Multi-dimensional upwind fluctuation splitting scheme with mesh adaption for hypersonic viscous flow
Wood, William Alfred, III
production is shown relative to DMFDSFV. Remarkably the fluctuation splitting scheme shows grid converged skin friction coefficients with only five points in the boundary layer for this case. A viscous Mach 17.6 (perfect gas) cylinder case demonstrates solution monotonicity and heat transfer capability with the fluctuation splitting scheme. While fluctuation splitting is recommended over DMFDSFV, the difference in performance between the schemes is not so great as to obsolete DMFDSFV. The second half of the dissertation develops a local, compact, anisotropic unstructured mesh adaption scheme in conjunction with the multi-dimensional upwind solver, exhibiting a characteristic alignment behavior for scalar problems. This alignment behavior stands in contrast to the curvature clustering nature of the local, anisotropic unstructured adaption strategy based upon a posteriori error estimation that is used for comparison. The characteristic alignment is most pronounced for linear advection, with reduced improvement seen for the more complex non-linear advection and advection-diffusion cases. The adaption strategy is extended to the two-dimensional and axisymmetric Navier-Stokes equations of motion through the concept of fluctuation minimization. The system test case for the adaption strategy is a sting mounted capsule at Mach-10 wind tunnel conditions, considered in both two-dimensional and axisymmetric configurations. For this complex flowfield the adaption results are disappointing since feature alignment does not emerge from the local operations. Aggressive adaption is shown to result in a loss of robustness for the solver, particularly in the bow shock/stagnation point interaction region. Reducing the adaption strength maintains solution robustness but fails to produce significant improvement in the surface heat transfer predictions.
Kovačević, Branko; Milosavljević, Milan
2013-01-01
“Adaptive Digital Filters” presents an important discipline applied to the domain of speech processing. The book first makes the reader acquainted with the basic terms of filtering and adaptive filtering, before introducing the field of advanced modern algorithms, some of which are contributed by the authors themselves. Working in the field of adaptive signal processing requires the use of complex mathematical tools. The book offers a detailed presentation of the mathematical models that is clear and consistent, an approach that allows everyone with a college level of mathematics knowledge to successfully follow the mathematical derivations and descriptions of algorithms. The algorithms are presented in flow charts, which facilitates their practical implementation. The book presents many experimental results and treats the aspects of practical application of adaptive filtering in real systems, making it a valuable resource for both undergraduate and graduate students, and for all others interested in m...
Grenga, Temistocle
The aim of this research is to further develop a dynamically adaptive algorithm based on wavelets that is able to solve efficiently multi-dimensional compressible reactive flow problems. This work demonstrates the great potential for the method to perform direct numerical simulation (DNS) of combustion with detailed chemistry and multi-component diffusion. In particular, it addresses the performance obtained using a massive parallel implementation and demonstrates important savings in memory storage and computational time over conventional methods. In addition, fully-resolved simulations of challenging three dimensional problems involving mixing and combustion processes are performed. These problems are particularly challenging due to their strong multiscale characteristics. For these solutions, it is necessary to combine the advanced numerical techniques applied to modern computational resources.
Target Response Adaptation for Correlation Filter Tracking
Bibi, Adel Aamer
2016-09-16
Most correlation filter (CF) based trackers utilize the circulant structure of the training data to learn a linear filter that best regresses this data to a hand-crafted target response. These circularly shifted patches are only approximations to actual translations in the image, which become unreliable in many realistic tracking scenarios including fast motion, occlusion, etc. In these cases, the traditional use of a single centered Gaussian as the target response impedes tracker performance and can lead to unrecoverable drift. To circumvent this major drawback, we propose a generic framework that can adaptively change the target response from frame to frame, so that the tracker is less sensitive to the cases where circular shifts do not reliably approximate translations. To do that, we reformulate the underlying optimization to solve for both the filter and target response jointly, where the latter is regularized by measurements made using actual translations. This joint problem has a closed form solution and thus allows for multiple templates, kernels, and multi-dimensional features. Extensive experiments on the popular OTB100 benchmark show that our target adaptive framework can be combined with many CF trackers to realize significant overall performance improvement (ranging from 3 %-13.5% in precision and 3.2 %-13% in accuracy), especially in categories where this adaptation is necessary (e.g. fast motion, motion blur, etc.). © Springer International Publishing AG 2016.
Adaptively robust filtering with classified adaptive factors
Institute of Scientific and Technical Information of China (English)
CUI Xianqiang; YANG Yuanxi
2006-01-01
The key problems in applying the adaptively robust filtering to navigation are to establish an equivalent weight matrix for the measurements and a suitable adaptive factor for balancing the contributions of the measurements and the predicted state information to the state parameter estimates. In this paper, an adaptively robust filtering with classified adaptive factors was proposed, based on the principles of the adaptively robust filtering and bi-factor robust estimation for correlated observations. According to the constant velocity model of Kalman filtering, the state parameter vector was divided into two groups, namely position and velocity. The estimator of the adaptively robust filtering with classified adaptive factors was derived, and the calculation expressions of the classified adaptive factors were presented. Test results show that the adaptively robust filtering with classified adaptive factors is not only robust in controlling the measurement outliers and the kinematic state disturbing but also reasonable in balancing the contributions of the predicted position and velocity, respectively, and its filtering accuracy is superior to the adaptively robust filter with single adaptive factor based on the discrepancy of the predicted position or the predicted velocity.
Wang, Cheng; Dong, XinZhuang; Shu, Chi-Wang
2015-10-01
For numerical simulation of detonation, computational cost using uniform meshes is large due to the vast separation in both time and space scales. Adaptive mesh refinement (AMR) is advantageous for problems with vastly different scales. This paper aims to propose an AMR method with high order accuracy for numerical investigation of multi-dimensional detonation. A well-designed AMR method based on finite difference weighted essentially non-oscillatory (WENO) scheme, named as AMR&WENO is proposed. A new cell-based data structure is used to organize the adaptive meshes. The new data structure makes it possible for cells to communicate with each other quickly and easily. In order to develop an AMR method with high order accuracy, high order prolongations in both space and time are utilized in the data prolongation procedure. Based on the message passing interface (MPI) platform, we have developed a workload balancing parallel AMR&WENO code using the Hilbert space-filling curve algorithm. Our numerical experiments with detonation simulations indicate that the AMR&WENO is accurate and has a high resolution. Moreover, we evaluate and compare the performance of the uniform mesh WENO scheme and the parallel AMR&WENO method. The comparison results provide us further insight into the high performance of the parallel AMR&WENO method.
Adaptive filtering and change detection
Gustafsson, Fredrik
2003-01-01
Adaptive filtering is a classical branch of digital signal processing (DSP). Industrial interest in adaptive filtering grows continuously with the increase in computer performance that allows ever more conplex algorithms to be run in real-time. Change detection is a type of adaptive filtering for non-stationary signals and is also the basic tool in fault detection and diagnosis. Often considered as separate subjects Adaptive Filtering and Change Detection bridges a gap in the literature with a unified treatment of these areas, emphasizing that change detection is a natural extensi
FLAG: A multi-dimensional adaptive free-Lagrange code for fully unstructured grids
Energy Technology Data Exchange (ETDEWEB)
Burton, D.E.; Miller, D.S.; Palmer, T. [Lawrence Livermore National Lab., CA (United States)
1995-07-01
The authors describe FLAG, a 3D adaptive free-Lagrange method for unstructured grids. The grid elements were 3D polygons, which move with the flow, and are refined or reconnected as necessary to achieve uniform accuracy. The authors stressed that they were able to construct a 3D hydro version of this code in 3 months, using an object-oriented FORTRAN approach.
Foam Multi-Dimensional General Purpose Monte Carlo Generator With Self-Adapting Symplectic Grid
Jadach, Stanislaw
2000-01-01
A new general purpose Monte Carlo event generator with self-adapting grid consisting of simplices is described. In the process of initialization, the simplex-shaped cells divide into daughter subcells in such a way that: (a) cell density is biggest in areas where integrand is peaked, (b) cells elongate themselves along hyperspaces where integrand is enhanced/singular. The grid is anisotropic, i.e. memory of the axes directions of the primary reference frame is lost. In particular, the algorithm is capable of dealing with distributions featuring strong correlation among variables (like ridge along diagonal). The presented algorithm is complementary to others known and commonly used in the Monte Carlo event generators. It is, in principle, more effective then any other one for distributions with very complicated patterns of singularities - the price to pay is that it is memory-hungry. It is therefore aimed at a small number of integration dimensions (<10). It should be combined with other methods for higher ...
Adaptive filtering prediction and control
Goodwin, Graham C
2009-01-01
Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o
Frequency domain FIR and IIR adaptive filters
Lynn, D. W.
1990-01-01
A discussion of the LMS adaptive filter relating to its convergence characteristics and the problems associated with disparate eigenvalues is presented. This is used to introduce the concept of proportional convergence. An approach is used to analyze the convergence characteristics of block frequency-domain adaptive filters. This leads to a development showing how the frequency-domain FIR adaptive filter is easily modified to provide proportional convergence. These ideas are extended to a block frequency-domain IIR adaptive filter and the idea of proportional convergence is applied. Experimental results illustrating proportional convergence in both FIR and IIR frequency-domain block adaptive filters is presented.
Adaptive Filter in SAR Interferometry Derived DEM
Institute of Scientific and Technical Information of China (English)
XU Caijun; WANG Hua; WANG Jianglin; GE Linlin
2005-01-01
In this paper, the performance of median filter, elevation dependent adaptive sigma median filter, and directionally dependent adaptive sigma median filter are tested on both InSAR Tandem DEM and simulated high-level noisy DEM. Through the comparison, the directionally dependent adaptive sigma median filter is proved to be the most effective one not only in the noise removing but also in the boundary preserve.
Directory of Open Access Journals (Sweden)
K. V. Dobrego
2015-01-01
Full Text Available Differential approximation is derived from radiation transfer equation by averaging over the solid angle. It is one of the more effective methods for engineering calculations of radia- tive heat transfer in complex three-dimensional thermal power systems with selective and scattering media. The new method for improvement of accuracy of the differential approximation based on using of auto-adaptable boundary conditions is introduced in the paper. The efficiency of the named method is proved for the test 2D-systems. Self-consistent auto-adaptable boundary conditions taking into consideration the nonorthogonal component of the incident to the boundary radiation flux are formulated. It is demonstrated that taking in- to consideration of the non- orthogonal incident flux in multi-dimensional systems, such as furnaces, boilers, combustion chambers improves the accuracy of the radiant flux simulations and to more extend in the zones adjacent to the edges of the chamber.Test simulations utilizing the differential approximation method with traditional boundary conditions, new self-consistent boundary conditions and “precise” discrete ordinates method were performed. The mean square errors of the resulting radiative fluxes calculated along the boundary of rectangular and triangular test areas were decreased 1.5–2 times by using auto- adaptable boundary conditions. Radiation flux gaps in the corner points of non-symmetric sys- tems are revealed by using auto-adaptable boundary conditions which can not be obtained by using the conventional boundary conditions.
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.
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...
Adaptive Filters for Muscle Response Suppression
DEFF Research Database (Denmark)
Sennels, Søren; Biering-Soerensen, Fin; Hansen, Steffen Duus
1996-01-01
are proposed, based on the observation that the shape of the muscle responses only exhibits moderate changes during a time window of up to 300 ms. The filters are derived and compared with a conventional fixed comb filter on both simulated and real data. For variations in amplitude of the muscle responses...... the performance of the adaptive filters are independent of the filter length, whereas for variations in the shape the performance is increased with the filter length. Using the adaptive filters it is possible to obtain a signal-to-noise ratio, which enables the EMG from a partly paralysed muscle to be used......To be able to use the voluntary EMG-signal from an electrically stimulated muscle as control signal for FES-applications, it is necessary to eliminate the muscle response evoked by the stimulation. The muscle response is a non-stationary signal, therefore a set of linear adaptive prediction filters...
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.
Javidi, Bahram; Andres, Pedro
2014-01-01
Provides a broad overview of advanced multidimensional imaging systems with contributions from leading researchers in the field Multi-dimensional Imaging takes the reader from the introductory concepts through to the latest applications of these techniques. Split into 3 parts covering 3D image capture, processing, visualization and display, using 1) a Multi-View Approach and 2.) a Holographic Approach, followed by a 3rd part addressing other 3D systems approaches, applications and signal processing for advanced 3D imaging. This book describes recent developments, as well as the prospects and
Adaptive Order-Statistic LMS Filters
Directory of Open Access Journals (Sweden)
S. Marchevsky
2001-04-01
Full Text Available The LMS-based adaptive order-statistic filters are presented in thispaper. The adaptive Ll-filters as extension of the adaptive L-filterfor two-dimensional filtering of noisy greyscale images is studied too.Their adaptation properties are studied by three types of noise, theadditive white Gaussian noise, the impulsive noise or both,respectively. Moreover, the impulsive noise has the fixed noise value(Salt & Pepper noise. The problem of pixel value multiplicity anddetermination its position in the ordered input vector for adaptiveLl-filter is shown in this article. The two types of images withdifferent of image complexity are used to demonstration of the power oftime-spatial ordering.
Adaptive Marginal Median Filter for Colour Images
Directory of Open Access Journals (Sweden)
Almanzor Sapena
2011-03-01
Full Text Available This paper describes a new filter for impulse noise reduction in colour images which is aimed at improving the noise reduction capability of the classical vector median filter. The filter is inspired by the application of a vector marginal median filtering process over a selected group of pixels in each filtering window. This selection, which is based on the vector median, along with the application of the marginal median operation constitutes an adaptive process that leads to a more robust filter design. Also, the proposed method is able to process colour images without introducing colour artifacts. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter.
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 Adaptive Iterated Nonlocal Interferometry Filtering Method
Directory of Open Access Journals (Sweden)
Lin Xue
2014-04-01
Full Text Available Interferometry filtering is one of the key steps in obtain high-precision Digital Elevation Model (DEM and Digital Orthophoto Map (DOM. In the case of low-correlation or complicated topography, traditional phase filtering methods fail in balancing noise elimination and phase preservation, which leads to inaccurate interferometric phase. This paper proposed an adaptive iterated nonlocal interferometry filtering method to deal with the problem. Based on the thought of nonlocal filtering, the proposed method filters the image with utilization of the image redundancy information. The smoothing parameter of the method is adaptive to the interferometry, and automatic iteration, in which the window size is adjusted, is applied to improve the filtering precision. Validity of the proposed method is verified by simulated and real data. Comparison with existed methods is given at the same time.
Erbach, G
1994-01-01
In this paper, we present an alternative approach to multiple inheritance for typed feature structures. In our approach, a feature structure can be associated with several types coming from different hierarchies (dimensions). In case of multiple inheritance a type has supertypes from different hierarchies. We contrast this approach with approaches based on a single type hierarchy where a feature structure has only one unique most general type, and multiple inheritance involves computation of greatest lower bounds in the hierarchy. The proposed approach supports current linguistic analyses in constraint-based formalisms like HPSG, inheritance in the lexicon, and knowledge representation for NLP systems. Finally, we show that multi-dimensional inheritance hierarchies can be compiled into a Prolog term representation, which allows to compute the conjunction of two types efficiently by Prolog term unification.
Performance Analysis of LMS Adaptive FIR Filter and RLS Adaptive FIR Filter for Noise Cancellation
Directory of Open Access Journals (Sweden)
Jyotsna Yadav
2013-06-01
Full Text Available Interest in adaptive filters continues to grow as they begin to find practical real-time applications in areas such as channel equalization, echo cancellation, noise cancellation and many other adaptive signal processing applications. The key to successful adaptive signal processing understands the fundamental properties of adaptive algorithms such as LMS, RLS etc. Adaptive filter is used for the cancellation of the noise component which is overlap with undesired signal in the same frequency range. This paper presents design, implementation and performance comparison of adaptive FIR filter using LMS and RMS algorithms. MATLAB Simulink environment are used for simulations.
Performance Analysis of LMS Adaptive FIR Filter and RLS Adaptive FIR Filter for Noise Cancellation
Directory of Open Access Journals (Sweden)
Jyotsna Yadav
2013-07-01
Full Text Available Interest in adaptive filters continues to grow as they begin to find practical real-time applications in areassuch as channel equalization, echo cancellation, noise cancellation and many other adaptive signalprocessing applications. The key to successful adaptive signal processing understands the fundamentalproperties of adaptive algorithms such as LMS, RLS etc. Adaptive filter is used for the cancellation of thenoise component which is overlap with undesired signal in the same frequency range. This paper presentsdesign, implementation and performance comparison of adaptive FIR filter using LMS and RMSalgorithms. MATLAB Simulink environment are used for simulations.
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.
Filters, reproducing kernel, and adaptive meshfree method
You, Y.; Chen, J.-S.; Lu, H.
Reproducing kernel, with its intrinsic feature of moving averaging, can be utilized as a low-pass filter with scale decomposition capability. The discrete convolution of two nth order reproducing kernels with arbitrary support size in each kernel results in a filtered reproducing kernel function that has the same reproducing order. This property is utilized to separate the numerical solution into an unfiltered lower order portion and a filtered higher order portion. As such, the corresponding high-pass filter of this reproducing kernel filter can be used to identify the locations of high gradient, and consequently serves as an operator for error indication in meshfree analysis. In conjunction with the naturally conforming property of the reproducing kernel approximation, a meshfree adaptivity method is also proposed.
Matched filter based iterative adaptive approach
Nepal, Ramesh; Zhang, Yan Rockee; Li, Zhengzheng; Blake, William
2016-05-01
Matched Filter sidelobes from diversified LPI waveform design and sensor resolution are two important considerations in radars and active sensors in general. Matched Filter sidelobes can potentially mask weaker targets, and low sensor resolution not only causes a high margin of error but also limits sensing in target-rich environment/ sector. The improvement in those factors, in part, concern with the transmitted waveform and consequently pulse compression techniques. An adaptive pulse compression algorithm is hence desired that can mitigate the aforementioned limitations. A new Matched Filter based Iterative Adaptive Approach, MF-IAA, as an extension to traditional Iterative Adaptive Approach, IAA, has been developed. MF-IAA takes its input as the Matched Filter output. The motivation here is to facilitate implementation of Iterative Adaptive Approach without disrupting the processing chain of traditional Matched Filter. Similar to IAA, MF-IAA is a user parameter free, iterative, weighted least square based spectral identification algorithm. This work focuses on the implementation of MF-IAA. The feasibility of MF-IAA is studied using a realistic airborne radar simulator as well as actual measured airborne radar data. The performance of MF-IAA is measured with different test waveforms, and different Signal-to-Noise (SNR) levels. In addition, Range-Doppler super-resolution using MF-IAA is investigated. Sidelobe reduction as well as super-resolution enhancement is validated. The robustness of MF-IAA with respect to different LPI waveforms and SNR levels is also demonstrated.
Partial update least-square adaptive filtering
Xie, Bei
2014-01-01
Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity (O(N)) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster a
Sparse adaptive filters for echo cancellation
Paleologu, Constantin
2011-01-01
Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati
A Rapid Introduction to Adaptive Filtering
Vega, Leonardo Rey
2013-01-01
In this book, the authors provide insights into the basics of adaptive filtering, which are particularly useful for students taking their first steps into this field. They start by studying the problem of minimum mean-square-error filtering, i.e., Wiener filtering. Then, they analyze iterative methods for solving the optimization problem, e.g., the Method of Steepest Descent. By proposing stochastic approximations, several basic adaptive algorithms are derived, including Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Sign-error algorithms. The authors provide a general framework to study the stability and steady-state performance of these algorithms. The affine Projection Algorithm (APA) which provides faster convergence at the expense of computational complexity (although fast implementations can be used) is also presented. In addition, the Least Squares (LS) method and its recursive version (RLS), including fast implementations are discussed. The book closes with the discussion of severa...
VSP wave separation by adaptive masking filters
Rao, Ying; Wang, Yanghua
2016-06-01
In vertical seismic profiling (VSP) data processing, the first step might be to separate the down-going wavefield from the up-going wavefield. When using a masking filter for VSP wave separation, there are difficulties associated with two termination ends of the up-going waves. A critical challenge is how the masking filter can restore the energy tails, the edge effect associated with these terminations uniquely exist in VSP data. An effective strategy is to implement masking filters in both τ-p and f-k domain sequentially. Meanwhile it uses a median filter, producing a clean but smooth version of the down-going wavefield, used as a reference data set for designing the masking filter. The masking filter is implemented adaptively and iteratively, gradually restoring the energy tails cut-out by any surgical mute. While the τ-p and the f-k domain masking filters target different depth ranges of VSP, this combination strategy can accurately perform in wave separation from field VSP data.
Fast adaptive elliptical filtering using box splines
Chaudhury, Kunal Narayan; Unser, Michael
2009-01-01
We demonstrate that it is possible to filter an image with an elliptic window of varying size, elongation and orientation with a fixed computational cost per pixel. Our method involves the application of a suitable global pre-integrator followed by a pointwise-adaptive localization mesh. We present the basic theory for the 1D case using a B-spline formalism and then appropriately extend it to 2D using radially-uniform box splines. The size and ellipticity of these radially-uniform box splines is adaptively controlled. Moreover, they converge to Gaussians as the order increases. Finally, we present a fast and practical directional filtering algorithm that has the capability of adapting to the local image features.
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.
Kernel adaptive filtering a comprehensive introduction
Liu, Weifeng; Haykin, Simon
2010-01-01
Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, O
Multi-Dimensional Path Queries
DEFF Research Database (Denmark)
Bækgaard, Lars
1998-01-01
We present the path-relationship model that supports multi-dimensional data modeling and querying. A path-relationship database is composed of sets of paths and sets of relationships. A path is a sequence of related elements (atoms, paths, and sets of paths). A relationship is a binary path...... to create nested path structures. We present an SQL-like query language that is based on path expressions and we show how to use it to express multi-dimensional path queries that are suited for advanced data analysis in decision support environments like data warehousing environments...
Adaptive Filter Based on Gradient Information
Institute of Scientific and Technical Information of China (English)
JINGXiaojun; LIJiangfeng; YANGYixian
2003-01-01
In this paper, an adaptive smoothing filter algorithm based on gradient information is proposed. The new method solves the problem of conventional filer that can't smooth noise and sharp edge simultaneously. It is based on the iterative convolution of local adaptive template and the original image signal, the template has the property of diffusing anisotropically. In each iteration, the weight coefficients of filter are determined by the gradient function of each pixel, and they vary with the variety of the gradient function, thus reflects the degree of continuity of the gray value. The weight coefficients also depend on one parameter, which controls the amplitude of the breaking point that needs to be preserved during the iteration. This algorithm sharps the edge of image by iterative computation, and after several iterations the image is adaptively smoothed according to the edge blocking. The simulation results demonstrate that this algorithm can perform filtering effectively. It has appropriate computation complexity and is suitable for real-time processing.
Adaptive Filtering Queueing for Improving Fairness
Directory of Open Access Journals (Sweden)
Jui-Pin Yang
2015-06-01
Full Text Available In this paper, we propose a scalable and efficient Active Queue Management (AQM scheme to provide fair bandwidth sharing when traffic is congested dubbed Adaptive Filtering Queueing (AFQ. First, AFQ identifies the filtering level of an arriving packet by comparing it with a flow label selected at random from the first level to an estimated level in the filtering level table. Based on the accepted traffic estimation and the previous fair filtering level, AFQ updates the fair filtering level. Next, AFQ uses a simple packet-dropping algorithm to determine whether arriving packets are accepted or discarded. To enhance AFQ’s feasibility in high-speed networks, we propose a two-layer mapping mechanism to effectively simplify the packet comparison operations. Simulation results demonstrate that AFQ achieves optimal fairness when compared with Rotating Preference Queues (RPQ, Core-Stateless Fair Queueing (CSFQ, CHOose and Keep for responsive flows, CHOose and Kill for unresponsive flows (CHOKe and First-In First-Out (FIFO schemes under a variety of traffic conditions.
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.
Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning
Institute of Scientific and Technical Information of China (English)
XIAO Kun; FANG Shao-ji; PANG Yong-jie
2007-01-01
To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.
Research on adaptive filtering method for electrostatic signals
Xu, Hongke; Pang, Yue; Yi, Yingmin
2017-05-01
The signal will be inevitably mixed with various types of noise in the process of transmission, which causes the distortion of information in different degree, in order to obtain accurate information, it's an important work to suppress random noise in the digital signal processing system. This paper mainly studies the adaptive filtering method, using LMS algorithm in adaptive filter (Least mean square LMS algorithm), when the filter starts reading the electrostatic signal, it also can estimate the statistical characteristics of electrostatic signal, adaptive adjust its filter parameters, filtering the electrostatic signal on time, attain the maximum noise suppression, to avoid distortion of information, and to achieve optimal filtering.
Adaptive ship autopilot with wave filter
Directory of Open Access Journals (Sweden)
Steinar Sælid
1983-01-01
Full Text Available This paper is concerned with analysis and design of an adaptive autopilot for ships. The design is based on a low and high frequency model of the vessel motion adequate to ship steering. The low frequency model describes the vessel response to rudder control and slowly varying environmental forces. The high frequency model represents the wave induced oscillatory part of the yaw motion. The models are used in a Kalman filter and the rudder control is computed from linear quadratic theory based on the low frequency part of the vector. This yields a very effective filtering of the wave component of the yaw motion. Proper operation of this filter/controller structure requires knowledge of the vessel model parameters and the dominating wave frequency. The vessel parameters are estimated on line by a recursive prediction error method. In order to reduce the computing requirements, the state estimator is operated using scheduled gains. This results in an easy and robust design. The convergence properties are investigated by using the method of Ljung. The performance is confirmed by simulation experiments.
Adaptive Linear Filtering Design with Minimum Symbol Error Probability Criterion
Institute of Scientific and Technical Information of China (English)
Sheng Chen
2006-01-01
Adaptive digital filtering has traditionally been developed based on the minimum mean square error (MMSE)criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate (MSER) criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non-Gaussian distribution of filter output effectively. Consequently, it provides significant performance gain in terms of smaller symbol error over the MMSE approach. Adopting Parzen window or kernel density estimation for a probability density function, a block-data gradient adaptive MSER algorithm is derived. A stochastic gradient adaptive MSER algorithm, referred to as the least symbol error rate, is further developed for sampleby-sample adaptive implementation of the MSER filtering. Two applications, involving single-user channel equalization and beamforming assisted receiver, are included to demonstrate the effectiveness and generality of the proposed adaptive MSER filtering approach.
Adaptive Local Image Registration: Analysis on Filter Size
Vishnukumar S; M.Wilscy
2012-01-01
Adaptive Local Image Registration is a Local Image Registration based on an Adaptive Filtering frame work. A filter of appropriate size convolves with reference image and gives the pixel values corresponding to the distorted image and the filter is updated in each stage of the convolution. When the filter converges to the system model, it provides the registered image. The filter size plays an important role in this method. The analysis on the filter size is done using Peak Signal-to-Noise Ra...
An Efficient Topography Adaptive Filter for Insar Processing
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
An efficient implementation of the topography adaptive filter based on local frequency estimation is proposed, where chirpz transform is applied to enhance the accuracy of the frequency estimation. As a byproduct of this adaptive filter, the linear approximated phase model of the interferogram is employed to improve the coherence estimation. The impacts of the adaptive filter on global and local phase unwrapping algorithms are discussed. Finally, aiming at the negative effect that the adaptive filter can bring to local phase unwrapping algorithms, a fusion scheme that takes advantage of least square and several local phase unwrapping algorithms is presented.
Adaptive information filtering for dynamic recommender systems
Jin, Ci-Hang; Zhang, Yi-Cheng; Zhou, Tao
2009-01-01
The dynamic environment in the real world calls for the adaptive techniques for information filtering, namely to provide real-time responses to the changes of system data. Where many incremental algorithms are designed for this purpose, they are usually challenged by the worse and worse performance resulted from the cumulative errors over time. In this Letter, we propose two incremental diffusion-based algorithms for the personalized recommendations, which integrate some pieces of local and fast updatings to achieve the approximate results. In addition to the fast responses, the errors of the proposed algorithms do not cumulate over time, that is to say, the global recomputing is unnecessary. This remarkable advantage is demonstrated by several metrics on algorithmic accuracy for two movie recommender systems and a social bookmarking system.
Adaptive Federal Kalman Filtering for SINS/GPS Integrated System
Institute of Scientific and Technical Information of China (English)
杨勇; 缪玲娟
2003-01-01
A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.
Simulation for noise cancellation using LMS adaptive filter
Lee, Jia-Haw; Ooi, Lu-Ean; Ko, Ying-Hao; Teoh, Choe-Yung
2017-06-01
In this paper, the fundamental algorithm of noise cancellation, Least Mean Square (LMS) algorithm is studied and enhanced with adaptive filter. The simulation of the noise cancellation using LMS adaptive filter algorithm is developed. The noise corrupted speech signal and the engine noise signal are used as inputs for LMS adaptive filter algorithm. The filtered signal is compared to the original noise-free speech signal in order to highlight the level of attenuation of the noise signal. The result shows that the noise signal is successfully canceled by the developed adaptive filter. The difference of the noise-free speech signal and filtered signal are calculated and the outcome implies that the filtered signal is approaching the noise-free speech signal upon the adaptive filtering. The frequency range of the successfully canceled noise by the LMS adaptive filter algorithm is determined by performing Fast Fourier Transform (FFT) on the signals. The LMS adaptive filter algorithm shows significant noise cancellation at lower frequency range.
Adaptive Notch filter based active damping for power converters using LCL filters
DEFF Research Database (Denmark)
Ciobotaru, M.; Rossé, A.; Bede, L.;
2016-01-01
This paper proposes an active damping technique for grid-connected converters using inductor-capacitor-inductor (LCL) filters. The technique relies on a discrete-time adaptive notch filter (NF) which is able to adapt its resonance frequency and bandwidth in real-time. The tuning function of this ......This paper proposes an active damping technique for grid-connected converters using inductor-capacitor-inductor (LCL) filters. The technique relies on a discrete-time adaptive notch filter (NF) which is able to adapt its resonance frequency and bandwidth in real-time. The tuning function...
A new adaptive filtering algorithm for systems with multiplicative noise
Institute of Scientific and Technical Information of China (English)
WANG Hui-li; CHEN Xi-xin; LU Qian-hao
2005-01-01
Presented here is a new adaptive state filtering algorithm for systems with multiplicative noise. This algorithm estimates the vector state of the system and the statistics of noise when all the statistics of noise are unknown. This filtering algorithm is a simple recursive structure. A simulation example is presented which demonstrates the effectiveness of this filtering algorithm.
Scheme of adaptive polarization filtering based on Kalman model
Institute of Scientific and Technical Information of China (English)
Song Lizhong; Qi Haiming; Qiao Xiaolin; Meng Xiande
2006-01-01
A new kind of adaptive polarization filtering algorithm in order to suppress the angle cheating interference for the active guidance radar is presented. The polarization characteristic of the interference is dynamically tracked by using Kalman estimator under variable environments with time. The polarization filter parameters are designed according to the polarization characteristic of the interference, and the polarization filtering is finished in the target cell. The system scheme of adaptive polarization filter is studied and the tracking performance of polarization filter and improvement of angle measurement precision are simulated. The research results demonstrate this technology can effectively suppress the angle cheating interference in guidance radar and is feasible in engineering.
An Adaptive Combinatorial Morphological Filter Based on Omnidirectional Structuring Elements
Institute of Scientific and Technical Information of China (English)
ZHAO Chunhui; HUI Junying; SUN Shenghe
2001-01-01
A new adaptive morphological filter is proposed in this paper. The filter utilizes the omnidirectional structuring elements and morphological open-closing or clos-opening operations. The outputs of the morphological operations by each structuring element are linear weighted processed by means of the adaptive method under the constrained least mean absolute (CLMA) error criterion. The new filter is applied to restore a noisy image and compared with the traditional morphological filters. The simulation results have shown that the new filter possesses effective noise suppression without blurring the geometrical features of the image.
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.
ADAPTIVE FILTER FOR SYSTEM IDENTIFICATION USING QUANTIZATION SCHEMES
Directory of Open Access Journals (Sweden)
Nitesh Mudgal
2012-03-01
Full Text Available The Least Mean Square (LMS Algorithm finds its application in System identification due to its simplicity.Reduction of the complexity of Adaptive Finite Impulse Response(FIR filter had received attention in the area of adative filter. This paper proposes methods of system identification using adaptive filter which are based on a Quantised version of the LMS, namely the Clipped Least Mean Square (CLMS and Modified Clipped Least Mean Square( QX-LMS algorithms. In both Algorithms coefficients of the adaptive filter are adjusted automatically by an adaptive algorithm based on the input signals. This property makes the adaptive filter has an important application in system identification.the Quantized version of Least Mean Square Algorithm increases covergence property as compared to normal Least Mean Square Algorithm.
A hybrid RNS adaptive filter for channel equalization
DEFF Research Database (Denmark)
Bernocchi, Gian Luca; Cardarilli, Gian Carlo; Re, Andrea Del
2006-01-01
In this work a hybrid Residue Number System (RNS) implementation of an adaptive FIR filter is presented. The used adaptation algorithm is the Least Mean Squares (LMS). The filter has been designed to meet the constraints of specific class of applications. In fact, it is suitable for applications...... requiring a large number of taps where a serial updating of the filter coefficients is feasible (channel equalization or echo cancellation). In the literature, it has been shown that the RNS implementation of FIR filters grants earnings in area ad power consumption due to the introduced arithmetic...... simplifications. Vice versa, the RNS implementation of the adaptation algorithm needs scaling circuits that are complex and expensive in RNS arithmetic. For this reason, a serial binary implementation of the adaptation algorithm is chosen. The advantages in terms of area and speed of the RNS adaptive filter...
Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion
Directory of Open Access Journals (Sweden)
Zongze Wu
2015-10-01
Full Text Available The maximum correntropy criterion (MCC has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error (MSE criterion, the proposed algorithm can achieve better convergence performance especially in the presence of impulsive non-Gaussian (e.g., α-stable noises. Additionally, some theoretical results concerning the convergence behavior are also obtained. Simulation examples are presented to confirm the superior performance of the new algorithm.
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.
Synthetically adaptive robust filtering for satellite orbit determination
Institute of Scientific and Technical Information of China (English)
YANG; Yuanxi
2004-01-01
The quality of the satellite orbit determination is rested on the knowledge of perturbing forces acting on the satellite and stochastic properties of the observations, and the ability of controlling various kinds of errors. After a brief discussion on the dynamic and geometric orbit determinations, Sage adaptive filtering and robust filtering are reviewed. A new synthetically adaptive robust filtering based on a combination of robust filtering and Sage filtering is developed. It is shown, by derivations and calculations, that the synthetically adaptive robust filtering for orbit determination is not only robust but also simple in calculation. It controls the effects of the outliers of tracking observations and the satellite dynamical disturbance on the parameter estimates of the satellite orbit.
Multi-Dimensional Aggregation for Temporal Data
DEFF Research Database (Denmark)
Böhlen, M. H.; Gamper, J.; Jensen, Christian Søndergaard
2006-01-01
Business Intelligence solutions, encompassing technologies such as multi-dimensional data modeling and aggregate query processing, are being applied increasingly to non-traditional data. This paper extends multi-dimensional aggregation to apply to data with associated interval values that capture...
Real time microcontroller implementation of an adaptive myoelectric filter.
Bagwell, P J; Chappell, P H
1995-03-01
This paper describes a real time digital adaptive filter for processing myoelectric signals. The filter time constant is automatically selected by the adaptation algorithm, giving a significant improvement over linear filters for estimating the muscle force and controlling a prosthetic device. Interference from mains sources often produces problems for myoelectric processing, and so 50 Hz and all harmonic frequencies are reduced by an averaging filter and differential process. This makes practical electrode placement and contact less critical and time consuming. An economic real time implementation is essential for a prosthetic controller, and this is achieved using an Intel 80C196KC microcontroller.
Multi-dimensional model order selection
Directory of Open Access Journals (Sweden)
Roemer Florian
2011-01-01
Full Text Available Abstract Multi-dimensional model order selection (MOS techniques achieve an improved accuracy, reliability, and robustness, since they consider all dimensions jointly during the estimation of parameters. Additionally, from fundamental identifiability results of multi-dimensional decompositions, it is known that the number of main components can be larger when compared to matrix-based decompositions. In this article, we show how to use tensor calculus to extend matrix-based MOS schemes and we also present our proposed multi-dimensional model order selection scheme based on the closed-form PARAFAC algorithm, which is only applicable to multi-dimensional data. In general, as shown by means of simulations, the Probability of correct Detection (PoD of our proposed multi-dimensional MOS schemes is much better than the PoD of matrix-based schemes.
Variable Step Size Maximum Correntropy Criteria Based Adaptive Filtering Algorithm
Directory of Open Access Journals (Sweden)
S. Radhika
2016-04-01
Full Text Available Maximum correntropy criterion (MCC based adaptive filters are found to be robust against impulsive interference. This paper proposes a novel MCC based adaptive filter with variable step size in order to obtain improved performance in terms of both convergence rate and steady state error with robustness against impulsive interference. The optimal variable step size is obtained by minimizing the Mean Square Deviation (MSD error from one iteration to the other. Simulation results in the context of a highly impulsive system identification scenario show that the proposed algorithm has faster convergence and lesser steady state error than the conventional MCC based adaptive filters.
Design and performance optimization of fiber optic adaptive filters.
Paparao, P; Ghosh, A; Allen, S D
1991-05-10
There is a great need for easy-to-fabricate and versatile fiber optic signal processing systems in which optical fibers are used for the delay and storage of wideband guided lightwave signals. We describe the design of the least-mean-square algorithm-based fiber optic adaptive filters for processing guided lightwave signals in real time. Fiber optic adaptive filters can learn to change their parameters or to process a set of characteristics of the input signal. In our realization we employ as few electronic devices as possible and use optical computation to utilize the advantages of optics in the processing speed, parallelism, and interconnection. Many schemes for optical adaptive filtering of electronic signals are available in the literature. The new optical adaptive filters described in this paper are for optical processing of guided lightwave signals, not electronic signals. We analyzed the convergence or learning characteristics of the adaptive filtering process as a function of the filter parameters and the fiber optic hardware errors. From this analysis we found that the effects of the optical round-off errors and noise can be reduced, and the learning speed can be comparatively increased in our design through an optimal selection of the filter parameters. A general knowledge of the fiber optic hardware, the statistics of the lightwave signal, and the desired goal of the adaptive processing are enough for this optimum selection of the parameters. Detailed computer simulations validate the theoretical results of performance optimization.
An Adaptive Filtering System Configurations and Architecture on Reconfigurable Platform
Directory of Open Access Journals (Sweden)
Dipen B. Patel
2014-03-01
Full Text Available This paper proposed the implementation of adaptive filters on reconfigurable platform. Adaptive filter is an essential part of digital signal systems, have been widely used and its implementation takes a great deal, there is no dedicated IC for adaptive filter. When FPGA implemented in such area, provides a lot of facilities to the designers and also offer a better solution for filtering the data. Adaptive signal processing evolved from the techniques developed to enable the adaptive control of time- varying systems. Filtering data in real-time requires dedicated hardware to meet demanding time requirements and provide the highest processing performance, but is inflexible for changes. When a design demands the use of a DSP, design adaptability is crucial, then FPGA may offer a better solution. Reconfigurable hardware devices offer both the flexibility of computer software, and the ability to construct custom high performance computing circuits. Adaptive filter implemented using Field Programmable Gate Arrays (FPGAs due to some of their attractive advantages include flexibility and programmability, availability of tens to hundreds of hardware multipliers available on a chip. Keywords -
An adaptive Kalman filter for ECG signal enhancement.
Vullings, Rik; de Vries, Bert; Bergmans, Jan W M
2011-04-01
The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate.
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...
Adaptive Subband Filtering Method for MEMS Accelerometer Noise Reduction
Directory of Open Access Journals (Sweden)
Piotr PIETRZAK
2008-12-01
Full Text Available Silicon microaccelerometers can be considered as an alternative to high-priced piezoelectric sensors. Unfortunately, relatively high noise floor of commercially available MEMS (Micro-Electro-Mechanical Systems sensors limits the possibility of their usage in condition monitoring systems of rotating machines. The solution of this problem is the method of signal filtering described in the paper. It is based on adaptive subband filtering employing Adaptive Line Enhancer. For filter weights adaptation, two novel algorithms have been developed. They are based on the NLMS algorithm. Both of them significantly simplify its software and hardware implementation and accelerate the adaptation process. The paper also presents the software (Matlab and hardware (FPGA implementation of the proposed noise filter. In addition, the results of the performed tests are reported. They confirm high efficiency of the solution.
A novel gradient adaptive step size LMS algorithm with dual adaptive filters.
Jiao, Yuzhong; Cheung, Rex Y P; Chow, Winnie W Y; Mok, Mark P C
2013-01-01
Least mean square (LMS) adaptive filter has been used to extract life signals from serious ambient noises and interferences in biomedical applications. However, a LMS adaptive filter with a fixed step size always suffers from slow convergence rate or large signal distortion due to the diversity of the application environments. An ideal adaptive filtering system should be able to adapt different environments and obtain the useful signals with low distortion. Adaptive filter with gradient adaptive step size is therefore more desirable in order to meet the demands of adaptation and convergence rate, which adjusts the step-size parameter automatically by using gradient descent technique. In this paper, a novel gradient adaptive step size LMS adaptive filter is presented. The proposed algorithm utilizes two adaptive filters to estimate gradients accurately, thus achieves good adaptation and performance. Though it uses two LMS adaptive filters, it has a low computational complexity. An active noise cancellation (ANC) system with two applications for extracting heartbeat and lung sound signals from noises is used to simulate the performance of the proposed algorithm.
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.
A hybrid method for optimization of the adaptive Goldstein filter
Jiang, Mi; Ding, Xiaoli; Tian, Xin; Malhotra, Rakesh; Kong, Weixue
2014-12-01
The Goldstein filter is a well-known filter for interferometric filtering in the frequency domain. The main parameter of this filter, alpha, is set as a power of the filtering function. Depending on it, considered areas are strongly or weakly filtered. Several variants have been developed to adaptively determine alpha using different indicators such as the coherence, and phase standard deviation. The common objective of these methods is to prevent areas with low noise from being over filtered while simultaneously allowing stronger filtering over areas with high noise. However, the estimators of these indicators are biased in the real world and the optimal model to accurately determine the functional relationship between the indicators and alpha is also not clear. As a result, the filter always under- or over-filters and is rarely correct. The study presented in this paper aims to achieve accurate alpha estimation by correcting the biased estimator using homogeneous pixel selection and bootstrapping algorithms, and by developing an optimal nonlinear model to determine alpha. In addition, an iteration is also merged into the filtering procedure to suppress the high noise over incoherent areas. The experimental results from synthetic and real data show that the new filter works well under a variety of conditions and offers better and more reliable performance when compared to existing approaches.
Weighted adaptive spatial filtering in digital holographic microscopy
Hong, Yuan; Shi, Tielin; Wang, Xiao; Zhang, Yichun; Chen, Kepeng; Liao, Guanglan
2017-01-01
Spatial filtering, a key point to realize real-time measurement, is used commonly in digital off-axis holography to extract desired terms. In this paper, we propose a weighted adaptive spatial filtering method by weighting the adaptive filtering window (obtained from image segmentation) based on signal to noise ratio. The advantages of this method are evaluated by simulations and further verified by recorded digital image plane holograms. The results demonstrate that our method is effective in suppressing noise and retaining the sharp edges in the reconstructed 3D profiles.
FPGA Implementation of Adaptive Filter and its Performance Analysis
Directory of Open Access Journals (Sweden)
J. Malarmannan
2013-06-01
Full Text Available Adaptive filters are used in various real-time applications such as echo cancellation, noise cancellation, system identification and prediction. Field -programmable gate arrays (FPGAs are alsoused most widely for applications where timing requirements are strict. Thus implementation of filter in real-time is needed. The objective of this paper is to design and implement an Adaptive filter which is robust to impulsive noise using hardware description language (HDL design. The design implementation and its performance analysis are presented. The targeted FPGA is Altera CycloneIV. The obtained design results in superior performance, greater data sample frequency and less logic occupation.
Adaptive filtering for air-to-ground surveillance
Rigling, Brian D.
2004-09-01
This paper introduces a new concept for air-to-ground noise radar based on adaptive filtering. A transmitting antenna illuminates a region of interest with a continuous, noise waveform. The processor within the receiver treats the illuminated scene as a linear system with unknown coefficients which filters the transmitted signal. Given access to the transmitted waveform and the digitized backscattered signal, the receiver adaptively estimates the unknown filter coefficients, using the same processing architecture as a wireless channel equalizer, and continues to update their values as the transmitter and receiver traverse their flight paths. The adapted filters correspond to range profiles of the illuminated scene which may be Doppler processed to yield synthetic aperture imagery.
Adaptive robust Kalman filtering for precise point positioning
Guo, Fei; Zhang, Xiaohong
2014-10-01
The optimality of precise point postioning (PPP) solution using a Kalman filter is closely connected to the quality of the a priori information about the process noise and the updated mesurement noise, which are sometimes difficult to obtain. Also, the estimation enviroment in the case of dynamic or kinematic applications is not always fixed but is subject to change. To overcome these problems, an adaptive robust Kalman filtering algorithm, the main feature of which introduces an equivalent covariance matrix to resist the unexpected outliers and an adaptive factor to balance the contribution of observational information and predicted information from the system dynamic model, is applied for PPP processing. The basic models of PPP including the observation model, dynamic model and stochastic model are provided first. Then an adaptive robust Kalmam filter is developed for PPP. Compared with the conventional robust estimator, only the observation with largest standardized residual will be operated by the IGG III function in each iteration to avoid reducing the contribution of the normal observations or even filter divergence. Finally, tests carried out in both static and kinematic modes have confirmed that the adaptive robust Kalman filter outperforms the classic Kalman filter by turning either the equivalent variance matrix or the adaptive factor or both of them. This becomes evident when analyzing the positioning errors in flight tests at the turns due to the target maneuvering and unknown process/measurement noises.
Lee, Boreom; Kee, Youngwook; Han, Jonghee; Yi, Won Jin
2011-01-01
Photoplethysmographic (PPG) signal can provide important information about cardiovascular and respiratory conditions of individuals in a hospital or daily life. However, PPG can be distorted by motion artifacts significantly. Therefore, the reduction of the effects of motion artifacts is very important procedure for monitoring cardio-respiratory system by PPG. There have been many adaptive techniques to reduce motion artifacts from PPG signal including normalized least mean squares (NLMS) method, recursive least squares (RLS) filter, and Kalman filter. In the present study, we propose the adaptive comb filter (ACF) for reducing the effects of motion artifacts from PPG signal. ACF with adaptive lattice infinite impulse response (IIR) notch filter (ALNF) successfully reduced the motion artifacts from the quasi-periodic PPG signal.
Adaptive Filtering for Aeroservoelastic Response Suppression Project
National Aeronautics and Space Administration — CSA Engineering proposes the design of an adaptive aeroelastic mode suppression for advanced fly-by-wire aircraft, which will partition the modal suppression...
Adaptive filters for color image processing
Directory of Open Access Journals (Sweden)
Papanikolaou V.
1998-01-01
Full Text Available The color filters that are used to attenuate noise are usually optimized to perform extremely well when dealing with certain noise distributions. Unfortunately it is often the case that the noise corrupting the image is not known. It is thus beneficial to know a priori the type of noise corrupting the image in order to select the optimal filter. A method of extracting and characterizing the noise within a digital color image using the generalized Gaussian probability density function (pdf (B.D. Jeffs and W.H. Pun, IEEE Transactions on Image Processing, 4(10, 1451–1456, 1995 and Proceedings of the Int. Conference on Image Processing, 465–468, 1996, is presented. In this paper simulation results are included to demonstrate the effectiveness of the proposed methodology.
Adaptive filters for color image processing
Directory of Open Access Journals (Sweden)
V. Papanikolaou
1999-01-01
Full Text Available The color filters that are used to attenuate noise are usually optimized to perform extremely well when dealing with certain noise distributions. Unfortunately it is often the case that the noise corrupting the image is not known. It is thus beneficial to know a priori the type of noise corrupting the image in order to select the optimal filter. A method of extracting and characterizing the noise within a digital color image using the generalized Gaussian probability density function (pdf (B.D. Jeffs and W.H. Pun, IEEE Transactions on Image Processing, 4(10, 1451–1456, 1995 and Proceedings of the Int. Conference on Image Processing, 465–468, 1996, is presented. In this paper simulation results are included to demonstrate the effectiveness of the proposed methodology.
Adaptive Control Using Residual Mode Filters Applied to Wind Turbines
Frost, Susan A.; Balas, Mark J.
2011-01-01
Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a model reference direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will augment the adaptive controller using a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. We apply these theoretical results to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine that has minimum phase zeros.
Adaptive texture filtering for defect inspection in ultrasound images
Zmola, Carl; Segal, Andrew C.; Lovewell, Brian; Nash, Charles
1993-05-01
The use of ultrasonic imaging to analyze defects and characterize materials is critical in the development of non-destructive testing and non-destructive evaluation (NDT/NDE) tools for manufacturing. To develop better quality control and reliability in the manufacturing environment advanced image processing techniques are useful. For example, through the use of texture filtering on ultrasound images, we have been able to filter characteristic textures from highly-textured C-scan images of materials. The materials have highly regular characteristic textures which are of the same resolution and dynamic range as other important features within the image. By applying texture filters and adaptively modifying their filter response, we have examined a family of filters for removing these textures.
Adaptive filtering for ECG rejection from surface EMG recordings.
Marque, C; Bisch, C; Dantas, R; Elayoubi, S; Brosse, V; Pérot, C
2005-06-01
Surface electromyograms (EMG) of back muscles are often corrupted by electrocardiogram (ECG) signals. This noise in the EMG signals does not allow to appreciate correctly the spectral content of the EMG signals and to follow its evolution during, for example, a fatigue process. Several methods have been proposed to reject the ECG noise from EMG recordings, but seldom taking into account the eventual changes in ECG characteristics during the experiment. In this paper we propose an adaptive filtering algorithm specifically developed for the rejection of the electrocardiogram corrupting surface electromyograms (SEMG). The first step of the study was to choose the ECG electrode position in order to record the ECG with a shape similar to that found in the noised SEMGs. Then, the efficiency of different algorithms were tested on 28 erector spinae SEMG recordings. The best algorithm belongs to the fast recursive least square family (FRLS). More precisely, the best results were obtained with the simplified formulation of a FRLS algorithm. As an application of the adaptive filtering, the paper compares the evolutions of spectral parameters of noised or denoised (after adaptive filtering) surface EMGs recorded on erector spinae muscles during a trunk extension. The fatigue test was analyzed on 16 EMG recordings. After adaptive filtering, mean initial values of energy and of mean power frequency (MPF) were significantly lower and higher respectively. The differences corresponded to the removal of the ECG components. Furthermore, classical fatigue criteria (increase in energy and decrease in MPF values over time during the fatigue test) were better observed on the denoised EMGs. The mean values of the slopes of the energy-time and MPF-time linear relationships differed significantly when established before and after adaptive filtering. These results account for the efficacy of the adaptive filtering method proposed here to denoise electrophysiological signals.
An Adaptive Filtering Algorithm using Mean Field Annealing Techniques
Persson, Per; Nordebo, Sven; Claesson, Ingvar
2002-01-01
We present a new approach to discrete adaptive filtering based on the mean field annealing algorithm. The main idea is to find the discrete filter vector that minimizes the matrix form of the Wiener-Hopf equations in a least-squares sense by a generalized mean field annealing algorithm. It is indicated by simulations that this approach, with complexity O(M^2) where M is the filter length, finds a solution comparable to the one obtained by the recursive least squares (RLS) algorithm but withou...
A reduced bias delay lock loop for adaptive filters
Fan, Guangteng; Huang, Yangbo; Su, Yingxue; Li, Jingyuan; Sun, Guangfu
2017-01-01
Narrowband interferences (NBIs) severely degrade the quality of a received signal and can hinder the operation of GPS receivers, and therefore, they are commonly excised using an adaptive transversal filter. This filter does not cause code tracking bias in the case of an ideal analog receiver channel when its magnitude and phase response are constant; however, distortion is induced by RF cables, amplifiers, and mixers that results in an asymmetric correlation function. This correlation function is further deformed by the adaptive transversal filter, resulting in a nonzero bias. Given the adaptive nature of this transversal filter, the bias varies based on the jamming pattern. For precision navigation applications, this bias must be mitigated. With this problem in mind, a new technique called amplitude estimating delay lock loop (AEDLL) is presented. By using data related to a known structure of the adaptive transversal filter, the proposed method only needs to estimate the amplitude of the correlation function and revise the correlation function for code tracking. Simulations show that the AEDLL method is capable of reducing the RMSE of code tracking bias to less than 0.12 ns, which is significantly smaller than that achieved using existing methods.
An Adaptive Kalman Filter Excisor for Suppressing Narrowband Interference
1993-11-01
interferences in- connues. Le filtre de Kalman doit alors "apprendre" ý ajuster un de ses param~tres pour effectuer le meilleur traitement. L’erreur est...4"L l B"• -- -- - - -.- ,_, . An~. A)7cQ 0 -QGOP II liii 111111 IIa( Naional 06fenso I ’ I Deence nitonals I "It AN ADAPTIVE KALMAN FILTER EXCISOR...Ottawa 0 A o~ oO Best Available COpy 4INational Defense Defence nationals AN ADAPTIVE KALMAN FILTER EXCISOR FOR SUPPRESSING NARROWBAND INTERFERENCE by
Adaptive filtering and indexing for image databases
Alexandrov, Albert D.; Ma, Wei Y.; El Abbadi, Amr; Manjunath, B. S.
1995-03-01
In this paper we combine image feature extraction with indexing techniques for efficient retrieval in large texture images databases. A 2D image signal is processed using a set of Gabor filters to derive a 120 component feature vector representing the image. The feature components are ordered based on the relative importance in characterizing a given texture pattern, and this facilitates the development of efficient indexing mechanisms. We propose three different sets of indexing features based on the best feature, the average feature and a combination of both. We investigate the tradeoff between accuracy and discriminating power using these different indexing approaches, and conclude that the combination of best feature and the average feature gives the best results.
Spatially adaptive morphological image filtering using intrinsic structuring elements:
Johan Debayle; Jean-Charles Pinoli
2005-01-01
This paper deals with spatially adaptive morphological filtering, extending the theory of mathematical morphology to the paradigm of adaptive neighborhood. The basic idea in this approach is to substitute the extrinsically-defined, fixed-shape, fixed-size structuring elements generally used by morphological operators, by intrinsically-defined, variable-shape, variable-size structuring elements. These last so-called intrinsic structuring elements fit to the local features of the image, with re...
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...
Design of Reliable Adaptive Filter with Fault Tolerance Using DSP
Energy Technology Data Exchange (ETDEWEB)
Ryoo, D. W.; Lee, J. W. [Electronics and Telecommunications Research Institute, Taejon (Korea); Seo, B. H. [Kyungbok National University, Taegu (Korea)
2001-01-01
LSM algorithm has been used for plant identifier and noise cancellation. This algorithm has been researched for performance enhancement of filtering. The design and development of a reliable system has been becoming a key issue in industry field because the reliability of a system is considered as an important factor to perform the system's function successfully. And the computing with reliability and fault tolerance is a important factor in the case of aviation, system communication, and nuclear plant. This paper presents design of reliable adaptive filter with fault tolerance. Generally, redundancy is used for reliability. In this case it needs computing or circuit for voting mechanism or computing for fault detection or switching part. But this presented Filter is not in need of computing for voting mechanism, or fault detection. Therefore it has simple computing , and practicality for application. And in this paper, reliability of adaptive filter is analyzed. The effectiveness of the proposed adaptive filter is demonstrated to the case studies of plant identifier and noise cancellation by using DSP. (author). 9 refs., 18 figs.
Adaptive Rate Sampling and Filtering Based on Level Crossing Sampling
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Saeed Mian Qaisar
2009-01-01
Full Text Available The recent sophistications in areas of mobile systems and sensor networks demand more and more processing resources. In order to maintain the system autonomy, energy saving is becoming one of the most difficult industrial challenges, in mobile computing. Most of efforts to achieve this goal are focused on improving the embedded systems design and the battery technology, but very few studies target to exploit the input signal time-varying nature. This paper aims to achieve power efficiency by intelligently adapting the processing activity to the input signal local characteristics. It is done by completely rethinking the processing chain, by adopting a non conventional sampling scheme and adaptive rate filtering. The proposed approach, based on the LCSS (Level Crossing Sampling Scheme presents two filtering techniques, able to adapt their sampling rate and filter order by online analyzing the input signal variations. Indeed, the principle is to intelligently exploit the signal local characteristics—which is usually never considered—to filter only the relevant signal parts, by employing the relevant order filters. This idea leads towards a drastic gain in the computational efficiency and hence in the processing power when compared to the classical techniques.
Robust visual tracking via adaptive kernelized correlation filter
Wang, Bo; Wang, Desheng; Liao, Qingmin
2016-10-01
Correlation filter based trackers have proved to be very efficient and robust in object tracking with a notable performance competitive with state-of-art trackers. In this paper, we propose a novel object tracking method named Adaptive Kernelized Correlation Filter (AKCF) via incorporating Kernelized Correlation Filter (KCF) with Structured Output Support Vector Machines (SOSVM) learning method in a collaborative and adaptive way, which can effectively handle severe object appearance changes with low computational cost. AKCF works by dynamically adjusting the learning rate of KCF and reversely verifies the intermediate tracking result by adopting online SOSVM classifier. Meanwhile, we bring Color Names in this formulation to effectively boost the performance owing to its rich feature information encoded. Experimental results on several challenging benchmark datasets reveal that our approach outperforms numerous state-of-art trackers.
BPSK Receiver Based on Recursive Adaptive Filter with Remodulation
Directory of Open Access Journals (Sweden)
N. Milosevic
2011-12-01
Full Text Available This paper proposes a new binary phase shift keying (BPSK signal receiver intended for reception under conditions of significant carrier frequency offsets. The recursive adaptive filter with least mean squares (LMS adaptation is used. The proposed receiver has a constant, defining the balance between the recursive and the nonrecursive part of the filter, whose proper choice allows a simple construction of the receiver. The correct choice of this parameter could result in unitary length of the filter. The proposed receiver has performance very close to the performance of the BPSK receiver with perfect frequency synchronization, in a wide range of frequency offsets (plus/minus quarter of the signal bandwidth. The results obtained by the software simulation are confirmed by the experimental results measured on the receiver realized with the universal software radio peripheral (USRP, with the baseband signal processing at personal computer (PC.
Adaptive Compensation of Reactive Power With Shunt Active Power Filters
DEFF Research Database (Denmark)
Blaabjerg, Frede; Asiminoaei, Lucian; Hansen, Steffan;
2008-01-01
This paper describes an adaptive method for compensating the reactive power with an active power filter (APF), which is initially rated for mitigation of only the harmonic currents given by a nonlinear industrial load. It is proven that, if the harmonic currents do not load the APF at the rated...
Regularized Adaptive Notch Filters for Acoustic Howling Suppression
DEFF Research Database (Denmark)
Gil-Cacho, Pepe; van Waterschoot, Toon; Moonen, Marc;
2009-01-01
In this paper, a method for the suppression of acoustic howling is developed, based on adaptive notch filters (ANF) with regularization (RANF). The method features three RANFs working in parallel to achieve frequency tracking, howling detection and suppression. The ANF-based approach to howling...
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
Interference Cancellation in Aircraft Cockpit by Adaptive Filters
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Arun C.
2016-01-01
Full Text Available This paper investigates on the development and implementation of adaptive noise cancellation (ANC algorithm meant for mitigating the high level engine noise in the cockpit of an aircraft, which makes the speech signal unintelligible. Adaptive filters configured as interference canceller have the potential application in mitigating the above interference. A comparative study of Gradient based adaptive Infinite Impulse Response (IIR algorithm and its modified version is performed using MATLAB simulator in terms of converging speed. From the simulation result the best IIR algorithm is used for implementation in Performance Optimized with Enhanced RISC PC (Power PC 7448.
Adaptive training of feedforward neural networks by Kalman filtering
Energy Technology Data Exchange (ETDEWEB)
Ciftcioglu, Oe. [Istanbul Technical Univ. (Turkey). Dept. of Electrical Engineering; Tuerkcan, E. [Netherlands Energy Research Foundation (ECN), Petten (Netherlands)
1995-02-01
Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.).
Non-commutative multi-dimensional cosmology
Khosravi, N; Sepangi, H R
2006-01-01
A non-commutative multi-dimensional cosmological model is introduced and used to address the issues of compactification and stabilization of extra dimensions and the cosmological constant problem. We show that in such a scenario these problems find natural solutions in a universe described by an increasing time parameter.
New bounds for multi-dimensional packing
S. Seiden; R. van Stee (Rob)
2001-01-01
textabstractNew upper and lower bounds are presented for a multi-dimensional generalization of bin packing called box packing. Several variants of this problem, including bounded space box packing, square packing, variable sized box packing and resource augmented box packing are also studied. The
New bounds for multi-dimensional packing
Seiden, S.; Stee, R. van
2001-01-01
New upper and lower bounds are presented for a multi-dimensional generalization of bin packing called box packing. Several variants of this problem, including bounded space box packing, square packing, variable sized box packing and resource augmented box packing are also studied. The main results,
Multi-dimensional quasitoeplitz Markov chains
Directory of Open Access Journals (Sweden)
Alexander N. Dudin
1999-01-01
Full Text Available This paper deals with multi-dimensional quasitoeplitz Markov chains. We establish a sufficient equilibrium condition and derive a functional matrix equation for the corresponding vector-generating function, whose solution is given algorithmically. The results are demonstrated in the form of examples and applications in queues with BMAP-input, which operate in synchronous random environment.
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.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
Directory of Open Access Journals (Sweden)
Chien-Hao Tseng
2016-07-01
Full Text Available This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF and fuzzy logic adaptive system (FLAS for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF, unscented Kalman filter (UKF, and CKF approaches.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-07-26
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.
Fast GPU based adaptive filtering of 4D echocardiography.
Broxvall, Mathias; Emilsson, Kent; Thunberg, Per
2012-06-01
Time resolved three-dimensional (3D) echocardiography generates four-dimensional (3D+time) data sets that bring new possibilities in clinical practice. Image quality of four-dimensional (4D) echocardiography is however regarded as poorer compared to conventional echocardiography where time-resolved 2D imaging is used. Advanced image processing filtering methods can be used to achieve image improvements but to the cost of heavy data processing. The recent development of graphics processing unit (GPUs) enables highly parallel general purpose computations, that considerably reduces the computational time of advanced image filtering methods. In this study multidimensional adaptive filtering of 4D echocardiography was performed using GPUs. Filtering was done using multiple kernels implemented in OpenCL (open computing language) working on multiple subsets of the data. Our results show a substantial speed increase of up to 74 times, resulting in a total filtering time less than 30 s on a common desktop. This implies that advanced adaptive image processing can be accomplished in conjunction with a clinical examination. Since the presented GPU processor method scales linearly with the number of processing elements, we expect it to continue scaling with the expected future increases in number of processing elements. This should be contrasted with the increases in data set sizes in the near future following the further improvements in ultrasound probes and measuring devices. It is concluded that GPUs facilitate the use of demanding adaptive image filtering techniques that in turn enhance 4D echocardiographic data sets. The presented general methodology of implementing parallelism using GPUs is also applicable for other medical modalities that generate multidimensional data.
Directory of Open Access Journals (Sweden)
Cahit Tağı ÇELİK
2004-01-01
Full Text Available Monitoring the Crustal Movement in Geodesy is performed by the deformation survey and analysis. If monitoring the crustal movements involves more than two epochs of survey campaign then from the plate tectonic theory, stations do not move randomly from one epoch to the other, therefore Kalman Filter may be suitable to use. However, if sudden movements happened in the crust in particular earthquake happened, the crust moves very fast in a very short period of time. When Kalman Filter used for monitoring these movements, from associated epoch, for a number of epochs the results may be biased. In the paper, comparison of two methods for elimination of the above mentioned biases have been performed. These methods are Fading Memory Filter and Adaptive Kalman Filter for an unknown bias.
Low-power adaptive filter based on RNS components
DEFF Research Database (Denmark)
Bernocchi, Gian Luca; Cardarilli, Gian Carlo; Del Re, Andrea
2007-01-01
on the least mean squares (LMS) algorithm, is allowed. Previous work showed that the use of the residue number system (RNS) for the variable FIR filter grants advantages both in area and power consumption. On the other hand, the use of a binary serial implementation of the adaptation algorithm eliminates...... the need for complex scaling circuits in RNS. The advantages in terms of area and speed of the presented filter, with respect to its two's complement counterpart, are evaluated for implementations in standard cells....
An Affine Combination of Adaptive Filters for Channels with Different Sparsity Levels
Directory of Open Access Journals (Sweden)
M. Butsenko
2016-06-01
Full Text Available In this paper we present an affine combination strategy for two adaptive filters. One filter is designed to handle sparse impulse responses and the other one performs better if impulse response is dispersive. Filter outputs are combined using an adaptive mixing parameter and the resulting output shows a better performance than each of the combining filters separately. We also demonstrate that affine combination results in faster convergence than a convex combination of two adaptive filters.
Kalman filtering to suppress spurious signals in Adaptive Optics control
Energy Technology Data Exchange (ETDEWEB)
Poyneer, L; Veran, J P
2010-03-29
In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.
Kalman filtering to suppress spurious signals in Adaptive Optics control
Energy Technology Data Exchange (ETDEWEB)
Poyneer, L; Veran, J P
2010-03-29
In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.
Queueing interpretation of adaptive reconstructive multiparameter τ-opening filters
Chen, Yidong; Dougherty, Edward R.
1998-04-01
A multiparameter binary (tau) -opening is a union of parameterized openings in which parameters for each opening are individually defined and a structuring element can be parameterized relative to both size and shape. The reconstructive filter corresponding to an opening is defined by fully passing any grain not eliminated by the opening and deleting all other grains. Adaptive design results from treating the parameter vector of a reconstructive multiparameter (tau) -opening as the state space of a Markov chain. The present paper considers the relationship between Markovian queueing networks and adaptive multiparameter (tau) - openings for the signal-union-noise model.
Model Adaptation for Prognostics in a Particle Filtering Framework
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Bhaskar Saha
2011-01-01
Full Text Available One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion and Li-Polymer batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.
Microseismic event denoising via adaptive directional vector median filters
Zheng, Jing; Lu, Ji-Ren; Jiang, Tian-Qi; Liang, Zhe
2017-03-01
We present a novel denoising scheme via Radon transform-based adaptive vector directional median filters named adaptive directional vector median filter (AD-VMF) to suppress noise for microseismic downhole dataset. AD-VMF contains three major steps for microseismic downhole data processing: (i) applying Radon transform on the microseismic data to obtain the parameters of the waves, (ii) performing S-transform to determine the parameters for filters, and (iii) applying the parameters for vector median filter (VMF) to denoise the data. The steps (i) and (ii) can realize the automatic direction detection. The proposed algorithm is tested with synthetic and field datasets that were recorded with a vertical array of receivers. The P-wave and S-wave direct arrivals are properly denoised for poor signal-to-noise ratio (SNR) records. In the simulation case, we also evaluate the performance with mean square error (MSE) in terms of signal-to-noise ratio (SNR). The result shows that the distortion of the proposed method is very low; the SNR is even less than 0 dB.
Using LMS Adaptive Filter in Direct Wave Cancellation
Institute of Scientific and Technical Information of China (English)
徐元军; 陶然; 王越; 单涛
2003-01-01
The way to use the least-mean-square (LMS) arithmetic to cancel the direct wave for a passive radar system is introduced. The model of the direct wave is deduced. By using the LMS adaptive FIR filter, the software solution for FM passive radar system is developed instead of the hardware consumption of the existent experiment system of passive radar. Further more some simulative results are given. The simulative results indicate that using LMS arithmetic to cancel the direct wave is effective.
Reduced Rank Adaptive Filtering in Impulsive Noise Environments
Soury, Hamza
2014-01-06
An impulsive noise environment is used in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction. The minimized objective function is defined using the Lp norm. The results are presented and the efficiency of each algorithm is discussed.
Parametric Adaptive Matched Filter for Multistatic MIMO Radar (Preprint)
2016-11-04
SUPPLEMENTARY NOTES Journal article submitted for publication to the IEEE Transactions on Aerospace and Electronic Systems. The U.S. Government is joint...Prescribed by ANSI Std. Z39-18 1 Parametric Adaptive Matched Filter for Multistatic MIMO Radar Tariq Qureshi Member, IEEE , Muralidhar Rangaswamy, Fellow... IEEE , and Kristine Bell, Fellow, IEEE Abstract A fully disrtibuted MIMO radar system can be treated in terms of all bistatic pairs. If a bistatic
Frequency-shift low-pass filtering and least mean square adaptive filtering for ultrasound imaging
Wang, Shanshan; Li, Chunyu; Ding, Mingyue; Yuchi, Ming
2016-04-01
Ultrasound image quality enhancement is a problem of considerable interest in medical imaging modality and an ongoing challenge to date. This paper investigates a method based on frequency-shift low-pass filtering (FSLF) and least mean square adaptive filtering (LMSAF) for ultrasound image quality enhancement. FSLF is used for processing the ultrasound signal in the frequency domain, while LMSAPF in the time domain. Firstly, FSLF shifts the center frequency of the focused signal to zero. Then the real and imaginary part of the complex data are filtered respectively by finite impulse response (FIR) low-pass filter. Thus the information around the center frequency are retained while the undesired ones, especially background noises are filtered. Secondly, LMSAF multiplies the signals with an automatically adjusted weight vector to further eliminate the noises and artifacts. Through the combination of the two filters, the ultrasound image is expected to have less noises and artifacts and higher resolution, and contrast. The proposed method was verified with the RF data of the CIRS phantom 055A captured by SonixTouch DAQ system. Experimental results show that the background noises and artifacts can be efficiently restrained, the wire object has a higher resolution and the contrast ratio (CR) can be enhanced for about 12dB to 15dB at different image depth comparing to delay-and-sum (DAS).
THE ADAPTIVE SMOOTHING FILTERS OF SENSOR SIGNALS IN THE MICROAVIONIC SYSTEMS
Directory of Open Access Journals (Sweden)
V. A. Malkin
2012-01-01
Full Text Available The adaptive for intensivity of measuring noise filters for smooth of sensor signals are considered. The adaptation are realized at the expense of the statistical processing of the filtering errors. The algorithm of adaptive filter coefficients calculation and modeling results are presented.
Multi-Dimensional Aggregation for Temporal Data
DEFF Research Database (Denmark)
Böhlen, M. H.; Gamper, J.; Jensen, Christian Søndergaard
2006-01-01
when the data hold. In temporal databases, intervals typically capture the states of reality that the data apply to, or capture when the data are, or were, part of the current database state. This paper proposes a new aggregation operator that addresses several challenges posed by interval data. First......Business Intelligence solutions, encompassing technologies such as multi-dimensional data modeling and aggregate query processing, are being applied increasingly to non-traditional data. This paper extends multi-dimensional aggregation to apply to data with associated interval values that capture......, the intervals to be associated with the result tuples may not be known in advance, but depend on the actual data. Such unknown intervals are accommodated by allowing result groups that are specified only partially. Second, the operator contends with the case where an interval associated with data expresses...
Multi-Dimensional Recurrent Neural Networks
Graves, Alex; Schmidhuber, Juergen
2007-01-01
Recurrent neural networks (RNNs) have proved effective at one dimensional sequence learning tasks, such as speech and online handwriting recognition. Some of the properties that make RNNs suitable for such tasks, for example robustness to input warping, and the ability to access contextual information, are also desirable in multidimensional domains. However, there has so far been no direct way of applying RNNs to data with more than one spatio-temporal dimension. This paper introduces multi-dimensional recurrent neural networks (MDRNNs), thereby extending the potential applicability of RNNs to vision, video processing, medical imaging and many other areas, while avoiding the scaling problems that have plagued other multi-dimensional models. Experimental results are provided for two image segmentation tasks.
New bounds for multi-dimensional packing
Seiden, S.; Stee, van, Rob
2001-01-01
New upper and lower bounds are presented for a multi-dimensional generalization of bin packing called box packing. Several variants of this problem, including bounded space box packing, square packing, variable sized box packing and resource augmented box packing are also studied. The main results, stated for d=2, are as follows: A new upper bound of 2.66013 for online box packing, a new $14/9 + varepsilon$ polynomial time offline approximation algorithm for square packing, a new upper bound ...
A New Adaptive Framework for Collaborative Filtering Prediction.
Almosallam, Ibrahim A; Shang, Yi
2008-06-01
Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system.
Adaptive distributed Kalman filtering with wind estimation for astronomical adaptive optics.
Massioni, Paolo; Gilles, Luc; Ellerbroek, Brent
2015-12-01
In the framework of adaptive optics (AO) for astronomy, it is a common assumption to consider the atmospheric turbulent layers as "frozen flows" sliding according to the wind velocity profile. For this reason, having knowledge of such a velocity profile is beneficial in terms of AO control system performance. In this paper we show that it is possible to exploit the phase estimate from a Kalman filter running on an AO system in order to estimate wind velocity. This allows the update of the Kalman filter itself with such knowledge, making it adaptive. We have implemented such an adaptive controller based on the distributed version of the Kalman filter, for a realistic simulation of a multi-conjugate AO system with laser guide stars on a 30 m telescope. Simulation results show that this approach is effective and promising and the additional computational cost with respect to the distributed filter is negligible. Comparisons with a previously published slope detection and ranging wind profiler are made and the impact of turbulence profile quantization is assessed. One of the main findings of the paper is that all flavors of the adaptive distributed Kalman filter are impacted more significantly by turbulence profile quantization than the static minimum mean square estimator which does not incorporate wind profile information.
Meng, Yang; Gao, Shesheng; Zhong, Yongmin; Hu, Gaoge; Subic, Aleksandar
2016-03-01
The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performance of the standard UKF is dependent on the accurate statistical characterizations of system noise. If the noise distributions of inertial instruments and GNSS receivers are not appropriately described, the standard UKF will produce deteriorated or even divergent navigation solutions. This paper presents an adaptive UKF with noise statistic estimator to overcome the limitation of the standard UKF. According to the covariance matching technique, the innovation and residual sequences are used to determine the covariance matrices of the process and measurement noises. The proposed algorithm can estimate and adjust the system noise statistics online, and thus enhance the adaptive capability of the standard UKF. Simulation and experimental results demonstrate that the performance of the proposed algorithm is significantly superior to that of the standard UKF and adaptive-robust UKF under the condition without accurate knowledge on system noise, leading to improved navigation precision.
Directory of Open Access Journals (Sweden)
Samuel Boudet
2014-01-01
Full Text Available Muscle artifacts constitute one of the major problems in electroencephalogram (EEG examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings.
Coevolution-Based Adaptive Particle Filters for Global Localization
Institute of Scientific and Technical Information of China (English)
LUORonghua; HONGBingrong; PIAOSonghao; DAIHuming
2005-01-01
A coevolution mechanism derived from competition relationships between ecological species is merged into Particle filters (PF). The new version of particle filters is termed Coevolutionbased adaptive particle filters (CEAPF). In CEAPF, samples are clustered into species, each of which represents a hypothesis of state of the system in a higher level than a single sample. Since the coevolution between the species ensures that the multiple distinct hypotheses can be tracked stably, the problem of premature convergence of PF can be solved. And the number of samples can be adjusted adaptively over time according to the uncertainty of the state of the system by using the population growth model. In addition, by using the crossover and mutation operators in evolutionary computation, intra-species evolution can drive the samples move towards the regions where the desired posterior density is large. So a small number of samples can represent the desired density well enough. And CEAPF is applied to robot localization in highly symmetric environments. Experiments prove that CEAPF can considerably improve the success rate and precision of localization.
An adaptive particle filter for mobile robot fault diagnosis
Institute of Scientific and Technical Information of China (English)
DUAN Zhuo-hua; FU Ming; CAI Zi-xing; YU Jin-xia
2006-01-01
An adaptive particle filter for fault diagnosis of dead-reckoning system was presented, which applied a general framework to integrate rule-based domain knowledge into particle filter. Domain knowledge was exploited to constrain the state space to certain subset. The state space was adjusted by setting the transition matrix. Firstly, the monitored mobile robot and its kinematics models,measurement models and fault models were given. Then, 5 kinds of planar movement states of the robot were estimated with driving speeds of left and right side. After that, the possible (or detectable) fault modes were obtained to modify the transitional probability.There are two typical advantages of this method, i.e. particles will never be drawn from hopeless area of the state space, and the particle number is reduced.
An Adaptive Multipath Mitigation Filter for GNSS Applications
Chang, Chung-Liang; Juang, Jyh-Ching
2008-12-01
Global navigation satellite system (GNSS) is designed to serve both civilian and military applications. However, the GNSS performance suffers from several errors, such as ionosphere delay, troposphere delay, ephemeris error, and receiver noise and multipath. Among these errors, the multipath is one of the most unpredictable error sources in high-accuracy navigation. This paper applies a modified adaptive filter to reduce code and carrier multipath errors in GPS. The filter employs a tap-delay line with an Adaline network to estimate the direction and the delayed-signal parameters. Then, the multipath effect is mitigated by subtracting the estimated multipath effects from the processed correlation function. The hardware complexity of the method is also compared with other existing methods. Simulation results show that the proposed method using field data has a significant reduction in multipath error especially in short-delay multipath scenarios.
An Adaptive Multipath Mitigation Filter for GNSS Applications
Directory of Open Access Journals (Sweden)
Jyh-Ching Juang
2008-02-01
Full Text Available Global navigation satellite system (GNSS is designed to serve both civilian and military applications. However, the GNSS performance suffers from several errors, such as ionosphere delay, troposphere delay, ephemeris error, and receiver noise and multipath. Among these errors, the multipath is one of the most unpredictable error sources in high-accuracy navigation. This paper applies a modified adaptive filter to reduce code and carrier multipath errors in GPS. The filter employs a tap-delay line with an Adaline network to estimate the direction and the delayed-signal parameters. Then, the multipath effect is mitigated by subtracting the estimated multipath effects from the processed correlation function. The hardware complexity of the method is also compared with other existing methods. Simulation results show that the proposed method using field data has a significant reduction in multipath error especially in short-delay multipath scenarios.
Fast Source Camera Identification Using Content Adaptive Guided Image Filter.
Zeng, Hui; Kang, Xiangui
2016-03-01
Source camera identification (SCI) is an important topic in image forensics. One of the most effective fingerprints for linking an image to its source camera is the sensor pattern noise, which is estimated as the difference between the content and its denoised version. It is widely believed that the performance of the sensor-based SCI heavily relies on the denoising filter used. This study proposes a novel sensor-based SCI method using content adaptive guided image filter (CAGIF). Thanks to the low complexity nature of the CAGIF, the proposed method is much faster than the state-of-the-art methods, which is a big advantage considering the potential real-time application of SCI. Despite the advantage of speed, experimental results also show that the proposed method can achieve comparable or better performance than the state-of-the-art methods in terms of accuracy.
Image denoising using a directional adaptive diffusion filter
Zhao, Cuifang; Shi, Caicheng; He, Peikun
2006-11-01
Partial differential equations (PDEs) are well-known due to their good processing results which it can not only smooth the noise but also preserve the edges. But the shortcomings of these processes came to being noticed by people. In some sense, PDE filter is called "cartoon model" as it produces an approximation of the input image, use the same diffusion model and parameters to process noise and signal because it can not differentiate them, therefore, the image is naturally modified toward piecewise constant functions. A new method called a directional adaptive diffusion filter is proposed in the paper, which combines PDE mode with wavelet transform. The undecimated discrete wavelet transform (UDWT) is carried out to get different frequency bands which have obviously directional selectivity and more redundancy details. Experimental results show that the proposed method provides a performance better to preserve textures, small details and global information.
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...
Reliable hydraulic turbine governor based on identification and adaptive filtering
Energy Technology Data Exchange (ETDEWEB)
Jiang, J.; Doraiswami, R.
1986-01-01
A scheme for improving reliable operation of a PID governor of a hydraulic turbine generating unit is proposed. The parameters of governor and actuators are identified on-line to, a) detect their anomalous behaviours, b) facilitate the calibration of the proportional integral and derivative gain settings. An adaptive filter is used to detect the lightly damped oscillations of the system. The proposed scheme was verified via simulation on the real data obtained from one of Mactaquac hydro-generating units of New Brunswick Electrical Power Commission. The simulation results show that the proposed scheme can indeed provide an accurate and rapid detection of the abnormal system operations.
Reduced rank adaptive filtering in impulsive noise environments
Soury, Hamza
2014-11-01
An impulsive noise environment is considered in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction, while the minimized objective function is defined using the L
Suppression of Mixed Noise in the Similar Images by Using Adaptive LMS L-filters
Directory of Open Access Journals (Sweden)
S. Marchevsky
2000-12-01
Full Text Available In this paper, several adaptive least mean squares (LMSlocation-invariant filter (L-filter modifications will be described.These filters are based on linear combination of order statistics. Theadaptive L-filters are able to adapt well to variety of noiseprobability distribution, including impulsive noise. They also performwell in the case of nonstationary signals and, therefore, they aresuitable for image processing, too. Following this L-filter property,applications of the adaptive LMS L-filters for filteringtwo-dimensional static images degraded by mixed noise consisting ofadditive Gaussian white noise and impulsive noise will be presented inthis paper. Based on conveniently selected experiments intent on imagefiltering, the properties of a several adaptive L-filters modificationswill be demonstrated and compared. It will follow from experimentresults, that the L-filter modification called signal-dependent LMSL-filter yields the best results.
Directory of Open Access Journals (Sweden)
Jinsoo Jeong
2011-06-01
Full Text Available This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure.
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.
Periodic Noise Suppression from ECG Signal using Novel Adaptive Filtering Techniques
Directory of Open Access Journals (Sweden)
Yogesh Sharma
2012-03-01
Full Text Available Electrocardiogram signal most commonly known recognized and used biomedical signal for medical examination of heart. The ECG signal is very sensitive in nature, and even if small noise mixed with original signal, the various characteristics of the signal changes, Data corrupted with noise must either filtered or discarded, filtering is important issue for design consideration of real time heart monitoring systems. Various filters used for removing the noise from ECG signals, most commonly used filters are Notch Filters, FIR filters, IIR filters, Wiener filter, Adaptive filters etc. Performance analysis shows that the best result is obtained by using Adaptive filter to remove various noises from ECG signal and get significant SNR andMSE results. In this paper a novel adaptive approach by using LMS algorithm and delay has shown whichcan be used for pre-processing of ECG signal and give appreciable result.
Intelligent neural-network-based adaptive power-line conditioner for real-time harmonics filtering
Energy Technology Data Exchange (ETDEWEB)
Lin, H.C. [Chien Kuo Institute of Technology (China). Dept. of Automation Engineering
2004-09-01
Conventional approaches for harmonic filtering usually employ either passive or active filtering techniques or a combination of both. The paper proposes an alternative intelligent adaptive power line conditioner (I-APLC), which. is a form of neural-network- based adaptive harmonic filtering. The I-APLC makes use of one supervised learning rule (backpropagation) which underlies the adaptive self-learning in realising the optimal filter weight vector. Experimental. results obtained via a prototype model of the DC variable-speed motor verified that I-APLC is feasible in terms of real-time tracking, adaptive harmonic filtering, faster training mid convergence speeds, and simplicity in the online hardware implementation. (author)
A stochastic total least squares solution of adaptive filtering problem.
Javed, Shazia; Ahmad, Noor Atinah
2014-01-01
An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs.
Multimodal Medical Image Fusion by Adaptive Manifold Filter
Directory of Open Access Journals (Sweden)
Peng Geng
2015-01-01
Full Text Available Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images.
Immune adaptive Gaussian mixture par ticle filter for state estimation
Institute of Scientific and Technical Information of China (English)
Wenlong Huang; Xiaodan Wang; Yi Wang; Guohong Li
2015-01-01
The particle filter (PF) is a flexible and powerful sequen-tial Monte Carlo (SMC) technique capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. However, the generic PF suffers from particle degeneracy and sample im-poverishment, which greatly affects its performance for nonlinear, non-Gaussian tracking problems. To deal with those issues, an improved PF is proposed. The algorithm consists of a PF that uses an immune adaptive Gaussian mixture model (IAGM) based immune algorithm to re-approximate the posterior density. At the same time, three immune antibody operators are embed in the new filter. Instead of using a resample strategy, the newest obser-vation and conditional likelihood are integrated into those immune antibody operators to update the particles, which can further im-prove the diversity of particles, and drive particles toward their close local maximum of the posterior probability. The improved PF algorithm can produce a closed-form expression for the posterior state distribution. Simulation results show the proposed algorithm can maintain the effectiveness and diversity of particles and avoid sample impoverishment, and its performance is superior to several PFs and Kalman filters.
An adaptive filtered back-projection for photoacoustic image reconstruction
Energy Technology Data Exchange (ETDEWEB)
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong, E-mail: jingyong.ye@utsa.edu [Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas 78249 (United States)
2015-05-15
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
Unbalance vibration suppression for AMBs system using adaptive notch filter
Chen, Qi; Liu, Gang; Han, Bangcheng
2017-09-01
The unbalance of rotor levitated by active magnetic bearings (AMBs) will cause synchronous vibration which greatly degrade the performance at high speeds in the rotating machinery. To suppress the unbalance vibration without angular velocity information, a novel modified adaptive notch filter (ANF) with phase shift in the AMBs system is presented in this study. Firstly, a 4-degree-of-freedom (DOF) radial unbalanced AMB rotor system is described and analyzed, and the solution of rotor vibration displacement is compared with the experimental data to verify the preciseness of the dynamic model. Then the principle and structure of the proposed notch filter used for the frequency estimation and online identification of synchronous component are presented. As well, the convergence property of the algorithm is investigated. In addition, the stability analysis of the closed-loop AMB system with the proposed ANF is conducted. Simulation and experiments on an AMB driveline system demonstrate the effectiveness and the adaptive characteristics of the proposed ANF on the elimination of synchronous controlled current in a widely operating speed range.
Multi Dimensional CTL and Stratified Datalog
Directory of Open Access Journals (Sweden)
Theodore Andronikos
2010-02-01
Full Text Available In this work we define Multi Dimensional CTL (MD-CTL in short by extending CTL which is thedominant temporal specification language in practice. The need for Multi Dimensional CTL is mainlydue to the advent of semi-structured data. The common path nature of CTL and XPath which provides asuitable model for semi-structured data, has caused the emergence of work on specifying a relation amongthem aiming at exploiting the nice properties of CTL. Although the advantages of such an approach havealready been noticed [36, 26, 5], no formal definition of MD-CTL has been given. The goal of this workis twofold; a we define MD-CTL and prove that the “nice” properties of CTL (linear model checking andbounded model property transfer also to MD-CTL, b we establish new results on stratified Datalog. Inparticular, we define a fragment of stratified Datalog called Multi Branching Temporal (MBT in shortprograms that has the same expressive power as MD-CTL. We prove that by devising a linear translationbetween MBT and MD-CTL. We actually give the exact translation rules for both directions. We furtherbuild on this relation to prove that query evaluation is linear and checking satisfiability, containment andequivalence are EXPTIME–complete for MBT programs. The class MBT is the largest fragment of stratifiedDatalog for which such results exist in the literature.
Performance Evaluation of 2D Adaptive Bilateral Filter For Removal of Noise From Robust Images
National Research Council Canada - National Science Library
B.Sridhar; Dr.K.V.V.S.Reddy
2013-01-01
.... The variance of range filter can also be adaptive. The filter is applied to improve the sharpens of a gray level and color image by increasing the slope of the edges without producing overshoot or undershoots...
Application of adaptive filters in denoising magnetocardiogram signals
Khan, Pathan Fayaz; Patel, Rajesh; Sengottuvel, S.; Saipriya, S.; Swain, Pragyna Parimita; Gireesan, K.
2017-05-01
Magnetocardiography (MCG) is the measurement of weak magnetic fields from the heart using Superconducting QUantum Interference Devices (SQUID). Though the measurements are performed inside magnetically shielded rooms (MSR) to reduce external electromagnetic disturbances, interferences which are caused by sources inside the shielded room could not be attenuated. The work presented here reports the application of adaptive filters to denoise MCG signals. Two adaptive noise cancellation approaches namely least mean squared (LMS) algorithm and recursive least squared (RLS) algorithm are applied to denoise MCG signals and the results are compared. It is found that both the algorithms effectively remove noisy wiggles from MCG traces; significantly improving the quality of the cardiac features in MCG traces. The calculated signal-to-noise ratio (SNR) for the denoised MCG traces is found to be slightly higher in the LMS algorithm as compared to the RLS algorithm. The results encourage the use of adaptive techniques to suppress noise due to power line frequency and its harmonics which occur frequently in biomedical measurements.
Application of adaptive inverse filtering approach in weigh-in-motion of vehicles
Yu, Jinsong; Wu, Jie; Wan, Jiuqing; Li, Xingshan
2006-11-01
In this paper an adaptive inverse filter is employed which suppress noise within the bandwidth of the desired signal with the particular aim to improve the accuracy of WIM systems. Within the framework of the FIR filter, the inverse system of WIM system is constructed by using LMS adaptive algorithm as an innovative filter. Moreover, an additional filter, a noise filter, is adopted as well, in order to best improvement the measurement accuracy. The final results processed by cascaded filter combination show a significant improvement in estimation of static weight of moving vehicles.
Adaptive integrated navigation filtering based on accelerometer calibration
Directory of Open Access Journals (Sweden)
Qifan Zhou
2012-11-01
Full Text Available In this paper, a novel GPS (Global Positioning System and DR (Dead Reckoning system which was based on the accelerometer and gyroscope integrated system was designed and implemented. In this system, the odometer used in traditional DR system was replaced by a MEMS tri-axis accelerometer in order to decrease the cost and the volume of the system. The system was integrated by the Kalman filter and a new mathematical model was introduced. In order to reasonably use the GPS information, an adaptive algorithm based on single measurement system which could estimate the measurement noise covariance was obtained. On the purpose of reducing the effect of the accumulated error caused by drift and bias of accelerometer, the accelerometer was calibrated online when GPS performed well. In this way, the integrated system could not only obtain the high-precision positioning in real time, but also perform stably in practice.
Adaptive Current Control Method for Hybrid Active Power Filter
Chau, Minh Thuyen
2016-09-01
This paper proposes an adaptive current control method for Hybrid Active Power Filter (HAPF). It consists of a fuzzy-neural controller, identification and prediction model and cost function. The fuzzy-neural controller parameters are adjusted according to the cost function minimum criteria. For this reason, the proposed control method has a capability on-line control clings to variation of the load harmonic currents. Compared to the single fuzzy logic control method, the proposed control method shows the advantages of better dynamic response, compensation error in steady-state is smaller, able to online control is better and harmonics cancelling is more effective. Simulation and experimental results have demonstrated the effectiveness of the proposed control method.
Adaptive filtering for white-light LED visible light communication
Hsu, Chin-Wei; Chen, Guan-Hong; Wei, Liang-Yu; Chow, Chi-Wai; Lu, I.-Cheng; Liu, Yen-Liang; Chen, Hsing-Yu; Yeh, Chien-Hung; Liu, Yang
2017-01-01
White-light phosphor-based light-emitting diode (LED) can be used to provide lighting and visible light communication (VLC) simultaneously. However, the long relaxation time of phosphor can reduce the modulation bandwidth and limit the VLC data rate. Recent VLC works focus on improving the LED modulation bandwidths. Here, we propose and demonstrate the use of adaptive Volterra filtering (AVF) to increase the data rate of a white-light LED VLC system. The detailed algorithm and implementation of the AVF for the VLC system have been discussed. Using our proposed electrical frontend circuit and the proposed AVF, a significant data rate enhancement to 700.68 Mbit/s is achieved after 1-m free-space transmission using a single white-light phosphor-based LED.
Channel Estimation using Adaptive Filtering for LTE-Advanced
Directory of Open Access Journals (Sweden)
Saqib Saleem
2011-05-01
Full Text Available For demand of high data rates, enhanced system capacity and coverage, ITU made proposal for the standardization of next generation wireless communication systems, known as IMT-Advanced. To achieve these targets, a priori knowledge of the channel is required at the transmitter side. In this paper, three adaptive channel estimation techniques: Least Mean Square (LMS, Recursive Least Square (RLS and Kalman-Filtering Based, are compared in terms of performance and complexity. For performance, Mean Square Error (MSE and Symbol Error Rate (SER while for complexity, computational time is measured for variable channel impulse response (CIR lengths and channel taps. MATLAB Monte-Carlo Simulations are used to evaluate these techniques.
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Directory of Open Access Journals (Sweden)
Sergueï Burykh
2003-01-01
Full Text Available A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.
Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth
Alam, Mushfiqul; Rohac, Jan
2015-01-01
MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing. PMID:25648711
Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth
Directory of Open Access Journals (Sweden)
Mushfiqul Alam
2015-02-01
Full Text Available MEMS (micro-electro-mechanical system-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU, which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor’s behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer’s data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing.
Application of adaptive Savitzky–Golay filter for EEG signal processing
Directory of Open Access Journals (Sweden)
Deepshikha Acharya
2016-09-01
Full Text Available A Savitzky–Golay filter typically requires pre-determined values of order and frame size for its fabrication. Generally, a random hit-and-trial method or prior experience is required to determine the suitable values of design parameters. However, the proposed adaptive Savitzky–Golay filter aims to provide a generic framework for optimal design of filter vis-à-vis the order and frame size of the filter. The algorithm uses all the possible combinations of these parameters in a certain range and the correlation coefficient is evaluated in each case to measure the filter efficiency. The parameters which provide the highest correlation coefficient are considered for filter design. In this paper the relative advantages of adaptive Savitzky–Golay filter over the standard models are also discussed. The proposed adaptive model of Savitzky–Golay filter is successfully tested for EEG signal processing.
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.
Theory of affine projection algorithms for adaptive filtering
Ozeki, Kazuhiko
2016-01-01
This book focuses on theoretical aspects of the affine projection algorithm (APA) for adaptive filtering. The APA is a natural generalization of the classical, normalized least-mean-squares (NLMS) algorithm. The book first explains how the APA evolved from the NLMS algorithm, where an affine projection view is emphasized. By looking at those adaptation algorithms from such a geometrical point of view, we can find many of the important properties of the APA, e.g., the improvement of the convergence rate over the NLMS algorithm especially for correlated input signals. After the birth of the APA in the mid-1980s, similar algorithms were put forward by other researchers independently from different perspectives. This book shows that they are variants of the APA, forming a family of APAs. Then it surveys research on the convergence behavior of the APA, where statistical analyses play important roles. It also reviews developments of techniques to reduce the computational complexity of the APA, which are important f...
Directory of Open Access Journals (Sweden)
Ida Bagus Ketut Surya Arnawa
2015-08-01
Full Text Available Balinese papyrus (Lontar is one of the most popular media to write for more than a hundred years in Indonesia. Balinese papyrus are used to document things that are considered important in the past. Most of the balinese papyrus suffered damage caused by weathering, edible fungus and insects making it is difficult to read. One of the efforts made to preserve the existence of balinese papyrus is to perform digitization of it. The problems most often encountered in the process of digitizing the image of the balinese papyrus is less good results as there is noise caused by its conditions that have been damaged and the uneven distribution illumination in this part of the image. In this study the authors propose to combine homomorphic filtering with adaptive median filtering to perform image enhancement. Surve results obtained show the percentage of the average respondents stated that the image enhancement results are good is 83.4%, the percentage of the average respondents stated that the image enhancement results are very good is 4% and the percentage of the average respondents stated that the image enhancement results are enough is 12, 6%.
Directory of Open Access Journals (Sweden)
Jan eKneissler
2015-04-01
Full Text Available Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF. PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than ten-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-07-16
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.
Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang
2016-02-01
Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses
Optimal adaptive normalized matched filter for large antenna arrays
Kammoun, Abla
2016-09-13
This paper focuses on the problem of detecting a target in the presence of a compound Gaussian clutter with unknown statistics. To this end, we focus on the design of the adaptive normalized matched filter (ANMF) detector which uses the regularized Tyler estimator (RTE) built from N-dimensional observations x, · · ·, x in order to estimate the clutter covariance matrix. The choice for the RTE is motivated by its possessing two major attributes: first its resilience to the presence of outliers, and second its regularization parameter that makes it more suitable to handle the scarcity in observations. In order to facilitate the design of the ANMF detector, we consider the regime in which n and N are both large. This allows us to derive closed-form expressions for the asymptotic false alarm and detection probabilities. Based on these expressions, we propose an asymptotically optimal setting for the regularization parameter of the RTE that maximizes the asymptotic detection probability while keeping the asymptotic false alarm probability below a certain threshold. Numerical results are provided in order to illustrate the gain of the proposed detector over a recently proposed setting of the regularization parameter.
Study of a new fast adaptive filtering algorithm
Institute of Scientific and Technical Information of China (English)
WANG Zhen-li; ZHANG Xiong-wei; YANG Ji-bin; CHEN Gong
2006-01-01
A new fast adaptive filtering algorithm was presented by using the correlations between the signal's former and latter sampling times.The proof of the new algorithm was also presented,which showed that its optimal weight vector was the solution of generalized Wiener equation.The new algorithm was of simple structure,fast convergence,and less stable maladjustment.It can handle many signals including both uncorrelated signal and strong correlation signal.However,its computational complexity was comparable to that of the normalized least-mean-square (NLMS) algorithm.Simulation results show that for uncorrelated signals,the stable maladjustment of the proposed algorithm is less than that of the VS-NLMS algorithm,and its convergence is comparable to that of the algorithm proposed in references but faster than that of L.E-LMS algorithm.For strong correlation signal,its performance is superior to those of the NLMS algorithm and DCR-LMS algorithm.
两种渐消滤波与自适应抗差滤波的综合比较分析%Comparison of Two Fading Filters and Adaptively Robust Filter
Institute of Scientific and Technical Information of China (English)
杨元喜; 高为广
2007-01-01
Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.
Adaptive Line Enhancers filters for Gravitational Waves Detection from coalescing binaries
Acernese, F; De Rosa, R; Eleuteri, A; Milano, L
2004-01-01
In this paper we propose a new strategy for gravitational waves detection from coalescing binaries, using IIR Adaptive Line Enhancer (ALE) filters. This strategy is a classical hierarchical strategy in which the ALE filters have the role of triggers, used to select data chunks which may contain gravitational events, to be further analyzed with more refined optimal techniques, like the the classical Matched Filter Technique. After a direct comparison of the performances of ALE filters with the Wiener-Komolgoroff optimum filters (matched filters), necessary to discuss their performance and to evaluate the statistical limitation in their use as triggers, we performed a series of tests, demonstrating that these filters are quite promising both for the relatively small computational power needed and for the robustness of the algorithms used. The performed tests have shown a weak point of ALE filters, that we fixed by introducing a further strategy, based on a dynamic bank of ALE filters, running simultaneously, bu...
A gradient-adaptive lattice-based complex adaptive notch filter
Zhu, Rui; Yang, Feiran; Yang, Jun
2016-12-01
This paper presents a new complex adaptive notch filter to estimate and track the frequency of a complex sinusoidal signal. The gradient-adaptive lattice structure instead of the traditional gradient one is adopted to accelerate the convergence rate. It is proved that the proposed algorithm results in unbiased estimations by using the ordinary differential equation approach. The closed-form expressions for the steady-state mean square error and the upper bound of step size are also derived. Simulations are conducted to validate the theoretical analysis and demonstrate that the proposed method generates considerably better convergence rates and tracking properties than existing methods, particularly in low signal-to-noise ratio environments.
Mean-square performance of a convex combination of two adaptive filters
DEFF Research Database (Denmark)
Garcia, Jeronimo; Figueiras-Vidal, A.R.; Sayed, A.H.
2006-01-01
Combination approaches provide an interesting way to improve adaptive filter performance. In this paper, we study the mean-square performance of a convex combination of two transversal filters. The individual filters are independently adapted using their own error signals, while the combination...... is adapted by means of a stochastic gradient algorithm in order to minimize the error of the overall structure. General expressions are derived that show that the method is universal with respect to the component filters, i.e., in steady-state, it performs at least as well as the best component filter....... Furthermore, when the correlation between the a priori errors of the components is low enough, their combination is able to outperform both of them. Using energy conservation relations, we specialize the results to a combination of least mean-square filters operating both in stationary and in nonstationary...
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.
An Adjoint-Based Adaptive Ensemble Kalman Filter
Song, Hajoon
2013-10-01
A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.
Analysis of dynamic deformation processes with adaptive KALMAN-filtering
Eichhorn, Andreas
2007-05-01
In this paper the approach of a full system analysis is shown quantifying a dynamic structural ("white-box"-) model for the calculation of thermal deformations of bar-shaped machine elements. The task was motivated from mechanical engineering searching new methods for the precise prediction and computational compensation of thermal influences in the heating and cooling phases of machine tools (i.e. robot arms, etc.). The quantification of thermal deformations under variable dynamic loads requires the modelling of the non-stationary spatial temperature distribution inside the object. Based upon FOURIERS law of heat flow the high-grade non-linear temperature gradient is represented by a system of partial differential equations within the framework of a dynamic Finite Element topology. It is shown that adaptive KALMAN-filtering is suitable to quantify relevant disturbance influences and to identify thermal parameters (i.e. thermal diffusivity) with a deviation of only 0,2%. As result an identified (and verified) parametric model for the realistic prediction respectively simulation of dynamic temperature processes is presented. Classifying the thermal bend as the main deformation quantity of bar-shaped machine tools, the temperature model is extended to a temperature deformation model. In lab tests thermal load steps are applied to an aluminum column. Independent control measurements show that the identified model can be used to predict the columns bend with a mean deviation (r.m.s.) smaller than 10 mgon. These results show that the deformation model is a precise predictor and suitable for realistic simulations of thermal deformations. Experiments with modified heat sources will be necessary to verify the model in further frequency spectra of dynamic thermal loads.
Development of XAFS for multi-dimensional structural information
Cheung, K
1998-01-01
time-resolved XAFS data on dilute metal systems. The development .also involved off-line commissioning of a UV/visible stopped-flow unit and adapting it to carry out time-resolved fluorescence XAFS data. The rest of the upgrade in implementing sagittal focusing optics are in progress. Similarly a novel fast count rate and compact solid state fluorescence detector has also been designed and is to be commissioned shortly. Our aim is to reduce sample size and data integration lime for obtaining high quality time-resolved XAFS data. There were two distinct projects which this thesis reports. The overall aim was to extend the XAFS technique to a multi-dimensional structural technique. First of the projects, concerned with the development of the EXAFS analysis method. This new approach in EXAFS analysis aims to extend it to give 3-D structural information with atomic precision and accuracy. The method involves making use of the structural information available from the vast library of the PDB database on protein mo...
Development of adaptive IIR filtered-e LMS algorithm for active noise control
Institute of Scientific and Technical Information of China (English)
SUN Xu; MENG Guang; TENG Pengxiao; CHEN Duanshi
2003-01-01
Compared to finite impulse response (FIR) filters, infinite impulse response (IIR)filters can match the system better with much fewer coefficients, and hence the computationload is saved and the performance improves. Therefore, it is attractive to use IIR filters insteadof FIR filters in active noise control (ANC). However, filtered-U LMS (FULMS) algorithm, theIIR filter-based algorithm commonly used so far cannot ensure global convergence. A new IIRfilter based adaptive algorithm, which can ensure global convergence with computation loadonly slightly increasing, is proposed in this paper. The new algorithm is called as filtered-eLMS algorithm since the error signal of which need to be filtered. Simulation results show thatthe FELMS algorithm presents better performance than the FULMS algorithm.
A multi-dimensional assessment of urban vulnerability to climate change in Sub-Saharan Africa
DEFF Research Database (Denmark)
Herslund, Lise Byskov; Jalyer, Fatameh; Jean-Baptiste, Nathalie
2016-01-01
In this paper, we develop and apply a multi-dimensional vulnerability assessment framework for understanding the impacts of climate change-induced hazards in Sub- Saharan African cities. The research was carried out within the European/African FP7 project CLimate change and Urban Vulnerability...... in Africa, which investigated climate change-induced risks, assessed vulnerability and proposed policy initiatives in five African cities. Dar es Salaam (Tanzania) was used as a main case with a particular focus on urban flooding. The multi-dimensional assessment covered the physical, institutional...... for strategic coordination and action. To better adapt to urban flooding andthereby reduce vulnerability and build resilience, we suggest working across dimensions and scales, integrating climate change issues in city-level plans and strategies and enabling local actions to initiate a ‘learning...
Comparison of Narrowband Adaptive Filter Technologies for GPS
2000-03-01
phase filter is developed by utilizing the M taps on each side of the center tap as a 2M tap linear predictor of the value at the center tap. This...linear phase filter is realized with M complex multipliers and 2M complex adders. Complex multiplies are modeled as 6 operations (ops) and complex
A Novel RSSE-PSP Equalizer with an Adaptive Pre-Filter
Institute of Scientific and Technical Information of China (English)
ZHANG Yong-qiang; RU Guo-bao; YANG Hao; XUE Ni
2005-01-01
A reduced-state sequence estimation (RSSE) receiver based on per-survivor processing (PSP) in conjunction with an adaptive pre-filter is proposed in this paper. In RSSE-PSP, each survivor path holds an estimated value of the channel impulse response (CIR), which is updated by adaptive algorithm during the data transmission. Based on the different estimated channel values of each survivor path, corresponding pre-filters are calculated via the Levinson-Durbin algorithm, which can track the time-varying channel adaptively. Computer simulations indicate that the RSSE-PSP equalizer with the new adaptive pre-filter works much better than those with the prevenient pre-filters in ISI channel.
Theelen, T.; Wesseling, P.; Keunen, J.E.E.; Klevering, B.J.
2007-01-01
BACKGROUND: Our study aims to identify anatomical characteristics of glaucoma filtering blebs by means of slit lamp-adapted optical coherence tomography (SL-OCT) and to identify new parameters for the functional prognosis of the filter in the early post-operative period. METHODS: Patients with
Institute of Scientific and Technical Information of China (English)
Li Zhuo; Chen Geng-Hua; Zhang Li-Hua; Yang Qian-Sheng; Feng Ji
2005-01-01
We present acomplementary least-mean-square algorithm of adaptive filtering for SQUID-based magnetocardiography, in which both rapid convergence and fine tracking are realized by switching the weight parameters back and forth between two filters according to the least mean square principle.
Directory of Open Access Journals (Sweden)
Eduardo Fernández
2010-01-01
Full Text Available In this paper, the fast least-mean-squares (LMS algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate.
Microwave Photonic Filters for Interference Cancellation and Adaptive Beamforming
Chang, John
Wireless communication has experienced an explosion of growth, especially in the past half- decade, due to the ubiquity of wireless devices, such as tablets, WiFi-enabled devices, and especially smartphones. Proliferation of smartphones with powerful processors and graphic chips have given an increasing amount of people the ability to access anything from anywhere. Unfortunately, this ease of access has greatly increased mobile wireless bandwidth and have begun to stress carrier networks and spectra. Wireless interference cancellation will play a big role alongside the popularity of wire- less communication. In this thesis, we will investigate optical signal processing methods for wireless interference cancellation methods. Optics provide the perfect backdrop for interference cancellation. Mobile wireless data is already aggregated and transported through fiber backhaul networks in practice. By sandwiching the signal processing stage between the receiver and the fiber backhaul, processing can easily be done locally in one location. Further, optics offers the advantages of being instantaneously broadband and size, weight, and power (SWAP). We are primarily concerned with two methods for interference cancellation, based on microwave photonic filters, in this thesis. The first application is for a co-channel situation, in which a transmitter and receiver are co-located and transmitting at the same frequency. A novel analog optical technique extended for multipath interference cancellation of broadband signals is proposed and experimentally demonstrated in this thesis. The proposed architecture was able to achieve a maximum of 40 dB of cancellation over 200 MHz and 50 dB of cancellation over 10 MHz. The broadband nature of the cancellation, along with its depth, demonstrates both the precision of the optical components and the validity of the architecture. Next, we are interested in a scenario with dynamically changing interference, which requires an adaptive photonic
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.
Multi-template Scale-Adaptive Kernelized Correlation Filters
Bibi, Adel Aamer
2015-12-07
This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous frame). In our approach, we update the scale of the tracker by maximizing over the posterior distribution of a grid of scales. As for the filter update, we prove and show that it is possible to use all previous training examples to update the filter taps very efficiently using fixed-point optimization. We validate the efficacy of our approach on two tracking datasets, VOT2014 and VOT2015.
Model Adaptation for Prognostics in a Particle Filtering Framework
National Aeronautics and Space Administration — One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated....
Shanmugavadivu, P.; Eliahim Jeevaraj, P. S.
2014-06-01
The Adaptive Iterated Functions Systems (AIFS) Filter presented in this paper has an outstanding potential to attenuate the fixed-value impulse noise in images. This filter has two distinct phases namely noise detection and noise correction which uses Measure of Statistics and Iterated Function Systems (IFS) respectively. The performance of AIFS filter is assessed by three metrics namely, Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index Matrix (MSSIM) and Human Visual Perception (HVP). The quantitative measures PSNR and MSSIM endorse the merit of this filter in terms of degree of noise suppression and details/edge preservation respectively, in comparison with the high performing filters reported in the recent literature. The qualitative measure HVP confirms the noise suppression ability of the devised filter. This computationally simple noise filter broadly finds application wherein the images are highly degraded by fixed-value impulse noise.
PSO Algorithm based Adaptive Median Filter for Noise Removal in Image Processing Application
Directory of Open Access Journals (Sweden)
Ruby Verma
2016-07-01
Full Text Available A adaptive Switching median filter for salt and pepper noise removal based on genetic algorithm is presented. Proposed filter consist of two stages, a noise detector stage and a noise filtering stage. Particle swarm optimization seems to be effective for single objective problem. Noise Dictation stage works on it. In contrast to the standard median filter, the proposed algorithm generates the noise map of corrupted Image. Noise map gives information about the corrupted and non-corrupted pixels of Image. In filtering, filter calculates the median of uncorrupted neighbouring pixels and replaces the corrupted pixels. Extensive simulations are performed to validate the proposed filter. Simulated results show refinement both in Peak signal to noise ratio (PSNR and Image Quality Index value (IQI. Experimental results shown that proposed method is more effective than existing methods.
Institute of Scientific and Technical Information of China (English)
李钊; 李意文
2016-01-01
A transmission mechanism based on the joint management of time, frequency and space domain resources is proposed in the Cooperative Cognitive Radio Network (CCRN), with which the cooperative cognitive users can obtain proper reward under the premise of improving the primary user’s transmission. The proposed scheme employs multi-antenna cognitive user as a relay. Via adaptive time-slot and bandwidth allocation, bottleneck in the two-hop transmission can be eliminated. Furthermore, a relay selection algorithm taking fairness into account is given for the situation where multiple cooperative cognitive users exist. On one hand, the relay selection utilizes proportional fair to achieve inter-cognitive-user fairness. On the other hand, proper reward is determined by adjusting the incentive factor. Simulation results show that the proposed mechanism can improve the data rate of both primary and cognitive systems, and afford fair reward to the cognitive relays.%该文针对协作认知无线电网络提出一种时域、频域和空域资源联合分配的传输机制，在改善授权用户传输性能的前提下使参与协作的认知用户获得合理的回报。所提机制利用多天线认知节点担任中继，通过自适应的时隙划分与带宽分配，对两跳传输链路中的瓶颈予以消除；并在包含多个协作认知用户的场景中，给出一种考虑公平性的中继选择算法。该方法一方面采用比例公平的思想实现认知用户间的公平性，另一方面通过调整奖励因子，使中继获得合理的回报。仿真结果表明，所提算法能够改善授权与认知系统的数据速率，同时给予认知中继公平的回报。
Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller.
Lin, Chih-Min; Yang, Ming-Shu; Chao, Fei; Hu, Xiao-Min; Zhang, Jun
2016-10-01
This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties, type-2 fuzzy sets can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so that the convergence of the filtering error can be guaranteed. In order to demonstrate the performance of the proposed adaptive T2FCMAC filter, it is tested in signal processing applications, including a nonlinear channel equalization system, a time-varying channel equalization system, and an adaptive noise cancellation system. The advantages of the proposed filter over the other adaptive filters are verified through simulations.
IAE-adaptive Kalman filter for INS/GPS integrated navigation system
Institute of Scientific and Technical Information of China (English)
Bian Hongwei; Jin Zhihua; Tian Weifeng
2006-01-01
A marine INS/GPS adaptive navigation system is presented in this paper. GPS with two antenna providing vessel's altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter (SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.
E-mail Spam Filtering Using Adaptive Genetic Algorithm
Directory of Open Access Journals (Sweden)
Jitendra Nath Shrivastava
2014-01-01
Full Text Available Now a day’s everybody email inbox is full with spam mails. The problem with spam mails is that they are not malicious in nature so generally don’t get blocked with firewall or filters etc., however, they are unwanted mails received by any internet users. In 2012, more that 50% emails of the total emails were spam emails. In this paper, a genetic algorithm based method for spam email filtering is discussed with its advantages and dis-advantages. The results presented in the paper are promising and suggested that GA can be a good option in conjunction with other e-mail filtering techniques can provide more robust solution.
Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
The alignment accuracy of the strap-down inertial navigation system (SINS) of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.
Directory of Open Access Journals (Sweden)
Qiguang Zhu
2014-05-01
Full Text Available To resolve the difficulty in establishing accurate priori noise model for the extended Kalman filtering algorithm, propose the fractional-order Darwinian particle swarm optimization (PSO algorithm has been proposed and introduced into the fuzzy adaptive extended Kalman filtering algorithm. The natural selection method has been adopted to improve the standard particle swarm optimization algorithm, which enhanced the diversity of particles and avoided the premature. In addition, the fractional calculus has been used to improve the evolution speed of particles. The PSO algorithm after improved has been applied to train fuzzy adaptive extended Kalman filter and achieve the simultaneous localization and mapping. The simulation results have shown that compared with the geese particle swarm optimization training of fuzzy adaptive extended Kalman filter localization and mapping algorithm, has been greatly improved in terms of localization and mapping.
Low-Complexity Lossless Compression of Hyperspectral Imagery via Adaptive Filtering
Klimesh, M.
2005-01-01
A low-complexity, adaptive predictive technique for lossless compression of hyperspectral data is presented. The technique relies on the sign algorithm from the repertoire of adaptive filtering. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.
Multi-dimensional sparse time series: feature extraction
Franciosi, Marco
2008-01-01
We show an analysis of multi-dimensional time series via entropy and statistical linguistic techniques. We define three markers encoding the behavior of the series, after it has been translated into a multi-dimensional symbolic sequence. The leading component and the trend of the series with respect to a mobile window analysis result from the entropy analysis and label the dynamical evolution of the series. The diversification formalizes the differentiation in the use of recurrent patterns, from a Zipf law point of view. These markers are the starting point of further analysis such as classification or clustering of large database of multi-dimensional time series, prediction of future behavior and attribution of new data. We also present an application to economic data. We deal with measurements of money investments of some business companies in advertising market for different media sources.
Computational Methods for Multi-dimensional Neutron Diffusion Problems
Energy Technology Data Exchange (ETDEWEB)
Song Han
2009-10-15
Lead-cooled fast reactor (LFR) has potential for becoming one of the advanced reactor types in the future. Innovative computational tools for system design and safety analysis on such NPP systems are needed. One of the most popular trends is coupling multi-dimensional neutron kinetics (NK) with thermal-hydraulic (T-H) to enhance the capability of simulation of the NPP systems under abnormal conditions or during rare severe accidents. Therefore, various numerical methods applied in the NK module should be reevaluated to adapt the scheme of coupled code system. In the author's present work a neutronic module for the solution of two dimensional steady-state multigroup diffusion problems in nuclear reactor cores is developed. The module can produce both direct fluxes as well as adjoints, i.e. neutron importances. Different numerical schemes are employed. A standard finite-difference (FD) approach is firstly implemented, mainly to serve as a reference for less computationally challenging schemes, such as transverse-integrated nodal methods (TINM) and boundary element methods (BEM), which are considered in the second part of the work. The validation of the methods proposed is carried out by comparisons of the results for some reference structures. In particular a critical problem for a homogeneous reactor for which an analytical solution exists is considered as a benchmark. The computational module is then applied to a fast spectrum system, having physical characteristics similar to the proposed European Lead-cooled System (ELSY) project. The results show the effectiveness of the numerical techniques presented. The flexibility and the possibility to obtain neutron importances allow the use of the module for parametric studies, design assessments and integral parameter evaluations, as well as for future sensitivity and perturbation analyses and as a shape solver for time-dependent procedures
Signal-adapted FIR Filter Banks Without Perfect-reconstruction Property
Institute of Scientific and Technical Information of China (English)
SHUIPenglang; ZHANGAihua
2005-01-01
A time-domain approach is proposed to design signal-adapted FIR (Finite impulse response) filter banks without the perfect reconstruction property. For a given Wide sense stationary (WSS) input process and a total bit budget, it is a highly nonlinear and large size optimization problem to design the optimal FIR filter bank that minimizes the sum of the quantization distortion and systematic distortion. Thus, the design algorithm is crucial, in particular, selection of the initial filter bank. Here, the FIR approximation of the optimal IIR biorthogonal filter bank is used as the initial filter bank and an ad hoc three-stage algorithm is developed to solve the optimization problem. The numerical results show: the design achieves large subband coding gains (GSBC) that are close to or exceed the GSBC's of the optimal IIR biorthogonal filter banks.
New Approach for IIR Adaptive Lattice Filter Structure Using Simultaneous Perturbation Algorithm
Martinez, Jorge Ivan Medina; Nakano, Kazushi; Higuchi, Kohji
Adaptive infinite impulse response (IIR), or recursive, filters are less attractive mainly because of the stability and the difficulties associated with their adaptive algorithms. Therefore, in this paper the adaptive IIR lattice filters are studied in order to devise algorithms that preserve the stability of the corresponding direct-form schemes. We analyze the local properties of stationary points, a transformation achieving this goal is suggested, which gives algorithms that can be efficiently implemented. Application to the Steiglitz-McBride (SM) and Simple Hyperstable Adaptive Recursive Filter (SHARF) algorithms is presented. Also a modified version of Simultaneous Perturbation Stochastic Approximation (SPSA) is presented in order to get the coefficients in a lattice form more efficiently and with a lower computational cost and complexity. The results are compared with previous lattice versions of these algorithms. These previous lattice versions may fail to preserve the stability of stationary points.
Adaptive multiple subtraction with wavelet-based complex unary Wiener filters
Ventosa, Sergi; Huard, Irène; Pica, Antonio; Rabeson, Hérald; Ricarte, Patrice; Duval, Laurent
2011-01-01
Multiple attenuation is a crucial task in seismic data processing because multiples usually cover primaries from fundamental reflectors. Predictive multiple suppression methods remove these multiples by building an adapted model, aiming at being subtracted from the original signal. However, before the subtraction is applied, a matching filter is required to minimize amplitude differences and misalignments between actual multiples and their prediction, and thus to minimize multiples in the input dataset after the subtraction. In this work we focus on the subtraction element. We propose an adaptive multiple removal technique in a 1-D complex wavelet frame combined with a non-stationary adaptation performed via single-sample (unary) Wiener filters, consistently estimated on overlapping windows in the transformed domain. This approach greatly simplifies the matching filter estimation and, despite its simplicity, compares promisingly with standard adaptive 2-D methods, both in terms of results and retained speed a...
Fast Packet Classification Using Multi-Dimensional Encoding
Huang, Chi Jia; Chen, Chien
Internet routers need to classify incoming packets quickly into flows in order to support features such as Internet security, virtual private networks and Quality of Service (QoS). Packet classification uses information contained in the packet header, and a predefined rule table in the routers. Packet classification of multiple fields is generally a difficult problem. Hence, researchers have proposed various algorithms. This study proposes a multi-dimensional encoding method in which parameters such as the source IP address, destination IP address, source port, destination port and protocol type are placed in a multi-dimensional space. Similar to the previously best known algorithm, i.e., bitmap intersection, multi-dimensional encoding is based on the multi-dimensional range lookup approach, in which rules are divided into several multi-dimensional collision-free rule sets. These sets are then used to form the new coding vector to replace the bit vector of the bitmap intersection algorithm. The average memory storage of this encoding is Θ (L · N · log N) for each dimension, where L denotes the number of collision-free rule sets, and N represents the number of rules. The multi-dimensional encoding practically requires much less memory than bitmap intersection algorithm. Additionally, the computation needed for this encoding is as simple as bitmap intersection algorithm. The low memory requirement of the proposed scheme means that it not only decreases the cost of packet classification engine, but also increases the classification performance, since memory represents the performance bottleneck in the packet classification engine implementation using a network processor.
Multi-dimensional Bin Packing Problems with Guillotine Constraints
DEFF Research Database (Denmark)
Amossen, Rasmus Resen; Pisinger, David
2010-01-01
parallel straight cuts that can recursively cut the bin into pieces so that each piece contains a box and no box has been intersected by a cut. The unrestricted problem is known to be NP-hard. In this paper we present a generalization of a constructive algorithm for the multi-dimensional bin packing......The problem addressed in this paper is the decision problem of determining if a set of multi-dimensional rectangular boxes can be orthogonally packed into a rectangular bin while satisfying the requirement that the packing should be guillotine cuttable. That is, there should exist a series of face...
Multi-Dimensional Customer Data Analysis in Online Auctions
Institute of Scientific and Technical Information of China (English)
LAO Guoling; XIONG Kuan; QIN Zheng
2007-01-01
In this paper, we designed a customer-centered data warehouse system with five subjects: listing, bidding, transaction,accounts, and customer contact based on the business process of online auction companies. For each subject, we analyzed its fact indexes and dimensions. Then take transaction subject as example,analyzed the data warehouse model in detail, and got the multi-dimensional analysis structure of transaction subject. At last, using data mining to do customer segmentation, we divided customers into four types: impulse customer, prudent customer, potential customer, and ordinary customer. By the result of multi-dimensional customer data analysis, online auction companies can do more target marketing and increase customer loyalty.
Multi-dimensional Bin Packing Problems with Guillotine Constraints
DEFF Research Database (Denmark)
Amossen, Rasmus Resen; Pisinger, David
2010-01-01
The problem addressed in this paper is the decision problem of determining if a set of multi-dimensional rectangular boxes can be orthogonally packed into a rectangular bin while satisfying the requirement that the packing should be guillotine cuttable. That is, there should exist a series of face...... parallel straight cuts that can recursively cut the bin into pieces so that each piece contains a box and no box has been intersected by a cut. The unrestricted problem is known to be NP-hard. In this paper we present a generalization of a constructive algorithm for the multi-dimensional bin packing...
Multi-dimensional procurement auction under uncertain and asymmetric information
DEFF Research Database (Denmark)
Papakonstantinou, Athanasios; Bogetoft, Peter
2017-01-01
This article addresses two important issues in public procurement: ex ante uncertainty about the participating agents’ qualities and costs and their strategic behaviour. We present a novel multi-dimensional auction that incentivises agents to make a partial inquiry into the procured task and to h......This article addresses two important issues in public procurement: ex ante uncertainty about the participating agents’ qualities and costs and their strategic behaviour. We present a novel multi-dimensional auction that incentivises agents to make a partial inquiry into the procured task...
Delay Estimator and Improved Proportionate Multi-Delay Adaptive Filtering Algorithm
Directory of Open Access Journals (Sweden)
E. Verteletskaya
2012-04-01
Full Text Available This paper pertains to speech and acoustic signal processing, and particularly to a determination of echo path delay and operation of echo cancellers. To cancel long echoes, the number of weights in a conventional adaptive filter must be large. The length of the adaptive filter will directly affect both the degree of accuracy and the convergence speed of the adaptation process. We present a new adaptive structure which is capable to deal with multiple dispersive echo paths. An adaptive filter according to the present invention includes means for storing an impulse response in a memory, the impulse response being indicative of the characteristics of a transmission line. It also includes a delay estimator for detecting ranges of samples within the impulse response having relatively large distribution of echo energy. These ranges of samples are being indicative of echoes on the transmission line. An adaptive filter has a plurality of weighted taps, each of the weighted taps having an associated tap weight value. A tap allocation/control circuit establishes the tap weight values in response to said detecting means so that only taps within the regions of relatively large distributions of echo energy are turned on. Thus, the convergence speed and the degree of estimation in the adaptation process can be improved.
Stent enhancement in digital x-ray fluoroscopy using an adaptive feature enhancement filter
Jiang, Yuhao; Zachary, Josey
2016-03-01
Fluoroscopic images belong to the classes of low contrast and high noise. Simply lowering radiation dose will render the images unreadable. Feature enhancement filters can reduce patient dose by acquiring images at low dose settings and then digitally restoring them to the original quality. In this study, a stent contrast enhancement filter is developed to selectively improve the contrast of stent contour without dramatically boosting the image noise including quantum noise and clinical background noise. Gabor directional filter banks are implemented to detect the edges and orientations of the stent. A high orientation resolution of 9° is used. To optimize the use of the information obtained from Gabor filters, a computerized Monte Carlo simulation followed by ROC study is used to find the best nonlinear operator. The next stage of filtering process is to extract symmetrical parts in the stent. The global and local symmetry measures are used. The information gathered from previous two filter stages are used to generate a stent contour map. The contour map is then scaled and added back to the original image to get a contrast enhanced stent image. We also apply a spatio-temporal channelized Hotelling observer model and other numerical measures to characterize the response of the filters and contour map to optimize the selections of parameters for image quality. The results are compared to those filtered by an adaptive unsharp masking filter previously developed. It is shown that stent enhancement filter can effectively improve the stent detection and differentiation in the interventional fluoroscopy.
An Improved Variable Structure Adaptive Filter Design and Analysis for Acoustic Echo Cancellation
Directory of Open Access Journals (Sweden)
A. Kar
2015-04-01
Full Text Available In this research an advance variable structure adaptive Multiple Sub-Filters (MSF based algorithm for single channel Acoustic Echo Cancellation (AEC is proposed and analyzed. This work suggests a new and improved direction to find the optimum tap-length of adaptive filter employed for AEC. The structure adaptation, supported by a tap-length based weight update approach helps the designed echo canceller to maintain a trade-off between the Mean Square Error (MSE and time taken to attain the steady state MSE. The work done in this paper focuses on replacing the fixed length sub-filters in existing MSF based AEC algorithms which brings refinements in terms of convergence, steady state error and tracking over the single long filter, different error and common error algorithms. A dynamic structure selective coefficient update approach to reduce the structural and computational cost of adaptive design is discussed in context with the proposed algorithm. Simulated results reveal a comparative performance analysis over proposed variable structure multiple sub-filters designs and existing fixed tap-length sub-filters based acoustic echo cancellers.
Secure Tracking in Sensor Networks using Adaptive Extended Kalman Filter
Fard, Ali P
2012-01-01
Location information of sensor nodes has become an essential part of many applications in Wireless Sensor Networks (WSN). The importance of location estimation and object tracking has made them the target of many security attacks. Various methods have tried to provide location information with high accuracy, while lots of them have neglected the fact that WSNs may be deployed in hostile environments. In this paper, we address the problem of securely tracking a Mobile Node (MN) which has been noticed very little previously. A novel secure tracking algorithm is proposed based on Extended Kalman Filter (EKF) that is capable of tracking a Mobile Node (MN) with high resolution in the presence of compromised or colluding malicious beacon nodes. It filters out and identifies the malicious beacon data in the process of tracking. The proposed method considerably outperforms the previously proposed secure algorithms in terms of either detection rate or MSE. The experimental data based on different settings for the netw...
Adaptive high-gain extended kalman filter and applications
Boizot, Nicolas Richard
2010-01-01
The work concerns the ``observability problem” --- the reconstruction of a dynamic process's full state from a partially measured state--- for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc… We propose a solution to...
Steady State Analysis of Convex Combination of Affine Projection Adaptive Filters
Directory of Open Access Journals (Sweden)
S. Radhika
2015-05-01
Full Text Available The aim of the study is to propose an adaptive algorithm using convex combinational approach to have both fast convergence and less steady state error simultaneously. For this purpose, we have used two affine projection adaptive filters with complementary nature (both in step size and projection order as the component filters. The first component filter has high projection order and large step size which makes it to have fast convergence at the cost of more steady state error. The second component filter has slow convergence and less steady state error due to the selection of small step size and projection order. Both are combined using convex combiner so as to have best final output with fast convergence and less steady state error. Each of the component filters are updated using their own error signals and stochastic gradient approach is used to update the convex combiner so as to have minimum overall error. By using energy conservation argument, analytical treatment of the combination stage is made in stationary environment. It is found that during initial stage the proposed scheme converges to the fast filter which has good convergence later it converges to either of the two (whichever has less steady state error and towards the end, the final output converges to slow filter which is superior in lesser steady state error. Experimental results proved that the proposed algorithm has adopted the best features of the component filters.
Chi-squared smoothed adaptive particle-filtering based prognosis
Ley, Christopher P.; Orchard, Marcos E.
2017-01-01
This paper presents a novel form of selecting the likelihood function of the standard sequential importance sampling/re-sampling particle filter (SIR-PF) with a combination of sliding window smoothing and chi-square statistic weighting, so as to: (a) increase the rate of convergence of a flexible state model with artificial evolution for online parameter learning (b) improve the performance of a particle-filter based prognosis algorithm. This is applied and tested with real data from oil total base number (TBN) measurements from three haul trucks. The oil data has high measurement uncertainty and an unknown phenomenological state model. Performance of the proposed algorithm is benchmarked against the standard form of SIR-PF estimation which utilises the Normal (Gaussian) likelihood function. Both implementations utilise the same particle filter based prognosis algorithm so as to provide a common comparison. A sensitivity analysis is also performed to further explore the effects of the combination of sliding window smoothing and chi-square statistic weighting to the SIR-PF.
Efficient implementation of adaptive filters using TMS320C6713 DSP platform
Directory of Open Access Journals (Sweden)
Diogo Kaoru Takayama
2011-06-01
Full Text Available This paper presents a methodology for accelerated development of solution associated to adaptive filtering using Matlab/Simulink, Code Composer Studio and DSK 6713 digital signal processing (DSP technologies. The purpose of this methodology is to provide an efficient and rapid method to develop and test adaptive filters in DSPs. The methodology development represents a very important tool for the engineer in charge of design-simulation-implementation of adaptive filters. Another important benefit is that it avoids low level hardware work that can be tedious and time consuming. An example of application is presented in this paper in order to illustrate the feasibility of this methodology. The methodology is applied in order to implement an adaptive filter in the DSP platform. The project steps are discussed in details, including the methods of transforming the Matlab code into DSP code. The results are analyzed in terms of accuracy and convergence speed. The TMS320C6713 development kit supplied by Spectrum Digital Inc., that includes a float point DSP, was used to implement the adaptive filter.
An adaptive fuzzy filter for coding artifacts removal in video and image
Institute of Scientific and Technical Information of China (English)
WU Jing; YE Xiu-qing; GU Wei-kang
2007-01-01
This paper proposes a new adaptive post-filtering algorithm to remove coding artifacts in block-based video coder. The proposed method concentrates on blocking and ringing artifacts removal. For de-blocking, the blocking strength is identified to determine the filtering range, and the maximum quantization parameter of the image is used to adapt the 1D fuzzy filter. For de-ringing, besides the edge detection, a complementary ringing detection method is proposed to locate the neglected ringing blocks, and the gradient threshold is adopted to adjust the parameter of 2D fuzzy filter. Experiments are performed on the MPEG-4 sequences. Compared with other methods, the proposed one achieves better detail preservation and artifacts removal performance with lower computational cost.
Kelly, D. A.; Fermelia, A.; Lee, G. K. F.
1990-01-01
An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter.
A multi-dimensional model for search intent.
Kraaij, W.; Hinne, M.; Heijden, M. van der; Verberne, S.
2011-01-01
The interaction of users with search engines is part of goal driven behaviour involving an underlying information need. Information needs range from simple lookups to complex long-term desk studies. This paper proposes a new multi-dimensional model for search intent, which can be used for the
A SLAM Algorithm Based on Adaptive Cubature Kalman Filter
Directory of Open Access Journals (Sweden)
Fei Yu
2014-01-01
CKF-SLAM and the adaptive estimator, the new ACKF-SLAM algorithm can reduce the state estimated error significantly and improve the navigation accuracy of the SLAM system effectively. The performance of this new algorithm has been examined through numerical simulations in different scenarios. The results have shown that the position error can be effectively reduced with the new adaptive CKF-SLAM algorithm. Compared with other traditional SLAM methods, the accuracy of the nonlinear SLAM system is significantly improved. It verifies that the proposed ACKF-SLAM algorithm is valid and feasible.
Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption
Xie, Qing
2016-01-12
The problem we aim to address is the optimization of cost management for executing multiple continuous queries on data streams, where each query is defined by several filters, each of which monitors certain status of the data stream. Specially the filter can be shared by different queries and expensive to evaluate. The conventional objective for such a problem is to minimize the overall execution cost to solve all queries, by planning the order of filter evaluation in shared strategy. However, in streaming scenario, the characteristics of data items may change in process, which can bring some uncertainty to the outcome of individual filter evaluation, and affect the plan of query execution as well as the overall execution cost. In our work, considering the influence of the uncertain variation of data characteristics, we propose a framework to deal with the dynamic adjustment of filter ordering for query execution on data stream, and focus on the issues of cost management. By incrementally monitoring and analyzing the results of filter evaluation, our proposed approach can be effectively adaptive to the varied stream behavior and adjust the optimal ordering of filter evaluation, so as to optimize the execution cost. In order to achieve satisfactory performance and efficiency, we also discuss the trade-off between the adaptivity of our framework and the overhead incurred by filter adaption. The experimental results on synthetic and two real data sets (traffic and multimedia) show that our framework can effectively reduce and balance the overall query execution cost and keep high adaptivity in streaming scenario.
Adaptive Filter Design for Stock Market Prediction Using a Correlation-based Criterion
J. E. Wesen; V. VV. Vermehren; de Oliveira, H. M.
2015-01-01
This paper presents a novel adaptive-filter approach for predicting assets on the stock markets. Concepts are introduced here, which allow understanding this method and computing of the corresponding forecast. This approach is applied, as an example, through the prediction over the actual valuation of the PETR3 shares (Petrobras ON) traded in the Brazilian Stock Market. The first-rate choices of the window length and the number of filter coefficient are evaluated. This is done by observing th...
Institute of Scientific and Technical Information of China (English)
Li Shu; Zhuo Jiashou; Ren Qingwen
2000-01-01
In this paper, an optimal criterion is presented for adaptive Kalman filter in a control sys tem with unknown variances of stochastic vibration by constructing a function of noise variances and minimizing the function. We solve the model and measure variances by using DFP optimal method to guarantee the results of Kalman filter to be optimized. Finally, the control of vibration can be implemented by LQG method.
Design of adaptive deblocking filter for H.264/AVC decoder SOC
Institute of Scientific and Technical Information of China (English)
YANG Kun; ZHANG Chun; WANG Zhi-hua
2009-01-01
In this article, a design for the adaptive deblocking filter is proposed. To understand the real-time performance, a FILTER unit that can process eight pixels beside an edge simultaneously is applied in this design to increase filtering efficiency, and local memory is used to store all temporary data generated by the FILTER to reduce access to system bus. The filter makes every 4×4 sample block pipelined through the process units and achieves an efficiency of 80% for both the FILTER unit and the bus access unit. It can fulfill filtering process for a crystallographic information file (CIF, 352×288) format picture in 95 k clock cycles. The proposed design is part of a H.264/AVC decoder system-on-chip (SOC), which is fabricated in 0.18 μm complementary metal oxide semiconductor (CMOS) process. The filter module consists of 60 k gates and 25.7 kb static random access memory (SRAM) and it can filter a macro-block in 240 clock cycles.
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.
Adaptive filter for a miniature MEMS based attitude and heading reference system
Institute of Scientific and Technical Information of China (English)
WANG Mei; WANG Yong-quan; ZHANG Yan-hua
2006-01-01
An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastic model. The adaptive filter has six states with a time variable transition matrix. When the system is in the non-acceleration mode, the accelerometer measurements of the gravity and the compass measurements of the heading have observability and yield good estimates of the states. When the system is in the high dynamic mode and the bias has converged to an accurate estimate, the attitude calculation will be maintained for a long interval of time. The adaptive filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance.
Adaptive-Fuzzy Controller Based Shunt Active Filter for Power Line Conditioners
Directory of Open Access Journals (Sweden)
KamalaKanta Mahapatra
2011-08-01
Full Text Available This paper presents a novel Fuzzy Logic Controller (FLC in conjunction with Phase Locked Loop (PLL based shunt active filter for Power Line Conditioners (PLCs to improve the power quality in the distribution system. The active filter is implemented with current controlled Voltage Source Inverter (VSI for compensating current harmonics and reactive power at the point of common coupling. The VSI gate control switching pulses are derived from proposed Adaptive-Fuzzy-Hysteresis Current Controller (HCC and this method calculates the hysteresis bandwidth effectively using fuzzy logic. The bandwidth can be adjusted based on compensation current variation, which is used to optimize the required switching frequency and improves active filter substantially. These shunt active power filter system is investigated and verified under steady and transient-state with non-linear load conditions. This shunt active filter is in compliance with IEEE 519 and IEC 61000-3 recommended harmonic standards.
Identification and adaptive control scheme using fuzzy parameterized linear filters
Papp, Z.
1998-01-01
A nonlinear fuzzy control structure enhanced with supervised learning and/or adaption is presented. Availability of at least a partial process model is assumed. Nonlinear process identification procedure is used to complete the partial model. Based on the identification model the system sensitivity
Adaptive Matrices and Filters for Color Texture Classification
Giotis, Ioannis; Bunte, Kerstin; Petkov, Nicolai; Biehl, Michael
In this paper we introduce an integrative approach towards color texture classification and recognition using a supervised learning framework. Our approach is based on Generalized Learning Vector Quantization (GLVQ), extended by an adaptive distance measure, which is defined in the Fourier domain,
Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI
Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R.
2017-04-01
Objective. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. Approach. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. Main results. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. Significance. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several evaluation criteria suggest superior
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...
New cardiac MRI gating method using event-synchronous adaptive digital filter.
Park, Hodong; Park, Youngcheol; Cho, Sungpil; Jang, Bongryoel; Lee, Kyoungjoung
2009-11-01
When imaging the heart using MRI, an artefact-free electrocardiograph (ECG) signal is not only important for monitoring the patient's heart activity but also essential for cardiac gating to reduce noise in MR images induced by moving organs. The fundamental problem in conventional ECG is the distortion induced by electromagnetic interference. Here, we propose an adaptive algorithm for the suppression of MR gradient artefacts (MRGAs) in ECG leads of a cardiac MRI gating system. We have modeled MRGAs by assuming a source of strong pulses used for dephasing the MR signal. The modeled MRGAs are rectangular pulse-like signals. We used an event-synchronous adaptive digital filter whose reference signal is synchronous to the gradient peaks of MRI. The event detection processor for the event-synchronous adaptive digital filter was implemented using the phase space method-a sort of topology mapping method-and least-squares acceleration filter. For evaluating the efficiency of the proposed method, the filter was tested using simulation and actual data. The proposed method requires a simple experimental setup that does not require extra hardware connections to obtain the reference signals of adaptive digital filter. The proposed algorithm was more effective than the multichannel approach.
Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat
2017-05-01
Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.
An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics.
Xin, Yao; Li, Will X Y; Zhang, Zhaorui; Cheung, Ray C C; Song, Dong; Berger, Theodore W
2015-01-01
Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance.
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.
Steganalysis of content-adaptive JPEG steganography based on Gauss partial derivative filter bank
Zhang, Yi; Liu, Fenlin; Yang, Chunfang; Luo, Xiangyang; Song, Xiaofeng; Lu, Jicang
2017-01-01
A steganalysis feature extraction method based on Gauss partial derivative filter bank is proposed in this paper to improve the detection performance for content-adaptive JPEG steganography. Considering that the embedding changes of content-adaptive steganographic schemes are performed in the texture and edge regions, the proposed method generates filtered images comprising rich texture and edge information using Gauss partial derivative filter bank, and histograms of absolute values of filtered subimages are extracted as steganalysis features. Gauss partial derivative filter bank can represent texture and edge information in multiple orientations with less computation load than conventional methods and prevent redundancy in different filtered images. These two properties are beneficial in the extraction of low-complexity sensitive features. The results of experiments conducted on three selected modern JPEG steganographic schemes-uniform embedding distortion, JPEG universal wavelet relative distortion, and side-informed UNIWARD-indicate that the proposed feature set is superior to the prior art feature sets-discrete cosine transform residual, phase aware rich model, and Gabor filter residual.
Huang, Quanzhen; Luo, Jun; Li, Hengyu; Wang, Xiaohua
2013-08-01
With the wide application of large-scale flexible structures in spacecraft, vibration control problems in these structures have become important design issues. The filtered-X least mean square (FXLMS) algorithm is the most popular one in current active vibration control using adaptive filtering. It assumes that the source of interference can be measured and the interference source is considered as the reference signal input to the controller. However, in the actual control system, this assumption is not accurate, because it does not consider the impact of the reference signal on the output feedback signal. In this paper, an adaptive vibration active control algorithm based on an infinite impulse response (IIR) filter structure (FULMS, filtered-U least mean square) is proposed. The algorithm is based on an FXLMS algorithm framework, which replaces the finite impulse response (FIR) filter with an IIR filter. This paper focuses on the structural design of the controller, the process of the FULMS filtering control method, the design of the experimental model object, and the experimental platform construction for the entire control system. The comparison of the FXLMS algorithm with FULMS is theoretically analyzed and experimentally validated. The results show that the FULMS algorithm converges faster and controls better. The design of the FULMS controller is feasible and effective and has greater value in practical applications of aerospace engineering.
Stent enhancement using a locally adaptive unsharp masking filter in digital x-ray fluoroscopy
Jiang, Yuhao; Ekanayake, Eranda
2014-03-01
Low exposure X-ray fluoroscopy is used to guide some complicate interventional procedures. Due to the inherent high levels of noise, improving the visibility of some interventional devices such as stent will greatly benefit those interventional procedures. Stent, which is made up of tiny steel wires, is also suffered from contrast dilutions of large flat panel detector pixels. A novel adaptive unsharp masking filter has been developed to improve stent contrast in real-time applications. In unsharp masking processing, the background is estimated and subtracted from the original input image to create a foreground image containing objects of interest. A background estimator is therefore critical in the unsharp masking processing. In this specific study, orientation filter kernels are used as the background estimator. To make the process simple and fast, the kernels average along a line of pixels. A high orientation resolution of 18° is used. A nonlinear operator is then used to combine the information from the images generated from convolving the original background and noise only images with orientation filters. A computerized Monte Carlo simulation followed by ROC study is used to identify the best nonlinear operator. We then apply the unsharp masking filter to the images with stents present. It is shown that the locally adaptive unsharp making filter is an effective filter for improving stent visibility in the interventional fluoroscopy. We also apply a spatio-temporal channelized human observer model to quantitatively optimize and evaluate the filter.
Directory of Open Access Journals (Sweden)
Mustafa Rahimie
2017-01-01
Full Text Available The method of least mean square (LMS is the commonly used algorithm in Adaptive filter due to its simplicity and robustness in implementation. In Active Noise Control application, a filtered reference signal is used prior to LMS algorithm to overcome the constraint on stability and convergence performance of the system due to the existence of the auxiliary path. This is known as Filtered-X LMS algorithm. In conventional Filtered-X LMS algorithm, each filter weight is updated once on every audio sample. This paper proposes the improved version of Filtered-X LMS algorithm with the use of multiple iteration of filter weight on every sample of audio signal. The proposed work uses field programmable gate arrays to realize real-time simulation on hardware for the noise signal of 500 Hz. Results from the real-time hardware simulations have shown much faster error convergence and better adaptation performance for different selections of learning constant μ, as compared with the conventional method.
Directory of Open Access Journals (Sweden)
Daniel A. Castello
2005-01-01
Full Text Available The present work is aimed at assessing the performance of adaptive Finite Impulse Response (FIR filters on the identification of vibrating structures. Four adaptive algorithms were used: Least Mean Squares (LMS, Normalized Least Mean Squares (NLMS, Transform-Domain Least Mean Squares (TD – LMS and Set-Membership Binormalized Data-Reusing LMS Algorithm (SM – BNDRLMS. The capability of these filters to perform the identification of vibrating structures is shown on real experiments. The first experiment consists of an aluminum cantilever beam containing piezoelectric sensors and actuators and the second one is a steel pinned-pinned beam instrumented with accelerometers and an electromechanical shaker.
Performance Analysis of Adaptive Volterra Filters in the Finite-Alphabet Input Case
Directory of Open Access Journals (Sweden)
Jaïdane Mériem
2004-01-01
Full Text Available This paper deals with the analysis of adaptive Volterra filters, driven by the LMS algorithm, in the finite-alphabet inputs case. A tailored approach for the input context is presented and used to analyze the behavior of this nonlinear adaptive filter. Complete and rigorous mean square analysis is provided without any constraining independence assumption. Exact transient and steady-state performances expressed in terms of critical step size, rate of transient decrease, optimal step size, excess mean square error in stationary mode, and tracking nonstationarities are deduced.
Institute of Scientific and Technical Information of China (English)
Xiaogu ZHENG
2009-01-01
An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate the efficacy of the proposed approach.
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 and tracking with trinion-valued adaptive algorithms
Institute of Scientific and Technical Information of China (English)
Xiao-ming GOU; Zhi-wen LIU; Wei LIU; You-gen XU
2016-01-01
A new model for three-dimensional processes based on the trinion algebra is introduced for the fi rst time. Compared to the pure quaternion model, the trinion model is more compact and computationally more eﬃcient, while having similar or comparable performance in terms of adaptive linear fi ltering. Moreover, the trinion model can effectively represent the general relationship of state evolution in Kalman fi ltering, where the pure quaternion model fails. Simulations on real-world wind recordings and synthetic data sets are provided to demonstrate the potential of this new modeling method.
Application of Adaptive Filters to Active Noise Control
Institute of Scientific and Technical Information of China (English)
PEI Bingnan; LI Chuanguang
2001-01-01
A modified LMS algorithm for noise-control is suggested after a mathematical model ofsound-cancellation is established, on the basis of thesound wave interference principle and the physicalmodel of progressive waves in a duct. Its applicationin controlling noise with the frequency range from 100to 800 Hz can be implemented by using the adaptivedigital signal processing technique. The experimentson a pink noise, a broadband noise and a noise takenfrom a tank were made, which show that there existsan attenuation of 11 dB at the frequency of 500 Hzor so, and that the proposed adaptive noise controltechnique is very effective and valid.
Energy Technology Data Exchange (ETDEWEB)
Zurbenko, I.; Chen, J.; Rao, S.T. [State Univ. of New York, Albany, NY (United States)] [and others
1997-11-01
The issue of global climate change due to increased anthropogenic emissions of greenhouse gases in the atmosphere has gained considerable attention and importance. Climate change studies require the interpretation of weather data collected in numerous locations and/or over the span of several decades. Unfortunately, these data contain biases caused by changes in instruments and data acquisition procedures. It is essential that biases are identified and/or removed before these data can be used confidently in the context of climate change research. The purpose of this paper is to illustrate the use of an adaptive moving average filter and compare it with traditional parametric methods. The advantage of the adaptive filter over traditional parametric methods is that it is less effected by seasonal patterns and trends. The filter has been applied to upper air relative humidity and temperature data. Applied to generated data, the filter has a root mean squared error accuracy of about 600 days when locating changes of 0.1 standard deviations and about 20 days for changes of 0.5 standard deviations. In some circumstances, the accuracy of location estimation can be improved through parametric techniques used in conjunction with the adaptive filter.
AN ADAPTIVE MORPHOLOGICAL MEAN FILTER FOR VERY HIGH-RESOLUTION REMOTE SENSING IMAGE PROCESSING
Directory of Open Access Journals (Sweden)
Z. Lv
2017-09-01
Full Text Available Very high resolution (VHR remote sensing imagery can reveal the ground object in greater detail, depicting their color, shape, size and structure. However, VHR also leads much original noise in spectra, and this original noise may reduce the reliability of the classification’s result. This paper presents an Adaptive Morphological Mean Filter (AMMF for smoothing the original noise of VHR imagery and improving the classification’s performance. AMMF is a shape-adaptive filter which is constructed by detecting gradually the spectral similarity between a kernel-anchored pixel and its contextual pixels through an extension-detector with 8-neighbouring pixels, and the spectral value of the kernel-anchored pixel is instead by the mean of group pixels within the adaptive region. The classification maps based on the AMMF are compared with the classification of VHR images based on the homologous filter processing, such as Mean Filter (MF and Median Filter(MedF. The experimental results suggest the following: 1 VHR image processed using AMMF can not only preserve the detail information among inter-classes but also smooth the noise within intra-class; 2 The proposed AMMF processing can improve the classification’s performance of VHR image, and it obtains a better visual performance and accuracy while comparing with MF and MedF.
Adaptive error covariances estimation methods for ensemble Kalman filters
Energy Technology Data Exchange (ETDEWEB)
Zhen, Yicun, E-mail: zhen@math.psu.edu [Department of Mathematics, The Pennsylvania State University, University Park, PA 16802 (United States); Harlim, John, E-mail: jharlim@psu.edu [Department of Mathematics and Department of Meteorology, The Pennsylvania State University, University Park, PA 16802 (United States)
2015-08-01
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.
Robust and Adaptive Block Tracking Method Based on Particle Filter
Directory of Open Access Journals (Sweden)
Bin Sun
2015-10-01
Full Text Available In the field of video analysis and processing, object tracking is attracting more and more attention especially in traffic management, digital surveillance and so on. However problems such as objects’ abrupt motion, occlusion and complex target structures would bring difficulties to academic study and engineering application. In this paper, a fragmentsbased tracking method using the block relationship coefficient is proposed. In this method, we use particle filter algorithm and object region is divided into blocks initially. The contribution of this method is that object features are not extracted just from a single block, the relationship between current block and its neighbor blocks are extracted to describe the variation of the block. Each block is weighted according to the block relationship coefficient when the block is voted on the most matched region in next frame. This method can make full use of the relationship between blocks. The experimental results demonstrate that our method can provide good performance in condition of occlusion and abrupt posture variation.
Boz, Utku; Basdogan, Ipek
2015-12-01
Structural vibrations is a major cause for noise problems, discomfort and mechanical failures in aerospace, automotive and marine systems, which are mainly composed of plate-like structures. In order to reduce structural vibrations on these structures, active vibration control (AVC) is an effective approach. Adaptive filtering methodologies are preferred in AVC due to their ability to adjust themselves for varying dynamics of the structure during the operation. The filtered-X LMS (FXLMS) algorithm is a simple adaptive filtering algorithm widely implemented in active control applications. Proper implementation of FXLMS requires availability of a reference signal to mimic the disturbance and model of the dynamics between the control actuator and the error sensor, namely the secondary path. However, the controller output could interfere with the reference signal and the secondary path dynamics may change during the operation. This interference problem can be resolved by using an infinite impulse response (IIR) filter which considers feedback of the one or more previous control signals to the controller output and the changing secondary path dynamics can be updated using an online modeling technique. In this paper, IIR filtering based filtered-U LMS (FULMS) controller is combined with online secondary path modeling algorithm to suppress the vibrations of a plate-like structure. The results are validated through numerical and experimental studies. The results show that the FULMS with online secondary path modeling approach has more vibration rejection capabilities with higher convergence rate than the FXLMS counterpart.
Thakur, A; Anand, R S
2007-01-01
This article discusses an adaptive filtering technique for reducing speckle using second order statistics of the speckle pattern in ultrasound medical images. Several region-based adaptive filter techniques have been developed for speckle noise suppression, but there are no specific criteria for selecting the region growing size in the post processing of the filter. The size appropriate for one local region may not be appropriate for other regions. Selection of the correct region size involves a trade-off between speckle reduction and edge preservation. Generally, a large region size is used to smooth speckle and a small size to preserve the edges into an image. In this paper, a smoothing procedure combines the first order statistics of speckle for the homogeneity test and second order statistics for selection of filters and desired region growth. Grey level co-occurrence matrix (GLCM) is calculated for every region during the region contraction and region growing for second order statistics. Further, these GLCM features determine the appropriate filter for the region smoothing. The performance of this approach is compared with the aggressive region-growing filter (ARGF) using edge preservation and speckle reduction tests. The processed image results show that the proposed method effectively reduces speckle noise and preserves edge details.
Adaptive Conflict-Free Optimization of Rule Sets for Network Security Packet Filtering Devices
Directory of Open Access Journals (Sweden)
Andrea Baiocchi
2015-01-01
Full Text Available Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices.
Energy Technology Data Exchange (ETDEWEB)
Maier, Andreas; Wigstroem, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu Lei; Strobel, Norbert; Fahrig, Rebecca [Department of Radiology, Stanford University, Stanford, California 94305 (United States); Department of Radiology, Stanford University, Stanford, California 94305 (United States) and Center for Medical Image Science and Visualization, Linkoeping University, Linkoeping (Sweden); Pattern Recognition Laboratory, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen (Germany); Nuclear and Radiological Engineering and Medical Physics Programs, George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States); Siemens AG Healthcare, Forchheim 91301 (Germany); Department of Radiology, Stanford University, Stanford, California 94305 (United States)
2011-11-15
Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8
Maier, Andreas; Wigstrom, Lars; Hofmann, Hannes G; Hornegger, Joachim; Zhu, Lei; Strobel, Norbert; Fahrig, Rebecca
2011-11-01
The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8.9-fold speed-up of the
Design of adaptive filter amplifier in UV communication based on DSP
Lv, Zhaoshun; Wu, Hanping; Li, Junyu
2016-10-01
According to the problem of the weak signal at receiving end in UV communication, we design a high gain, continuously adjustable adaptive filter amplifier. Based on proposing overall technical indicators and analyzing its working principle of the signal amplifier, we use chip LMH6629MF and two chips of AD797BN to achieve three-level cascade amplification. And apply hardware of DSP TMS320VC5509A to implement digital filtering. Design and verification by Multisim, Protel 99SE and CCS, the results show that: the amplifier can realize continuously adjustable amplification from 1000 to 10000 times without distortion. Magnification error is <=%4@1000 10000. And equivalent input noise voltage of amplification circuit is <=6 nV/ √Hz @30KHz 45KHz, and realizing function of adaptive filtering. The design provides theoretical reference and technical support for the UV weak signal processing.
Multidimensional Systolic Arrays of LMS AlgorithmAdaptive (FIR Digital Filters
Directory of Open Access Journals (Sweden)
Bakir A. R. Al-Hashemy
2009-01-01
Full Text Available A multidimensional systolic arrays realization of LMS algorithm by a method of mapping regular algorithm onto processor array, are designed. They are based on appropriately selected 1-D systolic array filter that depends on the inner product sum systolic implementation. Various arrays may be derived that exhibit a regular arrangement of the cells (processors and local interconnection pattern, which are important for VLSI implementation. It reduces latency time and increases the throughput rate in comparison to classical 1-D systolic arrays. The 3-D multilayered array consists of 2-D layers, which are connected with each other only by edges. Such arrays for LMS-based adaptive (FIR filter may be opposed the fundamental requirements of fast convergence rate in most adaptive filter applications.
Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method
Institute of Scientific and Technical Information of China (English)
杨海; 李威; 罗成名
2015-01-01
Pure inertial navigation system (INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network (WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter (KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system (FIS), and the fuzzy adaptive Kalman filter (FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.
Hayes, Charles E.; McClellan, James H.; Scott, Waymond R.; Kerr, Andrew J.
2016-05-01
This work introduces two advances in wide-band electromagnetic induction (EMI) processing: a novel adaptive matched filter (AMF) and matched subspace detection methods. Both advances make use of recent work with a subspace SVD approach to separating the signal, soil, and noise subspaces of the frequency measurements The proposed AMF provides a direct approach to removing the EMI self-response while improving the signal to noise ratio of the data. Unlike previous EMI adaptive downtrack filters, this new filter will not erroneously optimize the EMI soil response instead of the EMI target response because these two responses are projected into separate frequency subspaces. The EMI detection methods in this work elaborate on how the signal and noise subspaces in the frequency measurements are ideal for creating the matched subspace detection (MSD) and constant false alarm rate matched subspace detection (CFAR) metrics developed by Scharf The CFAR detection metric has been shown to be the uniformly most powerful invariant detector.
Methodology for adapting the parameters of a fuzzy system using the extended Kalman filter
2011-01-01
When we try to analyze and to control a system whose model was obtained only based on input/output data, accuracy is essential in the model. On the other hand, to make the procedure practical, the modeling stage must be computationally efficient. In this regard, this paper presents the application of extended Kalman filter for the parametric adaptation of a fuzzy model.
Adaptive Filter Techniques for Optical Beam Jitter Control and Target Tracking
2008-12-01
Analysis ......................................................51 5. Standard Deviation of Beam Position Error ...................................51 6...Organization of Analysis ...................................................................51 B. FEEDFORWARD ADAPTIVE FILTERS USING MULTIPLE...actuator (loud speaker or CFSM) before its effect reaches the error sensor. In ANC lingo , y(t) must first pass through the secondary plant dynamics of the
DEFF Research Database (Denmark)
Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar
2016-01-01
, an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...
Weighted Robust Adaptive Filtering in Krein Space and Its Application in Active Noise Control
Jayawardhana, Bayu; Yuan, Shuqing; Xie, Lihua
2002-01-01
Robust adaptive filtering ensures the minimization of the transfer function from the disturbance to the estimation error and thus, guarantees the robustness against the worst-case disturbance in the system. However, a more general approach will be given in this paper hy employing frequency weighting
Tap-length optimization of adaptive filters used in stereophonic acoustic echo cancellation
DEFF Research Database (Denmark)
Kar, Asutosh; Swamy, M.N.S.
2017-01-01
of acoustic echo paths. The tap-length optimization is applied to a single long adaptive filter with thousands of coefficients to decrease the total number of weights, which in turn reduces the computational load. To further increase the convergence rate, the proposed tap-length-optimization algorithm...
Wang, Shanshan; Song, Junjie; Peng, Yang; Zhou, Liang; Ding, Mingyue; Yuchi, Ming
2017-03-01
In recent years, many research studies have been carried out on ultrasound computed tomography (USCT) for improving the detection and management of breast cancer. This paper investigates a signal pre-processing method based on frequency-shift low-pass filtering (FSLF) and least mean square adaptive filtering (LMSAF) for USCT image quality enhancement (proposed in our previous work). FSLF is designed base on Zoom Fast Fourier Transform algorithm (ZFFT) for processing the ultrasound signals in the frequency domain, while LMSAPF is based on the least mean square (LMS) algorithm in the time domain. Through the combination of the two filters, the ultrasound image is expected to have less noises and artifacts, and higher resolution and contrast. The proposed method was verified with the radio-frequency (RF) data of the nylon threads and the breast phantom captured by the USCT system developed in the Medical Ultrasound Laboratory. Experimental results show that the reconstructed images of nylon threads by the proposed method had narrower main lobe width and lower side lobe level comparing to the delay-and-sum (DAS). The background noises and artifacts could also be efficiently restrained. The reconstructed image of breast phantom by the proposed method had a higher resolution and the contrast ratio (CR) could be enhanced for about 12dB to 18dB at different region of interest (ROI).
Multi-dimensional blind separation method for STBC systems
Institute of Scientific and Technical Information of China (English)
Minggang Luo; Liping Li; Guobing Qian; Huaguo Zhang
2013-01-01
Intercepted signal blind separation is a research topic with high importance for both military and civilian communication systems. A blind separation method for space-time block code (STBC) systems is proposed by using the ordinary independent component analysis (ICA). This method cannot work when spe-cific complex modulations are employed since the assumption of mutual independence cannot be satisfied. The analysis shows that source signals, which are group-wise independent and use multi-dimensional ICA (MICA) instead of ordinary ICA, can be applied in this case. Utilizing the block-diagonal structure of the cumulant matrices, the JADE algorithm is generalized to the multi-dimensional case to separate the received data into mutual y in-dependent groups. Compared with ordinary ICA algorithms, the proposed method does not introduce additional ambiguities. Sim-ulations show that the proposed method overcomes the drawback and achieves a better performance without utilizing coding infor-mation than channel estimation based algorithms.
Multi-Dimensional Top-k Dominating Queries
DEFF Research Database (Denmark)
Yiu, Man Lung; Mamoulis, Nikos
2009-01-01
The top-k dominating query returns k data objects which dominate the highest number of objects in a dataset. This query is an important tool for decision support since it provides data analysts an intuitive way for finding significant objects. In addition, it combines the advantages of top...... attention from the research community. This paper is an extensive study on the evaluation of topk dominating queries. First, we propose a set of algorithms that apply on indexed multi-dimensional data. Second, we investigate query evaluation on data that are not indexed. Finally, we study a relaxed variant...... of the query which considers dominance in dimensional subspaces. Experiments using synthetic and real datasets demonstrate that our algorithms significantly outperform a previous skyline-based approach. We also illustrate the applicability of this multi-dimensional analysis query by studying the meaningfulness...
Multi-dimensional LOCA Analysis, Status and Perspective
Energy Technology Data Exchange (ETDEWEB)
Lee, Sang Yong [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)
2015-10-15
The most extensive research project for LOCA was the 2D/3D program. It involved large or full size experiments such as CCTF (Cylindrical Core Test Facility), SCTF (Slab Core Test Facility) and UPTF (Upper Plenum Test Facility) as well as the most up-to-date two-fluid analysis code, TRAC. With the background of 2D/3D study, the topic of this paper is to investigate facts concerning LOCA application of the present day up-to-date code systems such as RELAP5, TRAC, CATHARE and COBRA-TF. Especially, focus has been put on the multi-dimensional phenomena. The rigorous conservative form of COBRA-TF is strongly recommended to handle the multi-dimensional multi-phase flow phenomena in the future. It is inevitable to develop the reasonable correlation for the covariance coefficients.
Multi-dimensional versions of a formula of Popoviciu
Institute of Scientific and Technical Information of China (English)
2007-01-01
In this paper, an explicit formulation for multivariate truncated power functions of degree one is given firstly. Based on multivariate truncated power functions of degree one, a formulation is presented which counts the number of non-negative integer solutions of s×(s + 1) linear Diophantine equations and it can be considered as a multi-dimensional versions of the formula counting the number of non-negative integer solutions of ax + by = n which is given by Popoviciu in 1953.
Multi-dimensional versions of a formula of Popoviciu
Institute of Scientific and Technical Information of China (English)
Zhi-qiang XU
2007-01-01
In this paper, an explicit formulation for multivariate truncated power functions of degree one is given firstly. Based on multivariate truncated power functions of degree one, a formulation is presented which counts the number of non-negative integer solutions of s × (s + 1) linear Diophantine equations and it can be considered as a multi-dimensional versions of the formula counting the number of non-negative integer solutions of ax + by = n which is given by Popoviciu in 1953.
Peer Pressure in Multi-Dimensional Work Tasks
Felix Ebeling; Gerlinde Fellner; Johannes Wahlig
2012-01-01
We study the influence of peer pressure in multi-dimensional work tasks theoretically and in a controlled laboratory experiment. Thereby, workers face peer pressure in only one work dimension. We find that effort provision increases in the dimension where peer pressure is introduced. However, not all of this increase translates into a productivity gain, since the effect is partly offset by a decrease of effort in the work dimension without peer pressure. Furthermore, this tradeoff is stronger...
Code Coupling for Multi-Dimensional Core Transient Analysis
Energy Technology Data Exchange (ETDEWEB)
Park, Jin-Woo; Park, Guen-Tae; Park, Min-Ho; Ryu, Seok-Hee; Um, Kil-Sup; Lee Jae-Il [KEPCO NF, Daejeon (Korea, Republic of)
2015-05-15
After the CEA ejection, the nuclear power of the reactor dramatically increases in an exponential behavior until the Doppler effect becomes important and turns the reactivity balance and power down to lower levels. Although this happens in a very short period of time, only few seconds, the energy generated can be very significant and cause fuel failures. The current safety analysis methodology which is based on overly conservative assumptions with the point kinetics model results in quite adverse consequences. Thus, KEPCO Nuclear Fuel(KNF) is developing the multi-dimensional safety analysis methodology to mitigate the consequences of the single CEA ejection accident. For this purpose, three-dimensional core neutron kinetics code ASTRA, sub-channel analysis code THALES, and fuel performance analysis code FROST, which have transient calculation performance, were coupled using message passing interface (MPI). This paper presents the methodology used for code coupling and the preliminary simulation results with the coupled code system (CHASER). Multi-dimensional core transient analysis code system, CHASER, has been developed and it was applied to simulate a single CEA ejection accident. CHASER gave a good prediction of multi-dimensional core transient behaviors during transient. In the near future, the multi-dimension CEA ejection analysis methodology using CHASER is planning to be developed. CHASER is expected to be a useful tool to gain safety margin for reactivity initiated accidents (RIAs), such as a single CEA ejection accident.
Ren, Hongwu; Dekany, Richard; Britton, Matthew
2005-05-01
We propose a new recursive filtering algorithm for wave-front reconstruction in a large-scale adaptive optics system. An embedding step is used in this recursive filtering algorithm to permit fast methods to be used for wave-front reconstruction on an annular aperture. This embedding step can be used alone with a direct residual error updating procedure or used with the preconditioned conjugate-gradient method as a preconditioning step. We derive the Hudgin and Fried filters for spectral-domain filtering, using the eigenvalue decomposition method. Using Monte Carlo simulations, we compare the performance of discrete Fourier transform domain filtering, discrete cosine transform domain filtering, multigrid, and alternative-direction-implicit methods in the embedding step of the recursive filtering algorithm. We also simulate the performance of this recursive filtering in a closed-loop adaptive optics system.
Adaptive Current Control with PI-Fuzzy Compound Controller for Shunt Active Power Filter
Directory of Open Access Journals (Sweden)
Juntao Fei
2013-01-01
Full Text Available An adaptive control technology and PI-fuzzy compound control technology are proposed to control an active power filter (APF. AC side current compensation and DC capacitor voltage tracking control strategy are discussed and analyzed. Model reference adaptive controller for the AC side current compensation is derived and established based on Lyapunov stability theory; proportional and integral (PI fuzzy compound controller is designed for the DC side capacitor voltage control. The adaptive current controller based on PI-fuzzy compound system is compared with the conventional PI controller for active power filter. Simulation results demonstrate the feasibility and satisfactory performance of the proposed control strategies. It is shown that the proposed control method has an excellent dynamic performance such as small current tracking error, reduced total harmonic distortion (THD, and strong robustness in the presence of parameters variation and nonlinear load.
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm.
A new learning statistic for adaptive filter based on predicted residuals
Institute of Scientific and Technical Information of China (English)
YANG Yuanxi; GAO Weiguang
2006-01-01
A key problem for an adaptive filter is to establish a suitable adaptive factor for balancing the contributions of the measurements and the predicted state information from some kinematic models. The reasonable adaptive factor needs a reliable learning statistics to judge the state kinematic model errors. After analyzing the existing two kinds of learning statistics based on the state discrepancy and variance component ratio, a new learning statistic based on predicted residuals is set up, which is different from the exiting learning statistics. The new learning statistic does not need to estimate the kinemetic state parameters before the filtering process, Of course, it does not need necessary measurements to estimate state parameters for all observation epochs. The new learning statistic can be applied together with the learning factor constructed by the state discrepancy. The advantages and shortcomings of the new learning factor are analyzed, and an example is given.
Directory of Open Access Journals (Sweden)
Lianhui Li
2015-12-01
Full Text Available Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.
Spors, Sascha; Buchner, Herbert; Rabenstein, Rudolf; Herbordt, Wolfgang
2007-07-01
The acoustic theory for multichannel sound reproduction systems usually assumes free-field conditions for the listening environment. However, their performance in real-world listening environments may be impaired by reflections at the walls. This impairment can be reduced by suitable compensation measures. For systems with many channels, active compensation is an option, since the compensating waves can be created by the reproduction loudspeakers. Due to the time-varying nature of room acoustics, the compensation signals have to be determined by an adaptive system. The problems associated with the successful operation of multichannel adaptive systems are addressed in this contribution. First, a method for decoupling the adaptation problem is introduced. It is based on a generalized singular value decomposition and is called eigenspace adaptive filtering. Unfortunately, it cannot be implemented in its pure form, since the continuous adaptation of the generalized singular value decomposition matrices to the variable room acoustics is numerically very demanding. However, a combination of this mathematical technique with the physical description of wave propagation yields a realizable multichannel adaptation method with good decoupling properties. It is called wave domain adaptive filtering and is discussed here in the context of wave field synthesis.
Automatic speech signal segmentation based on the innovation adaptive filter
Directory of Open Access Journals (Sweden)
Makowski Ryszard
2014-06-01
Full Text Available Speech segmentation is an essential stage in designing automatic speech recognition systems and one can ﬁnd several algorithms proposed in the literature. It is a difﬁcult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006, and it is a modiﬁed version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefﬁcients of an innovation adaptive ﬁlter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics deﬁned on the mel spectrum determined from the reﬂection coefﬁcients. The paper presents the structure of the algorithm, deﬁnes its properties, lists parameter values, describes detection efﬁciency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.
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.
Design of Semi-Adaptive 190-200 KHz Digital Band Pass Filters for SAR Applications
Directory of Open Access Journals (Sweden)
P Yadav
2013-04-01
Full Text Available Technologies have advanced rapidly in the field of digital signal processing due to advances made in high speed, low cost digital integrated chips. These technologies have further stimulated ever increasing use of signal representation in digital form for purposes of transmission, measurement, control and storage. Design of digital filters especially adaptive or semi adaptive is the necessity of the hour for SAR applications. The aim of this research work is to design and performance evaluation of 380-400 KHz Bartlett, Blackman and Chebyshev digital semi adaptive filters. For this work XILINX and MATLAB softwares were used for the design. As pert of practical research work these designs were translated using FPGA hardware SPARTAN-3E kit. These were optimized, analyzed, compared and evaluated keeping the sampling frequency at 5 MHz for 64 order. Both these filters designed using software and hardware were tested by passing a sinusoidal test signal of 381 KHz along with noise and the filtered output signals are presented.
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.
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.
Speed Estimation of Induction Motor Using Model Reference Adaptive System with Kalman Filter
Directory of Open Access Journals (Sweden)
Pavel Brandstetter
2013-01-01
Full Text Available The paper deals with a speed estimation of the induction motor using observer with Model Reference Adaptive System and Kalman Filter. For simulation, Hardware in Loop Simulation method is used. The first part of the paper includes the mathematical description of the observer for the speed estimation of the induction motor. The second part describes Kalman filter. The third part describes Hardware in Loop Simulation method and its realization using multifunction card MF 624. In the last section of the paper, simulation results are shown for different changes of the induction motor speed which confirm high dynamic properties of the induction motor drive with sensorless control.
An adaptive filter for studying the life cycle of optical rogue waves.
Liu, Chu; Rees, Eric J; Laurila, Toni; Jian, Shuisheng; Kaminski, Clemens F
2010-12-06
We present an adaptive numerical filter for analyzing fiber-length dependent properties of optical rogue waves, which are highly intense and extremely red-shifted solitons that arise during supercontinuum generation in photonic crystal fiber. We use this filter to study a data set of 1000 simulated supercontinuum pulses, produced from 5 ps pump pulses containing random noise. Optical rogue waves arise in different supercontinuum pulses at various positions along the fiber, and exhibit a lifecycle: their intensity peaks over a finite range of fiber length before declining slowly.
Optimal adaptive focusing through heterogeneous media with the minimally invasive inverse filter.
Vignon, François; de Rosny, Julien; Aubry, Jean-François; Fink, Mathias
2007-11-01
The inverse filter is a technique used to adaptively focus waves through heterogeneous media. It is based on the inversion of the Green's functions matrix between the M transducers of a focusing array and N control points in the focal area. The inverse filter minimizes the pressure deposited around the focal point. However it is highly invasive, requiring the presence of N transducers or hydrophones in the focal area at the control points' locations to measure the Green's functions. This paper presents a way of reaching the inverse filter's focusing quality with a minimally invasive setup: only one transducer (at the desired focal location) is needed. This minimally invasive inverse filter takes advantage of the fact all the information about the propagation medium can be retrieved from the signals backscattered by the medium towards the focusing array, if the propagation medium is lossless. A numerical simulation is performed to test this minimally invasive inverse filter through a scattering, lossless medium. The focusing quality equals the conventional, highly invasive inverse filter's. The average spatial and temporal contrast is increased by up to 10 dB compared to the time reversal focusing.
Adaptive filtering and feed-forward control for suppression of vibration and jitter
Anderson, Eric H.; Blankinship, Ross L.; Fowler, Leslie P.; Glaese, Roger M.; Janzen, Paul C.
2007-04-01
This paper describes the use of adaptive filtering to control vibration and optical jitter. Adaptive filtering is a class of signal processing techniques developed over the last several decades and applied since to applications ranging from communications to image processing. Basic concepts in adaptive filtering and feedforward control are reviewed. A series of examples in vibration, motion and jitter control, including cryocoolers, ground-based active optics systems, flight motion simulators, wind turbines and airborne optical beam control systems, illustrates the effectiveness of the adaptive methods. These applications make use of information and signals that originate from system disturbances and minimize the correlations between disturbance information and error and performance measures. The examples incorporate a variety of disturbance types including periodic, multi-tonal, broadband stationary and non-stationary. Control effectiveness with slowly-varying narrowband disturbances originating from cryocoolers can be extraordinary, reaching 60 dB of reduction or rejection. In other cases, performance improvements are only 30-50%, but such reductions effectively complement feedback servo performance in many applications.
Modified Log-LMS adaptive filter with low signal distortion for biomedical applications.
Jiao, Yuzhong; Cheung, Rex Y P; Mok, Mark P C
2012-01-01
Life signals from human body, e.g. heartbeat or electrocardiography (ECG), are usually weak and susceptible to external noise and interference. Adaptive filter is a good tool to reduce the influence of ambient noise/interference on the life signals. Least mean squares (LMS) algorithm, as one of most popular adaptive algorithms for active noise cancellation (ANC) by adaptive filtering, has the advantage of easy implementation. In order to further decrease the complexity of LMS algorithm based adaptive filter, a Log-LMS algorithm was proposed, which quantized signals by the function of log2. The algorithm can replace multipliers by simple shifting. However, both LMS algorithm and Log-LMS algorithm have the disadvantage of serious signal distortion in biomedical applications. In this paper, a modified Log-LMS algorithm is presented, which divides the convergence process into two different stages, and utilizes different quantization method in each stage. Two scenarios of biomedical applications are used for analysis, 1) using stethoscope in emergence medical helicopter and 2) measuring ECG under power line interference. The simulated results show that the modified algorithm can achieve fast convergence and low signal distortion in processing periodic life signals.
Rucci, Michael; Hardie, Russell C; Barnard, Kenneth J
2014-05-01
In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.
Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces
Lu, Jun; McFarland, Dennis J.; Wolpaw, Jonathan R.
2013-02-01
Objective. Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an ‘adaptive Laplacian (ALAP) filter’, can provide better performance for SMR-based BCIs. Approach. An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible. Main results. Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. Significance. Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.
Adaptive nonlocal means filtering based on local noise level for CT denoising
Energy Technology Data Exchange (ETDEWEB)
Li, Zhoubo; Trzasko, Joshua D.; Lake, David S.; Blezek, Daniel J.; Manduca, Armando, E-mail: manduca.armando@mayo.edu [Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905 (United States); Yu, Lifeng; Fletcher, Joel G.; McCollough, Cynthia H. [Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905 (United States)
2014-01-15
Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the
Train velocity estimation method based on an adaptive filter with fuzzy logic
Pichlík, Petr; Zděnek, Jiří
2017-03-01
The train velocity is difficult to determine when the velocity is measured only on the driven or braked locomotive wheelsets. In this case, the calculated train velocity is different from the actual train velocity due to slip velocity or skid velocity respectively. The train velocity is needed for a locomotive controller proper work. For this purpose, an adaptive filter that is tuned by a fuzzy logic is designed and described in the paper. The filter calculates the train longitudinal velocity based on locomotive wheelset velocity. The fuzzy logic is used for the tuning of the filter according to actual wheelset acceleration and wheelset jerk. The simulation results are based on real measured data on a freight train. The results show that the calculated velocity corresponds to the actual train velocity.
An adaptive nonlocal filtering for low-dose CT in both image and projection domains
Directory of Open Access Journals (Sweden)
Yingmei Wang
2015-04-01
Full Text Available An important problem in low-dose CT is the image quality degradation caused by photon starvation. There are a lot of algorithms in sinogram domain or image domain to solve this problem. In view of strong self-similarity contained in the special sinusoid-like strip data in the sinogram space, we propose a novel non-local filtering, whose average weights are related to both the image FBP (filtered backprojection reconstructed from restored sinogram data and the image directly FBP reconstructed from noisy sinogram data. In the process of sinogram restoration, we apply a non-local method with smoothness parameters adjusted adaptively to the variance of noisy sinogram data, which makes the method much effective for noise reduction in sinogram domain. Simulation experiments show that our proposed method by filtering in both image and projection domains has a better performance in noise reduction and details preservation in reconstructed images.
An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network
Directory of Open Access Journals (Sweden)
Kai Hu
2013-01-01
Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.
Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
Sannelli, Claudia; Vidaurre, Carmen; Müller, Klaus-Robert; Blankertz, Benjamin
2016-08-01
Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain-computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI
Adaptive RBF Neural Network Control for Three-Phase Active Power Filter
Directory of Open Access Journals (Sweden)
Juntao Fei
2013-05-01
Full Text Available Abstract An adaptive radial basis function (RBF neural network control system for three-phase active power filter (APF is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non-linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achieve the desired tracking task. The simulation results demonstrate good performance, for example showing small current tracking error, reduced total harmonic distortion (THD, improved accuracy and strong robustness in the presence of parameters variation and nonlinear load. It is shown that the adaptive RBF neural network control system for three-phase APF gives better control than hysteresis control.
Penland, Cecile; Ghil, Michael; Weickmann, Klaus M.
1991-01-01
The spectral resolution and statistical significance of a harmonic analysis obtained by low-order MEM can be improved by subjecting the data to an adaptive filter. This adaptive filter consists of projecting the data onto the leading temporal empirical orthogonal functions obtained from singular spectrum analysis (SSA). The combined SSA-MEM method is applied both to a synthetic time series and a time series of AAM data. The procedure is very effective when the background noise is white and less so when the background noise is red. The latter case obtains in the AAM data. Nevertheless, reliable evidence for intraseasonal and interannual oscillations in AAM is detected. The interannual periods include a quasi-biennial one and an LF one, of 5 years, both related to the El Nino/Southern Oscillation. In the intraseasonal band, separate oscillations of about 48.5 and 51 days are ascertained.
Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System
Directory of Open Access Journals (Sweden)
Xin Zhang
2014-01-01
Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.
Particle filter based visual tracking with multi-cue adaptive fusion
Institute of Scientific and Technical Information of China (English)
Anping Li; Zhongliang Jing; Shiqiang Hu
2005-01-01
@@ To improve the robustness of visual tracking in complex environments such as: cluttered backgrounds, partial occlusions, similar distraction and pose variations, a novel tracking method based on adaptive fusion and particle filter is proposed in this paper. In this method, the image color and shape cues are adaptively fused to represent the target observation; fuzzy logic is applied to dynamically adjust each cue weight according to its associated reliability in the past frame; particle filter is adopted to deal with non-linear and non-Gaussian problems in visual tracking. The method is demonstrated to be robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion for a test image sequence.
Directory of Open Access Journals (Sweden)
Wentao Yu
2013-01-01
high. In order to reduce the computation cost of UPF and meanwhile maintain the accuracy, we propose an adaptive unscented particle filter (AUPF algorithm through relative entropy. AUPF can adaptively adjust the number of particles during filtering to reduce the necessary computation and hence improve the real-time capability of UPF. In AUPF, the relative entropy is used to measure the distance between the empirical distribution and the true posterior distribution. The least number of particles for the next step is then decided according to the relative entropy. In order to offset the difference between the proposal distribution, and the true distribution the least number is adjusted thereafter. The ideal performance of AUPF in real robot self-localization is demonstrated.
Video Enhancement Using Adaptive Spatio-Temporal Connective Filter and Piecewise Mapping
Directory of Open Access Journals (Sweden)
Wang Chao
2008-01-01
Full Text Available This paper presents a novel video enhancement system based on an adaptive spatio-temporal connective (ASTC noise filter and an adaptive piecewise mapping function (APMF. For ill-exposed videos or those with much noise, we first introduce a novel local image statistic to identify impulse noise pixels, and then incorporate it into the classical bilateral filter to form ASTC, aiming to reduce the mixture of the most two common types of noises—Gaussian and impulse noises in spatial and temporal directions. After noise removal, we enhance the video contrast with APMF based on the statistical information of frame segmentation results. The experiment results demonstrate that, for diverse low-quality videos corrupted by mixed noise, underexposure, overexposure, or any mixture of the above, the proposed system can automatically produce satisfactory results.
Video Enhancement Using Adaptive Spatio-Temporal Connective Filter and Piecewise Mapping
Directory of Open Access Journals (Sweden)
Shi-Qiang Yang
2008-06-01
Full Text Available This paper presents a novel video enhancement system based on an adaptive spatio-temporal connective (ASTC noise filter and an adaptive piecewise mapping function (APMF. For ill-exposed videos or those with much noise, we first introduce a novel local image statistic to identify impulse noise pixels, and then incorporate it into the classical bilateral filter to form ASTC, aiming to reduce the mixture of the most two common types of noisesÃ¢Â€Â”Gaussian and impulse noises in spatial and temporal directions. After noise removal, we enhance the video contrast with APMF based on the statistical information of frame segmentation results. The experiment results demonstrate that, for diverse low-quality videos corrupted by mixed noise, underexposure, overexposure, or any mixture of the above, the proposed system can automatically produce satisfactory results.
A New Subband Adaptive Filtering Algorithm for Sparse System Identification with Impulsive Noise
Directory of Open Access Journals (Sweden)
Young-Seok Choi
2014-01-01
Full Text Available This paper presents a novel subband adaptive filter (SAF for system identification where an impulse response is sparse and disturbed with an impulsive noise. Benefiting from the uses of l1-norm optimization and l0-norm penalty of the weight vector in the cost function, the proposed l0-norm sign SAF (l0-SSAF achieves both robustness against impulsive noise and remarkably improved convergence behavior more than the classical adaptive filters. Simulation results in the system identification scenario confirm that the proposed l0-norm SSAF is not only more robust but also faster and more accurate than its counterparts in the sparse system identification in the presence of impulsive noise.
Adaptive control of a flexible beam using least square lattice filters
Sundararajan, N.; Montgomery, R. C.
1983-01-01
This paper presents an indirect adaptive control scheme for the control of flexible structures using recursive least square lattice filters. The identification scheme uses lattice filters which provide an on-line estimate of the number of modes, mode shapes and modal amplitudes. These modes are coupled and a transformation to decouple them in order to obtain the natural modes is presented. The decoupled modal amplitude time series are then used in an equation error identification scheme to identify the model parameters in an autoregressive moving average (ARMA) form. The control is based on modal pole placement scheme with the objective of vibration suppression. The control gains are calculated based on the identified ARMA parameters. Before using the identified parameters for control, detailed testing and validation procedures are carried out on the identified parameters. The full adaptive control scheme is demonstrated using the simulation for the 12 foot free-free beam apparatus at NASA Langley Research Center.
Directory of Open Access Journals (Sweden)
Jesmin F. Khan
2008-08-01
Full Text Available A novel approach for bidimensional empirical mode decomposition (BEMD is proposed in this paper. BEMD decomposes an image into multiple hierarchical components known as bidimensional intrinsic mode functions (BIMFs. In each iteration of the process, two-dimensional (2D interpolation is applied to a set of local maxima (minima points to form the upper (lower envelope. But, 2D scattered data interpolation methods cause huge computation time and other artifacts in the decomposition. This paper suggests a simple, but effective, method of envelope estimation that replaces the surface interpolation. In this method, order statistics filters are used to get the upper and lower envelopes, where filter size is derived from the data. Based on the properties of the proposed approach, it is considered as fast and adaptive BEMD (FABEMD. Simulation results demonstrate that FABEMD is not only faster and adaptive, but also outperforms the original BEMD in terms of the quality of the BIMFs.
Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators.
Hooshmand, Rahmat Allah; Torabian Esfahani, Mahdi
2011-04-01
Thyristor controlled reactor with fixed capacitor (TCR/FC) compensators have the capability of compensating reactive power and improving power quality phenomena. Delay in the response of such compensators degrades their performance. In this paper, a new method based on adaptive filters (AF) is proposed in order to eliminate delay and increase the response of the TCR compensator. The algorithm designed for the adaptive filters is performed based on the least mean square (LMS) algorithm. In this design, instead of fixed capacitors, band-pass LC filters are used. To evaluate the filter, a TCR/FC compensator was used for nonlinear and time varying loads of electric arc furnaces (EAFs). These loads caused occurrence of power quality phenomena in the supplying system, such as voltage fluctuation and flicker, odd and even harmonics and unbalancing in voltage and current. The above design was implemented in a realistic system model of a steel complex. The simulation results show that applying the proposed control in the TCR/FC compensator efficiently eliminated delay in the response and improved the performance of the compensator in the power system.
State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter
Directory of Open Access Journals (Sweden)
Bizhong Xia
2015-06-01
Full Text Available Accurate state of charge (SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST and New European Driving Cycle (NEDC are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF and cubature Kalman filter (CKF algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms.
Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.
Wilson, Emma D; Assaf, Tareq; Pearson, Martin J; Rossiter, Jonathan M; Dean, Paul; Anderson, Sean R; Porrill, John
2015-01-01
The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.
Adaptive Controller for Vehicle Active Suspension Generated Through LMS Filter Algorithms
Institute of Scientific and Technical Information of China (English)
SUN Jianmin; SHU Gequn
2006-01-01
The least means squares (LMS) adaptive filter algorithm was used in active suspension system.By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained.For two-degree-of-freedom vehicle suspension model, LMS adaptive controller was designed.The acceleration of the sprung mass,the dynamic tyre load between wheels and road,and the dynamic deflection between sprung mass and unsprung mass were determined as the evaluation targets of suspension performance.For LMS adaptive control suspension, compared with passive suspension, acceleration power spectral density of sprung mass acceleration under the road input model decreased 8-10 times in high frequency resonance band or low frequency resonance band.The simulation results show that LMS adaptive control is simple and remarkably effective.It further proves that the active control suspension system can improve both the riding comfort and handling safety in various operation conditions, and the method is fit for the active control of the suspension system.
Biohybrid control of general linear systems using the adaptive filter model of cerebellum
Directory of Open Access Journals (Sweden)
Emma D. Wilson
2015-07-01
Full Text Available The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems such as the vestibulo-ocular reflex (VOR and to sensory processing problems such as the adaptive cancellation of reafferent noise. It has also been successfully applied to problems in robotics such as adaptive camera stabilisation and sensor noise cancellation. In previous applications to inverse control problems the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity control of this plant results in unstable learning and control. To be more generally useful in engineering problems it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC scheme, which stabilises the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.
Real-time adaptive filtering of dental drill noise using a digital signal processor
2006-01-01
The application of noise reduction methods requires the integration of acoustics engineering and digital signal processing, which is well served by a mechatronic approach as described in this paper. The Normalised Least Mean Square (NLMS) algorithm is implemented on the Texas Instruments TMS320C6713 DSK Digital Signal Processor (DSP) as an adaptive digital filter for dental drill noise. Blocks within the Matlab/Simulink Signal Processing Blockset and the Embedded Target for TI C6000 DSP famil...
A MICROPAYMENT SCHEME BASED ON WEIGHTED MULTI DIMENSIONAL HASH CHAIN
Institute of Scientific and Technical Information of China (English)
Liu Yining; Hu Lei; Liu Heguo
2006-01-01
Hash chain and its generalization-Multi-Dimensional Hash Chain (MDHC) have been widely used in the design of micropayment due to its simplicity and efficiency. In this letter, a more efficient variant of MDHC, called WMDHC, which endows in the structure of MDHC a weight value for each hash value through a well-defined mapping, is proposed. The average hash operation number of WMDHC is log(2m/t), which is better than log(m) of MDHC when the parameter t is typically suggested as t = 7.
Multifunction myoelectric control using multi-dimensional dynamic time warping.
AbdelMaseeh, Meena; Tsu-Wei Chen; Stashuk, Daniel
2014-01-01
Myoelectric control can be used for a variety of applications including powered protheses and different human computer interface systems. The aim of this study is to investigate the formulation of myoelectric control as a multi-class distance-based classification of multidimensional sequences. More specifically, we investigate (1) estimation of multi-muscle activation sequences from multi-channel electromyographic signals in an online manner, and (2) classification using a distance metric based on multi-dimensional dynamic time warping. Subject-specific results across 5 subjects executing 10 different hand movements showed an accuracy of 95% using offline extracted trajectories and an accuracy of 84% using online extracted trajectories.
Subotić, Miško; Šarić, Zoran; Jovičić, Slobodan T
2012-03-01
Transient otoacoustic emission (TEOAE) is a method widely used in clinical practice for assessment of hearing quality. The main problem in TEOAE detection is its much lower level than the level of environmental and biological noise. While the environmental noise level can be controlled, the biological noise can be only reduced by appropriate signal processing. This paper presents a new two-probe preprocessing TEOAE system for suppression of the biological noise by adaptive filtering. The system records biological noises in both ears and applies a specific adaptive filtering approach for suppression of biological noise in the ear canal with TEOAE. The adaptive filtering approach includes robust sign error LMS algorithm, stimuli response summation according to the derived non-linear response (DNLR) technique, subtraction of the estimated TEOAE signal and residual noise suppression. The proposed TEOAE detection system is tested by three quality measures: signal-to-noise ratio (S/N), reproducibility of TEOAE, and measurement time. The maximal TEOAE detection improvement is dependent on the coherence function between biological noise in left and right ears. The experimental results show maximal improvement of 7 dB in S/N, improvement in reproducibility near 40% and reduction in duration of TEOAE measurement of over 30%.
1-D Systolic Arrays Design of LMS Adaptive (FIR Digital Filtering
Directory of Open Access Journals (Sweden)
Ali H. Mahdi
2010-01-01
Full Text Available This paper extends the 1-D systolic array approach with a method of systematic linear design of systolic algorithms. Past methods for mapping the Least-Mean-Square (LMS Adaptive Finite-Impulse-Response (FIR filter onto parallel and pipelined architectures either introduce delays in the coefficients updates or have excessive hardware requirements. In this article, we describe an efficient 1-D systolic array for the LMS adaptive FIR filter that produces the same output and error signals as produced by the standard LMS adaptive filter architecture with single assignment form of processor functions.The proposed systolic architectures that are designed operate on a block-by-block basis and makes use of the flexibility in the design, which takes the inner product step (convolution sum of the tap weight vector and the tap input vector in the design consideration. It enables us to extract more than one algorithm for the same problem. The input and output data flow sequentially and continuously into and out of the systolic arrays at the system clock rates, during each clock period, processing element of the same type operates in parallel. The most computationally demanding among them performs only two consecutive multiplications and two additions/subtractions per clock period, thereby allowing a very high throughput and very fast block signal processing to be achieved at the expense of a delay of L samples between the input and output and 100% utilization, L being the block size.
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.
DEFF Research Database (Denmark)
Gil-Cacho, Jose M.; van Waterschoot, Toon; Moonen, Marc
2014-01-01
In this paper, we propose a new framework to tackle the double-talk (DT) problem in acoustic echo cancellation (AEC). It is based on a frequency-domain adaptive filter (FDAF) implementation of the so-called prediction error method adaptive filtering using row operations (PEM-AFROW) leading...... to the FDAF-PEM-AFROW algorithm. We show that FDAF-PEM-AFROW is by construction related to the best linear unbiased estimate (BLUE) of the echo path. We depart from this framework to show an improvement in performance with respect to other adaptive filters minimizing the BLUE criterion, namely the PEM...
[Research of adaptive notch filter based on QRD-LS algorithm for power line interference in ECG].
Wang, Shuyan; Dong, Jian; Guan, Xin
2008-10-01
In this paper, an adaptive notch filter based on QRD-LS algorithm for power line interference in ECG is researched. It can automatically eliminate the power line interference in order to improve the signal-to-interference ratio. Furthermore, QLD-LS algorithm, which is recursive least-squares minimization using systolic arrays, is employed to adjust the weight vector. Compared with the adaptive notch filter based on LMS (least mean square) algorithm, it has good robustness. Simulation examples confirm the results. QRD-LS adaptive notch filter has better performance in comparison with LMS method.
Santarius, John; Navarro, Marcos; Michalak, Matthew; Fancher, Aaron; Kulcinski, Gerald; Bonomo, Richard
2016-10-01
A newly initiated research project will be described that investigates methods for detecting shielded special nuclear materials by combining multi-dimensional neutron sources, forward/adjoint calculations modeling neutron and gamma transport, and sparse data analysis of detector signals. The key tasks for this project are: (1) developing a radiation transport capability for use in optimizing adaptive-geometry, inertial-electrostatic confinement (IEC) neutron source/detector configurations for neutron pulses distributed in space and/or phased in time; (2) creating distributed-geometry, gas-target, IEC fusion neutron sources; (3) applying sparse data and noise reduction algorithms, such as principal component analysis (PCA) and wavelet transform analysis, to enhance detection fidelity; and (4) educating graduate and undergraduate students. Funded by DHS DNDO Project 2015-DN-077-ARI095.
Multi-dimensional structure of accreting young stars
Geroux, C; Viallet, M; Goffrey, T; Pratt, J; Constantino, T; Folini, D; Popov, M V; Walder, R
2016-01-01
This work is the first attempt to describe the multi-dimensional structure of accreting young stars based on fully compressible time implicit multi-dimensional hydrodynamics simulations. One major motivation is to analyse the validity of accretion treatment used in previous 1D stellar evolution studies. We analyse the effect of accretion on the structure of a realistic stellar model of the young Sun. Our work is inspired by the numerical work of Kley \\& Lin (1996, ApJ, 461, 933) devoted to the structure of the boundary layer in accretion disks. We analyse the redistribution of accreted material with a range of values of specific entropy relative to the bulk specific entropy of the material in the accreting object's convective envelope. A primary goal is to understand whether and how accreted energy deposited onto a stellar surface is redistributed in the interior. This study focusses on the high accretion rates characteristic of FU Ori systems. We find that the highest entropy cases produce a distinctive ...
Extended Darknet: Multi-Dimensional Internet Threat Monitoring System
Shimoda, Akihiro; Mori, Tatsuya; Goto, Shigeki
Internet threats caused by botnets/worms are one of the most important security issues to be addressed. Darknet, also called a dark IP address space, is one of the best solutions for monitoring anomalous packets sent by malicious software. However, since darknet is deployed only on an inactive IP address space, it is an inefficient way for monitoring a working network that has a considerable number of active IP addresses. The present paper addresses this problem. We propose a scalable, light-weight malicious packet monitoring system based on a multi-dimensional IP/port analysis. Our system significantly extends the monitoring scope of darknet. In order to extend the capacity of darknet, our approach leverages the active IP address space without affecting legitimate traffic. Multi-dimensional monitoring enables the monitoring of TCP ports with firewalls enabled on each of the IP addresses. We focus on delays of TCP syn/ack responses in the traffic. We locate syn/ack delayed packets and forward them to sensors or honeypots for further analysis. We also propose a policy-based flow classification and forwarding mechanism and develop a prototype of a monitoring system that implements our proposed architecture. We deploy our system on a campus network and perform several experiments for the evaluation of our system. We verify that our system can cover 89% of the IP addresses while darknet-based monitoring only covers 46%. On our campus network, our system monitors twice as many IP addresses as darknet.
Directory of Open Access Journals (Sweden)
Onur Jane
2016-01-01
Full Text Available Digital watermarking has been one of the focal points of research interests in order to provide multimedia security in the last decade. Watermark data, belonging to the user, are embedded on an original work such as text, audio, image, and video and thus, product ownership can be proved. Various robust watermarking algorithms have been developed in order to extract/detect the watermark against such attacks. Although watermarking algorithms in the transform domain differ from others by different combinations of transform techniques, it is difficult to decide on an algorithm for a specific application. Therefore, instead of developing a new watermarking algorithm with different combinations of transform techniques, we propose a novel and effective watermark extraction and detection method by pre-filtering, namely Adaptive Unsharp Masking (AUM. In spite of the fact that Unsharp Masking (UM based pre-filtering is used for watermark extraction/detection in the literature by causing the details of the watermarked image become more manifest, effectiveness of UM may decrease in some cases of attacks. In this study, AUM has been proposed for pre-filtering as a solution to the disadvantages of UM. Experimental results show that AUM performs better up to 11\\% in objective quality metrics than that of the results when pre-filtering is not used. Moreover; AUM proposed for pre-filtering in the transform domain image watermarking is as effective as that of used in image enhancement and can be applied in an algorithm-independent way for pre-filtering in transform domain image watermarking.
Adaptive system noise covariance for performance enhancement of Kalman filter-based algorithms
Lee, Vika; Chan, Keith C. C.; Leung, Henry
1996-06-01
Several designs of Kalman filters and the interacting multiple models algorithm were used in real tracking tasks involving high dynamic targets. The data were obtained through the joint effort of the defense departments of Canada and the US. Their performance, measured in terms of positional deviation and the number of track losses, are rather unsatisfactory even though they perform particularly well when using simulated data. To identify the reasons behind, we compared and analyzed the differences between the model assumptions behind the design of these Kalman filters and the model required for accurate tracking of these targets. In this paper, we discussed our findings. Moreover, based on the characteristics of real tracking data, we present an alternative methodology for measuring the effectiveness of various Kalman filter based trackers in stressful environmental. It can also be used to explain the well known characteristics of Kalman filter. A lower bound for the deviation, obtained from this equation, shows that deviation could be too large to manage if noise bandwidth is as high as the real data instead of a pre-assumed magnitude. Instead of having to redesign many existing Kalman filters to suit for stressful environment, we developed a design-independent module that can be added to different types of Kalman filters based trackers to enhance their performance in the tracking high dynamic targets. The module is called adaptive systems noise covariance estimation. It is not only safe (i.e. almost no negative effect) but it can sometimes even double the performance of trackers in stressful environment.
Directory of Open Access Journals (Sweden)
A. Konstantaras
2006-01-01
Full Text Available The method of Hybrid Adaptive Filtering (HAF aims to recover the recorded electric field signals from anomalies of magnetotelluric origin induced mainly by magnetic storms. An adaptive filter incorporating neuro-fuzzy technology has been developed to remove any significant distortions from the equivalent magnetic field signal, as retrieved from the original electric field signal by reversing the magnetotelluric method. Testing with further unseen data verifies the reliability of the model and demonstrates the effectiveness of the HAF method.
Use of 3D adaptive raw-data filter in CT of the lung: effect on radiation dose reduction.
Kubo, Takeshi; Ohno, Yoshiharu; Gautam, Shiva; Lin, Pei-Jan P; Kauczor, Hans-Ulrich; Hatabu, Hiroto
2008-10-01
The purpose of this study was to determine the effectiveness of a 3D adaptive raw-data filter in improving image quality and the role of the filter in radiation dose reduction in lung CT. Fifty-eight chest CT examinations were performed with a 16-MDCT scanner. Two acquisitions were performed with different tube current-exposure time settings (50 and 150 mAs, 120 kVp). Four series of lung images were prepared from two sets of raw data with and without application of a 3D adaptive filter (50 mAs, 50 mAs with filter, 150 mAs, 150 mAs with filter). Three blinded readers using a 5-point scale from 1 (nondiagnostic) to 5 (excellent) independently evaluated image quality in five lobes and the lingula. A set of images was considered acceptable when scores in all six regions were 3 (acceptable) or higher. The SD of attenuation was calculated in 24 regions of interest. The overall mean image quality scores were 3.09, 3.53, 4.02, and 4.38 for the 50 mAs, 50 mAs with filter, 150 mAs, and 150 mAs with filter sets, respectively. Scores were significantly better with filter application (p filter application (p images, 18, 52, 50, and 58 sets were judged acceptable with no significant difference in acceptability between images obtained at 50 mAs with a filter and at 150 mAs (p = 0.72). With filter application, the acceptability of 50-mAs images became comparable with that of 150-mAs images, making dose reduction to 50 mAs practical. Use of a 3D adaptive raw-data filter improved the quality of lung images, making dose reduction to 50 mAs attainable with use of the filter.
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-05-30
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback-Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity.
High performance 3D adaptive filtering for DSP based portable medical imaging systems
Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark
2015-03-01
Portable medical imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. Despite their constraints on power, size and cost, portable imaging devices must still deliver high quality images. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often cannot be run with sufficient performance on a portable platform. In recent years, advanced multicore digital signal processors (DSP) have been developed that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms on a portable platform. In this study, the performance of a 3D adaptive filtering algorithm on a DSP is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec with an Ultrasound 3D probe. Relative performance and power is addressed between a reference PC (Quad Core CPU) and a TMS320C6678 DSP from Texas Instruments.
Locally adaptive regression filter-based infrared focal plane array non-uniformity correction
Li, Jia; Qin, Hanlin; Yan, Xiang; Huang, He; Zhao, Yingjuan; Zhou, Huixin
2015-10-01
Due to the limitations of the manufacturing technology, the response rates to the same infrared radiation intensity in each infrared detector unit are not identical. As a result, the non-uniformity of infrared focal plane array, also known as fixed pattern noise (FPN), is generated. To solve this problem, correcting the non-uniformity in infrared image is a promising approach, and many non-uniformity correction (NUC) methods have been proposed. However, they have some defects such as slow convergence, ghosting and scene degradation. To overcome these defects, a novel non-uniformity correction method based on locally adaptive regression filter is proposed. First, locally adaptive regression method is used to separate the infrared image into base layer containing main scene information and the detail layer containing detailed scene with FPN. Then, the detail layer sequence is filtered by non-linear temporal filter to obtain the non-uniformity. Finally, the high quality infrared image is obtained by subtracting non-uniformity component from original image. The experimental results show that the proposed method can significantly eliminate the ghosting and the scene degradation. The results of correction are superior to the THPF-NUC and NN-NUC in the aspects of subjective visual and objective evaluation index.
Improving the response of accelerometers for automotive applications by using LMS adaptive filters.
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg; Fernández, Eduardo
2010-01-01
In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s(2)) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory.
Energy Technology Data Exchange (ETDEWEB)
Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng
2016-06-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so-called "curse of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF could be even more computationally expensive than EnKF. Motivated by most recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to eliminate the inconsistency between model parameters and states. The performance of RAPCKF is tested with numerical cases of unsaturated flow models. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.
Chen, Xiyuan; Wang, Xiying; Xu, Yuan
2014-12-09
This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.
Correia, Carlos M.; Bond, Charlotte Z.; Sauvage, Jean-François; Fusco, Thierry; Conan, Rodolphe; Wizinowich, Peter L.
2017-10-01
We build on a long-standing tradition in astronomical adaptive optics (AO) of specifying performance metrics and error budgets using linear systems modeling in the spatial-frequency domain. Our goal is to provide a comprehensive tool for the calculation of error budgets in terms of residual temporally filtered phase power spectral densities and variances. In addition, the fast simulation of AO-corrected point spread functions (PSFs) provided by this method can be used as inputs for simulations of science observations with next-generation instruments and telescopes, in particular to predict post-coronagraphic contrast improvements for planet finder systems. We extend the previous results and propose the synthesis of a distributed Kalman filter to mitigate both aniso-servo-lag and aliasing errors whilst minimizing the overall residual variance. We discuss applications to (i) analytic AO-corrected PSF modeling in the spatial-frequency domain, (ii) post-coronagraphic contrast enhancement, (iii) filter optimization for real-time wavefront reconstruction, and (iv) PSF reconstruction from system telemetry. Under perfect knowledge of wind velocities, we show that $\\sim$60 nm rms error reduction can be achieved with the distributed Kalman filter embodying anti- aliasing reconstructors on 10 m class high-order AO systems, leading to contrast improvement factors of up to three orders of magnitude at few ${\\lambda}/D$ separations ($\\sim1-5{\\lambda}/D$) for a 0 magnitude star and reaching close to one order of magnitude for a 12 magnitude star.
Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems
Imani, Mahdi; Braga-Neto, Ulisses M.
2017-01-01
Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear models with application in estimation and control of Boolean processes based on noisy and incomplete measurements. The optimal minimum mean square error (MMSE) algorithms for POBDS state estimation, namely, the Boolean Kalman filter (BKF) and Boolean Kalman smoother (BKS), are intractable in the case of large systems, due to computational and memory requirements. To address this, we propose approximate MMSE filtering and smoothing algorithms based on the auxiliary particle filter (APF) method from sequential Monte-Carlo theory. These algorithms are used jointly with maximum-likelihood (ML) methods for simultaneous state and parameter estimation in POBDS models. In the presence of continuous parameters, ML estimation is performed using the expectation-maximization (EM) algorithm; we develop for this purpose a special smoother which reduces the computational complexity of the EM algorithm. The resulting particle-based adaptive filter is applied to a POBDS model of Boolean gene regulatory networks observed through noisy RNA-Seq time series data, and performance is assessed through a series of numerical experiments using the well-known cell cycle gene regulatory model.
Energy Technology Data Exchange (ETDEWEB)
Ray, Jaideep; Lefantzi, Sophia; Najm, Habib N.; Kennedy, Christopher A.
2006-01-01
Block-structured adaptively refined meshes (SAMR) strive for efficient resolution of partial differential equations (PDEs) solved on large computational domains by clustering mesh points only where required by large gradients. Previous work has indicated that fourth-order convergence can be achieved on such meshes by using a suitable combination of high-order discretizations, interpolations, and filters and can deliver significant computational savings over conventional second-order methods at engineering error tolerances. In this paper, we explore the interactions between the errors introduced by discretizations, interpolations and filters. We develop general expressions for high-order discretizations, interpolations, and filters, in multiple dimensions, using a Fourier approach, facilitating the high-order SAMR implementation. We derive a formulation for the necessary interpolation order for given discretization and derivative orders. We also illustrate this order relationship empirically using one and two-dimensional model problems on refined meshes. We study the observed increase in accuracy with increasing interpolation order. We also examine the empirically observed order of convergence, as the effective resolution of the mesh is increased by successively adding levels of refinement, with different orders of discretization, interpolation, or filtering.
Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter
Directory of Open Access Journals (Sweden)
Hairong Chu
2017-01-01
Full Text Available In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF algorithm is proposed. First, the model of SINS transfer alignment is defined based on the “Velocity and Attitude” matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.
Adaptive Command-Filtered Backstepping Control for Linear Induction Motor via Projection Algorithm
Directory of Open Access Journals (Sweden)
Wenxu Yan
2016-01-01
Full Text Available A theoretical framework of the position control for linear induction motors (LIM has been proposed. First, indirect field-oriented control of LIM is described. Then, the backstepping approach is used to ensure the convergence and robustness of the proposed control scheme against the external time-varying disturbances via Lyapunov stability theory. At the same time, in order to solve the differential expansion and the control saturation problems in the traditional backstepping, command filter is designed in the control and compensating signals are presented to eliminate the influence of the errors caused by command filters. Next, unknown total mass of the mover, viscous friction, and load disturbances are estimated by the projection-based adaptive law which bounds the estimated function and simultaneously guarantees the robustness of the proposed controller against the parameter uncertainties. Finally, simulation results are given to illustrate the validity and potential of the designed control scheme.
A DSP-Based Beam Current Monitoring System for Machine Protection Using Adaptive Filtering
Energy Technology Data Exchange (ETDEWEB)
J. Musson; H. Dong; R. Flood; C. Hovater; J. Hereford
2001-06-01
The CEBAF accelerator at Jefferson Lab is currently using an analog beam current monitoring (BCM) system for its machine protection system (MPS), which has a loss accuracy of 2 micro-amps. Recent burn-through simulations predict catastrophic beam line component failures below 1 micro-amp of loss, resulting in a blind spot for the MPS. Revised MPS requirements target an ultimate beam loss accuracy of 250 nA. A new beam current monitoring system has been developed which utilizes modern digital receiver technology and digital signal processing concepts. The receiver employs a direct-digital down converter integrated circuit, mated with a Jefferson Lab digital signal processor VME card. Adaptive filtering is used to take advantage of current-dependent burn-through rates. Benefits of such a system include elimination of DC offsets, generic algorithm development, extensive filter options, and interfaces to UNIX-based control systems.
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.
Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter.
Chu, Hairong; Sun, Tingting; Zhang, Baiqiang; Zhang, Hongwei; Chen, Yang
2017-01-14
In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the "Velocity and Attitude" matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.
Institute of Scientific and Technical Information of China (English)
CHEN Shi-hai; WANG En-yuan
2012-01-01
The electromagnetic radiation (EMR) signal collected by monitoring system during coal or rock dynamic disaster may be interferred easily by electromagnetic noises in mines.The noises have a direct influence on the recognition and analysis of the EMR signal features during the disaster.With the aim of removing these noises,an ensemble empirical mode decomposition (EEMD) adaptive morphological filter was proposed.From the result of the simulation and the experiment,it is shown that the method can restrain the random noise and white Gaussian noise mixed with EMR signal effectively.The filter is highly useful for improving the robustness of the coal or rock dynamic disaster monitoring system.
Energy Technology Data Exchange (ETDEWEB)
Ben Youssef, C.; Dahhou, B.; Roux, G. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Rols, J.L. [Institut National des Sciences Appliquees (INSA), 31 - Toulouse (France)
1995-12-31
Controlling the process of a fixed bed bioreactor imply solving filtering and adaptative control problems. Estimation processes have been developed for unmeasurable parameters. An adaptative non linear control has been built, instead of conventional approaches trying to linearize the system and apply a linear control system. (D.L.) 10 refs.
Automatic balancing of AMB systems using plural notch filter and adaptive synchronous compensation
Xu, Xiangbo; Chen, Shao; Zhang, Yanan
2016-07-01
To achieve automatic balancing in active magnetic bearing (AMB) system, a control method with notch filters and synchronous compensators is widely employed. However, the control precision is significantly affected by the synchronous compensation error, which is caused by parameter errors and variations of the power amplifiers. Furthermore, the computation effort may become intolerable if a 4-degree-of-freedom (dof) AMB system is studied. To solve these problems, an adaptive automatic balancing control method in the AMB system is presented in this study. Firstly, a 4-dof radial AMB system is described and analyzed. To simplify the controller design, the 4-dof dynamic equations are transferred into two plural functions related to translation and rotation, respectively. Next, to achieve automatic balancing of the AMB system, two synchronous equations are formed. Solution of them leads to a control strategy based on notch filters and feedforward controllers with an inverse function of the power amplifier. The feedforward controllers can be simplified as synchronous phases and amplitudes. Then, a plural phase-shift notch filter which can identify the synchronous components in 2-dof motions is formulated, and an adaptive compensation method that can form two closed-loop systems to tune the synchronous amplitude of the feedforward controller and the phase of the plural notch filter is proposed. Finally, the proposed control strategy is verified by both simulations and experiments on a test rig of magnetically suspended control moment gyro. The results indicate that this method can fulfill the automatic balancing of the AMB system with a light computational load.
A Multi-Dimensional Classification Model for Scientific Workflow Characteristics
Energy Technology Data Exchange (ETDEWEB)
Ramakrishnan, Lavanya; Plale, Beth
2010-04-05
Workflows have been used to model repeatable tasks or operations in manufacturing, business process, and software. In recent years, workflows are increasingly used for orchestration of science discovery tasks that use distributed resources and web services environments through resource models such as grid and cloud computing. Workflows have disparate re uirements and constraints that affects how they might be managed in distributed environments. In this paper, we present a multi-dimensional classification model illustrated by workflow examples obtained through a survey of scientists from different domains including bioinformatics and biomedical, weather and ocean modeling, astronomy detailing their data and computational requirements. The survey results and classification model contribute to the high level understandingof scientific workflows.
Multi-dimensional technology-enabled social learning approach
DEFF Research Database (Denmark)
Petreski, Hristijan; Tsekeridou, Sofia; Prasad, Neeli R.
2013-01-01
in learning while socializing within their learning communities. However, their “educational” usage is still limited to facilitation of online learning communities and to collaborative authoring of learning material complementary to existing formal (e-) learning services. If the educational system doesn...... content on the Web, using social networks to keep in touch, express, distribute and publish their experiences, views and ideas. Although, since their birth, most of the social media tools were not intended for educational purposes, educational organizations have started to recognize their added value......’t respond to this systemic and structural changes and/or challenges and retains its status quo than it is jeopardizing its own existence or the existence of the education, as we know it. This paper aims to precede one step further by proposing a multi-dimensional approach for technology-enabled social...
Multi-dimensional technology-enabled social learning approach
DEFF Research Database (Denmark)
Petreski, Hristijan; Tsekeridou, Sofia; Prasad, Neeli R.
2013-01-01
content on the Web, using social networks to keep in touch, express, distribute and publish their experiences, views and ideas. Although, since their birth, most of the social media tools were not intended for educational purposes, educational organizations have started to recognize their added value...... in learning while socializing within their learning communities. However, their “educational” usage is still limited to facilitation of online learning communities and to collaborative authoring of learning material complementary to existing formal (e-) learning services. If the educational system doesn......’t respond to this systemic and structural changes and/or challenges and retains its status quo than it is jeopardizing its own existence or the existence of the education, as we know it. This paper aims to precede one step further by proposing a multi-dimensional approach for technology-enabled social...
Multi-Dimensional Thoughts on English Classroom Teaching Techniques
Institute of Scientific and Technical Information of China (English)
方丽
2013-01-01
English Language teaching involves various English teaching techniques in terms of listening, speaking, reading, writ-ing and the like skills. Traditional teaching ways are mostly discussed from that standpoint. Here, some new English classroom teaching techniques are introduced and evaluated in a dimensional perspective, which relate to such elements as the students, the teacher, classroom organization and management, and instructional strategies, etc. It makes English classroom teaching more effec-tive, thus improve English classroom teaching results. So, it’s advisable for English teachers to reconsider and reevaluate their teaching strategy and result in language classroom from a new multi-dimensional angle in order to improve English teaching effi-ciency.
Benchmarking the Multi-dimensional Stellar Implicit Code MUSIC
Goffrey, T; Viallet, M; Baraffe, I; Popov, M V; Walder, R; Folini, D; Geroux, C; Constantino, T
2016-01-01
We present the results of a numerical benchmark study for the MUlti-dimensional Stellar Implicit Code (MUSIC) based on widely applicable two- and three-dimensional compressible hydrodynamics problems relevant to stellar interiors. MUSIC is an implicit large eddy simulation code that uses implicit time integration, implemented as a Jacobian-free Newton Krylov method. A physics based preconditioning technique which can be adjusted to target varying physics is used to improve the performance of the solver. The problems used for this benchmark study include the Rayleigh-Taylor and Kelvin-Helmholtz instabilities, and the decay of the Taylor-Green vortex. Additionally we show a test of hydrostatic equilibrium, in a stellar environment which is dominated by radiative effects. In this setting the flexibility of the preconditioning technique is demonstrated. This work aims to bridge the gap between the hydrodynamic test problems typically used during development of numerical methods and the complex flows of stellar in...
A Multi-Dimensional approach towards Intrusion Detection System
Thakur, Manoj Rameshchandra
2012-01-01
In this paper, we suggest a multi-dimensional approach towards intrusion detection. Network and system usage parameters like source and destination IP addresses; source and destination ports; incoming and outgoing network traffic data rate and number of CPU cycles per request are divided into multiple dimensions. Rather than analyzing raw bytes of data corresponding to the values of the network parameters, a mature function is inferred during the training phase for each dimension. This mature function takes a dimension value as an input and returns a value that represents the level of abnormality in the system usage with respect to that dimension. This mature function is referred to as Individual Anomaly Indicator. Individual Anomaly Indicators recorded for each of the dimensions are then used to generate a Global Anomaly Indicator, a function with n variables (n is the number of dimensions) that provides the Global Anomaly Factor, an indicator of anomaly in the system usage based on all the dimensions consid...
The Multi-Dimensional Character of Core-Collapse Supernovae
Hix, W R; Bruenn, S W; Mezzacappa, A; Messer, O E B; Endeve, E; Blondin, J M; Harris, J A; Marronett, P; Yakunin, K N
2016-01-01
Core-collapse supernovae, the culmination of massive stellar evolution, are spectacular astronomical events and the principle actors in the story of our elemental origins. Our understanding of these events, while still incomplete, centers around a neutrino-driven central engine that is highly hydrodynamically unstable. Increasingly sophisticated simulations reveal a shock that stalls for hundreds of milliseconds before reviving. Though brought back to life by neutrino heating, the development of the supernova explosion is inextricably linked to multi-dimensional fluid flows. In this paper, the outcomes of three-dimensional simulations that include sophisticated nuclear physics and spectral neutrino transport are juxtaposed to learn about the nature of the three dimensional fluid flow that shapes the explosion. Comparison is also made between the results of simulations in spherical symmetry from several groups, to give ourselves confidence in the understanding derived from this juxtaposition.
Acceleration of multi-dimensional propagator measurements with compressed sensing.
Paulsen, Jeffrey L; Cho, HyungJoon; Cho, Gyunggoo; Song, Yi-Qiao
2011-12-01
NMR can probe the microstructures of anisotropic materials such as liquid crystals, stretched polymers and biological tissues through measurement of the diffusion propagator, where internal structures are indicated by restricted diffusion. Multi-dimensional measurements can probe the microscopic anisotropy, but full sampling can then quickly become prohibitively time consuming. However, for incompletely sampled data, compressed sensing is an effective reconstruction technique to enable accelerated acquisition. We demonstrate that with a compressed sensing scheme, one can greatly reduce the sampling and the experimental time with minimal effect on the reconstruction of the diffusion propagator with an example of anisotropic diffusion. We compare full sampling down to 64× sub-sampling for the 2D propagator measurement and reduce the acquisition time for the 3D experiment by a factor of 32 from ∼80 days to ∼2.5 days. Copyright Â© 2011 Elsevier Inc. All rights reserved.
Sequential optimizing strategy in multi-dimensional bounded forecasting games
Kumon, Masayuki; Takeuchi, Kei
2009-01-01
We propose a sequential optimizing betting strategy in the multi-dimensional bounded forecasting game in the framework of game-theoretic probability of Shafer and Vovk (2001). By studying the asymptotic behavior of its capital process, we prove a generalization of the strong law of large numbers, where the convergence rate of the sample mean vector depends on the growth rate of the quadratic variation process. The growth rate of the quadratic variation process may be slower than the number of rounds or may even be zero. We also introduce an information criterion for selecting efficient betting items. These results are then applied to multiple asset trading strategies in discrete-time and continuous-time games. In the case of continuous-time game we present a measure of the jaggedness of a vector-valued continuous process. Our results are examined by several numerical examples.
Adaptive Robust Tracking Control of Pressure Trajectory Based on Kalman Filter
Institute of Scientific and Technical Information of China (English)
CAO Jian; ZHU Xiaocong; TAO Guoliang; YAO Bin
2009-01-01
When adaptive robust control(ARC) strategy based on backstepping design is applied in pneumatic servo control, accurate pressure tracking in motion is especially necessary for both force and position trajectories tracking of rodless pneumatic cylinders, and therefore an adaptive robust pressure controller is developed in this paper to improve the tracking accuracy of pressure trajectory in the chamber when the pneumatic cylinder is moving. In the proposed adaptive robust pressure controller, off-line fitting of the orifice area and on-line parameter estimation of the flow coefficient are utilized to have improved model compensation, and meanwhile robust feedback and Kalman filter are used to have strong robustness against uncertain nonlinearities, parameter fluctuations and noise. Research results demonstrate that the adaptive robust pressure controller could not only track various pressure trajectories accurately even when the pneumatic cylinder is moving, but also obtain very smooth control input, which indicates the effectiveness of adaptive model compensation. Especially when a step pressure trajectory is tracked under the condition of the movement of a rodless pneumatic cylinder, maximum tracking error of ARC is 4.46 kPa and average tracking error is 0.99 kPa, and steady-state error of ARC could achieve 0.84 kPa, which is very close to the measurement accuracy of pressure transducer.
CT image artifacts from brachytherapy seed implants: a postprocessing 3D adaptive median filter.
Basran, Parminder S; Robertson, Andrew; Wells, Derek
2011-02-01
To design a postprocessing 3D adaptive median filter that minimizes streak artifacts and improves soft-tissue contrast in postoperative CT images of brachytherapy seed implantations. The filter works by identifying voxels that are likely streaks and estimating more reflective voxel intensity by using voxel intensities in adjacent CT slices and applying a median filter over voxels not identified as seeds. Median values are computed over a 5 x 5 x 5 mm region of interest (ROI) within the CT volume. An acrylic phantom simulating a clinical seed implant arrangement and containing nonradioactive seeds was created. Low contrast subvolumes of tissuelike material were also embedded in the phantom. Pre- and postprocessed image quality metrics were compared using the standard deviation of ROIs between the seeds, the CT numbers of low contrast ROIs embedded within the phantom, the signal to noise ratio (SNR), and the contrast to noise ratio (CNR) of the low contrast ROIs. The method was demonstrated with a clinical postimplant CT dataset. After the filter was applied, the standard deviation of CT values in streak artifact regions was significantly reduced from 76.5 to 7.2 HU. Within the observable low contrast plugs, the mean of all ROI standard deviations was significantly reduced from 60.5 to 3.9 HU, SNR significantly increased from 2.3 to 22.4, and CNR significantly increased from 0.2 to 4.1 (all P mean CT in the low contrast plugs remained within 5 HU of the original values. An efficient postprocessing filter that does not require access to projection data, which can be applied irrespective of CT scan parameters has been developed, provided the slice thickness and spacing is 3 mm or less.
CT image artifacts from brachytherapy seed implants: A postprocessing 3D adaptive median filter
Energy Technology Data Exchange (ETDEWEB)
Basran, Parminder S.; Robertson, Andrew; Wells, Derek [Department of Medical Physics, Vancouver Island Cancer Centre, 2410 Lee Avenue, Victoria, British Columbia V8R 6V5 (Canada) and Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia V8W 3P6 (Canada); Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia V8W 3P6 (Canada); Department of Medical Physics, Vancouver Island Cancer Centre, 2410 Lee Avenue, Victoria, British Columbia V8R 6V5 (Canada) and Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia V8W 3P6 (Canada)
2011-02-15
Purpose: To design a postprocessing 3D adaptive median filter that minimizes streak artifacts and improves soft-tissue contrast in postoperative CT images of brachytherapy seed implantations. Methods: The filter works by identifying voxels that are likely streaks and estimating more reflective voxel intensity by using voxel intensities in adjacent CT slices and applying a median filter over voxels not identified as seeds. Median values are computed over a 5x5x5 mm region of interest (ROI) within the CT volume. An acrylic phantom simulating a clinical seed implant arrangement and containing nonradioactive seeds was created. Low contrast subvolumes of tissuelike material were also embedded in the phantom. Pre- and postprocessed image quality metrics were compared using the standard deviation of ROIs between the seeds, the CT numbers of low contrast ROIs embedded within the phantom, the signal to noise ratio (SNR), and the contrast to noise ratio (CNR) of the low contrast ROIs. The method was demonstrated with a clinical postimplant CT dataset. Results: After the filter was applied, the standard deviation of CT values in streak artifact regions was significantly reduced from 76.5 to 7.2 HU. Within the observable low contrast plugs, the mean of all ROI standard deviations was significantly reduced from 60.5 to 3.9 HU, SNR significantly increased from 2.3 to 22.4, and CNR significantly increased from 0.2 to 4.1 (all P<0.01). The mean CT in the low contrast plugs remained within 5 HU of the original values. Conclusion: An efficient postprocessing filter that does not require access to projection data, which can be applied irrespective of CT scan parameters has been developed, provided the slice thickness and spacing is 3 mm or less.
DEFF Research Database (Denmark)
Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar
2016-01-01
Satellite tracking is a challenging task for marine applications due to the disturbance from ocean waves. An Attitude Heading and Reference System (AHRS) for measuring ship attitude, based on Microelectromechanical Systems (MEMS) sensors, is a key part for satellite tracking. In this paper......, an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...
Conventional Synchronous Reference Frame Phase-Locked Loop Is An Adaptive Complex Filter
DEFF Research Database (Denmark)
Golestan, Saeed; Guerrero, Josep M.
2015-01-01
Despite the wide acceptance and use of the conventional synchronous reference frame phase-locked loop (SRFPLL) no transfer function describing its actual input-output relationship has been developed so far. Arguably, the absence of such transfer function has hampered the application of SRF......-PLL as a filter or controller inside the closed-loop control systems. In this letter, the transfer function describing the actual inputoutput relationship of the conventional SRF-PLL is presented. Using this transfer function, it is shown that the conventional SRF-PLL is a first-order adaptive complex bandpass...
Liu, Yan; Pecht, Michael G
2006-01-01
The effectiveness of electrocardiogram (ECG) monitors can be significantly impaired by motion artifacts which can cause misdiagnoses, lead to inappropriate treatment decisions, and trigger false alarms. Skin stretch associated with patient motion is a significant source of motion artifacts in current ECG monitoring. In this study, motion artifacts are adaptively filtered by using skin strain as the reference variable. Skin strain is measured non-invasively using a light emitting diode (LED) and an optical sensor incorporated in an ECG electrode. The results demonstrate that this device and method can significantly reduce skin strain induced ECG artifacts.
SOGI-FLL Based Adaptive Filter for DSTATCOM Under Variable Supply Frequency
Puranik, Vishal; Arya, Sabha Raj
2016-12-01
This paper presents an adaptive filter based on second order generalized integrator-frequency locked loop (SOGI-FLL) for distribution static compensator (DSTATCOM) operating under variable supply frequency with nonlinear load. It is observed that under variable supply frequency, the FLL provides an excellent frequency tracking performance. Necessary compensation can be provided by DSTATCOM at any frequency with the help of SOGI-FLL. The MATLAB simulink model of DSTATCOM is developed with SOGI-FLL based control algorithm and rectifier based nonlinear load. This three wire system is simulated in power factor correction and zero voltage regulation mode under variable supply frequency.
SOGI-FLL Based Adaptive Filter for DSTATCOM Under Variable Supply Frequency
Puranik, Vishal; Arya, Sabha Raj
2017-08-01
This paper presents an adaptive filter based on second order generalized integrator-frequency locked loop (SOGI-FLL) for distribution static compensator (DSTATCOM) operating under variable supply frequency with nonlinear load. It is observed that under variable supply frequency, the FLL provides an excellent frequency tracking performance. Necessary compensation can be provided by DSTATCOM at any frequency with the help of SOGI-FLL. The MATLAB simulink model of DSTATCOM is developed with SOGI-FLL based control algorithm and rectifier based nonlinear load. This three wire system is simulated in power factor correction and zero voltage regulation mode under variable supply frequency.
Single Channel Fetal ECG Detection Using LMS and RLS Adaptive Filters
Institute of Scientific and Technical Information of China (English)
Alaa Aldoori; Ali Buniya; ZHENG Zheng
2015-01-01
ECG is an important tool for the primary diagnosis of heart diseases, which shows the electrophysiology of the heart. In our method, a single maternal abdominal ECG signal is taken as an input signal and the maternal P-QRS-T complexes of original signal is averaged and repeated and taken as a reference signal. LMS and RLS adaptive filters algorithms are applied. The results showed that the fetal ECGs have been successfully detected. The accuracy of Daisy database was up to 84%of LMS and 88%of RLS while PhysioNet was up to 98%and 96%for LMS and RLS respectively.
Scale-Adaptive filters for the detection/separation of compact sources
Herranz, D; Barreiro, R B; Martínez-González, E
2002-01-01
This paper presents scale-adaptive filters that optimize the detection/separation of compact sources on a background. We assume that the sources have a multiquadric profile, i. e. $\\tau (x) = {[1 + {(x/r_c)}^2]}^{-\\lambda}, \\lambda \\geq {1/2}, x\\equiv |\\vec{x}|$, and a background modeled by a homogeneous and isotropic random field, characterized by a power spectrum $P(q)\\propto q^{-\\gamma}, \\gamma \\geq 0, q\\equiv |\\vec{q}|$. We make an n-dimensional treatment but consider two interesting astrophysical applications related to clusters of galaxies (Sunyaev-Zel'dovich effect and X-ray emission).
Energy Technology Data Exchange (ETDEWEB)
Kaethner, Christian, E-mail: kaethner@imt.uni-luebeck.de; Ahlborg, Mandy; Buzug, Thorsten M., E-mail: buzug@imt.uni-luebeck.de [Institute of Medical Engineering, Universität zu Lübeck, 23562 Lübeck (Germany); Knopp, Tobias [Thorlabs GmbH, 23562 Lübeck (Germany); Sattel, Timo F. [Philips Medical Systems DMC GmbH, 22335 Hamburg (Germany)
2014-01-28
Magnetic Particle Imaging (MPI) is a tomographic imaging modality capable to visualize tracers using magnetic fields. A high magnetic gradient strength is mandatory, to achieve a reasonable image quality. Therefore, a power optimization of the coil configuration is essential. In order to realize a multi-dimensional efficient gradient field generator, the following improvements compared to conventionally used Maxwell coil configurations are proposed: (i) curved rectangular coils, (ii) interleaved coils, and (iii) multi-layered coils. Combining these adaptions results in total power reduction of three orders of magnitude, which is an essential step for the feasibility of building full-body human MPI scanners.
Weld Defect Extraction Based on Adaptive Morphology Filtering and Edge Detection by Wavelet Analysis
Institute of Scientific and Technical Information of China (English)
WANGDonghua; ZHOUYuanhua; GANGTie
2003-01-01
One of the most key steps in X-ray au-tomatic inspection and intelligent recognition systems is how to extract defects and detect their edges effectively.In this paper, a novel method of defect extraction based on the adaptive morphology filtering (DEAMF) is pro-posed, whose structuring elements can be changed with the sizes of defects adaptively. By this method, defects in X-ray weld inspection images are extracted with well-kept shapes and high speeds. Then according to the theory of edge detection based on wavelet transform modulus max-ima, a locally supported wavelet with good antisymmetry is developed to extract edges of defects and the results are satisfying.
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.
Sadjadi, Firooz A; Mahalanobis, Abhijit
2006-05-01
We report the development of a technique for adaptive selection of polarization ellipse tilt and ellipticity angles such that the target separation from clutter is maximized. From the radar scattering matrix [S] and its complex components, in phase and quadrature phase, the elements of the Mueller matrix are obtained. Then, by means of polarization synthesis, the radar cross section of the radar scatters are obtained at different transmitting and receiving polarization states. By designing a maximum average correlation height filter, we derive a target versus clutter distance measure as a function of four transmit and receive polarization state angles. The results of applying this method on real synthetic aperture radar imagery indicate a set of four transmit and receive angles that lead to maximum target versus clutter discrimination. These optimum angles are different for different targets. Hence, by adaptive control of the state of polarization of polarimetric radar, one can noticeably improve the discrimination of targets from clutter.
Adaptive filtering methods for identifying cross-frequency couplings in human EEG.
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Jérôme Van Zaen
Full Text Available Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.
Adaptive filtering methods for identifying cross-frequency couplings in human EEG.
Van Zaen, Jérôme; Murray, Micah M; Meuli, Reto A; Vesin, Jean-Marc
2013-01-01
Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.
Erp, J.B.F. van
2006-01-01
Designers are very well aware of the multi-dimensionality of visual stimuli and extensively use dimensions such as color, form and contrast to encode information. An analogous multi-dimensionality applies to haptic and tactile stimuli. Based on the fact that the human visual system only has two rece
On Multi-Dimensional Vocabulary Teaching Mode for College English Teaching
Zhou, Li-na
2010-01-01
This paper analyses the major approaches in EFL (English as a Foreign Language) vocabulary teaching from historical perspective and puts forward multi-dimensional vocabulary teaching mode for college English. The author stresses that multi-dimensional approaches of communicative vocabulary teaching, lexical phrase teaching method, the grammar…
Charisis, Vasileios S; Hadjileontiadis, Leontios J
2016-10-21
A new feature extraction technique for the detection of lesions created from mucosal inflammations in Crohn's disease, based on wireless capsule endoscopy (WCE) images processing is presented here. More specifically, a novel filtering process, namely Hybrid Adaptive Filtering (HAF), was developed for efficient extraction of lesion-related structural/textural characteristics from WCE images, by employing Genetic Algorithms to the Curvelet-based representation of images. Additionally, Differential Lacunarity (DLac) analysis was applied for feature extraction from the HAF-filtered images. The resulted scheme, namely HAF-DLac, incorporates support vector machines for robust lesion recognition performance. For the training and testing of HAF-DLac, an 800-image database was used, acquired from 13 patients who undertook WCE examinations, where the abnormal cases were grouped into mild and severe, according to the severity of the depicted lesion, for a more extensive evaluation of the performance. Experimental results, along with comparison with other related efforts, have shown that the HAF-DLac approach evidently outperforms them in the field of WCE image analysis for automated lesion detection, providing higher classification results, up to 93.8% (accuracy), 95.2% (sensitivity), 92.4% (specificity) and 92.6% (precision). The promising performance of HAF-DLac paves the way for a complete computer-aided diagnosis system that could support physicians' clinical practice.
Quaternion-Based Kalman Filter for AHRS Using an Adaptive-Step Gradient Descent Algorithm
Directory of Open Access Journals (Sweden)
Li Wang
2015-09-01
Full Text Available This paper presents a quaternion-based Kalman filter for real-time estimation of the orientation of a quadrotor. Quaternions are used to represent rotation relationship between navigation frame and body frame. Processing of a 3-axis accelerometer using Adaptive-Step Gradient Descent (ASGD produces a computed quaternion input to the Kalman filter. The step-size in GD is set in direct proportion to the physical orientation rate. Kalman filter combines 3-axis gyroscope and computed quaternion to determine pitch and roll angles. This combination overcomes linearization error of the measurement equations and reduces the calculation cost. 3-axis magnetometer is separated from ASGD to independently calculate yaw angle for Attitude Heading Reference System (AHRS. This AHRS algorithm is able to remove the magnetic distortion impact. Experiments are carried out in the small-size flight controller and the real world flying test shows the proposed AHRS algorithm is adequate for the real-time estimation of the orientation of a quadrotor.
Parametric adaptive filtering and data validation in the bar GW detector AURIGA
Ortolan, A; Cerdonio, M; Prodi, G A; Vedovato, G; Vitale, S
2002-01-01
We report on our experience gained in the signal processing of the resonant GW detector AURIGA. Signal amplitude and arrival time are estimated by means of a matched-adaptive Wiener filter. The detector noise, entering in the filter set-up, is modelled as a parametric ARMA process; to account for slow non-stationarity of the noise, the ARMA parameters are estimated on an hourly basis. A requirement of the set-up of an unbiased Wiener filter is the separation of time spans with 'almost Gaussian' noise from non-Gaussian and/or strongly non-stationary time spans. The separation algorithm consists basically of a variance estimate with the Chauvenet convergence method and a threshold on the Curtosis index. The subsequent validation of data is strictly connected with the separation procedure: in fact, by injecting a large number of artificial GW signals into the 'almost Gaussian' part of the AURIGA data stream, we have demonstrated that the effective probability distributions of the signal-to-noise ratio chi sup 2 ...
Steeb, P.; Krause, S.; Linke, P.; Hensen, C.; Dale, A. W.; Nuzzo, M.; Treude, T.
2014-11-01
Large amounts of methane are delivered by fluids through the erosive forearc of the convergent margin offshore Costa Rica and lead to the formation of cold seeps at the sediment surface. Besides mud extrusion, numerous cold seeps are created by landslides induced by seamount subduction or fluid migration along major faults. Most of the dissolved methane reaching the seafloor at cold seeps is oxidized within the benthic microbial methane filter by anaerobic oxidation of methane (AOM). Measurements of AOM and sulfate reduction as well as numerical modeling of porewater profiles revealed a highly active and efficient benthic methane filter at Quepos Slide site; a landslide on the continental slope between the Nicoya and Osa Peninsula. Integrated areal rates of AOM ranged from 12.9 ± 6.0 to 45.2 ± 11.5 mmol m-2 d-1, with only 1 to 2.5% of the upward methane flux being released into the water column. Additionally, two parallel sediment cores from Quepos Slide were used for in vitro experiments in a recently developed Sediment-F low-Through (SLOT) system to simulate an increased fluid and methane flux from the bottom of the sediment core. The benthic methane filter revealed a high adaptability whereby the methane oxidation efficiency responded to the increased fluid flow within 150-170 days. To our knowledge, this study provides the first estimation of the natural biogeochemical response of seep sediments to changes in fluid flow.
Color Image Denoising Using Stationary Wavelet Transform and Adaptive Wiener Filter
Directory of Open Access Journals (Sweden)
Iman M.G. Alwan
2012-01-01
Full Text Available The denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing. Much work has been done in the field of wavelet thresholding but most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds. This paper describes a new method for the suppression of noise in image by fusing the stationary wavelet denoising technique with adaptive wiener filter. The wiener filter is applied to the reconstructed image for the approximation coefficients only, while the thresholding technique is applied to the details coefficients of the transform, then get the final denoised image is obtained by combining the two results. The proposed method was applied by using MATLAB R2010a with color images contaminated by white Gaussian noise. Compared with stationary wavelet and wiener filter algorithms, the experimental results show that the proposed method provides better subjective and objective quality, and obtain up to 3.5 dB PSNR improvement.
Real-time scale-adaptive correlation filters tracker with depth information to handle occlusion
Pi, Jiatian; Gu, Yuzhang; Hu, Keli; Cheng, Xiaoliu; Zhan, Yunlong; Wang, Yingguan
2016-07-01
In visual object tracking, occlusions significantly undermine the performance of tracking algorithms. RGB-D cameras, such as Microsoft Kinect or the related PrimeSense camera, are widely available to consumers. Great attention has been focused on exploiting depth information for object tracking in recent years. We propose an algorithm that improves the existing correlation filter-based tracker for scale-adaptive tracking. Moreover, we utilize depth information provided by the Kinect camera to handle various types of occlusions. First, the optimal location of the target is obtained by the conventional kernelized correlation filter tracker. Then, we make use of the discriminative correlation filter for scale estimation as an independent part. At last, to further improve the tracking performance under occlusions, we present a simple yet effective occlusion handling mechanism to detect occlusion and recovery. In this mechanism, cluster analysis and object segmentation by K-means method have been applied to depth data. Numerous experiments on Princeton RGB-D tracking dataset demonstrate that the proposed algorithm outperforms several state-of-the-art trackers by successfully dealing with occlusions.
Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing
Directory of Open Access Journals (Sweden)
Jaakko T. Astola
2005-05-01
Full Text Available Textural features are one of the most important types of useful information contained in images. In practice, these features are commonly masked by noise. Relatively little attention has been paid to texture preserving properties of noise attenuation methods. This stimulates solving the following tasks: (1 to analyze the texture preservation properties of various filters; and (2 to design image processing methods capable to preserve texture features well and to effectively reduce noise. This paper deals with examining texture feature preserving properties of different filters. The study is performed for a set of texture samples and different noise variances. The locally adaptive three-state schemes are proposed for which texture is considered as a particular class. For Ã¢Â€ÂœdetectionÃ¢Â€Â of texture regions, several classifiers are proposed and analyzed. As shown, an appropriate trade-off of the designed filter properties is provided. This is demonstrated quantitatively for artificial test images and is confirmed visually for real-life images.
Point source detection and extraction from simulated Planck TOD using optimal adaptive filters
Herranz, D; Sanz, J L; Martínez-González, E
2002-01-01
Wavelet-related techniques have proven useful in the processing and analysis of one and two dimensional data sets (spectra in the former case, images in the latter). In this work we apply adaptive filters, introduced in a previous work (Sanz et al. 2001), to optimize the detection and extraction of point sources from a one-dimensional array of time-ordered data such as the one that will be produced by the future 30 GHz LFI28 channel of the ESA Planck mission. At a $4\\sigma$ detection level 224 sources over a flux of 0.88 Jy are detected with a mean relative error (in absolute value) of 21% and a systematic bias of -7.7%. The position of the sources in the sky is determined with errors inferior to the size of the pixel. The catalogue of detected sources is complete at fluxes $\\geq$ 4.3 Jy. The number of spurious detections is less than a 10% of the true detections. We compared the results with the ones obtained by filtering with a Gaussian filter and a Mexican Hat Wavelet of width equal to the scale of the sou...
A measurement-driven adaptive probability hypothesis density filter for multitarget tracking
Institute of Scientific and Technical Information of China (English)
Si Weijian; Wang Liwei; Qu Zhiyu
2015-01-01
This paper studies the dynamic estimation problem for multitarget tracking. A novel gat-ing strategy that is based on the measurement likelihood of the target state space is proposed to improve the overall effectiveness of the probability hypothesis density (PHD) filter. Firstly, a measurement-driven mechanism based on this gating technique is designed to classify the measure-ments. In this mechanism, only the measurements for the existing targets are considered in the update step of the existing targets while the measurements of newborn targets are used for exploring newborn targets. Secondly, the gating strategy enables the development of a heuristic state estima-tion algorithm when sequential Monte Carlo (SMC) implementation of the PHD filter is investi-gated, where the measurements are used to drive the particle clustering within the space gate. The resulting PHD filter can achieve a more robust and accurate estimation of the existing targets by reducing the interference from clutter. Moreover, the target birth intensity can be adaptive to detect newborn targets, which is in accordance with the birth measurements. Simulation results demonstrate the computational efficiency and tracking performance of the proposed algorithm. ? 2015 The Authors. Production and hosting by Elsevier Ltd. on behalf of CSAA&BUAA. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter
Song, Hajoon
2010-07-01
A new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new members chosen adaptively to add missing information that prevents the EnKF from fully fitting the data to the ensemble. The vectors to be added are obtained by back projecting the residuals of the observation misfits from the EnKF analysis step onto the state space. The back projection is done using an optimal interpolation (OI) scheme based on an estimated covariance of the subspace missing from the ensemble. In the experiments reported here, the OI uses a preselected stationary background covariance matrix, as in the hybrid EnKF–three-dimensional variational data assimilation (3DVAR) approach, but the resulting correction is included as a new ensemble member instead of being added to all existing ensemble members. The adaptive approach is tested with the Lorenz-96 model. The hybrid EnKF–3DVAR is used as a benchmark to evaluate the performance of the adaptive approach. Assimilation experiments suggest that the new adaptive scheme significantly improves the EnKF behavior when it suffers from small size ensembles and neglected model errors. It was further found to be competitive with the hybrid EnKF–3DVAR approach, depending on ensemble size and data coverage.
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.
Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang
2015-10-23
An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.
Dong, Gangqi; Zhu, Zheng H.
2016-05-01
This paper presents a real-time, vision-based algorithm for the pose and motion estimation of non-cooperative targets and its application in visual servo robotic manipulator to perform autonomous capture. A hybrid approach of adaptive extended Kalman filter and photogrammetry is developed for the real-time pose and motion estimation of non-cooperative targets. Based on the pose and motion estimates, the desired pose and trajectory of end-effector is defined and the corresponding desired joint angles of the robotic manipulator are derived by inverse kinematics. A close-loop visual servo control scheme is then developed for the robotic manipulator to track, approach and capture the target. Validating experiments are designed and performed on a custom-built six degrees of freedom robotic manipulator with an eye-in-hand configuration. The experimental results demonstrate the feasibility, effectiveness and robustness of the proposed adaptive extended Kalman filter enabled pose and motion estimation and visual servo strategy.
Dynamic Adaptive Median Filter (DAMF for Removal of High Density Impulse Noise
Directory of Open Access Journals (Sweden)
Punyaban Patel
2012-10-01
Full Text Available This paper proposes a novel adaptive filtering scheme to remove impulse noise from images. The scheme replaces the corrupted test pixel with the median value of non-corrupted neighboring pixels selected from a window dynamically. If the number of non-corrupted pixels in the selected window is not sufficient, a window of next higher size is chosen. Thus window size is automatically adapted based on the density of noise in the image as well as the density of corruption local to a window. As a result window size may vary pixel to pixel while filtering. The scheme is simple to implement and do not require multiple iterations. The efficacy of the proposed scheme is evaluated with respect to subjective as well as objective parameters on standard images on various noise densities. Comparative analysis reveals that the proposed scheme has improved performance over other schemes, preferably in high density impulse noise cases. Further, the computational overhead is also less as compared its competent scheme.
A Multidelay Double-Talk Detector Combined with the MDF Adaptive Filter
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Gänsler Tomas
2003-01-01
Full Text Available The multidelay block frequency-domain (MDF adaptive filter is an excellent candidate for both acoustic and network echo cancellation. There is a need for a very good double-talk detector (DTD to be combined efficiently with the MDF algorithm. Recently, a DTD based on a normalized cross-correlation vector was proposed and it was shown that this DTD performs much better than the Geigel algorithm and other DTDs based on the cross-correlation coefficient. In this paper, we show how to extend the definition of a normalized cross-correlation vector in the frequency domain for the general case where the block size of the Fourier transform is smaller than the length of the adaptive filter. The resulting DTD has an MDF structure, which makes it easy to implement, and a good fit with an echo canceler based on the MDF algorithm. We also analyze resource requirements (computational complexity and memory requirement and compare the MDF algorithm with the normalized least mean square algorithm (NLMS from this point of view.
Mahmood, Muhammad Tariq; Chu, Yeon-Ho; Choi, Young-Kyu
2016-06-01
This paper proposes a Rician noise reduction method for magnetic resonance (MR) images. The proposed method is based on adaptive non-local mean and guided image filtering techniques. In the first phase, a guidance image is obtained from the noisy image through an adaptive non-local mean filter. Sobel operators are applied to compute the strength of edges which is further used to control the spread of the kernel in non-local mean filtering. In the second phase, the noisy and the guidance images are provided to the guided image filter as input to restore the noise-free image. The improved performance of the proposed method is investigated using the simulated and real data sets of MR images. Its performance is also compared with the previously proposed state-of-the art methods. Comparative analysis demonstrates the superiority of the proposed scheme over the existing approaches.
Liu, Zong-Xiang; Wu, De-Hui; Xie, Wei-Xin; Li, Liang-Qun
2017-02-15
Tracking the target that maneuvers at a variable turn rate is a challenging problem. The traditional solution for this problem is the use of the switching multiple models technique, which includes several dynamic models with different turn rates for matching the motion mode of the target at each point in time. However, the actual motion mode of a target at any time may be different from all of the dynamic models, because these models are usually limited. To address this problem, we establish a formula for estimating the turn rate of a maneuvering target. By applying the estimation method of the turn rate to the multi-target Bayes (MB) filter, we develop a MB filter with an adaptive estimation of the turn rate, in order to track multiple maneuvering targets. Simulation results indicate that the MB filter with an adaptive estimation of the turn rate, is better than the existing filter at tracking the target that maneuvers at a variable turn rate.
Directory of Open Access Journals (Sweden)
Wang Wei
2016-01-01
Full Text Available The related theory and algorithm of adaptive inverse control were presented through the research which pointed out the adaptive inverse control strategy could effectively eliminate the noise influence on the system control. Proposed using a frequency domain filter-X LMS adaptive inverse control algorithm, and the control algorithm was applied to the two-exciter hydraulic vibration test system of random shock vibration control process and summarized the process of the adaptive inverse control strategies in the realization of the random shock vibration test. The self-closed-loop and field test show that using the frequency-domain filter-X LMS adaptive inverse control algorithm can realize high precision control of random shock vibration test.
Block-Sparsity-Induced Adaptive Filter for Multi-Clustering System Identification
Jiang, Shuyang; Gu, Yuantao
2015-10-01
In order to improve the performance of least mean square (LMS)-based adaptive filtering for identifying block-sparse systems, a new adaptive algorithm called block-sparse LMS (BS-LMS) is proposed in this paper. The basis of the proposed algorithm is to insert a penalty of block-sparsity, which is a mixed \\$l_{2, 0}\\$ norm of adaptive tap-weights with equal group partition sizes, into the cost function of traditional LMS algorithm. To describe a block-sparse system response, we first propose a Markov-Gaussian model, which can generate a kind of system responses of arbitrary average sparsity and arbitrary average block length using given parameters. Then we present theoretical expressions of the steady-state misadjustment and transient convergence behavior of BS-LMS with an appropriate group partition size for white Gaussian input data. Based on the above results, we theoretically demonstrate that BS-LMS has much better convergence behavior than \\$l_0\\$-LMS with the same small level of misadjustment. Finally, numerical experiments verify that all of the theoretical analysis agrees well with simulation results in a large range of parameters.
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.
Parallel acquisition of multi-dimensional spectra in protein NMR
Energy Technology Data Exchange (ETDEWEB)
Kupce, Eriks [Agilent Technologies (United Kingdom); Kay, Lewis E., E-mail: kay@pound.med.utoronto.ca [University of Toronto, Departments of Molecular Genetics, Biochemistry and Chemistry (Canada)
2012-09-15
We introduce the use of multiple receivers applied in parallel for simultaneously recording multi-dimensional data sets of proteins in a single experiment. The utility of the approach is established through the introduction of the 2D {sup 15}N,{sup 1}H{sup N} vertical bar vertical bar {sup 13}CO HSQC experiment in which a pair of two-dimensional {sup 15}N,{sup 1}H{sup N} and {sup 15}N,{sup 13}CO spectra are recorded. The methodology is further extended to higher dimensionality via the 3D {sup 1}H{sup N} vertical bar vertical bar {sup 13}CO HNCA in which a pair of data sets recording {sup 13}C{sup {alpha}},{sup 15}N,{sup 1}H{sup N} and {sup 13}C{sup {alpha}},{sup 15}N,{sup 13}CO chemical shifts are acquired. With the anticipated increases in probe sensitivity it is expected that multiple receiver experiments will become an important approach for efficient recording of NMR data.
Multi-dimensional dynamics of human electromagnetic brain activity
Directory of Open Access Journals (Sweden)
Tetsuo eKida
2016-01-01
Full Text Available Magnetoencephalography (MEG and electroencephalography (EEG are invaluable neuroscientific tools for unveiling human neural dynamics in three dimensions (space, time, and frequency, which are associated with a wide variety of perceptions, cognition, and actions. MEG/EEG also provides different categories of neuronal indices including activity magnitude, connectivity, and network properties along the three dimensions. In the last 20 years, interest has increased in inter-regional connectivity and complex network properties assessed by various sophisticated scientific analyses. We herein review the definition, computation, short history, and pros and cons of connectivity and complex network (graph-theory analyses applied to MEG/EEG signals. We briefly describe recent developments in source reconstruction algorithms essential for source-space connectivity and network analyses. Furthermore, we discuss a relatively novel approach used in MEG/EEG studies to examine the complex dynamics represented by human brain activity. The correct and effective use of these neuronal metrics provides a new insight into the multi-dimensional dynamics of the neural representations of various functions in the complex human brain.
Multi-Dimensional Dynamics of Human Electromagnetic Brain Activity.
Kida, Tetsuo; Tanaka, Emi; Kakigi, Ryusuke
2015-01-01
Magnetoencephalography (MEG) and electroencephalography (EEG) are invaluable neuroscientific tools for unveiling human neural dynamics in three dimensions (space, time, and frequency), which are associated with a wide variety of perceptions, cognition, and actions. MEG/EEG also provides different categories of neuronal indices including activity magnitude, connectivity, and network properties along the three dimensions. In the last 20 years, interest has increased in inter-regional connectivity and complex network properties assessed by various sophisticated scientific analyses. We herein review the definition, computation, short history, and pros and cons of connectivity and complex network (graph-theory) analyses applied to MEG/EEG signals. We briefly describe recent developments in source reconstruction algorithms essential for source-space connectivity and network analyses. Furthermore, we discuss a relatively novel approach used in MEG/EEG studies to examine the complex dynamics represented by human brain activity. The correct and effective use of these neuronal metrics provides a new insight into the multi-dimensional dynamics of the neural representations of various functions in the complex human brain.
QED multi-dimensional vacuum polarization finite-difference solver
Carneiro, Pedro; Grismayer, Thomas; Silva, Luís; Fonseca, Ricardo
2015-11-01
The Extreme Light Infrastructure (ELI) is expected to deliver peak intensities of 1023 - 1024 W/cm2 allowing to probe nonlinear Quantum Electrodynamics (QED) phenomena in an unprecedented regime. Within the framework of QED, the second order process of photon-photon scattering leads to a set of extended Maxwell's equations [W. Heisenberg and H. Euler, Z. Physik 98, 714] effectively creating nonlinear polarization and magnetization terms that account for the nonlinear response of the vacuum. To model this in a self-consistent way, we present a multi dimensional generalized Maxwell equation finite difference solver with significantly enhanced dispersive properties, which was implemented in the OSIRIS particle-in-cell code [R.A. Fonseca et al. LNCS 2331, pp. 342-351, 2002]. We present a detailed numerical analysis of this electromagnetic solver. As an illustration of the properties of the solver, we explore several examples in extreme conditions. We confirm the theoretical prediction of vacuum birefringence of a pulse propagating in the presence of an intense static background field [arXiv:1301.4918 [quant-ph
Multi-dimensional assessment of soccer coaching course effectiveness.
Hammond, J; Perry, J
The purpose of this study was to determine the relationship between the aims of course providers and events during the delivery of two soccer coaching accreditation courses. A secondary purpose was to evaluate performance-analysis methods for assessing the course instructor's performance. A case analysis approach was developed to evaluate the courses and the data-gathering process. This research approach was chosen to amalgamate the sources of evidence, providing a multi-dimensional view of course delivery. Data collection methods included simple hand notation and computer logging of events, together with video analysis. The hand notation and video analysis were employed for the first course with the hand notation being replaced with computer event logging for the second course. Questionnaires, focusing on course quality, were administered to participants. Interviews and document analysis provided the researchers with the instructors' main aims and priorities for course delivery. Results of the video analysis suggest a difference between these aims and the events of the courses. Analysis of the questionnaires indicated favourable perceptions of course content and delivery. This evidence is discussed in relation to intent and practice in coach education and the efficiency of employing performance-analysis techniques in logging instructional events.
MD-SeeGH: a platform for integrative analysis of multi-dimensional genomic data
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Ng Raymond T
2008-05-01
Full Text Available Abstract Background Recent advances in global genomic profiling methodologies have enabled multi-dimensional characterization of biological systems. Complete analysis of these genomic profiles require an in depth look at parallel profiles of segmental DNA copy number status, DNA methylation state, single nucleotide polymorphisms, as well as gene expression profiles. Due to the differences in data types it is difficult to conduct parallel analysis of multiple datasets from diverse platforms. Results To address this issue, we have developed an integrative genomic analysis platform MD-SeeGH, a software tool that allows users to rapidly and directly analyze genomic datasets spanning multiple genomic experiments. With MD-SeeGH, users have the flexibility to easily update datasets in accordance with new genomic builds, make a quality assessment of data using the filtering features, and identify genetic alterations within single or across multiple experiments. Multiple sample analysis in MD-SeeGH allows users to compare profiles from many experiments alongside tracks containing detailed localized gene information, microRNA, CpG islands, and copy number variations. Conclusion MD-SeeGH is a new platform for the integrative analysis of diverse microarray data, facilitating multiple profile analyses and group comparisons.
DEFF Research Database (Denmark)
Golestan, Saeed; Ebrahimzadeh, Esmaeil; Guerrero, Josep M.
2017-01-01
Without any doubt, phase-locked loops (PLLs) are the most popular and widely used technique for the synchronization purposes in the power and energy areas. They are also popular for the selective extraction of fundamental and harmonic/disturbance components of the grid voltage and current. Like...... most of the control algorithms, designing PLLs involves a tradeoff between the accuracy and dynamic response, and improving this tradeoff is what recent research efforts have focused on. These efforts are often based on designing advanced filters and using them as a preprocessing tool before the PLL...... input. A filtering technique that has received a little attention for this purpose is the least-error squares (LES)-based filter. In this paper, an adaptive LES filter-based PLL, briefly called the LES-PLL, for the synchronization and signal decomposition purposes is presented. The proposed LES filter...
Hu, Han; Ding, Yulin; Zhu, Qing; Wu, Bo; Lin, Hui; Du, Zhiqiang; Zhang, Yeting; Zhang, Yunsheng
2014-06-01
The filtering of point clouds is a ubiquitous task in the processing of airborne laser scanning (ALS) data; however, such filtering processes are difficult because of the complex configuration of the terrain features. The classical filtering algorithms rely on the cautious tuning of parameters to handle various landforms. To address the challenge posed by the bundling of different terrain features into a single dataset and to surmount the sensitivity of the parameters, in this study, we propose an adaptive surface filter (ASF) for the classification of ALS point clouds. Based on the principle that the threshold should vary in accordance to the terrain smoothness, the ASF embeds bending energy, which quantitatively depicts the local terrain structure to self-adapt the filter threshold automatically. The ASF employs a step factor to control the data pyramid scheme in which the processing window sizes are reduced progressively, and the ASF gradually interpolates thin plate spline surfaces toward the ground with regularization to handle noise. Using the progressive densification strategy, regularization and self-adaption, both performance improvement and resilience to parameter tuning are achieved. When tested against the benchmark datasets provided by ISPRS, the ASF performs the best in comparison with all other filtering methods, yielding an average total error of 2.85% when optimized and 3.67% when using the same parameter set.
An Improved WiFi/PDR Integrated System Using an Adaptive and Robust Filter for Indoor Localization
Directory of Open Access Journals (Sweden)
Zengke Li
2016-11-01
Full Text Available Location-based services (LBS are services offered through a mobile device that take into account a device’s geographical location. To provide position information for these services, location is a key process. GNSS (Global Navigation Satellite System can provide sub-meter accuracy in open-sky areas using satellite signals. However, for indoor and dense urban environments, the accuracy deteriorates significantly because of weak signals and dense multipaths. The situation becomes worse in indoor environments where the GNSS signals are unreliable or totally blocked. To improve the accuracy of indoor positioning for location-based services, an improved WiFi/Pedestrian Dead Reckoning (PDR integrated positioning and navigation system using an adaptive and robust filter is presented. The adaptive filter is based on scenario and motion state recognition and the robust filter is based on the Mahalanobis distance. They are combined and used in the WiFi/PDR integrated system to weaken the effect of gross errors on the dynamic and observation models. To validate their performance in the WiFi/PDR integrated system, a real indoor localization experiment is conducted. The results indicate that the adaptive filter is better able to adapt to the circumstances of the dynamic model by adjusting the covariance of the process noise and the robust Kalman filter is able to mitigate the harmful effect of gross errors from the WiFi positioning.
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.
Adaptive UAV attitude estimation employing unscented Kalman Filter, FOAM and low-cost MEMS sensors.
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.
Propagating adaptive-weighted vector median filter for motion-field smoothing
Institute of Scientific and Technical Information of China (English)
林梦冬; 余松煜
2004-01-01
In the field of predictive video coding and format conversion, there is an increasing attention towards estimation of the true inter-frame motion. The restoration of motion vector field computed by 3-D RS is addressed and a propagating adaptive-weighted vector median (PAWVM) post-filter is proposed. This approach decomposes blocks to make a betteres timation on object borders and propagates good vectors in the scanning direction. Furthermore, a hard-thresholding method is introduced into calculating vector weights to improve the propagating. By exploiting both the spatial correlation of the vector field and the matching error of candidate vectors, PAWVM makes a good balance between the smoothness of vector field and the prediction error, and the output vector field is more valid to reflect the true motion.
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Arslan Hüseyin
2009-01-01
Full Text Available Long-Term Evolution (LTE systems will employ single carrier frequency division multiple access (SC-FDMA for the uplink. Similar to the Orthogonal frequency-division multiple access (OFDMA technology, SC-FDMA is sensitive to frequency offsets leading to intercarrier interference (ICI. In this paper, we propose a Kalman filter-based approach in order to mitigate ICI under high Doppler spread scenarios by tracking the variation of channel taps jointly in time domain for LTE uplink systems. Upon acquiring the estimates of channel taps from the Kalman tracker, we employ an interpolation algorithm based on polynomial fitting whose order is changed adaptively. The proposed method is evaluated under four different scenarios with different settings in order to reflect the impact of various critical parameters on the performance such as propagation environment, speed, and size of resource block (RB assignments. Results are given along with discussions.
Ibey, Bennett; Subramanian, Hariharan; Ericson, Nance; Xu, Weijian; Wilson, Mark; Cote, Gerard L.
2005-03-01
A blood perfusion and oxygenation sensor has been developed for in situ monitoring of transplanted organs. In processing in situ data, motion artifacts due to increased perfusion can create invalid oxygenation saturation values. In order to remove the unwanted artifacts from the pulsatile signal, adaptive filtering was employed using a third wavelength source centered at 810nm as a reference signal. The 810 nm source resides approximately at the isosbestic point in the hemoglobin absorption curve where the absorbance of light is nearly equal for oxygenated and deoxygenated hemoglobin. Using an autocorrelation based algorithm oxygenation saturation values can be obtained without the need for large sampling data sets allowing for near real-time processing. This technique has been shown to be more reliable than traditional techniques and proven to adequately improve the measurement of oxygenation values in varying perfusion states.
Singh, Omkar; Sunkaria, Ramesh Kumar
2015-01-01
Separating an information-bearing signal from the background noise is a general problem in signal processing. In a clinical environment during acquisition of an electrocardiogram (ECG) signal, The ECG signal is corrupted by various noise sources such as powerline interference (PLI), baseline wander and muscle artifacts. This paper presents novel methods for reduction of powerline interference in ECG signals using empirical wavelet transform (EWT) and adaptive filtering. The proposed methods are compared with the empirical mode decomposition (EMD) based PLI cancellation methods. A total of six methods for PLI reduction based on EMD and EWT are analysed and their results are presented in this paper. The EWT-based de-noising methods have less computational complexity and are more efficient as compared with the EMD-based de-noising methods.
Subband Adaptive Filtering with l1-Norm Constraint for Sparse System Identification
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Young-Seok Choi
2013-01-01
Full Text Available This paper presents a new approach of the normalized subband adaptive filter (NSAF which directly exploits the sparsity condition of an underlying system for sparse system identification. The proposed NSAF integrates a weighted l1-norm constraint into the cost function of the NSAF algorithm. To get the optimum solution of the weighted l1-norm regularized cost function, a subgradient calculus is employed, resulting in a stochastic gradient based update recursion of the weighted l1-norm regularized NSAF. The choice of distinct weighted l1-norm regularization leads to two versions of the l1-norm regularized NSAF. Numerical results clearly indicate the superior convergence of the l1-norm regularized NSAFs over the classical NSAF especially when identifying a sparse system.
Adaptive update using visual models for lifting-based motion-compensated temporal filtering
Li, Song; Xiong, H. K.; Wu, Feng; Chen, Hong
2005-03-01
Motion compensated temporal filtering is a useful framework for fully scalable video compression schemes. However, when supposed motion models cannot represent a real motion perfectly, both the temporal high and the temporal low frequency sub-bands may contain artificial edges, which possibly lead to a decreased coding efficiency, and ghost artifacts appear in the reconstructed video sequence at lower bit rates or in case of temporal scaling. We propose a new technique that is based on utilizing visual models to mitigate ghosting artifacts in the temporal low frequency sub-bands. Specifically, we propose content adaptive update schemes where visual models are used to determine image dependent upper bounds on information to be updated. Experimental results show that the proposed algorithm can significantly improve subjective visual quality of the low-pass temporal frames and at the same time, coding performance can catch or exceed the classical update steps.
Directory of Open Access Journals (Sweden)
Chen Siyu
2017-01-01
Full Text Available Patrol UAV has poor aerial posture stability and is largely affected by anthropic factors, which lead to some shortages such as low power cable tracking precision, captured image loss and inconvenient temperature measurement, etc. In order to solve these disadvantages, this article puts forward a power cable intelligent patrol system. The core innovation of the system is a 360° platform. This collects the position information of power cables by using far infrared sensors and carries out real-time all-direction adjustment of UAV lifting platform through the adaptive Kalman filter fuzzy PID control algorithm, so that the precise tracking of power cables is achieved. An intelligent patrol system is established to detect the faults more accurately, so that a high intelligence degree of power cable patrol system is realized.
Model-Based Hand Tracking by Chamfer Distance and Adaptive Color Learning Using Particle Filter
Directory of Open Access Journals (Sweden)
Kerdvibulvech Chutisant
2009-01-01
Full Text Available We propose a new model-based hand tracking method for recovering of three-dimensional hand motion from an image sequence. We first build a three-dimensional hand model using truncated quadrics. The degrees of freedom (DOF for each joint correspond to the DOF of a real hand. This feature extraction is performed by using the Chamfer Distance function for the edge likelihood. The silhouette likelihood is performed by using a Bayesian classifier and the online adaptation of skin color probabilities. Therefore, it is to effectively deal with any illumination changes. Particle filtering is used to track the hand by predicting the next state of three-dimensional hand model. By using these techniques, this method adds the useful ability of automatic recovery from tracking failures. This method can also be used to track the guitarist's hand.
Adaptation of Gabor filters for simulation of human preattentive mechanism for a mobile robot
Kulkarni, Naren; Naghdy, Golshah A.
1993-08-01
Vision guided mobile robot navigation is complex and requires analysis of tremendous amounts of information in real time. In order to simplify the task and reduce the amount of information, human preattentive mechanism can be adapted [Nag90]. During the preattentive search the scene is analyzed rapidly but in sufficient detail for the attention to be focused on the `area of interest.' The `area of interest' can further be scrutinized in more detail for recognition purposes. This `area of interest' can be a text message to facilitate navigation. Gabor filters and an automated turning mechanism are used to isolate the `area of interest.' These regions are subsequently processed with optimal spatial resolution for perception tasks. This method has clear advantages over the global operators in that, after an initial search, it scans each region of interest with optimum resolution. This reduces the volume of information for recognition stages and ensures that no region is over or under estimated.
A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.
Shabbouei Hagh, Yashar; Mohammadi Asl, Reza; Cocquempot, Vincent
2017-01-01
In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used.
Adaptive wavelet packet-based de-speckling of ultrasound images with bilateral filter.
Esakkirajan, Sankaralingam; Vimalraj, Chinna Thambi; Muhammed, Rashad; Subramanian, Ganapathi
2013-12-01
A new adaptive wavelet packet-based approach to minimize speckle noise in ultrasound images is proposed. This method combines wavelet packet thresholding with a bilateral filter. Here, the best bases after wavelet packet decomposition are selected by comparing the first singular value of all sub-bands, and the noisy coefficients are thresholded using a modified NeighShrink technique. The algorithm is tested with various ultrasound images, and the results, in terms of peak signal-to-noise ratio and mean structural similarity values, are compared with those for some well-known de-speckling techniques. The simulation results indicate that the proposed method has better potential to minimize speckle noise and retain fine details of the ultrasound image. Copyright © 2013 World Federation for Ultrasound in Medicine & Biology. All rights reserved.
Yoon, Paul K; Zihajehzadeh, Shaghayegh; Bong-Soo Kang; Park, Edward J
2015-08-01
This paper proposes a novel indoor localization method using the Bluetooth Low Energy (BLE) and an inertial measurement unit (IMU). The multipath and non-line-of-sight errors from low-power wireless localization systems commonly result in outliers, affecting the positioning accuracy. We address this problem by adaptively weighting the estimates from the IMU and BLE in our proposed cascaded Kalman filter (KF). The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The performance of the proposed algorithm is compared against that of the standard KF experimentally. The results show that the proposed algorithm can maintain high accuracy for position tracking the sensor in the presence of the outliers.
Charisis, Vasileios S; Hadjileontiadis, Leontios J
2016-03-01
The aim of this Letter is to present a new capsule endoscopy (CE) image analysis scheme for the detection of small bowel ulcers that relate to Crohn's disease. More specifically, this scheme is based on: (i) a hybrid adaptive filtering (HAF) process, that utilises genetic algorithms to the curvelet-based representation of images for efficient extraction of the lesion-related morphological characteristics, (ii) differential lacunarity (DL) analysis for texture feature extraction from the HAF-filtered images and (iii) support vector machines for robust classification performance. For the training of the proposed scheme, namely HAF-DL, an 800-image database was used and the evaluation was based on ten 30-second long endoscopic videos. Experimental results, along with comparison with other related efforts, have shown that the HAF-DL approach evidently outperforms the latter in the field of CE image analysis for automated lesion detection, providing higher classification results. The promising performance of HAF-DL paves the way for a complete computer-aided diagnosis system that could support the physicians' clinical practice.
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Doo Yong Choi
2016-04-01
Full Text Available Rapid detection of bursts and leaks in water distribution systems (WDSs can reduce the social and economic costs incurred through direct loss of water into the ground, additional energy demand for water supply, and service interruptions. Many real-time burst detection models have been developed in accordance with the use of supervisory control and data acquisition (SCADA systems and the establishment of district meter areas (DMAs. Nonetheless, no consideration has been given to how frequently a flow meter measures and transmits data for predicting breaks and leaks in pipes. This paper analyzes the effect of sampling interval when an adaptive Kalman filter is used for detecting bursts in a WDS. A new sampling algorithm is presented that adjusts the sampling interval depending on the normalized residuals of flow after filtering. The proposed algorithm is applied to a virtual sinusoidal flow curve and real DMA flow data obtained from Jeongeup city in South Korea. The simulation results prove that the self-adjusting algorithm for determining the sampling interval is efficient and maintains reasonable accuracy in burst detection. The proposed sampling method has a significant potential for water utilities to build and operate real-time DMA monitoring systems combined with smart customer metering systems.
A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.
Zhao, Haiquan; Zhang, Jiashu
2009-12-01
To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.
Engineering of Multi-Dimensional Entangled States of Photon Pairs Using Hyper-Entanglement
Institute of Scientific and Technical Information of China (English)
REN Xi-Feng; GUO Guo-Ping; LI Jian; LI Chuan-Feng; GUO Guang-Can
2006-01-01
@@ Multi-dimensional entangled states have been proven to be more powerful in some quantum information processes.Down-converted photons from spontaneous parametric down-conversion are used to engineer multi-dimensional entangled states. A kind of multi-degree multi-dimensional Greenberger-Horne-Zeilinger states can also be generated. The hyper-entangled photons are entangled in energy-time, polarization and orbital angular momentum,which is proven to be useful to increase the dimension of systems and to investigate higher-dimensional entangled states.
Shih, Cheng-Ting; Wu, Jay; Lin, Hsin-Hon; Chang, Shu-Jun; Chuang, Keh-Shih
2014-08-01
Several positron emission tomography (PET) scanners with special detector block arrangements have been developed in recent years to improve the resolution of PET images. However, the discontinuous detector blocks cause gaps in the sinogram. This study proposes an adaptive discrete cosine transform-based (aDCT) filter for gap-inpainting. The gap-corrupted sinogram was morphologically closed and subsequently converted to the DCT domain. A certain number of the largest coefficients in the DCT spectrum were identified to determine the low-frequency preservation region. The weighting factors for the remaining coefficients were determined by an exponential weighting function. The aDCT filter was constructed and applied to two digital phantoms and a simulated phantom introduced with various levels of noise. For the Shepp-Logan head phantom, the aDCT filter filled the gaps effectively. For the Jaszczak phantom, no secondary artifacts were induced after aDCT filtering. The percent mean square error and mean structure similarity of the aDCT filter were superior to those of the DCT2 filter at all noise levels. For the simulated striatal dopamine innervation study, the aDCT filter recovered the shape of the striatum and restored the striatum to reference activity ratios to the ideal value. The proposed aDCT filter can recover the missing gap data in the sinogram and improve the image quality and quantitative accuracy of PET images.
Energy Technology Data Exchange (ETDEWEB)
Shih, Cheng-Ting; Lin, Hsin-Hon; Chuang, Keh-Shih [Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan (China); Wu, Jay, E-mail: jwu@mail.cmu.edu.tw [Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 40402, Taiwan (China); Chang, Shu-Jun [Health Physics Division, Institute of Nuclear Energy Research, Atomic Energy Council, Taoyuan 32546, Taiwan (China)
2014-08-15
Purpose: Several positron emission tomography (PET) scanners with special detector block arrangements have been developed in recent years to improve the resolution of PET images. However, the discontinuous detector blocks cause gaps in the sinogram. This study proposes an adaptive discrete cosine transform-based (aDCT) filter for gap-inpainting. Methods: The gap-corrupted sinogram was morphologically closed and subsequently converted to the DCT domain. A certain number of the largest coefficients in the DCT spectrum were identified to determine the low-frequency preservation region. The weighting factors for the remaining coefficients were determined by an exponential weighting function. The aDCT filter was constructed and applied to two digital phantoms and a simulated phantom introduced with various levels of noise. Results: For the Shepp-Logan head phantom, the aDCT filter filled the gaps effectively. For the Jaszczak phantom, no secondary artifacts were induced after aDCT filtering. The percent mean square error and mean structure similarity of the aDCT filter were superior to those of the DCT2 filter at all noise levels. For the simulated striatal dopamine innervation study, the aDCT filter recovered the shape of the striatum and restored the striatum to reference activity ratios to the ideal value. Conclusions: The proposed aDCT filter can recover the missing gap data in the sinogram and improve the image quality and quantitative accuracy of PET images.
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
Multi-dimensional discovery of biomarker and phenotype complexes
Directory of Open Access Journals (Sweden)
Huang Kun
2010-10-01
Full Text Available Abstract Background Given the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature. Results In response to the preceding gap in knowledge and practice, we report upon a prototypical series of experiments that utilize multi-modal approaches to network induction. These experiments are intended to elicit meaningful and significant biomarker-phenotype complexes spanning multiple levels of granularity. This work has been performed in the experimental context of a large-scale clinical and basic science data repository maintained by the National Cancer Institute (NCI funded Chronic Lymphocytic Leukemia Research Consortium. Conclusions Our results indicate that it is computationally tractable to link orthogonal networks of genes, clinical features, and conceptual knowledge to create multi-dimensional models of interrelated biomarkers and phenotypes. Further, our results indicate that such systems-level models contain interrelated bio-molecular and clinical markers capable of supporting hypothesis discovery and testing. Based on such findings, we propose a conceptual model intended to inform the cross-linkage of the results of such methods. This model has as its aim the identification of novel and knowledge-anchored biomarker-phenotype complexes.
Wu, Chunyan; Liu, Jian; Peng, Fuqiang; Yu, Dejie; Li, Rong
2013-07-01
When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.
Dong, Feng; Gunn, James E; Wechsler, Risa H
2007-01-01
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is ~85% complete and over 90% pure for clusters with masses above 1.0*10^{14} h^{-1} M_solar and redshifts up to z=0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensi...
Energy Technology Data Exchange (ETDEWEB)
Dong, Feng; Pierpaoli, Elena; Gunn, James E.; Wechsler, Risa H.
2007-10-29
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is {approx} 85% complete and over 90% pure for clusters with masses above 1.0 x 10{sup 14}h{sup -1} M and redshifts up to z = 0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensity of {Delta} = 200, we find the derived cluster richness {Lambda}{sub 200} a roughly linear indicator of its virial mass M{sub 200}, which well recovers the relation between total luminosity and cluster mass of the input simulation.
Directory of Open Access Journals (Sweden)
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.
Planetary gearbox fault feature enhancement based on combined adaptive filter method
Directory of Open Access Journals (Sweden)
Shuangshu Tian
2015-12-01
Full Text Available The reliability of vibration signals acquired from a planetary gear system (the indispensable part of wind turbine gearbox is directly related to the accuracy of fault diagnosis. The complex operation environment leads to lots of interference signals which are included in the vibration signals. Furthermore, both multiple gears meshing with each other and the differences in transmission rout produce strong nonlinearity in the vibration signals, which makes it difficult to eliminate the noise. This article presents a combined adaptive filter method by taking a delayed signal as reference signal, the Self-Adaptive Noise Cancellation method is adopted to eliminate the white noise. In the meanwhile, by applying Gaussian function to transform the input signal into high-dimension feature-space signal, the kernel least mean square algorithm is used to cancel the nonlinear interference. Effectiveness of the method has been verified by simulation signals and test rig signals. By dealing with simulation signal, the signal-to-noise ratio can be improved around 30 dB (white noise and the amplitude of nonlinear interference signal can be depressed up to 50%. Experimental results show remarkable improvements and enhance gear fault features.
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
There are two kinds of methods in researching the crust deformation: geophysical method and geometrical (or observational) method. Considerable differences usually exist between the two kinds of results, because of the datum differences, geophysical model errors, observational model errors, and so on. Thus, it is reasonable to combine the two kinds of information to collect the crust deformation information. To use the reliable geometrical and geophysical information, we have to control the observational and geophysical model error influences on the estimated deformation parameters, and to balance their contributions to the evaluated parameters. A hybrid estimation strategy is proposed here for evaluating the deformation parameters employing an adaptively robust filtering. The effects of measurement outliers on the estimated parameters are controlled by robust equivalent weights. Adaptive factors are introduced to balance the contribution of the geophysical model information and the geometrical measurements to the model parameters. The datum for the local deformation analysis is mainly determined by the highly accurate IGS station velocities. The hybrid estimation strategy is applied in an actual GPS monitoring network. It is shown that the hybrid technique employs locally repeated geometrical displacements to reduce the displacement errors caused by the mis-modeling of geophysical technique, and thus improves the precision of the estimated crust deformation parameters.
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.
Adaptive clutter filter in 2-D color flow imaging based on in vivo I/Q signal.
Zhou, Xiaoming; Zhang, Congyao; Liu, Dong C
2014-01-01
Color flow imaging has been well applied in clinical diagnosis. For the high quality color flow images, clutter filter is important to separate the Doppler signals from blood and tissue. Traditional clutter filters, such as finite impulse response, infinite impulse response and regression filters, were applied, which are based on the hypothesis that the clutter signal is stationary or tissue moves slowly. However, in realistic clinic color flow imaging, the signals are non-stationary signals because of accelerated moving tissue. For most related papers, simulated RF signals are widely used without in vivo I/Q signal. Hence, in this paper, adaptive polynomial regression filter, which is down mixing with instantaneous clutter frequency, was proposed based on in vivo carotid I/Q signal in realistic color flow imaging. To get the best performance, the optimal polynomial order of polynomial regression filter and the optimal polynomial order for estimation of instantaneous clutter frequency respectively were confirmed. Finally, compared with the mean blood velocity and quality of 2-D color flow image, the experiment results show that adaptive polynomial regression filter, which is down mixing with instantaneous clutter frequency, can significantly enhance the mean blood velocity and get high quality 2-D color flow image.
A fast image super-resolution algorithm using an adaptive Wiener filter.
Hardie, Russell
2007-12-01
A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented.
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.
Pham, Mai Quyen; Chaux, Caroline; Pesquet, Jean-Christophe
2014-01-01
Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations, based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a constrained minimization problem that alleviates standard regularization issues in finding hyperparameters. Th...
The Multi-Dimensional Application/Use of Branding in the Universe of Marketers
P. K. A. Ladipo; T. O. Olufayo; C. I. Omoera
2012-01-01
The article considers branding as an instrument of multi-dimensional importance to producers contrary to the view of mono-dimensional use, in the sense that branding merely serves as an instrument of product differentiation in the entire marketing process. The multi-dimensional application of branding is believed to be made possible through its capability to evolve a “deep-brand” with essentially six levels of meaning, each meaning serving to achieve one marketing objective or the other. In t...
Higher-order symmetries and conservation laws of multi-dimensional Gordon-type equations
Indian Academy of Sciences (India)
S Jamal; A H Kara
2011-09-01
In this paper a class of multi-dimensional Gordon-type equations are analysed using a multiplier and homotopy approach to construct conservation laws. The main focus is the analysis of the classical versions of the Gordon-type equations and obtaining higher-order variational symmetries and corresponding conserved quantities. The results are extended to the multi-dimensional Gordontype equations with the two-dimensional Klein–Gordon equation in particular yielding interesting results.
A multi-dimensional finite volume cell-centered direct ALE solver for hydrodynamics
Clair, G.; Ghidaglia, J.-M.; Perlat, J.-P.
2016-12-01
In this paper we describe a second order multi-dimensional scheme, belonging to the class of direct Arbitrary Lagrangian-Eulerian (ALE) methods, for the solution of non-linear hyperbolic systems of conservation law. The scheme is constructed upon a cell-centered explicit Lagrangian solver completed with an edge-based upwinded formulation of the numerical fluxes, computed from the MUSCL-Hancock method, to obtain a full ALE formulation. Numerical fluxes depend on nodal grid velocities which are either set or computed to avoid most of the mesh problems typically encountered in purely Lagrangian simulations. In order to assess the robustness of the scheme, most results proposed in this paper have been obtained by computing the grid velocities as a fraction of the Lagrangian nodal velocities, the ratio being set before running the test case. The last part of the paper describes preliminary results about the triple point test case run in the ALE framework by computing the grid velocities with the fully adaptive Large Eddy Limitation (L.E.L.) method proposed in [1]. Such a method automatically computes the grid velocities at each node defining the mesh from the local characteristics of the flow. We eventually discuss the advantages and the drawback of the coupling.
一种卡尔曼滤波自适应算法%An adaptive Algorithm on Kalman Filtering
Institute of Scientific and Technical Information of China (English)
黄波; 郑新星; 刘凤伟
2012-01-01
自适应滤波是指随着外部信号的变化,滤波器能够自我调节滤波参数,使得滤波器的某一性能指标达到最优。文章以卡尔曼滤波理论为基础,给出一种新的自适应卡尔曼滤波算法。%Adaptive-filtering means the filter could adjust filtration parameters by itself and make some performance index optimal when the external signals vary.This paper will give a new Kalman filter algorithm whose base is Kalman filter theory.
Kipka, H.; Pfennig, B.; Fink, M.; Kralisch, S.; Krause, P.; Flügel, W.
2010-12-01
Fully spatially distributed hydrological modeling requires a topological linkage of single modeling entities (e.g. Hydrological Response Units - HRU) in order to reproduce relevant attenuation and translation processes within the stream but also during the transport of water in form of lateral surface or subsurface flow. Most often such linkage is considered by a one dimensional (1D) approach which links one modeling entity to only one receiver that follows in flow direction. The comparison with actual lateral water movement in catchments show that such a 1D routing scheme is often too simple which can lead to an overestimation of the runoff concentration along the 1D flow paths. On the other hand an underestimation of runoff in flow cascades that do not reside next to the main 1D flow paths can occur as the affected HRUs don’t receive realistic inflow from their source entities above. As a catchment-wide consequence the 1D routing scheme can result in a significant over- or underestimation of the contributing area for specific parts of a catchment which can have important implications on the spatial distribution of accompanying processes such as spatial variation of soil moisture, soil erosion or nutrient/contaminant transport. To address the problems outlined above a new approach has been developed that allows a multi-dimensional linkage of model entities in such a way that each entity can have various receivers to which the water is passed. This extended routing scheme was implemented in the hydrological, nutrient transport and erosion modeling system J2000-S-E and was used for the simulation of the hydrological processes of a number of meso-scaled catchments in Thuringia, Germany. This work will present the most important facts of the extended routing scheme, the simulation results along with the comparison of those obtained with the 1D linkage and will highlight the impacts on the hydrological process dynamics as well as on the HRU-based mass transport and
Improved characterization of slow-moving landslides by means of adaptive NL-InSAR filtering
Albiol, David; Iglesias, Rubén.; Sánchez, Francisco; Duro, Javier
2014-10-01
Advanced remote sensing techniques based on space-borne Synthetic Aperture Radar (SAR) have been developed during the last decade showing their applicability for the monitoring of surface displacements in landslide areas. This paper presents an advanced Persistent Scatterer Interferometry (PSI) processing based on the Stable Point Network (SPN) technique, developed by the company Altamira-Information, for the monitoring of an active slowmoving landslide in the mountainous environment of El Portalet, Central Spanish Pyrenees. For this purpose, two TerraSAR-X data sets acquired in ascending mode corresponding to the period from April to November 2011, and from August to November 2013, respectively, are employed. The objective of this work is twofold. On the one hand, the benefits of employing Nonlocal Interferomtric SAR (NL-InSAR) adaptive filtering techniques over vegetated scenarios to maximize the chances of detecting natural distributed scatterers, such as bare or rocky areas, and deterministic point-like scatterers, such as man-made structures or poles, is put forward. In this context, the final PSI displacement maps retrieved with the proposed filtering technique are compared in terms of pixels' density and quality with classical PSI, showing a significant improvement. On the other hand, since SAR systems are only sensitive to detect displacements in the line-of-sight (LOS) direction, the importance of projecting the PSI displacement results retrieved along the steepest gradient of the terrain slope is discussed. The improvements presented in this paper are particularly interesting in these type of applications since they clearly allow to better determine the extension and dynamics of complex landslide phenomena.
Adaptive partial median filter for early CT signs of acute cerebral infarction
Energy Technology Data Exchange (ETDEWEB)
Lee, Yongbum; Tsai, Du-Yih [Niigata University, Department of Radiological Technology, School of Health Sciences, Niigata (Japan); Takahashi, Noriyuki; Ishii, Kiyoshi [Sendai City Hospital, Department of Radiology, Sendai (Japan)
2007-08-15
Purpose: Detection of early CT signs of infarct in non- enhanced CT image is mandatory in patients with acute ischemic stroke. Loss of the gray-white matter interface at the lentiform nucleus or the insular ribbon has been an important early CT sign of acute cerebral infarction, which affects decisions on thrombolytic therapy. However, its detection is difficult, since the principal early CT sign is subtle hypoattenuation. An image processing method to reduce local noise with edges preserved was developed to improve infarct detection. Rationale: An adaptive partial median filter (APMF) was selected for this application, since the APMF can markedly improve the visibility of the normal gray-white matter interface. APMF should enhance the conspicuity of gray-white matter interface changes due to hypoattenuation that accompanies cerebral infarction. Method: In a criterion referenced performance study using simulated CT images with gray-white matter interfaces, a total of 14 conventional smoothing filters were also used for comparison to validate the usefulness of the proposed APMF. The APMF indicated the highest performance among the compared methods. Then, observer performance study by receiver operator characteristic (ROC) analysis was performed with 4 radiologist observers using a database with 18 abnormal and 33 normal head CT images. The average A{sub z} values of ROC curves for all radiologists increased from 0.876 without the APMF images to 0.926 with the APMF images, and this difference was statistically significant (P = 0.04). The results from the two observer performance studies demonstrated that APMF has significant potential to improve the diagnosis of acute cerebral infarction using non-enhanced CT images. (orig.)
Rodríguez-Caballero, E.; Afana, A.; Chamizo, S.; Solé-Benet, A.; Canton, Y.
2016-07-01
Terrestrial laser scanning (TLS), widely known as light detection and ranging (LiDAR) technology, is increasingly used to provide highly detailed digital terrain models (DTM) with millimetric precision and accuracy. In order to generate a DTM, TLS data has to be filtered from undesired spurious objects, such as vegetation, artificial structures, etc., Early filtering techniques, successfully applied to airborne laser scanning (ALS), fail when applied to TLS data, as they heavily smooth the terrain surface and do not retain their real morphology. In this article, we present a new methodology for filtering TLS data based on the geometric and radiometric properties of the scanned surfaces. This methodology was built on previous morphological filters that select the minimum point height within a sliding window as the real surface. However, contrary to those methods, which use a fixed window size, the new methodology operates under different spatial scales represented by different window sizes, and can be adapted to different types and sizes of plants. This methodology has been applied to two study areas of differing vegetation type and density. The accuracy of the final DTMs was improved by ∼30% under dense canopy plants and over ∼40% on the open spaces between plants, where other methodologies drastically underestimated the real surface heights. This resulted in more accurate representation of the soil surface and microtopography than up-to-date techniques, eventually having strong implications in hydrological and geomorphological studies.
Directory of Open Access Journals (Sweden)
Hsuan-Yu Lin
2008-11-01
Full Text Available Adaptive reduced-rank (RR multistage matrix Wiener filtering (MMWF techniques, based on the minimum mean-square error (MMSE criterion, are proposed for direct-sequence (DS ultra-wideband (UWB communication systems. These RR-MMWF-based algorithms employ an adaptive fuzzy-inference determined filter stage. As a consequence, the proposed schemes achieve a substantial saving in complexity without compromising system performance and dynamic convergence/tracking capability. Additionally, the fuzzy-logic-controlled matrix conjugate gradient (MCG algorithm is developed for a robust and reduced-rank implementation of the full-rank MMWF. Simulations are conducted to illustrate the convergence/tracking superiority and to provide a comparative evaluation of the proposed algorithms with the MMWF-based schemes using other adaptive stage-selecting criteria.
Low-cost adaptive square-root cubature Kalman filter for systems with process model uncer tainty
Institute of Scientific and Technical Information of China (English)
An Zhang; Shuida Bao; Wenhao Bi; Yuan Yuan
2016-01-01
A novel low-cost adaptive square-root cubature Kalman filter (LCASCKF) is proposed to enhance the robustness of pro-cess models while only increasing the computational load slightly. It is wel-known that the Kalman filter cannot handle uncertainties in a process model, such as initial state estimation errors, parameter mismatch and abrupt state changes. These uncertainties severely affect filter performance and may even provoke divergence. A strong tracking filter (STF), which utilizes a suboptimal fading fac-tor, is an adaptive approach that is commonly adopted to solve this problem. However, if the strong tracking SCKF (STSCKF) uses the same method as the extended Kalman filter (EKF) to introduce the suboptimal fading factor, it greatly increases the computational load. To avoid this problem, a low-cost introductory method is proposed and a hypothesis testing theory is applied to detect uncertainties. The computational load analysis is performed by counting the total number of floating-point operations and it is found that the computational load of LCASCKF is close to that of SCKF. Experimental results prove that the LCASCKF performs as wel as STSCKF, while the increase in computational load is much lower than STSCKF.
Complex Membership Grades with an Application to the Design of Adaptive Filters
Directory of Open Access Journals (Sweden)
D. Moses
1999-12-01
Full Text Available In this paper, complex membership grades are introduced for the extension of fuzzy set theory to the complex domain. This model is based on the idea of viewing the complex domain in a linguistic manner, where two linguistic terms are required to define an object. Thus, as opposed to Buckley's model, after fuzzification the two- dimensionality of the universe of discourse is still apparent. One form for representing a complex fuzzy set is using the Cartesian Complex Fuzzy Set representation, which produces complex sets of the form [Z\\tilde]c = [X\\tilde] + j[Y\\tilde]. The motivation for this aberrant representation is oriented from the limitations in using a direct extension to Zadeh, that Buckley introduced. These limitations pose the guidelines for Complex Membership Grades and, therefore, are initially discussed in this paper. Complex Fuzzy Sets are defined and a technique for converting between Complex Fuzzy Sets and Fuzzy Relations is developed based on Cylindrical Extensions and Projections defined by Zadeh. Next, linguistic coordinate transformations are discussed and exemplified by a rule-base coordinate transformation between Polar and Cartesian Complex Fuzzy Sets. Arithmetic operations and defuzzification are demonstrated. The simplicity of these latter operations is crucial when considering implementation prospects. Finally, Complex Membership Grades are applied to the design of adaptive filters. It is shown that a logically derived rule-base can be described, using the linguistic complex domain, for the adaptation process. Emphasis, in this part, is put on the unique characteristics of the complex membership grades model.
Wiklund, Urban; Karlsson, Marcus; Ostlund, Nils; Berglin, Lena; Lindecrantz, Kaj; Karlsson, Stefan; Sandsjö, Leif
2007-06-01
Intermittent disturbances are common in ECG signals recorded with smart clothing: this is mainly because of displacement of the electrodes over the skin. We evaluated a novel adaptive method for spatio-temporal filtering for heartbeat detection in noisy multi-channel ECGs including short signal interruptions in single channels. Using multi-channel database recordings (12-channel ECGs from 10 healthy subjects), the results showed that multi-channel spatio-temporal filtering outperformed regular independent component analysis. We also recorded seven channels of ECG using a T-shirt with textile electrodes. Ten healthy subjects performed different sequences during a 10-min recording: resting, standing, flexing breast muscles, walking and pushups. Using adaptive multi-channel filtering, the sensitivity and precision was above 97% in nine subjects. Adaptive multi-channel spatio-temporal filtering can be used to detect heartbeats in ECGs with high noise levels. One application is heartbeat detection in noisy ECG recordings obtained by integrated textile electrodes in smart clothing.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-08
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.
Peña, M.
2016-10-01
Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.
Mitchell, D A; Sault, R J
2010-01-01
In radio astronomy, reference signals from auxiliary antennas that receive only the radio frequency interference (RFI) can be modified to model the RFI environment at the astronomy receivers. The RFI can then be canceled from the astronomy signal paths. However, astronomers typically only require signal statistics. If the RFI statistics are changing slowly, the cancellation can be applied to the signal correlations at a much lower rate than is required for standard adaptive filters. In this paper we describe five canceler setups; precorrelation and postcorrelation cancelers that use one or two reference signals in different ways. The theoretical residual RFI and added noise levels are examined and are demonstrated using microwave television RFI at the Australia Telescope Compact Array. The RFI is attenuated to below the system noise, a reduction of at least 20 dB. While dual-reference cancelers add more reference noise than single-reference cancelers, this noise is zero-mean and only adds to the system noise,...
Directory of Open Access Journals (Sweden)
Pipa Daniel
2010-01-01
Full Text Available Flexible riser is a class of flexible pipes which is used to connect subsea pipelines to floating offshore installations, such as FPSOs (floating production/storage/off-loading unit and SS (semisubmersible platforms, in oil and gas production. Flexible risers are multilayered pipes typically comprising an inner flexible metal carcass surrounded by polymer layers and spiral wound steel ligaments, also referred to as armor wires. Since these armor wires are made of steel, their magnetic properties are sensitive to the stress they are subjected to. By measuring their magnetic properties in a nonintrusive manner, it is possible to compare the stress in the armor wires, thus allowing the identification of damaged ones. However, one encounters several sources of noise when measuring electromagnetic properties contactlessly, such as movement between specimen and probe, and magnetic noise. This paper describes the development of a new technique for automatic monitoring of armor layers of flexible risers. The proposed approach aims to minimize these current uncertainties by combining electromagnetic measurements with optical strain gage data through a recursive least squares (RLSs adaptive filter.
A piezo-shunted kirigami auxetic lattice for adaptive elastic wave filtering
Ouisse, Morvan; Collet, Manuel; Scarpa, Fabrizio
2016-11-01
Tailoring the dynamical behavior of wave-guide structures can provide an efficient and physically elegant approach for optimizing mechanical components with regards to vibroacoustic propagation. Architectured materials as pyramidal core kirigami cells combined with smart systems may represent a promising way to improve the vibroacoustic quality of structural components. This paper describes the design and modeling of a pyramidal core with auxetic (negative Poisson’s ratio) characteristics and distributed shunted piezoelectric patches that allow for wave propagation control. The core is produced using a kirigami technique, inspired by the cutting/folding processes of the ancient Japanese art. The kirigami structure has a pyramidal unit cell shape that creates an in-plane negative Poisson’s ratio macroscopic behavior. This structure exhibits in-plane elastic properties (Young’s and shear modulus) which are higher than the out-of-plane ones, and hence this lattice has very specific properties in terms of wave propagation that are investigated in this work. The short-circuited configuration is first analyzed, before using negative capacitance and resistance as a shunt which provides impressive band gaps in the low frequency range. All configurations are investigated by using a full analysis of the Brillouin zone, rendering possible the deep understanding of the dynamical properties of the smart lattice. The results are presented in terms of dispersion and directivity diagrams, and the smart lattice shows quite interesting properties for the adaptive filtering of elastic waves at low frequencies bandwidths.
Directory of Open Access Journals (Sweden)
Muhammad Ammirrul Atiqi Mohd Zainuri
2016-05-01
Full Text Available This paper presents improvement of a harmonics extraction algorithm, known as the fundamental active current (FAC adaptive linear element (ADALINE neural network with the integration of photovoltaic (PV to shunt active power filters (SAPFs as active current source. Active PV injection in SAPFs should reduce dependency on grid supply current to supply the system. In addition, with a better and faster harmonics extraction algorithm, the SAPF should perform well, especially under dynamic PV and load conditions. The role of the actual injection current from SAPF after connecting PVs will be evaluated, and the better effect of using FAC ADALINE will be confirmed. The proposed SAPF was simulated and evaluated in MATLAB/Simulink first. Then, an experimental laboratory prototype was also developed to be tested with a PV simulator (CHROMA 62100H-600S, and the algorithm was implemented using a TMS320F28335 Digital Signal Processor (DSP. From simulation and experimental results, significant improvements in terms of total harmonic distortion (THD, time response and reduction of source power from grid have successfully been verified and achieved.
Fault detection method for railway wheel flat using an adaptive multiscale morphological filter
Li, Yifan; Zuo, Ming J.; Lin, Jianhui; Liu, Jianxin
2017-02-01
This study explores the capacity of the morphology analysis for railway wheel flat fault detection. A dynamic model of vehicle systems with 56 degrees of freedom was set up along with a wheel flat model to calculate the dynamic responses of axle box. The vehicle axle box vibration signal is complicated because it not only contains the information of wheel defect, but also includes track condition information. Thus, how to extract the influential features of wheels from strong background noise effectively is a typical key issue for railway wheel fault detection. In this paper, an algorithm for adaptive multiscale morphological filtering (AMMF) was proposed, and its effect was evaluated by a simulated signal. And then this algorithm was employed to study the axle box vibration caused by wheel flats, as well as the influence of track irregularity and vehicle running speed on diagnosis results. Finally, the effectiveness of the proposed method was verified by bench testing. Research results demonstrate that the AMMF extracts the influential characteristic of axle box vibration signals effectively and can diagnose wheel flat faults in real time.
Application of subband adaptive filtering techniques to ultrasonic detection in multilayers
Institute of Scientific and Technical Information of China (English)
MAO Jie; LI Mingxuan
2003-01-01
The ultrasonic testing for the defects of complete disbond in multi-layered structure with lower acoustic impedance beneath a high acoustic impedance overburden is one of the difficult problems in ultrasonic nondestructive testing field. A model of a multi-layered steel-rubber composite plate is depicted. Because the acoustic impedance of the steel differs far from that of the couplant water and the rubber, the energy of the signal reflected from the debonded rubber layers is very weak. More over, the flaw echoes are masked by the strong echoes reverberated in the steel plate. It's nearly impossible to identify the debonding echoes directly. The subband adaptive filtering method is discussed in the paper, where the subband decomposition is performed by mutual wavelet packets decomposition on the criterion of maximizing the cross-correlation between the signals. The simulations on both synthetic and real signals are presented. The echoes from the delaminated flaw at the depth of 5 mm in the rubber from the calculated signal, and echoes from the flaw at the depth of 3 mm from the real signal are extracted successfully.
Wang, Xudong; Syrmos, Vassilis L.
2004-07-01
In this paper, an adaptive reconfigurable control system based on extended Kalman filter approach and eigenstructure assignments is proposed. System identification is carried out using an extended Kalman filter (EKF) approach. An eigenstructure assignment (EA) technique is applied for reconfigurable feedback control law design to recover the system dynamic performance. The reconfigurable feedforward controllers are designed to achieve the steady-state tracking using input weighting approach. The proposed scheme can identify not only actuator and sensor variations, but also changes in the system structures using the extended Kalman filtering method. The overall design is robust with respect to uncertainties in the state-space matrices of the reconfigured system. To illustrate the effectiveness of the proposed reconfigurable control system design technique, an aircraft longitudinal vertical takeoff and landing (VTOL) control system is used to demonstrate the reconfiguration procedure.
Ushaq, Muhammad; Fang, Jiancheng
2013-10-01
Integrated navigation systems for various applications, generally employs the centralized Kalman filter (CKF) wherein all measured sensor data are communicated to a single central Kalman filter. The advantage of CKF is that there is a minimal loss of information and high precision under benign conditions. But CKF may suffer computational overloading, and poor fault tolerance. The alternative is the federated Kalman filter (FKF) wherein the local estimates can deliver optimal or suboptimal state estimate as per certain information fusion criterion. FKF has enhanced throughput and multiple level fault detection capability. The Standard CKF or FKF require that the system noise and the measurement noise are zero-mean and Gaussian. Moreover it is assumed that covariance of system and measurement noises remain constant. But if the theoretical and actual statistical features employed in Kalman filter are not compatible, the Kalman filter does not render satisfactory solutions and divergence problems also occur. To resolve such problems, in this paper, an adaptive Kalman filter scheme strengthened with fuzzy inference system (FIS) is employed to adapt the statistical features of contributing sensors, online, in the light of real system dynamics and varying measurement noises. The excessive faults are detected and isolated by employing Chi Square test method. As a case study, the presented scheme has been implemented on Strapdown Inertial Navigation System (SINS) integrated with the Celestial Navigation System (CNS), GPS and Doppler radar using FKF. Collectively the overall system can be termed as SINS/CNS/GPS/Doppler integrated navigation system. The simulation results have validated the effectiveness of the presented scheme with significantly enhanced precision, reliability and fault tolerance. Effectiveness of the scheme has been tested against simulated abnormal errors/noises during different time segments of flight. It is believed that the presented scheme can be
DEFF Research Database (Denmark)
Prince, Kamau; Presi, Marco; Chiuchiarelli, Andrea
2009-01-01
on a directly-modulated reflective emiconductor amplifier (R-SOA) and exploits the interplay between transmission-line dispersion and tunable optical filtering to achieve flexible true time delay, with $2pi$ beam steering at the different antennas. The system was characterized, then successfully tested with two......We present an all-optical adaptive-antenna radio over fiber transport system that uses proven, commercially-available components to effectively deliver standard-compliant optical signaling to adaptive multiantenna arrays for current and emerging radio technology implementations. The system is based...
Deng, Feiyue; Yang, Shaopu; Tang, Guiji; Hao, Rujiang; Zhang, Mingliang
2017-04-01
Wheel bearings are essential mechanical components of trains, and fault detection of the wheel bearing is of great significant to avoid economic loss and casualty effectively. However, considering the operating conditions, detection and extraction of the fault features hidden in the heavy noise of the vibration signal have become a challenging task. Therefore, a novel method called adaptive multi-scale AVG-Hat morphology filter (MF) is proposed to solve it. The morphology AVG-Hat operator not only can suppress the interference of the strong background noise greatly, but also enhance the ability of extracting fault features. The improved envelope spectrum sparsity (IESS), as a new evaluation index, is proposed to select the optimal filtering signal processed by the multi-scale AVG-Hat MF. It can present a comprehensive evaluation about the intensity of fault impulse to the background noise. The weighted coefficients of the different scale structural elements (SEs) in the multi-scale MF are adaptively determined by the particle swarm optimization (PSO) algorithm. The effectiveness of the method is validated by analyzing the real wheel bearing fault vibration signal (e.g. outer race fault, inner race fault and rolling element fault). The results show that the proposed method could improve the performance in the extraction of fault features effectively compared with the multi-scale combined morphological filter (CMF) and multi-scale morphology gradient filter (MGF) methods.
Adaptive Iterated Square-Root Cubature Kalman Filter and Its Application to SLAM of a Mobile Robot
Directory of Open Access Journals (Sweden)
Zuguo Chen
2013-07-01
Full Text Available For the mobile robot Simultaneous Localization and Mapping(SLAM，a new algorithm is proposed, and named Adaptive Iterated Square-Root Cubature Kalman Filter based SLAM algorithm(AISRCKF-SLAM. The main contribution of the algorithm is that the numerical integration method based on cubature rule is directly used to calculate the SLAM posterior probability density. To improve innovation covariance and cross-covariance, the latest measurements are iteratively used in the measurement updating. The algorithm can reduce linearization error and improve the accuracy of the SLAM algorithm. The algorithm also used adaptive iterating estimation restricted by the iterative sentencing guideline to adjust the proportion of the observation and dynamic model, to make the estimated square root of the error covariance more accurate and reasonable. In experiments, the proposed algorithm is compared with Extended Kalman Filter based SLAM algorithm (EKF-SLAM, Unscented Kalman Filter based SLAM algorithm (UKF-SLAM and Square-Root Cubature Kalman Filter based SLAM algorithm (SRCKF-SLAM. The results indicate that the proposed algorithm having with the higher accuracy of the state estimation is obtained to compare with the EKF-SLAM algorithm, the UKF-SLAM algorithm and the SRCKF-SLAM algorithm.
Tankanag, Arina V; Chemeris, Nikolay K
2009-10-01
The paper describes an original method for analysis of the peripheral blood flow oscillations measured with the laser Doppler flowmetry (LDF) technique. The method is based on the continuous wavelet transform and adaptive wavelet theory and applies an adaptive wavelet filtering to the LDF data. The method developed allows one to examine the dynamics of amplitude oscillations in a wide frequency range (from 0.007 to 2 Hz) and to process both stationary and non-stationary short (6 min) signals. The capabilities of the method have been demonstrated by analyzing LDF signals registered in the state of rest and upon humeral occlusion. The paper shows the main advantage of the method proposed, which is the significant reduction of 'border effects', as compared to the traditional wavelet analysis. It was found that the low-frequency amplitudes obtained by adaptive wavelets are significantly higher than those obtained by non-adaptive ones. The method suggested would be useful for the analysis of low-frequency components of the short-living transitional processes under the conditions of functional tests. The method of adaptive wavelet filtering can be used to process stationary and non-stationary biomedical signals (cardiograms, encephalograms, myograms, etc), as well as signals studied in the other fields of science and engineering.
Ogle, K.; Fell, M.; Barber, J. J.
2016-12-01
Empirical, field studies of plant functional traits have revealed important trade-offs among pairs or triplets of traits, such as the leaf (LES) and wood (WES) economics spectra. Trade-offs include correlations between leaf longevity (LL) vs specific leaf area (SLA), LL vs mass-specific leaf respiration rate (RmL), SLA vs RmL, and resistance to breakage vs wood density. Ordination analyses (e.g., PCA) show groupings of traits that tend to align with different life-history strategies or taxonomic groups. It is unclear, however, what underlies such trade-offs and emergent spectra. Do they arise from inherent physiological constraints on growth, or are they more reflective of environmental filtering? The relative importance of these mechanisms has implications for predicting biogeochemical cycling, which is influenced by trait distributions of the plant community. We address this question using an individual-based model of tree growth (ACGCA) to quantify the theoretical trait space of trees that emerges from physiological constraints. ACGCA's inputs include 32 physiological, anatomical, and allometric traits, many of which are related to the LES and WES. We fit ACGCA to 1.6 million USFS FIA observations of tree diameters and heights to obtain vectors of trait values that produce realistic growth, and we explored the structure of this trait space. No notable correlations emerged among the 496 trait pairs, but stepwise regressions revealed complicated multi-variate structure: e.g., relationships between pairs of traits (e.g., RmL and SLA) are governed by other traits (e.g., LL, radiation-use efficiency [RUE]). We also simulated growth under various canopy gap scenarios that impose varying degrees of environmental filtering to explore the multi-dimensional trait space (hypervolume) of trees that died vs survived. The centroid and volume of the hypervolumes differed among dead and live trees, especially under gap conditions leading to low mortality. Traits most predictive
Energy Technology Data Exchange (ETDEWEB)
Ren, Qingguo, E-mail: renqg83@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Dewan, Sheilesh Kumar, E-mail: sheilesh_d1@hotmail.com [Department of Geriatrics, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Li, Ming, E-mail: minli77@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Li, Jianying, E-mail: Jianying.Li@med.ge.com [CT Imaging Research Center, GE Healthcare China, Beijing (China); Mao, Dingbiao, E-mail: maodingbiao74@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Wang, Zhenglei, E-mail: Williswang_doc@yahoo.com.cn [Department of Radiology, Shanghai Electricity Hospital, Shanghai 200050 (China); Hua, Yanqing, E-mail: cjr.huayanqing@vip.163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China)
2012-10-15
Purpose: To compare image quality and visualization of normal structures and lesions in brain computed tomography (CT) with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) reconstruction techniques in different X-ray tube current–time products. Materials and methods: In this IRB-approved prospective study, forty patients (nineteen men, twenty-one women; mean age 69.5 ± 11.2 years) received brain scan at different tube current–time products (300 and 200 mAs) in 64-section multi-detector CT (GE, Discovery CT750 HD). Images were reconstructed with FBP and four levels of ASIR-FBP blending. Two radiologists (please note that our hospital is renowned for its geriatric medicine department, and these two radiologists are more experienced in chronic cerebral vascular disease than in neoplastic disease, so this research did not contain cerebral tumors but as a discussion) assessed all the reconstructed images for visibility of normal structures, lesion conspicuity, image contrast and diagnostic confidence in a blinded and randomized manner. Volume CT dose index (CTDI{sub vol}) and dose-length product (DLP) were recorded. All the data were analyzed by using SPSS 13.0 statistical analysis software. Results: There was no statistically significant difference between the image qualities at 200 mAs with 50% ASIR blending technique and 300 mAs with FBP technique (p > .05). While between the image qualities at 200 mAs with FBP and 300 mAs with FBP technique a statistically significant difference (p < .05) was found. Conclusion: ASIR provided same image quality and diagnostic ability in brain imaging with greater than 30% dose reduction compared with FBP reconstruction technique.
Towards Semantic Web Services on Large, Multi-Dimensional Coverages
Baumann, P.
2009-04-01
Observed and simulated data in the Earth Sciences often come as coverages, the general term for space-time varying phenomena as set forth by standardization bodies like the Open GeoSpatial Consortium (OGC) and ISO. Among such data are 1-d time series, 2-D surface data, 3-D surface data time series as well as x/y/z geophysical and oceanographic data, and 4-D metocean simulation results. With increasing dimensionality the data sizes grow exponentially, up to Petabyte object sizes. Open standards for exploiting coverage archives over the Web are available to a varying extent. The OGC Web Coverage Service (WCS) standard defines basic extraction operations: spatio-temporal and band subsetting, scaling, reprojection, and data format encoding of the result - a simple interoperable interface for coverage access. More processing functionality is available with products like Matlab, Grid-type interfaces, and the OGC Web Processing Service (WPS). However, these often lack properties known as advantageous from databases: declarativeness (describe results rather than the algorithms), safe in evaluation (no request can keep a server busy infinitely), and optimizable (enable the server to rearrange the request so as to produce the same result faster). WPS defines a geo-enabled SOAP interface for remote procedure calls. This allows to webify any program, but does not allow for semantic interoperability: a function is identified only by its function name and parameters while the semantics is encoded in the (only human readable) title and abstract. Hence, another desirable property is missing, namely an explicit semantics which allows for machine-machine communication and reasoning a la Semantic Web. The OGC Web Coverage Processing Service (WCPS) language, which has been adopted as an international standard by OGC in December 2008, defines a flexible interface for the navigation, extraction, and ad-hoc analysis of large, multi-dimensional raster coverages. It is abstract in that it
Multi-Dimensional Piece-Wise Self-Affine Fractal Interpolation Model
Institute of Scientific and Technical Information of China (English)
ZHANG Tong; ZHUANG Zhuo
2007-01-01
Iterated function system (IFS) models have been used to represent discrete sequences where the attractor of the IFS is piece-wise self-affine in R2 or R3 (R is the set of real numbers). In this paper, the piece-wise self-affine IFS model is extended from R3 to Rn (n is an integer greater than 3), which is called the multi-dimensional piece-wise self-affine fractal interpolation model. This model uses a "mapping partial derivative", and a constrained inverse algorithm to identify the model parameters. The model values depend continuously on all the model parameters, and represent most data which are not multi-dimensional self-affine in Rn. Therefore, the result is very general. The class of functions obtained is much more diverse because their values depend continuously on all of the variables, with all the coefficients of the possible multi-dimensional affine maps determining the functions.
Directory of Open Access Journals (Sweden)
Soon A Kim
2013-01-01
Full Text Available There are limited examples of population-based approaches that engage a broad range of stakeholders for prevention of pneumonia. In 2010, a multi-dimensional public-private partnership was established around World Pneumonia Day (WPD in Seoul, Korea and included the following components: a formation of an expert advisory group, b creation of educational materials tailored for lay persons, c creation of a dedicated WPD internet website in the local language, d organisation of a WPD venue in central Seoul, e creation of video and social networking messages for wide distribution, and f engagement of parents, health-care professionals, public health agencies and policymakers. This project directly engaged 7 expert health professionals, five national- and city-level health facilities, and parents from communities. The program reached out to 70,560 persons including 25,200 persons who were contacted in person at publicly-held WPD events. An educational video produced for WPD was aired in the Seoul subway and visible to several million persons riding subway lines that aired the pneumonia public service announcements over a two-month period (February to March, 2011. In addition, the Korean WPD website experienced 4,975 page views with 3,338 visitors and the micro blog associated with this site hosted 82 posts from site visitors. Based on participant numbers and contact volumes achieved in this project, the Korean WPD program was widely accepted and proved to be a highly effective in reaching a large audience to advocate for pneumonia prevention. One key to success of this program appears to be the unique public-private partnership around a major health issue. The methods and tools developed in this program have excellent potential for adaptation and application in other countries where pneumonia may be an under recognised problem among the general public.
Piretzidis, Dimitrios; Sideris, Michael G.
2017-09-01
Filtering and signal processing techniques have been widely used in the processing of satellite gravity observations to reduce measurement noise and correlation errors. The parameters and types of filters used depend on the statistical and spectral properties of the signal under investigation. Filtering is usually applied in a non-real-time environment. The present work focuses on the implementation of an adaptive filtering technique to process satellite gravity gradiometry data for gravity field modeling. Adaptive filtering algorithms are commonly used in communication systems, noise and echo cancellation, and biomedical applications. Two independent studies have been performed to introduce adaptive signal processing techniques and test the performance of the least mean-squared (LMS) adaptive algorithm for filtering satellite measurements obtained by the gravity field and steady-state ocean circulation explorer (GOCE) mission. In the first study, a Monte Carlo simulation is performed in order to gain insights about the implementation of the LMS algorithm on data with spectral behavior close to that of real GOCE data. In the second study, the LMS algorithm is implemented on real GOCE data. Experiments are also performed to determine suitable filtering parameters. Only the four accurate components of the full GOCE gravity gradient tensor of the disturbing potential are used. The characteristics of the filtered gravity gradients are examined in the time and spectral domain. The obtained filtered GOCE gravity gradients show an agreement of 63-84 mEötvös (depending on the gravity gradient component), in terms of RMS error, when compared to the gravity gradients derived from the EGM2008 geopotential model. Spectral-domain analysis of the filtered gradients shows that the adaptive filters slightly suppress frequencies in the bandwidth of approximately 10-30 mHz. The limitations of the adaptive LMS algorithm are also discussed. The tested filtering algorithm can be
Piretzidis, Dimitrios; Sideris, Michael G.
2017-03-01
Filtering and signal processing techniques have been widely used in the processing of satellite gravity observations to reduce measurement noise and correlation errors. The parameters and types of filters used depend on the statistical and spectral properties of the signal under investigation. Filtering is usually applied in a non-real-time environment. The present work focuses on the implementation of an adaptive filtering technique to process satellite gravity gradiometry data for gravity field modeling. Adaptive filtering algorithms are commonly used in communication systems, noise and echo cancellation, and biomedical applications. Two independent studies have been performed to introduce adaptive signal processing techniques and test the performance of the least mean-squared (LMS) adaptive algorithm for filtering satellite measurements obtained by the gravity field and steady-state ocean circulation explorer (GOCE) mission. In the first study, a Monte Carlo simulation is performed in order to gain insights about the implementation of the LMS algorithm on data with spectral behavior close to that of real GOCE data. In the second study, the LMS algorithm is implemented on real GOCE data. Experiments are also performed to determine suitable filtering parameters. Only the four accurate components of the full GOCE gravity gradient tensor of the disturbing potential are used. The characteristics of the filtered gravity gradients are examined in the time and spectral domain. The obtained filtered GOCE gravity gradients show an agreement of 63-84 mEötvös (depending on the gravity gradient component), in terms of RMS error, when compared to the gravity gradients derived from the EGM2008 geopotential model. Spectral-domain analysis of the filtered gradients shows that the adaptive filters slightly suppress frequencies in the bandwidth of approximately 10-30 mHz. The limitations of the adaptive LMS algorithm are also discussed. The tested filtering algorithm can be
Pentexonomy: A Multi-Dimensional Taxonomy of Educational Online Technologies
Tuapawa, Kimberley; Sher, William; Gu, Ning
2014-01-01
Educational online technologies (EOTs) have revolutionised the delivery of online education, making a large contribution towards the global increase in demand for higher learning. Educationalists have striven to adapt through knowledge development and application of online tools, but making educationally sound choices about technology has proved…
Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter
Directory of Open Access Journals (Sweden)
Álvaro Moreno
2014-08-01
Full Text Available Time series of remotely sensed data are an important source of information for understanding land cover dynamics. In particular, the fraction of absorbed photosynthetic active radiation (fAPAR is a key variable in the assessment of vegetation primary production over time. However, the fAPAR series derived from polar orbit satellites are not continuous and consistent in space and time. Filtering methods are thus required to fill in gaps and produce high-quality time series. This study proposes an adapted (iteratively reweighted local regression filter (LOESS and performs a benchmarking intercomparison with four popular and generally applicable smoothing methods: Double Logistic (DLOG, smoothing spline (SSP, Interpolation for Data Reconstruction (IDR and adaptive Savitzky-Golay (ASG. This paper evaluates the main advantages and drawbacks of the considered techniques. The results have shown that ASG and the adapted LOESS perform better in recovering fAPAR time series over multiple controlled noisy scenarios. Both methods can robustly reconstruct the fAPAR trajectories, reducing the noise up to 80% in the worst simulation scenario, which might be attributed to the quality control (QC MODIS information incorporated into these filtering algorithms, their flexibility and adaptation to the upper envelope. The adapted LOESS is particularly resistant to outliers. This method clearly outperforms the other considered methods to deal with the high presence of gaps and noise in satellite data records. The low RMSE and biases obtained with the LOESS method (|rMBE| < 8%; rRMSE < 20% reveals an optimal reconstruction even in most extreme situations with long seasonal gaps. An example of application of the LOESS method to fill in invalid values in real MODIS images presenting persistent cloud and snow coverage is also shown. The LOESS approach is recommended in most remote sensing applications, such as gap-filling, cloud-replacement, and observing temporal
DEFF Research Database (Denmark)
Nadernejad, Ehsan; Forchhammer, Søren; Korhonen, Jari
2011-01-01
Fuzzy filtering is one of the recently developed methods for reducing distortion in compressed images and video. In this paper, we combine the powerful anisotropic diffusion equations with fuzzy filtering in order to reduce the impact of artifacts. Based on the directional nature of the blocking ...
Analysis and case study on multi-dimensional scalability of the Internet architecture
Institute of Scientific and Technical Information of China (English)
XU Ke; XU MingWei; LI Qi; LIN Song
2008-01-01
This paper presents the definition of multi-dimensional scalability of the Internet architecture, and puts forward a mathematical method to evaluate Internet scalability based on a variety of constraints. Then, the method is employed to study the Internet scalability problem in performance, scale and service scalability. Based on the examples, theoretical analysis and experimental simulation are conducted to address the scalability issue. The results show that the proposed definition and evaluation method of multi-dimensional Internet scalability can effectively evaluate the scalability of the Internet in every aspect, thus providing rational suggestions and methods for evaluation of the next generation Internet architecture.
An Analogue of the L\\'Evy-Cram\\'Er Theorem for Multi-Dimensional Rayleigh Distributions
Nguyen, Thu
2009-01-01
In the present paper we prove that every k-dimensional Cartesian product of Kingman convolutions can be embedded into a k-dimensional symmetric convolution (k=1, 2, ...) and obtain an analogue of the Cram\\'er-L\\'evy theorem for multi-dimensional Rayleigh distributions. A new and more general class of multi-dimensional Rayleygh distributions and associated higher dimensional Bessel processes are introduced and studied. This class of processes inherits the well-known characteristics of Brownian motions: They are independent stationary "increments" processes with continuous sample paths.
Pan, M.-Ch.; Chu, W.-Ch.; Le, Duc-Do
2016-12-01
The paper presents an alternative Vold-Kalman filter order tracking (VKF_OT) method, i.e. adaptive angular-velocity VKF_OT technique, to extract and characterize order components in an adaptive manner for the condition monitoring and fault diagnosis of rotary machinery. The order/spectral waveforms to be tracked can be recursively solved by using Kalman filter based on the one-step state prediction. The paper comprises theoretical derivation of computation scheme, numerical implementation, and parameter investigation. Comparisons of the adaptive VKF_OT scheme with two other ones are performed through processing synthetic signals of designated order components. Processing parameters such as the weighting factor and the correlation matrix of process noise, and data conditions like the sampling frequency, which influence tracking behavior, are explored. The merits such as adaptive processing nature and computation efficiency brought by the proposed scheme are addressed although the computation was performed in off-line conditions. The proposed scheme can simultaneously extract multiple spectral components, and effectively decouple close and crossing orders associated with multi-axial reference rotating speeds.
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-07-12
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.
Adaptive Filtering for FSCW Signal-to-noise Ratio Enhancement of SAW Interrogation Units
Directory of Open Access Journals (Sweden)
Díaz Luis
2016-01-01
Full Text Available A digital filter that improves the signal-to-noise ratio of the response of a FSCW (Frequency Stepped Continuous Wave scheme is presented. An improvement in signal-to-noise ratio represents an enhanced readout distance. This work considers this architecture as an interrogation unit for SAW tags with time and phase encoding. The parameters of the proposed digital filter, which is a non-linear edge preserving filter, were studied and tested for this specific application. An improvement of around 20dB in the SNR level was achieved. This filter preserves the phase of the signal at the time position of the reflectors, which is critical for correct identification of the code in phase encoding schemes.
A Replication Study on the Multi-Dimensionality of Online Social Presence
Mykota, David B.
2015-01-01
The purpose of the present study is to conduct an external replication into the multi-dimensionality of social presence as measured by the Computer-Mediated Communication Questionnaire (Tu, 2005). Online social presence is one of the more important constructs for determining the level of interaction and effectiveness of learning in an online…
Energy Technology Data Exchange (ETDEWEB)
T. Downar
2009-03-31
The overall objective of the work here has been to eliminate the approximations used in current resonance treatments by developing continuous energy multi-dimensional transport calculations for problem dependent self-shielding calculations. The work here builds on the existing resonance treatment capabilities in the ORNL SCALE code system.
Developing a Hypothetical Multi-Dimensional Learning Progression for the Nature of Matter
Stevens, Shawn Y.; Delgado, Cesar; Krajcik, Joseph S.
2010-01-01
We describe efforts toward the development of a hypothetical learning progression (HLP) for the growth of grade 7-14 students' models of the structure, behavior and properties of matter, as it relates to nanoscale science and engineering (NSE). This multi-dimensional HLP, based on empirical research and standards documents, describes how students…
Li, Hai-Sheng; Zhu, Qingxin; Zhou, Ri-Gui; Song, Lan; Yang, Xing-jiang
2014-04-01
Multi-dimensional color image processing has two difficulties: One is that a large number of bits are needed to store multi-dimensional color images, such as, a three-dimensional color image of needs bits. The other one is that the efficiency or accuracy of image segmentation is not high enough for some images to be used in content-based image search. In order to solve the above problems, this paper proposes a new representation for multi-dimensional color image, called a -qubit normal arbitrary quantum superposition state (NAQSS), where qubits represent colors and coordinates of pixels (e.g., represent a three-dimensional color image of only using 30 qubits), and the remaining 1 qubit represents an image segmentation information to improve the accuracy of image segmentation. And then we design a general quantum circuit to create the NAQSS state in order to store a multi-dimensional color image in a quantum system and propose a quantum circuit simplification algorithm to reduce the number of the quantum gates of the general quantum circuit. Finally, different strategies to retrieve a whole image or the target sub-image of an image from a quantum system are studied, including Monte Carlo sampling and improved Grover's algorithm which can search out a coordinate of a target sub-image only running in where and are the numbers of pixels of an image and a target sub-image, respectively.
Liu, Gi-Zen; Liu, Zih-Hui; Hwang, Gwo-Jen
2011-01-01
Many English learning websites have been developed worldwide, but little research has been conducted concerning the development of comprehensive evaluation criteria. The main purpose of this study is thus to construct a multi-dimensional set of criteria to help learners and teachers evaluate the quality of English learning websites. These…
A Master Equation for Multi-Dimensional Non-Linear Field Theories
Park, Q H
1992-01-01
A master equation ( $n$ dimensional non--Abelian current conservation law with mutually commuting current components ) is introduced for multi-dimensional non-linear field theories. It is shown that the master equation provides a systematic way to understand 2-d integrable non-linear equations as well as 4-d self-dual equations and, more importantly, their generalizations to higher dimensions.
DEFF Research Database (Denmark)
Abdul Samad, Noor Asma Fazli; Singh, Ravendra; Sin, Gürkan
2011-01-01
A generic multi-dimensional modeling framework for studying batch cooling crystallization processes under generated operational policies is presented. The generic nature of the modeling allows the study of a wide range of chemical systems under different operational scenarios, enabling thereby, t...
Development and empirical validation of symmetric component measures of multi-dimensional constructs
DEFF Research Database (Denmark)
Sørensen, Hans Eibe; Slater, Stanley F.
2008-01-01
Atheoretical measure purification may lead to construct deficient measures. The purpose of this paper is to provide a theoretically driven procedure for the development and empirical validation of symmetric component measures of multi-dimensional constructs. We place particular emphasis on establ...
Institute of Scientific and Technical Information of China (English)
Shuxia Guo; Yafeng Wang; Ruibing Liu; Ying Gao
2015-01-01
A typical electronic communication system, such as GPS receiver, unmanned aerial vehicle’s (UAV’s) data link, and radar, faces multi-dimensional and complicated electromagnetic interference in operating environment. To measure the anti-interference performance of the electronic communication system in the complicated electromagnetic interference environment, a method of multi-dimensional and complicated electromagnetic in-terference hardware-in-the-loop simulation in an anechoic room is proposed. It takes into account the characteristics of interference signals and the positional relationship among interference, the re-ceiver and the transmitter of the electronic communication system. It uses the grey relational method and the angular domain map-ping error correction method to control the relevant parameters, the microwave switch and so on, thus achieving the approximately actual mapping of the outdoor multi-dimensional and complicated electromagnetic interference in the anechoic room. To verify the ef-fectiveness of this method, the multi-dimensional and complicated electromagnetic interference of the UAV’s data link is simulated as an example. The results show that the degree of correlation between the calculated signal to interference ratio of the data link receiver in the actual scene and the measured signal to interfer-ence ratio of the data link receiver simulated with this method in the anechoic room is 0.968 1, proving that the method is effective for simulating the complicated electromagnetic interference.
Multi-dimensional Gain Scheduling with Application to Power Plant Control
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Stoustrup, Jakob; Trangbæk, Klaus
2003-01-01
This paper deals with gain scheduling control of a power plant model, which is an example of a multi-dimensional nonlinear system. Linear observer-based controllers are designed for a number of linear approximations of the system model in a set of operating points, and gain-scheduling control can...
Evidencing Learning Outcomes: A Multi-Level, Multi-Dimensional Course Alignment Model
Sridharan, Bhavani; Leitch, Shona; Watty, Kim
2015-01-01
This conceptual framework proposes a multi-level, multi-dimensional course alignment model to implement a contextualised constructive alignment of rubric design that authentically evidences and assesses learning outcomes. By embedding quality control mechanisms at each level for each dimension, this model facilitates the development of an aligned…
Multi-dimensional database design and implementation of dam safety monitoring system
Directory of Open Access Journals (Sweden)
Er-feng ZHAO
2008-09-01
Full Text Available To improve the effectiveness of dam safety monitoring database systems, the development process of a multi-dimensional conceptual data model was analyzed and a logic design was achieved in multi-dimensional database mode. The optimal data model was confirmed by identifying data objects, defining relations and reviewing entities. The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely. On this basis, a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established, for which factual tables and dimensional tables have been designed. Finally, based on service design and user interface design, the dam safety monitoring system has been developed with Delphi as the development tool. This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design. It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.
Multi-dimensional database design and implementation of dam safety monitoring system
Institute of Scientific and Technical Information of China (English)
Zhao Erfeng; Wang Yachao; Jiang Yufeng; Zhang Lei; Yu Hong
2008-01-01
To improve the effectiveness of dam safety monitoring database systems, the development process of a multi-dimensional conceptual data model was analyzed and a logic design was achieved in multi-dimensional database mode. The optimal data model was confirmed by identifying data objects, defining relations and reviewing entities. The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely. On this basis, a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established, for which factual tables and dimensional tables have been designed. Finally, based on service design and user interface design, the dam safety monitoring system has been developed with Delphi as the development tool. This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design. It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.
Multi-dimensional information diffusion and balancing market supply: an agent-based approach
Osinga, S.A.; Kramer, M.R.; Hofstede, G.J.; Beulens, A.J.M.
2013-01-01
This agent-based information management model is designed to explore how multi-dimensional information, spreading through a population of agents (for example farmers) affects market supply. Farmers make quality decisions that must be aligned with available markets. Markets distinguish themselves by
Building dynamic thermal simulation of low-order multi-dimensional heat transfer
Institute of Scientific and Technical Information of China (English)
高岩; 范蕊; 张群力
2014-01-01
Multi-dimensional heat transfers modeling is crucial for building simulations of insulated buildings, which are widely used and have multi-dimensional heat transfers characteristics. For this work, state-model-reduction techniques were used to develop a reduced low-order model of multi-dimensional heat transfers. With hot box experiment of hollow block wall, heat flow relative errors between experiment and low-order model predication were less than 8%and the largest errors were less than 3%. Also, frequency responses of five typical walls, each with different thermal masses or insulation modes, the low-order model and the complete model showed that the low-order model results agree very well in the lower excitation frequency band with deviations appearing only at high frequency. Furthermore, low-order model was used on intersection thermal bridge of a floor slab and exterior wall. Results show that errors between the two models are very small. This low-order model could be coupled with most existing simulation software for different thermal mass envelope analyses to make up for differences between the multi-dimensional and one-dimensional models, simultaneously simplifying simulation calculations.
Zeng, Bangze; Zhu, Youpan; Li, Zemin; Hu, Dechao; Luo, Lin; Zhao, Deli; Huang, Juan
2014-11-01
Duo to infrared image with low contrast, big noise and unclear visual effect, target is very difficult to observed and identified. This paper presents an improved infrared image detail enhancement algorithm based on adaptive histogram statistical stretching and gradient filtering (AHSS-GF). Based on the fact that the human eyes are very sensitive to the edges and lines, the author proposed to extract the details and textures by using the gradient filtering. New histogram could be acquired by calculating the sum of original histogram based on fixed window. With the minimum value for cut-off point, author carried on histogram statistical stretching. After the proper weights given to the details and background, the detail-enhanced results could be acquired finally. The results indicate image contrast could be improved and the details and textures could be enhanced effectively as well.
Mihajlovic, Vojkan; Patki, Shrishail; Grundlehner, Bernard
2014-01-01
Designing and developing a comfortable and convenient EEG system for daily usage that can provide reliable and robust EEG signal, encompasses a number of challenges. Among them, the most ambitious is the reduction of artifacts due to body movements. This paper studies the effect of head movement artifacts on the EEG signal and on the dry electrode-tissue impedance (ETI), monitored continuously using the imec's wireless EEG headset. We have shown that motion artifacts have huge impact on the EEG spectral content in the frequency range lower than 20 Hz. Coherence and spectral analysis revealed that ETI is not capable of describing disturbances at very low frequencies (below 2 Hz). Therefore, we devised a motion artifact reduction (MAR) method that uses a combination of a band-pass filtering and multi-channel adaptive filtering (AF), suitable for real-time MAR. This method was capable of substantially reducing artifacts produced by head movements.
Sheng-Hui, Rong; Hui-Xin, Zhou; Han-Lin, Qin; Rui, Lai; Kun, Qian
2016-05-01
Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.
Directory of Open Access Journals (Sweden)
Sindhu M R
2014-12-01
Full Text Available Variable speed drives are mostly preferred in industries, while considering energy saving, smooth control, flexible operation and fast response. On the other hand, this equipment generates dynamic harmonic distortions in source currents and draws variable reactive power demand depending on variation in load condition. These distortions propagate throughout the system and affect all the loads connected to the point of common coupling. Hence a dynamic harmonic and reactive compensator is necessary to enhance power quality to meet current and voltage distortion limits at point of common coupling as per IEEE standard. The most common choice for adjustable speed drives in industries is three phase voltage source inverter based induction motor drives which uses PWM technique for voltage, frequency and current control. This paper presents harmonic analysis of an induction motor drive in an industrial plant and development of adaptive dynamic harmonic and reactive compensator to improve power quality. A digital controller which is programmed with adaptive Artificial Neural Network (ANN based control algorithm is used for controlling shunt active filter. The simulation and experimental results show that the adaptive shunt active filter provides effective harmonic and reactive compensation in the system under steady state and dynamic load conditions.
Directory of Open Access Journals (Sweden)
Radek Martinek
2017-04-01
Full Text Available This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS, and the Normalized Least Mean Square (NLMS Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs, filtered from abdominal maternal phonocardiograms (mPCGs by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV.
Adaptive hyperspectral imaging with a MEMS-based full-frame programmable spectral filter
Graff, David L.; Love, Steven P.
2014-05-01
Rapidly programmable spatial light modulation devices based on MEMS technology have opened an exciting new arena in spectral imaging: rapidly reprogrammable, high spectral resolution, multi-band spectral filters that enable hyperspectral processing directly in the optical hardware of an imaging sensor. Implemented as a multiplexing spectral selector, a digital micro-mirror device (DMD) can independently choose or reject dozens or hundreds of spectral bands and present them simultaneously to an imaging sensor, forming a complete 2D image. The result is a high-speed, highresolution, programmable spectral filter that gives the user complete control over the spectral content of the image formed at the sensor. This technology enables a wide variety of rapidly reprogrammable operational capabilities within the same sensor including broadband, color, false color, multispectral, hyperspectral and target specific, matched filter imaging. Of particular interest is the ability to implement target-specific hyperspectral matched filters directly into the optical train of the sensor, producing an image highlighting a target within a spectrally cluttered scene in real time without further processing. By performing the hyperspectral image processing at the sensor, such a system can operate with high performance, greatly reduced data volume, and at a fraction of the cost of traditional push broom hyperspectral instruments. Examples of color, false color and target-specific matched-filter images recorded with our visible-spectrum prototype will be displayed, and extensions to other spectral regions will be discussed.
Energy Technology Data Exchange (ETDEWEB)
Sun, Winston Y. [Univ. of California, Berkeley, CA (United States)
1993-04-01
This thesis solves the problem of finding the optimal linear noise-reduction filter for linear tomographic image reconstruction. The optimization is data dependent and results in minimizing the mean-square error of the reconstructed image. The error is defined as the difference between the result and the best possible reconstruction. Applications for the optimal filter include reconstructions of positron emission tomographic (PET), X-ray computed tomographic, single-photon emission tomographic, and nuclear magnetic resonance imaging. Using high resolution PET as an example, the optimal filter is derived and presented for the convolution backprojection, Moore-Penrose pseudoinverse, and the natural-pixel basis set reconstruction methods. Simulations and experimental results are presented for the convolution backprojection method.
Digital Repository Service at National Institute of Oceanography (India)
Hassani, V.; Sorensen, A.J.; Pascoal, A.M.
feedback loop should be filtered by using a so-called wave filtering technique so as to prevent excessive control activity in response to wave frequency components. Furthermore, only the slowly- varying disturbances should be counterbalanced... by the propulsion system, whereas the oscillatory motion induced by the waves (1st-order wave induced loads) should not enter the feedback control loop. To this effect, DP control systems should be designed so as to react to the low fre- quency forces on the vessel...
Directory of Open Access Journals (Sweden)
Marie Ramon
2009-01-01
Full Text Available Systematic lossy error protection (SLEP is a robust error resilient mechanism based on principles of Wyner-Ziv (WZ coding for video transmission over error-prone networks. In an SLEP scheme, the video bitstream is separated into two parts: a systematic part consisting of a video sequence transmitted without channel coding, and additional information consisting of a WZ supplementary stream. This paper presents an adaptive SLEP scheme in which the WZ stream is obtained by frequency filtering in the transform domain. Additionally, error resilience varies adaptively depending on the characteristics of compressed video. We show that the proposed SLEP architecture achieves graceful degradation of reconstructed video quality in the presence of increasing transmission errors. Moreover, it provides good performances in terms of error protection as well as reconstructed video quality if compared to solutions based on coarser quantization, while offering an interesting embedded scheme to apply digital video format conversion.
Improved prediction error filters for adaptive feedback cancellation in hearing aids
DEFF Research Database (Denmark)
Ngo, Kim; van Waterschoot, Toon; Christensen, Mads Græsbøll;
2013-01-01
and the loudspeaker signal caused by the closed signal loop, in particular when the near-end signal is spectrally colored as is the case for a speech signal. This paper adopts a prediction-error method (PEM)-based approach to AFC, which is based on the use of decorrelating prediction error filters (PEFs). We propose...
Optical Filter Effects on Night Vision Goggle Acuity and Preservation of Dark Adaptation
2010-09-01
2010 NVG FILTER STUDY — THOMAS ET AL. uncorrected or best corrected Snellen visual acuities of 20/20 (6/6) or better and were evaluated with their ha... Snellen equivalent of the mean in parenthesis. * Indicates a signifi cant difference ( P , 0.05). TABLE II. TIME REQUIRED TO ACHIEVE BASELINE
Steeb, P.; Krause, S.; Linke, P.; Hensen, C.; Dale, A. W.; Nuzzo, M.; Treude, T.
2015-11-01
Large amounts of methane are delivered by fluids through the erosive forearc of the convergent margin offshore of Costa Rica and lead to the formation of cold seeps at the sediment surface. Besides mud extrusion, numerous cold seeps are created by landslides induced by seamount subduction or fluid migration along major faults. Most of the dissolved methane migrating through the sediments of cold seeps is oxidized within the benthic microbial methane filter by anaerobic oxidation of methane (AOM). Measurements of AOM and sulfate reduction as well as numerical modeling of porewater profiles revealed a highly active and efficient benthic methane filter at the Quepos Slide site, a landslide on the continental slope between the Nicoya and Osa Peninsula. Integrated areal rates of AOM ranged from 12.9 ± 6.0 to 45.2 ± 11.5 mmol m-2 d-1, with only 1 to 2.5 % of the upward methane flux being released into the water column. Additionally, two parallel sediment cores from Quepos Slide were used for in vitro experiments in a recently developed sediment-flow-through (SLOT) system to simulate an increased fluid and methane flux from the bottom of the sediment core. The benthic methane filter revealed a high adaptability whereby the methane oxidation efficiency responded to the increased fluid flow within ca. 170 d. To our knowledge, this study provides the first estimation of the natural biogeochemical response of seep sediments to changes in fluid flow.
Dai, Haifeng; Zhu, Letao; Zhu, Jiangong; Wei, Xuezhe; Sun, Zechang
2015-10-01
The accurate monitoring of battery cell temperature is indispensible to the design of battery thermal management system. To obtain the internal temperature of a battery cell online, an adaptive temperature estimation method based on Kalman filtering and an equivalent time-variant electrical network thermal (EENT) model is proposed. The EENT model uses electrical components to simulate the battery thermodynamics, and the model parameters are obtained with a least square algorithm. With a discrete state-space description of the EENT model, a Kalman filtering (KF) based internal temperature estimator is developed. Moreover, considering the possible time-varying external heat exchange coefficient, a joint Kalman filtering (JKF) based estimator is designed to simultaneously estimate the internal temperature and the external thermal resistance. Several experiments using the hard-cased LiFePO4 cells with embedded temperature sensors have been conducted to validate the proposed method. Validation results show that, the EENT model expresses the battery thermodynamics well, the KF based temperature estimator tracks the real central temperature accurately even with a poor initialization, and the JKF based estimator can simultaneously estimate both central temperature and external thermal resistance precisely. The maximum estimation errors of the KF- and JKF-based estimators are less than 1.8 °C and 1 °C respectively.
Directory of Open Access Journals (Sweden)
Xin Li
2016-02-01
Full Text Available Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning, which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF.
Energy Technology Data Exchange (ETDEWEB)
Takahashi, N.; Lee, Y.; Tsai, D. Y.; Ishii, K.; Kinoshita, T.; Tamura, H.; Kimura, M. (Dept. of Radiological Technology, School of Health Sciences, Niigata Univ., Niigata (Japan))
2008-09-15
Background: Much attention has been directed toward identifying early signs of cerebral ischemia on computed tomography (CT) images. Hypoattenuation of ischemic brain parenchyma has been found to be the most frequent early sign. Purpose: To evaluate the effect of a previously proposed adaptive smoothing filter for improving detection of parenchymal hypoattenuation of acute ischemic stroke on unenhanced CT images. Material and Methods: Twenty-six patients with parenchymal hypoattenuation and 49 control subjects without hypoattenuation were retrospectively selected in this study. The adaptive partial median filter (APMF) designed for improving detectability of hypoattenuation areas on unenhanced CT images was applied. Seven radiologists, including four certified radiologists and three radiology residents, indicated their confidence level regarding the presence (or absence) of hypoattenuation on CT images, first without and then with the APMF processed images. Their performances without and with the APMF processed images were evaluated by receiver operating characteristic (ROC) analysis. Results: The mean areas under the ROC curves (AUC) for all observers increased from 0.875 to 0.929 (P=0.002) when the radiologists observed with the APMF processed images. The mean sensitivity in the detection of hypoattenuation significantly improved, from 69% (126 of 182 observations) to 89% (151 of 182 observations), when employing the APMF (P=0.012). The specificity, however, was unaffected by the APMF (P=0.41). Conclusion: The APMF has the potential to improve the detection of parenchymal hypoattenuation of acute ischemic stroke on unenhanced CT images
Institute of Scientific and Technical Information of China (English)
Han Wenhua; Que Peiwen
2006-01-01
With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage (MFL) method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise (SPN). SPN can in some cases completely mask MFL signals from certain type of defects,and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.
Simulation and Performance Analysis of Adaptive Filtering Algorithms in Noise Cancellation
Ferdouse, Lilatul; Nipa, Tamanna Haque; Jaigirdar, Fariha Tasmin
2011-01-01
Noise problems in signals have gained huge attention due to the need of noise-free output signal in numerous communication systems. The principal of adaptive noise cancellation is to acquire an estimation of the unwanted interfering signal and subtract it from the corrupted signal. Noise cancellation operation is controlled adaptively with the target of achieving improved signal to noise ratio. This paper concentrates upon the analysis of adaptive noise canceller using Recursive Least Square (RLS), Fast Transversal Recursive Least Square (FTRLS) and Gradient Adaptive Lattice (GAL) algorithms. The performance analysis of the algorithms is done based on convergence behavior, convergence time, correlation coefficients and signal to noise ratio. After comparing all the simulated results we observed that GAL performs the best in noise cancellation in terms of Correlation Coefficient, SNR and Convergence Time. RLS, FTRLS and GAL were never evaluated and compared before on their performance in noise cancellation in ...
Design of the Adaptive Low-pass Filter%自适应低通滤波器的设计
Institute of Scientific and Technical Information of China (English)
马胜前; 冉兴萍; 范满红; 张维昭
2013-01-01
This paper presents the structure and implementation of an adaptive low-pass filter. After the input signal is pre-processed and shaped,the frequency signal is generated; the frequency signal then is converted into voltage signal through F/V circuit. Then the voltage signal is input into the voltage-controlled low-pass filter circuit which is mainly constituted by the analog multiplier MLT04 and the current feedback amplifier AD844. The cutoff frequency of the low-pass filter can be adjusted by the voltage signal, thus the frequency of the filter can be tracking atuomatically. In this paper,the design principle is introduced in detail,the design formulas are derived and the circuit of second order from tracking low-pass filter is given. When the input signal's frequency is in the range of 100 Hz to 10 kHz, the measured results are in good agreement with the theoretical results. If the value of timeing resistance in the F/V circuit is changed,the operating frequency of the filter can be extended to 100 kHz.%提出了一种自适应低通滤波器的结构和实现方法,输入信号预处理并整形后产生频率信号,频率信号经频率电压转换(F/V)电路转换成电压信号,再将该电压信号输入到模拟乘法器MLT04和电流反馈运算放大器AD844为核心构成的压控低通滤波电路.通过该电压信号调节滤波器的截止频率,从而实现滤波器频率的自动跟踪.介绍了设计原理,推导出设计公式并设计了自适应二阶低通滤波器电路.经过测试,输入信号的频率为100 Hz～10 kHz,实测结果与理论符合良好.改变F/V电路的定时电阻的阻值,电路工作频率可扩展到100 kHz.
Tehsin, Sara; Rehman, Saad; Awan, Ahmad B.; Chaudry, Qaiser; Abbas, Muhammad; Young, Rupert; Asif, Afia
2016-04-01
Sensitivity to the variations in the reference image is a major concern when recognizing target objects. A combinational framework of correlation filters and logarithmic transformation has been previously reported to resolve this issue alongside catering for scale and rotation changes of the object in the presence of distortion and noise. In this paper, we have extended the work to include the influence of different logarithmic bases on the resultant correlation plane. The meaningful changes in correlation parameters along with contraction/expansion in the correlation plane peak have been identified under different scenarios. Based on our research, we propose some specific log bases to be used in logarithmically transformed correlation filters for achieving suitable tolerance to different variations. The study is based upon testing a range of logarithmic bases for different situations and finding an optimal logarithmic base for each particular set of distortions. Our results show improved correlation and target detection accuracies.
Noise adaptive fading Kalman filter for free-space laser communication beacon tracking.
Li, Lixing; Huang, Yongmei; Wang, Qiang; Yang, Fasheng
2016-10-20
We proposed a prediction algorithm for laser communication pointing, acquisition, and tracking (PAT) subsystems in order to further improve PAT accuracy and reduce the effect of processing delay. In terms of this prediction algorithm, a fading Kalman filter is employed, with the observation noise obtained by the gray value distribution of the laser images. Moreover, to better fit the dynamics of a laser target, the two-stage dynamic model has been chosen as the state transition model for Kalman filtering. In addition, the two-stage dynamic model has been modified by accommodating its form to a change of time lag, thereby compensating the effect of time delay. A series of horizontal path (17 km) experiments under different atmospheric conditions were conducted in the fields. According to the experimental results, the algorithm we proposed could effectively reduce the tracking error and improve pointing accuracy.
Comparison of various schema of filter adaptivity for the tracking of maneuvering targets
Jouan, Alexandre; Bosse, Eloi; Simard, Marc-Alain; Shahbazian, Elisa
1998-09-01
Tracking maneuvering targets is a complex problem which has generated a great deal of effort over the past several years. It has now been well established that in terms of tracking accuracy, the Interacting Multiple Model (IMM) algorithm, where state estimates are mixed, performs significantly better for maneuvering targets than other types of filters. However, the complexity of the IMM algorithm can prohibit its use in these applications of which similar algorithms cannot provide the necessary accuracy and which can ont afford the computational load of IMM algorithm. This paper presents the evaluation of the tracking accuracy of a multiple model track filter using three different constant-velocity models running in parallel and a maneuver detector. The output estimate is defined by selecting the model whose likelihood function is lower than a target maneuver threshold.