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...
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
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...
International Nuclear Information System (INIS)
Dikusar, N.D.
1993-01-01
The new approach to solving of the finding problem is proposed. The method is based on Discrete Projective Transformations (DPT), the List Square Fitting (LSF) and uses the information feedback in tracing for linear or quadratic track segments (TS). The fast and stable with respect to measurement errors and background points recurrent algorithm is suggested. The algorithm realizes the family of digital adaptive projective filters (APF) with known nonlinear weight functions-projective invariants. APF can be used in adequate control systems for collection, processing and compression of data, including tracking problems for the wide class of detectors. 10 refs.; 9 figs
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
Digital Repository Service at National Institute of Oceanography (India)
Hassani, V.; Sorensen, A.J.; Pascoal, A.M.
This paper addresses a filtering problem that arises in the design of dynamic positioning systems for ships and offshore rigs subjected to the influence of sea waves. The dynamic model of the vessel captures explicitly the sea state as an uncertain...
A novel methodology for adaptive wave filtering of marine vessels: Theory and experiments
Digital Repository Service at National Institute of Oceanography (India)
Hassani, V.; Pascoal, A.M.; Sorensen, A.J.
This paper addresses a filtering problem that arises in the design of dynamic positioning systems for ships and offshore rigs subjected to the influence of sea waves. The vessel`s dynamic model adopted captures the sea state as an uncertain...
Implicit LES using adaptive filtering
Sun, Guangrui; Domaradzki, Julian A.
2018-04-01
In implicit large eddy simulations (ILES) numerical dissipation prevents buildup of small scale energy in a manner similar to the explicit subgrid scale (SGS) models. If spectral methods are used the numerical dissipation is negligible but it can be introduced by applying a low-pass filter in the physical space, resulting in an effective ILES. In the present work we provide a comprehensive analysis of the numerical dissipation produced by different filtering operations in a turbulent channel flow simulated using a non-dissipative, pseudo-spectral Navier-Stokes solver. The amount of numerical dissipation imparted by filtering can be easily adjusted by changing how often a filter is applied. We show that when the additional numerical dissipation is close to the subgrid-scale (SGS) dissipation of an explicit LES the overall accuracy of ILES is also comparable, indicating that periodic filtering can replace explicit SGS models. A new method is proposed, which does not require any prior knowledge of a flow, to determine the filtering period adaptively. Once an optimal filtering period is found, the accuracy of ILES is significantly improved at low implementation complexity and computational cost. The method is general, performing well for different Reynolds numbers, grid resolutions, and filter shapes.
Stochastic processes and filtering theory
Jazwinski, Andrew H
1970-01-01
This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well.Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probab
Czech Academy of Sciences Publication Activity Database
Kroupa, Tomáš
2008-01-01
Roč. 159, č. 14 (2008), s. 1773-1787 ISSN 0165-0114 R&D Projects: GA MŠk 1M0572; GA AV ČR KJB100300502 Institutional research plan: CEZ:AV0Z10750506 Keywords : filter * prime filter * fuzzy class theory Subject RIV: BA - General Mathematics Impact factor: 1.833, year: 2008
Analysis on Influence Factors of Adaptive Filter Acting on ANC
Zhang, Xiuqun; Zou, Liang; Ni, Guangkui; Wang, Xiaojun; Han, Tao; Zhao, Quanfu
The noise problem has become more and more serious in recent years. The adaptive filter theory which is applied in ANC [1] (active noise control) has also attracted more and more attention. In this article, the basic principle and algorithm of adaptive theory are both researched. And then the influence factor that affects its covergence rate and noise reduction is also simulated.
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...
Superresolution restoration of an image sequence: adaptive filtering approach.
Elad, M; Feuer, A
1999-01-01
This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters, least mean squares (LMS) or recursive least squares (RLS). The adaptation enables the treatment of linear space and time-variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the superresolution restoration algorithms are presented.
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.
Adaptivni digitalni filtri / Adaptive digital filters
Directory of Open Access Journals (Sweden)
Dragan Petković
2002-01-01
Full Text Available Rad opisuje osnove funkcionisanja adaptivnih filtara. U uvodnim razmatranjima obra-đene su osnove matematičke obrade diskretnih signala i z-transformacije kod adaptivnih filtara. Izložen je Wienerov problem filtracije. Predstavljeni su CCL petlja i Widrow-Hoffov LMS algoritam i razmotrena brzina konvergencije adaptivnih filtara. Praktično je realizova-na CCL petlja sa osvrtom na brzinu konvergencije. / The paper describes the basis of adaptive filter functioning. The first considerations deal with the mathematical processing of discrete signals and the Z-transform in adaptive filters. The Wieners filter processing problem was exposed. The Correlation Canceler Loop (CCL was presented as well as the Widrow-Hoffs adaptive Least Mean Squares (LMS step-by-step procedure. The convergence rate of adaptive filters was considered as well. The CCL simulations were obtained pointing out the convergence rate.
Adaptive filtering primer with Matlab
Poularikas, Alexander D
2006-01-01
INTRODUCTIONSignal ProcessingAn ExampleOutline of the TextDISCRETE-TIME SIGNAL PROCESSINGDiscrete Time SignalsTransform-Domain Representation of Discrete-Time SignalsThe Z-TransformDiscrete-Time SystemsProblemsHints-Solutions-SuggestionsRANDOM VARIABLES, SEQUENCES, AND STOCHASTIC PROCESSESRandom Signals and DistributionsAveragesStationary ProcessesSpecial Random Signals and Probability Density FunctionsWiener-Khinchin RelationsFiltering Random ProcessesSpecial Types of Random ProcessesNonparametric Spectra EstimationParametric Methods of power Spectral EstimationProblemsHints-Solutions-SuggestionsWIENER FILTERSThe Mean-Square ErrorThe FIR Wiener FilterThe Wiener SolutionWiener Filtering ExamplesProblemsHints-Solutions-SuggestionsEIGENVALUES OF RX - PROPERTIES OF THE ERROR SURFACEThe Eigenvalues of the Correlation MatrixGeometrical Properties of the Error SurfaceProblemsHints-Solutions-SuggestionsNEWTON AND STEEPEST-DESCENT METHODOne-Dimensional Gradient Search MethodSteepest-Descent AlgorithmProblemsHints-Sol...
Spherical Tensor Calculus for Local Adaptive Filtering
Reisert, Marco; Burkhardt, Hans
In 3D image processing tensors play an important role. While rank-1 and rank-2 tensors are well understood and commonly used, higher rank tensors are rare. This is probably due to their cumbersome rotation behavior which prevents a computationally efficient use. In this chapter we want to introduce the notion of a spherical tensor which is based on the irreducible representations of the 3D rotation group. In fact, any ordinary cartesian tensor can be decomposed into a sum of spherical tensors, while each spherical tensor has a quite simple rotation behavior. We introduce so called tensorial harmonics that provide an orthogonal basis for spherical tensor fields of any rank. It is just a generalization of the well known spherical harmonics. Additionally we propose a spherical derivative which connects spherical tensor fields of different degree by differentiation. Based on the proposed theory we present two applications. We propose an efficient algorithm for dense tensor voting in 3D, which makes use of tensorial harmonics decomposition of the tensor-valued voting field. In this way it is possible to perform tensor voting by linear-combinations of convolutions in an efficient way. Secondly, we propose an anisotropic smoothing filter that uses a local shape and orientation adaptive filter kernel which can be computed efficiently by the use spherical derivatives.
Spectral analysis and filter theory in applied geophysics
Buttkus, Burkhard
2000-01-01
This book is intended to be an introduction to the fundamentals and methods of spectral analysis and filter theory and their appli cations in geophysics. The principles and theoretical basis of the various methods are described, their efficiency and effectiveness eval uated, and instructions provided for their practical application. Be sides the conventional methods, newer methods arediscussed, such as the spectral analysis ofrandom processes by fitting models to the ob served data, maximum-entropy spectral analysis and maximum-like lihood spectral analysis, the Wiener and Kalman filtering methods, homomorphic deconvolution, and adaptive methods for nonstation ary processes. Multidimensional spectral analysis and filtering, as well as multichannel filters, are given extensive treatment. The book provides a survey of the state-of-the-art of spectral analysis and fil ter theory. The importance and possibilities ofspectral analysis and filter theory in geophysics for data acquisition, processing an...
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.
Theory and design of microwave filters
Hunter, Ian
2000-01-01
This is a thorough, graduate-level text which provides a single source for filter design including basic circuit theory, network synthesis and the design of a variety of microwave filter structures. The aim is to present design theories followed by specific examples with numerical simulations of the designs, with pictures of real devices wherever possible. The book is aimed at designers, engineers and researchers working in microwave electronics who need to design or specify filters.
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...
Analog Electronic Filters Theory, Design and Synthesis
Dimopoulos, Hercules G
2012-01-01
Filters are essential subsystems in a huge variety of electronic systems. Filter applications are innumerable; they are used for noise reduction, demodulation, signal detection, multiplexing, sampling, sound and speech processing, transmission line equalization and image processing, to name just a few. In practice, no electronic system can exist without filters. They can be found in everything from power supplies to mobile phones and hard disk drives and from loudspeakers and MP3 players to home cinema systems and broadband Internet connections. This textbook introduces basic concepts and methods and the associated mathematical and computational tools employed in electronic filter theory, synthesis and design. This book can be used as an integral part of undergraduate courses on analog electronic filters. Includes numerous, solved examples, applied examples and exercises for each chapter. Includes detailed coverage of active and passive filters in an independent but correlated manner. Emphasizes real filter...
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.
Convergence Performance of Adaptive Algorithms of L-Filters
Directory of Open Access Journals (Sweden)
Robert Hudec
2003-01-01
Full Text Available This paper deals with convergence parameters determination of adaptive algorithms, which are used in adaptive L-filters design. Firstly the stability of adaptation process, convergence rate or adaptation time, and behaviour of convergence curve belong among basic properties of adaptive algorithms. L-filters with variety of adaptive algorithms were used to their determination. Convergence performances finding of adaptive filters is important mainly for their hardware applications, where filtration in real time or adaptation of coefficient filter with low capacity of input data are required.
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.
Musical noise reduction using an adaptive filter
Hanada, Takeshi; Murakami, Takahiro; Ishida, Yoshihisa; Hoya, Tetsuya
2003-10-01
This paper presents a method for reducing a particular noise (musical noise). The musical noise is artificially produced by Spectral Subtraction (SS), which is one of the most conventional methods for speech enhancement. The musical noise is the tin-like sound and annoying in human auditory. We know that the duration of the musical noise is considerably short in comparison with that of speech, and that the frequency components of the musical noise are random and isolated. In the ordinary SS-based methods, the musical noise is removed by the post-processing. However, the output of the ordinary post-processing is delayed since the post-processing uses the succeeding frames. In order to improve this problem, we propose a novel method using an adaptive filter. In the proposed system, the observed noisy signal is used as the input signal to the adaptive filter and the output of SS is used as the reference signal. In this paper we exploit the normalized LMS (Least Mean Square) algorithm for the adaptive filter. Simulation results show that the proposed method has improved the intelligibility of the enhanced speech in comparison with the conventional method.
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.
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.
Normalised subband adaptive filtering with extended adaptiveness on degree of subband filters
Samuyelu, Bommu; Rajesh Kumar, Pullakura
2017-12-01
This paper proposes an adaptive normalised subband adaptive filtering (NSAF) to accomplish the betterment of NSAF performance. In the proposed NSAF, an extended adaptiveness is introduced from its variants in two ways. In the first way, the step-size is set adaptive, and in the second way, the selection of subbands is set adaptive. Hence, the proposed NSAF is termed here as variable step-size-based NSAF with selected subbands (VS-SNSAF). Experimental investigations are carried out to demonstrate the performance (in terms of convergence) of the VS-SNSAF against the conventional NSAF and its state-of-the-art adaptive variants. The results report the superior performance of VS-SNSAF over the traditional NSAF and its variants. It is also proved for its stability, robustness against noise and substantial computing complexity.
Applying Maxi-adjustment to Adaptive Information Filtering Agents
Lau, Raymond; ter Hofstede, Arthur H. M.; Bruza, Peter D.
2000-01-01
Learning and adaptation is a fundamental property of intelligent agents. In the context of adaptive information filtering, a filtering agent's beliefs about a user's information needs have to be revised regularly with reference to the user's most current information preferences. This learning and adaptation process is essential for maintaining the agent's filtering performance. The AGM belief revision paradigm provides a rigorous foundation for modelling rational and minimal changes to an age...
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.
1982-02-01
concept of motivation at all. Nuttin (1973) placed -5- Landy: ONR Annual Report certain hedonistic overtones on White’s principle of effectance motivation...deficiencies of motivation theories become particularly apparent in dealing with the issue of boredom . Tn terms of objective reality, it would seem...with expcsure to a c nstant stimulus set, we might justifiably conclude that there is a regular sequence which characterizes the appearance of boredom
Awareness, training and trust in interaction with adaptive spam filters
Cramer, H.S.M.; Evers, V.; van Someren, M.W.; Wielinga, B.J.; Greenberg, S.; Hudson, S.E.; Hinckley, K.; Morris, M.R.; Olsen Jr., D.R.
2009-01-01
Even though adaptive (trainable) spam filters are a common example of systems that make (semi-)autonomous decisions on behalf of the user, trust in these filters has been underexplored. This paper reports a study of usage of spam filters in the daily workplace and user behaviour in training these
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.
User interaction with user-adaptive information filters
Cramer, H.S.M.; Evers, V.; Someren, van M.W.; Wielinga, B.J.; Besselink, S.; Rutledge, L.W.; Stash, N.; Aroyo, L.M.; Aykin, N.M.
2007-01-01
User-adaptive information filters can be a tool to achieve timely delivery of the right information to the right person, a feat critical in crisis management. This paper explores interaction issues that need to be taken into account when designing a user-adaptive information filter. Two case studies
User Interaction with User-Adaptive Information Filters
H. Cramer; V. Evers; M. van Someren; B. Wielinga; S. Besselink; L. Rutledge (Lloyd); N. Stash; L. Aroyo (Lora)
2007-01-01
htmlabstractUser-adaptive information filters can be a tool to achieve timely delivery of the right information to the right person, a feat critical in crisis management. This paper explores interaction issues that need to be taken into account when designing a user-adaptive information filter. Two
Target Response Adaptation for Correlation Filter Tracking
Bibi, Adel Aamer; Mueller, Matthias; Ghanem, Bernard
2016-01-01
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
Applications of adaptive filters in active noise control
Darlington, Paul
The active reduction of acoustic noise is achieved by the addition of a cancelling acoustic signal to the unwanted sound. Successful definition of the cancelling signal amounts to a system identification problem. Recent advances in adaptive signal processing have allowed this problem to be tackled using adaptive filters, which offer significant advantages over conventional solutions. The extension of adaptive noise cancelling techniques, which were developed in the electrical signal conditioning context, to the control of acoustic systems is studied. An analysis is presented of the behavior of the Widrow-Hoff LMS adaptive noise canceller with a linear filter in its control loop. The active control of plane waves propagating axially in a hardwalled duct is used as a motivating model problem. The model problem also motivates the study of the effects of feedback around an LMS adaptive filter. An alternative stochastic gradient algorithm for controlling adaptive filters in the presence of feedback is presented.
Diagnostic analysis of vibration signals using adaptive digital filtering techniques
Jewell, R. E.; Jones, J. H.; Paul, J. E.
1983-01-01
Signal enhancement techniques are described using recently developed digital adaptive filtering equipment. Adaptive filtering concepts are not new; however, as a result of recent advances in microprocessor-based electronics, hardware has been developed that has stable characteristics and of a size exceeding 1000th order. Selected data processing examples are presented illustrating spectral line enhancement, adaptive noise cancellation, and transfer function estimation in the presence of corrupting noise.
Restoration of nuclear medicine images using adaptive Wiener filters
International Nuclear Information System (INIS)
Meinel, G.
1989-01-01
An adaptive Wiener filter implementation for restoration of nuclear medicine images is described. These are considerably disturbed both deterministically (definition) and stochastically (Poisson's quantum noise). After introduction of an image model, description of necessary parameter approximations and information on optimum design methods the implementation is described. The filter operates adaptively as concerns the local signal-to-noise ratio and is based on a filter band concept. To verify the restoration effect size numbers are introduced and the filter is tested against these numbers. (author)
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.
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.
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.
Filter. Remix. Make.: Cultivating Adaptability through Multimodality
Dusenberry, Lisa; Hutter, Liz; Robinson, Joy
2015-01-01
This article establishes traits of adaptable communicators in the 21st century, explains why adaptability should be a goal of technical communication educators, and shows how multimodal pedagogy supports adaptability. Three examples of scalable, multimodal assignments (infographics, research interviews, and software demonstrations) that evidence…
Speech Enhancement by Modified Convex Combination of Fractional Adaptive Filtering
Directory of Open Access Journals (Sweden)
M. Geravanchizadeh
2014-12-01
Full Text Available This paper presents new adaptive filtering techniques used in speech enhancement system. Adaptive filtering schemes are subjected to different trade-offs regarding their steady-state misadjustment, speed of convergence, and tracking performance. Fractional Least-Mean-Square (FLMS is a new adaptive algorithm which has better performance than the conventional LMS algorithm. Normalization of LMS leads to better performance of adaptive filter. Furthermore, convex combination of two adaptive filters improves its performance. In this paper, new convex combinational adaptive filtering methods in the framework of speech enhancement system are proposed. The proposed methods utilize the idea of normalization and fractional derivative, both in the design of different convex mixing strategies and their related component filters. To assess our proposed methods, simulation results of different LMS-based algorithms based on their convergence behavior (i.e., MSE plots and different objective and subjective criteria are compared. The objective and subjective evaluations include examining the results of SNR improvement, PESQ test, and listening tests for dual-channel speech enhancement. The powerful aspects of proposed methods are their low complexity, as expected with all LMS-based methods, along with a high convergence rate.
Czech Academy of Sciences Publication Activity Database
Ökzan, E.; Šmídl, Václav; Saha, S.; Lundquist, C.; Gustafsson, F.
2013-01-01
Roč. 49, č. 6 (2013), s. 1566-1575 ISSN 0005-1098 R&D Projects: GA ČR(CZ) GAP102/11/0437 Keywords : Unknown Noise Statistics * Adaptive Filtering * Marginalized Particle Filter * Bayesian Conjugate prior Subject RIV: BC - Control Systems Theory Impact factor: 3.132, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/smidl-0393047.pdf
A Nonlinear Adaptive Filter for Gyro Thermal Bias Error Cancellation
Galante, Joseph M.; Sanner, Robert M.
2012-01-01
Deterministic errors in angular rate gyros, such as thermal biases, can have a significant impact on spacecraft attitude knowledge. In particular, thermal biases are often the dominant error source in MEMS gyros after calibration. Filters, such as J\\,fEKFs, are commonly used to mitigate the impact of gyro errors and gyro noise on spacecraft closed loop pointing accuracy, but often have difficulty in rapidly changing thermal environments and can be computationally expensive. In this report an existing nonlinear adaptive filter is used as the basis for a new nonlinear adaptive filter designed to estimate and cancel thermal bias effects. A description of the filter is presented along with an implementation suitable for discrete-time applications. A simulation analysis demonstrates the performance of the filter in the presence of noisy measurements and provides a comparison with existing techniques.
Low-power adaptive filter based on RNS components
DEFF Research Database (Denmark)
Bernocchi, Gian Luca; Cardarilli, Gian Carlo; Del Re, Andrea
2007-01-01
In this paper a low-power implementation of an adaptive FIR filter is presented. The filter is designed to meet the constraints of channel equalization for fixed wireless communications that typically requires a large number of taps, but a serial updating of the filter coefficients, based...... 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....
Adaptive robust Kalman filtering for precise point positioning
International Nuclear Information System (INIS)
Guo, Fei; Zhang, Xiaohong
2014-01-01
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. (paper)
Adaptive Filtering for Non-Gaussian Processes
DEFF Research Database (Denmark)
Kidmose, Preben
2000-01-01
A new stochastic gradient robust filtering method, based on a non-linear amplitude transformation, is proposed. The method requires no a priori knowledge of the characteristics of the input signals and it is insensitive to the signals distribution and to the stationarity of the signals. A simulat...
A generalized adaptive mathematical morphological filter for LIDAR data
Cui, Zheng
Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in
A bag adapted for the handling of a filtering element or filter unit
International Nuclear Information System (INIS)
Marshall, D.A.G.
1980-01-01
The invention relates to a transparent, flexible, synthetic plastics bag adapted to contain a filter element or filter unit so that the latter can be inserted into or removed from a filter casing or duct while being contained in the bag. The bag has a neck portion which is capable of being removably secured in an air-tight manner on to a part of the casing, and gloves or glove portions are provided in, or are formed in, the wall of the bag to permit handles on the filter element or unit to be grasped. (author)
Interference suppression using a SAW-based adaptive filter
Saulnier, Gary J.; Grant, Calvin J.; Das, Pankaj K.
The structure and performance of a transversal filter interference suppressor that has been constructed using a surface acoustic wave (SAW) delay line are described. The delay line operates at a center frequency of 300 MHz and has eight equally spaced taps with an intertap delay of 150 ns. In the programmable mode, the tap weights are externally controllable, and in the adaptive mode, the tap weights are adjusted using the Widrow-Hoff least-mean-squared algorithm. Experimental results are provided that illustrate the performance of the filter in both the adaptive and programmable modes. Filter responses obtained in the adaptive mode are shown, along with spectra demonstrating the corresponding interference suppression. Bit-error-rate performance results for a single-tone jammer interfering with a direct sequence spread spectrum signal are presented.
Artifact removal from EEG signals using adaptive filters in cascade
Garcés Correa, A.; Laciar, E.; Patiño, H. D.; Valentinuzzi, M. E.
2007-11-01
Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.
Artifact removal from EEG signals using adaptive filters in cascade
International Nuclear Information System (INIS)
Garces Correa, A; Laciar, E; Patino, H D; Valentinuzzi, M E
2007-01-01
Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records
Artifact removal from EEG signals using adaptive filters in cascade
Energy Technology Data Exchange (ETDEWEB)
Garces Correa, A [Gabinete de TecnologIa Medica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Laciar, E [Gabinete de TecnologIa Medica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Patino, H D [Instituto de Automatica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Valentinuzzi, M E [Instituto Superior de Investigaciones Biologicas (INSIBIO), UNT-CONICET, Tucuman (Argentina)
2007-11-15
Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.
Reversible wavelet filter banks with side informationless spatially adaptive low-pass filters
Abhayaratne, Charith
2011-07-01
Wavelet transforms that have an adaptive low-pass filter are useful in applications that require the signal singularities, sharp transitions, and image edges to be left intact in the low-pass signal. In scalable image coding, the spatial resolution scalability is achieved by reconstructing the low-pass signal subband, which corresponds to the desired resolution level, and discarding other high-frequency wavelet subbands. In such applications, it is vital to have low-pass subbands that are not affected by smoothing artifacts associated with low-pass filtering. We present the mathematical framework for achieving 1-D wavelet transforms that have a spatially adaptive low-pass filter (SALP) using the prediction-first lifting scheme. The adaptivity decisions are computed using the wavelet coefficients, and no bookkeeping is required for the perfect reconstruction. Then, 2-D wavelet transforms that have a spatially adaptive low-pass filter are designed by extending the 1-D SALP framework. Because the 2-D polyphase decompositions are used in this case, the 2-D adaptivity decisions are made nonseparable as opposed to the separable 2-D realization using 1-D transforms. We present examples using the 2-D 5/3 wavelet transform and their lossless image coding and scalable decoding performances in terms of quality and resolution scalability. The proposed 2-D-SALP scheme results in better performance compared to the existing adaptive update lifting schemes.
Adaptive Rate Sampling and Filtering Based on Level Crossing Sampling
Directory of Open Access Journals (Sweden)
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.
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.
An adaptive Kalman filter for speckle reductions in ultrasound images
International Nuclear Information System (INIS)
Castellini, G.; Labate, D.; Masotti, L.; Mannini, E.; Rocchi, S.
1988-01-01
Speckle is the term used to describe the granular appearance found in ultrasound images. The presence of speckle reduces the diagnostic potential of the echographic technique because it tends to mask small inhomogeneities of the investigated tissue. We developed a new method of speckle reductions that utilizes an adaptive one-dimensional Kalman filter based on the assumption that the observed image can be considered as a superimposition of speckle on a ''true images''. The filter adaptivity, necessary to avoid loss of resolution, has been obtained by statistical considerations on the local signal variations. The results of the applications of this particular Kalman filter, both on A-Mode and B-MODE images, show a significant speckle reduction
Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation
DEFF Research Database (Denmark)
Mahmoudi, Zeinab; Poulsen, Niels Kjølstad; Madsen, Henrik
2017-01-01
The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated...
Application of adaptive Kalman filter in vehicle laser Doppler velocimetry
Fan, Zhe; Sun, Qiao; Du, Lei; Bai, Jie; Liu, Jingyun
2018-03-01
Due to the variation of road conditions and motor characteristics of vehicle, great root-mean-square (rms) error and outliers would be caused. Application of Kalman filter in laser Doppler velocimetry(LDV) is important to improve the velocity measurement accuracy. In this paper, the state-space model is built by using current statistical model. A strategy containing two steps is adopted to make the filter adaptive and robust. First, the acceleration variance is adaptively adjusted by using the difference of predictive observation and measured observation. Second, the outliers would be identified and the measured noise variance would be adjusted according to the orthogonal property of innovation to reduce the impaction of outliers. The laboratory rotating table experiments show that adaptive Kalman filter greatly reduces the rms error from 0.59 cm/s to 0.22 cm/s and has eliminated all the outliers. Road experiments compared with a microwave radar show that the rms error of LDV is 0.0218 m/s, and it proves that the adaptive Kalman filtering is suitable for vehicle speed signal processing.
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...
Adaptive training of feedforward neural networks by Kalman filtering
International Nuclear Information System (INIS)
Ciftcioglu, Oe.
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.)
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.
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.
Selected annotated bibliographies for adaptive filtering of digital image data
Mayers, Margaret; Wood, Lynnette
1988-01-01
Digital spatial filtering is an important tool both for enhancing the information content of satellite image data and for implementing cosmetic effects which make the imagery more interpretable and appealing to the eye. Spatial filtering is a context-dependent operation that alters the gray level of a pixel by computing a weighted average formed from the gray level values of other pixels in the immediate vicinity.Traditional spatial filtering involves passing a particular filter or set of filters over an entire image. This assumes that the filter parameter values are appropriate for the entire image, which in turn is based on the assumption that the statistics of the image are constant over the image. However, the statistics of an image may vary widely over the image, requiring an adaptive or "smart" filter whose parameters change as a function of the local statistical properties of the image. Then a pixel would be averaged only with more typical members of the same population. This annotated bibliography cites some of the work done in the area of adaptive filtering. The methods usually fall into two categories, (a) those that segment the image into subregions, each assumed to have stationary statistics, and use a different filter on each subregion, and (b) those that use a two-dimensional "sliding window" to continuously estimate the filter either the spatial or frequency domain, or may utilize both domains. They may be used to deal with images degraded by space variant noise, to suppress undesirable local radiometric statistics while enforcing desirable (user-defined) statistics, to treat problems where space-variant point spread functions are involved, to segment images into regions of constant value for classification, or to "tune" images in order to remove (nonstationary) variations in illumination, noise, contrast, shadows, or haze.Since adpative filtering, like nonadaptive filtering, is used in image processing to accomplish various goals, this bibliography
Restricted Kalman Filtering Theory, Methods, and Application
Pizzinga, Adrian
2012-01-01
In statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variations and other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measurement alone. This Brief offers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions: new proofs for results already established; new results within the subject; and applications in investment analysis and macroeconomics, where th
Adaptive particle filter for localization problem in service robotics
Directory of Open Access Journals (Sweden)
Heilig Alexander
2018-01-01
Full Text Available In this paper we present a statistical approach to the likelihood computation and adaptive resampling algorithm for particle filters using low cost ultrasonic sensors in the context of service robotics. This increases the efficiency of the particle filter in the Monte Carlo Localization problem by means of preventing sample impoverishment and ensuring it converges towards the most likely particle and simultaneously keeping less likely ones by systematic resampling. Proposed algorithms were developed in the ROS framework, simulation was done in Gazebo environment. Experiments using a differential drive mobile platform with 4 ultrasonic sensors in the office environment show that our approach provides strong improvement over particle filters with fixed sample sizes.
A synthesis theory for self-oscillating adaptive systems /SOAS/
Horowitz, I.; Smay, J.; Shapiro, A.
1974-01-01
A quantitative synthesis theory is presented for the Self-Oscillating Adaptive System (SOAS), whose nonlinear element has a static, odd character with hard saturation. The synthesis theory is based upon the quasilinear properties of the SOAS to forced inputs, which permits the extension of quantitative linear feedback theory to the SOAS. A reasonable definition of optimum design is shown to be the minimization of the limit cycle frequency. The great advantages of the SOAS is its zero sensitivity to pure gain changes. However, quasilinearity and control of the limit cycle amplitude at the system output, impose additional constraints which partially or completely cancel this advantage, depending on the numerical values of the design parameters. By means of narrow-band filtering, an additional factor is introduced which permits trade-off between filter complexity and limit cycle frequency minimization.
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.
Chained function filters - theory and applications.
Chrisostomidis, Christos E.
2003-01-01
For the first time, the new class of filter transfer functions, called Chained Functions is described, in detail. With Chained functions, one may define a new polynomial generating function that is given by the product of a combination of low order functions, called seed functions. The chained function concept provides with a variety of transfer functions, having the same order but different frequency-domain, time-domain and implementation characteristics. When compared to the conventional Ch...
Model Adaptation for Prognostics in a Particle Filtering Framework
Directory of Open Access Journals (Sweden)
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.
Model Adaptation for Prognostics in a Particle Filtering Framework
Saha, Bhaskar; Goebel, Kai Frank
2011-01-01
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 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.
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.
Reduced Rank Adaptive Filtering in Impulsive Noise Environments
Soury, Hamza; Abed-Meraim, Karim; Alouini, Mohamed-Slim
2014-01-01
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.
Improved pulsed photoacoustic detection by means of an adapted filter
González, M.; Santiago, G.; Peuriot, A.; Slezak, V.; Mosquera, C.
2005-06-01
We present a numerical and experimental study of two adapted filters devised to the quantitative analysis of weak photoacoustic signals. The first one is a simple convolution-type one and the other is based on neural networks of the multilayer perceptron type. The theoretical signal used as one of the inputs in both filters is derived from the solution of the transient response of the acoustic cell modeled with a simple transmission-line analogue. The filters were tested numerically by using the theoretical signal corrupted with white noise. After 500 iterations it was possible to define an average error for the returned value of each filter. Since the neural network outperformed the convolution-type, we assessed its performance by measuring SF6 traces diluted in N2 and excited by tuned TEA CO2 laser. The results show the use of the neural network filter allows recovering a signal with poor signal-to-noise ratio without resorting to extensive averaging, thus reducing the acquisition time while improving the precision of the measurement.
Adaptive multiresolution Hermite-Binomial filters for image edge and texture analysis
Gu, Y.H.; Katsaggelos, A.K.
1994-01-01
A new multiresolution image analysis approach using adaptive Hermite-Binomial filters is presented in this paper. According to the local image structural and textural properties, the analysis filter kernels are made adaptive both in their scales and orders. Applications of such an adaptive filtering
Combination of Adaptive Feedback Cancellation and Binaural Adaptive Filtering in Hearing Aids
Directory of Open Access Journals (Sweden)
Anthony Lombard
2009-01-01
Full Text Available We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaural scenario is presented, some already existing and commonly used feedback cancellation performance measures for the unilateral case are adapted to the binaural case, and possible interaction effects between the algorithms are identified. For illustration purposes, a blind source separation algorithm has been chosen as an example for adaptive binaural spatial filtering. Experimental results for binaural hearing aids confirm the theoretical findings and the validity of the new measures.
Sodium and potassium vapor Faraday filters revisited: theory and applications
International Nuclear Information System (INIS)
Harrell, S. D.; She, C.-Y.; Yuan Tao; Krueger, David A.; Chen, H.; Chen, S. S.; Hu, Z. L.
2009-01-01
A complete theory describing the transmission of atomic vapor Faraday filters is developed. The dependence of the filter transmission on atomic density and external magnetic field strength, as well as the frequency dependence of transmission, are explained in physical terms. As examples, applications of the computed results to ongoing research to suppress sky background, thus allowing Na lidar operation under sunlit conditions, and to enable measurement of the density of mesospheric oxygen atoms are briefly discussed
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.
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.
Adaptive anisotropic diffusion filtering of Monte Carlo dose distributions
International Nuclear Information System (INIS)
Miao Binhe; Jeraj, Robert; Bao Shanglian; Mackie, Thomas R
2003-01-01
The Monte Carlo method is the most accurate method for radiotherapy dose calculations, if used correctly. However, any Monte Carlo dose calculation is burdened with statistical noise. In this paper, denoising of Monte Carlo dose distributions with a three-dimensional adaptive anisotropic diffusion method was investigated. The standard anisotropic diffusion method was extended by changing the filtering parameters adaptively according to the local statistical noise. Smoothing of dose distributions with different noise levels in an inhomogeneous phantom, a conventional and an IMRT treatment case is shown. The resultant dose distributions were analysed using several evaluating criteria. It is shown that the adaptive anisotropic diffusion method can reduce statistical noise significantly (two to five times, corresponding to the reduction of simulation time by a factor of up to 20), while preserving important gradients of the dose distribution well. The choice of free parameters of the method was found to be fairly robust
Adaptive filters and internal models: multilevel description of cerebellar function.
Porrill, John; Dean, Paul; Anderson, Sean R
2013-11-01
Cerebellar function is increasingly discussed in terms of engineering schemes for motor control and signal processing that involve internal models. To address the relation between the cerebellum and internal models, we adopt the chip metaphor that has been used to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections. This metaphor indicates that identifying the function of a particular cerebellar chip requires knowledge of both the general microcircuit algorithm and the chip's individual connections. Here we use a popular candidate algorithm as embodied in the adaptive filter, which learns to decorrelate its inputs from a reference ('teaching', 'error') signal. This algorithm is computationally powerful enough to be used in a very wide variety of engineering applications. However, the crucial issue is whether the external connectivity required by such applications can be implemented biologically. We argue that some applications appear to be in principle biologically implausible: these include the Smith predictor and Kalman filter (for state estimation), and the feedback-error-learning scheme for adaptive inverse control. However, even for plausible schemes, such as forward models for noise cancellation and novelty-detection, and the recurrent architecture for adaptive inverse control, there is unlikely to be a simple mapping between microzone function and internal model structure. This initial analysis suggests that cerebellar involvement in particular behaviours is therefore unlikely to have a neat classification into categories such as 'forward model'. It is more likely that cerebellar microzones learn a task-specific adaptive-filter operation which combines a number of signal-processing roles. Copyright © 2012 Elsevier Ltd. All rights reserved.
Adaptive probabilistic collocation based Kalman filter for unsaturated flow problem
Man, J.; Li, W.; Zeng, L.; Wu, L.
2015-12-01
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 "cure of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF is even more computationally expensive than EnKF. Motivated by recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problem. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to alleviate the inconsistency between model parameters and states. The performance of RAPCKF is tested by unsaturated flow numerical cases. 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.
Implementation of an Improved Adaptive Testing Theory
Al-A'ali, Mansoor
2007-01-01
Computer adaptive testing is the study of scoring tests and questions based on assumptions concerning the mathematical relationship between examinees' ability and the examinees' responses. Adaptive student tests, which are based on item response theory (IRT), have many advantages over conventional tests. We use the least square method, a…
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.
Statistical-uncertainty-based adaptive filtering of lidar signals
International Nuclear Information System (INIS)
Fuehrer, P. L.; Friehe, C. A.; Hristov, T. S.; Cooper, D. I.; Eichinger, W. E.
2000-01-01
An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometerological humidity data were used to calibrate the ratio of the lidar gains of the H 2 O and the N 2 photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem. (c) 2000 Optical Society of America
Adaptive Kalman filtering for diagnosis of multiple component degradations
International Nuclear Information System (INIS)
Aumeier, S. E.; Alpay, B.; Lee, J. C.
2005-01-01
We have developed an adaptive Kalman filtering algorithm for the diagnosis of faults or degradations of multiple components in nuclear power plants. We propose to detect the presence and magnitude of the fault(s) through noisy system observations when the measurements indicate significant deviations from predictions. Our diagnostic algorithm uses the measurement residuals, i.e., the difference between the measurements and predictions, to generate a noise input to the uncertain component state in an adaptive Kalman filtering algorithm so that various postulated component transitions or degradations may be statistically represented. The diagnostic algorithm has been tested with a balance of plant (BOP) model of a boiling water reactor (BWR). We have presented a set of algorithms for the detection and diagnosis of component faults of arbitrary magnitude and type within a multi-component system. By analyzing a number of transients including the one example illustrated in the paper, we find that these algorithms are not only capable of determining the correct component fault and magnitude for single components but also they can be used to determine binary faults satisfactorily. Additional study is under way to evaluate the performance of the proposed algorithm including the sensitivity of the diagnostic time to adaptive noise matrix introduced (see equations 7 and 8 illustrated in the paper)
Real Time Adaptive Stream-oriented Geo-data Filtering
Directory of Open Access Journals (Sweden)
A. A. Golovkov
2016-01-01
Full Text Available The cutting-edge engineering maintenance software systems of various objects are aimed at processing of geo-location data coming from the employees’ mobile devices in real time. To reduce the amount of transmitted data such systems, usually, use various filtration methods of geo-coordinates recorded directly on mobile devices.The paper identifies the reasons for errors of geo-data coming from different sources, and proposes an adaptive dynamic method to filter geo-location data. Compared with the static method previously described in the literature [1] the approach offers to align adaptively the filtering threshold with changing characteristics of coordinates from many sources of geo-location data.To evaluate the efficiency of the developed filter method have been involved about 400 thousand points, representing motion paths of different type (on foot, by car and high-speed train and parking (indoors, outdoors, near high-rise buildings to take data from different mobile devices. Analysis of results has shown that the benefits of the proposed method are the more precise location of long parking (up to 6 hours and coordinates when user is in motion, the capability to provide steam-oriented filtering of data from different sources that allows to use the approach in geo-information systems, providing continuous monitoring of the location in streamoriented data processing in real time. The disadvantage is a little bit more computational complexity and increasing amount of points of the final track as compared to other filtration techniques.In general, the developed approach enables a significant quality improvement of displayed paths of moving mobile objects.
An adaptive orienting theory of error processing.
Wessel, Jan R
2018-03-01
The ability to detect and correct action errors is paramount to safe and efficient goal-directed behaviors. Existing work on the neural underpinnings of error processing and post-error behavioral adaptations has led to the development of several mechanistic theories of error processing. These theories can be roughly grouped into adaptive and maladaptive theories. While adaptive theories propose that errors trigger a cascade of processes that will result in improved behavior after error commission, maladaptive theories hold that error commission momentarily impairs behavior. Neither group of theories can account for all available data, as different empirical studies find both impaired and improved post-error behavior. This article attempts a synthesis between the predictions made by prominent adaptive and maladaptive theories. Specifically, it is proposed that errors invoke a nonspecific cascade of processing that will rapidly interrupt and inhibit ongoing behavior and cognition, as well as orient attention toward the source of the error. It is proposed that this cascade follows all unexpected action outcomes, not just errors. In the case of errors, this cascade is followed by error-specific, controlled processing, which is specifically aimed at (re)tuning the existing task set. This theory combines existing predictions from maladaptive orienting and bottleneck theories with specific neural mechanisms from the wider field of cognitive control, including from error-specific theories of adaptive post-error processing. The article aims to describe the proposed framework and its implications for post-error slowing and post-error accuracy, propose mechanistic neural circuitry for post-error processing, and derive specific hypotheses for future empirical investigations. © 2017 Society for Psychophysiological Research.
Edge and line enhancement by adaptive lattice filtering
International Nuclear Information System (INIS)
Brolley, J.E.
1979-01-01
Digitized images have been two-dimensionally transformed to the Haar sequency domain. High-sequency boosting was performed and the inverse Haar two-dimensional transform applied. The resulting image was then raster-scanned with a continuously adaptive lattice filter. This procedure was applied to a simple image of a photographic step tablet and a complex scene. All of the lines of the step tablet were well defined over the whole dynamic range. Useful definition of lines in the complex scene was obtained
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 Lp norm. The results are presented and the efficiency of each method is discussed. © 2014 IEEE.
Linguistic Derivations and Filtering. Minimalism and Optimality Theory
Broekhuis, H.; Vogel, R.
2013-01-01
This volume focuses on the role of the postulated derivational and filtering devices in current linguistic theory and aims to promote the exchange of ideas between the proponents of MP and OT in order to evaluate the role of these devices in the two frameworks. It sheds more light on the tenability
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 i...
Adaptive filtering for hidden node detection and tracking in networks.
Hamilton, Franz; Setzer, Beverly; Chavez, Sergio; Tran, Hien; Lloyd, Alun L
2017-07-01
The identification of network connectivity from noisy time series is of great interest in the study of network dynamics. This connectivity estimation problem becomes more complicated when we consider the possibility of hidden nodes within the network. These hidden nodes act as unknown drivers on our network and their presence can lead to the identification of false connections, resulting in incorrect network inference. Detecting the parts of the network they are acting on is thus critical. Here, we propose a novel method for hidden node detection based on an adaptive filtering framework with specific application to neuronal networks. We consider the hidden node as a problem of missing variables when model fitting and show that the estimated system noise covariance provided by the adaptive filter can be used to localize the influence of the hidden nodes and distinguish the effects of different hidden nodes. Additionally, we show that the sequential nature of our algorithm allows for tracking changes in the hidden node influence over time.
Microseismic Event Location Improvement Using Adaptive Filtering for Noise Attenuation
de Santana, F. L., Sr.; do Nascimento, A. F.; Leandro, W. P. D. N., Sr.; de Carvalho, B. M., Sr.
2017-12-01
In this work we show how adaptive filtering noise suppression improves the effectiveness of the Source Scanning Algorithm (SSA; Kao & Shan, 2004) in microseism location in the context of fracking operations. The SSA discretizes the time and region of interest in a 4D vector and, for each grid point and origin time, a brigthness value (seismogram stacking) is calculated. For a given set of velocity model parameters, when origin time and hypocenter of the seismic event are correct, a maximum value for coherence (or brightness) is achieved. The result is displayed on brightness maps for each origin time. Location methods such as SSA are most effective when the noise present in the seismograms is incoherent, however, the method may present false positives when the noise present in the data is coherent as occurs in fracking operations. To remove from the seismograms, the coherent noise from the pump and engines used in the operation, we use an adaptive filter. As the noise reference, we use the seismogram recorded at the station closest to the machinery employed. Our methodology was tested on semi-synthetic data. The microseismic was represented by Ricker pulses (with central frequency of 30Hz) on synthetics seismograms, and to simulate real seismograms on a surface microseismic monitoring situation, we added real noise recorded in a fracking operation to these synthetics seismograms. The results show that after the filtering of the seismograms, we were able to improve our detection threshold and to achieve a better resolution on the brightness maps of the located events.
Superconducting Magnetometry for Cardiovascular Studies and AN Application of Adaptive Filtering.
Leifer, Mark Curtis
Sensitive magnetic detectors utilizing Superconducting Quantum Interference Devices (SQUID's) have been developed and used for studying the cardiovascular system. The theory of magnetic detection of cardiac currents is discussed, and new experimental data supporting the validity of the theory is presented. Measurements on both humans and dogs, in both healthy and diseased states, are presented using the new technique, which is termed vector magnetocardiography. In the next section, a new type of superconducting magnetometer with a room temperature pickup is analyzed, and techniques for optimizing its sensitivity to low-frequency sub-microamp currents are presented. Performance of the actual device displays significantly improved sensitivity in this frequency range, and the ability to measure currents in intact, in vivo biological fibers. The final section reviews the theoretical operation of a digital self-optimizing filter, and presents a four-channel software implementation of the system. The application of the adaptive filter to enhancement of geomagnetic signals for earthquake forecasting is discussed, and the adaptive filter is shown to outperform existing techniques in suppressing noise from geomagnetic records.
Random matrix theory filters and currency portfolio optimisation
Daly, J.; Crane, M.; Ruskin, H. J.
2010-04-01
Random matrix theory (RMT) filters have recently been shown to improve the optimisation of financial portfolios. This paper studies the effect of three RMT filters on realised portfolio risk, using bootstrap analysis and out-of-sample testing. We considered the case of a foreign exchange and commodity portfolio, weighted towards foreign exchange, and consisting of 39 assets. This was intended to test the limits of RMT filtering, which is more obviously applicable to portfolios with larger numbers of assets. We considered both equally and exponentially weighted covariance matrices, and observed that, despite the small number of assets involved, RMT filters reduced risk in a way that was consistent with a much larger S&P 500 portfolio. The exponential weightings indicated showed good consistency with the value suggested by Riskmetrics, in contrast to previous results involving stocks. This decay factor, along with the low number of past moves preferred in the filtered, equally weighted case, displayed a trend towards models which were reactive to recent market changes. On testing portfolios with fewer assets, RMT filtering provided less or no overall risk reduction. In particular, no long term out-of-sample risk reduction was observed for a portfolio consisting of 15 major currencies and commodities.
National Research Council Canada - National Science Library
Dhillon, Santpal
2001-01-01
...) notch filter with a simplified adaptation algorithm for removal of power line frequency from ECG signals, The performance of this filter is better as compared to a second order infinite impulse response (IIR...
Two-antenna GNSS Aided-INS Alignment Using Adaptive Control of Filter Noise Covariance
Directory of Open Access Journals (Sweden)
HAO Yushi
2018-04-01
Full Text Available This paper developed a theory of INS fine alignment in order to restrain the divergence of yaw angle,two antennas GNSS aided-INS integrated alignment algorithm was utilized.An attitude error measurement equation was conducted based on the relationship between baseline vectors calculated by two sensors and attitude error.The algorithm was executed by EKF using adaptive control of filter noise covariance.The experimental results showed that stability of the integrated system was improved under the system noise covariance adaptive control mechanism;The measurement noise covariance adaptive control mechanism can reduce the influence of measurement noise and improve the alignment absolute accuracy;Further improvement was achieved under the condition of minim bias of baseline length.The accuracy of roll and pitch was 0.02°,the accuracy of yaw was 0.04°.
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.
Babič, Rudolf; Horvat, Bogomir; Osebik, Davorin
2001-01-01
Adaptive digital filters have a wide range of applications in the area of signal processing where only minimum a priori knowledge of signal characteristics is available. In this article the adaptive FIR digital filter implementation based on the distributed arithmetic technique is described. The major problem with conventional adaptive digital filter is the need for fast multipliers. When using a hardware implementation. These multipliers take up the disproportional amount of the overall cost...
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.
Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting
Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.
2014-12-01
Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.
International Nuclear Information System (INIS)
Zhao, Yibo; Jiang, Yi; Feng, Jiuchao; Wu, Lifu
2016-01-01
Highlights: • A novel nonlinear Wiener adaptive filters based on the backslash operator are proposed. • The identification approach to the memristor-based chaotic systems using the proposed adaptive filters. • The weight update algorithm and convergence characteristics for the proposed adaptive filters are derived. - Abstract: Memristor-based chaotic systems have complex dynamical behaviors, which are characterized as nonlinear and hysteresis characteristics. Modeling and identification of their nonlinear model is an important premise for analyzing the dynamical behavior of the memristor-based chaotic systems. This paper presents a novel nonlinear Wiener adaptive filtering identification approach to the memristor-based chaotic systems. The linear part of Wiener model consists of the linear transversal adaptive filters, the nonlinear part consists of nonlinear adaptive filters based on the backslash operator for the hysteresis characteristics of the memristor. The weight update algorithms for the linear and nonlinear adaptive filters are derived. Final computer simulation results show the effectiveness as well as fast convergence characteristics. Comparing with the adaptive nonlinear polynomial filters, the proposed nonlinear adaptive filters have less identification error.
Adaptive Core Simulation Employing Discrete Inverse Theory - Part I: Theory
International Nuclear Information System (INIS)
Abdel-Khalik, Hany S.; Turinsky, Paul J.
2005-01-01
Use of adaptive simulation is intended to improve the fidelity and robustness of important core attribute predictions such as core power distribution, thermal margins, and core reactivity. Adaptive simulation utilizes a selected set of past and current reactor measurements of reactor observables, i.e., in-core instrumentation readings, to adapt the simulation in a meaningful way. A meaningful adaption will result in high-fidelity and robust adapted core simulator models. To perform adaption, we propose an inverse theory approach in which the multitudes of input data to core simulators, i.e., reactor physics and thermal-hydraulic data, are to be adjusted to improve agreement with measured observables while keeping core simulator models unadapted. At first glance, devising such adaption for typical core simulators with millions of input and observables data would spawn not only several prohibitive challenges but also numerous disparaging concerns. The challenges include the computational burdens of the sensitivity-type calculations required to construct Jacobian operators for the core simulator models. Also, the computational burdens of the uncertainty-type calculations required to estimate the uncertainty information of core simulator input data present a demanding challenge. The concerns however are mainly related to the reliability of the adjusted input data. The methodologies of adaptive simulation are well established in the literature of data adjustment. We adopt the same general framework for data adjustment; however, we refrain from solving the fundamental adjustment equations in a conventional manner. We demonstrate the use of our so-called Efficient Subspace Methods (ESMs) to overcome the computational and storage burdens associated with the core adaption problem. We illustrate the successful use of ESM-based adaptive techniques for a typical boiling water reactor core simulator adaption problem
Linear theory for filtering nonlinear multiscale systems with model error.
Berry, Tyrus; Harlim, John
2014-07-08
In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online , as part of a filtering
Locally-adaptive Myriad Filters for Processing ECG Signals in Real Time
Directory of Open Access Journals (Sweden)
Nataliya Tulyakova
2017-03-01
Full Text Available The locally adaptive myriad filters to suppress noise in electrocardiographic (ECG signals in almost in real time are proposed. Statistical estimates of efficiency according to integral values of such criteria as mean square error (MSE and signal-to-noise ratio (SNR for the test ECG signals sampled at 400 Hz embedded in additive Gaussian noise with different values of variance are obtained. Comparative analysis of adaptive filters is carried out. High efficiency of ECG filtering and high quality of signal preservation are demonstrated. It is shown that locally adaptive myriad filters provide higher degree of suppressing additive Gaussian noise with possibility of real time implementation.
Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V
2015-01-01
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 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.
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.
Preprocessing of PHERMEX flash radiographic images with Haar and adaptive filtering
International Nuclear Information System (INIS)
Brolley, J.E.
1978-11-01
Work on image preparation has continued with the application of high-sequency boosting via Haar filtering. This is useful in developing line or edge structures. Widrow LMS adaptive filtering has also been shown to be useful in developing edge structure in special problems. Shadow effects can be obtained with the latter which may be useful for some problems. Combined Haar and adaptive filtering is illustrated for a PHERMEX image
Adaptive kernels in approximate filtering of state-space models
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil
2017-01-01
Roč. 31, č. 6 (2017), s. 938-952 ISSN 0890-6327 R&D Projects: GA ČR(CZ) GP14-06678P Institutional support: RVO:67985556 Keywords : filtering * nonlinear filters * Bayesian filtering * sequential Monte Carlo * approximate filtering Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 1.708, year: 2016 http://library.utia.cs.cz/separaty/2016/AS/dedecius-0466448.pdf
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
An adaptive filter with a large number of weights or taps is necessary for stereophonic acoustic echo cancellation (SAEC), depending on the room impulse response and acoustic path where the cancellation is performed. However, a large tap-length results in slow convergence and increases...... the complexity of the tapped delay line structure for FIR adaptive filters. To overcome this problem, there is a need for an optimum tap-length-estimation algorithm that provides better convergence for the adaptive filters used in SAEC. This paper presents a solution to the problem of balancing convergence...... and steady-state performance of long length adaptive filters used for SAEC by proposing a new tap-length-optimization algorithm. The optimum tap length and step size of the adaptive filter are derived considering an impulse response with an exponentially-decaying envelope, which models a wide range...
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.
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.
Adaptive spatial filtering for daytime satellite quantum key distribution
Gruneisen, Mark T.; Sickmiller, Brett A.; Flanagan, Michael B.; Black, James P.; Stoltenberg, Kurt E.; Duchane, Alexander W.
2014-11-01
The rate of secure key generation (SKG) in quantum key distribution (QKD) is adversely affected by optical noise and loss in the quantum channel. In a free-space atmospheric channel, the scattering of sunlight into the channel can lead to quantum bit error ratios (QBERs) sufficiently large to preclude SKG. Furthermore, atmospheric turbulence limits the degree to which spatial filtering can reduce sky noise without introducing signal losses. A system simulation quantifies the potential benefit of tracking and higher-order adaptive optics (AO) technologies to SKG rates in a daytime satellite engagement scenario. The simulations are performed assuming propagation from a low-Earth orbit (LEO) satellite to a terrestrial receiver that includes an AO system comprised of a Shack-Hartmann wave-front sensor (SHWFS) and a continuous-face-sheet deformable mirror (DM). The effects of atmospheric turbulence, tracking, and higher-order AO on the photon capture efficiency are simulated using statistical representations of turbulence and a time-domain waveoptics hardware emulator. Secure key generation rates are then calculated for the decoy state QKD protocol as a function of the receiver field of view (FOV) for various pointing angles. The results show that at FOVs smaller than previously considered, AO technologies can enhance SKG rates in daylight and even enable SKG where it would otherwise be prohibited as a consequence of either background optical noise or signal loss due to turbulence effects.
Optimal adaptive normalized matched filter for large antenna arrays
Kammoun, Abla; Couillet, Romain; Pascal, Fré dé ric; Alouini, Mohamed-Slim
2016-01-01
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.
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
International Nuclear Information System (INIS)
Zhang, Yan; Tang, Baoping; Chen, Rengxiang; Liu, Ziran
2016-01-01
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
Directory of Open Access Journals (Sweden)
Mingyue Cui
2016-09-01
Full Text Available A novel control approach is proposed for trajectory tracking of a wheeled mobile robot with unknown wheels’ slipping. The longitudinal and lateral slipping are considered and processed as three time-varying parameters. The adaptive unscented Kalman filter is then designed to estimate the slipping parameters online, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the adaptive unscented Kalman filter context. Considering the practical physical constrains, a stable tracking control law for this robot system is proposed by the backstepping method. Asymptotic stability is guaranteed by Lyapunov stability theory. Control gains are determined online by applying pole placement method. Simulation and real experiment results show the effectiveness and robustness of the proposed control method.
International Nuclear Information System (INIS)
Iwamoto, Hiroyuki; Tanaka, Nobuo; Hill, Simon G
2010-01-01
This paper concerns the active vibration control of a rectangular panel using smart sensors from the viewpoint of an active wave control theory. The objective of this paper is to present a new type of filter which enables the measurement of the wave amplitude of a rectangular panel in real time for the application of an adaptive feedforward control system which inactivates vibration modes. Firstly, a novel wave filtering method using smart PVDF sensors is proposed. It is found that the shaping function of smart sensors is a complex function. To realize the smart sensor in a practical situation, a Hilbert transformer is utilized to implement a phase shifter of 90° for broadband frequencies. Then, from the viewpoint of a numerical analysis, the characteristics of the proposed wave filter and the performance of the adaptive feedforward control system using the wave filter are discussed. Finally, experiments implementing the active wave control theory which uses the proposed wave filter are conducted, demonstrating the validity of the proposed method in suppressing the vibration of a rectangular panel
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.
An emergent theory of digital library metadata enrich then filter
Stevens, Brett
2015-01-01
An Emergent Theory of Digital Library Metadata is a reaction to the current digital library landscape that is being challenged with growing online collections and changing user expectations. The theory provides the conceptual underpinnings for a new approach which moves away from expert defined standardised metadata to a user driven approach with users as metadata co-creators. Moving away from definitive, authoritative, metadata to a system that reflects the diversity of users’ terminologies, it changes the current focus on metadata simplicity and efficiency to one of metadata enriching, which is a continuous and evolving process of data linking. From predefined description to information conceptualised, contextualised and filtered at the point of delivery. By presenting this shift, this book provides a coherent structure in which future technological developments can be considered.
Benardini, James N.; Koukol, Robert C.; Schubert, Wayne W.; Morales, Fabian; Klatte, Marlin F.
2012-01-01
A report describes an adaptation of a filter assembly to enable it to be used to filter out microorganisms from a propulsion system. The filter assembly has previously been used for particulates greater than 2 micrometers. Projects that utilize large volumes of nonmetallic materials of planetary protection concern pose a challenge to their bioburden budget, as a conservative specification value of 30 spores per cubic centimeter is typically used. Helium was collected utilizing an adapted filtration approach employing an existing Millipore filter assembly apparatus used by the propulsion team for particulate analysis. The filter holder on the assembly has a 47-mm diameter, and typically a 1.2-5 micrometer pore-size filter is used for particulate analysis making it compatible with commercially available sterilization filters (0.22 micrometers) that are necessary for biological sampling. This adaptation to an existing technology provides a proof-of-concept and a demonstration of successful use in a ground equipment system. This adaptation has demonstrated that the Millipore filter assembly can be utilized to filter out microorganisms from a propulsion system, whereas in previous uses the filter assembly was utilized for particulates greater than 2 micrometers.
A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation
Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao
2016-01-01
The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361
Adaptive mean filtering for noise reduction in CT polymer gel dosimetry
International Nuclear Information System (INIS)
Hilts, Michelle; Jirasek, Andrew
2008-01-01
X-ray computed tomography (CT) as a method of extracting 3D dose information from irradiated polymer gel dosimeters is showing potential as a practical means to implement gel dosimetry in a radiation therapy clinic. However, the response of CT contrast to dose is weak and noise reduction is critical in order to achieve adequate dose resolutions with this method. Phantom design and CT imaging technique have both been shown to decrease image noise. In addition, image postprocessing using noise reduction filtering techniques have been proposed. This work evaluates in detail the use of the adaptive mean filter for reducing noise in CT gel dosimetry. Filter performance is systematically tested using both synthetic patterns mimicking a range of clinical dose distribution features as well as actual clinical dose distributions. Both low and high signal-to-noise ratio (SNR) situations are examined. For all cases, the effects of filter kernel size and the number of iterations are investigated. Results indicate that adaptive mean filtering is a highly effective tool for noise reduction CT gel dosimetry. The optimum filtering strategy depends on characteristics of the dose distributions and image noise level. For low noise images (SNR ∼20), the filtered results are excellent and use of adaptive mean filtering is recommended as a standard processing tool. For high noise images (SNR ∼5) adaptive mean filtering can also produce excellent results, but filtering must be approached with more caution as spatial and dose distortions of the original dose distribution can occur
Energy Technology Data Exchange (ETDEWEB)
Donohoe, G.W.
1977-01-01
Sandia Laboratories' Digital Systems Division/1734, as part of its work on the Base and Installation Security Systems (BISS) program has been making use of adaptive digital filters to improve the signal-to-noise ratio of perimeter sensor signals. In particular, the Widrow-Hoff least-mean-squares algorithm has been used extensively. This non-recursive linear predictor has been successful in extracting aperiodic signals from periodic noise. The adaptive filter generates a predictor signal which is subtracted from the input signal to produce an error signal. The value of this error is fed back to the filter to improve the quality of the next prediction. Implementation of the Widrow adaptive filter using a Charge-Coupled Device tapped analog delay line, analog voltage multipliers and operational amplifiers is described. The resulting filter adapts to signals with frequency components as high as several megahertz.
Stupin, Daniil D.; Koniakhin, Sergei V.; Verlov, Nikolay A.; Dubina, Michael V.
2017-05-01
The time-domain technique for impedance spectroscopy consists of computing the excitation voltage and current response Fourier images by fast or discrete Fourier transformation and calculating their relation. Here we propose an alternative method for excitation voltage and current response processing for deriving a system impedance spectrum based on a fast and flexible adaptive filtering method. We show the equivalence between the problem of adaptive filter learning and deriving the system impedance spectrum. To be specific, we express the impedance via the adaptive filter weight coefficients. The noise-canceling property of adaptive filtering is also justified. Using the RLC circuit as a model system, we experimentally show that adaptive filtering yields correct admittance spectra and elements ratings in the high-noise conditions when the Fourier-transform technique fails. Providing the additional sensitivity of impedance spectroscopy, adaptive filtering can be applied to otherwise impossible-to-interpret time-domain impedance data. The advantages of adaptive filtering are justified with practical living-cell impedance measurements.
Adaptive Control of Non-Minimum Phase Modal Systems Using Residual Mode Filters2. Parts 1 and 2
Balas, Mark J.; Frost, Susan
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 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 modify the adaptive controller with 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. This paper will be divided into two parts. Here in Part I we will review the basic adaptive control approach and introduce the primary ideas. In Part II, we will present the RMF methodology and complete the proofs of all our results. Also, we will apply the above theoretical results to a simple flexible structure example to illustrate the behavior with and without the residual mode filter.
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.
An Adjoint-Based Adaptive Ensemble Kalman Filter
Song, Hajoon; Hoteit, Ibrahim; Cornuelle, Bruce D.; Luo, Xiaodong; Subramanian, Aneesh C.
2013-01-01
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.
Foussier, Jerome; Teichmann, Daniel; Jia, Jing; Misgeld, Berno; Leonhardt, Steffen
2014-05-09
Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min(-1) (0.3 min(-1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min(-1) (0.4 min(-1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed
Zhou, Qiuling; Tang, Chen; Li, Biyuan; Wang, Linlin; Lei, Zhenkun; Tang, Shuwei
2018-01-01
The filtering of discontinuous optical fringe patterns is a challenging problem faced in this area. This paper is concerned with oriented partial differential equations (OPDEs)-based image filtering methods for discontinuous optical fringe patterns. We redefine a new controlling speed function to depend on the orientation coherence. The orientation coherence can be used to distinguish the continuous regions and the discontinuous regions, and can be calculated by utilizing fringe orientation. We introduce the new controlling speed function to the previous OPDEs and propose adaptive OPDEs filtering models. According to our proposed adaptive OPDEs filtering models, the filtering in the continuous and discontinuous regions can be selectively carried out. We demonstrate the performance of the proposed adaptive OPDEs via application to the simulated and experimental fringe patterns, and compare our methods with the previous OPDEs.
Slice image pretreatment for cone-beam computed tomography based on adaptive filter
International Nuclear Information System (INIS)
Huang Kuidong; Zhang Dinghua; Jin Yanfang
2009-01-01
According to the noise properties and the serial slice image characteristics in Cone-Beam Computed Tomography (CBCT) system, a slice image pretreatment for CBCT based on adaptive filter was proposed. The judging criterion for the noise is established firstly. All pixels are classified into two classes: adaptive center weighted modified trimmed mean (ACWMTM) filter is used for the pixels corrupted by Gauss noise and adaptive median (AM) filter is used for the pixels corrupted by impulse noise. In ACWMTM filtering algorithm, the estimated Gauss noise standard deviation in the current slice image with offset window is replaced by the estimated standard deviation in the adjacent slice image to the current with the corresponding window, so the filtering accuracy of the serial images is improved. The pretreatment experiment on CBCT slice images of wax model of hollow turbine blade shows that the method makes a good performance both on eliminating noises and on protecting details. (authors)
An adaptive deep-coupled GNSS/INS navigation system with hybrid pre-filter processing
Wu, Mouyan; Ding, Jicheng; Zhao, Lin; Kang, Yingyao; Luo, Zhibin
2018-02-01
The deep-coupling of a global navigation satellite system (GNSS) with an inertial navigation system (INS) can provide accurate and reliable navigation information. There are several kinds of deeply-coupled structures. These can be divided mainly into coherent and non-coherent pre-filter based structures, which have their own strong advantages and disadvantages, especially in accuracy and robustness. In this paper, the existing pre-filters of the deeply-coupled structures are analyzed and modified to improve them firstly. Then, an adaptive GNSS/INS deeply-coupled algorithm with hybrid pre-filters processing is proposed to combine the advantages of coherent and non-coherent structures. An adaptive hysteresis controller is designed to implement the hybrid pre-filters processing strategy. The simulation and vehicle test results show that the adaptive deeply-coupled algorithm with hybrid pre-filters processing can effectively improve navigation accuracy and robustness, especially in a GNSS-challenged environment.
Ahmad, Muneer; Jung, Low Tan; Bhuiyan, Al-Amin
2017-10-01
Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals. This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise. Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms. This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary
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....
Multi-template Scale-Adaptive Kernelized Correlation Filters
Bibi, Adel Aamer; Ghanem, Bernard
2015-01-01
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.
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
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.
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.
SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering
International Nuclear Information System (INIS)
Iliopoulos, AS; Sun, X; Floros, D; Zhang, Y; Yin, FF; Ren, L; Pitsianis, N
2016-01-01
Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial
SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering
Energy Technology Data Exchange (ETDEWEB)
Iliopoulos, AS; Sun, X [Duke University, Durham, NC (United States); Floros, D [Aristotle University of Thessaloniki (Greece); Zhang, Y; Yin, FF; Ren, L [Duke University Medical Center, Durham, NC (United States); Pitsianis, N [Aristotle University of Thessaloniki (Greece); Duke University, Durham, NC (United States)
2016-06-15
Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial
Dim small targets detection based on self-adaptive caliber temporal-spatial filtering
Fan, Xiangsuo; Xu, Zhiyong; Zhang, Jianlin; Huang, Yongmei; Peng, Zhenming
2017-09-01
To boost the detect ability of dim small targets, this paper began by using improved anisotropy for background prediction (IABP), followed by target enhancement by improved high-order cumulates (HQS). Finally, on the basis of image pre-processing, to address the problem of missed and wrong detection caused by fixed caliber of traditional pipeline filtering, this paper used targets' multi-frame movement correlation in the time-space domain, combined with the scale-space theory, to propose a temporal-spatial filtering algorithm which allows the caliber to make self-adaptive changes according to the changes of the targets' scale, effectively solving the detection-related issues brought by unchanged caliber and decreased/increased size of the targets. Experiments showed that the improved anisotropic background predication could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods; the improved HQS significantly increased the signal-noise ratio of images; when the signal-noise ratio was lower than 2.6 dB, this detection algorithm could effectively eliminate noise and detect targets. For the algorithm, the lowest signal-to-noise ratio of the detectable target is 0.37.
International Nuclear Information System (INIS)
Semwal, Girish; Rastogi, Vipul
2014-01-01
We present design optimization of wavelength filters based on long period waveguide gratings (LPWGs) using the adaptive particle swarm optimization (APSO) technique. We demonstrate optimization of the LPWG parameters for single-band, wide-band and dual-band rejection filters for testing the convergence of APSO algorithms. After convergence tests on the algorithms, the optimization technique has been implemented to design more complicated application specific filters such as erbium doped fiber amplifier (EDFA) amplified spontaneous emission (ASE) flattening, erbium doped waveguide amplifier (EDWA) gain flattening and pre-defined broadband rejection filters. The technique is useful for designing and optimizing the parameters of LPWGs to achieve complicated application specific spectra. (paper)
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.
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.
DSP based adaptive hysteresis-band current controlled active filter ...
African Journals Online (AJOL)
The use of non-linear loads critically affects the quality of supply by drawing harmonic currents and reactive power from the electrical distribution system. Active power filters are the most viable solution for solving such power quality problems in compliance with the harmonic standards. This article presents a digital signal ...
Learning Motivation and Adaptive Video Caption Filtering for EFL Learners Using Handheld Devices
Hsu, Ching-Kun
2015-01-01
The aim of this study was to provide adaptive assistance to improve the listening comprehension of eleventh grade students. This study developed a video-based language learning system for handheld devices, using three levels of caption filtering adapted to student needs. Elementary level captioning excluded 220 English sight words (see Section 1…
Identification of chaotic memristor systems based on piecewise adaptive Legendre filters
International Nuclear Information System (INIS)
Zhao, Yibo; Zhang, Xiuzai; Xu, Jin; Guo, Yecai
2015-01-01
Memristor is a nonlinear device, which plays an important role in the design and implementation of chaotic systems. In order to be able to understand in-depth the complex nonlinear dynamic behaviors in chaotic memristor systems, modeling or identification of its nonlinear model is very important premise. This paper presents a chaotic memristor system identification method based on piecewise adaptive Legendre filters. The threshold decomposition is carried out for the input vector, and also the input signal subintervals via decomposition satisfy the convergence condition of the adaptive Legendre filters. Then the adaptive Legendre filter structure and adaptive weight update algorithm are derived. Final computer simulation results show the effectiveness as well as fast convergence characteristics.
Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators
Kammoun, Abla; Couillet, Romain; Pascal, Frederic; Alouini, Mohamed-Slim
2017-01-01
This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.
Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators
Kammoun, Abla
2017-10-25
This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.
Adaptive Filtering for Aeroservoelastic Response Suppression, Phase I
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...
Hierarchical Threshold Adaptive for Point Cloud Filter Algorithm of Moving Surface Fitting
Directory of Open Access Journals (Sweden)
ZHU Xiaoxiao
2018-02-01
Full Text Available In order to improve the accuracy,efficiency and adaptability of point cloud filtering algorithm,a hierarchical threshold adaptive for point cloud filter algorithm of moving surface fitting was proposed.Firstly,the noisy points are removed by using a statistic histogram method.Secondly,the grid index is established by grid segmentation,and the surface equation is set up through the lowest point among the neighborhood grids.The real height and fit are calculated.The difference between the elevation and the threshold can be determined.Finally,in order to improve the filtering accuracy,hierarchical filtering is used to change the grid size and automatically set the neighborhood size and threshold until the filtering result reaches the accuracy requirement.The test data provided by the International Photogrammetry and Remote Sensing Society (ISPRS is used to verify the algorithm.The first and second error and the total error are 7.33%,10.64% and 6.34% respectively.The algorithm is compared with the eight classical filtering algorithms published by ISPRS.The experiment results show that the method has well-adapted and it has high accurate filtering result.
The role of adaptive immunity as an ecological filter on the gut microbiota in zebrafish.
Stagaman, Keaton; Burns, Adam R; Guillemin, Karen; Bohannan, Brendan Jm
2017-07-01
All animals live in intimate association with communities of microbes, collectively referred to as their microbiota. Certain host traits can influence which microbial taxa comprise the microbiota. One potentially important trait in vertebrate animals is the adaptive immune system, which has been hypothesized to act as an ecological filter, promoting the presence of some microbial taxa over others. Here we surveyed the intestinal microbiota of 68 wild-type zebrafish, with functional adaptive immunity, and 61 rag1 - zebrafish, lacking functional B- and T-cell receptors, to test the role of adaptive immunity as an ecological filter on the intestinal microbiota. In addition, we tested the robustness of adaptive immunity's filtering effects to host-host interaction by comparing the microbiota of fish populations segregated by genotype to those containing both genotypes. The presence of adaptive immunity individualized the gut microbiota and decreased the contributions of neutral processes to gut microbiota assembly. Although mixing genotypes led to increased phylogenetic diversity in each, there was no significant effect of adaptive immunity on gut microbiota composition in either housing condition. Interestingly, the most robust effect on microbiota composition was co-housing within a tank. In all, these results suggest that adaptive immunity has a role as an ecological filter of the zebrafish gut microbiota, but it can be overwhelmed by other factors, including transmission of microbes among hosts.
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.
Saulnier, G. J.; Das, P.; Milstein, L. B.
1984-11-01
Analytical results have shown that adaptive filtering can be a powerful tool for the rejection of narrow-band interference in a spread-spectrum receiver. However, the complexity of adaptive filtering hardware has hindered the experimental verification of these results. This paper describes a new adaptive filter architecture for implementing the Widrow-Hoff LMS algorithm while using only two multipliers regardless of filter order. This hardware simplification is achieved through the use of a burst processing technique. A 16-tap version of this adaptive filter constructed using charge-transfer devices (CTD's) is used to suppress a single tone jammer in a direct sequence spread-spectrum receiver. Probability of error measurements demonstrating the effectiveness of the adaptive filter for suppressing the single tone jammer along with simulation results for the optimal Weiner-Hopf filter are presented and discussed.
International Nuclear Information System (INIS)
Riaz, Nadeem; Wiersma, Rodney; Mao Weihua; Xing Lei; Shanker, Piyush; Gudmundsson, Olafur; Widrow, Bernard
2009-01-01
Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.
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.
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.
SUPPRESSION OF POWERLINE INTERFERENCE IN ECG USING ADAPTIVE DIGITAL FILTER BY
Mbachu C.B; Onoh G. N; Idigo V.E; Oguejiofor O.S
2011-01-01
Artifacts in electrocardiogram (ECG) records are caused by various factors, such as powerline interference, electroencephalogram (EEG), electromyogram (EMG) and baseline wander. These noise sources increase the difficulty in analyzing the ECG and to obtaining clinical information. For that reason, it is necessary to designspecific filters to decrease such artifacts in ECG records. In this paper, FIR adaptive filter based on a least mean square (LMS) algorithm for eliminating 50Hz powerline in...
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.
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,
Adaptive Kalman Filter Applied to Vision Based Head Gesture Tracking for Playing Video Games
Directory of Open Access Journals (Sweden)
Mohammadreza Asghari Oskoei
2017-11-01
Full Text Available This paper proposes an adaptive Kalman filter (AKF to improve the performance of a vision-based human machine interface (HMI applied to a video game. The HMI identifies head gestures and decodes them into corresponding commands. Face detection and feature tracking algorithms are used to detect optical flow produced by head gestures. Such approaches often fail due to changes in head posture, occlusion and varying illumination. The adaptive Kalman filter is applied to estimate motion information and reduce the effect of missing frames in a real-time application. Failure in head gesture tracking eventually leads to malfunctioning game control, reducing the scores achieved, so the performance of the proposed vision-based HMI is examined using a game scoring mechanism. The experimental results show that the proposed interface has a good response time, and the adaptive Kalman filter improves the game scores by ten percent.
An Adaptive Filter for the Removal of Drifting Sinusoidal Noise Without a Reference.
Kelly, John W; Siewiorek, Daniel P; Smailagic, Asim; Wang, Wei
2016-01-01
This paper presents a method for filtering sinusoidal noise with a variable bandwidth filter that is capable of tracking a sinusoid's drifting frequency. The method, which is based on the adaptive noise canceling (ANC) technique, will be referred to here as the adaptive sinusoid canceler (ASC). The ASC eliminates sinusoidal contamination by tracking its frequency and achieving a narrower bandwidth than typical notch filters. The detected frequency is used to digitally generate an internal reference instead of relying on an external one as ANC filters typically do. The filter's bandwidth adjusts to achieve faster and more accurate convergence. In this paper, the focus of the discussion and the data is physiological signals, specifically electrocorticographic (ECoG) neural data contaminated with power line noise, but the presented technique could be applicable to other recordings as well. On simulated data, the ASC was able to reliably track the noise's frequency, properly adjust its bandwidth, and outperform comparative methods including standard notch filters and an adaptive line enhancer. These results were reinforced by visual results obtained from real ECoG data. The ASC showed that it could be an effective method for increasing signal to noise ratio in the presence of drifting sinusoidal noise, which is of significant interest for biomedical applications.
Detection of circuit-board components with an adaptive multiclass correlation filter
Diaz-Ramirez, Victor H.; Kober, Vitaly
2008-08-01
A new method for reliable detection of circuit-board components is proposed. The method is based on an adaptive multiclass composite correlation filter. The filter is designed with the help of an iterative algorithm using complex synthetic discriminant functions. The impulse response of the filter contains information needed to localize and classify geometrically distorted circuit-board components belonging to different classes. Computer simulation results obtained with the proposed method are provided and compared with those of known multiclass correlation based techniques in terms of performance criteria for recognition and classification of objects.
DEFF Research Database (Denmark)
Nadernejad, Ehsan; Forchhammer, Søren; Korhonen, Jari
2011-01-01
and ringing artifacts, we have applied directional anisotropic diffusion. Besides that, the selection of the adaptive threshold parameter for the diffusion coefficient has also improved the performance of the algorithm. Experimental results on JPEG compressed images as well as MJPEG and H.264 compressed......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...... videos show improvement in artifact reduction of the proposed algorithm over other directional and spatial fuzzy filters....
Robust adaptive extended Kalman filtering for real time MR-thermometry guided HIFU interventions.
Roujol, Sébastien; de Senneville, Baudouin Denis; Hey, Silke; Moonen, Chrit; Ries, Mario
2012-03-01
Real time magnetic resonance (MR) thermometry is gaining clinical importance for monitoring and guiding high intensity focused ultrasound (HIFU) ablations of tumorous tissue. The temperature information can be employed to adjust the position and the power of the HIFU system in real time and to determine the therapy endpoint. The requirement to resolve both physiological motion of mobile organs and the rapid temperature variations induced by state-of-the-art high-power HIFU systems require fast MRI-acquisition schemes, which are generally hampered by low signal-to-noise ratios (SNRs). This directly limits the precision of real time MR-thermometry and thus in many cases the feasibility of sophisticated control algorithms. To overcome these limitations, temporal filtering of the temperature has been suggested in the past, which has generally an adverse impact on the accuracy and latency of the filtered data. Here, we propose a novel filter that aims to improve the precision of MR-thermometry while monitoring and adapting its impact on the accuracy. For this, an adaptive extended Kalman filter using a model describing the heat transfer for acoustic heating in biological tissues was employed together with an additional outlier rejection to address the problem of sparse artifacted temperature points. The filter was compared to an efficient matched FIR filter and outperformed the latter in all tested cases. The filter was first evaluated on simulated data and provided in the worst case (with an approximate configuration of the model) a substantial improvement of the accuracy by a factor 3 and 15 during heat up and cool down periods, respectively. The robustness of the filter was then evaluated during HIFU experiments on a phantom and in vivo in porcine kidney. The presence of strong temperature artifacts did not affect the thermal dose measurement using our filter whereas a high measurement variation of 70% was observed with the FIR filter.
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.
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.
Environmental Adaptations, Ecological Filtering, and Dispersal Central to Insect Invasions.
Renault, David; Laparie, Mathieu; McCauley, Shannon J; Bonte, Dries
2018-01-07
Insect invasions, the establishment and spread of nonnative insects in new regions, can have extensive economic and environmental consequences. Increased global connectivity accelerates rates of introductions, while climate change may decrease the barriers to invader species' spread. We follow an individual-level insect- and arachnid-centered perspective to assess how the process of invasion is influenced by phenotypic heterogeneity associated with dispersal and stress resistance, and their coupling, across the multiple steps of the invasion process. We also provide an overview and synthesis on the importance of environmental filters during the entire invasion process for the facilitation or inhibition of invasive insect population spread. Finally, we highlight important research gaps and the relevance and applicability of ongoing natural range expansions in the context of climate change to gain essential mechanistic insights into insect invasions.
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.
Adapted wavelet analysis from theory to software
Wickerhauser, Mladen Victor
1994-01-01
This detail-oriented text is intended for engineers and applied mathematicians who must write computer programs to perform wavelet and related analysis on real data. It contains an overview of mathematical prerequisites and proceeds to describe hands-on programming techniques to implement special programs for signal analysis and other applications. From the table of contents: - Mathematical Preliminaries - Programming Techniques - The Discrete Fourier Transform - Local Trigonometric Transforms - Quadrature Filters - The Discrete Wavelet Transform - Wavelet Packets - The Best Basis Algorithm - Multidimensional Library Trees - Time-Frequency Analysis - Some Applications - Solutions to Some of the Exercises - List of Symbols - Quadrature Filter Coefficients
Directory of Open Access Journals (Sweden)
GAN Yu
2015-09-01
Full Text Available Attitude determination directly by carrier phase observation makes optimal use of observation and attitude constraints. The phase models based on misalignment angle and multiplicative quaternion error are derived. The state models for attitude estimation with and without external angular rate sensors are both erected. The attitude errors are estimated by adaptively robust filtering, in which the adaptive factors of ambiguity and attitude error are decided respectively following the idea of multi adaptive factor filtering. The factor of attitude is determined by a three-section function containing Ratio. Adaptively robust filtering makes the best use of constraint and historical information, fusing them in the calculation of float solution. As the accuracy of float solution and the structure of covariance matrix are improved greatly, the fix solution can be searched efficiently using LAMBDA (least-squares ambiguity decorrelation adjustment method merely, perfectly fulfilling the real-time requirement. Field test of a ship-based three-antenna attitude system is used to validate the proposed method. It is showed that direct attitude determination based on adaptively robust filtering has obvious advantages in efficiency and reliability.
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.
Cannistraci, Carlo Vittorio; Abbas, Ahmed; Gao, Xin
2015-01-01
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.
Transmissive/Reflective Structural Color Filters: Theory and Applications
Directory of Open Access Journals (Sweden)
Yan Yu
2014-01-01
Full Text Available Structural color filters, which obtain color selection by varying structures, have attracted extensive research interest in recent years due to the advantages of compactness, stability, multifunctions, and so on. In general, the mechanisms of structural colors are based on the interaction between light and structures, including light diffraction, cavity resonance, and surface plasmon resonance. This paper reviews recent progress of various structural color techniques and the integration applications of structural color filters in CMOS image sensors, solar cells, and display.
Lisano, Michael E.
2007-01-01
Recent literature in applied estimation theory reflects growing interest in the sigma-point (also called unscented ) formulation for optimal sequential state estimation, often describing performance comparisons with extended Kalman filters as applied to specific dynamical problems [c.f. 1, 2, 3]. Favorable attributes of sigma-point filters are described as including a lower expected error for nonlinear even non-differentiable dynamical systems, and a straightforward formulation not requiring derivation or implementation of any partial derivative Jacobian matrices. These attributes are particularly attractive, e.g. in terms of enabling simplified code architecture and streamlined testing, in the formulation of estimators for nonlinear spaceflight mechanics systems, such as filter software onboard deep-space robotic spacecraft. As presented in [4], the Sigma-Point Consider Filter (SPCF) algorithm extends the sigma-point filter algorithm to the problem of consider covariance analysis. Considering parameters in a dynamical system, while estimating its state, provides an upper bound on the estimated state covariance, which is viewed as a conservative approach to designing estimators for problems of general guidance, navigation and control. This is because, whether a parameter in the system model is observable or not, error in the knowledge of the value of a non-estimated parameter will increase the actual uncertainty of the estimated state of the system beyond the level formally indicated by the covariance of an estimator that neglects errors or uncertainty in that parameter. The equations for SPCF covariance evolution are obtained in a fashion similar to the derivation approach taken with standard (i.e. linearized or extended) consider parameterized Kalman filters (c.f. [5]). While in [4] the SPCF and linear-theory consider filter (LTCF) were applied to an illustrative linear dynamics/linear measurement problem, in the present work examines the SPCF as applied to
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.
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.
Directory of Open Access Journals (Sweden)
Ke Li
2016-01-01
Full Text Available 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.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
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. PMID:26761006
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.
Development of an adaptive bilateral filter for evaluating color image difference
Wang, Zhaohui; Hardeberg, Jon Yngve
2012-04-01
Spatial filtering, which aims to mimic the contrast sensitivity function (CSF) of the human visual system (HVS), has previously been combined with color difference formulae for measuring color image reproduction errors. These spatial filters attenuate imperceptible information in images, unfortunately including high frequency edges, which are believed to be crucial in the process of scene analysis by the HVS. The adaptive bilateral filter represents a novel approach, which avoids the undesirable loss of edge information introduced by CSF-based filtering. The bilateral filter employs two Gaussian smoothing filters in different domains, i.e., spatial domain and intensity domain. We propose a method to decide the parameters, which are designed to be adaptive to the corresponding viewing conditions, and the quantity and homogeneity of information contained in an image. Experiments and discussions are given to support the proposal. A series of perceptual experiments were conducted to evaluate the performance of our approach. The experimental sample images were reproduced with variations in six image attributes: lightness, chroma, hue, compression, noise, and sharpness/blurriness. The Pearson's correlation values between the model-predicted image difference and the observed difference were employed to evaluate the performance, and compare it with that of spatial CIELAB and image appearance model.
From Narrative Slave to Movie: Adaptation Theory
Fathu Rahman, Dr.,M.Hum.
2017-01-01
Twelve Years a Slave is one of literary works that adapted in movie version with the same title. Twelve Years a Slave is a work was written by Solomon Northup which is based on his journey life when he was become slave for Twelve Years. In 2013, this work was adapted into movie by English film director with the same title. In the process of adapting a literary works into movie, it will be occurs many changes due the differences between two mediums. Although it ...
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
International Nuclear Information System (INIS)
Maier, Andreas; Wigstroem, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu Lei; Strobel, Norbert; Fahrig, Rebecca
2011-01-01
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 8.9-fold
International Nuclear Information System (INIS)
Chen, Lin; Fan, Xiangtao; Du, Xiaoping
2014-01-01
Point cloud filtering is the basic and key step in LiDAR data processing. Adaptive Triangle Irregular Network Modelling (ATINM) algorithm and Threshold Segmentation on Elevation Statistics (TSES) algorithm are among the mature algorithms. However, few researches concentrate on the parameter selections of ATINM and the iteration condition of TSES, which can greatly affect the filtering results. First the paper presents these two key problems under two different terrain environments. For a flat area, small height parameter and angle parameter perform well and for areas with complex feature changes, large height parameter and angle parameter perform well. One-time segmentation is enough for flat areas, and repeated segmentations are essential for complex areas. Then the paper makes comparisons and analyses of the results by these two methods. ATINM has a larger I error in both two data sets as it sometimes removes excessive points. TSES has a larger II error in both two data sets as it ignores topological relations between points. ATINM performs well even with a large region and a dramatic topology while TSES is more suitable for small region with flat topology. Different parameters and iterations can cause relative large filtering differences
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.
A multi-stage noise adaptive switching filter for extremely corrupted images
Dinh, Hai; Adhami, Reza; Wang, Yi
2015-07-01
A multi-stage noise adaptive switching filter (MSNASF) is proposed for the restoration of images extremely corrupted by impulse and impulse-like noise. The filter consists of two steps: noise detection and noise removal. The proposed extrema-based noise detection scheme utilizes the false contouring effect to get better over detection rate at low noise density. It is adaptive and will detect not only impulse but also impulse-like noise. In the noise removal step, a novel multi-stage filtering scheme is proposed. It replaces corrupted pixel with the nearest uncorrupted median to preserve details. When compared with other methods, MSNASF provides better peak signal to noise ratio (PSNR) and structure similarity index (SSIM). A subjective evaluation carried out online also demonstrates that MSNASF yields higher fidelity.
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.
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...
Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.
Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan
2018-02-06
This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.
Morphological adaptations in filtering screens of Daphnia galeata to food quantity and food quality
Repka, S.; Veen, A.; Vijverberg, J.
1999-01-01
We reared clones of the waterflea Daphnia galeata, a common grazer in many types of lakes, under several food regimes to study adaptations to feeding conditions in filter screen morphology and life history. As food regimes, we used low and high concentrations of the green alga Scenedesmus, a high
Super-resolution pupil filtering for visual performance enhancement using adaptive optics
Zhao, Lina; Dai, Yun; Zhao, Junlei; Zhou, Xiaojun
2018-05-01
Ocular aberration correction can significantly improve visual function of the human eye. However, even under ideal aberration correction conditions, pupil diffraction restricts the resolution of retinal images. Pupil filtering is a simple super-resolution (SR) method that can overcome this diffraction barrier. In this study, a 145-element piezoelectric deformable mirror was used as a pupil phase filter because of its programmability and high fitting accuracy. Continuous phase-only filters were designed based on Zernike polynomial series and fitted through closed-loop adaptive optics. SR results were validated using double-pass point spread function images. Contrast sensitivity was further assessed to verify the SR effect on visual function. An F-test was conducted for nested models to statistically compare different CSFs. These results indicated CSFs for the proposed SR filter were significantly higher than the diffraction correction (p vision optical correction of the human eye.
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.
Adaptive management of natural resources: theory, concepts, and management institutions.
George H. Stankey; Roger N. Clark; Bernard T. Bormann
2005-01-01
This report reviews the extensive and growing literature on the concept and application of adaptive management. Adaptive management is a central element of the Northwest Forest Plan and there is a need for an informed understanding of the key theories, concepts, and frameworks upon which it is founded. Literature from a diverse range of fields including social learning...
Evaluation of non-linear adaptive smoothing filter by digital phantom
International Nuclear Information System (INIS)
Sato, Kazuhiro; Ishiya, Hiroki; Oshita, Ryosuke; Yanagawa, Isao; Goto, Mitsunori; Mori, Issei
2008-01-01
As a result of the development of multi-slice CT, diagnoses based on three-dimensional reconstruction images and multi-planar reconstruction have spread. For these applications, which require high z-resolution, thin slice imaging is essential. However, because z-resolution is always based on a trade-off with image noise, thin slice imaging is necessarily accompanied by an increase in noise level. To improve the quality of thin slice images, a non-linear adaptive smoothing filter has been developed, and is being widely applied to clinical use. We developed a digital bar pattern phantom for the purpose of evaluating the effect of this filter and attempted evaluation from an addition image of the bar pattern phantom and the image of the water phantom. The effect of this filter was changed in a complex manner by the contrast and spatial frequency of the original image. We have confirmed the reduced effect of image noise in the low frequency component of the image, but decreased contrast or increased quantity of noise in the image of the high frequency component. This result represents the effect of change in the adaptation of this filter. The digital phantom was useful for this evaluation, but to understand the total effect of filtering, much improvement of the shape of the digital phantom is required. (author)
Tan, Jun; Song, Peng; Li, Jinshan; Wang, Lei; Zhong, Mengxuan; Zhang, Xiaobo
2017-06-01
The surface-related multiple elimination (SRME) method is based on feedback formulation and has become one of the most preferred multiple suppression methods used. However, some differences are apparent between the predicted multiples and those in the source seismic records, which may result in conventional adaptive multiple subtraction methods being barely able to effectively suppress multiples in actual production. This paper introduces a combined adaptive multiple attenuation method based on the optimized event tracing technique and extended Wiener filtering. The method firstly uses multiple records predicted by SRME to generate a multiple velocity spectrum, then separates the original record to an approximate primary record and an approximate multiple record by applying the optimized event tracing method and short-time window FK filtering method. After applying the extended Wiener filtering method, residual multiples in the approximate primary record can then be eliminated and the damaged primary can be restored from the approximate multiple record. This method combines the advantages of multiple elimination based on the optimized event tracing method and the extended Wiener filtering technique. It is an ideal method for suppressing typical hyperbolic and other types of multiples, with the advantage of minimizing damage of the primary. Synthetic and field data tests show that this method produces better multiple elimination results than the traditional multi-channel Wiener filter method and is more suitable for multiple elimination in complicated geological areas.
Adaptive vs. eductive learning : Theory and evidence
Bao, T.; Duffy, J.
2014-01-01
Adaptive learning and eductive learning are two widely used ways of modeling learning behavior in macroeconomics. Both approaches yield restrictions on model parameters under which agents are able to learn a rational expectation equilibrium (REE) but these restrictions do not always overlap with one
"Theory of food" as a neurocognitive adaptation.
Allen, John S
2012-01-01
Human adult cognition emerges over the course of development via the interaction of multiple critical neurocognitive networks. These networks evolved in response to various selection pressures, many of which were modified or intensified by the intellectual, technological, and sociocultural environments that arose in connection with the evolution of genus Homo. Networks related to language and theory of mind clearly play an important role in adult cognition. Given the critical importance of food to both basic survival and cultural interaction, a "theory of food" (analogous to theory of mind) may represent another complex network essential for normal cognition. I propose that theory of food evolved as an internal, cognitive representation of our diets in our minds. Like other complex cognitive abilities, it relies on complex and overlapping dedicated neural networks that develop in childhood under familial and cultural influences. Normative diets are analogous to first languages in that they are acquired without overt teaching; they are also difficult to change or modify once a critical period in development is passed. Theory of food suggests that cognitive activities related to food may be cognitive enhancers, which could have implications for maintaining healthy brain function in aging. Copyright © 2012 Wiley Periodicals, Inc.
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.
Adaptive integral equation methods in transport theory
International Nuclear Information System (INIS)
Kelley, C.T.
1992-01-01
In this paper, an adaptive multilevel algorithm for integral equations is described that has been developed with the Chandrasekhar H equation and its generalizations in mind. The algorithm maintains good performance when the Frechet derivative of the nonlinear map is singular at the solution, as happens in radiative transfer with conservative scattering and in critical neutron transport. Numerical examples that demonstrate the algorithm's effectiveness are presented
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.
Xiao, Mengli; Zhang, Yongbo; Wang, Zhihua; Fu, Huimin
2018-04-01
Considering the performances of conventional Kalman filter may seriously degrade when it suffers stochastic faults and unknown input, which is very common in engineering problems, a new type of adaptive three-stage extended Kalman filter (AThSEKF) is proposed to solve state and fault estimation in nonlinear discrete-time system under these conditions. The three-stage UV transformation and adaptive forgetting factor are introduced for derivation, and by comparing with the adaptive augmented state extended Kalman filter, it is proven to be uniformly asymptotically stable. Furthermore, the adaptive three-stage extended Kalman filter is applied to a two-dimensional radar tracking scenario to illustrate the effect, and the performance is compared with that of conventional three stage extended Kalman filter (ThSEKF) and the adaptive two-stage extended Kalman filter (ATEKF). The results show that the adaptive three-stage extended Kalman filter is more effective than these two filters when facing the nonlinear discrete-time systems with information of unknown inputs not perfectly known. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
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.
Directory of Open Access Journals (Sweden)
Wang Liang
2018-01-01
Full Text Available This paper proposes a novel integrated guidance and control (IGC method for hypersonic vehicle in terminal phase. Firstly, the system model is developed with a second order actuator dynamics. Then the back-stepping controller is designed hierarchically with command filters, where the first order command filters are implemented to construct the virtual control input with ideal states predicted by an adaptive estimator, and the nonlinear command filter is designed to produce magnitude, rate and bandwidth limited control surface deflection finally tracked by a terminal sliding mode controller with finite convergence time. Through a series of 6-DOF numerical simulations, it’s indicated that the proposed method successfully cancels out the large aerodynamics coefficient uncertainties and disturbances in hypersonic flight under limited control surface deflection. The contribution of this paper lies in the application and determination of nonlinear integrated design of guidance and control system for hypersonic vehicle.
Adaptive nonlocal means filtering based on local noise level for CT denoising
International Nuclear Information System (INIS)
Li, Zhoubo; Trzasko, Joshua D.; Lake, David S.; Blezek, Daniel J.; Manduca, Armando; Yu, Lifeng; Fletcher, Joel G.; McCollough, Cynthia H.
2014-01-01
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
Selection vector filter framework
Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.
2003-10-01
We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.
Scaled unscented transform Gaussian sum filter: Theory and application
Luo, Xiaodong
2010-05-01
In this work we consider the state estimation problem in nonlinear/non-Gaussian systems. We introduce a framework, called the scaled unscented transform Gaussian sum filter (SUT-GSF), which combines two ideas: the scaled unscented Kalman filter (SUKF) based on the concept of scaled unscented transform (SUT) (Julier and Uhlmann (2004) [16]), and the Gaussian mixture model (GMM). The SUT is used to approximate the mean and covariance of a Gaussian random variable which is transformed by a nonlinear function, while the GMM is adopted to approximate the probability density function (pdf) of a random variable through a set of Gaussian distributions. With these two tools, a framework can be set up to assimilate nonlinear systems in a recursive way. Within this framework, one can treat a nonlinear stochastic system as a mixture model of a set of sub-systems, each of which takes the form of a nonlinear system driven by a known Gaussian random process. Then, for each sub-system, one applies the SUKF to estimate the mean and covariance of the underlying Gaussian random variable transformed by the nonlinear governing equations of the sub-system. Incorporating the estimations of the sub-systems into the GMM gives an explicit (approximate) form of the pdf, which can be regarded as a "complete" solution to the state estimation problem, as all of the statistical information of interest can be obtained from the explicit form of the pdf (Arulampalam et al. (2002) [7]). In applications, a potential problem of a Gaussian sum filter is that the number of Gaussian distributions may increase very rapidly. To this end, we also propose an auxiliary algorithm to conduct pdf re-approximation so that the number of Gaussian distributions can be reduced. With the auxiliary algorithm, in principle the SUT-GSF can achieve almost the same computational speed as the SUKF if the SUT-GSF is implemented in parallel. As an example, we will use the SUT-GSF to assimilate a 40-dimensional system due to
Sealing Clay Text Segmentation Based on Radon-Like Features and Adaptive Enhancement Filters
Directory of Open Access Journals (Sweden)
Xia Zheng
2015-01-01
Full Text Available Text extraction is a key issue in sealing clay research. The traditional method based on rubbings increases the risk of sealing clay damage and is unfavorable to sealing clay protection. Therefore, using digital image of sealing clay, a new method for text segmentation based on Radon-like features and adaptive enhancement filters is proposed in this paper. First, adaptive enhancement LM filter bank is used to get the maximum energy image; second, the edge image of the maximum energy image is calculated; finally, Radon-like feature images are generated by combining maximum energy image and its edge image. The average image of Radon-like feature images is segmented by the image thresholding method. Compared with 2D Otsu, GA, and FastFCM, the experiment result shows that this method can perform better in terms of accuracy and completeness of the text.
Directory of Open Access Journals (Sweden)
Manish Jaiswal
2015-03-01
Full Text Available An energy efficient high-speed adaptive finite impulse response filter with novel architecture is developed. Synthesis results along with novel architecture on different complementary metal–oxide semiconductor (CMOS families are presented. Analysis is performed using Artix-7, Spartan-6 and Virtex-4 for most popular adaptive least mean square filter for different orders such as N = 8, 16, 32. The presented work is done using MATLAB (2013b and Xilinx (14.2. From the synthesis results, it can be found that CMOS (28 nm achieves the lowest power and critical path delay compared to others, and thus proves its efficiency in terms of energy. Different parameters are considered such as look up tables and input–output blocks, along with their optimised results.
Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery
International Nuclear Information System (INIS)
Zheng Hong; Liu Xu; Wei Min
2015-01-01
In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded. (paper)
State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter
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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.
Actuator Fault Diagnosis in a Boeing 747 Model via Adaptive Modified Two-Stage Kalman Filter
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Fikret Caliskan
2014-01-01
Full Text Available An adaptive modified two-stage linear Kalman filtering algorithm is utilized to identify the loss of control effectiveness and the magnitude of low degree of stuck faults in a closed-loop nonlinear B747 aircraft. Control effectiveness factors and stuck magnitudes are used to quantify faults entering control systems through actuators. Pseudorandom excitation inputs are used to help distinguish partial loss and stuck faults. The partial loss and stuck faults in the stabilizer are isolated and identified successfully.
Adaptive Networks Theory, Models and Applications
Gross, Thilo
2009-01-01
With adaptive, complex networks, the evolution of the network topology and the dynamical processes on the network are equally important and often fundamentally entangled. Recent research has shown that such networks can exhibit a plethora of new phenomena which are ultimately required to describe many real-world networks. Some of those phenomena include robust self-organization towards dynamical criticality, formation of complex global topologies based on simple, local rules, and the spontaneous division of "labor" in which an initially homogenous population of network nodes self-organizes into functionally distinct classes. These are just a few. This book is a state-of-the-art survey of those unique networks. In it, leading researchers set out to define the future scope and direction of some of the most advanced developments in the vast field of complex network science and its applications.
Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm
Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong
2018-06-01
The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.
International Nuclear Information System (INIS)
Sun, Fengchun; Hu, Xiaosong; Zou, Yuan; Li, Siguang
2011-01-01
An accurate battery State of Charge estimation is of great significance for battery electric vehicles and hybrid electric vehicles. This paper presents an adaptive unscented Kalman filtering method to estimate State of Charge of a lithium-ion battery for battery electric vehicles. The adaptive adjustment of the noise covariances in the State of Charge estimation process is implemented by an idea of covariance matching in the unscented Kalman filter context. Experimental results indicate that the adaptive unscented Kalman filter-based algorithm has a good performance in estimating the battery State of Charge. A comparison with the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms shows that the proposed State of Charge estimation method has a better accuracy. -- Highlights: → Adaptive unscented Kalman filtering is proposed to estimate State of Charge of a lithium-ion battery for electric vehicles. → The proposed method has a good performance in estimating the battery State of Charge. → A comparison with three other Kalman filtering algorithms shows that the proposed method has a better accuracy.
Middle-Range Theory: Coping and Adaptation with Active Aging.
Salazar-Barajas, Martha Elba; Salazar-González, Bertha Cecilia; Gallegos-Cabriales, Esther Carlota
2017-10-01
Various disciplines focus on a multiplicity of aspects of aging: lifestyles, personal biological factors, psychological conditions, health conditions, physical environment, and social and economic factors. The aforementioned are all related to the determinants of active aging. The aim is to describe the development of a middle-range theory based on coping and adaptation with active aging. Concepts and relationships derived from Roy's model of adaptation are included. The proposed concepts are hope, health habits, coping with aging, social relations, and active aging.
Conformal prediction for reliable machine learning theory, adaptations and applications
Balasubramanian, Vineeth; Vovk, Vladimir
2014-01-01
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detecti
Ship detection for high resolution optical imagery with adaptive target filter
Ju, Hongbin
2015-10-01
Ship detection is important due to both its civil and military use. In this paper, we propose a novel ship detection method, Adaptive Target Filter (ATF), for high resolution optical imagery. The proposed framework can be grouped into two stages, where in the first stage, a test image is densely divided into different detection windows and each window is transformed to a feature vector in its feature space. The Histograms of Oriented Gradients (HOG) is accumulated as a basic feature descriptor. In the second stage, the proposed ATF highlights all the ship regions and suppresses the undesired backgrounds adaptively. Each detection window is assigned a score, which represents the degree of the window belonging to a certain ship category. The ATF can be adaptively obtained by the weighted Logistic Regression (WLR) according to the distribution of backgrounds and targets of the input image. The main innovation of our method is that we only need to collect positive training samples to build the filter, while the negative training samples are adaptively generated by the input image. This is different to other classification method such as Support Vector Machine (SVM) and Logistic Regression (LR), which need to collect both positive and negative training samples. The experimental result on 1-m high resolution optical images shows the proposed method achieves a desired ship detection performance with higher quality and robustness than other methods, e.g., SVM and LR.
An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps
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Tongyang LI
2018-05-01
Full Text Available An accurate estimation of the remaining useful life (RUL not only contributes to an effective application of an aviation piston pump, but also meets the necessity of condition based maintenance (CBM. For the current RUL evaluation methods, a model-based method is inappropriate for the degradation process of an aviation piston pump due to difficulties of modeling, while a data-based method rarely presents high-accuracy prediction in a long period of time. In this work, an adaptive-order particle filter (AOPF prognostic process is proposed aiming at improving long-term prediction accuracy of RUL by combining both kinds of methods. A dynamic model is initialized by a data-driven or empirical method. When a new observation comes, the prior state distribution is approximated by a current model. The order of the current model is updated adaptively by fusing the information of the observation. Monte Carlo simulation is employed for estimating the posterior probability density function of future states of the pump’s degradation. With updating the order number adaptively, the method presents a higher precision in contrast with those of traditional methods. In a case study, the proposed AOPF method is adopted to forecast the degradation status of an aviation piston pump with experimental return oil flow data, and the analytical results show the effectiveness of the proposed AOPF method. Keywords: Adaptive prognosis, Condition based maintenance (CBM, Particle filter (PF, Piston pump, Remaining useful life (RUL
Building a Middle-Range Theory of Adaptive Spirituality.
Dobratz, Marjorie C
2016-04-01
The purpose of this article is to describe a Roy adaptation model based- research abstraction, the findings of which were synthesized into a middle-range theory (MRT) of adaptive spirituality. The published literature yielded 21 empirical studies that investigated religion/spirituality. Quantitative results supported the influence of spirituality on quality of life, psychosocial adjustment, well-being, adaptive coping, and the self-concept mode. Qualitative findings showed the importance of spiritual expressions, values, and beliefs in adapting to chronic illness, bereavement, death, and other life transitions. These findings were abstracted into six theoretical statements, a conceptual definition of adaptive spirituality, and three hypotheses for future testing. © The Author(s) 2016.
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Yazhe Tang
2015-01-01
Full Text Available This paper presents a novel surveillance system named thermal omnidirectional vision (TOV system which can work in total darkness with a wild field of view. Different to the conventional thermal vision sensor, the proposed vision system exhibits serious nonlinear distortion due to the effect of the quadratic mirror. To effectively model the inherent distortion of omnidirectional vision, an equivalent sphere projection is employed to adaptively calculate parameterized distorted neighborhood of an object in the image plane. With the equivalent projection based adaptive neighborhood calculation, a distortion-invariant gradient coding feature is proposed for thermal catadioptric vision. For robust tracking purpose, a rotational kinematic modeled adaptive particle filter is proposed based on the characteristic of omnidirectional vision, which can handle multiple movements effectively, including the rapid motions. Finally, the experiments are given to verify the performance of the proposed algorithm for human tracking in TOV system.
Random matrix theory filters in portfolio optimisation: A stability and risk assessment
Daly, J.; Crane, M.; Ruskin, H. J.
2008-07-01
Random matrix theory (RMT) filters, applied to covariance matrices of financial returns, have recently been shown to offer improvements to the optimisation of stock portfolios. This paper studies the effect of three RMT filters on the realised portfolio risk, and on the stability of the filtered covariance matrix, using bootstrap analysis and out-of-sample testing. We propose an extension to an existing RMT filter, (based on Krzanowski stability), which is observed to reduce risk and increase stability, when compared to other RMT filters tested. We also study a scheme for filtering the covariance matrix directly, as opposed to the standard method of filtering correlation, where the latter is found to lower the realised risk, on average, by up to 6.7%. We consider both equally and exponentially weighted covariance matrices in our analysis, and observe that the overall best method out-of-sample was that of the exponentially weighted covariance, with our Krzanowski stability-based filter applied to the correlation matrix. We also find that the optimal out-of-sample decay factors, for both filtered and unfiltered forecasts, were higher than those suggested by Riskmetrics [J.P. Morgan, Reuters, Riskmetrics technical document, Technical Report, 1996. http://www.riskmetrics.com/techdoc.html], with those for the latter approaching a value of α=1. In conclusion, RMT filtering reduced the realised risk, on average, and in the majority of cases when tested out-of-sample, but increased the realised risk on a marked number of individual days-in some cases more than doubling it.
Li, Wei; Huang, Zhitong; Li, Haoyue; Ji, Yuefeng
2018-04-01
Visible light communication (VLC) is a promising candidate for short-range broadband access due to its integration of advantages for both optical communication and wireless communication, whereas multi-user access is a key problem because of the intra-cell and inter-cell interferences. In addition, the non-flat channel effect results in higher losses for users in high frequency bands, which leads to unfair qualities. To solve those issues, we propose a power adaptive multi-filter carrierless amplitude and phase access (PA-MF-CAPA) scheme, and in the first step of this scheme, the MF-CAPA scheme utilizing multiple filters as different CAP dimensions is used to realize multi-user access. The character of orthogonality among the filters in different dimensions can mitigate the effect of intra-cell and inter-cell interferences. Moreover, the MF-CAPA scheme provides different channels modulated on the same frequency bands, which further increases the transmission rate. Then, the power adaptive procedure based on MF-CAPA scheme is presented to realize quality fairness. As demonstrated in our experiments, the MF-CAPA scheme yields an improved throughput compared with multi-band CAP access scheme, and the PA-MF-CAPA scheme enhances the quality fairness and further improves the throughput compared with the MF-CAPA scheme.
Processing-Efficient Distributed Adaptive RLS Filtering for Computationally Constrained Platforms
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Noor M. Khan
2017-01-01
Full Text Available In this paper, a novel processing-efficient architecture of a group of inexpensive and computationally incapable small platforms is proposed for a parallely distributed adaptive signal processing (PDASP operation. The proposed architecture runs computationally expensive procedures like complex adaptive recursive least square (RLS algorithm cooperatively. The proposed PDASP architecture operates properly even if perfect time alignment among the participating platforms is not available. An RLS algorithm with the application of MIMO channel estimation is deployed on the proposed architecture. Complexity and processing time of the PDASP scheme with MIMO RLS algorithm are compared with sequentially operated MIMO RLS algorithm and liner Kalman filter. It is observed that PDASP scheme exhibits much lesser computational complexity parallely than the sequential MIMO RLS algorithm as well as Kalman filter. Moreover, the proposed architecture provides an improvement of 95.83% and 82.29% decreased processing time parallely compared to the sequentially operated Kalman filter and MIMO RLS algorithm for low doppler rate, respectively. Likewise, for high doppler rate, the proposed architecture entails an improvement of 94.12% and 77.28% decreased processing time compared to the Kalman and RLS algorithms, respectively.
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.
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.
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.
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.
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.
Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory
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Lichuan Zhang
2017-10-01
Full Text Available Cooperative localization (CL is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs. In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD filter, aiming to enhance the global localization accuracy of the follower. In the proposed framework, the follower carries lower cost navigation systems, whereas the leaders carry better ones. Meanwhile, the leaders acquire the followers’ observations, including both measurements and clutter. Then, the PHD filters are utilized on the leaders and the results are communicated to the followers. The followers then perform weighted summation based on all received messages and obtain a final positioning result. Based on the information entropy theory and the PHD filter, the follower is able to acquire a precise knowledge of its position.
Contraction theory based adaptive synchronization of chaotic systems
International Nuclear Information System (INIS)
Sharma, B.B.; Kar, I.N.
2009-01-01
Contraction theory based stability analysis exploits the incremental behavior of trajectories of a system with respect to each other. Application of contraction theory provides an alternative way for stability analysis of nonlinear systems. This paper considers the design of a control law for synchronization of certain class of chaotic systems based on backstepping technique. The controller is selected so as to make the error dynamics between the two systems contracting. Synchronization problem with and without uncertainty in system parameters is discussed and necessary stability proofs are worked out using contraction theory. Suitable adaptation laws for unknown parameters are proposed based on the contraction principle. The numerical simulations verify the synchronization of the chaotic systems. Also parameter estimates converge to their true values with the proposed adaptation laws.
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.
A DSP-Based Beam Current Monitoring System for Machine Protection Using Adaptive Filtering
International Nuclear Information System (INIS)
J. Musson; H. Dong; R. Flood; C. Hovater; J. Hereford
2001-01-01
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
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.
White noise theory of robust nonlinear filtering with correlated state and observation noises
Bagchi, Arunabha; Karandikar, Rajeeva
1992-01-01
In the direct white noise theory of nonlinear filtering, the state process is still modeled as a Markov process satisfying an Ito stochastic differential equation, while a finitely additive white noise is used to model the observation noise. In the present work, this asymmetry is removed by modeling
White noise theory of robust nonlinear filtering with correlated state and observation noises
Bagchi, Arunabha; Karandikar, Rajeeva
1994-01-01
In the existing `direct¿ white noise theory of nonlinear filtering, the state process is still modelled as a Markov process satisfying an Itô stochastic differential equation, while a `finitely additive¿ white noise is used to model the observation noise. We remove this asymmetry by modelling the
Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.
Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram
2014-12-01
Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.
Neilson, Peter D; Neilson, Megan D
2005-09-01
Adaptive model theory (AMT) is a computational theory that addresses the difficult control problem posed by the musculoskeletal system in interaction with the environment. It proposes that the nervous system creates motor maps and task-dependent synergies to solve the problems of redundancy and limited central resources. These lead to the adaptive formation of task-dependent feedback/feedforward controllers able to generate stable, noninteractive control and render nonlinear interactions unobservable in sensory-motor relationships. AMT offers a unified account of how the nervous system might achieve these solutions by forming internal models. This is presented as the design of a simulator consisting of neural adaptive filters based on cerebellar circuitry. It incorporates a new network module that adaptively models (in real time) nonlinear relationships between inputs with changing and uncertain spectral and amplitude probability density functions as is the case for sensory and motor signals.
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)
1996-12-31
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.
Directory of Open Access Journals (Sweden)
Biçer Cenker
2016-01-01
Full Text Available In this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable.
Temporal Scalability through Adaptive -Band Filter Banks for Robust H.264/MPEG-4 AVC Video Coding
Directory of Open Access Journals (Sweden)
Pau G
2006-01-01
Full Text Available This paper presents different structures that use adaptive -band hierarchical filter banks for temporal scalability. Open-loop and closed-loop configurations are introduced and illustrated using existing video codecs. In particular, it is shown that the H.264/MPEG-4 AVC codec allows us to introduce scalability by frame shuffling operations, thus keeping backward compatibility with the standard. The large set of shuffling patterns introduced here can be exploited to adapt the encoding process to the video content features, as well as to the user equipment and transmission channel characteristics. Furthermore, simulation results show that this scalability is obtained with no degradation in terms of subjective and objective quality in error-free environments, while in error-prone channels the scalable versions provide increased robustness.
Design of 2-D Recursive Filters Using Self-adaptive Mutation Differential Evolution Algorithm
Directory of Open Access Journals (Sweden)
Lianghong Wu
2011-08-01
Full Text Available This paper investigates a novel approach to the design of two-dimensional recursive digital filters using differential evolution (DE algorithm. The design task is reformulated as a constrained minimization problem and is solved by an Self-adaptive Mutation DE algorithm (SAMDE, which adopts an adaptive mutation operator that combines with the advantages of the DE/rand/1/bin strategy and the DE/best/2/bin strategy. As a result, its convergence performance is improved greatly. Numerical experiment results confirm the conclusion. The proposedSAMDE approach is effectively applied to test a numerical example and is compared with previous design methods. The computational experiments show that the SAMDE approach can obtain better results than previous design methods.
Energy Technology Data Exchange (ETDEWEB)
Duval, L.
2000-11-01
Wavelet and wavelet packet transforms are the most commonly used algorithms for seismic data compression. Wavelet coefficients are generally quantized and encoded by classical entropy coding techniques. We first propose in this work a compression algorithm based on the wavelet transform. The wavelet transform is used together with a zero-tree type coding, with first use in seismic applications. Classical wavelet transforms nevertheless yield a quite rigid approach, since it is often desirable to adapt the transform stage to the properties of each type of signal. We thus propose a second algorithm using, instead of wavelets, a set of so called 'extended transforms'. These transforms, originating from the filter bank theory, are parameterized. Classical examples are Malvar's Lapped Orthogonal Transforms (LOT) or de Queiroz et al. Generalized Lapped Orthogonal Transforms (GenLOT). We propose several optimization criteria to build 'extended transforms' which are adapted the properties of seismic signals. We further show that these transforms can be used with the same zero-tree type coding technique as used with wavelets. Both proposed algorithms provide exact compression rate choice, block-wise compression (in the case of extended transforms) and partial decompression for quality control or visualization. Performances are tested on a set of actual seismic data. They are evaluated for several quality measures. We also compare them to other seismic compression algorithms. (author)
Parametric adaptive filtering and data validation in the bar GW detector AURIGA
Ortolan, A.; Baggio, L.; Cerdonio, M.; Prodi, G. A.; Vedovato, G.; Vitale, S.
2002-04-01
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 χ2 and the time of arrival are those that are expected.
Parametric adaptive filtering and data validation in the bar GW detector AURIGA
International Nuclear Information System (INIS)
Ortolan, A; Baggio, L; 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 χ 2 and the time of arrival are those that are expected
Parametric adaptive filtering and data validation in the bar GW detector AURIGA
Energy Technology Data Exchange (ETDEWEB)
Ortolan, A [INFN - Laboratori Nazionali di Legnaro, Via Romea, 4 I-35020 Legnaro, Padova (Italy); Baggio, L [Department of Physics, University of Trento and INFN Gruppo Collegato di Trento, I-38050 Povo, Trento (Italy); Cerdonio, M [Department of Physics, University of Padova and INFN Sezione di Padova, Via Marzolo 8, I-35131 Padova (Italy); Prodi, G A [Department of Physics, University of Trento and INFN Gruppo Collegato di Trento, I-38050 Povo, Trento (Italy); Vedovato, G [INFN - Laboratori Nazionali di Legnaro, Via Romea, 4 I-35020 Legnaro, Padova (Italy); Vitale, S [Department of Physics, University of Trento and INFN Gruppo Collegato di Trento, I-38050 Povo, Trento (Italy)
2002-04-07
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} and the time of arrival are those that are expected.
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 ...
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.
Detection of network attacks based on adaptive resonance theory
Bukhanov, D. G.; Polyakov, V. M.
2018-05-01
The paper considers an approach to intrusion detection systems using a neural network of adaptive resonant theory. It suggests the structure of an intrusion detection system consisting of two types of program modules. The first module manages connections of user applications by preventing the undesirable ones. The second analyzes the incoming network traffic parameters to check potential network attacks. After attack detection, it notifies the required stations using a secure transmission channel. The paper describes the experiment on the detection and recognition of network attacks using the test selection. It also compares the obtained results with similar experiments carried out by other authors. It gives findings and conclusions on the sufficiency of the proposed approach. The obtained information confirms the sufficiency of applying the neural networks of adaptive resonant theory to analyze network traffic within the intrusion detection system.
A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance.
Zheng, Binqi; Fu, Pengcheng; Li, Baoqing; Yuan, Xiaobing
2018-03-07
The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.
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.
TECRA: C2 Application of Adaptive Automation Theory
2010-03-01
1 TECRA: C2 Application of Adaptive Automation Theory Ewart J. de Visser 1,2 , Melanie LeGoullon 1 , Don Horvath 1 , Gershon Weltman 1 , Amos...this second trip served to inform a modified version of Klein , Calderwood, and MacGregor’s [7] Critical Decision Method Cognitive Task Analysis (CTA...NSN:1550-01-538-9256, EIC:ICB, January 9. [7] Klein , G. A., Calderwood, R., & MacGregor, D. (1989). Critical decision method for eliciting
Directory of Open Access Journals (Sweden)
Karthikrjan Senthilnathan
2018-02-01
Full Text Available This paper describes about the Hybrid Shunt Active Power Filter (HSAPF for the elimination of the current harmonics in the line side of the three phase three wire systems. The Active Power Filter is based on the Voltage Source Converter (VSC topology. The control strategy for the converter is based on Synchronous Reference Frame (SRF theory. The compensation of harmonics is done by the APF which is connected in the shunt configuration to the system. The Shunt APF has the better compensation of current harmonics. The design and implementation of Shunt active power filter is done by MATLAB/Simulink. The real time implementation by using the ATMEGA 8 Microcontroller. The Simulation and Hardware results shows that the current harmonics are eliminated in the system
Directory of Open Access Journals (Sweden)
Ming-Hung Chen
2015-01-01
Full Text Available This paper proposes a new adaptive filter for wind generators that combines instantaneous reactive power compensation technology and current prediction controller, and therefore this system is characterized by low harmonic distortion, high power factor, and small DC-link voltage variations during load disturbances. The performance of the system was first simulated using MATLAB/Simulink, and the possibility of an adaptive digital low-pass filter eliminating current harmonics was confirmed in steady and transient states. Subsequently, a digital signal processor was used to implement an active power filter. The experimental results indicate, that for the rated operation of 2 kVA, the system has a total harmonic distortion of current less than 5.0% and a power factor of 1.0 on the utility side. Thus, the transient performance of the adaptive filter is superior to the traditional digital low-pass filter and is more economical because of its short computation time compared with other types of adaptive filters.
Chen, Ming-Hung
2015-01-01
This paper proposes a new adaptive filter for wind generators that combines instantaneous reactive power compensation technology and current prediction controller, and therefore this system is characterized by low harmonic distortion, high power factor, and small DC-link voltage variations during load disturbances. The performance of the system was first simulated using MATLAB/Simulink, and the possibility of an adaptive digital low-pass filter eliminating current harmonics was confirmed in steady and transient states. Subsequently, a digital signal processor was used to implement an active power filter. The experimental results indicate, that for the rated operation of 2 kVA, the system has a total harmonic distortion of current less than 5.0% and a power factor of 1.0 on the utility side. Thus, the transient performance of the adaptive filter is superior to the traditional digital low-pass filter and is more economical because of its short computation time compared with other types of adaptive filters.
International Nuclear Information System (INIS)
Ye, Min; Guo, Hui; Cao, Binggang
2017-01-01
Highlights: • Propose an improved adaptive particle swarm filter method. • The SoC estimation method for the battery based on the adaptive particle swarm filter is presented. • The algorithm is validated by the case study of different aged extent batteries. • The effectiveness and applicability of the algorithm are validated by the LiPB batteries. - Abstract: Obtaining accurate parameters, state of charge (SoC) and capacity of a lithium-ion battery is crucial for a battery management system, and establishing a battery model online is complex. In addition, the errors and perturbations of the battery model dramatically increase throughout the battery lifetime, making it more challenging to model the battery online. To overcome these difficulties, this paper provides three contributions: (1) To improve the robustness of the adaptive particle filter algorithm, an error analysis method is added to the traditional adaptive particle swarm algorithm. (2) An online adaptive SoC estimator based on the improved adaptive particle filter is presented; this estimator can eliminate the estimation error due to battery degradation and initial SoC errors. (3) The effectiveness of the proposed method is verified using various initial states of lithium nickel manganese cobalt oxide (NMC) cells and lithium-ion polymer (LiPB) batteries. The experimental analysis shows that the maximum errors are less than 1% for both the voltage and SoC estimations and that the convergence time of the SoC estimation decreased to 120 s.
Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.
Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui
2017-01-01
To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.
DEFF Research Database (Denmark)
Gil-Cacho, Jose M.; van Waterschoot, Toon; Moonen, Marc
2014-01-01
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......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...... regularization (VR) algorithms. The FDAF-PEM-AFROW versions significantly outperform the original versions in every simulation. In terms of computational complexity, the FDAF-PEM-AFROW versions are themselves about two orders of magnitude cheaper than the original versions....
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.
Adaptive Kalman filtering for real-time mapping of the visual field
Ward, B. Douglas; Janik, John; Mazaheri, Yousef; Ma, Yan; DeYoe, Edgar A.
2013-01-01
This paper demonstrates the feasibility of real-time mapping of the visual field for clinical applications. Specifically, three aspects of this problem were considered: (1) experimental design, (2) statistical analysis, and (3) display of results. Proper experimental design is essential to achieving a successful outcome, particularly for real-time applications. A random-block experimental design was shown to have less sensitivity to measurement noise, as well as greater robustness to error in modeling of the hemodynamic impulse response function (IRF) and greater flexibility than common alternatives. In addition, random encoding of the visual field allows for the detection of voxels that are responsive to multiple, not necessarily contiguous, regions of the visual field. Due to its recursive nature, the Kalman filter is ideally suited for real-time statistical analysis of visual field mapping data. An important feature of the Kalman filter is that it can be used for nonstationary time series analysis. The capability of the Kalman filter to adapt, in real time, to abrupt changes in the baseline arising from subject motion inside the scanner and other external system disturbances is important for the success of clinical applications. The clinician needs real-time information to evaluate the success or failure of the imaging run and to decide whether to extend, modify, or terminate the run. Accordingly, the analytical software provides real-time displays of (1) brain activation maps for each stimulus segment, (2) voxel-wise spatial tuning profiles, (3) time plots of the variability of response parameters, and (4) time plots of activated volume. PMID:22100663
Subband Adaptive Filtering with l1-Norm Constraint for Sparse System Identification
Directory of Open Access Journals (Sweden)
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.
Extraction of ECG signal with adaptive filter for hearth abnormalities detection
Turnip, Mardi; Saragih, Rijois. I. E.; Dharma, Abdi; Esti Kusumandari, Dwi; Turnip, Arjon; Sitanggang, Delima; Aisyah, Siti
2018-04-01
This paper demonstrates an adaptive filter method for extraction ofelectrocardiogram (ECG) feature in hearth abnormalities detection. In particular, electrocardiogram (ECG) is a recording of the heart's electrical activity by capturing a tracingof cardiac electrical impulse as it moves from the atrium to the ventricles. The applied algorithm is to evaluate and analyze ECG signals for abnormalities detection based on P, Q, R and S peaks. In the first phase, the real-time ECG data is acquired and pre-processed. In the second phase, the procured ECG signal is subjected to feature extraction process. The extracted features detect abnormal peaks present in the waveform. Thus the normal and abnormal ECG signal could be differentiated based on the features extracted.
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. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Ciftcioglu, O.
1991-03-01
Detection of failure in the operational status of a NPP is described. The method uses lattice form of the signal modelling established by means of Kalman filtering methodology. In this approach each lattice parameter is considered to be a state and the minimum variance estimate of the states is performed adaptively by optimal parameter estimation together with fast convergence and favourable statistical properties. In particular, the state covariance is also the covariance of the error committed by that estimate of the state value and the Mahalanobis distance formed for pattern comparison takes x 2 distribution for normally distributed signals. The failure detection is performed after a decision making process by probabilistic assessments based on the statistical information provided. The failure detection system is implemented in multi-channel signal environment of Borssele NPP and its favourable features are demonstrated. (author). 29 refs.; 7 figs
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.
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.
Directory of Open Access Journals (Sweden)
Byeong Hak Kim
2017-12-01
Full Text Available Unmanned aerial vehicles (UAVs are equipped with optical systems including an infrared (IR camera such as electro-optical IR (EO/IR, target acquisition and designation sights (TADS, or forward looking IR (FLIR. However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC and scene-based NUC (SBNUC. However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA. In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR and long wave infrared (LWIR images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.
Kim, Byeong Hak; Kim, Min Young; Chae, You Seong
2017-01-01
Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC. PMID:29280970
Kim, Byeong Hak; Kim, Min Young; Chae, You Seong
2017-12-27
Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.
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.
Directory of Open Access Journals (Sweden)
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.
Gruneisen, Mark T.; Sickmiller, Brett A.; Flanagan, Michael B.; Black, James P.; Stoltenberg, Kurt E.; Duchane, Alexander W.
2016-02-01
Spatial filtering is an important technique for reducing sky background noise in a satellite quantum key distribution downlink receiver. Atmospheric turbulence limits the extent to which spatial filtering can reduce sky noise without introducing signal losses. Using atmospheric propagation and compensation simulations, the potential benefit of adaptive optics (AO) to secure key generation (SKG) is quantified. Simulations are performed assuming optical propagation from a low-Earth-orbit satellite to a terrestrial receiver that includes AO. Higher-order AO correction is modeled assuming a Shack-Hartmann wavefront sensor and a continuous-face-sheet deformable mirror. The effects of atmospheric turbulence, tracking, and higher-order AO on the photon capture efficiency are simulated using statistical representations of turbulence and a time-domain wave-optics hardware emulator. SKG rates are calculated for a decoy-state protocol as a function of the receiver field of view for various strengths of turbulence, sky radiances, and pointing angles. The results show that at fields of view smaller than those discussed by others, AO technologies can enhance SKG rates in daylight and enable SKG where it would otherwise be prohibited as a consequence of background optical noise and signal loss due to propagation and turbulence effects.
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Dalei Song
2012-10-01
Full Text Available The adaptive extended set-membership filter (AESMF for nonlinear ellipsoidal estimation suffers a mismatch between real process noise and its set boundaries, which may result in unstable estimation. In this paper, a MIT method-based adaptive set-membership filter, for the optimization of the set boundaries of process noise, is developed and applied to the nonlinear joint estimation of both time-varying states and parameters. As a result of using the proposed MIT-AESMF, the estimation effectiveness and boundary accuracy of traditional AESMF are substantially improved. Simulation results have shown the efficiency and robustness of the proposed method.
Technical Note: A fast online adaptive replanning method for VMAT using flattening filter free beams
Energy Technology Data Exchange (ETDEWEB)
Ates, Ozgur; Ahunbay, Ergun E.; Li, X. Allen, E-mail: ali@mcw.edu [Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226 (United States); Moreau, Michel [Elekta, Inc., Maryland Heights, Missouri 63043 (United States)
2016-06-15
Purpose: To develop a fast replanning algorithm based on segment aperture morphing (SAM) for online replanning of volumetric modulated arc therapy (VMAT) with flattening filter free (FFF) beams. Methods: A software tool was developed to interface with a VMAT research planning system, which enables the input and output of beam and machine parameters of VMAT plans. The SAM algorithm was used to modify multileaf collimator positions for each segment aperture based on the changes of the target from the planning (CT/MR) to daily image [CT/CBCT/magnetic resonance imaging (MRI)]. The leaf travel distance was controlled for large shifts to prevent the increase of VMAT delivery time. The SAM algorithm was tested for 11 patient cases including prostate, pancreatic, and lung cancers. For each daily image set, three types of VMAT plans, image-guided radiation therapy (IGRT) repositioning, SAM adaptive, and full-scope reoptimization plans, were generated and compared. Results: The SAM adaptive plans were found to have improved the plan quality in target and/or critical organs when compared to the IGRT repositioning plans and were comparable to the reoptimization plans based on the data of planning target volume (PTV)-V100 (volume covered by 100% of prescription dose). For the cases studied, the average PTV-V100 was 98.85% ± 1.13%, 97.61% ± 1.45%, and 92.84% ± 1.61% with FFF beams for the reoptimization, SAM adaptive, and repositioning plans, respectively. The execution of the SAM algorithm takes less than 10 s using 16-CPU (2.6 GHz dual core) hardware. Conclusions: The SAM algorithm can generate adaptive VMAT plans using FFF beams with comparable plan qualities as those from the full-scope reoptimization plans based on daily CT/CBCT/MRI and can be used for online replanning to address interfractional variations.
International Nuclear Information System (INIS)
Shih, Cheng-Ting; Lin, Hsin-Hon; Chuang, Keh-Shih; Wu, Jay; Chang, Shu-Jun
2014-01-01
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
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 preprocessing filtering on Decision Tree C4.5 and rough set theory
Chan, Joseph C. C.; Lin, Tsau Y.
2001-03-01
This paper compares two artificial intelligence methods: the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the feature (attribute) transformations, which allows users to input formulas and create new attributes. Also, the application produces three varieties of data set with delaying, averaging, and summation. The results prove the improvement of pre-processing by applying feature (attribute) transformations on Decision Tree C4.5. Moreover, the comparison between Decision Tree C4.5 and Rough Set Theory is based on the clarity, automation, accuracy, dimensionality, raw data, and speed, which is supported by the rules sets generated by both algorithms on three different sets of data.
Planetary gearbox fault feature enhancement based on combined adaptive filter method
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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.
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Hongjian Wang
2014-01-01
Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.
Takahashi, Masahiro; Kimura, Fumiko; Umezawa, Tatsuya; Watanabe, Yusuke; Ogawa, Harumi
2016-01-01
Adaptive statistical iterative reconstruction (ASIR) has been used to reduce radiation dose in cardiac computed tomography. However, change of image parameters by ASIR as compared to filtered back projection (FBP) may influence quantification of coronary calcium. To investigate the influence of ASIR on calcium quantification in comparison to FBP. In 352 patients, CT images were reconstructed using FBP alone, FBP combined with ASIR 30%, 50%, 70%, and ASIR 100% based on the same raw data. Image noise, plaque density, Agatston scores and calcium volumes were compared among the techniques. Image noise, Agatston score, and calcium volume decreased significantly with ASIR compared to FBP (each P ASIR reduced Agatston score by 10.5% to 31.0%. In calcified plaques both of patients and a phantom, ASIR decreased maximum CT values and calcified plaque size. In comparison to FBP, adaptive statistical iterative reconstruction (ASIR) may significantly decrease Agatston scores and calcium volumes. Copyright © 2016 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.
Wang, Xin; Wu, Linhui; Yi, Xi; Zhang, Yanqi; Zhang, Limin; Zhao, Huijuan; Gao, Feng
2015-01-01
Due to both the physiological and morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic diffuse fluorescence tomography (DFT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. In this regime, the extended Kalman filtering (EKF) based method shows numerous advantages including accurate modeling, online estimation of multiparameters, and universal applicability to any optical fluorophore. Nevertheless the performance of the conventional EKF highly hinges on the exact and inaccessible prior knowledge about the initial values. To address the above issues, an adaptive-EKF scheme is proposed based on a two-compartmental model for the enhancement, which utilizes a variable forgetting-factor to compensate the inaccuracy of the initial states and emphasize the effect of the current data. It is demonstrated using two-dimensional simulative investigations on a circular domain that the proposed adaptive-EKF can obtain preferable estimation of the pharmacokinetic-rates to the conventional-EKF and the enhanced-EKF in terms of quantitativeness, noise robustness, and initialization independence. Further three-dimensional numerical experiments on a digital mouse model validate the efficacy of the method as applied in realistic biological systems.
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Li N.
2017-01-01
Full Text Available Affected by the unstable pulse radiation and the pulsar directional errors, the statistical characteristics of the pulsar measurement noise may vary with time slowly and cannot be accurately determined, which cause the filtering accuracy of the extended Kalman filter(EKF in pulsar navigation positioning system decline sharply or even diverge. To solve this problem, an adaptive extended Kalman filtering algorithm based on the empirical mode decomposition(EMD is proposed. In this method, the high frequency noise is separated from measurement information of pulsar by the method of EMD, and the noise variance can be estimated to update the parameters of EKF. The simulation results demonstrate that compared with conventional EKF, the proposed method can adaptively track the change of the measurement noise, and still keeps high estimation accuracy with unknown measurement noise, the positioning accuracy of the pulsar navigation is improved simultaneously.
Adaptive partial median filter for early CT signs of acute cerebral infarction
International Nuclear Information System (INIS)
Lee, Yongbum; Tsai, Du-Yih; Takahashi, Noriyuki; Ishii, Kiyoshi
2007-01-01
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 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.)
Kuznetsova, T. A.
2018-05-01
The methods for increasing gas-turbine aircraft engines' (GTE) adaptive properties to interference based on empowerment of automatic control systems (ACS) are analyzed. The flow pulsation in suction and a discharge line of the compressor, which may cause the stall, are considered as the interference. The algorithmic solution to the problem of GTE pre-stall modes’ control adapted to stability boundary is proposed. The aim of the study is to develop the band-pass filtering algorithms to provide the detection functions of the compressor pre-stall modes for ACS GTE. The characteristic feature of pre-stall effect is the increase of pressure pulsation amplitude over the impeller at the multiples of the rotor’ frequencies. The used method is based on a band-pass filter combining low-pass and high-pass digital filters. The impulse response of the high-pass filter is determined through a known low-pass filter impulse response by spectral inversion. The resulting transfer function of the second order band-pass filter (BPF) corresponds to a stable system. The two circuit implementations of BPF are synthesized. Designed band-pass filtering algorithms were tested in MATLAB environment. Comparative analysis of amplitude-frequency response of proposed implementation allows choosing the BPF scheme providing the best quality of filtration. The BPF reaction to the periodic sinusoidal signal, simulating the experimentally obtained pressure pulsation function in the pre-stall mode, was considered. The results of model experiment demonstrated the effectiveness of applying band-pass filtering algorithms as part of ACS to identify the pre-stall mode of the compressor for detection of pressure fluctuations’ peaks, characterizing the compressor’s approach to the stability boundary.
Scott, Robert C.; Perry, Boyd, III; Pototzky, Anthony S.
1991-01-01
This paper describes and illustrates two matched-filter-theory based schemes for obtaining maximized and time-correlated gust-loads for a nonlinear airplane. The first scheme is computationally fast because it uses a simple one-dimensional search procedure to obtain its answers. The second scheme is computationally slow because it uses a more complex multidimensional search procedure to obtain its answers, but it consistently provides slightly higher maximum loads than the first scheme. Both schemes are illustrated with numerical examples involving a nonlinear control system.
On Convergence of the Unscented Kalman-Bucy Filter using Contraction Theory
DEFF Research Database (Denmark)
Maree, J.P.; Imsland, Lars; Jouffroy, Jerome
2016-01-01
Contraction theory entails a theoretical framework in which convergence of a nonlinear system can be analysed differentially in an appropriate contraction metric. This paper is concerned with utilizing stochastic contraction theory to conclude on exponential convergence of the Unscented Kalman......-Bucy Filter. The underlying process and measurement models of interest are Itô-type stochastic differential equations. In particular, statistical linearisation techniques are employed in a virtual-actual systems framework to establish deterministic contraction of the estimated expected mean of process values....... Under mild conditions of bounded process noise, we extend the results on deterministic contraction to stochastic contraction of the estimated expected mean of process state. It follows that for regions of contraction, a result on convergence, and thereby incremental stability, is concluded...
Complex adaptive systems and game theory: An unlikely union
Hadzikadic, M.; Carmichael, T.; Curtin, C.
2010-01-01
A Complex Adaptive System is a collection of autonomous, heterogeneous agents, whose behavior is defined with a limited number of rules. A Game Theory is a mathematical construct that assumes a small number of rational players who have a limited number of actions or strategies available to them. The CAS method has the potential to alleviate some of the shortcomings of GT. On the other hand, CAS researchers are always looking for a realistic way to define interactions among agents. GT offers an attractive option for defining the rules of such interactions in a way that is both potentially consistent with observed real-world behavior and subject to mathematical interpretation. This article reports on the results of an effort to build a CAS system that utilizes GT for determining the actions of individual agents. ?? 2009 Wiley Periodicals, Inc. Complexity, 16,24-42, 2010.
Olivares, A.; Górriz, J. M.; Ramírez, J.; Olivares, G.
2011-02-01
Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed.
International Nuclear Information System (INIS)
Olivares, A; Olivares, G; Górriz, J M; Ramírez, J
2011-01-01
Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed
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Daniel Pipa
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.
International Nuclear Information System (INIS)
Zhang, Y; Sun, J W; Rolfe, P
2012-01-01
The non-invasive measurement of cerebral functional haemodynamics using near-infrared spectroscopy (NIRS) instruments is often affected by physiological interference. The suppression of this interference is crucial for reliable recovery of brain activity measurements because it can significantly affect the signal quality. In this study, we present a recursive least-squares (RLS) algorithm for adaptive filtering to reduce the magnitude of the physiological interference component. To evaluate it, we implemented Monte Carlo simulations based on a five-layer slab model of a human adult head with a multidistance source–detector arrangement, of a short pair and a long pair, for NIRS measurement. We derived measurements by adopting different interoptode distances, which is relevant to the process of optimizing the NIRS probe configuration. Both RLS and least mean squares (LMS) algorithms were used to attempt the removal of physiological interference. The results suggest that the RLS algorithm is more capable of minimizing the effect of physiological interference due to its advantages of faster convergence and smaller mean squared error (MSE). The influence of superficial layer thickness on the performance of the RLS algorithm was also investigated. We found that the near-detector position is an important variable in minimizing the MSE and a short source–detector separation less than 9 mm is robust to superficial layer thickness variation. (paper)
Gearbox Fault Features Extraction Using Vibration Measurements and Novel Adaptive Filtering Scheme
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Ghalib R. Ibrahim
2012-01-01
Full Text Available Vibration signals measured from a gearbox are complex multicomponent signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures, and a substantial amount of noise. This paper presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS algorithm is examined and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio, which makes meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a healthy pair of gears and a pair suffering from a tooth breakage with severity fault 1 (25% tooth removal and fault 2 (50% tooth removal under loads (0%, and 80% of the total load. The experimental results show remarkable improvements and enhance gear condition features. This paper illustrates that the new approach offers a more effective way to detect early faults.
Lesmanne, Emeline; Espiau de Lamaestre, Roch; Boutami, Salim; Durantin, Cédric; Dussopt, Laurent; Badano, Giacomo
2016-09-01
Multispectral infrared (IR) detection is of great interest to enhance our ability to gather information from a scene. Filtering is a low-cost alternative to the complex multispectral device architectures to which the IR community has devoted much attention. Multilayer dielectric filters are standard in industry, but they require changing the thickness of at least one layer to tune the wavelength. Here, we pursue an approach based on apertures in a metallic layer of fixed thickness, in which the filtered wavelengths are selected by varying the aperture geometry. In particular, we study filters made of at least one sheet of resonating apertures in metal embedded in dielectrics. We will discuss two interesting problems that arise when one attempts to design such filters. First, metallic absorption must be taken into account. Second, the form and size of the pattern is limited by lithography. We will present some design examples and an attempt at explaining the filtering behavior based on the temporal coupled mode theory. That theory models the filter as a resonator interacting with the environment via loss channels. The transmission is solely determined by the loss rates associated with those channels. This model allows us to give a general picture of the filtering performance and compare their characteristics at different wavelength bands.
Phase Coordinate System and p-q Theory Based Methods in Active Filtering Implementation
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POPESCU, M.
2013-02-01
Full Text Available This paper is oriented towards implementation of the main theories of powers in the compensating current generation stage of a three-phase three-wire shunt active power system. The system control is achieved through a dSPACE 1103 platform which is programmed under the Matlab/Simulink environment. Four calculation blocks included in a specifically designed Simulink library are successively implemented in the experimental setup. The first two approaches, namely those based on the Fryze-Buchholz-Depenbrock theory and the generalized instantaneous reactive power theory, make use of phase quantities without any transformation of the coordinate system and provide the basis for calculating the compensating current when total compensation is desired. The others are based on the p-q theory concepts and require the direct and reverse transformation to/from the two-phases stationary reference frame. They are used for total compensation and partial compensation of the current harmonic distortion. The experimental results, in terms of active filtering performances, validate the control strategies implementation and provide arguments in choosing the most appropriate method.
Sang, Hongqiang; Yang, Chenghao; Liu, Fen; Yun, Jintian; Jin, Guoguang; Chen, Fa
2016-12-01
Hand physiological tremor of surgeons can cause vibration at the surgical instrument tip, which may make it difficult for the surgeon to perform fine manipulations of tissue, needles, and sutures. A zero phase adaptive fuzzy Kalman filter (ZPAFKF) is proposed to suppress hand tremor and vibration of a robotic surgical system. The involuntary motion can be reduced by adding a compensating signal that has the same magnitude and frequency but opposite phase with the tremor signal. Simulations and experiments using different filters were performed. Results show that the proposed filter can avoid the loss of useful motion information and time delay, and better suppress minor and varying tremor. The ZPAFKF can provide less error, preferred accuracy, better tremor estimation, and more desirable compensation performance, to suppress hand tremor and decrease vibration at the surgical instrument tip. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Tassé, Marc J.; Schalock, Robert L.; Thissen, David; Balboni, Giulia; Bersani, Henry, Jr.; Borthwick-Duffy, Sharon A.; Spreat, Scott; Widaman, Keith F.; Zhang, Dalun; Navas, Patricia
2016-01-01
The Diagnostic Adaptive Behavior Scale (DABS) was developed using item response theory (IRT) methods and was constructed to provide the most precise and valid adaptive behavior information at or near the cutoff point of making a decision regarding a diagnosis of intellectual disability. The DABS initial item pool consisted of 260 items. Using IRT…
Directory of Open Access Journals (Sweden)
Lisi Tian
2016-12-01
Full Text Available This paper presents a novel position and speed estimation method for low-speed sensorless control of interior permanent-magnet synchronous machines (IPMSMs. The parameter design of the position and speed estimator is based on the sampled current rather than the motor electrical parameters. The proposed method not only simplifies the parameter design, it enables the estimator to work normally even in the condition that the electrical parameters are uncertain or varied. The adaptive filters are adopted to extract the desired high frequency current. The structure and corresponding transfer function are analyzed. To address the shortage of insufficient stop-band attenuation, the structure of the adaptive filter is modified to provide suitable bandwidth and stop-band attenuation simultaneously. The effectiveness of the proposed sensorless control strategy has been verified by simulations and experiments.
Yang, Yanfu; Xiang, Qian; Zhang, Qun; Zhou, Zhongqing; Jiang, Wen; He, Qianwen; Yao, Yong
2017-09-01
We propose a joint estimation scheme for fast, accurate, and robust frequency offset (FO) estimation along with phase estimation based on modified adaptive Kalman filter (MAKF). The scheme consists of three key modules: extend Kalman filter (EKF), lock detector, and FO cycle slip recovery. The EKF module estimates time-varying phase induced by both FO and laser phase noise. The lock detector module makes decision between acquisition mode and tracking mode and consequently sets the EKF tuning parameter in an adaptive manner. The third module can detect possible cycle slip in the case of large FO and make proper correction. Based on the simulation and experimental results, the proposed MAKF has shown excellent estimation performance featuring high accuracy, fast convergence, as well as the capability of cycle slip recovery.
A Novel Robust H∞ Filter Based on Krein Space Theory in the SINS/CNS Attitude Reference System
Directory of Open Access Journals (Sweden)
Fei Yu
2016-03-01
Full Text Available Owing to their numerous merits, such as compact, autonomous and independence, the strapdown inertial navigation system (SINS and celestial navigation system (CNS can be used in marine applications. What is more, due to the complementary navigation information obtained from two different kinds of sensors, the accuracy of the SINS/CNS integrated navigation system can be enhanced availably. Thus, the SINS/CNS system is widely used in the marine navigation field. However, the CNS is easily interfered with by the surroundings, which will lead to the output being discontinuous. Thus, the uncertainty problem caused by the lost measurement will reduce the system accuracy. In this paper, a robust H∞ filter based on the Krein space theory is proposed. The Krein space theory is introduced firstly, and then, the linear state and observation models of the SINS/CNS integrated navigation system are established reasonably. By taking the uncertainty problem into account, in this paper, a new robust H∞ filter is proposed to improve the robustness of the integrated system. At last, this new robust filter based on the Krein space theory is estimated by numerical simulations and actual experiments. Additionally, the simulation and experiment results and analysis show that the attitude errors can be reduced by utilizing the proposed robust filter effectively when the measurements are missing discontinuous. Compared to the traditional Kalman filter (KF method, the accuracy of the SINS/CNS integrated system is improved, verifying the robustness and the availability of the proposed robust H∞ filter.
Adaptive Kalman filtering for histogram-based appearance learning in infrared imagery.
Venkataraman, Vijay; Fan, Guoliang; Havlicek, Joseph P; Fan, Xin; Zhai, Yan; Yeary, Mark B
2012-11-01
Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors, including complex target maneuvers, ego-motion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online. In this paper, we adopt a recent appearance model that estimates the pixel intensity histograms as well as the distribution of local standard deviations in both the foreground and background regions for robust target representation. Appearance learning is then cast as an adaptive Kalman filtering problem where the process and measurement noise variances are both unknown. We formulate this problem using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Although convergence of the ALS algorithm is guaranteed only for the case of globally wide sense stationary process and measurement noises, we demonstrate for the first time that the technique can often be applied with great effectiveness under the much weaker assumption of piecewise stationarity. The performance advantages of the ALS method relative to the classical covariance matching are illustrated by means of simulated stationary and nonstationary systems. Against real data, our results show that the ALS-based algorithm outperforms the covariance matching as well as the traditional histogram similarity-based methods, achieving sub-pixel tracking accuracy against the well-known AMCOM closure sequences and the recent SENSIAC automatic target recognition dataset.
International Nuclear Information System (INIS)
Ren, Qingguo; Dewan, Sheilesh Kumar; Li, Ming; Li, Jianying; Mao, Dingbiao; Wang, Zhenglei; Hua, Yanqing
2012-01-01
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 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
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.
Adaptive trials for tuberculosis: early reflections on theory and practice.
Montgomery, C M
2016-08-01
Adaptive designs (ADs) have been proposed for anti-tuberculosis treatment trials. This call for innovation occurs against the backdrop of fundamental changes in the acceptable evidence base in anti-tuberculosis treatment. To contextualise ADs for tuberculosis (TB) and explore early responses from those working in the field. In this qualitative study investigating processes of theoretical and practical change in randomised controlled trials, 24 interviews were conducted with professionals involved in AD trials, half of whom worked in the TB field. Clinical trialists working on AD trials in TB are positive about the efficiency these designs offer, but remain cautious about their suitability. In addition to technical concerns, informants discussed the challenges of implementing AD in developing countries, including limited regulatory capacity to evaluate proposals, investments needed in infrastructure and site capacity, and challenges regarding informed consent. Respondents identified funding, interdisciplinary communication and regulatory and policy responses as additional concerns potentially affecting the success of AD for TB. Empirical research is needed into patient experiences of AD, including informed consent. Further consideration of the contexts of innovation in trial design is needed. These are fundamental to the successful translation of theory into practice.
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
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
Computerized Adaptive Test (CAT) Applications and Item Response Theory Models for Polytomous Items
Aybek, Eren Can; Demirtasli, R. Nukhet
2017-01-01
This article aims to provide a theoretical framework for computerized adaptive tests (CAT) and item response theory models for polytomous items. Besides that, it aims to introduce the simulation and live CAT software to the related researchers. Computerized adaptive test algorithm, assumptions of item response theory models, nominal response…
Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption
Xie, Qing; Zhang, Xiangliang; Li, Zhixu; Zhou, Xiaofang
2016-01-01
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
Liu, Yahui; Fan, Xiaoqian; Lv, Chen; Wu, Jian; Li, Liang; Ding, Dawei
2018-02-01
Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments were collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional Adaptive Kalman Filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise.
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.
Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.
2012-04-01
Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.
Kuhn, John R., Jr.
2009-01-01
Drawing upon the theories of complexity and complex adaptive systems and the Singerian Inquiring System from C. West Churchman's seminal work "The Design of Inquiring Systems" the dissertation herein develops a systems design theory for continuous auditing systems. The dissertation consists of discussion of the two foundational theories,…
An application of adaption-innovation theory to bioremediation
Energy Technology Data Exchange (ETDEWEB)
Guerin, L.J. [World Trade Centre, Melbourne, Victoria (Australia); Guerin, T.F. [Minenco Bioremediation Services, Bundoora, Victoria (Australia)
1995-12-31
This paper provides a discussion of the potential application of the Kirton Adaption-Innovation Inventory (KAI) for assessing the adaptive-innovative cognitive style of individuals and organizations within the bioremediation industry. Human-resource and line managers, or other individuals responsible for staff evaluation, selection, and project planning, should consider using the KAI to assist them in selecting individuals for specific roles requiring either an innovative or adaptive style. The KAI, a measure for assessing adaption-innovation at the individual employee level, is introduced and its potential value in the bioremediation industry is discussed.
Yano, Ken'ichi; Ohara, Eiichi; Horihata, Satoshi; Aoki, Takaaki; Nishimoto, Yutaka
A robot that supports independent living by assisting with eating and other activities which use the operator's own hand would be helpful for people suffering from tremors of the hand or any other body part. The proposed system using adaptive filter estimates tremor frequencies with a time-varying property and individual differences online. In this study, the estimated frequency is used to adjusting the tremor suppression filter which insulates the voluntary motion signal from the sensor signal containing tremor components. These system are integrated into the control system of the Meal-Assist Robot. As a result, the developed system makes it possible for the person with a tremor to manipulate the supporting robot without causing operability to deteriorate and without hazards due to improper operation.
Oulhen, Marianne; Pailler, Emma; Faugeroux, Vincent; Farace, Françoise
2017-01-01
Circulating tumor cells (CTCs) may represent an easily accessible source of tumor material to assess genetic aberrations such as gene-rearrangements or gene-amplifications and screen cancer patients eligible for targeted therapies. As the number of CTCs is a critical parameter to identify such biomarkers, we developed fluorescent in situ hybridization (FISH) for CTCs enriched on filters (filter-adapted-FISH, FA-FISH). Here, we describe the FA-FISH protocol, the combination of immunofluorescent staining (DAPI/CD45) and FA-FISH techniques, as well as the semi-automated microscopy method that we developed to improve the feasibility and reliability of FISH analyses in filtration-enriched CTC.
Directory of Open Access Journals (Sweden)
Baofeng Wang
Full Text Available Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.
Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng
2017-01-01
Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.
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.
Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R
2018-01-01
Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.
International Nuclear Information System (INIS)
Takahashi, N.; Lee, Y.; Tsai, D. Y.; Ishii, K.; Kinoshita, T.; Tamura, H.; K imura, M.
2008-01-01
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
Finger, R.; Curotto, F.; Fuentes, R.; Duan, R.; Bronfman, L.; Li, D.
2018-02-01
Radio Frequency Interference (RFI) is a growing concern in the radio astronomy community. Single-dish telescopes are particularly susceptible to RFI. Several methods have been developed to cope with RF-polluted environments, based on flagging, excision, and real-time blanking, among others. All these methods produce some degree of data loss or require assumptions to be made on the astronomical signal. We report the development of a real-time, digital adaptive filter implemented on a Field Programmable Gate Array (FPGA) capable of processing 4096 spectral channels in a 1 GHz of instantaneous bandwidth. The filter is able to cancel a broad range of interference signals and quickly adapt to changes on the RFI source, minimizing the data loss without any assumption on the astronomical or interfering signal properties. The speed of convergence (for a decrease to a 1%) was measured to be 208.1 μs for a broadband noise-like RFI signal and 125.5 μs for a multiple-carrier RFI signal recorded at the FAST radio telescope.
Zheng, Shiqiang; Feng, Rui
2016-03-01
This paper introduces a feedforward control strategy combined with a novel adaptive notch filter to solve the problem of rotor imbalance in high-speed Magnetically Suspended Centrifugal Compressors (MSCCs). Unbalance vibration force of rotor in MSCC is mainly composed of current stiffness force and displacement stiffness force. In this paper, the mathematical model of the unbalance vibration with the proportional-integral-derivative (PID) control laws is presented. In order to reduce the unbalance vibration, a novel adaptive notch filter is proposed to identify the synchronous frequency displacement of the rotor as a compensation signal to eliminate the current stiffness force. In addition, a feedforward channel from position component to control output is introduced to compensate displacement stiffness force to achieve a better performance. A simplified inverse model of power amplifier is included in the feedforward channel to reject the degrade performance caused by its low-pass characteristic. Simulation and experimental results on a MSCC demonstrate a significant effect on the synchronous vibration suppression of the magnetically suspended rotor at a high speed.
Czech Academy of Sciences Publication Activity Database
Macháček, Jiří; Seďa, Jaromír
2016-01-01
Roč. 38, č. 5 (2016), s. 1269-1280 ISSN 0142-7873 R&D Projects: GA ČR(CZ) GA206/09/1325; GA ČR(CZ) GA15-24309S Institutional support: RVO:60077344 Keywords : Daphnia * filtering screen morphology * transgenerational plasticity * ontogenetic plasticity * taxonspecific trait Subject RIV: EH - Ecology, Behaviour Impact factor: 1.983, year: 2016
Adaptive Mean and Trend Removal of Heart Rate Variability Using Kalman Filtering
National Research Council Canada - National Science Library
Schloegl, A
2001-01-01
Analysis of heart rate van ability requires the calculation of the mean heart rate, Adaptive methods are important for online and real-time parameter estimation, In this paper we demonstrate the use...
Applications of decision theory to computer-based adaptive instructional systems
Vos, Hendrik J.
1988-01-01
This paper considers applications of decision theory to the problem of instructional decision-making in computer-based adaptive instructional systems, using the Minnesota Adaptive Instructional System (MAIS) as an example. The first section indicates how the problem of selecting the appropriate
A Theory of Secondary Teachers' Adaptations When Implementing a Reading Intervention Program
Leko, Melinda M.; Roberts, Carly A.; Pek, Yvonne
2015-01-01
This study examined the causes and consequences of secondary teachers' adaptations when implementing a research-based reading intervention program. Interview, observation, and artifact data were collected on five middle school intervention teachers, leading to a grounded theory composed of the core component, reconciliation through adaptation, and…
A Lyapunov Stability Theory-Based Control Strategy for Three-Level Shunt Active Power Filter
Directory of Open Access Journals (Sweden)
Yijia Cao
2017-01-01
Full Text Available The three-phase three-wire neutral-point-clamped shunt active power filter (NPC-SAPF, which most adopts classical closed-loop feedback control methods such as proportional-integral (PI, proportional-resonant (PR and repetitive control, can only output 1st–25th harmonic currents with 10–20 kHz switching frequency. The reason for this is that the controller design must make a compromise between system stability and harmonic current compensation ability under the condition of less than 20 kHz switching frequency. To broaden the bandwidth of the compensation current, a Lyapunov stability theory-based control strategy is presented in this paper for NPC-SAPF. The proposed control law is obtained by constructing the switching function on the basis of the mathematical model and the Lyapunov candidate function, which can avoid introducing closed-loop feedback control and keep the system globally asymptotically stable. By means of the proposed method, the NPC-SAPF has compensation ability for the 1st–50th harmonic currents, the total harmonic distortion (THD and each harmonic content of grid currents satisfy the requirements of IEEE Standard 519-2014. In order to verify the superiority of the proposed control strategy, stability conditions of the proposed strategy and the representative PR controllers are compared. The simulation results in MATLAB/Simulink (MathWorks, Natick, MA, USA and the experimental results obtained on a 6.6 kVA NPC-SAPF laboratory prototype validate the proposed control strategy.
Moment-tensor solutions estimated using optimal filter theory: Global seismicity, 2001
Sipkin, S.A.; Bufe, C.G.; Zirbes, M.D.
2003-01-01
This paper is the 12th in a series published yearly containing moment-tensor solutions computed at the US Geological Survey using an algorithm based on the theory of optimal filter design (Sipkin, 1982 and Sipkin, 1986b). An inversion has been attempted for all earthquakes with a magnitude, mb or MS, of 5.5 or greater. Previous listings include solutions for earthquakes that occurred from 1981 to 2000 (Sipkin, 1986b; Sipkin and Needham, 1989, Sipkin and Needham, 1991, Sipkin and Needham, 1992, Sipkin and Needham, 1993, Sipkin and Needham, 1994a and Sipkin and Needham, 1994b; Sipkin and Zirbes, 1996 and Sipkin and Zirbes, 1997; Sipkin et al., 1998, Sipkin et al., 1999, Sipkin et al., 2000a, Sipkin et al., 2000b and Sipkin et al., 2002).The entire USGS moment-tensor catalog can be obtained via anonymous FTP at ftp://ghtftp.cr.usgs.gov. After logging on, change directory to “momten”. This directory contains two compressed ASCII files that contain the finalized solutions, “mt.lis.Z” and “fmech.lis.Z”. “mt.lis.Z” contains the elements of the moment tensors along with detailed event information; “fmech.lis.Z” contains the decompositions into the principal axes and best double-couples. The fast moment-tensor solutions for more recent events that have not yet been finalized and added to the catalog, are gathered by month in the files “jan01.lis.Z”, etc. “fmech.doc.Z” describes the various fields.
Brief Report: Translation and Adaptation of the Theory of Mind Inventory to Spanish
Pujals, Elena; Batlle, Santiago; Camprodon, Ester; Pujals, Sílvia; Estrada, Xavier; Aceña, Marta; Petrizan, Araitz; Duñó, Lurdes; Martí, Josep; Martin, Luis Miguel; Pérez-Solá, Víctor
2016-01-01
The Theory of Mind Inventory is an informant measure designed to evaluate children's theory of mind competence. We describe the translation and cultural adaptation of the inventory by the following process: (1) translation from English to Spanish by two independent certified translators; (2) production of an agreed version by a multidisciplinary…
Adapting Structuration Theory as a Comprehensive Theory for Distance Education: The ASTIDE Model
Aktaruzzaman, Md; Plunkett, Margaret
2016-01-01
Distance Education (DE) theorists have argued about the requirement for a theory to be comprehensive in a way that can explicate many of the activities associated with DE. Currently, Transactional Distance Theory (TDT) (Moore, 1993) and the Theory of Instructional Dialogue (IDT) (Caspi & Gorsky, 2006) are the most prominent theories, yet they…
Lee, Christina S.; Colby, Suzanne M.; Magill, Molly; Almeida, Joanna; Tavares, Tonya; Rohsenow, Damaris J.
2016-01-01
Background The NIH Strategic Plan prioritizes health disparities research for socially disadvantaged Hispanics, to reduce the disproportionate burden of alcohol-related negative consequences compared to other racial/ethnic groups. Cultural adaptation of evidence-based treatments, such as motivational interviewing (MI), can improve access and response to alcohol treatment. However, the lack of rigorous clinical trials designed to test the efficacy and theoretical underpinnings of cultural adaptation has made proof of concept difficult. Objective The CAMI2 (Culturally Adapted Motivational Interviewing) study design and its theoretical model, is described to illustrate how MI adapted to social and cultural factors (CAMI) can be discriminated against non-adapted MI. Methods and Design CAMI2, a large, 12 month randomized prospective trial, examines the efficacy of CAMI and MI among heavy drinking Hispanics recruited from the community (n=257). Outcomes are reductions in heavy drinking days (Time Line Follow-Back) and negative consequences of drinking among Hispanics (Drinkers Inventory of Consequences). A second aim examines perceived acculturation stress as a moderator of treatment outcomes in the CAMI condition. Summary The CAMI2 study design protocol is presented and the theory of adaptation is presented. Findings from the trial described may yield important recommendations on the science of cultural adaptation and improve MI dissemination to Hispanics with alcohol risk. PMID:27565832
An embedded implementation based on adaptive filter bank for brain-computer interface systems.
Belwafi, Kais; Romain, Olivier; Gannouni, Sofien; Ghaffari, Fakhreddine; Djemal, Ridha; Ouni, Bouraoui
2018-07-15
Brain-computer interface (BCI) is a new communication pathway for users with neurological deficiencies. The implementation of a BCI system requires complex electroencephalography (EEG) signal processing including filtering, feature extraction and classification algorithms. Most of current BCI systems are implemented on personal computers. Therefore, there is a great interest in implementing BCI on embedded platforms to meet system specifications in terms of time response, cost effectiveness, power consumption, and accuracy. This article presents an embedded-BCI (EBCI) system based on a Stratix-IV field programmable gate array. The proposed system relays on the weighted overlap-add (WOLA) algorithm to perform dynamic filtering of EEG-signals by analyzing the event-related desynchronization/synchronization (ERD/ERS). The EEG-signals are classified, using the linear discriminant analysis algorithm, based on their spatial features. The proposed system performs fast classification within a time delay of 0.430 s/trial, achieving an average accuracy of 76.80% according to an offline approach and 80.25% using our own recording. The estimated power consumption of the prototype is approximately 0.7 W. Results show that the proposed EBCI system reduces the overall classification error rate for the three datasets of the BCI-competition by 5% compared to other similar implementations. Moreover, experiment shows that the proposed system maintains a high accuracy rate with a short processing time, a low power consumption, and a low cost. Performing dynamic filtering of EEG-signals using WOLA increases the recognition rate of ERD/ERS patterns of motor imagery brain activity. This approach allows to develop a complete prototype of a EBCI system that achieves excellent accuracy rates. Copyright © 2018 Elsevier B.V. All rights reserved.
Adaptive Core Simulation Employing Discrete Inverse Theory - Part II: Numerical Experiments
International Nuclear Information System (INIS)
Abdel-Khalik, Hany S.; Turinsky, Paul J.
2005-01-01
Use of adaptive simulation is intended to improve the fidelity and robustness of important core attribute predictions such as core power distribution, thermal margins, and core reactivity. Adaptive simulation utilizes a selected set of past and current reactor measurements of reactor observables, i.e., in-core instrumentation readings, to adapt the simulation in a meaningful way. The companion paper, ''Adaptive Core Simulation Employing Discrete Inverse Theory - Part I: Theory,'' describes in detail the theoretical background of the proposed adaptive techniques. This paper, Part II, demonstrates several computational experiments conducted to assess the fidelity and robustness of the proposed techniques. The intent is to check the ability of the adapted core simulator model to predict future core observables that are not included in the adaption or core observables that are recorded at core conditions that differ from those at which adaption is completed. Also, this paper demonstrates successful utilization of an efficient sensitivity analysis approach to calculate the sensitivity information required to perform the adaption for millions of input core parameters. Finally, this paper illustrates a useful application for adaptive simulation - reducing the inconsistencies between two different core simulator code systems, where the multitudes of input data to one code are adjusted to enhance the agreement between both codes for important core attributes, i.e., core reactivity and power distribution. Also demonstrated is the robustness of such an application
Decentralized adaptive control of manipulators - Theory, simulation, and experimentation
Seraji, Homayoun
1989-01-01
The author presents a simple decentralized adaptive-control scheme for multijoint robot manipulators based on the independent joint control concept. The control objective is to achieve accurate tracking of desired joint trajectories. The proposed control scheme does not use the complex manipulator dynamic model, and each joint is controlled simply by a PID (proportional-integral-derivative) feedback controller and a position-velocity-acceleration feedforward controller, both with adjustable gains. Simulation results are given for a two-link direct-drive manipulator under adaptive independent joint control. The results illustrate trajectory tracking under coupled dynamics and varying payload. The proposed scheme is implemented on a MicroVAX II computer for motion control of the three major joints of a PUMA 560 arm. Experimental results are presented to demonstrate that trajectory tracking is achieved despite coupled nonlinear joint dynamics.
Block-structured Adaptive Mesh Refinement - Theory, Implementation and Application
Directory of Open Access Journals (Sweden)
Deiterding Ralf
2011-12-01
Full Text Available Structured adaptive mesh refinement (SAMR techniques can enable cutting-edge simulations of problems governed by conservation laws. Focusing on the strictly hyperbolic case, these notes explain all algorithmic and mathematical details of a technically relevant implementation tailored for distributed memory computers. An overview of the background of commonly used finite volume discretizations for gas dynamics is included and typical benchmarks to quantify accuracy and performance of the dynamically adaptive code are discussed. Large-scale simulations of shock-induced realistic combustion in non-Cartesian geometry and shock-driven fluid-structure interaction with fully coupled dynamic boundary motion demonstrate the applicability of the discussed techniques for complex scenarios.
Zhao, Qinglin; Hu, Bin; Shi, Yujun; Li, Yang; Moore, Philip; Sun, Minghou; Peng, Hong
2014-06-01
Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-05-13
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.
Directory of Open Access Journals (Sweden)
Yibo Feng
2015-05-01
Full Text Available We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF, the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.
Azarpour, Masoumeh; Enzner, Gerald
2017-12-01
Binaural noise reduction, with applications for instance in hearing aids, has been a very significant challenge. This task relates to the optimal utilization of the available microphone signals for the estimation of the ambient noise characteristics and for the optimal filtering algorithm to separate the desired speech from the noise. The additional requirements of low computational complexity and low latency further complicate the design. A particular challenge results from the desired reconstruction of binaural speech input with spatial cue preservation. The latter essentially diminishes the utility of multiple-input/single-output filter-and-sum techniques such as beamforming. In this paper, we propose a comprehensive and effective signal processing configuration with which most of the aforementioned criteria can be met suitably. This relates especially to the requirement of efficient online adaptive processing for noise estimation and optimal filtering while preserving the binaural cues. Regarding noise estimation, we consider three different architectures: interaural (ITF), cross-relation (CR), and principal-component (PCA) target blocking. An objective comparison with two other noise PSD estimation algorithms demonstrates the superiority of the blocking-based noise estimators, especially the CR-based and ITF-based blocking architectures. Moreover, we present a new noise reduction filter based on minimum mean-square error (MMSE), which belongs to the class of common gain filters, hence being rigorous in terms of spatial cue preservation but also efficient and competitive for the acoustic noise reduction task. A formal real-time subjective listening test procedure is also developed in this paper. The proposed listening test enables a real-time assessment of the proposed computationally efficient noise reduction algorithms in a realistic acoustic environment, e.g., considering time-varying room impulse responses and the Lombard effect. The listening test outcome
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
feedback cancellation (AFC) where the goal is to adaptively model the acoustic feedback path and estimate the feedback signal, which is then subtracted from the microphone signal. The main problem in identifying the acoustic feedback path model is the correlation between the near-end signal...
DEFF Research Database (Denmark)
Leza, Cristina; Puthusserypady, Sadasivan
2017-01-01
Brain Computer Interfaces (BCIs) use brain signals to communicate with the external world. The main challenges to address are speed, accuracy and adaptability. Here, a novel algorithm for P300 based BCI spelling system is presented, speciﬁcally suited for single-trial detection of Event...
An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter
Song, Hajoon; Hoteit, Ibrahim; Cornuelle, Bruce D.; Subramanian, Aneesh C.
2010-01-01
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 En
A quantitative evolutionary theory of adaptive behavior dynamics.
McDowell, J J
2013-10-01
The idea that behavior is selected by its consequences in a process analogous to organic evolution has been discussed for over 100 years. A recently proposed theory instantiates this idea by means of a genetic algorithm that operates on a population of potential behaviors. Behaviors in the population are represented by numbers in decimal integer (phenotypic) and binary bit string (genotypic) forms. One behavior from the population is emitted at random each time tick, after which a new population of potential behaviors is constructed by recombining parent behavior bit strings. If the emitted behavior produced a benefit to the organism, then parents are chosen on the basis of their phenotypic similarity to the emitted behavior; otherwise, they are chosen at random. After parent behavior recombination, the population is subjected to a small amount of mutation by flipping random bits in the population's bit strings. The behavior generated by this process of selection, reproduction, and mutation reaches equilibrium states that conform to every empirically valid equation of matching theory, exactly and without systematic error. These equations are known to describe the behavior of many vertebrate species, including humans, in a variety of experimental, naturalistic, natural, and social environments. The evolutionary theory also generates instantaneous dynamics and patterns of preference change in constantly changing environments that are consistent with the dynamics of live-organism behavior. These findings support the assertion that the world of behavior we observe and measure is generated by evolutionary dynamics. PsycINFO Database Record (c) 2013 APA, all rights reserved
Han, Dongju
2018-05-01
Safe and efficient flight powered by an aircraft turbojet engine relies on the performance of the engine controller preventing compressor surge with robustness from noises or disturbances. This paper proposes the effective nonlinear controller associated with the nonlinear filter for the real turbojet engine with highly nonlinear dynamics. For the feasible controller study the nonlinearity of the engine dynamics was investigated by comparing the step responses from the linearized model with the original nonlinear dynamics. The fuzzy-based PID control logic is introduced to control the engine efficiently and FAUKF is applied for robustness from noises. The simulation results prove the effectiveness of FAUKF applied to the proposed controller such that the control performances are superior over the conventional controller and the filer performance using FAUKF indicates the satisfactory results such as clearing the defects by reducing the distortions without compressor surge, whereas the conventional UKF is not fully effective as occurring some distortions with compressor surge due to a process noise.
Li, Yongbo; Li, Guoyan; Yang, Yuantao; Liang, Xihui; Xu, Minqiang
2018-05-01
The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.
Directory of Open Access Journals (Sweden)
Denis Delisle-Rodriguez
2017-11-01
Full Text Available This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs based on Canonical Correlation Analysis (CCA to recognize 40 targets of steady-state visual evoked potential (SSVEP, providing an accuracy (ACC of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly ( p < 0.01 improved for most of the subjects ( A C C ≥ 74.79 % , when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.
Directory of Open Access Journals (Sweden)
Mosbeh R. Kaloop
2015-10-01
Full Text Available This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2 the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and (3 The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.
Laverick, Kiarn T.; Wiseman, Howard M.; Dinani, Hossein T.; Berry, Dominic W.
2018-04-01
The problem of measuring a time-varying phase, even when the statistics of the variation is known, is considerably harder than that of measuring a constant phase. In particular, the usual bounds on accuracy, such as the 1 /(4 n ¯) standard quantum limit with coherent states, do not apply. Here, by restricting to coherent states, we are able to analytically obtain the achievable accuracy, the equivalent of the standard quantum limit, for a wide class of phase variation. In particular, we consider the case where the phase has Gaussian statistics and a power-law spectrum equal to κp -1/|ω| p for large ω , for some p >1 . For coherent states with mean photon flux N , we give the quantum Cramér-Rao bound on the mean-square phase error as [psin(π /p ) ] -1(4N /κ ) -(p -1 )/p . Next, we consider whether the bound can be achieved by an adaptive homodyne measurement in the limit N /κ ≫1 , which allows the photocurrent to be linearized. Applying the optimal filtering for the resultant linear Gaussian system, we find the same scaling with N , but with a prefactor larger by a factor of p . By contrast, if we employ optimal smoothing we can exactly obtain the quantum Cramér-Rao bound. That is, contrary to previously considered (p =2 ) cases of phase estimation, here the improvement offered by smoothing over filtering is not limited to a factor of 2 but rather can be unbounded by a factor of p . We also study numerically the performance of these estimators for an adaptive measurement in the limit where N /κ is not large and find a more complicated picture.
Briefer E McElligott EG
2011-01-01
The source–filter theory is an important framework recently applied to the study of animal vocalisations which links the mode of vocal production to call parameters. Vocalisations can be good indicators of a sender’s characteristics such as identity body size age and even hormonal status and affective states. For these reasons applied vocal communication research would greatly benefit from adopting the source–filter theory approach to identify key call parameters linked to physical and physio...
Adaptive control theory of concentration in the uranium enrichment plant
International Nuclear Information System (INIS)
Sugitsue, Noritake; Miyagawa, Hiroshi; Yokoyama, Kaoru; Nakakura, Hiroyuki
1999-01-01
This paper presents the new adaptive control of concentration in the uranium enrichment plant. The purpose of this control system is average concentration control in production tram. As a result the accuracy and practical use of this control system have already been confirmed by the operation of the uranium enrichment demonstration plant. Three elements of technology are required to this method. The first is the measurement of the concentration using product flow quantity change. This technology shall be called 'Qp difference to Xp transform method'. The second is the relationship between temperature change and flow quantity using G.M.D.H. (Groupe Method of Data Handling) and the third is the estimation of temperature change using AR (Auto-regressive) model. (author)
GSFAP Adaptive Filtering Using Log Arithmetic for Resource-Constrained Embedded Systems
Czech Academy of Sciences Publication Activity Database
Tichý, Milan; Schier, Jan; Gregg, D.
2010-01-01
Roč. 9, č. 3 (2010), s. 1-31 ISSN 1539-9087 R&D Projects: GA MŠk 7H09005 Institutional research plan: CEZ:AV0Z10750506 Keywords : FPGA * DSP * logarithmic arithmetic * affine projection Subject RIV: BD - Theory of Information Impact factor: 1.057, year: 2010 http://library.utia.cas.cz/separaty/2010/ZS/tichy-0341115.pdf
An adaptive digital suppression filter for direct-sequence spread-spectrum communications
Saulnier, G. J.; Das, P. K.; Milstein, L. B.
1985-09-01
This paper describes the structure of a digital implementation of the Widrow-Hoff LMS algorithm which uses a burst processing technique to obtain some hardware simplification. This adaptive system is used to suppress narrow-band interference in a direct-sequence spread-spectrum communication system. Several different narrow-band interferers are considered, and probability of error results are presented for all cases. While, in general, the results show significant improvement in performance when the LMS algorithm is used, certain disadvantages are also present and are discussed in this paper.
Human tracking in thermal images using adaptive particle filters with online random forest learning
Ko, Byoung Chul; Kwak, Joon-Young; Nam, Jae-Yeal
2013-11-01
This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented center-symmetric local binary patterns (OCS-LBP). Specifically, we design a real-time random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other methods.
Hesar, Hamed Danandeh; Mohebbi, Maryam
2017-11-01
Model-based Bayesian frameworks have a common problem in processing electrocardiogram (ECG) signals with sudden morphological changes. This situation often happens in the case of arrhythmias where ECGs do not obey the predefined state models. To solve this problem, in this paper, a model-based Bayesian denoising framework is proposed using marginalized particle-extended Kalman filter (MP-EKF), variational mode decomposition, and a novel fuzzy-based adaptive particle weighting strategy. This strategy helps MP-EKF to perform well even when the morphology of signal does not comply with the predefined dynamic model. In addition, this strategy adapts MP-EKF's behavior to the acquired measurements in different input signal to noise ratios (SNRs). At low input SNRs, this strategy decreases the particles' trust level to the measurements while increasing their trust level to a synthetic ECG constructed with the feature parameters of ECG dynamic model. At high input SNRs, the particles' trust level to the measurements is increased and the trust level to synthetic ECG is decreased. The proposed method was evaluated on MIT-BIH normal sinus rhythm database and compared with EKF/EKS frameworks and previously proposed MP-EKF. It was also evaluated on ECG segments extracted from MIT-BIH arrhythmia database, which contained ventricular and atrial arrhythmia. The results showed that the proposed algorithm had a noticeable superiority over benchmark methods from both SNR improvement and multiscale entropy based weighted distortion (MSEWPRD) viewpoints at low input SNRs.
Directory of Open Access Journals (Sweden)
Yuan Xu
2014-01-01
Full Text Available As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF which used the noise statistics estimator in the iterated extended Kalman (IEKF, and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS/wireless sensors networks (WSNs-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.
The Role of Theory Adaptation in the Making of a Reference Discipline
DEFF Research Database (Denmark)
Kjærgaard, Annemette; Vendelø, Morten Thanning
2015-01-01
Information Systems (IS) scholars repeatedly debate the nature of the IS discipline. A series of articles have debated whether the IS field has become a reference discipline. While many scholars have argued this question from a perceptual point of view, we address it by examining the role of theo...... adaptation, have a higher probability of being referenced by other disciplines. Finally, we discuss the implications of the manner in which IS scholars borrow theory regarding the IS discipline's prospects of becoming a reference discipline.......Information Systems (IS) scholars repeatedly debate the nature of the IS discipline. A series of articles have debated whether the IS field has become a reference discipline. While many scholars have argued this question from a perceptual point of view, we address it by examining the role of theory...... adaptation in the making of a reference discipline. Based on a review of how the sensemaking theory from organization studies is adapted and used in IS research, we show that papers that adapt and use sensemaking theory as a central construct in the theoretical framework – in other words – engaging in theory...
Bergner, Yoav; Droschler, Stefan; Kortemeyer, Gerd; Rayyan, Saif; Seaton, Daniel; Pritchard, David E.
2012-01-01
We apply collaborative filtering (CF) to dichotomously scored student response data (right, wrong, or no interaction), finding optimal parameters for each student and item based on cross-validated prediction accuracy. The approach is naturally suited to comparing different models, both unidimensional and multidimensional in ability, including a…
Szadkowski, Zbigniew; Fraenkel, E. D.; van den Berg, Ad M.
2013-01-01
We present the FPGA/NIOS implementation of an adaptive finite impulse response (FIR) filter based on linear prediction to suppress radio frequency interference (RFI). This technique will be used for experiments that observe coherent radio emission from extensive air showers induced by
1974-01-01
Communications equipment for use with the Skylab project is examined to show compliance with contract requirements. The items of equipment considered are: (1) communications carrier assemblies, (2) filter bypass adapter assemblies, and (3) sub-assemblies, parts, and repairs. Additional information is provided concerning contract requirements, test requirements, and failure investigation actions.
High-dynamic range compressive spectral imaging by grayscale coded aperture adaptive filtering
Directory of Open Access Journals (Sweden)
Nelson Eduardo Diaz
2015-09-01
Full Text Available The coded aperture snapshot spectral imaging system (CASSI is an imaging architecture which senses the three dimensional informa-tion of a scene with two dimensional (2D focal plane array (FPA coded projection measurements. A reconstruction algorithm takes advantage of the compressive measurements sparsity to recover the underlying 3D data cube. Traditionally, CASSI uses block-un-block coded apertures (BCA to spatially modulate the light. In CASSI the quality of the reconstructed images depends on the design of these coded apertures and the FPA dynamic range. This work presents a new CASSI architecture based on grayscaled coded apertu-res (GCA which reduce the FPA saturation and increase the dynamic range of the reconstructed images. The set of GCA is calculated in a real-time adaptive manner exploiting the information from the FPA compressive measurements. Extensive simulations show the attained improvement in the quality of the reconstructed images when GCA are employed. In addition, a comparison between traditional coded apertures and GCA is realized with respect to noise tolerance.
Adaptive Restoration of Airborne Daedalus AADS1268 ATM Thermal Data
International Nuclear Information System (INIS)
D. Yuan; E. Doak; P. Guss; A. Will
2002-01-01
To incorporate the georegistration and restoration processes into airborne data processing in support of U.S. Department of Energy's nuclear emergency response task, we developed an adaptive restoration filter for airborne Daedalus AADS1268 ATM thermal data based on the Wiener filtering theory. Preliminary assessment shows that this filter enhances the detectability of small weak thermal anomalies in AADS1268 thermal images
Directory of Open Access Journals (Sweden)
Yu-Chai Lai
2015-10-01
Full Text Available Transmedia narratives are a key topic of communication research. Transmedia adaptations occur when a narrator transposes an original work by using various media platforms. Considering pictorial turn, this study employed the intermediality of an animated work that was adapted from an award-winning picture book as an example to propose an analysis for theory building. After examining the literature on transmedia narratives, intermediality, and aesthetic communication, this study proposed analyses for the dimensions of transmedia adaptations of pictorial narratives and aesthetic pole interpretations. Focusing on the three layers of the artistic pole, aesthetic pole, and interactions and effects, this study cited the cases of award-winning picture books and adapted animated works as the basis for reflecting on aesthetic communication. The artistic pole of transmedia adaptation was used as an example of how a narrator employs intermediality by citing the pictures, plot, or art forms from an original work to reinvent structures and then adapts them according to intermediality. If the aesthetic pole views the adapted animated work after reading the picture book or forms an expectation of the adaptation because of intermediality, then when the audience watches the adapted animated work, their imagination could be stimulated by the intermediality (i.e., picture book graphics and scene depictions. For transmedia narrative interaction, the aesthetic pole must “fill blanks” or “negate” to continue to view the adaptation. For filling blanks, because of intermediality, the aesthetic pole must construct an “intracompositional intermediality” to connect with the visual and audio links of the same work or construct “extracompositional intermedialtiy” to associate the original work with the transmedia adaptation. For negation, when viewing the adaptation (i.e., presentation of picture book graphics, added music, or theme song, the aesthetic pole
Kou, Jisheng; Sun, Shuyu
2014-01-01
The gradient theory for the surface tension of simple fluids and mixtures is rigorously analyzed based on mathematical theory. The finite element approximation of surface tension is developed and analyzed, and moreover, an adaptive finite element method based on a physical-based estimator is proposed and it can be coupled efficiently with Newton's method as well. The numerical tests are carried out both to verify the proposed theory and to demonstrate the efficiency of the proposed method. © 2013 Elsevier B.V. All rights reserved.
Kou, Jisheng
2014-01-01
The gradient theory for the surface tension of simple fluids and mixtures is rigorously analyzed based on mathematical theory. The finite element approximation of surface tension is developed and analyzed, and moreover, an adaptive finite element method based on a physical-based estimator is proposed and it can be coupled efficiently with Newton\\'s method as well. The numerical tests are carried out both to verify the proposed theory and to demonstrate the efficiency of the proposed method. © 2013 Elsevier B.V. All rights reserved.
Beyond the Hedonic Treadmill: Revising the Adaptation Theory of Well-Being
Diener, Ed; Lucas, Richard E.; Napa, Christine
2006-01-01
According to the hedonic treadmill model, good and bad events temporarily affect happiness, but people quickly adapt back to hedonic neutrality. The theory, which has gained widespread acceptance in recent years, implies that individual and societal efforts to increase happiness are doomed to failure. The recent empirical work outlined here…
When do mixotrophs specialize? Adaptive dynamics theory applied to a dynamic energy budget model.
Troost, T.A.; Kooi, B.W.; Kooijman, S.A.L.M.
2005-01-01
In evolutionary history, several events have occurred at which mixotrophs specialized into pure autotrophs and heterotrophs. We studied the conditions under which such events take place, using the Dynamic Energy Budget (DEB) theory for physiological rules of the organisms' metabolism and Adaptive
Bolkan, San; Goodboy, Alan K.
2016-01-01
Protection motivation theory (PMT) explains people's adaptive behavior in response to personal threats. In this study, PMT was used to predict rhetorical dissent episodes related to 210 student reports of perceived classroom problems. In line with theoretical predictions, a moderated moderation analysis revealed that students were likely to voice…
Seeing Coloured Fruits: Utilisation of the Theory of Adaptive Memory in Teaching Botany
Prokop, Pavol; Fancovicová, Jana
2014-01-01
Plants are characterised by a great diversity of easily observed features such as colours or shape, but children show low interest in learning about them. Here, we integrated modern theory of adaptive memory and evolutionary views of the function of fruit colouration on children's retention of information. Survival-relevant (fruit toxicity) and…
Adaptive capacity of geographical clusters: Complexity science and network theory approach
Albino, Vito; Carbonara, Nunzia; Giannoccaro, Ilaria
This paper deals with the adaptive capacity of geographical clusters (GCs), that is a relevant topic in the literature. To address this topic, GC is considered as a complex adaptive system (CAS). Three theoretical propositions concerning the GC adaptive capacity are formulated by using complexity theory. First, we identify three main properties of CAS s that affect the adaptive capacity, namely the interconnectivity, the heterogeneity, and the level of control, and define how the value of these properties influence the adaptive capacity. Then, we associate these properties with specific GC characteristics so obtaining the key conditions of GCs that give them the adaptive capacity so assuring their competitive advantage. To test these theoretical propositions, a case study on two real GCs is carried out. The considered GCs are modeled as networks where firms are nodes and inter-firms relationships are links. Heterogeneity, interconnectivity, and level of control are considered as network properties and thus measured by using the methods of the network theory.
Miniaturized dielectric waveguide filters
Sandhu, MY; Hunter, IC
2016-01-01
Design techniques for a new class of integrated monolithic high-permittivity ceramic waveguide filters are presented. These filters enable a size reduction of 50% compared to air-filled transverse electromagnetic filters with the same unloaded Q-factor. Designs for Chebyshev and asymmetric generalised Chebyshev filter and a diplexer are presented with experimental results for an 1800 MHz Chebyshev filter and a 1700 MHz generalised Chebyshev filter showing excellent agreement with theory.
International Nuclear Information System (INIS)
Buerck, J.; Kraemer, K.; Koenig, W.
1990-02-01
The multicomponent version of the interference filter photometer SPECTRAN was adapted by radiation resistant quartz glass optical fibers to in-line flow cells in the aqueous and organic bypass stream of an uranium laboratory extraction column. A combined photometric/electrolytical conductivity measurement allows this modified process instrument to be used as uranium/plutonium in-line monitor in radioactive process streams. By applying a high performance 100 W quartz halogen lamp and suitable light focussing optics the light intensity, attenuated by coupling losses, could be increased to the desired level even when 1000 μm-single strand fibers (2x18 m) were used to transmit the light. In a series of calibration experiments the U(VI)- and U(IV)-extinction coefficients were determined as a function of nitric acid molarity (for U(VI) also in TBP/kerosene). Furthermore the validity of Lambert-Beer's law was examined for both oxidation states at different optical path lengths and nitric acid/electrolytical conductivity calibration functions between 0-100 g/l U(VI) and 0-4 mol/l HNO 3 were set up. (orig./EF) [de
Luo, Yong; Wu, Wenqi; Babu, Ravindra; Tang, Kanghua; Luo, Bing
2012-01-01
COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System). Since the ultra-tight GPS/INS (Inertial Navigation System) integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF), and INS's accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load. PMID:23012564
Adaptive bilateral filter for image denoising and its application to in-vitro Time-of-Flight data
Seitel, Alexander; dos Santos, Thiago R.; Mersmann, Sven; Penne, Jochen; Groch, Anja; Yung, Kwong; Tetzlaff, Ralf; Meinzer, Hans-Peter; Maier-Hein, Lena
2011-03-01
Image-guided therapy systems generally require registration of pre-operative planning data with the patient's anatomy. One common approach to achieve this is to acquire intra-operative surface data and match it to surfaces extracted from the planning image. Although increasingly popular for surface generation in general, the novel Time-of-Flight (ToF) technology has not yet been applied in this context. This may be attributed to the fact that the ToF range images are subject to considerable noise. The contribution of this study is two-fold. Firstly, we present an adaption of the well-known bilateral filter for denoising ToF range images based on the noise characteristics of the camera. Secondly, we assess the quality of organ surfaces generated from ToF range data with and without bilateral smoothing using corresponding high resolution CT data as ground truth. According to an evaluation on five porcine organs, the root mean squared (RMS) distance between the denoised ToF data points and the reference computed tomography (CT) surfaces ranged from 3.0 mm (lung) to 9.0 mm (kidney). This corresponds to an error-reduction of up to 36% compared to the error of the original ToF surfaces.
Directory of Open Access Journals (Sweden)
Bing Luo
2012-07-01
Full Text Available COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System. Since the ultra-tight GPS/INS (Inertial Navigation System integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF, and INS’s accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load.
Theory and validation of a liquid radiation filter greenhouse simulation for performance prediction
International Nuclear Information System (INIS)
Feuermann, D.; Kopel, R.; Zeroni, M.; Levi, S.; Gale, J.
1997-01-01
A greenhouse is described which has a selectively absorbing liquid radiation filter (LRF) circulating in double layered cladding. The filter removes much of the near infrared wave band of solar radiation (700 nm) while transmitting most of the photosynthetic radiation (400-700 nm). This greatly reduces the heat input to the greenhouse and, by transferring heat from day to night, facilitates better temperature control. This is particularly important for CO2 fertilization, which requires that the greenhouse should remain closed during daylight hours. A computer simulation model was developed to study the relationship between design parameters of such a LRF greenhouse and its thermal performance under different climatic conditions. The model was based on a small number of governing equations describing the major physical phenomena responsible for the greenhouse climate. Validation of the simulation was performed with data from a 330 m2 LRF greenhouse, operating in the Negev (Israel) desert highlands. The predicted greenhouse temperatures were found to agree with measured values to within one to two degrees Celsius. Performances of a LRF and a conventional greenhouse were compared using the simulation and hourly meteorological data for central Israel. For the summer season of May to October, the number of daylight hours during which the LRF greenhouse could remain closed was larger by about two-thirds than that of the conventional greenhouse
Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms.
Harikumar, G; Bresler, Y
1999-01-01
We address the problem of restoring an image from its noisy convolutions with two or more unknown finite impulse response (FIR) filters. We develop theoretical results about the existence and uniqueness of solutions, and show that under some generically true assumptions, both the filters and the image can be determined exactly in the absence of noise, and stably estimated in its presence. We present efficient algorithms to estimate the blur functions and their sizes. These algorithms are of two types, subspace-based and likelihood-based, and are extensions of techniques proposed for the solution of the multichannel blind deconvolution problem in one dimension. We present memory and computation-efficient techniques to handle the very large matrices arising in the two-dimensional (2-D) case. Once the blur functions are determined, they are used in a multichannel deconvolution step to reconstruct the unknown image. The theoretical and practical implications of edge effects, and "weakly exciting" images are examined. Finally, the algorithms are demonstrated on synthetic and real data.
Favaro, Livio; Gamba, Marco; Alfieri, Chiara; Pessani, Daniela; McElligott, Alan G
2015-11-25
The African penguin is a nesting seabird endemic to southern Africa. In penguins of the genus Spheniscus vocalisations are important for social recognition. However, it is not clear which acoustic features of calls can encode individual identity information. We recorded contact calls and ecstatic display songs of 12 adult birds from a captive colony. For each vocalisation, we measured 31 spectral and temporal acoustic parameters related to both source and filter components of calls. For each parameter, we calculated the Potential of Individual Coding (PIC). The acoustic parameters showing PIC ≥ 1.1 were used to perform a stepwise cross-validated discriminant function analysis (DFA). The DFA correctly classified 66.1% of the contact calls and 62.5% of display songs to the correct individual. The DFA also resulted in the further selection of 10 acoustic features for contact calls and 9 for display songs that were important for vocal individuality. Our results suggest that studying the anatomical constraints that influence nesting penguin vocalisations from a source-filter perspective, can lead to a much better understanding of the acoustic cues of individuality contained in their calls. This approach could be further extended to study and understand vocal communication in other bird species.
Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.
2000-01-01
Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Yamada, Yoshitake, E-mail: yamada@rad.med.keio.ac.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Jinzaki, Masahiro, E-mail: jinzaki@rad.med.keio.ac.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Hosokawa, Takahiro, E-mail: hosokawa@rad.med.keio.ac.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Tanami, Yutaka, E-mail: tanami@rad.med.keio.ac.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Sugiura, Hiroaki, E-mail: hsugiura@rad.med.keio.ac.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Abe, Takayuki, E-mail: tabe@z5.keio.jp [Center for Clinical Research, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan); Kuribayashi, Sachio, E-mail: skuribay@a5.keio.jp [Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582 (Japan)
2012-12-15
Objectives: To assess the effectiveness of adaptive iterative dose reduction (AIDR) and AIDR 3D in improving the image quality in low-dose chest CT (LDCT). Materials and methods: Fifty patients underwent standard-dose chest CT (SDCT) and LDCT simultaneously, performed under automatic exposure control with noise index of 19 and 38 (for a 2-mm slice thickness), respectively. The SDCT images were reconstructed with filtered back projection (SDCT-FBP images), and the LDCT images with FBP, AIDR and AIDR 3D (LDCT-FBP, LDCT-AIDR and LDCT-AIDR 3D images, respectively). On all the 200 lung and 200 mediastinal image series, objective image noise and signal-to-noise ratio (SNR) were measured in several regions, and two blinded radiologists independently assessed the subjective image quality. Wilcoxon's signed rank sum test with Bonferroni's correction was used for the statistical analyses. Results: The mean dose reduction in LDCT was 64.2% as compared with the dose in SDCT. LDCT-AIDR 3D images showed significantly reduced objective noise and significantly increased SNR in all regions as compared to the SDCT-FBP, LDCT-FBP and LDCT-AIDR images (all, P ≤ 0.003). In all assessments of the image quality, LDCT-AIDR 3D images were superior to LDCT-AIDR and LDCT-FBP images. The overall diagnostic acceptability of both the lung and mediastinal LDCT-AIDR 3D images was comparable to that of the lung and mediastinal SDCT-FBP images. Conclusions: AIDR 3D is superior to AIDR. Intra-individual comparisons between SDCT and LDCT suggest that AIDR 3D allows a 64.2% reduction of the radiation dose as compared to SDCT, by substantially reducing the objective image noise and increasing the SNR, while maintaining the overall diagnostic acceptability.
Real-time single image dehazing based on dark channel prior theory and guided filtering
Zhang, Zan
2017-10-01
Images and videos taken outside the foggy day are serious degraded. In order to restore degraded image taken in foggy day and overcome traditional Dark Channel prior algorithms problems of remnant fog in edge, we propose a new dehazing method.We first find the fog area in the dark primary color map to obtain the estimated value of the transmittance using quadratic tree. Then we regard the gray-scale image after guided filtering as atmospheric light map and remove haze based on it. Box processing and image down sampling technology are also used to improve the processing speed. Finally, the atmospheric light scattering model is used to restore the image. A plenty of experiments show that algorithm is effective, efficient and has a wide range of application.
A convergence result for the unscented Kalman-Bucy filter using contraction theory
DEFF Research Database (Denmark)
Maree, J. P.; Imsland, L.; Jouffroy, J.
2013-01-01
-type stochastic differential equations. By employing a virtual/actual system framework, a special relation is established between sigma-point dynamics, and observed process states, with respect to contraction and convergence. The proposed theory is illustrated on an isothermal, non-linear CSTR process. © 2013...
Singh, Yashi; Hussain, Ikhlaq; Singh, Bhim; Mishra, Sukumar
2018-03-01
In this paper, power quality features such as harmonics mitigation, power factor correction with active power filtering are addressed in a single-stage, single-phase solar photovoltaic (PV) grid tied system. The Power Balance Theory (PBT) with perturb and observe based maximum power point tracking algorithm is proposed for the mitigation of power quality problems in a solar PV grid tied system. The solar PV array is interfaced to a single phase AC grid through a Voltage Source Converter (VSC), which provides active power flow from a solar PV array to the grid as well as to the load and it performs harmonics mitigation using PBT based control. The solar PV array power varies with sunlight and due to this, the solar PV grid tied VSC works only 8-10 h per day. At night, when PV power is zero, the VSC works as an active power filter for power quality improvement, and the load active power is delivered by the grid to the load connected at the point of common coupling. This increases the effective utilization of a VSC. The system is modelled and simulated using MATLAB and simulated responses of the system at nonlinear loads and varying environmental conditions are also validated experimentally on a prototype developed in the laboratory.
International Nuclear Information System (INIS)
Kachelriess, Marc; Watzke, Oliver; Kalender, Willi A.
2001-01-01
In modern computed tomography (CT) there is a strong desire to reduce patient dose and/or to improve image quality by increasing spatial resolution and decreasing image noise. These are conflicting demands since increasing resolution at a constant noise level or decreasing noise at a constant resolution level implies a higher demand on x-ray power and an increase of patient dose. X-ray tube power is limited due to technical reasons. We therefore developed a generalized multi-dimensional adaptive filtering approach that applies nonlinear filters in up to three dimensions in the raw data domain. This new method differs from approaches in the literature since our nonlinear filters are applied not only in the detector row direction but also in the view and in the z-direction. This true three-dimensional filtering improves the quantum statistics of a measured projection value proportional to the third power of the filter size. Resolution tradeoffs are shared among these three dimensions and thus are considerably smaller as compared to one-dimensional smoothing approaches. Patient data of spiral and sequential single- and multi-slice CT scans as well as simulated spiral cone-beam data were processed to evaluate these new approaches. Image quality was assessed by evaluation of difference images, by measuring the image noise and the noise reduction, and by calculating the image resolution using point spread functions. The use of generalized adaptive filters helps to reduce image noise or, alternatively, patient dose. Image noise structures, typically along the direction of the highest attenuation, are effectively reduced. Noise reduction values of typically 30%-60% can be achieved in noncylindrical body regions like the shoulder. The loss in image resolution remains below 5% for all cases. In addition, the new method has a great potential to reduce metal artifacts, e.g., in the hip region
On residual stresses and homeostasis: an elastic theory of functional adaptation in living matter.
Ciarletta, P; Destrade, M; Gower, A L
2016-04-26
Living matter can functionally adapt to external physical factors by developing internal tensions, easily revealed by cutting experiments. Nonetheless, residual stresses intrinsically have a complex spatial distribution, and destructive techniques cannot be used to identify a natural stress-free configuration. This work proposes a novel elastic theory of pre-stressed materials. Imposing physical compatibility and symmetry arguments, we define a new class of free energies explicitly depending on the internal stresses. This theory is finally applied to the study of arterial remodelling, proving its potential for the non-destructive determination of the residual tensions within biological materials.
Directory of Open Access Journals (Sweden)
Mark Frogley
2013-01-01
Full Text Available To reduce the maintenance cost, avoid catastrophic failure, and improve the wind transmission system reliability, online condition monitoring system is critical important. In the real applications, many rotating mechanical faults, such as bearing surface defect, gear tooth crack, chipped gear tooth and so on generate impulsive signals. When there are these types of faults developing inside rotating machinery, each time the rotating components pass over the damage point, an impact force could be generated. The impact force will cause a ringing of the support structure at the structural natural frequency. By effectively detecting those periodic impulse signals, one group of rotating machine faults could be detected and diagnosed. However, in real wind turbine operations, impulsive fault signals are usually relatively weak to the background noise and vibration signals generated from other healthy components, such as shaft, blades, gears and so on. Moreover, wind turbine transmission systems work under dynamic operating conditions. This will further increase the difficulties in fault detection and diagnostics. Therefore, developing advanced signal processing methods to enhance the impulsive signals is in great needs.In this paper, an adaptive filtering technique will be applied for enhancing the fault impulse signals-to-noise ratio in wind turbine gear transmission systems. Multiple statistical features designed to quantify the impulsive signals of the processed signal are extracted for bearing fault detection. The multiple dimensional features are then transformed into one dimensional feature. A minimum error rate classifier will be designed based on the compressed feature to identify the gear transmission system with defect. Real wind turbine vibration signals will be used to demonstrate the effectiveness of the presented methodology.
Quantum Information Biology: From Theory of Open Quantum Systems to Adaptive Dynamics
Asano, Masanari; Basieva, Irina; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro
This chapter reviews quantum(-like) information biology (QIB). Here biology is treated widely as even covering cognition and its derivatives: psychology and decision making, sociology, and behavioral economics and finances. QIB provides an integrative description of information processing by bio-systems at all scales of life: from proteins and cells to cognition, ecological and social systems. Mathematically QIB is based on the theory of adaptive quantum systems (which covers also open quantum systems). Ideologically QIB is based on the quantum-like (QL) paradigm: complex bio-systems process information in accordance with the laws of quantum information and probability. This paradigm is supported by plenty of statistical bio-data collected at all bio-scales. QIB re ects the two fundamental principles: a) adaptivity; and, b) openness (bio-systems are fundamentally open). In addition, quantum adaptive dynamics provides the most generally possible mathematical representation of these principles.
Directory of Open Access Journals (Sweden)
Joshua Rodewald
2016-10-01
Full Text Available Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network structure and dynamics. With minimal information about the network nodes, transfer entropy can be used to reverse engineer the network structure while local transfer entropy can be used to analyze the network structure’s dynamics. Both simulated and real-world networks were analyzed using this methodology. Applying the methodology to CASNs allows the practitioner to capitalize on observations from the highly multidisciplinary field of information theory which provides insights into CASN’s self-organization, emergence, stability/instability, and distributed computation. This not only provides managers with a more thorough understanding of a system’s structure and dynamics for management purposes, but also opens up research opportunities into eventual strategies to monitor and manage emergence and adaption within the environment.
PHOTOVOLTAIC BASED SHUNT ACTIVE FILTER FOR POWER QUALITY IMPROVEMENT USING ICOSΦ THEORY
Directory of Open Access Journals (Sweden)
VIJAYAKUMAR G.
2015-11-01
Full Text Available This paper presents an optimal operation of Photovoltaic based Shunt Active Filter as (PV-SAF for significant energy conservation, harmonic mitigation and reactive power compensation. When the PV system generates excessive or equal power required to the load demand, then the coordinating logic disconnects the service grid from the load and with a consequent reduction of panel tariff and global warming gasses. The PV module is connected to the DC side of SAF through the DC-DC converter. Converter switch is controlled by fuzzy based Perturb & Observe (P&O Maximum Power Point Tracking (MPPT algorithm and it eliminates the drawback in the conventional PV system. The reference currents are extracted by the Fuzzy logic controller based ICosΦ control strategy. This proposed PV-SAF, if connected at the terminals of a small industry or a home or a small enlightening institution can avoid the use of interruptible power supply and individual stabilizer. An emulation using MATLAB Simulink is presented to validate the advantage of the proposed system.
Li, Zhendong; Liu, Wenjian
2010-08-14
The spin-adaptation of single-reference quantum chemical methods for excited states of open-shell systems has been nontrivial. The primary reason is that the configuration space, generated by a truncated rank of excitations from only one component of a reference multiplet, is spin-incomplete. Those "missing" configurations are of higher ranks and can, in principle, be recaptured by a particular class of excitation operators. However, the resulting formalisms are then quite involved and there are situations [e.g., time-dependent density functional theory (TD-DFT) under the adiabatic approximation] that prevent one from doing so. To solve this issue, we propose here a tensor-coupling scheme that invokes all the components of a reference multiplet (i.e., a tensor reference) rather than increases the excitation ranks. A minimal spin-adapted n-tuply excited configuration space can readily be constructed by tensor products between the n-tuple tensor excitation operators and the chosen tensor reference. Further combined with the tensor equation-of-motion formalism, very compact expressions for excitation energies can be obtained. As a first application of this general idea, a spin-adapted open-shell random phase approximation is first developed. The so-called "translation rule" is then adopted to formulate a spin-adapted, restricted open-shell Kohn-Sham (ROKS)-based TD-DFT (ROKS-TD-DFT). Here, a particular symmetry structure has to be imposed on the exchange-correlation kernel. While the standard ROKS-TD-DFT can access only excited states due to singlet-coupled single excitations, i.e., only some of the singly excited states of the same spin (S(i)) as the reference, the new scheme can capture all the excited states of spin S(i)-1, S(i), or S(i)+1 due to both singlet- and triplet-coupled single excitations. The actual implementation and computation are very much like the (spin-contaminated) unrestricted Kohn-Sham-based TD-DFT. It is also shown that spin-contaminated spin
Directory of Open Access Journals (Sweden)
Haoqian Huang
2014-12-01
Full Text Available High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF based on the quaternion expanded to the state variable (BD-AEKF. The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.
Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping
2014-12-03
High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.
Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping
2014-01-01
High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method. PMID:25479331
Monolithic Integrated Ceramic Waveguide Filters
Hunter, IC; Sandhu, MY
2014-01-01
Design techniques for a new class of integrated monolithic high permittivity ceramic waveguide filters are presented. These filters enable a size reduction of 50% compared to air-filled TEM filters with the same unloaded Q-Factor. Designs for both chebyshev and asymmetric generalized chebyshev filter are presented, with experimental results for an 1800 MHz chebyshev filter showing excellent agreement with theory.
Spin-adapted open-shell time-dependent density functional theory. II. Theory and pilot application.
Li, Zhendong; Liu, Wenjian; Zhang, Yong; Suo, Bingbing
2011-04-07
The excited states of open-shell systems calculated by unrestricted Kohn-Sham-based time-dependent density functional theory (U-TD-DFT) are often heavily spin-contaminated and hence meaningless. This is solved ultimately by the recently proposed spin-adapted time-dependent density functional theory (TD-DFT) (S-TD-DFT) [J. Chem. Phys. 133, 064106 (2010)]. Unlike the standard restricted open-shell Kohn-Sham-based TD-DFT (R-TD-DFT) which can only access the singlet-coupled single excitations, the S-TD-DFT can capture both the singlet- and triplet-coupled single excitations with the same computational effort as the U-TD-DFT. The performances of the three approaches (U-TD-DFT, R-TD-DFT, and S-TD-DFT) are compared for both the spin-conserving and spin-flip excitations of prototypical open-shell systems, the nitrogen (N(2)(+)) and naphthalene (C(10)H(8)(+)) cations. The results show that the S-TD-DFT gives rise to balanced descriptions of excited states of open-shell systems.
Adaptability of the Logistics System in National Economic Mobilization Based on Blocking Flow Theory
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Xiangyuan Jing
2014-01-01
Full Text Available In the process of national economic mobilization, the logistics system usually suffers from negative impact and/or threats of such emergency events as wars and accidents, which implies that adaptability of the logistics system directly determines realization of economic mobilization. And where the real-time rescue operation is concerned, heavy traffic congestion is likely to cause a great loss of or damage to human beings and their properties. To deal with this situation, this article constructs a blocking-resistance optimum model and an optimum restructuring model based on blocking flow theories, of which both are illustrated by numerical cases and compared in characteristics and application. The design of these two models is expected to eliminate or alleviate the congestion situation occurring in the logistics system, thus effectively enhancing its adaptability in the national economic mobilization process.
Ho, Ching S.; Liou, Juin J.; Georgiopoulos, Michael; Christodoulou, Christos G.
1994-03-01
This paper presents an analog circuit design and implementation for an adaptive resonance theory neural network architecture called the augmented ART1 neural network (AART1-NN). Practical monolithic operational amplifiers (Op-Amps) LM741 and LM318 are selected to implement the circuit, and a simple compensation scheme is developed to adjust the Op-Amp electrical characteristics to meet the design requirement. A 7-node prototype circuit has been designed and verified using the Pspice circuit simulator run on a Sun workstation. Results simulated from the AART1-NN circuit using the LM741, LM318, and ideal Op-Amps are presented and compared.
Intermolecular symmetry-adapted perturbation theory study of large organic complexes
International Nuclear Information System (INIS)
Heßelmann, Andreas; Korona, Tatiana
2014-01-01
Binding energies for the complexes of the S12L database by Grimme [Chem. Eur. J. 18, 9955 (2012)] were calculated using intermolecular symmetry-adapted perturbation theory combined with a density-functional theory description of the interacting molecules. The individual interaction energy decompositions revealed no particular change in the stabilisation pattern as compared to smaller dimer systems at equilibrium structures. This demonstrates that, to some extent, the qualitative description of the interaction of small dimer systems may be extrapolated to larger systems, a method that is widely used in force-fields in which the total interaction energy is decomposed into atom-atom contributions. A comparison of the binding energies with accurate experimental reference values from Grimme, the latter including thermodynamic corrections from semiempirical calculations, has shown a fairly good agreement to within the error range of the reference binding energies
Generalized Net Model of the Cognitive and Neural Algorithm for Adaptive Resonance Theory 1
Directory of Open Access Journals (Sweden)
Todor Petkov
2013-12-01
Full Text Available The artificial neural networks are inspired by biological properties of human and animal brains. One of the neural networks type is called ART [4]. The abbreviation of ART stands for Adaptive Resonance Theory that has been invented by Stephen Grossberg in 1976 [5]. ART represents a family of Neural Networks. It is a cognitive and neural theory that describes how the brain autonomously learns to categorize, recognize and predict objects and events in the changing world. In this paper we introduce a GN model that represent ART1 Neural Network learning algorithm [1]. The purpose of this model is to explain when the input vector will be clustered or rejected among all nodes by the network. It can also be used for explanation and optimization of ART1 learning algorithm.
Howes, Andrew; Lewis, Richard L; Vera, Alonso
2009-10-01
The authors assume that individuals adapt rationally to a utility function given constraints imposed by their cognitive architecture and the local task environment. This assumption underlies a new approach to modeling and understanding cognition-cognitively bounded rational analysis-that sharpens the predictive acuity of general, integrated theories of cognition and action. Such theories provide the necessary computational means to explain the flexible nature of human behavior but in doing so introduce extreme degrees of freedom in accounting for data. The new approach narrows the space of predicted behaviors through analysis of the payoff achieved by alternative strategies, rather than through fitting strategies and theoretical parameters to data. It extends and complements established approaches, including computational cognitive architectures, rational analysis, optimal motor control, bounded rationality, and signal detection theory. The authors illustrate the approach with a reanalysis of an existing account of psychological refractory period (PRP) dual-task performance and the development and analysis of a new theory of ordered dual-task responses. These analyses yield several novel results, including a new understanding of the role of strategic variation in existing accounts of PRP and the first predictive, quantitative account showing how the details of ordered dual-task phenomena emerge from the rational control of a cognitive system subject to the combined constraints of internal variance, motor interference, and a response selection bottleneck.
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Roohollah Roozbeh
2017-06-01
Full Text Available Hollywood has been fascinated with the Thousand and One Nights. It has adapted it from the very outset of Cinema itself. Based on Linda Hutcheon’s (2006 A Theory of Adaptation, this paper tries to examine Rawlins’ Arabian Nights (1942 to see how Hollywood adapts from this Oriental work. Hutcheon argues that adapters adapt differently; some adapt to pay tribute and homage to the author, some adapt because they want to make money; some adapt to gain cultural authority and canonicity; others adapt to critique and subvert the ideology of the text. In this paper we will argue that Hollywood occupies the fourth category in adapting Thousand and One Nights in Arabian Nights directed by Rawlins, that is to say Hollywood seeks to subvert the ideology of the text and critique what it stands for and what the text stands for is the East and mostly Arabic culture. To do that, Hollywood does not indigenize the culture of the East and critiques the culture of the Arabs through the tropes of belly-dance, veil, and harem. The methodology of this article is wholly based on Edward Said’s theory of Orientalism in his book Orientalism.
Balas, Mark J.; Thapa Magar, Kaman S.; Frost, Susan A.
2013-01-01
A theory called Adaptive Disturbance Tracking Control (ADTC) is introduced and used to track the Tip Speed Ratio (TSR) of 5 MW Horizontal Axis Wind Turbine (HAWT). Since ADTC theory requires wind speed information, a wind disturbance generator model is combined with lower order plant model to estimate the wind speed as well as partial states of the wind turbine. In this paper, we present a proof of stability and convergence of ADTC theory with lower order estimator and show that the state feedback can be adaptive.
DEFF Research Database (Denmark)
Mu, Xiaobin; Wang, Jiuhe; Wu, Weimin
2018-01-01
The passivity-based control (PBC) has a better control performance using an accurate mathematical model of the control object. It can offer an alternative tracking control scheme for the shunt active power filter (SAPF). However, the conventional PBC-based SAPF cannot achieve zero steady...
Apramian, Tavis; Watling, Christopher; Lingard, Lorelei; Cristancho, Sayra
2015-10-01
Surgical research struggles to describe the relationship between procedural variations in daily practice and traditional conceptualizations of evidence. The problem has resisted simple solutions, in part, because we lack a solid understanding of how surgeons conceptualize and interact around variation, adaptation, innovation, and evidence in daily practice. This grounded theory study aims to describe the social processes that influence how procedural variation is conceptualized in the surgical workplace. Using the constructivist grounded theory methodology, semi-structured interviews with surgeons (n = 19) from four North American academic centres were collected and analysed. Purposive sampling targeted surgeons with experiential knowledge of the role of variations in the workplace. Theoretical sampling was conducted until a theoretical framework representing key processes was conceptually saturated. Surgical procedural variation was influenced by three key processes. Seeking improvement was shaped by having unsolved procedural problems, adapting in the moment, and pursuing personal opportunities. Orienting self and others to variations consisted of sharing stories of variations with others, taking stock of how a variation promoted personal interests, and placing trust in peers. Acting under cultural and material conditions was characterized by being wary, positioning personal image, showing the logic of a variation, and making use of academic resources to do so. Our findings include social processes that influence how adaptations are incubated in surgical practice and mature into innovations. This study offers a language for conceptualizing the sociocultural influences on procedural variations in surgery. Interventions to change how surgeons interact with variations on a day-to-day basis should consider these social processes in their design. © 2015 John Wiley & Sons, Ltd.
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Yuanyuan Liu
2013-08-01
Full Text Available Accurate estimation of the state of charge (SOC of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li-ion batteries. First, an enhanced battery model is proposed to include the impacts due to different discharge rates and temperatures. An adaptive joint estimation of the battery SOC and battery internal resistance is then presented to enhance system robustness with battery aging. The SOC estimation algorithm has been developed and verified through experiments on different types of Li-ion batteries. The results indicate that the proposed method provides an accurate SOC estimation and is computationally efficient, making it suitable for embedded system implementation.
Guo, Xiaoting; Sun, Changku; Wang, Peng
2017-08-01
This paper investigates the multi-rate inertial and vision data fusion problem in nonlinear attitude measurement systems, where the sampling rate of the inertial sensor is much faster than that of the vision sensor. To fully exploit the high frequency inertial data and obtain favorable fusion results, a multi-rate CKF (Cubature Kalman Filter) algorithm with estimated residual compensation is proposed in order to adapt to the problem of sampling rate discrepancy. During inter-sampling of slow observation data, observation noise can be regarded as infinite. The Kalman gain is unknown and approaches zero. The residual is also unknown. Therefore, the filter estimated state cannot be compensated. To obtain compensation at these moments, state error and residual formulas are modified when compared with the observation data available moments. Self-propagation equation of the state error is established to propagate the quantity from the moments with observation to the moments without observation. Besides, a multiplicative adjustment factor is introduced as Kalman gain, which acts on the residual. Then the filter estimated state can be compensated even when there are no visual observation data. The proposed method is tested and verified in a practical setup. Compared with multi-rate CKF without residual compensation and single-rate CKF, a significant improvement is obtained on attitude measurement by using the proposed multi-rate CKF with inter-sampling residual compensation. The experiment results with superior precision and reliability show the effectiveness of the proposed method.
Cassista, Julianne; Payne-Gagnon, Julie; Martel, Brigitte; Gagnon, Marie-Pierre
2014-01-01
The manipulation of glass ampoules involves risk of particle contamination of parenteral medication, and the use of filter needles has often been recommended in order to reduce the number of particles in these solutions. This study aims to develop a theory-based intervention to increase nurse intention to use filter needles according to clinical guideline recommendations produced by a large university medical centre in Quebec (Canada). Using the Intervention Mapping framework, we first identified the psychosocial determinants of nurse intention to use filter needles according to these recommendations. Second, we developed and implemented an intervention targeting nurses from five care units in order to increase their intention to adhere to recommendations on the use of filter needles. We also assessed nurse satisfaction with the intervention. In total, 270 nurses received the intervention and 169 completed the posttest questionnaire. The two determinants of intention, that is, attitude and perceived behavioral control, were significantly higher after the intervention, but only perceived behavioral control remained a predictor of intention. In general, nurses were highly satisfied with the intervention. This study provides support for the use of Intervention Mapping to develop, implement, and evaluate theory-based interventions in order to improve healthcare professional adherence to clinical recommendations.
Directory of Open Access Journals (Sweden)
Julianne Cassista
2014-01-01
Full Text Available The manipulation of glass ampoules involves risk of particle contamination of parenteral medication, and the use of filter needles has often been recommended in order to reduce the number of particles in these solutions. This study aims to develop a theory-based intervention to increase nurse intention to use filter needles according to clinical guideline recommendations produced by a large university medical centre in Quebec (Canada. Using the Intervention Mapping framework, we first identified the psychosocial determinants of nurse intention to use filter needles according to these recommendations. Second, we developed and implemented an intervention targeting nurses from five care units in order to increase their intention to adhere to recommendations on the use of filter needles. We also assessed nurse satisfaction with the intervention. In total, 270 nurses received the intervention and 169 completed the posttest questionnaire. The two determinants of intention, that is, attitude and perceived behavioral control, were significantly higher after the intervention, but only perceived behavioral control remained a predictor of intention. In general, nurses were highly satisfied with the intervention. This study provides support for the use of Intervention Mapping to develop, implement, and evaluate theory-based interventions in order to improve healthcare professional adherence to clinical recommendations.
Directory of Open Access Journals (Sweden)
Yap Hoon
2017-02-01
Full Text Available In this paper, a refined reference current generation algorithm based on instantaneous power (pq theory is proposed, for operation of an indirect current controlled (ICC three-level neutral-point diode clamped (NPC inverter-based shunt active power filter (SAPF under non-sinusoidal source voltage conditions. SAPF is recognized as one of the most effective solutions to current harmonics due to its flexibility in dealing with various power system conditions. As for its controller, pq theory has widely been applied to generate the desired reference current due to its simple implementation features. However, the conventional dependency on self-tuning filter (STF in generating reference current has significantly limited mitigation performance of SAPF. Besides, the conventional STF-based pq theory algorithm is still considered to possess needless features which increase computational complexity. Furthermore, the conventional algorithm is mostly designed to suit operation of direct current controlled (DCC SAPF which is incapable of handling switching ripples problems, thereby leading to inefficient mitigation performance. Therefore, three main improvements are performed which include replacement of STF with mathematical-based fundamental real power identifier, removal of redundant features, and generation of sinusoidal reference current. To validate effectiveness and feasibility of the proposed algorithm, simulation work in MATLAB-Simulink and laboratory test utilizing a TMS320F28335 digital signal processor (DSP are performed. Both simulation and experimental findings demonstrate superiority of the proposed algorithm over the conventional algorithm.
Tsay, Crystal Han-Huei
2012-01-01
The contemporary business world demands adaptive individuals (Friedman & Wyman, 2005). Adaptation is essential for any life transition. It often involves developing coping mechanisms, strategies, and seeking of social support. Adaptation occurs in many settings from moving to a new culture, taking a new job, starting or finishing an…
Rigatos, Gerasimos G
2015-01-01
This monograph presents recent advances in differential flatness theory and analyzes its use for nonlinear control and estimation. It shows how differential flatness theory can provide solutions to complicated control problems, such as those appearing in highly nonlinear multivariable systems and distributed-parameter systems. Furthermore, it shows that differential flatness theory makes it possible to perform filtering and state estimation for a wide class of nonlinear dynamical systems and provides several descriptive test cases. The book focuses on the design of nonlinear adaptive controllers and nonlinear filters, using exact linearization based on differential flatness theory. The adaptive controllers obtained can be applied to a wide class of nonlinear systems with unknown dynamics, and assure reliable functioning of the control loop under uncertainty and varying operating conditions. The filters obtained outperform other nonlinear filters in terms of accuracy of estimation and computation speed. The bo...
Intramolecular symmetry-adapted perturbation theory with a single-determinant wavefunction
Energy Technology Data Exchange (ETDEWEB)
Pastorczak, Ewa; Prlj, Antonio; Corminboeuf, Clémence, E-mail: clemence.corminboeuf@epfl.ch [Laboratory for Computational Molecular Design, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne (Switzerland); Gonthier, Jérôme F. [Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry, School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400 (United States)
2015-12-14
We introduce an intramolecular energy decomposition scheme for analyzing non-covalent interactions within molecules in the spirit of symmetry-adapted perturbation theory (SAPT). The proposed intra-SAPT approach is based upon the Chemical Hamiltonian of Mayer [Int. J. Quantum Chem. 23(2), 341–363 (1983)] and the recently introduced zeroth-order wavefunction [J. F. Gonthier and C. Corminboeuf, J. Chem. Phys. 140(15), 154107 (2014)]. The scheme decomposes the interaction energy between weakly bound fragments located within the same molecule into physically meaningful components, i.e., electrostatic-exchange, induction, and dispersion. Here, we discuss the key steps of the approach and demonstrate that a single-determinant wavefunction can already deliver a detailed and insightful description of a wide range of intramolecular non-covalent phenomena such as hydrogen bonds, dihydrogen contacts, and π − π stacking interactions. Intra-SAPT is also used to shed the light on competing intra- and intermolecular interactions.
Symmetry-adapted perturbation theory interaction energy decomposition for some noble gas complexes
Cukras, Janusz; Sadlej, Joanna
2008-06-01
This Letter contains a study of the interaction energy in HArF⋯N 2 and HArF⋯P 2 complexes. Symmetry-adapted perturbation theory (SAPT) has been applied to analyze the electrostatic, induction, dispersion and exchange contributions to the total interaction energy. The interaction energy has also been obtained by supermolecular method at the MP2, MP4, CCSD, CCSD(T) levels. The interaction energy for the studied complexes results from a partial cancelation of large attractive electrostatic, induction, dispersion terms by a strong repulsive exchange contribution. The induction and dispersion effects proved to be crucial in establishing the preference for the colinear HArF⋯N 2 and HArF⋯P 2 structures and shift direction of νHAr stretching vibrations.
Broom, Donald M
2006-01-01
The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and
Cognitive adaptation theory and quality of life in late-stage cancer patients.
Christianson, Heidi Fowell; Weis, Jo M; Fouad, Nadya A
2013-01-01
In this study, the question of whether using slightly illusionary, positive attributions of self, control, and meaning (e.g., cognitive adaptation theory), in the face of disconfirmatory evidence, facilitates quality of life in late-stage cancer patients was examined. Eighty late-stage cancer patients (Mean age = 59.7, SD = 12.5; 48.8% male, 51.2% female; varying cancer diagnoses) who recently failed or refused first line anti-neoplastic treatment completed questionnaires assessing meaning, control, self-esteem, and optimism, as well as physical and psychological quality of life. Findings suggest that greater self-esteem, control, and meaning predicted physical and psychological quality of life, with physical quality of life being influenced by control beliefs and psychological quality of life influenced by self-esteem. Optimism independently predicted physical quality of life and neither mediated nor moderated the relationship between cognitive adaptation and quality of life. Findings suggest that slightly positive, illusionary beliefs of self, control, and meaning predicted quality of life even in the presence of clear, disconfirmatory environmental evidence.
Adapting Modern Portfolio Theory For Prioritising Asset Care Planning In Industry
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Van den Honert, Andrew Francis
2014-05-01
Full Text Available Productivity improvement within any organisation can lead to increased turnover. This study focuses on developing a maintenance productivity improvement model that is based upon an established financial investment portfolio technique known as the Modern Portfolio Theory (MPT. The model can be used as a tool to minimise and diversify the long term risk associated with variances or fluctuations in the increase in productivity in multiple maintenance service centres. This is achieved by optimising the most efficient way of splitting resources, such as time and money, between these multiple service centres, resulting in increased productivity and a more constant maintenance work load. This model is verified through the use of an efficient frontier, resulting in a graphical method to determine the link between the expected increase in productivity and the standard deviation of the increase in productivity. Ultimately this model can be adapted for use in many sectors within an organisation, over and above the application in maintenance prioritisation. This study concludes that the model offers a simple tool to aid decision-making among various combinations of assets within a maintenance context; and this model, adapted from MPT, was successfully validated with the use of an efficient frontier.
DEFF Research Database (Denmark)
Prince, Kamau; Presi, Marco; Chiuchiarelli, Andrea
2009-01-01
types of signals defined in IEEE 802.16 (WiMAX) standard for wireless networks: a 90 Mbps single-carrier signal (64-QAM at 2.4 GHz) and a 78 Mbps multitone orthogonal frequency-division multiple access (OFDMA) signal. The power budget of this configuration supports a 4-element antenna array....... 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...
Bolea, Mario; Mora, José; Ortega, Beatriz; Capmany, José
2009-03-30
We propose theoretically and demonstrate experimentally an optical architecture for flexible Ultra-Wideband pulse generation. It is based on an N-tap reconfigurable microwave photonic filter fed by a laser array by using phase inversion in a Mach-Zehnder modulator. Since a large number of positive and negative coefficients can be easily implemented, UWB pulses fitted to the FCC mask requirements can be generated. As an example, a four tap pulse generator is experimentally demonstrated which complies with the FCC regulation. The proposed pulse generator allows different pulse modulation formats since the amplitude, polarity and time delay of generated pulse is controlled.
International Development Research Centre (IDRC) Digital Library (Canada)
building skills, knowledge or networks on adaptation, ... the African partners leading the AfricaAdapt network, together with the UK-based Institute of Development Studies; and ... UNCCD Secretariat, Regional Coordination Unit for Africa, Tunis, Tunisia .... 26 Rural–urban Cooperation on Water Management in the Context of.
Adapting evidence-based interventions using a common theory, practices, and principles.
Rotheram-Borus, Mary Jane; Swendeman, Dallas; Becker, Kimberly D
2014-01-01
Hundreds of validated evidence-based intervention programs (EBIP) aim to improve families' well-being; however, most are not broadly adopted. As an alternative diffusion strategy, we created wellness centers to reach families' everyday lives with a prevention framework. At two wellness centers, one in a middle-class neighborhood and one in a low-income neighborhood, popular local activity leaders (instructors of martial arts, yoga, sports, music, dancing, Zumba), and motivated parents were trained to be Family Mentors. Trainings focused on a framework that taught synthesized, foundational prevention science theory, practice elements, and principles, applied to specific content areas (parenting, social skills, and obesity). Family Mentors were then allowed to adapt scripts and activities based on their cultural experiences but were closely monitored and supervised over time. The framework was implemented in a range of activities (summer camps, coaching) aimed at improving social, emotional, and behavioral outcomes. Successes and challenges are discussed for (a) engaging parents and communities; (b) identifying and training Family Mentors to promote children and families' well-being; and (c) gathering data for supervision, outcome evaluation, and continuous quality improvement. To broadly diffuse prevention to families, far more experimentation is needed with alternative and engaging implementation strategies that are enhanced with knowledge harvested from researchers' past 30 years of experience creating EBIP. One strategy is to train local parents and popular activity leaders in applying robust prevention science theory, common practice elements, and principles of EBIP. More systematic evaluation of such innovations is needed.
Universal dynamics of complex adaptive systems: Gauge theory of things alive
International Nuclear Information System (INIS)
Mack, G.
1994-04-01
A universal dynamics of objects and their relations - a kind of ''universal chemistry'' - is discussed which satisfies general principles of locality and relativity. Einsteins theory of gravitation and the gauge theory of elementary particles are prototypes, but complex adaptive systems - anything that is alive in the widest sense - fall under the same paradigma. Frustration and gauge symmetry arise naturally in this context. Besides a nondissipative deterministic dynamics, which is thought to operate at a fundamental levle, a Thermo-Dynamics in sense of Prigogine is introduced by adding a diffusion process. It introduces irreversibility and entropy production. It equilibrates the chaotic local model of the time development (only) and is designed to be undetectable under continued observation with given finite measuring accuracy. Compositeness and the development of structure can be described in this framework. The existence of a critical equilibrium state may be postulated which is invariant under the dynamics. But it is usually not reached in a finite time from a given starting configuration, because local dynamics suffers from critical slowing down, especially in the presence of frustration. (orig.)
Goodenberger, Martin H; Wagner-Bartak, Nicolaus A; Gupta, Shiva; Liu, Xinming; Yap, Ramon Q; Sun, Jia; Tamm, Eric P; Jensen, Corey T
The purpose of this study was to compare abdominopelvic computed tomography images reconstructed with adaptive statistical iterative reconstruction-V (ASIR-V) with model-based iterative reconstruction (Veo 3.0), ASIR, and filtered back projection (FBP). Abdominopelvic computed tomography scans for 36 patients (26 males and 10 females) were reconstructed using FBP, ASIR (80%), Veo 3.0, and ASIR-V (30%, 60%, 90%). Mean ± SD patient age was 32 ± 10 years with mean ± SD body mass index of 26.9 ± 4.4 kg/m. Images were reviewed by 2 independent readers in a blinded, randomized fashion. Hounsfield unit, noise, and contrast-to-noise ratio (CNR) values were calculated for each reconstruction algorithm for further comparison. Phantom evaluation of low-contrast detectability (LCD) and high-contrast resolution was performed. Adaptive statistical iterative reconstruction-V 30%, ASIR-V 60%, and ASIR 80% were generally superior qualitatively compared with ASIR-V 90%, Veo 3.0, and FBP (P ASIR-V 60% with respective CNR values of 5.54 ± 2.39, 8.78 ± 3.15, and 3.49 ± 1.77 (P ASIR 80% had the best and worst spatial resolution, respectively. Adaptive statistical iterative reconstruction-V 30% and ASIR-V 60% provided the best combination of qualitative and quantitative performance. Adaptive statistical iterative reconstruction 80% was equivalent qualitatively, but demonstrated inferior spatial resolution and LCD.
Sen, Avijit; Sen, Sangita; Samanta, Pradipta Kumar; Mukherjee, Debashis
2015-04-05
We present here a comprehensive account of the formulation and pilot applications of the second-order perturbative analogue of the recently proposed unitary group adapted state-specific multireference coupled cluster theory (UGA-SSMRCC), which we call as the UGA-SSMRPT2. We also discuss the essential similarities and differences between the UGA-SSMRPT2 and the allied SA-SSMRPT2. Our theory, like its parent UGA-SSMRCC formalism, is size-extensive. However, because of the noninvariance of the theory with respect to the transformation among the active orbitals, it requires the use of localized orbitals to ensure size-consistency. We have demonstrated the performance of the formalism with a set of pilot applications, exploring (a) the accuracy of the potential energy surface (PES) of a set of small prototypical difficult molecules in their various low-lying states, using natural, pseudocanonical and localized orbitals and compared the respective nonparallelity errors (NPE) and the mean average deviations (MAD) vis-a-vis the full CI results with the same basis; (b) the efficacy of localized active orbitals to ensure and demonstrate manifest size-consistency with respect to fragmentation. We found that natural orbitals lead to the best overall PES, as evidenced by the NPE and MAD values. The MRMP2 results for individual states and of the MCQDPT2 for multiple states displaying avoided curve crossings are uniformly poorer as compared with the UGA-SSMRPT2 results. The striking aspect of the size-consistency check is the complete insensitivity of the sum of fragment energies with given fragment spin-multiplicities, which are obtained as the asymptotic limit of super-molecules with different coupled spins. © 2015 Wiley Periodicals, Inc.
Krengel, Annette; Hauth, Jan; Taskinen, Marja-Riitta; Adiels, Martin; Jirstrand, Mats
2013-01-19
When mathematical modelling is applied to many different application areas, a common task is the estimation of states and parameters based on measurements. With this kind of inference making, uncertainties in the time when the measurements have been taken are often neglected, but especially in applications taken from the life sciences, this kind of errors can considerably influence the estimation results. As an example in the context of personalized medicine, the model-based assessment of the effectiveness of drugs is becoming to play an important role. Systems biology may help here by providing good pharmacokinetic and pharmacodynamic (PK/PD) models. Inference on these systems based on data gained from clinical studies with several patient groups becomes a major challenge. Particle filters are a promising approach to tackle these difficulties but are by itself not ready to handle uncertainties in measurement times. In this article, we describe a variant of the standard particle filter (PF) algorithm which allows state and parameter estimation with the inclusion of measurement time uncertainties (MTU). The modified particle filter, which we call MTU-PF, also allows the application of an adaptive stepsize choice in the time-continuous case to avoid degeneracy problems. The modification is based on the model assumption of uncertain measurement times. While the assumption of randomness in the measurements themselves is common, the corresponding measurement times are generally taken as deterministic and exactly known. Especially in cases where the data are gained from measurements on blood or tissue samples, a relatively high uncertainty in the true measurement time seems to be a natural assumption. Our method is appropriate in cases where relatively few data are used from a relatively large number of groups or individuals, which introduce mixed effects in the model. This is a typical setting of clinical studies. We demonstrate the method on a small artificial example
DEFF Research Database (Denmark)
Hammoud, Riad; Hansen, Dan Witzner
2005-01-01
. The tracker overcomes the issues of prior selection of static thresholds during the detection of feature observations in the bright-dark difference images. The auto-initialization process is performed using cascaded classifier trained using adaboost and adapted to IR eye images. Experiments show good...... performance in challenging sequences with test subjects showing large head movements and under significant light changes....
Rotationally invariant correlation filtering
International Nuclear Information System (INIS)
Schils, G.F.; Sweeney, D.W.
1985-01-01
A method is presented for analyzing and designing optical correlation filters that have tailored rotational invariance properties. The concept of a correlation of an image with a rotation of itself is introduced. A unified theory of rotation-invariant filtering is then formulated. The unified approach describes matched filters (with no rotation invariance) and circular-harmonic filters (with full rotation invariance) as special cases. The continuum of intermediate cases is described in terms of a cyclic convolution operation over angle. The angular filtering approach allows an exact choice for the continuous trade-off between loss of the correlation energy (or specificity regarding the image) and the amount of rotational invariance desired
Directory of Open Access Journals (Sweden)
Jianhua Cheng
2017-10-01
Full Text Available Because of the harsh polar environment, the master strapdown inertial navigation system (SINS has low accuracy and the system model information becomes abnormal. In this case, existing polar transfer alignment (TA algorithms which use the measurement information provided by master SINS would lose their effectiveness. In this paper, a new polar TA algorithm with the aid of a star sensor and based on an adaptive unscented Kalman filter (AUKF is proposed to deal with the problems. Since the measurement information provided by master SINS is inaccurate, the accurate information provided by the star sensor is chosen as the measurement. With the compensation of lever-arm effect and the model of star sensor, the nonlinear navigation equations are derived. Combined with the attitude matching method, the filter models for polar TA are designed. An AUKF is introduced to solve the abnormal information of system model. Then, the AUKF is used to estimate the states of TA. Results have demonstrated that the performance of the new polar TA algorithm is better than the state-of-the-art polar TA algorithms. Therefore, the new polar TA algorithm proposed in this paper is effectively to ensure and improve the accuracy of TA in the harsh polar environment.
Cheng, Jianhua; Wang, Tongda; Wang, Lu; Wang, Zhenmin
2017-10-23
Because of the harsh polar environment, the master strapdown inertial navigation system (SINS) has low accuracy and the system model information becomes abnormal. In this case, existing polar transfer alignment (TA) algorithms which use the measurement information provided by master SINS would lose their effectiveness. In this paper, a new polar TA algorithm with the aid of a star sensor and based on an adaptive unscented Kalman filter (AUKF) is proposed to deal with the problems. Since the measurement information provided by master SINS is inaccurate, the accurate information provided by the star sensor is chosen as the measurement. With the compensation of lever-arm effect and the model of star sensor, the nonlinear navigation equations are derived. Combined with the attitude matching method, the filter models for polar TA are designed. An AUKF is introduced to solve the abnormal information of system model. Then, the AUKF is used to estimate the states of TA. Results have demonstrated that the performance of the new polar TA algorithm is better than the state-of-the-art polar TA algorithms. Therefore, the new polar TA algorithm proposed in this paper is effectively to ensure and improve the accuracy of TA in the harsh polar environment.
Wei, Jingwen; Dong, Guangzhong; Chen, Zonghai
2017-10-01
With the rapid development of battery-powered electric vehicles, the lithium-ion battery plays a critical role in the reliability of vehicle system. In order to provide timely management and protection for battery systems, it is necessary to develop a reliable battery model and accurate battery parameters estimation to describe battery dynamic behaviors. Therefore, this paper focuses on an on-board adaptive model for state-of-charge (SOC) estimation of lithium-ion batteries. Firstly, a first-order equivalent circuit battery model is employed to describe battery dynamic characteristics. Then, the recursive least square algorithm and the off-line identification method are used to provide good initial values of model parameters to ensure filter stability and reduce the convergence time. Thirdly, an extended-Kalman-filter (EKF) is applied to on-line estimate battery SOC and model parameters. Considering that the EKF is essentially a first-order Taylor approximation of battery model, which contains inevitable model errors, thus, a proportional integral-based error adjustment technique is employed to improve the performance of EKF method and correct model parameters. Finally, the experimental results on lithium-ion batteries indicate that the proposed EKF with proportional integral-based error adjustment method can provide robust and accurate battery model and on-line parameter estimation.
International Nuclear Information System (INIS)
Wang, Tongda; Cheng, Jianhua; Guan, Dongxue; Kang, Yingyao; Zhang, Wei
2017-01-01
Due to the lever-arm effect and flexural deformation in the practical application of transfer alignment (TA), the TA performance is decreased. The existing polar TA algorithm only compensates a fixed lever-arm without considering the dynamic lever-arm caused by flexural deformation; traditional non-polar TA algorithms also have some limitations. Thus, the performance of existing compensation algorithms is unsatisfactory. In this paper, a modified compensation algorithm of the lever-arm effect and flexural deformation is proposed to promote the accuracy and speed of the polar TA. On the basis of a dynamic lever-arm model and a noise compensation method for flexural deformation, polar TA equations are derived in grid frames. Based on the velocity-plus-attitude matching method, the filter models of polar TA are designed. An adaptive Kalman filter (AKF) is improved to promote the robustness and accuracy of the system, and then applied to the estimation of the misalignment angles. Simulation and experiment results have demonstrated that the modified compensation algorithm based on the improved AKF for polar TA can effectively compensate the lever-arm effect and flexural deformation, and then improve the accuracy and speed of TA in the polar region. (paper)
Hirano, Akihiro; Nakayama, Kenji
2008-01-01
This paper presents an computationally ef cient implementation of sparse-tap FIR adaptive lters with tapposition control on Intel IA-32 processors with single-instruction multiple-data (SIMD) capability. In order to overcome randomorder memory access which prevents a ectorization, a blockbased processing and a re-ordering buffer are introduced. A dynamic register allocation and the use of memory-to-register operations help the maximization of the loop-unrolling level. Up to 66percent speedup ...
Broussard, John R.
1987-01-01
Relationships between observers, Kalman Filters and dynamic compensators using feedforward control theory are investigated. In particular, the relationship, if any, between the dynamic compensator state and linear functions of a discrete plane state are investigated. It is shown that, in steady state, a dynamic compensator driven by the plant output can be expressed as the sum of two terms. The first term is a linear combination of the plant state. The second term depends on plant and measurement noise, and the plant control. Thus, the state of the dynamic compensator can be expressed as an estimator of the first term with additive error given by the second term. Conditions under which a dynamic compensator is a Kalman filter are presented, and reduced-order optimal estimaters are investigated.
Kim, Sooyeon; Moses, Tim; Yoo, Hanwook Henry
2015-01-01
The purpose of this inquiry was to investigate the effectiveness of item response theory (IRT) proficiency estimators in terms of estimation bias and error under multistage testing (MST). We chose a 2-stage MST design in which 1 adaptation to the examinees' ability levels takes place. It includes 4 modules (1 at Stage 1, 3 at Stage 2) and 3 paths…
The puzzle of partial migration: Adaptive dynamics and evolutionary game theory perspectives.
De Leenheer, Patrick; Mohapatra, Anushaya; Ohms, Haley A; Lytle, David A; Cushing, J M
2017-01-07
We consider the phenomenon of partial migration which is exhibited by populations in which some individuals migrate between habitats during their lifetime, but others do not. First, using an adaptive dynamics approach, we show that partial migration can be explained on the basis of negative density dependence in the per capita fertilities alone, provided that this density dependence is attenuated for increasing abundances of the subtypes that make up the population. We present an exact formula for the optimal proportion of migrants which is expressed in terms of the vital rates of migrant and non-migrant subtypes only. We show that this allocation strategy is both an evolutionary stable strategy (ESS) as well as a convergence stable strategy (CSS). To establish the former, we generalize the classical notion of an ESS because it is based on invasion exponents obtained from linearization arguments, which fail to capture the stabilizing effects of the nonlinear density dependence. These results clarify precisely when the notion of a "weak ESS", as proposed in Lundberg (2013) for a related model, is a genuine ESS. Secondly, we use an evolutionary game theory approach, and confirm, once again, that partial migration can be attributed to negative density dependence alone. In this context, the result holds even when density dependence is not attenuated. In this case, the optimal allocation strategy towards migrants is the same as the ESS stemming from the analysis based on the adaptive dynamics. The key feature of the population models considered here is that they are monotone dynamical systems, which enables a rather comprehensive mathematical analysis. Copyright © 2016 Elsevier Ltd. All rights reserved.
Bargatze, L. F.
2015-12-01
Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted
International Nuclear Information System (INIS)
Tabuchi, Motohiro; Yamane, Nobumoto; Morikawa, Yoshitaka
2008-01-01
In recent decades, X-ray CT imaging has become more important as a result of its high-resolution performance. However, it is well known that the X-ray dose is insufficient in the techniques that use low-dose imaging in health screening or thin-slice imaging in work-up. Therefore, the degradation of CT images caused by the streak artifact frequently becomes problematic. In this study, we applied a Wiener filter (WF) using the universal Gaussian mixture distribution model (UNI-GMM) as a statistical model to remove streak artifact. In designing the WF, it is necessary to estimate the statistical model and the precise co-variances of the original image. In the proposed method, we obtained a variety of chest X-ray CT images using a phantom simulating a chest organ, and we estimated the statistical information using the images for training. The results of simulation showed that it is possible to fit the UNI-GMM to the chest X-ray CT images and reduce the specific noise. (author)
CR-Calculus and adaptive array theory applied to MIMO random vibration control tests
Musella, U.; Manzato, S.; Peeters, B.; Guillaume, P.
2016-09-01
Performing Multiple-Input Multiple-Output (MIMO) tests to reproduce the vibration environment in a user-defined number of control points of a unit under test is necessary in applications where a realistic environment replication has to be achieved. MIMO tests require vibration control strategies to calculate the required drive signal vector that gives an acceptable replication of the target. This target is a (complex) vector with magnitude and phase information at the control points for MIMO Sine Control tests while in MIMO Random Control tests, in the most general case, the target is a complete spectral density matrix. The idea behind this work is to tailor a MIMO random vibration control approach that can be generalized to other MIMO tests, e.g. MIMO Sine and MIMO Time Waveform Replication. In this work the approach is to use gradient-based procedures over the complex space, applying the so called CR-Calculus and the adaptive array theory. With this approach it is possible to better control the process performances allowing the step-by-step Jacobian Matrix update. The theoretical bases behind the work are followed by an application of the developed method to a two-exciter two-axis system and by performance comparisons with standard methods.
Breakdown of the single-exchange approximation in third-order symmetry-adapted perturbation theory.
Lao, Ka Un; Herbert, John M
2012-03-22
We report third-order symmetry-adapted perturbation theory (SAPT) calculations for several dimers whose intermolecular interactions are dominated by induction. We demonstrate that the single-exchange approximation (SEA) employed to derive the third-order exchange-induction correction (E(exch-ind)((30))) fails to quench the attractive nature of the third-order induction (E(ind)((30))), leading to one-dimensional potential curves that become attractive rather than repulsive at short intermolecular separations. A scaling equation for (E(exch-ind)((30))), based on an exact formula for the first-order exchange correction, is introduced to approximate exchange effects beyond the SEA, and qualitatively correct potential energy curves that include third-order induction are thereby obtained. For induction-dominated systems, our results indicate that a "hybrid" SAPT approach, in which a dimer Hartree-Fock calculation is performed in order to obtain a correction for higher-order induction, is necessary not only to obtain quantitative binding energies but also to obtain qualitatively correct potential energy surfaces. These results underscore the need to develop higher-order exchange-induction formulas that go beyond the SEA. © 2012 American Chemical Society
Jordon, Michelle; Lanham, Holly Jordan; Anderson, Ruth A; McDaniel, Reuben R
2010-02-01
Data about health care organizations (HCOs) are not useful until they are interpreted. Such interpretations are influenced by the theoretical lenses used by the researcher. Our purpose was to suggest the usefulness of theories of complex adaptive systems (CASs) in guiding research interpretation. Specifically, we addressed two questions: (1) What are the implications for interpreting research observations in HCOs of the fact that we are observing relationships among diverse agents? (2) What are the implications for interpreting research observations in HCOs of the fact that we are observing relationships among agents that learn? We defined diversity and learning and the implications of the non-linear relationships among agents from a CAS perspective. We then identified some common analytical practices that were problematic and may lead to conceptual and methodological errors. Then we described strategies for interpreting the results of research observations. We suggest that the task of interpreting research observations of HCOs could be improved if researchers take into account that the systems they study are CASs with non-linear relationships among diverse, learning agents. Our analysis points out how interpretation of research results might be shaped by the fact that HCOs are CASs. We described how learning is, in fact, the result of interactions among diverse agents and that learning can, by itself, reduce or increase agent diversity. We encouraged researchers to be persistent in their attempts to reason about complex systems and learn to attend not only to structures, but also to processes and functions of complex systems.
Directory of Open Access Journals (Sweden)
Budhitama Subagdja
2016-06-01
Full Text Available One of the fundamental challenges in reinforcement learning is to setup a proper balance between exploration and exploitation to obtain the maximum cummulative reward in the long run. Most protocols for exploration bound the overall values to a convergent level of performance. If new knowledge is inserted or the environment is suddenly changed, the issue becomes more intricate as the exploration must compromise the pre-existing knowledge. This paper presents a type of multi-channel adaptive resonance theory (ART neural network model called fusion ART which serves as a fuzzy approximator for reinforcement learning with inherent features that can regulate the exploration strategy. This intrinsic regulation is driven by the condition of the knowledge learnt so far by the agent. The model offers a stable but incremental reinforcement learning that can involve prior rules as bootstrap knowledge for guiding the agent to select the right action. Experiments in obstacle avoidance and navigation tasks demonstrate that in the configuration of learning wherein the agent learns from scratch, the inherent exploration model in fusion ART model is comparable to the basic E-greedy policy. On the other hand, the model is demonstrated to deal with prior knowledge and strike a balance between exploration and exploitation.
Institute of Scientific and Technical Information of China (English)
叶军; 陈坚; 石国祥
2011-01-01
针对自标定加速度计组合动基座试验数据中存在的数据异常问题,推导并运用自适应Kalman滤波算法剔除异常数据,通过对不同Kalman滤波算法自标定精度解算结果的均值和标准差进行比较,表明自适应Kalman滤波算法更加有效.%Aiming at the problems of abnormal data in the test data of autonomous calibration accelerometer-unit on dynamicbase,deducing and using adaptive Kalman filtering algorithm eliminates abnormal data, according the comparison of results from calibration precision by different Kalman filtering algorithm, it shows that the adaptive Kalman filtering algorithm is more effective.
Girard, Corentin; Rinaudo, Jean-Daniel; Pulido-Velazquez, Manuel
2016-10-01
The adaptation of water resource systems to the potential impacts of climate change requires mixed portfolios of supply and demand adaptation measures. The issue is not only to select efficient, robust, and flexible adaptation portfolios but also to find equitable strategies of cost allocation among the stakeholders. Our work addresses such cost allocation problems by applying two different theoretical approaches: social justice and cooperative game theory in a real case study. First of all, a cost-effective portfolio of adaptation measures at the basin scale is selected using a least-cost optimization model. Cost allocation solutions are then defined based on economic rationality concepts from cooperative game theory (the Core). Second, interviews are conducted to characterize stakeholders' perceptions of social justice principles associated with the definition of alternatives cost allocation rules. The comparison of the cost allocation scenarios leads to contrasted insights in order to inform the decision-making process at the river basin scale and potentially reap the efficiency gains from cooperation in the design of river basin adaptation portfolios.
Verbist, Bie M P; Thys, Kim; Reumers, Joke; Wetzels, Yves; Van der Borght, Koen; Talloen, Willem; Aerssens, Jeroen; Clement, Lieven; Thas, Olivier
2015-01-01
In virology, massively parallel sequencing (MPS) opens many opportunities for studying viral quasi-species, e.g. in HIV-1- and HCV-infected patients. This is essential for understanding pathways to resistance, which can substantially improve treatment. Although MPS platforms allow in-depth characterization of sequence variation, their measurements still involve substantial technical noise. For Illumina sequencing, single base substitutions are the main error source and impede powerful assessment of low-frequency mutations. Fortunately, base calls are complemented with quality scores (Qs) that are useful for differentiating errors from the real low-frequency mutations. A variant calling tool, Q-cpileup, is proposed, which exploits the Qs of nucleotides in a filtering strategy to increase specificity. The tool is imbedded in an open-source pipeline, VirVarSeq, which allows variant calling starting from fastq files. Using both plasmid mixtures and clinical samples, we show that Q-cpileup is able to reduce the number of false-positive findings. The filtering strategy is adaptive and provides an optimized threshold for individual samples in each sequencing run. Additionally, linkage information is kept between single-nucleotide polymorphisms as variants are called at the codon level. This enables virologists to have an immediate biological interpretation of the reported variants with respect to their antiviral drug responses. A comparison with existing SNP caller tools reveals that calling variants at the codon level with Q-cpileup results in an outstanding sensitivity while maintaining a good specificity for variants with frequencies down to 0.5%. The VirVarSeq is available, together with a user's guide and test data, at sourceforge: http://sourceforge.net/projects/virtools/?source=directory. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Hu, Chia-Chang; Lin, Hsuan-Yu; Chen, Yu-Fan; Wen, Jyh-Horng
2006-12-01
An adaptive minimum mean-square error (MMSE) array receiver based on the fuzzy-logic recursive least-squares (RLS) algorithm is developed for asynchronous DS-CDMA interference suppression in the presence of frequency-selective multipath fading. This receiver employs a fuzzy-logic control mechanism to perform the nonlinear mapping of the squared error and squared error variation, denoted by ([InlineEquation not available: see fulltext.],[InlineEquation not available: see fulltext.]), into a forgetting factor[InlineEquation not available: see fulltext.]. For the real-time applicability, a computationally efficient version of the proposed receiver is derived based on the least-mean-square (LMS) algorithm using the fuzzy-inference-controlled step-size[InlineEquation not available: see fulltext.]. This receiver is capable of providing both fast convergence/tracking capability as well as small steady-state misadjustment as compared with conventional LMS- and RLS-based MMSE DS-CDMA receivers. Simulations show that the fuzzy-logic LMS and RLS algorithms outperform, respectively, other variable step-size LMS (VSS-LMS) and variable forgetting factor RLS (VFF-RLS) algorithms at least 3 dB and 1.5 dB in bit-error-rate (BER) for multipath fading channels.
Directory of Open Access Journals (Sweden)
Chen Yu-Fan
2006-01-01
Full Text Available An adaptive minimum mean-square error (MMSE array receiver based on the fuzzy-logic recursive least-squares (RLS algorithm is developed for asynchronous DS-CDMA interference suppression in the presence of frequency-selective multipath fading. This receiver employs a fuzzy-logic control mechanism to perform the nonlinear mapping of the squared error and squared error variation, denoted by ( , , into a forgetting factor . For the real-time applicability, a computationally efficient version of the proposed receiver is derived based on the least-mean-square (LMS algorithm using the fuzzy-inference-controlled step-size . This receiver is capable of providing both fast convergence/tracking capability as well as small steady-state misadjustment as compared with conventional LMS- and RLS-based MMSE DS-CDMA receivers. Simulations show that the fuzzy-logic LMS and RLS algorithms outperform, respectively, other variable step-size LMS (VSS-LMS and variable forgetting factor RLS (VFF-RLS algorithms at least 3 dB and 1.5 dB in bit-error-rate (BER for multipath fading channels.
Socio-Emotional Adaptation Theory: Charting the Emotional Process of Alzheimer's Disease.
Halpin, Sean N; Dillard, Rebecca L; Puentes, William J
2017-08-01
The emotional reactions to the progression of Mild Cognitive Impairment and Alzheimer's disease (MCI/AD) oftentimes present as cognitive or behavioral changes, leading to misguided interventions by Formal Support (paid health care providers). Despite a rich body of literature identifying cognitive and behavioral staging of MCI/AD, the emotional changes that accompany these diagnoses have been largely ignored. The objective of this study was to develop a model of the emotional aspects of MCI/AD. One hour, semistructured interviews, with 14 patient-Informal Support Partner dyads (N = 28) interviewed concurrently; patients were in various stages of MCI/AD. An interdisciplinary team employed a grounded theory coding process to detect emotional characteristics of the participants with MCI/AD. Emotional reactions were categorized into depression/sadness, apathy, concern/fear, anger/frustration, and acceptance. The emotions did not present linearly along the course of the disease and were instead entwined within a set of complex (positive/negative) interactions including: relationship with the Informal Support Partner (i.e., teamwork vs infantilization), relationship with the Formal Support (i.e., patient vs disengaged), coping (i.e., adaptive vs nonadaptive), and perceived control (i.e., internal vs external locus-of-control). For example, a person with poor formal and informal support and external locus-of-control may become depressed, a condition that is known to negatively affect cognitive status. Understanding the emotional reactions of individuals diagnosed with MCI/AD will provide clinicians with information needed to develop treatments suited to current needs of the patient and provide Informal Support Partners insight into cognitive and physical changes associated with MCI/AD. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Item response theory, computerized adaptive testing, and PROMIS: assessment of physical function.
Fries, James F; Witter, James; Rose, Matthias; Cella, David; Khanna, Dinesh; Morgan-DeWitt, Esi
2014-01-01
Patient-reported outcome (PRO) questionnaires record health information directly from research participants because observers may not accurately represent the patient perspective. Patient-reported Outcomes Measurement Information System (PROMIS) is a US National Institutes of Health cooperative group charged with bringing PRO to a new level of precision and standardization across diseases by item development and use of item response theory (IRT). With IRT methods, improved items are calibrated on an underlying concept to form an item bank for a "domain" such as physical function (PF). The most informative items can be combined to construct efficient "instruments" such as 10-item or 20-item PF static forms. Each item is calibrated on the basis of the probability that a given person will respond at a given level, and the ability of the item to discriminate people from one another. Tailored forms may cover any desired level of the domain being measured. Computerized adaptive testing (CAT) selects the best items to sharpen the estimate of a person's functional ability, based on prior responses to earlier questions. PROMIS item banks have been improved with experience from several thousand items, and are calibrated on over 21,000 respondents. In areas tested to date, PROMIS PF instruments are superior or equal to Health Assessment Questionnaire and Medical Outcome Study Short Form-36 Survey legacy instruments in clarity, translatability, patient importance, reliability, and sensitivity to change. Precise measures, such as PROMIS, efficiently incorporate patient self-report of health into research, potentially reducing research cost by lowering sample size requirements. The advent of routine IRT applications has the potential to transform PRO measurement.
Directory of Open Access Journals (Sweden)
Houzeng Han
2016-07-01
Full Text Available Precise Point Positioning (PPP makes use of the undifferenced pseudorange and carrier phase measurements with ionospheric-free (IF combinations to achieve centimeter-level positioning accuracy. Conventionally, the IF ambiguities are estimated as float values. To improve the PPP positioning accuracy and shorten the convergence time, the integer phase clock model with between-satellites single-difference (BSSD operation is used to recover the integer property. However, the continuity and availability of stand-alone PPP is largely restricted by the observation environment. The positioning performance will be significantly degraded when GPS operates under challenging environments, if less than five satellites are present. A commonly used approach is integrating a low cost inertial sensor to improve the positioning performance and robustness. In this study, a tightly coupled (TC algorithm is implemented by integrating PPP with inertial navigation system (INS using an Extended Kalman filter (EKF. The navigation states, inertial sensor errors and GPS error states are estimated together. The troposphere constrained approach, which utilizes external tropospheric delay as virtual observation, is applied to further improve the ambiguity-fixed height positioning accuracy, and an improved adaptive filtering strategy is implemented to improve the covariance modelling considering the realistic noise effect. A field vehicular test with a geodetic GPS receiver and a low cost inertial sensor was conducted to validate the improvement on positioning performance with the proposed approach. The results show that the positioning accuracy has been improved with inertial aiding. Centimeter-level positioning accuracy is achievable during the test, and the PPP/INS TC integration achieves a fast re-convergence after signal outages. For troposphere constrained solutions, a significant improvement for the height component has been obtained. The overall positioning accuracies
Kahle, L R; Kulka, R A; Klingel, D M
1980-09-01
This article reports the results of a study that annually monitored the self-esteem and interpersonal problems of over 100 boys during their sophomore, junior, and senior years of high school. Cross-lagged panel correlation differences show that low self-esteem leads to interpersonal problems in all three time lags when multiple interpersonal problems constitute the dependent variable but not when single interpersonal problem criteria constitute the dependent variable. These results are interpreted as supporting social-adaptation theory rather than self-perception theory. Implications for the conceptual status of personality variables as causal antecedents and for the assessment of individual differences are discussed.
Adaptive Nonlinear Tracking for Robotic Walking
Czech Academy of Sciences Publication Activity Database
Dolinský, Kamil; Čelikovský, Sergej
2012-01-01
Roč. 1, č. 1 (2012), s. 28-35 ISSN 2223-7038 Institutional research plan: CEZ:AV0Z10750506 Keywords : Adaptive control * Kalman filter * walking robots Subject RIV: BC - Control Systems Theory http://lib.physcon.ru/doc?id=9e51935aa5bc
Use of adaptive diffusion theory based monitors in optimizing boiling water reactor core designs
International Nuclear Information System (INIS)
Congdon, S.P.; Martin, C.L.; Crowther, R.L.
1988-01-01
Three-dimensional coarse mesh models are routinely used to predict the performance of boiling water reactors. In the adaptive monitory model, the three-dimensional solutions are permanently adapted to incore probe data. The corrections resulting from the adaptive process lead to reliable predictions of future reactor states. The corrections can also be carried forward to future operating cycles. This can shorten the time required to introduce an validate new design and operating strategy improvements. (orig.) [de
International Nuclear Information System (INIS)
Kim, Milim; Lee, Jeong Min; Son, Hyo Shin; Han, Joon Koo; Choi, Byung Ihn; Yoon, Jeong Hee; Choi, Jin Woo
2014-01-01
To evaluate the impact of the adaptive iterative dose reduction (AIDR) three-dimensional (3D) algorithm in CT on noise reduction and the image quality compared to the filtered back projection (FBP) algorithm and to compare the effectiveness of AIDR 3D on noise reduction according to the body habitus using phantoms with different sizes. Three different-sized phantoms with diameters of 24 cm, 30 cm, and 40 cm were built up using the American College of Radiology CT accreditation phantom and layers of pork belly fat. Each phantom was scanned eight times using different mAs. Images were reconstructed using the FBP and three different strengths of the AIDR 3D. The image noise, the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) of the phantom were assessed. Two radiologists assessed the image quality of the 4 image sets in consensus. The effectiveness of AIDR 3D on noise reduction compared with FBP were also compared according to the phantom sizes. Adaptive iterative dose reduction 3D significantly reduced the image noise compared with FBP and enhanced the SNR and CNR (p < 0.05) with improved image quality (p < 0.05). When a stronger reconstruction algorithm was used, greater increase of SNR and CNR as well as noise reduction was achieved (p < 0.05). The noise reduction effect of AIDR 3D was significantly greater in the 40-cm phantom than in the 24-cm or 30-cm phantoms (p < 0.05). The AIDR 3D algorithm is effective to reduce the image noise as well as to improve the image-quality parameters compared by FBP algorithm, and its effectiveness may increase as the phantom size increases.
Energy Technology Data Exchange (ETDEWEB)
Kim, Milim; Lee, Jeong Min; Son, Hyo Shin; Han, Joon Koo; Choi, Byung Ihn [College of Medicine, Seoul National University, Seoul (Korea, Republic of); Yoon, Jeong Hee; Choi, Jin Woo [Dept. of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of)
2014-04-15
To evaluate the impact of the adaptive iterative dose reduction (AIDR) three-dimensional (3D) algorithm in CT on noise reduction and the image quality compared to the filtered back projection (FBP) algorithm and to compare the effectiveness of AIDR 3D on noise reduction according to the body habitus using phantoms with different sizes. Three different-sized phantoms with diameters of 24 cm, 30 cm, and 40 cm were built up using the American College of Radiology CT accreditation phantom and layers of pork belly fat. Each phantom was scanned eight times using different mAs. Images were reconstructed using the FBP and three different strengths of the AIDR 3D. The image noise, the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) of the phantom were assessed. Two radiologists assessed the image quality of the 4 image sets in consensus. The effectiveness of AIDR 3D on noise reduction compared with FBP were also compared according to the phantom sizes. Adaptive iterative dose reduction 3D significantly reduced the image noise compared with FBP and enhanced the SNR and CNR (p < 0.05) with improved image quality (p < 0.05). When a stronger reconstruction algorithm was used, greater increase of SNR and CNR as well as noise reduction was achieved (p < 0.05). The noise reduction effect of AIDR 3D was significantly greater in the 40-cm phantom than in the 24-cm or 30-cm phantoms (p < 0.05). The AIDR 3D algorithm is effective to reduce the image noise as well as to improve the image-quality parameters compared by FBP algorithm, and its effectiveness may increase as the phantom size increases.
Directory of Open Access Journals (Sweden)
Luciane Stallivieri
2015-11-01
Full Text Available Students from around the world are increasingly interested in learning new languages and be inserted in different cultures, increasing the numbers of international mobility. However, when traveling to another country, cultural differences are strongly perceived, even if in different ways, influencing the adaptation process. Given the importance and growth of the international academic mobility, this study analyzes the process of cultural adaptation of students in mobility, considering the assumptions of the theory of curves "U" and "W". As for the methodology, it is a descriptive research with quantitative approach. Data collection was conducted through closed questionnaires sent to international students from different countries who have studied or are still studying in a Brazilian Institution of Higher Education. The results showed that after the stage of culture shock, students feel more connected to local culture, make more friends and feel more confident. It was confirmed also that almost all international students managed to adapt well to local customs.
Interpretive Research Aiming at Theory Building: Adopting and Adapting the Case Study Design
Diaz Andrade, Antonio
2009-01-01
Although the advantages of case study design are widely recognised, its original positivist underlying assumptions may mislead interpretive researchers aiming at theory building. The paper discusses the limitations of the case study design for theory building and explains how grounded theory systemic process adds to the case study design. The…
Robust Ensemble Filtering and Its Relation to Covariance Inflation in the Ensemble Kalman Filter
Luo, Xiaodong; Hoteit, Ibrahim
2011-01-01
A robust ensemble filtering scheme based on the H∞ filtering theory is proposed. The optimal H∞ filter is derived by minimizing the supremum (or maximum) of a predefined cost function, a criterion different from the minimum variance used
International Nuclear Information System (INIS)
Choo, Ji Yung; Goo, Jin Mo; Park, Chang Min; Park, Sang Joon; Lee, Chang Hyun; Shim, Mi-Suk
2014-01-01
To evaluate filtered back projection (FBP) and two iterative reconstruction (IR) algorithms and their effects on the quantitative analysis of lung parenchyma and airway measurements on computed tomography (CT) images. Low-dose chest CT obtained in 281 adult patients were reconstructed using three algorithms: FBP, adaptive statistical IR (ASIR) and model-based IR (MBIR). Measurements of each dataset were compared: total lung volume, emphysema index (EI), airway measurements of the lumen and wall area as well as average wall thickness. Accuracy of airway measurements of each algorithm was also evaluated using an airway phantom. EI using a threshold of -950 HU was significantly different among the three algorithms in decreasing order of FBP (2.30 %), ASIR (1.49 %) and MBIR (1.20 %) (P < 0.01). Wall thickness was also significantly different among the three algorithms with FBP (2.09 mm) demonstrating thicker walls than ASIR (2.00 mm) and MBIR (1.88 mm) (P < 0.01). Airway phantom analysis revealed that MBIR showed the most accurate value for airway measurements. The three algorithms presented different EIs and wall thicknesses, decreasing in the order of FBP, ASIR and MBIR. Thus, care should be taken in selecting the appropriate IR algorithm on quantitative analysis of the lung. (orig.)
Seitz, F.; Kirschner, S.; Neubersch, D.
2012-09-01
The geophysical interpretation of observed time series of Earth rotation parameters (ERP) is commonly based on numerical models that describe and balance variations of angular momentum in various subsystems of the Earth. Naturally, models are dependent on geometrical, rheological and physical parameters. Many of these are weakly determined from other models or observations. In our study we present an adaptive Kalman filter approach for the improvement of parameters of the dynamic Earth system model DyMEG which acts as a simulator of ERP. In particular we focus on the improvement of the pole tide Love number k2. In the frame of a sensitivity analysis k2 has been identified as one of the most crucial parameters of DyMEG since it directly influences the modeled Chandler oscillation. At the same time k2 is one of the most uncertain parameters in the model. Our simulations with DyMEG cover a period of 60 years after which a steady state of k2 is reached. The estimate for k2, accounting for the anelastic response of the Earth's mantle and the ocean, is 0.3531 + 0.0030i. We demonstrate that the application of the improved parameter k2 in DyMEG leads to significantly better results for polar motion than the original value taken from the Conventions of the International Earth Rotation and Reference Systems Service (IERS).
Seitz, F.; Kirschner, S.; Neubersch, D.
2012-12-01
Earth rotation has been monitored using space geodetic techniques since many decades. The geophysical interpretation of observed time series of Earth rotation parameters (ERP) polar motion and length-of-day is commonly based on numerical models that describe and balance variations of angular momentum in various subsystems of the Earth. Naturally, models are dependent on geometrical, rheological and physical parameters. Many of these are weakly determined from other models or observations. In our study we present an adaptive Kalman filter approach for the improvement of parameters of the dynamic Earth system model DyMEG which acts as a simulator of ERP. In particular we focus on the improvement of the pole tide Love number k2. In the frame of a sensitivity analysis k2 has been identified as one of the most crucial parameters of DyMEG since it directly influences the modeled Chandler oscillation. At the same time k2 is one of the most uncertain parameters in the model. Our simulations with DyMEG cover a period of 60 years after which a steady state of k2 is reached. The estimate for k2, accounting for the anelastic response of the Earth's mantle and the ocean, is 0.3531 + 0.0030i. We demonstrate that the application of the improved parameter k2 in DyMEG leads to significantly better results for polar motion than the original value taken from the Conventions of the International Earth Rotation and Reference Systems Service (IERS).
International Nuclear Information System (INIS)
Kim, Jin Hyeok; Choo, Ki Seok; Moon, Tae Yong; Lee, Jun Woo; Jeon, Ung Bae; Kim, Tae Un; Hwang, Jae Yeon; Yun, Myeong-Ja; Jeong, Dong Wook; Lim, Soo Jin
2016-01-01
To evaluate the subjective and objective qualities of computed tomography (CT) venography images at 80 kVp using model-based iterative reconstruction (MBIR) and to compare these with those of filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR) using the same CT data sets. Forty-four patients (mean age: 56.1 ± 18.1) who underwent 80 kVp CT venography (CTV) for the evaluation of deep vein thrombosis (DVT) during 4 months were enrolled in this retrospective study. The same raw data were reconstructed using FBP, ASIR, and MBIR. Objective and subjective image analysis were performed at the inferior vena cava (IVC), femoral vein, and popliteal vein. The mean CNR of MBIR was significantly greater than those of FBP and ASIR and images reconstructed using MBIR had significantly lower objective image noise (p <.001). Subjective image quality and confidence of detecting DVT by MBIR group were significantly greater than those of FBP and ASIR (p <.005), and MBIR had the lowest score for subjective image noise (p <.001). CTV at 80 kVp with MBIR was superior to FBP and ASIR regarding subjective and objective image qualities. (orig.)
International Nuclear Information System (INIS)
Mitsumori, Lee M.; Shuman, William P.; Busey, Janet M.; Kolokythas, Orpheus; Koprowicz, Kent M.
2012-01-01
To compare routine dose liver CT reconstructed with filtered back projection (FBP) versus low dose images reconstructed with FBP and adaptive statistical iterative reconstruction (ASIR). In this retrospective study, patients had a routine dose protocol reconstructed with FBP, and again within 17 months (median 6.1 months), had a low dose protocol reconstructed twice, with FBP and ASIR. These reconstructions were compared for noise, image quality, and radiation dose. Nineteen patients were included. (12 male, mean age 58). Noise was significantly lower in low dose images reconstructed with ASIR compared to routine dose images reconstructed with FBP (liver: p <.05, aorta: p < 0.001). Low dose FBP images were scored significantly lower for subjective image quality than low dose ASIR (2.1 ± 0.5, 3.2 ± 0.8, p < 0.001). There was no difference in subjective image quality scores between routine dose FBP images and low dose ASIR images (3.6 ± 0.5, 3.2 ± 0.8, NS).Radiation dose was 41% less for the low dose protocol (4.4 ± 2.4 mSv versus 7.5 ± 5.5 mSv, p < 0.05). Our initial results suggest low dose CT images reconstructed with ASIR may have lower measured noise, similar image quality, yet significantly less radiation dose compared with higher dose images reconstructed with FBP. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Choo, Ji Yung [Seoul National University Medical Research Center, Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul (Korea, Republic of); Korea University Ansan Hospital, Ansan-si, Department of Radiology, Gyeonggi-do (Korea, Republic of); Goo, Jin Mo; Park, Chang Min; Park, Sang Joon [Seoul National University Medical Research Center, Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul (Korea, Republic of); Seoul National University, Cancer Research Institute, Seoul (Korea, Republic of); Lee, Chang Hyun; Shim, Mi-Suk [Seoul National University Medical Research Center, Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul (Korea, Republic of)
2014-04-15
To evaluate filtered back projection (FBP) and two iterative reconstruction (IR) algorithms and their effects on the quantitative analysis of lung parenchyma and airway measurements on computed tomography (CT) images. Low-dose chest CT obtained in 281 adult patients were reconstructed using three algorithms: FBP, adaptive statistical IR (ASIR) and model-based IR (MBIR). Measurements of each dataset were compared: total lung volume, emphysema index (EI), airway measurements of the lumen and wall area as well as average wall thickness. Accuracy of airway measurements of each algorithm was also evaluated using an airway phantom. EI using a threshold of -950 HU was significantly different among the three algorithms in decreasing order of FBP (2.30 %), ASIR (1.49 %) and MBIR (1.20 %) (P < 0.01). Wall thickness was also significantly different among the three algorithms with FBP (2.09 mm) demonstrating thicker walls than ASIR (2.00 mm) and MBIR (1.88 mm) (P < 0.01). Airway phantom analysis revealed that MBIR showed the most accurate value for airway measurements. The three algorithms presented different EIs and wall thicknesses, decreasing in the order of FBP, ASIR and MBIR. Thus, care should be taken in selecting the appropriate IR algorithm on quantitative analysis of the lung. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Mitsumori, Lee M.; Shuman, William P.; Busey, Janet M.; Kolokythas, Orpheus; Koprowicz, Kent M. [University of Washington School of Medicine, Department of Radiology, Seattle, WA (United States)
2012-01-15
To compare routine dose liver CT reconstructed with filtered back projection (FBP) versus low dose images reconstructed with FBP and adaptive statistical iterative reconstruction (ASIR). In this retrospective study, patients had a routine dose protocol reconstructed with FBP, and again within 17 months (median 6.1 months), had a low dose protocol reconstructed twice, with FBP and ASIR. These reconstructions were compared for noise, image quality, and radiation dose. Nineteen patients were included. (12 male, mean age 58). Noise was significantly lower in low dose images reconstructed with ASIR compared to routine dose images reconstructed with FBP (liver: p <.05, aorta: p < 0.001). Low dose FBP images were scored significantly lower for subjective image quality than low dose ASIR (2.1 {+-} 0.5, 3.2 {+-} 0.8, p < 0.001). There was no difference in subjective image quality scores between routine dose FBP images and low dose ASIR images (3.6 {+-} 0.5, 3.2 {+-} 0.8, NS).Radiation dose was 41% less for the low dose protocol (4.4 {+-} 2.4 mSv versus 7.5 {+-} 5.5 mSv, p < 0.05). Our initial results suggest low dose CT images reconstructed with ASIR may have lower measured noise, similar image quality, yet significantly less radiation dose compared with higher dose images reconstructed with FBP. (orig.)
How adaptive learning affects evolution: reviewing theory on the Baldwin effect
Sznajder, B.; Sabelis, M.W.; Egas, M.
2012-01-01
We review models of the Baldwin effect, i.e., the hypothesis that adaptive learning (i.e., learning to improve fitness) accelerates genetic evolution of the phenotype. Numerous theoretical studies scrutinized the hypothesis that a non-evolving ability of adaptive learning accelerates evolution of
Durai, Mithila; O'Keeffe, Mary G; Searchfield, Grant D
2017-03-01
Existing evidence suggests a strong relationship between tinnitus and emotion. The objective of this study was to examine the effects of short-term emotional changes along valence and arousal dimensions on tinnitus outcomes. Emotional stimuli were presented in two different modalities: auditory and visual. The authors hypothesized that (1) negative valence (unpleasant) stimuli and/or high arousal stimuli will lead to greater tinnitus loudness and annoyance than positive valence and/or low arousal stimuli, and (2) auditory emotional stimuli, which are in the same modality as the tinnitus, will exhibit a greater effect on tinnitus outcome measures than visual stimuli. Auditory and visual emotive stimuli were administered to 22 participants (12 females and 10 males) with chronic tinnitus, recruited via email invitations send out to the University of Auckland Tinnitus Research Volunteer Database. Emotional stimuli used were taken from the International Affective Digital Sounds- Version 2 (IADS-2) and the International Affective Picture System (IAPS) (Bradley and Lang, 2007a, 2007b). The Emotion Regulation Questionnaire (Gross and John, 2003) was administered alongside subjective ratings of tinnitus loudness and annoyance, and psychoacoustic sensation level matches to external sounds. Males had significantly different emotional regulation scores than females. Negative valence emotional auditory stimuli led to higher tinnitus loudness ratings in males and females and higher annoyance ratings in males only; loudness matches of tinnitus remained unchanged. The visual stimuli did not have an effect on tinnitus ratings. The results are discussed relative to the Adaptation Level Theory Model of Tinnitus. The results indicate that the negative valence dimension of emotion is associated with increased tinnitus magnitude judgements and gender effects may also be present, but only when the emotional stimulus is in the auditory modality. Sounds with emotional associations may be
Interactive Teaching of Adaptive Signal Processing
Stewart, R W; Harteneck, M; Weiss, S
2000-01-01
Over the last 30 years adaptive digital signal processing has progressed from being a strictly graduate level advanced class in signal processing theory to a topic that is part of the core curriculum for many undergraduate signal processing classes. The key reason is the continued advance of communications technology, with its need for echo control and equalisation, and the widespread use of adaptive filters in audio, biomedical, and control applications. In this paper we will review the basi...
An adaptive observer for on-line tool wear estimation in turning, Part I: Theory
Danai, Kourosh; Ulsoy, A. Galip
1987-04-01
On-line sensing of tool wear has been a long-standing goal of the manufacturing engineering community. In the absence of any reliable on-line tool wear sensors, a new model-based approach for tool wear estimation has been proposed. This approach is an adaptive observer, based on force measurement, which uses both parameter and state estimation techniques. The design of the adaptive observer is based upon a dynamic state model of tool wear in turning. This paper (Part I) presents the model, and explains its use as the basis for the adaptive observer design. This model uses flank wear and crater wear as state variables, feed as the input, and the cutting force as the output. The suitability of the model as the basis for adaptive observation is also verified. The implementation of the adaptive observer requires the design of a state observer and a parameter estimator. To obtain the model parameters for tuning the adaptive observer procedures for linearisation of the non-linear model are specified. The implementation of the adaptive observer in turning and experimental results are presented in a companion paper (Part II).
International Nuclear Information System (INIS)
Böning, G.; Schäfer, M.; Grupp, U.; Kaul, D.; Kahn, J.; Pavel, M.; Maurer, M.; Denecke, T.; Hamm, B.; Streitparth, F.
2015-01-01
Highlights: • Iterative reconstruction (IR) in staging CT provides equal objective image quality compared to filtered back projection (FBP). • IR delivers excellent subjective quality and reduces effective dose compared to FBP. • In patients with neuroendocrine tumor (NET) or may other hypervascular abdominal tumors IR can be used without scarifying diagnostic confidence. - Abstract: Objective: To investigate whether dose reduction via adaptive statistical iterative reconstruction (ASIR) affects image quality and diagnostic accuracy in neuroendocrine tumor (NET) staging. Methods: A total of 28 NET patients were enrolled in the study. Inclusion criteria were histologically proven NET and visible tumor in abdominal computed tomography (CT). In an intraindividual study design, the patients underwent a baseline CT (filtered back projection, FBP) and follow-up CT (ASIR 40%) using matched scan parameters. Image quality was assessed subjectively using a 5-grade scoring system and objectively by determining signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNRs). Applied volume computed tomography dose index (CTDI vol ) of each scan was taken from the dose report. Results: ASIR 40% significantly reduced CTDI vol (10.17 ± 3.06 mGy [FBP], 6.34 ± 2.25 mGy [ASIR] (p < 0.001) by 37.6% and significantly increased CNRs (complete tumor-to-liver, 2.76 ± 1.87 [FBP], 3.2 ± 2.32 [ASIR]) (p < 0.05) (complete tumor-to-muscle, 2.74 ± 2.67 [FBP], 4.31 ± 4.61 [ASIR]) (p < 0.05) compared to FBP. Subjective scoring revealed no significant changes for diagnostic confidence (5.0 ± 0 [FBP], 5.0 ± 0 [ASIR]), visibility of suspicious lesion (4.8 ± 0.5 [FBP], 4.8 ± 0.5 [ASIR]) and artifacts (5.0 ± 0 [FBP], 5.0 ± 0 [ASIR]). ASIR 40% significantly decreased scores for noise (4.3 ± 0.6 [FBP], 4.0 ± 0.8 [ASIR]) (p < 0.05), contrast (4.4 ± 0.6 [FBP], 4.1 ± 0.8 [ASIR]) (p < 0.001) and visibility of small structures (4.5 ± 0.7 [FBP], 4.3 ± 0.8 [ASIR]) (p < 0
Energy Technology Data Exchange (ETDEWEB)
Böning, G., E-mail: georg.boening@charite.de [Department of Radiology, Charité, Humboldt-University Medical School, Charitéplatz 1, 10117 Berlin (Germany); Schäfer, M.; Grupp, U. [Department of Radiology, Charité, Humboldt-University Medical School, Charitéplatz 1, 10117 Berlin (Germany); Kaul, D. [Department of Radiation Oncology, Charité, Humboldt-University Medical School, Charitéplatz 1, 10117 Berlin (Germany); Kahn, J. [Department of Radiology, Charité, Humboldt-University Medical School, Charitéplatz 1, 10117 Berlin (Germany); Pavel, M. [Department of Gastroenterology, Charité, Humboldt-University Medical School, Charitéplatz 1, 10117 Berlin (Germany); Maurer, M.; Denecke, T.; Hamm, B.; Streitparth, F. [Department of Radiology, Charité, Humboldt-University Medical School, Charitéplatz 1, 10117 Berlin (Germany)
2015-08-15
Highlights: • Iterative reconstruction (IR) in staging CT provides equal objective image quality compared to filtered back projection (FBP). • IR delivers excellent subjective quality and reduces effective dose compared to FBP. • In patients with neuroendocrine tumor (NET) or may other hypervascular abdominal tumors IR can be used without scarifying diagnostic confidence. - Abstract: Objective: To investigate whether dose reduction via adaptive statistical iterative reconstruction (ASIR) affects image quality and diagnostic accuracy in neuroendocrine tumor (NET) staging. Methods: A total of 28 NET patients were enrolled in the study. Inclusion criteria were histologically proven NET and visible tumor in abdominal computed tomography (CT). In an intraindividual study design, the patients underwent a baseline CT (filtered back projection, FBP) and follow-up CT (ASIR 40%) using matched scan parameters. Image quality was assessed subjectively using a 5-grade scoring system and objectively by determining signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNRs). Applied volume computed tomography dose index (CTDI{sub vol}) of each scan was taken from the dose report. Results: ASIR 40% significantly reduced CTDI{sub vol} (10.17 ± 3.06 mGy [FBP], 6.34 ± 2.25 mGy [ASIR] (p < 0.001) by 37.6% and significantly increased CNRs (complete tumor-to-liver, 2.76 ± 1.87 [FBP], 3.2 ± 2.32 [ASIR]) (p < 0.05) (complete tumor-to-muscle, 2.74 ± 2.67 [FBP], 4.31 ± 4.61 [ASIR]) (p < 0.05) compared to FBP. Subjective scoring revealed no significant changes for diagnostic confidence (5.0 ± 0 [FBP], 5.0 ± 0 [ASIR]), visibility of suspicious lesion (4.8 ± 0.5 [FBP], 4.8 ± 0.5 [ASIR]) and artifacts (5.0 ± 0 [FBP], 5.0 ± 0 [ASIR]). ASIR 40% significantly decreased scores for noise (4.3 ± 0.6 [FBP], 4.0 ± 0.8 [ASIR]) (p < 0.05), contrast (4.4 ± 0.6 [FBP], 4.1 ± 0.8 [ASIR]) (p < 0.001) and visibility of small structures (4.5 ± 0.7 [FBP], 4.3 ± 0.8 [ASIR]) (p < 0
Page, Ralph H.; Doty, Patrick F.
2017-08-01
The various technologies presented herein relate to a tiled filter array that can be used in connection with performance of spatial sampling of optical signals. The filter array comprises filter tiles, wherein a first plurality of filter tiles are formed from a first material, the first material being configured such that only photons having wavelengths in a first wavelength band pass therethrough. A second plurality of filter tiles is formed from a second material, the second material being configured such that only photons having wavelengths in a second wavelength band pass therethrough. The first plurality of filter tiles and the second plurality of filter tiles can be interspersed to form the filter array comprising an alternating arrangement of first filter tiles and second filter tiles.
Statistically-Efficient Filtering in Impulsive Environments: Weighted Myriad Filters
Directory of Open Access Journals (Sweden)
Juan G. Gonzalez
2002-01-01
Full Text Available Linear filtering theory has been largely motivated by the characteristics of Gaussian signals. In the same manner, the proposed Myriad Filtering methods are motivated by the need for a flexible filter class with high statistical efficiency in non-Gaussian impulsive environments that can appear in practice. Myriad filters have a solid theoretical basis, are inherently more powerful than median filters, and are very general, subsuming traditional linear FIR filters. The foundation of the proposed filtering algorithms lies in the definition of the myriad as a tunable estimator of location derived from the theory of robust statistics. We prove several fundamental properties of this estimator and show its optimality in practical impulsive models such as the ÃŽÂ±-stable and generalized-t. We then extend the myriad estimation framework to allow the use of weights. In the same way as linear FIR filters become a powerful generalization of the mean filter, filters based on running myriads reach all of their potential when a weighting scheme is utilized. We derive the Ã¢Â€ÂœnormalÃ¢Â€Â equations for the optimal myriad filter, and introduce a suboptimal methodology for filter tuning and design. The strong potential of myriad filtering and estimation in impulsive environments is illustrated with several examples.
Directory of Open Access Journals (Sweden)
Carlos David Navarrete
2005-01-01
Full Text Available Adherence to ingroup ideology increases after exposure to death-related stimuli, a reaction that proponents of terror management theory (TMT explain as a psychological defense against the uniquely human existential fear of death. We argue that existential concerns are not the relevant issue; rather, such concepts can be subsumed under a larger category of adaptive challenges that prime coalitional thinking. We suggest that increases in adherence to ingroup ideology in response to adaptive challenges are manifestations of normative mental representations emanating from psychological systems designed to enhance coordination and membership in social groups. In providing an alternative to TMT, we (1 explain why the theory is inconsistent with contemporary evolutionary biology, (2 demonstrate that mortality-salience does not have the unique evocative powers ascribed to it by TMT advocates, and (3 discuss our approach to coalitional psychology, a framework consistent with modern evolutionary theory and informed by a broad understanding of cultural variation, can be employed to help account for both the corpus of results in TMT research and the growing body of findings inconsistent with TMT's predictions.
Bayesian signal processing classical, modern, and particle filtering methods
Candy, James V
2016-01-01
This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on "Sequential Bayesian Detection," a new section on "Ensemble Kalman Filters" as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to "fill-in-the gaps" of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical "sanity testing" lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed an...
A CLT on the SNR of Diagonally Loaded MVDR Filters
Rubio, Francisco; Mestre, Xavier; Hachem, Walid
2012-08-01
This paper studies the fluctuations of the signal-to-noise ratio (SNR) of minimum variance distorsionless response (MVDR) filters implementing diagonal loading in the estimation of the covariance matrix. Previous results in the signal processing literature are generalized and extended by considering both spatially as well as temporarily correlated samples. Specifically, a central limit theorem (CLT) is established for the fluctuations of the SNR of the diagonally loaded MVDR filter, under both supervised and unsupervised training settings in adaptive filtering applications. Our second-order analysis is based on the Nash-Poincar\\'e inequality and the integration by parts formula for Gaussian functionals, as well as classical tools from statistical asymptotic theory. Numerical evaluations validating the accuracy of the CLT confirm the asymptotic Gaussianity of the fluctuations of the SNR of the MVDR filter.
Bagagnan, A. R.
2016-12-01
In the Gambia, Changes in the climate pattern has affected and continue to affect the agriculture sector and therefore calling for effective adaptation policies. The present study aimed to explain farmers' adoption of climate change adaptation measure through the protection motivation theory in The Central River Region of The Gambia. Primary data were collected in all the eight communities of the study area. A transect walk was conducted first followed by a survey with 283 informants. The perception variables were referring to the past 20 years while the stated implementation was addressing the current adaptation practices. Results showed that on one hand, most of the perception variables such as severity, ability to withstand, and internal barriers are significantly correlated to protection motivation and on the other hand Protection motivation and stated implementation for water conservation technique are strongly correlated. Structural Equation Modeling confirms the mediation role of Protection motivation between Farmers stated implementation and their perception of climate variability. Decrease in soil water storage capacity, degradation of the quality of soil surface structure, decrease of the length of the growing season are factors that motivate farmers to implement an adaptation measure. Cost of the implementation and farmers' vulnerability are factors that prevent farmers to implement an adaptation measure. The cost of the implementation is the main barrier to farmers `protection motivation. Therefore the study suggested that farmers' awareness about climate change/variability should be increased through farmers' field school and awareness campaigns, farmers' resilience should be improved and adaptation measures should be made accessible to farmers through loans facilities and subsidizes application.
Böning, G; Schäfer, M; Grupp, U; Kaul, D; Kahn, J; Pavel, M; Maurer, M; Denecke, T; Hamm, B; Streitparth, F
2015-08-01
To investigate whether dose reduction via adaptive statistical iterative reconstruction (ASIR) affects image quality and diagnostic accuracy in neuroendocrine tumor (NET) staging. A total of 28 NET patients were enrolled in the study. Inclusion criteria were histologically proven NET and visible tumor in abdominal computed tomography (CT). In an intraindividual study design, the patients underwent a baseline CT (filtered back projection, FBP) and follow-up CT (ASIR 40%) using matched scan parameters. Image quality was assessed subjectively using a 5-grade scoring system and objectively by determining signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNRs). Applied volume computed tomography dose index (CTDIvol) of each scan was taken from the dose report. ASIR 40% significantly reduced CTDIvol (10.17±3.06mGy [FBP], 6.34±2.25mGy [ASIR] (pASIR]) (pASIR]) (pASIR]), visibility of suspicious lesion (4.8±0.5 [FBP], 4.8±0.5 [ASIR]) and artifacts (5.0±0 [FBP], 5.0±0 [ASIR]). ASIR 40% significantly decreased scores for noise (4.3±0.6 [FBP], 4.0±0.8 [ASIR]) (pASIR]) (pASIR]) (pASIR can be used to reduce radiation dose without sacrificing image quality and diagnostic confidence in staging CT of NET patients. This may be beneficial for patients with frequent follow-up and significant cumulative radiation exposure. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Moore, Tria; Norman, Paul; Harris, Peter R; Makris, Michael
2006-11-01
Venous thrombosis is a common and life-threatening disease that has received little attention in health psychology. The present study applied the theory of cognitive adaptation (TCA) to examine patients' reactions to venous thrombosis. Patients (N = 123) aged 16-84 recruited from anticoagulation units in the north of England completed measures of TCA constructs (meaning, mastery, self-esteem and optimism) and various outcome variables (anxiety, depression, thrombosis worries and quality of life) within 1 month of their thrombosis. The TCA explained large and significant amounts of variance in the outcome variables. In line with expectations, mastery, self-esteem and optimism were associated with positive adjustment. However, meaning was associated with elevated levels of distress. The results are discussed in relation to the search for meaning and the use of different control strategies in the early phases of adaptation to thrombosis.
Bedford, Felice L
2012-02-01
A new theory of mind-body interaction in healing is proposed based on considerations from the field of perception. It is suggested that the combined effect of visual imagery and mindful meditation on physical healing is simply another example of cross-modal adaptation in perception, much like adaptation to prism-displaced vision. It is argued that psychological interventions produce a conflict between the perceptual modalities of the immune system and vision (or touch), which leads to change in the immune system in order to realign the modalities. It is argued that mind-body interactions do not exist because of higher-order cognitive thoughts or beliefs influencing the body, but instead result from ordinary interactions between lower-level perceptual modalities that function to detect when sensory systems have made an error. The theory helps explain why certain illnesses may be more amenable to mind-body interaction, such as autoimmune conditions in which a sensory system (the immune system) has made an error. It also renders sensible erroneous changes, such as those brought about by "faith healers," as conflicts between modalities that are resolved in favor of the wrong modality. The present view provides one of very few psychological theories of how guided imagery and mindfulness meditation bring about positive physical change. Also discussed are issues of self versus non-self, pain, cancer, body schema, attention, consciousness, and, importantly, developing the concept that the immune system is a rightful perceptual modality. Recognizing mind-body healing as perceptual cross-modal adaptation implies that a century of cross-modal perception research is applicable to the immune system.
Non-identifier based adaptive control in mechatronics theory and application
Hackl, Christoph M
2017-01-01
This book introduces non-identifier-based adaptive control (with and without internal model) and its application to the current, speed and position control of mechatronic systems such as electrical synchronous machines, wind turbine systems, industrial servo systems, and rigid-link, revolute-joint robots. In mechatronics, there is often only rough knowledge of the system. Due to parameter uncertainties, nonlinearities and unknown disturbances, model-based control strategies can reach their performance or stability limits without iterative controller design and performance evaluation, or system identification and parameter estimation. The non-identifier-based adaptive control presented is an alternative that neither identifies the system nor estimates its parameters but ensures stability. The adaptive controllers are easy to implement, compensate for disturbances and are inherently robust to parameter uncertainties and nonlinearities. For controller implementation only structural system knowledge (like relativ...
Directory of Open Access Journals (Sweden)
Langemeyer, Ines
2017-03-01
Full Text Available In the first half of the 20th century, the field concept was part of theoretical and methodological innovations in physics, gestalt theory as well as epic theatre as introduced by Bertolt Brecht. Another reference is the psychology of Kurt Lewin. In what ways Brecht took notice of Lewin’s research, especially his demand of a transition from Aristotelian to Galileian thought is reconstructed within the context of paradigm shifts fostered by logical empiricism, gestalt theory and physics. Lewin’s argumentation of an advanced understanding of the lawfulness of societal and psychological processes is placed in the center and traced back as an inspiration to Brecht’s writings. Vice versa, the article investigates in what ways Brecht’s theoretical writings and adaptations of Lewin’s approach can be reconsidered as a source for psychological theorizing.
Designing manufacturable filters for a 16-band plenoptic camera using differential evolution
Doster, Timothy; Olson, Colin C.; Fleet, Erin; Yetzbacher, Michael; Kanaev, Andrey; Lebow, Paul; Leathers, Robert
2017-05-01
A 16-band plenoptic camera allows for the rapid exchange of filter sets via a 4x4 filter array on the lens's front aperture. This ability to change out filters allows for an operator to quickly adapt to different locales or threat intelligence. Typically, such a system incorporates a default set of 16 equally spaced at-topped filters. Knowing the operating theater or the likely targets of interest it becomes advantageous to tune the filters. We propose using a modified beta distribution to parameterize the different possible filters and differential evolution (DE) to search over the space of possible filter designs. The modified beta distribution allows us to jointly optimize the width, taper and wavelength center of each single- or multi-pass filter in the set over a number of evolutionary steps. Further, by constraining the function parameters we can develop solutions which are not just theoretical but manufacturable. We examine two independent tasks: general spectral sensing and target detection. In the general spectral sensing task we utilize the theory of compressive sensing (CS) and find filters that generate codings which minimize the CS reconstruction error based on a fixed spectral dictionary of endmembers. For the target detection task and a set of known targets, we train the filters to optimize the separation of the background and target signature. We compare our results to the default 16 at-topped non-overlapping filter set which comes with the plenoptic camera and full hyperspectral resolution data which was previously acquired.
Adaptive sex allocation in birds : The complexities of linking theory and practice
Komdeur, Jan; Pen, Ido
2002-01-01
We review some recent theoretical and empirical developments in the study of sex allocation in birds. The advent of reliable molecular sexing techniques has led to a sharp increase in the number of studies that report biased offspring sex ratios in birds. However, compelling evidence for adaptive
Howes, Andrew; Lewis, Richard L.; Vera, Alonso
2009-01-01
The authors assume that individuals adapt rationally to a utility function given constraints imposed by their cognitive architecture and the local task environment. This assumption underlies a new approach to modeling and understanding cognition--cognitively bounded rational analysis--that sharpens the predictive acuity of general, integrated…
Developing Online Graduate Coursework in Adapted Physical Education Utilizing Andragogy Theory
Sato, Takahiro; Haegele, Justin Anthony; Foot, Rachel
2017-01-01
Graduate adapted physical education (APE) courses have typically been taught using face-to-face formats where the instructor and learners physically meet in a classroom and engage in discussions and experiential exercises. However, because in-service physical educators have time demands associated with teaching, coaching, and family commitments,…
Livneh, Hanoch; Bishop, Malachy; Anctil, Tina M.
2014-01-01
Purpose: In this article, we describe how four recent models of psychosocial adaptation to chronic illness and disability (CID) could be fruitfully conceptualized and compared by resorting to the general framework of Lewin's field theory--a theory frequently regarded as a precursor and the primary impetus to the development of the field of…
Energy Technology Data Exchange (ETDEWEB)
Szadkowski, Zbigniew [University of Lodz, Department of Physics and Applied Informatics, 90-236 Lodz, (Poland)
2015-07-01
We present the new approach to a filtering of radio frequency interferences (RFI) in the Auger Engineering Radio Array (AERA) which study the electromagnetic part of the Extensive Air Showers. The radio stations can observe radio signals caused by coherent emissions due to geomagnetic radiation and charge excess processes. AERA observes frequency band from 30 to 80 MHz. This range is highly contaminated by human-made RFI. In order to improve the signal to noise ratio RFI filters are used in AERA to suppress this contamination. The first kind of filter used by AERA was the Median one, based on the Fast Fourier Transform (FFT) technique. The second one, which is currently in use, is the infinite impulse response (IIR) notch filter. The proposed new filter is a finite impulse response (FIR) filter based on a linear prediction (LP). A periodic contamination hidden in a registered signal (digitized in the ADC) can be extracted and next subtracted to make signal cleaner. The FIR filter requires a calculation of n=32, 64 or even 128 coefficients (dependent on a required speed or accuracy) by solving of n linear equations with coefficients built from the covariance Toeplitz matrix. This matrix can be solved by the Levinson recursion, which is much faster than the Gauss procedure. The filter has been already tested in the real AERA radio stations on Argentinean pampas with a very successful results. The linear equations were solved either in the virtual soft-core NIOSR processor (implemented in the FPGA chip as a net of logic elements) or in the external Voipac PXA270M ARM processor. The NIOS processor is relatively slow (50 MHz internal clock), calculations performed in an external processor consume a significant amount of time for data exchange between the FPGA and the processor. Test showed a very good efficiency of the RFI suppression for stationary (long-term) contaminations. However, we observed a short-time contaminations, which could not be suppressed either by the
Generalized Selection Weighted Vector Filters
Directory of Open Access Journals (Sweden)
Rastislav Lukac
2004-09-01
Full Text Available This paper introduces a class of nonlinear multichannel filters capable of removing impulsive noise in color images. The here-proposed generalized selection weighted vector filter class constitutes a powerful filtering framework for multichannel signal processing. Previously defined multichannel filters such as vector median filter, basic vector directional filter, directional-distance filter, weighted vector median filters, and weighted vector directional filters are treated from a global viewpoint using the proposed framework. Robust order-statistic concepts and increased degree of freedom in filter design make the proposed method attractive for a variety of applications. Introduced multichannel sigmoidal adaptation of the filter parameters and its modifications allow to accommodate the filter parameters to varying signal and noise statistics. Simulation studies reported in this paper indicate that the proposed filter class is computationally attractive, yields excellent performance, and is able to preserve fine details and color information while efficiently suppressing impulsive noise. This paper is an extended version of the paper by Lukac et al. presented at the 2003 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03 in Grado, Italy.
Directory of Open Access Journals (Sweden)
Y. A. Bladyko
2010-01-01
Full Text Available The paper contains definition of a smoothing factor which is suitable for any rectifier filter. The formulae of complex smoothing factors have been developed for simple and complex passive filters. The paper shows conditions for application of calculation formulae and filters.
Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A
2014-01-01
Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...
EXTERNALITIES IN EXCHANGE NETWORKS AN ADAPTATION OF EXISTING THEORIES OF EXCHANGE NETWORKS
Dijkstra, Jacob
2009-01-01
The present paper extends the focus of network exchange research to externalities in exchange networks. Externalities of exchange are defined as direct effects on an actor's utility, of an exchange in which this actor is not involved. Existing theories in the field of network exchange do not inform
Free Choice or Adaptable Choice: Self-Determination Theory and Play
King, Pete; Howard, Justine
2016-01-01
The authors explore the use of three basic tenets from Self-Determination Theory--competence, relatedness, and autonomy--for a definition of play that resists the current popular call for play to be freely chosen. They explore whether free play truly exists and whether complete choice constitutes an absolute requirement for children to consider…
Adaptive State Feedback—Theory and Application for Wind Turbine Control
Directory of Open Access Journals (Sweden)
Kaman Thapa Magar
2017-12-01
Full Text Available A class of adaptive disturbance tracking controllers (ADTCs is augmented with disturbance and state estimation and adaptive state feedback, in which a controller and estimator, which are designed on the basis of a lower-order model, are used to control a higher-order nonlinear plant. The ADTC requires that the plant be almost strict positive real (ASPR to ensure stability. In this paper, we show that the ASPR property of a plant is retained with the addition of disturbance and state estimation and state feedback, thereby ensuring the stability of the augmented system. The proposed adaptive controller with augmentation is presented in the context of maximum power extraction from a wind turbine in a low-wind-speed operation region. A simulation and comparative study on the National Renewable Energy Laboratory’s (NREL’s 5 MW nonlinear wind turbine model with an existing baseline Proportional-Integral-Derivative(PID controller shows that the proposed controller is more effective than the existing baseline PID controller.
International Nuclear Information System (INIS)
Yao, Jianyong; Jiao, Zongxia; Yao, Bin
2014-01-01
High performance robust force control of hydraulic load simulator with constant but unknown hydraulic parameters is considered. In contrast to the linear control based on hydraulic linearization equations, hydraulic inherent nonlinear properties and uncertainties make the conventional feedback proportional-integral-derivative (PID) control not yield to high performance requirements. Furthermore, the hydraulic system may be subjected to non-smooth and discontinuous nonlinearities due to the directional change of valve opening. In this paper, based on a nonlinear system model of hydraulic load simulator, a discontinuous projection-based nonlinear adaptive robust back stepping controller is developed with servo valve dynamics. The proposed controller constructs a novel stable adaptive controller and adaptation laws with additional pressure dynamic related unknown parameters, which can compensate for the system nonlinearities and uncertain parameters, meanwhile a well-designed robust controller is also synthesized to dominate the model uncertainties coming from both parametric uncertainties and uncertain nonlinearities including unmodeled and ignored system dynamics. The controller theoretically guarantee a prescribed transient performance and final tracking accuracy in presence of both parametric uncertainties and uncertain nonlinearities; while achieving asymptotic output tracking in the absence of unstructured uncertainties. The implementation issues are also discussed for controller simplification. Some comparative results are obtained to verify the high-performance nature of the proposed controller.