the subject is narrowed down to a small pool of competitive attitude filters. Amongst these filters is a second-order optimal minimum-energy filter recently proposed by the authors. Easily comparable discretized unit quaternion implementati...
Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters
Hoteit, Ibrahim
2010-09-19
Optimal nonlinear filtering consists of sequentially determining the conditional probability distribution functions (pdf) of the system state, given the information of the dynamical and measurement processes and the previous measurements. Once the pdfs are obtained, one can determine different estimates, for instance, the minimum variance estimate, or the maximum a posteriori estimate, of the system state. It can be shown that, many filters, including the Kalman filter (KF) and the particle filter (PF), can be derived based on this sequential Bayesian estimation framework. In this contribution, we present a Gaussian mixture‐based framework, called the particle Kalman filter (PKF), and discuss how the different EnKF methods can be derived as simplified variants of the PKF. We also discuss approaches to reducing the computational burden of the PKF in order to make it suitable for complex geosciences applications. We use the strongly nonlinear Lorenz‐96 model to illustrate the performance of the PKF.
Interpolation and Iteration for Nonlinear Filters
Chorin, Alexandre J
2009-01-01
We present a general form of the iteration and interpolation process used in implicit particle filters. Implicit filters are based on a pseudo-Gaussian representation of posterior densities, and are designed to focus the particle paths so as to reduce the number of particles needed in nonlinear data assimilation. Examples are given.
Nonlinear Filtering and Approximation Techniques
1988-10-01
e par des iquations de dimension finie, les 6quations du filtre de Kalman : X +h~pklk Xk=(1 + bAt).kk..I + e2+h2 pl_(k - h( + b~t)Xk-... (6 -kIj (1...Equation. 3. Piecewise Linear Filtering with Small Observation Noise. 4. Filtres Approches pour un Probleme de Fitrage Nonlineaire Discretise avec Petit...finite dimensional solution, namely the Kalman filter (which is the extended Kalman filter for (0.1) ). The above considerations tend to indicate that
An Adaptive Nonlinear Filter for System Identification
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Tokunbo Ogunfunmi
2009-01-01
Full Text Available The primary difficulty in the identification of Hammerstein nonlinear systems (a static memoryless nonlinear system in series with a dynamic linear system is that the output of the nonlinear system (input to the linear system is unknown. By employing the theory of affine projection, we propose a gradient-based adaptive Hammerstein algorithm with variable step-size which estimates the Hammerstein nonlinear system parameters. The adaptive Hammerstein nonlinear system parameter estimation algorithm proposed is accomplished without linearizing the systems nonlinearity. To reduce the effects of eigenvalue spread as a result of the Hammerstein system nonlinearity, a new criterion that provides a measure of how close the Hammerstein filter is to optimum performance was used to update the step-size. Experimental results are presented to validate our proposed variable step-size adaptive Hammerstein algorithm given a real life system and a hypothetical case.
Numerical discretization for nonlinear diffusion filter
Mustaffa, I.; Mizuar, I.; Aminuddin, M. M. M.; Dasril, Y.
2015-05-01
Nonlinear diffusion filters are famously used in machine vision for image denoising and restoration. This paper presents a study on the effects of different numerical discretization of nonlinear diffusion filter. Several numerical discretization schemes are presented; namely semi-implicit, AOS, and fully implicit schemes. The results of these schemes are compared by visual results, objective measurement e.g. PSNR and MSE. The results are also compared to a Daubechies wavelet denoising method. It is acknowledged that the two preceding scheme have already been discussed in literature, however comparison to the latter scheme has not been made. The semi-implicit scheme uses an additive operator splitting (AOS) developed to overcome the shortcoming of the explicit scheme i.e., stability for very small time steps. Although AOS has proven to be efficient, from the nonlinear diffusion filter results with different discretization schemes, examples shows that implicit schemes are worth pursuing.
Testing of Nonlinear Filters For Coloured Noise
Macek, Wieslaw M.; Redaelli, Stefano; Plewczynski, Dariusz
We focus on nonlinearity and deterministic behaviour of classical model systems cor- rupted by white or coloured noise. Therefore, we use nonlinear filters to give a faith- ful representation of nonlinear behaviour of the systems. We also analyse time series of a real system, namely, we study velocities of of the solar wind plasma including Alfvénic fluctuations measured in situ by the Helios spacecraft in the inner helio- sphere. We demonstrate that the influence of white and coloured noise in the data records can be efficiently reduced by a nonlinear filter. We show that due to this non- linear noise reduction we get with much reliability estimates of the largest Lyapunov exponent and the Kolmogorov entropy.
Nonlinear Kalman Filtering in Affine Term Structure Models
DEFF Research Database (Denmark)
Christoffersen, Peter; Dorion, Christian; Jacobs, Kris;
2014-01-01
The extended Kalman filter, which linearizes the relationship between security prices and state variables, is widely used in fixed-income applications. We investigate whether the unscented Kalman filter should be used to capture nonlinearities and compare the performance of the Kalman filter...... with that of the particle filter. We analyze the cross section of swap rates, which are mildly nonlinear in the states, and cap prices, which are highly nonlinear. When caps are used to filter the states, the unscented Kalman filter significantly outperforms its extended counterpart. The unscented Kalman filter also...
Modified nonlinear complex diffusion filter (MNCDF).
Saini, Kalpana; Dewal, M L; Rohit, Manojkumar
2012-06-01
Speckle noise removal is the most important step in the processing of echocardiographic images. A speckle-free image produces useful information to diagnose heart-related diseases. Images which contain low noise and sharp edges are more easily analyzed by the clinicians. This noise removal stage is also a preprocessing stage in segmentation techniques. A new formulation has been proposed for a well-known nonlinear complex diffusion filter (NCDF). Its diffusion coefficient and the time step size are modified to give fast processing and better results. An investigation has been performed among nine patients suffering from mitral regurgitation. Images have been taken with 2D echo in apical and parasternal views. The peak signal-to-noise ratio (PSNR), universal quality index (Qi), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) have been calculated, and the results show that the proposed method is much better than the previous filters for echocardiographic images. The proposed method, modified nonlinear complex diffusion filter (MNCDF), smooths the homogeneous area and enhances the fine details.
The Behavior of Filters and Smoothers for Strongly Nonlinear Dynamics
Zhu, Yanqiu; Cohn, Stephen E.; Todling, Ricardo
1999-01-01
The Kalman filter is the optimal filter in the presence of known Gaussian error statistics and linear dynamics. Filter extension to nonlinear dynamics is non trivial in the sense of appropriately representing high order moments of the statistics. Monte Carlo, ensemble-based, methods have been advocated as the methodology for representing high order moments without any questionable closure assumptions (e.g., Miller 1994). Investigation along these lines has been conducted for highly idealized dynamics such as the strongly nonlinear Lorenz (1963) model as well as more realistic models of the oceans (Evensen and van Leeuwen 1996) and atmosphere (Houtekamer and Mitchell 1998). A few relevant issues in this context are related to the necessary number of ensemble members to properly represent the error statistics and, the necessary modifications in the usual filter equations to allow for correct update of the ensemble members (Burgers 1998). The ensemble technique has also been applied to the problem of smoothing for which similar questions apply. Ensemble smoother examples, however, seem to quite puzzling in that results of state estimate are worse than for their filter analogue (Evensen 1997). In this study, we use concepts in probability theory to revisit the ensemble methodology for filtering and smoothing in data assimilation. We use Lorenz (1963) model to test and compare the behavior of a variety implementations of ensemble filters. We also implement ensemble smoothers that are able to perform better than their filter counterparts. A discussion of feasibility of these techniques to large data assimilation problems will be given at the time of the conference.
Nonlinear optical properties of induced transmission filters.
Owens, Daniel T; Fuentes-Hernandez, Canek; Hales, Joel M; Perry, Joseph W; Kippelen, Bernard
2010-08-30
The nonlinear optical (NLO) properties of induced transmission filters (ITFs) based on Ag are experimentally determined using white light continuum pump-probe measurements. The experimental results are supported using simulations based on the matrix transfer method. The magnitude of the NLO response is shown to be 30 times that of an isolated Ag film of comparable thickness. The impacts of design variations on the linear and NLO response are simulated. It is shown that the design can be modified to enhance the NLO response of an ITF by a factor of 2 or more over a perfectly matched ITF structure.
Nonlinear Filtering Preserves Chaotic Synchronization via Master-Slave System
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J. S. González-Salas
2013-01-01
Full Text Available We present a study on a class of interconnected nonlinear systems and give some criteria for them to behave like a filter. Some chaotic systems present this kind of interconnected nonlinear structure, which enables the synchronization of a master-slave system. Interconnected nonlinear filters have been defined in terms of interconnected nonlinear systems. Furthermore, their behaviors have been studied numerically and theoretically on different input signals.
Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters
Hoteit, Ibrahim; Pham, Dinh-Tuan
2011-01-01
This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that, the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated covariance matrices in the Gaussian mixture. We show that this filter is an algorithm in between the Kalman filter and the particle filter, and therefore is referred to as the particle Kalman filter (PKF). In the PKF, the solution of a nonlinear filtering problem is expressed as the weighted average of an "ensemble of Kalman filters" operating in parallel. Running an ensemble of Kalman filters is, however, computationally prohibitive for realistic atmospheric and oceanic data assimilation problems. For this reason, we consider the construction of the PKF through an "ensemble" of ensemble Kalman filters (EnKFs) instead, ...
Nonlinear filtering properties of detrended fluctuation analysis
Kiyono, Ken; Tsujimoto, Yutaka
2016-11-01
Detrended fluctuation analysis (DFA) has been widely used for quantifying long-range correlation and fractal scaling behavior. In DFA, to avoid spurious detection of scaling behavior caused by a nonstationary trend embedded in the analyzed time series, a detrending procedure using piecewise least-squares fitting has been applied. However, it has been pointed out that the nonlinear filtering properties involved with detrending may induce instabilities in the scaling exponent estimation. To understand this issue, we investigate the adverse effects of the DFA detrending procedure on the statistical estimation. We show that the detrending procedure using piecewise least-squares fitting results in the nonuniformly weighted estimation of the root-mean-square deviation and that this property could induce an increase in the estimation error. In addition, for comparison purposes, we investigate the performance of a centered detrending moving average analysis with a linear detrending filter and sliding window DFA and show that these methods have better performance than the standard DFA.
A nested sampling particle filter for nonlinear data assimilation
Elsheikh, Ahmed H.
2014-04-15
We present an efficient nonlinear data assimilation filter that combines particle filtering with the nested sampling algorithm. Particle filters (PF) utilize a set of weighted particles as a discrete representation of probability distribution functions (PDF). These particles are propagated through the system dynamics and their weights are sequentially updated based on the likelihood of the observed data. Nested sampling (NS) is an efficient sampling algorithm that iteratively builds a discrete representation of the posterior distributions by focusing a set of particles to high-likelihood regions. This would allow the representation of the posterior PDF with a smaller number of particles and reduce the effects of the curse of dimensionality. The proposed nested sampling particle filter (NSPF) iteratively builds the posterior distribution by applying a constrained sampling from the prior distribution to obtain particles in high-likelihood regions of the search space, resulting in a reduction of the number of particles required for an efficient behaviour of particle filters. Numerical experiments with the 3-dimensional Lorenz63 and the 40-dimensional Lorenz96 models show that NSPF outperforms PF in accuracy with a relatively smaller number of particles. © 2013 Royal Meteorological Society.
A new extended H∞ filter for discrete nonlinear systems
Institute of Scientific and Technical Information of China (English)
张永安; 周荻; 段广仁
2004-01-01
Nonlinear estimation problem is investigated in this paper. By extension of a linear H∞ estimation with corrector-predictor form to nonlinear cases, a new extended H∞ filter is proposed for time-varying discretetime nonlinear systems. The new filter has a simple observer structure based on a local linearization model, and can be viewed as a general case of the extended Kalman filter (EKF). An example demonstrates that the new filter with a suitable-chosen prescribed H∞ bound performs better than the EKF.
Optimal Nonlinear Filter for INS Alignment
Institute of Scientific and Technical Information of China (English)
赵瑞; 顾启泰
2002-01-01
All the methods to handle the inertial navigation system (INS) alignment were sub-optimal in the past. In this paper, particle filtering (PF) as an optimal method is used for solving the problem of INS alignment. A sub-optimal two-step filtering algorithm is presented to improve the real-time performance of PF. The approach combines particle filtering with Kalman filtering (KF). Simulation results illustrate the superior performance of these approaches when compared with extended Kalman filtering (EKF).
IMAGE RESTORATION: DESIGN OF NON-LINEAR FILTER (LR
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Shenbagarajan Anantharajan
2012-11-01
Full Text Available In this proposed method, various types of noise models are subjected to an image and apply the nonlinear filter to reconstruct the original image from degraded image. Image restoration is a technique to attempt of reconstructs the original image by using a degraded phenomenon. In this paper the Lucy-Richardson filter is reconstruct the degraded image which closely resembles the original image. This paper deals with the various noise models and nonlinear filter. Objective of this paper is to study the various noise models and restoration filters in depth at restoration area.
Autonomous Navigation System Using a Fuzzy Adaptive Nonlinear H∞ Filter
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Fariz Outamazirt
2014-09-01
Full Text Available Although nonlinear H∞ (NH∞ filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞ filter is proposed for the Unmanned Aerial Vehicle (UAV localization problem. Based on a real-time Fuzzy Inference System (FIS, the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter.
Nonlinear Adaptive Filter for MEMS Gyro Error Cancellation Project
National Aeronautics and Space Administration — The Nonlinear adaptive filters (NAF) can learn deterministic gyro errors and cancel the error’s effect from attitude estimates. By completely canceling...
Federated nonlinear predictive filtering for the gyroless attitude determination system
Zhang, Lijun; Qian, Shan; Zhang, Shifeng; Cai, Hong
2016-11-01
This paper presents a federated nonlinear predictive filter (NPF) for the gyroless attitude determination system with star sensor and Global Positioning System (GPS) sensor. This approach combines the good qualities of both the NPF and federated filter. In order to combine them, the equivalence relationship between the NPF and classical Kalman filter (KF) is demonstrated from algorithm structure and estimation criterion. The main features of this approach include a nonlinear predictive filtering algorithm to estimate uncertain model errors and determine the spacecraft attitude by using attitude kinematics and dynamics, and a federated filtering algorithm to process measurement data from multiple attitude sensors. Moreover, a fault detection and isolation algorithm is applied to the proposed federated NPF to improve the estimation accuracy even when one sensor fails. Numerical examples are given to verify the navigation performance and fault-tolerant performance of the proposed federated nonlinear predictive attitude determination algorithm.
Nonlinear H∞ filtering for interconnected Markovian jump systems
Institute of Scientific and Technical Information of China (English)
Zhang Xiaomei; Zheng Yufan
2006-01-01
The problem of nonlinear H∞ filtering for interconnected Markovian jump systems is discussed. The aim of this note is the design of a nonlinear Markovian jump filter such that the resulting error system is exponentially meansquare stable and ensures a prescribed H∞ performance. A sufficient condition for the solvability of this problem is given in terms of linear matrix inequalities(LMIs). A simulation example is presented to demonstrate the effectiveness of the proposed design approach.
On filter-successive linearization methods for nonlinear semidefinite programming
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
In this paper we present a filter-successive linearization method with trust region for solutions of nonlinear semidefinite programming. Such a method is based on the concept of filter for nonlinear programming introduced by Fletcher and Leyffer in 2002. We describe the new algorithm and prove its global convergence under weaker assumptions. Some numerical results are reported and show that the new method is potentially effcient.
On filter-successive linearization methods for nonlinear semidefinite programming
Institute of Scientific and Technical Information of China (English)
LI ChengJin; SUN WenYui
2009-01-01
In this paper we present a filter-successive linearization method with trust region for solutions of nonlinear semidefinite programming. Such a method is based on the concept of filter for nonlinear programming introduced by Fletcher and Leyffer in 2002. We describe the new algorithm and prove its global convergence under weaker assumptions. Some numerical results are reported and show that the new method is potentially efficient.
Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters*
Hoteit, Ibrahim
2012-02-01
This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated covariance matrices in the Gaussian mixture. The authors show that this filter is an algorithm in between the Kalman filter and the particle filter, and therefore is referred to as the particle Kalman filter (PKF). In the PKF, the solution of a nonlinear filtering problem is expressed as the weighted average of an “ensemble of Kalman filters” operating in parallel. Running an ensemble of Kalman filters is, however, computationally prohibitive for realistic atmospheric and oceanic data assimilation problems. For this reason, the authors consider the construction of the PKF through an “ensemble” of ensemble Kalman filters (EnKFs) instead, and call the implementation the particle EnKF (PEnKF). It is shown that different types of the EnKFs can be considered as special cases of the PEnKF. Similar to the situation in the particle filter, the authors also introduce a resampling step to the PEnKF in order to reduce the risk of weights collapse and improve the performance of the filter. Numerical experiments with the strongly nonlinear Lorenz-96 model are presented and discussed.
A fast n-dimensional nonlinear filter
Tremblais, Benoit; Augereau, Bertrand
2004-05-01
In this communication, we propose an original approach for the diffusion paradigm in image processing. Our starting point is the iterative resolution of partial differential equations (PDE) according to the explicit resolution scheme. We simply consider that this iterative process is nothing but a fixed point search. So we obtain a convergence condition which applies to a large set of image processing PDE. That allows to introduce a new smoothing process with strong abilities to preserve any structure of interest in the images. As an example we choose a linear isotropic diffusion for the denoising performances. Thus while resolving the equation of isotropic diffusion and by using an adaptive resolution parameter, we obtain a filtering process which can preserve arbitrary dimension object edges as one-dimensional signals, gray level images, color images, volumes, films, etc. We show the edge localization preserving property of the process. And we compare the complexity of the process with the Perona and Malik explicit scheme, and the Weickert AOS scheme. We establish that the computational effort of our scheme is lower than this of the two others. For illustration, we apply this new process to denoising of different kinds of medical images.
Nonlinear Diffusion Filtering of the GOCE-Based Satellite-Only Mean Dynamic Topography
Cunderlik, Robert; Mikula, Karol
2015-03-01
The paper presents nonlinear diffusion filtering of the GOCE-based satellite-only mean dynamic topography (MDT). Our approach is based on a numerical solution to the nonlinear diffusion equation defined on the discretized Earth’s surface using the regularized surface Perona-Malik Model. For its numerical discretization we use a surface finite volume method. A key idea is that the diffusivity coefficient depends on the edge detector. It allows effectively reduce the stripping noise while preserve important gradients in filtered data. Numerical experiments present nonlinear filtering of the geopotential evaluated from the GO_CONS_GCF_2_ DIR_R5 model on the DTU13 mean sea surface. After filtering the geopotential is transformed into the MDT.
Nonlinear Kalman filtering in the presence of additive noise
Kraszewski, Tomasz; Czopik, Grzegorz
2017-04-01
Each modern navigation or localization system designed for ground, water or air objects should provide information on the estimated parameters continuously and as accurately as possible. The implementation of such a process requires the application to operate on non-linear transformations. The defined expectations necessitate the use of nonlinear filtering elements with particular emphasis on the extended Kalman filter. The article presents the simulation research elements of this filter type in the aspect of the possibility of its practical implementation. In the initial phase of the study the conclusion was based on nonlinear one-dimensional model. The possibility of improving the precision of the output through the use of unscented Kalman filters was also analyzed.
Ensemble-based Kalman Filters in Strongly Nonlinear Dynamics
Institute of Scientific and Technical Information of China (English)
Zhaoxia PU; Joshua HACKER
2009-01-01
This study examines the effectiveness of ensemble Kalman filters in data assimilation with the strongly nonlinear dynamics of the Lorenz-63 model, and in particular their use in predicting the regime transition that occurs when the model jumps from one basin of attraction to the other. Four configurations of the ensemble-based Kalman filtering data assimilation techniques, including the ensemble Kalman filter, ensemble adjustment Kalman filter, ensemble square root filter and ensemble transform Kalman filter, are evaluated with their ability in predicting the regime transition (also called phase transition) and also are compared in terms of their sensitivity to both observational and sampling errors. The sensitivity of each ensemble-based filter to the size of the ensemble is also examined.
Non-linear DSGE Models and The Optimized Particle Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
This paper improves the accuracy and speed of particle filtering for non-linear DSGE models with potentially non-normal shocks. This is done by introducing a new proposal distribution which i) incorporates information from new observables and ii) has a small optimization step that minimizes...... the distance to the optimal proposal distribution. A particle filter with this proposal distribution is shown to deliver a high level of accuracy even with relatively few particles, and this filter is therefore much more efficient than the standard particle filter....
Nonlinear filtering in ECG Signal Enhancement
Directory of Open Access Journals (Sweden)
N. Siddiah
2012-02-01
Full Text Available High resolution ECG signals are needed in measuring cardiac abnormalities analysis. Generally baseline wander is one of the important artifact occurred in ECG signal extraction, this strongly affects the signal quality. In order to facilitate proper diagnosis these artifacts have to be removed. In this paper various non linear, non adaptive filtering techniques are presented for the removal of baseline wander removal from ECG signals. The performance characteristics of various filtering techniques are measured in terms of signal to noise ratio.
Nonlinear dynamical system identification using unscented Kalman filter
Rehman, M. Javvad ur; Dass, Sarat Chandra; Asirvadam, Vijanth Sagayan
2016-11-01
Kalman Filter is the most suitable choice for linear state space and Gaussian error distribution from decades. In general practical systems are not linear and Gaussian so these assumptions give inconsistent results. System Identification for nonlinear dynamical systems is a difficult task to perform. Usually, Extended Kalman Filter (EKF) is used to deal with non-linearity in which Jacobian method is used for linearizing the system dynamics, But it has been observed that in highly non-linear environment performance of EKF is poor. Unscented Kalman Filter (UKF) is proposed here as a better option because instead of analytical linearization of state space, UKF performs statistical linearization by using sigma point calculated from deterministic samples. Formation of the posterior distribution is based on the propagation of mean and covariance through sigma points.
Non-linear and signal energy optimal asymptotic filter design
Directory of Open Access Journals (Sweden)
Josef Hrusak
2003-10-01
Full Text Available The paper studies some connections between the main results of the well known Wiener-Kalman-Bucy stochastic approach to filtering problems based mainly on the linear stochastic estimation theory and emphasizing the optimality aspects of the achieved results and the classical deterministic frequency domain linear filters such as Chebyshev, Butterworth, Bessel, etc. A new non-stochastic but not necessarily deterministic (possibly non-linear alternative approach called asymptotic filtering based mainly on the concepts of signal power, signal energy and a system equivalence relation plays an important role in the presentation. Filtering error invariance and convergence aspects are emphasized in the approach. It is shown that introducing the signal power as the quantitative measure of energy dissipation makes it possible to achieve reasonable results from the optimality point of view as well. The property of structural energy dissipativeness is one of the most important and fundamental features of resulting filters. Therefore, it is natural to call them asymptotic filters. The notion of the asymptotic filter is carried in the paper as a proper tool in order to unify stochastic and non-stochastic, linear and nonlinear approaches to signal filtering.
Approximations and Implementations of Nonlinear Filtering Schemes.
1988-02-01
SYSTEMS 13/14 (Blank) JP{ I LDMX MAJ=V AMOnUXIO rOQO v F 3LES LXIMKR STOCASTIC STSTEN A. a. Uaded an L 1. Verriest School of flectrical Engineering...1975. (15] P. G. Hoel , S. C. Port, and C. J. Stone, "Introduction to Stochastic Processes", Houghton Mifflin Co., 1972. (161 A. Isidori, "Nonlinear
A Filter Method for Nonlinear Semidefinite Programming with Global Convergence
Institute of Scientific and Technical Information of China (English)
Zhi Bin ZHU; Hua Li ZHU
2014-01-01
In this study, a new filter algorithm is presented for solving the nonlinear semidefinite programming. This algorithm is inspired by the classical sequential quadratic programming method. Unlike the traditional filter methods, the suffi cient descent is ensured by changing the step size instead of the trust region radius. Under some suitable conditions, the global convergence is obtained. In the end, some numerical experiments are given to show that the algorithm is eff ective.
Filtering nonlinear dynamical systems with linear stochastic models
Harlim, J.; Majda, A. J.
2008-06-01
An important emerging scientific issue is the real time filtering through observations of noisy signals for nonlinear dynamical systems as well as the statistical accuracy of spatio-temporal discretizations for filtering such systems. From the practical standpoint, the demand for operationally practical filtering methods escalates as the model resolution is significantly increased. For example, in numerical weather forecasting the current generation of global circulation models with resolution of 35 km has a total of billions of state variables. Numerous ensemble based Kalman filters (Evensen 2003 Ocean Dyn. 53 343-67 Bishop et al 2001 Mon. Weather Rev. 129 420-36 Anderson 2001 Mon. Weather Rev. 129 2884-903 Szunyogh et al 2005 Tellus A 57 528-45 Hunt et al 2007 Physica D 230 112-26) show promising results in addressing this issue; however, all these methods are very sensitive to model resolution, observation frequency, and the nature of the turbulent signals when a practical limited ensemble size (typically less than 100) is used. In this paper, we implement a radical filtering approach to a relatively low (40) dimensional toy model, the L-96 model (Lorenz 1996 Proc. on Predictability (ECMWF, 4-8 September 1995) pp 1-18) in various chaotic regimes in order to address the 'curse of ensemble size' for complex nonlinear systems. Practically, our approach has several desirable features such as extremely high computational efficiency, filter robustness towards variations of ensemble size (we found that the filter is reasonably stable even with a single realization) which makes it feasible for high dimensional problems, and it is independent of any tunable parameters such as the variance inflation coefficient in an ensemble Kalman filter. This radical filtering strategy decouples the problem of filtering a spatially extended nonlinear deterministic system to filtering a Fourier diagonal system of parametrized linear stochastic differential equations (Majda and Grote
Nonlinear Principal Component Analysis Using Strong Tracking Filter
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.
Nonlinear Filter Based Image Denoising Using AMF Approach
Thivakaran, T K
2010-01-01
This paper proposes a new technique based on nonlinear Adaptive Median filter (AMF) for image restoration. Image denoising is a common procedure in digital image processing aiming at the removal of noise, which may corrupt an image during its acquisition or transmission, while retaining its quality. This procedure is traditionally performed in the spatial or frequency domain by filtering. The aim of image enhancement is to reconstruct the true image from the corrupted image. The process of image acquisition frequently leads to degradation and the quality of the digitized image becomes inferior to the original image. Filtering is a technique for enhancing the image. Linear filter is the filtering in which the value of an output pixel is a linear combination of neighborhood values, which can produce blur in the image. Thus a variety of smoothing techniques have been developed that are non linear. Median filter is the one of the most popular non-linear filter. When considering a small neighborhood it is highly e...
Nonlinear projective filtering; 1, Application to real time series
Schreiber, T
1998-01-01
We discuss applications of nonlinear filtering of time series by locally linear phase space projections. Noise can be reduced whenever the error due to the manifold approximation is smaller than the noise in the system. Examples include the real time extraction of the fetal electrocardiogram from abdominal recordings.
Linear and nonlinear filters under high power microwave conditions
Directory of Open Access Journals (Sweden)
F. Brauer
2009-05-01
Full Text Available The development of protection circuits against a variety of electromagnetic disturbances is important to assure the immunity of an electronic system. In this paper the behavior of linear and nonlinear filters is measured and simulated with high power microwave (HPM signals to achieve a comprehensive protection against different high power electromagnetic (HPEM threats.
Nonlinear Statistical Signal Processing: A Particle Filtering Approach
Energy Technology Data Exchange (ETDEWEB)
Candy, J
2007-09-19
A introduction to particle filtering is discussed starting with an overview of Bayesian inference from batch to sequential processors. Once the evolving Bayesian paradigm is established, simulation-based methods using sampling theory and Monte Carlo realizations are discussed. Here the usual limitations of nonlinear approximations and non-gaussian processes prevalent in classical nonlinear processing algorithms (e.g. Kalman filters) are no longer a restriction to perform Bayesian inference. It is shown how the underlying hidden or state variables are easily assimilated into this Bayesian construct. Importance sampling methods are then discussed and shown how they can be extended to sequential solutions implemented using Markovian state-space models as a natural evolution. With this in mind, the idea of a particle filter, which is a discrete representation of a probability distribution, is developed and shown how it can be implemented using sequential importance sampling/resampling methods. Finally, an application is briefly discussed comparing the performance of the particle filter designs with classical nonlinear filter implementations.
Exploiting nonlinearities of micro-machined resonators for filtering applications
Ilyas, Saad
2017-06-21
We demonstrate the exploitation of the nonlinear behavior of two electrically coupled microbeam resonators to realize a band-pass filter. More specifically, we combine their nonlinear hardening and softening responses to realize a near flat pass band filter with sharp roll-off characteristics. The device is composed of two near identical doubly clamped and electrostatically actuated microbeams made of silicon. One of the resonators is buckled via thermal loading to produce a softening frequency response. It is then further tuned to create the desired overlap with the second resonator response of hardening behavior. This overlapping improves the pass band flatness. Also, the sudden jumps due to the softening and hardening behaviors create sharp roll-off characteristics. This approach can be promising for the future generation of filters with superior characteristics.
A Girsanov particle filter in nonlinear engineering dynamics
Energy Technology Data Exchange (ETDEWEB)
Saha, Nilanjan [Structures Lab, Department of Civil Engineering, Indian Institute of Science, Bangalore-560012 (India); Roy, D. [Structures Lab, Department of Civil Engineering, Indian Institute of Science, Bangalore-560012 (India)], E-mail: royd@civil.iisc.ernet.in
2009-02-02
In this Letter, we propose a novel variant of the particle filter (PF) for state and parameter estimations of nonlinear engineering dynamical systems, modelled through stochastic differential equations (SDEs). The aim is to address a possible loss of accuracy in the estimates due to the discretization errors, which are inevitable during numerical integration of the SDEs. In particular, we adopt an explicit local linearization of the governing nonlinear SDEs and the resulting linearization errors in the estimates are corrected using Girsanov transformation of measures. Indeed, the linearization scheme via transformation of measures provides a weak framework for computing moments and this fits in well with any stochastic filtering strategy wherein estimates are themselves statistical moments. We presently implement the strategy using a bootstrap PF and numerically illustrate its performance for state and parameter estimations of the Duffing oscillator with linear and nonlinear measurement equations.
Modified unscented particle filter for nonlinear Bayesian tracking
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A modified unscented particle filtering scheme for nonlinear tracking is proposed,in view of the potential drawbacks (such as,particle impoverishment and numerical sensitivity in calculating the prior) of the conventional unscented particle filter (UPF) confronted in practice.Specifically,a different derivation of the importance weight is presented in detail.The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution.Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one.
A neural architecture for nonlinear adaptive filtering of time series
DEFF Research Database (Denmark)
Hoffmann, Nils; Larsen, Jan
1991-01-01
A neural architecture for adaptive filtering which incorporates a modularization principle is proposed. It facilitates a sparse parameterization, i.e. fewer parameters have to be estimated in a supervised training procedure. The main idea is to use a preprocessor which determines the dimension...... of the input space and can be designed independently of the subsequent nonlinearity. Two suggestions for the preprocessor are presented: the derivative preprocessor and the principal component analysis. A novel implementation of fixed Volterra nonlinearities is given. It forces the boundedness...
Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
This paper shows how non-linear DSGE models with potential non-normal shocks can be estimated by Quasi-Maximum Likelihood based on the Central Difference Kalman Filter (CDKF). The advantage of this estimator is that evaluating the quasi log-likelihood function only takes a fraction of a second. T...
Rigatos, Gerasimos G; Rigatou, Efthymia G; Djida, Jean Daniel
2015-10-01
A method for early diagnosis of parametric changes in intracellular protein synthesis models (e.g. the p53 protein - mdm2 inhibitor model) is developed with the use of a nonlinear Kalman Filtering approach (Derivative-free nonlinear Kalman Filter) and of statistical change detection methods. The intracellular protein synthesis dynamic model is described by a set of coupled nonlinear differential equations. It is shown that such a dynamical system satisfies differential flatness properties and this allows to transform it, through a change of variables (diffeomorphism), to the so-called linear canonical form. For the linearized equivalent of the dynamical system, state estimation can be performed using the Kalman Filter recursion. Moreover, by applying an inverse transformation based on the previous diffeomorphism it becomes also possible to obtain estimates of the state variables of the initial nonlinear model. By comparing the output of the Kalman Filter (which is assumed to correspond to the undistorted dynamical model) with measurements obtained from the monitored protein synthesis system, a sequence of differences (residuals) is obtained. The statistical processing of the residuals with the use of x2 change detection tests, can provide indication within specific confidence intervals about parametric changes in the considered biological system and consequently indications about the appearance of specific diseases (e.g. malignancies).
A Particle Filtering Approach to Change Detection for Nonlinear Systems
Directory of Open Access Journals (Sweden)
P. S. Krishnaprasad
2004-11-01
Full Text Available We present a change detection method for nonlinear stochastic systems based on particle filtering. We assume that the parameters of the system before and after change are known. The statistic for this method is chosen in such a way that it can be calculated recursively while the computational complexity of the method remains constant with respect to time. We present simulation results that show the advantages of this method compared to linearization techniques.
Fast recursive filters for simulating nonlinear dynamic systems.
van Hateren, J H
2008-07-01
A fast and accurate computational scheme for simulating nonlinear dynamic systems is presented. The scheme assumes that the system can be represented by a combination of components of only two different types: first-order low-pass filters and static nonlinearities. The parameters of these filters and nonlinearities may depend on system variables, and the topology of the system may be complex, including feedback. Several examples taken from neuroscience are given: phototransduction, photopigment bleaching, and spike generation according to the Hodgkin-Huxley equations. The scheme uses two slightly different forms of autoregressive filters, with an implicit delay of zero for feedforward control and an implicit delay of half a sample distance for feedback control. On a fairly complex model of the macaque retinal horizontal cell, it computes, for a given level of accuracy, one to two orders of magnitude faster than the fourth-order Runge-Kutta. The computational scheme has minimal memory requirements and is also suited for computation on a stream processor, such as a graphical processing unit.
Non-linear Kalman filters for calibration in radio interferometry
Tasse, Cyril
2014-01-01
We present a new calibration scheme based on a non-linear version of Kalman filter that aims at estimating the physical terms appearing in the Radio Interferometry Measurement Equation (RIME). We enrich the filter's structure with a tunable data representation model, together with an augmented measurement model for regularization. We show using simulations that it can properly estimate the physical effects appearing in the RIME. We found that this approach is particularly useful in the most extreme cases such as when ionospheric and clock effects are simultaneously present. Combined with the ability to provide prior knowledge on the expected structure of the physical instrumental effects (expected physical state and dynamics), we obtain a fairly cheap algorithm that we believe to be robust, especially in low signal-to-noise regime. Potentially the use of filters and other similar methods can represent an improvement for calibration in radio interferometry, under the condition that the effects corrupting visib...
A nonlinear optoelectronic filter for electronic signal processing
Loh, William; Yegnanarayanan, Siva; Ram, Rajeev J.; Juodawlkis, Paul W.
2014-01-01
The conversion of electrical signals into modulated optical waves and back into electrical signals provides the capacity for low-loss radio-frequency (RF) signal transfer over optical fiber. Here, we show that the unique properties of this microwave-photonic link also enable the manipulation of RF signals beyond what is possible in conventional systems. We achieve these capabilities by realizing a novel nonlinear filter, which acts to suppress a stronger RF signal in the presence of a weaker signal independent of their separation in frequency. Using this filter, we demonstrate a relative suppression of 56 dB for a stronger signal having a 1-GHz center frequency, uncovering the presence of otherwise undetectable weaker signals located as close as 3.5 Hz away. The capabilities of the optoelectronic filter break the conventional limits of signal detection, opening up new possibilities for radar and communication systems, and for the field of precision frequency metrology. PMID:24402418
Nonlinear Filtering Techniques Comparison for Battery State Estimation
Directory of Open Access Journals (Sweden)
Aspasia Papazoglou
2014-09-01
Full Text Available The performance of estimation algorithms is vital for the correct functioning of batteries in electric vehicles, as poor estimates will inevitably jeopardize the operations that rely on un-measurable quantities, such as State of Charge and State of Health. This paper compares the performance of three nonlinear estimation algorithms: the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter, where a lithium-ion cell model is considered. The effectiveness of these algorithms is measured by their ability to produce accurate estimates against their computational complexity in terms of number of operations and execution time required. The trade-offs between estimators' performance and their computational complexity are analyzed.
Nonlinear stochastic systems with incomplete information filtering and control
Shen, Bo; Shu, Huisheng
2013-01-01
Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H∞ filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: · a unified framework for handling filtering and control problems in complex communication networks with limited bandwidth; · new concepts such as random sensor and signal saturations for more realistic modeling; and · demonstration of the use of techniques such...
On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles
Luo, Xiaodong
2010-09-19
The ensemble square root filter (EnSRF) [1, 2, 3, 4] is a popular method for data assimilation in high dimensional systems (e.g., geophysics models). Essentially the EnSRF is a Monte Carlo implementation of the conventional Kalman filter (KF) [5, 6]. It is mainly different from the KF at the prediction steps, where it is some ensembles, rather then the means and covariance matrices, of the system state that are propagated forward. In doing this, the EnSRF is computationally more efficient than the KF, since propagating a covariance matrix forward in high dimensional systems is prohibitively expensive. In addition, the EnSRF is also very convenient in implementation. By propagating the ensembles of the system state, the EnSRF can be directly applied to nonlinear systems without any change in comparison to the assimilation procedures in linear systems. However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].
Nonlinear temporal filtering of time-resolved digital particle image velocimetry data
Energy Technology Data Exchange (ETDEWEB)
Fore, L.B.; Tung, A.T.; Buchanan, J.R.; Welch, J.W. [Bechtel Bettis Inc., West Mifflin, PA (United States)
2005-07-01
Nonlinear filtering methods have been developed to identify and replace outlying data points in velocity time series obtained with time-resolved digital particle image velocimetry (PIV) of the flow around a surface-mounted cube at a Reynolds number of 20,000. Nuances associated with the spectral computation of the cross-correlation are highlighted, including the requirement of zero-padding an image interrogation area to eliminate the circular components of the cross-correlation. Three nonlinear filtering methods for the replacement of outliers are applied to the velocity time series sampled at 1,000 Hz: a median filter, a decision-based Hampel filter, and a PIV-specific Hampel filter. The particular benefit of the PIV-specific Hampel filter is that it allows the retention of actual measured data, sometimes derived from alternate peaks in the cross-correlation function, while still providing for the removal of outliers when a consistent, nonoutlying measurement is not available. (orig.)
Nonlinear ultrasonic measurements based on cross-correlation filtering techniques
Yee, Andrew; Stewart, Dylan; Bunget, Gheorghe; Kramer, Patrick; Farinholt, Kevin; Friedersdorf, Fritz; Pepi, Marc; Ghoshal, Anindya
2017-02-01
Cyclic loading of mechanical components promotes the formation of dislocation dipoles in metals, which can serve as precursors to crack nucleation and ultimately lead to failure. In the laboratory setting, an acoustic nonlinearity parameter has been assessed as an effective indicator for characterizing the progression of fatigue damage precursors. However, the need to use monochromatic waves of medium-to-high acoustic energy has presented a constraint, making it problematic for use in field applications. This paper presents a potential approach for field measurement of acoustic nonlinearity by using general purpose ultrasonic pulser-receivers. Nonlinear ultrasonic measurements during fatigue testing were analyzed by the using contact and immersion pulse-through method. A novel cross-correlation filtering technique was developed to extract the fundamental and higher harmonic waves from the signals. As in the case of the classic harmonic generation, the nonlinearity parameters of the second and third harmonics indicate a strong correlation with fatigue cycles. Consideration was given to potential nonlinearities in the measurement system, and tests have confirmed that measured second harmonic signals exhibit a linear dependence on the input signal strength, further affirming the conclusion that this parameter relates to damage precursor formation from cyclic loading.
Filtering Non-Linear Transfer Functions on Surfaces.
Heitz, Eric; Nowrouzezahrai, Derek; Poulin, Pierre; Neyret, Fabrice
2014-07-01
Applying non-linear transfer functions and look-up tables to procedural functions (such as noise), surface attributes, or even surface geometry are common strategies used to enhance visual detail. Their simplicity and ability to mimic a wide range of realistic appearances have led to their adoption in many rendering problems. As with any textured or geometric detail, proper filtering is needed to reduce aliasing when viewed across a range of distances, but accurate and efficient transfer function filtering remains an open problem for several reasons: transfer functions are complex and non-linear, especially when mapped through procedural noise and/or geometry-dependent functions, and the effects of perspective and masking further complicate the filtering over a pixel's footprint. We accurately solve this problem by computing and sampling from specialized filtering distributions on the fly, yielding very fast performance. We investigate the case where the transfer function to filter is a color map applied to (macroscale) surface textures (like noise), as well as color maps applied according to (microscale) geometric details. We introduce a novel representation of a (potentially modulated) color map's distribution over pixel footprints using Gaussian statistics and, in the more complex case of high-resolution color mapped microsurface details, our filtering is view- and light-dependent, and capable of correctly handling masking and occlusion effects. Our approach can be generalized to filter other physical-based rendering quantities. We propose an application to shading with irradiance environment maps over large terrains. Our framework is also compatible with the case of transfer functions used to warp surface geometry, as long as the transformations can be represented with Gaussian statistics, leading to proper view- and light-dependent filtering results. Our results match ground truth and our solution is well suited to real-time applications, requires only a few
State estimation of connected vehicles using a nonlinear ensemble filter
Institute of Scientific and Technical Information of China (English)
刘江; 陈华展; 蔡伯根; 王剑
2015-01-01
The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of dynamic states of the vehicles under the cooperative environments is a fundamental issue. By integrating multiple sensors, localization modules in OBUs (on-board units) require effective estimation solutions to cope with various operation conditions. Based on the filtering estimation framework for sensor fusion, an ensemble Kalman filter (EnKF) is introduced to estimate the vehicle’s state with observations from navigation satellites and neighborhood vehicles, and the original EnKF solution is improved by using the cubature transformation to fulfill the requirements of the nonlinearity approximation capability, where the conventional ensemble analysis operation in EnKF is modified to enhance the estimation performance without increasing the computational burden significantly. Simulation results from a nonlinear case and the cooperative vehicle localization scenario illustrate the capability of the proposed filter, which is crucial to realize the active safety of connected vehicles in future intelligent transportation.
On a nonlinear Kalman filter with simplified divided difference approximation
Luo, Xiaodong
2012-03-01
We present a new ensemble-based approach that handles nonlinearity based on a simplified divided difference approximation through Stirling\\'s interpolation formula, which is hence called the simplified divided difference filter (sDDF). The sDDF uses Stirling\\'s interpolation formula to evaluate the statistics of the background ensemble during the prediction step, while at the filtering step the sDDF employs the formulae in an ensemble square root filter (EnSRF) to update the background to the analysis. In this sense, the sDDF is a hybrid of Stirling\\'s interpolation formula and the EnSRF method, while the computational cost of the sDDF is less than that of the EnSRF. Numerical comparison between the sDDF and the EnSRF, with the ensemble transform Kalman filter (ETKF) as the representative, is conducted. The experiment results suggest that the sDDF outperforms the ETKF with a relatively large ensemble size, and thus is a good candidate for data assimilation in systems with moderate dimensions. © 2011 Elsevier B.V. All rights reserved.
Nonlinear filtering for character recognition in low quality document images
Diaz-Escobar, Julia; Kober, Vitaly
2014-09-01
Optical character recognition in scanned printed documents is a well-studied task, where the captured conditions like sheet position, illumination, contrast and resolution are controlled. Nowadays, it is more practical to use mobile devices for document capture than a scanner. So as a consequence, the quality of document images is often poor owing to presence of geometric distortions, nonhomogeneous illumination, low resolution, etc. In this work we propose to use multiple adaptive nonlinear composite filters for detection and classification of characters. Computer simulation results obtained with the proposed system are presented and discussed.
Kypraios, Ioannis; Young, Rupert C. D.; Birch, Philip M.; Chatwin, Christopher R.
2003-08-01
The various types of synthetic discriminant function (sdf) filter result in a weighted linear superposition of the training set images. Neural network training procedures result in a non-linear superposition of the training set images or, effectively, a feature extraction process, which leads to better interpolation properties than achievable with the sdf filter. However, generally, shift invariance is lost since a data dependant non-linear weighting function is incorporated in the input data window. As a compromise, we train a non-linear superposition filter via neural network methods with the constraint of a linear input to allow for shift invariance. The filter can then be used in a frequency domain based optical correlator. Simulation results are presented that demonstrate the improved training set interpolation achieved by the non-linear filter as compared to a linear superposition filter.
Neural network-based H∞ filtering for nonlinear systems with time-delays
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed.Firstly,neural networks are employed to approximate the nonlinearities.Next,the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion (LDI).Finally,based on the LDI model,a neural network-based nonlinear filter (NNBNF) is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality (LMI) constraints.Compared with the existing nonlinear filters,NNBNF is time-invariant and numerically tractable.The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.
A 3-D nonlinear recursive digital filter for video image processing
Bauer, P. H.; Qian, W.
1991-01-01
This paper introduces a recursive 3-D nonlinear digital filter, which is capable of performing noise suppression without degrading important image information such as edges in space or time. It also has the property of unnoticeable bandwidth reduction immediately after a scene change, which makes the filter an attractive preprocessor to many interframe compression algorithms. The filter consists of a nonlinear 2-D spatial subfilter and a 1-D temporal filter. In order to achieve the required computational speed and increase the flexibility of the filter, all of the linear shift-variant filter modules are of the IIR type.
Rigatos, Gerasimos G
2015-01-01
This monograph presents recent advances in differential flatness theory and analyzes its use for nonlinear control and estimation. It shows how differential flatness theory can provide solutions to complicated control problems, such as those appearing in highly nonlinear multivariable systems and distributed-parameter systems. Furthermore, it shows that differential flatness theory makes it possible to perform filtering and state estimation for a wide class of nonlinear dynamical systems and provides several descriptive test cases. The book focuses on the design of nonlinear adaptive controllers and nonlinear filters, using exact linearization based on differential flatness theory. The adaptive controllers obtained can be applied to a wide class of nonlinear systems with unknown dynamics, and assure reliable functioning of the control loop under uncertainty and varying operating conditions. The filters obtained outperform other nonlinear filters in terms of accuracy of estimation and computation speed. The bo...
Advanced nonlinear control of three phase series active power filter
Directory of Open Access Journals (Sweden)
Abouelmahjoub Y.
2014-01-01
Full Text Available The problem of controlling three-phase series active power filter (TPSAPF is addressed in this paper in presence of the perturbations in the voltages of the electrical supply network. The control objective of the TPSAPF is twofold: (i compensation of all voltage perturbations (voltage harmonics, voltage unbalance and voltage sags, (ii regulation of the DC bus voltage of the inverter. A controller formed by two nonlinear regulators is designed, using the Backstepping technique, to provide the above compensation. The regulation of the DC bus voltage of the inverter is ensured by the use of a diode bridge rectifier which its output is in parallel with the DC bus capacitor. The Analysis of controller performances is illustrated by numerical simulation in Matlab/Simulink environment.
Nonlinear Optical Materials for the Smart Filtering of Optical Radiation.
Dini, Danilo; Calvete, Mário J F; Hanack, Michael
2016-11-23
The control of luminous radiation has extremely important implications for modern and future technologies as well as in medicine. In this Review, we detail chemical structures and their relevant photophysical features for various groups of materials, including organic dyes such as metalloporphyrins and metallophthalocyanines (and derivatives), other common organic materials, mixed metal complexes and clusters, fullerenes, dendrimeric nanocomposites, polymeric materials (organic and/or inorganic), inorganic semiconductors, and other nanoscopic materials, utilized or potentially useful for the realization of devices able to filter in a smart way an external radiation. The concept of smart is referred to the characteristic of those materials that are capable to filter the radiation in a dynamic way without the need of an ancillary system for the activation of the required transmission change. In particular, this Review gives emphasis to the nonlinear optical properties of photoactive materials for the function of optical power limiting. All known mechanisms of optical limiting have been analyzed and discussed for the different types of materials.
Empirical intrinsic geometry for nonlinear modeling and time series filtering.
Talmon, Ronen; Coifman, Ronald R
2013-07-30
In this paper, we present a method for time series analysis based on empirical intrinsic geometry (EIG). EIG enables one to reveal the low-dimensional parametric manifold as well as to infer the underlying dynamics of high-dimensional time series. By incorporating concepts of information geometry, this method extends existing geometric analysis tools to support stochastic settings and parametrizes the geometry of empirical distributions. However, the statistical models are not required as priors; hence, EIG may be applied to a wide range of real signals without existing definitive models. We show that the inferred model is noise-resilient and invariant under different observation and instrumental modalities. In addition, we show that it can be extended efficiently to newly acquired measurements in a sequential manner. These two advantages enable us to revisit the Bayesian approach and incorporate empirical dynamics and intrinsic geometry into a nonlinear filtering framework. We show applications to nonlinear and non-Gaussian tracking problems as well as to acoustic signal localization.
Reduction of nonlinear patterning effects in SOA-based All-optical Switches using Optical filtering
DEFF Research Database (Denmark)
Nielsen, Mads Lønstrup; Mørk, Jesper; Skaguchi, J.
2005-01-01
We explain theoretically, and demonstrate and quantify experimentally, how appropriate filtering can reduce the dominant nonlinear patterning effect, which limits the performance of differential-mode SOA-based switches.......We explain theoretically, and demonstrate and quantify experimentally, how appropriate filtering can reduce the dominant nonlinear patterning effect, which limits the performance of differential-mode SOA-based switches....
Hu, Jun; Gao, Huijun
2014-01-01
This monograph introduces methods for handling filtering and control problems in nonlinear stochastic systems arising from network-induced phenomena consequent on limited communication capacity. Such phenomena include communication delay, packet dropout, signal quantization or saturation, randomly occurring nonlinearities and randomly occurring uncertainties.The text is self-contained, beginning with an introduction to nonlinear stochastic systems, network-induced phenomena and filtering and control, moving through a collection of the latest research results which focuses on the three aspects
Local-instantaneous filtering in the integral transform solution of nonlinear diffusion problems
Macêdo, E. N.; Cotta, R. M.; Orlande, H. R. B.
A novel filtering strategy is proposed to be utilized in conjunction with the Generalized Integral Transform Technique (GITT), in the solution of nonlinear diffusion problems. The aim is to optimize convergence enhancement, yielding computationally efficient eigenfunction expansions. The proposed filters include space and time dependence, extracted from linearized versions of the original partial differential system. The scheme automatically updates the filter along the time integration march, as the required truncation orders for the user requested accuracy begin to exceed a prescribed maximum system size. A fully nonlinear heat conduction example is selected to illustrate the computational performance of the filtering strategy, against the classical single-filter solution behavior.
A novel extended Kalman filter for a class of nonlinear systems
Institute of Scientific and Technical Information of China (English)
DONG Zhe; YOU Zheng
2006-01-01
Estimation of the state variables of nonlinear systems is one of the fundamental and significant problems in control and signal processing. A new extended Kalman filtering approach for a class of nonlinear discrete-time systems in engineering is presented in this paper. In contrast to the celebrated extended Kalman filter (EKF), there is no linearization operation in the design procedure of the filter, and the parameters of the filter are obtained through minimizing a proper upper bound of the mean-square estimation error. Simulation results show that this filter can provide higher estimation precision than that provided by the EKF.
Highway traffic estimation of improved precision using the derivative-free nonlinear Kalman Filter
Rigatos, Gerasimos; Siano, Pierluigi; Zervos, Nikolaos; Melkikh, Alexey
2015-12-01
The paper proves that the PDE dynamic model of the highway traffic is a differentially flat one and by applying spatial discretization its shows that the model's transformation into an equivalent linear canonical state-space form is possible. For the latter representation of the traffic's dynamics, state estimation is performed with the use of the Derivative-free nonlinear Kalman Filter. The proposed filter consists of the Kalman Filter recursion applied on the transformed state-space model of the highway traffic. Moreover, it makes use of an inverse transformation, based again on differential flatness theory which enables to obtain estimates of the state variables of the initial nonlinear PDE model. By avoiding approximate linearizations and the truncation of nonlinear terms from the PDE model of the traffic's dynamics the proposed filtering methods outperforms, in terms of accuracy, other nonlinear estimators such as the Extended Kalman Filter. The article's theoretical findings are confirmed through simulation experiments.
Donnet, Benoît; Baynat, Bruno; Friedman, Timur
2006-01-01
Where distributed agents must share voluminous set mem- bership information, Bloom filters provide a compact, though lossy, way for them to do so. Numerous recent networking papers have examined the trade-offs between the bandwidth consumed by the transmission of Bloom filters, and the er- ror rate, which takes the form of false positives, and which rises the more the filters are compressed. In this paper, we introduce the retouched Bloom filter (RBF), an extension that makes the Bloom filter...
An improved exponential filter for fast nonlinear registration of brain magnetic resonance images
Institute of Scientific and Technical Information of China (English)
Zhiying Long; Li Yao; Kewei Chen; Danling Peng
2009-01-01
A linear elastic convolution filter was derived from the eigenfunctions of the Navier-Stokes differential operator by Bro-Nielsen in order to match images with large deformations. Due to the complexity of constructing the elastic convolution filter, the algorithm's effi-ciency reduces rapidly with the increase in the image's size. In our previous work, a simple two-sided exponential filter with high efficiency was proposed to approximate an elastic filter. However, its poor smoothness may degenerate the performance. In this paper, a new expo-nential filter was constructed by utilizing a modified nonlinear curve fitting method to approximate the elastic filter. The new filter's good smoothness makes its performance comparable to an elastic filter. Its simple and separable form makes the algorithm's speed faster than the elastic filter. Furthermore, our experiments demonstrated that the new filter was suitable for both the elastic and fluid models.
Rigatos, Gerasimos
2016-07-01
The Derivative-free nonlinear Kalman Filter is used for developing a communication system that is based on a chaotic modulator such as the Duffing system. In the transmitter's side, the source of information undergoes modulation (encryption) in which a chaotic signal generated by the Duffing system is the carrier. The modulated signal is transmitted through a communication channel and at the receiver's side demodulation takes place, after exploiting the estimation provided about the state vector of the chaotic oscillator by the Derivative-free nonlinear Kalman Filter. Evaluation tests confirm that the proposed filtering method has improved performance over the Extended Kalman Filter and reduces significantly the rate of transmission errors. Moreover, it is shown that the proposed Derivative-free nonlinear Kalman Filter can work within a dual Kalman Filtering scheme, for performing simultaneously transmitter-receiver synchronisation and estimation of unknown coefficients of the communication channel.
Adaptive Filters with Error Nonlinearities: Mean-Square Analysis and Optimum Design
Directory of Open Access Journals (Sweden)
Ali H. Sayed
2001-01-01
Full Text Available This paper develops a unified approach to the analysis and design of adaptive filters with error nonlinearities. In particular, the paper performs stability and steady-state analysis of this class of filters under weaker conditions than what is usually encountered in the literature, and without imposing any restriction on the color or statistics of the input. The analysis results are subsequently used to derive an expression for the optimum nonlinearity, which turns out to be a function of the probability density function of the estimation error. Some common nonlinearities are shown to be approximations to the optimum nonlinearity. The framework pursued here is based on energy conservation arguments.
[Radiation dose reduction using a non-linear image filter in MDCT].
Nakashima, Junya; Takahashi, Toshiyuki; Takahashi, Yoshimasa; Imai, Yasuhiro; Ishihara, Yotaro; Kato, Kyoichi; Nakazawa, Yasuo
2010-05-20
The development of MDCT enabled various high-quality 3D imaging and optimized scan timing with contrast injection in a multi-phase dynamic study. Since radiation dose tends to increase to yield such high-quality images, we have to make an effort to reduce radiation dose. A non-linear image filter (Neuro 3D filter: N3D filter) has been developed in order to improve image noise. The purpose of this study was to evaluate the physical performance and effectiveness of this non-linear image filter using phantoms, and show how we can reduce radiation dose in clinical use of this filter. This N3D filter reduced radiation dose by about 50%, with minimum deterioration of spatial reduction in phantom and clinical studies. This filter shows great potential for clinical application.
Hypersonic entry vehicle state estimation using nonlinearity-based adaptive cubature Kalman filters
Sun, Tao; Xin, Ming
2017-05-01
Guidance, navigation, and control of a hypersonic vehicle landing on the Mars rely on precise state feedback information, which is obtained from state estimation. The high uncertainty and nonlinearity of the entry dynamics make the estimation a very challenging problem. In this paper, a new adaptive cubature Kalman filter is proposed for state trajectory estimation of a hypersonic entry vehicle. This new adaptive estimation strategy is based on the measure of nonlinearity of the stochastic system. According to the severity of nonlinearity along the trajectory, the high degree cubature rule or the conventional third degree cubature rule is adaptively used in the cubature Kalman filter. This strategy has the benefit of attaining higher estimation accuracy only when necessary without causing excessive computation load. The simulation results demonstrate that the proposed adaptive filter exhibits better performance than the conventional third-degree cubature Kalman filter while maintaining the same performance as the uniform high degree cubature Kalman filter but with lower computation complexity.
Estimation in continuous-time stochastic| volatility models using nonlinear filters
DEFF Research Database (Denmark)
Nielsen, Jan Nygaard; Vestergaard, M.; Madsen, Henrik
2000-01-01
Presents a correction to the authorship of the article 'Estimation in Continuous-Time Stochastic Volatility Models Using Nonlinear Filters,' published in the periodical 'International Journal of Theoretical and Applied Finance,' Vol. 3, No. 2., pp. 279-308.......Presents a correction to the authorship of the article 'Estimation in Continuous-Time Stochastic Volatility Models Using Nonlinear Filters,' published in the periodical 'International Journal of Theoretical and Applied Finance,' Vol. 3, No. 2., pp. 279-308....
A Bayes Formula for Nonlinear Filtering with Gaussian and Cox Noise
Directory of Open Access Journals (Sweden)
Vidyadhar Mandrekar
2011-01-01
Full Text Available A Bayes-type formula is derived for the nonlinear filter where the observation contains both general Gaussian noise as well as Cox noise whose jump intensity depends on the signal. This formula extends the well-known Kallianpur-Striebel formula in the classical non-linear filter setting. We also discuss Zakai-type equations for both the unnormalized conditional distribution as well as unnormalized conditional density in case the signal is a Markovian jump diffusion.
The Use of Nonlinear Constitutive Equations to Evaluate Draw Resistance and Filter Ventilation
Eitzinger B; Ederer G
2014-01-01
This study investigates by nonlinear constitutive equations the influence of tipping paper, cigarette paper, filter, and tobacco rod on the degree of filter ventilation and draw resistance. Starting from the laws of conservation, the path to the theory of fluid dynamics in porous media and Darcy's law is reviewed and, as an extension to Darcy's law, two different nonlinear pressure drop-flow relations are proposed. It is proven that these relations are valid constitutive equations and the par...
1989-10-30
In this Phase I SBIR study, new methods are developed for the system identification and stochastic filtering of nonlinear controlled Markov processes...state space Markov process models and canonical variate analysis (CVA) for obtaining optimal nonlinear procedures for system identification and stochastic
An Unscented Kalman Filter Approach to the Estimation of Nonlinear Dynamical Systems Models
Chow, Sy-Miin; Ferrer, Emilio; Nesselroade, John R.
2007-01-01
In the past several decades, methodologies used to estimate nonlinear relationships among latent variables have been developed almost exclusively to fit cross-sectional models. We present a relatively new estimation approach, the unscented Kalman filter (UKF), and illustrate its potential as a tool for fitting nonlinear dynamic models in two ways:…
Institute of Scientific and Technical Information of China (English)
ZHANG JIA-SHU; XIAO XIAN-CI
2001-01-01
A multistage adaptive higher-order nonlinear finite impulse response (MAHONFIR) filter is proposed to predict chaotic time series. Using this approach, we may readily derive the decoupled parallel algorithm for the adaptation of the coefficients of the MAHONFIR filter, to guarantee a more rapid convergence of the adaptive weights to their optimal values. Numerical simulation results show that the MAHONFIR filters proposed here illustrate a very good performance for making an adaptive prediction of chaotic time series.
Nonlinear performance characterization in an eight-pole quasi-elliptic bandpass filter
Energy Technology Data Exchange (ETDEWEB)
Mateu, J [Centre Tecnologic de Telecomunicacions de Catalunya, Edifici Nexus, Gran Capita, 2nd Floor, Room 202-203, 08034 Barcelona (Spain); Collado, C [Universitat Politecnica de Catalunya, Department of Signal Theory and Communications, Campus Nord UPC, D3-Jordi Girona, 1-3, 08034 Barcelona (Spain); Menendez, O [Universitat Politecnica de Catalunya, Department of Signal Theory and Communications, Campus Nord UPC, D3-Jordi Girona, 1-3, 08034 Barcelona (Spain); O' Callaghan, J M [Universitat Politecnica de Catalunya, Department of Signal Theory and Communications, Campus Nord UPC, D3-Jordi Girona, 1-3, 08034 Barcelona (Spain)
2004-05-01
In this work we predict the nonlinear behaviour of an eight-pole quasi-elliptic bandpass high temperature superconducting (HTS) filter with an equivalent circuit extracted from intermodulation measurements performed at the centre of the filter passband. We present measurements that show that the equivalent circuit is able to predict the intermodulation products produced by the filter when driven by two in-band or out-of-band sinusoidal signals. Numerical techniques based on harmonic balance are used to extract the elements of the equivalent circuit and to simulate its nonlinear performance.
2-D nonlinear IIR-filters for image processing - An exploratory analysis
Bauer, P. H.; Sartori, M.
1991-01-01
A new nonlinear IIR filter structure is introduced and its deterministic properties are analyzed. It is shown to be better suited for image processing applications than its linear shift-invariant counterpart. The new structure is obtained from causality inversion of a 2D quarterplane causal linear filter with respect to the two directions of propagation. It is demonstrated, that by using this design, a nonlinear 2D lowpass filter can be constructed, which is capable of effectively suppressing Gaussian or impulse noise without destroying important image information.
2-D nonlinear IIR-filters for image processing - An exploratory analysis
Bauer, P. H.; Sartori, M.
1991-01-01
A new nonlinear IIR filter structure is introduced and its deterministic properties are analyzed. It is shown to be better suited for image processing applications than its linear shift-invariant counterpart. The new structure is obtained from causality inversion of a 2D quarterplane causal linear filter with respect to the two directions of propagation. It is demonstrated, that by using this design, a nonlinear 2D lowpass filter can be constructed, which is capable of effectively suppressing Gaussian or impulse noise without destroying important image information.
Directory of Open Access Journals (Sweden)
Jingjing Wu
2015-01-01
Full Text Available A robust particle filter (PF and its application to fault/defect detection of nonlinear system are investigated in this paper. First, an adaptive parametric model is exploited as the observation model for a nonlinear system. Second, by incorporating the parametric model, particle filter is employed to estimate more accurate hidden states for the nonlinear stochastic system. Third, by formulating the problem of defect detection within the hypothesis testing framework, the statistical properties of the proposed testing are established. Finally, experimental results demonstrate the effectiveness and robustness of the proposed detector on real defect detection and localization in images.
Command Filtering-Based Fuzzy Control for Nonlinear Systems With Saturation Input.
Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Lin, Chong
2016-12-13
In this paper, command filtering-based fuzzy control is designed for uncertain multi-input multioutput (MIMO) nonlinear systems with saturation nonlinearity input. First, the command filtering method is employed to deal with the explosion of complexity caused by the derivative of virtual controllers. Then, fuzzy logic systems are utilized to approximate the nonlinear functions of MIMO systems. Furthermore, error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach. The developed method will guarantee all signals of the systems are bounded. The effectiveness and advantages of the theoretic result are obtained by a simulation example.
Grey Box Non-Linearities Modeling and Characterization of a BandPass BAW Filter
Directory of Open Access Journals (Sweden)
M. Mabrouk
2016-06-01
Full Text Available In this work, the non-linearities of a 3G/UMTS geared BandPass Bulk Acoustic Wave ladder filter composed of five resonators were modeled using non-linear modified Butterworth-Van Dyke model. The non-linear characteristics were measured and simulated, and they were compared and found to be fairly identical. The filter's central frequency is 2.12 GHz, the corresponding bandwidth is 61.55 MHz, and the quality factor is 34.55.
Decentralized neural identifier and control for nonlinear systems based on extended Kalman filter.
Castañeda, Carlos E; Esquivel, P
2012-07-01
A time-varying learning algorithm for recurrent high order neural network in order to identify and control nonlinear systems which integrates the use of a statistical framework is proposed. The learning algorithm is based in the extended Kalman filter, where the associated state and measurement noises covariance matrices are composed by the coupled variance between the plant states. The formulation allows identification of interactions associate between plant state and the neural convergence. Furthermore, a sliding window-based method for dynamical modeling of nonstationary systems is presented to improve the neural identification in the proposed methodology. The efficiency and accuracy of the proposed method is assessed to a five degree of freedom (DOF) robot manipulator where based on the time-varying neural identifier model, the decentralized discrete-time block control and sliding mode techniques are used to design independent controllers and develop the trajectory tracking for each DOF.
Method and system for training dynamic nonlinear adaptive filters which have embedded memory
Rabinowitz, Matthew (Inventor)
2002-01-01
Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.
A novel strong tracking finite-difference extended Kalman filter for nonlinear eye tracking
Institute of Scientific and Technical Information of China (English)
ZHANG ZuTao; ZHANG JiaShu
2009-01-01
Non-Intrusive methods for eye tracking are Important for many applications of vision-based human computer interaction. However, due to the high nonlinearity of eye motion, how to ensure the robust-ness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into today's interfaces. In this paper, we present a strong tracking finite-difference extended Kalman filter algorithm, aiming to overcome the difficulty In modeling nonlinear eye tracking. In filtering calculation, strong tracking factor is introduced to modify a priori covariance matrix and im-prove the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions for eye tracking. The latest experimental results show the validity of our method for eye tracking under realistic conditions.
NONLINEAR FILTER METHOD OF GPS DYNAMIC POSITIONING BASED ON BANCROFT ALGORITHM
Institute of Scientific and Technical Information of China (English)
ZHANGQin; TAOBen-zao; ZHAOChao-ying; WANGLi
2005-01-01
Because of the ignored items after linearization, the extended Kalman filter (EKF) becomes a form of suboptimal gradient descent algorithm. The emanative tendency exists in GPS solution when the filter equations are ill-posed. The deviation in the estimation cannot be avoided. Furthermore, the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions. To solve the above problems in GPS dynamic positioning by using EKF, a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American. The method separates the spatial parts from temporal parts during processing the GPS filter problems, and solves the nonlinear GPS dynamic positioning, thus getting stable and reliable dynamic positioning solutions.
Finite-time H∞ filtering for non-linear stochastic systems
Hou, Mingzhe; Deng, Zongquan; Duan, Guangren
2016-09-01
This paper describes the robust H∞ filtering analysis and the synthesis of general non-linear stochastic systems with finite settling time. We assume that the system dynamic is modelled by Itô-type stochastic differential equations of which the state and the measurement are corrupted by state-dependent noises and exogenous disturbances. A sufficient condition for non-linear stochastic systems to have the finite-time H∞ performance with gain less than or equal to a prescribed positive number is established in terms of a certain Hamilton-Jacobi inequality. Based on this result, the existence of a finite-time H∞ filter is given for the general non-linear stochastic system by a second-order non-linear partial differential inequality, and the filter can be obtained by solving this inequality. The effectiveness of the obtained result is illustrated by a numerical example.
A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems with Additive Noise
Directory of Open Access Journals (Sweden)
Yong Zhou
2015-01-01
Full Text Available The Kalman filter (KF, extended KF, and unscented KF all lack a self-adaptive capacity to deal with system noise. This paper describes a new adaptive filtering approach for nonlinear systems with additive noise. Based on the square-root unscented KF (SRUKF, traditional Maybeck’s estimator is modified and extended to nonlinear systems. The square root of the process noise covariance matrix Q or that of the measurement noise covariance matrix R is estimated straightforwardly. Because positive semidefiniteness of Q or R is guaranteed, several shortcomings of traditional Maybeck’s algorithm are overcome. Thus, the stability and accuracy of the filter are greatly improved. In addition, based on three different nonlinear systems, a new adaptive filtering technique is described in detail. Specifically, simulation results are presented, where the new filter was applied to a highly nonlinear model (i.e., the univariate nonstationary growth model (UNGM. The UNGM is compared with the standard SRUKF to demonstrate its superior filtering performance. The adaptive SRUKF (ASRUKF algorithm can complete direct recursion and calculate the square roots of the variance matrixes of the system state and noise, which ensures the symmetry and nonnegative definiteness of the matrixes and greatly improves the accuracy, stability, and self-adaptability of the filter.
Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
. The second contribution of this paper is to derive a new particle filter which we term the Mean Shifted Particle Filter (MSPFb). We show that the MSPFb outperforms the standard Particle Filter by delivering more precise state estimates, and in general the MSPFb has lower Monte Carlo variation in the reported...
A filter algorithm for multi-measurement nonlinear system with parameter perturbation
Institute of Scientific and Technical Information of China (English)
GUO Yun-fei; WEI Wei; XUE An-ke; MAO Dong-cai
2006-01-01
An improved interacting multiple models particle filter (IMM-PF) algorithm is proposed for multi-measurement nonlinear system with parameter perturbation. It divides the perturbation region into sub-regions and assigns each of them a particle filter. Hence the perturbation problem is converted into a multi-model filters problem. It combines the multiple measurements into a fusion value according to their likelihood function. In the simulation study, we compared it with the IMM-KF and the H-infinite filter; the results testify to its advantage over the other two methods.
Signal-to-noise-ratio analysis for nonlinear N-ary phase filters.
Miller, Paul C
2007-09-01
The problem of recognizing targets in nonoverlapping clutter using nonlinear N-ary phase filters is addressed. Using mathematical analysis, expressions were derived for an N-ary phase filter and the intensity variance of an optical correlator output. The N-ary phase filter was shown to consist of an infinite sum of harmonic terms whose periodicity was determined by N. For the intensity variance, it was found that under certain conditions the variance was minimized due to a previously undiscovered phase quadrature effect. Comparison showed that optimal real filters produced greater signal-to-noise-ratio values than the continuous phase versions as a consequence of this effect.
IDENTIFICATION OF NONLINEAR DYNAMIC SYSTEMS:TIME-FREQUENCY FILTERING AND SKELETON CURVES
Institute of Scientific and Technical Information of China (English)
王丽丽; 张景绘
2001-01-01
The nonlinear behavior varying with the instantaneous response was analyzed through the joint time-frequency analysis method for a class of S. D. O . F nonlinear system.A masking operator on definite regions is defined and two theorems are presented. Based on these, the nonlinear system is modeled with a special time-varying linear one, called the generalized skeleton linear system ( GSLS ). The frequency skeleton curve and the damping skeleton curve are defined to describe the main feature of the non-linearity as well. More over, an identification method is proposed through the skeleton curves and the time frequency filtering technique.
Nonlinear Imaging of Microbubble Contrast Agent Using the Volterra Filter: In Vivo Results.
Du, Juan; Liu, Dalong; Ebbini, Emad S
2016-12-01
A nonlinear filtering approach to imaging the dynamics of microbubble ultrasound contrast agents (UCAs) in microvessels is presented. The approach is based on the adaptive third-order Volterra filter (TVF), which separates the linear, quadratic, and cubic components from beamformed pulse-echo ultrasound data. The TVF captures polynomial nonlinearities utilizing the full spectral components of the echo data and not from prespecified bands, e.g., second or third harmonics. This allows for imaging using broadband pulse transmission to preserve the axial resolution and the SNR. In this paper, we present the results from imaging the UCA activity in a 200- [Formula: see text] cellulose tube embedded in a tissue-mimicking phantom using a linear array diagnostic probe. The contrast enhancement was quantified by computing the contrast-to-tissue ratio (CTR) for the different imaging components, i.e., B-mode, pulse inversion (PI), and the TVF components. The temporal mean and standard deviation of the CTR values were computed for all frames in a given data set. Quadratic and cubic images, referred to as QB-mode and CB-mode, produced higher mean CTR values than B-mode, which showed improved sensitivity. Compared with PI, they produced similar or higher mean CTR values with greater spatial specificity. We also report in vivo results from imaging UCA activity in an implanted LNCaP tumor with heterogeneous perfusion. The temporal means and standard deviations of the echogenicity were evaluated in small regions with different perfusion levels in the presence and absence of UCA. The in vivo measurements behaved consistently with the corresponding calculations obtained under microflow conditions in vitro. Specifically, the nonlinear VF components produced larger increases in the temporal mean and standard deviation values compared with B-mode in regions with low to relatively high perfusion. These results showed that polynomial filters such as the TVF can provide an important tool
Interaction of Lyapunov vectors in the formulation of the nonlinear extension of the Kalman filter.
Palatella, Luigi; Trevisan, Anna
2015-04-01
When applied to strongly nonlinear chaotic dynamics the extended Kalman filter (EKF) is prone to divergence due to the difficulty of correctly forecasting the forecast error probability density function. In operational forecasting applications ensemble Kalman filters circumvent this problem with empirical procedures such as covariance inflation. This paper presents an extension of the EKF that includes nonlinear terms in the evolution of the forecast error estimate. This is achieved starting from a particular square-root implementation of the EKF with assimilation confined in the unstable subspace (EKF-AUS), that is, the span of the Lyapunov vectors with non-negative exponents. When the error evolution is nonlinear, the space where it is confined is no more restricted to the unstable and neutral subspace causing filter divergence. The algorithm presented here, denominated EKF-AUS-NL, includes the nonlinear terms in the error dynamics: These result from the nonlinear interaction among the leading Lyapunov vectors and account for all directions where the error growth may take place. Numerical results show that with the nonlinear terms included, filter divergence can be avoided. We test the algorithm on the Lorenz96 model, showing very promising results.
Weighted Ensemble Square Root Filters for Non-linear, Non-Gaussian, Data Assimilation
Livings, D. M.; van Leeuwen, P.
2012-12-01
In recent years the Ensemble Kalman Filter (EnKF) has become widely-used in both operational and research data assimilation systems. The particle filter is an alternative ensemble-based algorithm that offers the possibility of improved performance in non-linear and non-Gaussian problems. Papadakis et al (2010) introduced the Weighted Ensemble Kalman Filter (WEnKF) as a combination of the best features of the EnKF and the particle filter. Published work on the WEnKF has so far concentrated on the formulation of the EnKF in which observations are perturbed; no satisfactory general framework has been given for particle filters based on the alternative formulation of the EnKF known as the ensemble square root filter. This presentation will provide such a framework and show how several popular ensemble square root filters fit into it. No linear or Gaussian assumptions about the dynamical or observational models will be necessary. By examining the algorithms closely, shortcuts will be identified that increase both the simplicity and the efficiency of the resulting particle filter in comparison with a naive implementation. A procedure will be given for simply converting an existing ensemble square root filter into a particle filter. The procedure will not be limited to basic ensemble square root filters, but will be able to incorporate common variations such as covariance inflation without making any approximations.
Directory of Open Access Journals (Sweden)
Hua-Ming Qian
2014-01-01
Full Text Available A robust filtering problem is formulated and investigated for a class of nonlinear systems with correlated noises, packet losses, and multiplicative noises. The packet losses are assumed to be independent Bernoulli random variables. The multiplicative noises are described as random variables with bounded variance. Different from the traditional robust filter based on the assumption that the process noises are uncorrelated with the measurement noises, the objective of the addressed robust filtering problem is to design a recursive filter such that, for packet losses and multiplicative noises, the state prediction and filtering covariance matrices have the optimized upper bounds in the case that there are correlated process and measurement noises. Two examples are used to illustrate the effectiveness of the proposed filter.
Generation of Long Waves using Non-Linear Digital Filters
DEFF Research Database (Denmark)
Høgedal, Michael; Frigaard, Peter; Christensen, Morten
1994-01-01
transform of the 1st order surface elevation and subsequently inverse Fourier transformed. Hence, the methods are unsuitable for real-time applications, for example where white noise are filtered digitally to obtain a wave spectrum with built-in stochastic variabillity. In the present paper an approximative...... method for including the correct 2nd order bound terms in such applications is presented. The technique utilizes non-liner digital filters fitted to the appropriate transfer function is derived only for bounded 2nd order subharmonics, as they laboratory experiments generally are considered the most...
Improvement of nonlinear diffusion equation using relaxed geometric mean filter for low PSNR images
DEFF Research Database (Denmark)
Nadernejad, Ehsan
2013-01-01
A new method to improve the performance of low PSNR image denoising is presented. The proposed scheme estimates edge gradient from an image that is regularised with a relaxed geometric mean filter. The proposed method consists of two stages; the first stage consists of a second order nonlinear...... anisotropic diffusion equation with new neighboring structure and the second is a relaxed geometric mean filter, which processes the output of nonlinear anisotropic diffusion equation. The proposed algorithm enjoys the benefit of both nonlinear PDE and relaxed geometric mean filter. In addition, the algorithm...... will not introduce any artefacts, and preserves image details, sharp corners, curved structures and thin lines. Comparison of the results obtained by the proposed method, with those of other methods, shows that a noticeable improvement in the quality of the denoised images, that were evaluated subjectively...
Iterative nonlinear ISI cancellation in optical tilted filter-based Nyquist 4-PAM system
Ju, Cheng; Liu, Na
2016-09-01
The conventional double sideband (DSB) modulation and direct detection scheme suffers from severer power fading, linear and nonlinear inter-symbol interference (ISI) caused by fiber dispersion and square-law direct detection. The system's frequency response deteriorates at high frequencies owing to the limited device bandwidth. Moreover, the linear and nonlinear ISI is enhanced induced by the bandwidth limited effect. In this paper, an optical tilted filter is used to mitigate the effect of power fading, and improve the high frequency response of bandwidth limited device in Nyquist 4-ary pulse amplitude modulation (4-PAM) system. Furtherly, iterative technique is introduced to mitigate the nonlinear ISI caused by the combined effects of electrical Nyquist filter, limited device bandwidth, optical tilted filter, dispersion, and square-law photo-detection. Thus, the system's frequency response is greatly improved and the delivery distance can be extended.
Nonlinear Kalman Filtering in Affine Term Structure Models
DEFF Research Database (Denmark)
Christoffersen, Peter; Dorion, Christian; Jacobs, Kris;
When the relationship between security prices and state variables in dynamic term structure models is nonlinear, existing studies usually linearize this relationship because nonlinear fi…ltering is computationally demanding. We conduct an extensive investigation of this linearization and analyze...... Monte Carlo experiment demonstrates that the unscented Kalman fi…lter is much more accurate than its extended counterpart in fi…ltering the states and forecasting swap rates and caps. Our fi…ndings suggest that the unscented Kalman fi…lter may prove to be a good approach for a number of other problems...... in fi…xed income pricing with nonlinear relationships between the state vector and the observations, such as the estimation of term structure models using coupon bonds and the estimation of quadratic term structure models....
Control of underactuated robotic systems with the use of the derivative-free nonlinear Kalman filter
Rigatos, Gerasimos G.; Siano, Pierluigi
2013-10-01
The Derivative-free nonlinear Kalman Filter is used for developing a robust controller which can be applied to underactuated MIMO robotic systems. Using differential flatness theory it is shown that the model of a closed-chain 2-DOF robotic manipulator can be transformed to linear canonical form. For the linearized equivalent of the robotic system it is shown that a state feedback controller can be designed. Since certain elements of the state vector of the linearized system can not be measured directly, it is proposed to estimate them with the use of a novel filtering method, the so-called Derivative-free nonlinear Kalman Filter. Moreover, by redesigning the Kalman Filter as a disturbance observer, it is is shown that one can estimate simultaneously external disturbances terms that affect the robotic model or disturbance terms which are associated with parametric uncertainty.
Robust Filtering for a Class of Networked Nonlinear Systems With Switching Communication Channels.
Zhang, Lixian; Yin, Xunyuan; Ning, Zepeng; Ye, Dong
2016-02-15
This paper is concerned with the problem of robust filter design for a class of discrete-time networked nonlinear systems. The Takagi-Sugeno fuzzy model is employed to represent the underlying nonlinear dynamics. A multi-channel communication scheme that involves a channel switching phenomenon described by a Markov chain is proposed for data transmission. Two typical communication imperfections, network-induced time-varying delays and packet dropouts are considered in each channel. The objective of this paper is to design an admissible filter such that the filter error system is stochastically stable and ensures a prescribed disturbance attenuation level bound. Based on the Lyapunov-Krasovskii functional method and matrix inequality techniques, sufficient conditions on the existence of the desired filter are obtained. A numerical example is provided to illustrate the effectiveness of the proposed design approach.
Institute of Scientific and Technical Information of China (English)
Shuo Zhang,Yan Zhao,Min Li,; Jianhui Zhao
2015-01-01
The global y optimal recursive filtering problem is stu-died for a class of systems with random parameter matrices, stochastic nonlinearities, correlated noises and missing measure-ments. The stochastic nonlinearities are presented in the system model to reflect multiplicative random disturbances, and the addi-tive noises, process noise and measurement noise, are assumed to be one-step autocorrelated as wel as two-step cross-correlated. A series of random variables is introduced as the missing rates governing the intermittent measurement losses caused by un-favorable network conditions. The aim of the addressed filtering problem is to design an optimal recursive filter for the uncertain systems based on an innovation approach such that the filtering error is global y minimized at each sampling time. A numerical simulation example is provided to il ustrate the effectiveness and applicability of the proposed algorithm.
Hybrid three-dimensional variation and particle filtering for nonlinear systems
Institute of Scientific and Technical Information of China (English)
Leng Hong-Ze; Song Jun-Qiang
2013-01-01
This work addresses the problem of estimating the states of nonlinear dynamic systems with sparse observations.We present a hybrid three-dimensional variation (3DVar) and particle piltering (PF) method,which combines the advantages of 3DVar and particle-based filters.By minimizing the cost function,this approach will produce a better proposal distribution of the state.Afterwards the stochastic resampling step in standard PF can be avoided through a deterministic scheme.The simulation results show that the performance of the new method is superior to the traditional ensemble Kalman filtering (EnKF) and the standard PF,especially in highly nonlinear systems.
Nikitin, Alexei V.; Epard, Marc; Lancaster, John B.; Lutes, Robert L.; Shumaker, Eric A.
2012-12-01
A strong digital communication transmitter in close physical proximity to a receiver of a weak signal can noticeably interfere with the latter even when the respective channels are tens or hundreds of megahertz apart. When time domain observations are made in the signal chain of the receiver between the first mixer and the baseband, this interference is likely to appear impulsive. The impulsive nature of this interference provides an opportunity to reduce its power by nonlinear filtering, improving the quality of the receiver channel. This article describes the mitigation, by a particular nonlinear filter, of the impulsive out-of-band (OOB) interference induced in High Speed Downlink Packet Access (HSDPA) by WiFi transmissions, protocols which coexist in many 3G smartphones and mobile hotspots. Our measurements show a decrease in the maximum error-free bit rate of a 1.95 GHz HSDPA receiver caused by the impulsive interference from an OOB 2.4 GHz WiFi transmission, sometimes down to a small fraction of the rate observed in the absence of the interference. We apply a nonlinear SPART filter to recover a noticeable portion of the lost rate and maintain an error-free connection under much higher levels of the WiFi interference than a receiver that does not contain such a filter. These measurements support our wider investigation of OOB interference resulting from digital modulation, which appears impulsive in a receiver, and its mitigation by nonlinear filters.
A simple new filter for nonlinear high-dimensional data assimilation
Tödter, Julian; Kirchgessner, Paul; Ahrens, Bodo
2015-04-01
The ensemble Kalman filter (EnKF) and its deterministic variants, mostly square root filters such as the ensemble transform Kalman filter (ETKF), represent a popular alternative to variational data assimilation schemes and are applied in a wide range of operational and research activities. Their forecast step employs an ensemble integration that fully respects the nonlinear nature of the analyzed system. In the analysis step, they implicitly assume the prior state and observation errors to be Gaussian. Consequently, in nonlinear systems, the analysis mean and covariance are biased, and these filters remain suboptimal. In contrast, the fully nonlinear, non-Gaussian particle filter (PF) only relies on Bayes' theorem, which guarantees an exact asymptotic behavior, but because of the so-called curse of dimensionality it is exposed to weight collapse. This work shows how to obtain a new analysis ensemble whose mean and covariance exactly match the Bayesian estimates. This is achieved by a deterministic matrix square root transformation of the forecast ensemble, and subsequently a suitable random rotation that significantly contributes to filter stability while preserving the required second-order statistics. The forecast step remains as in the ETKF. The proposed algorithm, which is fairly easy to implement and computationally efficient, is referred to as the nonlinear ensemble transform filter (NETF). The properties and performance of the proposed algorithm are investigated via a set of Lorenz experiments. They indicate that such a filter formulation can increase the analysis quality, even for relatively small ensemble sizes, compared to other ensemble filters in nonlinear, non-Gaussian scenarios. Furthermore, localization enhances the potential applicability of this PF-inspired scheme in larger-dimensional systems. Finally, the novel algorithm is coupled to a large-scale ocean general circulation model. The NETF is stable, behaves reasonably and shows a good
Sequential nonlinear tracking filter without requirement of measurement decorrelation
Institute of Scientific and Technical Information of China (English)
Taifan Quan
2015-01-01
Sequential measurement processing is of benefit to both estimation accuracy and computational efficiency. When the noises are correlated across the measurement components, decorrelation based on covariance matrix factorization is required in the previous methods in order to perform sequential updates properly. A new sequential processing method, which carries out the sequential updates directly using the correlated measurement components, is proposed. And a typical sequential processing example is investigated, where the converted position measure-ments are used to estimate target states by standard Kalman filtering equations and the converted Doppler measurements are then incorporated into a minimum mean squared error (MMSE) estimator with the updated cross-covariance involved to account for the correlated errors. Numerical simulations demonstrate the superiority of the proposed new sequential processing in terms of better accuracy and consistency than the conventional sequential filter based on measurement decorrelation.
A Finitely Additive White Noise Approach to Nonlinear Filtering.
1982-10-01
processes, J. of Multivariate Analysis, 1 (1971). [181. J.L. Lions, Equation differentielles operationnelles et problemes aux limites, Springer-Verlag (1961...Verlag, (1979). [24]. J. Szpirglas, Sur l’equivalence d’equations differentielles stochastiques a valeurs mesures intervenant dans le filtrage Markovien...filtering.I Thi s re ea rch h s e u p p o rted by AF OSR Gran t No . 49620 82 r 0009 . 0. Introduction The theory of Ito stochastic differential equations
Directory of Open Access Journals (Sweden)
Y. Orlov
2002-01-01
Full Text Available The paper is intended to be of tutorial value for Schwartz' distributions theory in nonlinear setting. Mathematical models are presented for nonlinear systems which admit both standard and impulsive inputs. These models are governed by differential equations in distributions whose meaning is generalized to involve nonlinear, non single-valued operating over distributions. The set of generalized solutions of these differential equations is defined via closure, in a certain topology, of the set of the conventional solutions corresponding to standard integrable inputs. The theory is exemplified by mechanical systems with impulsive phenomena, optimal impulsive feedback synthesis, sampled-data filtering of stochastic and deterministic dynamic systems.
3D early embryogenesis image filtering by nonlinear partial differential equations.
Krivá, Z; Mikula, K; Peyriéras, N; Rizzi, B; Sarti, A; Stasová, O
2010-08-01
We present nonlinear diffusion equations, numerical schemes to solve them and their application for filtering 3D images obtained from laser scanning microscopy (LSM) of living zebrafish embryos, with a goal to identify the optimal filtering method and its parameters. In the large scale applications dealing with analysis of 3D+time embryogenesis images, an important objective is a correct detection of the number and position of cell nuclei yielding the spatio-temporal cell lineage tree of embryogenesis. The filtering is the first and necessary step of the image analysis chain and must lead to correct results, removing the noise, sharpening the nuclei edges and correcting the acquisition errors related to spuriously connected subregions. In this paper we study such properties for the regularized Perona-Malik model and for the generalized mean curvature flow equations in the level-set formulation. A comparison with other nonlinear diffusion filters, like tensor anisotropic diffusion and Beltrami flow, is also included. All numerical schemes are based on the same discretization principles, i.e. finite volume method in space and semi-implicit scheme in time, for solving nonlinear partial differential equations. These numerical schemes are unconditionally stable, fast and naturally parallelizable. The filtering results are evaluated and compared first using the Mean Hausdorff distance between a gold standard and different isosurfaces of original and filtered data. Then, the number of isosurface connected components in a region of interest (ROI) detected in original and after the filtering is compared with the corresponding correct number of nuclei in the gold standard. Such analysis proves the robustness and reliability of the edge preserving nonlinear diffusion filtering for this type of data and lead to finding the optimal filtering parameters for the studied models and numerical schemes. Further comparisons consist in ability of splitting the very close objects which
A Nonlinear Entropic Variational Model for Image Filtering
Directory of Open Access Journals (Sweden)
Krim Hamid
2004-01-01
Full Text Available We propose an information-theoretic variational filter for image denoising. It is a result of minimizing a functional subject to some noise constraints, and takes a hybrid form of a negentropy variational integral for small gradient magnitudes and a total variational integral for large gradient magnitudes. The core idea behind this approach is to use geometric insight in helping to construct regularizing functionals and avoiding a subjective choice of a prior in maximum a posteriori estimation. Illustrative experimental results demonstrate a much improved performance of the approach in the presence of Gaussian and heavy-tailed noise.
Sequential Monte Carlo methods for nonlinear discrete-time filtering
Bruno, Marcelo GS
2013-01-01
In these notes, we introduce particle filtering as a recursive importance sampling method that approximates the minimum-mean-square-error (MMSE) estimate of a sequence of hidden state vectors in scenarios where the joint probability distribution of the states and the observations is non-Gaussian and, therefore, closed-form analytical expressions for the MMSE estimate are generally unavailable.We begin the notes with a review of Bayesian approaches to static (i.e., time-invariant) parameter estimation. In the sequel, we describe the solution to the problem of sequential state estimation in line
Variance-Constrained Multiobjective Control and Filtering for Nonlinear Stochastic Systems: A Survey
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Lifeng Ma
2013-01-01
Full Text Available The multiobjective control and filtering problems for nonlinear stochastic systems with variance constraints are surveyed. First, the concepts of nonlinear stochastic systems are recalled along with the introduction of some recent advances. Then, the covariance control theory, which serves as a practical method for multi-objective control design as well as a foundation for linear system theory, is reviewed comprehensively. The multiple design requirements frequently applied in engineering practice for the use of evaluating system performances are introduced, including robustness, reliability, and dissipativity. Several design techniques suitable for the multi-objective variance-constrained control and filtering problems for nonlinear stochastic systems are discussed. In particular, as a special case for the multi-objective design problems, the mixed H2/H∞ control and filtering problems are reviewed in great detail. Subsequently, some latest results on the variance-constrained multi-objective control and filtering problems for the nonlinear stochastic systems are summarized. Finally, conclusions are drawn, and several possible future research directions are pointed out.
White noise theory of robust nonlinear filtering with correlated state and observation noises
Bagchi, Arunabha; Karandikar, Rajeeva
1994-01-01
In the existing `direct¿ white noise theory of nonlinear filtering, the state process is still modelled as a Markov process satisfying an Itô stochastic differential equation, while a `finitely additive¿ white noise is used to model the observation noise. We remove this asymmetry by modelling the st
White noise theory of robust nonlinear filtering with correlated state and observation noises
Bagchi, Arunabha; Karandikar, Rajeeva
1992-01-01
In the direct white noise theory of nonlinear filtering, the state process is still modeled as a Markov process satisfying an Ito stochastic differential equation, while a finitely additive white noise is used to model the observation noise. In the present work, this asymmetry is removed by modeling
Astroza, Rodrigo; Ebrahimian, Hamed; Conte, Joel P.
2015-03-01
This paper describes a novel framework that combines advanced mechanics-based nonlinear (hysteretic) finite element (FE) models and stochastic filtering techniques to estimate unknown time-invariant parameters of nonlinear inelastic material models used in the FE model. Using input-output data recorded during earthquake events, the proposed framework updates the nonlinear FE model of the structure. The updated FE model can be directly used for damage identification and further used for damage prognosis. To update the unknown time-invariant parameters of the FE model, two alternative stochastic filtering methods are used: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). A three-dimensional, 5-story, 2-by-1 bay reinforced concrete (RC) frame is used to verify the proposed framework. The RC frame is modeled using fiber-section displacement-based beam-column elements with distributed plasticity and is subjected to the ground motion recorded at the Sylmar station during the 1994 Northridge earthquake. The results indicate that the proposed framework accurately estimate the unknown material parameters of the nonlinear FE model. The UKF outperforms the EKF when the relative root-mean-square error of the recorded responses are compared. In addition, the results suggest that the convergence of the estimate of modeling parameters is smoother and faster when the UKF is utilized.
ESTIMATE ACCURACY OF NONLINEAR COEFFICIENTS OF SQUEEZEFILM DAMPER USING STATE VARIABLE FILTER METHOD
Institute of Scientific and Technical Information of China (English)
1998-01-01
The estimate model for a nonlinear system of squeeze-film damper (SFD) is described.The method of state variable filter (SVF) is used to estimate the coefficients of SFD.The factors which are critical to the estimate accuracy are discussed.
A nonlinear filtering algorithm for denoising HR(S)TEM micrographs
Energy Technology Data Exchange (ETDEWEB)
Du, Hongchu, E-mail: h.du@fz-juelich.de [Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons, Jülich Research Centre, Jülich, 52425 (Germany); Central Facility for Electron Microscopy (GFE), RWTH Aachen University, Aachen 52074 (Germany); Peter Grünberg Institute, Jülich Research Centre, Jülich 52425 (Germany)
2015-04-15
Noise reduction of micrographs is often an essential task in high resolution (scanning) transmission electron microscopy (HR(S)TEM) either for a higher visual quality or for a more accurate quantification. Since HR(S)TEM studies are often aimed at resolving periodic atomistic columns and their non-periodic deviation at defects, it is important to develop a noise reduction algorithm that can simultaneously handle both periodic and non-periodic features properly. In this work, a nonlinear filtering algorithm is developed based on widely used techniques of low-pass filter and Wiener filter, which can efficiently reduce noise without noticeable artifacts even in HR(S)TEM micrographs with contrast of variation of background and defects. The developed nonlinear filtering algorithm is particularly suitable for quantitative electron microscopy, and is also of great interest for beam sensitive samples, in situ analyses, and atomic resolution EFTEM. - Highlights: • A nonlinear filtering algorithm for denoising HR(S)TEM images is developed. • It can simultaneously handle both periodic and non-periodic features properly. • It is particularly suitable for quantitative electron microscopy. • It is of great interest for beam sensitive samples, in situ analyses, and atomic resolution EFTEM.
Directory of Open Access Journals (Sweden)
E. L. Dmitrieva
2016-05-01
Full Text Available Basic peculiarities of nonlinear Kalman filtering algorithm applied to processing of interferometric signals are considered. Analytical estimates determining statistical characteristics of signal values prediction errors were obtained and analysis of errors histograms taking into account variations of different parameters of interferometric signal was carried out. Modeling of the signal prediction procedure with known fixed parameters and variable parameters of signal in the algorithm of nonlinear Kalman filtering was performed. Numerical estimates of prediction errors for interferometric signal values were obtained by formation and analysis of the errors histograms under the influence of additive noise and random variations of amplitude and frequency of interferometric signal. Nonlinear Kalman filter is shown to provide processing of signals with randomly variable parameters, however, it does not take into account directly the linearization error of harmonic function representing interferometric signal that is a filtering error source. The main drawback of the linear prediction consists in non-Gaussian statistics of prediction errors including cases of random deviations of signal amplitude and/or frequency. When implementing stochastic filtering of interferometric signals, it is reasonable to use prediction procedures based on local statistics of a signal and its parameters taken into account.
Shen, Zheqi; Tang, Youmin
2016-04-01
The ensemble Kalman particle filter (EnKPF) is a combination of two Bayesian-based algorithms, namely, the ensemble Kalman filter (EnKF) and the sequential importance resampling particle filter(SIR-PF). It was recently introduced to address non-Gaussian features in data assimilation for highly nonlinear systems, by providing a continuous interpolation between the EnKF and SIR-PF analysis schemes. In this paper, we first extend the EnKPF algorithm by modifying the formula for the computation of the covariancematrix, making it suitable for nonlinear measurement functions (we will call this extended algorithm nEnKPF). Further, a general form of the Kalman gain is introduced to the EnKPF to improve the performance of the nEnKPF when the measurement function is highly nonlinear (this improved algorithm is called mEnKPF). The Lorenz '63 model and Lorenz '96 model are used to test the two modified EnKPF algorithms. The experiments show that the mEnKPF and nEnKPF, given an affordable ensemble size, can perform better than the EnKF for the nonlinear systems with nonlinear observations. These results suggest a promising opportunity to develop a non-Gaussian scheme for realistic numerical models.
The Use of Nonlinear Constitutive Equations to Evaluate Draw Resistance and Filter Ventilation
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Eitzinger B
2014-12-01
Full Text Available This study investigates by nonlinear constitutive equations the influence of tipping paper, cigarette paper, filter, and tobacco rod on the degree of filter ventilation and draw resistance. Starting from the laws of conservation, the path to the theory of fluid dynamics in porous media and Darcy's law is reviewed and, as an extension to Darcy's law, two different nonlinear pressure drop-flow relations are proposed. It is proven that these relations are valid constitutive equations and the partial differential equations for the stationary flow in an unlit cigarette covering anisotropic, inhomogeneous and nonlinear behaviour are derived. From these equations a system of ordinary differential equations for the one-dimensional flow in the cigarette is derived by averaging pressure and velocity over the cross section of the cigarette. By further integration, the concept of an electrical analog is reached and discussed in the light of nonlinear pressure drop-flow relations. By numerical calculations based on the system of ordinary differential equations, it is shown that the influence of nonlinearities cannot be neglected because variations in the degree of filter ventilation can reach up to 20% of its nominal value.
Nonlinear filtering techniques for noisy geophysical data: Using big data to predict the future
Moore, J. M.
2014-12-01
Chaos is ubiquitous in physical systems. Within the Earth sciences it is readily evident in seismology, groundwater flows and drilling data. Models and workflows have been applied successfully to understand and even to predict chaotic systems in other scientific fields, including electrical engineering, neurology and oceanography. Unfortunately, the high levels of noise characteristic of our planet's chaotic processes often render these frameworks ineffective. This contribution presents techniques for the reduction of noise associated with measurements of nonlinear systems. Our ultimate aim is to develop data assimilation techniques for forward models that describe chaotic observations, such as episodic tremor and slip (ETS) events in fault zones. A series of nonlinear filters are presented and evaluated using classical chaotic systems. To investigate whether the filters can successfully mitigate the effect of noise typical of Earth science, they are applied to sunspot data. The filtered data can be used successfully to forecast sunspot evolution for up to eight years (see figure).
Adaptive Non-Linear Bayesian Filter for ECG Denoising
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Mitesh Kumar Sao
2014-06-01
Full Text Available The cycles of an electrocardiogram (ECG signal contain three components P-wave, QRS complex and the T-wave. Noise is present in cardiograph as signals being measured in which biological resources (muscle contraction, base line drift, motion noise and environmental resources (power line interference, electrode contact noise, instrumentation noise are normally pollute ECG signal detected at the electrode. Visu-Shrink thresholding and Bayesian thresholding are the two filters based technique on wavelet method which is denoising the PLI noisy ECG signal. So thresholding techniques are applied for the effectiveness of ECG interval and compared the results with the wavelet soft and hard thresholding methods. The outputs are evaluated by calculating the root mean square (RMS, signal to noise ratio (SNR, correlation coefficient (CC and power spectral density (PSD using MATLAB software. The clean ECG signal shows Bayesian thresholding technique is more powerful algorithm for denoising.
Undithering using linear filtering and non-linear diffusion techniques
Asha, V
2011-01-01
Data compression is a method of improving the efficiency of transmission and storage of images. Dithering, as a method of data compression, can be used to convert an 8-bit gray level image into a 1-bit / binary image. Undithering is the process of reconstruction of gray image from binary image obtained from dithering of gray image. In the present paper, I propose a method of undithering using linear filtering followed by anisotropic diffusion which brings the advantage of smoothing and edge enhancement. First-order statistical parameters, second-order statistical parameters, mean-squared error (MSE) between reconstructed image and the original image before dithering, and peak signal to noise ratio (PSNR) are evaluated at each step of diffusion. Results of the experiments show that the reconstructed image is not as sharp as the image before dithering but a large number of gray values are reproduced with reference to those of the original image prior to dithering.
Wang, Changyuan; Zhang, Jing; Mu, Jing
2012-01-01
A new filter named the maximum likelihood-based iterated divided difference filter (MLIDDF) is developed to improve the low state estimation accuracy of nonlinear state estimation due to large initial estimation errors and nonlinearity of measurement equations. The MLIDDF algorithm is derivative-free and implemented only by calculating the functional evaluations. The MLIDDF algorithm involves the use of the iteration measurement update and the current measurement, and the iteration termination criterion based on maximum likelihood is introduced in the measurement update step, so the MLIDDF is guaranteed to produce a sequence estimate that moves up the maximum likelihood surface. In a simulation, its performance is compared against that of the unscented Kalman filter (UKF), divided difference filter (DDF), iterated unscented Kalman filter (IUKF) and iterated divided difference filter (IDDF) both using a traditional iteration strategy. Simulation results demonstrate that the accumulated mean-square root error for the MLIDDF algorithm in position is reduced by 63% compared to that of UKF and DDF algorithms, and by 7% compared to that of IUKF and IDDF algorithms. The new algorithm thus has better state estimation accuracy and a fast convergence rate.
Directory of Open Access Journals (Sweden)
Changyuan Wang
2012-06-01
Full Text Available A new filter named the maximum likelihood-based iterated divided difference filter (MLIDDF is developed to improve the low state estimation accuracy of nonlinear state estimation due to large initial estimation errors and nonlinearity of measurement equations. The MLIDDF algorithm is derivative-free and implemented only by calculating the functional evaluations. The MLIDDF algorithm involves the use of the iteration measurement update and the current measurement, and the iteration termination criterion based on maximum likelihood is introduced in the measurement update step, so the MLIDDF is guaranteed to produce a sequence estimate that moves up the maximum likelihood surface. In a simulation, its performance is compared against that of the unscented Kalman filter (UKF, divided difference filter (DDF, iterated unscented Kalman filter (IUKF and iterated divided difference filter (IDDF both using a traditional iteration strategy. Simulation results demonstrate that the accumulated mean-square root error for the MLIDDF algorithm in position is reduced by 63% compared to that of UKF and DDF algorithms, and by 7% compared to that of IUKF and IDDF algorithms. The new algorithm thus has better state estimation accuracy and a fast convergence rate.
Li, Yuan; Quan, Mingran; Tian, Jiajun; Yao, Yong
2015-05-01
A tunable multiwavelength erbium-doped fiber laser (MWEDFL) based on nonlinear optical loop mirror (NOLM) and tunable birefringence fiber filter (BFF) is proposed and demonstrated. By combination of intensity-dependent loss modulation induced by NOLM and pump power adjustment, the proposed laser can achieve independent control over the number of lasing lines, without affecting other important characteristics such as channel spacing and peak location. In addition, the laser allows wavelength tuning with both the peak location and the spectral range of lasing lines controllable. Specifically, the peak location of lasing lines can be controlled to scan the whole spectral range between adjacent channels of comb filter by adjusting the BFF. Moreover, the spectral range of lasing lines can be controlled by adjusting NOLM. This tunable MWEDFL may be useful for fiber-optic communication and fiber-optic sensing.
Discrete-time filtering for nonlinear polynomial systems over linear observations
Hernandez-Gonzalez, M.; Basin, M. V.
2014-07-01
This paper designs a discrete-time filter for nonlinear polynomial systems driven by additive white Gaussian noises over linear observations. The solution is obtained by computing the time-update and measurement-update equations for the state estimate and the error covariance matrix. A closed form of this filter is obtained by expressing the conditional expectations of polynomial terms as functions of the estimate and the error covariance. As a particular case, a third-degree polynomial is considered to obtain the finite-dimensional filtering equations. Numerical simulations are performed for a third-degree polynomial system and an induction motor model. Performance of the designed filter is compared with the extended Kalman one to verify its effectiveness.
Nonlinear Inverse Problem for an Ion-Exchange Filter Model: Numerical Recovery of Parameters
Directory of Open Access Journals (Sweden)
Balgaisha Mukanova
2015-01-01
Full Text Available This paper considers the problem of identifying unknown parameters for a mathematical model of an ion-exchange filter via measurement at the outlet of the filter. The proposed mathematical model consists of a material balance equation, an equation describing the kinetics of ion-exchange for the nonequilibrium case, and an equation for the ion-exchange isotherm. The material balance equation includes a nonlinear term that depends on the kinetics of ion-exchange and several parameters. First, a numerical solution of the direct problem, the calculation of the impurities concentration at the outlet of the filter, is provided. Then, the inverse problem, finding the parameters of the ion-exchange process in nonequilibrium conditions, is formulated. A method for determining the approximate values of these parameters from the impurities concentration measured at the outlet of the filter is proposed.
Nonlinear Kalman Filtering for acoustic emission source localization in anisotropic panels.
Dehghan Niri, E; Farhidzadeh, A; Salamone, S
2014-02-01
Nonlinear Kalman Filtering is an established field in applied probability and control systems, which plays an important role in many practical applications from target tracking to weather and climate prediction. However, its application for acoustic emission (AE) source localization has been very limited. In this paper, two well-known nonlinear Kalman Filtering algorithms are presented to estimate the location of AE sources in anisotropic panels: the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). These algorithms are applied to two cases: velocity profile known (CASE I) and velocity profile unknown (CASE II). The algorithms are compared with a more traditional nonlinear least squares method. Experimental tests are carried out on a carbon-fiber reinforced polymer (CFRP) composite panel instrumented with a sparse array of piezoelectric transducers to validate the proposed approaches. AE sources are simulated using an instrumented miniature impulse hammer. In order to evaluate the performance of the algorithms, two metrics are used: (1) accuracy of the AE source localization and (2) computational cost. Furthermore, it is shown that both EKF and UKF can provide a confidence interval of the estimated AE source location and can account for uncertainty in time of flight measurements.
Luo, Xiaodong
2014-10-01
The ensemble Kalman filter (EnKF) is an efficient algorithm for many data assimilation problems. In certain circumstances, however, divergence of the EnKF might be spotted. In previous studies, the authors proposed an observation-space-based strategy, called residual nudging, to improve the stability of the EnKF when dealing with linear observation operators. The main idea behind residual nudging is to monitor and, if necessary, adjust the distances (misfits) between the real observations and the simulated ones of the state estimates, in the hope that by doing so one may be able to obtain better estimation accuracy. In the present study, residual nudging is extended and modified in order to handle nonlinear observation operators. Such extension and modification result in an iterative filtering framework that, under suitable conditions, is able to achieve the objective of residual nudging for data assimilation problems with nonlinear observation operators. The 40-dimensional Lorenz-96 model is used to illustrate the performance of the iterative filter. Numerical results show that, while a normal EnKF may diverge with nonlinear observation operators, the proposed iterative filter remains stable and leads to reasonable estimation accuracy under various experimental settings.
Panourgias, Konstantinos T.; Ekaterinaris, John A.
2016-12-01
The nonlinear filter introduced by Yee et al. (1999) [27] and extensively used in the development of low dissipative well-balanced high order accurate finite-difference schemes is adapted to the finite element context of discontinuous Galerkin (DG) discretizations. The filter operator is constructed in the canonical computational domain for the standard cubical element where it is applied to the computed conservative variables in a direction per direction basis. Filtering becomes possible for all element types in unstructured meshes using collapsed coordinate transformations. The performance of the proposed nonlinear filter for DG discretizations is demonstrated and evaluated for different orders of expansions for one-dimensional and multidimensional problems with exact solutions. It is shown that for higher order discretizations discontinuity resolution within the cell is achieved and the design order of accuracy is preserved. The filter is applied for a number of standard inviscid flow test problems including strong shocks interactions to demonstrate that the proposed dissipative mechanism for DG discretizations yields superior results compared to the results obtained with the total variation bounded (TVB) limiter and high-order hierarchical limiting. The proposed approach is suitable for p-adaptivity in order to locally enhance resolution of three-dimensional flow simulations that include discontinuities and complex flow features.
Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang
2016-07-21
The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.
A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.
Zhao, Haiquan; Zhang, Jiashu
2009-12-01
To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.
Chaotic keyed hash function based on feedforward feedback nonlinear digital filter
Zhang, Jiashu; Wang, Xiaomin; Zhang, Wenfang
2007-03-01
In this Letter, we firstly construct an n-dimensional chaotic dynamic system named feedforward feedback nonlinear filter (FFNF), and then propose a novel chaotic keyed hash algorithm using FFNF. In hashing process, the original message is modulated into FFNF's chaotic trajectory by chaotic shift keying (CSK) mode, and the final hash value is obtained by the coarse-graining quantization of chaotic trajectory. To expedite the avalanche effect of hash algorithm, a cipher block chaining (CBC) mode is introduced. Theoretic analysis and numerical simulations show that the proposed hash algorithm satisfies the requirement of keyed hash function, and it is easy to implement by the filter structure.
A NEW SQP-FILTER METHOD FOR SOLVING NONLINEAR PROGRAMMING PROBLEMS
Institute of Scientific and Technical Information of China (English)
Duoquan Li
2006-01-01
In [4],Fletcher and Leyffer present a new method that solves nonlinear programming problems without a penalty function by SQP-Filter algorithm. It has attracted much attention due to its good numerical results. In this paper we propose a new SQP-Filter method which can overcome Maratos effect more effectively. We give stricter acceptant criteria when the iterative points are far from the optimal points and looser ones vice-versa. About this new method,the proof of global convergence is also presented under standard assumptions. Numerical results show that our method is efficient.
Dimensional Reduction for Filters of Nonlinear Systems with Time-Scale Separation
2013-03-01
Rapp, Edwin Kreuzer and N. Sri Namachchivaya, “Reduced Nor- mal Forms for Nonlinear Control of Underactuated Hoisting Systems ,” Archive of Applied Mechanics , Vol.82, 2012, pp. 297 - 315. 7 ... Mechanics , Vol. 78(6), 2011, pp. 61001-1 - 61001-10. 8. Lee DeVille, N. Sri Namachchivaya and Zoi Rapti, “Noisy Two Dimensional Non-Hamiltonian System ...AFRL-OSR-VA-TR-2013-0009 Dimensional Reduction for Filters of Nonlinear Systems with Time- Scale Separation Namachchivaya, N
Huang, Guanghui; Wan, Jianping; Chen, Hui
2013-02-01
Nonlinear stochastic differential equation models with unobservable state variables are now widely used in analysis of PK/PD data. Unobservable state variables are usually estimated with extended Kalman filter (EKF), and the unknown pharmacokinetic parameters are usually estimated by maximum likelihood estimator. However, EKF is inadequate for nonlinear PK/PD models, and MLE is known to be biased downwards. A density-based Monte Carlo filter (DMF) is proposed to estimate the unobservable state variables, and a simulation-based M estimator is proposed to estimate the unknown parameters in this paper, where a genetic algorithm is designed to search the optimal values of pharmacokinetic parameters. The performances of EKF and DMF are compared through simulations for discrete time and continuous time systems respectively, and it is found that the results based on DMF are more accurate than those given by EKF with respect to mean absolute error.
Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games
Directory of Open Access Journals (Sweden)
Emmanuel García
2014-01-01
Full Text Available This paper deals with the problem of identifying and filtering a class of continuous-time nonlinear dynamic games (nonlinear differential games subject to additive and undesired deterministic perturbations. Moreover, the mathematical model of this class is completely unknown with the exception of the control actions of each player, and even though the deterministic noises are known, their power (or their effect is not. Therefore, two differential neural networks are designed in order to obtain a feedback (perfect state information pattern for the mentioned class of games. In this way, the stability conditions for two state identification errors and for a filtering error are established, the upper bounds of these errors are obtained, and two new learning laws for each neural network are suggested. Finally, an illustrating example shows the applicability of this approach.
3-D zebrafish embryo image filtering by nonlinear partial differential equations.
Rizzi, Barbara; Campana, Matteo; Zanella, Cecilia; Melani, Camilo; Cunderlik, Robert; Krivá, Zuzana; Bourgine, Paul; Mikula, Karol; Peyriéras, Nadine; Sarti, Alessandro
2007-01-01
We discuss application of nonlinear PDE based methods to filtering of 3-D confocal images of embryogenesis. We focus on the mean curvature driven and the regularized Perona-Malik equations, where standard as well as newly suggested edge detectors are used. After presenting the related mathematical models, the practical results are given and discussed by visual inspection and quantitatively using the mean Hausdorff distance.
Directory of Open Access Journals (Sweden)
B. Shank
2014-11-01
Full Text Available We present a detailed thermal and electrical model of superconducting transition edge sensors (TESs connected to quasiparticle (qp traps, such as the W TESs connected to Al qp traps used for CDMS (Cryogenic Dark Matter Search Ge and Si detectors. We show that this improved model, together with a straightforward time-domain optimal filter, can be used to analyze pulses well into the nonlinear saturation region and reconstruct absorbed energies with optimal energy resolution.
Recovery of systems with a linear filter and nonlinear delay feedback in periodic regimes.
Ponomarenko, V I; Prokhorov, M D
2008-12-01
We propose a set of methods for the estimation of the parameters of time-delay systems with a linear filter and nonlinear delay feedback performing periodic oscillations. The methods are based on an analysis of the system response to regular external perturbations and are valid only for systems whose dynamics can be perturbed. The efficiency of the methods is illustrated using both numerical and experimental data.
Direct heuristic dynamic programming for nonlinear tracking control with filtered tracking error.
Yang, Lei; Si, Jennie; Tsakalis, Konstantinos S; Rodriguez, Armando A
2009-12-01
This paper makes use of the direct heuristic dynamic programming design in a nonlinear tracking control setting with filtered tracking error. A Lyapunov stability approach is used for the stability analysis of the tracking system. It is shown that the closed-loop tracking error and the approximating neural network weight estimates retain the property of uniformly ultimate boundedness under the presence of neural network approximation error and bounded unknown disturbances under certain conditions.
Shank, B; Cabrera, B; Kreikebaum, J M; Moffatt, R; Redl, P; Young, B A; Brink, P L; Cherry, M; Tomada, A
2014-01-01
We present a detailed thermal and electrical model of superconducting transition edge sensors (TESs) connected to quasiparticle (qp) traps, such as the W TESs connected to Al qp traps used for CDMS (Cryogenic Dark Matter Search) Ge and Si detectors. We show that this improved model, together with a straightforward time-domain optimal filter, can be used to analyze pulses well into the nonlinear saturation region and reconstruct absorbed energies with optimal energy resolution.
The effect of compression on tuning estimates in a simple nonlinear auditory filter model
DEFF Research Database (Denmark)
Marschall, Marton; MacDonald, Ewen; Dau, Torsten
2013-01-01
, there is evidence that human frequency-selectivity estimates depend on whether an iso-input or an iso-response measurement paradigm is used (Eustaquio-Martin et al., 2011). This study presents simulated tuning estimates using a simple compressive auditory filter model, the bandpass nonlinearity (BPNL), which......, then compression alone may explain a large part of the behaviorally observed differences in tuning between simultaneous and forward-masking conditions....
Aguirre, Luis Antonio; Billings, S. A.
This paper investigates the identification of global models from chaotic data corrupted by additive noise. It is verified that noise has a strong influence on the identification of chaotic systems. In particular, there seems to be a critical noise level beyond which the accurate estimation of polynomial models from chaotic data becomes very difficult. Similarities with the estimation of the largest Lyapunov exponent from noisy data suggest that part of the problem might be related to the limited ability of predicting the data records when these are chaotic. A nonlinear filtering scheme is suggested in order to reduce the noise in the data and thereby enable the estimation of good models. This prediction-based filtering incorporates a resetting mechanism which enables the filtering of chaotic data and which is also applicable to non-chaotic data.
Gaussian Sum PHD Filtering Algorithm for Nonlinear Non-Gaussian Models
Institute of Scientific and Technical Information of China (English)
Yin Jianjun; Zhang Jianqiu; Zhuang Zesen
2008-01-01
A new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis density (GSPHD) filter, is proposed for nonlinear non-Gaussian tracking models. Provided that the initial prior intensity of the states is Gaussian or can be identified as a Gaussiaa sum, the analytical results of the algorithm show that the posterior intensity at any subsequent time step remains a Gaussian sum under the assumption that the state noise, the measurement noise, target spawn intensity, new target birth intensity, target survival probability, and detection probability are all Gaussian sums. The analysis also shows that the existing Gaassian mixture probability hypothesis density (GMPHD) filter, which is unsuitable for handling the non-Gaussian noise cases, is no more than a special ease of the proposed algorithm, which fills the shortage of incapability of treating non-Gaussian noise. The multi-target tracking simulation results verify the effectiveness of the proposed GSPHD.
Target tracking by distributed autonomous vessels using the derivative-free nonlinear Kalman filter
Rigatos, Gerasimos; Siano, Pierluigi; Raffo, Guilerme
2015-12-01
In this paper a distributed control problem for unmanned surface vessels (USVs) is formulated as follows: there are N USVs which pursue another vessel (moving target). At each time instant each USV can obtain measurements of the target's cartesian coordinates. The objective is to make the USVs converge in a synchronized manner towards the target, while avoiding collisions between them and avoiding collisions with obstacles in their motion plane. A distributed control law is developed for the USVs which enables not only convergence of the USVs to the goal position, but also makes possible to maintain the cohesion of the USVs fleet. Moreover, distributed filtering is performed, so as to obtain an estimate of the target vessel's state vector. This provides the desirable state vector to be tracked by each one of the USVs. To this end, a new distributed nonlinear filtering method of improved accuracy and computation speed is introduced. This filtering approach, under the name Derivative-free distributed nonlinear Kalman Filter is based on differential flatness theory and on an exact linearization of the target vessel's dynamic/kinematic model.
A derivative-free distributed filtering approach for sensorless control of nonlinear systems
Rigatos, Gerasimos G.
2012-09-01
This article examines the problem of sensorless control for nonlinear dynamical systems with the use of derivative-free Extended Information Filtering (EIF). The system is first subject to a linearisation transformation and next state estimation is performed by applying the standard Kalman Filter to the linearised model. At a second level, the standard Information Filter is used to fuse the state estimates obtained from local derivative-free Kalman filters running at the local information processing nodes. This approach has significant advantages because unlike the EIF (i) is not based on local linearisation of the nonlinear dynamics (ii) does not assume truncation of higher order Taylor expansion terms thus preserving the accuracy and robustness of the performed estimation and (iii) does not require the computation of Jacobian matrices. As a case study a robotic manipulator is considered and a cameras network consisting of multiple vision nodes is assumed to provide the visual information to be used in the control loop. A derivative-free implementation of the EIF is used to produce the aggregate state vector of the robot by processing local state estimates coming from the distributed vision nodes. The performance of the considered sensorless control scheme is evaluated through simulation experiments.
Pozdeev, V. A.; Olefirenko, O. Yu.
2016-06-01
The problem of harmonic pressure wave generation by a moving piston is solved for the first time. An initial boundary value problem for the Riemann equation is formulated, and a boundary condition for the current position of a contact boundary is set. Physical effects caused by the allowance for mobility of the contact boundary and nonlinearity of the medium are considered in the framework of the obtained analytical solution.
Recovering the nonlinear density field from the galaxy distribution with a Poisson-Lognormal filter
Kitaura, Francisco S; Metcalf, R Benton
2009-01-01
We present a general expression for a lognormal filter given an arbitrary nonlinear galaxy bias. We derive this filter as the maximum a posteriori solution assuming a lognormal prior distribution for the matter field with a given mean field and modeling the observed galaxy distribution by a Poissonian process. We have performed a three-dimensional implementation of this filter with a very efficient Newton-Krylov inversion scheme. Furthermore, we have tested it with a dark matter N-body simulation assuming a unit galaxy bias relation and compared the results with previous density field estimators like the inverse weighting scheme and Wiener filtering. Our results show good agreement with the underlying dark matter field for overdensities even above delta~1000 which exceeds by one order of magnitude the regime in which the lognormal is expected to be valid. The reason is that for our filter the lognormal assumption enters as a prior distribution function, but the maximum a posteriori solution is also conditione...
Liu, Yajuan; Park, Ju H; Guo, Bao-Zhu
2016-07-01
In this paper,the problem of H∞ filtering for a class of nonlinear discrete-time delay systems is investigated. The time delay is assumed to be belonging to a given interval, and the designed filter includes additive gain variations which are supposed to be random and satisfy the Bernoulli distribution. By the augmented Lyapunov functional approach, a sufficient condition is developed to ensure that the filtering error system is asymptotically mean-square stable with a prescribed H∞ performance. In addition, an improved result of H∞ filtering for linear system is also derived. The filter parameters are obtained by solving a set of linear matrix inequalities. For nonlinear systems, the applicability of the developed filtering result is confirmed by a longitudinal flight system, and an additional example for linear system is presented to demonstrate the less conservativeness of the proposed design method.
Energy Technology Data Exchange (ETDEWEB)
Harlim, John, E-mail: jharlim@psu.edu [Department of Mathematics and Department of Meteorology, the Pennsylvania State University, University Park, PA 16802, Unites States (United States); Mahdi, Adam, E-mail: amahdi@ncsu.edu [Department of Mathematics, North Carolina State University, Raleigh, NC 27695 (United States); Majda, Andrew J., E-mail: jonjon@cims.nyu.edu [Department of Mathematics and Center for Atmosphere and Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012 (United States)
2014-01-15
A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partial noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model.
Miao, Zhiyong; Shi, Hongyang; Zhang, Yi; Xu, Fan
2017-10-01
In this paper, a new variational Bayesian adaptive cubature Kalman filter (VBACKF) is proposed for nonlinear state estimation. Although the conventional VBACKF performs better than cubature Kalman filtering (CKF) in solving nonlinear systems with time-varying measurement noise, its performance may degrade due to the uncertainty of the system model. To overcome this drawback, a multilayer feed-forward neural network (MFNN) is used to aid the conventional VBACKF, generalizing it to attain higher estimation accuracy and robustness. In the proposed neural-network-aided variational Bayesian adaptive cubature Kalman filter (NN-VBACKF), the MFNN is used to turn the state estimation of the VBACKF adaptively, and it is used for both state estimation and in the online training paradigm simultaneously. To evaluate the performance of the proposed method, it is compared with CKF and VBACKF via target tracking problems. The simulation results demonstrate that the estimation accuracy and robustness of the proposed method are better than those of the CKF and VBACKF.
Denoising of single-trial matrix representations using 2D nonlinear diffusion filtering.
Mustaffa, I; Trenado, C; Schwerdtfeger, K; Strauss, D J
2010-01-15
In this paper we present a novel application of denoising by means of nonlinear diffusion filters (NDFs). NDFs have been successfully applied for image processing and computer vision areas, particularly in image denoising, smoothing, segmentation, and restoration. We apply two types of NDFs for the denoising of evoked responses in single-trials in a matrix form, the nonlinear isotropic and the anisotropic diffusion filters. We show that by means of NDFs we are able to denoise the evoked potentials resulting in a better extraction of physiologically relevant morphological features over the ongoing experiment. This technique offers the advantage of translation-invariance in comparison to other well-known methods, e.g., wavelet denoising based on maximally decimated filter banks, due to an adaptive diffusion feature. We compare the proposed technique with a wavelet denoising scheme that had been introduced before for evoked responses. It is concluded that NDFs represent a promising and useful approach in the denoising of event related potentials. Novel NDF applications of single-trials of auditory brain responses (ABRs) and the transcranial magnetic stimulation (TMS) evoked electroencephalographic responses denoising are presented in this paper.
Zha, Yikun; Wei, Jingsong; Gan, Fuxi
2013-04-01
With the continuous development of the field of information technology, there has been a demand for recording mark size of optical data storage, optical imaging resolving power, and characteristic linewidth of photolithography to reach nanoscale. However, it is very difficult to realize the goal due to the optical diffraction limit restrictions. Much interest has focused on the study of optical far-field super-resolution spot by using pupil filters. However, common concerns have continued to plague super-resolving pupil filters based on either scalar diffraction theory or vector diffraction theory. These concerns include the fact that the side lobe becomes non-negligible when the central lobe is squeezed to a certain extent. Moreover, it is difficult to reduce the super-resolving spot to nanoscale. In this work, we proposed a novel method to combine the super-resolving pupil filters with nonlinear saturable absorption thin films to reduce the central spot size to nanoscale, lower the intensity ratio of side lobe to central lobe, and elongate the depth of focus or tunable tolerance distance between the super-resolving spot and sample. The simulated results indicate that by using the three-zone annular binary phase filter as the super-resolving pupil filter and Sb2Te3 as the nonlinear saturable absorption thin films, the central spot size can be reduced to nanoscale, the side lobe intensity is squeezed to about 10% of the central lobe intensity, and the tunable tolerance distance between the super-resolving spot and the sample is about two times that of the depth of focus of the diffraction limited spot at the incident laser wavelength of 405 nm and the numerical aperture of focusing lens of 0.95. The combination of the super-resolving pupil filters with the nonlinear saturable absorption thin films is very useful for nano-optical data storage, maskless nanolithography, and nano-optical imaging. It is also easy to use in actual applications because of the operation
Identification of parameters in nonlinear geotechnical models using extenden Kalman filter
Directory of Open Access Journals (Sweden)
Nestorović Tamara
2014-01-01
Full Text Available Direct measurement of relevant system parameters often represents a problem due to different limitations. In geomechanics, measurement of geotechnical material constants which constitute a material model is usually a very diffcult task even with modern test equipment. Back-analysis has proved to be a more effcient and more economic method for identifying material constants because it needs measurement data such as settlements, pore pressures, etc., which are directly measurable, as inputs. Among many model parameter identification methods, the Kalman filter method has been applied very effectively in recent years. In this paper, the extended Kalman filter – local iteration procedure incorporated with finite element analysis (FEA software has been implemented. In order to prove the effciency of the method, parameter identification has been performed for a nonlinear geotechnical model.
State estimation of nonlinear stochastic systems using a novel meta-heuristic particle filter
DEFF Research Database (Denmark)
Ahmadi, Mohamadreza; Mojallali, Hamed; Izadi-Zamanabadi, Roozbeh
2012-01-01
This paper proposes a new version of the particle filtering (PF) algorithm based on the invasive weed optimization (IWO) method. The sub-optimality of the sampling step in the PF algorithm is prone to estimation errors. In order to avert such approximation errors, this paper suggests applying...... the IWO algorithm by translating the sampling step into a nonlinear optimization problem. By introducing an appropriate fitness function, the optimization problem is properly treated. The validity of the proposed method is evaluated against three distinct examples: the stochastic volatility estimation...... problem in finance, the severely nonlinear waste water sludge treatment plant, and the benchmark target tracking on re-entry problem. By simulation analysis and evaluation, it is verified that applying the suggested IWO enhanced PF algorithm (PFIWO) would contribute to significant estimation performance...
Design of robust fault detection filter for nonlinear time-delay systems
Institute of Scientific and Technical Information of China (English)
BAI Lei-shi; HE Li-ming; TIAN Zuo-hua; SHI Song-jiao
2006-01-01
In this paper, the robust fault detection filter (RFDF) design problems are studied for nonlinear time-delay systems with unknown inputs. First, a reference residual model is introduced to formulate the RFDF design problem as an H∞model-matching problem. Then appropriate input/output selection matrices are introduced to extend a performance index to the time-delay systems in time domain. The reference residual model designed according to the performance index is an optimal residual generator, which takes into account the robustness against disturbances and sensitivity to faults simultaneously. Applying robust H∞ optimization control technique, the existence conditions of the RFDF for nonlinear time-delay systems with unknown inputs are presented in terms of linear matrix inequality (LMI) formulation, independently of time delay. An illustrative design example is used to demonstrate the validity and applicability of the proposed approach.
Institute of Scientific and Technical Information of China (English)
Juming CHEN; Feng LIU; Shengwei MEI
2006-01-01
Active power filter (APF) based on voltage source inverter (VSI) is one of the important measures for handling the power quality problem. Mathematically, the APF model in a power grid is a typical nonlinear one. The idea of passivity is a powerful tool to study the stabilization of such a nonlinear system. In this paper, a state-space model of the four-leg APF is derived, based on which a new H-infinity controller for current tracking is proposed from the passivity point of view. It can achieve not only asymptotic tracking, but also disturbance attenuation in the sense of L2-gain. Subsequently,a sufficient condition to guarantee the boundedness and desired mean of the DC voltage is also given. This straightforward condition is consistent with the power-balancing law of electrical circuits. Simulations performed on PSCAD platform verify the validity of the new approach.
Non-linear DSGE Models and The Central Difference Kalman Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
solved up to third order. A Monte Carlo study shows that this QML estimator is basically unbiased and normally distributed infi…nite samples for DSGE models solved using a second order or a third order approximation. These results hold even when structural shocks are Gaussian, Laplace distributed......This paper introduces a Quasi Maximum Likelihood (QML) approach based on the Cen- tral Difference Kalman Filter (CDKF) to estimate non-linear DSGE models with potentially non-Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models...
Cubic generalized B-splines for interpolation and nonlinear filtering of images
Tshughuryan, Heghine
1997-04-01
This paper presents the introduction and using of the generalized or parametric B-splines, namely the cubic generalized B-splines, in various signal processing applications. The theory of generalized B-splines is briefly reviewed and also some important properties of generalized B-splines are investigated. In this paper it is shown the use of generalized B-splines as a tool to solve the quasioptimal algorithm problem for nonlinear filtering. Finally, the experimental results are presented for oscillatory and other signals and images.
Spatio-Temporal Nonlinear Filtering With Applications to Information Assurance and Counter Terrorism
2011-11-14
International Conference on Infor- mation Fusion, Hyatt Regency Hotel , Cologne, Germany, 2008, pp. 878-885 (Invited). 7. A.G. Tartakovsky, M. Pollak, and...probability kernel Qt (x, y) and v̇t is white noise. For example, if the state process is given by the noisy kinematic equation ẋt = a (t, xt) + σε̇t, where...targets with evolving appearance in noisy and cluttered environments. Our method is based on combination of nonlinear filtering for interacting
Out-of-band and adjacent-channel interference reduction by analog nonlinear filters
Nikitin, Alexei V.; Davidchack, Ruslan L.; Smith, Jeffrey E.
2015-12-01
In a perfect world, we would have `brick wall' filters, no-distortion amplifiers and mixers, and well-coordinated spectrum operations. The real world, however, is prone to various types of unintentional and intentional interference of technogenic (man-made) origin that can disrupt critical communication systems. In this paper, we introduce a methodology for mitigating technogenic interference in communication channels by analog nonlinear filters, with an emphasis on the mitigation of out-of-band and adjacent-channel interference. Interference induced in a communications receiver by external transmitters can be viewed as wide-band non-Gaussian noise affecting a narrower-band signal of interest. This noise may contain a strong component within the receiver passband, which may dominate over the thermal noise. While the total wide-band interference seen by the receiver may or may not be impulsive, we demonstrate that the interfering component due to power emitted by the transmitter into the receiver channel is likely to appear impulsive under a wide range of conditions. We give an example of mechanisms of impulsive interference in digital communication systems resulting from the nonsmooth nature of any physically realizable modulation scheme for transmission of a digital (discontinuous) message. We show that impulsive interference can be effectively mitigated by nonlinear differential limiters (NDLs). An NDL can be configured to behave linearly when the input signal does not contain outliers. When outliers are encountered, the nonlinear response of the NDL limits the magnitude of the respective outliers in the output signal. The signal quality is improved in excess of that achievable by the respective linear filter, increasing the capacity of a communications channel. The behavior of an NDL, and its degree of nonlinearity, is controlled by a single parameter in a manner that enables significantly better overall suppression of the noise-containing impulsive components
Fuzzy predictive filtering in nonlinear economic model predictive control for demand response
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.;
2016-01-01
The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...... problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...
Sun, Xiaodian; Jin, Li; Xiong, Momiao
2008-01-01
It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.
Set-membership fuzzy filtering for nonlinear discrete-time systems.
Yang, Fuwen; Li, Yongmin
2010-02-01
This paper is concerned with the set-membership filtering (SMF) problem for discrete-time nonlinear systems. We employ the Takagi-Sugeno (T-S) fuzzy model to approximate the nonlinear systems over the true value of state and to overcome the difficulty with the linearization over a state estimate set rather than a state estimate point in the set-membership framework. Based on the T-S fuzzy model, we develop a new nonlinear SMF estimation method by using the fuzzy modeling approach and the S-procedure technique to determine a state estimation ellipsoid that is a set of states compatible with the measurements, the unknown-but-bounded process and measurement noises, and the modeling approximation errors. A recursive algorithm is derived for computing the ellipsoid that guarantees to contain the true state. A smallest possible estimate set is recursively computed by solving the semidefinite programming problem. An illustrative example shows the effectiveness of the proposed method for a class of discrete-time nonlinear systems via fuzzy switch.
Rigatos, Gerasimos G
2016-06-01
It is proven that the model of the p53-mdm2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p53 protein at the desirable levels. To estimate the non-measurable elements of the state vector describing the p53-mdm2 system dynamics, the derivative-free non-linear Kalman filter is used. Moreover, to compensate for modelling uncertainties and external disturbances that affect the p53-mdm2 system, the derivative-free non-linear Kalman filter is re-designed as a disturbance observer. The derivative-free non-linear Kalman filter consists of the Kalman filter recursion applied on the linearised equivalent of the protein synthesis model together with an inverse transformation based on differential flatness theory that enables to retrieve estimates for the state variables of the initial non-linear model. The proposed non-linear feedback control and perturbations compensation method for the p53-mdm2 system can result in more efficient chemotherapy schemes where the infusion of medication will be better administered.
Rigatos, G; Rigatou, E; Djida, J D
2015-01-01
The derivative-free nonlinear Kalman filter is proposed for state estimation and fault diagnosis in distributed parameter systems of the wave-type and particularly in the Peyrard-Bishop-Dauxois model of DNA dynamics. At a first stage, a nonlinear filtering approach is introduced for estimating the dynamics of the Peyrard-Bishop-Dauxois 1D nonlinear wave equation, through the processing of a small number of measurements. It is shown that the numerical solution of the associated partial differential equation results in a set of nonlinear ordinary differential equations. With the application of a diffeomorphism that is based on differential flatness theory it is shown that an equivalent description of the system is obtained in the linear canonical (Brunovsky) form. This transformation enables to obtain local estimates about the state vector of the DNA model through the application us of the standard Kalman filter recursion. At a second stage, the local statistical approach to fault diagnosis is used to perform fault diagnosis for this distributed parameter system by processing with statistical tools the differences (residuals) between the output of the Kalman filter and the measurements obtained from the distributed parameter system. Optimal selection of the fault threshold is succeeded by using the local statistical approach to fault diagnosis. The efficiency of the proposed filtering approach in the problem of fault diagnosis for parametric change detection, in nonlinear wave-type models of DNA dynamics, is confirmed through simulation experiments.
Two-dimensional nonlinear geophysical data filtering using the multidimensional EEMD method
Chen, Chih-Sung; Jeng, Yih
2014-12-01
A variety of two-dimensional (2D) empirical mode decomposition (EMD) methods have been proposed in the last decade. Furthermore, the multidimensional EMD algorithm and its parallel class, multivariate EMD (MEMD), are available in recent years. From those achievements, it is possible to design an efficient 2D nonlinear filter for geophysical data processing. We introduce a robust 2D nonlinear filter which can be applied to enhance the signal of 2D geophysical data or to highlight the feature component on an image. We did this by replacing the conventionally used smooth interpolation in the ensemble empirical mode decomposition (EEMD) algorithm with a piecewise interpolation method. The one-dimensional (1D) EEMD procedures were consecutively performed in all directions, and then the comparable minimal scale combination technique was applied to the decomposed components. The theoretical derivation, model simulation, and real data applications are demonstrated in this paper. The proposed filtering method is effective in improving the image resolution by suppressing the random noise added in the simulation example and strong low frequency track corrugation noise bands with background noise in the field example. Furthermore, the algorithm can be easily extended to higher dimensions by repeating the same procedure in the succeeding dimension. To evaluate the proposed method, one data set is processed separately by using the enhanced analytic signal method and the multivariate EMD (MEMD) algorithm, and the results from these two methods are compared with that of the proposed method. A general equation for generating three-dimensional (3D) EEMD components based on the comparable minimal scale combination principle is derived for further applications.
Cusenza, Monica; Accardo, Agostino; Monti, Fabrizio; Bramanti, Placido
2010-01-01
Simultaneous EEG-fMRI is a powerful emerging tool in functional neuroimaging that exploits the relationship between neuronal electrophysiological activity and its hemodynamic response. It has found application in the study of both spontaneous and evoked brain activity. Combining the complementary advantages of the two techniques it provides a measurement with high temporal and spatial resolution, allowing a reliable localization of event generators. However, EEG data recorded inside MRI scanner are heavily corrupted by different types of artifacts due to the interactions between the patient, EEG electrodes wires and the magnetic fields inside the scanner. In particular, gradient switching and RF pulses, necessary to acquire fMRI data, generate large artifacts that can completely obscure EEG signals. Many methods have been proposed to eliminate or at least reduce gradient artifact. In this paper both a qualitative and a quantitative evaluation of two different algorithms used for gradient artifact removal are presented. Linear and non-linear characteristics of EEG, such as power spectra, fractal dimension and beta scaling exponent, are evaluated for EEGs recorded outside and inside the scanner, in MR static and dynamic conditions. The study highlights how residual artifacts after correction and artifacts induced by correction itself could still considerably affect EEG signals. The results suggest that the quality of both these gradient artifact filtering methods is not yet sufficient to preserve EEG characteristics and thus it must be further improved. The aim of this study is to make neurophysiologists aware of the filtering effects that can compromise linear and non-linear analysis of EEG recorded during functional MRI.
Nonlinear filtering for autonomous navigation of spacecraft in highly elliptical orbit
Vigneron, Adam C.; de Ruiter, Anton H. J.; Burlton, Bruce V.; Soh, Warren K. H.
2016-05-01
In support of Canada's proposed Polar Communication and Weather mission, this study examined the accuracy to which GPS-based autonomous navigation might be realized for spacecraft in a Molniya orbit. A navigation algorithm based on the Extended Kalman Filter was demonstrated to achieve a three-dimensional root-mean-square accuracy of 58.9 m over a Molniya orbit with 500 km and 40,000 km perigee and apogee altitudes, respectively. Despite the inclusion of biased and non-white error models in the generated GPS pseudorange measurements - a first for navigation studies in this orbital regime - algorithms based on the Unscented Kalman Filter and the Cubature Kalman Filter were not found to improve this result; their benefits were eclipsed due to the accurate pseudorange measurements which were available during periods of highly nonlinear dynamics. This study revealed receiver clock bias error to be a significant source of navigation solution error. For reasons of geometry, the navigation algorithm is not able to differentiate between this error and a radial position error. A novel dual-mode dynamic clock model was proposed and implemented as a means to minimize receiver clock bias error over the entire orbital regime.
Detection of broken rotor bars in induction motors using nonlinear Kalman filters.
Karami, Farzaneh; Poshtan, Javad; Poshtan, Majid
2010-04-01
This paper presents a model-based fault detection approach for induction motors. A new filtering technique using Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) is utilized as a state estimation tool for on-line detection of broken bars in induction motors based on rotor parameter value estimation from stator current and voltage processing. The hypothesis on which the detection is based is that the failure events are detected by jumps in the estimated parameter values of the model. Both UKF and EKF are used to estimate the value of rotor resistance. Upon breaking a bar the estimated rotor resistance is increased instantly, thus providing two values of resistance after and before bar breakage. In order to compare the estimation performance of the EKF and UKF, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. Computer simulations are carried out for a squirrel cage induction motor. The results show the superiority of UKF over EKF in nonlinear system (such as induction motors) as it provides better estimates for rotor fault detection.
Penalized Ensemble Kalman Filters for High Dimensional Non-linear Systems
Hou, Elizabeth; Hero, Alfred O
2016-01-01
The ensemble Kalman filter (EnKF) is a data assimilation technique that uses an ensemble of models, updated with data, to track the time evolution of a non-linear system. It does so by using an empirical approximation to the well-known Kalman filter. Unfortunately, its performance suffers when the ensemble size is smaller than the state space, as is often the case for computationally burdensome models. This scenario means that the empirical estimate of the state covariance is not full rank and possibly quite noisy. To solve this problem in this high dimensional regime, a computationally fast and easy to implement algorithm called the penalized ensemble Kalman filter (PEnKF) is proposed. Under certain conditions, it can be proved that the PEnKF does not require more ensemble members than state dimensions in order to have good performance. Further, the proposed approach does not require special knowledge of the system such as is used by localization methods. These theoretical results are supported with superior...
Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network.
Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou
2011-09-01
To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.
Directory of Open Access Journals (Sweden)
M. Manimozhi
2014-05-01
Full Text Available Fault Detection and Isolation (FDI using Linear Kalman Filter (LKF is not sufficient for effective monitoring of nonlinear processes. Most of the chemical plants are nonlinear in nature while operating the plant in a wide range of process variables. In this study we present an approach for designing of Multi Model Adaptive Linear Kalman Filter (MMALKF for Fault Detection and Isolation (FDI of a nonlinear system. The uses a bank of adaptive Kalman filter, with each model based on different fault hypothesis. In this study the effectiveness of the MMALKF has been demonstrated on a spherical tank system. The proposed method is detecting and isolating the sensor and actuator soft faults which occur sequentially or simultaneously.
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Dalei Song
2012-10-01
Full Text Available The adaptive extended set‐membership filter (AESMF for nonlinear ellipsoidal estimation suffers a mismatch between real process noise and its set boundaries, which may result in unstable estimation. In this paper, a MIT method‐based adaptive set‐membership filter, for the optimization of the set boundaries of process noise, is developed and applied to the nonlinear joint estimation of both time‐varying states and parameters. As a result of using the proposed MIT‐AESMF, the estimation effectiveness and boundary accuracy of traditional AESMF are substantially improved. Simulation results have shown the efficiency and robustness of the proposed method.
An inertia-free filter line-search algorithm for large-scale nonlinear programming
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Chiang, Nai-Yuan; Zavala, Victor M.
2016-02-15
We present a filter line-search algorithm that does not require inertia information of the linear system. This feature enables the use of a wide range of linear algebra strategies and libraries, which is essential to tackle large-scale problems on modern computing architectures. The proposed approach performs curvature tests along the search step to detect negative curvature and to trigger convexification. We prove that the approach is globally convergent and we implement the approach within a parallel interior-point framework to solve large-scale and highly nonlinear problems. Our numerical tests demonstrate that the inertia-free approach is as efficient as inertia detection via symmetric indefinite factorizations. We also demonstrate that the inertia-free approach can lead to reductions in solution time because it reduces the amount of convexification needed.
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Zhaohui Chen
2013-01-01
Full Text Available The delay-dependent exponential L2-L∞ performance analysis and filter design are investigated for stochastic systems with mixed delays and nonlinear perturbations. Based on the delay partitioning and integral partitioning technique, an improved delay-dependent sufficient condition for the existence of the L2-L∞ filter is established, by choosing an appropriate Lyapunov-Krasovskii functional and constructing a new integral inequality. The full-order filter design approaches are obtained in terms of linear matrix inequalities (LMIs. By solving the LMIs and using matrix decomposition, the desired filter gains can be obtained, which ensure that the filter error system is exponentially stable with a prescribed L2-L∞ performance γ. Numerical examples are provided to illustrate the effectiveness and significant improvement of the proposed method.
Li, Tao; Yuan, Gannan; Li, Wang
2016-03-15
The derivation of a conventional error model for the miniature gyroscope-based measurement while drilling (MGWD) system is based on the assumption that the errors of attitude are small enough so that the direction cosine matrix (DCM) can be approximated or simplified by the errors of small-angle attitude. However, the simplification of the DCM would introduce errors to the navigation solutions of the MGWD system if the initial alignment cannot provide precise attitude, especially for the low-cost microelectromechanical system (MEMS) sensors operated in harsh multilateral horizontal downhole drilling environments. This paper proposes a novel nonlinear error model (NNEM) by the introduction of the error of DCM, and the NNEM can reduce the propagated errors under large-angle attitude error conditions. The zero velocity and zero position are the reference points and the innovations in the states estimation of particle filter (PF) and Kalman filter (KF). The experimental results illustrate that the performance of PF is better than KF and the PF with NNEM can effectively restrain the errors of system states, especially for the azimuth, velocity, and height in the quasi-stationary condition.
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Tao Li
2016-03-01
Full Text Available The derivation of a conventional error model for the miniature gyroscope-based measurement while drilling (MGWD system is based on the assumption that the errors of attitude are small enough so that the direction cosine matrix (DCM can be approximated or simplified by the errors of small-angle attitude. However, the simplification of the DCM would introduce errors to the navigation solutions of the MGWD system if the initial alignment cannot provide precise attitude, especially for the low-cost microelectromechanical system (MEMS sensors operated in harsh multilateral horizontal downhole drilling environments. This paper proposes a novel nonlinear error model (NNEM by the introduction of the error of DCM, and the NNEM can reduce the propagated errors under large-angle attitude error conditions. The zero velocity and zero position are the reference points and the innovations in the states estimation of particle filter (PF and Kalman filter (KF. The experimental results illustrate that the performance of PF is better than KF and the PF with NNEM can effectively restrain the errors of system states, especially for the azimuth, velocity, and height in the quasi-stationary condition.
Mode Coupling and Nonlinear Resonances of MEMS Arch Resonators for Bandpass Filters
Hajjaj, Amal Z.
2017-01-30
We experimentally demonstrate an exploitation of the nonlinear softening, hardening, and veering phenomena (near crossing), where the frequencies of two vibration modes get close to each other, to realize a bandpass filter of sharp roll off from the passband to the stopband. The concept is demonstrated based on an electrothermally tuned and electrostatically driven MEMS arch resonator operated in air. The in-plane resonator is fabricated from a silicon-on-insulator wafer with a deliberate curvature to form an arch shape. A DC current is applied through the resonator to induce heat and modulate its stiffness, and hence its resonance frequencies. We show that the first resonance frequency increases up to twice of the initial value while the third resonance frequency decreases until getting very close to the first resonance frequency. This leads to the phenomenon of veering, where both modes get coupled and exchange energy. We demonstrate that by driving both modes nonlinearly and electrostatically near the veering regime, such that the first and third modes exhibit softening and hardening behavior, respectively, sharp roll off from the passband to the stopband is achievable. We show a flat, wide, and tunable bandwidth and center frequency by controlling the electrothermal actuation voltage.
Application of adaptive non-linear 2D and 3D postprocessing filters for reduced dose abdominal CT.
Borgen, Lars; Kalra, Mannudeep K; Laerum, Frode; Hachette, Isabelle W; Fredriksson, Carina H; Sandborg, Michael; Smedby, Orjan
2012-04-01
Abdominal computed tomography (CT) is a frequently performed imaging procedure, resulting in considerable radiation doses to the patient population. Postprocessing filters are one of several dose reduction measures that might help to reduce radiation doses without loss of image quality. To assess and compare the effect of two- and three-dimensional (2D, 3D) non-linear adaptive filters on reduced dose abdominal CT images. Two baseline abdominal CT image series with a volume computer tomography dose index (CTDI (vol)) of 12 mGy and 6 mGy were acquired for 12 patients. Reduced dose images were postprocessed with 2D and 3D filters. Six radiologists performed blinded randomized, side-by-side image quality assessments. Objective noise was measured. Data were analyzed using visual grading regression and mixed linear models. All image quality criteria were rated as superior for 3D filtered images compared to reduced dose baseline and 2D filtered images (P 0.05). There were no significant variations of objective noise between standard dose and 2D or 3D filtered images. The quality of 3D filtered reduced dose abdominal CT images is superior compared to reduced dose unfiltered and 2D filtered images. For patients with BMI < 30 kg/m(2), 3D filtered images are comparable to standard dose images.
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G. Wu
2014-04-01
Full Text Available The Ensemble Transform Kalman Filter (ETKF assimilation scheme has recently seen rapid development and wide application. As a specific implementation of the Ensemble Kalman Filter (EnKF, the ETKF is computationally more efficient than the conventional EnKF. However, the current implementation of the ETKF still has some limitations when the observation operator is strongly nonlinear. One problem is that the nonlinear operator and its tangent-linear operator are iteratively calculated in the minimization of a nonlinear objective function similar to 4DVAR, which may be computationally expensive. Another problem is that it uses the tangent-linear approximation of the observation operator to estimate the multiplicative inflation factor of the forecast errors, which may not be sufficiently accurate. This study seeks a way to avoid these problems. First, we apply the second-order Taylor approximation of the nonlinear observation operator to avoid iteratively calculating the operator and its tangent-linear operator. The related computational cost is also discussed. Second, we propose a scheme to estimate the inflation factor when the observation operator is strongly nonlinear. Experimentation with the Lorenz-96 model shows that using the second-order Taylor approximation of the nonlinear observation operator leads to a reduction of the analysis error compared with the traditional linear approximation. Similarly, the proposed inflation scheme leads to a reduction of the analysis error compared with the procedure using the traditional inflation scheme.
Subramanian, Aneesh C.
2012-11-01
This paper investigates the role of the linear analysis step of the ensemble Kalman filters (EnKF) in disrupting the balanced dynamics in a simple atmospheric model and compares it to a fully nonlinear particle-based filter (PF). The filters have a very similar forecast step but the analysis step of the PF solves the full Bayesian filtering problem while the EnKF analysis only applies to Gaussian distributions. The EnKF is compared to two flavors of the particle filter with different sampling strategies, the sequential importance resampling filter (SIRF) and the sequential kernel resampling filter (SKRF). The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode. It can also be configured either to evolve on a so-called slow manifold, where the fast motion is suppressed, or such that the fast-varying variables are diagnosed from the slow-varying variables as slaved modes. Identical twin experiments show that EnKF and PF capture the variables on the slow manifold well as the dynamics is very stable. PFs, especially the SKRF, capture slaved modes better than the EnKF, implying that a full Bayesian analysis estimates the nonlinear model variables better. The PFs perform significantly better in the fully coupled nonlinear model where fast and slow variables modulate each other. This suggests that the analysis step in the PFs maintains the balance in both variables much better than the EnKF. It is also shown that increasing the ensemble size generally improves the performance of the PFs but has less impact on the EnKF after a sufficient number of members have been used.
Application of adaptive non-linear 2D and 3D postprocessing filters for reduced dose abdominal CT
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Borgen, Lars (Dept. of Radiology, Drammen Hospital, Drammen and Buskerud Univ. College, Drammen (Norway)), Email: lars.borgen@vestreviken.no; Kalra, Mannudeep K. (Massachusetts General Hospital Imaging, Harvard Medical School, Massachusetts General Hospital, Boston (United States)); Laerum, Frode (Dept. of Radiology, Akershus Univ. Hospital, Loerenskog (Norway)); Hachette, Isabelle W.; Fredriksson, Carina H. (ContextVision AB, Linkoeping (Sweden)); Sandborg, Michael (Dept. of Medical Physics, IMH, Faculty of Health Sciences, Linkoeping Univ., County Council of Oestergoetland, Linkoeping (Sweden); Center for Medical Image Science and Visualization, Linkoeping (Sweden)); Smedby, Oerjan (Center for Medical Image Science and Visualization, Linkoeping (Sweden); Dept. of Radiology, Linkoeping Univ., Linkoeping (Sweden))
2012-04-15
Background: Abdominal computed tomography (CT) is a frequently performed imaging procedure, resulting in considerable radiation doses to the patient population. Postprocessing filters are one of several dose reduction measures that might help to reduce radiation doses without loss of image quality. Purpose: To assess and compare the effect of two- and three-dimensional (2D, 3D) non-linear adaptive filters on reduced dose abdominal CT images. Material and Methods: Two baseline abdominal CT image series with a volume computer tomography dose index (CTDI{sub vol}) of 12 mGy and 6 mGy were acquired for 12 patients. Reduced dose images were postprocessed with 2D and 3D filters. Six radiologists performed blinded randomized, side-by-side image quality assessments. Objective noise was measured. Data were analyzed using visual grading regression and mixed linear models. Results: All image quality criteria were rated as superior for 3D filtered images compared to reduced dose baseline and 2D filtered images (P < 0.01). Standard dose images had better image quality than reduced dose 3D filtered images (P < 0.01), but similar image noise. For patients with body mass index (BMI) < 30 kg/m2 however, 3D filtered images were rated significantly better than normal dose images for two image criteria (P < 0.05), while no significant difference was found for the remaining three image criteria (P > 0.05). There were no significant variations of objective noise between standard dose and 2D or 3D filtered images. Conclusion: The quality of 3D filtered reduced dose abdominal CT images is superior compared to reduced dose unfiltered and 2D filtered images. For patients with BMI < 30 kg/m2, 3D filtered images are comparable to standard dose images
Van Leeuwen, Peter Jan; Reich, Sebastian
2015-01-01
This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.
SPATIO-TEMPORAL DATA ANALYSIS WITH NON-LINEAR FILTERS: BRAIN MAPPING WITH fMRI DATA
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Karsten Rodenacker
2011-05-01
Full Text Available Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D is gathered during relatively long time ranges (3-5 min. From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters. Filters applied are compared by classifications of activations.
Khaki, Mehdi; Forootan, Ehsan; Kuhn, Michael; Awange, Joseph; Pattiaratchi, Charitha
2016-04-01
Quantifying large-scale (basin/global) water storage changes is essential to understand the Earth's hydrological water cycle. Hydrological models have usually been used to simulate variations in storage compartments resulting from changes in water fluxes (i.e., precipitation, evapotranspiration and runoff) considering physical or conceptual frameworks. Models however represent limited skills in accurately simulating the storage compartments that could be the result of e.g., the uncertainty of forcing parameters, model structure, etc. In this regards, data assimilation provides a great chance to combine observational data with a prior forecast state to improve both the accuracy of model parameters and to improve the estimation of model states at the same time. Various methods exist that can be used to perform data assimilation into hydrological models. The one more frequently used particle-based algorithms suitable for non-linear systems high-dimensional systems is the Ensemble Kalman Filtering (EnKF). Despite efficiency and simplicity (especially in EnKF), this method indicate some drawbacks. To implement EnKF, one should use the sample covariance of observations and model state variables to update a priori estimates of the state variables. The sample covariance can be suboptimal as a result of small ensemble size, model errors, model nonlinearity, and other factors. Small ensemble can also lead to the development of correlations between state components that are at a significant distance from one another where there is no physical relation. To investigate the under-sampling issue raise by EnKF, covariance inflation technique in conjunction with localization was implemented. In this study, a comparison between latest methods used in the data assimilation framework, to overcome the mentioned problem, is performed. For this, in addition to implementing EnKF, we introduce and apply the Local Ensemble Kalman Filter (LEnKF) utilizing covariance localization to remove
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Muammar Sadrawi
2016-01-01
Full Text Available Good quality cardiopulmonary resuscitation (CPR is the mainstay of treatment for managing patients with out-of-hospital cardiac arrest (OHCA. Assessment of the quality of the CPR delivered is now possible through the electrocardiography (ECG signal that can be collected by an automated external defibrillator (AED. This study evaluates a nonlinear approximation of the CPR given to the asystole patients. The raw ECG signal is filtered using ensemble empirical mode decomposition (EEMD, and the CPR-related intrinsic mode functions (IMF are chosen to be evaluated. In addition, sample entropy (SE, complexity index (CI, and detrended fluctuation algorithm (DFA are collated and statistical analysis is performed using ANOVA. The primary outcome measure assessed is the patient survival rate after two hours. CPR pattern of 951 asystole patients was analyzed for quality of CPR delivered. There was no significant difference observed in the CPR-related IMFs peak-to-peak interval analysis for patients who are younger or older than 60 years of age, similarly to the amplitude difference evaluation for SE and DFA. However, there is a difference noted for the CI (p<0.05. The results show that patients group younger than 60 years have higher survival rate with high complexity of the CPR-IMFs amplitude differences.
Mustaffa, Izadora; Trenado, Carlos; Schwerdtfeger, Karsten; Strauss, Daniel J
2008-01-01
Recent progress in mathematical image processing shows a remarkable success when applying numerical methods to ill-posed partial differential equations (PDE). In particular, nonlinear diffusion filtering (NDF)process is an approach that belongs to such family of differential equations. It has been successfully applied in many recent methods for image processing and computer vision areas, particularly in denoising, smoothing, segmentation, and restoration. In this paper we focus on a novel NDF application, namely denoising of single-trials of auditory brainstem responses (ABRs) and the analysis of transcranial magnetic stimulation (TMS) responses.We show that by applying NDF on a matrix-form image of single-trials, we were able to denoise the single-trials, resulting in a better extraction of information over the ongoing experiment; morphology, eg. the latency of the single-trials according to different stimuli paradigms at different stimulation intensity levels. It is concluded that NDF represents a novel and useful approach for the analysis of single-trials in brain imaging.
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Hongjian Wang
2014-01-01
Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.
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Yi-Ming Chen
2017-01-01
Full Text Available Noninvasive medical procedures are usually preferable to their invasive counterparts in the medical community. Anemia examining through the palpebral conjunctiva is a convenient noninvasive procedure. The procedure can be automated to reduce the medical cost. We propose an anemia examining approach by using a Kalman filter (KF and a regression method. The traditional KF is often used in time-dependent applications. Here, we modified the traditional KF for the time-independent data in medical applications. We simply compute the mean value of the red component of the palpebral conjunctiva image as our recognition feature and use a penalty regression algorithm to find a nonlinear curve that best fits the data of feature values and the corresponding levels of hemoglobin (Hb concentration. To evaluate the proposed approach and several relevant approaches, we propose a risk evaluation scheme, where the entire Hb spectrum is divided into high-risk, low-risk, and doubtful intervals for anemia. The doubtful interval contains the Hb threshold, say 11 g/dL, separating anemia and nonanemia. A suspect sample is the sample falling in the doubtful interval. For the anemia screening purpose, we would like to have as less suspect samples as possible. The experimental results show that the modified KF reduces the number of suspect samples significantly for all the approaches considered here.
Kee, Chul-Sik; Lee, Yeong Lak; Lee, Jongmin
2008-04-28
We investigate electro- and thermo-optic effects on multi-wavelength Solc filters based on chi(2) nonlinear quasi-periodic photonic crystals. The multi-wavelength Solc filters are composed of two building blocks A and B, in which each containing a pair of antiparallel poled domains, arranged as a Fibonacci sequence. The transmittances at filtering wavelengths can be modulated from 0 to 100% by applying an external voltage but the filtering wave-lengths are unchanged. The filtering wavelengths can be tuned by varying temperature. As temperature decreases, the filtering wavelengths increase (approximately -0.45 nm/degrees C).
Ilyas, Muhammad; Hong, Beomjin; Cho, Kuk; Baeg, Seung-Ho; Park, Sangdeok
2016-05-23
This paper provides algorithms to fuse relative and absolute microelectromechanical systems (MEMS) navigation sensors, suitable for micro planetary rovers, to provide a more accurate estimation of navigation information, specifically, attitude and position. Planetary rovers have extremely slow speed (~1 cm/s) and lack conventional navigation sensors/systems, hence the general methods of terrestrial navigation may not be applicable to these applications. While relative attitude and position can be tracked in a way similar to those for ground robots, absolute navigation information is hard to achieve on a remote celestial body, like Moon or Mars, in contrast to terrestrial applications. In this study, two absolute attitude estimation algorithms were developed and compared for accuracy and robustness. The estimated absolute attitude was fused with the relative attitude sensors in a framework of nonlinear filters. The nonlinear Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) were compared in pursuit of better accuracy and reliability in this nonlinear estimation problem, using only on-board low cost MEMS sensors. Experimental results confirmed the viability of the proposed algorithms and the sensor suite, for low cost and low weight micro planetary rovers. It is demonstrated that integrating the relative and absolute navigation MEMS sensors reduces the navigation errors to the desired level.
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Muhammad Ilyas
2016-05-01
Full Text Available This paper provides algorithms to fuse relative and absolute microelectromechanical systems (MEMS navigation sensors, suitable for micro planetary rovers, to provide a more accurate estimation of navigation information, specifically, attitude and position. Planetary rovers have extremely slow speed (~1 cm/s and lack conventional navigation sensors/systems, hence the general methods of terrestrial navigation may not be applicable to these applications. While relative attitude and position can be tracked in a way similar to those for ground robots, absolute navigation information is hard to achieve on a remote celestial body, like Moon or Mars, in contrast to terrestrial applications. In this study, two absolute attitude estimation algorithms were developed and compared for accuracy and robustness. The estimated absolute attitude was fused with the relative attitude sensors in a framework of nonlinear filters. The nonlinear Extended Kalman filter (EKF and Unscented Kalman filter (UKF were compared in pursuit of better accuracy and reliability in this nonlinear estimation problem, using only on-board low cost MEMS sensors. Experimental results confirmed the viability of the proposed algorithms and the sensor suite, for low cost and low weight micro planetary rovers. It is demonstrated that integrating the relative and absolute navigation MEMS sensors reduces the navigation errors to the desired level.
Ilyas, Muhammad; Hong, Beomjin; Cho, Kuk; Baeg, Seung-Ho; Park, Sangdeok
2016-01-01
This paper provides algorithms to fuse relative and absolute microelectromechanical systems (MEMS) navigation sensors, suitable for micro planetary rovers, to provide a more accurate estimation of navigation information, specifically, attitude and position. Planetary rovers have extremely slow speed (~1 cm/s) and lack conventional navigation sensors/systems, hence the general methods of terrestrial navigation may not be applicable to these applications. While relative attitude and position can be tracked in a way similar to those for ground robots, absolute navigation information is hard to achieve on a remote celestial body, like Moon or Mars, in contrast to terrestrial applications. In this study, two absolute attitude estimation algorithms were developed and compared for accuracy and robustness. The estimated absolute attitude was fused with the relative attitude sensors in a framework of nonlinear filters. The nonlinear Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) were compared in pursuit of better accuracy and reliability in this nonlinear estimation problem, using only on-board low cost MEMS sensors. Experimental results confirmed the viability of the proposed algorithms and the sensor suite, for low cost and low weight micro planetary rovers. It is demonstrated that integrating the relative and absolute navigation MEMS sensors reduces the navigation errors to the desired level. PMID:27223293
Sajedi, Salar; Kamal Asl, Alireza; Ay, Mohammad R; Farahani, Mohammad H; Rahmim, Arman
2013-06-01
Applications in imaging and spectroscopy rely on pulse processing methods for appropriate data generation. Often, the particular method utilized does not highly impact data quality, whereas in some scenarios, such as in the presence of high count rates or high frequency pulses, this issue merits extra consideration. In the present study, a new approach for pulse processing in nuclear medicine imaging and spectroscopy is introduced and evaluated. The new non-linear recursive filter (NLRF) performs nonlinear processing of the input signal and extracts the main pulse characteristics, having the powerful ability to recover pulses that would ordinarily result in pulse pile-up. The filter design defines sampling frequencies lower than the Nyquist frequency. In the literature, for systems involving NaI(Tl) detectors and photomultiplier tubes (PMTs), with a signal bandwidth considered as 15 MHz, the sampling frequency should be at least 30 MHz (the Nyquist rate), whereas in the present work, a sampling rate of 3.3 MHz was shown to yield very promising results. This was obtained by exploiting the known shape feature instead of utilizing a general sampling algorithm. The simulation and experimental results show that the proposed filter enhances count rates in spectroscopy. With this filter, the system behaves almost identically as a general pulse detection system with a dead time considerably reduced to the new sampling time (300 ns). Furthermore, because of its unique feature for determining exact event times, the method could prove very useful in time-of-flight PET imaging.
On Power Factor Improvement by Lossless Linear Filters in the Nonlinear Nonsinusoidal Case
Puerto-Flores, Dunstano del; Scherpen, Jacquelien M.A.; Ortega, Romeo
2010-01-01
Recently, it has been established that the problem of power factor compensation (PFC) for nonlinear loads with non-sinusoidal source voltage can be recast in terms of the property of cyclodissipativity. Using this framework we study the PFC for nonlinear loads containing a memoryless nonlinearity. W
On Power Factor Improvement by Lossless Linear Filters in the Nonlinear Nonsinusoidal Case
Puerto-Flores, Dunstano del; Scherpen, Jacquelien M.A.; Ortega, Romeo
2010-01-01
Recently, it has been established that the problem of power factor compensation (PFC) for nonlinear loads with non-sinusoidal source voltage can be recast in terms of the property of cyclodissipativity. Using this framework we study the PFC for nonlinear loads containing a memoryless nonlinearity.
Aguirre, Luis Antonio; Teixeira, Bruno Otávio S.; Tôrres, Leonardo Antônio B.
2005-08-01
This paper addresses the problem of state estimation for nonlinear systems by means of the unscented Kalman filter (UKF). Compared to the traditional extended Kalman filter, the UKF does not require the local linearization of the system equations used in the propagation stage. Important results using the UKF have been reported recently but in every case the system equations used by the filter were considered known. Not only that, such models are usually considered to be differential equations, which requires that numerical integration be performed during the propagation phase of the filter. In this paper the dynamical equations of the system are taken to be difference equations—thus avoiding numerical integration—and are built from data without prior knowledge. The identified models are subsequently implemented in the filter in order to accomplish state estimation. The paper discusses the impact of not knowing the exact equations and using data-driven models in the context of state and joint state-and-parameter estimation. The procedure is illustrated by means of examples that use simulated and measured data.
DEFF Research Database (Denmark)
Yu, Jianjun; Jeppesen, Palle
2001-01-01
Using cross-phase modulation in a 1-km high-nonlinearity dispersion-shifted fiber with subsequent filtering by a tunable optical filter, 80-Gb/s pulsewidth maintained wavelength conversion is realized. Penalty-free transmission over 80-km conventional single-mode fiber and 12-km dispersion...
Applications of Kalman filters based on non-linear functions to numerical weather predictions
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G. Galanis
2006-10-01
Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.
Modeling of racetrack-resonator add-drop filters with arbitrary nonlinear directional couplers.
Gómez-Alcalá, Rafael; Fraile-Peláez, F Javier; Chamorro-Posada, Pedro; Díaz-Otero, Francisco J
2012-06-01
In this Letter we employ the general coupled-mode equations of the nonlinear directional coupler and demonstrate that the switching characteristics of prototypical nonlinear racetrack-resonator structures may differ considerably from those obtained when the standard, generally incorrect, coupled-mode equations are used.
Ma, Lifeng; Wang, Zidong; Lam, Hak-Keung; Kyriakoulis, Nikos
2016-07-07
In this paper, the distributed set-membership filtering problem is investigated for a class of discrete time-varying system with an event-based communication mechanism over sensor networks. The system under consideration is subject to sector-bounded nonlinearity, unknown but bounded noises and sensor saturations. Each intelligent sensing node transmits the data to its neighbors only when certain triggering condition is violated. By means of a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired distributed event-based filter which is capable of confining the system state in certain ellipsoidal regions centered at the estimates. Within the established theoretical framework, two additional optimization problems are formulated: one is to seek the minimal ellipsoids (in the sense of matrix trace) for the best filtering performance, and the other is to maximize the triggering threshold so as to reduce the triggering frequency with satisfactory filtering performance. A numerically attractive chaos algorithm is employed to solve the optimization problems. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.
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Esfandiar, Habib; KoraYem, Moharam Habibnejad [Islamic Azad University, Tehran (Iran, Islamic Republic of)
2015-09-15
In this study, the researchers try to examine nonlinear dynamic analysis and determine Dynamic load carrying capacity (DLCC) in flexible manipulators. Manipulator modeling is based on Timoshenko beam theory (TBT) considering the effects of shear and rotational inertia. To get rid of the risk of shear locking, a new procedure is presented based on mixed finite element formulation. In the method proposed, shear deformation is free from the risk of shear locking and independent of the number of integration points along the element axis. Dynamic modeling of manipulators will be done by taking into account small and large deformation models and using extended Hamilton method. System motion equations are obtained by using nonlinear relationship between displacements-strain and 2nd PiolaKirchoff stress tensor. In addition, a comprehensive formulation will be developed to calculate DLCC of the flexible manipulators during the path determined considering the constraints end effector accuracy, maximum torque in motors and maximum stress in manipulators. Simulation studies are conducted to evaluate the efficiency of the method proposed taking two-link flexible and fixed base manipulators for linear and circular paths into consideration. Experimental results are also provided to validate the theoretical model. The findings represent the efficiency and appropriate performance of the method proposed.
Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation
Kollmann, Robert
2013-01-01
This paper develops a novel approach for estimating latent state variables of Dynamic Stochastic General Equilibrium (DSGE) models that are solved using a second-order accurate approximation. I apply the Kalman filter to a state-space representation of the second-order solution based on the ‘pruning’ scheme of Kim, Kim, Schaumburg and Sims (2008). By contrast to particle filters, no stochastic simulations are needed for the filter here--the present method is thus much faster. In Monte Carlo e...
Akhbari, Mahsa; Shamsollahi, Mohammad B; Jutten, Christian; Armoundas, Antonis A; Sayadi, Omid
2016-02-01
In this paper we propose an efficient method for denoising and extracting fiducial point (FP) of ECG signals. The method is based on a nonlinear dynamic model which uses Gaussian functions to model ECG waveforms. For estimating the model parameters, we use an extended Kalman filter (EKF). In this framework called EKF25, all the parameters of Gaussian functions as well as the ECG waveforms (P-wave, QRS complex and T-wave) in the ECG dynamical model, are considered as state variables. In this paper, the dynamic time warping method is used to estimate the nonlinear ECG phase observation. We compare this new approach with linear phase observation models. Using linear and nonlinear EKF25 for ECG denoising and nonlinear EKF25 for fiducial point extraction and ECG interval analysis are the main contributions of this paper. Performance comparison with other EKF-based techniques shows that the proposed method results in higher output SNR with an average SNR improvement of 12 dB for an input SNR of -8 dB. To evaluate the FP extraction performance, we compare the proposed method with a method based on partially collapsed Gibbs sampler and an established EKF-based method. The mean absolute error and the root mean square error of all FPs, across all databases are 14 ms and 22 ms, respectively, for our proposed method, with an advantage when using a nonlinear phase observation. These errors are significantly smaller than errors obtained with other methods. For ECG interval analysis, with an absolute mean error and a root mean square error of about 22 ms and 29 ms, the proposed method achieves better accuracy and smaller variability with respect to other methods.
Cannistraci, Carlo Vittorio
2015-01-26
Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet\\'s performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis.
McDonald, Alison C; Sanei, Kia; Keir, Peter J
2013-06-01
Muscle force estimates are important for full understanding of the musculoskeletal system and EMG is a modeling method used to estimate muscle force. The purpose of this investigation was to examine the effect of high pass filtering and non-linear normalization on the EMG-force relationship of sub-maximal finger exertions. Sub-maximal isometric ramp exertions were performed under three conditions (i) extension with restraint at the mid-proximal phalanx, (ii) flexion at the proximal phalanx and (iii) flexion at the distal phalanx. Thirty high pass filter designs were compared to a standardized processing procedure and an exponential fit equation was used for non-linear normalization. High pass filtering significantly reduced the %RMS error and increased the peak cross correlation between EMG and force in the distal flexion condition and in the other two conditions there was a trend towards improving force predictions with high pass filtering. The degree of linearity differed between the three contraction conditions and high pass filtering improved the linearity in all conditions. Non-linear normalization had greater impact on the EMG-force relationship than high pass filtering. The difference in optimal processing parameters suggests that high pass filtering and linearity are dependent on contraction mode as well as the muscle analyzed.
Directory of Open Access Journals (Sweden)
Yin Hua
2015-04-01
Full Text Available Estimation of state of charge (SOC is of great importance for lithium-ion (Li-ion batteries used in electric vehicles. This paper presents a state of charge estimation method using nonlinear predictive filter (NPF and evaluates the proposed method on the lithium-ion batteries with different chemistries. Contrary to most conventional filters which usually assume a zero mean white Gaussian process noise, the advantage of NPF is that the process noise in NPF is treated as an unknown model error and determined as a part of the solution without any prior assumption, and it can take any statistical distribution form, which improves the estimation accuracy. In consideration of the model accuracy and computational complexity, a first-order equivalent circuit model is applied to characterize the battery behavior. The experimental test is conducted on the LiCoO2 and LiFePO4 battery cells to validate the proposed method. The results show that the NPF method is able to accurately estimate the battery SOC and has good robust performance to the different initial states for both cells. Furthermore, the comparison study between NPF and well-established extended Kalman filter for battery SOC estimation indicates that the proposed NPF method has better estimation accuracy and converges faster.
Bds/gps Integrated Positioning Method Research Based on Nonlinear Kalman Filtering
Ma, Y.; Yuan, W.; Sun, H.
2017-09-01
In order to realize fast and accurate BDS/GPS integrated positioning, it is necessary to overcome the adverse effects of signal attenuation, multipath effect and echo interference to ensure the result of continuous and accurate navigation and positioning. In this paper, pseudo-range positioning is used as the mathematical model. In the stage of data preprocessing, using precise and smooth carrier phase measurement value to promote the rough pseudo-range measurement value without ambiguity. At last, the Extended Kalman Filter(EKF), the Unscented Kalman Filter(UKF) and the Particle Filter(PF) algorithm are applied in the integrated positioning method for higher positioning accuracy. The experimental results show that the positioning accuracy of PF is the highest, and UKF is better than EKF.
Particle filter initialization in non-linear non-Gaussian radar target tracking
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
When particle filter is applied in radar target tracking,the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles,a new method called"competition strategy algorithm"is presented.In this method,initial measurements give birth to several particle groups around them,regularly.Each of the groups is tested several times,separately,in the beginning periods,and the group that has the most number of efficient particles is selected as the initial particles.For this method,sample initial particles selected are on the basis of several measurements instead of only one first measurement,which surely improves the accuracy of initial particles.The method sacrifices initialization time and computation cost for accuracy of initial particles. Results of simulation show that it greely improves the accuracy of initial particles,which makes the effect of filtering much better.
Directory of Open Access Journals (Sweden)
Umar Iqbal
2010-01-01
Full Text Available Present land vehicle navigation relies mostly on the Global Positioning System (GPS that may be interrupted or deteriorated in urban areas. In order to obtain continuous positioning services in all environments, GPS can be integrated with inertial sensors and vehicle odometer using Kalman filtering (KF. For car navigation, low-cost positioning solutions based on MEMS-based inertial sensors are utilized. To further reduce the cost, a reduced inertial sensor system (RISS consisting of only one gyroscope and speed measurement (obtained from the car odometer is integrated with GPS. The MEMS-based gyroscope measurement deteriorates over time due to different errors like the bias drift. These errors may lead to large azimuth errors and mitigating the azimuth errors requires robust modeling of both linear and nonlinear effects. Therefore, this paper presents a solution based on Parallel Cascade Identification (PCI module that models the azimuth errors and is augmented to KF. The proposed augmented KF-PCI method can handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear and residual parts of the azimuth errors are modeled by PCI. The performance of this method is examined using road test experiments in a land vehicle.
Zeng, Nianyin; Wang, Zidong; Li, Yurong; Du, Min; Liu, Xiaohui
2011-07-01
In this paper, a mathematical model for sandwich-type lateral flow immunoassay is developed via short available time series. A nonlinear dynamic stochastic model is considered that consists of the biochemical reaction system equations and the observation equation. After specifying the model structure, we apply the extended Kalman filter (EKF) algorithm for identifying both the states and parameters of the nonlinear state-space model. It is shown that the EKF algorithm can accurately identify the parameters and also predict the system states in the nonlinear dynamic stochastic model through an iterative procedure by using a small number of observations. The identified mathematical model provides a powerful tool for testing the system hypotheses and also for inspecting the effects from various design parameters in both rapid and inexpensive way. Furthermore, by means of the established model, the dynamic changes in the concentration of antigens and antibodies can be predicted, thereby making it possible for us to analyze, optimize, and design the properties of lateral flow immunoassay devices. © 2011 IEEE
Nonlinear Control of Back-to-Back VSC-HVDC System via Command-Filter Backstepping
Directory of Open Access Journals (Sweden)
Jie Huang
2017-01-01
Full Text Available This paper proposed a command-filtered backstepping controller to improve the dynamic performance of back-to-back voltage-source-converter high voltage direct current (BTB VSC-HVDC. First, the principle and model of BTB VSC-HVDC in abc and d-q frame are described. Then, backstepping method is applied to design a controller to maintain the voltage balance and realize coordinated control of active and reactive power. Meanwhile, command filter is introduced to deal with the problem of input saturation and explosion of complexity in conventional backstepping, and a filter compensation signal is designed to diminish the adverse effects caused by the command filter. Next, the stability and convergence of the whole system are proved via the Lyapunov theorem of asymptotic stability. Finally, simulation results are given to demonstrate that proposed controller has a better dynamic performance and stronger robustness compared to the traditional PID algorithm, which also proves the effectiveness and possibility of the designed controller.
Energy Technology Data Exchange (ETDEWEB)
Daouas, N.; Radhouani, M.S. [Ecole Nationale d' Ingenieurs de Monastir, Dept. de Genie-Energetique, Monastir (Tunisia)
2000-02-01
Nonlinear inverse heat conduction problem is resolved by using a formulation of the Kalman filter based on a statistical approach and extended to nonlinear systems. The time evolution of a surface heat flux density is reconstructed from a numerical simulation which allowed us to analyse the influence of some parameters, that condition the running of the filter, on the estimation result. A suitable choice of these parameters, guided by the filter behaviour observations, leads to a solution that remains stable when using noisy data, but that is slightly time-lagged compared to the exact function. This time-lag depends on the location of the interior temperature measurement needed for the inversion and on the model error caused by the approximation of the heat flux with a piece-wide constant function. The application of the extended Kalman filter with real measurements recorded from an experimental set-up, shows that this technique fits the stochastic structure of experimental measurements. The provided results are validated by using the Raynaud's and Bransier's inverse method and are in good agreement with the flux density estimated with this method. (authors)
On Power Factor Improvement by Lossless Linear Filters under Nonlinear Nonsinusoidal Conditions
Puerto-Flores, D. del; Ortega, R.; Scherpen, J.M.A.
2011-01-01
Recently, it has been established that the problem of power factor compensation for nonlinear loads with nonsinusoidal source voltage can be recast in terms of the property of cyclodissipativity. The purpose of this brief note is to review and to illustrate the application of this framework to the p
Directory of Open Access Journals (Sweden)
Carlos Pozo
Full Text Available Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study
Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Sorribas, Albert; Jiménez, Laureano
2012-01-01
Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the
Accurate 3D maps from depth images and motion sensors via nonlinear Kalman filtering
Hervier, Thibault; Goulette, François
2012-01-01
This paper investigates the use of depth images as localisation sensors for 3D map building. The localisation information is derived from the 3D data thanks to the ICP (Iterative Closest Point) algorithm. The covariance of the ICP, and thus of the localization error, is analysed, and described by a Fisher Information Matrix. It is advocated this error can be much reduced if the data is fused with measurements from other motion sensors, or even with prior knowledge on the motion. The data fusion is performed by a recently introduced specific extended Kalman filter, the so-called Invariant EKF, and is directly based on the estimated covariance of the ICP. The resulting filter is very natural, and is proved to possess strong properties. Experiments with a Kinect sensor and a three-axis gyroscope prove clear improvement in the accuracy of the localization, and thus in the accuracy of the built 3D map.
Self-tuning control with a filter and a neural compensator for a class of nonlinear systems.
Fu, Yue; Chai, Tianyou
2013-05-01
Considering the mismatching of model-process order, in this brief, a self-tuning proportional-integral-derivative (PID)-like controller is proposed by combining a pole assignment self-tuning PID controller with a filter and a neural compensator. To design the PID controller, a reduced order model is introduced, whose linear parameters are identified by a normalized projection algorithm with a deadzone. The higher order nonlinearity is estimated by a high order neural network. The gains of the PID controller are obtained by pole assignment, which together with other parameters are tuned on-line. The bounded-input bounded-output stability condition and convergence condition of the closed-loop system are presented. Simulations are conducted on the continuous stirred tank reactors system. The results show the effectiveness of the proposed method.
Nonlinear tracking in a diffusion process with a Bayesian filter and the finite element method
DEFF Research Database (Denmark)
Pedersen, Martin Wæver; Thygesen, Uffe Høgsbro; Madsen, Henrik
2011-01-01
A new approach to nonlinear state estimation and object tracking from indirect observations of a continuous time process is examined. Stochastic differential equations (SDEs) are employed to model the dynamics of the unobservable state. Tracking problems in the plane subject to boundaries...... become complicated using SMC because Monte Carlo randomness is introduced. The finite element (FE) method solves the Kolmogorov equations of the SDE numerically on a triangular unstructured mesh for which boundary conditions to the state-space are simple to incorporate. The FE approach to nonlinear state...... estimation is suited for off-line data analysis because the computed smoothed state densities, maximum a posteriori parameter estimates and state sequence are deterministic conditional on the finite element mesh and the observations. The proposed method is conceptually similar to existing point...
Becis-Aubry, Yasmina; Boutayeb, Mohamed; Darouach, Mohamed
2006-01-01
International audience; This contribution proposes a recursive and easily implementable online algorithm for state estimation of multi-output discrete-time systems with nonlinear dynamics and linear measurements in presence of unknown but bounded disturbances corrupting both the state and measurement equations. The proposed algorithm is based on state bounding techniques and is decomposed into two steps : time update and observation update that uses a switching estimation Kalman-like gain mat...
Krcmarík, David; Slavík, Radan; Park, Yongwoo; Azaña, José
2009-04-27
tract: We demonstrate high quality pulse compression at high repetition rates by use of spectral broadening of short parabolic-like pulses in a normally-dispersive highly nonlinear fiber (HNLF) followed by linear dispersion compensation with a conventional SMF-28 fiber. The key contribution of this work is on the use of a simple and efficient long-period fiber grating (LPFG) filter for synthesizing the desired parabolic-like pulses from sech(2)-like input optical pulses; this all-fiber low-loss filter enables reducing significantly the required input pulse power as compared with the use of previous all-fiber pulse re-shaping solutions (e.g. fiber Bragg gratings). A detailed numerical analysis has been performed in order to optimize the system's performance, including investigation of the optimal initial pulse shape to be launched into the HNLF fiber. We found that the pulse shape launched into the HNLF is critically important for suppressing the undesired wave breaking in the nonlinear spectral broadening process. The optimal shape is found to be independent on the parameters of normally dispersive HNLFs. In our experiments, 1.5-ps pulses emitted by a 10-GHz mode-locked laser are first reshaped into 3.2-ps parabolic-like pulses using our LPFG-based pulse reshaper. Flat spectrum broadening of the amplified initial parabolic-like pulses has been generated using propagation through a commercially-available HNLF. Pulses of 260 fs duration with satellite peak and pedestal suppression greater than 17 dB have been obtained after the linear dispersion compensation fiber. The generated pulses exhibit a 20-nm wide supercontinuum energy spectrum that has almost a square-like spectral profile with >85% of the pulse energy contained in its FWHM spectral bandwidth.
He, Tiancheng; Xue, Zhong; Alvarado, Miguel Valdivia y.; Wong, Kelvin K.; Xie, Weixin; Wong, Stephen T. C.
2013-01-01
Fluorescence microendoscopy can potentially be a powerful modality in minimally invasive percutaneous intervention for cancer diagnosis because it has an exceptional ability to provide micron-scale resolution images in tissues inaccessible to traditional microscopy. After targeting the tumor with guidance by macroscopic images such as computed tomorgraphy or magnetic resonance imaging, fluorescence microendoscopy can help select the biopsy spots or perform an on-site molecular imaging diagnosis. However, one challenge of this technique for percutaneous lung intervention is that the respiratory and hemokinesis motion often renders instability of the sequential image visualization and results in inaccurate quantitative measurement. Motion correction on such serial microscopy image sequences is, therefore, an important post-processing step. We propose a nonlinear motion compensation algorithm using a cubature Kalman filter (NMC-CKF) to correct these periodic spatial and intensity changes, and validate the algorithm using preclinical imaging experiments. The algorithm integrates a longitudinal nonlinear system model using the CKF in the serial image registration algorithm for robust estimation of the longitudinal movements. Experiments were carried out using simulated and real microendoscopy videos captured from the CellVizio 660 system in rabbit VX2 cancer intervention. The results show that the NMC-CKF algorithm yields more robust and accurate alignment results.
Duifhuis, H
This letter concerns the paper "An approximate transfer function for the dual-resonance nonlinear filter model of auditory frequency selectivity" [E. A. Lopez-Poveda, J. Acoust. Soc. Am. 114, 2112-2117 (2003)]. It proposes a correction of the historical framework in which the paper is presented.
Nonlinear dynamic positioning of ships with gain-scheduled wave filtering
DEFF Research Database (Denmark)
Torsetnes, Guttorm; Jouffroy, Jerome; Fossen, Thor I.
This paper presents a globally contracting controller for regulation and dynamic positioning of ships, using only position measurements. For this purpose a globally contracting observer which reconstructs the unmeasured states is constructed. The observer produces accurate estimates of position......, slowly varying environmental disturbances (bias terms) and velocity. The estimates are automatically adjusted to the present sea state by gain-scheduling the wave model parameters in the observer. Finally, the estimates are used in a nonlinear PID control law and the stability proof of the observer...
Chaos synchronization in noisy environment using nonlinear filtering and sliding mode control
Energy Technology Data Exchange (ETDEWEB)
Behzad, Mehdi [Center of Excellence in Design, Robotics, and Automation (CEDRA), Department of Mechanical Engineering, Sharif University of Technology, Postal Code 11365-9567, Azadi Avenue, Tehran (Iran, Islamic Republic of)], E-mail: m_behzad@sharif.edu; Salarieh, Hassan [Center of Excellence in Design, Robotics, and Automation (CEDRA), Department of Mechanical Engineering, Sharif University of Technology, Postal Code 11365-9567, Azadi Avenue, Tehran (Iran, Islamic Republic of)], E-mail: salarieh@mech.sharif.edu; Alasty, Aria [Center of Excellence in Design, Robotics, and Automation (CEDRA), Department of Mechanical Engineering, Sharif University of Technology, Postal Code 11365-9567, Azadi Avenue, Tehran (Iran, Islamic Republic of)], E-mail: aalasti@sharif.edu
2008-06-15
This paper presents an algorithm for synchronizing two different chaotic systems, using a combination of the extended Kalman filter and the sliding mode controller. It is assumed that the drive chaotic system has a random excitation with a stochastically chaotic behavior. Two different cases are considered in this study. At first it is assumed that all state variables of the drive system are available, i.e. complete state measurement, and a sliding mode controller is designed for synchronization. For the second case, it is assumed that the output of the drive system does not contain the whole state variables of the drive system, and it is also affected by some random noise. By combination of extended Kalman filter and the sliding mode control, a synchronizing control law is proposed. As a case study, the presented algorithm is applied to the Lur'e-Genesio chaotic systems as the drive-response dynamic systems. Simulation results show the good performance of the algorithm in synchronizing the chaotic systems in presence of noisy environment.
Rigatos, Gerasimos
2014-12-01
A synchronizing control scheme for coupled neural oscillators of the FitzHugh-Nagumo type is proposed. Using differential flatness theory the dynamical model of two coupled neural oscillators is transformed into an equivalent model in the linear canonical (Brunovsky) form. A similar linearized description is succeeded using differential geometry methods and the computation of Lie derivatives. For such a model it becomes possible to design a state feedback controller that assures the synchronization of the membrane's voltage variations for the two neurons. To compensate for disturbances that affect the neurons' model as well as for parametric uncertainties and variations a disturbance observer is designed based on Kalman Filtering. This consists of implementation of the standard Kalman Filter recursion on the linearized equivalent model of the coupled neurons and computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. After estimating the disturbance terms in the neurons' model their compensation becomes possible. The performance of the synchronization control loop is tested through simulation experiments.
Estimation and filtering of nonlinear systems application to a waste-water treatment process
Energy Technology Data Exchange (ETDEWEB)
Ben Youssef, C.; Dahhou, B. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France). Lab. d`Automatique et d`Analyse des Systemes]|[Institut National des Sciences Appliquees (INSA), 31 - Toulouse (France); Zeng, F.Y.; Rols, J.L. [Institut National des Sciences Appliquees (INSA), 31 - Toulouse (France)
1994-04-01
A fundamental task in design and control of biotechnological processes is system modelling. This task is made difficult by the scarceness of on-line direct sensors for some key variables and by the fact that identifiability of models including Michaelis-Menten type of nonlinearities is not straightforward. The use of adaptive estimation approaches constitutes an interesting alternative to circumvent these kind of problems. This paper discusses an identification technique derived to solve the problem of estimating simultaneously inaccessible state variables and time-varying parameters of a nonlinear wastewater treatment process. An extended linearization technique using Kronecker`s calculation provides the error model of the joint observer-estimator procedure which convergence is proved via Lyapunov`s method. Sufficient conditions for stability of this joint identification scheme are given and discussed according to the persistence excitation conditions of the signals. A simulation study with measurement noises and abrupt jumps of the process parameters shows the feasibility and significant robustness of the proposed adaptive estimation methodologies. (author). (author). 10 refs., 3 figs.
Urban and Indoor Weak Signal Tracking Using an Array Tracker with MVA and Nonlinear Filtering
Directory of Open Access Journals (Sweden)
Jicheng Ding
2014-01-01
Full Text Available We focus on the need for weak GPS signal tracking technique at a receiver powered on in urban or indoor environment; the tracking loop is unlocked and data bit edge position is unknown. A modified Viterbi algorithm (MVA based on dynamic programming is developed and it is applied to GPS bit synchronization to improve bit edge position detection probability. Meanwhile, two combination carrier tracking schemes based on central difference Kalman filter (CDKF and MVA module are designed for tracking very weak GPS signal. The testing results indicate that the methods can successfully detect bit edge position with high detection probability whether or not the tracking loop is locked. The tested combination tracking scheme is still able to work well when the signal quality deteriorates to 20 dB-Hz without additional large store space.
Usefulness of Nonlinear Interpolation and Particle Filter in Zigbee Indoor Positioning
Zhang, Xiang; Wu, Helei; Uradziński, Marcin
2014-12-01
The key to fingerprint positioning algorithm is establishing effective fingerprint information database based on different reference nodes of received signal strength indicator (RSSI). Traditional method is to set the location area calibration multiple information sampling points, and collection of a large number sample data what is very time consuming. With Zigbee sensor networks as platform, considering the influence of positioning signal interference, we proposed an improved algorithm of getting virtual database based on polynomial interpolation, while the pre-estimated result was disposed by particle filter. Experimental result shows that this method can generate a quick, simple fine-grained localization information database, and improve the positioning accuracy at the same time. Kluczem do algorytmu pozycjonowania wykorzystującego metodę fi ngerprinting jest ustanowienie skutecznej bazy danych na podstawie informacji z radiowych nadajników referencyjnych przy wykorzystaniu wskaźnika mocy odbieranego sygnału (RSSI). Tradycyjna metoda oparta jest na przeprowadzeniu kalibracji obszaru lokalizacji na podstawie wielu punktów pomiarowych i otrzymaniu dużej liczby próbek, co jest bardzo czasochłonne.
Indian Academy of Sciences (India)
R BALASUBRAMANIAN; R SANKARAN; S PALANI
2017-09-01
This paper deals with design and simulation of a three-phase shunt hybrid power filter consisting of a pair of 5th and 7th selective harmonic elimination passive power filters connected in series with a conventional active power filter with reduced kVA rating. The objective is to enhance the power quality in a distributionnetwork feeding variety of non-linear, time-varying and unbalanced loads. The theory and modelling of the entire power circuit in terms of synchronously rotating reference frame and leading to a non-linear control scheme is presented. This work involves introduction of individual fuzzy logic controllers for d and q axiscurrent control and for voltage regulation of the DC link capacitor. The simulation schematic covering the power and control circuits have been developed taking into account severe harmonic distortion caused by non-linear and unbalanced loads. The effectiveness of the fuzzy logic controller for the compensation of harmonics and reactive power has been verified by successive simulation runs and analysis of the results. The proposed controller is also able to compensate the distortion generated by the voltage- and current-fed non-linear loads, unbalanced and dynamically varying loads. Further, excellent regulation of the DC link voltage is accomplished, which significantly contributes to improvement of power quality.
Comparative Study on Some Nonlinear Filtering Algorithms%几种非线性滤波算法的比较研究
Institute of Scientific and Technical Information of China (English)
王庆欣; 史连艳
2011-01-01
针对组合导航等非线性系统,扩展卡尔曼滤波算法(EKF)在初值不准确时存在滤波发散的现象,故提出U-卡尔曼滤波(UKF);粒子滤波算法(PF)适合于强非线性、非高斯噪声系统,但同时存在退化现象,故提出2种改进算法.前人的工作多集中在单一算法的研究,而在此是将上述各种算法应用到同一典型非线性系统,通过应用Matlab进行仿真实验得出具体滤渡效果数据,综合对比分析了各算法的优缺点,得出一些有用的结论,为组合导航系统中非线性滤波算法的选择提供了参考.%For the nonlinear systems such as integrated navigation systems, since the extended Kalman filtering ( EKF) has a dispersing phenomenon when the initial state value is inaccurate, the unscented Kalman filiering ( UKF) is proposed,and although particle filtering (PF) is suitable for any nonlinear non-Gaussian systems, it has a degeneracy phenomenon, then two kinds of improved filtering algorithms are put forward. Scientific researchers focused on single filtering before. The filtering algorithms mentioned above are adopted in a same typical model of nonlinear system in this paper. The detailed data of the filtering algorithms were obtained by emulational experiments with Matlab. Some useful conclusios were acquired after the contrast and analysis of their advantages and disadvantages. A reference is offered in choosing a suitable nonlinearfiltering algorithm for integrated navigation systems.
Zheng, Ziyi; Sun, Mingshan; Pavkovich, John; Star-Lack, Josh
2011-03-01
A challenge in using on-board cone beam computed tomography (CBCT) to image lung tumor motion prior to radiation therapy treatment is acquiring and reconstructing high quality 4D images in a sufficiently short time for practical use. For the 1 minute rotation times typical of Linacs, severe view aliasing artifacts, including streaks, are created if a conventional phase-correlated FDK reconstruction is performed. The McKinnon-Bates (MKB) algorithm provides an efficient means of reducing streaks from static tissue but can suffer from low SNR and other artifacts due to data truncation and noise. We have added truncation correction and bilateral nonlinear filtering to the MKB algorithm to reduce streaking and improve image quality. The modified MKB algorithm was implemented on a graphical processing unit (GPU) to maximize efficiency. Results show that a nearly 4x improvement in SNR is obtained compared to the conventional FDK phase-correlated reconstruction and that high quality 4D images with 0.4 second temporal resolution and 1 mm3 isotropic spatial resolution can be reconstructed in less than 20 seconds after data acquisition completes.
Institute of Scientific and Technical Information of China (English)
李振华; 宁磊; 徐胜男
2012-01-01
Based on analyzing divided difference filter（DDF） and Gaussian sum filter（GSF）, a GSF-based DDF algorithm is developed for nonlinear dynamic state space（DSS） models with non-Gaussian noise, which is suitable for the filtering problem of nonlinear/non-Gaussian systems. When the likelihood function appeares at the tall of the transfer probability density, the proposed algorithm can improve the precision of nonlinear/non-Gaussian filtering compared with the traditional particle filter（PF）. Experiments show that the proposed method works well in the filtering for DSS models with non-Gaussian noise.%针对一类非线性非高斯系统的滤波问题，在分析均差滤波算法和高斯和滤波算法的基础上，提出一种基于均差滤波的高斯和滤波算法，适于处理非线性非高斯系统的滤波问题．对于似然密度位于条件转移概率密度拖尾处的情况，与传统的粒子滤波算法相比，所提算法能提高滤波的精度和实时性．仿真实验验证了新算法的有效性．
模型不确定非线性Markov跳变系统的滤波算法%Filter algorithm for nonlinear Markov jump systems with uncertain models
Institute of Scientific and Technical Information of China (English)
赵顺毅; 刘飞
2012-01-01
Considering the state estimation problem for the nonlinear Markov jump system with uncertain model, a novel filtering algorithm is proposed. Compared with the traditional interacting multiple particle filter method, in this method, a term of filtering error at previous time instant is introduced to increase the effect of the particles which are true but with small weights due to the inaccuracy model to improve the estimation performance in the filtering process. Simulation results show the effectiveness of this method in handling with the state estimation problem for the nonlinear Markov jump systems with uncertain model parameter.%针对模型不确定非线性Markov跳变系统,提出一种新的滤波算法.相比于传统交互多模型粒子滤波,该方法通过引入前一时刻的滤波误差来增强原先由于不精确模型而造成权值较小的真实粒子在滤波过程中的作用,以此来改善算法的估计性能.仿真结果表明,该方法在处理含不确定模型参数的非线性Markov跳变系统状态估计问题时具有较好的性能.
Uzunov, Ivan M.; Georgiev, Zhivko D.; Arabadzhev, Todor N.
2015-01-01
In this paper we present numerical investigation of the influence of intrapulse Raman scattering (IRS) on the stable stationary pulses. Our basic equation, namely cubic-quintic Ginzburg-Landau equation describes the propagation of ultra-short optical pulses under the effect of IRS in the presence of linear and nonlinear gain as well as spectral filtering. Our aim is to examine numerically the influence of IRS, on the stable stationary pulses in the presence of constant linear and nonlinear gain as well as spectral filtering. Numerical solution of our basic equation is performed by means of the "fourth-order Runge-Kutta method in the interaction picture method" method. We found that the small change of the value of the parameter which describes IRS leads to qualitatively different behavior of the evolution of pulse amplitudes. In order to study the observed strong dependence on the IRS, the perturbation method of conserved quantities of the nonlinear Schrodinger equation is applied. The numerical analysis of the derived nonlinear system of ordinary differential equations has shown that our numerical findings are related to the existence of the Poincare-Andronov-Hopf bifurcation.
Connolly, Joseph W.; Csank, Jeffrey Thomas; Chicatelli, Amy; Kilver, Jacob
2013-01-01
This paper covers the development of a model-based engine control (MBEC) methodology featuring a self tuning on-board model applied to an aircraft turbofan engine simulation. Here, the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) serves as the MBEC application engine. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC over a wide range of operating points. The on-board model is a piece-wise linear model derived from CMAPSS40k and updated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. Investigations using the MBEC to provide a stall margin limit for the controller protection logic are presented that could provide benefits over a simple acceleration schedule that is currently used in traditional engine control architectures.
Institute of Scientific and Technical Information of China (English)
金中; 濮定国; 张宇; 蔡力
2008-01-01
A mechanism for proving global convergence in filter-SQP(sequence of quadratic programming)method with the nonlinear complementarity problem(NCP)function is described for constrained nonlinear optimization problem.We introduce an NCP function into the filter and construct a new SQP-filter algorithm.Such methods are characterized by their use of the dominance concept of multi-objective optimization,instead of a penalty parameter whose adjustment can be problematic.We prove that the algorithm has global convergence and superlinear convergence rates under some mild conditions.
Zhang, M; Kelleher, E J R; Popov, S V; Taylor, J R
2013-05-20
The nonlinear saturable absorption of an ionically-doped colored glass filter is measured directly using a Z-scan technique. For the first time, we demonstrate the potential of this material as a saturable asborber in fiber lasers. We achieve mode-locking of an ytterbium doped system. Mode-locking of cavities with all-positive and net-negative group velocity dispersion are demonstrated, achieving pulse durations of 60 ps and 4.1 ps, respectively. This inexpensive and optically robust material, with the potential for broadband operation, could surplant other saturable absorber devices in affordable mode-locked fiber lasers.
Institute of Scientific and Technical Information of China (English)
ZHOU En-Bo; ZHANG Xin-Liang; YU Yu; HUANG De-Xiu
2009-01-01
Nonlinear patterning (NLP) effect in wavelength conversion based on transient cross-phase modulation (XPM) in semiconductor optical amplifier (SOA) assisted with a detuning filter is theoretically investigated.A nonadiabatic model is used to estimate the ultrafast dynamics o[ gain,phase and electron temperature in the SOA.Simulation results show that the NLP can be greatly suppressed by introducing an assist light,especially for the probe wavelength distant from gain peak.Furthermore,the results also indicate that the improvement is more evident for long wavelength probe light and assist light in counter-propagating configuration.
分维自适应稀疏网格积分非线性滤波器%Dimension-wise Adaptive Spare Grid Quadrature Nonlinear Filter
Institute of Scientific and Technical Information of China (English)
徐嵩; 孙秀霞; 刘树光; 刘希; 蔡鸣
2014-01-01
For nonlinear discrete systems with addictive Gaus-sian noises, a new quadrature filter is proposed, which can fix sample points according to each dimension0s nonlinear function, respectively. In order to match higher-order terms of the nonlin-ear function0s Taylor expanding with reusing the sample points matching lower-order ones, an adaptive sampled multi variable quadrature rule is designed based on the embedded Gaussian sampled quadrature and the spare grid quadrature (SGQ) for-mula. A group of effective data structures and traversal algo-rithms are proposed for the sampled quadrature rule to be used for calculating the predict expectations of the states and mea-surements with their covariances. This filter could not only fix sampled points for different dimensions separately, but also reuse these points and their weights completely, thus enhancing the ef-ficiency of the filter. This filter achieves a higher accuracy than the unscented Kalman filter (UKF) , more effciency than the fixed SGQ filter, as well as generalized form of these two filters. The calculating cost of adaptive steps is much less than comput-ing the function sampled values. Simulations also validates the accuracy and effciency of this filter.%针对含加性高斯噪声的非线性离散系统，提出了可分别根据各维状态及量测方程的非线性函数特性来确定采样点及其权重的积分滤波器。设计了基于嵌入式高斯采样积分和稀疏网格法则的自适应多变量采样积分方法，可在匹配函数高阶泰勒展开项时，利用低阶采样点，提出了高效的数据结构和遍历算法，便于采用该积分方法分别估计系统状态/量测的预测均值和协方差矩阵。该滤波器既能根据各维非线性函数的特性确定采样点，又实现了对采样值和权重的完全复用，保证了算法效率。理论分析和仿真表明，该滤波算法中自适应调整的运算量小于计算非线性函数采样值。该滤
Filtering algorithms using shiftable kernels
Chaudhury, Kunal Narayan
2011-01-01
It was recently demonstrated in [4][arxiv:1105.4204] that the non-linear bilateral filter \\cite{Tomasi} can be efficiently implemented using an O(1) or constant-time algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in [4] can be extended to few other linear and non-linear filters [18,21,2]. While some of these filters have received a lot of attention in recent years, they are known to be computationally intensive. To extend the idea in \\cite{Chaudhury2011}, we identify a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations. In particular, using shiftable kernels, we show how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images. Each image in the stack is obtained through an elementary pointwise transform of the input image. Thi...
Barber, Jared; Tanase, Roxana; Yotov, Ivan
2016-06-01
Several Kalman filter algorithms are presented for data assimilation and parameter estimation for a nonlinear diffusion model of epithelial cell migration. These include the ensemble Kalman filter with Monte Carlo sampling and a stochastic collocation (SC) Kalman filter with structured sampling. Further, two types of noise are considered -uncorrelated noise resulting in one stochastic dimension for each element of the spatial grid and correlated noise parameterized by the Karhunen-Loeve (KL) expansion resulting in one stochastic dimension for each KL term. The efficiency and accuracy of the four methods are investigated for two cases with synthetic data with and without noise, as well as data from a laboratory experiment. While it is observed that all algorithms perform reasonably well in matching the target solution and estimating the diffusion coefficient and the growth rate, it is illustrated that the algorithms that employ SC and KL expansion are computationally more efficient, as they require fewer ensemble members for comparable accuracy. In the case of SC methods, this is due to improved approximation in stochastic space compared to Monte Carlo sampling. In the case of KL methods, the parameterization of the noise results in a stochastic space of smaller dimension. The most efficient method is the one combining SC and KL expansion.
Fast and Provably Accurate Bilateral Filtering.
Chaudhury, Kunal N; Dabhade, Swapnil D
2016-06-01
The bilateral filter is a non-linear filter that uses a range filter along with a spatial filter to perform edge-preserving smoothing of images. A direct computation of the bilateral filter requires O(S) operations per pixel, where S is the size of the support of the spatial filter. In this paper, we present a fast and provably accurate algorithm for approximating the bilateral filter when the range kernel is Gaussian. In particular, for box and Gaussian spatial filters, the proposed algorithm can cut down the complexity to O(1) per pixel for any arbitrary S . The algorithm has a simple implementation involving N+1 spatial filterings, where N is the approximation order. We give a detailed analysis of the filtering accuracy that can be achieved by the proposed approximation in relation to the target bilateral filter. This allows us to estimate the order N required to obtain a given accuracy. We also present comprehensive numerical results to demonstrate that the proposed algorithm is competitive with the state-of-the-art methods in terms of speed and accuracy.
Clutter filter design for ultrasound color flow imaging.
Bjaerum, Steinar; Torp, Hans; Kristoffersen, Kjell
2002-02-01
For ultrasound color flow images with high quality, it is important to suppress the clutter signals originating from stationary and slowly moving tissue sufficiently. Without sufficient clutter rejection, low velocity blood flow cannot be measured, and estimates of higher velocities will have a large bias. The small number of samples available (8 to 16) makes clutter filtering in color flow imaging a challenging problem. In this paper, we review and analyze three classes of filters: finite impulse response (FIR), infinite impulse response (IIR), and regression filters. The quality of the filters was assessed based on the frequency response, as well as on the bias and variance of a mean blood velocity estimator using an autocorrelation technique. For FIR filters, the frequency response was improved by allowing a non-linear phase response. By estimating the mean blood flow velocity from two vectors filtered in the forward and backward direction, respectively, the standard deviation was significantly lower with a minimum phase filter than with a linear phase filter. For IIR filters applied to short signals, the transient part of the output signal is important. We analyzed zero, step, and projection initialization, and found that projection initialization gave the best filters. For regression filters, polynomial basis functions provide effective clutter suppression. The best filters from each of the three classes gave comparable bias and variance of the mean blood velocity estimates. However, polynomial regression filters and projection-initialized IIR filters had a slightly better frequency response than could be obtained with FIR filters.
Nuclear counting filter based on a centered Skellam test and a double exponential smoothing
Energy Technology Data Exchange (ETDEWEB)
Coulon, Romain; Kondrasovs, Vladimir; Dumazert, Jonathan; Rohee, Emmanuel; Normand Stephane [CEA, LIST, Laboratoire Capteurs et Architectures Electroniques, F-91191 Gif-sur-Yvette, (France)
2015-07-01
Online nuclear counting represents a challenge due to the stochastic nature of radioactivity. The count data have to be filtered in order to provide a precise and accurate estimation of the count rate, this with a response time compatible with the application in view. An innovative filter is presented in this paper addressing this issue. It is a nonlinear filter based on a Centered Skellam Test (CST) giving a local maximum likelihood estimation of the signal based on a Poisson distribution assumption. This nonlinear approach allows to smooth the counting signal while maintaining a fast response when brutal change activity occur. The filter has been improved by the implementation of a Brown's double Exponential Smoothing (BES). The filter has been validated and compared to other state of the art smoothing filters. The CST-BES filter shows a significant improvement compared to all tested smoothing filters. (authors)
Human Resources Division
2001-01-01
HR Division wishes to clarify to members of the personnel that the allowance for a dependent child continues to be paid during all training courses ('stages'), apprenticeships, 'contrats de qualification', sandwich courses or other courses of similar nature. Any payment received for these training courses, including apprenticeships, is however deducted from the amount reimbursable as school fees. HR Division would also like to draw the attention of members of the personnel to the fact that any contract of employment will lead to the suppression of the child allowance and of the right to reimbursement of school fees.
Institute of Scientific and Technical Information of China (English)
龙君; 曾三云
2014-01-01
先将非线性互补问题（NCP ）转化为与其等价且有可行解的辅助问题，再将引入了信赖域方法思想的SQP方法与Filter技术相结合，提出一种求解NCP问题的信赖域-SQP-filter算法，并讨论了解的存在性和算法的全局收敛性。数值结果表明我们的算法是有效并收敛的。%This paper constructs an auxiliary problem with feasible solution , which is equivalent to the nonlinear complementarity problem . Through combining the trust region -SQP method and filter technology , a trust region -SQP-filter algorithm for solving NCP is proposed . Finally , we discuss the global convergence of the algorithm and the existence of solution for NCP . The numerical results show that our algorithm is effective and convergent .
Adaptive Filtering Algorithms and Practical Implementation
Diniz, Paulo S R
2013-01-01
In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation. The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes: Analytical and simulation examples in Chapters 4, 5, 6 and 10 Appendix E, which summarizes the analysis of set-membership algorithm Updated problems and references Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Several problems are...
Charvat, Hadrien; Remontet, Laurent; Bossard, Nadine; Roche, Laurent; Dejardin, Olivier; Rachet, Bernard; Launoy, Guy; Belot, Aurélien
2016-08-15
The excess hazard regression model is an approach developed for the analysis of cancer registry data to estimate net survival, that is, the survival of cancer patients that would be observed if cancer was the only cause of death. Cancer registry data typically possess a hierarchical structure: individuals from the same geographical unit share common characteristics such as proximity to a large hospital that may influence access to and quality of health care, so that their survival times might be correlated. As a consequence, correct statistical inference regarding the estimation of net survival and the effect of covariates should take this hierarchical structure into account. It becomes particularly important as many studies in cancer epidemiology aim at studying the effect on the excess mortality hazard of variables, such as deprivation indexes, often available only at the ecological level rather than at the individual level. We developed here an approach to fit a flexible excess hazard model including a random effect to describe the unobserved heterogeneity existing between different clusters of individuals, and with the possibility to estimate non-linear and time-dependent effects of covariates. We demonstrated the overall good performance of the proposed approach in a simulation study that assessed the impact on parameter estimates of the number of clusters, their size and their level of unbalance. We then used this multilevel model to describe the effect of a deprivation index defined at the geographical level on the excess mortality hazard of patients diagnosed with cancer of the oral cavity. Copyright © 2016 John Wiley & Sons, Ltd.
Institute of Scientific and Technical Information of China (English)
王秋平; 左玲; 康顺
2011-01-01
为解决非线性部分状态卡尔曼滤波算法中由于线性化误差所导致的滤波精度下降问题,提出采用UT变换方法计算系统状态误差方差,及基于新息自适应调整系统噪声方差,进而构成一种新的非线性自适应部分状态卡尔曼滤波算法,并总结出详细算法结构.同时,将此方法应用到非线性测量光电跟踪系统中,并与U卡尔曼滤波和非线性部分状态卡尔曼滤波进行性能对比.仿真实验结果证明,将UT变换和基于新息自适应调整系统噪声方差方法引入部分状态卡尔曼滤波是有效的,而且其性能明显优于U卡尔曼滤波和非线性部分状态卡尔曼滤波.%In order to solve the problem of accuracy decline caused by the linearization error in nonlinear reduced state Kalman filter, a new nonlinear adaptive reduced state Kalman filter algorithm is provided by using UT transformation to calculate the covariance of the system state error and modify adaptively the system noise covariance based on innovation,and the algorithm structure is summarized in detail. Then, the algorithm is applied in nonlinear measurement electro-optical tracking system and the performances of nonlinear adaptive reduced state Kalman filter were compared with unscented Kalman filter and nonlinear reduced state Kalman filter. The Matlab simulation results show that applying UT transformation and modifying adaptively the system noise covariance based on innovation in reduced state Kalman filter is valid, and the performance outperforms those of the unscented Kalman filter and nonlinear reduced state Kalman filter.
Derivative free filtering using Kalmtool
DEFF Research Database (Denmark)
Bayramoglu, Enis; Hansen, Søren; Ravn, Ole;
2010-01-01
In this paper we present a toolbox enabling easy evaluation and comparison of different filtering algorithms. The toolbox is called Kalmtool 4 and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox contains functions for extended Kalman filtering as well as for DD1...... filter and the DD2 filter. It also contains functions for Unscented Kalman filters as well as several versions of particle filters. The toolbox requires MATLAB version 7, but no additional toolboxes are required....
Concrete ensemble Kalman filters with rigorous catastrophic filter divergence.
Kelly, David; Majda, Andrew J; Tong, Xin T
2015-08-25
The ensemble Kalman filter and ensemble square root filters are data assimilation methods used to combine high-dimensional, nonlinear dynamical models with observed data. Ensemble methods are indispensable tools in science and engineering and have enjoyed great success in geophysical sciences, because they allow for computationally cheap low-ensemble-state approximation for extremely high-dimensional turbulent forecast models. From a theoretical perspective, the dynamical properties of these methods are poorly understood. One of the central mysteries is the numerical phenomenon known as catastrophic filter divergence, whereby ensemble-state estimates explode to machine infinity, despite the true state remaining in a bounded region. In this article we provide a breakthrough insight into the phenomenon, by introducing a simple and natural forecast model that transparently exhibits catastrophic filter divergence under all ensemble methods and a large set of initializations. For this model, catastrophic filter divergence is not an artifact of numerical instability, but rather a true dynamical property of the filter. The divergence is not only validated numerically but also proven rigorously. The model cleanly illustrates mechanisms that give rise to catastrophic divergence and confirms intuitive accounts of the phenomena given in past literature.
Concrete ensemble Kalman filters with rigorous catastrophic filter divergence
Kelly, David; Majda, Andrew J.; Tong, Xin T.
2015-01-01
The ensemble Kalman filter and ensemble square root filters are data assimilation methods used to combine high-dimensional, nonlinear dynamical models with observed data. Ensemble methods are indispensable tools in science and engineering and have enjoyed great success in geophysical sciences, because they allow for computationally cheap low-ensemble-state approximation for extremely high-dimensional turbulent forecast models. From a theoretical perspective, the dynamical properties of these methods are poorly understood. One of the central mysteries is the numerical phenomenon known as catastrophic filter divergence, whereby ensemble-state estimates explode to machine infinity, despite the true state remaining in a bounded region. In this article we provide a breakthrough insight into the phenomenon, by introducing a simple and natural forecast model that transparently exhibits catastrophic filter divergence under all ensemble methods and a large set of initializations. For this model, catastrophic filter divergence is not an artifact of numerical instability, but rather a true dynamical property of the filter. The divergence is not only validated numerically but also proven rigorously. The model cleanly illustrates mechanisms that give rise to catastrophic divergence and confirms intuitive accounts of the phenomena given in past literature. PMID:26261335
Advanced Filtering Techniques Applied to Spaceflight Project
National Aeronautics and Space Administration — IST-Rolla developed two nonlinear filters for spacecraft orbit determination during the Phase I contract. The theta-D filter and the cost based filter, CBF, were...
Directory of Open Access Journals (Sweden)
Karl Friston
2010-01-01
Full Text Available We describe a Bayesian filtering scheme for nonlinear state-space models in continuous time. This scheme is called Generalised Filtering and furnishes posterior (conditional densities on hidden states and unknown parameters generating observed data. Crucially, the scheme operates online, assimilating data to optimize the conditional density on time-varying states and time-invariant parameters. In contrast to Kalman and Particle smoothing, Generalised Filtering does not require a backwards pass. In contrast to variational schemes, it does not assume conditional independence between the states and parameters. Generalised Filtering optimises the conditional density with respect to a free-energy bound on the model's log-evidence. This optimisation uses the generalised motion of hidden states and parameters, under the prior assumption that the motion of the parameters is small. We describe the scheme, present comparative evaluations with a fixed-form variational version, and conclude with an illustrative application to a nonlinear state-space model of brain imaging time-series.
Directory of Open Access Journals (Sweden)
Meleiro L.A.C.
2000-01-01
Full Text Available Most advanced computer-aided control applications rely on good dynamics process models. The performance of the control system depends on the accuracy of the model used. Typically, such models are developed by conducting off-line identification experiments on the process. These experiments for identification often result in input-output data with small output signal-to-noise ratio, and using these data results in inaccurate model parameter estimates [1]. In this work, a multivariable adaptive self-tuning controller (STC was developed for a biotechnological process application. Due to the difficulties involving the measurements or the excessive amount of variables normally found in industrial process, it is proposed to develop "soft-sensors" which are based fundamentally on artificial neural networks (ANN. A second approach proposed was set in hybrid models, results of the association of deterministic models (which incorporates the available prior knowledge about the process being modeled with artificial neural networks. In this case, kinetic parameters - which are very hard to be accurately determined in real time industrial plants operation - were obtained using ANN predictions. These methods are especially suitable for the identification of time-varying and nonlinear models. This advanced control strategy was applied to a fermentation process to produce ethyl alcohol (ethanol in industrial scale. The reaction rate considered for substratum consumption, cells and ethanol productions are validated with industrial data for typical operating conditions. The results obtained show that the proposed procedure in this work has a great potential for application.
Fan, Jiajie; Mohamed, Moumouni Guero; Qian, Cheng; Fan, Xuejun; Zhang, Guoqi; Pecht, Michael
2017-07-18
With the expanding application of light-emitting diodes (LEDs), the color quality of white LEDs has attracted much attention in several color-sensitive application fields, such as museum lighting, healthcare lighting and displays. Reliability concerns for white LEDs are changing from the luminous efficiency to color quality. However, most of the current available research on the reliability of LEDs is still focused on luminous flux depreciation rather than color shift failure. The spectral power distribution (SPD), defined as the radiant power distribution emitted by a light source at a range of visible wavelength, contains the most fundamental luminescence mechanisms of a light source. SPD is used as the quantitative inference of an LED's optical characteristics, including color coordinates that are widely used to represent the color shift process. Thus, to model the color shift failure of white LEDs during aging, this paper first extracts the features of an SPD, representing the characteristics of blue LED chips and phosphors, by multi-peak curve-fitting and modeling them with statistical functions. Then, because the shift processes of extracted features in aged LEDs are always nonlinear, a nonlinear state-space model is then developed to predict the color shift failure time within a self-adaptive particle filter framework. The results show that: (1) the failure mechanisms of LEDs can be identified by analyzing the extracted features of SPD with statistical curve-fitting and (2) the developed method can dynamically and accurately predict the color coordinates, correlated color temperatures (CCTs), and color rendering indexes (CRIs) of phosphor-converted (pc)-white LEDs, and also can estimate the residual color life.
A hybrid method for optimization of the adaptive Goldstein filter
Jiang, Mi; Ding, Xiaoli; Tian, Xin; Malhotra, Rakesh; Kong, Weixue
2014-12-01
The Goldstein filter is a well-known filter for interferometric filtering in the frequency domain. The main parameter of this filter, alpha, is set as a power of the filtering function. Depending on it, considered areas are strongly or weakly filtered. Several variants have been developed to adaptively determine alpha using different indicators such as the coherence, and phase standard deviation. The common objective of these methods is to prevent areas with low noise from being over filtered while simultaneously allowing stronger filtering over areas with high noise. However, the estimators of these indicators are biased in the real world and the optimal model to accurately determine the functional relationship between the indicators and alpha is also not clear. As a result, the filter always under- or over-filters and is rarely correct. The study presented in this paper aims to achieve accurate alpha estimation by correcting the biased estimator using homogeneous pixel selection and bootstrapping algorithms, and by developing an optimal nonlinear model to determine alpha. In addition, an iteration is also merged into the filtering procedure to suppress the high noise over incoherent areas. The experimental results from synthetic and real data show that the new filter works well under a variety of conditions and offers better and more reliable performance when compared to existing approaches.
Air Filter Simulation by Geodict
Institute of Scientific and Technical Information of China (English)
WANG Xin-peng; Kitai Kim; Changhwan Lee; Jooyong Kim
2006-01-01
In this paper, we discussed the relationship of filter efficiency and pressure drop with the porosity, fiber diameter and filter thickness by Geodict. We found that filter efficiency will increase when filter porosity and fiber diameter decreasing or filter thickness increasing. And the pressure drop has a linear relationship with filter thickness and non-linear relationship with filter porosity and fiber diameter. We also compared the simulation results with the real test results by TSI 3160. Although there are some differences, I think Geodict can be used to predict filter efficiency and pressure drop.
Nonlinear regime-switching state-space (RSSS) models.
Chow, Sy-Miin; Zhang, Guangjian
2013-10-01
Nonlinear dynamic factor analysis models extend standard linear dynamic factor analysis models by allowing time series processes to be nonlinear at the latent level (e.g., involving interaction between two latent processes). In practice, it is often of interest to identify the phases--namely, latent "regimes" or classes--during which a system is characterized by distinctly different dynamics. We propose a new class of models, termed nonlinear regime-switching state-space (RSSS) models, which subsumes regime-switching nonlinear dynamic factor analysis models as a special case. In nonlinear RSSS models, the change processes within regimes, represented using a state-space model, are allowed to be nonlinear. An estimation procedure obtained by combining the extended Kalman filter and the Kim filter is proposed as a way to estimate nonlinear RSSS models. We illustrate the utility of nonlinear RSSS models by fitting a nonlinear dynamic factor analysis model with regime-specific cross-regression parameters to a set of experience sampling affect data. The parallels between nonlinear RSSS models and other well-known discrete change models in the literature are discussed briefly.
Bauer, Peter H.; Sartori, Michael A.; Bryden, Timothy M.
1992-01-01
A new class of nonlinear filters, the so-called class of multidirectional infinite impulse response median hybrid filters, is presented and analyzed. The input signal is processed twice using a linear shift-invariant infinite impulse response filtering module: once with normal causality and a second time with inverted causality. The final output of the MIMH filter is the median of the two-directional outputs and the original input signal. Thus, the MIMH filter is a concatenation of linear filtering and nonlinear filtering (a median filtering module). Because of this unique scheme, the MIMH filter possesses many desirable properties which are both proven and analyzed (including impulse removal, step preservation, and noise suppression). A comparison to other existing median type filters is also provided.
Improvement Particle Filtering Algorithm for Nonlinear Non-Gaussian models%非线性非高斯模型的改进粒子滤波算法
Institute of Scientific and Technical Information of China (English)
周航; 冯新喜; 王蓉
2012-01-01
An improved particle filter algorithm is proposed for the highly non-linear passive location and tracking system in which the common tracking filters often faile to catch and keep tracking of the emitter. Firstly, the algorithm uses limite gaussian mixture model to approximate the posterior density of states. Secondly, in order to solve the problem in which the stochastic observation noise influence the accuracy of particle weights, an improved based on averaging likelihood functions with diverse proportion is proposed. According to x2 testing, the new method combines multi-observations to compute the likelihood functions of each particle and then average them with single observation computes the likelihood functions of each particle to update particle weights. The method not only reduces the influence of the stochastic observation noise to particle weight but also improves real-time. Finally, the traditional process of particle filter resampling is replaced by the aitken-de-terministic annealing expectation maximization ( A-DAEM) algorithm, avoiding to a local maximum and reducing the effects cause by sampling depletion. Simulation results show that the algorithm outperforms the one based on PF-ALDP and the other based on EM-GMPF in tracking accuracy and stability. Therefore it is more suitable to the nonlinear state estimation.%针对被动定位跟踪系统非线性强、传统跟踪滤波方法收敛速度慢且容易发散的问题,给出了一种用于纯方位目标跟踪的改进粒子滤波算法.该算法首先用有限的高斯混合模型来近似后验状态密度；其次针对随机噪声对粒子权值准确性的影响,给出了改进的变权平均似然函数.根据X2检验,对每个粒子权值的更新,采取由多次观测值计算粒子似然函数并对其求变权平均和单一观测值求似然函数相结合的方式进行,既减小随机观测噪声对权值的影响也提高了算法实时性；最后利用基于退火机制的Aitken
Synchronization Phenomena and Epoch Filter of Electroencephalogram
Matani, Ayumu
Nonlinear electrophysiological synchronization phenomena in the brain, such as event-related (de)synchronization, long distance synchronization, and phase-reset, have received much attention in neuroscience over the last decade. These phenomena contain more electrical than physiological keywords and actually require electrical techniques to capture with electroencephalography (EEG). For instance, epoch filters, which have just recently been proposed, allow us to investigate such phenomena. Moreover, epoch filters are still developing and would hopefully generate a new paradigm in neuroscience from an electrical engineering viewpoint. Consequently, electrical engineers could be interested in EEG once again or from now on.
CHANGE DETECTION VIA SELECTIVE GUIDED CONTRASTING FILTERS
Directory of Open Access Journals (Sweden)
Y. V. Vizilter
2017-05-01
Full Text Available Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC. The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC, mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All
Change Detection via Selective Guided Contrasting Filters
Vizilter, Y. V.; Rubis, A. Y.; Zheltov, S. Y.
2017-05-01
Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented
基于Gabor特征分解的高斯混合非线性滤波算法%Gauss Hybrid Nonlinear Filter Design Based on Gabor Feature Decomposition
Institute of Scientific and Technical Information of China (English)
高菲菲
2015-01-01
传统的窄带信号检测滤波器采用IIR自适应线谱增强滤波算法,对信号特征分解的阶数要求高,导致非线性失真,提出一种基于Gabor特征分解的高斯混合非线性滤波器设计算法,在IIR滤波器设计的基础上,对信号进行尺度和时延估计,构建自适应高阶累积量滤波设计方法,采用高阶累积量对窄带信号进行均方一致估计,对Gabor特征函数Taylor级数展开,求得高斯混合非线性滤波器的带宽参数,最后实现高斯混合非线性滤波器设计改进,提高对窄带信号的检测性能.仿真结果表明,该算法具有较好的滤波性能,可以明显地抑制色噪声的影响,提高信号增益达到20 dB.%Narrow band signal detection filter is used in the traditional IIR adaptive line enhancement algorithm, order de-composition on signal feature requirements, resulting in nonlinear distortion, this paper puts forward a Gabor feature decom-position algorithm based on Gauss mixture nonlinear filter design, based on IIR filter design, scale and time delay estima-tion of signal, to construct an adaptive high order cumulants filter design method, using high order cumulant of mean square consistent estimation of narrowband signals, the characteristic function expansion on the Gabor Taylor series, the band-width parameter obtained Gauss mixed nonlinear filter, finally realize the Gauss improvement of mixed nonlinear filter de-sign, improve the detection performance of the narrowband signal. The simulation results show that the proposed algorithm has good filtering performance and can obviously suppress the color noise and improve the signal gain of 20 dB.
Multifunction nonlinear signal processor - Deconvolution and correlation
Javidi, Bahram; Horner, Joseph L.
1989-08-01
A multifuncional nonlinear optical signal processor is described that allows different types of operations, such as image deconvolution and nonlinear correlation. In this technique, the joint power spectrum of the input signal is thresholded with varying nonlinearity to produce different specific operations. In image deconvolution, the joint power spectrum is modified and hard-clip thresholded to remove the amplitude distortion effects and to restore the correct phase of the original image. In optical correlation, the Fourier transform interference intensity is thresholded to provide higher correlation peak intensity and a better-defined correlation spot. Various types of correlation signals can be produced simply by varying the severity of the nonlinearity, without the need for synthesis of specific matched filter. An analysis of the nonlinear processor for image deconvolution is presented.
Feng, Jie; Ding, Ruiqiang; Li, Jianping; Liu, Deqiang
2016-09-01
The breeding method has been widely used to generate ensemble perturbations in ensemble forecasting due to its simple concept and low computational cost. This method produces the fastest growing perturbation modes to catch the growing components in analysis errors. However, the bred vectors (BVs) are evolved on the same dynamical flow, which may increase the dependence of perturbations. In contrast, the nonlinear local Lyapunov vector (NLLV) scheme generates flow-dependent perturbations as in the breeding method, but regularly conducts the Gram-Schmidt reorthonormalization processes on the perturbations. The resulting NLLVs span the fast-growing perturbation subspace efficiently, and thus may grasp more components in analysis errors than the BVs. In this paper, the NLLVs are employed to generate initial ensemble perturbations in a barotropic quasi-geostrophic model. The performances of the ensemble forecasts of the NLLV method are systematically compared to those of the random perturbation (RP) technique, and the BV method, as well as its improved version—the ensemble transform Kalman filter (ETKF) method. The results demonstrate that the RP technique has the worst performance in ensemble forecasts, which indicates the importance of a flow-dependent initialization scheme. The ensemble perturbation subspaces of the NLLV and ETKF methods are preliminarily shown to catch similar components of analysis errors, which exceed that of the BVs. However, the NLLV scheme demonstrates slightly higher ensemble forecast skill than the ETKF scheme. In addition, the NLLV scheme involves a significantly simpler algorithm and less computation time than the ETKF method, and both demonstrate better ensemble forecast skill than the BV scheme.
Maximum likelihood channel estimation based on nonlinear filter%基于非线性滤波器的最大似然信道估计
Institute of Scientific and Technical Information of China (English)
沈壁川; 郑建宏; 申敏
2008-01-01
For long finite channel impulse response,accurate maximum likelihood channel estimation is computationally high cost due to high dimension of parameter space,and approximate approaches are usually adopted.By utilizing the suppression of noise and extraction of signal of the nonlinear Teager-Kaiser filter,a likelihood ratio of channel estimation is defined to represent the probability distribution of ehannel parameters.Maximization of this likelihood funetion 1eads to initially searching the extrema of path delays and then the complex attenuation.Computer simulation iS conducted and the results show periormance improvements of ioint detection as compared to the non-likelihood approach.%在有限信道冲激响应较长的情况,由于待估计参数空间的高维数,准确计算最大似然信道估计的复杂度较高,在实际应用中通常采用近似的方法.利用非线性Teager-Kaiser滤波器在抑制噪声的同时可以有效提取信号的特征,定义了一个表征信道参数概率分布的似然比,对该似然函数的最大化是首先得到路径延迟的极值,然后求得复路径衰耗.计算机仿真结果表明,与非似然方法相比,采用该似然函数方法能使联合检测性能得到提高.
Nonlinear Filtering in High Dimension
2014-06-02
dimension cardV . Remark 4.9. In the language of statistical mechanics, we exploit the fact that the smoothing distribution Px(X0, . . . , Xn ∈ · |Y1...does the mixing property of the random field X imply the conditional mixing property of (X, Y )? It will be insightful to reformulate the problem in...edge observations in Example 7.17 is merely cosmetic: the same example can be reformulated in terms of vertex observations. Indeed, let us define the
Filtering of Systems with Nonlinearities
1982-03-01
IEEE Transactions on Automatic Control , Vol. AC - 15, No. 1, February -1970, 74-81. 41 .’ w 7, 1.%U.-.. j...1972, 439 - 448. •IA 35. D. T. Magill, ’"ptimal Adaptive Estimation of Sampled Stochastic I .,"* , Processes," IEEE Transactions on Automatic Control , Vol...F. L. Sims, "Performance Measure for Adaptive Kalman Estimators," IEEE Transactions on Automatic Control , April 1970, pp. 249-250.
Adaptive Strategies in Nonlinear Filtering.
1974-01-01
IEEE Transactions on Automatic Control , Vol. AC-17...Systems" (with C.W. Sanders, T.D. Linton), IEEE Transactions on Automatic Control , Vol. AC-18, No. 3, June, 1973. 7. "Automatic Generation Control of...Systems" (with T.D. Linton, C.W. Sanders), IEEE Transactions on Automatic Control , Submitted for Publication. 15. "Trajectory Sensitivity Design
A backtracking algorithm that deals with particle filter degeneracy
Baarsma, Rein; Schmitz, Oliver; Karssenberg, Derek
2016-04-01
Particle filters are an excellent way to deal with stochastic models incorporating Bayesian data assimilation. While they are computationally demanding, the particle filter has no problem with nonlinearity and it accepts non-Gaussian observational data. In the geoscientific field it is this computational demand that creates a problem, since dynamic grid-based models are often already quite computationally demanding. As such it is of the utmost importance to keep the amount of samples in the filter as small as possible. Small sample populations often lead to filter degeneracy however, especially in models with high stochastic forcing. Filter degeneracy renders the sample population useless, as the population is no longer statistically informative. We have created an algorithm in an existing data assimilation framework that reacts to and deals with filter degeneracy based on Spiller et al. [2008]. During the Bayesian updating step of the standard particle filter, the algorithm tests the sample population for filter degeneracy. If filter degeneracy has occurred, the algorithm resets to the last time the filter did work correctly and recalculates the failed timespan of the filter with an increased sample population. The sample population is then reduced to its original size and the particle filter continues as normal. This algorithm was created in the PCRaster Python framework, an open source tool that enables spatio-temporal forward modelling in Python [Karssenberg et al., 2010] . The framework already contains several data assimilation algorithms, including a standard particle filter and a Kalman filter. The backtracking particle filter algorithm has been added to the framework, which will make it easy to implement in other research. The performance of the backtracking particle filter is tested against a standard particle filter using two models. The first is a simple nonlinear point model, and the second is a more complex geophysical model. The main testing
Nonlinear interaction of meta-atoms through optical coupling
Energy Technology Data Exchange (ETDEWEB)
Slobozhanyuk, A. P.; Kapitanova, P. V.; Filonov, D. S.; Belov, P. A. [National Research University of Information Technologies, Mechanics and Optics (ITMO), St. Petersburg 197101 (Russian Federation); Powell, D. A. [Nonlinear Physics Centre and Centre for Ultrahigh-bandwidth Devices for Optical Systems (CUDOS), Australian National University, Canberra, ACT 0200 (Australia); Shadrivov, I. V.; Kivshar, Yu. S. [National Research University of Information Technologies, Mechanics and Optics (ITMO), St. Petersburg 197101 (Russian Federation); Nonlinear Physics Centre and Centre for Ultrahigh-bandwidth Devices for Optical Systems (CUDOS), Australian National University, Canberra, ACT 0200 (Australia); Lapine, M., E-mail: mlapine@physics.usyd.edu.au [National Research University of Information Technologies, Mechanics and Optics (ITMO), St. Petersburg 197101 (Russian Federation); Centre for Ultrahigh-bandwidth Devices for Optical Systems (CUDOS), School of Physics, University of Sydney, New South Wales 2006 (Australia); McPhedran, R. C. [Centre for Ultrahigh-bandwidth Devices for Optical Systems (CUDOS), School of Physics, University of Sydney, New South Wales 2006 (Australia)
2014-01-06
We propose and experimentally demonstrate a multi-frequency nonlinear coupling mechanism between split-ring resonators. We engineer the coupling between two microwave resonators through optical interaction, whilst suppressing the direct electromagnetic coupling. This allows for a power-dependent interaction between the otherwise independent resonators, opening interesting opportunities to address applications in signal processing, filtering, directional coupling, and electromagnetic compatibility.
Kowalski, J
2003-01-01
In this paper, a very large scale integration chip of an analog image weighted-order statistic (WOS) filter based on cellular neural network (CNN) architecture for real-time applications is described. The chip has been implemented in CMOS AMS 0.8 /spl mu/m technology. CNN-based filter consists of feedforward nonlinear template B operating within the window of 3 /spl times/ 3 pixels around the central pixel being filtered. The feedforward nonlinear CNN coefficients have been realized using programmable nonlinear coupler circuits. The WOS filter chip allows for processing of images with 300 pixels horizontal resolution. The resolution can be increased by cascading of the chips. Experimental results of basic circuit building blocks measurements are presented. Functional tests of the chip have been performed using a special test setup for PAL composite video signal processing. Using the setup real images have been filtered by WOS filter chip under test.
Fundamentals of Stochastic Filtering
Crisan, Dan
2008-01-01
The objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide a rigorous mathematical treatment of the non-linear stochastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis of numerical methods for the solution of the filtering problem via particle methods. The book should provide sufficient
Institute of Scientific and Technical Information of China (English)
蔡敏
2015-01-01
In order to improve the image resolution and recognition ability by image filtering, image filtering algorithm using the traditional wavelet denoising method, due to the interference of background color noise, wavelet decomposition in the fil-tering performance of low-frequency image parameters is not good. This paper puts forward a Gabor feature decomposition nonlinear image filtering algorithm based on Gauss mixture. Firstly, image smoothing preprocessing and wavelet decomposi-tion, obtained along the gradient direction information of image edge, wavelet decomposition characteristics in scale transla-tional plane, Gabor wavelet transform coefficients of image filtering process, using the Gauss hybrid nonlinear filtering algo-rithm and improved image filtering method. The simulation results show that, using the method of image filtering, can effec-tively suppress speckle noise in images, improve image resolution performance, with edge to edge features and details of the ability to maintain performance, especially suitable for synthetic aperture radar imaging processing.%通过图像滤波提高图像的分辨和识别能力,传统的图像滤波算法采用小波降噪方法,由于受到背景色噪声的干扰,小波分解中对低频图像参量的滤波性能不好.提出一种基于Gabor特征分解的高斯混合非线性图像滤波算法.首先进行图像平滑和小波分解预处理,沿梯度方向求得图像边缘信息,在尺度平移平面上进行小波特征分解,得到图像滤波过程中的Gabor小波变换系数,采用高斯混合非线性滤波算法实现图像滤波方法改进.仿真结果表明,采用该方法进行图像滤波,能有效抑制图像斑点噪声,提高图像的分辨性能,对边缘特征和细节的保持能力方面性能有优越,特别适用于对合成孔径雷达成像的滤波处理.
Institute of Scientific and Technical Information of China (English)
王小旭; 梁彦; 潘泉; 赵春晖; 李汉舟
2012-01-01
Traditional unscented Kalman filter (UKF) calls for that noise should be Gaussian white one, and can not solve nonlinear filtering problem with colored noise. For this reason, a new UKF filtering algorithm with colored measurement noise is proposed. Firstly, optimal filtering framework for a class of nonlinear discrete-time systems with colored measurement noise is derived on the basis of augmented measurement information and minimum mean square error estimation. Secondly, filtering recursive formula of UKF with colored noise is proposed through applying unscented transformation (UT) to calculation the posterior mean and covariance of the nonlinear state in this optimal framework. The proposed UKF can effectively deal with the issue that traditional UKF is failure under the condition that measurement noise is colored. A numerical simulation example also shows its feasibility and effectiveness.%传统Unscented卡尔曼滤波器(Unscented Kalman filter,UKF)要求噪声必须为高斯白噪声,无法解决带有色噪声的非线性系统滤波问题.为此,本文提出了一种带有色量测噪声的UKF滤波新算法.首先,基于量测信息增广和最小方差估计,推导出一类带有色量测噪声的非线性离散系统状态的最优滤波框架,接着采用Unscented变换(Unscented transformation,UT)来计算最优框架中的非线性状态后验均值和协方差,进而得到有色量测噪声下UKF滤波递推公式.所设计的UKF新方法能有效地解决传统UKF在量测噪声有色情况下非线性滤波失效的问题,数值仿真实例验证了其可行性和有效性.
采用非线性量子比特的形态滤波及其应用%Morphological Filtering Using Nonlinear Quantum Bit and Its Application
Institute of Scientific and Technical Information of China (English)
陈彦龙; 张培林; 李兵; 李胜
2015-01-01
针对数学形态学结构元素无法动态调整尺寸的问题，结合量子理论提出一种基于非线性量子比特的形态滤波方法，提升形态学的机械振动信号处理效果。分析机械信号与量子理论结合的可行性，并在此基础上构建机械振动信号的峰值波谷的量子表达形式；结合振动信号的最大值和最小值，通过数学分析提出非线性量子比特的表达式，用于表达振动信号的瞬时状态；根据振动信号邻域的关联性，分析振动信号的局部特点，建立振动信号的三量子位系统；根据机械振动信号的峰值波谷的量子表达形式，在三量子位系统的框架内，提出机械振动信号在量子概率特征下的结构元素尺寸收缩算子，并基于尺寸收缩算子实现结构元素长度的自适应调整。运用轴承故障信号进行分析，结果表明，该方法能够比传统方法更加有效地提取出故障脉冲信息。%Aiming at the problem that mathematical morphology structuring element is unable to adjust its length dynamically, a morphological filtering method using nonlinear quantum bit integrating quantum theory is presented, to enhance mechanical vibration signal processing effect of morphology. Firstly the feasibility of combination between mechanical signal and quantum theory is analyzed. Based on the analysis, the quantum expressions of crest and trough in mechanical vibration signal are presented. Then combining both maximum and minimum of vibration signal, an expression of nonlinear quantum bit is proposed after mathematical analysis, which is used to depict the instantaneous state of vibration signal. The next according to the relevance in the neighbourhood of mechanical vibration signal, a quantum system with multiple quantum bits for mechanical vibration signals is proposed after local characteristics of vibration signals are analyzed. Based on the quantum expressions of crest and trough in
Position USBL/DVL Sensor-based Navigation Filter in the presence of Unknown Ocean Currents
Morgado, M; Oliveira, P; Silvestre, C
2010-01-01
This paper presents a novel approach to the design of globally asymptotically stable (GAS) position filters for Autonomous Underwater Vehicles (AUVs) based directly on the nonlinear sensor readings of an Ultra-short Baseline (USBL) and a Doppler Velocity Log (DVL). Central to the proposed solution is the derivation of a linear time-varying (LTV) system that fully captures the dynamics of the nonlinear system, allowing for the use of powerful linear system analysis and filtering design tools that yield GAS filter error dynamics. Simulation results reveal that the proposed filter is able to achieve the same level of performance of more traditional solutions, such as the Extended Kalman Filter (EKF), while providing, at the same time, GAS guarantees, which are absent for the EKF.
Detection of Harmonic Occurring using Kalman Filtering
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar; Shoro, Ghulam Mustafa; Imran, Raja Muhammed
2014-01-01
As long as the load to a power system is linear which has been the case before 80's, typically no harmonics are produced. However, the modern power electronic equipment for controlled power consumption produces harmonic disturbances, these devices/equipment possess nonlinear voltage/current chara...... using Kalman filter. This may be very useful for example to quickly switching on certain filters based on the harmonic present. We are using a unique technique to detect the occurrence of harmonics......./current characteristic. These harmonics are not to be allowed to grow beyond a certain limit to avoid any grave consequence to the customer’s main supply. Filters can be implemented at the power source or utility location to eliminate these harmonics. In this paper we detect the instance at which these harmonics occur...
Sander, W. A., III
1973-01-01
Dc to dc static power conditioning systems on unmanned spacecraft have as their inputs highly fluctuating dc voltages which they condition to regulated dc voltages. These input voltages may be less than or greater than the desired regulated voltages. The design of two circuits which address specific problems in the design of these power conditioning systems and a nonlinear analysis of one of the circuits are discussed. The first circuit design is for a nondissipative active ripple filter which uses an operational amplifier to amplify and cancel the sensed ripple voltage. A dc to dc converter operating at a switching frequency of 1 MHz is the second circuit discussed. A nonlinear analysis of the type of dc to dc converter utilized in designing the 1 MHz converter is included.
Institute of Scientific and Technical Information of China (English)
王丽丽; 张景绘
2001-01-01
利用非平稳信号的时频分析方法研究了一类非线性系统的频率特性和阻尼特性随运动形态的变化规律，得到了能简洁、直观地反映系统基本非线性动力学特性的广义骨架线性系统(简称GSLS)和骨架曲线,在此基础上，利用时频滤波方法根据系统非平稳响应信号对非线性系统进行辨识,该项工作为非线性系统反问题的研究提供了一条新的途径,%The nonlinear behavior varying with the instantaneous response was analyzed through the joint time_frequency analysis method for a class of S.D.O.F nonlinear system. A masking operator on definite regions is defined and two theorems are presented. Based on these, the nonlinear system is modeled with a special time_varying linear one, called the generalized skeleton linear system(GSLS). The frequency skeleton curve and the damping skeleton curve are defined to describe the main feature of the non_linearity as well. Moreover, an identification method is proposed through the skeleton curves and the time_frequency filtering technique.
Crosta, Giovanni Franco; Pan, Yong-Le; Aptowicz, Kevin B.; Casati, Caterina; Pinnick, Ronald G.; Chang, Richard K.; Videen, Gorden W.
2013-12-01
Measurement of two-dimensional angle-resolved optical scattering (TAOS) patterns is an attractive technique for detecting and characterizing micron-sized airborne particles. In general, the interpretation of these patterns and the retrieval of the particle refractive index, shape or size alone, are difficult problems. By reformulating the problem in statistical learning terms, a solution is proposed herewith: rather than identifying airborne particles from their scattering patterns, TAOS patterns themselves are classified through a learning machine, where feature extraction interacts with multivariate statistical analysis. Feature extraction relies on spectrum enhancement, which includes the discrete cosine FOURIER transform and non-linear operations. Multivariate statistical analysis includes computation of the principal components and supervised training, based on the maximization of a suitable figure of merit. All algorithms have been combined together to analyze TAOS patterns, organize feature vectors, design classification experiments, carry out supervised training, assign unknown patterns to classes, and fuse information from different training and recognition experiments. The algorithms have been tested on a data set with more than 3000 TAOS patterns. The parameters that control the algorithms at different stages have been allowed to vary within suitable bounds and are optimized to some extent. Classification has been targeted at discriminating aerosolized Bacillus subtilis particles, a simulant of anthrax, from atmospheric aerosol particles and interfering particles, like diesel soot. By assuming that all training and recognition patterns come from the respective reference materials only, the most satisfactory classification result corresponds to 20% false negatives from B. subtilis particles and classification method may be adapted into a real-time operation technique, capable of detecting and characterizing micron-sized airborne particles.
Ruszczynski, Andrzej
2011-01-01
Optimization is one of the most important areas of modern applied mathematics, with applications in fields from engineering and economics to finance, statistics, management science, and medicine. While many books have addressed its various aspects, Nonlinear Optimization is the first comprehensive treatment that will allow graduate students and researchers to understand its modern ideas, principles, and methods within a reasonable time, but without sacrificing mathematical precision. Andrzej Ruszczynski, a leading expert in the optimization of nonlinear stochastic systems, integrates t
Suelzer, Joseph S.; Prasad, Awadhesh; Ghosh, Rupamanjari; Vemuri, Gautam
2016-07-01
We report on a theoretical and computational investigation of the complex dynamics that arise in a semiconductor laser that is subject to two external, time-delayed, filtered optical feedbacks with special attention to the effect of quantum noise. In particular, we focus on the dynamics of the instantaneous optical frequency (wavelength) and its behavior for a wide range of feedback strengths and filter parameters. In the case of two intermediate filter bandwidths, the most significant results are that in the presence of noise, the feedback strengths required for the onset of chaos in a period doubling route are higher than in the absence of noise. We find that the inclusion of noise changes the dominant frequency of the wavelength oscillations, and that certain attractors do not survive in the presence of noise for a range of filter parameters. The results are interpreted by use of a combination of phase portraits, rf spectra, and first return maps.
Institute of Scientific and Technical Information of China (English)
陈岁生; 卢建刚; 楼晓春
2012-01-01
New localization algorithms for wireless sensor networks which combine multidimensional scal-ing-map (MDS-MAP) and nonlinear filtering were studied to improve the localization accuracy of sensor nodes. According to the nonlinear relationship between the sensor node distances and the node localized coordinates, the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) were applied to refine the localized coordinates obtained by the MDS-MAP algorithm. The localization accuracies of these three different localization algorithms, MDS-MAP, MDS-EKF (combination of MDS-MAP and EKF) and MDS-UKF (combination of MDS-MAP and UKF), were compared. Experimental results show that the implementation of nonlinear filtering algorithms (EKF and UKF) can improve the localization accuracy. Under the same conditions, the MDS-UKF localization algorithm achieves the best accuracy and its generated network topology is the closest to the actual network topology.%为提高传感器网络节点的定位精度,对MDS-MAP结合非线性滤波方法的多种传感器网络定位算法进行研究.根据传感器节点间距离与节点定位坐标之间存在的非线性关系,在MDS-MAP定位算法的基础上,引入扩展卡尔曼滤波(EKF)求精算法和不敏卡尔曼滤波(UKF)求精算法,对MDS- MAP求得的节点坐标进行求精.对MDS-MAP定位算法、MDS-MAP和EKF相结合的定位算法(MDS-EKF)、MDS-MAP和UKF相结合的定位算法(MDS-UKF)的定位精度进行比较.实验结果表明:EKF和UKF等非线性滤波方法的应用可以提高定位精度,在相同条件下MDS-UKF定位算法的定位精度更高并且其生成的网络拓扑图最接近于实际网络拓扑图.
Directory of Open Access Journals (Sweden)
Jianping Gao
2015-01-01
Full Text Available Accurate state of charge (SoC estimation is of great significance for the lithium-ion battery to ensure its safety operation and to prevent it from overcharging or overdischarging. To achieve reliable SoC estimation for Li4Ti5O12 lithium-ion battery cell, three filtering methods have been compared and evaluated. A main contribution of this study is that a general three-step model-based battery SoC estimation scheme has been proposed. It includes the processes of battery data measurement, parametric modeling, and model-based SoC estimation. With the proposed general scheme, multiple types of model-based SoC estimators have been developed and evaluated for battery management system application. The detailed comparisons on three advanced adaptive filter techniques, which include extend Kalman filter, unscented Kalman filter, and adaptive extend Kalman filter (AEKF, have been implemented with a Li4Ti5O12 lithium-ion battery. The experimental results indicate that the proposed model-based SoC estimation approach with AEKF algorithm, which uses the covariance matching technique, performs well with good accuracy and robustness; the mean absolute error of the SoC estimation is within 1% especially with big SoC initial error.
Institute of Scientific and Technical Information of China (English)
李雄杰; 周东华
2009-01-01
针对在工程实践中发生的测量数据随机丢失情况,提出了一种应用于非线性系统的滤波方法,该方法将基于序贯重要性采样的粒子滤波器应用于非线性、非高斯系统状态的在线状态估计.首先将测量数据丢失描述成满足一定条件概率分布的二元开关序列;然后基于似然函数设计方法,设计出测量数据丢失时的粒子滤波器算法;最后用本文方法对倒立摆系统状态估计进行了仿真.仿真实验表明,测量数据丢失时的粒子滤波器算法是有效的.%Aimed at the case that sensor data may be missing randomly in practice, a filtering approach was proposed for the nonlinear systems, which applies a particle filter based on sequential importance sampling to the on-line state estimation of non-Gauss and nonlinear systems. The missing sensor data were described as a binary switching sequence which satisfies a certain conditional probability distribution; a particle filter algorithm in the presence of missing sensor data was designed based on likelihood function; the state estimation of a upside-down pendulum system was simulated by the proposed approach. The simulated results show the effectiveness of the proposed algorithm.
Use of dominant harmonic active filters in high power applications
Cheng, Po-Tai
The application of power electronics equipment is increasing rapidly. It is estimated that 60% of electrical power will be processed by power electronics equipment by year 2000. These equipments typically require rectifiers for AC-DC power conversion. Due to their nonlinear nature, most rectifiers draw harmonic current from the utility grid. The harmonic current causes higher energy losses, and may excite resonance conditions in the utility grid. Harmonic standards such as IEEE 519 and IEC 1000-3-2 have been proposed to regulate the harmonic current and voltage levels. This work is to develop a dominant harmonic active filter (DHAF) to realize a cost-effective active filtering solution for nonlinear loads in the range of megawatt and above. The DHAF system achieves harmonic isolation at dominant harmonic frequencies, e.g. the 5th and 7th. This approach allows use of low switching frequency and small rating active filter inverters (1%--2% of the load MVA rating) for implementation. Review of conventional passive filters and various active filters based on high bandwidth PWM inverters is provided. The control theory of the DHAF system is presented. Comparison of the DHAF system and other dominant harmonic filtering approach is provided. Simulation results and laboratory prototype test results are presented to validate the effectiveness of the proposed DHAF system.
A new method for adaptive color image filtering
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
An adaptive color image filter (ACIF) is proposed in this note. Through analyzing noise corruption of color image, efficient locally adaptive filters are chosen for image enhancement. The proposed adaptive color image filter combines advantages of both nonlinear vector filters and linear filters, it attenuates noise and preserves edges and details very well. Experimental results show that the proposed filter performs better than vector median filter, directional-distance filter, directional-magnitude vector filter, adaptive nearest-neighbor filter, and -trimmed mean filter.
Bridging the ensemble Kalman filter and particle filters: the adaptive Gaussian mixture filter
Stordal, Andreas Størksen; Karlsen, Hans A.; Nævdal, Geir; Hans J. Skaug; Vallès, Brice
2010-01-01
The nonlinear filtering problem occurs in many scientific areas. Sequential Monte Carlo solutions with the correct asymptotic behavior such as particle filters exist, but they are computationally too expensive when working with high-dimensional systems. The ensemble Kalman filter (EnKF) is a more robust method that has shown promising results with a small sample size, but the samples are not guaranteed to come from the true posterior distribution. By approximating the model error with a Gauss...
Generic Kalman Filter Software
Lisano, Michael E., II; Crues, Edwin Z.
2005-01-01
the basis of the aforementioned templates. The GKF software can be used to develop many different types of unfactorized Kalman filters. A developer can choose to implement either a linearized or an extended Kalman filter algorithm, without having to modify the GKF software. Control dynamics can be taken into account or neglected in the filter-dynamics model. Filter programs developed by use of the GKF software can be made to propagate equations of motion for linear or nonlinear dynamical systems that are deterministic or stochastic. In addition, filter programs can be made to operate in user-selectable "covariance analysis" and "propagation-only" modes that are useful in design and development stages.
Scattering-angle based filtering of the waveform inversion gradients
Alkhalifah, Tariq Ali
2014-11-22
Full waveform inversion (FWI) requires a hierarchical approach to maneuver the complex non-linearity associated with the problem of velocity update. In anisotropic media, the non-linearity becomes far more complex with the potential trade-off between the multiparameter description of the model. A gradient filter helps us in accessing the parts of the gradient that are suitable to combat the potential non-linearity and parameter trade-off. The filter is based on representing the gradient in the time-lag normalized domain, in which the low scattering angle of the gradient update is initially muted out in the FWI implementation, in what we may refer to as a scattering angle continuation process. The result is a low wavelength update dominated by the transmission part of the update gradient. In this case, even 10 Hz data can produce vertically near-zero wavenumber updates suitable for a background correction of the model. Relaxing the filtering at a later stage in the FWI implementation allows for smaller scattering angles to contribute higher-resolution information to the model. The benefits of the extended domain based filtering of the gradient is not only it\\'s ability in providing low wavenumber gradients guided by the scattering angle, but also in its potential to provide gradients free of unphysical energy that may correspond to unrealistic scattering angles.
Langevin Monte Carlo filtering for target tracking
Iglesias Garcia, Fernando; Bocquel, Melanie; Driessen, Hans
2015-01-01
This paper introduces the Langevin Monte Carlo Filter (LMCF), a particle filter with a Markov chain Monte Carlo algorithm which draws proposals by simulating Hamiltonian dynamics. This approach is well suited to non-linear filtering problems in high dimensional state spaces where the bootstrap filte
Energy Technology Data Exchange (ETDEWEB)
Page, Ralph H.; Doty, Patrick F.
2017-08-01
The various technologies presented herein relate to a tiled filter array that can be used in connection with performance of spatial sampling of optical signals. The filter array comprises filter tiles, wherein a first plurality of filter tiles are formed from a first material, the first material being configured such that only photons having wavelengths in a first wavelength band pass therethrough. A second plurality of filter tiles is formed from a second material, the second material being configured such that only photons having wavelengths in a second wavelength band pass therethrough. The first plurality of filter tiles and the second plurality of filter tiles can be interspersed to form the filter array comprising an alternating arrangement of first filter tiles and second filter tiles.
Noise Shaping Filter Compensating PWM Distortion for Fully Digital Amplifier
Yoneya, Akihiko
The full-digital audio amplifiers have several merits such as a high power enabling a small size of the amplifier and digital implementation of the signal processing which allows desired precision of the processing except for the final stage switching amplifiers. Unfortunately, the pulse width modulation (PWM) causes signal distortions because of the non-linearity of the modulation from the viewpoint of the transient response. This paper proposes a compensation method of the PWM distortion with feedback approach. In the noise-shaping filter of the delta-sigma modulator to calculate the pulse codes for the PWM, the distortion caused by the PWM is evaluated and fed it back to compensate the distortion. Eventually the filter is implemented as a state-variable filter with non-linear feedback from the quantizer. The calculation of the filter elements is also described. By using proposed filters, PWM signals with small distortions and small floor noise can be obtained to realize high-fidelity audio amplifiers.
Scattering angle base filtering of the inversion gradients
Alkhalifah, Tariq Ali
2014-01-01
Full waveform inversion (FWI) requires a hierarchical approach based on the availability of low frequencies to maneuver the complex nonlinearity associated with the problem of velocity inversion. I develop a model gradient filter to help us access the parts of the gradient more suitable to combat this potential nonlinearity. The filter is based on representing the gradient in the time-lag normalized domain, in which low scattering angles of the gradient update are initially muted. The result are long-wavelength updates controlled by the ray component of the wavefield. In this case, even 10 Hz data can produce near zero wavelength updates suitable for a background correction of the model. Allowing smaller scattering angle to contribute provides higher resolution information to the model.
Chebabhi, Ali; Fellah, Mohammed Karim; Kessal, Abdelhalim; Benkhoris, Mohamed F
2016-07-01
In this paper is proposed a new balancing three-level three dimensional space vector modulation (B3L-3DSVM) strategy which uses a redundant voltage vectors to realize precise control and high-performance for a three phase three-level four-leg neutral point clamped (NPC) inverter based Shunt Active Power Filter (SAPF) for eliminate the source currents harmonics, reduce the magnitude of neutral wire current (eliminate the zero-sequence current produced by single-phase nonlinear loads), and to compensate the reactive power in the three-phase four-wire electrical networks. This strategy is proposed in order to gate switching pulses generation, dc bus voltage capacitors balancing (conserve equal voltage of the two dc bus capacitors), and to switching frequency reduced and fixed of inverter switches in same times. A Nonlinear Back Stepping Controllers (NBSC) are used for regulated the dc bus voltage capacitors and the SAPF injected currents to robustness, stabilizing the system and to improve the response and to eliminate the overshoot and undershoot of traditional PI (Proportional-Integral). Conventional three-level three dimensional space vector modulation (C3L-3DSVM) and B3L-3DSVM are calculated and compared in terms of error between the two dc bus voltage capacitors, SAPF output voltages and THDv, THDi of source currents, magnitude of source neutral wire current, and the reactive power compensation under unbalanced single phase nonlinear loads. The success, robustness, and the effectiveness of the proposed control strategies are demonstrated through simulation using Sim Power Systems and S-Function of MATLAB/SIMULINK.
Directory of Open Access Journals (Sweden)
Yintao Wang
2013-01-01
measurements. Our method consists of two parts. In first phase, using the unscented sigma-point transformation techniques and information filter framework, a class of algorithms denoted as unscented information filters was developed to estimate the states of a target to be tracked. These techniques exhibit robustness and accuracy of sigma-point filters for nonlinear dynamic inference while being as easily fused as the information filters. In the second phase, we proposed a novel consensus protocol which allows each sensor node to find a consistent estimate of the value of the target. Under this protocol, the final estimate of the value of the target at each time step is iteratively updated only by fusing the neighbors’ measurements when one sensor node is out of the measurement scope of the target. Performance of the distributed unscented information filter is demonstrated and discussed on a target tracking task.
Hybrid Kalman Filter: A New Approach for Aircraft Engine In-Flight Diagnostics
Kobayashi, Takahisa; Simon, Donald L.
2006-01-01
In this paper, a uniquely structured Kalman filter is developed for its application to in-flight diagnostics of aircraft gas turbine engines. The Kalman filter is a hybrid of a nonlinear on-board engine model (OBEM) and piecewise linear models. The utilization of the nonlinear OBEM allows the reference health baseline of the in-flight diagnostic system to be updated to the degraded health condition of the engines through a relatively simple process. Through this health baseline update, the effectiveness of the in-flight diagnostic algorithm can be maintained as the health of the engine degrades over time. Another significant aspect of the hybrid Kalman filter methodology is its capability to take advantage of conventional linear and nonlinear Kalman filter approaches. Based on the hybrid Kalman filter, an in-flight fault detection system is developed, and its diagnostic capability is evaluated in a simulation environment. Through the evaluation, the suitability of the hybrid Kalman filter technique for aircraft engine in-flight diagnostics is demonstrated.
An analysis of a new nonlinear estimation technique: The state-dependent Ricatti equation method
Ewing, Craig Michael
1999-10-01
Research into nonlinear estimation techniques for terminal homing missiles has been conducted for many decades. The terminal state estimator, also called the guidance filter, is responsible for providing accurate estimates of target motion for use in guiding the missile to a collision course with the target. Some form of the extended-Kalman filter (EKF) has become the standard estimation technique employed in most modern weapon guidance systems. EKF linearization of nonlinear dynamics and/or measurements can cause problems of divergence when confronted by highly nonlinear conditions. The objective of this dissertation is to analyze a new nonlinear estimation technique that is based on the parameterization of the nonlinearities. This parameterization converts the nonlinear estimation problem into the form of a steady-state continuous Kalman filtering problem with state-dependent coefficients. This new technique, called the state-dependent Ricatti equation filter (SDREF), allows the nonlinearities of the system to be fully incorporated into the filter design, before stochastic uncertainties are imposed, without the need for linearization. The SDREF was investigated in three problems: an exoatmospheric, terminal homing, ballistic-missile intercept problem; a highly nonlinear pendulum example; and an algorithmic loss of observability problem. The exoatmospheric guidance problem examined nonlinear measurements with linear dynamics. To investigate the SDREF when used with a combination of nonlinear dynamics and nonlinear measurements, a highly nonlinear, two-state pendulum problem was also examined. While these problems were useful in gaining insight into the performance characteristics of the SDREF, no formal proof of stability could be determined for the original formulation of the estimator. The original SDREF solved an algebraic SDRE that arose from an infinite-time horizon formulation of the nonlinear filtering problem. A modification to the SDREF formulation was
Bayesian target tracking based on particle filter
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter (EKF) proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo (MCMC) method, the resampling step, etc novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.
Kullback-Leibler Divergence Approach to Partitioned Update Kalman Filter
Raitoharju, Matti; García-Fernández, Ángel F.; Piché, Robert
2016-01-01
Kalman filtering is a widely used framework for Bayesian estimation. The partitioned update Kalman filter applies a Kalman filter update in parts so that the most linear parts of measurements are applied first. In this paper, we generalize partitioned update Kalman filter, which requires the use oft the second order extended Kalman filter, so that it can be used with any Kalman filter extension. To do so, we use a Kullback-Leibler divergence approach to measure the nonlinearity of the measure...
Robustifying Vector Median Filter
Directory of Open Access Journals (Sweden)
Valentín Gregori
2011-08-01
Full Text Available This paper describes two methods for impulse noise reduction in colour images that outperform the vector median filter from the noise reduction capability point of view. Both methods work by determining first the vector median in a given filtering window. Then, the use of complimentary information from componentwise analysis allows to build robust outputs from more reliable components. The correlation among the colour channels is taken into account in the processing and, as a result, a more robust filter able to process colour images without introducing colour artifacts is obtained. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter. Objective measures demonstrate the goodness of the achieved improvement.
Generating nonlinear FM chirp waveforms for radar.
Energy Technology Data Exchange (ETDEWEB)
Doerry, Armin Walter
2006-09-01
Nonlinear FM waveforms offer a radar matched filter output with inherently low range sidelobes. This yields a 1-2 dB advantage in Signal-to-Noise Ratio over the output of a Linear FM waveform with equivalent sidelobe filtering. This report presents design and implementation techniques for Nonlinear FM waveforms.
Institute of Scientific and Technical Information of China (English)
黄湘远; 汤霞清; 武萌; 高军强
2015-01-01
为了降低非线性对准的计算量而不损失对准精度，针对容积卡尔曼滤波( CKF)采样点数与状态维数成正比、计算量较大的问题，提出了基于简化CKF/降维CKF混合滤波的非线性对准方法。利用大失准角模型和基于线性观测方程的简化CKF算法进行水平对准；使用大方位失准角模型和降维CKF完成精对准。仿真结果表明，该方法摆脱了CKF算法的“维数灾难”和降维CKF对准应用条件限制，能够完成任意失准角下的初始对准并获得较高对准精度，具有重要的工程应用价值。%In order to reduce calculation amount and keep alignment precision of nonlinear alignment, the problems that the sample points are directly proportional to state dimension and the calculation amount is large in cubature Kalman filter ( CKF) , a new alignment algorithm with mixed filter based on simplified CKF(SCKF) and reduced dimension CKF(RDCKF) proposed. The level alignment finished by a large misalignment angle model and SCKF without coarse alignment; the fine alignment fulfilled by a large azimuth misalignment angle model and RDCKF based on the level alignment. The simulation result shows that this way two disadvantages that CKF’ s“dimension prob-lem” and RDCKF’ s application limitation. It is available on any misalignment angle and has higher precision, and with important engi-neering application value.
Institute of Scientific and Technical Information of China (English)
王祝君; 朱德通
2012-01-01
本文提供了一簇新的过滤线搜索修正正割方法求解非线性等式约束优化问题.新算法簇的特点是:用修正正割算法簇中的一个算法获得搜索方向,回代线搜索技术得到步长,过滤准则用来决定是否接受步长,引入二阶校正技术减少不可行性并克服Maratos效应.在合理的假设条件下,分析了算法的总体收敛性.并证明了,通过附加二阶校正步,算法簇克服了Maratos效应,并二步Q-超线性收敛到满足二阶充分最优条件的局部解.数值结果表明了所提供的算法具有有效性.%This paper proposes a new class of line search filter improved secant methods for general nonlinear equality constrained optimization. The feature of these new algorithms is that one of the improved secant algorithms is used to produce a search direction, a backtracking line search procedure to generate step size, some filtered rules to determine step acceptance, second order correction technique to reduce infeasibility and overcome the Maratos effects. Under mild assumptions the global convergence is established. Moreover, it is also established that the Maratos effect are overcome in our new approaches by adding second order correction steps so that two-step Q-superlinear convergence to second order sufficient local solution is achieved. The results of numerical experiments are reported to show the effectiveness of these proposed algorithms.
Anderson, Brian D O
2005-01-01
This graduate-level text augments and extends beyond undergraduate studies of signal processing, particularly in regard to communication systems and digital filtering theory. Vital for students in the fields of control and communications, its contents are also relevant to students in such diverse areas as statistics, economics, bioengineering, and operations research.Topics include filtering, linear systems, and estimation; the discrete-time Kalman filter; time-invariant filters; properties of Kalman filters; computational aspects; and smoothing of discrete-time signals. Additional subjects e
Stochastic processes and filtering theory
Jazwinski, Andrew H
2007-01-01
This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well.Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probab
DSP Approach to the Design of Nonlinear Optical Devices
Directory of Open Access Journals (Sweden)
Steve Blair
2005-06-01
Full Text Available Discrete-time signal processing (DSP tools have been used to analyze numerous optical filter configurations in order to optimize their linear response. In this paper, we propose a DSP approach to design nonlinear optical devices by treating the desired nonlinear response in the weak perturbation limit as a discrete-time filter. Optimized discrete-time filters can be designed and then mapped onto a specific optical architecture to obtain the desired nonlinear response. This approach is systematic and intuitive for the design of nonlinear optical devices. We demonstrate this approach by designing autoregressive (AR and autoregressive moving average (ARMA lattice filters to obtain a nonlinear phase shift response.
Statistically-Efficient Filtering in Impulsive Environments: Weighted Myriad Filters
Directory of Open Access Journals (Sweden)
Gonzalez Juan G
2002-01-01
Full Text Available Linear filtering theory has been largely motivated by the characteristics of Gaussian signals. In the same manner, the proposed Myriad Filtering methods are motivated by the need for a flexible filter class with high statistical efficiency in non-Gaussian impulsive environments that can appear in practice. Myriad filters have a solid theoretical basis, are inherently more powerful than median filters, and are very general, subsuming traditional linear FIR filters. The foundation of the proposed filtering algorithms lies in the definition of the myriad as a tunable estimator of location derived from the theory of robust statistics. We prove several fundamental properties of this estimator and show its optimality in practical impulsive models such as the -stable and generalized- . We then extend the myriad estimation framework to allow the use of weights. In the same way as linear FIR filters become a powerful generalization of the mean filter, filters based on running myriads reach all of their potential when a weighting scheme is utilized. We derive the "normal" equations for the optimal myriad filter, and introduce a suboptimal methodology for filter tuning and design. The strong potential of myriad filtering and estimation in impulsive environments is illustrated with several examples.
High Dynamic GPS Positioning Model Based on Nonlinear Filter Algorithm%基于非线性滤波算法的高动态GPS定位模型
Institute of Scientific and Technical Information of China (English)
范韬; 茅旭初
2011-01-01
运动载体上的GPS接收机在高动态环境下运行时,由于接收机与卫星的相对加速度和速度均过大,测得的伪距和多普勒频移均存在较大的误差,而现有的GPS定位模型动态建模较为简单,导致系统在高动态环境下定位精度很低.提出一种改进的GPS系统模型,将接收机加速度信息引入到系统状态变量中进行估计,测量模型和状态模型均随接收到的卫星颗数而动态改变,并使用了平淡卡尔曼滤波进行定位解算,结果证明,使GPS系统在高动态环境下仍能得出较高的定位精度,并有定位模型的有效性和较强的鲁棒性.%In high dynamic environment, there are large errors in Pseudorange and the Doppler Shift due to the high speed and relative acceleration of the satellite and the receiver. The existing GPS models are too simple that the positioning accuracy becomes low in the high motion environment. In this paper, an improved CPS positioning model is presented. The accelerations of the receiver are added to the system state variables. The measurement model and the state model both vary according to the numbers of available satellites. Furthermore, Unscented Kalman Filter ( UKF) is employed, which maintains the high positioning accuracy in the high dynamic environment and has a reliable robustness.
Nonlinearities in Microwave Superconductivity
Ledenyov, Dimitri O.; Ledenyov, Viktor O.
2012-01-01
The research is focused on the modeling of nonlinear properties of High Temperature Superconducting (HTS) thin films, using Bardeen, Cooper, Schrieffer and Lumped Element Circuit theories, with purpose to enhance microwave power handling capabilities of microwave filters and optimize design of microwave circuits in micro- and nano- electronics.
Kalman Filtering with Real-Time Applications
Chui, Charles K
2009-01-01
Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering.
Edge detection by nonlinear dynamics
Energy Technology Data Exchange (ETDEWEB)
Wong, Yiu-fai
1994-07-01
We demonstrate how the formulation of a nonlinear scale-space filter can be used for edge detection and junction analysis. By casting edge-preserving filtering in terms of maximizing information content subject to an average cost function, the computed cost at each pixel location becomes a local measure of edgeness. This computation depends on a single scale parameter and the given image data. Unlike previous approaches which require careful tuning of the filter kernels for various types of edges, our scheme is general enough to be able to handle different edges, such as lines, step-edges, corners and junctions. Anisotropy in the data is handled automatically by the nonlinear dynamics.
Indian Academy of Sciences (India)
A H Mazinan; M Sarikhani
2014-02-01
With a focus on new researches in the area of intelligent transportation systems (ITS), an efficient approach has been investigated here. Based on the present view point, analysis of traffic signs are first considered via intelligence based approach, which is carried out through three main stages including detection, tracking and recognition, respectively, in this research. The key role of detection is to identify traffic signs by classification of road sign shapes in accordance with their signatures. This classification consists of four different shapes of circle, semicircle, triangle and square, as well. The linear classification of traffic sign is also carried out via support vector machine (SVM) by using one against all (OAA), since the present SVMs classifiers realized via linear kernel. The next step is to track traffic sign. It should be noted that this technique is now developed to reduce the searching mode in case of the whole area to be optimized its computational processing, consequently. This research work is investigated by realizing Kalman filter approach, where, finally, in recognition step, a feature of the region of interest (ROI) has been extracted for SVM classification. Histogram of oriented gradient (HOG) is realized in organizing the approach, as long as Gaussian kernel is also developed for non-linear SVM classifier.
Institute of Scientific and Technical Information of China (English)
周泽民; 曾新吾; 龚昌超; 田章福; 孙海洋
2013-01-01
针对调制气流声源存在较强的谐波畸变，将声源系统等效为 Hammerstein 非线性模型，利用该模型下的预失真技术对声源进行非线性补偿研究。根据辨识的 Hammerstein 模型中静态非线性部分带有直流分量的特点，给出了考虑直流分量补偿的预失真算法，并用数值仿真验证了算法的准确性和直流分量补偿的必要性。在非线性补偿实验中，根据单频信号辨识得到 Hammerstein 模型参数，采用 NFxPEM算法求得对应的预失真 Wiener 模型参数和预失真波形。实验结果表明，与直接发射相比，补偿发射后声波的功率谱中谐波能量有所下降，而基频能量有小幅度的上升，说明了研究思路的正确性。%Aimed at the harmonic distortion problem in the air-modulated speaker(AMS),the AMS behavioral model was represented by a Hammerstein structure,and the research on predistortion of AMS based on this model was made.As the DC offset exists in the nonlinearity of the Hammerstein model,a predistortion algorithm considering the DC offset compensation was developed.The validity of the algorithm and the necessity of the DC offset compensation were verified by computer simulation.In the experiment,a single sinusoidal excitation signal was first used to identify the Hammerstein model.Then,using the identified system parameters,the NFxPEMalgorithm was performed to obtain the parameters of Wiener predistorter and to predistort the excitation signal.From the experiment results,it is found that our approach is effective in reducing the harmonic power with a relatively small upgrade in the fundamental frequency power.
Image denoising using modified nonlinear diffusion approach
Upadhyay, Akhilesh R.; Talbar, Sanjay N.; Sontakke, Trimbak R.
2006-01-01
Partial Differential Equation (PDE) based, non-linear diffusion approaches are an effective way to denoise the images. In this paper, the work is extended to include anisotropic diffusion, where the diffusivity is a tensor valued function, which can be adapted to local edge orientation. This allows smoothing along the edges, but not perpendicular to it. The diffusion tensor is a function of differential structure of the evolving image itself. Such a feedback leads to nonlinear diffusion filters. It shows improved performance in the presence of noise. The original anisotropic diffusion algorithm updates each point based on four nearest-neighbor differences, the progress of diffusion results in improved edges. In the proposed method the edges are better preserved because diffusion is controlled by the gray level differences of diagonal neighbors in addition to 4 nearest neighbors using coupled PDF formulation. The proposed algorithm gives excellent results for MRI images, Biomedical images and Fingerprint images with noise.
IMM Iterated Extended Particle Filter Algorithm
Yang Wan; Shouyong Wang; Xing Qin
2013-01-01
In order to solve the tracking problem of radar maneuvering target in nonlinear system model and non-Gaussian noise background, this paper puts forward one interacting multiple model (IMM) iterated extended particle filter algorithm (IMM-IEHPF). The algorithm makes use of multiple modes to model the target motion form to track any maneuvering target and each mode uses iterated extended particle filter (IEHPF) to deal with the state estimation problem of nonlinear non-Gaussian system. IEH...
Adaptable Iterative and Recursive Kalman Filter Schemes
Zanetti, Renato
2014-01-01
Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. The Iterated Kalman filter (IKF) and the Recursive Update Filter (RUF) are two algorithms that reduce the consequences of the linearization assumption of the EKF by performing N updates for each new measurement, where N is the number of recursions, a tuning parameter. This paper introduces an adaptable RUF algorithm to calculate N on the go, a similar technique can be used for the IKF as well.
An overview on hybrid active filters
Energy Technology Data Exchange (ETDEWEB)
Libano, Fausto B.; Uceda, Javier [Universidad Politecnica de Madrid (Spain). Division de Ingenieria Electronica; Simonetti, Domingos S.L. [Espirito Santo Univ., Vitoria, ES (Brazil). Dept. de Engenharia Eletrica
1995-12-31
This paper summarizes the main hybrid filter methods. A special attention is given to series active filter associations. Nowadays, an hybrid filtering is the preferred choice to improve line performance when feeding high-power non-linear loads. In addition, the use of an independent reference frame leads to a better response comparing to the initial proposition of p-q theory. A comparison of possible filter associations is given, presenting the expected function of each one. The work represents an interesting overview on the state-of-the-art of hybrid filters. (author) 16 refs., 8 figs., 2 tabs.
Institute of Scientific and Technical Information of China (English)
孙宇新; 杨玉伟
2016-01-01
For agricultural motor drive applications, reliability and stability are very significant, and even under disturbance condition, stable drive operation is essential. In view of the characteristics of the bearingless induction motor, which includes multi -variables, nonlinearity and high coupling, an adaptive inverse decoupling control strategy for the bearingless induction motor based on the nonlinear adaptive filter was proposed to improve the efficiency and reliability of the motor drives. First, the mathematical model of a bearingless induction motor was deduced through analyzing the generation mechanism of a bearingless induction motor’s radial levitation force. By adopting the control theory of an adaptive inverse control system and the principle of a nonlinear adaptive filter, the model and inverse model of the torque system and levitation system were established respectively, including the option of the structure of nonlinear adaptive filter and the adaptive algorithm. Based on the inverse model, the adaptive inverse controller which cascaded in front of the corresponding system was designed by making use of the algorithm of variable step size least mean square (LMS) to adjust the weighting factors online. The difference between the given input signal and the system output signal was used as the error signal of the adaptive algorithm of variable step size LMS. In addition, compared to the traditional field oriented control method, this method did not need to rely on torque system to transfer flux information, which avoided the mutual restriction among the control strategies, and solved the coupling problem between the variables in the modeling process. Then, aiming at the performances of rotor flux, speed, torque and levitation response, the simulation and analysis of the adaptive inverse control system for the bearingless induction motor wew carried out on the basis of MATLAB/Simulink simulation platform. Moreover, the initial given value of motor speed
Shaath, Nadim A
2010-04-01
The chemistry, photostability and mechanism of action of ultraviolet filters are reviewed. The worldwide regulatory status of the 55 approved ultraviolet filters and their optical properties are documented. The photostabilty of butyl methoxydibenzoyl methane (avobenzone) is considered and methods to stabilize it in cosmetic formulations are presented.
Extending particle filters to higher dimensional problems
Weir, B.; Miller, R.; Spitz, Y. H.
2013-12-01
Particle filters are attractive solutions to nonlinear and non-Gaussian data assimilation problems since they avoid making parametric assumptions. Nevertheless, in very many dimensions their ensembles collapse onto a single particle unless the number of particles grows exponentially as a function of the dimension. This talk investigates three techniques that, used in conjunction, show the potential of preventing ensemble collapse: optimization, mixture models, and covariance refinement. Optimization is the basis of implicit sampling algorithms. By itself, it significantly reduces the growth of the necessary ensemble size, yet not to a sub-exponential function of dimension. Mixture models, which introduce a semi-parametric assumption, allow the technique to adjust the initial position of the particles. For linear and Gaussian problems, the combination of optimization and mixture models reduces the necessary ensemble size to a sub-exponential function of dimension. Covariance refinement adjusts local approximations of the second moment of the particle distributions to account for its global variation. This is especially effective for problems that are strongly nonlinear. In numerical experiments, covariance refinement used alongside optimization and mixture models shows the potential to extend the prevention of collapse to a general class of nonlinear and non-Gaussian problems.
Malfense Fierro, Gian Piero; Meo, Michele
2017-02-01
Recently, there has been high interest in the capabilities of nonlinear ultrasound techniques for damage/defect detection as these techniques have been shown to be quite accurate in imaging some particular type of damage. This paper presents a Constructive Nonlinear Array (CNA) method, for the detection and imaging of material defects/damage in a complex composite stiffened panel. CNA requires the construction of an ultrasound array in a similar manner to standard phased arrays systems, which require multiple transmitting and receiving elements. The method constructively phase-match multiple captured signals at a particular position given multiple transmit positions, similar to the total focusing method (TFM) method. Unlike most of the ultrasonic linear techniques, a longer excitation signal was used to achieve a steady-state excitation at each capturing position, so that compressive and tensile stress at defect/crack locations increases the likelihood of the generation of nonlinear elastic waves. Moreover, the technique allows the reduction of instrumentation nonlinear wave generation by relying on signal attenuation to naturally filter these errors. Experimental tests were carried out on a stiffened panel with manufacturing defects. Standard industrial linear ultrasonic test were carried out for comparison. The proposed new method allows to image damages/defects in a reliable and reproducible manner and overcomes some of the main limitations of nonlinear ultrasound techniques. In particular, the effectiveness and robustness of CNA and the advantages over linear ultrasonic were clearly demonstrated allowing a better resolution and imaging of complex and realistic flaws.
Institute of Scientific and Technical Information of China (English)
李荣冰; 黄隽祎; 刘建业; 谢非
2014-01-01
Doppler shift and signal power attenuation in the complex environment both can make damage in the accuracy of carrier tracking. Therefore, the non-linear Kalman filter for carrier tracking is designed, which makes correlated observations in the EKF and UKF model based on the analysis of the structure of BeiDou B1 signal. By using measurements from the estimation of filtering in feedback control of the carrier tracking loop, higher and more stable performance can be given in high dynamic and weak signal environments. Finally, the test results show that the feedback control-based EKF and UKF model can perform precise carrier tracking, and make a good limitation of loop error, both of which lead to realization of high performance of signal tracking.%复杂环境下的多普勒频移变化及信号功率衰减均会对载波准确跟踪造成影响。在研究北斗卫星B1频点信号结构的基础上，建立以环路中相关积分值为观测量的非线性EKF模型和UKF模型，并提出利用滤波估计状态量进行状态反馈控制的方法，从而解决了载波跟踪环路在高动态及弱信号环境中难以高性能工作的问题。实验结果表明，状态反馈控制的EKF模型和UKF滤波模型能准确地跟踪弱信号及高动态下的信号变化，从而有效控制跟踪误差，为实现快速准确的载波跟踪奠定了基础。
Kalman filtering with real-time applications
Chui, Charles K
2017-01-01
This new edition presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering. Over 100 exercises and problems with solutions help de...
Modular Filter Convergence Theorems for Urysohn Integral Operators and Applications
Institute of Scientific and Technical Information of China (English)
Antonio BOCCUTO; Xenofon DIMITRIOU
2013-01-01
We prove some versions of modular convergence theorems for nonlinear Urysohn-type integral operators with respect to filter convergence.We consider pointwise filter convergence of functions giving also some applications to linear and nonlinear Mellin operators.We show that our results are strict extensions of the classical ones.
Particle Filters for Positioning, Navigation and Tracking
Gustafsson, Fredrik; Gunnarsson, Fredrik; Bergman, Niclas; Forssell, Urban; Jansson, Jonas; Karlsson, Rickard; Nordlund, Per-Johan
2001-01-01
A framework for positioning, navigation and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general non-linear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low-dimensional. This is of utmost importance for high...
Unscented Kalman filter for SINS alignment
Institute of Scientific and Technical Information of China (English)
Zhou Zhanxin; Gao Yanan; Chen Jiabin
2007-01-01
In order to improve the filter accuracy for the nonlinear error model of strapdown inertial navigation system (SINS) alignment, Unscented Kalman Filter (UKF) is presented for simulation with stationary base and moving base of SINS alignment.Simulation results show the superior performance of this approach when compared with classical suboptimal techniques such as extended Kalman filter in cases of large initial misalignment.The UKF has good performance in case of small initial misalignment.
Polarizing Filter for Integrated Optics
Ramer, O. G.; Goss, W. C.; Goldstein, R.
1986-01-01
Polarizing filter for titanium-doped lithium niobate light waveguide suppresses transverse magnetic (TM) mode of light propagation while allowing transverse electric (TE) mode to continue on its way. Filter - lithium niobate crystal - is expected to find many applications in integrated optical circuits.
Cubication of Conservative Nonlinear Oscillators
Belendez, Augusto; Alvarez, Mariela L.; Fernandez, Elena; Pascual, Immaculada
2009-01-01
A cubication procedure of the nonlinear differential equation for conservative nonlinear oscillators is analysed and discussed. This scheme is based on the Chebyshev series expansion of the restoring force, and this allows us to approximate the original nonlinear differential equation by a Duffing equation in which the coefficients for the linear…
Institute of Scientific and Technical Information of China (English)
履之
1995-01-01
A typical food-processing plant produces about 500,000 gallons of waste water daily. Laden with organic compounds, this water usually is evaporated or discharged into sewers.A better solution is to filter the water through
Optimization of integrated polarization filters
Gagnon, Denis; Déziel, Jean-Luc; Dubé, Louis J
2014-01-01
This study reports on the design of small footprint, integrated polarization filters based on engineered photonic lattices. Using a rods-in-air lattice as a basis for a TE filter and a holes-in-slab lattice for the analogous TM filter, we are able to maximize the degree of polarization of the output beams up to 98 % with a transmission efficiency greater than 75 %. The proposed designs allow not only for logical polarization filtering, but can also be tailored to output an arbitrary transverse beam profile. The lattice configurations are found using a recently proposed parallel tabu search algorithm for combinatorial optimization problems in integrated photonics.
Adaptive filtering and change detection
Gustafsson, Fredrik
2003-01-01
Adaptive filtering is a classical branch of digital signal processing (DSP). Industrial interest in adaptive filtering grows continuously with the increase in computer performance that allows ever more conplex algorithms to be run in real-time. Change detection is a type of adaptive filtering for non-stationary signals and is also the basic tool in fault detection and diagnosis. Often considered as separate subjects Adaptive Filtering and Change Detection bridges a gap in the literature with a unified treatment of these areas, emphasizing that change detection is a natural extensi
Filtering, control and fault detection with randomly occurring incomplete information
Dong, Hongli; Gao, Huijun
2013-01-01
This book investigates the filtering, control and fault detection problems for several classes of nonlinear systems with randomly occurring incomplete information. It proposes new concepts, including RVNs, ROMDs, ROMTCDs, and ROQEs. The incomplete information under consideration primarily includes missing measurements, time-delays, sensor and actuator saturations, quantization effects and time-varying nonlinearities. The first part of this book focuses on the filtering, control and fault detection problems for several classes of nonlinear stochastic discrete-time systems and
Quantification and prediction of rare events in nonlinear waves
Sapsis, Themistoklis; Cousins, Will; Mohamad, Mustafa
2014-11-01
The scope of this work is the quantification and prediction of rare events characterized by extreme intensity, in nonlinear dispersive models that simulate water waves. In particular we are interested for the understanding and the short-term prediction of rogue waves in the ocean and to this end, we consider 1-dimensional nonlinear models of the NLS type. To understand the energy transfers that occur during the development of an extreme event we perform a spatially localized analysis of the energy distribution along different wavenumbers by means of the Gabor transform. A stochastic analysis of the Gabor coefficients reveals i) the low-dimensionality of the intermittent structures, ii) the interplay between non-Gaussian statistical properties and nonlinear energy transfers between modes, as well as iii) the critical scales (or Gabor coefficients) where a critical energy can trigger the formation of an extreme event. The unstable character of these critical localized modes is analysed directly through the system equation and it is shown that it is defined as the result of the system nonlinearity and the wave dissipation (that mimics wave breaking). These unstable modes are randomly triggered through the dispersive ``heat bath'' of random waves that propagate in the nonlinear medium. Using these properties we formulate low-dimensional functionals of these Gabor coefficients that allow for the prediction of extreme event well before the strongly nonlinear interactions begin to occur. The prediction window is further enhanced by the combination of the developed scheme with traditional filtering schemes.
Parameter estimation, model reduction and quantum filtering
Chase, Bradley A.
rise to effective non-linearities which enhance the effect of Larmor precession allowing for improved magnetic field estimation. I then turn to the topic of model reduction, which is the search for a reduced computational model of a dynamical system. This is a particularly important task for quantum mechanical systems, whose state grows exponentially in the number of subsystems. In the quantum filtering setting, I study the use of model reduction in developing a feedback controller for continuous-time quantum error correction. By studying the propagation of errors in a noisy quantum memory, I present a computation model which scales polynomially, rather than exponentially, in the number of physical qubits of the system. Although inexact, a feedback controller using this model performs almost indistinguishably from one using the full model. I finally review an exact but polynomial model of collective qubit systems undergoing arbitrary symmetric dynamics which allows for the efficient simulation of spontaneous-emission and related open quantum system phenomenon.
Design and control of LCL-filter with active damping for Active Power Filter
DEFF Research Database (Denmark)
Zeng, Guohong; Rasmussen, Tonny Wederberg; Ma, L
2010-01-01
In the application of shunt Active Power Filter (APF) to compensate nonlinear load's harmonic, reactive and negative sequence current, it is more effective to use a LCL-filter than an L-filter as an interface between the Voltage Source Converter (VSC) and grid. In this paper, a designing procedure...... of LCL-filter for APF is introduced, which is aimed for simplified the implementation. To suppress the resonance that may be excited in the system, which brings in stability problems, an active damping control strategy using the current feed-back of the filter capacitor is adopted. By selecting two equal...
Fast cartoon + texture image filters.
Buades, Antoni; Le, Triet M; Morel, Jean-Michel; Vese, Luminita A
2010-08-01
Can images be decomposed into the sum of a geometric part and a textural part? In a theoretical breakthrough, [Y. Meyer, Oscillating Patterns in Image Processing and Nonlinear Evolution Equations. Providence, RI: American Mathematical Society, 2001] proposed variational models that force the geometric part into the space of functions with bounded variation, and the textural part into a space of oscillatory distributions. Meyer's models are simple minimization problems extending the famous total variation model. However, their numerical solution has proved challenging. It is the object of a literature rich in variants and numerical attempts. This paper starts with the linear model, which reduces to a low-pass/high-pass filter pair. A simple conversion of the linear filter pair into a nonlinear filter pair involving the total variation is introduced. This new-proposed nonlinear filter pair retains both the essential features of Meyer's models and the simplicity and rapidity of the linear model. It depends upon only one transparent parameter: the texture scale, measured in pixel mesh. Comparative experiments show a better and faster separation of cartoon from texture. One application is illustrated: edge detection.
A Concept of Approximated Densities for Efficient Nonlinear Estimation
Directory of Open Access Journals (Sweden)
Virginie F. Ruiz
2002-10-01
Full Text Available This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD. The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques, the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy principle subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes theorem. Through simulation on a generic exponential model, the proposed nonlinear filter is implemented and the results prove to be superior to that of the extended Kalman filter and a class of nonlinear filters based on partitioning algorithms.
Development of a noise reduction filter algorithm for pediatric body images in multidetector CT.
Nishimaru, Eiji; Ichikawa, Katsuhiro; Okita, Izumi; Tomoshige, Yukihiro; Kurokawa, Takehiro; Nakamura, Yuko; Suzuki, Masayuki
2010-12-01
Recently, several types of post-processing image filter which was designed to reduce noise allowing a corresponding dose reduction in CT images have been proposed and these were reported to be useful for noise reduction of CT images of adult patients. However, these have not been reported on adaptation for pediatric patients. Because they are not very effective with small (<20 cm) display fields of view, they could not be used for pediatric (e.g., premature babies and infants) body CT images. In order to solve this restriction, we have developed a new noise reduction filter algorithm which can be applicable for pediatric body CT images. This algorithm is based on a three-dimensional post processing, in which output pixel values are calculated by multi-directional, one-dimensional median filters on original volumetric datasets. The processed directions were selected except in in-plane (axial plane) direction, and consequently the in-plane spatial resolution was not affected by the filter. Also, in other directions, the spatial resolutions including slice thickness were almost maintained due to a characteristic of non-linear filtering of the median filter. From the results of phantom studies, the proposed algorithm could reduce standard deviation values as a noise index by up to 30% without affecting the spatial resolution of all directions, and therefore, contrast-to-noise ratio was improved by up to 30%. This newly developed filter algorithm will be useful for the diagnosis and radiation dose reduction of pediatric body CT images.
Kovačević, Branko; Milosavljević, Milan
2013-01-01
“Adaptive Digital Filters” presents an important discipline applied to the domain of speech processing. The book first makes the reader acquainted with the basic terms of filtering and adaptive filtering, before introducing the field of advanced modern algorithms, some of which are contributed by the authors themselves. Working in the field of adaptive signal processing requires the use of complex mathematical tools. The book offers a detailed presentation of the mathematical models that is clear and consistent, an approach that allows everyone with a college level of mathematics knowledge to successfully follow the mathematical derivations and descriptions of algorithms. The algorithms are presented in flow charts, which facilitates their practical implementation. The book presents many experimental results and treats the aspects of practical application of adaptive filtering in real systems, making it a valuable resource for both undergraduate and graduate students, and for all others interested in m...
Robust fault detection filter design
Douglas, Randal Kirk
The detection filter is a specially tuned linear observer that forms the residual generation part of an analytical redundancy system designed for model-based fault detection and identification. The detection filter has an invariant state subspace structure that produces a residual with known and fixed directional characteristics in response to a known design fault direction. In addition to a parameterization of the detection filter gain, three methods are given for improving performance in the presence of system disturbances, sensor noise, model mismatch and sensitivity to small parameter variations. First, it is shown that by solving a modified algebraic Riccati equation, a stabilizing detection filter gain is found that bounds the H-infinity norm of the transfer matrix from system disturbances and sensor noise to the detection filter residual. Second, a specially chosen expanded-order detection filter is formed with fault detection properties identical to a set of independent reduced-order filters that have no structural constraints. This result is important to the practitioner because the difficult problem of finding a detection filter insensitive to disturbances and sensor noise is converted to the easier problem of finding a set of uncoupled noise insensitive filters. Furthermore, the statistical properties of the reduced-order filter residuals are easier to find than the statistical properties of the structurally constrained detection filter residual. Third, an interpretation of the detection filter as a special case of the dual of the restricted decoupling problem leads to a new detection filter eigenstructure assignment algorithm. The new algorithm places detection filter left eigenvectors, which annihilate the detection spaces, rather than right eigenvectors, which span the detection spaces. This allows for a more flexible observer based fault detection system structure that could not be formulated as a detection filter. Furthermore, the link to the dual
Artificial spectral filtering in dissipative soliton fiber lasers with invisible bandpass filters
Institute of Scientific and Technical Information of China (English)
Kong Ling-Jie; Xiao Xiao-Sheng; Yang Chang-Xi
2012-01-01
We numerically study the artificial spectral-filtering effect in dissipative soliton fiber lasers without intracavity spectral filters.It is found that in dissipative soliton lasers with real saturable absorbers (SAs),the dynamic spectral filtering of the real SAs serves as an artificial spectral filter and contributes to the pulse shaping.While in the dissipative soliton lasers with artificial SAs,such as nonlinear polarization rotation,the spectral filtering introduced by the intracavity polarization-dependent components acts as an artificial spectral filter and shapes the pulses to obtain modelocking. An investigation of the artificial spectral-filtering effect reveals the operating mechanisms of the dissipative soliton fiber lasers without visible bandpass filters.
Harmonic Detection at Initialization With Kalman Filter
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar; Imran, Raja Muhammad; Shoro, Ghulam Mustafa
2014-01-01
the affect of harmonics on the supply. For the detection of these harmonics various techniques are available and one of that technique is the Kalman filter. In this paper we investigate that what are the consequences when harmonic detection system based on Kalman Filtering is initialized......Most power electronic equipment these days generate harmonic disturbances, these devices hold nonlinear voltage/current characteristic. The harmonics generated can potentially be harmful to the consumer supply. Typically, filters are integrated at the power source or utility location to filter out...
Energy Technology Data Exchange (ETDEWEB)
Sorrentino, A [Dipartimento di Fisica, Universita di Genova (Italy); Pascarella, A; Campi, C [Dipartimento di Matematica, Universita di Genova (Italy); Piana, M [Dipartimento di Informatica, Universita di Verona (Italy)], E-mail: sorrentino@fisica.unige.it
2008-07-15
We consider the problem of dynamically estimating the parameters of point-like neural sources from magnetoencephalography data. Since the problem is non-linear, we apply the sequential Monte Carlo algorithms known as particle filters for solving the Bayesian filtering problem. We suggest that the linear dependence of the data on a subset of the parameters allows the analytic computation of the posterior density for these parameters, i.e. Rao-Blackwellization; this considerably improves the accuracy of the method and its statistical efficiency.
Shelton, G. B. (Inventor)
1977-01-01
A notch filter for the selective attenuation of a narrow band of frequencies out of a larger band was developed. A helical resonator is connected to an input circuit and an output circuit through discrete and equal capacitors, and a resistor is connected between the input and the output circuits.
Utilizing Time Redundancy for Particle Filter-Based Transfer Alignment
Chattaraj, Suvendu; Mukherjee, Abhik
2016-07-01
Signal detection in the presence of high noise is a challenge in natural sciences. From understanding signals emanating out of deep space probes to signals in protein interactions for systems biology, domain specific innovations are needed. The present work is in the domain of transfer alignment (TA), which deals with estimation of the misalignment of deliverable daughter munitions with respect to that of the delivering mother platform. In this domain, the design of noise filtering scheme has to consider a time varying and nonlinear system dynamics at play. The accuracy of conventional particle filter formulation suffers due to deviations from modeled system dynamics. An evolutionary particle filter can overcome this problem by evolving multiple system models through few support points per particle. However, this variant has even higher time complexity for real-time execution. As a result, measurement update gets deferred and the estimation accuracy is compromised. By running these filter algorithms on multiple processors, the execution time can be reduced, to allow frequent measurement updates. Such scheme ensures better system identification so that performance improves in case of simultaneous ejection of multiple daughters and also results in better convergence of TA algorithms for single daughter.
Nonlinear graphene metamaterial
Nikolaenko, Andrey E; Atmatzakis, Evangelos; Luo, Zhiqiang; Shen, Ze Xiang; De Angelis, Francesco; Boden, Stuart A; Di Fabrizio, Enzo; Zheludev, Nikolay I
2012-01-01
We demonstrate that the broadband nonlinear optical response of graphene can be resonantly enhanced by more than an order of magnitude through hybridization with a plasmonic metamaterial,while retaining an ultrafast nonlinear response time of ~1 ps. Transmission modulation close to ~1% is seen at a pump uence of ~0.03 mJ/cm^2 at the wavelength of ~1600 nm. This approach allows to engineer and enhance graphene's nonlinearity within a broad wavelength range enabling applications in optical switching, mode-locking and pulse shaping.
Mendoza, John Cadiz
1995-01-01
The computational fluid dynamics code, PARC3D, is tested to see if its use of non-physical artificial dissipation affects the accuracy of its results. This is accomplished by simulating a shock-laminar boundary layer interaction and several hypersonic flight conditions of the Pegasus(TM) launch vehicle using full artificial dissipation, low artificial dissipation, and the Engquist filter. Before the filter is applied to the PARC3D code, it is validated in one-dimensional and two-dimensional form in a MacCormack scheme against the Riemann and convergent duct problem. For this explicit scheme, the filter shows great improvements in accuracy and computational time as opposed to the nonfiltered solutions. However, for the implicit PARC3D code it is found that the best estimate of the Pegasus experimental heat fluxes and surface pressures is the simulation utilizing low artificial dissipation and no filter. The filter does improve accuracy over the artificially dissipative case but at a computational expense greater than that achieved by the low artificial dissipation case which has no computational time penalty and shows better results. For the shock-boundary layer simulation, the filter does well in terms of accuracy for a strong impingement shock but not as well for weaker shock strengths. Furthermore, for the latter problem the filter reduces the required computational time to convergence by 18.7 percent.
Power System Harmonic Compensation Using Shunt Active Power Filter.
Directory of Open Access Journals (Sweden)
Shiuly Mukherjee
2014-07-01
Full Text Available This paper shows the method of improving the power quality using shunt active power filter. The proposedtopic comprises of PI controller, filter hysteresis current control loop, dc link capacitor. The switching signal generation for filter is fromhysteresis current controller techniques. With the all these element shunt active power filter reduce the total harmonic distortion. Thispaper represents the simulation and analysis of the using three phase three wire system active filter to compensate harmonics .Theproposed shunt active filter model uses balanced non-linear load. This paper successfully lowers the THD within IEEE norms and satisfactorily works to compensatecurrent harmonics.
Identification Filtering with fuzzy estimations
Directory of Open Access Journals (Sweden)
J.J Medel J
2012-10-01
Full Text Available A digital identification filter interacts with an output reference model signal known as a black-box output system. The identification technique commonly needs the transition and gain matrixes. Both estimation cases are based on mean square criterion obtaining of the minimum output error as the best estimation filtering. The evolution system represents adaptive properties that the identification mechanism includes considering the fuzzy logic strategies affecting in probability sense the evolution identification filter. The fuzzy estimation filter allows in two forms describing the transition and the gain matrixes applying actions that affect the identification structure. Basically, the adaptive criterion conforming the inference mechanisms set, the Knowledge and Rule bases, selecting the optimal coefficients in distribution form. This paper describes the fuzzy strategies applied to the Kalman filter transition function, and gain matrixes. The simulation results were developed using Matlab©.
Fast Numerical Nonlinear Fourier Transforms
Wahls, Sander
2014-01-01
The nonlinear Fourier transform, which is also known as the forward scattering transform, decomposes a periodic signal into nonlinearly interacting waves. In contrast to the common Fourier transform, these waves no longer have to be sinusoidal. Physically relevant waveforms are often available for the analysis instead. The details of the transform depend on the waveforms underlying the analysis, which in turn are specified through the implicit assumption that the signal is governed by a certain evolution equation. For example, water waves generated by the Korteweg-de Vries equation can be expressed in terms of cnoidal waves. Light waves in optical fiber governed by the nonlinear Schr\\"dinger equation (NSE) are another example. Nonlinear analogs of classic problems such as spectral analysis and filtering arise in many applications, with information transmission in optical fiber, as proposed by Yousefi and Kschischang, being a very recent one. The nonlinear Fourier transform is eminently suited to address them ...
Filter based phase distortions in extracellular spikes.
Yael, Dorin; Bar-Gad, Izhar
2017-01-01
Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results.
Filter based phase distortions in extracellular spikes
Yael, Dorin
2017-01-01
Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results. PMID:28358895
Impulse control in Kalman-like filtering problems
Directory of Open Access Journals (Sweden)
Michael V. Basin
1998-01-01
Full Text Available This paper develops the impulse control approach to the observation process in Kalman-like filtering problems, which is based on impulsive modeling of the transition matrix in an observation equation. The impulse control generates the jumps of the estimate variance from its current position down to zero and, as a result, enables us to obtain the filtering equations for the Kalman estimate with zero variance for all post-jump time moments. The filtering equations for the estimates with zero variances are obtained in the conventional linear filtering problem and in the case of scalar nonlinear state and nonlinear observation equations.
基于样条插值的非线性滤波器的分析与设计%Analysis and Design of Non-linear filters Based on Cubic Spline Function
Institute of Scientific and Technical Information of China (English)
伍小芹; 张宏科; 邓家先
2011-01-01
在理论分析和实际应用中,信号分析具有重要的理论意义和实际应用价值.非平稳信号的分析及处理一直是学术和工程界关注的热点问题之一.由于传统数据分析方法受线性或者平稳性假设的限制,无法有效地应用于图像处理、语音处理及雷达信号处理等实际应用中.本文通过对非线性、非平稳数据的建模,研究了适合非平稳数据分析的经验数据分解算法.建立了可行的经验数据分解滤波器的设计准则,并利用三次样条插值预测滤波器的参数.使用超光谱图像数据进行测试分析,在一次经验数据分解后,分析了高频子带数值在规定范围内的概率分布及相应的熵值.实验结果表明:经验数据分解算法产生的高频系数在0附近更集中,这对图像压缩有利,从而证明经验数据分解是一种对非平稳数据有效的分析方法.%Signal analysis has important theoretical and practical application. Non-stationary signal analysis and processing is one of the hot topics in the scientific and engineering research area. Because of the limit of linearity and stationarity assumption, the traditional methods can not be effectively used in image processing, speech processing and radar signal processing. A model suiting for nonlinear and non-stationary is established. The empirical data decomposition algorithm is discussed. A suitable design criteria is established. The use of cubic spline functions to predict the parameters of the predictive filter is discussed. Making a test on spectrum image data with empirical data decomposition. The system is simulated in Matlab. The probability distribution of the samples in high-frequency subbands whose values are within the specified range and the corresponding entropy are analyzed through simulation. The results show that the high-frequency coefficients produed by empirical data decomposition algorithm is more concentrated than those of 5/3 wavelet and 9
Bloembergen, Nicolaas
1996-01-01
Nicolaas Bloembergen, recipient of the Nobel Prize for Physics (1981), wrote Nonlinear Optics in 1964, when the field of nonlinear optics was only three years old. The available literature has since grown by at least three orders of magnitude.The vitality of Nonlinear Optics is evident from the still-growing number of scientists and engineers engaged in the study of new nonlinear phenomena and in the development of new nonlinear devices in the field of opto-electronics. This monograph should be helpful in providing a historical introduction and a general background of basic ideas both for expe
Tunable Imaging Filters in Astronomy
Bland-Hawthorn, J
2000-01-01
While tunable filters are a recent development in night time astronomy, they have long been used in other physical sciences, e.g. solar physics, remote sensing and underwater communications. With their ability to tune precisely to a given wavelength using a bandpass optimized for the experiment, tunable filters are already producing some of the deepest narrowband images to date of astrophysical sources. Furthermore, some classes of tunable filters can be used in fast telescope beams and therefore allow for narrowband imaging over angular fields of more than a degree over the sky.
Accuracy and Stability of Filters for Dissipative PDEs
Brett, C E A; Law, K J H; McCormick, D S; Scott, M R; Stuart, A M
2012-01-01
Data assimilation methodologies are designed to incorporate noisy observations of a physical system into an underlying model in order to infer the properties of the state of the system. Filters refer to a class of data assimilation algorithms designed to update the estimation of the state as data is acquired sequentially. For linear problems subject to Gaussian noise filtering can be performed exactly using the Kalman filter. For nonlinear systems it can be approximated in a systematic way by particle filters. However in high dimensions these particle filtering methods can break down. Hence, for the large nonlinear systems arising in applications such as oceanography and weather forecasting, various ad hoc filters are used, based on Gaussian approximations. In this work, we study the accuracy and stability of these ad hoc filters in the context of the 2D incompressible Navier-Stokes equation. The ideas readily generalize to a range of dissipative partial differential equations (PDEs). By working in this infin...
Energy Technology Data Exchange (ETDEWEB)
Mitchell, M A; Bergman, W; Haslam, J; Brown, E P; Sawyer, S; Beaulieu, R; Althouse, P; Meike, A
2012-04-30
Potential benefits of ceramic filters in nuclear facilities: (1) Short term benefit for DOE, NRC, and industry - (a) CalPoly HTTU provides unique testing capability to answer questions for DOE - High temperature testing of materials, components, filter, (b) Several DNFSB correspondences and presentations by DNFSB members have highlighted the need for HEPA filter R and D - DNFSB Recommendation 2009-2 highlighted a nuclear facility response to an evaluation basis earthquake followed by a fire (aka shake-n-bake) and CalPoly has capability for a shake-n-bake test; (2) Intermediate term benefit for DOE and industry - (a) Filtration for specialty applications, e.g., explosive applications at Nevada, (b) Spin-off technologies applicable to other commercial industries; and (3) Long term benefit for DOE, NRC, and industry - (a) Across industry, strong desire for better performance filter, (b) Engineering solution to safety problem will improve facility safety and decrease dependence on associated support systems, (c) Large potential life-cycle cost savings, and (d) Facilitates development and deployment of LLNL process innovations to allow continuous ventilation system operation during a fire.
Harmonic distortion in microwave photonic filters.
Rius, Manuel; Mora, José; Bolea, Mario; Capmany, José
2012-04-09
We present a theoretical and experimental analysis of nonlinear microwave photonic filters. Far from the conventional condition of low modulation index commonly used to neglect high-order terms, we have analyzed the harmonic distortion involved in microwave photonic structures with periodic and non-periodic frequency responses. We show that it is possible to design microwave photonic filters with reduced harmonic distortion and high linearity even under large signal operation.
Adaptive Filtering Using Recurrent Neural Networks
Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.
2005-01-01
A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.
Energy Technology Data Exchange (ETDEWEB)
Geniet, F; Leon, J [Physique Mathematique et Theorique, CNRS-UMR 5825, 34095 Montpellier (France)
2003-05-07
A nonlinear system possessing a natural forbidden band gap can transmit energy of a signal with a frequency in the gap, as recently shown for a nonlinear chain of coupled pendulums (Geniet and Leon 2002 Phys. Rev. Lett. 89 134102). This process of nonlinear supratransmission, occurring at a threshold that is exactly predictable in many cases, is shown to have a simple experimental realization with a mechanical chain of pendulums coupled by a coil spring. It is then analysed in more detail. First we go to different (nonintegrable) systems which do sustain nonlinear supratransmission. Then a Josephson transmission line (a one-dimensional array of short Josephson junctions coupled through superconducting wires) is shown to also sustain nonlinear supratransmission, though being related to a different class of boundary conditions, and despite the presence of damping, finiteness, and discreteness. Finally, the mechanism at the origin of nonlinear supratransmission is found to be a nonlinear instability, and this is briefly discussed here.
Signal interference RF photonic bandstop filter.
Aryanfar, Iman; Choudhary, Amol; Shahnia, Shayan; Pagani, Mattia; Liu, Yang; Marpaung, David; Eggleton, Benjamin J
2016-06-27
In the microwave domain, signal interference bandstop filters with high extinction and wide stopbands are achieved through destructive interference of two signals. Implementation of this filtering concept using RF photonics will lead to unique filters with high performance, enhanced tuning range and reconfigurability. Here we demonstrate an RF photonic signal interference filter, achieved through the combination of precise synthesis of stimulated Brillouin scattering (SBS) loss with advanced phase and amplitude tailoring of RF modulation sidebands. We achieve a square-shaped, 20-dB extinction RF photonic filter over a tunable bandwidth of up to 1 GHz with a central frequency tuning range of 16 GHz using a low SBS loss of ~3 dB. Wideband destructive interference in this novel filter leads to the decoupling of the filter suppression from its bandwidth and shape factor. This allows the creation of a filter with all-optimized qualities.
CRYSTAL FILTERS, *HIGH FREQUENCY, *RADIOFREQUENCY FILTERS, AMPLIFIERS, ELECTRIC POTENTIAL, FREQUENCY, IMPEDANCE MATCHING , INSTRUMENTATION, RADIOFREQUENCY, RADIOFREQUENCY AMPLIFIERS, TEST EQUIPMENT, TEST METHODS
Intrinsic nonlinear response of surface plasmon polaritons
Im, Song-Jin; Kim, Gum-Hyok
2015-01-01
We offer a model to describe the intrinsic nonlinear response of surface plasmon polaritons (SPPs). Relation of the complex nonlinear coefficient of SPPs to the third-order nonlinear susceptibility of the metal is provided. As reported in a recent study, gold is highly lossy and simultaneously highly nonlinear due to interband absorption and interband thermo-modulation at a wavelength shorter than 700 nm. The effect of the high loss of the metal on the SPP nonlinear propagation is taken into account in our model. With the model we show difference in sign of real and imaginary parts between the nonlinear propagation coefficient and the nonlinear susceptibility of component material for the first time to our knowledge. Our model could have practical importance in studying plasmonic devices utilizing the nonlinear phase modulation and the nonlinear absorption of SPPs. For example, it allows one to extract the complex nonlinear susceptibility of gold through a measurement of SPP nonlinear propagation at the visib...
Hamming, Richard W
1997-01-01
Digital signals occur in an increasing number of applications: in telephone communications; in radio, television, and stereo sound systems; and in spacecraft transmissions, to name just a few. This introductory text examines digital filtering, the processes of smoothing, predicting, differentiating, integrating, and separating signals, as well as the removal of noise from a signal. The processes bear particular relevance to computer applications, one of the focuses of this book.Readers will find Hamming's analysis accessible and engaging, in recognition of the fact that many people with the s
Nonlinear Ultrasonic Phased Array Imaging
Potter, J. N.; Croxford, A. J.; Wilcox, P. D.
2014-10-01
This Letter reports a technique for the imaging of acoustic nonlinearity. By contrasting the energy of the diffuse field produced through the focusing of an ultrasonic array by delayed parallel element transmission with that produced by postprocessing of sequential transmission data, acoustic nonlinearity local to the focal point is measured. Spatially isolated wave distortion is inferred without requiring interrogation of the wave at the inspection point, thereby allowing nonlinear imaging through depth.
Nonlinear ultrasonic phased array imaging
Potter, J N; Croxford, A.J.; Wilcox, P. D.
2014-01-01
This Letter reports a technique for the imaging of acoustic nonlinearity. By contrasting the energy of the diffuse field produced through the focusing of an ultrasonic array by delayed parallel element transmission with that produced by postprocessing of sequential transmission data, acoustic nonlinearity local to the focal point is measured. Spatially isolated wave distortion is inferred without requiring interrogation of the wave at the inspection point, thereby allowing nonlinear imaging t...
Nonlinear ultrasonic phased array imaging.
Potter, J N; Croxford, A J; Wilcox, P D
2014-10-03
This Letter reports a technique for the imaging of acoustic nonlinearity. By contrasting the energy of the diffuse field produced through the focusing of an ultrasonic array by delayed parallel element transmission with that produced by postprocessing of sequential transmission data, acoustic nonlinearity local to the focal point is measured. Spatially isolated wave distortion is inferred without requiring interrogation of the wave at the inspection point, thereby allowing nonlinear imaging through depth.
Linearization of conservative nonlinear oscillators
Energy Technology Data Exchange (ETDEWEB)
Belendez, A; Alvarez, M L [Departamento de Fisica, IngenierIa de Sistemas y TeorIa de la Senal, Universidad de Alicante, Apartado 99, E-03080 Alicante (Spain); Fernandez, E; Pascual, I [Departamento de Optica, FarmacologIa y AnatomIa, Universidad de Alicante, Apartado 99, E-03080 Alicante (Spain)], E-mail: a.belendez@ua.es
2009-03-11
A linearization method of the nonlinear differential equation for conservative nonlinear oscillators is analysed and discussed. This scheme is based on the Chebyshev series expansion of the restoring force which allows us to obtain a frequency-amplitude relation which is valid not only for small but also for large amplitudes and, sometimes, for the complete range of oscillation amplitudes. Some conservative nonlinear oscillators are analysed to illustrate the usefulness and effectiveness of the technique.
Turbine Engine Performance Estimation using Particle Filters Project
National Aeronautics and Space Administration — Development of a nonlinear particle filter for engine performance is proposed. The approach employs NASA high-fidelity C-MAPSS40K engine model as the central...
Continuous-Discrete Path Integral Filtering
Directory of Open Access Journals (Sweden)
Bhashyam Balaji
2009-08-01
Full Text Available A summary of the relationship between the Langevin equation, Fokker-Planck-Kolmogorov forward equation (FPKfe and the Feynman path integral descriptions of stochastic processes relevant for the solution of the continuous-discrete filtering problem is provided in this paper. The practical utility of the path integral formula is demonstrated via some nontrivial examples. Specifically, it is shown that the simplest approximation of the path integral formula for the fundamental solution of the FPKfe can be applied to solve nonlinear continuous-discrete filtering problems quite accurately. The Dirac-Feynman path integral filtering algorithm is quite simple, and is suitable for real-time implementation.
Dominant Correlogram Based Particle Filter Tracking
Institute of Scientific and Technical Information of China (English)
MAO Yan-fen; SHI Peng-fei
2005-01-01
A novel dominant correlogram based particle filter was proposed for an object tracking in visual surveillance. Particle filter outperforms the Kalman filter in non-linear and non-Gaussian estimation problem. This paper proposed incorporating spatial information into visual feature, and yields a reliable likelihood description of the observation and prediction. A similarity-ratio is defined to evaluate the effectivity of different similarity measurements in weighing samples. The experimental results demonstrate the effective and robust performance compared with the histogram based tracking in traffic scenes.
Adaptive filtering using Higher Order Statistics (HOS
Directory of Open Access Journals (Sweden)
Abdelghani Manseur
2012-03-01
Full Text Available The performed job, in this study, consists in studying adaptive filters and higher order statistics (HOS to ameliorate their performances, by extension of linear case to non linear filters via Volterra series. This study is, principally, axed on: „ Choice of the adaptation step and convergence conditions. „ Convergence rate. „ Adaptive variation of the convergence factor, according to the input signal. The obtained results, with real signals, have shown computationally efficient and numerically stable algorithms for adaptive nonlinear filtering while keeping relatively simple computational complexity.
2016-07-01
Advanced Research Projects Agency (DARPA) Dynamics-Enabled Frequency Sources (DEFYS) program is focused on the convergence of nonlinear dynamics and...Early work in this program has shown that nonlinear dynamics can provide performance advantages. However, the pathway from initial results to...dependent nonlinear stiffness observed in these devices. This work is ongoing, and will continue through the final period of this program . Reference 9
A Buried Vertical Filter for Micro and Nanoparticle Filtration
Li, S.J.; Shen, C.; Sarro, P.M.
2011-01-01
This paper presents a silicon micromachined filter for micro- and nanoparticles. The filter is vertical and completely buried beneath the surface. The buried aspect allows additional features to be integrated above the filter, while the vertical aspect allows the creation of highly uniform pores and
Nayfeh, Ali Hasan
1995-01-01
Nonlinear Oscillations is a self-contained and thorough treatment of the vigorous research that has occurred in nonlinear mechanics since 1970. The book begins with fundamental concepts and techniques of analysis and progresses through recent developments and provides an overview that abstracts and introduces main nonlinear phenomena. It treats systems having a single degree of freedom, introducing basic concepts and analytical methods, and extends concepts and methods to systems having degrees of freedom. Most of this material cannot be found in any other text. Nonlinear Oscillations uses sim
Yoshida, Zensho
2010-01-01
This book gives a general, basic understanding of the mathematical structure "nonlinearity" that lies in the depths of complex systems. Analyzing the heterogeneity that the prefix "non" represents with respect to notions such as the linear space, integrability and scale hierarchy, "nonlinear science" is explained as a challenge of deconstruction of the modern sciences. This book is not a technical guide to teach mathematical tools of nonlinear analysis, nor a zoology of so-called nonlinear phenomena. By critically analyzing the structure of linear theories, and cl
Nanda, Sudarsan
2013-01-01
"Nonlinear analysis" presents recent developments in calculus in Banach space, convex sets, convex functions, best approximation, fixed point theorems, nonlinear operators, variational inequality, complementary problem and semi-inner-product spaces. Nonlinear Analysis has become important and useful in the present days because many real world problems are nonlinear, nonconvex and nonsmooth in nature. Although basic concepts have been presented here but many results presented have not appeared in any book till now. The book could be used as a text for graduate students and also it will be useful for researchers working in this field.
Particle filters for random set models
Ristic, Branko
2013-01-01
“Particle Filters for Random Set Models” presents coverage of state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. The resulting algorithms, known as particle filters, in the last decade have become one of the essential tools for stochastic filtering, with applications ranging from navigation and autonomous vehicles to bio-informatics and finance. While particle filters have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. These recent developments have dramatically widened the scope of applications, from single to multiple appearing/disappearing objects, from precise to imprecise measurements and measurement models. This book...
Image Filtering Based on Improved Information Entropy
Institute of Scientific and Technical Information of China (English)
JINGXiaojun; LIUYulin; XIONGYuqing
2004-01-01
An image filtering based on improved information entropy is proposed in this paper, which can overcome the shortcomings of hybrid linear and non-linear filtering algorithm. Due to the shortcomings of information entropy in the field of data fusion, we introduce the consistency constraint factor of sub-source report and subsource performance difference parameter, propose the concept of fusion entropy, utilize its amendment and regularity function on sub-source decision-making matrix, bring into play the competency, redundency and complementarity of information fusion, suppress and delete fault and invalid information, strengthen and preserve correct and useful information, overcome the risk of error reporting on single source critical point and the shortcomings of reliability and error tolerating, add the decision-making criteria of multiple sub-source fusion, finally improve filtering quality. Subsequent experiments show its validity and improved filtering performance, thus providing a new way of image filtering technique.
Kalman filtering theory and practice with MATLAB
Grewal, M
2015-01-01
The definitive textbook and professional reference on Kalman Filtering fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.
Passive target tracking using marginalized particle filter
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A marginalized particle filtering(MPF)approach is proposed for target tracking under the background of passive measurement.Essentially,the MPF is a combination of particle filtering technique and Kalman filter.By making full use of marginalization,the distributions of the tractable linear part of the total state variables are updated analytically using Kalman filter,and only the lower-dimensional nonlinear state variable needs to be dealt with using particle filter.Simulation studies are performed on an illustrative example,and the results show that the MPF method leads to a significant reduction of the tracking errors when compared with the direct particle implementation.Real data test results also validate the effectiveness of the presented method.
Institute of Scientific and Technical Information of China (English)
Wu Xinhui; Huang Gaoming; Gao Jun
2013-01-01
In Bayesian multi-target filtering, knowledge of measurement noise variance is very important. Significant mismatches in noise parameters will result in biased estimates. In this paper, a new particle filter for a probability hypothesis density (PHD) filter handling unknown measure-ment noise variances is proposed. The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian (VB) methods. Moreover, the sequential Monte Carlo method is used to approximate the posterior intensity considering non-lin-ear and non-Gaussian conditions. Unlike other particle filters for this challenging class of PHD fil-ters, the proposed method can adaptively learn the unknown and time-varying noise variances while filtering. Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.
Generalized design of high performance shunt active power filter with output LCL filter
DEFF Research Database (Denmark)
Tang, Yi; Loh, Poh Chiang; Wang, Peng
2012-01-01
, the proposed SAPF offers superior switching harmonic suppression using much reduced passive filtering elements. Its output currents thus have high slew rate for tracking the targeted reference closely. Smaller inductance of the LCL filter also means smaller harmonic voltage drop across the passive output......This paper concentrates on the design, control, and implementation of an LCL-filter-based shunt active power filter (SAPF), which can effectively compensate for harmonic currents produced by nonlinear loads in a three-phase three-wire power system. With an LCL filter added at its output...... filter, which in turn minimizes the possibility of overmodulation, particularly for cases where high modulation index is desired. These advantages, together with overall system stability, are guaranteed only through proper consideration of critical design and control issues, like the selection of LCL...
Nonlinear edge: preserving smoothing by PDEs
Ha, Yan; Liu, Jiejing
2008-12-01
This work introduces a new algorithm for image smoothing. Nonlinear partial differential equations (PDEs) are employed to smooth the image while preserving the edges and corners. Compared with other filters such as average filter and median filter, it is found that the effects of image denoising by the new algorithm are better than that by other filters. The experimental results show that this method can not only remove the noise but also preserve the edges and corners. Due to its simplicity and efficiency, the algorithm becomes extremely attractive.
A Simple and Fast Spline Filtering Algorithm for Surface Metrology.
Zhang, Hao; Ott, Daniel; Song, John; Tong, Mingsi; Chu, Wei
2015-01-01
Spline filters and their corresponding robust filters are commonly used filters recommended in ISO (the International Organization for Standardization) standards for surface evaluation. Generally, these linear and non-linear spline filters, composed of symmetric, positive-definite matrices, are solved in an iterative fashion based on a Cholesky decomposition. They have been demonstrated to be relatively efficient, but complicated and inconvenient to implement. A new spline-filter algorithm is proposed by means of the discrete cosine transform or the discrete Fourier transform. The algorithm is conceptually simple and very convenient to implement.
Estimating dynamic equilibrium economies: linear versus nonlinear likelihood
2004-01-01
This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a sequential Monte Carlo filter proposed by Fernández-Villaverde and Rubio-Ramírez (2004) and the Kalman filter. The sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by simulation methods. The Kalman filter estimates a linearization of the economy around the steady state. The authors report two main results...
Device Applications of Nonlinear Dynamics
Baglio, Salvatore
2006-01-01
This edited book is devoted specifically to the applications of complex nonlinear dynamic phenomena to real systems and device applications. While in the past decades there has been significant progress in the theory of nonlinear phenomena under an assortment of system boundary conditions and preparations, there exist comparatively few devices that actually take this rich behavior into account. "Device Applications of Nonlinear Dynamics" applies and exploits this knowledge to make devices which operate more efficiently and cheaply, while affording the promise of much better performance. Given the current explosion of ideas in areas as diverse as molecular motors, nonlinear filtering theory, noise-enhanced propagation, stochastic resonance and networked systems, the time is right to integrate the progress of complex systems research into real devices.
Topics in particle filtering and smoothing
Saha, Saikat
2009-01-01
Particle filtering/smoothing is a relatively new promising class of algorithms to deal with the estimation problems in nonlinear and/or non- Gaussian systems. Currently, this is a very active area of research and there are many issues that are not either properly addressed or are still open. One of
Q-Method Extended Kalman Filter
Zanetti, Renato; Ainscough, Thomas; Christian, John; Spanos, Pol D.
2012-01-01
A new algorithm is proposed that smoothly integrates non-linear estimation of the attitude quaternion using Davenport s q-method and estimation of non-attitude states through an extended Kalman filter. The new method is compared to a similar existing algorithm showing its similarities and differences. The validity of the proposed approach is confirmed through numerical simulations.
Recursive Filtering And Smoothing In Robot Dynamics
Rodriguez, Guillermo
1992-01-01
Techniques developed originally for electronic systems also useful for multibody mechanical systems. Report summarizes methods developed to solve nonlinear forward-dynamics problem for robot of multiple-link arms connected by joints. Primary objective to show equivalence between recursive methods of dynamical analysis and some filtering and smoothing techniques from state-estimation theory.
Signal enhancement with variable span linear filters
Benesty, Jacob; Jensen, Jesper R
2016-01-01
This book introduces readers to the novel concept of variable span speech enhancement filters, and demonstrates how it can be used for effective noise reduction in various ways. Further, the book provides the accompanying Matlab code, allowing readers to easily implement the main ideas discussed. Variable span filters combine the ideas of optimal linear filters with those of subspace methods, as they involve the joint diagonalization of the correlation matrices of the desired signal and the noise. The book shows how some well-known filter designs, e.g. the minimum distortion, maximum signal-to-noise ratio, Wiener, and tradeoff filters (including their new generalizations) can be obtained using the variable span filter framework. It then illustrates how the variable span filters can be applied in various contexts, namely in single-channel STFT-based enhancement, in multichannel enhancement in both the time and STFT domains, and, lastly, in time-domain binaural enhancement. In these contexts, the properties of ...
Performance analysis and design of filtering hydrocyclones
Directory of Open Access Journals (Sweden)
L. G. M. Vieira
2005-03-01
Full Text Available The filtering hydrocyclone is a solid-liquid separation device patented by the Chemical Engineering Department at the Federal University of Uberlândia, which consists of a hydrocyclone whose conical section was replaced by a conical filtering wall. The objective of this work is to compare the performances of the filtering hydrocyclones designed by Bradley and by Rietema. The experimental results obtained with the filtering hydrocyclones under the same operational conditions as those used with the conventional device allow the conclusion that performance of the Bradley and Rietema types is significantly influenced by the filtering medium. Rietema's filtering hydrocyclones had a lower volumetric feed flowrate than the conventional device and Bradley's filtering hydrocyclones showed increases in this same variable. In both designs, overall efficiency was influenced by the underflow-to-throughput ratio.
Signal Enhancement with Variable Span Linear Filters
DEFF Research Database (Denmark)
Benesty, Jacob; Christensen, Mads Græsbøll; Jensen, Jesper Rindom
-to-noise ratio, Wiener, and tradeoff filters (including their new generalizations) can be obtained using the variable span filter framework. It then illustrates how the variable span filters can be applied in various contexts, namely in single-channel STFT-based enhancement, in multichannel enhancement in both......This book introduces readers to the novel concept of variable span speech enhancement filters, and demonstrates how it can be used for effective noise reduction in various ways. Further, the book provides the accompanying Matlab code, allowing readers to easily implement the main ideas discussed....... Variable span filters combine the ideas of optimal linear filters with those of subspace methods, as they involve the joint diagonalization of the correlation matrices of the desired signal and the noise. The book shows how some well-known filter designs, e.g. the minimum distortion, maximum signal...
Method and apparatus for a self-cleaning filter
Diebold, James P.; Lilley, Arthur; Browne, III, Kingsbury; Walt, Robb Ray; Duncan, Dustin; Walker, Michael; Steele, John; Fields, Michael
2010-11-16
A method and apparatus for removing fine particulate matter from a fluid stream without interrupting the overall process or flow. The flowing fluid inflates and expands the flexible filter, and particulate is deposited on the filter media while clean fluid is permitted to pass through the filter. This filter is cleaned when the fluid flow is stopped, the filter collapses, and a force is applied to distort the flexible filter media to dislodge the built-up filter cake. The dislodged filter cake falls to a location that allows undisrupted flow of the fluid after flow is restored. The shed particulate is removed to a bin for periodic collection. A plurality of filter cells can operate independently or in concert, in parallel, or in series to permit cleaning the filters without shutting off the overall fluid flow. The self-cleaning filter is low cost, has low power consumption, and exhibits low differential pressures.
Comparison of Sigma-Point and Extended Kalman Filters on a Realistic Orbit Determination Scenario
Gaebler, John; Hur-Diaz. Sun; Carpenter, Russell
2010-01-01
Sigma-point filters have received a lot of attention in recent years as a better alternative to extended Kalman filters for highly nonlinear problems. In this paper, we compare the performance of the additive divided difference sigma-point filter to the extended Kalman filter when applied to orbit determination of a realistic operational scenario based on the Interstellar Boundary Explorer mission. For the scenario studied, both filters provided equivalent results. The performance of each is discussed in detail.
Comparison of Sigma-Point and Extended Kalman Filters on a Realistic Orbit Determination Scenario
Gaebler, John; Hur-Diaz. Sun; Carpenter, Russell
2010-01-01
Sigma-point filters have received a lot of attention in recent years as a better alternative to extended Kalman filters for highly nonlinear problems. In this paper, we compare the performance of the additive divided difference sigma-point filter to the extended Kalman filter when applied to orbit determination of a realistic operational scenario based on the Interstellar Boundary Explorer mission. For the scenario studied, both filters provided equivalent results. The performance of each is discussed in detail.
Simulation of non-linear ultrasound fields
DEFF Research Database (Denmark)
Jensen, Jørgen Arendt; Fox, Paul D.; Wilhjelm, Jens E.
2002-01-01
An approach for simulating non-linear ultrasound imaging using Field II has been implemented using the operator splitting approach, where diffraction, attenuation, and non-linear propagation can be handled individually. The method uses the Earnshaw/Poisson solution to Burgcrs' equation for the non......-linear ultrasound imaging in 3D using filters or pulse inversion for any kind of transducer, focusing, apodization, pulse emission and scattering phantom. This is done by first simulating the non-linear emitted field and assuming that the scattered field is weak and linear. The received signal is then the spatial...
Coghetto Roland
2015-01-01
We are inspired by the work of Henri Cartan [16], Bourbaki [10] (TG. I Filtres) and Claude Wagschal [34]. We define the base of filter, image filter, convergent filter bases, limit filter and the filter base of tails (fr: filtre des sections).
Directory of Open Access Journals (Sweden)
Coghetto Roland
2015-09-01
Full Text Available We are inspired by the work of Henri Cartan [16], Bourbaki [10] (TG. I Filtres and Claude Wagschal [34]. We define the base of filter, image filter, convergent filter bases, limit filter and the filter base of tails (fr: filtre des sections.
Srivastava, A.; Srivastava, O. N.; Talapatra, S.; Vajtai, R.; Ajayan, P. M.
2004-09-01
Over the past decade of nanotube research, a variety of organized nanotube architectures have been fabricated using chemical vapour deposition. The idea of using nanotube structures in separation technology has been proposed, but building macroscopic structures that have controlled geometric shapes, density and dimensions for specific applications still remains a challenge. Here we report the fabrication of freestanding monolithic uniform macroscopic hollow cylinders having radially aligned carbon nanotube walls, with diameters and lengths up to several centimetres. These cylindrical membranes are used as filters to demonstrate their utility in two important settings: the elimination of multiple components of heavy hydrocarbons from petroleum-a crucial step in post-distillation of crude oil-with a single-step filtering process, and the filtration of bacterial contaminants such as Escherichia coli or the nanometre-sized poliovirus (~25 nm) from water. These macro filters can be cleaned for repeated filtration through ultrasonication and autoclaving. The exceptional thermal and mechanical stability of nanotubes, and the high surface area, ease and cost-effective fabrication of the nanotube membranes may allow them to compete with ceramic- and polymer-based separation membranes used commercially.
Yang, Qianli; Pitkow, Xaq
2015-03-01
Most interesting natural sensory stimuli are encoded in the brain in a form that can only be decoded nonlinearly. But despite being a core function of the brain, nonlinear population codes are rarely studied and poorly understood. Interestingly, the few existing models of nonlinear codes are inconsistent with known architectural features of the brain. In particular, these codes have information content that scales with the size of the cortical population, even if that violates the data processing inequality by exceeding the amount of information entering the sensory system. Here we provide a valid theory of nonlinear population codes by generalizing recent work on information-limiting correlations in linear population codes. Although these generalized, nonlinear information-limiting correlations bound the performance of any decoder, they also make decoding more robust to suboptimal computation, allowing many suboptimal decoders to achieve nearly the same efficiency as an optimal decoder. Although these correlations are extremely difficult to measure directly, particularly for nonlinear codes, we provide a simple, practical test by which one can use choice-related activity in small populations of neurons to determine whether decoding is suboptimal or optimal and limited by correlated noise. We conclude by describing an example computation in the vestibular system where this theory applies. QY and XP was supported by a grant from the McNair foundation.
Computation of Filters by Sampling and Quantization.
1987-09-01
gbnbral de filtrage non-Lin6aire et equations differentielles stochastiques associees, Ann. Inst. Henri Poincare, B 15(2). 147-173. [23] J.H. Van Schuppen...motivation of research has been the solution of the filtering equation . Since in the linear case the filtering equation has an explicit solution, the early...probability method ([1], (22]. [25]) allowing the derivation of the Zakai equation , has opened a new way to considering the filtering problem. Although there
Beams Propagation Modelled by Bi-filters
Lacaze, Bernard
2010-01-01
In acoustic, ultrasonic or electromagnetic propagation, crossed media are often modelled by linear filters with complex gains in accordance with the Beer-Lambert law. This paper addresses the problem of propagation in media where polarization has to be taken into account. Because waves are now bi-dimensional, an unique filter is not sufficient to represent the effects of the medium. We propose a model which uses four linear invariant filters, which allows to take into account exchanges betwee...
40 CFR 35.2025 - Allowance and advance of allowance.
2010-07-01
... facilities planning and design of the project and Step 7 agreements will include an allowance for facility planning in accordance with appendix B of this subpart. (b) Advance of allowance to potential grant... grant applicants for facilities planning and project design. (2) The State may request that the right to...
Fuzzy neural network image filter based on GA
Institute of Scientific and Technical Information of China (English)
刘涵; 刘丁; 李琦
2004-01-01
A new nonlinear image filter using fuzzy neural network based on genetic algorithm is proposed. The learning of network parameters is performed by genetic algorithm with the efficient binary encoding scheme. In the following,fuzzy reasoning embedded in the network aims at restoring noisy pixels without degrading the quality of fine details. It is shown by experiments that the filter is very effective in removing impulse noise and significantly outperforms conventional filters.
An Optimal Transport Formulation of the Linear Feedback Particle Filter
Taghvaei, Amirhossein; Mehta, Prashant G.
2015-01-01
Feedback particle filter (FPF) is an algorithm to numerically approximate the solution of the nonlinear filtering problem in continuous time. The algorithm implements a feedback control law for a system of particles such that the empirical distribution of particles approximates the posterior distribution. However, it has been noted in the literature that the feedback control law is not unique. To find a unique control law, the filtering task is formulated here as an optimal transportation pro...
Channel Equalization in Filter Bank Based Multicarrier Modulation for Wireless Communications
Directory of Open Access Journals (Sweden)
Markku Renfors
2007-01-01
Full Text Available Channel equalization in filter bank based multicarrier (FBMC modulation is addressed. We utilize an efficient oversampled filter bank concept with 2x-oversampled subcarrier signals that can be equalized independently of each other. Due to Nyquist pulse shaping, consecutive symbol waveforms overlap in time, which calls for special means for equalization. Two alternative linear low-complexity subcarrier equalizer structures are developed together with straightforward channel estimation-based methods to calculate the equalizer coefficients using pointwise equalization within each subband (in a frequency-sampled manner. A novel structure, consisting of a linear-phase FIR amplitude equalizer and an allpass filter as phase equalizer, is found to provide enhanced robustness to timing estimation errors. This allows the receiver to be operated without time synchronization before the filter bank. The coded error-rate performance of FBMC with the studied equalization scheme is compared to a cyclic prefix OFDM reference in wireless mobile channel conditions, taking into account issues like spectral regrowth with practical nonlinear transmitters and sensitivity to frequency offsets. It is further emphasized that FBMC provides flexible means for high-quality frequency selective filtering in the receiver to suppress strong interfering spectral components within or close to the used frequency band.
Channel Equalization in Filter Bank Based Multicarrier Modulation for Wireless Communications
Ihalainen, Tero; Hidalgo Stitz, Tobias; Rinne, Mika; Renfors, Markku
2006-12-01
Channel equalization in filter bank based multicarrier (FBMC) modulation is addressed. We utilize an efficient oversampled filter bank concept with 2x-oversampled subcarrier signals that can be equalized independently of each other. Due to Nyquist pulse shaping, consecutive symbol waveforms overlap in time, which calls for special means for equalization. Two alternative linear low-complexity subcarrier equalizer structures are developed together with straightforward channel estimation-based methods to calculate the equalizer coefficients using pointwise equalization within each subband (in a frequency-sampled manner). A novel structure, consisting of a linear-phase FIR amplitude equalizer and an allpass filter as phase equalizer, is found to provide enhanced robustness to timing estimation errors. This allows the receiver to be operated without time synchronization before the filter bank. The coded error-rate performance of FBMC with the studied equalization scheme is compared to a cyclic prefix OFDM reference in wireless mobile channel conditions, taking into account issues like spectral regrowth with practical nonlinear transmitters and sensitivity to frequency offsets. It is further emphasized that FBMC provides flexible means for high-quality frequency selective filtering in the receiver to suppress strong interfering spectral components within or close to the used frequency band.
RB Particle Filter Time Synchronization Algorithm Based on the DPM Model
Directory of Open Access Journals (Sweden)
Chunsheng Guo
2015-09-01
Full Text Available Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM model. In a state-space equation with a linear substructure, state variables are divided into linear and non-linear variables by the RB particle filter algorithm. These two variables can be estimated using Kalman filter and particle filter, respectively, which improves the computational efficiency more so than if only the particle filter was used. In addition, the DPM model is used to describe the distribution of non-deterministic delays and to automatically adjust the number of Gaussian mixture model components based on the observational data. This improves the estimation accuracy of clock offset and skew, which allows achieving the time synchronization. The time synchronization performance of this algorithm is also validated by computer simulations and experimental measurements. The results show that the proposed algorithm has a higher time synchronization precision than traditional time synchronization algorithms.
Cubication of conservative nonlinear oscillators
Energy Technology Data Exchange (ETDEWEB)
Belendez, Augusto; Alvarez, Mariela L [Departamento de Fisica, Ingenieria de Sistemas y Teoria de la Senal, Universidad de Alicante, Apartado 99, E-03080 Alicante (Spain); Fernandez, Elena; Pascual, Inmaculada [Departamento de Optica, FarmacologIa y Anatomia, Universidad de Alicante, Apartado 99, E-03080 Alicante (Spain)], E-mail: a.belendez@ua.es
2009-09-15
A cubication procedure of the nonlinear differential equation for conservative nonlinear oscillators is analysed and discussed. This scheme is based on the Chebyshev series expansion of the restoring force, and this allows us to approximate the original nonlinear differential equation by a Duffing equation in which the coefficients for the linear and cubic terms depend on the initial amplitude, A, while in a Taylor expansion of the restoring force these coefficients are independent of A. The replacement of the original nonlinear equation by an approximate Duffing equation allows us to obtain an approximate frequency-amplitude relation as a function of the complete elliptic integral of the first kind. Some conservative nonlinear oscillators are analysed to illustrate the usefulness and effectiveness of this scheme.
A Comparison of PDE-based Non-Linear Anisotropic Diffusion Techniques for Image Denoising
Energy Technology Data Exchange (ETDEWEB)
Weeratunga, S K; Kamath, C
2003-01-06
PDE-based, non-linear diffusion techniques are an effective way to denoise images. In a previous study, we investigated the effects of different parameters in the implementation of isotropic, non-linear diffusion. Using synthetic and real images, we showed that for images corrupted with additive Gaussian noise, such methods are quite effective, leading to lower mean-squared-error values in comparison with spatial filters and wavelet-based approaches. In this paper, we extend this work to include anisotropic diffusion, where the diffusivity is a tensor valued function which can be adapted to local edge orientation. This allows smoothing along the edges, but not perpendicular to it. We consider several anisotropic diffusivity functions as well as approaches for discretizing the diffusion operator that minimize the mesh orientation effects. We investigate how these tensor-valued diffusivity functions compare in image quality, ease of use, and computational costs relative to simple spatial filters, the more complex bilateral filters, wavelet-based methods, and isotropic non-linear diffusion based techniques.
Comparison of PDE-based non-linear anistropic diffusion techniques for image denoising
Weeratunga, Sisira K.; Kamath, Chandrika
2003-05-01
PDE-based, non-linear diffusion techniques are an effective way to denoise images.In a previous study, we investigated the effects of different parameters in the implementation of isotropic, non-linear diffusion. Using synthetic and real images, we showed that for images corrupted with additive Gaussian noise, such methods are quite effective, leading to lower mean-squared-error values in comparison with spatial filters and wavelet-based approaches. In this paper, we extend this work to include anisotropic diffusion, where the diffusivity is a tensor valued function which can be adapted to local edge orientation. This allows smoothing along the edges, but not perpendicular to it. We consider several anisotropic diffusivity functions as well as approaches for discretizing the diffusion operator that minimize the mesh orientation effects. We investigate how these tensor-valued diffusivity functions compare in image quality, ease of use, and computational costs relative to simple spatial filters, the more complex bilateral filters, wavelet-based methods, and isotropic non-linear diffusion based techniques.
2011-09-01
several in- dependent, locally stationary processes with simple parametric stationary (or isotropic) covariance func- tions ( Fuentes 2001). Parametric...230, 99–111. ——, and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimi- lations and...Q. Yao, 2003: Nonlinear Time Series: Nonparametric and Parametric Methods. Springer-Verlag, 552 pp. Fuentes , M., 2001: A high frequency kriging
NOVEL MICROWAVE FILTER DESIGN TECHNIQUES.
ELECTROMAGNETIC WAVE FILTERS, MICROWAVE FREQUENCY, PHASE SHIFT CIRCUITS, BANDPASS FILTERS, TUNED CIRCUITS, NETWORKS, IMPEDANCE MATCHING , LOW PASS FILTERS, MULTIPLEXING, MICROWAVE EQUIPMENT, WAVEGUIDE FILTERS, WAVEGUIDE COUPLERS.
Filtered-X Affine Projection Algorithms for Active Noise Control Using Volterra Filters
Directory of Open Access Journals (Sweden)
Sicuranza Giovanni L
2004-01-01
Full Text Available We consider the use of adaptive Volterra filters, implemented in the form of multichannel filter banks, as nonlinear active noise controllers. In particular, we discuss the derivation of filtered-X affine projection algorithms for homogeneous quadratic filters. According to the multichannel approach, it is then easy to pass from these algorithms to those of a generic Volterra filter. It is shown in the paper that the AP technique offers better convergence and tracking capabilities than the classical LMS and NLMS algorithms usually applied in nonlinear active noise controllers, with a limited complexity increase. This paper extends in two ways the content of a previous contribution published in Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03, Grado, Italy, June 2003. First of all, a general adaptation algorithm valid for any order of affine projections is presented. Secondly, a more complete set of experiments is reported. In particular, the effects of using multichannel filter banks with a reduced number of channels are investigated and relevant results are shown.
Blended particle filters for large-dimensional chaotic dynamical systems.
Majda, Andrew J; Qi, Di; Sapsis, Themistoklis P
2014-05-27
A major challenge in contemporary data science is the development of statistically accurate particle filters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particle filters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particle filters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particle filter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below.
Reduced nonlinearities in 100-nm high SOI waveguides
Lacava, C.; Marchetti, R.; Vitali, V.; Cristiani, I.; Giuliani, G.; Fournier, M.; Bernabe, S.; Minzioni, P.
2016-03-01
Here we show the results of an experimental analysis dedicated to investigate the impact of optical non linear effects, such as two-photon absorption (TPA), free-carrier absorption (FCA) and free-carrier dispersion (FCD), on the performance of integrated micro-resonator based filters for application in WDM telecommunication systems. The filters were fabricated using SOI (Silicon-on-Insulator) technology by CEA-Leti, in the frame of the FP7 Fabulous Project, which aims to develop low-cost and high-performance integrated optical devices to be used in new generation passive optical- networks (NG-PON2). Different designs were tested, including both ring-based structures and racetrack-based structures, with single-, double- or triple- resonator configuration, and using different waveguide cross-sections (from 500 x 200 nm to 825 x 100 nm). Measurements were carried out using an external cavity tunable laser source operating in the extended telecom bandwidth, using both continuous wave signals and 10 Gbit/s modulated signals. Results show that the use 100-nm high waveguide allows reducing the impact of non-linear losses, with respect to the standard waveguides, thus increasing by more than 3 dB the maximum amount of optical power that can be injected into the devices before causing significant non-linear effects. Measurements with OOK-modulated signals at 10 Gbit/s showed that TPA and FCA don't affect the back-to-back BER of the signal, even when long pseudo-random-bit-sequences (PRBS) are used, as the FCD-induced filter-detuning increases filter losses but "prevents" excessive signal degradation.
Miniaturized dielectric waveguide filters
Sandhu, Muhammad Y.; Hunter, Ian C.
2016-10-01
Design techniques for a new class of integrated monolithic high-permittivity ceramic waveguide filters are presented. These filters enable a size reduction of 50% compared to air-filled transverse electromagnetic filters with the same unloaded Q-factor. Designs for Chebyshev and asymmetric generalised Chebyshev filter and a diplexer are presented with experimental results for an 1800 MHz Chebyshev filter and a 1700 MHz generalised Chebyshev filter showing excellent agreement with theory.
Microorganisms interacting in a bio filter
Energy Technology Data Exchange (ETDEWEB)
Barba-Avila, M. D.; Flores-Tene, F. J.; Moreno-Terrazas, R.; Ramirez-Lopez, E. M.
2009-07-01
Biofilm microorganisms developed on a bio filter support media allow the metabolism of volatile organic compounds (VOCs) to carbon dioxide and water. VOCs are present in polluted gaseous streams for varied industrial activities. The main objective of this study was to identify the microorganisms present in the biofilm developed on a bio filter support media using molecular biology techniques. (Author)
Zhu, Hong-Ming; Pen, Ue-Li; Chen, Xuelei; Yu, Hao-Ran
2016-01-01
We present a direct approach to non-parametrically reconstruct the linear density field from an observed non-linear map. We solve for the unique displacement potential consistent with the non-linear density and positive definite coordinate transformation using a multigrid algorithm. We show that we recover the linear initial conditions up to $k\\sim 1\\ h/\\mathrm{Mpc}$ with minimal computational cost. This reconstruction approach generalizes the linear displacement theory to fully non-linear fields, potentially substantially expanding the BAO and RSD information content of dense large scale structure surveys, including for example SDSS main sample and 21cm intensity mapping.
Boyd, Robert W
2013-01-01
Nonlinear Optics is an advanced textbook for courses dealing with nonlinear optics, quantum electronics, laser physics, contemporary and quantum optics, and electrooptics. Its pedagogical emphasis is on fundamentals rather than particular, transitory applications. As a result, this textbook will have lasting appeal to a wide audience of electrical engineering, physics, and optics students, as well as those in related fields such as materials science and chemistry.Key Features* The origin of optical nonlinearities, including dependence on the polarization of light* A detailed treatment of the q
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter.
Zhang, Zhen; Ma, Yaopeng
2016-02-06
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively.
Higher-order chaotic oscillator using active bessel filter
DEFF Research Database (Denmark)
Lindberg, Erik; Mykolaitis, Gytis; Bumelien, Skaidra;
2010-01-01
A higher-order oscillator, including a nonlinear unit and an 8th-order low-pass active Bessel filter is described. The Bessel unit plays the role of "three-in-one": a delay line, an amplifier and a filter. Results of hardware experiments and numerical simulation are presented. Depending on the pa...... on the parameters of the nonlinear unit the oscillator operates either in a one-scroll or two-scroll mode. Two positive Lyapunov exponents, found at larger values of the negative slopes of the nonlinear function, characterize the oscillations as hyperchaotic....
Sampling Versus Filtering in Large-Eddy Simulations
Debliquy, O.; Knaepen, B.; Carati, D.; Wray, A. A.
2004-01-01
A LES formalism in which the filter operator is replaced by a sampling operator is proposed. The unknown quantities that appear in the LES equations originate only from inadequate resolution (Discretization errors). The resulting viewpoint seems to make a link between finite difference approaches and finite element methods. Sampling operators are shown to commute with nonlinearities and to be purely projective. Moreover, their use allows an unambiguous definition of the LES numerical grid. The price to pay is that sampling never commutes with spatial derivatives and the commutation errors must be modeled. It is shown that models for the discretization errors may be treated using the dynamic procedure. Preliminary results, using the Smagorinsky model, are very encouraging.
Nonlinear dynamics of structures
Oller, Sergio
2014-01-01
This book lays the foundation of knowledge that will allow a better understanding of nonlinear phenomena that occur in structural dynamics. This work is intended for graduate engineering students who want to expand their knowledge on the dynamic behavior of structures, specifically in the nonlinear field, by presenting the basis of dynamic balance in non‐linear behavior structures due to the material and kinematics mechanical effects. Particularly, this publication shows the solution of the equation of dynamic equilibrium for structure with nonlinear time‐independent materials (plasticity, damage and frequencies evolution), as well as those time dependent non‐linear behavior materials (viscoelasticity and viscoplasticity). The convergence conditions for the non‐linear dynamic structure solution are studied, and the theoretical concepts and its programming algorithms are presented.
Filter quality of pleated filter cartridges.
Chen, Chun-Wan; Huang, Sheng-Hsiu; Chiang, Che-Ming; Hsiao, Ta-Chih; Chen, Chih-Chieh
2008-04-01
The performance of dust cartridge filters commonly used in dust masks and in room ventilation depends both on the collection efficiency of the filter material and the pressure drop across the filter. Currently, the optimization of filter design is based only on minimizing the pressure drop at a set velocity chosen by the manufacturer. The collection efficiency, an equally important factor, is rarely considered in the optimization process. In this work, a filter quality factor, which combines the collection efficiency and the pressure drop, is used as the optimization criterion for filter evaluation. Most respirator manufacturers pleat the filter to various extents to increase the filtration area in the limit space within the dust cartridge. Six sizes of filter holders were fabricated to hold just one pleat of filter, simulating six different pleat counts, ranging from 0.5 to 3.33 pleats cm(-1). The possible electrostatic charges on the filter were removed by dipping in isopropyl alcohol, and the air velocity is fixed at 100 cm s(-1). Liquid dicotylphthalate particles generated by a constant output atomizer were used as challenge aerosols to minimize particle loading effects. A scanning mobility particle sizer was used to measure the challenge aerosol number concentrations and size distributions upstream and downstream of the pleated filter. The pressure drop across the filter was monitored by using a calibrated pressure transducer. The results showed that the performance of pleated filters depend not only on the size of the particle but also on the pleat count of the pleated filter. Based on filter quality factor, the optimal pleat count (OPC) is always higher than that based on pressure drop by about 0.3-0.5 pleats cm(-1). For example, the OPC is 2.15 pleats cm(-1) from the standpoint of pressure drop, but for the highest filter quality factor, the pleated filter needed to have a pleat count of 2.65 pleats cm(-1) at particle diameter of 122 nm. From the aspect of
Moireau, Philippe; Chapelle, Dominique; LeTallec, Patrick
2009-03-01
We propose an effective filtering methodology designed to perform estimation in a distributed mechanical system using position measurements. As in a previously introduced method, the filter is inspired by robust control feedback, but here we take full advantage of the estimation specificity to choose a feedback law that can act on displacements instead of velocities and still retain the same kind of dissipativity property which guarantees robustness. This is very valuable in many applications for which positions are more readily available than velocities, as in medical imaging. We provide an in-depth analysis of the proposed procedure, as well as detailed numerical assessments using a test problem inspired by cardiac biomechanics, as medical diagnosis assistance is an important perspective for this approach. The method is formulated first for measurements based on Lagrangian displacements, but we then derive a nonlinear extension allowing us to instead consider segmented images, which of course is even more relevant in medical applications.
GABA(A) receptor mediated inhibition contributes to corticostriatal frequency filtering.
Jelinek, Devin A; Partridge, L Donald
2012-11-21
The striatum plays an important role in the initiation and learning of skilled motor behavior [6] and receives topographic input from most areas of the cortex. Cortical afferents make divergent contact with many striatal medium spiny neurons while individual medium spiny neurons receive tens of thousands of these glutamatergic synapses [13]. Temporal filtering of frequency information within synaptic fields plays an important role in the processing of neuronal signals. We have previously shown differential filtering characteristics within CA1, CA3, and the dentate gyrus of the hippocampus [26] and have now extended these studies to the cortical input to the dorsal striatum in order to address the network filtering characteristics in this important synaptic field. We measured field potentials of striatal medium spiny neurons in response to layer V cortical input over a range of stimulus frequencies from 2Hz to 100Hz. The average population spike amplitude in response to these stimulus trains exhibited a non-linear relationship to frequency, with characteristics of a low pass filter. In order to assess potential modulation of these filter properties, we examined the frequency response in the presence of antagonists to CB1, D2, nACh, and GABA(A) receptors, which are all known to be expressed at these synapses [13]. Of these, only GABA(A) receptor antagonists significantly modulated the frequency filtering characteristics over the examined frequency range. High frequency stimulation induces long term plasticity at corticostriatal synapses [4] and this process is strengthened when GABA(A) receptors are blocked [7,20,29]. Our results suggest a model whereby a temporary decrease in GABA level would modulate the filtering parameters of the corticostriatal circuit, allowing a more robust induction of high frequency-dependent plasticity.
Composing morphological filters
H.J.A.M. Heijmans (Henk)
1995-01-01
textabstractA morphological filter is an operator on a complete lattice which is increasing and idempotent. Two well-known classes of morphological filters are openings and closings. Furthermore, an interesting class of filters, the alternating sequential filters, is obtained if one composes openin
Composing morphological filters
Heijmans, H.J.A.M.
1995-01-01
A morphological filter is an operator on a complete lattice which is increasing and idempotent. Two well-known classes of morphological filters are openings and closings. Furthermore, an interesting class of filters, the alternating sequential filters, is obtained if one composes openings and closi
Optimization-based particle filter for state and parameter estimation
Institute of Scientific and Technical Information of China (English)
Li Fu; Qi Fei; Shi Guangming; Zhang Li
2009-01-01
In recent years, the theory of particle filter has been developed and widely used for state and parameter estimation in nonlinear/non-Gaussian systems. Choosing good importance density is a critical issue in particle filter design. In order to improve the approximation of posterior distribution, this paper provides an optimization-based algorithm (the steepest descent method) to generate the proposal distribution and then sample particles from the distribution. This algorithm is applied in 1-D case, and the simulation results show that the proposed particle filter performs better than the extended Kalman filter (EKF), the standard particle filter (PF), the extended Kalman particle filter (PF-EKF) and the unscented particle filter (UPF) both in efficiency and in estimation precision.
Institute of Scientific and Technical Information of China (English)
钟慧敏; 房建成
2008-01-01
针对采用星载GPS(Global Position System)定位的LEO(Low Each Orbiter)微纳卫星获得的位置速度数据不连续,而使用轨道动力学获得的连续的位置速度信息误差快速发散的问题,提出了一种基于非线性MPF(Model Predict Filter)的LEO微纳卫星定位方法,它采用非线性MPF预测的模型误差作为一步状态估计,同时使用GPS信息作为观测量,并与改进的扩展卡尔曼滤波组合,既可获得连续的卫星位置速度信息,又可获得相当于GPS单点定位的精度.仿真结果表明,此种方法可以有效地获得连续的微纳卫星位置速度信息,并且精度优于EKF(Extended Kalman Filter).
Künzi, R
2015-01-01
Power converters require passive low-pass filters which are capable of reducing voltage ripples effectively. In contrast to signal filters, the components of power filters must carry large currents or withstand large voltages, respectively. In this paper, three different suitable filter struc tures for d.c./d.c. power converters with inductive load are introduced. The formulas needed to calculate the filter components are derived step by step and practical examples are given. The behaviour of the three discussed filters is compared by means of the examples. P ractical aspects for the realization of power filters are also discussed.
In, Visarath; Longhini, Patrick; Kho, Andy; Neff, Joseph D.; Leung, Daniel; Liu, Norman; Meadows, Brian K.; Gordon, Frank; Bulsara, Adi R.; Palacios, Antonio
2012-12-01
The nonlinear channelizer is an integrated circuit made up of large parallel arrays of analog nonlinear oscillators, which, collectively, serve as a broad-spectrum analyzer with the ability to receive complex signals containing multiple frequencies and instantaneously lock-on or respond to a received signal in a few oscillation cycles. The concept is based on the generation of internal oscillations in coupled nonlinear systems that do not normally oscillate in the absence of coupling. In particular, the system consists of unidirectionally coupled bistable nonlinear elements, where the frequency and other dynamical characteristics of the emergent oscillations depend on the system's internal parameters and the received signal. These properties and characteristics are being employed to develop a system capable of locking onto any arbitrary input radio frequency signal. The system is efficient by eliminating the need for high-speed, high-accuracy analog-to-digital converters, and compact by making use of nonlinear coupled systems to act as a channelizer (frequency binning and channeling), a low noise amplifier, and a frequency down-converter in a single step which, in turn, will reduce the size, weight, power, and cost of the entire communication system. This paper covers the theory, numerical simulations, and some engineering details that validate the concept at the frequency band of 1-4 GHz.
de Jong, Roelof
2005-07-01
This program incorporates a number of tests to analyse the count rate dependent non-linearity seen in NICMOS spectro-photometric observations. In visit 1 we will observe a few fields with stars of a range in luminosity in NGC1850 with NICMOS in NIC1 in F090M, F110W and F160W and NIC2 F110W, F160W, and F180W. We will repeat the observations with flatfield lamp on, creating artificially high count-rates, allowing tests of NICMOS linearity as function of count rate. To access the effect of charge trapping and persistence, we first take darks {so there is not too much charge already trapped}, than take exposures with the lamp off, exposures with the lamp on, and repeat at the end with lamp off. Finally, we continue with taking darks during occultation. In visit 2 we will observe spectro-photometric standard P041C using the G096 and G141 grisms in NIC3, and repeat the lamp off/on/off test to artificially create a high background. In visits 3&4 we repeat photometry measurements of faint standard stars SNAP-2 and WD1657+343, on which the NICMOS non-linearity was originally discovered using grism observations. These measurements are repeated, because previous photometry was obtained with too short exposure times, hence substantially affected by charge trapping non-linearity. Measurements will be made with NIC1: Visit 5 forms the persistence test of the program. The bright star GL-390 {used in a previous persistence test} will iluminate the 3 NICMOS detectors in turn for a fixed time, saturating the center many times, after which a series of darks will be taken to measure the persistence {i.e. trapped electrons and the decay time of the traps}. To determine the wavelength dependence of the trap chance, exposures of the bright star in different filters will be taken, as well as one in the G096 grism with NIC3. Most exposures will be 128s long, but two exposures in the 3rd orbit will be 3x longer, to seperate the effects of count rate versus total counts of the trap
Energy Technology Data Exchange (ETDEWEB)
Turchetti, G. (Bologna Univ. (Italy). Dipt. di Fisica)
1989-01-01
Research in nonlinear dynamics is rapidly expanding and its range of applications is extending beyond the traditional areas of science where it was first developed. Indeed while linear analysis and modelling, which has been very successful in mathematical physics and engineering, has become a mature science, many elementary phenomena of intrinsic nonlinear nature were recently experimentally detected and investigated, suggesting new theoretical work. Complex systems, as turbulent fluids, were known to be governed by intrinsically nonlinear laws since a long time ago, but received purely phenomenological descriptions. The pioneering works of Boltzmann and Poincare, probably because of their intrinsic difficulty, did not have a revolutionary impact at their time; it is only very recently that their message is reaching a significant number of mathematicians and physicists. Certainly the development of computers and computer graphics played an important role in developing geometric intuition of complex phenomena through simple numerical experiments, while a new mathematical framework to understand them was being developed.
Institute of Scientific and Technical Information of China (English)
任智源; 张海林
2009-01-01
为了解决宽带正交频分复用系统中功率放大器频率选择非线性失真问题,在Jardin等提出的Filter Look-Up Table(FLUT)结构基础上提出一种新的双预失真器结构.在FLUT结构基础上增加一个频域预失真器,同时利用二维查表预失真器更精确地弥补了功率放大器频率选择非线性失真对信号造成的影响.仿真结果表明,新结构在提高系统性能和收敛精度的同时,可以提高收敛速度50%左右.
Normal incidence narrowband transmission filtering in zero-contrast gratings
Cui, Xuan; Du, Yan; Tan, Peng; Shi, Guang; Zhou, Zhongxiang
2015-01-01
We report narrowband transmission filtering based on zero-contrast grating (ZCG) reflectors at normal incidence. Computational results show that the filtering is realized through symmetry-protected modes coupling. The guided modes introduced by the slab layer make the filter frequencies flexible to modify. The rectangular structure of the filter allows simple fabrication and integration into optical systems. The quality factor of the filters could exceed 106. Owing to the low refraction index dispersion of the semiconductor and their scale-invariant operations, these filters can be applied in a broad infrared range from near infrared to terahertz wavelengths.
An Applied Method for Designing Maximally Decimating Non-uniform Filter Banks
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Assembling individual line phase filters to form a multi-channel filter bank allows the synthesis filter to be similar to corresponding analysis filters, and the design calculation can be simple. The appropriate relations between synthesis filters and analysis filters eliminate most aliasing resulting from decimation in non-uniform maximally decimating filter banks, and LS algorithm and Remez algorithm are used to optimize the composite character. This design method can achieve approximate Perfect-Reconstruction. An example is given in which the general parameter filters with approximate line phase are used as units of a filter bank.
Stochastic nonlinear mixed effects: a metformin case study.
Matzuka, Brett; Chittenden, Jason; Monteleone, Jonathan; Tran, Hien
2016-02-01
In nonlinear mixed effect (NLME) modeling, the intra-individual variability is a collection of errors due to assay sensitivity, dosing, sampling, as well as model misspecification. Utilizing stochastic differential equations (SDE) within the NLME framework allows the decoupling of the measurement errors from the model misspecification. This leads the SDE approach to be a novel tool for model refinement. Using Metformin clinical pharmacokinetic (PK) data, the process of model development through the use of SDEs in population PK modeling was done to study the dynamics of absorption rate. A base model was constructed and then refined by using the system noise terms of the SDEs to track model parameters and model misspecification. This provides the unique advantage of making no underlying assumptions about the structural model for the absorption process while quantifying insufficiencies in the current model. This article focuses on implementing the extended Kalman filter and unscented Kalman filter in an NLME framework for parameter estimation and model development, comparing the methodologies, and illustrating their challenges and utility. The Kalman filter algorithms were successfully implemented in NLME models using MATLAB with run time differences between the ODE and SDE methods comparable to the differences found by Kakhi for their stochastic deconvolution.
Method of securing filter elements
Energy Technology Data Exchange (ETDEWEB)
Brown, Erik P.; Haslam, Jeffery L.; Mitchell, Mark A.
2016-10-04
A filter securing system including a filter unit body housing; at least one tubular filter element positioned in the filter unit body housing, the tubular filter element having a closed top and an open bottom; a dimple in either the filter unit body housing or the top of the tubular filter element; and a socket in either the filter unit body housing or the top of the tubular filter element that receives the dimple in either the filter unit body housing or the top of the tubular filter element to secure the tubular filter element to the filter unit body housing.
Seider, Warren D.; Ungar, Lyle H.
1987-01-01
Describes a course in nonlinear mathematics courses offered at the University of Pennsylvania which provides an opportunity for students to examine the complex solution spaces that chemical engineers encounter. Topics include modeling many chemical processes, especially those involving reaction and diffusion, auto catalytic reactions, phase…
Fault Tolerant Parallel Filters Based On Bch Codes
Directory of Open Access Journals (Sweden)
K.Mohana Krishna
2015-04-01
Full Text Available Digital filters are used in signal processing and communication systems. In some cases, the reliability of those systems is critical, and fault tolerant filter implementations are needed. Over the years, many techniques that exploit the filters’ structure and properties to achieve fault tolerance have been proposed. As technology scales, it enables more complex systems that incorporate many filters. In those complex systems, it is common that some of the filters operate in parallel, for example, by applying the same filter to different input signals. Recently, a simple technique that exploits the presence of parallel filters to achieve multiple fault tolerance has been presented. In this brief, that idea is generalized to show that parallel filters can be protected using Bose– Chaudhuri–Hocquenghem codes (BCH in which each filter is the equivalent of a bit in a traditional ECC. This new scheme allows more efficient protection when the number of parallel filters is large.
Linear Phase Perfect Reconstruction Filters and Wavelets with Even Symmetry
Monzon, Lucas
2011-01-01
Perfect reconstruction filter banks can be used to generate a variety of wavelet bases. Using IIR linear phase filters one can obtain symmetry properties for the wavelet and scaling functions. In this paper we describe all possible IIR linear phase filters generating symmetric wavelets with any prescribed number of vanishing moments. In analogy with the well known FIR case, we construct and study a new family of wavelets obtained by considering maximal number of vanishing moments for each fixed order of the IIR filter. Explicit expressions for the coefficients of numerator, denominator, zeroes, and poles are presented. This new parameterization allows one to design linear phase quadrature mirror filters with many other properties of interest such as filters that have any preassigned set of zeroes in the stopband or that satisfy an almost interpolating property. Using Beylkin's approach, it is indicated how to implement these IIR filters not as recursive filters but as FIR filters.
Design of Tunable Anisotropic Photonic Crystal Filter as Photonic Switch
Majid Seifan; Alireza Malekijavan; Alireza Monajati Kashani
2014-01-01
By creating point defects and line defects in photonic crystals, we reach the new sort of photonic crystals. Which allow us to design photonic crystals filters. In this type of photonic crystals the ability to tune up central frequency of filter is important to attention. In this paper, we use foregoing points for designing photonic crystal filters. The main function of this type of filters is coupling between shield of point defect modes and directional line defect modes. By using liquid cry...
Nonlinear phononics using atomically thin membranes
Midtvedt, Daniel; Isacsson, Andreas; Croy, Alexander
2014-09-01
Phononic crystals and acoustic metamaterials are used to tailor phonon and sound propagation properties by utilizing artificial, periodic structures. Analogous to photonic crystals, phononic band gaps can be created, which influence wave propagation and, more generally, allow engineering of the acoustic properties of a system. Beyond that, nonlinear phenomena in periodic structures have been extensively studied in photonic crystals and atomic Bose-Einstein condensates in optical lattices. However, creating nonlinear phononic crystals or nonlinear acoustic metamaterials remains challenging and only few examples have been demonstrated. Here, we show that atomically thin and periodically pinned membranes support coupled localized modes with nonlinear dynamics. The proposed system provides a platform for investigating nonlinear phononics.
NONLINEAR ESTIMATION METHODS FOR AUTONOMOUS TRACKED VEHICLE WITH SLIP
Institute of Scientific and Technical Information of China (English)
ZHOU Bo; HAN Jianda
2007-01-01
In order to achieve precise, robust autonomous guidance and control of a tracked vehicle, a kinematic model with longitudinal and lateral slip is established. Four different nonlinear filters are used to estimate both state vector and time-varying parameter vector of the created model jointly. The first filter is the well-known extended Kalman filter. The second filter is an unscented version of the Kalman filter. The third one is a particle filter using the unscented Kalman filter to generate the importance proposal distribution. The last one is a novel and guaranteed filter that uses a linear set-membership estimator and can give an ellipsoid set in which the true state lies. The four different approaches have different complexities, behavior and advantages that are surveyed and compared.
A Hierarchical Bayes Ensemble Kalman Filter
Tsyrulnikov, Michael; Rakitko, Alexander
2017-01-01
A new ensemble filter that allows for the uncertainty in the prior distribution is proposed and tested. The filter relies on the conditional Gaussian distribution of the state given the model-error and predictability-error covariance matrices. The latter are treated as random matrices and updated in a hierarchical Bayes scheme along with the state. The (hyper)prior distribution of the covariance matrices is assumed to be inverse Wishart. The new Hierarchical Bayes Ensemble Filter (HBEF) assimilates ensemble members as generalized observations and allows ordinary observations to influence the covariances. The actual probability distribution of the ensemble members is allowed to be different from the true one. An approximation that leads to a practicable analysis algorithm is proposed. The new filter is studied in numerical experiments with a doubly stochastic one-variable model of "truth". The model permits the assessment of the variance of the truth and the true filtering error variance at each time instance. The HBEF is shown to outperform the EnKF and the HEnKF by Myrseth and Omre (2010) in a wide range of filtering regimes in terms of performance of its primary and secondary filters.
Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent
Directory of Open Access Journals (Sweden)
Ibnkahla Mohamed
2003-01-01
Full Text Available We use natural gradient (NG learning neural networks (NNs for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity . The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM procedure in terms of convergence speed and mean squared error (MSE performance.
Vibrations of Nonlinear Systems. The Method of Integral Equations,
Many diverse applied methods of investigating oscillations of nonlinear systems often in different mathematical formulations and outwardly not...parameter classical methods and the methods of investigating nonlinear systems of automatic control based on the so-called filter hypothesis, and to
A Modal Model to Simulate Typical Structural Dynamic Nonlinearity
Energy Technology Data Exchange (ETDEWEB)
Pacini, Benjamin Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mayes, Randall L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Roettgen, Daniel R [Univ. of Wisconsin, Madison, WI (United States)
2015-10-01
Some initial investigations have been published which simulate nonlinear response with almost traditional modal models: instead of connecting the modal mass to ground through the traditional spring and damper, a nonlinear Iwan element was added. This assumes that the mode shapes do not change with amplitude and there are no interactions between modal degrees of freedom. This work expands on these previous studies. An impact experiment is performed on a structure which exhibits typical structural dynamic nonlinear response, i.e. weak frequency dependence and strong damping dependence on the amplitude of vibration. Use of low level modal test results in combination with high level impacts are processed using various combinations of modal filtering, the Hilbert Transform and band-pass filtering to develop response data that are then fit with various nonlinear elements to create a nonlinear pseudo-modal model. Simulations of forced response are compared with high level experimental data for various nonlinear element assumptions.
Pearson, Ronald K.; Neuvo, Yrjö; Astola, Jaakko; Gabbouj, Moncef
2016-12-01
The standard median filter based on a symmetric moving window has only one tuning parameter: the window width. Despite this limitation, this filter has proven extremely useful and has motivated a number of extensions: weighted median filters, recursive median filters, and various cascade structures. The Hampel filter is a member of the class of decsion filters that replaces the central value in the data window with the median if it lies far enough from the median to be deemed an outlier. This filter depends on both the window width and an additional tuning parameter t, reducing to the median filter when t=0, so it may be regarded as another median filter extension. This paper adopts this view, defining and exploring the class of generalized Hampel filters obtained by applying the median filter extensions listed above: weighted Hampel filters, recursive Hampel filters, and their cascades. An important concept introduced here is that of an implosion sequence, a signal for which generalized Hampel filter performance is independent of the threshold parameter t. These sequences are important because the added flexibility of the generalized Hampel filters offers no practical advantage for implosion sequences. Partial characterization results are presented for these sequences, as are useful relationships between root sequences for generalized Hampel filters and their median-based counterparts. To illustrate the performance of this filter class, two examples are considered: one is simulation-based, providing a basis for quantitative evaluation of signal recovery performance as a function of t, while the other is a sequence of monthly Italian industrial production index values that exhibits glaring outliers.
High Degree Cubature Federated Filter for Multisensor Information Fusion with Correlated Noises
Directory of Open Access Journals (Sweden)
Lijun Wang
2016-01-01
Full Text Available This paper proposes an improved high degree cubature federated filter for the nonlinear fusion system with cross-correlation between process and measurement noises at the same time using the fifth-degree cubature rule and the decorrelated principle in its local filters. The master filter of the federated filter adopts the no-reset mode to fuse local estimates of local filters to generate a global estimate according to the scalar weighted rule. The air-traffic maneuvering target tracking simulations are performed between the proposed filter and the fifth-degree cubature federated filter. Simulations results demonstrate that the proposed filter not only can achieve almost the same accuracy as the fifth-degree cubature federated filter with independent white noises, but also has superior performance to the fifth-degree cubature federated filter while the noises are cross-correlated at the same time.
Generalized Nonlinear Yule Models
Lansky, Petr; Polito, Federico; Sacerdote, Laura
2016-10-01
With the aim of considering models related to random graphs growth exhibiting persistent memory, we propose a fractional nonlinear modification of the classical Yule model often studied in the context of macroevolution. Here the model is analyzed and interpreted in the framework of the development of networks such as the World Wide Web. Nonlinearity is introduced by replacing the linear birth process governing the growth of the in-links of each specific webpage with a fractional nonlinear birth process with completely general birth rates. Among the main results we derive the explicit distribution of the number of in-links of a webpage chosen uniformly at random recognizing the contribution to the asymptotics and the finite time correction. The mean value of the latter distribution is also calculated explicitly in the most general case. Furthermore, in order to show the usefulness of our results, we particularize them in the case of specific birth rates giving rise to a saturating behaviour, a property that is often observed in nature. The further specialization to the non-fractional case allows us to extend the Yule model accounting for a nonlinear growth.
Generalized Nonlinear Yule Models
Lansky, Petr; Polito, Federico; Sacerdote, Laura
2016-11-01
With the aim of considering models related to random graphs growth exhibiting persistent memory, we propose a fractional nonlinear modification of the classical Yule model often studied in the context of macroevolution. Here the model is analyzed and interpreted in the framework of the development of networks such as the World Wide Web. Nonlinearity is introduced by replacing the linear birth process governing the growth of the in-links of each specific webpage with a fractional nonlinear birth process with completely general birth rates. Among the main results we derive the explicit distribution of the number of in-links of a webpage chosen uniformly at random recognizing the contribution to the asymptotics and the finite time correction. The mean value of the latter distribution is also calculated explicitly in the most general case. Furthermore, in order to show the usefulness of our results, we particularize them in the case of specific birth rates giving rise to a saturating behaviour, a property that is often observed in nature. The further specialization to the non-fractional case allows us to extend the Yule model accounting for a nonlinear growth.
Tsia, Kevin K.; Jalali, Bahram
2010-05-01
An intriguing optical property of silicon is that it exhibits a large third-order optical nonlinearity, with orders-ofmagnitude larger than that of silica glass in the telecommunication band. This allows efficient nonlinear optical interaction at relatively low power levels in a small footprint. Indeed, we have witnessed a stunning progress in harnessing the Raman and Kerr effects in silicon as the mechanisms for enabling chip-scale optical amplification, lasing, and wavelength conversion - functions that until recently were perceived to be beyond the reach of silicon. With all the continuous efforts developing novel techniques, nonlinear silicon photonics is expected to be able to reach even beyond the prior achievements. Instead of providing a comprehensive overview of this field, this manuscript highlights a number of new branches of nonlinear silicon photonics, which have not been fully recognized in the past. In particular, they are two-photon photovoltaic effect, mid-wave infrared (MWIR) silicon photonics, broadband Raman effects, inverse Raman scattering, and periodically-poled silicon (PePSi). These novel effects and techniques could create a new paradigm for silicon photonics and extend its utility beyond the traditionally anticipated applications.
Radar Image Texture Classification based on Gabor Filter Bank
Directory of Open Access Journals (Sweden)
Mbainaibeye Jérôme
2014-01-01
Full Text Available The aim of this paper is to design and develop a filter bank for the detection and classification of radar image texture with 4.6m resolution obtained by airborne synthetic Aperture Radar. The textures of this kind of images are more correlated and contain forms with random disposition. The design and the developing of the filter bank is based on Gabor filter. We have elaborated a set of filters applied to each set of feature texture allowing its identification and enhancement in comparison with other textures. The filter bank which we have elaborated is represented by a combination of different texture filters. After processing, the selected filter bank is the filter bank which allows the identification of all the textures of an image with a significant identification rate. This developed filter is applied to radar image and the obtained results are compared with those obtained by using filter banks issue from the generalized Gaussian models (GGM. We have shown that Gabor filter developed in this work gives the classification rate greater than the results obtained by Generalized Gaussian model. The main contribution of this work is the generation of the filter banks able to give an optimal filter bank for a given texture and in particular for radar image textures
Degradation of HEPA filters exposed to DMSO
Energy Technology Data Exchange (ETDEWEB)
Bergman, W.; Wilson, K.; Larsen, G. [Lawrence Livermore National Laboratory, CA (United States)] [and others
1995-02-01
Dimethyl sulfoxide (DMSO) sprays are being used to remove the high explosive (HE) from nuclear weapons in the process of their dismantlement. A boxed 50 cmf HEPA filter with an integral prefilter was exposed to DMSO vapor and aerosols that were generated by a spray nozzle to simulate conditions expected in the HE dissolution operation. After 198 hours of operation, the pressure drop of the filter had increased form 1.15 inches to 2,85 inches, and the efficiency for 0.3 {mu}m dioctyl sebacate (DOS) aerosols decreased form 99.992% to 98.6%. Most of the DMSO aerosols had collected as a liquid pool inside the boxed HEPA. The liquid was blown out of the filter exit with 100 cmf air flow at the end of the test. Since the filter still met the minimum allowed efficiency of 99.97% after 166 hours of exposure, we recommend replacing the filter every 160 hours of operation or sooner if the pressure drop increases by 50%. Examination of the filter showed that visible cracks appeared at the joints of the wooden frame and a portion of the sealant had pulled away from the frame. Since all of the DMSO will be trapped in the first HEPA filter, the second HEPA filter should not suffer from DMSO degradation. Thus the combined efficiency for the first filter (98.6%) and the second filter (99.97%) is 99.99996% for 0.3 {mu}m particles. If the first filter is replaced prior to its degradation, each of the filters will have 99.97% efficiency, and the combined efficiency will be 99.999991%. The collection efficiency for DMSO/HE aerosols will be much higher because the particle size is much greater.
Adaptive error covariances estimation methods for ensemble Kalman filters
Energy Technology Data Exchange (ETDEWEB)
Zhen, Yicun, E-mail: zhen@math.psu.edu [Department of Mathematics, The Pennsylvania State University, University Park, PA 16802 (United States); Harlim, John, E-mail: jharlim@psu.edu [Department of Mathematics and Department of Meteorology, The Pennsylvania State University, University Park, PA 16802 (United States)
2015-08-01
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.
Optimal Energy Measurement in Nonlinear Systems: An Application of Differential Geometry
Fixsen, Dale J.; Moseley, S. H.; Gerrits, T.; Lita, A.; Nam, S. W.
2014-01-01
Design of TES microcalorimeters requires a tradeoff between resolution and dynamic range. Often, experimenters will require linearity for the highest energy signals, which requires additional heat capacity be added to the detector. This results in a reduction of low energy resolution in the detector. We derive and demonstrate an algorithm that allows operation far into the nonlinear regime with little loss in spectral resolution. We use a least squares optimal filter that varies with photon energy to accommodate the nonlinearity of the detector and the non-stationarity of the noise. The fitting process we use can be seen as an application of differential geometry. This recognition provides a set of well-developed tools to extend our work to more complex situations. The proper calibration of a nonlinear microcalorimeter requires a source with densely spaced narrow lines. A pulsed laser multi-photon source is used here, and is seen to be a powerful tool for allowing us to develop practical systems with significant detector nonlinearity. The combination of our analysis techniques and the multi-photon laser source create a powerful tool for increasing the performance of future TES microcalorimeters.
Assaf, Tareq; Rossiter, Jonathan M.; Porrill, John
2016-01-01
Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training. PMID:27655667
An area efficient low noise 100 Hz low-pass filter
DEFF Research Database (Denmark)
Ølgaard, Christian; Sassene, Haoues; Perch-Nielsen, Ivan R.
1996-01-01
scaling technique. The two filters utilize approximately the same silicon area. The scaled filter implements the scaling by use of a MOS based current conveyor type CCII. Measurements indicate that the current scaled filter results in a noise improvement of approximately 5.5 dB over the reference filter......A technique based on scaling a filter's capacitor currents to improve the noise performance of low frequency continuous-time filters is presented. Two 100 Hz low-pass filters have been implemented: a traditional low pass filter (as reference), and a filter utilizing the above mentioned current...... when a class A/B biasing scheme is used in the current divider. Obtaining identical noise performance from the reference filter would require a 3.6 times larger filter capacitor. This would increase the reference filter's die area by 100%. Therefore, the current scaling technique allows filters...
A Sensor-based Long Baseline Position and Velocity Navigation Filter for Underwater Vehicles
Batista, Pedro; Oliveira, Paulo
2010-01-01
This paper presents a novel Long Baseline (LBL) position and velocity navigation filter for underwater vehicles based directly on the sensor measurements. The solution departs from previous approaches as the range measurements are explicitly embedded in the filter design, therefore avoiding inversion algorithms. Moreover, the nonlinear system dynamics are considered to their full extent and no linearizations are carried out whatsoever. The filter error dynamics are globally asymptotically stable (GAS) and it is shown, under simulation environment, that the filter achieves similar performance to the Extended Kalman Filter (EKF) and outperforms linear position and velocity filters based on algebraic estimates of the position obtained from the range measurements.
An Unbiased Unscented Transform Based Kalman Filter for 3D Radar
Institute of Scientific and Technical Information of China (English)
WANGGuohong; XIUJianjuan; HEYou
2004-01-01
As a derivative-free alternative to the Extended Kalman filter (EKF) in the framework of state estimation, the Unscented Kalman filter (UKF) has potential applications in nonlinear filtering. By noting the fact that the unscented transform is generally biased when converting the radar measurements from spherical coordinates into Cartesian coordinates, a new filtering algorithm for 3D radar, called Unbiased unscented Kalman filter (UUKF), is proposed. The new algorithm is validated by Monte Carlo simulation runs. Simulation results show that the UUKF is more effective than the UKF, EKF and the Converted measurement Kalman filter (CMKF).
An Extended Kalman Filter with a Computed Mean Square Error Bound
Hexner, Gyorgy; Weiss, Haim
2014-01-01
The paper proposes a new recursive filter for non-linear systems that inherently computes a valid bound on the mean square estimation error. The proposed filter, bound based extended Kalman, (BEKF) is in the form of an extended Kalman filter. The main difference of the proposed filter from the conventional extended Kalman filter is in the use of a computed mean square error bound matrix, to calculate the filter gain, and to serve as bound on the actual mean square error. The paper shows that ...
An SQP Algorithm for Recourse-based Stochastic Nonlinear Programming
Directory of Open Access Journals (Sweden)
Xinshun Ma
2016-05-01
Full Text Available The stochastic nonlinear programming problem with completed recourse and nonlinear constraints is studied in this paper. We present a sequential quadratic programming method for solving the problem based on the certainty extended nonlinear model. This algorithm is obtained by combing the active set method and filter method. The convergence of the method is established under some standard assumptions. Moreover, a practical design is presented and numerical results are provided.
Robust Filtering and Smoothing with Gaussian Processes
Deisenroth, Marc Peter; Turner, Ryan; Huber, Marco F.; Hanebeck, Uwe D.; Rasmussen, Carl Edward
2012-01-01
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of "system identification" is more robust than finding p...
Improved image filter based on SPCNN
Institute of Scientific and Technical Information of China (English)
ZHANG YuDong; WU LeNan
2008-01-01
By extraction of the thoughts of non-linear model and adaptive model match, an improved Nagao filter is brought. Meanwhile a technique based on simplified pulse coupled neural network and used for noise positioning, is put forward. Combining the two methods above, we acquire a new method that can restore images corrupted by salt and pepper noise. Experiments show that this method is more preferable than other popular ones, and still works well while noise density fluctuates severely.
Energy Technology Data Exchange (ETDEWEB)
Poirier, M. R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Burket, P. R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Duignan, M. R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)
2015-03-12
The Savannah River Site (SRS) is currently treating radioactive liquid waste with the Actinide Removal Process (ARP) and the Modular Caustic Side Solvent Extraction Unit (MCU). The low filter flux through the ARP has limited the rate at which radioactive liquid waste can be treated. Recent filter flux has averaged approximately 5 gallons per minute (gpm). Salt Batch 6 has had a lower processing rate and required frequent filter cleaning. Savannah River Remediation (SRR) has a desire to understand the causes of the low filter flux and to increase ARP/MCU throughput. In addition, at the time the testing started, SRR was assessing the impact of replacing the 0.1 micron filter with a 0.5 micron filter. This report describes testing of MST filterability to investigate the impact of filter pore size and MST particle size on filter flux and testing of filter enhancers to attempt to increase filter flux. The authors constructed a laboratory-scale crossflow filter apparatus with two crossflow filters operating in parallel. One filter was a 0.1 micron Mott sintered SS filter and the other was a 0.5 micron Mott sintered SS filter. The authors also constructed a dead-end filtration apparatus to conduct screening tests with potential filter aids and body feeds, referred to as filter enhancers. The original baseline for ARP was 5.6 M sodium salt solution with a free hydroxide concentration of approximately 1.7 M.3 ARP has been operating with a sodium concentration of approximately 6.4 M and a free hydroxide concentration of approximately 2.5 M. SRNL conducted tests varying the concentration of sodium and free hydroxide to determine whether those changes had a significant effect on filter flux. The feed slurries for the MST filterability tests were composed of simple salts (NaOH, NaNO_{2}, and NaNO_{3}) and MST (0.2 – 4.8 g/L). The feed slurry for the filter enhancer tests contained simulated salt batch 6 supernate, MST, and filter enhancers.
NONLINEAR DATA RECONCILIATION METHOD BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
In the industrial process situation, principal component analysis (PCA) is a general method in data reconciliation.However, PCA sometime is unfeasible to nonlinear feature analysis and limited in application to nonlinear industrial process.Kernel PCA (KPCA) is extension of PCA and can be used for nonlinear feature analysis.A nonlinear data reconciliation method based on KPCA is proposed.The basic idea of this method is that firstly original data are mapped to high dimensional feature space by nonlinear function, and PCA is implemented in the feature space.Then nonlinear feature analysis is implemented and data are reconstructed by using the kernel.The data reconciliation method based on KPCA is applied to ternary distillation column.Simulation results show that this method can filter the noise in measurements of nonlinear process and reconciliated data can represent the true information of nonlinear process.
Vehicle Sideslip Angle Estimation Based on Hybrid Kalman Filter
Directory of Open Access Journals (Sweden)
Jing Li
2016-01-01
Full Text Available Vehicle sideslip angle is essential for active safety control systems. This paper presents a new hybrid Kalman filter to estimate vehicle sideslip angle based on the 3-DoF nonlinear vehicle dynamic model combined with Magic Formula tire model. The hybrid Kalman filter is realized by combining square-root cubature Kalman filter (SCKF, which has quick convergence and numerical stability, with square-root cubature based receding horizon Kalman FIR filter (SCRHKF, which has robustness against model uncertainty and temporary noise. Moreover, SCKF and SCRHKF work in parallel, and the estimation outputs of two filters are merged by interacting multiple model (IMM approach. Experimental results show the accuracy and robustness of the hybrid Kalman filter.
A GENERIC SYNTHESIS THEORY AND REALIZATION CONDITIONS FOR ELIN FILTERS
Directory of Open Access Journals (Sweden)
Remzi ARSLANALP
2007-01-01
Full Text Available In this paper, a generic synthesis method of Externally Linear Internally Nonlinear (ELIN filters is considered. Previously developed theories are summarized and the weak sides of these theories are discussed. Based on the state space synthesis method, an nth order filter synthesis method is developed for ELIN filters. This new theory does not only cover the previously proposed theories but also overcomes their problems. In this paper, static and dynamic constraints associated with these filters are discussed. Prerequisites, necessary conditions and satisfactory conditions are defined. The developed theory gives one to modify system equations of a filter that does not satisfy these conditions. For this process, differential type Class AB filters are preferred. The theory is applied to two examples to verify the validity of the proposed approach.
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter
Zhen Zhang; Yaopeng Ma
2016-01-01
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) al...
Ma, Jinxiang; Fan, Xinnan; Ni, Jianjun; Zhu, Xifang; Xiong, Chao
2017-07-01
In order to restore image color and enhance contrast of remote sensing image without suffering from color cast and insufficient detail enhancement, a novel improved multi-scale retinex with color restoration (MSRCR) image enhancement algorithm based on Gaussian filtering and guided filtering was proposed in this paper. Firstly, multi-scale Gaussian filtering functions were used to deal with the original image to obtain the rough illumination components. Secondly, accurate illumination components were acquired by using the guided filtering functions. Then, combining with four-direction Sobel edge detector, a self-adaptive weight selection nonlinear image enhancement was carried out. Finally, a series of evaluate metrics such as mean, MSE, PSNR, contrast and information entropy were used to assess the enhancement algorithm. The results showed that the proposed algorithm can suppress effectively noise interference, enhance the image quality and restore image color effectively.
Field of Particle Filters Image Inpainting
DEFF Research Database (Denmark)
Cuzol, Anne; Pedersen, Kim Steenstrup; Nielsen, Mads
2008-01-01
We present a novel algorithm for solving the image inpainting problem based on a field of locally interacting particle filters. Image inpainting, also known as image completion, is concerned with the problem of filling image regions with new visually plausible data. In order to avoid the difficulty...... of solving the problem globally for the region to be inpainted, we introduce a field of local particle filters. The states of the particle filters are image patches. Global consistency is enforced by a Markov random field image model which connects neighbouring particle filters. The benefit of using locally...... interacting particle filters is that several competing hypotheses on inpainting solutions are kept active, allowing the method to provide globally consistent solutions on problems where other local methods may fail. We provide examples of applications of the developed method. Keywords: Inpainting · Image...
He, Kaiming; Sun, Jian; Tang, Xiaoou
2013-06-01
In this paper, we propose a novel explicit image filter called guided filter. Derived from a local linear model, the guided filter computes the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter [1], but it has better behaviors near edges. The guided filter is also a more generic concept beyond smoothing: It can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and nonapproximate linear time algorithm, regardless of the kernel size and the intensity range. Currently, it is one of the fastest edge-preserving filters. Experiments show that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications, including edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, joint upsampling, etc.
2015-01-01
From the Back Cover: The emphasis throughout the present volume is on the practical application of theoretical mathematical models helping to unravel the underlying mechanisms involved in processes from mathematical physics and biosciences. It has been conceived as a unique collection of abstract methods dealing especially with nonlinear partial differential equations (either stationary or evolutionary) that are applied to understand concrete processes involving some important applications re...