WorldWideScience

Sample records for lms adaptive filters

  1. Simulation for noise cancellation using LMS adaptive filter

    Science.gov (United States)

    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.

  2. Research on the Random Shock Vibration Test Based on the Filter-X LMS Adaptive Inverse Control Algorithm

    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.

  3. SD LMS L-Filters for Filtration of Gray Level Images in Timespatial Domain Based on GLCM Features

    Directory of Open Access Journals (Sweden)

    Robert Hudec

    2008-01-01

    Full Text Available In this paper, the new kind of adaptive signal-dependent LMS L-filter for suppression of a mixed noise in greyscale images is developed. It is based on the texture parameter measurement as modification of spatial impulse detector structure. Moreover, the one of GLCM (Gray Level Co-occurrence Matrix features, namely, the contrast or inertia adjusted by threshold as switch between partial filters is utilised. Finally, at the positions of partial filters the adaptive LMS versions of L-filters are chosen.

  4. Performance study of LMS based adaptive algorithms for unknown system identification

    Energy Technology Data Exchange (ETDEWEB)

    Javed, Shazia; Ahmad, Noor Atinah [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Penang (Malaysia)

    2014-07-10

    Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.

  5. Performance study of LMS based adaptive algorithms for unknown system identification

    International Nuclear Information System (INIS)

    Javed, Shazia; Ahmad, Noor Atinah

    2014-01-01

    Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment

  6. Adaptive Noise Canceling Menggunakan Algoritma Least Mean Square (Lms)

    OpenAIRE

    Nardiana, Anita; Sumaryono, Sari Sujoko

    2011-01-01

    Noise is inevitable in communication system. In some cases, noise can disturb signal. It is veryannoying as the received signal is jumbled with the noise itself. To reduce or remove noise, filter lowpass,highpass or bandpass can solve the problems, but this method cannot reach a maximum standard. One ofthe alternatives to solve the problem is by using adaptive filter. Adaptive algorithm frequently used is LeastMean Square (LMS) Algorithm which is compatible to Finite Impulse Response (FIR). T...

  7. 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.

  8. Modified Clipped LMS Algorithm

    Directory of Open Access Journals (Sweden)

    Lotfizad Mojtaba

    2005-01-01

    Full Text Available Abstract A new algorithm is proposed for updating the weights of an adaptive filter. The proposed algorithm is a modification of an existing method, namely, the clipped LMS, and uses a three-level quantization ( scheme that involves the threshold clipping of the input signals in the filter weight update formula. Mathematical analysis shows the convergence of the filter weights to the optimum Wiener filter weights. Also, it can be proved that the proposed modified clipped LMS (MCLMS algorithm has better tracking than the LMS algorithm. In addition, this algorithm has reduced computational complexity relative to the unmodified one. By using a suitable threshold, it is possible to increase the tracking capability of the MCLMS algorithm compared to the LMS algorithm, but this causes slower convergence. Computer simulations confirm the mathematical analysis presented.

  9. 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.

  10. Hardware Implementation of LMS-Based Adaptive Noise Cancellation Core with Low Resource Utilization

    Directory of Open Access Journals (Sweden)

    Omid Sharifi Tehrani

    2011-10-01

    Full Text Available A hardware implementation of adaptive noise cancellation (ANC core is proposed. Adaptive filters are widely used in different applications such as adaptive noise cancellation, prediction, equalization, inverse modeling and system identification. FIR adaptive filters are mostly used because of their low computation costs and their linear phase. Least mean squared algorithm (LMS is used to train FIR adaptive filter weights. Advances in semiconductor technology especially in digital signal processors (DSP and field programmable gate arrays (FPGA with hundreds of mega hertz in speed, will allow digital designers to embed essential digital signal processing units in small chips. But designing a synthesizable core on an FPGA is not always as simple as DSP chips due to complexity and limitations of FPGAs. In this paper we design anLMS-based FIR adaptive filter for adaptive noise cancellation based on VHDL97 hardware description language (HDL and Xilinx SPARTAN3E (XC3S500E which utilizes low resources and is high performance and FPGA-brand independent so can be implemented on different FPGA brands (Xilinx, ALTERA, ACTEL. Simulations are done in MODELSIM and MATLAB and implementation is done with Xilinx ISE. Finally, result are compared with other papers for better judgment.

  11. Applications of adaptive filters in active noise control

    Science.gov (United States)

    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.

  12. Partial update least-square adaptive filtering

    CERN Document Server

    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

  13. Adaptive Filtering Algorithms and Practical Implementation

    CERN Document Server

    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...

  14. Superresolution restoration of an image sequence: adaptive filtering approach.

    Science.gov (United States)

    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.

  15. 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

  16. Adaptive filtering of GOCE-derived gravity gradients of the disturbing potential in the context of the space-wise approach

    Science.gov (United States)

    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

  17. 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....

  18. A Rapid Introduction to Adaptive Filtering

    CERN Document Server

    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...

  19. Artifact removal from EEG signals using adaptive filters in cascade

    Science.gov (United States)

    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.

  20. 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

  1. 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.

  2. SUPPRESSION OF POWERLINE INTERFERENCE IN ECG USING ADAPTIVE DIGITAL FILTER BY

    OpenAIRE

    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...

  3. Active vibration suppression of self-excited structures using an adaptive LMS algorithm

    Science.gov (United States)

    Danda Roy, Indranil

    The purpose of this investigation is to study the feasibility of an adaptive feedforward controller for active flutter suppression in representative linear wing models. The ability of the controller to suppress limit-cycle oscillations in wing models having root springs with freeplay nonlinearities has also been studied. For the purposes of numerical simulation, mathematical models of a rigid and a flexible wing structure have been developed. The rigid wing model is represented by a simple three-degree-of-freedom airfoil while the flexible wing is modelled by a multi-degree-of-freedom finite element representation with beam elements for bending and rod elements for torsion. Control action is provided by one or more flaps attached to the trailing edge and extending along the entire wing span for the rigid model and a fraction of the wing span for the flexible model. Both two-dimensional quasi-steady aerodynamics and time-domain unsteady aerodynamics have been used to generate the airforces in the wing models. An adaptive feedforward controller has been designed based on the filtered-X Least Mean Squares (LMS) algorithm. The control configuration for the rigid wing model is single-input single-output (SISO) while both SISO and multi-input multi-output (MIMO) configurations have been applied on the flexible wing model. The controller includes an on-line adaptive system identification scheme which provides the LMS controller with a reasonably accurate model of the plant. This enables the adaptive controller to track time-varying parameters in the plant and provide effective control. The wing models in closed-loop exhibit highly damped responses at airspeeds where the open-loop responses are destructive. Simulations with the rigid and the flexible wing models in a time-varying airstream show a 63% and 53% increase, respectively, over their corresponding open-loop flutter airspeeds. The ability of the LMS controller to suppress wing store flutter in the two models has

  4. The steady-state of the (Normalized) LMS is schur convex

    KAUST Repository

    Al-Hujaili, Khaled A.

    2016-06-24

    In this work, we demonstrate how the theory of majorization and schur-convexity can be used to assess the impact of input-spread on the Mean Squares Error (MSE) performance of adaptive filters. First, we show that the concept of majorization can be utilized to measure the spread in input-regressors and subsequently order the input-regressors according to their spread. Second, we prove that the MSE of the Least Mean Squares Error (LMS) and Normalized LMS (NLMS) algorithms are schur-convex, that is, the MSE of the LMS and the NLMS algorithms preserve the majorization order of the inputs which provide an analytical justification to why and how much the MSE performance of the LMS and the NLMS algorithms deteriorate as the spread in input increases. © 2016 IEEE.

  5. The steady-state of the (Normalized) LMS is schur convex

    KAUST Repository

    Al-Hujaili, Khaled A.; Al-Naffouri, Tareq Y.; Moinuddin, Muhammad

    2016-01-01

    In this work, we demonstrate how the theory of majorization and schur-convexity can be used to assess the impact of input-spread on the Mean Squares Error (MSE) performance of adaptive filters. First, we show that the concept of majorization can be utilized to measure the spread in input-regressors and subsequently order the input-regressors according to their spread. Second, we prove that the MSE of the Least Mean Squares Error (LMS) and Normalized LMS (NLMS) algorithms are schur-convex, that is, the MSE of the LMS and the NLMS algorithms preserve the majorization order of the inputs which provide an analytical justification to why and how much the MSE performance of the LMS and the NLMS algorithms deteriorate as the spread in input increases. © 2016 IEEE.

  6. Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

    Science.gov (United States)

    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.

  7. Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

    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

  8. Musical noise reduction using an adaptive filter

    Science.gov (United States)

    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.

  9. Suppression of narrow-band interference in a PN spread-spectrum receiver using a CTD-based adaptive filter

    Science.gov (United States)

    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.

  10. 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.

  11. Wavelet-LMS algorithm-based echo cancellers

    Science.gov (United States)

    Seetharaman, Lalith K.; Rao, Sathyanarayana S.

    2002-12-01

    This paper presents Echo Cancellers based on the Wavelet-LMS Algorithm. The performance of the Least Mean Square Algorithm in Wavelet transform domain is observed and its application in Echo cancellation is analyzed. The Widrow-Hoff Least Mean Square Algorithm is most widely used algorithm for Adaptive filters that function as Echo Cancellers. The present day communication signals are widely non-stationary in nature and some errors crop up when Least Mean Square Algorithm is used for the Echo Cancellers handling such signals. The analysis of non-stationary signals often involves a compromise between how well transitions or discontinuities can be located. The multi-scale or multi-resolution of signal analysis, which is the essence of wavelet transform, makes Wavelets popular in non-stationary signal analysis. In this paper, we present a Wavelet-LMS algorithm wherein the wavelet coefficients of a signal are modified adaptively using the Least Mean Square Algorithm and then reconstructed to give an Echo-free signal. The Echo Canceller based on this Algorithm is found to have a better convergence and a comparatively lesser MSE (Mean Square error).

  12. Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

    Directory of Open Access Journals (Sweden)

    Ranjan Walia

    2018-04-01

    Full Text Available Active noise control is an efficient technique for noise cancellation of the system, which has been defined in this paper with the aid of Modified Filtered-s Least Mean Square (MFsLMS algorithm. The Hybrid Particle Swarm Optimization and Firefly (HPSOFF algorithm are used to identify the stability factor of the MFsLMS algorithm. The computational difficulty of the modified algorithm is reduced when compared with the original Filtered-s Least Mean Square (FsLMS algorithm. The noise sources are removed from the signal and it is compared with the existing FsLMS algorithm. The performance of the system is established with the normalized mean square error for two different types of noises. The proposed method has also been compared with the existing algorithms for the same purposes.

  13. Floating-point-based hardware accelerator of a beam phase-magnitude detector and filter for a beam phase control system in a heavy-ion synchrotron application

    International Nuclear Information System (INIS)

    Samman, F.A.; Pongyupinpanich Surapong; Spies, C.; Glesner, M.

    2012-01-01

    A hardware implementation of an adaptive phase and magnitude detector and filter of a beam-phase control system in a heavy ion synchrotron application is presented in this paper. The main components of the hardware are adaptive LMS (Least-Mean-Square) filters and phase and magnitude detectors. The phase detectors are implemented by using a CORDIC (Coordinate Rotation Digital Computer) algorithm based on 32-bit binary floating-point arithmetic data formats. The floating-point-based hardware is designed to improve the precision of the past hardware implementation that were based on fixed-point arithmetics. The hardware of the detector and the adaptive LMS filter have been implemented on a programmable logic device (FPGA) for hardware acceleration purpose. The ideal Matlab/Simulink model of the hardware and the VHDL model of the adaptive LMS filter and the phase and magnitude detector are compared. The comparison result shows that the output signal of the floating-point based adaptive FIR filter as well as the phase and magnitude detector agree with the expected output signal of the ideal Matlab/Simulink model. (authors)

  14. Adaptive prediction applied to seismic event detection

    International Nuclear Information System (INIS)

    Clark, G.A.; Rodgers, P.W.

    1981-01-01

    Adaptive prediction was applied to the problem of detecting small seismic events in microseismic background noise. The Widrow-Hoff LMS adaptive filter used in a prediction configuration is compared with two standard seismic filters as an onset indicator. Examples demonstrate the technique's usefulness with both synthetic and actual seismic data

  15. Adaptive prediction applied to seismic event detection

    Energy Technology Data Exchange (ETDEWEB)

    Clark, G.A.; Rodgers, P.W.

    1981-09-01

    Adaptive prediction was applied to the problem of detecting small seismic events in microseismic background noise. The Widrow-Hoff LMS adaptive filter used in a prediction configuration is compared with two standard seismic filters as an onset indicator. Examples demonstrate the technique's usefulness with both synthetic and actual seismic data.

  16. Investigation of Diesel’s Residual Noise on Predictive Vehicles Noise Cancelling using LMS Adaptive Algorithm

    Science.gov (United States)

    Arttini Dwi Prasetyowati, Sri; Susanto, Adhi; Widihastuti, Ida

    2017-04-01

    Every noise problems require different solution. In this research, the noise that must be cancelled comes from roadway. Least Mean Square (LMS) adaptive is one of the algorithm that can be used to cancel that noise. Residual noise always appears and could not be erased completely. This research aims to know the characteristic of residual noise from vehicle’s noise and analysis so that it is no longer appearing as a problem. LMS algorithm was used to predict the vehicle’s noise and minimize the error. The distribution of the residual noise could be observed to determine the specificity of the residual noise. The statistic of the residual noise close to normal distribution with = 0,0435, = 1,13 and the autocorrelation of the residual noise forming impulse. As a conclusion the residual noise is insignificant.

  17. Adaptive elimination of optical fiber transmission noise in fiber ocean bottom seismic system

    Science.gov (United States)

    Zhong, Qiuwen; Hu, Zhengliang; Cao, Chunyan; Dong, Hongsheng

    2017-10-01

    In this paper, a pressure and acceleration insensitive reference Interferometer is used to obtain laser and public noise introduced by transmission fiber and laser. By using direct subtraction and adaptive filtering, this paper attempts to eliminate and estimation the transmission noise of sensing probe. This paper compares the noise suppression effect of four methods, including the direct subtraction (DS), the least mean square error adaptive elimination (LMS), the normalized least mean square error adaptive elimination (NLMS) and the least square (RLS) adaptive filtering. The experimental results show that the noise reduction effect of RLS and NLMS are almost the same, better than LMS and DS, which can reach 8dB (@100Hz). But considering the workload, RLS is not conducive to the real-time operating system. When it comes to the same treatment effect, the practicability of NLMS is higher than RLS. The noise reduction effect of LMS is slightly worse than that of RLS and NLMS, about 6dB (@100Hz), but its computational complexity is small, which is beneficial to the real time system implementation. It can also be seen that the DS method has the least amount of computational complexity, but the noise suppression effect is worse than that of the adaptive filter due to the difference of the noise amplitude between the RI and the SI, only 4dB (@100Hz) can be reached. The adaptive filter can basically eliminate the influence of the transmission noise, and the simulation signal of the sensor is kept intact.

  18. Full Gradient Solution to Adaptive Hybrid Control

    Science.gov (United States)

    Bean, Jacob; Schiller, Noah H.; Fuller, Chris

    2017-01-01

    This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

  19. Filtered-X Affine Projection Algorithms for Active Noise Control Using Volterra Filters

    Directory of Open Access Journals (Sweden)

    Sicuranza Giovanni L

    2004-01-01

    Full Text Available We consider the use of adaptive Volterra filters, implemented in the form of multichannel filter banks, as nonlinear active noise controllers. In particular, we discuss the derivation of filtered-X affine projection algorithms for homogeneous quadratic filters. According to the multichannel approach, it is then easy to pass from these algorithms to those of a generic Volterra filter. It is shown in the paper that the AP technique offers better convergence and tracking capabilities than the classical LMS and NLMS algorithms usually applied in nonlinear active noise controllers, with a limited complexity increase. This paper extends in two ways the content of a previous contribution published in Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03, Grado, Italy, June 2003. First of all, a general adaptation algorithm valid for any order of affine projections is presented. Secondly, a more complete set of experiments is reported. In particular, the effects of using multichannel filter banks with a reduced number of channels are investigated and relevant results are shown.

  20. Adaptive filtering and change detection

    CERN Document Server

    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

  1. Adaptive digital filters

    CERN Document Server

    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...

  2. An adaptive digital suppression filter for direct-sequence spread-spectrum communications

    Science.gov (United States)

    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.

  3. Discrete linear canonical transform computation by adaptive method.

    Science.gov (United States)

    Zhang, Feng; Tao, Ran; Wang, Yue

    2013-07-29

    The linear canonical transform (LCT) describes the effect of quadratic phase systems on a wavefield and generalizes many optical transforms. In this paper, the computation method for the discrete LCT using the adaptive least-mean-square (LMS) algorithm is presented. The computation approaches of the block-based discrete LCT and the stream-based discrete LCT using the LMS algorithm are derived, and the implementation structures of these approaches by the adaptive filter system are considered. The proposed computation approaches have the inherent parallel structures which make them suitable for efficient VLSI implementations, and are robust to the propagation of possible errors in the computation process.

  4. A Two-Stage Approach for Improving the Convergence of Least-Mean-Square Adaptive Decision-Feedback Equalizers in the Presence of Severe Narrowband Interference

    Science.gov (United States)

    Batra, Arun; Zeidler, James R.; Beex, A. A. Louis

    2007-12-01

    It has previously been shown that a least-mean-square (LMS) decision-feedback filter can mitigate the effect of narrowband interference (L.-M. Li and L. Milstein, 1983). An adaptive implementation of the filter was shown to converge relatively quickly for mild interference. It is shown here, however, that in the case of severe narrowband interference, the LMS decision-feedback equalizer (DFE) requires a very large number of training symbols for convergence, making it unsuitable for some types of communication systems. This paper investigates the introduction of an LMS prediction-error filter (PEF) as a prefilter to the equalizer and demonstrates that it reduces the convergence time of the two-stage system by as much as two orders of magnitude. It is also shown that the steady-state bit-error rate (BER) performance of the proposed system is still approximately equal to that attained in steady-state by the LMS DFE-only. Finally, it is shown that the two-stage system can be implemented without the use of training symbols. This two-stage structure lowers the complexity of the overall system by reducing the number of filter taps that need to be adapted, while incurring a slight loss in the steady-state BER.

  5. A Two-Stage Approach for Improving the Convergence of Least-Mean-Square Adaptive Decision-Feedback Equalizers in the Presence of Severe Narrowband Interference

    Directory of Open Access Journals (Sweden)

    A. A. (Louis Beex

    2008-02-01

    Full Text Available It has previously been shown that a least-mean-square (LMS decision-feedback filter can mitigate the effect of narrowband interference (L.-M. Li and L. Milstein, 1983. An adaptive implementation of the filter was shown to converge relatively quickly for mild interference. It is shown here, however, that in the case of severe narrowband interference, the LMS decision-feedback equalizer (DFE requires a very large number of training symbols for convergence, making it unsuitable for some types of communication systems. This paper investigates the introduction of an LMS prediction-error filter (PEF as a prefilter to the equalizer and demonstrates that it reduces the convergence time of the two-stage system by as much as two orders of magnitude. It is also shown that the steady-state bit-error rate (BER performance of the proposed system is still approximately equal to that attained in steady-state by the LMS DFE-only. Finally, it is shown that the two-stage system can be implemented without the use of training symbols. This two-stage structure lowers the complexity of the overall system by reducing the number of filter taps that need to be adapted, while incurring a slight loss in the steady-state BER.

  6. Transform Domain Robust Variable Step Size Griffiths' Adaptive Algorithm for Noise Cancellation in ECG

    Science.gov (United States)

    Hegde, Veena; Deekshit, Ravishankar; Satyanarayana, P. S.

    2011-12-01

    The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality of ECG is utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts or noise. Noise severely limits the utility of the recorded ECG and thus needs to be removed, for better clinical evaluation. In the present paper a new noise cancellation technique is proposed for removal of random noise like muscle artifact from ECG signal. A transform domain robust variable step size Griffiths' LMS algorithm (TVGLMS) is proposed for noise cancellation. For the TVGLMS, the robust variable step size has been achieved by using the Griffiths' gradient which uses cross-correlation between the desired signal contaminated with observation or random noise and the input. The algorithm is discrete cosine transform (DCT) based and uses symmetric property of the signal to represent the signal in frequency domain with lesser number of frequency coefficients when compared to that of discrete Fourier transform (DFT). The algorithm is implemented for adaptive line enhancer (ALE) filter which extracts the ECG signal in a noisy environment using LMS filter adaptation. The proposed algorithm is found to have better convergence error/misadjustment when compared to that of ordinary transform domain LMS (TLMS) algorithm, both in the presence of white/colored observation noise. The reduction in convergence error achieved by the new algorithm with desired signal decomposition is found to be lower than that obtained without decomposition. The experimental results indicate that the proposed method is better than traditional adaptive filter using LMS algorithm in the aspects of retaining geometrical characteristics of ECG signal.

  7. Adaptive filtering prediction and control

    CERN Document Server

    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

  8. 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.

  9. Detection Capability Evaluation on Chang'e-5 Lunar Mineralogical Spectrometer (LMS)

    Science.gov (United States)

    Liu, Bin; Ren, Xin; Yan, Wei; Xu, Xuesen; Cai, Tingni; Liu, Dawei; Liu, Jianjun; Li, Chunlai

    2016-04-01

    The Chang'e-5 (CE-5) lunar sample return mission is scheduled to launch in 2017 to bring back lunar regolith and drill samples. The Chang'e-5 Lunar Mineralogical Spectrometer (LMS), as one of the three sets of scientific payload installed on the lander, is used to collect in-situ spectrum and analyze the mineralogical composition of the sampling site. It can also help to select the sampling site , and to compare the measured laboratory spectrum of returned sample with in-situ data. LMS employs acousto-optic tunable filters (AOTFs) and is composed of a VIS/NIR module (0.48μm-1.45μm) and an IR module (1.4μm -3.2μm). It has spectral resolution ranging from 3 to 25 nm, with a field of view (FOV) of 4.24°×4.24°. Unlike Chang'e-3 VIS/NIR Imaging Spectrometer (VNIS), the spectral coverage of LMS is extended from 2.4μm to 3.2μm, which has capability to identify H2O/OH absorption features around 2.7μm. An aluminum plate and an Infragold plate are fixed in the dust cover, being used as calibration targets in the VIS/NIR and IR spectral range respectively when the dust cover is open. Before launch, a ground verification test of LMS needs to be conducted in order to: 1) test and verify the detection capability of LMS through evaluation on the quality of image and spectral data collected for the simulated lunar samples; and 2) evaluate the accuracy of data processing methods by the simulation of instrument working on the moon. The ground verification test will be conducted both in the lab and field. The spectra of simulated lunar regolith/mineral samples will be collected simultaneously by the LMS and two calibrated spectrometers: a FTIR spectrometer (Model 102F) and an ASD FieldSpec 4 Hi-Res spectrometer. In this study, the results of the LMS ground verification test will be reported including the evaluation on the LMS spectral and image data quality, mineral identification and inversion ability, accuracy of calibration and geometric positioning .

  10. Space-Time Joint Interference Cancellation Using Fuzzy-Inference-Based Adaptive Filtering Techniques in Frequency-Selective Multipath 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.

  11. Space-Time Joint Interference Cancellation Using Fuzzy-Inference-Based Adaptive Filtering Techniques in Frequency-Selective Multipath Channels

    Science.gov (United States)

    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.

  12. A new LMS algorithm for analysis of atrial fibrillation signals.

    Science.gov (United States)

    Ciaccio, Edward J; Biviano, Angelo B; Whang, William; Garan, Hasan

    2012-03-26

    error for paroxysmal versus persistent data. From all trials, the mean convergence time was approximately 1 second for both algorithms. The new LMS algorithm was useful to enhance the electrocardiogram F wave by subtraction of an adaptively weighted prototypical reference signal from the aVF lead. The extrinsic weighting over 25 s demonstrated that time-varying functions such as patient respiration could be identified and monitored. A new LMS algorithm was derived and used for normalization of the extrinsic features in CFAE and for electrocardiogram monitoring. The weighting at convergence provides an estimate of the degree of similarity between two signals in terms of x-axis and y-axis shift and scale. The algorithm is computationally efficient with low estimation error. Based on the results, proposed applications include monitoring of extrinsic and intrinsic features of repetitive patterns in CFAE, enhancement of the electrocardiogram F wave and monitoring of time-varying signal properties, and to quantitatively characterize mechanistic differences in paroxysmal versus persistent AF.

  13. A new LMS algorithm for analysis of atrial fibrillation signals

    Directory of Open Access Journals (Sweden)

    Ciaccio Edward J

    2012-03-01

    -Hoff LMS. There were no significant differences in estimation error for paroxysmal versus persistent data. From all trials, the mean convergence time was approximately 1 second for both algorithms. The new LMS algorithm was useful to enhance the electrocardiogram F wave by subtraction of an adaptively weighted prototypical reference signal from the aVF lead. The extrinsic weighting over 25 s demonstrated that time-varying functions such as patient respiration could be identified and monitored. Conclusions A new LMS algorithm was derived and used for normalization of the extrinsic features in CFAE and for electrocardiogram monitoring. The weighting at convergence provides an estimate of the degree of similarity between two signals in terms of x-axis and y-axis shift and scale. The algorithm is computationally efficient with low estimation error. Based on the results, proposed applications include monitoring of extrinsic and intrinsic features of repetitive patterns in CFAE, enhancement of the electrocardiogram F wave and monitoring of time-varying signal properties, and to quantitatively characterize mechanistic differences in paroxysmal versus persistent AF.

  14. Applying Maxi-adjustment to Adaptive Information Filtering Agents

    OpenAIRE

    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...

  15. A generalized leaky FxLMS algorithm for tuning the waterbed effect of feedback active noise control systems

    Science.gov (United States)

    Wu, Lifu; Qiu, Xiaojun; Guo, Yecai

    2018-06-01

    To tune the noise amplification in the feedback system caused by the waterbed effect effectively, an adaptive algorithm is proposed in this paper by replacing the scalar leaky factor of the leaky FxLMS algorithm with a real symmetric Toeplitz matrix. The elements in the matrix are calculated explicitly according to the noise amplification constraints, which are defined based on a simple but efficient method. Simulations in an ANC headphone application demonstrate that the proposed algorithm can adjust the frequency band of noise amplification more effectively than the FxLMS algorithm and the leaky FxLMS algorithm.

  16. User Interaction with User-Adaptive Information Filters

    NARCIS (Netherlands)

    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

  17. User interaction with user-adaptive information filters

    NARCIS (Netherlands)

    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

  18. Normalised subband adaptive filtering with extended adaptiveness on degree of subband filters

    Science.gov (United States)

    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.

  19. Performance Analysis of the Consensus-Based Distributed LMS Algorithm

    Directory of Open Access Journals (Sweden)

    Gonzalo Mateos

    2009-01-01

    Full Text Available Low-cost estimation of stationary signals and reduced-complexity tracking of nonstationary processes are well motivated tasks than can be accomplished using ad hoc wireless sensor networks (WSNs. To this end, a fully distributed least mean-square (D-LMS algorithm is developed in this paper, in which sensors exchange messages with single-hop neighbors to consent on the network-wide estimates adaptively. The novel approach does not require a Hamiltonian cycle or a special bridge subset of sensors, while communications among sensors are allowed to be noisy. A mean-square error (MSE performance analysis of D-LMS is conducted in the presence of a time-varying parameter vector, which adheres to a first-order autoregressive model. For sensor observations that are related to the parameter vector of interest via a linear Gaussian model and after adopting simplifying independence assumptions, exact closed-form expressions are derived for the global and sensor-level MSE evolution as well as its steady-state (s.s. values. Mean and MSE-sense stability of D-LMS are also established. Interestingly, extensive numerical tests demonstrate that for small step-sizes the results accurately extend to the pragmatic setting whereby sensors acquire temporally correlated, not necessarily Gaussian data.

  20. 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.

  1. Reversible wavelet filter banks with side informationless spatially adaptive low-pass filters

    Science.gov (United States)

    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.

  2. 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.

  3. 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)

  4. Sparse adaptive filters for echo cancellation

    CERN Document Server

    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

  5. A kernel adaptive algorithm for quaternion-valued inputs.

    Science.gov (United States)

    Paul, Thomas K; Ogunfunmi, Tokunbo

    2015-10-01

    The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations.

  6. 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.

  7. Measured results of polarization crosstalk cancellation using LMS control. [Least Mean Square

    Science.gov (United States)

    Baird, C. A.; Rassweiler, G. G.

    1977-01-01

    This paper discusses the use of wideband decoupling networks for the cancellation of polarization crosstalk in dual-polarized communication links. Measured cancellation performance for an all-electronic IF network and an RF electro-mechanical waveguide network are presented. Each of these networks utilizes LMS-type adaptive control techniques to adjust the cancellation network.

  8. Adaptive multiresolution Hermite-Binomial filters for image edge and texture analysis

    NARCIS (Netherlands)

    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

  9. The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.

    Science.gov (United States)

    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.

  10. 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)

  11. OH/H2O Detection Capability Evaluation on Chang'e-5 Lunar Mineralogical Spectrometer (LMS)

    Science.gov (United States)

    Liu, Bin; Ren, Xin; Liu, Jianjun; Li, Chunlai; Mu, Lingli; Deng, Liyan

    2016-10-01

    The Chang'e-5 (CE-5) lunar sample return mission is scheduled to launch in 2017 to bring back lunar regolith and drill samples. The Chang'e-5 Lunar Mineralogical Spectrometer (LMS), as one of the three sets of scientific payload installed on the lander, is used to collect in-situ spectrum and analyze the mineralogical composition of the samplingsite. It can also help to select the sampling site, and to compare the measured laboratory spectrum of returned sample with in-situ data. LMS employs acousto-optic tunable filters (AOTFs) and is composed of a VIS/NIR module (0.48μm-1.45μm) and an IR module (1.4μm -3.2μm). It has spectral resolution ranging from 3 to 25 nm, with a field of view (FOV) of 4.24°×4.24°. Unlike Chang'e-3 VIS/NIR Imaging Spectrometer (VNIS), the spectral coverage of LMS is extended from 2.4μm to 3.2μm, which has capability to identify H2O/OH absorption features around 2.7μm. An aluminum plate and an Infragold plate are fixed in the dust cover, being used as calibration targets in the VIS/NIR and IR spectral range respectively when the dust cover is open. Before launch, a ground verification test of LMS needs to be conducted in order to: 1) test and verify the detection capability of LMS through evaluation on the quality of image and spectral data collected for the simulated lunar samples; and 2) evaluate the accuracy of data processing methods by the simulation of instrument working on the moon. The ground verification test will be conducted both in the lab and field. The spectra of simulated lunar regolith/mineral samples will be collected simultaneously by the LMS and two calibrated spectrometers: a FTIR spectrometer (Model 102F) and an ASD FieldSpec 4 Hi-Res spectrometer. In this study, the results of the LMS ground verification test will be reported, and OH/H2O Detection Capability will be evaluated especially.

  12. 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.

  13. 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.

  14. Implicit LES using adaptive filtering

    Science.gov (United States)

    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.

  15. Interference suppression using a SAW-based adaptive filter

    Science.gov (United States)

    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.

  16. Real time microcontroller implementation of an adaptive myoelectric filter.

    Science.gov (United States)

    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.

  17. 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.

  18. 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...

  19. 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.

  20. Gearbox Fault Features Extraction Using Vibration Measurements and Novel Adaptive Filtering Scheme

    Directory of Open Access Journals (Sweden)

    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.

  1. Adaptable Iterative and Recursive Kalman Filter Schemes

    Science.gov (United States)

    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.

  2. Wavelet Adaptive Algorithm and Its Application to MRE Noise Control System

    Directory of Open Access Journals (Sweden)

    Zhang Yulin

    2015-01-01

    Full Text Available To address the limitation of conventional adaptive algorithm used for active noise control (ANC system, this paper proposed and studied two adaptive algorithms based on Wavelet. The twos are applied to a noise control system including magnetorheological elastomers (MRE, which is a smart viscoelastic material characterized by a complex modulus dependent on vibration frequency and controllable by external magnetic fields. Simulation results reveal that the Decomposition LMS algorithm (D-LMS and Decomposition and Reconstruction LMS algorithm (DR-LMS based on Wavelet can significantly improve the noise reduction performance of MRE control system compared with traditional LMS algorithm.

  3. Asynchronous LMS adaptive equalization

    NARCIS (Netherlands)

    Bergmans, J.W.M.; Lin, M.Y.; Modrie, D.; Otte, R.

    2005-01-01

    Digital data receivers often operate at a fixed sampling rate 1/Ts that is asynchronous to the baud rate 1/T. A digital equalizer that processes the incoming signal will also operate in the asynchronous clock domain. Existing adaptation techniques for this equalizer involve an error sequence ek that

  4. 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.

  5. Evaluation of an Acoustic Charge Transport (ACT) device for adaptive interference suppression in spread spectrum communications systems

    Science.gov (United States)

    Mills, Michael S.

    1993-12-01

    Analytical results have shown that adaptive filtering can be a powerful tool for the rejection of narrowband interference in a direct sequence spread spectrum receiver. However, the complexity of adaptive filtering hardware has hindered the experimental validation of these results. This thesis describes a unique adaptive filter architecture for implementing the Widrow-Hoff least mean square (LMS) algorithm using two state of the art acoustic charge transport (ACT) programmable transversal filters (PTF's). Signal to noise ratio improvement measurements demonstrate the effectiveness of the adaptive filter for suppressing single- and dual-tone jammers at jammer to signal ratios (JSR's) of up to 30 dB. It is shown that the ACT adaptive interference rejection system can consistently produce 55 dB notch depths with 3-dB bandwidths as low as 300 kHz with minimal degradation to the spread spectrum signal. It is also shown that the adaptive system can eliminate single tone jammers at any frequency within the spread spectrum bandwidth at any of 10, 20, or 30 dB JSRs within 10 to 15 iterations of the adaptive algorithm. The only drawback with the adaptive system as tested is the amount of time taken to perform an iteration because of the requirement to update the PTF tap weights sequentially. Suggestions are given as to how this particular parameter of the adaptive interference system could be optimized.

  6. 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...

  7. A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

    Science.gov (United States)

    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

  8. Diagnostic analysis of vibration signals using adaptive digital filtering techniques

    Science.gov (United States)

    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.

  9. 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.

  10. Modeling of memristor-based chaotic systems using nonlinear Wiener adaptive filters based on backslash operator

    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.

  11. 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.

  12. A generalized adaptive mathematical morphological filter for LIDAR data

    Science.gov (United States)

    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

  13. 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)

  14. 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

  15. 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

  16. 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

  17. Adaptive projective filters

    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

  18. Application of adaptive Kalman filter in vehicle laser Doppler velocimetry

    Science.gov (United States)

    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.

  19. 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.

  20. Development and Interaction between LMS Services and Remote Labs

    Directory of Open Access Journals (Sweden)

    Manuel Castro

    2008-03-01

    Full Text Available Nowadays there is a great number of universities and organizations working in e-learning and i-learning solutions. One of the most well-known is the learning management system or LMS that allows displaying theoretical content in an organized and controlled way. In some jobs and studies it is necessary for the student to get a practical knowledge as well as a theoretical one. To obtain this practical knowledge, the universities and organizations are developing Virtual, Remote and Web labs. At these moments the LMS and Web labs are working independently. We are studying a new architecture allowing the integration of the LMS with different Web labs. This architecture must allow the students, teachers and administrators to use the services of LMS and virtual lab’s features as if they were working with the same software.

  1. LMS Use and Instructor Performance: The Role of Task-Technology Fit

    Science.gov (United States)

    McGill, Tanya; Klobas, Jane; Renzi, Stefano

    2011-01-01

    The introduction of learning management systems (LMS) has changed the way in which instructors work. This paper uses Goodhue and Thompson's (1995) technology-to-performance chain (TPC) to explore the roles of task-technology fit (TTF) and level of LMS use in the performance impacts of LMS for instructors. A mixed method approach was used: an…

  2. 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.

  3. Adaptive Filtering to Enhance Noise Immunity of Impedance and Admittance Spectroscopy: Comparison with Fourier Transformation

    Science.gov (United States)

    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.

  4. Target Response Adaptation for Correlation Filter Tracking

    KAUST Repository

    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.

  5. A Nonlinear Adaptive Filter for Gyro Thermal Bias Error Cancellation

    Science.gov (United States)

    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.

  6. 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.

  7. 47 CFR 90.355 - LMS operations below 512 MHz.

    Science.gov (United States)

    2010-10-01

    ... PRIVATE LAND MOBILE RADIO SERVICES Intelligent Transportation Systems Radio Service § 90.355 LMS... LMS station and the nearest co-channel base station of another licensee operating a voice system is 75... MHz, 150-170 MHz, and 450-512 MHz bands may use either base-mobile frequencies currently assigned the...

  8. Awareness, training and trust in interaction with adaptive spam filters

    NARCIS (Netherlands)

    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

  9. New approaches to improve a WCDMA SIR estimator by employing different post-processing stages

    Directory of Open Access Journals (Sweden)

    Amnart Chaichoet

    2008-09-01

    Full Text Available For effective control of transmission power in WCDMA mobile systems, a good estimate of signal-to-interference ratio (SIR is needed. Conventionally, an adaptive SIR estimator employs a moving average (MA filter (Yoon et al., 2002 to encounter fading channel distortion. However, the resulting estimate seems to have high estimation error due to fluctuation in the channel variation. In this paper, an additional post-processing stage is proposed to improve the estimation accuracy by reducing the variation of the estimate. Four variations of post-processing stages, namely 1 a moving average (MA postfilter,2 an exponential moving average (EMA post-filter, 3 an IIR post-filter and 4 least-mean-squared (LMS adaptive post-filter, are proposed and their optimal performance in terms of root-mean-square error (RMSE are then compared by simulation. The results show the best comparable performance when the MA and LMS post-filter are used. However, the MA post-filter requires a lookup table of filter order for optimal performance at different channel conditions, while the LMS post-filter can be used conveniently without a lookup table.

  10. Hardware implementation of adaptive filtering using charge-coupled devices. [For perimeter security sensors

    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.

  11. Contrasting LMS Marketing Approaches

    Science.gov (United States)

    Carriere, Brain; Challborn, Carl; Moore, James; Nibourg, Theodorus

    2005-01-01

    The first section of this report examines the CourseCompass learning management system (LMS), made available to educators by the Pearson publishing group as a vehicle for the company's extensive content library. The product's features are discussed, and the implications of Pearson's software/textbook "bundling" policy for the integrity of course…

  12. Radiotherapy-induced xerostomia, pre-clinical promise of LMS-611.

    Science.gov (United States)

    Paterson, Claire; Caldwell, B; Porteous, S; McLean, A; Messow, C M; Thomson, M

    2016-02-01

    Radiotherapy-induced xerostomia (RIX) is the most common permanent side effect of radiotherapy (RT) to the head and neck (H&N). There is no effective topical treatment. LMS-611 is a mimetic of a natural lamellar body which prevents thick secretions like saliva from congesting organs. The primary objective of this study was to assess saliva properties before and during RT to the H&N. The secondary objectives were to re-assess saliva properties with the addition of LMS-611, measure inter-patient variability, correlate patient-reported symptoms with laboratory measurements and design subsequent first-in-human clinical trial of LMS-611. Patients with H&N cancer receiving RT as primary treatment were recruited. Patients completed the Groningen RIX (GRIX) questionnaire and provided saliva samples at baseline and weeks 2, 4 and 6 of RT. Saliva adhesiveness and viscosity were tested by measuring time taken to travel 5 cm down an inclined plane. Thirty patients were enrolled. The inclined plane test (IPT) results (s) were as follows: baseline 31.3, week 2 49.7, week 4 51.1 and week 6 55.7. Wide inter-patient variability was seen at baseline. GRIX scores increased as RT progressed. Spearman rank correlation coefficient of inclined plane tests with GRIX scores was -0.06 at baseline, 0.25 at week 2, 0.12 at week 4 and 0.08 at week 6. LMS-611 concentrations of 10 and 20 mg/ml significantly reduced IPT times on saliva samples. Saliva becomes more visco-adhesive and RIX worsens as RT progresses. There is little correlation between objective and subjective measures of RIX. The addition of LMS-611 to thick, sticky saliva restores its fluidity ex vivo. This warrants in vivo analysis of the effect of LMS-611 upon RIX.

  13. Adaptive Filtering Using Recurrent Neural Networks

    Science.gov (United States)

    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.

  14. A New Adaptive Framework for Collaborative Filtering Prediction.

    Science.gov (United States)

    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.

  15. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.

    Science.gov (United States)

    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.

  16. 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.

  17. An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors.

    Science.gov (United States)

    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

  18. 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.

  19. 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

  20. 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)

  1. Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption

    KAUST Repository

    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.

  2. Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression

    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.

  3. 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.

  4. Model Adaptation for Prognostics in a Particle Filtering Framework

    Science.gov (United States)

    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.

  5. e-Learning Management System (eLMS) -

    Data.gov (United States)

    Department of Transportation — DOT's electronic Learning Management System (eLMS) is a state-of-the-art web-based system that meets the needs of training administrators, learners, and managers and...

  6. Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

    Science.gov (United States)

    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.

  7. Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft

    Institute of Scientific and Technical Information of China (English)

    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.

  8. Technological Evaluation Report 42. Contrasting LMS Marketing Approaches

    Directory of Open Access Journals (Sweden)

    Brian Carriere

    2005-03-01

    Full Text Available The first section of this report examines the CourseCompass learning management system (LMS, made available to educators by the Pearson publishing group as a vehicle for the company’s extensive content library. The product’s features are discussed, and the implications of Pearson’s software/ textbook “bundling” policy for the integrity of course design. The second section of the report reviews Wordcircle, an open source (OS LMS provided as a free hosted service or as source code downloadable for installation on the user’s own server. The contrasting marketing approaches of these two products are discussed in the light of the increasing move by distance educators from proprietary to open source LMS products. As in previous reports in the series, the reviews are based on the six evaluation criteria of the American Society for Training and Development (ASTD: cost, complexity, control, clarity, common technical framework, and features.

  9. Technical Evaluation Report 44: Breaking Down the LMS Walls

    Directory of Open Access Journals (Sweden)

    Michael Hotrum

    2005-03-01

    Full Text Available The traditional packaging of electronic learning — the learning management system (LMS — is progressively being regarded as a hindrance to effective online learning. Its design, functionality, complexity, price, and value are being questioned. A new generation of Web-based tools and approaches is evolving that are better suited to meet the need for dynamic online learning content, interaction, collaboration, and networking. Whereas traditional LMS approaches traditionally tend to relegate students to the role of passive recipients of information, these new tools and collaborative approaches allow learners to take proactive control of their own learning. This paper will discuss how the LMS has affected the design and delivery of e-learning, and the emerging technologies that are better suited to current learning and performance needs.

  10. Online Estimation of Time-Varying Volatility Using a Continuous-Discrete LMS Algorithm

    Directory of Open Access Journals (Sweden)

    Jacques Oksman

    2008-09-01

    Full Text Available The following paper addresses a problem of inference in financial engineering, namely, online time-varying volatility estimation. The proposed method is based on an adaptive predictor for the stock price, built from an implicit integration formula. An estimate for the current volatility value which minimizes the mean square prediction error is calculated recursively using an LMS algorithm. The method is then validated on several synthetic examples as well as on real data. Throughout the illustration, the proposed method is compared with both UKF and offline volatility estimation.

  11. LTSA Conformance Testing to Architectural Design of LMS Using Ontology

    Science.gov (United States)

    Sengupta, Souvik; Dasgupta, Ranjan

    2017-01-01

    This paper proposes a new methodology for checking conformance of the software architectural design of Learning Management System (LMS) to Learning Technology System Architecture (LTSA). In our approach, the architectural designing of LMS follows the formal modeling style of Acme. An ontology is built to represent the LTSA rules and the software…

  12. Adaptation of a Filter Assembly to Assess Microbial Bioburden of Pressurant Within a Propulsion System

    Science.gov (United States)

    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.

  13. Selected annotated bibliographies for adaptive filtering of digital image data

    Science.gov (United States)

    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

  14. Fort Hood Land Management System (LMS) Military Field Application Site FY99 In-progress Review

    National Research Council Canada - National Science Library

    Anderson, Alan

    1999-01-01

    .... LMS demonstration efforts provide opportunities to test, evaluate, modify, and document how LMS capabilities help to address specific user problems and how LMS capabilities fit into decision processes at user sites...

  15. A multichannel nonlinear adaptive noise canceller based on generalized FLANN for fetal ECG extraction

    International Nuclear Information System (INIS)

    Ma, Yaping; Wei, Guo; Sun, Jinwei; Xiao, Yegui

    2016-01-01

    In this paper, a multichannel nonlinear adaptive noise canceller (ANC) based on the generalized functional link artificial neural network (FLANN, GFLANN) is proposed for fetal electrocardiogram (FECG) extraction. A FIR filter and a GFLANN are equipped in parallel in each reference channel to respectively approximate the linearity and nonlinearity between the maternal ECG (MECG) and the composite abdominal ECG (AECG). A fast scheme is also introduced to reduce the computational cost of the FLANN and the GFLANN. Two (2) sets of ECG time sequences, one synthetic and one real, are utilized to demonstrate the improved effectiveness of the proposed nonlinear ANC. The real dataset is derived from the Physionet non-invasive FECG database (PNIFECGDB) including 55 multichannel recordings taken from a pregnant woman. It contains two subdatasets that consist of 14 and 8 recordings, respectively, with each recording being 90 s long. Simulation results based on these two datasets reveal, on the whole, that the proposed ANC does enjoy higher capability to deal with nonlinearity between MECG and AECG as compared with previous ANCs in terms of fetal QRS (FQRS)-related statistics and morphology of the extracted FECG waveforms. In particular, for the second real subdataset, the F1-measure results produced by the PCA-based template subtraction (TS pca ) technique and six (6) single-reference channel ANCs using LMS- and RLS-based FIR filters, Volterra filter, FLANN, GFLANN, and adaptive echo state neural network (ESN a ) are 92.47%, 93.70%, 94.07%, 94.22%, 94.90%, 94.90%, and 95.46%, respectively. The same F1-measure statistical results from five (5) multi-reference channel ANCs (LMS- and RLS-based FIR filters, Volterra filter, FLANN, and GFLANN) for the second real subdataset turn out to be 94.08%, 94.29%, 94.68%, 94.91%, and 94.96%, respectively. These results indicate that the ESN a and GFLANN perform best, with the ESN a being slightly better than the GFLANN but about four times

  16. Corporate knowledge repository: Adopting academic LMS into corporate environment

    Science.gov (United States)

    Bakar, Muhamad Shahbani Abu; Jalil, Dzulkafli

    2017-10-01

    The growth of Knowledge Economy has transformed human capital to be the vital asset in business organization of the 21st century. Arguably, due to its white-collar nature, knowledge-based industry is more favorable than traditional manufacturing business. However, over dependency on human capital can also be a major challenge as any workers will inevitably leave the company or retire. This situation will possibly create knowledge gap that may impact business continuity of the enterprise. Knowledge retention in the corporate environment has been of many research interests. Learning Management System (LMS) refers to the system that provides the delivery, assessment and management tools for an organization to handle its knowledge repository. By using the aspirations of a proven LMS implemented in an academic environment, this paper proposes LMS model that can be used to enable peer-to-peer knowledge capture and sharing in the knowledge-based organization. Cloud Enterprise Resource Planning (ERP), referred to an ERP solution in the internet cloud environment was chosen as the domain knowledge. The complexity of the Cloud ERP business and its knowledge make it very vulnerable to the knowledge retention problem. This paper discusses how the company's essential knowledge can be retained using the LMS system derived from academic environment into the corporate model.

  17. RLS adaptive filtering for physiological interference reduction in NIRS brain activity measurement: a Monte Carlo study

    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)

  18. An adaptive deep-coupled GNSS/INS navigation system with hybrid pre-filter processing

    Science.gov (United States)

    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.

  19. An adaptive three-stage extended Kalman filter for nonlinear discrete-time system in presence of unknown inputs.

    Science.gov (United States)

    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.

  20. A comparative evaluation of adaptive noise cancellation algorithms for minimizing motion artifacts in a forehead-mounted wearable pulse oximeter.

    Science.gov (United States)

    Comtois, Gary; Mendelson, Yitzhak; Ramuka, Piyush

    2007-01-01

    Wearable physiological monitoring using a pulse oximeter would enable field medics to monitor multiple injuries simultaneously, thereby prioritizing medical intervention when resources are limited. However, a primary factor limiting the accuracy of pulse oximetry is poor signal-to-noise ratio since photoplethysmographic (PPG) signals, from which arterial oxygen saturation (SpO2) and heart rate (HR) measurements are derived, are compromised by movement artifacts. This study was undertaken to quantify SpO2 and HR errors induced by certain motion artifacts utilizing accelerometry-based adaptive noise cancellation (ANC). Since the fingers are generally more vulnerable to motion artifacts, measurements were performed using a custom forehead-mounted wearable pulse oximeter developed for real-time remote physiological monitoring and triage applications. This study revealed that processing motion-corrupted PPG signals by least mean squares (LMS) and recursive least squares (RLS) algorithms can be effective to reduce SpO2 and HR errors during jogging, but the degree of improvement depends on filter order. Although both algorithms produced similar improvements, implementing the adaptive LMS algorithm is advantageous since it requires significantly less operations.

  1. Simultaneous Learning and Filtering without Delusions: A Bayes-Optimal Derivation of Combining Predictive Inference and AdaptiveFiltering

    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.

  2. Fault estimation of satellite reaction wheels using covariance based adaptive unscented Kalman filter

    Science.gov (United States)

    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.

  3. Mixed Skyhook and FxLMS Control of a Half-Car Model with Magnetorheological Dampers

    Directory of Open Access Journals (Sweden)

    Piotr Krauze

    2016-01-01

    Full Text Available The problem of vibration attenuation in a semiactive vehicle suspension is considered. The proposed solution is based on usage of the information about the road roughness coming from the sensor installed on the front axle of the vehicle. It does not need any preview sensor to measure the road roughness as other preview control strategies do. Here, the well-known Skyhook algorithm is used for control of the front magnetorheological (MR damper. This algorithm is tuned to a quarter-car model of the front part of the vehicle. The rear MR damper is controlled by the FxLMS (Filtered-x LMS taking advantage of the information about the motion of the front vehicle axle. The goal of this algorithm is to minimize pitch of the vehicle body. The strategy is applied for a four-degree-of-freedom (4-DOF vehicle model equipped with magnetorheological dampers which were described using the Bouc-Wen model. The suspension model was subjected to the road-induced excitation in the form of a series of bumps within the frequency range 1.0–10 Hz. Different solutions are compared based on the transmissibility function and simulation results show the usefulness of the proposed solution.

  4. Adaptivna digitalna sita v strukturi porazdeljene aritmetike: Adaptive digital filter implementation with distributed arithmetic structure:

    OpenAIRE

    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...

  5. A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing.

    Science.gov (United States)

    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

  6. 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.

  7. Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

    Science.gov (United States)

    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.

  8. Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles

    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.

  9. A multi-stage noise adaptive switching filter for extremely corrupted images

    Science.gov (United States)

    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.

  10. Instantaneous Attitude Determination Based on Original Multi-antenna Observations Using Adaptively Robust Kalman Filtering

    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.

  11. 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...

  12. 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...

  13. A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter

    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.

  14. Development of Shunt-Type Three-Phase Active Power Filter with Novel Adaptive Control for Wind Generators

    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.

  15. Development of Shunt-Type Three-Phase Active Power Filter with Novel Adaptive Control for Wind Generators.

    Science.gov (United States)

    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.

  16. Attitude determination and calibration using a recursive maximum likelihood-based adaptive Kalman filter

    Science.gov (United States)

    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.

  17. Development of an adaptive bilateral filter for evaluating color image difference

    Science.gov (United States)

    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.

  18. 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.

  19. An Adaptive Filter for the Removal of Drifting Sinusoidal Noise Without a Reference.

    Science.gov (United States)

    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.

  20. Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra

    KAUST Repository

    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.

  1. Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra

    KAUST Repository

    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.

  2. Method and system for training dynamic nonlinear adaptive filters which have embedded memory

    Science.gov (United States)

    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.

  3. 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.)

  4. 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)

  5. Research and Application on Fractional-Order Darwinian PSO Based Adaptive Extended Kalman Filtering Algorithm

    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.

  6. 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.

  7. Computationally efficient video restoration for Nyquist sampled imaging sensors combining an affine-motion-based temporal Kalman filter and adaptive Wiener filter.

    Science.gov (United States)

    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.

  8. 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

  9. Low complexity adaptive equalizers for underwater acoustic communications

    Science.gov (United States)

    Soflaei, Masoumeh; Azmi, Paeiz

    2014-08-01

    Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm (APA), selective regressor APA(SR-APA), family of selective partial update (SPU) algorithms, family of set-membership (SM) algorithms and selective partial update selective regressor APA (SPU-SR-APA) are compared with conventional algorithms such as the least mean square (LMS) in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA, SPU-APA, SPU-normalized least mean square (SPU-NLMS), SPU-SR-APA, SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.

  10. Analysis on Influence Factors of Adaptive Filter Acting on ANC

    Science.gov (United States)

    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.

  11. 47 CFR 90.353 - LMS operations in the 902-928 MHz band.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 5 2010-10-01 2010-10-01 false LMS operations in the 902-928 MHz band. 90.353... operations in the 902-928 MHz band. LMS systems may be authorized within the 902-928 MHz band, subject to the..., scientific, and medical (ISM) devices and radiolocation Government stations that operate in the 902-928 MHz...

  12. Data-Aided Frequency-Domain 2×2 MIMO Equalizer for 112 Gbit/s PDM-QPSK Coherent Transmission Systems

    DEFF Research Database (Denmark)

    Pittalà, Fabio; Hauske, Fabian N.; Ye, Yabin

    2012-01-01

    Benefits of a low-complexity adaptive 32-tap 2×2 MIMO frequency-domain filter update by data-aided channel estimation over a time-domain filter with DD-LMS are shown. Superior stability and convergence speed is demonstrated with identical impairment tolerance....

  13. Adaptive oriented PDEs filtering methods based on new controlling speed function for discontinuous optical fringe patterns

    Science.gov (United States)

    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.

  14. Design of adaptive filter amplifier in UV communication based on DSP

    Science.gov (United States)

    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.

  15. 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.

  16. Performance behavior of prediction filters for respiratory motion compensation in radiotherapy

    Directory of Open Access Journals (Sweden)

    Jöhl Alexander

    2017-09-01

    Full Text Available Introduction: In radiotherapy, tumors may move due to the patient’s respiration, which decreases treatment accuracy. Some motion mitigation methods require measuring the tumor position during treatment. Current available sensors often suffer from time delays, which degrade the motion mitigation performance. However, the tumor motion is often periodic and continuous, which allows predicting the motion ahead. Method and Materials: A couch tracking system was simulated in MATLAB and five prediction filters selected from literature were implemented and tested on 51 respiration signals (median length: 103 s. The five filters were the linear filter (LF, the local regression (LOESS, the neural network (NN, the support vector regression (SVR, and the wavelet least mean squares (wLMS. The time delay to compensate was 320 ms. The normalized root mean square error (nRMSE was calculated for all prediction filters and respiration signals. The correlation coefficients between the nRMSE of the prediction filters were computed. Results: The prediction filters were grouped into a low and a high nRMSE group. The low nRMSE group consisted of the LF, the NN, and the wLMS with a median nRMSE of 0.14, 0.15, and 0.14, respectively. The high nRMSE group consisted of the LOESS and the SVR with both a median nRMSE of 0.34. The correlations between the low nRMSE filters were above 0.87 and between the high nRMSE filters it was 0.64. Conclusion: The low nRMSE prediction filters not only have similar median nRMSEs but also similar nRMSEs for the same respiration signals as the high correlation shows. Therefore, good prediction filters perform similarly for identical respiration patterns, which might indicate a minimally achievable nRMSE for a given respiration pattern.

  17. EFL Instructors' Perceptions of Blackboard Learning Management System (LMS) at University Level

    Science.gov (United States)

    Tawalbeh, Thaer Issa

    2018-01-01

    The present paper aims to investigate EFL instructors' perceptions of Blackboard learning management system (LMS) at Taif University in Saudi Arabia. To achieve this purposes, the researcher attempted to answer two questions. The first question investigates EFL instructors' perceptions of Blackboard LMS. The second question aims to identify…

  18. Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT

    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

  19. Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT

    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

  20. The role of adaptive immunity as an ecological filter on the gut microbiota in zebrafish.

    Science.gov (United States)

    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.

  1. 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.

  2. Handover aspects for a Low Earth Orbit (LEO) CDMA Land Mobile Satellite (LMS) system

    Science.gov (United States)

    Carter, P.; Beach, M. A.

    1993-01-01

    This paper addresses the problem of handoff in a land mobile satellite (LMS) system between adjacent satellites in a low earth orbit (LEO) constellation. In particular, emphasis is placed on the application of soft handoff in a direct sequence code division multiple access (DS-CDMA) LMS system. Soft handoff is explained in terms of terrestrial macroscopic diversity, in which signals transmitted via several independent fading paths are combined to enhance the link quality. This concept is then reconsidered in the context of a LEO LMS system. A two-state Markov channel model is used to simulate the effects of shadowing on the communications path from the mobile to each satellite during handoff. The results of the channel simulation form a platform for discussion regarding soft handoff, highlighting the potential merits of the scheme when applied in a LEO LMS environment.

  3. Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm

    Science.gov (United States)

    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.

  4. Detection of circuit-board components with an adaptive multiclass correlation filter

    Science.gov (United States)

    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.

  5. Calculation of Gilbert damping in ferromagnetic films

    Directory of Open Access Journals (Sweden)

    Edwards D. M.

    2013-01-01

    Full Text Available The Gilbert damping constant in the phenomenological Landau-Lifshitz-Gilbert equation which describes the dynamics of magnetization, is calculated for Fe, Co and Ni bulk ferromagnets, Co films and Co/Pd bilayers within a nine-band tight-binding model with spin-orbit coupling included. The calculational effciency is remarkably improved by introducing finite temperature into the electronic occupation factors and subsequent summation over the Matsubara frequencies. The calculated dependence of Gilbert damping constant on scattering rate for bulk Fe, Co and Ni is in good agreement with the results of previous ab initio calculations. Calculations are reported for ferromagnetic Co metallic films and Co/Pd bilayers. The dependence of the Gilbert damping constant on Co film thickness, for various scattering rates, is studied and compared with recent experiments.

  6. Adaptive filters and internal models: multilevel description of cerebellar function.

    Science.gov (United States)

    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.

  7. 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)

  8. Processing-Efficient Distributed Adaptive RLS Filtering for Computationally Constrained Platforms

    Directory of Open Access Journals (Sweden)

    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.

  9. Adaptive probabilistic collocation based Kalman filter for unsaturated flow problem

    Science.gov (United States)

    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.

  10. 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

  11. Adaptive Control Using Residual Mode Filters Applied to Wind Turbines

    Science.gov (United States)

    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.

  12. On-line adaptive line frequency noise cancellation from a nuclear power measuring channel

    Directory of Open Access Journals (Sweden)

    Qadir Javed

    2011-01-01

    Full Text Available On-line software for adaptively canceling 50 Hz line frequency noise has been designed and tested at Pakistan Research Reactor 1. Line frequency noise causes much problem in weak signals acquisition. Sometimes this noise is so dominant that original signal is totally corrupted. Although notch filter can be used for eliminating this noise, but if signal of interest is in close vicinity of 50 Hz, then original signal is also attenuated and hence overall performance is degraded. Adaptive noise removal is a technique which could be employed for removing line frequency without degrading the desired signal. In this paper line frequency noise has been eliminated on-line from a nuclear power measuring channel. The adaptive LMS algorithm has been used to cancel 50 Hz noise. The algorithm has been implemented in labVIEW with NI 6024 data acquisition card. The quality of the acquired signal has been improved much as can be seen in experimental results.

  13. PENERAPAN METODE LEAST MEDIAN SQUARE-MINIMUM COVARIANCE DETERMINANT (LMS-MCD DALAM REGRESI KOMPONEN UTAMA

    Directory of Open Access Journals (Sweden)

    I PUTU EKA IRAWAN

    2013-11-01

    Full Text Available Principal Component Regression is a method to overcome multicollinearity techniques by combining principal component analysis with regression analysis. The calculation of classical principal component analysis is based on the regular covariance matrix. The covariance matrix is optimal if the data originated from a multivariate normal distribution, but is very sensitive to the presence of outliers. Alternatives are used to overcome this problem the method of Least Median Square-Minimum Covariance Determinant (LMS-MCD. The purpose of this research is to conduct a comparison between Principal Component Regression (RKU and Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD in dealing with outliers. In this study, Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD has a bias and mean square error (MSE is smaller than the parameter RKU. Based on the difference of parameter estimators, still have a test that has a difference of parameter estimators method LMS-MCD greater than RKU method.

  14. An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation

    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.

  15. Superconducting Magnetometry for Cardiovascular Studies and AN Application of Adaptive Filtering.

    Science.gov (United States)

    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.

  16. 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.

  17. Improved pulsed photoacoustic detection by means of an adapted filter

    Science.gov (United States)

    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.

  18. Evaluating Learning Management System (LMS)-facilitated Delivery of Universal Design for Learning (UDL)

    Science.gov (United States)

    Bryans Bongey, Sarah

    This quantitative study involved 157 students in two sections of an undergraduate class in general biology, as well as one instructor who taught both sections of the course. It used resources from the Center for Applied Special Technologies (CAST) to evaluate the viability of a Learning Management System (LMS) to provide Universal Design for Learning (UDL). It also measured and tracked the instructor's level of efficacy in sustaining UDL approaches throughout the semester. In an effort to identify the UDL's specific outcomes or benefits to students, this study used a pre- and post- test to identify the treatment's impact on student engagement. Findings indicated that the LMS could be designed to comply with UDL guidelines, and the instructor was able to establish a high level of efficacy in maintaining that UDL design. However, based on the statistical analysis of pre- and post-test responses from control vs. treatment groups of students, the treatment was seen to have no significant effect in the area of student engagement. Overall, the study added to the literature by suggesting (a) the viability of the LMS as a means of providing UDL approaches, (b) the promise of the LMS as a tool faculty can use to deliver UDL with a high level of efficacy, and (c) the design's lack of effect in the area of student engagement. The fact that this study was limited to a single brand of LMS (Blackboard), a single instructor, and a single group of students underscores the need for further research.

  19. Real-time Kalman filter implementation for active feedforward control of time-varying broadband noise and vibrations

    NARCIS (Netherlands)

    Ophem, S. van; Berkhoff, A.P.

    2012-01-01

    Tracking behavior and the rate of convergence are critical properties in active noise control applications with time-varying disturbance spectra. As compared to the standard filtered-reference Least Mean Square (LMS) algorithm, improved convergence can be obtained with schemes based on

  20. FxLMS Method for Suppressing In-Wheel Switched Reluctance Motor Vertical Force Based on Vehicle Active Suspension System

    Directory of Open Access Journals (Sweden)

    Yan-yang Wang

    2014-01-01

    Full Text Available The vibration of SRM obtains less attention for in-wheel motor applications according to the present research works. In this paper, the vertical component of SRM unbalanced radial force, which is named as SRM vertical force, is taken into account in suspension performance for in-wheel motor driven electric vehicles (IWM-EV. The analysis results suggest that SRM vertical force has a great effect on suspension performance. The direct cause for this phenomenon is that SRM vertical force is directly exerted on the wheel, which will result in great variation in tyre dynamic load and the tyre will easily jump off the ground. Furthermore, the frequency of SRM vertical force is broad which covers the suspension resonance frequencies. So it is easy to arouse suspension resonance and greatly damage suspension performance. Aiming at the new problem, FxLMS (filtered-X least mean square controller is proposed to improve suspension performance. The FxLMS controller is based on active suspension system which can generate the controllable force to suppress the vibration caused by SRM vertical force. The conclusion shows that it is effective to take advantage of active suspensions to reduce the effect of SRM vertical force on suspension performance.

  1. Pengembangan LMS (Learning Management System Berbasis Web untuk Mengukur Pemahaman Konsep dan Karakter Siswa

    Directory of Open Access Journals (Sweden)

    Agung Tri Wibowo

    2015-10-01

    Full Text Available Perkembangan teknologi informasi telah berkembang pesat dalam bidang pendidikan dengan lahirnya e-learning. E-learning dapat membantu guru dalam memantau keaktifan siswa dengan penugasan, forum diskusi maupun aktivitas lain, sehingga karakter dapat dideskripsikan melalui e-learning. Tujuan dari penelitian ini adalah mengembangkan Software Learning Management System (LMS. LMS adalah aplikasi perangkat lunak untuk kegiatan online, program pembelajaran elektronik (e-learning program dan isi pelatihan. Selain itu, penelitian ini juga menyelidiki respon dari siswa terhadap LMS dan menguji keefektifannya dalam meningkatkan pemahaman konsep serta mengembangkan karakter siswa. Metode yang digunakan dalam penelitian ini adalah metode penelitian pengembangan. Uji produk menggunakan Pre Experimental Design dengan jenis Pretest and Posttest One Group Design. Instrumen penelitian berupa angket uji ahli, angket tanggapan, tes tertulis dan lembar observasi karakter. Teknik analisis data uji kefektifan menggunakan uji gain. Hasil tanggapan siswa untuk keseluruhan aspek mendapatkan prosentase diatas 82,5% kategori sangat baik. Hasil uji gain pemahaman konsep sebesar 0,56 dengan kategori sedang, artinya LMS efektif meningkatkan pemahaman konsep siswa. Sedangkan uji gain karakter 0,16 kategori rendah, artinya belum efektif mengembangkan karakter siswa.

  2. Adaptive filtering for hidden node detection and tracking in networks.

    Science.gov (United States)

    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.

  3. Power Line Interference Removal from Electrocardiogram Using a Simplified Lattice Based Adaptive IIR Notch Filter

    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...

  4. Robust adaptive extended Kalman filtering for real time MR-thermometry guided HIFU interventions.

    Science.gov (United States)

    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.

  5. Jüüzli du Muotatal. Quatre films de Hugo Zemp*

    OpenAIRE

    Baily, John

    2012-01-01

    Ces quatre films sur la youtse et le yodel du Muotatal en Suisse centrale représentent une sorte de tour de force, car ils mettent en évidence quatre approches de la « mise en film » de la musique avec les paramètres d’une seule philosophie stylistique. Bien que nous attendions encore le guide (promis) qui doit accompagner les films, Zemp (1988) nous a déjà donné quelques informations de base sur le tournage. Dans l’article en question, il éclaire de nombreux aspects de son style cinématographiq...

  6. Architectural Design of a LMS with LTSA-Conformance

    Science.gov (United States)

    Sengupta, Souvik; Dasgupta, Ranjan

    2017-01-01

    This paper illustrates an approach for architectural design of a Learning Management System (LMS), which is verifiable against the Learning Technology System Architecture (LTSA) conformance rules. We introduce a new method for software architectural design that extends the Unified Modeling Language (UML) component diagram with the formal…

  7. Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters

    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

  8. 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.

  9. Towards a more pedagogical use of a new LMS

    DEFF Research Database (Denmark)

    Hansen, Janne Saltoft

    2015-01-01

    Background In September 2013 the implementation of the new learning management system (LMS) started at Aarhus University. Contained in the central project each faculty was responsible for their own implementation; projects running for approximately two years. Summary of work The project work showed...... of super users to assure relevant, local support for the departments together with continuous communication between the departments and the project. 2) How all academic and administrative staff is offered an education in central principles and functionality of the system to assure a base for pedagogical...... use (cf. Salmon’s Five Stage Model). 3) How the pilot project are used as pedagogical inspiration. We conclude that all principles above are important in the use of the LMS because these steps make the users feel safe and motivated to explore the pedagogical possibilities Take-home message The users...

  10. Realistic Measurement of Student Attendance in LMS Using Biometrics

    Directory of Open Access Journals (Sweden)

    Elisardo Gonzalez-Agulla

    2010-10-01

    Full Text Available In this paper we propose a solution to obtain useful and reliable student session logs in a Learning Management System (LMS combining current logs with biometrics-based logs that show the student behaviour during the whole learning session. The aims of our solution are to guarantee that the online student is who he/she claims to be, and also to know exactly how much time he/she spends in front of the computer reading each LMS content. Even when the proposed solution does not completely avoid cheating, the use of biometric data during authentication and face tracking provides additional help to validate student performance during learning sessions. In this way it is possible to improve security for specific contents, to gain feedback of the student effort and to check the actual time spent in learning.

  11. Super-resolution pupil filtering for visual performance enhancement using adaptive optics

    Science.gov (United States)

    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.

  12. 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

  13. Reduced Rank Adaptive Filtering in Impulsive Noise Environments

    KAUST Repository

    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.

  14. Reduced Rank Adaptive Filtering in Impulsive Noise Environments

    KAUST Repository

    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.

  15. Learning Motivation and Adaptive Video Caption Filtering for EFL Learners Using Handheld Devices

    Science.gov (United States)

    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…

  16. Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study

    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.

  17. Combined adaptive multiple subtraction based on optimized event tracing and extended wiener filtering

    Science.gov (United States)

    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.

  18. Reduced rank adaptive filtering in impulsive noise environments

    KAUST Repository

    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.

  19. A pilot study on slit lamp-adapted optical coherence tomography imaging of trabeculectomy filtering blebs.

    NARCIS (Netherlands)

    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

  20. 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

  1. Design and implementation of efficient low complexity biomedical artifact canceller for nano devices

    Directory of Open Access Journals (Sweden)

    Md Zia Ur RAHMAN

    2016-07-01

    Full Text Available In the current day scenario, with the rapid development of communication technology remote health care monitoring becomes as an intense research area. In remote health care monitoring, the primary aim is to facilitate the doctor with high resolution biomedical data. In order to cancel various artifacts in clinical environment in this paper we propose some efficient adaptive noise cancellation techniques. To obtain low computational complexity we combine clipping the data or error with Least Mean Square (LMS algorithm. This results sign regressor LMS (SRLMS, sign LMS (SLMS and sign LMS (SSLMS algorithms. Using these algorithms, we design Very-large-scale integration (VLSI architectures of various Biomedical Noise Cancellers (BNCs. In addition, the filtering capabilities of the proposed implementations are measured using real biomedical signals. Among the various BNCs tested, SRLMS based BNC is found to be better with reference to convergence speed, filtering capability and computational complexity. The main advantage of this technique is it needs only one multiplication to compute next weight. In this manner SRLMS based BNC is independent of filter length with reference to its computations. Whereas, the average signal to noise ratio achieved in the noise cancellation experiments are recorded as 7.1059dBs, 7.1776dBs, 6.2795dBs and 5.8847dBs for various BNCs based on LMS, SRLMS, SLMS and SSSLMS algorithms respectively. Based on the filtering characteristics, convergence and computational complexity, the proposed SRLMS based BNC architecture is well suited for nanotechnology applications.

  2. Adaptive iterated function systems filter for images highly corrupted with fixed - Value impulse noise

    Science.gov (United States)

    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.

  3. Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold

    Directory of Open Access Journals (Sweden)

    Hamed Komari Alaie

    2018-01-01

    Full Text Available This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf and noise less vessels. Generally, in passive sonar, the targets are detected by sonar equation (with constant threshold that increases the detection error in shallow water. The purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound is processed in time and frequency domain. For classifying, Bayesian classification is used and posterior distribution is estimated by Maximum Likelihood Estimation algorithm. Finally, target was detected by combining the detection points in both domains using Least Mean Square (LMS adaptive filter. Results of this paper has showed that the proposed method has improved true detection rate by about 24% when compared other the best detection method.

  4. Ship detection for high resolution optical imagery with adaptive target filter

    Science.gov (United States)

    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.

  5. An adaptive unscented Kalman filter-based adaptive tracking control for wheeled mobile robots with control constrains in the presence of wheel slipping

    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.

  6. An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy

    Science.gov (United States)

    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.

  7. Asynchronous zero-forcing adaptive equalization

    NARCIS (Netherlands)

    Bergmans, J.W.M.; Pozidis, H.; Lin, M.Y.

    2005-01-01

    Digital data receivers often operate at a fixed sampling rate 1/Ts that is asynchronous to the baud rate 1/T. A digital equalizer that processes the incoming signal will also be asynchronous, and its adaptation is commonly based on extensions of the LMS algorithm. In this paper, we develop and

  8. Microseismic Event Location Improvement Using Adaptive Filtering for Noise Attenuation

    Science.gov (United States)

    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.

  9. Determining the Effects of LMS Learning Behaviors on Academic Achievement in a Learning Analytic Perspective

    OpenAIRE

    Mehmet FIRAT

    2016-01-01

    Two of the most important outcomes of learning analytics are predicting students’ learning and providing effective feedback. Learning Management Systems (LMS), which are widely used to support online and face-to-face learning, provide extensive research opportunities with detailed records of background data regarding users’ behaviors. The purpose of this study was to investigate the effects of undergraduate students’ LMS learning behaviors on their academic achievements. In line with this pur...

  10. 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.

  11. Acoustic emission leak monitoring system LMS-96

    International Nuclear Information System (INIS)

    Liska, J.; Cvrcek, M.; Mueller, L.

    1997-01-01

    On-line acoustic emission leak monitoring under industrial conditions of nuclear power plants is a problem with specific features setting specific demands on the leak monitoring system. The paper briefly reviews those problems (attenuation pattern of a real structure, acoustic background, alarm system, etc.) and the solution of some of them is discussed. Information is presented on the Acoustic Emission Leak Monitoring System LMS-96 by SKODA NUCLEAR MACHINERY and the system's function is briefly described. (author)

  12. AN ADAPTIVE OPTIMAL KALMAN FILTER FOR STOCHASTIC VIBRATION CONTROL SYSTEM WITH UNKNOWN NOISE VARIANCES

    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.

  13. Theory of affine projection algorithms for adaptive filtering

    CERN Document Server

    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...

  14. A Frequency-Domain Adaptive Filter (FDAF) Prediction Error Method (PEM) Framework for Double-Talk-Robust Acoustic Echo Cancellation

    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....

  15. Adaptive Electronic Dispersion Compensator for Chromatic and Polarization-Mode Dispersions in Optical Communication Systems

    Directory of Open Access Journals (Sweden)

    Koc Ut-Va

    2005-01-01

    Full Text Available The widely-used LMS algorithm for coefficient updates in adaptive (feedforward/decision-feedback equalizers is found to be suboptimal for ASE-dominant systems but various coefficient-dithering approaches suffer from slow adaptation rate without guarantee of convergence. In view of the non-Gaussian nature of optical noise after the square-law optoelectronic conversion, we propose to apply the higher-order least-mean 2 th-order (LMN algorithms resulting in OSNR penalty which is 1.5–2 dB less than that of LMS. Furthermore, combined with adjustable slicer threshold control, the proposed equalizer structures are demonstrated through extensive Monte Carlo simulations to achieve better performance.

  16. Development of an improved MATLAB GUI for the prediction of coefficients of restitution, and integration into LMS.

    Energy Technology Data Exchange (ETDEWEB)

    Baca, Renee Nicole [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Congdon, Michael L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brake, Matthew Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2014-07-01

    In 2012, a Matlab GUI for the prediction of the coefficient of restitution was developed in order to enable the formulation of more accurate Finite Element Analysis (FEA) models of components. This report details the development of a new Rebound Dynamics GUI, and how it differs from the previously developed program. The new GUI includes several new features, such as source and citation documentation for the material database, as well as a multiple materials impact modeler for use with LMS Virtual.Lab Motion (LMS VLM), and a rigid body dynamics modeling software. The Rebound Dynamics GUI has been designed to work with LMS VLM to enable straightforward incorporation of velocity-dependent coefficients of restitution in rigid body dynamics simulations.

  17. High performance 3D adaptive filtering for DSP based portable medical imaging systems

    Science.gov (United States)

    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.

  18. 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.

  19. Implementation of high-speed–low-power adaptive finite impulse response filter with novel architecture

    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.

  20. State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

    Directory of Open Access Journals (Sweden)

    Bizhong Xia

    2015-06-01

    Full Text Available Accurate state of charge (SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST and New European Driving Cycle (NEDC are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF and cubature Kalman filter (CKF algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms.

  1. Design of application specific long period waveguide grating filters using adaptive particle swarm optimization algorithms

    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)

  2. Determining the Effects of LMS Learning Behaviors on Academic Achievement in a Learning Analytic Perspective

    Directory of Open Access Journals (Sweden)

    Mehmet FIRAT

    2016-02-01

    Full Text Available Two of the most important outcomes of learning analytics are predicting students’ learning and providing effective feedback. Learning Management Systems (LMS, which are widely used to support online and face-to-face learning, provide extensive research opportunities with detailed records of background data regarding users’ behaviors. The purpose of this study was to investigate the effects of undergraduate students’ LMS learning behaviors on their academic achievements. In line with this purpose, the participating students’ online learning behaviors in LMS were examined by using learning analytics for 14 weeks, and the relationship between students’ behaviors and their academic achievements was analyzed, followed by an analysis of their views about the influence of LMS on their academic achievement. The present study, in which quantitative and qualitative data were collected, was carried out with the explanatory mixed method. A total of 71 undergraduate students participated in the study. The results revealed that the students used LMSs as a support to face-to-face education more intensively on course days (at the beginning of the related lessons and at nights on course days and that they activated the content elements the most. Lastly, almost all the students agreed that LMSs helped increase their academic achievement only when LMSs included such features as effectiveness, interaction, reinforcement, attractive design, social media support, and accessibility.

  3. Efficient block processing of long duration biotelemetric brain data for health care monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Soumya, I. [Department of E.I.E, GITAM University, Visakhapatnam (India); Zia Ur Rahman, M., E-mail: mdzr-5@ieee.org [Department of E.C.E, K.L. University, Vaddeswaram, Green Fields, Guntur, Andhra Pradesh (India); Rama Koti Reddy, D. V. [Department of Instrumentation Engineering, College of Engineering, Andhra University, Visakhapatnam (India); Lay-Ekuakille, A. [Department of Innovation Engineering, University of Salento, Lecce (Italy)

    2015-03-15

    In real time clinical environment, the brain signals which doctor need to analyze are usually very long. Such a scenario can be made simple by partitioning the input signal into several blocks and applying signal conditioning. This paper presents various block based adaptive filter structures for obtaining high resolution electroencephalogram (EEG) signals, which estimate the deterministic components of the EEG signal by removing noise. To process these long duration signals, we propose Time domain Block Least Mean Square (TDBLMS) algorithm for brain signal enhancement. In order to improve filtering capability, we introduce normalization in the weight update recursion of TDBLMS, which results TD-B-normalized-least mean square (LMS). To increase accuracy and resolution in the proposed noise cancelers, we implement the time domain cancelers in frequency domain which results frequency domain TDBLMS and FD-B-Normalized-LMS. Finally, we have applied these algorithms on real EEG signals obtained from human using Emotive Epoc EEG recorder and compared their performance with the conventional LMS algorithm. The results show that the performance of the block based algorithms is superior to the LMS counter-parts in terms of signal to noise ratio, convergence rate, excess mean square error, misadjustment, and coherence.

  4. Efficient block processing of long duration biotelemetric brain data for health care monitoring

    International Nuclear Information System (INIS)

    Soumya, I.; Zia Ur Rahman, M.; Rama Koti Reddy, D. V.; Lay-Ekuakille, A.

    2015-01-01

    In real time clinical environment, the brain signals which doctor need to analyze are usually very long. Such a scenario can be made simple by partitioning the input signal into several blocks and applying signal conditioning. This paper presents various block based adaptive filter structures for obtaining high resolution electroencephalogram (EEG) signals, which estimate the deterministic components of the EEG signal by removing noise. To process these long duration signals, we propose Time domain Block Least Mean Square (TDBLMS) algorithm for brain signal enhancement. In order to improve filtering capability, we introduce normalization in the weight update recursion of TDBLMS, which results TD-B-normalized-least mean square (LMS). To increase accuracy and resolution in the proposed noise cancelers, we implement the time domain cancelers in frequency domain which results frequency domain TDBLMS and FD-B-Normalized-LMS. Finally, we have applied these algorithms on real EEG signals obtained from human using Emotive Epoc EEG recorder and compared their performance with the conventional LMS algorithm. The results show that the performance of the block based algorithms is superior to the LMS counter-parts in terms of signal to noise ratio, convergence rate, excess mean square error, misadjustment, and coherence

  5. A MIT-Based Nonlinear Adaptive Set-Membership Filter for the Ellipsoidal Estimation of Mobile Robots' States

    Directory of Open Access Journals (Sweden)

    Dalei Song

    2012-10-01

    Full Text Available The adaptive extended set-membership filter (AESMF for nonlinear ellipsoidal estimation suffers a mismatch between real process noise and its set boundaries, which may result in unstable estimation. In this paper, a MIT method-based adaptive set-membership filter, for the optimization of the set boundaries of process noise, is developed and applied to the nonlinear joint estimation of both time-varying states and parameters. As a result of using the proposed MIT-AESMF, the estimation effectiveness and boundary accuracy of traditional AESMF are substantially improved. Simulation results have shown the efficiency and robustness of the proposed method.

  6. Improved beam jitter control methods for high energy laser systems

    OpenAIRE

    Frist, Duane C.

    2009-01-01

    Approved for public release, distribution unlimited The objective of this research was to develop beam jitter control methods for a High Energy Laser (HEL) testbed. The first step was to characterize the new HEL testbed at NPS. This included determination of natural frequencies and component models which were used to create a Matlab/Simulink model of the testbed. Adaptive filters using Filtered-X Least Mean Squares (FX-LMS) and Filtered-X Recursive Least Square (FX-RLS) were then implement...

  7. Genetic Algorithm Applied to the Eigenvalue Equalization Filtered-x LMS Algorithm (EE-FXLMS

    Directory of Open Access Journals (Sweden)

    Stephan P. Lovstedt

    2008-01-01

    Full Text Available The FXLMS algorithm, used extensively in active noise control (ANC, exhibits frequency-dependent convergence behavior. This leads to degraded performance for time-varying tonal noise and noise with multiple stationary tones. Previous work by the authors proposed the eigenvalue equalization filtered-x least mean squares (EE-FXLMS algorithm. For that algorithm, magnitude coefficients of the secondary path transfer function are modified to decrease variation in the eigenvalues of the filtered-x autocorrelation matrix, while preserving the phase, giving faster convergence and increasing overall attenuation. This paper revisits the EE-FXLMS algorithm, using a genetic algorithm to find magnitude coefficients that give the least variation in eigenvalues. This method overcomes some of the problems with implementing the EE-FXLMS algorithm arising from finite resolution of sampled systems. Experimental control results using the original secondary path model, and a modified secondary path model for both the previous implementation of EE-FXLMS and the genetic algorithm implementation are compared.

  8. 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.

  9. Wave field synthesis, adaptive wave field synthesis and ambisonics using decentralized transformed control: Potential applications to sound field reproduction and active noise control

    Science.gov (United States)

    Gauthier, Philippe-Aubert; Berry, Alain; Woszczyk, Wieslaw

    2005-09-01

    Sound field reproduction finds applications in listening to prerecorded music or in synthesizing virtual acoustics. The objective is to recreate a sound field in a listening environment. Wave field synthesis (WFS) is a known open-loop technology which assumes that the reproduction environment is anechoic. Classical WFS, therefore, does not perform well in a real reproduction space such as room. Previous work has suggested that it is physically possible to reproduce a progressive wave field in-room situation using active control approaches. In this paper, a formulation of adaptive wave field synthesis (AWFS) introduces practical possibilities for an adaptive sound field reproduction combining WFS and active control (with WFS departure penalization) with a limited number of error sensors. AWFS includes WFS and closed-loop ``Ambisonics'' as limiting cases. This leads to the modification of the multichannel filtered-reference least-mean-square (FXLMS) and the filtered-error LMS (FELMS) adaptive algorithms for AWFS. Decentralization of AWFS for sound field reproduction is introduced on the basis of sources' and sensors' radiation modes. Such decoupling may lead to decentralized control of source strength distributions and may reduce computational burden of the FXLMS and the FELMS algorithms used for AWFS. [Work funded by NSERC, NATEQ, Université de Sherbrooke and VRQ.] Ultrasound/Bioresponse to

  10. Spherical Tensor Calculus for Local Adaptive Filtering

    Science.gov (United States)

    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.

  11. Adaptive Control of Non-Minimum Phase Modal Systems Using Residual Mode Filters2. Parts 1 and 2

    Science.gov (United States)

    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.

  12. Comparisons of adaptive TIN modelling filtering method and threshold segmentation filtering method of LiDAR point cloud

    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

  13. An adaptive filtering method based on EMD for X-ray pulsar navigation with uncertain measurement noise

    Directory of Open Access Journals (Sweden)

    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.

  14. 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)

  15. Estimation, filtering and adaptative control of a waste water processing process; Estimation, filtrage et commande adaptive d`un procede de traitement des eaux usees

    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.

  16. Online learning management systems (LMS and sense of community: A pre-service practicum perspective

    Directory of Open Access Journals (Sweden)

    Glenn Rideout

    2008-05-01

    Full Text Available This paper examines the impact of the implementation of an online learning management system (LMS on pre-service teachers during their major pre-service practicum. The LMS enabled students to remain connected to peers, professors, and supervisors while new relationships were formed with in-school personnel such as mentor teachers, principals, and students. The quantitative data analysis revealed that pre-service teachers perceived a higher sense of community when it included pre-service teachers only than when it included teaching faculty and part-time practicum supervisors. The online LMS was identified as the best predictor of a sense of community among pre-service teachers, when compared to other variables, and not a significant predictor when community included part-time practicum supervisors. The paper concludes with an examination of the perspectives of provincial Ministries of Education and Teachers Federations in relation to the role and evaluation of educational technology initiatives. Résumé: Cet article examine l’impact de l’implantation d’une plateforme eLearning (LMS sur de futurs enseignants en stage. La plateforme a permis aux étudiants de rester en contact avec leurs pairs, leur professeurs et leurs superviseurs, alors que de nouvelles relations se formaient avec la collectivité de l’école les accueillant pour leur stage (mentor, directeur d’école et élèves. L’analyse des données quantitatives a révélé que les futurs enseignants percevaient un meilleur esprit communautaire quand seuls les futurs enseignants en faisaient partie par opposition à une communauté composée des futurs enseignants, des professeurs de leur programme et les superviseurs à temps partiel de leur stage. La plateforme LMS a été identifiée comme le meilleur prédicteur de l’esprit communautaire chez les futurs enseignants par comparaison avec d’autres variables, et un prédicteur non significatif quand la communauté incluait

  17. ANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO Algorithm

    Directory of Open Access Journals (Sweden)

    Jie-Sheng Wang

    2015-06-01

    Full Text Available In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO algorithm combined with the least squares method (LMS to optimize the adaptive network-based fuzzy inference system (ANFIS model parameters. Through the introduction of metric function of population diversity to ensure the diversity of population and adaptive changes in inertia weight and learning factors, the optimization ability of the particle swarm optimization (PSO algorithm is improved, which avoids the premature convergence problem of the PSO algorithm. The simulation comparison experiments are carried out with BP-LMS algorithm and standard PSO-LMS by adopting real commercial banks’ cash flow data to verify the effectiveness of the proposed time series prediction of bank cash flow based on improved PSO-ANFIS optimization method. Simulation results show that the optimization speed is faster and the prediction accuracy is higher.

  18. Application of adaptive digital signal processing to speech enhancement for the hearing impaired.

    Science.gov (United States)

    Chabries, D M; Christiansen, R W; Brey, R H; Robinette, M S; Harris, R W

    1987-01-01

    A major complaint of individuals with normal hearing and hearing impairments is a reduced ability to understand speech in a noisy environment. This paper describes the concept of adaptive noise cancelling for removing noise from corrupted speech signals. Application of adaptive digital signal processing has long been known and is described from a historical as well as technical perspective. The Widrow-Hoff LMS (least mean square) algorithm developed in 1959 forms the introduction to modern adaptive signal processing. This method uses a "primary" input which consists of the desired speech signal corrupted with noise and a second "reference" signal which is used to estimate the primary noise signal. By subtracting the adaptively filtered estimate of the noise, the desired speech signal is obtained. Recent developments in the field as they relate to noise cancellation are described. These developments include more computationally efficient algorithms as well as algorithms that exhibit improved learning performance. A second method for removing noise from speech, for use when no independent reference for the noise exists, is referred to as single channel noise suppression. Both adaptive and spectral subtraction techniques have been applied to this problem--often with the result of decreased speech intelligibility. Current techniques applied to this problem are described, including signal processing techniques that offer promise in the noise suppression application.

  19. An adaptive Multiplicative Extened Kalman Filter for Attitude Estimation of Marine Satellite Tracking Antenna

    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...

  20. Parametric adaptive filtering and data validation in the bar GW detector AURIGA

    CERN Document Server

    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 ...

  1. Artifact reduction of compressed images and video combining adaptive fuzzy filtering and directional anisotropic diffusion

    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....

  2. Hybrid Adaptive Filter development for the minimisation of transient fluctuations superimposed on electrotelluric field recordings mainly by magnetic storms

    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.

  3. Experimental low-frequency sound equalization in an extended region of an enclosure using adaptive filters

    DEFF Research Database (Denmark)

    Orozco-Santillán, Arturo

    1998-01-01

    An experimental investigation of sound equalization in an extended region inside an enclosure is presented. DSP and the multiple LMS algorithm is used to obtain the optimal solution. Equalization at single frequencies in the stationary state is studied. Also analyzed is the equalization of band...

  4. 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.

  5. Band-pass filtering algorithms for adaptive control of compressor pre-stall modes in aircraft gas-turbine engine

    Science.gov (United States)

    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.

  6. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    Science.gov (United States)

    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

  7. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    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

  8. Smart wave filtering method of a rectangular panel using Hilbert transformers and its application to an adaptive control system

    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

  9. Regulation and functions of the lms homeobox gene during development of embryonic lateral transverse muscles and direct flight muscles in Drosophila.

    Directory of Open Access Journals (Sweden)

    Dominik Müller

    Full Text Available BACKGROUND: Patterning and differentiation of developing musculatures require elaborate networks of transcriptional regulation. In Drosophila, significant progress has been made into identifying the regulators of muscle development and defining their interactive networks. One major family of transcription factors involved in these processes consists of homeodomain proteins. In flies, several members of this family serve as muscle identity genes to specify the fates of individual muscles, or groups thereof, during embryonic and/or adult muscle development. Herein, we report on the expression and function of a new Drosophila homeobox gene during both embryonic and adult muscle development. METHODOLOGY/PRINCIPAL FINDINGS: The newly described homeobox gene, termed lateral muscles scarcer (lms, which has yet uncharacterized orthologs in other invertebrates and primitive chordates but not in vertebrates, is expressed exclusively in subsets of developing muscle tissues. In embryos, lms is expressed specifically in the four lateral transverse (LT muscles and their founder cells in each hemisegment, whereas in larval wing imaginal discs, it is expressed in myoblasts that develop into direct flight muscles (DFMs, which are important for proper wing positioning. We have analyzed the regulatory inputs of various other muscle identity genes with overlapping or complementary expression patterns towards the cell type specific regulation of lms expression. Further we demonstrate that lms null mutants exhibit reduced numbers of embryonic LT muscles, and null mutant adults feature held-out-wing phenotypes. We provide a detailed description of the pattern and morphology of the direct flight muscles in the wild type and lms mutant flies by using the recently-developed ultramicroscopy and show that, in the mutants, all DFMs are present and present normal morphologies. CONCLUSIONS/SIGNIFICANCE: We have identified the homeobox gene lms as a new muscle identity gene

  10. Adaptive filtering primer with Matlab

    CERN Document Server

    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...

  11. 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

  12. Parametric adaptive filtering and data validation in the bar GW detector AURIGA

    Science.gov (United States)

    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.

  13. 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

  14. 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.

  15. Morphological adaptations in filtering screens of Daphnia galeata to food quantity and food quality

    NARCIS (Netherlands)

    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

  16. An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps

    Directory of Open Access Journals (Sweden)

    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

  17. Modeling, estimation and optimal filtration in signal processing

    CERN Document Server

    Najim, Mohamed

    2010-01-01

    The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed.Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented.Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and the

  18. Sensorless Control of Interior Permanent Magnet Synchronous Motor in Low-Speed Region Using Novel Adaptive Filter

    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.

  19. Adaptive Equalizer Based on Second-Order Cone Programming in Underwater Acoustic Communication

    Directory of Open Access Journals (Sweden)

    Yang CHEN

    2014-01-01

    Full Text Available An improved adaptive equalizer based on the principle of minimum mean square error (MMSE is proposed. This optimization problem which is shown to be convex, is transformed to second-order cone (SOC and solved using the interior point method instead of conventional iterative methods such as least mean squares (LMS or recursive least squares (RLS. To validate its performance a single-carrier system for underwater acoustic communication with digital phase-locked loop and the adaptive fractional spaced equalizers was designed and a lake trial was carried out. According to the results, comparing with traditional equalizers based on LMS and RLS algorithms, the equalizer proposed needs no iterative process and gets rid of the contradiction between convergent rate and precision. Therefore it overcomes the difficulty of parameters setting. Furthermore, the algorithm needs much less training codes to achieve the same equalization performance and improves the communication efficiency.

  20. Constructing Proxy Variables to Measure Adult Learners' Time Management Strategies in LMS

    Science.gov (United States)

    Jo, Il-Hyun; Kim, Dongho; Yoon, Meehyun

    2015-01-01

    This study describes the process of constructing proxy variables from recorded log data within a Learning Management System (LMS), which represents adult learners' time management strategies in an online course. Based on previous research, three variables of total login time, login frequency, and regularity of login interval were selected as…

  1. Power adaptive multi-filter carrierless amplitude and phase access scheme for visible light communication network

    Science.gov (United States)

    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.

  2. Filter-Adapted Fluorescent In Situ Hybridization (FA-FISH) for Filtration-Enriched Circulating Tumor Cells.

    Science.gov (United States)

    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.

  3. 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°.

  4. Optical properties of the plasma hydrogenated ZnO thin films

    Czech Academy of Sciences Publication Activity Database

    Chang, Yu-Ying; Stuchlík, Jiří; Neykova, Neda; Souček, J.; Remeš, Zdeněk

    2017-01-01

    Roč. 68, č. 7 (2017), s. 70-73 ISSN 1335-3632 R&D Projects: GA ČR GC16-10429J Institutional support: RVO:68378271 Keywords : metal oxide * magnetron sputtering * thin films * reflectance interferometry * photothermal deflection spectroscopy Subject RIV: BM - Solid Matter Physics ; Magnetism OBOR OECD: Condensed matter physics (including formerly solid state physics, supercond.) Impact factor: 0.483, year: 2016

  5. A Comparison between LMS tools to support e-health educational activities.

    Science.gov (United States)

    de Araújo Novaes, Magdala; Soares de Vasconcelos, Gabriel; Florencio da Silva, Jackson Raniel

    2015-01-01

    The objective of this study is to understand how a Learning Management System (LMS) plataform is used in a telehealth center to support two virtual learning enviroments focused on the education of the healthcare professionals and the students of a medical schoool. The study outcome is expected to provide indications towards choosing a better LSM for the telehelath center to support their educacional activities.

  6. 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 “detection” 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.

  7. 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.

  8. A new LMS algorithm for analysis of atrial fibrillation signals

    OpenAIRE

    Ciaccio Edward J; Biviano Angelo B; Whang William; Garan Hasan

    2012-01-01

    Abstract Background A biomedical signal can be defined by its extrinsic features (x-axis and y-axis shift and scale) and intrinsic features (shape after normalization of extrinsic features). In this study, an LMS algorithm utilizing the method of differential steepest descent is developed, and is tested by normalization of extrinsic features in complex fractionated atrial electrograms (CFAE). Method Equations for normalization of x-axis and y-axis shift and scale are first derived. The algori...

  9. Using high-order methods on adaptively refined block-structured meshes - discretizations, interpolations, and filters.

    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.

  10. A novel adaptive discrete cosine transform-domain filter for gap-inpainting of high resolution PET scanners

    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

  11. A novel adaptive discrete cosine transform-domain filter for gap-inpainting of high resolution PET scanners

    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.

  12. Performance enhancement for a GPS vector-tracking loop utilizing an adaptive iterated extended Kalman filter.

    Science.gov (United States)

    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.

  13. A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance.

    Science.gov (United States)

    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.

  14. Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting

    Science.gov (United States)

    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.

  15. Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking

    Institute of Scientific and Technical Information of China (English)

    Changyun Liu; Penglang Shui; Gang Wei; Song Li

    2014-01-01

    To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneu-vers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive scale factor of state noise is pre-sented. In every filter process, the estimated scale factor is used to update the state noise covariance Qk, and the improved filter gain is obtained in the filter process of unscented Kalman filter (UKF) via predicted variance Pk|k-1, which is similar to the standard Kalman filter. Simulation results show that the proposed algorithm provides better accuracy and ability to adapt to the highly maneu-vering target compared with the standard UKF.

  16. Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT

    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

  17. AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.

    Science.gov (United States)

    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.

  18. An improved AE detection method of rail defect based on multi-level ANC with VSS-LMS

    Science.gov (United States)

    Zhang, Xin; Cui, Yiming; Wang, Yan; Sun, Mingjian; Hu, Hengshan

    2018-01-01

    In order to ensure the safety and reliability of railway system, Acoustic Emission (AE) method is employed to investigate rail defect detection. However, little attention has been paid to the defect detection at high speed, especially for noise interference suppression. Based on AE technology, this paper presents an improved rail defect detection method by multi-level ANC with VSS-LMS. Multi-level noise cancellation based on SANC and ANC is utilized to eliminate complex noises at high speed, and tongue-shaped curve with index adjustment factor is proposed to enhance the performance of variable step-size algorithm. Defect signals and reference signals are acquired by the rail-wheel test rig. The features of noise signals and defect signals are analyzed for effective detection. The effectiveness of the proposed method is demonstrated by comparing with the previous study, and different filter lengths are investigated to obtain a better noise suppression performance. Meanwhile, the detection ability of the proposed method is verified at the top speed of the test rig. The results clearly illustrate that the proposed method is effective in detecting rail defects at high speed, especially for noise interference suppression.

  19. An FPGA-based DS-CDMA multiuser demodulator employing adaptive multistage parallel interference cancellation

    Science.gov (United States)

    Li, Xinhua; Song, Zhenyu; Zhan, Yongjie; Wu, Qiongzhi

    2009-12-01

    Since the system capacity is severely limited, reducing the multiple access interfere (MAI) is necessary in the multiuser direct-sequence code division multiple access (DS-CDMA) system which is used in the telecommunication terminals data-transferred link system. In this paper, we adopt an adaptive multistage parallel interference cancellation structure in the demodulator based on the least mean square (LMS) algorithm to eliminate the MAI on the basis of overviewing various of multiuser dectection schemes. Neither a training sequence nor a pilot signal is needed in the proposed scheme, and its implementation complexity can be greatly reduced by a LMS approximate algorithm. The algorithm and its FPGA implementation is then derived. Simulation results of the proposed adaptive PIC can outperform some of the existing interference cancellation methods in AWGN channels. The hardware setup of mutiuser demodulator is described, and the experimental results based on it demonstrate that the simulation results shows large performance gains over the conventional single-user demodulator.

  20. FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

    Science.gov (United States)

    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.

  1. Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter.

    Science.gov (United States)

    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.

  2. 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.

  3. Multi-template Scale-Adaptive Kernelized Correlation Filters

    KAUST Repository

    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.

  4. Multi-template Scale-Adaptive Kernelized Correlation Filters

    KAUST Repository

    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.

  5. Target Response Adaptation for Correlation Filter Tracking

    KAUST Repository

    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

  6. Fast, accurate, and robust frequency offset estimation based on modified adaptive Kalman filter in coherent optical communication system

    Science.gov (United States)

    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.

  7. Adaptive noise cancellation

    International Nuclear Information System (INIS)

    Akram, N.

    1999-01-01

    In this report we describe the concept of adaptive noise canceling, an alternative method of estimating signals corrupted by additive noise of interference. The method uses 'primary' input containing the corrupted signal and a 'reference' input containing noise correlated in some unknown way with the primary noise, the reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. Adaptive filtering before subtraction allows the treatment of inputs that are deterministic or stochastic, stationary or time variable. When the reference input is free of signal and certain other conditions are met then noise in the primary input can be essentially eliminated without signal distortion. It is further shown that the adaptive filter also acts as notch filter. Simulated results illustrate the usefulness of the adaptive noise canceling technique. (author)

  8. Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system.

    Science.gov (United States)

    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.

  9. 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

  10. Modeling astronomical adaptive optics performance with temporally filtered Wiener reconstruction of slope data

    Science.gov (United States)

    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.

  11. Kernel-based noise filtering of neutron detector signals

    International Nuclear Information System (INIS)

    Park, Moon Ghu; Shin, Ho Cheol; Lee, Eun Ki

    2007-01-01

    This paper describes recently developed techniques for effective filtering of neutron detector signal noise. In this paper, three kinds of noise filters are proposed and their performance is demonstrated for the estimation of reactivity. The tested filters are based on the unilateral kernel filter, unilateral kernel filter with adaptive bandwidth and bilateral filter to show their effectiveness in edge preservation. Filtering performance is compared with conventional low-pass and wavelet filters. The bilateral filter shows a remarkable improvement compared with unilateral kernel and wavelet filters. The effectiveness and simplicity of the unilateral kernel filter with adaptive bandwidth is also demonstrated by applying it to the reactivity measurement performed during reactor start-up physics tests

  12. An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles

    Science.gov (United States)

    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.

  13. Adaptive Kalman filtering for real-time mapping of the visual field

    Science.gov (United States)

    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

  14. Examining the Effect of Academic Procrastination on Achievement Using LMS Data in E-Learning

    Science.gov (United States)

    You, Ji Won

    2015-01-01

    This study aimed to investigate the effect of academic procrastination on e-learning course achievement. Because all of the interactions among students, instructors, and contents in an e-learning environment were automatically recorded in a learning management system (LMS), procrastination such as the delays in weekly scheduled learning and late…

  15. LMS learning algorithms: misconceptions and new results on converence.

    Science.gov (United States)

    Wang, Z Q; Manry, M T; Schiano, J L

    2000-01-01

    The Widrow-Hoff delta rule is one of the most popular rules used in training neural networks. It was originally proposed for the ADALINE, but has been successfully applied to a few nonlinear neural networks as well. Despite its popularity, there exist a few misconceptions on its convergence properties. In this paper we consider repetitive learning (i.e., a fixed set of samples are used for training) and provide an in-depth analysis in the least mean square (LMS) framework. Our main result is that contrary to common belief, the nonbatch Widrow-Hoff rule does not converge in general. It converges only to a limit cycle.

  16. Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.

    Science.gov (United States)

    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.

  17. Adaptive Command Filtered Integrated Guidance and Control for Hypersonic Vehicle with Magnitude, Rate and Bandwidth Constraints

    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.

  18. Extraction of ECG signal with adaptive filter for hearth abnormalities detection

    Science.gov (United States)

    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.

  19. Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system.

    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.

  20. 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

  1. Adaptive Electronic Dispersion Compensator for Chromatic and Polarization-Mode Dispersions in Optical Communication Systems

    OpenAIRE

    Koc Ut-Va

    2005-01-01

    The widely-used LMS algorithm for coefficient updates in adaptive (feedforward/decision-feedback) equalizers is found to be suboptimal for ASE-dominant systems but various coefficient-dithering approaches suffer from slow adaptation rate without guarantee of convergence. In view of the non-Gaussian nature of optical noise after the square-law optoelectronic conversion, we propose to apply the higher-order least-mean 2 th-order (LMN) algorithms resulting in OSNR penalty which is 1.5–2 d...

  2. Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach

    Science.gov (United States)

    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.

  3. Rotational Kinematics Model Based Adaptive Particle Filter for Robust Human Tracking in Thermal Omnidirectional Vision

    Directory of Open Access Journals (Sweden)

    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.

  4. Planetary gearbox fault feature enhancement based on combined adaptive filter method

    Directory of Open Access Journals (Sweden)

    Shuangshu Tian

    2015-12-01

    Full Text Available The reliability of vibration signals acquired from a planetary gear system (the indispensable part of wind turbine gearbox is directly related to the accuracy of fault diagnosis. The complex operation environment leads to lots of interference signals which are included in the vibration signals. Furthermore, both multiple gears meshing with each other and the differences in transmission rout produce strong nonlinearity in the vibration signals, which makes it difficult to eliminate the noise. This article presents a combined adaptive filter method by taking a delayed signal as reference signal, the Self-Adaptive Noise Cancellation method is adopted to eliminate the white noise. In the meanwhile, by applying Gaussian function to transform the input signal into high-dimension feature-space signal, the kernel least mean square algorithm is used to cancel the nonlinear interference. Effectiveness of the method has been verified by simulation signals and test rig signals. By dealing with simulation signal, the signal-to-noise ratio can be improved around 30 dB (white noise and the amplitude of nonlinear interference signal can be depressed up to 50%. Experimental results show remarkable improvements and enhance gear fault features.

  5. Adaptive cancellation of motion artifact in wearable biosensors.

    Science.gov (United States)

    Yousefi, Rasoul; Nourani, Mehrdad; Panahi, Issa

    2012-01-01

    The performance of wearable biosensors is highly influenced by motion artifact. In this paper, a model is proposed for analysis of motion artifact in wearable photoplethysmography (PPG) sensors. Using this model, we proposed a robust real-time technique to estimate fundamental frequency and generate a noise reference signal. A Least Mean Square (LMS) adaptive noise canceler is then designed and validated using our synthetic noise generator. The analysis and results on proposed technique for noise cancellation shows promising performance.

  6. Dim small targets detection based on self-adaptive caliber temporal-spatial filtering

    Science.gov (United States)

    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.

  7. A zero phase adaptive fuzzy Kalman filter for physiological tremor suppression in robotically assisted minimally invasive surgery.

    Science.gov (United States)

    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.

  8. Development of Tremor Suppression Control System Using Adaptive Filter and Its Application to Meal-assist Robot

    Science.gov (United States)

    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.

  9. A comparative study of the energy resolution achievable with digital signal processors in x-ray spectroscopy

    International Nuclear Information System (INIS)

    Geraci, A.; Zambusi, M.; Ripamonti, G.

    1996-01-01

    Interest for digital processing of signals from radiation detectors is subject to a growing attention due to its intrinsic adaptivity, easiness of calibration, etc. This work compares two digital processing methods: a multiple-delay-line (DL) N filter and a least-mean-squares (LMS) adaptive filter for applications in high resolution X-ray spectroscopy. The signal pulse, as appears at the output of a proper analog conditioning circuit, is digitized; the samples undergo a digital filtering procedure. Both digital filters take advantage of the possibility of synthesizing the best possible weighting function with respect to the actual noise conditions. A noticeable improvement of more than 10% in energy resolution has been achieved with both systems with respect to state-of-the-art systems based on analog circuitry. In particular, the two digital processors are shown to be the best choice respectively; for on-line use with critical ballistic deficit conditions and for very-high-resolution spectroscopy systems, ultimately limited by 1/f noise

  10. Adaptive Equalizer Using Selective Partial Update Algorithm and Selective Regressor Affine Projection Algorithm over Shallow Water Acoustic Channels

    Directory of Open Access Journals (Sweden)

    Masoumeh Soflaei

    2014-01-01

    Full Text Available One of the most important problems of reliable communications in shallow water channels is intersymbol interference (ISI which is due to scattering from surface and reflecting from bottom. Using adaptive equalizers in receiver is one of the best suggested ways for overcoming this problem. In this paper, we apply the family of selective regressor affine projection algorithms (SR-APA and the family of selective partial update APA (SPU-APA which have low computational complexity that is one of the important factors that influences adaptive equalizer performance. We apply experimental data from Strait of Hormuz for examining the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE of SR-APA and SPU-APA decrease by 5.8 (dB and 5.5 (dB, respectively, in comparison with least mean square (LMS algorithm. Also the families of SPU-APA and SR-APA have better convergence speed than LMS type algorithm.

  11. Using the Leitz LMS 2000 for monitoring and improvement of an e-beam

    Science.gov (United States)

    Blaesing-Bangert, Carola; Roeth, Klaus-Dieter; Ogawa, Yoichi

    1994-11-01

    Kaizen--a continuously improving--is a philosophy lived in Japan which is also becoming more and more important in Western companies. To implement this philosophy in the semiconductor industry, a high performance metrology tool is essential to determine the status of production quality periodically. An important prerequisite for statistical process control is the high stability of the metrology tool over several months or years; the tool-induced shift should be as small as possible. The pattern placement metrology tool Leitz LMS 2000 has been used in a major European mask house for several years now to qualify masks within the tightest specifications and to monitor the MEBES III and its cassettes. The mask shop's internal specification for the long term repeatability of the pattern placement metrology tool is 19 nm instead of 42 nm as specified by the supplier of the tool. Then the process capability of the LMS 2000 over 18 months is represented by an average cpk value of 2.8 for orthogonality, 5.2 for x-scaling, and 3.0 for y-scaling. The process capability of the MEBES III and its cassettes was improved in the past years. For instance, 100% of the masks produced with a process tolerance of +/- 200 nm are now within this limit.

  12. Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems

    Directory of Open Access Journals (Sweden)

    Hongjian Wang

    2014-01-01

    Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.

  13. An adaptive prediction and detection algorithm for multistream syndromic surveillance

    Directory of Open Access Journals (Sweden)

    Magruder Steve F

    2005-10-01

    Full Text Available Abstract Background Surveillance of Over-the-Counter pharmaceutical (OTC sales as a potential early indicator of developing public health conditions, in particular in cases of interest to biosurvellance, has been suggested in the literature. This paper is a continuation of a previous study in which we formulated the problem of estimating clinical data from OTC sales in terms of optimal LMS linear and Finite Impulse Response (FIR filters. In this paper we extend our results to predict clinical data multiple steps ahead using OTC sales as well as the clinical data itself. Methods The OTC data are grouped into a few categories and we predict the clinical data using a multichannel filter that encompasses all the past OTC categories as well as the past clinical data itself. The prediction is performed using FIR (Finite Impulse Response filters and the recursive least squares method in order to adapt rapidly to nonstationary behaviour. In addition, we inject simulated events in both clinical and OTC data streams to evaluate the predictions by computing the Receiver Operating Characteristic curves of a threshold detector based on predicted outputs. Results We present all prediction results showing the effectiveness of the combined filtering operation. In addition, we compute and present the performance of a detector using the prediction output. Conclusion Multichannel adaptive FIR least squares filtering provides a viable method of predicting public health conditions, as represented by clinical data, from OTC sales, and/or the clinical data. The potential value to a biosurveillance system cannot, however, be determined without studying this approach in the presence of transient events (nonstationary events of relatively short duration and fast rise times. Our simulated events superimposed on actual OTC and clinical data allow us to provide an upper bound on that potential value under some restricted conditions. Based on our ROC curves we argue that a

  14. Regenerative-filter-incinerator device

    Energy Technology Data Exchange (ETDEWEB)

    Rosebrock, T.L.

    1977-10-18

    A regenerative-filter-incinerator device, for use in the exhaust system of a diesel engine, includes a drum-like regenerative-heat exchanger-filter assembly rotatably mounted within a housing that is adapted to be installed directly in the exhaust gas stream discharged from a diesel engine as close to the engine as possible. The regenerative-heat exchanger-filter assembly provides an inner chamber which serves as a reaction chamber for the secondary combustion of exhaust gases including particulates discharged from the engine. The regenerative-heat exchanger-filter assembly includes separately rotatable heat exchange-filter elements pervious to radial flow of fluid therethrough and adapted to filter out particulates from the exhaust gases and to carry them into the reaction chamber. During engine operation, the reaction chamber is provided with a quantity of heat, as necessary, to effect secondary combustion of the exhaust gases and particulates by means of an auxiliary heat source and the heat generated within the reaction chamber is stored in the individual heat exchange-filter elements during the discharge of exhaust gases therethrough from the reaction chamber and this heat is then transferred to the inflowing volume of the exhaust gases so that, in effect, exhaust gas is discharged from the device at substantially the same temperature as it was during its inlet into the device from the engine.

  15. The Development of E-Portfolio Evaluation Criteria and Application to the Blackboard LMS E-Portfolio

    Science.gov (United States)

    McKenna, Gary F.; Stansfield, Mark H.

    2012-01-01

    The purpose of this paper is to develop e-portfolio evaluation criteria which will be used to review the Blackboard LMS e-portfolio being used at one Higher Education (HE) institution in the UK as evaluation criteria for reviewing e-portfolio provision does not exist in the literature. The approach taken was to initiate a wide literature search…

  16. Orthonormal filters for identification in active control systems

    International Nuclear Information System (INIS)

    Mayer, Dirk

    2015-01-01

    Many active noise and vibration control systems require models of the control paths. When the controlled system changes slightly over time, adaptive digital filters for the identification of the models are useful. This paper aims at the investigation of a special class of adaptive digital filters: orthonormal filter banks possess the robust and simple adaptation of the widely applied finite impulse response (FIR) filters, but at a lower model order, which is important when considering implementation on embedded systems. However, the filter banks require prior knowledge about the resonance frequencies and damping of the structure. This knowledge can be supposed to be of limited precision, since in many practical systems, uncertainties in the structural parameters exist. In this work, a procedure using a number of training systems to find the fixed parameters for the filter banks is applied. The effect of uncertainties in the prior knowledge on the model error is examined both with a basic example and in an experiment. Furthermore, the possibilities to compensate for the imprecise prior knowledge by a higher filter order are investigated. Also comparisons with FIR filters are implemented in order to assess the possible advantages of the orthonormal filter banks. Numerical and experimental investigations show that significantly lower computational effort can be reached by the filter banks under certain conditions. (paper)

  17. Adaptive piezoelectric sensoriactuators for active structural acoustic control

    Science.gov (United States)

    Vipperman, Jeffrey Stuart

    1997-09-01

    A new transducer technology with application to active control systems, modal analysis, and autonomous system health monitoring, is brought to fruition in this work. It has the advantages of being lightweight, potentially cost-effective, self-tuning, has negligible dynamics, and most importantly (from a robustness perspective), it provides a colocated sensor/actuator pair. The transducer consists of a piezoceramic element which serves as both an actuator and a sensor and will be referred to in this work as a sensoriactuator. Simple, adaptive signal processing in conjunction with a voltage controlled amplifier, reference capacitor, and a common-mode rejection circuit extract the mechanical response from the total response of the piezoelectric sensoriactuator for sensing. The digital portion of the adaptive piezoelectric sensoriactuator merely serves to tune the circuit, avoiding the potentially destabilizing effects of introducing a digital delay in the signal path, when used for feedback control applications. Adaptive compensation of the sensoriactuator is necessary since the signal to noise ratio is typically greater than 40 dB, making it prohibitive to tune the circuit manually. In addition, the constitutive properties of piezoceramics vary with time and environment, necessitating that the circuit be periodically re-tuned. The analog portion of the hardware is based upon op-amp circuits and an AD632 analog multiplier chip, which serves as both a voltage controlled amplifier (VCA) and a common mode rejection (CMR) circuit. A single coefficient least-mean square (LMS) adaptive filter continuously adjusts the gain of the VCA circuit as necessary. Nonideal behavior of piezoceramics is discussed along with methods to counter the consequential deterioration in circuit performance. A multiple input multiple output (MIMO) implementation of the adaptive piezoelectric sensoriactuator is developed using orthogonal white noise training signals for each sensoriactuator. Two

  18. Actuator Fault Diagnosis in a Boeing 747 Model via Adaptive Modified Two-Stage Kalman Filter

    Directory of Open Access Journals (Sweden)

    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.

  19. An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter

    KAUST Repository

    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.

  20. A computer simulation of an adaptive noise canceler with a single input

    Science.gov (United States)

    Albert, Stuart D.

    1991-06-01

    A description of an adaptive noise canceler using Widrows' LMS algorithm is presented. A computer simulation of canceler performance (adaptive convergence time and frequency transfer function) was written for use as a design tool. The simulations, assumptions, and input parameters are described in detail. The simulation is used in a design example to predict the performance of an adaptive noise canceler in the simultaneous presence of both strong and weak narrow-band signals (a cosited frequency hopping radio scenario). On the basis of the simulation results, it is concluded that the simulation is suitable for use as an adaptive noise canceler design tool; i.e., it can be used to evaluate the effect of design parameter changes on canceler performance.

  1. A FPGA-based Fast Converging Digital Adaptive Filter for Real-time RFI Mitigation on Ground Based Radio Telescopes

    Science.gov (United States)

    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.

  2. Utilizing LMS tools to help with student assessment in an online course

    Directory of Open Access Journals (Sweden)

    Dudley B. Turner

    2015-07-01

    Full Text Available In online learning, feedback to students is important in their progress. Assessments are often final or summative assessments that do not allow the student to adjust or improve their learning progress. Assessment, however, takes valuable time. This paper describes how using the tools available in a LMS can assist faculty in assessing student work and provide helpful feedback to students in an online course. The tools available for faculty to use can be set up to save time for the faculty during assessments. The assignments in this study were developed based on previous research indicating assessment can be an aid to student learning, and students who know how well they are doing can make needed adjustments. Students used the feedback from these LMS tools to decide whether or not to try again or move on. Students had the opportunity for multiple attempts at assignments and received feedback on each to help measure their learning. The rubric tool was used to not only grade student papers but also to provide appropriate feedback for student performance on the levels of achievement. Quizzes can be automatically graded. Any additional attempts are drawn from a bank of questions. Results from this pilot study show the benefits of multiple attempts at quizzes and assignments. Students who took advantage of multiple attempts did improve their scores. The paper also discusses further research that to help support this practice.

  3. Evaluation of LMS Moodle's functionality for the purpose of supporting the application administrator

    OpenAIRE

    Šperková, Lucie

    2009-01-01

    This bachelor thesis deals with administrator environment of the Learning Management System Moodle. The aim is to provide a basic outline of the alternatives for administrative tools supporting the learning procedure in Moodle and to create a user's guide for LMS Moodle. The characterisation focuses on the administration of users, courses, modules, appearances, security and some other basic settings. A brief survey on functioning of the system among Czech universities is also a part of this t...

  4. 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.

  5. Two-dimensional FIR compaction filter design

    NARCIS (Netherlands)

    Vijayakumar, N.; Prabhu, K.M.M.

    2001-01-01

    The design of signal-adapted multirate filter banks has been an area of research interest. The authors present the design of a 2-D finite impulse response (FIR) compaction filter followed by a 2-D FIR filter bank that packs the maximum energy of the input process into a few subbands. The energy

  6. 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.

  7. 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...

  8. Optimal adaptive normalized matched filter for large antenna arrays

    KAUST Repository

    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.

  9. Optimal adaptive normalized matched filter for large antenna arrays

    KAUST Repository

    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.

  10. Implementation Issues of Adaptive Energy Detection in Heterogeneous Wireless Networks

    Science.gov (United States)

    Sobron, Iker; Eizmendi, Iñaki; Martins, Wallace A.; Diniz, Paulo S. R.; Ordiales, Juan Luis; Velez, Manuel

    2017-01-01

    Spectrum sensing (SS) enables the coexistence of non-coordinated heterogeneous wireless systems operating in the same band. Due to its computational simplicity, energy detection (ED) technique has been widespread employed in SS applications; nonetheless, the conventional ED may be unreliable under environmental impairments, justifying the use of ED-based variants. Assessing ED algorithms from theoretical and simulation viewpoints relies on several assumptions and simplifications which, eventually, lead to conclusions that do not necessarily meet the requirements imposed by real propagation environments. This work addresses those problems by dealing with practical implementation issues of adaptive least mean square (LMS)-based ED algorithms. The paper proposes a new adaptive ED algorithm that uses a variable step-size guaranteeing the LMS convergence in time-varying environments. Several implementation guidelines are provided and, additionally, an empirical assessment and validation with a software defined radio-based hardware is carried out. Experimental results show good performance in terms of probabilities of detection (Pd>0.9) and false alarm (Pf∼0.05) in a range of low signal-to-noise ratios around [-4,1] dB, in both single-node and cooperative modes. The proposed sensing methodology enables a seamless monitoring of the radio electromagnetic spectrum in order to provide band occupancy information for an efficient usage among several wireless communications systems. PMID:28441751

  11. An Adjoint-Based Adaptive Ensemble Kalman Filter

    KAUST Repository

    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.

  12. An Adjoint-Based Adaptive Ensemble Kalman Filter

    KAUST Repository

    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.

  13. Directional Joint Bilateral Filter for Depth Images

    Directory of Open Access Journals (Sweden)

    Anh Vu Le

    2014-06-01

    Full Text Available Depth maps taken by the low cost Kinect sensor are often noisy and incomplete. Thus, post-processing for obtaining reliable depth maps is necessary for advanced image and video applications such as object recognition and multi-view rendering. In this paper, we propose adaptive directional filters that fill the holes and suppress the noise in depth maps. Specifically, novel filters whose window shapes are adaptively adjusted based on the edge direction of the color image are presented. Experimental results show that our method yields higher quality filtered depth maps than other existing methods, especially at the edge boundaries.

  14. Feedforward compensation control of rotor imbalance for high-speed magnetically suspended centrifugal compressors using a novel adaptive notch filter

    Science.gov (United States)

    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.

  15. Spin Torques in Systems with Spin Filtering and Spin Orbit Interaction

    KAUST Repository

    Ortiz Pauyac, Christian

    2016-01-01

    filtering. In Chap. 3 we discuss the Rashba torque in ferromagnetic films, and in Chap. 4 we study spin Hall effect and spin swapping in ferromagnetic films, exploring the nature of spin-orbit torques based on these mechanisms. Conclusions and perspectives

  16. Design and implementation of predictive filtering system for current reference generation of active power filter

    Energy Technology Data Exchange (ETDEWEB)

    Kilic, Tomislav; Milun, Stanko; Petrovic, Goran [FESB University of Split, Faculty of Electrical Engineering, Machine Engineering and Naval Architecture, R. Boskovica bb, 21000, Split (Croatia)

    2007-02-15

    The shunt active power filters are used to attenuate the harmonic currents in power systems by injecting equal but opposite compensating currents. Successful control of the active filters requires an accurate current reference. In this paper the current reference determination based on predictive filtering structure is presented. Current reference was obtained by taking the difference of load current and its fundamental harmonic. For fundamental harmonic determination with no time delay a combination of digital predictive filter and low pass filter is used. The proposed method was implemented on a laboratory prototype of a three-phase active power filter. The algorithm for current reference determination was adapted and implemented on DSP controller. Simulation and experimental results show that the active power filter with implemented predictive filtering structure gives satisfactory performance in power system harmonic attenuation. (author)

  17. Active Vibration Control of Plate Partly Treated with ACLD Using Hybrid Control

    Directory of Open Access Journals (Sweden)

    Dongdong Zhang

    2014-01-01

    Full Text Available A finite element model of plate partly treated with ACLD treatments is developed based on the constitutive equations of elastic, piezoelectric, viscoelastic materials and Hamilton’s principle. The Golla-Hughes-Mctavish (GHM method is employed to describe the frequency-dependent characteristics of viscoelastic material (VEM. A model reduction is completed by using iterative dynamic condensation and balance model reduction method to design an effective control system. The emphasis is concerned on hybrid (combined feedback/feedforward control system to attenuate the vibration of plates with ACLD treatments. The optimal linear quadratic Gaussian (LQG controller is considered as a feedback channel and the adaptive filtered-reference LMS (FxLMS controller is used as a feedforward channel. They can be utilized individually or in a hybrid way to suppress the vibration of plate/ACLD system. The results show that the hybrid controller which combines feedback/feedforward together can reduce the displacement amplitude of plate/ACLD system subjected to a complicated disturbance substantially without requiring more control effort. Furthermore, the hybrid controller has more rapid and stable convergence rate than the adaptive feedforward FxLMS controller. Meanwhile, perfect robustness to phase error of the cancellation path in feedforward controller and the weight matrices in feedback LQG controller is demonstrated in proposed hybrid controller. Therefore, its application in structural engineering can be highly appreciated.

  18. 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.

  19. Using LMS Method in Smoothing Reference Centile Curves for Lipid Profile of Iranian Children and Adolescents: A CASPIAN Study

    Directory of Open Access Journals (Sweden)

    M Hoseini

    2012-05-01

    Full Text Available

    Background and Objectives: LMS is a general monitoring method for fitting smooth reference centile curves in medical sciences. They provide the distribution of a measurement as it changes according to some covariates like age or time. This method describes the distribution of changes by three parameters; Mean, Coefficient of variation and Cox-Box power (skewness. Applying maximum penalized likelihood and spline function, the three curves are estimated and fitted and optimum smoothness is expressed by three curves. This study was conducted to provide the percentiles of lipid profile of Iranian children and adolescents by LMS.

     

    Methods: Smoothed reference centile curves of four groups of lipids (triglycerides, total-LDL- and HDL-cholesterol were developed from the data of 4824 Iranian school students, aged 6-18 years, living in six cities (Tabriz, Rasht, Gorgan, Mashad, Yazd and Tehran-Firouzkouh in Iran. Demographic and laboratory data were taken from the national study of the surveillance and prevention of non-communicable diseases from childhood (CASPIAN Study. After data management, data of 4824 students were included in the statistical analysis, which was conducted by the modified LMS method proposed by Cole. The curves were developed with a degree of freedom of four to ten with some tools such as deviance, Q tests, and detrended Q-Q plot were used for monitoring goodness of fit models.

     

    Results: All tools confirmed the model, and the LMS method was used as an appropriate method in smoothing reference centile. This method revealed the distributing features of variables serving as an objective tool to determine their relative importance.

     

    Conclusion: This study showed that the triglycerides level is higher and

  20. A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.

    Science.gov (United States)

    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.

  1. Particle filtering for passive fathometer tracking.

    Science.gov (United States)

    Michalopoulou, Zoi-Heleni; Yardim, Caglar; Gerstoft, Peter

    2012-01-01

    Seabed interface depths and fathometer amplitudes are tracked for an unknown and changing number of sub-bottom reflectors. This is achieved by incorporating conventional and adaptive fathometer processors into sequential Monte Carlo methods for a moving vertical line array. Sediment layering information and time-varying fathometer response amplitudes are tracked by using a multiple model particle filter with an uncertain number of reflectors. Results are compared to a classical particle filter where the number of reflectors is considered to be known. Reflector tracking is demonstrated for both conventional and adaptive processing applied to the drifting array data from the Boundary 2003 experiment. The layering information is successfully tracked by the multiple model particle filter even for noisy fathometer outputs. © 2012 Acoustical Society of America.

  2. Filter. Remix. Make.: Cultivating Adaptability through Multimodality

    Science.gov (United States)

    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…

  3. Binaural noise reduction via cue-preserving MMSE filter and adaptive-blocking-based noise PSD estimation

    Science.gov (United States)

    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

  4. Maritime adaptive optics beam control

    OpenAIRE

    Corley, Melissa S.

    2010-01-01

    The Navy is interested in developing systems for horizontal, near ocean surface, high-energy laser propagation through the atmosphere. Laser propagation in the maritime environment requires adaptive optics control of aberrations caused by atmospheric distortion. In this research, a multichannel transverse adaptive filter is formulated in Matlab's Simulink environment and compared to a complex lattice filter that has previously been implemented in large system simulations. The adaptive fil...

  5. FPGA/NIOS Implementation of an Adaptive FIR Filter Using Linear Prediction to Reduce Narrow-Band RFI for Radio Detection of Cosmic Rays

    NARCIS (Netherlands)

    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

  6. Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques in quantifying coronary calcium.

    Science.gov (United States)

    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.

  7. Nonlinear Principal Component Analysis Using Strong Tracking Filter

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.

  8. Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization

    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.

  9. Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

    Science.gov (United States)

    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.

  10. Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

    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.

  11. Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm

    Science.gov (United States)

    Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui

    2017-05-01

    The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.

  12. Skylab communications carrier 16536G and filter bypass adapter assembly 12535G. [development of communications equipment for use with Skylab spacecraft

    Science.gov (United States)

    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.

  13. Discrete stochastic processes and optimal filtering

    CERN Document Server

    Bertein, Jean-Claude

    2012-01-01

    Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from the extraction of noise signals. Moreover, it is a fundamental feature in a range of applications, such as in navigation in aerospace and aeronautics, filter processing in the telecommunications industry, etc. This book provides a comprehensive overview of this area, discussing random and Gaussian vectors, outlining the results necessary for the creation of Wiener and adaptive filters used for stationary signals, as well as examining Kalman filters which ar

  14. A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.

    Science.gov (United States)

    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.

  15. Quick-change filter cartridge

    Science.gov (United States)

    Rodgers, John C.; McFarland, Andrew R.; Ortiz, Carlos A.

    1995-01-01

    A quick-change filter cartridge. In sampling systems for measurement of airborne materials, a filter element is introduced into the sampled airstream such that the aerosol constituents are removed and deposited on the filter. Fragile sampling media often require support in order to prevent rupture during sampling, and careful mounting and sealing to prevent misalignment, tearing, or creasing which would allow the sampled air to bypass the filter. Additionally, handling of filter elements may introduce cross-contamination or exposure of operators to toxic materials. Moreover, it is desirable to enable the preloading of filter media into quick-change cartridges in clean laboratory environments, thereby simplifying and expediting the filter-changing process in the field. The quick-change filter cartridge of the present invention permits the application of a variety of filter media in many types of instruments and may also be used in automated systems. The cartridge includes a base through which a vacuum can be applied to draw air through the filter medium which is located on a porous filter support and held there by means of a cap which forms an airtight seal with the base. The base is also adapted for receiving absorbing media so that both particulates and gas-phase samples may be trapped for investigation, the latter downstream of the aerosol filter.

  16. Selection vector filter framework

    Science.gov (United States)

    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.

  17. Reference Values for Spirometry Derived Using Lambda, Mu, Sigma (LMS) Method in Korean Adults: in Comparison with Previous References.

    Science.gov (United States)

    Jo, Bum Seak; Myong, Jun Pyo; Rhee, Chin Kook; Yoon, Hyoung Kyu; Koo, Jung Wan; Kim, Hyoung Ryoul

    2018-01-15

    The present study aimed to update the prediction equations for spirometry and their lower limits of normal (LLN) by using the lambda, mu, sigma (LMS) method and to compare the outcomes with the values of previous spirometric reference equations. Spirometric data of 10,249 healthy non-smokers (8,776 females) were extracted from the fourth and fifth versions of the Korea National Health and Nutrition Examination Survey (KNHANES IV, 2007-2009; V, 2010-2012). Reference equations were derived using the LMS method which allows modeling skewness (lambda [L]), mean (mu [M]), and coefficient of variation (sigma [S]). The outcome equations were compared with previous reference values. Prediction equations were presented in the following form: predicted value = e{a + b × ln(height) + c × ln(age) + M - spline}. The new predicted values for spirometry and their LLN derived using the LMS method were shown to more accurately reflect transitions in pulmonary function in young adults than previous prediction equations derived using conventional regression analysis in 2013. There were partial discrepancies between the new reference values and the reference values from the Global Lung Function Initiative in 2012. The results should be interpreted with caution for young adults and elderly males, particularly in terms of the LLN for forced expiratory volume in one second/forced vital capacity in elderly males. Serial spirometry follow-up, together with correlations with other clinical findings, should be emphasized in evaluating the pulmonary function of individuals. Future studies are needed to improve the accuracy of reference data and to develop continuous reference values for spirometry across all ages. © 2018 The Korean Academy of Medical Sciences.

  18. Blended particle filters for large-dimensional chaotic dynamical systems

    Science.gov (United States)

    Majda, Andrew J.; Qi, Di; Sapsis, Themistoklis P.

    2014-01-01

    A major challenge in contemporary data science is the development of statistically accurate particle filters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particle filters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particle filters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particle filter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below. PMID:24825886

  19. Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications

    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.

  20. Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications

    Science.gov (United States)

    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

  1. Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications.

    Science.gov (United States)

    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.

  2. OPUS One: An Intelligent Adaptive Learning Environment Using Artificial Intelligence Support

    Science.gov (United States)

    Pedrazzoli, Attilio

    2010-06-01

    AI based Tutoring and Learning Path Adaptation are well known concepts in e-Learning scenarios today and increasingly applied in modern learning environments. In order to gain more flexibility and to enhance existing e-learning platforms, the OPUS One LMS Extension package will enable a generic Intelligent Tutored Adaptive Learning Environment, based on a holistic Multidimensional Instructional Design Model (PENTHA ID Model), allowing AI based tutoring and adaptation functionality to existing Web-based e-learning systems. Relying on "real time" adapted profiles, it allows content- / course authors to apply a dynamic course design, supporting tutored, collaborative sessions and activities, as suggested by modern pedagogy. The concept presented combines a personalized level of surveillance, learning activity- and learning path adaptation suggestions to ensure the students learning motivation and learning success. The OPUS One concept allows to implement an advanced tutoring approach combining "expert based" e-tutoring with the more "personal" human tutoring function. It supplies the "Human Tutor" with precise, extended course activity data and "adaptation" suggestions based on predefined subject matter rules. The concept architecture is modular allowing a personalized platform configuration.

  3. Adaptive spatial filtering for daytime satellite quantum key distribution

    Science.gov (United States)

    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.

  4. Adaptive spatial filtering of daytime sky noise in a satellite quantum key distribution downlink receiver

    Science.gov (United States)

    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.

  5. Performance Enhancement of Pharmacokinetic Diffuse Fluorescence Tomography by Use of Adaptive Extended Kalman Filtering.

    Science.gov (United States)

    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.

  6. Improving Assessment Processes in Higher Education: Student and Teacher Perceptions of the Effectiveness of a Rubric Embedded in a LMS

    Science.gov (United States)

    Atkinson, Doug; Lim, Siew Leng

    2013-01-01

    Students and teachers play different roles and thus have different perceptions about the effectiveness of assessment including structure, feedback, consistency, fairness and efficiency. In an undergraduate Business Information Systems course, a rubric was designed and semi-automated through a learning management system (LMS) to provide formative…

  7. Failure detection by adaptive lattice modelling using Kalman filtering methodology : application to NPP

    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

  8. Critique and sensitivity analysis of the compensation function used in the LMS Hudson River striped bass models. Environmental Sciences Division publication No. 944

    International Nuclear Information System (INIS)

    Van Winkle, W.; Christensen, S.W.; Kauffman, G.

    1976-12-01

    The description and justification for the compensation function developed and used by Lawler, Matusky and Skelly Engineers (LMS) (under contract to Consolidated Edison Company of New York) in their Hudson River striped bass models are presented. A sensitivity analysis of this compensation function is reported, based on computer runs with a modified version of the LMS completely mixed (spatially homogeneous) model. Two types of sensitivity analysis were performed: a parametric study involving at least five levels for each of the three parameters in the compensation function, and a study of the form of the compensation function itself, involving comparison of the LMS function with functions having no compensation at standing crops either less than or greater than the equilibrium standing crops. For the range of parameter values used in this study, estimates of percent reduction are least sensitive to changes in YS, the equilibrium standing crop, and most sensitive to changes in KXO, the minimum mortality rate coefficient. Eliminating compensation at standing crops either less than or greater than the equilibrium standing crops results in higher estimates of percent reduction. For all values of KXO and for values of YS and KX at and above the baseline values, eliminating compensation at standing crops less than the equilibrium standing crops results in a greater increase in percent reduction than eliminating compensation at standing crops greater than the equilibrium standing crops

  9. Critique and sensitivity analysis of the compensation function used in the LMS Hudson River striped bass models. Environmental Sciences Division publication No. 944

    Energy Technology Data Exchange (ETDEWEB)

    Van Winkle, W.; Christensen, S.W.; Kauffman, G.

    1976-12-01

    The description and justification for the compensation function developed and used by Lawler, Matusky and Skelly Engineers (LMS) (under contract to Consolidated Edison Company of New York) in their Hudson River striped bass models are presented. A sensitivity analysis of this compensation function is reported, based on computer runs with a modified version of the LMS completely mixed (spatially homogeneous) model. Two types of sensitivity analysis were performed: a parametric study involving at least five levels for each of the three parameters in the compensation function, and a study of the form of the compensation function itself, involving comparison of the LMS function with functions having no compensation at standing crops either less than or greater than the equilibrium standing crops. For the range of parameter values used in this study, estimates of percent reduction are least sensitive to changes in YS, the equilibrium standing crop, and most sensitive to changes in KXO, the minimum mortality rate coefficient. Eliminating compensation at standing crops either less than or greater than the equilibrium standing crops results in higher estimates of percent reduction. For all values of KXO and for values of YS and KX at and above the baseline values, eliminating compensation at standing crops less than the equilibrium standing crops results in a greater increase in percent reduction than eliminating compensation at standing crops greater than the equilibrium standing crops.

  10. 自适应Kalman滤波算法在加速度计自标定中的应用%Application of adaptive Kalman filtering algorithm in autonomous calibration accelerometer

    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.

  11. An Examination of Faculty and Student Online Activity: Predictive Relationships of Student Academic Success in a Learning Management System (LMS)

    Science.gov (United States)

    Stamm, Randy Lee

    2013-01-01

    The purpose of this mixed method research study was to examine relationships in student and instructor activity logs and student performance benchmarks specific to enabling early intervention by the instructor in a Learning Management System (LMS). Instructor feedback was collected through a survey instrument to demonstrate perceived importance of…

  12. Computerized tomographic scanner with shaped radiation filter

    International Nuclear Information System (INIS)

    Carlson, R.W.; Walters, R.G.

    1981-01-01

    The invention comprises a shaped filter and a filter correction circuitry for computerized tomographic scanners. The shaped filter is a generally u-shaped block of filter material which is adapted to be mounted between the source of radiation and the scan circle. The u-shaped block has a parabolic recess. The filter material may be beryllium, aluminum, sulphur, calcium, titanium, erbium, copper, and compounds including oxides and alloys thereof. The filter correction circuit comprises a first filter correction profile adding circuit for adding a first scaler valve to each intensity valve in a data line. The data line is operated on by a beam hardness correction polynomial. After the beam hardness polynomial correction operation, a second filter correction circuit adds a second filter correction profile consisting of a table of scalor values, one corresponding to each intensity reading in the data line

  13. Tracking of multiple objects with time-adjustable composite correlation filters

    Science.gov (United States)

    Ruchay, Alexey; Kober, Vitaly; Chernoskulov, Ilya

    2017-09-01

    An algorithm for tracking of multiple objects in video based on time-adjustable adaptive composite correlation filtering is proposed. For each frame a bank of composite correlation filters are designed in such a manner to provide invariance to pose, occlusion, clutter, and illumination changes. The filters are synthesized with the help of an iterative algorithm, which optimizes the discrimination capability for each object. The filters are adapted to the objects changes online using information from the current and past scene frames. Results obtained with the proposed algorithm using real-life scenes are presented and compared with those obtained with state-of-the-art tracking methods in terms of detection efficiency, tracking accuracy, and speed of processing.

  14. Rapid estimation of high-parameter auditory-filter shapes

    Science.gov (United States)

    Shen, Yi; Sivakumar, Rajeswari; Richards, Virginia M.

    2014-01-01

    A Bayesian adaptive procedure, the quick-auditory-filter (qAF) procedure, was used to estimate auditory-filter shapes that were asymmetric about their peaks. In three experiments, listeners who were naive to psychoacoustic experiments detected a fixed-level, pure-tone target presented with a spectrally notched noise masker. The qAF procedure adaptively manipulated the masker spectrum level and the position of the masker notch, which was optimized for the efficient estimation of the five parameters of an auditory-filter model. Experiment I demonstrated that the qAF procedure provided a convergent estimate of the auditory-filter shape at 2 kHz within 150 to 200 trials (approximately 15 min to complete) and, for a majority of listeners, excellent test-retest reliability. In experiment II, asymmetric auditory filters were estimated for target frequencies of 1 and 4 kHz and target levels of 30 and 50 dB sound pressure level. The estimated filter shapes were generally consistent with published norms, especially at the low target level. It is known that the auditory-filter estimates are narrower for forward masking than simultaneous masking due to peripheral suppression, a result replicated in experiment III using fewer than 200 qAF trials. PMID:25324086

  15. Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    T. Panigrahi

    2012-01-01

    Full Text Available In a distributed parameter estimation problem, during each sampling instant, a typical sensor node communicates its estimate either by the diffusion algorithm or by the incremental algorithm. Both these conventional distributed algorithms involve significant communication overheads and, consequently, defeat the basic purpose of wireless sensor networks. In the present paper, we therefore propose two new distributed algorithms, namely, block diffusion least mean square (BDLMS and block incremental least mean square (BILMS by extending the concept of block adaptive filtering techniques to the distributed adaptation scenario. The performance analysis of the proposed BDLMS and BILMS algorithms has been carried out and found to have similar performances to those offered by conventional diffusion LMS and incremental LMS algorithms, respectively. The convergence analyses of the proposed algorithms obtained from the simulation study are also found to be in agreement with the theoretical analysis. The remarkable and interesting aspect of the proposed block-based algorithms is that their communication overheads per node and latencies are less than those of the conventional algorithms by a factor as high as the block size used in the algorithms.

  16. 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.)

  17. Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators

    KAUST Repository

    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.

  18. Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators

    KAUST Repository

    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.

  19. Comparative Study of Online Open Circuit Voltage Estimation Techniques for State of Charge Estimation of Lithium-Ion Batteries

    Directory of Open Access Journals (Sweden)

    Hicham Chaoui

    2017-04-01

    Full Text Available Online estimation techniques are extensively used to determine the parameters of various uncertain dynamic systems. In this paper, online estimation of the open-circuit voltage (OCV of lithium-ion batteries is proposed by two different adaptive filtering methods (i.e., recursive least square, RLS, and least mean square, LMS, along with an adaptive observer. The proposed techniques use the battery’s terminal voltage and current to estimate the OCV, which is correlated to the state of charge (SOC. Experimental results highlight the effectiveness of the proposed methods in online estimation at different charge/discharge conditions and temperatures. The comparative study illustrates the advantages and limitations of each online estimation method.

  20. Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System

    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.

  1. Improvement of Detection of Hypoattenuation in Acute Ischemic Stroke in Unenhanced Computed Tomography Using an Adaptive Smoothing Filter

    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

  2. Apparatus for filtering radioactive fluids

    International Nuclear Information System (INIS)

    Gischel, E.H.

    1975-01-01

    Apparatus is provided for filtering radioactive particles from the cooling and/or auxiliary process water of a nuclear reactor, or nuclear fuel processing plant, or other installations wherein radioactive fluid systems are known to exist. The apparatus affords disposal of the captured particles in a manner which minimizes the exposure of operating personnel to radioactivity. The apparatus comprises a housing adapted to contain a removable filter cartridge assembly, a valve normally closing the lower end of the housing, an upwardly-open shipping cask located below the valve, and an elongated operating rod assembly projecting upwardly from the filter cartridge assembly and through the upper end of the housing to enable a workman to dismount the filter cartridge assembly from its housing and to lower the filter cartridge assembly through the valve and into the cask from a remote location above the housing. (U.S.)

  3. Using an LMS for Foreign Language Teaching/Learning: An Attempt Based on the "Cyclic Model of Learning"

    OpenAIRE

    SUMI, Seijiro; TAKEUCHI, Osamu

    2008-01-01

    The purposes of the study are (a) to put the “cyclic model of learning” into practice by means of an LMS (Learning Management System) for foreign language teaching /learning, and (b) to examine how the “cyclic model of learning” influences improvement of students' English ability in both proficiency and achievement. Current major concerns of CALL (Computer Assisted Language learning) research have shifted from piecemeal and experimental tests of the use of technology in a single computer lab ...

  4. Neural network training by Kalman filtering in process system monitoring

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1996-03-01

    Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

  5. Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model

    Directory of Open Access Journals (Sweden)

    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.

  6. Automatic identification and removal of ocular artifacts in EEG--improved adaptive predictor filtering for portable applications.

    Science.gov (United States)

    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.

  7. Integrating adaptive functionality in a LMS

    NARCIS (Netherlands)

    Sluijs, van der K.A.M.; Höver, K.M.

    2009-01-01

    Learning management systems are becoming more and more important in the learning process in both educational and corporate settings. They can nowadays even be used to server actual courses to the learner. However, one important feature is lacking in learning management systems: personalization. In

  8. 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.

  9. Comparison of cryogenic low-pass filters

    Science.gov (United States)

    Thalmann, M.; Pernau, H.-F.; Strunk, C.; Scheer, E.; Pietsch, T.

    2017-11-01

    Low-temperature electronic transport measurements with high energy resolution require both effective low-pass filtering of high-frequency input noise and an optimized thermalization of the electronic system of the experiment. In recent years, elaborate filter designs have been developed for cryogenic low-level measurements, driven by the growing interest in fundamental quantum-physical phenomena at energy scales corresponding to temperatures in the few millikelvin regime. However, a single filter concept is often insufficient to thermalize the electronic system to the cryogenic bath and eliminate spurious high frequency noise. Moreover, the available concepts often provide inadequate filtering to operate at temperatures below 10 mK, which are routinely available now in dilution cryogenic systems. Herein we provide a comprehensive analysis of commonly used filter types, introduce a novel compact filter type based on ferrite compounds optimized for the frequency range above 20 GHz, and develop an improved filtering scheme providing adaptable broad-band low-pass characteristic for cryogenic low-level and quantum measurement applications at temperatures down to few millikelvin.

  10. Comparison of cryogenic low-pass filters.

    Science.gov (United States)

    Thalmann, M; Pernau, H-F; Strunk, C; Scheer, E; Pietsch, T

    2017-11-01

    Low-temperature electronic transport measurements with high energy resolution require both effective low-pass filtering of high-frequency input noise and an optimized thermalization of the electronic system of the experiment. In recent years, elaborate filter designs have been developed for cryogenic low-level measurements, driven by the growing interest in fundamental quantum-physical phenomena at energy scales corresponding to temperatures in the few millikelvin regime. However, a single filter concept is often insufficient to thermalize the electronic system to the cryogenic bath and eliminate spurious high frequency noise. Moreover, the available concepts often provide inadequate filtering to operate at temperatures below 10 mK, which are routinely available now in dilution cryogenic systems. Herein we provide a comprehensive analysis of commonly used filter types, introduce a novel compact filter type based on ferrite compounds optimized for the frequency range above 20 GHz, and develop an improved filtering scheme providing adaptable broad-band low-pass characteristic for cryogenic low-level and quantum measurement applications at temperatures down to few millikelvin.

  11. Two-dimensional filtering of SPECT images using the Metz and Wiener filters

    International Nuclear Information System (INIS)

    King, M.A.; Schwinger, R.B.; Penney, B.C.; Doherty, P.W.

    1984-01-01

    Presently, single photon emission computed tomographic (SPECT) images are usually reconstructed by arbitrarily selecting a one-dimensional ''window'' function for use in reconstruction. A better method would be to automatically choose among a family of two-dimensional image restoration filters in such a way as to produce ''optimum'' image quality. Two-dimensional image processing techniques offer the advantages of a larger statistical sampling of the data for better noise reduction, and two-dimensional image deconvolution to correct for blurring during acquisition. An investigation of two such ''optimal'' digital image restoration techniques (the count-dependent Metz filter and the Wiener filter) was made. They were applied both as two-dimensional ''window'' functions for preprocessing SPECT images, and for filtering reconstructed images. Their performance was compared by measuring image contrast and per cent fractional standard deviation (% FSD) in multiple-acquisitions of the Jaszczak SPECT phantom at two different count levels. A statistically significant increase in image contrast and decrease in % FSD was observed with these techniques when compared to the results of reconstruction with a ramp filter. The adaptability of the techniques was manifested in a lesser % reduction in % FSD at the high count level coupled with a greater enhancement in image contrast. Using an array processor, processing time was 0.2 sec per image for the Metz filter and 3 sec for the Wiener filter. It is concluded that two-dimensional digital image restoration with these techniques can produce a significant increase in SPECT image quality

  12. Quantized, piecewise linear filter network

    DEFF Research Database (Denmark)

    Sørensen, John Aasted

    1993-01-01

    A quantization based piecewise linear filter network is defined. A method for the training of this network based on local approximation in the input space is devised. The training is carried out by repeatedly alternating between vector quantization of the training set into quantization classes...... and equalization of the quantization classes linear filter mean square training errors. The equalization of the mean square training errors is carried out by adapting the boundaries between neighbor quantization classes such that the differences in mean square training errors are reduced...

  13. A current detection based on an extension of the Prony's method

    Energy Technology Data Exchange (ETDEWEB)

    Deng, C.; Xia, X.; Gong, F. [Changsha Univ. of Science and Technology, Changsha (China). College of Electrical Engineering

    2009-07-01

    The Prony method for spectrum estimation was combined with an adaptive frequency tracking and current frequency demultiplication method as a harmonic detection system. An injective active power filter was used for effective harmonic wave management. The automated system includes a digital signal processor and a high-speed interfacing device. An algorithm was developed to consider the slow voltage changes in the power grid as well as potential mutations in current harmonics. Widrow-Hoff's LMS algorithm was used to develop rolling steps for the filter. The method was used to detect harmonic waves with a sampling frequency of 400 Hz. Results of the study showed that the detection method can be used in real time to detect fundamental and first harmonics in electric power grids. 10 refs., 2 tabs., 1 fig.

  14. Model Adaptation for Prognostics in a Particle Filtering Framework

    Data.gov (United States)

    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....

  15. Nonrigid registration with tissue-dependent filtering of the deformation field

    International Nuclear Information System (INIS)

    Staring, Marius; Klein, Stefan; Pluim, Josien P W

    2007-01-01

    In present-day medical practice it is often necessary to nonrigidly align image data. Current registration algorithms do not generally take the characteristics of tissue into account. Consequently, rigid tissue, such as bone, can be deformed elastically, growth of tumours may be concealed, and contrast-enhanced structures may be reduced in volume. We propose a method to locally adapt the deformation field at structures that must be kept rigid, using a tissue-dependent filtering technique. This adaptive filtering of the deformation field results in locally linear transformations without scaling or shearing. The degree of filtering is related to tissue stiffness: more filtering is applied at stiff tissue locations, less at parts of the image containing nonrigid tissue. The tissue-dependent filter is incorporated in a commonly used registration algorithm, using mutual information as a similarity measure and cubic B-splines to model the deformation field. The new registration algorithm is compared with this popular method. Evaluation of the proposed tissue-dependent filtering is performed on 3D computed tomography (CT) data of the thorax and on 2D digital subtraction angiography (DSA) images. The results show that tissue-dependent filtering of the deformation field leads to improved registration results: tumour volumes and vessel widths are preserved rather than affected

  16. Language Model Combination and Adaptation Using Weighted Finite State Transducers

    Science.gov (United States)

    Liu, X.; Gales, M. J. F.; Hieronymus, J. L.; Woodland, P. C.

    2010-01-01

    In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaption may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences

  17. A hand tracking algorithm with particle filter and improved GVF snake model

    Science.gov (United States)

    Sun, Yi-qi; Wu, Ai-guo; Dong, Na; Shao, Yi-zhe

    2017-07-01

    To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow (GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.

  18. Study of the algorithm of backtracking decoupling and adaptive extended Kalman filter based on the quaternion expanded to the state variable for underwater glider navigation.

    Science.gov (United States)

    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.

  19. Study of the Algorithm of Backtracking Decoupling and Adaptive Extended Kalman Filter Based on the Quaternion Expanded to the State Variable for Underwater Glider Navigation

    Science.gov (United States)

    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

  20. BaTiO3 thick fi lms obtained by tape casting from powders prepared by the oxalate route

    Directory of Open Access Journals (Sweden)

    Adelina Ianculescu

    2009-06-01

    Full Text Available BaTiO3 powders were prepared by co-precipitation via oxalate route. The size, morphology and particle size distribution of the oxalate powders have been optimized by the control of different synthesis parameters during the precipitation reaction (nature of salts, concentration of different solutions, aging time. The single phase BaTiO3 oxide particles were obtained after a thermal decomposition of the as-synthesized powders at 850°C for 4 hours under air atmosphere. Oxide powders with a suitable specifi c surface area were selected in order to obtain thick fi lms by the tape casting technique. The microstructure and dielectric properties of the thick films varied obviously depending on the deposition-calcination-sintering cycle used. A double depositioncalcination cycle followed by sintering, as well as a two step deposition-calcination-sintering procedure was used in order to improve the compactness and therefore, the dielectric behaviour. A higher dielectric constant value (~ 750 and lower dielectric losses (~ 2 % were achieved at room temperature and at 1 kHz frequency for the dense, double-deposited fi lm obtained after two deposition-calcination-sintering cycles. For this film, a superior value of the dielectric constant (~ 1100, almost frequency independent in the frequency range of 100 Hz – 10 kHz was gained also at the ferroelectric-paraelectric phase transition temperature of 130°C.

  1. Nonlinear Filtering with IMM Algorithm for Ultra-Tight GPS/INS Integration

    Directory of Open Access Journals (Sweden)

    Dah-Jing Jwo

    2013-05-01

    Full Text Available Abstract This paper conducts a performance evaluation for the ultra-tight integration of a Global positioning system (GPS and an inertial navigation system (INS, using nonlinear filtering approaches with an interacting multiple model (IMM algorithm. An ultra-tight GPS/INS architecture involves the integration of in-phase and quadrature components from the correlator of a GPS receiver with INS data. An unscented Kalman filter (UKF, which employs a set of sigma points by deterministic sampling, avoids the error caused by linearization as in an extended Kalman filter (EKF. Based on the filter structural adaptation for describing various dynamic behaviours, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the ultra-tight GPS/INS integration. The use of IMM enables tuning of an appropriate value for the process of noise covariance so as to maintain good estimation accuracy and tracking capability. Two examples are provided to illustrate the effectiveness of the design and demonstrate the effective improvement in navigation estimation accuracy. A performance comparison among various filtering methods for ultra-tight integration of GPS and INS is also presented. The IMM based nonlinear filtering approach demonstrates the effectiveness of the algorithm for improved positioning performance.

  2. Fast digital zooming system using directionally adaptive image interpolation and restoration.

    Science.gov (United States)

    Kang, Wonseok; Jeon, Jaehwan; Yu, Soohwan; Paik, Joonki

    2014-01-01

    This paper presents a fast digital zooming system for mobile consumer cameras using directionally adaptive image interpolation and restoration methods. The proposed interpolation algorithm performs edge refinement along the initially estimated edge orientation using directionally steerable filters. Either the directionally weighted linear or adaptive cubic-spline interpolation filter is then selectively used according to the refined edge orientation for removing jagged artifacts in the slanted edge region. A novel image restoration algorithm is also presented for removing blurring artifacts caused by the linear or cubic-spline interpolation using the directionally adaptive truncated constrained least squares (TCLS) filter. Both proposed steerable filter-based interpolation and the TCLS-based restoration filters have a finite impulse response (FIR) structure for real time processing in an image signal processing (ISP) chain. Experimental results show that the proposed digital zooming system provides high-quality magnified images with FIR filter-based fast computational structure.

  3. An adaptive compensation algorithm for temperature drift of micro-electro-mechanical systems gyroscopes using a strong tracking Kalman filter.

    Science.gov (United States)

    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.

  4. An Adaptive Compensation Algorithm for Temperature Drift of Micro-Electro-Mechanical Systems Gyroscopes Using a Strong Tracking Kalman Filter

    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.

  5. [Investigation of fast filter of ECG signals with lifting wavelet and smooth filter].

    Science.gov (United States)

    Li, Xuefei; Mao, Yuxing; He, Wei; Yang, Fan; Zhou, Liang

    2008-02-01

    The lifting wavelet is used to decompose the original ECG signals and separate them into the approach signals with low frequency and the detail signals with high frequency, based on frequency characteristic. Parts of the detail signals are ignored according to the frequency characteristic. To avoid the distortion of QRS Complexes, the approach signals are filtered by an adaptive smooth filter with a proper threshold value. Through the inverse transform of the lifting wavelet, the reserved approach signals are reconstructed, and the three primary kinds of noise are limited effectively. In addition, the method is fast and there is no time delay between input and output.

  6. An Adaptive Noise Cancellation System Based on Linear and Widely Linear Complex Valued Least Mean Square Algorithms for Removing Electrooculography Artifacts from Electroencephalography Signals

    Directory of Open Access Journals (Sweden)

    Engin Cemal MENGÜÇ

    2018-03-01

    Full Text Available In this study, an adaptive noise cancellation (ANC system based on linear and widely linear (WL complex valued least mean square (LMS algorithms is designed for removing electrooculography (EOG artifacts from electroencephalography (EEG signals. The real valued EOG and EEG signals (Fp1 and Fp2 given in dataset are primarily expressed as a complex valued signal in the complex domain. Then, using the proposed ANC system, the EOG artifacts are eliminated in the complex domain from the EEG signals. Expression of these signals in the complex domain allows us to remove EOG artifacts from two EEG channels simultaneously. Moreover, in this study, it has been shown that the complex valued EEG signal exhibits noncircular behavior, and in the case, the WL-CLMS algorithm enhances the performance of the ANC system compared to real-valued LMS and CLMS algorithms. Simulation results support the proposed approach.

  7. Design of a Flexible and Adaptable LMS Engine in Conformance with PoEML

    Directory of Open Access Journals (Sweden)

    Roberto Pérez-Rodríguez

    2009-03-01

    Full Text Available This paper describes the support of the Structural, Functional, Order and Temporal perspectives in PoEML. PoEML is a modeling language devoted to support a broad range of pedagogical approaches, from content-based, to collaborative and practical oriented. At this point, a main issue is to provide a good level of adaptability and flexibility. The final goal is to support changes in the educational process development, enabling the provision of different learning experiences depending on the learning goals, the learner needs and features, the previous results, etc. The introduced solution is based on the separation of concerns principle adopted in PoEML. Basically, the solution facilitates the use of a set of educational resources in different ways by separating the form in which such resources are organized (Structural perspective from the decisions of what has to be done (Functional perspective and when (Order and Temporal perspectives.

  8. Design of Power Cable UAV Intelligent Patrol System Based on Adaptive Kalman Filter Fuzzy PID Control

    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.

  9. Adaptive lattice decision-feedback equalizers - Their performance and application to time-variant multipath channnels

    Science.gov (United States)

    Ling, F.; Proakis, J. G.

    1985-04-01

    This paper presents two types of adaptive lattice decision-feedback equalizers (DFE), the least squares (LS) lattice DFE and the gradient lattice DFE. Their performance has been investigated on both time-invariant and time-variant channels through computer simulations and compared to other kinds of equalizers. An analysis of the self-noise and tracking characteristics of the LS DFE and the DFE employing the Widrow-Hoff least mean square adaptive algorithm (LMS DFE) are also given. The analysis and simulation results show that the LS lattice DFE has the faster initial convergence rate, while the gradient lattice DFE is computationally more efficient. The main advantages of the lattice DFE's are their numerical stability, their computational efficiency, the flexibility to change their length, and their excellent capabilities for tracking rapidly time-variant channels.

  10. Design and control of hybrid active power filters

    CERN Document Server

    Lam, Chi-Seng

    2014-01-01

    Design and Control of Hybrid Active Power Filters presents an overview of the current quality problems and their compensators. To get a balance between the system cost and performance, hybrid active power filters (HAPFs) are valuable. The book presents the coverage of resonance phenomena prevention capability, filtering performance and system robustness analysis of HAPF; nonlinear inverter current slope characteristics and their linear operation region requirement analysis of the hysteresis PWM for the HAPF; minimum inverter capacity design procedure of HAPF, adaptive dc-link voltage controller for the HAPF and the real design example of a 220V 10kVA HAPF, in which the system performance analysis method, minimum dc voltage deduction concept and adaptive dc voltage idea can be further extended into the other active compensators, such as APF, static synchronous compensator STATCOM, etc. This book will benefit researchers, graduate students, and electrical power engineers in the field of power-quality compensati...

  11. Frequency Adaptability and Waveform Design for OFDM Radar Space-Time Adaptive Processing

    Energy Technology Data Exchange (ETDEWEB)

    Sen, Satyabrata [ORNL; Glover, Charles Wayne [ORNL

    2012-01-01

    We propose an adaptive waveform design technique for an orthogonal frequency division multiplexing (OFDM) radar signal employing a space-time adaptive processing (STAP) technique. We observe that there are inherent variabilities of the target and interference responses in the frequency domain. Therefore, the use of an OFDM signal can not only increase the frequency diversity of our system, but also improve the target detectability by adaptively modifying the OFDM coefficients in order to exploit the frequency-variabilities of the scenario. First, we formulate a realistic OFDM-STAP measurement model considering the sparse nature of the target and interference spectra in the spatio-temporal domain. Then, we show that the optimal STAP-filter weight-vector is equal to the generalized eigenvector corresponding to the minimum generalized eigenvalue of the interference and target covariance matrices. With numerical examples we demonstrate that the resultant OFDM-STAP filter-weights are adaptable to the frequency-variabilities of the target and interference responses, in addition to the spatio-temporal variabilities. Hence, by better utilizing the frequency variabilities, we propose an adaptive OFDM-waveform design technique, and consequently gain a significant amount of STAP-performance improvement.

  12. Filters for radioactive liquid wastes

    International Nuclear Information System (INIS)

    Koshiba, Yukihiko; Kawashima, Akio

    1980-01-01

    In the crud generated in the reactor cooling water for nuclear power plants, iron oxides (hematite and magnetite) are contained as the main components, and also Co, Mn, Fe, Cr exist as radioactive nuclides. A new filter to separate these cruds, nuclepore membrane filter (NPMF), was investigated for its adaptability, and has been adopted as a practical filter for radioactive liquid wastes. The NPMF has such features as the possibility of complete automation of operation, no generation of secondary wastes, and easy maintenance, because the NPMF has uniform circular holes in poly-carbonate thin films, and shows the properties of stable filtering of particulates, capability of back washing, and others. The elements mounted in a practical system have such construction that the membrane is cut in the form of doughnut, and sandwiched with 100 mesh polyester nets (spacer); the obtained unit filter (cassette) is mounted on the stackable plate of the same size; and 80 pieces of this cassette are formed in a filter of 4 m 2 filtering area. The performance varies with the properties of suspended matters and the turbidity of wastes. For example, the filtered liquid of 0.1 ppm or less can be obtained when the 1 μm filter material is used to treat the liquid waste containing 1 to 100 ppm suspended matters. Usually back washed water is produced by about 1/100 of treated liquid wastes. The lifetime of the membrane is expected to be 1 or 2 years if crud is the main component. (Wakatsuki, Y.)

  13. Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

    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.

  14. Use of containers with sterilizing filter in autologous serum eyedrops.

    Science.gov (United States)

    López-García, José S; García-Lozano, Isabel

    2012-11-01

    To assess the effect of the use of containers with an adapted sterilizing filter on the contamination of autologous serum eyedrops. Prospective, consecutive, comparative, and randomized study. Thirty patients with Sjögren syndrome. One hundred seventy-six autologous serum containers used in home therapy were studied; 48 of them included an adapted filter (Hyabak; Thea, Clermont-Ferrand, France), and the other 128 were conventional containers. Containers equipped with a filter were tested at 7, 14, 21, and 28 days of use, whereas conventional containers were studied after 7 days of use. In addition, testing for contamination was carried out in 14 conventional containers used during in-patient therapy every week for 4 weeks. In all cases, the preparation of the autologous serum was similar. Blood agar and chocolate agar were used as regular culture media for the microbiologic studies, whereas Sabouraud agar with chloramphenicol was the medium for fungal studies. Microbiologic contamination of containers with autologous serum eyedrops. Only one of the containers with an adapted sterilizing filter (2.1%) became contaminated with Staphylococcus epidermidis after 1 month of treatment, whereas the contamination rate among conventional containers reached 28.9% after 7 days of treatment. The most frequent germs found in the samples were coagulase-negative Staphylococcus (48.6%). With regard the containers used in the in-patient setting, 2 (14.3%) became contaminated after 2 weeks, 5 (35.7%) became contaminated after 3 weeks, and 5 (50%) became contaminated after 4 weeks, leaving 7 (50%) that did not become contaminated after 1 month of treatment. Using containers with an adapted filter significantly reduces the contamination rates in autologous serum eyedrops, thus extending the use of such container by the patients for up to 4 weeks with virtually no contamination risks. Copyright © 2012 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

  15. An efficient incremental learning mechanism for tracking concept drift in spam filtering.

    Directory of Open Access Journals (Sweden)

    Jyh-Jian Sheu

    Full Text Available This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email's header and apply decision tree data mining technique to look for the association rules about spams. Then, we propose an efficient systematic filtering method based on these association rules. Our systematic method has the following major advantages: (1 Checking only the header sections of emails, which is different from those spam filtering methods at present that have to analyze fully the email's content. Meanwhile, the email filtering accuracy is expected to be enhanced. (2 Regarding the solution to the problem of concept drift, we propose a window-based technique to estimate for the condition of concept drift for each unknown email, which will help our filtering method in recognizing the occurrence of spam. (3 We propose an incremental learning mechanism for our filtering method to strengthen the ability of adapting to the dynamic environment.

  16. Adaptive Colour Feature Identification in Image for Object Tracking

    Directory of Open Access Journals (Sweden)

    Feng Su

    2012-01-01

    Full Text Available Identification and tracking of a moving object using computer vision techniques is important in robotic surveillance. In this paper, an adaptive colour filtering method is introduced for identifying and tracking a moving object appearing in image sequences. This filter is capable of automatically identifying the most salient colour feature of the moving object in the image and using this for a robot to track the object. The method enables the selected colour feature to adapt to surrounding condition when it is changed. A method of determining the region of interest of the moving target is also developed for the adaptive colour filter to extract colour information. Experimental results show that by using a camera mounted on a robot, the proposed methods can perform robustly in tracking a randomly moving object using adaptively selected colour features in a crowded environment.

  17. Study of the Algorithm of Backtracking Decoupling and Adaptive Extended Kalman Filter Based on the Quaternion Expanded to the State Variable for Underwater Glider Navigation

    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.

  18. RLS Channel Estimation with Adaptive Forgetting Factor for DS-CDMA Frequency-Domain Equalization

    Science.gov (United States)

    Kojima, Yohei; Tomeba, Hiromichi; Takeda, Kazuaki; Adachi, Fumiyuki

    Frequency-domain equalization (FDE) based on the minimum mean square error (MMSE) criterion can increase the downlink bit error rate (BER) performance of DS-CDMA beyond that possible with conventional rake combining in a frequency-selective fading channel. FDE requires accurate channel estimation. Recently, we proposed a pilot-assisted channel estimation (CE) based on the MMSE criterion. Using MMSE-CE, the channel estimation accuracy is almost insensitive to the pilot chip sequence, and a good BER performance is achieved. In this paper, we propose a channel estimation scheme using one-tap recursive least square (RLS) algorithm, where the forgetting factor is adapted to the changing channel condition by the least mean square (LMS)algorithm, for DS-CDMA with FDE. We evaluate the BER performance using RLS-CE with adaptive forgetting factor in a frequency-selective fast Rayleigh fading channel by computer simulation.

  19. Adaptive estimation of a time-varying phase with coherent states: Smoothing can give an unbounded improvement over filtering

    Science.gov (United States)

    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.

  20. Ground roll attenuation using a curvelet-SVD filter: a case study from the west of Iran

    International Nuclear Information System (INIS)

    Boustani, Bahareh; Javaherian, Abdorahim; Mortazavi, Seyed Ahmad; Torabi, Siyavash

    2013-01-01

    In reflection seismology, a ground roll is a low frequency, low velocity and high amplitude surface wave. It usually has stronger amplitude than reflections, and masks valuable information carried by signals. Many filters in different domains have been used for ground roll attenuation such as tau-p and f-k filters. Recently, in many studies, the curvelet transform has been used for ground roll attenuation. The curvelet transform creates a good separation between ground roll and reflections in dip and frequency, especially in high frequency subbands. In this paper, based on the adaptive curvelet filter, a new method is introduced through a combination of the adaptive curvelet and adaptive singular value decomposition (ASVD) filters and is called a curvelet-SVD filter. In this filter, the subbands in a curvelet domain are divided into three categories based on the ground roll energy in each subband. These categories are subbands (1) with high energy containing only ground roll, (2) with medium energy that contains both ground roll and reflections, and (3) with low energy containing only reflections. The category that contains only ground roll will be muted, as in common usage of the adaptive curvelet filter. If the category that contains both ground roll and reflections is unchanged, part of the ground roll will not be attenuated. If this category is muted, part of the reflections will be damaged. To overcome this problem, ASVD is applied to attenuate ground roll in the subbands of this category. The category that contains only reflections will not be touched. This filter was applied to a synthetic and to a real data set from the west of Iran. The synthetic data contained dispersed and aliased ground roll. A curvelet-SVD filter could attenuate dispersed ground roll but it could not completely attenuate aliased ground roll. Because of the damage to the reflections, the energy threshold for applying ASVD in the curvelet domain could not be selected any lower. In real

  1. Receding-horizon adaptive contyrol of aero-optical wavefronts

    NARCIS (Netherlands)

    Tesch, J.; Gibson, S.; Verhaegen, M.

    2013-01-01

    A new method for adaptive prediction and correction of wavefront errors in adaptive optics (AO) is introduced. The new method is based on receding-horizon control design and an adaptive lattice filter. Experimental results presented illustrate the capability of the new adaptive controller to predict

  2. Wave-filter-based approach for generation of a quiet space in a rectangular cavity

    Science.gov (United States)

    Iwamoto, Hiroyuki; Tanaka, Nobuo; Sanada, Akira

    2018-02-01

    This paper is concerned with the generation of a quiet space in a rectangular cavity using active wave control methodology. It is the purpose of this paper to present the wave filtering method for a rectangular cavity using multiple microphones and its application to an adaptive feedforward control system. Firstly, the transfer matrix method is introduced for describing the wave dynamics of the sound field, and then feedforward control laws for eliminating transmitted waves is derived. Furthermore, some numerical simulations are conducted that show the best possible result of active wave control. This is followed by the derivation of the wave filtering equations that indicates the structure of the wave filter. It is clarified that the wave filter consists of three portions; modal group filter, rearrangement filter and wave decomposition filter. Next, from a numerical point of view, the accuracy of the wave decomposition filter which is expressed as a function of frequency is investigated using condition numbers. Finally, an experiment on the adaptive feedforward control system using the wave filter is carried out, demonstrating that a quiet space is generated in the target space by the proposed method.

  3. 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 ...

  4. Processing of pulse oximeter signals using adaptive filtering and autocorrelation to isolate perfusion and oxygenation components

    Science.gov (United States)

    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.

  5. Flexible Riser Monitoring Using Hybrid Magnetic/Optical Strain Gage Techniques through RLS Adaptive Filtering

    Directory of Open Access Journals (Sweden)

    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.

  6. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    Science.gov (United States)

    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.

  7. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    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.

  8. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Science.gov (United States)

    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

  9. Digital implementation of the preloaded filter pulse processor

    International Nuclear Information System (INIS)

    Westphal, G.P.; Cadek, G.R.; Keroe, N.; Sauter, TH.; Thorwartl, P.C.

    1995-01-01

    Adapting it's processing time to the respective pulse intervals, the Preloaded Filter (PLF) pulse processor offers optimum resolution together with highest possible throughput rates. The PLF algorithm could be formulated in a recursive manner which made possible it's implementation by means of a large field-programmable gate array, as a fast, pipe-lined digital processor with 10 MHz maximum throughput rate. While pre-filter digitization by an ADC with 12 bit resolution and 10M Hz sampling rate resulted in a poorer resolution than that of an analog filter, a digital PLF based on an ADC with 14 bit resolution and 10 MHz sampling rate, surpassed high-quality analog filters in resolution, throughput rate and long-term stability. (author) 6 refs.; 7 figs

  10. A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty

    International Nuclear Information System (INIS)

    Li, Yanwen; Wang, Chao; Gong, Jinfeng

    2016-01-01

    An accurate battery State of Charge estimation plays an important role in battery electric vehicles. This paper makes two contributions to the existing literature. (1) A recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly. (2) The statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty, and a novel combination algorithm of strong tracking unscented Kalman filter and adaptive unscented Kalman filter has been developed to estimate SOC (State of Charge). Experimental results indicate that the novel algorithm has a good performance in estimating the battery SOC against initial SOC errors and voltage sensor drift. A comparison with the unscented Kalman filter-based algorithms and adaptive unscented Kalman filter-based algorithms shows that the proposed SOC estimation method has better accuracy, robustness and convergence behavior. - Highlights: • Recursive least squares method with fuzzy adaptive forgetting factor is presented. • The innovation obeying chi-square distribution is used to identify uncertainty. • A combination Karman filter approach for State of Charge estimation is presented. • The performance of the proposed method is verified by comparison results.

  11. Effectual switching filter for removing impulse noise using a SCM detector

    Science.gov (United States)

    Yuan, Jin-xia; Zhang, Hong-juan; Ma, Yi-de

    2012-03-01

    An effectual method is proposed to remove impulse noise from corrupted color images. The spiking cortical model (SCM) is adopted as a noise detector to identify noisy pixels in each channel of color images, and detected noise pixels are saved in three marking matrices. According to the three marking matrices, the detected noisy pixels are divided into two types (type I and type II). They are filtered differently: an adaptive median filter is used for type I and an adaptive vector median for type II. Noise-free pixels are left unchanged. Extensive experiments show that the proposed method outperforms most of the other well-known filters in the aspects of both visual and objective quality measures, and this method can also reduce the possibility of generating color artifacts while preserving image details.

  12. Nonlinear control and filtering using differential flatness approaches applications to electromechanical systems

    CERN Document Server

    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...

  13. Multi-Stage Adaptive Noise Cancellation Technique for Synthetic Hard-α Inclusion

    International Nuclear Information System (INIS)

    Kim, Jae Joon

    2003-01-01

    Adaptive noise cancellation techniques are ideally suitable for reducing spatially varying noise due to the grain structure of material in ultrasonic nondestructive evaluation. Grain noises have an un-correlation property, while flaw echoes are correlated. Thus, adaptive filtering algorithms use the correlation properties of signals to enhance the signal-to-noise ratio (SNR) of the output signal. In this paper, a multi-stage adaptive noise cancellation (MANC) method using adaptive least mean square error (LMSE) filter for enhancing flaw detection in ultrasonic signals is proposed

  14. Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing

    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.

  15. A Fixed-Lag Kalman Smoother to Filter Power Line Interference in Electrocardiogram Recordings.

    Science.gov (United States)

    Warmerdam, G J J; Vullings, R; Schmitt, L; Van Laar, J O E H; Bergmans, J W M

    2017-08-01

    Filtering power line interference (PLI) from electrocardiogram (ECG) recordings can lead to significant distortions of the ECG and mask clinically relevant features in ECG waveform morphology. The objective of this study is to filter PLI from ECG recordings with minimal distortion of the ECG waveform. In this paper, we propose a fixed-lag Kalman smoother with adaptive noise estimation. The performance of this Kalman smoother in filtering PLI is compared to that of a fixed-bandwidth notch filter and several adaptive PLI filters that have been proposed in the literature. To evaluate the performance, we corrupted clean neonatal ECG recordings with various simulated PLI. Furthermore, examples are shown of filtering real PLI from an adult and a fetal ECG recording. The fixed-lag Kalman smoother outperforms other PLI filters in terms of step response settling time (improvements that range from 0.1 to 1 s) and signal-to-noise ratio (improvements that range from 17 to 23 dB). Our fixed-lag Kalman smoother can be used for semi real-time applications with a limited delay of 0.4 s. The fixed-lag Kalman smoother presented in this study outperforms other methods for filtering PLI and leads to minimal distortion of the ECG waveform.

  16. Assessing a robust ensemble-based Kalman filter for efficient ecosystem data assimilation of the Cretan Sea

    KAUST Repository

    Triantafyllou, George N.; Hoteit, Ibrahim; Luo, Xiaodong; Tsiaras, Kostas P.; Petihakis, George

    2013-01-01

    An application of an ensemble-based robust filter for data assimilation into an ecosystem model of the Cretan Sea is presented and discussed. The ecosystem model comprises two on-line coupled sub-models: the Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM). The filtering scheme is based on the Singular Evolutive Interpolated Kalman (SEIK) filter which is implemented with a time-local H∞ filtering strategy to enhance robustness and performances during periods of strong ecosystem variability. Assimilation experiments in the Cretan Sea indicate that robustness can be achieved in the SEIK filter by introducing an adaptive inflation scheme of the modes of the filter error covariance matrix. Twin-experiments are performed to evaluate the performance of the assimilation system and to study the benefits of using robust filtering in an ensemble filtering framework. Pseudo-observations of surface chlorophyll, extracted from a model reference run, were assimilated every two days. Simulation results suggest that the adaptive inflation scheme significantly improves the behavior of the SEIK filter during periods of strong ecosystem variability. © 2012 Elsevier B.V.

  17. Assessing a robust ensemble-based Kalman filter for efficient ecosystem data assimilation of the Cretan Sea

    KAUST Repository

    Triantafyllou, George N.

    2013-09-01

    An application of an ensemble-based robust filter for data assimilation into an ecosystem model of the Cretan Sea is presented and discussed. The ecosystem model comprises two on-line coupled sub-models: the Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM). The filtering scheme is based on the Singular Evolutive Interpolated Kalman (SEIK) filter which is implemented with a time-local H∞ filtering strategy to enhance robustness and performances during periods of strong ecosystem variability. Assimilation experiments in the Cretan Sea indicate that robustness can be achieved in the SEIK filter by introducing an adaptive inflation scheme of the modes of the filter error covariance matrix. Twin-experiments are performed to evaluate the performance of the assimilation system and to study the benefits of using robust filtering in an ensemble filtering framework. Pseudo-observations of surface chlorophyll, extracted from a model reference run, were assimilated every two days. Simulation results suggest that the adaptive inflation scheme significantly improves the behavior of the SEIK filter during periods of strong ecosystem variability. © 2012 Elsevier B.V.

  18. Neural Networks and Their Applications in Noise - Information Storage and Retrieval Systems, and in the Rejection of Narrow-Band Interference in Direct Sequence Spread Spectrum Receivers.

    Science.gov (United States)

    Bijjani, Richard

    1990-01-01

    The introduction of neural network models has created new algorithms and application opportunities in parallel signal processing. Here, an M-ary extension of the Hopfield model is presented and is shown to have a substantially higher error correction capability, when compared to the Hopfield model. A digital image processing experiment is successfully conducted to illustrate the new model, and a holographic implementation is proposed. The use of neural networks and of linear combination filters are investigated in connection with the problem of user identification in code division multiple access systems. A multi-layer back-propagation perceptron model is then presented as a means of detecting a wideband signal in the presence of narrowband jammers and additive white Gaussian noise. The performance of the neural network is compared to that of the estimation type filter that uses a least mean squared adaptive filter, in terms of the interference rejection capability, the bit error rate and the overall robustness of the system. The nonlinear neural network filter is found to offer a faster convergence rate and an overall better performance over the LMS Widrow-Hoff filter.

  19. Students' acceptance of learning management systems: i-LearnV3 ...

    African Journals Online (AJOL)

    learning to more advanced software of LMS. This study was an advanced study from the previous to identify the new factors that contributed to the students' acceptance of a new verssion of i-Learn as a medium of LMS. The study adapted the ...

  20. A Framework of Finite-model Kalman Filter with Case Study: MVDP-FMKF Algorithm%A Framework of Finite-model Kalman Filter with Case Study:MVDP-FMKF Algorithm

    Institute of Scientific and Technical Information of China (English)

    FENG Bo; MA Hong-Bin; FU Meng-Yin; WANG Shun-Ting

    2013-01-01

    Kalman filtering techniques have been widely used in many applications,however,standard Kalman filters for linear Gaussian systems usually cannot work well or even diverge in the presence of large model uncertainty.In practical applications,it is expensive to have large number of high-cost experiments or even impossible to obtain an exact system model.Motivated by our previous pioneering work on finite-model adaptive control,a framework of finite-model Kalman filtering is introduced in this paper.This framework presumes that large model uncertainty may be restricted by a finite set of known models which can be very different from each other.Moreover,the number of known models in the set can be flexibly chosen so that the uncertain model may always be approximated by one of the known models,in other words,the large model uncertainty is "covered" by the "convex hull" of the known models.Within the presented framework according to the idea of adaptive switching via the minimizing vector distance principle,a simple finite-model Kalman filter,MVDP-FMKF,is mathematically formulated and illustrated by extensive simulations.An experiment of MEMS gyroscope drift has verified the effectiveness of the proposed algorithm,indicating that the mechanism of finite-model Kalman filter is useful and efficient in practical applications of Kalman filters,especially in inertial navigation systems.

  1. Adaptive Digital Predistortion Schemes to Linearize RF Power Amplifiers with Memory Effects

    Institute of Scientific and Technical Information of China (English)

    ZHANG Peng; WU Si-liang; ZHANG Qin

    2008-01-01

    To compensate for nonlinear distortion introduced by RF power amplifiers (PAs) with memory effects, two correlated models, namely an extended memory polynomial (EMP) model and a memory lookup table (LUT) model, are proposed for predistorter design. Two adaptive digital predistortion (ADPD) schemes with indirect learning architecture are presented. One adopts the EMP model and the recursive least square (RLS) algorithm, and the other utilizes the memory LUT model and the least mean square (LMS) algorithm. Simulation results demonstrate that the EMP-based ADPD yields the best linearization performance in terms of suppressing spectral regrowth. It is also shown that the ADPD based on memory LUT makes optimum tradeoff between performance and computational complexity.

  2. Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

    Directory of Open Access Journals (Sweden)

    M. Kumar

    2016-01-01

    Full Text Available Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN and the Cat Swarm Optimization (CSO is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR, have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.

  3. Practical feasibility of Kalman filters for the state estimation of lithium-ion batteries

    OpenAIRE

    Campestrini, Christian

    2018-01-01

    This work investigates the feasibility of the Kalman filter for the state estimation of lithium-ion cells and modules under real conditions. Therefore, the dependencies of the cells during ageing are shown and various Kalman filter types are compared. The strongly varying model parameters, as well as the temperature and ageing dependent open circuit voltage, require an empirical adaptation of the inconstant and non-linear filter tuning parameters. The performance of the Kalman filter in a rea...

  4. Digital high-pass filter deconvolution by means of an infinite impulse response filter

    Energy Technology Data Exchange (ETDEWEB)

    Födisch, P., E-mail: p.foedisch@hzdr.de [Helmholtz-Zentrum Dresden - Rossendorf, Department of Research Technology, Bautzner Landstr. 400, 01328 Dresden (Germany); Wohsmann, J. [Helmholtz-Zentrum Dresden - Rossendorf, Department of Research Technology, Bautzner Landstr. 400, 01328 Dresden (Germany); Dresden University of Applied Sciences, Faculty of Electrical Engineering, Friedrich-List-Platz 1, 01069 Dresden (Germany); Lange, B. [Helmholtz-Zentrum Dresden - Rossendorf, Department of Research Technology, Bautzner Landstr. 400, 01328 Dresden (Germany); Schönherr, J. [Dresden University of Applied Sciences, Faculty of Electrical Engineering, Friedrich-List-Platz 1, 01069 Dresden (Germany); Enghardt, W. [OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstr. 74, PF 41, 01307 Dresden (Germany); Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology, Bautzner Landstr. 400, 01328 Dresden (Germany); German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg (Germany); Kaever, P. [Helmholtz-Zentrum Dresden - Rossendorf, Department of Research Technology, Bautzner Landstr. 400, 01328 Dresden (Germany); Dresden University of Applied Sciences, Faculty of Electrical Engineering, Friedrich-List-Platz 1, 01069 Dresden (Germany)

    2016-09-11

    In the application of semiconductor detectors, the charge-sensitive amplifier is widely used in front-end electronics. The output signal is shaped by a typical exponential decay. Depending on the feedback network, this type of front-end electronics suffers from the ballistic deficit problem, or an increased rate of pulse pile-ups. Moreover, spectroscopy applications require a correction of the pulse-height, while a shortened pulse-width is desirable for high-throughput applications. For both objectives, digital deconvolution of the exponential decay is convenient. With a general method and the signals of our custom charge-sensitive amplifier for cadmium zinc telluride detectors, we show how the transfer function of an amplifier is adapted to an infinite impulse response (IIR) filter. This paper investigates different design methods for an IIR filter in the discrete-time domain and verifies the obtained filter coefficients with respect to the equivalent continuous-time frequency response. Finally, the exponential decay is shaped to a step-like output signal that is exploited by a forward-looking pulse processing.

  5. Adaptive Kalman filtering for histogram-based appearance learning in infrared imagery.

    Science.gov (United States)

    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.

  6. Unified Digital Periodic Signal Filters for Power Converter Systems

    DEFF Research Database (Denmark)

    Yang, Yongheng; Xin, Zhen; Zhou, Keliang

    2017-01-01

    Periodic signal controllers like repetitive and resonant controllers have demonstrated much potential in the control of power electronic converters, where periodic signals (e.g., ac voltages and currents) can be precisely regulated to follow references. Beyond the control of periodic signals, ac...... signal processing (e.g., in synchronization and pre-filtering) is also very important for power converter systems. Hence, this paper serves to unify digital periodic signal filters so as to maximize their roles in power converter systems (e.g., enhance the control of ac signals). The unified digital...... periodic signal filters behave like a comb filter, but it can also be configured to selectively filter out the harmonics of interest (e.g., the odd-order harmonics in single-phase power converter systems). Moreover, a virtual variable-sampling-frequency unit delay that enables frequency adaptive periodic...

  7. Spectral analysis and filter theory in applied geophysics

    CERN Document Server

    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...

  8. Correlation Filter Learning Toward Peak Strength for Visual Tracking.

    Science.gov (United States)

    Sui, Yao; Wang, Guanghui; Zhang, Li

    2018-04-01

    This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering. A new peak strength metric is proposed to measure the discriminative capability of the learned correlation filter. It is demonstrated that the proposed approach effectively strengthens the peak of the correlation response, leading to more discriminative performance than previous methods. Extensive experiments on a challenging visual tracking benchmark demonstrate that the proposed tracker outperforms most state-of-the-art methods.

  9. A granular refillable filter for glas-flows contaminated by radioactive impurities

    International Nuclear Information System (INIS)

    Bonn, J.W.

    1975-01-01

    Description is given of a granular charcoal refillable filter adapted to adsorb the radioactive impurities of a gaseous flow. That flow comprises a number of filtering layers, the consumed charcoal of which can be discharged by a pneumatic device without exposing the personnel to radioactivity. This can be applied to emergency devices in nuclear facilities [fr

  10. Improved picture rate conversion using classification based LMS-filters.

    NARCIS (Netherlands)

    An, L.; Heinrich, A.; Cordes, C.N.; Haan, de G.; Rabbani, Majid

    2009-01-01

    Due to the recent explosion of multimedia formats and the need to convert between them, more attention is drawn to picture rate conversion. Moreover, growing demands on video motion portrayal without judder or blur requires improved format conversion. The simplest conversion repeats the latest

  11. Feature-Based Nonlocal Polarimetric SAR Filtering

    Directory of Open Access Journals (Sweden)

    Xiaoli Xing

    2017-10-01

    Full Text Available Polarimetric synthetic aperture radar (PolSAR images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often affected by the distribution parameters and modeling texture components. In this paper, a novel filtering method introduces the coefficient of variance ( CV and Pauli basis (PB to measure the similarity, and the two features are combined with the framework of the nonlocal mean filtering. The CV is used to describe the complexity of various scenes and distinguish the scene heterogeneity; moreover, the Pauli basis is able to express the polarimetric information in PolSAR image processing. This proposed filtering combines the CV and Pauli basis to improve the estimation accuracy of the similarity weights. Then, the similarity of the features is deduced according to the test statistic. Subsequently, the filtering is proceeded by using the nonlocal weighted estimation. The performance of the proposed filter is tested with the simulated images and real PolSAR images, which are acquired by AIRSAR system and ESAR system. The qualitative and quantitative experiments indicate the validity of the proposed method by comparing with the widely-used despeckling methods.

  12. Compression of seismic data: filter banks and extended transforms, synthesis and adaptation; Compression de donnees sismiques: bancs de filtres et transformees etendues, synthese et adaptation

    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)

  13. A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy

    Science.gov (United States)

    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.

  14. Implementation and evaluation of LMS mobile application: scele mobile based on user-centered design

    Science.gov (United States)

    Banimahendra, R. D.; Santoso, H. B.

    2018-03-01

    The development of mobile technology is now increasing rapidly, demanding all activities including learning should be done on mobile devices. It shows that the implementation of mobile application as a learning medium needs to be done. This study describes the process of developing and evaluating the Moodle-based mobile Learning Management System (LMS) application called Student Centered e-Learning Environment (SCeLE). This study discusses the process of defining features, implementing features into the application, and evaluating the application. We define the features using user research and literature study, then we implement the application with user-centered design basis, at the last phase we evaluated the application using usability testing and system usability score (SUS). The purpose of this study is to determine the extent to which this application can help the users doing their tasks and provide recommendation for the next research and development.

  15. Properties of predictor based on relative neighborhood graph localized FIR filters

    DEFF Research Database (Denmark)

    Sørensen, John Aasted

    1995-01-01

    A time signal prediction algorithm based on relative neighborhood graph (RNG) localized FIR filters is defined. The RNG connects two nodes, of input space dimension D, if their lune does not contain any other node. The FIR filters associated with the nodes, are used for local approximation...... of the training vectors belonging to the lunes formed by the nodes. The predictor training is carried out by iteration through 3 stages: initialization of the RNG of the training signal by vector quantization, LS estimation of the FIR filters localized in the input space by RNG nodes and adaptation of the RNG...... nodes by equalizing the LS approximation error among the lunes formed by the nodes of the RNG. The training properties of the predictor is exemplified on a burst signal and characterized by the normalized mean square error (NMSE) and the mean valence of the RNG nodes through the adaptation...

  16. THE ADAPTIVE NATURE OF MANAGING SOFTWARE INNOVATION

    OpenAIRE

    Mihai Liviu Despa

    2013-01-01

    The focus of this article is pointed at adaptive management in the context of innovative software projects. Software development is presented through the filter of innovation. The aspects that differentiate software innovation from any other kind of innovation are highlighted. Adaptive management is addressed from a general point of view. The circumstances that require adaptive management are emphasized. Methods of implementing adaptive management in innovation oriented software projects are ...

  17. Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN)

    International Nuclear Information System (INIS)

    Ernst, Floris; Schweikard, Achim

    2008-01-01

    Forecasting of respiration motion in image-guided radiotherapy requires algorithms that can accurately and efficiently predict target location. Improved methods for respiratory motion forecasting were developed and tested. MULIN, a new family of prediction algorithms based on linear expansions of the prediction error, was developed and tested. Computer-generated data with a prediction horizon of 150 ms was used for testing in simulation experiments. MULIN was compared to Least Mean Squares-based predictors (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS) and a multi-frequency Extended Kalman Filter (EKF) approach. The in vivo performance of the algorithms was tested on data sets of patients who underwent radiotherapy. The new MULIN methods are highly competitive, outperforming the LMS and the EKF prediction algorithms in real-world settings and performing similarly to optimized nLMS and wLMS prediction algorithms. On simulated, periodic data the MULIN algorithms are outperformed only by the EKF approach due to its inherent advantage in predicting periodic signals. In the presence of noise, the MULIN methods significantly outperform all other algorithms. The MULIN family of algorithms is a feasible tool for the prediction of respiratory motion, performing as well as or better than conventional algorithms while requiring significantly lower computational complexity. The MULIN algorithms are of special importance wherever high-speed prediction is required. (orig.)

  18. Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN)

    Energy Technology Data Exchange (ETDEWEB)

    Ernst, Floris; Schweikard, Achim [University of Luebeck, Institute for Robotics and Cognitive Systems, Luebeck (Germany)

    2008-06-15

    Forecasting of respiration motion in image-guided radiotherapy requires algorithms that can accurately and efficiently predict target location. Improved methods for respiratory motion forecasting were developed and tested. MULIN, a new family of prediction algorithms based on linear expansions of the prediction error, was developed and tested. Computer-generated data with a prediction horizon of 150 ms was used for testing in simulation experiments. MULIN was compared to Least Mean Squares-based predictors (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS) and a multi-frequency Extended Kalman Filter (EKF) approach. The in vivo performance of the algorithms was tested on data sets of patients who underwent radiotherapy. The new MULIN methods are highly competitive, outperforming the LMS and the EKF prediction algorithms in real-world settings and performing similarly to optimized nLMS and wLMS prediction algorithms. On simulated, periodic data the MULIN algorithms are outperformed only by the EKF approach due to its inherent advantage in predicting periodic signals. In the presence of noise, the MULIN methods significantly outperform all other algorithms. The MULIN family of algorithms is a feasible tool for the prediction of respiratory motion, performing as well as or better than conventional algorithms while requiring significantly lower computational complexity. The MULIN algorithms are of special importance wherever high-speed prediction is required. (orig.)

  19. Multireference adaptive noise canceling applied to the EEG.

    Science.gov (United States)

    James, C J; Hagan, M T; Jones, R D; Bones, P J; Carroll, G J

    1997-08-01

    The technique of multireference adaptive noise canceling (MRANC) is applied to enhance transient nonstationarities in the electroeancephalogram (EEG), with the adaptation implemented by means of a multilayer-perception artificial neural network (ANN). The method was applied to recorded EEG segments and the performance on documented nonstationarities recorded. The results show that the neural network (nonlinear) gives an improvement in performance (i.e., signal-to-noise ratio (SNR) of the nonstationarities) compared to a linear implementation of MRANC. In both cases an improvement in the SNR was obtained. The advantage of the spatial filtering aspect of MRANC is highlighted when the performance of MRANC is compared to that of the inverse auto-regressive filtering of the EEG, a purely temporal filter.

  20. Disturbance Accommodating Adaptive Control with Application to Wind Turbines

    Science.gov (United States)

    Frost, Susan

    2012-01-01

    Adaptive control techniques are well suited to applications that have unknown modeling parameters and poorly known operating conditions. Many physical systems experience external disturbances that are persistent or continually recurring. Flexible structures and systems with compliance between components often form a class of systems that fail to meet standard requirements for adaptive control. For these classes of systems, a residual mode filter can restore the ability of the adaptive controller to perform in a stable manner. New theory will be presented that enables adaptive control with accommodation of persistent disturbances using residual mode filters. After a short introduction to some of the control challenges of large utility-scale wind turbines, this theory will be applied to a high-fidelity simulation of a wind turbine.

  1. 2 keV filters of quasi-mono-energetic neutrons

    International Nuclear Information System (INIS)

    Adib, M.; Habib, N.; El-Mesiry, M.S.; Bashter, I.I.; Saleh, A.; Fathallah, M.

    2013-01-01

    A simulation study for the production of 2 keV filters of quasi-mono-energetic neutrons based on the deep interference minima in the 45 Sc total cross-section was carried out. A computer code QMENF-II was adapted to calculate the optimum amounts of the 45 Sc as a main filter element and additional component ones to obtain sufficient intensity at high resolution and purity of the filtered quasi-mono-energetic neutrons. The emitted neutron spectrum from nuclear reactor and from the reaction of 2.6 MeV protons on a lithium fluoride target at the accelerator beam port, are used for simulation

  2. Adaptive Filtering for Aeroservoelastic Response Suppression, Phase I

    Data.gov (United States)

    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...

  3. Fuzzy Logic-Based Filter for Removing Additive and Impulsive Noise from Color Images

    Science.gov (United States)

    Zhu, Yuhong; Li, Hongyang; Jiang, Huageng

    2017-12-01

    This paper presents an efficient filter method based on fuzzy logics for adaptively removing additive and impulsive noise from color images. The proposed filter comprises two parts including noise detection and noise removal filtering. In the detection part, the fuzzy peer group concept is applied to determine what type of noise is added to each pixel of the corrupted image. In the filter part, the impulse noise is deducted by the vector median filter in the CIELAB color space and an optimal fuzzy filter is introduced to reduce the Gaussian noise, while they can work together to remove the mixed Gaussian-impulse noise from color images. Experimental results on several color images proves the efficacy of the proposed fuzzy filter.

  4. Unscented Kalman filtering in the additive noise case

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The unscented Kalman filter(UKF) has four implementations in the additive noise case,according to whether the state is augmented with noise vectors and whether a new set of sigma points is redrawn from the predicted state(which is so-called resampling) for the observation prediction.This paper concerns the differences of performances for those implementations,such as accuracy,adaptability,computational complexity,etc.The conditionally equivalent relationships between the augmented and non-augmented unscented transforms(UTs) are proved for several sampling strategies that are commonly used.Then,we find that the augmented and non-augmented UKFs have the same filter results with the additive measurement noise,but only have the same state predictions with the additive process noise.Resampling is not believed to be necessary in some researches.However,we find out that resampling can be helpful for an adaptive Kalman gain.This will improve the convergence and accuracy of the filter when the large scale state modeling bias or unknown maneuvers occur.Finally,some universal designing principles for a practical UKF are given as follows:1) for the additive observation noise case,it’s better to use the non-augmented UKF;2) for the additive process noise case,when the small state modeling bias or maneuvers are involved,the non-resampling algorithms with state whether augmented or not are candidates for filters;3) the resampling and non-augmented algorithm is the only choice while the large state modeling bias or maneuvers are latent.

  5. Assessing clustering strategies for Gaussian mixture filtering a subsurface contaminant model

    KAUST Repository

    Liu, Bo

    2016-02-03

    An ensemble-based Gaussian mixture (GM) filtering framework is studied in this paper in term of its dependence on the choice of the clustering method to construct the GM. In this approach, a number of particles sampled from the posterior distribution are first integrated forward with the dynamical model for forecasting. A GM representation of the forecast distribution is then constructed from the forecast particles. Once an observation becomes available, the forecast GM is updated according to Bayes’ rule. This leads to (i) a Kalman filter-like update of the particles, and (ii) a Particle filter-like update of their weights, generalizing the ensemble Kalman filter update to non-Gaussian distributions. We focus on investigating the impact of the clustering strategy on the behavior of the filter. Three different clustering methods for constructing the prior GM are considered: (i) a standard kernel density estimation, (ii) clustering with a specified mixture component size, and (iii) adaptive clustering (with a variable GM size). Numerical experiments are performed using a two-dimensional reactive contaminant transport model in which the contaminant concentration and the heterogenous hydraulic conductivity fields are estimated within a confined aquifer using solute concentration data. The experimental results suggest that the performance of the GM filter is sensitive to the choice of the GM model. In particular, increasing the size of the GM does not necessarily result in improved performances. In this respect, the best results are obtained with the proposed adaptive clustering scheme.

  6. Adaptive Maneuvering Frequency Method of Current Statistical Model

    Institute of Scientific and Technical Information of China (English)

    Wei Sun; Yongjian Yang

    2017-01-01

    Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.

  7. Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization

    Directory of Open Access Journals (Sweden)

    Fujun Pei

    2014-01-01

    Full Text Available The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter, especially in SLAM problem that involves a large number of dimensions. In this paper, particle filter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particle filter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particle filter. Second, an information fusion method was proposed by mixing the innovation method and the number of effective particles method, which combined the advantages of these two methods. And this paper extends the previously known convergence results for particle filter to prove that improved particle filter converges to the optimal filter in mean square as the number of particles goes to infinity. The experiment results show that the proposed algorithm improved the virtue of the DPF-SLAM system in isolate faults and enabled the system to have a better tolerance and robustness.

  8. A comparative study of Kalman filter and Linear Matrix Inequality based H infinity filter for SPND delay compensation

    International Nuclear Information System (INIS)

    Tamboli, P.K.; Duttagupta, Siddhartha P.; Roy, Kallol

    2016-01-01

    Highlights: • Derivation for delay compensation algorithm using recursive Kalman filter. • Derivation for delay compensation algorithm using Linear Matrix Inequality based H infinity filter. • Process modeling suitable for delay compensation. • Dynamic tuning of the delay compensation algorithm for both Kalman and H infinity filter. • Simulations and trade-off curve for Kalman and H infinity filter. - Abstract: This paper deals with delay compensation of vanadium Self Powered Neutron Detectors (SPNDs) using Linear Matrix Inequality (LMI) based H-infinity filtering method and compares the results with Kalman filtering method. The entire study is established upon the framework of neutron flux estimation in large core Pressurized Heavy Water Reactor (PHWR) in which delayed SPNDs such as vanadium SPNDs are used as in-core flux monitoring detectors. The use of vanadium SPNDs are limited to 3-D flux mapping despite of providing better Signal to Noise Ratio as compared to other prompt SPNDs, due to their small prompt component in the signal. The use of an appropriate delay compensation technique has been always considered to be an effective strategy to build a prompt and accurate estimate of the neutron flux. We also indicate the noise-response trade-off curve for both the techniques. Since all the delay compensation algorithms always suffer from noise amplification, we propose an efficient adaptive parameter tuning technique for improving performance of the filtering algorithm against noise in the measurement.

  9. Frequency tracking and variable bandwidth for line noise filtering without a reference.

    Science.gov (United States)

    Kelly, John W; Collinger, Jennifer L; Degenhart, Alan D; Siewiorek, Daniel P; Smailagic, Asim; Wang, Wei

    2011-01-01

    This paper presents a method for filtering line noise using an adaptive noise canceling (ANC) technique. This method effectively eliminates the sinusoidal contamination while achieving a narrower bandwidth than typical notch filters and without relying on the availability of a noise reference signal as ANC methods normally do. A sinusoidal reference is instead digitally generated and the filter efficiently tracks the power line frequency, which drifts around a known value. The filter's learning rate is also automatically adjusted to achieve faster and more accurate convergence and to control the filter's bandwidth. In this paper the focus of the discussion and the data will be electrocorticographic (ECoG) neural signals, but the presented technique is applicable to other recordings.

  10. Optimization of OT-MACH Filter Generation for Target Recognition

    Science.gov (United States)

    Johnson, Oliver C.; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of alpha, beta, gamma values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.

  11. Alternating minimisation for glottal inverse filtering

    International Nuclear Information System (INIS)

    Bleyer, Ismael Rodrigo; Lybeck, Lasse; Auvinen, Harri; Siltanen, Samuli; Airaksinen, Manu; Alku, Paavo

    2017-01-01

    A new method is proposed for solving the glottal inverse filtering (GIF) problem. The goal of GIF is to separate an acoustical speech signal into two parts: the glottal airflow excitation and the vocal tract filter. To recover such information one has to deal with a blind deconvolution problem. This ill-posed inverse problem is solved under a deterministic setting, considering unknowns on both sides of the underlying operator equation. A stable reconstruction is obtained using a double regularization strategy, alternating between fixing either the glottal source signal or the vocal tract filter. This enables not only splitting the nonlinear and nonconvex problem into two linear and convex problems, but also allows the use of the best parameters and constraints to recover each variable at a time. This new technique, called alternating minimization glottal inverse filtering (AM-GIF), is compared with two other approaches: Markov chain Monte Carlo glottal inverse filtering (MCMC-GIF), and iterative adaptive inverse filtering (IAIF), using synthetic speech signals. The recent MCMC-GIF has good reconstruction quality but high computational cost. The state-of-the-art IAIF method is computationally fast but its accuracy deteriorates, particularly for speech signals of high fundamental frequency ( F 0). The results show the competitive performance of the new method: With high F 0, the reconstruction quality is better than that of IAIF and close to MCMC-GIF while reducing the computational complexity by two orders of magnitude. (paper)

  12. Oriented diffusion filtering for enhancing low-quality fingerprint images

    KAUST Repository

    Gottschlich, C.; Schönlieb, C.-B.

    2012-01-01

    To enhance low-quality fingerprint images, we present a novel method that first estimates the local orientation of the fingerprint ridge and valley flow and next performs oriented diffusion filtering, followed by a locally adaptive contrast enhancement step. By applying the authors' new approach to low-quality images of the FVC2004 fingerprint databases, the authors are able to show its competitiveness with other state-of-the-art enhancement methods for fingerprints like curved Gabor filtering. A major advantage of oriented diffusion filtering over those is its computational efficiency. Combining oriented diffusion filtering with curved Gabor filters led to additional improvements and, to the best of the authors' knowledge, the lowest equal error rates achieved so far using MINDTCT and BOZORTH3 on the FVC2004 databases. The recognition performance and the computational efficiency of the method suggest to include oriented diffusion filtering as a standard image enhancement add-on module for real-time fingerprint recognition systems. In order to facilitate the reproduction of these results, an implementation of the oriented diffusion filtering for Matlab and GNU Octave is made available for download. © 2012 The Institution of Engineering and Technology.

  13. Oriented diffusion filtering for enhancing low-quality fingerprint images

    KAUST Repository

    Gottschlich, C.

    2012-01-01

    To enhance low-quality fingerprint images, we present a novel method that first estimates the local orientation of the fingerprint ridge and valley flow and next performs oriented diffusion filtering, followed by a locally adaptive contrast enhancement step. By applying the authors\\' new approach to low-quality images of the FVC2004 fingerprint databases, the authors are able to show its competitiveness with other state-of-the-art enhancement methods for fingerprints like curved Gabor filtering. A major advantage of oriented diffusion filtering over those is its computational efficiency. Combining oriented diffusion filtering with curved Gabor filters led to additional improvements and, to the best of the authors\\' knowledge, the lowest equal error rates achieved so far using MINDTCT and BOZORTH3 on the FVC2004 databases. The recognition performance and the computational efficiency of the method suggest to include oriented diffusion filtering as a standard image enhancement add-on module for real-time fingerprint recognition systems. In order to facilitate the reproduction of these results, an implementation of the oriented diffusion filtering for Matlab and GNU Octave is made available for download. © 2012 The Institution of Engineering and Technology.

  14. Construction of Low Dissipative High Order Well-Balanced Filter Schemes for Non-Equilibrium Flows

    Science.gov (United States)

    Wang, Wei; Yee, H. C.; Sjogreen, Bjorn; Magin, Thierry; Shu, Chi-Wang

    2009-01-01

    The goal of this paper is to generalize the well-balanced approach for non-equilibrium flow studied by Wang et al. [26] to a class of low dissipative high order shock-capturing filter schemes and to explore more advantages of well-balanced schemes in reacting flows. The class of filter schemes developed by Yee et al. [30], Sjoegreen & Yee [24] and Yee & Sjoegreen [35] consist of two steps, a full time step of spatially high order non-dissipative base scheme and an adaptive nonlinear filter containing shock-capturing dissipation. A good property of the filter scheme is that the base scheme and the filter are stand alone modules in designing. Therefore, the idea of designing a well-balanced filter scheme is straightforward, i.e., choosing a well-balanced base scheme with a well-balanced filter (both with high order). A typical class of these schemes shown in this paper is the high order central difference schemes/predictor-corrector (PC) schemes with a high order well-balanced WENO filter. The new filter scheme with the well-balanced property will gather the features of both filter methods and well-balanced properties: it can preserve certain steady state solutions exactly; it is able to capture small perturbations, e.g., turbulence fluctuations; it adaptively controls numerical dissipation. Thus it shows high accuracy, efficiency and stability in shock/turbulence interactions. Numerical examples containing 1D and 2D smooth problems, 1D stationary contact discontinuity problem and 1D turbulence/shock interactions are included to verify the improved accuracy, in addition to the well-balanced behavior.

  15. Fir Filters Compliant with the IEEE Standard for M Class PMU

    Directory of Open Access Journals (Sweden)

    Duda Krzysztof

    2016-12-01

    Full Text Available In this paper it is shown that M class PMU (Phasor Measurement Unit reference model for phasor estimation recommended by the IEEE Standard C37.118.1 with the Amendment 1 is not compliant with the Standard. The reference filter preserves only the limits for TVE (total vector error, and exceeds FE (frequency error and RFE (rate of frequency error limits. As a remedy we propose new filters for phasor estimation for M class PMU that are fully compliant with the Standard requirements. The proposed filters are designed: 1 by the window method; 2 as flat-top windows; or as 3 optimal min-max filters. The results for all Standard compliance tests are presented, confirming good performance of the proposed filters. The proposed filters are fixed at the nominal frequency, i.e. frequency tracking and adaptive filter tuning are not required, therefore they are well suited for application in lowcost popular PMUs.

  16. Spin Torques in Systems with Spin Filtering and Spin Orbit Interaction

    KAUST Repository

    Ortiz Pauyac, Christian

    2016-06-19

    In the present thesis we introduce the reader to the field of spintronics and explore new phenomena, such as spin transfer torques, spin filtering, and three types of spin-orbit torques, Rashba, spin Hall, and spin swapping, which have emerged very recently and are promising candidates for a new generation of memory devices in computer technology. A general overview of these phenomena is presented in Chap. 1. In Chap. 2 we study spin transfer torques in tunnel junctions in the presence of spin filtering. In Chap. 3 we discuss the Rashba torque in ferromagnetic films, and in Chap. 4 we study spin Hall effect and spin swapping in ferromagnetic films, exploring the nature of spin-orbit torques based on these mechanisms. Conclusions and perspectives are summarized in Chap. 5.

  17. Modified compensation algorithm of lever-arm effect and flexural deformation for polar shipborne transfer alignment based on improved adaptive Kalman filter

    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)

  18. Building blocks of temporal filters in retinal synapses.

    Directory of Open Access Journals (Sweden)

    Bongsoo Suh

    2014-10-01

    Full Text Available Sensory systems must be able to extract features of a stimulus to detect and represent properties of the world. Because sensory signals are constantly changing, a critical aspect of this transformation relates to the timing of signals and the ability to filter those signals to select dynamic properties, such as visual motion. At first assessment, one might think that the primary biophysical properties that construct a temporal filter would be dynamic mechanisms such as molecular concentration or membrane electrical properties. However, in the current issue of PLOS Biology, Baden et al. identify a mechanism of temporal filtering in the zebrafish and goldfish retina that is not dynamic but is in fact a structural building block-the physical size of a synapse itself. The authors observe that small, bipolar cell synaptic terminals are fast and highly adaptive, whereas large ones are slower and adapt less. Using a computational model, they conclude that the volume of the synaptic terminal influences the calcium concentration and the number of available vesicles. These results indicate that the size of the presynaptic terminal is an independent control for the dynamics of a synapse and may reveal aspects of synaptic function that can be inferred from anatomical structure.

  19. Harmonic Active Filtering and Impedance-based Stability Analysis in Offshore Wind Power Plants

    DEFF Research Database (Denmark)

    Dhua, Debasish; Yang, Guangya; Zhang, Zhe

    2017-01-01

    installation and provides effectively similar functionality as passive filters. This work is focused on harmonic propagation studies in wind power plants, power quality evaluation at the point of connection and harmonic mitigation by active filtering. Finally, an impedance-based stability analysis......Nowadays, to eliminate harmonics injected by the wind turbines in offshore wind power plants there is a need to install passive filters. Moreover, the passive filters are not adaptive to harmonic profile changes due to topology changes, grid loading etc. Therefore, active filters in wind turbines...... are proposed as a flexible harmonic mitigation measure. The motivation of this study is to explore the possibility of embedding active filtering in wind turbine grid-side converters without having to change the system electrical infrastructure. The active filtering method can prevent additional equipment...

  20. A User-Oriented Splog Filtering Based on a Machine Learning

    Science.gov (United States)

    Yoshinaka, Takayuki; Ishii, Soichi; Fukuhara, Tomohiro; Masuda, Hidetaka; Nakagawa, Hiroshi

    A method for filtering spam blogs (splogs) based on a machine learning technique, and its evaluation results are described. Today, spam blogs (splogs) became one of major issues on the Web. The problem of splogs is that values of blog sites are different by people. We propose a novel user-oriented splog filtering method that can adapt each user's preference for valuable blogs. We use the SVM(Support Vector Machine) for creating a personalized splog filter for each user. We had two experiments: (1) an experiment of individual splog judgement, and (2) an experiment for user oriented splog filtering. From the former experiment, we found existence of 'gray' blogs that are needed to treat by persons. From the latter experiment, we found that we can provide appropriate personalized filters by choosing the best feature set for each user. An overview of proposed method, and evaluation results are described.