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 theory of kinetic instabilities near threshold
Energy Technology Data Exchange (ETDEWEB)
Berk, H.L.; Pekker, M.S. [Univ. of Texas, Austin, TX (United States). Inst. for Fusion Studies; Breizman, B.N. [Texas Univ., Austin, TX (United States). Inst. for Fusion Studies]|[Budker Inst. of Nuclear Physics, Novosibirsk (Russian Federation)
1997-05-01
A new nonlinear equation has been derived and solved for the evolution of an unstable collective mode in a kinetic system close to the threshold of linear instability. The resonant particle response produces the dominant nonlinearity, which can be calculated iteratively in the near-threshold regime as long as the mode doe snot trap resonant particles. With sources and classical relaxation processes included, the theory describes both soft nonlinear regimes, where the mode saturation level is proportional to an increment above threshold, and explosive nonlinear regimes, where the mode grows to a level that is independent of the closeness to threshold. The explosive solutions exhibit mode frequency shifting. For modes that exist in the absence of energetic particles, the frequency shift is both upward and downward. For modes that require energetic particles for their existence, there is a preferred direction of the frequency shift. The frequency shift continues even after the mode traps resonant particles.
Nonlinear filtering for LIDAR signal processing
Directory of Open Access Journals (Sweden)
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.
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...
Nonlinear Psychometric Thresholds for Physics and Mathematics
Hsu, Stephen D H
2010-01-01
We analyze 5 years of student records at the University of Oregon to estimate the probability of success (as defined by superior undergraduate record; sufficient for admission to graduate school) in Physics and Mathematics as a function of SAT-M score. We find evidence of a nonlinear threshold: below SAT-M score of roughly 600, the probability of success is very low. Interestingly, no similar threshold exists in other majors, such as Sociology, History, English or Biology, whether on SAT combined, SAT-R or SAT-M. Our findings have significant implications for the demographic makeup of graduate populations in mathematically intensive subjects, given the current distribution of SAT-M scores.
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.
Adaptive Threshold Median Filter for Multiple-Impulse Noise
Institute of Scientific and Technical Information of China (English)
JIANG Bo; HUANG Wei
2007-01-01
Attenuating the noises plays an essential role in the image processing. Almost all the traditional median filters concern the removal of impulse noise having a single layer, whose noise gray level value is constant. In this paper, a new adaptive median filter is proposed to handle those images corrupted not only by single layer noise. The adaptive threshold median filter(ATMF) has been developed by combining the adaptive median filter (AMF) and two dynamic thresholds. Because of the dynamic threshold being used, the ATMF is able to balance the removal of the multiple-impulse noise and the quality of image. Comparison of the proposed method with traditional median filters is provided. Some visual examples are given to demonstrate the performance of the proposed Filter.
Highly nonlinear photoluminescence threshold in porous silicon
Energy Technology Data Exchange (ETDEWEB)
Nayfeh, M. [Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States); Akcakir, O. [Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States); Therrien, J. [Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States); Yamani, Z. [Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States); Barry, N. [Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States); Yu, W. [Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States); Gratton, E. [Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States)
1999-12-27
Porous silicon is excited using near-infrared femtosecond pulsed and continuous wave radiation at an average intensity of {approx}10{sup 6} W/cm{sup 2} (8x10{sup 10} W/cm{sup 2} peak intensity in pulsed mode). Our results demonstrate the presence of micron-size regions for which the intensity of the photoluminescence has a highly nonlinear threshold, rising by several orders of magnitude near this incident intensity for both the pulsed and continuous wave cases. These results are discussed in terms of stimulated emission from quantum confinement engineered intrinsic Si-Si radiative traps in ultrasmall nanocrystallites, populated following two-photon absorption. (c) 1999 American Institute of Physics.
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
Directory of Open Access Journals (Sweden)
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 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.
Excitation Thresholds for Nonlinear Localized Modes on Lattices
Weinstein, M I
1999-01-01
Breathers are spatially localized and time periodic solutions of extended Hamiltonian dynamical systems. In this paper we study excitation thresholds for (nonlinearly dynamically stable) ground state breather or standing wave solutions for networks of coupled nonlinear oscillators and wave equations of nonlinear Schrödinger (NLS) type. Excitation thresholds are rigorously characterized by variational methods. The excitation threshold is related to the optimal (best) constant in a class of discr ete interpolation inequalities related to the Hamiltonian energy. We establish a precise connection among $d$, the dimensionality of the lattice, $2\\sigma+1$, the degree of the nonlinearity and the existence of an excitation threshold for discrete nonlinear Schrödinger systems (DNLS). We prove that if $\\sigma\\ge 2/d$, then ground state standing waves exist if and only if the total power is larger than some strictly positive threshold, the context of DNLS. We also discuss upper and lower bounds for excitation threshol...
Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding
Desmal, Abdulla
2015-04-13
A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.
Segmentation of Vessels by Morphological Filters and Dynamic Thresholding
Institute of Scientific and Technical Information of China (English)
YUAN Hui-jing; XIAO Jie; WANG Yong-tian; LIU Yue
2006-01-01
A method of segmenting vessels by morphological filters and dynamic thresholding for digital subtraction angiography (DSA) images is presented. The first step is to reduce the noise and enhance the details of image by using morphological operators. The second is to segment vessels by dynamic thresholding combined with global thresholding based on the properties of DSA images. Artificial images and actual images have been tested. Experiment results show that the proposed method is efficient and is of great potential for the segmentation of vessels in medical images.
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.
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
Directory of Open Access Journals (Sweden)
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.
Threshold resonance and controlled filtering in quantum star graphs
Turek, Ondřej
2011-01-01
We design two simple quantum devices applicable as an adjustable quantum spectral filter and as a flux controller. Their function is based upon the threshold resonance in a F\\"ul\\"op-Tsutsui type star graph with an external potential added on one of the lines. Adjustment of the potential strength directly controls the quantum flow from the input to the output line. This is the first example to date in which the quantum flow control is achieved by addition of an external field not on the channel itself, but on other lines connected to the channel at a vertex.
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.
Ciattoni, Alessandro
2014-01-01
Strong nonlinear optical mechanisms operating in a miniaturized environment have a key role in photonics since they allow complex and versatile light manipulation within subwavelength devices. On the other hand, due to its two-dimensional planar geometry, graphene can easily be embedded within miniaturized structures and has fascinating linear and nonlinear optical properties arising from its relativistic electron dynamics. However, very few light steering graphene-based setups with a strong nonlinear behavior have been proposed since, due to its intrinsic planar localization, graphene nonlinearity has to be exploited through novel schemes not available in standard bulk nonlinear optics. Here we show that an active cavity hosting a graphene sheet, when tuned near its lasing threshold, is able to isolate the spatially localized graphene nonlinearity thus producing a very strong nonlinear device response with multi-valued features. The proposed strategy for exploiting graphene nonlinearity through its baring co...
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.
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).
Establishing nonlinearity thresholds with ultraintense X-ray pulses.
Szlachetko, Jakub; Hoszowska, Joanna; Dousse, Jean-Claude; Nachtegaal, Maarten; Błachucki, Wojciech; Kayser, Yves; Sà, Jacinto; Messerschmidt, Marc; Boutet, Sebastien; Williams, Garth J; David, Christian; Smolentsev, Grigory; van Bokhoven, Jeroen A; Patterson, Bruce D; Penfold, Thomas J; Knopp, Gregor; Pajek, Marek; Abela, Rafael; Milne, Christopher J
2016-09-13
X-ray techniques have evolved over decades to become highly refined tools for a broad range of investigations. Importantly, these approaches rely on X-ray measurements that depend linearly on the number of incident X-ray photons. The advent of X-ray free electron lasers (XFELs) is opening the ability to reach extremely high photon numbers within ultrashort X-ray pulse durations and is leading to a paradigm shift in our ability to explore nonlinear X-ray signals. However, the enormous increase in X-ray peak power is a double-edged sword with new and exciting methods being developed but at the same time well-established techniques proving unreliable. Consequently, accurate knowledge about the threshold for nonlinear X-ray signals is essential. Herein we report an X-ray spectroscopic study that reveals important details on the thresholds for nonlinear X-ray interactions. By varying both the incident X-ray intensity and photon energy, we establish the regimes at which the simplest nonlinear process, two-photon X-ray absorption (TPA), can be observed. From these measurements we can extract the probability of this process as a function of photon energy and confirm both the nature and sub-femtosecond lifetime of the virtual intermediate electronic state.
Establishing nonlinearity thresholds with ultraintense X-ray pulses
Szlachetko, Jakub; Hoszowska, Joanna; Dousse, Jean-Claude; Nachtegaal, Maarten; Błachucki, Wojciech; Kayser, Yves; Sà, Jacinto; Messerschmidt, Marc; Boutet, Sebastien; Williams, Garth J.; David, Christian; Smolentsev, Grigory; van Bokhoven, Jeroen A.; Patterson, Bruce D.; Penfold, Thomas J.; Knopp, Gregor; Pajek, Marek; Abela, Rafael; Milne, Christopher J.
2016-09-01
X-ray techniques have evolved over decades to become highly refined tools for a broad range of investigations. Importantly, these approaches rely on X-ray measurements that depend linearly on the number of incident X-ray photons. The advent of X-ray free electron lasers (XFELs) is opening the ability to reach extremely high photon numbers within ultrashort X-ray pulse durations and is leading to a paradigm shift in our ability to explore nonlinear X-ray signals. However, the enormous increase in X-ray peak power is a double-edged sword with new and exciting methods being developed but at the same time well-established techniques proving unreliable. Consequently, accurate knowledge about the threshold for nonlinear X-ray signals is essential. Herein we report an X-ray spectroscopic study that reveals important details on the thresholds for nonlinear X-ray interactions. By varying both the incident X-ray intensity and photon energy, we establish the regimes at which the simplest nonlinear process, two-photon X-ray absorption (TPA), can be observed. From these measurements we can extract the probability of this process as a function of photon energy and confirm both the nature and sub-femtosecond lifetime of the virtual intermediate electronic state.
IMAGE RESTORATION: DESIGN OF NON-LINEAR FILTER (LR
Directory of Open Access Journals (Sweden)
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
Directory of Open Access Journals (Sweden)
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.
Wang, Zhengzi; Ren, Zhong; Liu, Guodong
2016-10-01
In this paper, the wavelet threshold denoising method was used into the filtered back-projection algorithm of imaging reconstruction. To overcome the drawbacks of the traditional soft- and hard-threshold functions, a modified wavelet threshold function was proposed. The modified wavelet threshold function has two threshold values and two variants. To verify the feasibility of the modified wavelet threshold function, the standard test experiments were performed by using the software platform of MATLAB. Experimental results show that the filtered back-projection reconstruction algorithm based on the modified wavelet threshold function has better reconstruction effect because of more flexible advantage.
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.
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.
Adaptive Non-Linear Bayesian Filter for ECG Denoising
Directory of Open Access Journals (Sweden)
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.
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.
Simple nonlinear interferometer-based all-optical thresholder and its applications for optical CDMA.
Kravtsov, Konstantin; Prucnal, Paul R; Bubnov, Mikhail M
2007-10-01
We present an experimental demonstration of an ultrafast all-optical thresholder based on a nonlinear Sagnac interferometer. The proposed design is intended for operation at very small nonlinear phase shifts. Therefore, it requires an in-loop nonlinearity lower than for the classical nonlinear loop mirror scheme. Only 15 meters of conventional (non-holey) silica-based fiber is used as a nonlinear element. The proposed thresholder is polarization insensitive and is good for multi-wavelength operation, meeting all the requirements for autocorrelation detection in various optical CDMA communication systems. The observed cubic transfer function is superior to the quadratic transfer function of second harmonic generation-based thresholders.
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.
Image restoration using regularized inverse filtering and adaptive threshold wavelet denoising
Directory of Open Access Journals (Sweden)
Mr. Firas Ali
2007-01-01
Full Text Available Although the Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case when the blurring filter is singular, the Wiener filtering actually amplify the noise. This suggests that a denoising step is needed to remove the amplified noise .Wavelet-based denoising scheme provides a natural technique for this purpose .In this paper a new image restoration scheme is proposed, the scheme contains two separate steps : Fourier-domain inverse filtering and wavelet-domain image denoising. The first stage is Wiener filtering of the input image , the filtered image is inputted to adaptive threshold wavelet denoising stage . The choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet sub band coefficients like standard deviation, arithmetic mean and geometrical mean . The noisy image is first decomposed into many levels to obtain different frequency bands. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum thresholding value by this method .Experimental results on test image by using this method show that this method yields significantly superior image quality and better Peak Signal to Noise Ratio (PSNR. Here, to prove the efficiency of this method in image restoration , we have compared this with various restoration methods like Wiener filter alone and inverse filter.
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.
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.
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...
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].
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.
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.
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.
Speckle Filtering in PolSAR Images by Enhanced Wavelet Thresholding
Boutarfa, Souhila; Bouchemakh, Lynda; Smara, Youcef
2016-08-01
The PolSAR images are affected by a noise called speckle, which deteriorates image quality and complicates image interpretation. The polarimetric filtering is a necessary treatment prior to analysis that allows to reduce speckle and to obtain an improved image quality.In this paper, we present a polarimetric speckle filtering method based on enhancement of wavelet thresholding, hard and soft thresholding using directional coefficients improvement to reduce speckle without destroying the information. This algorithm is based on the classification of significant coefficients and applying the thresholding to obtain a better image quality.The methods are applied to the three polarimetric E-SAR images acquired on Oberpfaffenhofen area located in Munich, Germany, in P-band and the fully polarimetric RADARSAT-2 images acquired on Algiers, Algeria, in C-band.To evaluate the performance of each filter, we based it on the following criteria: smoothing homogeneous areas, preserving structural characteristics of objects and maintaining the information.
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.
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.
Nonlinear threshold behavior during the loss of Arctic sea ice.
Eisenman, I; Wettlaufer, J S
2009-01-06
In light of the rapid recent retreat of Arctic sea ice, a number of studies have discussed the possibility of a critical threshold (or "tipping point") beyond which the ice-albedo feedback causes the ice cover to melt away in an irreversible process. The focus has typically been centered on the annual minimum (September) ice cover, which is often seen as particularly susceptible to destabilization by the ice-albedo feedback. Here, we examine the central physical processes associated with the transition from ice-covered to ice-free Arctic Ocean conditions. We show that although the ice-albedo feedback promotes the existence of multiple ice-cover states, the stabilizing thermodynamic effects of sea ice mitigate this when the Arctic Ocean is ice covered during a sufficiently large fraction of the year. These results suggest that critical threshold behavior is unlikely during the approach from current perennial sea-ice conditions to seasonally ice-free conditions. In a further warmed climate, however, we find that a critical threshold associated with the sudden loss of the remaining wintertime-only sea ice cover may be likely.
GROUND FILTERING LiDAR DATA BASED ON MULTI-SCALE ANALYSIS OF HEIGHT DIFFERENCE THRESHOLD
Directory of Open Access Journals (Sweden)
P. Rashidi
2017-09-01
Full Text Available Separating point clouds into ground and non-ground points is a necessary step to generate digital terrain model (DTM from LiDAR dataset. In this research, a new method based on multi-scale analysis of height difference threshold is proposed for ground filtering of LiDAR data. The proposed method utilizes three windows with different sizes in small, average and large to cover the entire LiDAR point clouds, then with a height difference threshold, point clouds can be separated to ground and non-ground in each local window. Meanwhile, the best threshold values for size of windows are considered based on physical characteristics of the ground surface and size of objects. Also, the minimum of height of object in each window selected as height difference threshold. In order to evaluate the performance of the proposed algorithm, two datasets in rural and urban area were applied. The overall accuracy in rural and urban area was 96.06% and 94.88% respectively. These results of the filtering showed that the proposed method can successfully filters non-ground points from LiDAR point clouds despite of the data area.
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...
Nonlinear threshold behavior during the loss of Arctic sea ice
Eisenman, I; Wettlaufer, J. S.
2008-01-01
In light of the rapid recent retreat of Arctic sea ice, a number of studies have discussed the possibility of a critical threshold (or “tipping point”) beyond which the ice–albedo feedback causes the ice cover to melt away in an irreversible process. The focus has typically been centered on the annual minimum (September) ice cover, which is often seen as particularly susceptible to destabilization by the ice–albedo feedback. Here, we examine the central physical processes associated with the ...
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 threshold behavior during the loss of Arctic sea ice
Eisenman, I; 10.1073/pnas.0806887106
2008-01-01
In light of the rapid recent retreat of Arctic sea ice, a number of studies have discussed the possibility of a critical threshold (or "tipping point") beyond which the ice-albedo feedback causes the ice cover to melt away in an irreversible process. The focus has typically been centered on the annual minimum (September) ice cover, which is often seen as particularly susceptible to destabilization by the ice-albedo feedback. Here we examine the central physical processes associated with the transition from ice-covered to ice-free Arctic Ocean conditions. We show that while the ice-albedo feedback promotes the existence of multiple ice cover states, the stabilizing thermodynamic effects of sea ice mitigate this when the Arctic Ocean is ice-covered during a sufficiently large fraction of the year. These results suggest that critical threshold behavior is unlikely during the approach from current perennial sea ice conditions to seasonally ice-free conditions. In a further warmed climate, however, we find that a ...
A Critical, Nonlinear Threshold Dictates Bacterial Invasion and Initial Kinetics During Influenza
Smith, Amber M.; Smith, Amanda P.
2016-12-01
Secondary bacterial infections increase morbidity and mortality of influenza A virus (IAV) infections. Bacteria are able to invade due to virus-induced depletion of alveolar macrophages (AMs), but this is not the only contributing factor. By analyzing a kinetic model, we uncovered a nonlinear initial dose threshold that is dependent on the amount of virus-induced AM depletion. The threshold separates the growth and clearance phenotypes such that bacteria decline for dose-AM depletion combinations below the threshold, stay constant near the threshold, and increase above the threshold. In addition, the distance from the threshold correlates to the growth rate. Because AM depletion changes throughout an IAV infection, the dose requirement for bacterial invasion also changes accordingly. Using the threshold, we found that the dose requirement drops dramatically during the first 7d of IAV infection. We then validated these analytical predictions by infecting mice with doses below or above the predicted threshold over the course of IAV infection. These results identify the nonlinear way in which two independent factors work together to support successful post-influenza bacterial invasion. They provide insight into coinfection timing, the heterogeneity in outcome, the probability of acquiring a coinfection, and the use of new therapeutic strategies to combat viral-bacterial coinfections.
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.
Tereshchenko, S. A.; Savelyev, M. S.; Podgaetsky, V. M.; Gerasimenko, A. Yu.; Selishchev, S. V.
2016-09-01
A threshold model is described which permits one to determine the properties of limiters for high-powered laser light. It takes into account the threshold characteristics of the nonlinear optical interaction between the laser beam and the limiter working material. The traditional non-threshold model is a particular case of the threshold model when the limiting threshold is zero. The nonlinear characteristics of carbon nanotubes in liquid and solid media are obtained from experimental Z-scan data. Specifically, the nonlinear threshold effect was observed for aqueous dispersions of nanotubes, but not for nanotubes in solid polymethylmethacrylate. The threshold model fits the experimental Z-scan data better than the non-threshold model. Output characteristics were obtained that integrally describe the nonlinear properties of the optical limiters.
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....
Tsui, Po-Hsiang; Wan, Yung-Liang; Huang, Chih-Chung; Wang, Ming-Chen
2010-10-01
The Nakagami parameter is associated with the Nakagami distribution estimated from ultrasonic backscattered signals and closely reflects the scatterer concentrations in tissues. There is an interest in exploring the possibility of enhancing the ability of the Nakagami parameter to characterize tissues. In this paper, we explore the effect of adaptive thresholdfiltering based on the noise-assisted empirical mode decomposition of the ultrasonic backscattered signals on the Nakagami parameter as a function of scatterer concentration for improving the Nakagami parameter performance. We carried out phantom experiments using 5 MHz focused and nonfocused transducers. Before filtering, the dynamic ranges of the Nakagami parameter, estimated using focused and nonfocused transducers between the scatterer concentrations of 2 and 32 scatterers/mm3, were 0.44 and 0.1, respectively. After filtering, the dynamic ranges of the Nakagami parameter, using the focused and nonfocused transducers, were 0.71 and 0.79, respectively. The experimental results showed that the adaptive threshold filter makes the Nakagami parameter measured by a focused transducer more sensitive to the variation in the scatterer concentration. The proposed method also endows the Nakagami parameter measured by a nonfocused transducer with the ability to differentiate various scatterer concentrations. However, the Nakagami parameters estimated by focused and nonfocused transducers after adaptive threshold filtering have different physical meanings: the former represents the statistics of signals backscattered from unresolvable scatterers while the latter is associated with stronger resolvable scatterers or local inhomogeneity due to scatterer aggregation.
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.
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.
UNBALANCE RESPONSE AND TOUCH-RUBBING THRESHOLD SPEED OF ROTOR SUBJECTED TO NONLINEAR MAGNETIC FORCES
Institute of Scientific and Technical Information of China (English)
JING Minqing; LI Zixin; LUO Min; YU Lie
2008-01-01
Because of the effect of unbalance excitation and nonlinear magnetic force, the large vibration of the rotor supported by active magnetic bearing(AMB) will go beyond the radial gap of the bearing, even causing mechanical touch-rubbing when the system works at an operational speed closer to the critical speed. In order to investigate this problem, the linear model and nonlinear model of the single mass symmetric rigid rotor system supported by AMB are established respectively and the corresponding transfer functions of close-loop system are given. To pass through the numerical calculation by using MATLAB/Simulink, the effect of both the unbalance response and threshold speed of touch-rubbing of the system subjected to nonlinear magnetic forces and nonlinear output current of power amplifier are studied. Furthermore, threshold speed of touch-rubbing of the rotor-bearing system is defined and the results of numerical simulation are presented. Finally, based on above studies, two methods of increasing the touch-rubbing threshold speed are discussed.
Kim, Joo-Von; Mistral, Q.; Chappert, C.; Tiberkevich, V. S.; Slavin, A. N.
2007-01-01
The lineshape in an auto-oscillator with a large nonlinear frequency shift in the presence of thermal noise is calculated. Near the generation threshold, this lineshape becomes strongly non-Lorentzian, broadened, and asymmetric. A Lorentzian lineshape is recovered far below and far above threshold, which suggests that lineshape distortions provide a signature of the generation threshold. The theory developed adequately describes the observed behavior of a strongly nonlinear spin-torque nano-o...
Tracking Infection Diffusion in Social Networks: Filtering Algorithms and Threshold Bounds
Krishnamurthy, Vikram; Pedersen, Tavis
2016-01-01
This paper deals with the statistical signal pro- cessing over graphs for tracking infection diffusion in social networks. Infection (or Information) diffusion is modeled using the Susceptible-Infected-Susceptible (SIS) model. Mean field approximation is employed to approximate the discrete valued infected degree distribution evolution by a deterministic ordinary differential equation for obtaining a generative model for the infection diffusion. The infected degree distribution is shown to follow polynomial dynamics and is estimated using an exact non- linear Bayesian filter. We compute posterior Cramer-Rao bounds to obtain the fundamental limits of the filter which depend on the structure of the network. Considering the time-varying nature of the real world networks, the relationship between the diffusion thresholds and the degree distribution is investigated using generative models for real world networks. In addition, we validate the efficacy of our method with the diffusion data from a real-world online s...
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.
Energy Technology Data Exchange (ETDEWEB)
Bessec, Marie [CGEMP, Universite Paris-Dauphine, Place du Marechal de Lattre de Tassigny Paris (France); Fouquau, Julien [LEO, Universite d' Orleans, Faculte de Droit, d' Economie et de Gestion, Rue de Blois, BP 6739, 45067 Orleans Cedex 2 (France)
2008-09-15
This paper investigates the relationship between electricity demand and temperature in the European Union. We address this issue by means of a panel threshold regression model on 15 European countries over the last two decades. Our results confirm the non-linearity of the link between electricity consumption and temperature found in more limited geographical areas in previous studies. By distinguishing between North and South countries, we also find that this non-linear pattern is more pronounced in the warm countries. Finally, rolling regressions show that the sensitivity of electricity consumption to temperature in summer has increased in the recent period. (author)
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:…
Optical nonlinearity in Ar and N$_2$ near the ionization threshold
Wahlstrand, J K; Milchberg, H M
2011-01-01
We directly measure the nonlinear refractive index in argon and nitrogen in a thin gas target at laser intensities near the ionization threshold. No instantaneous negative nonlinear refractive index is observed, nor is saturation, in contrast with a previous measurement [Loriot et al., Opt. Express v. 17, 13429 (2009)] and calculations [Br\\'ee et al., Phys. Rev. Lett. v. 106, 183902 (2011)]. In addition, we are able to cleanly separate the electronic and rotational components of the nonlinear response in nitrogen. In both Ar and N$_2$, we observe the peak instantaneous index response scale linearly with the laser intensity until the point of ionization, whereupon it turns abruptly negative and ~constant, consistent with plasma generation.
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.
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.
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.
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...
Directory of Open Access Journals (Sweden)
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.
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.
Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter
Directory of Open Access Journals (Sweden)
Hilal Naimi
2015-01-01
Full Text Available Image denoising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images, etc. The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values. The wavelet methods are based on shrinking the wavelet coefficients in the wavelet domain. We propose in this paper, a denoising approach basing on dual tree complex wavelet and shrinkage with the Wiener filter technique (where either hard or soft thresholding operators of dual tree complex wavelet transform for the denoising of medical images are used. The results proved that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform with Wiener filter have a better balance between smoothness and accuracy than the DWT and are less redundant than SWT (StationaryWavelet Transform. We used the SSIM (Structural Similarity Index Measure along with PSNR (Peak Signal to Noise Ratio and SSIM map to assess the quality of denoised images.
Threshold Based Kernel Level HTTP Filter (TBHF for DDoS Mitigation
Directory of Open Access Journals (Sweden)
Mohamed Ibrahim AK
2012-11-01
Full Text Available HTTP flooding attack has a unique feature of interrupting application level services rather than depleting the network resources as in any other flooding attacks. Bombarding of HTTP GET requests to a target results in Denial of Service (DoS of the web server. Usage of shortened Uniform Resource Locator (URL is one of the best ways to unknowingly trap users for their participation in HTTP GET flooding attack. The existing solutions for HTTP attacks are based on browser level cache maintenance, CAPTCHA technique, and usage of Access Control Lists (ACL. Such techniques fail to prevent dynamic URL based HTTP attacks. To come up with a solution for the prevention of such kind of HTTP flooding attack, a real time HTTP GET flooding attack was generated using d0z-me, a malicious URL shortener tool. When user clicked the shortened URL, it was found that the user intended web page was displayed in the web browser. But simultaneously, an avalanche of HTTP GET requests were generated at the backdrop to the web server based on the scripts downloaded from the attacker. Since HTTP GET request traffic are part of any genuine internet traffic, it becomes difficult for the firewall to detect such kind of attacks. This motivated us to propose a Threshold Based Kernel Level HTTP Filter (TBHF, which would prevent internet users from taking part in such kind of Distributed Denial of Service (DDoS attacks unknowingly. Windows Filtering Platform (WFP, which is an Application Programming Interface (API, was used to develop TBHF. The proposed solution was tested by installing TBHF on a victim machine and generating the DDoS attack. It was observed that the TBHF completely prevented the user from participating in DDoS attack by filtering out the malicious HTTP GET requests while allowing other genuine HTTP GET requests generated from that system
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.
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.
A general derivation of the subharmonic threshold for non-linear bubble oscillations.
Prosperetti, Andrea
2013-06-01
The paper describes an approximate but rather general derivation of the acoustic threshold for a subharmonic component to be possible in the sound scattered by an insonified gas bubble. The general result is illustrated with several specific models for the mechanical behavior of the surface coating of bubbles used as acoustic contrast agents. The approximate results are found to be in satisfactory agreement with fully non-linear numerical results in the literature. The amplitude of the first harmonic is also found by the same method. A fundamental feature identified by the analysis is that the subharmonic threshold can be considerably lowered with respect to that of an uncoated free bubble if the mechanical response of the coating varies rapidly in the neighborhood of certain specific values of the bubble radius, e.g., because of buckling.
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.
Threshold Dynamics in Stochastic SIRS Epidemic Models with Nonlinear Incidence and Vaccination.
Wang, Lei; Teng, Zhidong; Tang, Tingting; Li, Zhiming
2017-01-01
In this paper, the dynamical behaviors for a stochastic SIRS epidemic model with nonlinear incidence and vaccination are investigated. In the models, the disease transmission coefficient and the removal rates are all affected by noise. Some new basic properties of the models are found. Applying these properties, we establish a series of new threshold conditions on the stochastically exponential extinction, stochastic persistence, and permanence in the mean of the disease with probability one for the models. Furthermore, we obtain a sufficient condition on the existence of unique stationary distribution for the model. Finally, a series of numerical examples are introduced to illustrate our main theoretical results and some conjectures are further proposed.
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
Analysis of schizophrenia data using a nonlinear threshold index logistic model.
Jiang, Zhenyu; Du, Chengan; Jablensky, Assen; Liang, Hua; Lu, Zudi; Ma, Yang; Teo, Kok Lay
2014-01-01
Genetic information, such as single nucleotide polymorphism (SNP) data, has been widely recognized as useful in prediction of disease risk. However, how to model the genetic data that is often categorical in disease class prediction is complex and challenging. In this paper, we propose a novel class of nonlinear threshold index logistic models to deal with the complex, nonlinear effects of categorical/discrete SNP covariates for Schizophrenia class prediction. A maximum likelihood methodology is suggested to estimate the unknown parameters in the models. Simulation studies demonstrate that the proposed methodology works viably well for moderate-size samples. The suggested approach is therefore applied to the analysis of the Schizophrenia classification by using a real set of SNP data from Western Australian Family Study of Schizophrenia (WAFSS). Our empirical findings provide evidence that the proposed nonlinear models well outperform the widely used linear and tree based logistic regression models in class prediction of schizophrenia risk with SNP data in terms of both Types I/II error rates and ROC curves.
Analysis of schizophrenia data using a nonlinear threshold index logistic model.
Directory of Open Access Journals (Sweden)
Zhenyu Jiang
Full Text Available Genetic information, such as single nucleotide polymorphism (SNP data, has been widely recognized as useful in prediction of disease risk. However, how to model the genetic data that is often categorical in disease class prediction is complex and challenging. In this paper, we propose a novel class of nonlinear threshold index logistic models to deal with the complex, nonlinear effects of categorical/discrete SNP covariates for Schizophrenia class prediction. A maximum likelihood methodology is suggested to estimate the unknown parameters in the models. Simulation studies demonstrate that the proposed methodology works viably well for moderate-size samples. The suggested approach is therefore applied to the analysis of the Schizophrenia classification by using a real set of SNP data from Western Australian Family Study of Schizophrenia (WAFSS. Our empirical findings provide evidence that the proposed nonlinear models well outperform the widely used linear and tree based logistic regression models in class prediction of schizophrenia risk with SNP data in terms of both Types I/II error rates and ROC curves.
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
Directory of Open Access Journals (Sweden)
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
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).
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).
Threshold Dynamics in Stochastic SIRS Epidemic Models with Nonlinear Incidence and Vaccination
Wang, Lei; Tang, Tingting
2017-01-01
In this paper, the dynamical behaviors for a stochastic SIRS epidemic model with nonlinear incidence and vaccination are investigated. In the models, the disease transmission coefficient and the removal rates are all affected by noise. Some new basic properties of the models are found. Applying these properties, we establish a series of new threshold conditions on the stochastically exponential extinction, stochastic persistence, and permanence in the mean of the disease with probability one for the models. Furthermore, we obtain a sufficient condition on the existence of unique stationary distribution for the model. Finally, a series of numerical examples are introduced to illustrate our main theoretical results and some conjectures are further proposed. PMID:28194223
Synchronization transitions in coupled time-delay electronic circuits with a threshold nonlinearity.
Srinivasan, K; Senthilkumar, D V; Murali, K; Lakshmanan, M; Kurths, J
2011-06-01
Experimental observations of typical kinds of synchronization transitions are reported in unidirectionally coupled time-delay electronic circuits with a threshold nonlinearity and two time delays, namely feedback delay τ(1) and coupling delay τ(2). We have observed transitions from anticipatory to lag via complete synchronization and their inverse counterparts with excitatory and inhibitory couplings, respectively, as a function of the coupling delay τ(2). The anticipating and lag times depend on the difference between the feedback and the coupling delays. A single stability condition for all the different types of synchronization is found to be valid as the stability condition is independent of both the delays. Further, the existence of different kinds of synchronizations observed experimentally is corroborated by numerical simulations and from the changes in the Lyapunov exponents of the coupled time-delay systems.
The Singularity Threshold of the Nonlinear Sigma Model Using 3D Adaptive Mesh Refinement
Liebling, S L
2002-01-01
Numerical solutions to the nonlinear sigma model (NLSM), a wave map from 3+1 Minkowski space to S^3, are computed in three spatial dimensions (3D) using adaptive mesh refinement (AMR). For initial data with compact support the model is known to have two regimes, one in which regular initial data forms a singularity and another in which the energy is dispersed to infinity. The transition between these regimes has been shown in spherical symmetry to demonstrate threshold behavior similar to that between black hole formation and dispersal in gravitating theories. Here, I generalize the result by removing the assumption of spherical symmetry. The evolutions suggest that the spherically symmetric critical solution remains an intermediate attractor separating the two end states.
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.
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.)
Qian, Xi-Yuan; Song, Fu-Tie; Zhou, Wei-Xing
2008-01-01
We have investigated the behaviour of the Shanghai Stock Exchange Composite (SSEC) index for the period from 1990:12 to 2007:06 using an unconstrained two-regime threshold autoregressive (TAR) model with a unit root developed by Caner and Hansen. The method allows us to simultaneously consider nonstationarity and nonlinearity in time series that has regime switching. Our finding indicates that the Shanghai stock market exhibits nonlinear behaviour with two regimes and has unit roots in both regimes. The important implications of the threshold effect in stock markets are also discussed.
Etching, micro hardness and laser damage threshold studies of a nonlinear optical material L-valine
Anbuchezhiyan, M.; Ponnusamy, S.; Muthamizhchelvan, C.; Kanakam, C. C.; Singh, S. P.; Pal, P. K.; Datta, P. K.
2012-04-01
A nonlinear optical crystal of L-valine was grown from an aqueous solution containing a small amount of phosphoric acid by the slow evaporation method. The grown crystal was characterized by a single crystal X-ray diffraction to determine the unit cell parameters. The powder X-ray diffraction analysis also confirmed the lattice parameters to be a = 9.6687(7) Å, b = 5.2709(4) Å, c = 12.0371(10) Å and β = 90.805(4)°. The results of the Inductively Coupled Plasma Optical Emission Spectrometry (ICPOES) indicate the presence of a small amount of phosphorus in the grown crystal. The Vickers micro hardness test was performed to study the mechanical strength of the crystals. Chemical etching studies were carried out to analyze the dislocation structure. The laser damaged threshold of the grown crystal was measured to be 11.11 GW/cm2 for 10 ns pulse at 1064 nm, which is higher than that of the standard nonlinear optical crystals like KDP. Second harmonic generation of the grown crystals was also 1.44 times that of KDP.
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
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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.
Energy Technology Data Exchange (ETDEWEB)
Peeters, A. G.; Rath, F.; Buchholz, R.; Grosshauser, S. R.; Strintzi, D.; Weikl, A. [Physics Department, University of Bayreuth, Universitätsstrasse 30, Bayreuth (Germany); Camenen, Y. [Aix Marseille Univ, CNRS, PIIM, UMR 7345, Marseille (France); Candy, J. [General Atomics, PO Box 85608, San Diego, California 92186-5608 (United States); Casson, F. J. [CCFE, Culham Science Centre, Abingdon OX14 3DB, Oxon (United Kingdom); Hornsby, W. A. [Max Planck Institut für Plasmaphysik, Boltzmannstrasse 2 85748 Garching (Germany)
2016-08-15
It is shown that Ion Temperature Gradient turbulence close to the threshold exhibits a long time behaviour, with smaller heat fluxes at later times. This reduction is connected with the slow growth of long wave length zonal flows, and consequently, the numerical dissipation on these flows must be sufficiently small. Close to the nonlinear threshold for turbulence generation, a relatively small dissipation can maintain a turbulent state with a sizeable heat flux, through the damping of the zonal flow. Lowering the dissipation causes the turbulence, for temperature gradients close to the threshold, to be subdued. The heat flux then does not go smoothly to zero when the threshold is approached from above. Rather, a finite minimum heat flux is obtained below which no fully developed turbulent state exists. The threshold value of the temperature gradient length at which this finite heat flux is obtained is up to 30% larger compared with the threshold value obtained by extrapolating the heat flux to zero, and the cyclone base case is found to be nonlinearly stable. Transport is subdued when a fully developed staircase structure in the E × B shearing rate forms. Just above the threshold, an incomplete staircase develops, and transport is mediated by avalanche structures which propagate through the marginally stable regions.
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.
Kallman, Robert R.
1986-12-01
Phase-only (PO) and binary phase only (BPO) versions of recently developed Synthetic Discriminant Filters, SDFs, (Kallman, 1986) are discussed which are potentially useful for threshold optical correlation detectors. A formulation of the performance or SNR of a filter against a training set is first presented which takes into account the POF or BPOF, unlike the SDF, being unable to control the actual size of the recognition spike of the output correlation plane when a valid target is centered in the filter input plane. Numerical tests of the present recipes for POFs and BPOFs have been carried out on four SDFs made from tank imagery, and the SNR for 12 POFs and 24 BPOFs were computed.
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.
Climate shocks and rural-urban migration in Mexico: Exploring nonlinearities and thresholds.
Nawrotzki, Raphael J; DeWaard, Jack; Bakhtsiyarava, Maryia; Ha, Jasmine Trang
2017-01-01
Adverse climatic conditions may differentially drive human migration patterns between rural and urban areas, with implications for changes in population composition and density, access to infrastructure and resources, and the delivery of essential goods and services. However, there is little empirical evidence to support this notion. In this study, we investigate the relationship between climate shocks and migration between rural and urban areas within Mexico. We combine individual records from the 2000 and 2010 Mexican censuses (n=683,518) with high-resolution climate data from Terra Populus that are linked to census data at the municipality level (n=2,321). We measure climate shocks as monthly deviation from a 30-year (1961-1990) long-term climate normal period, and uncover important nonlinearities using quadratic and cubic specifications. Satellite-based measures of urban extents allow us to classify migrant-sending and migrant-receiving municipalities as rural or urban to examine four internal migration patterns: rural-urban, rural-rural, urban-urban, and urban-rural. Among our key findings, results from multilevel models reveal that each additional drought month increases the odds of rural-urban migration by 3.6%. In contrast, the relationship between heat months and rural-urban migration is nonlinear. After a threshold of ~34 heat months is surpassed, the relationship between heat months and rural-urban migration becomes positive and progressively increases in strength. Policy and programmatic interventions may therefore reduce climate induced rural-urban migration in Mexico through rural climate change adaptation initiatives, while also assisting rural migrants in finding employment and housing in urban areas to offset population impacts.
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....
Wang, Jun; Meng, Xiaohong; Guo, Lianghui; Chen, Zhaoxi; Li, Fang
2014-10-01
We present a correlation coefficient analysis (CCA) method for obtaining threshold when using singular value decomposition (SVD) filtering method to reduce noise in potential field data. Before computation of correlation coefficients, SVD is performed on the gridded potential field data with the purpose of obtaining singular values of the data. A sliding window is utilized to truncate the acquired singular values, which allows us to obtain different singular value sequences. The lower limit of the sliding window is generally set to zero and the upper limit of the sliding window is the threshold. Then, we calculate and plot the correlation coefficients associated with the initial sequence and the newly obtained sequences, choosing the inflection point of the plotted correlation coefficients as the threshold. The CCA method offers a quantitative way to determine a threshold, which can be easily implemented by a computer program. We illustrate the method using synthetic datasets and field data from a metallic deposit area in the middle-lower reaches of the Yangtze River in China. The results show that the proposed method is effective and is able to provide an optimal threshold.
Evolutionary geomorphology: thresholds and nonlinearity in landform response to environmental change
Directory of Open Access Journals (Sweden)
J. D. Phillips
2006-04-01
Full Text Available Geomorphic systems are typically nonlinear, owing largely to their threshold-dominated nature (but due to other factors as well. Nonlinear geomorphic systems may exhibit complex behaviors not possible in linear systems, including dynamical instability and deterministic chaos. The latter are common in geomorphology, indicating that small, short-lived changes may produce disproportionately large and long-lived results; that evidence of geomorphic change may not reflect proportionally large external forcings; and that geomorphic systems may have multiple potential response trajectories or modes of adjustment to change. Instability and chaos do not preclude predictability, but do modify the context of predictability. The presence of chaotic dynamics inhibits or excludes some forms of predicability and prediction techniques, but does not preclude, and enables, others. These dynamics also make spatial and historical contingency inevitable: geography and history matter. Geomorphic systems are thus governed by a combination of ''global'' laws, generalizations and relationships that are largely (if not wholly independent of time and place, and ''local'' place and/or time-contingent factors. The more factors incorporated in the representation of any geomorphic system, the more singular the results or description are. Generalization is enhanced by reducing rather than increasing the number of factors considered. Prediction of geomorphic responses calls for a recursive approach whereby global laws and local contingencies are used to constrain each other. More specifically a methodology whereby local details are embedded within simple but more highly general phenomenological models is advocated. As landscapes and landforms change in response to climate and other forcings, it cannot be assumed that geomorphic systems progress along any particular pathway. Geomorphic systems are evolutionary in the sense of being path
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.
Low Threshold Bistability In TiO2-SiO2 Interference Filters
Mitschke, Fedor M.; Ankerhold, George; Lange, Wulfhard K.
1989-03-01
We have studied optical bistability in Ti02/Si02 interference filters ("hard coatings"). These systems compare favourably with the more conventional ZnSe filters in important characteristics, particularly in durability, switching contrast and long term stability. Unfortunately, switching is very slow. Our analysis reveals a unique mechanism: water molecu)es in pores of the coating are reversibly desorbed from well below the outside surface as the spot temperature is driven up and down by the irradiated light.
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.
Directory of Open Access Journals (Sweden)
K. Malathi
2014-09-01
Full Text Available A digital imaging technique is utilized in almost all the fields. Based on image processing concept image particle shape can be analyzed in detail. Nowadays, in eye clinics, imaging of the eye fundus with modern technology is in high demand because of its worth and expected lifetime. Eye fundus imaging is considered a non-invasive and painless route to screen and monitor the micro vascular distinction of diabetes and diabetic retinopathy. In general, Optic Disc (OD signifies the creation of the optic nerve. It is the point where the axons of retinal ganglion cells gain nearer. The Optic Disc is an access point of major blood vessels which provides the retina. In this study a method is introduced to automatically detect the position of the OD in digital retinal fundus images. The OD detection algorithm is based on the identical expected directional pattern of the retinal blood vessels. In this study two types of filters are proposed, one is Gaussian based bilateral filter, to reduce/eliminate the noise of the fundus images and another is a Haar filter to detect the diabetic retinopathy in the fundus images. The most excellent method to segment the images is thresholding based connected component pixels. The results have been taken from many diabetic retinopathy images. In this study for implementation efficient image filtering was used and named as OpenCV 2.4.9.0 and cvblobslib to accomplish successful result. In future development, the fovea detection will be applied.
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
Evolution of Channels Draining Mount St. Helens: Linking Non-Linear and Rapid, Threshold Responses
Simon, A.
2010-12-01
The catastrophic eruption of Mount St. Helens buried the valley of the North Fork Toutle River (NFT) to a depth of up to 140 m. Initial integration of a new drainage network took place episodically by the “filling and spilling” (from precipitation and seepage) of depressions formed during emplacement of the debris avalanche deposit. Channel incision to depths of 20-30 m occurred in the debris avalanche and extensive pyroclastic flow deposits, and headward migration of the channel network followed, with complete integration taking place within 2.5 years. Downstream reaches were converted from gravel-cobble streams with step-pool sequences to smoothed, infilled channels dominated by sand-sized materials. Subsequent channel evolution was dominated by channel widening with the ratio of changes in channel width to changes in channel depth ranging from about 60 to 100. Widening resulted in significant adjustment of hydraulic variables that control sediment-transport rates. For a given discharge over time, flow depths were reduced, relative roughness increased and flow velocity and boundary shear stress decreased non-linearly. These changes, in combination with coarsening of the channel bed with time resulted in systematically reduced rates of degradation (in upstream reaches), aggradation (in downstream reaches) and sediment-transport rates through much of the 1990s. Vertical adjustments were, therefore, easy to characterize with non-linear decay functions with bed-elevation attenuating with time. An empirical model of bed-level response was then created by plotting the total dimensionless change in elevation against river kilometer for both initial and secondary vertical adjustments. High magnitude events generated from the generated from upper part of the mountain, however, can cause rapid (threshold) morphologic changes. For example, a rain-on-snow event in November 2006 caused up to 9 m of incision along a 6.5 km reach of Loowit Creek and the upper NFT. The event
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.
Denoising lidar signal by combining wavelet improved threshold with wavelet domain spatial filtering
Institute of Scientific and Technical Information of China (English)
Shirong Yin; Weiran Wang
2006-01-01
Lidar is an effective tool for remotely monitoring target or object, but the lidar signal is often affected by various noises or interferences. Therefore, detecting the weak signals buried in noises is a fundamental and important problem in the lidar systems. In this paper, an effective noise reduction method combining wavelet improved threshold with wavelet domain spatial filtration is presented to denoise pulse lidar signal and is investigated by detecting the simulating pulse lidar signals in noise. The simulation results show that this method can effectively identify the edge of signal and detect the weak lidar signal buried in noises.
A silica based highly nonlinear fibre with improved threshold for stimulated brillouin scattering
DEFF Research Database (Denmark)
Grüner-Nielsen, Lars; Dasguta, Sonali; D. Mermelstein, Marc
2010-01-01
8.8 dB improvement in figure of merit for SBS limited highly nonlinear fibres is reported by using a combination of Al-doping and straining of the fibre......8.8 dB improvement in figure of merit for SBS limited highly nonlinear fibres is reported by using a combination of Al-doping and straining of the fibre...
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.
Directory of Open Access Journals (Sweden)
2007-01-01
Full Text Available Hysteresis is a rate-independent non-linearity that is expressed through thresholds, switches, and branches. Exceedance of a threshold, or the occurrence of a turning point in the input, switches the output onto a particular output branch. Rate-independent branching on a very large set of switches with non-local memory is the central concept in the new definition of hysteresis. Hysteretic loops are a special case. A self-consistent mathematical description of hydrological systems with hysteresis demands a new non-linear systems theory of adequate generality. The goal of this paper is to establish this and to show how this may be done. Two results are presented: a conceptual model for the hysteretic soil-moisture characteristic at the pedon scale and a hysteretic linear reservoir at the catchment scale. Both are based on the Preisach model. A result of particular significance is the demonstration that the independent domain model of the soil moisture characteristic due to Childs, Poulavassilis, Mualem and others, is equivalent to the Preisach hysteresis model of non-linear systems theory, a result reminiscent of the reduction of the theory of the unit hydrograph to linear systems theory in the 1950s. A significant reduction in the number of model parameters is also achieved. The new theory implies a change in modelling paradigm.
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.
Directory of Open Access Journals (Sweden)
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.
Saripalli, Ravi Kiran; Bhat, H. L.; Elizabeth, Suja
2016-09-01
Bulk, transparent organic nonlinear optical (NLO) single-crystals of imidazolium L-Ascorbate (ImLA) were grown using slow-evaporation. Crystal structure was determined by single crystal X-ray diffraction analysis. Preliminary linear optical measurements through UV-Visible and infrared spectroscopy revealed good optical transmittance and a low near-UV cutoff wavelength at 256 nm. Kurtz and Perry powder test revealed that ImLA is a phase-matchable NLO material with a second harmonic generation (SHG) efficiency of 1.2 times larger than that of standard KH2PO4 (KDP). Laser damage thresholds were determined for ImLA.
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
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.
An inertia-free filter line-search algorithm for large-scale nonlinear programming
Energy Technology Data Exchange (ETDEWEB)
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.
Hsu, Chih-Chieh; Parker, Alice C
2014-01-01
We present an electronic cortical neuron incorporating dynamic spike threshold and active dendritic properties. The circuit is simulated using a carbon nanotube field-effect transistor SPICE model. We demonstrate that our neuron has lower spike threshold for coincident synaptic inputs; however when the synaptic inputs are not in synchrony, it requires larger depolarization to evoke the neuron to fire. We also demonstrate that a dendritic spike is key to precisely-timed input-output transformation, produces reliable firing and results in more resilience to input jitter within an individual neuron.
Directory of Open Access Journals (Sweden)
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.
A general derivation of the subharmonic threshold for non-linear bubble oscillations
Prosperetti, A.
2013-01-01
The paper describes an approximate but rather general derivation of the acoustic threshold for a subharmonic component to be possible in the sound scattered by an insonified gas bubble. The general result is illustrated with several specific models for the mechanical behavior of the surface coating
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.
Directory of Open Access Journals (Sweden)
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.
Non-linear Target Adjustment in Corporate Liquidity Management: An Endogenous Thresholds Approach
Bruinshoofd, A.; Kool, C.J.M.
2006-01-01
We provide new empirical evidence on non-linear liquidity management in Dutch firms. Our results reveal that liquidity adjustment from below the target is significantly faster than from above. We find no evidence for bands of inaction around the target.
Bifurcations and chaotic threshold for a nonlinear system with an irrational restoring force
Institute of Scientific and Technical Information of China (English)
Tian Rui-Lan; Yang Xin-Wei; Cao Qing-Jie; Wu Qi-Liang
2012-01-01
Nonlinear dynamical systems with an irrational restoring force often occur in both science and engineering,and always lead to a barrier for conventional nonlinear techniques.In this paper,we have investigated the global bifurcations and the chaos directly for a nonlinear system with irrational nonlinearity avoiding the conventional Taylor's expansion to retain the natural characteristics of the system.A series of transformations are proposed to convert the homoclinic orbits of the unperturbed system to the heteroclinic orbits in the new coordinate,which can be transformed back to the analytical expressions of the homoclinic orbits.Melnikov's method is employed to obtain the criteria for chaotic motion,which implies that the existence of homoclinic orbits to chaos arose from the breaking of homoclinic orbits under the perturbation of damping and external forcing.The efficiency of the criteria for chaotic motion obtained in this paper is verified via bifurcation diagrams,Lyapunov exponents,and numerical simulations.It is worthwhile noting that our study is an attempt to make a step toward the solution of the problem proposed by Cao Q Jet al.(Cao Q J,Wiercigroch M,Pavlovskaia E E,Thompson J M T and Grebogi C 2008 Phil.Trans.R.Soc.A 366 635).
Bifurcations and chaotic threshold for a nonlinear system with an irrational restoring force
Tian, Rui-Lan; Yang, Xin-Wei; Cao, Qing-Jie; Wu, Qi-Liang
2012-02-01
Nonlinear dynamical systems with an irrational restoring force often occur in both science and engineering, and always lead to a barrier for conventional nonlinear techniques. In this paper, we have investigated the global bifurcations and the chaos directly for a nonlinear system with irrational nonlinearity avoiding the conventional Taylor's expansion to retain the natural characteristics of the system. A series of transformations are proposed to convert the homoclinic orbits of the unperturbed system to the heteroclinic orbits in the new coordinate, which can be transformed back to the analytical expressions of the homoclinic orbits. Melnikov's method is employed to obtain the criteria for chaotic motion, which implies that the existence of homoclinic orbits to chaos arose from the breaking of homoclinic orbits under the perturbation of damping and external forcing. The efficiency of the criteria for chaotic motion obtained in this paper is verified via bifurcation diagrams, Lyapunov exponents, and numerical simulations. It is worthwhile noting that our study is an attempt to make a step toward the solution of the problem proposed by Cao Q J et al. (Cao Q J, Wiercigroch M, Pavlovskaia E E, Thompson J M T and Grebogi C 2008 Phil. Trans. R. Soc. A 366 635).
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.
Directory of Open Access Journals (Sweden)
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
Energy Technology Data Exchange (ETDEWEB)
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
SPATIO-TEMPORAL DATA ANALYSIS WITH NON-LINEAR FILTERS: BRAIN MAPPING WITH fMRI DATA
Directory of Open Access Journals (Sweden)
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.
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).
Janulewicz, K. A.; Hapiddin, A.; Joseph, D.; Geckeler, K. E.; Sung, J. H.; Nickles, P. V.
2014-12-01
Physical processes in laser-matter interaction used to be determined by generation of fast electrons resulting from efficient conversion of the absorbed laser radiation. Composite materials offer the possibility to control the absorption by choice of the host material and dopants. Reported here strong absorption of ultrashort laser pulse in a composite carbon-based nanomaterial including single-walled carbon nanotubes (SWCNTs) or multilayer graphene was measured in the intensity range between 1012 and 1016 W cm-2. A protein (lysozyme) was used as the host. The maximum absorption of femtosecond laser pulse has reached 92-96 %. The optical damage thresholds of the coatings were registered at an intensity of (1.1 ± 0.5) × 1013 W cm-2 for the embedded SWCNTs and at (3.4 ± 0.3) × 1013 W cm-2 for the embedded graphene. Encapsulated variant of the dispersed nanomaterial was investigated as well. It was found that supernatant protein in the coating material tends to dominate the absorption process, independently of the embedded nanomaterial. The opposite was observed for the encapsulated material.
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
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Alex S Baldwin
Full Text Available The internal noise present in a linear system can be quantified by the equivalent noise method. By measuring the effect that applying external noise to the system's input has on its output one can estimate the variance of this internal noise. By applying this simple "linear amplifier" model to the human visual system, one can entirely explain an observer's detection performance by a combination of the internal noise variance and their efficiency relative to an ideal observer. Studies using this method rely on two crucial factors: firstly that the external noise in their stimuli behaves like the visual system's internal noise in the dimension of interest, and secondly that the assumptions underlying their model are correct (e.g. linearity. Here we explore the effects of these two factors while applying the equivalent noise method to investigate the contrast sensitivity function (CSF. We compare the results at 0.5 and 6 c/deg from the equivalent noise method against those we would expect based on pedestal masking data collected from the same observers. We find that the loss of sensitivity with increasing spatial frequency results from changes in the saturation constant of the gain control nonlinearity, and that this only masquerades as a change in internal noise under the equivalent noise method. Part of the effect we find can be attributed to the optical transfer function of the eye. The remainder can be explained by either changes in effective input gain, divisive suppression, or a combination of the two. Given these effects the efficiency of our observers approaches the ideal level. We show the importance of considering these factors in equivalent noise studies.
Tongal, Hakan; Booij, Martijn J.
2016-01-01
A nonlinear stochastic self-exciting threshold autoregressive (SETAR) model and a chaotic k-nearest neighbour (k-nn) model, for the first time, were compared in one and multi-step ahead daily flow forecasting for nine rivers with low, medium, and high flows in the western United States. The
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.
一种改进的去噪阈值混合滤波算法%A hybrid filtering algorithm based on denoising threshold
Institute of Scientific and Technical Information of China (English)
周绍光; 贾凯华; 王港淼; 刘会珍
2013-01-01
中值滤波和均值滤波通常被分别用来处理脉冲噪声和高斯噪声,但当图像同时存在高斯噪声和脉冲噪声时,单独用任何一种滤波方法都不能达到最好的去噪效果.针对这一问题,本文提出了一种改进的基于去噪阈值的图像混合滤波算法,可以更有效地减少噪声,又可以较好地保持图像的边缘细节信息.%Median filter and average filter are usually used to process impulse noise and Gaussian noise respectively.But when image is corrupted by Gaussian noise and impulse noise simultaneously,good filtering effect cannot be obtained if only using median filter or average filter.In allusion to this question,an improved hybrid image filtering based on the denoising threshold was proposed in this paper.This method would suppress noise efficiently and preserve edge details of image at the same time.
Selection of unstable patterns and control of optical turbulence by Fourier plane filtering
DEFF Research Database (Denmark)
Mamaev, A.V.; Saffman, M.
1998-01-01
We report on selection and stabilization of transverse optical patterns in a feedback mirror experiment. Amplitude filtering in the Fourier plane is used to select otherwise unstable spatial patterns. Optical turbulence observed for nonlinearities far above the pattern formation threshold...
Xu, Jin-Long; Sun, Yi-Jian; He, Jing-Liang; Wang, Yan; Zhu, Zhao-Jie; You, Zhen-Yu; Li, Jian-Fu; Chou, Mitch M C; Lee, Chao-Kuei; Tu, Chao-Yang
2015-10-07
Dirac-like topological insulators have attracted strong interest in optoelectronic application because of their unusual and startling properties. Here we report for the first time that the pure topological insulator Bi2Te3 exhibited a naturally ultrasensitive nonlinear absorption response to photoexcitation. The Bi2Te3 sheets with lateral size up to a few micrometers showed extremely low saturation absorption intensities of only 1.1 W/cm(2) at 1.0 and 1.3 μm, respectively. Benefiting from this sensitive response, a Q-switching pulsed laser was achieved in a 1.0 μm Nd:YVO4 laser where the threshold absorbed pump power was only 31 mW. This is the lowest threshold in Q-switched solid-state bulk lasers to the best of our knowledge. A pulse duration of 97 ns was observed with an average power of 26.1 mW. A Q-switched laser at 1.3 μm was also realized with a pulse duration as short as 93 ns. Moreover, the mode locking operation was demonstrated. These results strongly exhibit that Bi2Te3 is a promising optical device for constructing broadband, miniature and integrated high-energy pulsed laser systems with low power consumption. Our work clearly points out a significantly potential avenue for the development of two-dimensional-material-based broadband ultrasensitive photodetector and other optoelectronic devices.
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.
Zhang, Ming-Jian; Li, Bing-Xuan; Liu, Bin-Wen; Fan, Yu-Hang; Li, Xiao-Guo; Zeng, Hi-Yi; Guo, Guo-Cong
2013-10-21
Two new ternary rare earth chalcogenides, Dy3GaS6 (1) and Y3GaS6 (2), are reported here. They both crystallize in the orthorhombic space group Cmc21 (no. 36). Both are synthesized in pure phase and show phase-matchable second harmonic generation (SHG) of about 0.2 and 0.5 times, respectively for 1 and 2, as strong as that of KTiOPO4 (KTP) based on the powder SHG measurement at the wavelength of 1910 nm. They possess high powder laser induced damage thresholds (LIDTs), respectively, about 14 and 18 times that of AgGaS2 (AGS) based on the powder LIDT measurements under 1064 nm laser irradiation. They both exhibit wide transparency in the IR region (2.5–25 μm). It is believed that the title compounds are new candidates for nonlinear optical (NLO) materials in the IR region. To gain further insights into the NLO and LIDT properties of 1 and 2, the calculations of second-order NLO susceptibility and lattice energy density (LED) were also performed to explain their SHG efficiencies and high LIDTs.
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
Directory of Open Access Journals (Sweden)
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.
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.
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.
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...
Norris, G; McConnell, G
2010-03-01
A novel bi-directional pump geometry that nonlinearly increases the nonlinear optical conversion efficiency of a synchronously pumped optical parametric oscillator (OPO) is reported. This bi-directional pumping method synchronizes the circulating signal pulse with two counter-propagating pump pulses within a linear OPO resonator. Through this pump scheme, an increase in nonlinear optical conversion efficiency of 22% was achieved at the signal wavelength, corresponding to a 95% overall increase in average power. Given an almost unchanged measured pulse duration of 260 fs under optimal performance conditions, this related to a signal wavelength peak power output of 18.8 kW, compared with 10 kW using the traditional single-pass geometry. In this study, a total effective peak intensity pump-field of 7.11 GW/cm(2) (corresponding to 3.55 GW/cm(2) from each pump beam) was applied to a 3 mm long periodically poled lithium niobate crystal, which had a damage threshold intensity of 4 GW/cm(2), without impairing crystal integrity. We therefore prove the application of this novel pump geometry provides opportunities for power-scaling of synchronously pumped OPO systems together with enhanced nonlinear conversion efficiency through relaxed damage threshold intensity conditions.
He, Xuefei; Nguyen, Chuong Vinh; Pratap, Mrinalini; Zheng, Yujie; Wang, Yi; Nisbet, David R.; Rug, Melanie; Maier, Alexander G.; Lee, Woei Ming
2016-12-01
Here we propose a region-recognition approach with iterative thresholding, which is adaptively tailored to extract the appropriate region or shape of spatial frequency. In order to justify the method, we tested it with different samples and imaging conditions (different objectives). We demonstrate that our method provides a useful method for rapid imaging of cellular dynamics in microfluidic and cell cultures.
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.
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
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.
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...
DEFF Research Database (Denmark)
Da Ros, Francesco; Rottwitt, Karsten; Peucheret, Christophe
2012-01-01
Combining Al-doped and Ge-doped HNLFs as gain media in FOPAs is proposed and optimized, resulting in efficient SBS mitigation while circumventing the additional loss of the high SBS threshold Al-doped fiber.......Combining Al-doped and Ge-doped HNLFs as gain media in FOPAs is proposed and optimized, resulting in efficient SBS mitigation while circumventing the additional loss of the high SBS threshold Al-doped fiber....
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...
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.
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.
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...
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.
Saripalli, Ravi K.; Kumar, Sanath; Bhat, H. L.; Elizabeth, Suja
2015-05-01
Single crystals of Guanidinium L-Ascorbate (GuLA) were grown and crystal structure was determined by direct methods. GuLA crystallizes in orthorhombic, non-centrosymmetric space group P212121. The UV-cutoff was determined as 325 nm. The morphology was generated and the interplanar angles estimated and compared with experimental values. Second harmonic generation conversion efficiency was measured and compared with other salts of L-Ascorbic acid. Surface laser damage threshold was calculated as 11.3GW/cm2 for a single shot of laser of 1064 nm wavelength.
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．
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
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.
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.
Directory of Open Access Journals (Sweden)
Farshad Fathian
2017-01-01
Full Text Available Introduction: Time series models are generally categorized as a data-driven method or mathematically-based method. These models are known as one of the most important tools in modeling and forecasting of hydrological processes, which are used to design and scientific management of water resources projects. On the other hand, a better understanding of the river flow process is vital for appropriate streamflow modeling and forecasting. One of the main concerns of hydrological time series modeling is whether the hydrologic variable is governed by the linear or nonlinear models through time. Although the linear time series models have been widely applied in hydrology research, there has been some recent increasing interest in the application of nonlinear time series approaches. The threshold autoregressive (TAR method is frequently applied in modeling the mean (first order moment of financial and economic time series. Thise type of the model has not received considerable attention yet from the hydrological community. The main purposes of this paper are to analyze and to discuss stochastic modeling of daily river flow time series of the study area using linear (such as ARMA: autoregressive integrated moving average and non-linear (such as two- and three- regime TAR models. Material and Methods: The study area has constituted itself of four sub-basins namely, Saghez Chai, Jighato Chai, Khorkhoreh Chai and Sarogh Chai from west to east, respectively, which discharge water into the Zarrineh Roud dam reservoir. River flow time series of 6 hydro-gauge stations located on upstream basin rivers of Zarrineh Roud dam (located in the southern part of Urmia Lake basin were considered to model purposes. All the data series used here to start from January 1, 1997, and ends until December 31, 2011. In this study, the daily river flow data from January 01 1997 to December 31 2009 (13 years were chosen for calibration and data for January 01 2010 to December 31 2011
ASIC DESIGN OF ADAPTIVE THRESHOLD DENOISE DWT CHIP
Institute of Scientific and Technical Information of China (English)
Luo Feng; Wu Shunjun; Jiao Licheng; ZhangLinrang
2002-01-01
According to the relationship of wavelet transform and perfect reconstructive FIR filter banks, this paper presents a real-time chip with adaptive Donoho's non-linear soft-threshold for denoising in different levels of multi-scale space through rearranging the input data during convolving, filtering and sub-sampling. And more important, it gives a simple iterative algorithm to calculate the variance of the noise in interregna with no signal. It works well whether the signal or noise is stationary or not.
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.
Thirumurugan, Ramaiah; Anitha, Kandasamy
2017-05-01
In this work, a systematic study of an organic nonlinear optical (NLO) material, trans-4-hydroxy-l-proline (THP), C5H9NO3 is reported. An optical quality single crystals of THP have been successfully grown by using slow evaporation solution growth technique (SEST). The single crystal x-ray diffraction (SXRD) analysis reveals that grown crystal belongs to the orthorhombic system with non-centrosymmetric space group (NCS), P212121. Powder x-ray diffraction (PXRD) analysis shows relatively a good crystalline nature. The molecular structure of THP was recognized by NMR (1H and 13C) studies and its vibrational modes were confirmed by FTIR and FT-Raman vibrational studies. UV-Vis-NIR spectrum of grown crystal shows high optical transparency in the visible and near-IR region with low near-UV cut-off wavelength at 218 nm. Photoluminescence study confirms ultraviolet wavelength emission of THP crystal. The second harmonic generation (SHG) efficiency of grown crystal is 1.6 times greater with respect to standard potassium dihydrogen phosphate (KDP). Nonlinear refractive index (n 2) and nonlinear absorption coefficient (β) were determined using the Z-scan technique. The title compound owns high thermal stability of 294 °C and specific heat capacity (C P) of 1.21 J g-1 K-1 at 300 K and 11.33 J g-1 K-1 at 539 K (melting point). The laser-induced damage threshold (LDT) value of grown crystal was measured as 7.25 GW cm-2. The crystal growth mechanism and defects of grown crystal were studied by chemical etching technique. Mechanical strength was extensively studied by Vickers microhardness test and crystal void percentage analysis. Moreover, density functional theory (DFT) studies were carried out to probe the Mulliken charge distribution, frontier molecular orbitals (FMOs) and first order hyperpolarizability (β) of the optimized molecular structure to get a better insight of the molecular properties. These characterization results endorse that grown THP crystal as a
Rajesh, K.; Arun, A.; Mani, A.; Praveen Kumar, P.
2016-10-01
The 4-methylimidazolium picrate has been synthesized and characterized successfully. Single and powder x-ray diffraction studies were conducted which confirmed the crystal structure, and the value of the strain was calculated. The crystal perfection was determined by a HRXR diffractometer. The transmission spectrum exhibited a better transmittance of the crystal in the entire visible region with a lower cut-off wavelength of 209 nm. The linear absorption value was calculated by the optical limiting method. A birefringence study was also carried out. Second and third order nonlinear optical properties of the crystal were found by second harmonic generation and the z-scan technique. The crystals were also characterized by dielectric measurement and a photoconductivity analyzer to determine the dielectric property and the optical conductivity of the crystal. The laser damage threshold activity of the grown crystal was studied by a Q-switched Nd:YAG laser beam. Thermal studies established that the compound did not undergo a phase transition and was stable up to 240 °C.
Kolesik, M; Wright, E M; Andreasen, J; Brown, J M; Carlson, D R; Jones, R J
2012-07-02
We introduce a new computational approach for femtosecond pulse propagation in the transparency region of gases that permits full resolution in three space dimensions plus time while fully incorporating quantum coherent effects such as high-harmonic generation and strong-field ionization in a holistic fashion. This is achieved by utilizing a one-dimensional model atom with a delta-function potential which allows for a closed-form solution for the nonlinear optical response due to ground-state to continuum transitions. It side-steps evaluation of the wave function, and offers more than one hundred-fold reduction in computation time in comparison to direct solution of the atomic Schrödinger equation. To illustrate the capability of our new computational approach, we apply it to the example of near-threshold harmonic generation in Xenon, and we also present a qualitative comparison between our model and results from an in-house experiment on extreme ultraviolet generation in a femtosecond enhancement cavity.
Highly nonlinear property and threshold voltage of Sc2O3 doped ZnO-Bi2O3-based varistor ceramics
Institute of Scientific and Technical Information of China (English)
XU Dong; WU Jieting; JIAO Lei; XU Hongxing; ZHANG Peimei; YU Renhong; CHENG Xiaonong
2013-01-01
A series of ZnO-Bi2O3-based varistor ceramics doped with 0-0.4 mol.％ Sc2O3 were prepared by high-energy ball milling and sintered at temperatures between 1000 and 1150 ℃.X-ray diffractometry (XRD) and scanning electron microscopy (SEM) were applied to characterize the phases and microstructure of the varistor ceramics.A DC parameter instrument for varistor ceramics was applied to investigate the electronic properties and Ⅴ-Ⅰ characteristics.The results showed that there were no changes in crystal structure with Sc2O3-doped varistor ceramics and that the average size of ZnO grain increased first and then decreased.The best electronic charactefistcs of the varistor ceramics prepared by high-energy ball milling were found in 0.3 mol.％ Sc2O3-doped ZnO-Bi2O3-based ceramics sintered at 1000 ℃,which exhibited a threshold voltage of 821 V/mm,nonlinear coefficient of 62.1 and leakage current of 0.16 μA.
Kolesik, M; Andreasen, J; Brown, J M; Carlson, D R; Jones, R J; 10.1364/OE.20.016113
2012-01-01
We introduce a new computational approach for femtosecond pulse propagation in the transparency region of gases that permits full resolution in three space dimensions plus time while fully incorporating quantum coherent effects such as high-harmonic generation and strong-field ionization in a holistic fashion. This is achieved by utilizing a one-dimensional model atom with a delta-function potential which allows for a closed-form solution for the nonlinear optical response due to ground-state to continuum transitions. It side-steps evaluation of the wave function, and offers more than one hundred-fold reduction in computation time in comparison to direct solution of the atomic Schr\\"odinger equation. To illustrate the capability of our new computational approach, we apply it to the example of near-threshold harmonic generation in Xenon, and we also present a qualitative comparison between our model and results from an in-house experiment on extreme ultraviolet generation in a femtosecond enhancement cavity.
Institute of Scientific and Technical Information of China (English)
刘心旸; 王杰; 王昕; 姚钢
2011-01-01
A fuzzy threshold mutative bandwidth hysteresis current tracking control method for the Active Power Filter (APF) current tracking module is presented. Based on the construction of APF device model and the analysis of the variation of hysteresis width, the purpose to achieve a limited frequency of switching is reached by using an internal fuzzy controller to dynamically adjust the loop width of hysteresis comparator device. Compared with the traditional hysteresis method, the proposed method has more excellent dynamic response and tracking precision, taking full advantage of conduction capability of the power switches, moreover,a reduction of the maximum switching frequency ensures that the device operates safely. Finally, the current tracking efficiency of the method above and the harmonic compensating capability to nonlinear loads are demonstrated by the simulation and experiment results in this paper.This work is supported by National Natural Science Foundation of China (No. 60674035).%针对有源电力滤波器(APF)的电流跟踪功能模块,提出了一种模糊阈值变环宽滞环电流跟踪控制算法.在构建APF装置模型并分析滞环宽度变化规律的基础上,通过使用内部模糊控制器用以动态地调节滞环比较器的环宽,从而达到了限定开关频率的目的.该方法相比于传统滞环比较方法具有更好的跟踪精度和动态性能,充分利用了开关器件的导通能力;同时由于降低了最大开关频率,从而保证了装置的安全运行.通过仿真与实际实验结果进一步验证了该算法的电流跟踪效果以及对于非线性装置产生的系统谐波的补偿能力.
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.
Institute of Scientific and Technical Information of China (English)
莫晓齐; 何爱
2014-01-01
工程施工图像在获取、传输等过程中存在一定程度的噪声干扰。通过对标准中值滤波算法进行分析，得到了一种基于自适应开关中值的图像滤波算法，能够在去除椒盐噪声的同时保持图像细节。该算法用迭代实现了开关中值滤波算法中阈值的自动选取，可有效提高噪声点检测的准确率。通过MATLAB仿真实验，证实了该算法相较于传统滤波算法能够更好地保护图像细节和改进图像清晰度。%After obtaining and transmission processing ,Engineering image will be always disturbed by a certain degree of noise pollution .Through analysis of standard median filtering algorithm ,an adaptive threshold filtering algorithm the is proposed in this paper .This filtering algorithm could automatically chose the threshold for removing mixed noise in digital image .The noise detection veracity is improved obviously .The noise filtering algorithm is tested on the matlab software platform ,validate that the algorithm better than the traditional filtering algorithm .It makes the image detail is better pro-tect and the images show more clearly .
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
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.
Institute of Scientific and Technical Information of China (English)
黄俊; 吴普特; 赵西宁
2011-01-01
降雨产流阈值是受雨下垫面能够产流的最小降雨量值,是产流产沙规律研究的重要参数.采用室外人工模拟降雨试验,用传统直线回归法推求了4种下垫面条件下坡面降雨产流阈值,并综合考虑了其他因素对降雨产流阈值的影响,建立了一种多参数非线性降雨产流阈值模型.结果表明:传统直线回归法得到的4种不同调控措施下坡面的降雨产流阈值分别为:裸坡9.4 mm、黑麦草23.6 mm、苜蓿15.8 mm和春小麦19.5 mm.结合直线回归法并充分考虑降雨强度、植被覆盖度和前期土壤含水量3个因素对降雨产流阈值的影响,通过多元回归分析建立了一种多参数非线性降雨产流阈值模型,由该模型得到的4种不同调控措施下坡面的降雨产流阈值分别为:裸坡13.4 mm、黑麦草23.7 mm、苜蓿18.8 mm和春小麦19.7 mm.用实测数据对模型进行检验,计算值与实测值吻合程度较高,证实了该多参数非线性模型的适合性与可行性.%Rainfall-runoff threshold is the minimum rainfall producing surface runoff and it is an important parameter for the research of runoff and sediment yield law. In this paper, the field artificial rainfall simulation experiments were carried out, and the rainfall threshold for different vegetation covers was obtained using the traditional regression method. Moreover, a nonlinear multi-parameter rainfall-runoff threshold model was established to analyze the effects of other factors on rainfall-runoff threshold. The results indicated that the rainfall-runoff thresholds of four different control measurements (bare slope,ryegrass slope, purple medic slope, and spring wheat slope ) determined by the traditional linear regression method were 9.4, 23.6, 15.8 and 19.5mm, respectively. Considering the effects of rainfall intensity, vegetation coverage and antecedent soil water content on the rainfall-runoff threshold, a nonlinear multi-parameter rainfall-runoff threshold
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 .
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.
一种基于自适应阈值的图像去噪新方法%Adaptive Wavelet Thresholding for Image Denoising
Institute of Scientific and Technical Information of China (English)
尚晓清; 王军锋; 宋国乡
2003-01-01
Selecting threshold is the most important in threshold-based nonlinear filtering by wavelet transform. In this paper, a novel adaptive threshold is proposed by minimizing a Bayesian risk(It is adaptive to subband because it depends on data-driven estimates of the parameters). Combining this thresholding method with Wiener fitting can re-sult a new denoising method. Expermental results show that the proposed method indeed remove noise significantly and retaining most image edges. The results compare favorably with the reported results in the recent denoising liter-ature.
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....
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.
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.
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.
Institute of Scientific and Technical Information of China (English)
刘光达; 赵立荣
2001-01-01
Disturbance noises in medical X-ray imaging systems consist of inherent and quantum noises, which obey random Gauss and Polsson distributions, respectively. This paper theoretically provided an optimum threshold selection method for medical X-ray images filter. Through practical process of CT images, medical X-ray images filter based on minimum even-square error has been accomplished in this work.%固有噪声和量子噪声构成了医学X光影像系统的干扰噪声。它们在统计规律上分别是依从高斯分布和泊松分布的随机空间波动。本文从理论上推导出了基于小波变换原理的医学X光影像的固有噪声抑制和消除处理中的最优滤波阈值选择。通过对实际CT影像的消噪处理应用,实现了基于最小均方误差原理的医学X光影像的滤波处理。
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.
Adaptive weighted median filter utilizing impulsive noise detection
Ishihara, Jun; Meguro, Mitsuhiko; Hamada, Nozomu
1999-10-01
The removal of noise in image is one of the current important issues. It is also useful as a preprocessing for edge detection, motion estimation and so on. In this paper, an adaptive weighted median filter utilizing impulsive noise detection is proposed for the removal of impulsive noise in digital images. The aim of our proposed method is to eliminate impulsive noise effectively preserving original fine detail in images. This aim is same for another median-type nonlinear filters try to realized. In our method, we use weighted median filter whose weights should be determined by balancing between the signal preserving ability and noise reduction performance. The trade off between these two inconsistent properties is realized using the noise detection mechanism and optimized adaptation process. In the previous work, threshold value between the signal and the output of the median filter have to be decided for the noise detection. Adaptive algorithm for optimizing WM filters uses the teacher image for training process. In our method, following two new approaches are introduced in the filtering. (1) The noise detection process uses the discriminant method to the histogram distribution of the derivation from median filter output. (2) Filter weights which have been learned by uncorrupted pixels and their neighborhood without the original image are used for the restoration filtering for noise corrupted pixels. The validity of the proposed method is shown through some experimental results.
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.
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
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.
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)
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.
Institute of Scientific and Technical Information of China (English)
胡海燕; 翟永前; 李春鹏
2014-01-01
针对太阳能利用率不高的现状，设计了以MSP430F169单片机为核心的智能型太阳能自动追踪系统，采用基于阈值滤波的最大功率点追踪控制算法，提高了系统太阳能板追踪太阳的灵敏度，采用风速监测模块，增强了系统的稳定性。结果表明，相比固定电池板，系统吸收太阳能的转换效率提高了约95％，对提高太阳能的吸收效率，合理地利用太阳能具有重要的研究价值。%An automatic tracking system for the solar panel based on MSP430F169 and the angle sensor and wind speed sensor is designed in this paper,maximum power point tracking control algorithm based on threshold filtering is introduced,the tracking sensitivity for the solar panel is improved,the tracking stability of the system is enhanced by using the monitoring module on wind speed.Compared with the fixed solar panel.
Institute of Scientific and Technical Information of China (English)
黄杰卿; 谢新宇; 王文军; 刘开富
2013-01-01
In reduced coordinates, a one-dimensional finite strain consolidation equation for saturated soils is derived with threshold gradient. The new equation shows that threshold gradient and variation of permeability coefficient with void ratio should be considered. Using the two empirical relations proposed by Mesri, a new governing equation is obtained. Then three examples are analyzed by applying the partial differential finite element software FlexPDE. The results show that excess pore water pressure slightly increases at the beginning of consolidation progress and then dissipates. This phenomenon is similar to the Mandel-Cryer effect. It will be more significant if threshold gradient is greater or location of soil is deeper. Even though the maximum excess pore water pressure increases when threshold gradient increases, the increment is very small. It is too difficult for us to observe the tiny increments in laboratory and practical engineerings. Therefore, there is no need to consider threshold gradient in vast majority of actual projects. In other words, classical Carey's law is applicable. This research shows that it is very important to consider threshold gradient, geometric nonlinearity and material nonlinearity in studying the Mandel-Cryer effect so as to further understand the consolidation properties of saturated soils.%在固相物质坐标下推导出了考虑起始比降的饱和土体一维大应变固结控制方程.从新方程可以看出,要综合考虑土体固结过程中的渗流非线性,要同时考虑起始比降和渗透系数随孔隙比的变化.采用Mesri提出的两个经验关系式进一步推导出了新的控制方程.借助偏微分有限元软件FlexPDE对3个算例进行了分析.分析结果表明,土体固结开始阶段超静孔压先略微增大,然后减小,类似于Mandel-Cryer效应.起始比降越大,土层越深,该现象越显著.虽然超静孔压最大值随起始比降的增大而增大,但增量很小.如此微小的
Azhar, S. M.; Anis, Mohd; Hussaini, S. S.; Kalainathan, S.; Shirsat, M. D.; Rabbani, G.
2017-01-01
The present article is focused to investigate the influence of L-cystine (LC) on linear-non-linear optical and laser damage threshold of cadmium thiourea acetate (CTA) crystal. The structural parameters of pure and LC doped CTA crystals have been determined using the single crystal X-ray diffraction technique. The functional groups of grown crystals have been identified by means of fourier transform infrared (FT-IR) analysis. The UV-visible spectral analysis has been done in the range of 200-900 nm to ascertain the uplifting influence of LC on optical properties of CTA crystal. The second harmonic generation (SHG) efficiency of LC doped CTA crystal is found to be higher than CTA and KDP crystal. The Z-scan technique has been employed to determine the third order nonlinear optical (TONLO) nature of LC doped CTA crystal at 632.8 nm. The self focusing tendency confirmed the strong kerr lensing ability of LC doped CTA crystal. The TONLO susceptibility (χ3), refraction (n2) and absorption coefficient (β) has been calculated using the Z-scan data. The laser damage threshold of pure and LC doped CTA crystals has been measured using the Q-switched Nd:YAG laser and its is found to be in range of GW/cm2. The surface analysis has been done by means of etching studies.
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.
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
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.
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.
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
Energy Technology Data Exchange (ETDEWEB)
Jayaprakash, P. [PG & Research Department of Physics, Arignar Anna Govt. Arts College, Cheyyar 604 407, Tamil Nadu (India); Peer Mohamed, M. [PG & Research Department of Physics, Arignar Anna Govt. Arts College, Cheyyar 604 407, Tamil Nadu (India); Department of Physics, C. Abdul Hakeem College, Melvisharam 632 509, Tamil Nadu (India); Krishnan, P. [Department of Physics, St. Joseph’s College of Engineering, Chennai 600 119, Tamil Nadu (India); Nageshwari, M.; Mani, G. [PG & Research Department of Physics, Arignar Anna Govt. Arts College, Cheyyar 604 407, Tamil Nadu (India); Lydia Caroline, M., E-mail: lydiacaroline2006@yahoo.co.in [PG & Research Department of Physics, Arignar Anna Govt. Arts College, Cheyyar 604 407, Tamil Nadu (India)
2016-12-15
Single crystals of L-phenylalanine dl-mandelic acid [C{sub 9}H{sub 11}NO{sub 2}. C{sub 8}H{sub 8}O{sub 3}], have been grown by the slow evaporation technique at room temperature using aqueous solution. The single crystal XRD study confirms monoclinic system for the grown crystal. The functional groups present in the grown crystal have been identified by FTIR and FT-Raman analyses. The optical absorption studies show that the crystal is transparent in the visible region with a lower cut-off wavelength of 257 nm and the optical band gap energy E{sub g} is determined to be 4.62 eV. The Kurtz powder second harmonic generation was confirmed using Nd:YAG laser with fundamental wavelength of 1064 nm. Further, the thermal studies confirmed no weight loss up to 150°C for the as-grown crystal. The photoluminescence spectrum exhibited three peaks (414 nm, 519 nm, 568 nm) due to the donation of protons from carboxylic acid to amino group. Laser damage threshold value was found to be 4.98 GW/cm{sup 2}. The Vickers microhardness test was carried out on the grown crystals and there by Vickers hardness number (H{sub v}), work hardening coefficient (n), yield strength (σ{sub y}), stiffness constant C{sub 11} were evaluated. The dielectric behavior of the crystal has been determined in the frequency range 50 Hz–5 MHz at various temperatures.
MSTAR图像2D Gabor滤波增强与自适应阈值分割%2D Gabor Filter Enhancing and Adaptive Thresholding for MSTAR Image
Institute of Scientific and Technical Information of China (English)
倪维平; 严卫东; 吴俊政; 芦颖; 郑刚; 马心璐
2013-01-01
Image segmentation is a hot point in the research field of automatic target recognition of SAR image. In order to segment the MSTAR image automatically, a new adaptive method is proposed. Firstly, 2D Gabor filters with various orientations and scales are used to enhance the original image, which can effectually reduce speckle noise in the background, and smooth the interior of the homogeneous regions. Then the analysis of the statistical characteristics of the enhanced image is made, based on which the adaptive thresholding rules is presented for the automatically segmentation of the images. Experiment results with the MSTAR images indicate that the algorithm presented has advantage of segmentation accuracy, calculation efficiency and noise robustness over the traditional methods, such as OTSU, FCM and MRF.% 为实现MSTAR图像无监督分割,并提高分割精度和计算效率,提出了一种基于Gabor滤波增强的自适应阈值分割算法。首先利用多尺度、多方向的Gabor滤波器组对待分割图像进行滤波处理,抑制目标、阴影和背景区域内部的斑噪起伏,同时增强区域间的差异性；在此基础上,通过对增强图像统计特性的分析,给出了灰度阈值计算形式,实现了MSTAR图像的自适应分割。实验结果表明,本文算法对不同斑噪强度的MSTAR图像均具有良好的处理效果,在分割精度、计算效率等方面优于传统的OTSU,以及FCM、MRF等分割方法。
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.
The Effect of Nonlinear Threshold between the Public Service and Housing Price%公共服务供给与房价关系的非线性门限效应
Institute of Scientific and Technical Information of China (English)
范新英; 张所地
2015-01-01
This paper studies the impact of the urban public service on the housing prices, using the Tiebout theory and the panel data threshold regression. The results show that there is an obvious nonlinear threshold relationship between the level of public service and the housing prices. With the improvement of the level of pubic service, the impact on housing prices also increases gradually. The imbalance of urban public resource allocation is a main reason of some urban housing prices grow fast. So it is put forward that the real estate regulation is system engineering. The tax policy and fiscal transfer policy pegged to the level of public service should be explored.%基于Tiebout理论，采用构建基础理论模型及面板门限回归方法，检验城市公共服务供给水平对房价变动的影响。结果表明，公共服务供给与房价之间存在显著的非线性门限关系，随着公共服务供给水平的增加，其对房价影响也逐步增大，城市间公共资源配置失衡是造成一些城市房价分化的主要原因。据此提出房地产市场调控是一个系统工程，应探索与公共服务水平挂钩的房地产税收政策、财政转移支付政策等。
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.
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.
Li, Guangmao; Wu, Kui; Liu, Qiong; Yang, Zhihua; Pan, Shilie
2016-06-15
The development of frequency-conversion technology in the infrared region is in urgent need of new excellent infrared nonlinear optical (IR NLO) materials. How to achieve a good balance between laser damage threshold (LDT) and NLO coefficient (dij) for new IR NLO candidates is still a challenge. The combination of the highly electropositive alkali metal (Na) and Zn with d(10) electronic configuration into crystal structure affords one new IR NLO material, Na2ZnGe2S6. It exhibits excellent properties including a wide transparent region (0.38-22 μm), large band gap (3.25 eV), and especially a balance between a strong NLO coefficient (30-fold that of KDP) and a high LDT (6-fold that of AgGaS2), indicating a promising application in the IR region. Moreover, novel common-vertex-linked wavelike ∞[GeS3]n chains are interestingly discovered in Na2ZnGe2S6, which rarely exist in the reported thiogermanides containing alkali metals. In addition, calculated SHG density and dipole moment demonstrate that the large NLO response is mainly attributed to the cooperative effects of the [GeS4] and [ZnS4] units.
Iterative truncated arithmetic mean filter and its properties.
Jiang, Xudong
2012-04-01
The arithmetic mean and the order statistical median are two fundamental operations in signal and image processing. They have their own merits and limitations in noise attenuation and image structure preservation. This paper proposes an iterative algorithm that truncates the extreme values of samples in the filter window to a dynamic threshold. The resulting nonlinear filter shows some merits of both the fundamental operations. Some dynamic truncation thresholds are proposed that guarantee the filter output, starting from the mean, to approach the median of the input samples. As a by-product, this paper unveils some statistics of a finite data set as the upper bounds of the deviation of the median from the mean. Some stopping criteria are suggested to facilitate edge preservation and noise attenuation for both the long- and short-tailed distributions. Although the proposed iterative truncated mean (ITM) algorithm is not aimed at the median, it offers a way to estimate the median by simple arithmetic computing. Some properties of the ITM filters are analyzed and experimentally verified on synthetic data and real images.
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.
Optimal Threshold Functions for Fault Detection and Isolation
DEFF Research Database (Denmark)
Stoustrup, Jakob; Niemann, H.; Harbo, Anders La-Cour
2003-01-01
Fault diagnosis systems usually comprises two parts: a filtering part and a decision part, the latter typically based on threshold functions. In this paper, systematic ways to choose the threshold values are proposed. Two different test functions for the filtered signals are discussed and a method...
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.
Directory of Open Access Journals (Sweden)
Paulchamy Balaiah
2012-01-01
Full Text Available Problem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time, time domain biological signals, which are not reproducible. It is supposed to contain information about what is going on in the ensemble of excitatory pyramidal neuron level, at millisecond temporal resolution scale. Since scalp EEG contains considerable amount of noise and artifacts and exactly where it is coming from is poorly determined, extracting information from it is extremely challenging. For this reason it is necessary to design specific filters to decrease such artifacts in EEG records. Approach: Some of the other methods that are really appealing are artifact removal through Independent Component Analysis (ICA, Wavelet Transforms, Linear filtering and Artificial Neural Networks. ICA method could be used in situations, where large numbers of noises need to be distinguished, but it is not suitable for on-line real time application like Brain Computer Interface (BCI. Wavelet transforms are suitable for real-time application, but there all success lies in the selection of the threshold function. Linear filtering is best when; the frequency of noises does not interfere or overlap with each other. In this study we proposed adaptive filtering and neuro-fuzzy filtering method to remove artifacts from EEG. Adaptive filter performs linear filtering. Neuro-fuzzy approaches are very promising for non-linear filtering of noisy image. The multiple-output structure is based on recursive processing. It is able to adapt the filtering action to different kinds of corrupting noise. Fuzzy reasoning embedded into the network structure aims at reducing errors
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...
Threshold electric field in unconventional density waves
Dóra, Balázs; Virosztek, Attila; Maki, Kazumi
2001-07-01
As it is well known most charge-density waves (CDW's) and spin-density waves exhibit nonlinear transport with well-defined threshold electric field ET. Here we study theoretically the threshold electric field of unconventional density waves. We find that the threshold field increases monotonically with temperature without divergent behavior at Tc, unlike the one in conventional CDW. The present result in the three-dimensional weak pinning limit appears to describe rather well the threshold electric field observed recently in the low-temperature phase of α-(BEDT-TTF)2KHg(SCN)4.
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.
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
Multiscale Recognition Algorithm for Eye Ground Texture Based on Fusion Threshold Equalization
Directory of Open Access Journals (Sweden)
Zhongsheng Qiu
2014-09-01
Full Text Available The eye ground texture is disturbed by non ideal imaging factor such as noise, it will affect the clinical diagnosis in practice, an improved multi scale retina eye ground texture recognition algorithm is proposed based on fusion area threshold. The nonlinear sampling multi-scale transform is used to analyze the geometric space coefficient of retinal vessels with multi direction and shift invariant features, the regional threshold filtering is integrated, it is used to suppress the effect of non-uniform blocks for texture recognition. The maximum likelihood local mean standard deviation analysis is used for texture parameters estimation and recognition. The noise reduced greatly, accurate identification of texture feature is obtained. Simulation results show that the algorithm can well characterize the retinal vascular texture, it has good performance in different texture feature recognition, the recognition accuracy is improved, and it has good robustness.
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.
A solid-state single-photon filter
de Santis, Lorenzo; Antón, Carlos; Reznychenko, Bogdan; Somaschi, Niccolo; Coppola, Guillaume; Senellart, Jean; Gómez, Carmen; Lemaître, Aristide; Sagnes, Isabelle; White, Andrew G.; Lanco, Loïc; Auffèves, Alexia; Senellart, Pascale
2017-07-01
A strong limitation of linear optical quantum computing is the probabilistic operation of two-quantum-bit gates based on the coalescence of indistinguishable photons. A route to deterministic operation is to exploit the single-photon nonlinearity of an atomic transition. Through engineering of the atom-photon interaction, phase shifters, photon filters and photon-photon gates have been demonstrated with natural atoms. Proofs of concept have been reported with semiconductor quantum dots, yet limited by inefficient atom-photon interfaces and dephasing. Here, we report a highly efficient single-photon filter based on a large optical nonlinearity at the single-photon level, in a near-optimal quantum-dot cavity interface. When probed with coherent light wavepackets, the device shows a record nonlinearity threshold around 0.3 ± 0.1 incident photons. We demonstrate that 80% of the directly reflected light intensity consists of a single-photon Fock state and that the two- and three-photon components are strongly suppressed compared with the single-photon one.
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.
Thresholded Range Aggregation in Sensor Networks
DEFF Research Database (Denmark)
Yiu, Man Lung; Lin, Zhifeng; Mamoulis, Nikos
2010-01-01
called thresholded range aggregate query (TRA), which retrieves the IDs of the sensors for which the average measurement in their neighborhood exceeds a user-given threshold. This query provides results that they are robust against individual sensor abnormality, and yet precisely summarize the sensors......' status in each local region. In order to process the (snapshot) TRA query, we develop energy-efficient protocols based on appropriate operators and filters in sensor nodes. The design of these operators and filters is non-trivial, due to the fact that each sensor measurement influences the actual results...
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...
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.
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.
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.
Design of Threshold Controller Based Chaotic Circuits
DEFF Research Database (Denmark)
Mohamed, I. Raja; Murali, K.; Sinha, Sudeshna
2010-01-01
We propose a very simple implementation of a second-order nonautonomous chaotic oscillator, using a threshold controller as the only source of nonlinearity. We demonstrate the efficacy and simplicity of our design through numerical and experimental results. Further, we show that this approach of ...
Psychophysical thresholds of face visibility during infancy
DEFF Research Database (Denmark)
Gelskov, Sofie; Kouider, Sid
2010-01-01
. By contrast, 15 month-olds not only revealed adult-like thresholds, but also improved their performance through memory-based strategies. Our results imply that the development of face visibility follows a non-linear course and is determined by a radical improvement occurring between 10 and 15 months....
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.
Nonlinear time series modelling: an introduction
Simon M. Potter
1999-01-01
Recent developments in nonlinear time series modelling are reviewed. Three main types of nonlinear models are discussed: Markov Switching, Threshold Autoregression and Smooth Transition Autoregression. Classical and Bayesian estimation techniques are described for each model. Parametric tests for nonlinearity are reviewed with examples from the three types of models. Finally, forecasting and impulse response analysis is developed.
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...
Modeling human perceptual thresholds in self-motion perception
Valente Pais, A.R.; Mulder, M.; Paassen, M.M. van; Wentink, M.; Groen, E.L.
2006-01-01
Knowledge of thresholds for perception of inertial motion is needed for the design of simulator motion filters. Experiments have generally been done to measure these thresholds in isolation, one motion at the time. In vehicle simulation however, several motions occur concurrently. In a flight
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.
Zhou, J.; Ashouei, M.; Kinniment, D.; Huisken, J.; Russell, G.; Yakovlev, A.
2011-01-01
Sub-threshold operation has been proven to be very effective to reduce the power consumption of circuits when high performance is not required. Future low power systems on chip are likely to consist of many sub-systems operating at different frequencies and VDDs from super-threshold to sub-threshold
Threshold Concepts in Biochemistry
Loertscher, Jennifer
2011-01-01
Threshold concepts can be identified for any discipline and provide a framework for linking student learning to curricular design. Threshold concepts represent a transformed understanding of a discipline, without which the learner cannot progress and are therefore pivotal in learning in a discipline. Although threshold concepts have been…
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.
Institute of Scientific and Technical Information of China (English)
刘文栋; 王飞
2016-01-01
基于我国41家银行2004－2012年的平衡面板数据，采用面板门槛模型实证检验资本监管对银行风险非线性的影响。研究发现：资本监管对银行风险表现出明显的门槛特征，两者呈非线性关系，但资本监管与银行风险在不同的阈值范围内都呈正相关关系，验证了“监管假说”；本文估计出门槛值大于最低资本监管水平，这一发现启示监管当局应对银行施加适当的压力，提出银行达到最低资本监管标准具体的时限。%Based on the balance panel data of 41 banks in China during 2004-2012, this paper uses panel threshold model to empirically test the nonlinear effect of capital regulation on bank risk. The main conclusions are as follows:reg⁃ulatory capital has a clear threshold characteristic on bank risk and they show a nonlinear relationship, but in the thresh⁃old range of different regulatory capitals, bank risk is correlated with regulatory capital positively, verifying the hypothesis of “supervision”;the estimated threshold value is greater than the minimum regulatory capital levels, which has some enlightenment to the regulatory authorities:regulatory authorities should apply appropriate pressure to allow banks to meet regulatory capital standards and put forward specific period of time for banks.
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...
Institute of Scientific and Technical Information of China (English)
孙建
2011-01-01
The paper studies the innovation convergence and its club convergence effects during the process of regional innovation according to the theory of spatial filtering and threshold regression model with panel data and by means of the Chinese regional patent data from 1998-2008.After the spatial correlativity is filtered,the results of the threshold regression model show that there exists innovation convergence effects and club convergence effects in the process of regional innovation and there also exists threshold effects for RD human capital that is about equal to 0.5680 and 0.5829（indicated by the proportion of the number of scientists and engineers to the science and technology activities personnel）.And there are probable three kinds of the club convergence in China regional innovation.%借鉴经济收敛理论,基于空间分析和门槛面板回归模型,根据中国区域1998—2008年的省际专利创新数据,研究了中国区域创新敛散性问题。空间分析结果表明,中国区域研发创新存在着较强的空间相关性。在利用空间过滤技术消除样本数据的空间相关性后,门槛面板回归模型估计结果表明,中国区域创新存在绝对收敛和条件收敛共存的特性;以区域科学家工程师人数占区域科技活动人数的比例来表示的区域R＆D人力资本大约等于0.568 0和0.582 9为门槛,中国区域创新存在着三大俱乐部收敛现象。
GENERALIZED FUZZY FILTERS OF BL-ALGEBRAS
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The concept of quasi-coincidence of a fuzzy interval value with an interval valued fuzzy set is considered. In fact, this is a generalization of quasi-coincidence of a fuzzy point with a fuzzy set. By using this new idea, the notion of interval valued (∈, ∈∨q)-fuzzy filters in BL-algebras which is a generalization of fuzzy filters of BL-algebras, is defined, and related properties are investigated. In particular, the concept of a fuzzy subgroup with thresholds is extended to the concept of an interval valued fuzzy filter with thresholds in BL-algebras.
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.
DEFF Research Database (Denmark)
Hansen, Kristoffer Arnsfelt; Podolskii, Vladimir V.
2010-01-01
We initiate a systematic study of constant depth Boolean circuits built using exact threshold gates. We consider both unweighted and weighted exact threshold gates and introduce corresponding circuit classes. We next show that this gives a hierarchy of classes that seamlessly interleave with the ......We initiate a systematic study of constant depth Boolean circuits built using exact threshold gates. We consider both unweighted and weighted exact threshold gates and introduce corresponding circuit classes. We next show that this gives a hierarchy of classes that seamlessly interleave...... with the well-studied corresponding hierarchies defined using ordinary threshold gates. A major open problem in Boolean circuit complexity is to provide an explicit super-polynomial lower bound for depth two threshold circuits. We identify the class of depth two exact threshold circuits as a natural subclass...
Nonlinear Peltier effect in semiconductors
Zebarjadi, Mona; Esfarjani, Keivan; Shakouri, Ali
2007-09-01
Nonlinear Peltier coefficient of a doped InGaAs semiconductor is calculated numerically using the Monte Carlo technique. The Peltier coefficient is also obtained analytically for single parabolic band semiconductors assuming a shifted Fermi-Dirac electronic distribution under an applied bias. Analytical results are in agreement with numerical simulations. Key material parameters affecting the nonlinear behavior are doping concentration, effective mass, and electron-phonon coupling. Current density thresholds at which nonlinear behavior is observable are extracted from numerical data. It is shown that the nonlinear Peltier effect can be used to enhance cooling of thin film microrefrigerator devices especially at low temperatures.
ICA Based Speckle Filtering for Target Extraction in SAR Images Using Adaptive Space Separation
Institute of Scientific and Technical Information of China (English)
LI Yu-tong; ZHOU Yue; YANG Lei
2008-01-01
A novel approach based on independent component analysis (ICA) for speckle filtering and target extraction of synthetic aperture radar (SAR) images is proposed using adaptive space separation with weighted information entropy (WIE) incorporated. First the basis and the independent components are respectively obtained by ICA technique, and WIE of the image is computed; then based on the threshold computed from function T-WIE (threshold versus weighted-information-entropy), independent components are adaptively separated and the bases are classified accordingly. Thus, the image space is separated into two subspaces: "clean" and "noise". Then, a proposed nonlinear operator ABO is applied on each component of the 'clean' subspace for further optimization. Finally, recovery image is obtained reconstructing this subspace and target is easily extracted with binarisation. Note that here T-WIE is an interpolated function based on several representative target SAR images using proposed space separation algorithm.
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...
DEFF Research Database (Denmark)
Petersen, Sidsel Rübner; Alkeskjold, Thomas Tanggaard; Lægsgaard, Jesper
can reach the yellow‐orange light regime through frequency doubling. Yellow‐orange light has applications within the medical industry, high‐resolution spectroscopy and for laser‐guide stars [2]. To achieve amplification at these wavelengths, the larger gain at shorter wavelengths must be suppressed...... to avoid parasitic lasing due to Amplified Spontaneous Emission (ASE) build‐up. Nonlinear effects, such as stimulated Raman scattering, stimulated Brillouin scattering and four‐wave mixing, set the upper limit for achievable powers in fiber amplifiers. To increase the nonlinear threshold, Large...... to a large degree be controlled through index guidance by tuning the air hole diameter. Suppression of unwanted spectral components is realized through bandgap guidance by tailoring the high‐index inclusions. A filter of ASE is thereby incorporated in the PCF cladding. Furthermore the inclusions on one side...
Polynomial threshold functions and Boolean threshold circuits
DEFF Research Database (Denmark)
Hansen, Kristoffer Arnsfelt; Podolskii, Vladimir V.
2013-01-01
We study the complexity of computing Boolean functions on general Boolean domains by polynomial threshold functions (PTFs). A typical example of a general Boolean domain is 12n . We are mainly interested in the length (the number of monomials) of PTFs, with their degree and weight being...... of secondary interest. We show that PTFs on general Boolean domains are tightly connected to depth two threshold circuits. Our main results in regard to this connection are: PTFs of polynomial length and polynomial degree compute exactly the functions computed by THRMAJ circuits. An exponential length lower...
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
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
Institute of Scientific and Technical Information of China (English)
廖敏; 洪国彬
2015-01-01
This paper uses the DEA model to evaluate the efficiency of logistics in China’s 30 provincial units from 2004 to 2012, and by using the nonlinear panel threshold autoregressive model ( PTAR) empirical to research the relation between the environmental regulation and the efficiency of the logistics, when environmental regulation as the threshold variable .The research results show that the environmental regulation has three thresh-old effects on China's efficiency of logistics, when the environmental regulation is less than the first threshold value of 72.6697,that the interval one, it has weak significant role in promoting; When environmental regulation between the first threshold value of 72.6697 and second threshold value of 186.1485, that the interval two, environmental regulation has a significant positive influence on logistics efficiency , and the influence de-gree is much more than the interval one;When environmental regulation between the second threshold value of 186.1485 and third threshold value of 259.3606(the interval three) and the threshold is greater than the third value of 259.3606(the interval four), environmental regulation has in-hibitory effect on efficiency of logistics, the degree of the latter is greater but not notable.%本文首先运用DEA模型测算了我国30个省级单位2004—2012年的物流业效率，并利用非线性面板门槛模型（ PTAR）研究了环境规制对我国物流业效率的影响。研究结果表明：环境规制对我国物流业效率存在三门槛效应，当环境规制（环境污染治理投资）小于第一门槛值72．6697时（区间1），其具有促进作用；当环境规制介于第一门槛值72．6697与第二门槛值186．1485时（区间2），其具有显著正向影响，影响程度相比区间1大幅提升；当环境规制介于第二门槛值186．1485与第三门槛值259．3606（区间3）以及大于第三门槛259．3606时（区间4），环境规制对物流业效率
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.
Threshold Concepts in Economics
Shanahan, Martin
2016-01-01
Purpose: The purpose of this paper is to examine threshold concepts in the context of teaching and learning first-year university economics. It outlines some of the arguments for using threshold concepts and provides examples using opportunity cost as an exemplar in economics. Design/ Methodology/Approach: The paper provides an overview of the…
Monocular and binocular depth discrimination thresholds.
Kaye, S B; Siddiqui, A; Ward, A; Noonan, C; Fisher, A C; Green, J R; Brown, M C; Wareing, P A; Watt, P
1999-11-01
(distance) stereoacuity test. The method described for calculating the BT provides one simple nonlinear solution for determining the respective contributions of binocular and monocular (MT) depth discrimination to the combined depth threshold.
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...
Institute of Scientific and Technical Information of China (English)
王华; 祝树金; 赖明勇
2012-01-01
By introducing double-edged roles of technology gap into the quality- ladder endogenous growth model, this paper shows that there may exist the threshold value, which results in the non-linear evolution property of technology spillovers of FDI. Based on 14474 firms level panel date in China, the empirical results are robust to support there are two technology gap＇s threshold values, which lead to the FDI spillover effects vary cross firms who belong to different range of technology gap.%本文通过引入内外资技术差距的“双刃性”改进质量阶梯型内生增长模型，从理论上证明了存在内外资技术差距的门槛值，使得外商直接投资技术溢出受技术差距的影响而呈现非线性动态演进规律；进一步结合我国14474家微观企业面板数据，证明了存在两个技术差距的门槛值，使得外商直接投资企业对于落在不同技术差距范围内的内资企业具有不同的技术溢出效应。
Nonlinear Multiantenna Detection Methods
Directory of Open Access Journals (Sweden)
Chen Sheng
2004-01-01
Full Text Available A nonlinear detection technique designed for multiple-antenna assisted receivers employed in space-division multiple-access systems is investigated. We derive the optimal solution of the nonlinear spatial-processing assisted receiver for binary phase shift keying signalling, which we refer to as the Bayesian detector. It is shown that this optimal Bayesian receiver significantly outperforms the standard linear beamforming assisted receiver in terms of a reduced bit error rate, at the expense of an increased complexity, while the achievable system capacity is substantially enhanced with the advent of employing nonlinear detection. Specifically, when the spatial separation expressed in terms of the angle of arrival between the desired and interfering signals is below a certain threshold, a linear beamformer would fail to separate them, while a nonlinear detection assisted receiver is still capable of performing adequately. The adaptive implementation of the optimal Bayesian detector can be realized using a radial basis function network. Two techniques are presented for constructing block-data-based adaptive nonlinear multiple-antenna assisted receivers. One of them is based on the relevance vector machine invoked for classification, while the other on the orthogonal forward selection procedure combined with the Fisher ratio class-separability measure. A recursive sample-by-sample adaptation procedure is also proposed for training nonlinear detectors based on an amalgam of enhanced -means clustering techniques and the recursive least squares algorithm.
Chen, Yangkang
2016-07-01
The seislet transform has been demonstrated to have a better compression performance for seismic data compared with other well-known sparsity promoting transforms, thus it can be used to remove random noise by simply applying a thresholding operator in the seislet domain. Since the seislet transform compresses the seismic data along the local structures, the seislet thresholding can be viewed as a simple structural filtering approach. Because of the dependence on a precise local slope estimation, the seislet transform usually suffers from low compression ratio and high reconstruction error for seismic profiles that have dip conflicts. In order to remove the limitation of seislet thresholding in dealing with conflicting-dip data, I propose a dip-separated filtering strategy. In this method, I first use an adaptive empirical mode decomposition based dip filter to separate the seismic data into several dip bands (5 or 6). Next, I apply seislet thresholding to each separated dip component to remove random noise. Then I combine all the denoised components to form the final denoised data. Compared with other dip filters, the empirical mode decomposition based dip filter is data-adaptive. One only needs to specify the number of dip components to be separated. Both complicated synthetic and field data examples show superior performance of my proposed approach than the traditional alternatives. The dip-separated structural filtering is not limited to seislet thresholding, and can also be extended to all those methods that require slope information.
Universal threshold enhancement
Patkós, András; Szépfalusy, P; Szep, Zs.
2003-01-01
By assuming certain analytic properties of the propagator, it is shown that universal features of the spectral function including threshold enhancement arise if a pole describing a particle at high temperature approaches in the complex energy plane the threshold position of its two-body decay with the variation of T. The case is considered, when one can disregard any other decay processes. The quality of the proposed description is demonstrated by comparing it with the detailed large N solution of the linear sigma model around the pole-threshold coincidence.
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.
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
Quantum threshold group signature
Institute of Scientific and Technical Information of China (English)
2008-01-01
In most situations, the signer is generally a single person. However, when the message is written on behalf of an organization, a valid message may require the approval or consent of several persons. Threshold signature is a solution to this problem. Generally speaking, as an authority which can be trusted by all members does not exist, a threshold signature scheme without a trusted party appears more attractive. Following some ideas of the classical Shamir’s threshold signature scheme, a quantum threshold group signature one is proposed. In the proposed scheme, only t or more of n persons in the group can generate the group signature and any t-1 or fewer ones cannot do that. In the verification phase, any t or more of n signature receivers can verify the message and any t-1 or fewer receivers cannot verify the validity of the signature.
Efficient circular thresholding.
Lai, Yu-Kun; Rosin, Paul L
2014-03-01
Otsu's algorithm for thresholding images is widely used, and the computational complexity of determining the threshold from the histogram is O(N) where N is the number of histogram bins. When the algorithm is adapted to circular rather than linear histograms then two thresholds are required for binary thresholding. We show that, surprisingly, it is still possible to determine the optimal threshold in O(N) time. The efficient optimal algorithm is over 300 times faster than traditional approaches for typical histograms and is thus particularly suitable for real-time applications. We further demonstrate the usefulness of circular thresholding using the adapted Otsu criterion for various applications, including analysis of optical flow data, indoor/outdoor image classification, and non-photorealistic rendering. In particular, by combining circular Otsu feature with other colour/texture features, a 96.9% correct rate is obtained for indoor/outdoor classification on the well known IITM-SCID2 data set, outperforming the state-of-the-art result by 4.3%.
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.
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.
Efficient particle filtering through residual nudging
Luo, Xiaodong
2013-05-15
We introduce an auxiliary technique, called residual nudging, to the particle filter to enhance its performance in cases where it performs poorly. The main idea of residual nudging is to monitor and, if necessary, adjust the residual norm of a state estimate in the observation space so that it does not exceed a pre-specified threshold. We suggest a rule to choose the pre-specified threshold, and construct a state estimate accordingly to achieve this objective. Numerical experiments suggest that introducing residual nudging to a particle filter may (substantially) improve its performance, in terms of filter accuracy and/or stability against divergence, especially when the particle filter is implemented with a relatively small number of particles. © 2013 Royal Meteorological Society.
Multipole vector solitons in nonlocal nonlinear media.
Kartashov, Yaroslav V; Torner, Lluis; Vysloukh, Victor A; Mihalache, Dumitru
2006-05-15
We show that multipole solitons can be made stable via vectorial coupling in bulk nonlocal nonlinear media. Such vector solitons are composed of mutually incoherent nodeless and multipole components jointly inducing a nonlinear refractive index profile. We found that stabilization of the otherwise highly unstable multipoles occurs below certain maximum energy flow. Such a threshold is determined by the nonlocality degree.
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
Hydrodynamics of sediment threshold
Ali, Sk Zeeshan; Dey, Subhasish
2016-07-01
A novel hydrodynamic model for the threshold of cohesionless sediment particle motion under a steady unidirectional streamflow is presented. The hydrodynamic forces (drag and lift) acting on a solitary sediment particle resting over a closely packed bed formed by the identical sediment particles are the primary motivating forces. The drag force comprises of the form drag and form induced drag. The lift force includes the Saffman lift, Magnus lift, centrifugal lift, and turbulent lift. The points of action of the force system are appropriately obtained, for the first time, from the basics of micro-mechanics. The sediment threshold is envisioned as the rolling mode, which is the plausible mode to initiate a particle motion on the bed. The moment balance of the force system on the solitary particle about the pivoting point of rolling yields the governing equation. The conditions of sediment threshold under the hydraulically smooth, transitional, and rough flow regimes are examined. The effects of velocity fluctuations are addressed by applying the statistical theory of turbulence. This study shows that for a hindrance coefficient of 0.3, the threshold curve (threshold Shields parameter versus shear Reynolds number) has an excellent agreement with the experimental data of uniform sediments. However, most of the experimental data are bounded by the upper and lower limiting threshold curves, corresponding to the hindrance coefficients of 0.2 and 0.4, respectively. The threshold curve of this study is compared with those of previous researchers. The present model also agrees satisfactorily with the experimental data of nonuniform sediments.
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.
Explaining threshold effects of globalization on poverty: An institutional perspective
Sindzingre, Alice
2005-01-01
The paper focuses on the non-linearity of the transmission of the impact of globalization on poverty and the existence of threshold effects. Institutions constitute a critical factor for the creation of threshold effects in the impact of globalization on poverty. Institutions—their credibility, ability to be transformed by globalization, and the ways they give the poor access to the beneficial effects of globalization—determine whether the benefits of globalization are spread to the poor or a...
Threshold effect of fiscal policy on private consumption :
Directory of Open Access Journals (Sweden)
Wissem Khanfir
2016-03-01
Full Text Available Using a threshold regression model, we analyse the impact of fiscal policy on private consumption in Tunisia, over the 1975-2010 period. Our empirical results revealed that public expenditure and tax revenues have Keynesian effects on consumption, when private debt/GDP ratio is below 48 %. This effect becomes non-Keynesian once this threshold is exceeded. We provide empirical evidence that private consumption reacts in non-linear fashion to changes in fiscal policy.
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
Polynomial threshold functions and Boolean threshold circuits
DEFF Research Database (Denmark)
Hansen, Kristoffer Arnsfelt; Podolskii, Vladimir V.
2013-01-01
of secondary interest. We show that PTFs on general Boolean domains are tightly connected to depth two threshold circuits. Our main results in regard to this connection are: PTFs of polynomial length and polynomial degree compute exactly the functions computed by THRMAJ circuits. An exponential length lower...... bound for PTFs that holds regardless of degree, thereby extending known lower bounds for THRMAJ circuits. We generalize two-party unbounded error communication complexity to the multi-party number-on-the-forehead setting, and show that communication lower bounds for 3-player protocols would yield size...... lower bounds for THRTHR circuits. We obtain several other results about PTFs. These include relationships between weight and degree of PTFs, and a degree lower bound for PTFs of constant length. We also consider a variant of PTFs over the max-plus algebra. We show that they are connected to PTFs over...
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...
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.
CRYSTAL FILTERS, *HIGH FREQUENCY, *RADIOFREQUENCY FILTERS, AMPLIFIERS, ELECTRIC POTENTIAL, FREQUENCY, IMPEDANCE MATCHING , INSTRUMENTATION, RADIOFREQUENCY, RADIOFREQUENCY AMPLIFIERS, TEST EQUIPMENT, TEST METHODS
Automated thresholding in radiographic image for welded joints
Yazid, Haniza; Arof, Hamzah; Yazid, Hafizal
2012-03-01
Automated detection of welding defects in radiographic images becomes non-trivial when uneven illumination, contrast and noise are present. In this paper, a new surface thresholding method is introduced to detect defects in radiographic images of welding joints. In the first stage, several image processing techniques namely fuzzy c means clustering, region filling, mean filtering, edge detection, Otsu's thresholding and morphological operations method are utilised to locate the area in which defects might exist. This is followed by the implementation of inverse surface thresholding with partial differential equation to locate isolated areas that represent the defects in the second stage. The proposed method obtained a promising result with high precision.
Resolution enhancement in nonlinear photoacoustic imaging
Energy Technology Data Exchange (ETDEWEB)
Goy, Alexandre S.; Fleischer, Jason W. [Department of Electrical Engineering, Princeton University, Olden St., Princeton, New Jersey 08544 (United States)
2015-11-23
Nonlinear processes can be exploited to gain access to more information than is possible in the linear regime. Nonlinearity modifies the spectra of the excitation signals through harmonic generation, frequency mixing, and spectral shifting, so that features originally outside the detector range can be detected. Here, we present an experimental study of resolution enhancement for photoacoustic imaging of thin metal layers immersed in water. In this case, there is a threshold in the excitation below which no acoustic signal is detected. Above threshold, the nonlinearity reduces the width of the active area of the excitation beam, resulting in a narrower absorption region and thus improved spatial resolution. This gain is limited only by noise, as the active area of the excitation can be arbitrarily reduced when the fluence becomes closer to the threshold. Here, we demonstrate a two-fold improvement in resolution and quantify the image quality as the excitation fluence goes through threshold.
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
Institute of Scientific and Technical Information of China (English)
2013-01-01
产业集聚能否促进技术溢出，国内外学者的实证检验结果存在分歧。本文从人力资本视角研究产业集聚的技术溢出效应，选取中国1986－2011年各省数据，采用门限非线性估计，验证在不同人力资本条件下，空间集聚对全要素生产率增长的门槛效应。实证结果表明，在低人力资本条件下，产业集聚对全要素生产率增长没有影响，当人力资本跨越“门槛值”，产业集聚显著促进全要素生产率增长，且随着人力资本的进一步提高，促进效果明显加强。此外，研究表明产业集聚对全要素生产率增长的影响途径主要是促进技术进步，对于技术效率的影响不明显。%There exists divergence of empirical results about whether the industrial agglomeration can pro-mote the technology spillover.This paper analyses the technology spillover effect of industrial agglomeration from the perspective of human capital .Based on the Chinese provincial data from 1986 to 2011, the paper verifies the threshold effect of spatial agglomeration on the total factor productivity growth under different hu-man capital conditions by using threshold nonlinear estimation .The empirical results show that , the industrial concentration has no effect on total factor productivity growth at a low level of human capital .Once exceeding the "threshold value"of human capital, the industrial concentration will significantly promote the total factor productivity, and with the further increase of human capital , the effect is particularly obvious.In addition, this paper indicates that, the industrial agglomeration promotes the total factor productivity growth mainly by promoting technological progress rather than technical efficiency .
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
Drainage filter technologies to mitigate site-specific phosphorus losses
DEFF Research Database (Denmark)
Kjærgaard, Charlotte; Heckrath, Goswin Johann; Iversen, Bo Vangsø
2014-01-01
of implementing the drainage filter technologies including surface-flow constructed wetlands, subsurface flow constructed wetlands, and drainage well filters (www.supremetech.dk). We will present results on P retention from (i) controlled column experiments with permeable filter substrates, and (ii) a full......-scale surface-flow constructed wetland. In the former, various natural and industrial P filter substrates have been tested for their ability to reduce inlet P concentrations to below environmental threshold values (... on laboratory experiments and field scale monitoring, the different filter technology approaches will be compared and evaluated from a case study perspective....
Edge-Detected Guided Morphological Filter for Image Sharpening
Directory of Open Access Journals (Sweden)
S. Marshall
2009-02-01
Full Text Available A new edge-guided morphological filter is proposed to sharpen digital images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradient-based operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the performance of these detected edge deblurring filters is superior to that of other sharpener-type filters.
Edge-Detected Guided Morphological Filter for Image Sharpening
Directory of Open Access Journals (Sweden)
Mahmoud TA
2008-01-01
Full Text Available Abstract A new edge-guided morphological filter is proposed to sharpen digital images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradient-based operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the performance of these detected edge deblurring filters is superior to that of other sharpener-type filters.
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.
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...
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.
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.
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.
Saletes, Izella; Gilles, Bruno; Bera, Jean-Christophe
2011-01-01
Enhancing cavitation activity with minimal acoustic intensities could be interesting in a variety of therapeutic applications where mechanical effects of cavitation are needed with minimal heating of surrounding tissues. The present work focuses on the relative efficiency of a signal combining two neighbouring frequencies and a one-frequency signal for initiating ultrasound inertial cavitation. Experiments were carried out in a water tank, using a 550kHz piezoelectric composite spherical transducer focused on targets with 46μm roughness. The acoustic signal scattered, either by the target or by the cavitation bubbles, is filtered using a spectral and cepstral-like method to obtain an inertial cavitation activity measurement. The ultrasound excitations consist of 1.8ms single bursts of single frequency f(0)=550kHz excitation, in the monofrequency case, and of dual frequency f(1)=535kHz and f(2)=565kHz excitation, in the bifrequency case. It is shown that depending on the value of the monofrequency cavitation threshold intensity the bifrequency excitation can increase or reduce the cavitation threshold. The analysis of the thresholds indicates that the mechanisms involved are nonlinear. The progress of the cavitation activity beyond the cavitation threshold is also studied. The slope of the cavitation activity considered as a function of the acoustic intensity is always steeper in the case of the bifrequency excitation. This means that the delimitation of the region where cavitation occurs should be cleaner than with a classical monofrequency excitation.
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.
Roberge, Raymond J; Kim, Jung-Hyun; Powell, Jeffrey B; Shaffer, Ronald E; Ylitalo, Caroline M; Sebastian, John M
2013-01-01
Ten subjects underwent treadmill exercise at 5.6 km/h over one hour while wearing each of three identical appearing, cup-shaped, prototype filtering facepiece respirators that differed only in their filter resistances (3 mm, 6 mm, and 9 mm H2O pressure drop). There were no statistically significant differences between filtering facepiece respirators with respect to impact on physiological parameters (i.e., heart rate, respiratory rate, oxygen saturation, transcutaneous carbon dioxide levels, tympanic membrane temperature), pulmonary function variables (i.e., tidal volume, respiratory rate, volume of carbon dioxide production, oxygen consumption, or ventilation), and subjective ratings (i.e., exertion, thermal comfort, inspiratory effort, expiratory effort and overall breathing comfort). The nominal filter resistances of the prototype filtering facepiece respirators correspond to airflow resistances ranging from 2.1 - 6.6 mm H2O/L/s which are less than, or minimally equivalent to, previously reported values for the normal threshold for detection of inspiratory breathing resistance (6 - 7.6 mm H2O/L/sec). Therefore, filtering facepiece respirators with filter resistances at, or below, this level may not impact the wearer differently physiologically or subjectively from those with filter resistances only slightly above this threshold at low-moderate work rates over one hour.
Directory of Open Access Journals (Sweden)
Raymond J Roberge
Full Text Available Ten subjects underwent treadmill exercise at 5.6 km/h over one hour while wearing each of three identical appearing, cup-shaped, prototype filtering facepiece respirators that differed only in their filter resistances (3 mm, 6 mm, and 9 mm H2O pressure drop. There were no statistically significant differences between filtering facepiece respirators with respect to impact on physiological parameters (i.e., heart rate, respiratory rate, oxygen saturation, transcutaneous carbon dioxide levels, tympanic membrane temperature, pulmonary function variables (i.e., tidal volume, respiratory rate, volume of carbon dioxide production, oxygen consumption, or ventilation, and subjective ratings (i.e., exertion, thermal comfort, inspiratory effort, expiratory effort and overall breathing comfort. The nominal filter resistances of the prototype filtering facepiece respirators correspond to airflow resistances ranging from 2.1 - 6.6 mm H2O/L/s which are less than, or minimally equivalent to, previously reported values for the normal threshold for detection of inspiratory breathing resistance (6 - 7.6 mm H2O/L/sec. Therefore, filtering facepiece respirators with filter resistances at, or below, this level may not impact the wearer differently physiologically or subjectively from those with filter resistances only slightly above this threshold at low-moderate work rates over one hour.
Roberge, Raymond J.; Kim, Jung-Hyun; Powell, Jeffrey B.; Shaffer, Ronald E.; Ylitalo, Caroline M.; Sebastian, John M.
2013-01-01
Ten subjects underwent treadmill exercise at 5.6 km/h over one hour while wearing each of three identical appearing, cup-shaped, prototype filtering facepiece respirators that differed only in their filter resistances (3 mm, 6 mm, and 9 mm H2O pressure drop). There were no statistically significant differences between filtering facepiece respirators with respect to impact on physiological parameters (i.e., heart rate, respiratory rate, oxygen saturation, transcutaneous carbon dioxide levels, tympanic membrane temperature), pulmonary function variables (i.e., tidal volume, respiratory rate, volume of carbon dioxide production, oxygen consumption, or ventilation), and subjective ratings (i.e., exertion, thermal comfort, inspiratory effort, expiratory effort and overall breathing comfort). The nominal filter resistances of the prototype filtering facepiece respirators correspond to airflow resistances ranging from 2.1 - 6.6 mm H2O/L/s which are less than, or minimally equivalent to, previously reported values for the normal threshold for detection of inspiratory breathing resistance (6 - 7.6 mm H2O/L/sec). Therefore, filtering facepiece respirators with filter resistances at, or below, this level may not impact the wearer differently physiologically or subjectively from those with filter resistances only slightly above this threshold at low-moderate work rates over one hour. PMID:24386434
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.
Efficient Threshold Signature Scheme
Directory of Open Access Journals (Sweden)
Sattar J Aboud
2012-01-01
Full Text Available In this paper, we introduce a new threshold signature RSA-typed scheme. The proposed scheme has the characteristics of un-forgeable and robustness in random oracle model. Also, signature generation and verification is entirely non-interactive. In addition, the length of the entity signature participate is restricted by a steady times of the length of the RSA signature modulus. Also, the signing process of the proposed scheme is more efficient in terms of time complexity and interaction.
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.