Likelihood Methods for Adaptive Filtering and Smoothing. Technical Report #455.
ERIC Educational Resources Information Center
Butler, Ronald W.
The dynamic linear model or Kalman filtering model provides a useful methodology for predicting the past, present, and future states of a dynamic system, such as an object in motion or an economic or social indicator that is changing systematically with time. Recursive likelihood methods for adaptive Kalman filtering and smoothing are developed.…
A hybrid method for optimization of the adaptive Goldstein filter
NASA Astrophysics Data System (ADS)
Jiang, Mi; Ding, Xiaoli; Tian, Xin; Malhotra, Rakesh; Kong, Weixue
2014-12-01
The Goldstein filter is a well-known filter for interferometric filtering in the frequency domain. The main parameter of this filter, alpha, is set as a power of the filtering function. Depending on it, considered areas are strongly or weakly filtered. Several variants have been developed to adaptively determine alpha using different indicators such as the coherence, and phase standard deviation. The common objective of these methods is to prevent areas with low noise from being over filtered while simultaneously allowing stronger filtering over areas with high noise. However, the estimators of these indicators are biased in the real world and the optimal model to accurately determine the functional relationship between the indicators and alpha is also not clear. As a result, the filter always under- or over-filters and is rarely correct. The study presented in this paper aims to achieve accurate alpha estimation by correcting the biased estimator using homogeneous pixel selection and bootstrapping algorithms, and by developing an optimal nonlinear model to determine alpha. In addition, an iteration is also merged into the filtering procedure to suppress the high noise over incoherent areas. The experimental results from synthetic and real data show that the new filter works well under a variety of conditions and offers better and more reliable performance when compared to existing approaches.
A New Method to Cancel RFI---The Adaptive Filter
NASA Astrophysics Data System (ADS)
Bradley, R.; Barnbaum, C.
1996-12-01
An increasing amount of precious radio frequency spectrum in the VHF, UHF, and microwave bands is being utilized each year to support new commercial and military ventures, and all have the potential to interfere with radio astronomy observations. Some radio spectral lines of astronomical interest occur outside the protected radio astronomy bands and are unobservable due to heavy interference. Conventional approaches to deal with RFI include legislation, notch filters, RF shielding, and post-processing techniques. Although these techniques are somewhat successful, each suffers from insufficient interference cancellation. One concept of interference excision that has not been used before in radio astronomy is adaptive interference cancellation. The concept of adaptive interference canceling was first introduced in the mid-1970s as a way to reduce unwanted noise in low frequency (audio) systems. Examples of such systems include the canceling of maternal ECG in fetal electrocardiography and the reduction of engine noise in the passenger compartment of automobiles. Only recently have high-speed digital filter chips made adaptive filtering possible in a bandwidth as large a few megahertz, finally opening the door to astronomical uses. The system consists of two receivers: the main beam of the radio telescope receives the desired signal corrupted by RFI coming in the sidelobes, and the reference antenna receives only the RFI. The reference antenna is processed using a digital adaptive filter and then subtracted from the signal in the main beam, thus producing the system output. The weights of the digital filter are adjusted by way of an algorithm that minimizes, in a least-squares sense, the power output of the system. Through an adaptive-iterative process, the interference canceler will lock onto the RFI and the filter will adjust itself to minimize the effect of the RFI at the system output. We are building a prototype 100 MHz receiver and will measure the cancellation
An adaptive filter method for spacecraft using gravity assist
NASA Astrophysics Data System (ADS)
Ning, Xiaolin; Huang, Panpan; Fang, Jiancheng; Liu, Gang; Ge, Shuzhi Sam
2015-04-01
Celestial navigation (CeleNav) has been successfully used during gravity assist (GA) flyby for orbit determination in many deep space missions. Due to spacecraft attitude errors, ephemeris errors, the camera center-finding bias, and the frequency of the images before and after the GA flyby, the statistics of measurement noise cannot be accurately determined, and yet have time-varying characteristics, which may introduce large estimation error and even cause filter divergence. In this paper, an unscented Kalman filter (UKF) with adaptive measurement noise covariance, called ARUKF, is proposed to deal with this problem. ARUKF scales the measurement noise covariance according to the changes in innovation and residual sequences. Simulations demonstrate that ARUKF is robust to the inaccurate initial measurement noise covariance matrix and time-varying measurement noise. The impact factors in the ARUKF are also investigated.
An Adaptive Kalman Filter using a Simple Residual Tuning Method
NASA Technical Reports Server (NTRS)
Harman, Richard R.
1999-01-01
One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.
An Adaptive Kalman Filter Using a Simple Residual Tuning Method
NASA Technical Reports Server (NTRS)
Harman, Richard R.
1999-01-01
One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. A. H. Jazwinski developed a specialized version of this technique for estimation of process noise. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.
Adaptive error covariances estimation methods for ensemble Kalman filters
Zhen, Yicun; Harlim, John
2015-08-01
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.
Method and system for training dynamic nonlinear adaptive filters which have embedded memory
NASA Technical Reports Server (NTRS)
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.
New cardiac MRI gating method using event-synchronous adaptive digital filter.
Park, Hodong; Park, Youngcheol; Cho, Sungpil; Jang, Bongryoel; Lee, Kyoungjoung
2009-11-01
When imaging the heart using MRI, an artefact-free electrocardiograph (ECG) signal is not only important for monitoring the patient's heart activity but also essential for cardiac gating to reduce noise in MR images induced by moving organs. The fundamental problem in conventional ECG is the distortion induced by electromagnetic interference. Here, we propose an adaptive algorithm for the suppression of MR gradient artefacts (MRGAs) in ECG leads of a cardiac MRI gating system. We have modeled MRGAs by assuming a source of strong pulses used for dephasing the MR signal. The modeled MRGAs are rectangular pulse-like signals. We used an event-synchronous adaptive digital filter whose reference signal is synchronous to the gradient peaks of MRI. The event detection processor for the event-synchronous adaptive digital filter was implemented using the phase space method-a sort of topology mapping method-and least-squares acceleration filter. For evaluating the efficiency of the proposed method, the filter was tested using simulation and actual data. The proposed method requires a simple experimental setup that does not require extra hardware connections to obtain the reference signals of adaptive digital filter. The proposed algorithm was more effective than the multichannel approach. PMID:19644754
Blended particle methods with adaptive subspaces for filtering turbulent dynamical systems
NASA Astrophysics Data System (ADS)
Qi, Di; Majda, Andrew J.
2015-04-01
It is a major challenge throughout science and engineering to improve uncertain model predictions by utilizing noisy data sets from nature. Hybrid methods combining the advantages of traditional particle filters and the Kalman filter offer a promising direction for filtering or data assimilation in high dimensional turbulent dynamical systems. In this paper, blended particle filtering methods that exploit the physical structure of turbulent dynamical systems are developed. Non-Gaussian features of the dynamical system are captured adaptively in an evolving-in-time low dimensional subspace through particle methods, while at the same time statistics in the remaining portion of the phase space are amended by conditional Gaussian mixtures interacting with the particles. The importance of both using the adaptively evolving subspace and introducing conditional Gaussian statistics in the orthogonal part is illustrated here by simple examples. For practical implementation of the algorithms, finding the most probable distributions that characterize the statistics in the phase space as well as effective resampling strategies is discussed to handle realizability and stability issues. To test the performance of the blended algorithms, the forty dimensional Lorenz 96 system is utilized with a five dimensional subspace to run particles. The filters are tested extensively in various turbulent regimes with distinct statistics and with changing observation time frequency and both dense and sparse spatial observations. In real applications perfect dynamical models are always inaccessible considering the complexities in both modeling and computation of high dimensional turbulent system. The effects of model errors from imperfect modeling of the systems are also checked for these methods. The blended methods show uniformly high skill in both capturing non-Gaussian statistics and achieving accurate filtering results in various dynamical regimes with and without model errors.
Gong, Yushun; Yu, Tao; Chen, Bihua; He, Mi; Li, Yongqin
2014-01-01
Current automated external defibrillators mandate interruptions of chest compression to avoid the effect of artifacts produced by CPR for reliable rhythm analyses. But even seconds of interruption of chest compression during CPR adversely affects the rate of restoration of spontaneous circulation and survival. Numerous digital signal processing techniques have been developed to remove the artifacts or interpret the corrupted ECG with promising result, but the performance is still inadequate, especially for nonshockable rhythms. In the present study, we suppressed the CPR artifacts with an enhanced adaptive filtering method. The performance of the method was evaluated by comparing the sensitivity and specificity for shockable rhythm detection before and after filtering the CPR corrupted ECG signals. The dataset comprised 283 segments of shockable and 280 segments of nonshockable ECG signals during CPR recorded from 22 adult pigs that experienced prolonged cardiac arrest. For the unfiltered signals, the sensitivity and specificity were 99.3% and 46.8%, respectively. After filtering, a sensitivity of 93.3% and a specificity of 96.0% were achieved. This animal trial demonstrated that the enhanced adaptive filtering method could significantly improve the detection of nonshockable rhythms without compromising the ability to detect a shockable rhythm during uninterrupted CPR. PMID:24795878
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.
Nonlinear Attitude Filtering Methods
NASA Technical Reports Server (NTRS)
Markley, F. Landis; Crassidis, John L.; Cheng, Yang
2005-01-01
This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filter, but employ various modifications in order to provide better convergence or improve other performance characteristics. Examples of such approaches include: filter QUEST, extended QUEST, the super-iterated extended Kalman filter, the interlaced extended Kalman filter, and the second-order Kalman filter. Filters that propagate and update a discrete set of sigma points rather than using linearized equations for the mean and covariance are also reviewed. A two-step approach is discussed with a first-step state that linearizes the measurement model and an iterative second step to recover the desired attitude states. These approaches are all based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance. Other approaches that do not require this assumption are reviewed, including particle filters and a Bayesian filter based on a non-Gaussian, finite-parameter probability density function on SO(3). Finally, the predictive filter, nonlinear observers and adaptive approaches are shown. The strengths and weaknesses of the various approaches are discussed.
NASA Astrophysics Data System (ADS)
Stevens, Mark R.; Gutchess, Dan; Checka, Neal; Snorrason, Magnús
2006-05-01
Image exploitation algorithms for Intelligence, Surveillance and Reconnaissance (ISR) and weapon systems are extremely sensitive to differences between the operating conditions (OCs) under which they are trained and the extended operating conditions (EOCs) in which the fielded algorithms are tested. As an example, terrain type is an important OC for the problem of tracking hostile vehicles from an airborne camera. A system designed to track cars driving on highways and on major city streets would probably not do well in the EOC of parking lots because of the very different dynamics. In this paper, we present a system we call ALPS for Adaptive Learning in Particle Systems. ALPS takes as input a sequence of video images and produces labeled tracks. The system detects moving targets and tracks those targets across multiple frames using a multiple hypothesis tracker (MHT) tightly coupled with a particle filter. This tracker exploits the strengths of traditional MHT based tracking algorithms by directly incorporating tree-based hypothesis considerations into the particle filter update and resampling steps. We demonstrate results in a parking lot domain tracking objects through occlusions and object interactions.
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
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006
Adaptive filtering in biological signal processing.
Iyer, V K; Ploysongsang, Y; Ramamoorthy, P A
1990-01-01
The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed. PMID:2180633
Adaptive Mallow's optimization for weighted median filters
NASA Astrophysics Data System (ADS)
Rachuri, Raghu; Rao, Sathyanarayana S.
2002-05-01
This work extends the idea of spectral optimization for the design of Weighted Median filters and employ adaptive filtering that updates the coefficients of the FIR filter from which the weights of the median filters are derived. Mallows' theory of non-linear smoothers [1] has proven to be of great theoretical significance providing simple design guidelines for non-linear smoothers. It allows us to find a set of positive weights for a WM filter whose sample selection probabilities (SSP's) are as close as possible to a SSP set predetermined by Mallow's. Sample selection probabilities have been used as a basis for designing stack smoothers as they give a measure of the filter's detail preserving ability and give non-negative filter weights. We will extend this idea to design weighted median filters admitting negative weights. The new method first finds the linear FIR filter coefficients adaptively, which are then used to determine the weights of the median filter. WM filters can be designed to have band-pass, high-pass as well as low-pass frequency characteristics. Unlike the linear filters, however, the weighted median filters are robust in the presence of impulsive noise, as shown by the simulation results.
NASA Astrophysics Data System (ADS)
Rodríguez-Caballero, E.; Afana, A.; Chamizo, S.; Solé-Benet, A.; Canton, Y.
2016-07-01
Terrestrial laser scanning (TLS), widely known as light detection and ranging (LiDAR) technology, is increasingly used to provide highly detailed digital terrain models (DTM) with millimetric precision and accuracy. In order to generate a DTM, TLS data has to be filtered from undesired spurious objects, such as vegetation, artificial structures, etc., Early filtering techniques, successfully applied to airborne laser scanning (ALS), fail when applied to TLS data, as they heavily smooth the terrain surface and do not retain their real morphology. In this article, we present a new methodology for filtering TLS data based on the geometric and radiometric properties of the scanned surfaces. This methodology was built on previous morphological filters that select the minimum point height within a sliding window as the real surface. However, contrary to those methods, which use a fixed window size, the new methodology operates under different spatial scales represented by different window sizes, and can be adapted to different types and sizes of plants. This methodology has been applied to two study areas of differing vegetation type and density. The accuracy of the final DTMs was improved by ∼30% under dense canopy plants and over ∼40% on the open spaces between plants, where other methodologies drastically underestimated the real surface heights. This resulted in more accurate representation of the soil surface and microtopography than up-to-date techniques, eventually having strong implications in hydrological and geomorphological studies.
Mie Light-Scattering Granulometer with an Adaptive Numerical Filtering Method. II. Experiments.
Hespel, L; Delfour, A; Guillame, B
2001-02-20
A nephelometer is presented that theoretically requires no absolute calibration. This instrument is used for determining the particle-size distribution of various scattering media (aerosols, fogs, rocket exhausts, engine plumes, and the like) from angular static light-scattering measurements. An inverse procedure is used, which consists of a least-squares method and a regularization scheme based on numerical filtering. To retrieve the distribution function one matches the experimental data with theoretical patterns derived from Mie theory. The main principles of the inverse method are briefly presented, and the nephelometer is then described with the associated partial calibration procedure. Finally, the whole granulometer system (inverse method and nephelometer) is validated by comparison of measurements of scattering media with calibrated monodisperse or known size distribution functions. PMID:18357082
Filtering Algebraic Multigrid and Adaptive Strategies
Nagel, A; Falgout, R D; Wittum, G
2006-01-31
Solving linear systems arising from systems of partial differential equations, multigrid and multilevel methods have proven optimal complexity and efficiency properties. Due to shortcomings of geometric approaches, algebraic multigrid methods have been developed. One example is the filtering algebraic multigrid method introduced by C. Wagner. This paper proposes a variant of Wagner's method with substantially improved robustness properties. The method is used in an adaptive, self-correcting framework and tested numerically.
Frequency domain FIR and IIR adaptive filters
NASA Technical Reports Server (NTRS)
Lynn, D. W.
1990-01-01
A discussion of the LMS adaptive filter relating to its convergence characteristics and the problems associated with disparate eigenvalues is presented. This is used to introduce the concept of proportional convergence. An approach is used to analyze the convergence characteristics of block frequency-domain adaptive filters. This leads to a development showing how the frequency-domain FIR adaptive filter is easily modified to provide proportional convergence. These ideas are extended to a block frequency-domain IIR adaptive filter and the idea of proportional convergence is applied. Experimental results illustrating proportional convergence in both FIR and IIR frequency-domain block adaptive filters is presented.
Adaptive Filtering Using Recurrent Neural Networks
NASA Technical Reports Server (NTRS)
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.
Musical noise reduction using an adaptive filter
NASA Astrophysics Data System (ADS)
Hanada, Takeshi; Murakami, Takahiro; Ishida, Yoshihisa; Hoya, Tetsuya
2003-10-01
This paper presents a method for reducing a particular noise (musical noise). The musical noise is artificially produced by Spectral Subtraction (SS), which is one of the most conventional methods for speech enhancement. The musical noise is the tin-like sound and annoying in human auditory. We know that the duration of the musical noise is considerably short in comparison with that of speech, and that the frequency components of the musical noise are random and isolated. In the ordinary SS-based methods, the musical noise is removed by the post-processing. However, the output of the ordinary post-processing is delayed since the post-processing uses the succeeding frames. In order to improve this problem, we propose a novel method using an adaptive filter. In the proposed system, the observed noisy signal is used as the input signal to the adaptive filter and the output of SS is used as the reference signal. In this paper we exploit the normalized LMS (Least Mean Square) algorithm for the adaptive filter. Simulation results show that the proposed method has improved the intelligibility of the enhanced speech in comparison with the conventional method.
Adaptive WMMR filters for edge enhancement
NASA Astrophysics Data System (ADS)
Zhou, Jun; Longbotham, Harold G.
1993-05-01
In this paper, an adaptive WMMR filter is introduced, which adaptively changes its window size to accommodate edge width variations. We prove that for any given one dimensional input signal convergence is to fixed points, which are PICO (piecewise constant), by iterative application of the adaptive WMMR filter. An application of the filters to one-D data (non- PICO) and images of printed circuit boards are then provided. Application to images in general is discussed.
Reduction of MPEG ringing artifacts using adaptive sigma filter
NASA Astrophysics Data System (ADS)
Pan, Hao
2006-01-01
In this paper, we propose a novel computationally efficient post-processing algorithm to reduce ringing artifacts in the decoded DCT-coded video without using coding information. While the proposed algorithm is based on edge information as most filtering-based de-ringing algorithms do, this algorithm solely uses one single computationally efficient nonlinear filter, namely sigma filter, for both edge detection and smoothing. Specifically, the sigma filter, which was originally designed for nonlinear filtering, is extended to generate edge proximity information. Different from other adaptive filtering-based methods, whose filters typically use a fixed small window but flexible weights, this sigma filter adaptively switches between small and large windows. The adaptation is designed for removing ringing artifacts only, so the algorithm cannot be used for de-blocking. Overall, the proposed algorithm achieves a good balance among removing ringing artifacts, preserving edges and details, and computational complexity.
Objects tracking with adaptive correlation filters and Kalman filtering
NASA Astrophysics Data System (ADS)
Ontiveros-Gallardo, Sergio E.; Kober, Vitaly
2015-09-01
Object tracking is commonly used for applications such as video surveillance, motion based recognition, and vehicle navigation. In this work, a tracking system using adaptive correlation filters and robust Kalman prediction of target locations is proposed. Tracking is performed by means of multiple object detections in reduced frame areas. A bank of filters is designed from multiple views of a target using synthetic discriminant functions. An adaptive approach is used to improve discrimination capability of the synthesized filters adapting them to multiple types of backgrounds. With the help of computer simulation, the performance of the proposed algorithm is evaluated in terms of detection efficiency and accuracy of object tracking.
Burattini, Laura; Zareba, Wojciech; Burattini, Roberto
2008-09-01
To develop a new method for non-invasive identification of patients prone to ventricular tachyarrhythmia and sudden cardiac death, an adaptive match-filter (AMF) was applied to detect and characterize T-wave alternans (TWA) in 200 coronary artery diseased (CAD) patients compared with 176 healthy (H) subjects. TWA was characterized in terms of duration (TWAD), amplitude (TWAA), and magnitude (TWAM, defined as the product of TWAD times TWAA). A criterion derived from these parameters, estimated over the H-population, allowed discrimination between a risk (TWA+) and a normality (NO TWA) zone in the TWAD-TWAA plane. To gain further ability to discriminate among different risk levels, the TWA+ zone was divided into four sub-zones respectively characterized by low duration and low amplitude (LDLA), low duration and high amplitude (LDHA), high duration and low amplitude (HDLA), and high duration and high amplitude (HDHA). With our methodology, 21 CAD-patients (10.5%) were identified as TWA+, 9 falling in the LDLA zone, 4 in the HDLA, 7 in the LDHA, and 1 in the HDHA. These results are in agreement with clinical expectations and pave the way to further clinical follow-up studies finalized to analyze pathophysiological implications and risk factors associated to each TWA+ zone. PMID:18618261
Electronic filters, hearing aids and methods
NASA Technical Reports Server (NTRS)
Engebretson, A. Maynard (Inventor); O'Connell, Michael P. (Inventor); Zheng, Baohua (Inventor)
1991-01-01
An electronic filter for an electroacoustic system. The system has a microphone for generating an electrical output from external sounds and an electrically driven transducer for emitting sound. Some of the sound emitted by the transducer returns to the microphone means to add a feedback contribution to its electical output. The electronic filter includes a first circuit for electronic processing of the electrical output of the microphone to produce a filtered signal. An adaptive filter, interconnected with the first circuit, performs electronic processing of the filtered signal to produce an adaptive output to the first circuit to substantially offset the feedback contribution in the electrical output of the microphone, and the adaptive filter includes means for adapting only in response to polarities of signals supplied to and from the first circuit. Other electronic filters for hearing aids, public address systems and other electroacoustic systems, as well as such systems, and methods of operating them are also disclosed.
Adaptable Iterative and Recursive Kalman Filter Schemes
NASA Technical Reports Server (NTRS)
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.
Enhancement of Electrolaryngeal Speech by Adaptive Filtering.
ERIC Educational Resources Information Center
Espy-Wilson, Carol Y.; Chari, Venkatesh R.; MacAuslan, Joel M.; Huang, Caroline B.; Walsh, Michael J.
1998-01-01
A study tested the quality and intelligibility, as judged by several listeners, of four users' electrolaryngeal speech, with and without filtering to compensate for perceptually objectionable acoustic characteristics. Results indicated that an adaptive filtering technique produced a noticeable improvement in the quality of the Transcutaneous…
Electronic filters, hearing aids and methods
NASA Technical Reports Server (NTRS)
Engebretson, A. Maynard (Inventor)
1995-01-01
An electronic filter for an electroacoustic system. The system has a microphone for generating an electrical output from external sounds and an electrically driven transducer for emitting sound. Some of the sound emitted by the transducer returns to the microphone means to add a feedback contribution to its electrical output. The electronic filter includes a first circuit for electronic processing of the electrical output of the microphone to produce a first signal. An adaptive filter, interconnected with the first circuit, performs electronic processing of the first signal to produce an adaptive output to the first circuit to substantially offset the feedback contribution in the electrical output of the microphone, and the adaptive filter includes means for adapting only in response to polarities of signals supplied to and from the first circuit. Other electronic filters for hearing aids, public address systems and other electroacoustic systems, as well as such systems and methods of operating them are also disclosed.
NASA Astrophysics Data System (ADS)
Flad, David; Beck, Andrea; Munz, Claus-Dieter
2016-05-01
Scale-resolving simulations of turbulent flows in complex domains demand accurate and efficient numerical schemes, as well as geometrical flexibility. For underresolved situations, the avoidance of aliasing errors is a strong demand for stability. For continuous and discontinuous Galerkin schemes, an effective way to prevent aliasing errors is to increase the quadrature precision of the projection operator to account for the non-linearity of the operands (polynomial dealiasing, overintegration). But this increases the computational costs extensively. In this work, we present a novel spatially and temporally adaptive dealiasing strategy by projection filtering. We show this to be more efficient for underresolved turbulence than the classical overintegration strategy. For this novel approach, we discuss the implementation strategy and the indicator details, show its accuracy and efficiency for a decaying homogeneous isotropic turbulence and the transitional Taylor-Green vortex and compare it to the original overintegration approach and a state of the art variational multi-scale eddy viscosity formulation.
Recursive total-least-squares adaptive filtering
NASA Astrophysics Data System (ADS)
Dowling, Eric M.; DeGroat, Ronald D.
1991-12-01
In this paper a recursive total least squares (RTLS) adaptive filter is introduced and studied. The TLS approach is more appropriate and provides more accurate results than the LS approach when there is error on both sides of the adaptive filter equation; for example, linear prediction, AR modeling, and direction finding. The RTLS filter weights are updated in time O(mr) where m is the filter order and r is the dimension of the tracked subspace. In conventional adaptive filtering problems, r equals 1, so that updates can be performed with complexity O(m). The updates are performed by tracking an orthonormal basis for the smaller of the signal or noise subspaces using a computationally efficient subspace tracking algorithm. The filter is shown to outperform both LMS and RLS in terms of tracking and steady state tap weight error norms. It is also more versatile in that it can adapt its weight in the absence of persistent excitation, i.e., when the input data correlation matrix is near rank deficient. Through simulation, the convergence and tracking properties of the filter are presented and compared with LMS and RLS.
Superresolution restoration of an image sequence: adaptive filtering approach.
Elad, M; Feuer, A
1999-01-01
This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters, least mean squares (LMS) or recursive least squares (RLS). The adaptation enables the treatment of linear space and time-variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the superresolution restoration algorithms are presented. PMID:18262881
Real time adaptive filtering for digital X-ray applications.
Bockenbach, Olivier; Mangin, Michel; Schuberth, Sebastian
2006-01-01
Over the last decade, many methods for adaptively filtering a data stream have been proposed. Those methods have applications in two dimensional imaging as well as in three dimensional image reconstruction. Although the primary objective of this filtering technique is to reduce the noise while avoiding to blur the edges, diagnostic, automated segmentation and surgery show a growing interest in enhancing the features contained in the image flow. Most of the methods proposed so far emerged from thorough studies of the physics of the considered modality and therefore show only a marginal capability to be extended across modalities. Moreover, adaptive filtering belongs to the family of processing intensive algorithms. Existing technology has often driven to simplifications and modality specific optimization to sustain the expected performances. In the specific case of real time digital X-ray as used surgery, the system has to sustain a throughput of 30 frames per second. In this study, we take a generalized approach for adaptive filtering based on multiple oriented filters. Mapping the filtering part to the embedded real time image processing while a user/application defined adaptive recombination of the filter outputs allow to change the smoothing and edge enhancement properties of the filter without changing the oriented filter parameters. We have implemented the filtering on a Cell Broadband Engine processor and the adaptive recombination on an off-the-shelf PC, connected via Gigabit Ethernet. This implementation is capable of filtering images of 5122 pixels at a throughput in excess of 40 frames per second while allowing to change the parameters in real time. PMID:17354937
Estimated spectrum adaptive postfilter and the iterative prepost filtering algirighms
NASA Technical Reports Server (NTRS)
Linares, Irving (Inventor)
2004-01-01
The invention presents The Estimated Spectrum Adaptive Postfilter (ESAP) and the Iterative Prepost Filter (IPF) algorithms. These algorithms model a number of image-adaptive post-filtering and pre-post filtering methods. They are designed to minimize Discrete Cosine Transform (DCT) blocking distortion caused when images are highly compressed with the Joint Photographic Expert Group (JPEG) standard. The ESAP and the IPF techniques of the present invention minimize the mean square error (MSE) to improve the objective and subjective quality of low-bit-rate JPEG gray-scale images while simultaneously enhancing perceptual visual quality with respect to baseline JPEG images.
Filter and method of fabricating
Janney, Mark A.
2006-02-14
A method of making a filter includes the steps of: providing a substrate having a porous surface; applying to the porous surface a coating of dry powder comprising particles to form a filter preform; and heating the filter preform to bind the substrate and the particles together to form a filter.
VSP wave separation by adaptive masking filters
NASA Astrophysics Data System (ADS)
Rao, Ying; Wang, Yanghua
2016-06-01
In vertical seismic profiling (VSP) data processing, the first step might be to separate the down-going wavefield from the up-going wavefield. When using a masking filter for VSP wave separation, there are difficulties associated with two termination ends of the up-going waves. A critical challenge is how the masking filter can restore the energy tails, the edge effect associated with these terminations uniquely exist in VSP data. An effective strategy is to implement masking filters in both τ-p and f-k domain sequentially. Meanwhile it uses a median filter, producing a clean but smooth version of the down-going wavefield, used as a reference data set for designing the masking filter. The masking filter is implemented adaptively and iteratively, gradually restoring the energy tails cut-out by any surgical mute. While the τ-p and the f-k domain masking filters target different depth ranges of VSP, this combination strategy can accurately perform in wave separation from field VSP data.
Adaptive noise Wiener filter for scanning electron microscope imaging system.
Sim, K S; Teh, V; Nia, M E
2016-01-01
Noise on scanning electron microscope (SEM) images is studied. Gaussian noise is the most common type of noise in SEM image. We developed a new noise reduction filter based on the Wiener filter. We compared the performance of this new filter namely adaptive noise Wiener (ANW) filter, with four common existing filters as well as average filter, median filter, Gaussian smoothing filter and the Wiener filter. Based on the experiments results the proposed new filter has better performance on different noise variance comparing to the other existing noise removal filters in the experiments. PMID:26235517
White, H.R.
1985-04-09
This invention provides a non-fibrous combustible filter aid for vacuum pre-coat filter units which is effective to maintain a filter bed coating on rotary filter drums just sufficiently porous to pass the liquid of a slurry while retaining the slurry solids thereon and is capable of being scraped off the bed in thin film form with the slurry solids thereon. The filter aid comprises relatively non-compressible non-fibrous granular charcoal or nut shell particles having a dry bulk density from about 0.3 to about 0.6 grams per cubic centimeter, a particle density of about 1.4 to 1.7 grams per cubis centimeter and a particle size range of 30 to 125 microns. The filter aid of this invention is capable of being burned with the solids deposited thereon to recover the heat value thereof or to dispose of the solids, is free from silicates or other non-combustible ingredients, and is devoid of fibers which will interfere with the proper operation of the filter unit.
Fault-tolerant adaptive FIR filters using variable detection threshold
NASA Astrophysics Data System (ADS)
Lin, L. K.; Redinbo, G. R.
1994-10-01
Adaptive filters are widely used in many digital signal processing applications, where tap weight of the filters are adjusted by stochastic gradient search methods. Block adaptive filtering techniques, such as block least mean square and block conjugate gradient algorithm, were developed to speed up the convergence as well as improve the tracking capability which are two important factors in designing real-time adaptive filter systems. Even though algorithm-based fault tolerance can be used as a low-cost high level fault-tolerant technique to protect the aforementioned systems from hardware failures with minimal hardware overhead, the issue of choosing a good detection threshold remains a challenging problem. First of all, the systems usually only have limited computational resources, i.e., concurrent error detection and correction is not feasible. Secondly, any prior knowledge of input data is very difficult to get in practical settings. We propose a checksum-based fault detection scheme using two-level variable detection thresholds that is dynamically dependent on the past syndromes. Simulations show that the proposed scheme reduces the possibility of false alarms and has a high degree of fault coverage in adaptive filter systems.
NASA Astrophysics Data System (ADS)
Chen, Yangkang
2016-07-01
The seislet transform has been demonstrated to have a better compression performance for seismic data compared with other well-known sparsity promoting transforms, thus it can be used to remove random noise by simply applying a thresholding operator in the seislet domain. Since the seislet transform compresses the seismic data along the local structures, the seislet thresholding can be viewed as a simple structural filtering approach. Because of the dependence on a precise local slope estimation, the seislet transform usually suffers from low compression ratio and high reconstruction error for seismic profiles that have dip conflicts. In order to remove the limitation of seislet thresholding in dealing with conflicting-dip data, I propose a dip-separated filtering strategy. In this method, I first use an adaptive empirical mode decomposition based dip filter to separate the seismic data into several dip bands (5 or 6). Next, I apply seislet thresholding to each separated dip component to remove random noise. Then I combine all the denoised components to form the final denoised data. Compared with other dip filters, the empirical mode decomposition based dip filter is data-adaptive. One only needs to specify the number of dip components to be separated. Both complicated synthetic and field data examples show superior performance of my proposed approach than the traditional alternatives. The dip-separated structural filtering is not limited to seislet thresholding, and can also be extended to all those methods that require slope information.
NASA Astrophysics Data System (ADS)
Chen, Yangkang
2016-04-01
The seislet transform has been demonstrated to have a better compression performance for seismic data compared with other well-known sparsity promoting transforms, thus it can be used to remove random noise by simply applying a thresholding operator in the seislet domain. Since the seislet transform compresses the seismic data along the local structures, the seislet thresholding can be viewed as a simple structural filtering approach. Because of the dependence on a precise local slope estimation, the seislet transform usually suffers from low compression ratio and high reconstruction error for seismic profiles that have dip conflicts. In order to remove the limitation of seislet thresholding in dealing with conflicting-dip data, I propose a dip-separated filtering strategy. In this method, I first use an adaptive empirical mode decomposition based dip filter to separate the seismic data into several dip bands (5 or 6). Next, I apply seislet thresholding to each separated dip component to remove random noise. Then I combine all the denoised components to form the final denoised data. Compared with other dip filters, the empirical mode decomposition based dip filter is data-adaptive. One only need to specify the number of dip components to be separated. Both complicated synthetic and field data examples show superior performance of my proposed approach than the traditional alternatives. The dip-separated structural filtering is not limited to seislet thresholding, and can also be extended to all those methods that require slope information.
Adaptive control of large space structures using recursive lattice filters
NASA Technical Reports Server (NTRS)
Sundararajan, N.; Goglia, G. L.
1985-01-01
The use of recursive lattice filters for identification and adaptive control of large space structures is studied. Lattice filters were used to identify the structural dynamics model of the flexible structures. This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures is control engaged. This type of validation scheme prevents instability when the overall loop is closed. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods. The method uses the Linear Quadratic Guassian/Loop Transfer Recovery (LQG/LTR) approach to ensure stability against unmodeled higher frequency modes and achieves the desired performance.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-01-01
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. PMID:27472336
Adaptive filters for detection of gravitational waves from coalescing binaries
Eleuteri, Antonio; Milano, Leopoldo; De Rosa, Rosario; Garufi, Fabio; Acernese, Fausto; Barone, Fabrizio; Giordano, Lara; Pardi, Silvio
2006-06-15
In this work we propose use of infinite impulse response adaptive line enhancer (IIR ALE) filters for detection of gravitational waves from coalescing binaries. We extend our previous work and define an adaptive matched filter structure. Filter performance is analyzed in terms of the tracking capability and determination of filter parameters. Furthermore, following the Neyman-Pearson strategy, receiver operating characteristics are derived, with closedform expressions for detection threshold, false alarm, and detection probability. Extensive tests demonstrate the effectiveness of adaptive filters both in terms of small computational cost and robustness.
Infinite impulse response modal filtering in visible adaptive optics
NASA Astrophysics Data System (ADS)
Agapito, G.; Arcidiacono, C.; Quirós-Pacheco, F.; Puglisi, A.; Esposito, S.
2012-07-01
Diffraction limited resolution adaptive optics (AO) correction in visible wavelengths requires a high performance control. In this paper we investigate infinite impulse response filters that optimize the wavefront correction: we tested these algorithms through full numerical simulations of a single-conjugate AO system comprising an adaptive secondary mirror with 1127 actuators and a pyramid wavefront sensor (WFS). The actual practicability of the algorithms depends on both robustness and knowledge of the real system: errors in the system model may even worsen the performance. In particular we checked the robustness of the algorithms in different conditions, proving that the proposed method can reject both disturbance and calibration errors.
Image super-resolution via adaptive filtering and regularization
NASA Astrophysics Data System (ADS)
Ren, Jingbo; Wu, Hao; Dong, Weisheng; Shi, Guangming
2014-11-01
Image super-resolution (SR) is widely used in the fields of civil and military, especially for the low-resolution remote sensing images limited by the sensor. Single-image SR refers to the task of restoring a high-resolution (HR) image from the low-resolution image coupled with some prior knowledge as a regularization term. One classic method regularizes image by total variation (TV) and/or wavelet or some other transform which introduce some artifacts. To compress these shortages, a new framework for single image SR is proposed by utilizing an adaptive filter before regularization. The key of our model is that the adaptive filter is used to remove the spatial relevance among pixels first and then only the high frequency (HF) part, which is sparser in TV and transform domain, is considered as the regularization term. Concretely, through transforming the original model, the SR question can be solved by two alternate iteration sub-problems. Before each iteration, the adaptive filter should be updated to estimate the initial HF. A high quality HF part and HR image can be obtained by solving the first and second sub-problem, respectively. In experimental part, a set of remote sensing images captured by Landsat satellites are tested to demonstrate the effectiveness of the proposed framework. Experimental results show the outstanding performance of the proposed method in quantitative evaluation and visual fidelity compared with the state-of-the-art methods.
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter.
Zhang, Zhen; Ma, Yaopeng
2016-01-01
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively. PMID:26861349
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter
Zhang, Zhen; Ma, Yaopeng
2016-01-01
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively. PMID:26861349
A New Adaptive Framework for Collaborative Filtering Prediction.
Almosallam, Ibrahim A; Shang, Yi
2008-06-01
Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system. PMID:21572924
Fast Source Camera Identification Using Content Adaptive Guided Image Filter.
Zeng, Hui; Kang, Xiangui
2016-03-01
Source camera identification (SCI) is an important topic in image forensics. One of the most effective fingerprints for linking an image to its source camera is the sensor pattern noise, which is estimated as the difference between the content and its denoised version. It is widely believed that the performance of the sensor-based SCI heavily relies on the denoising filter used. This study proposes a novel sensor-based SCI method using content adaptive guided image filter (CAGIF). Thanks to the low complexity nature of the CAGIF, the proposed method is much faster than the state-of-the-art methods, which is a big advantage considering the potential real-time application of SCI. Despite the advantage of speed, experimental results also show that the proposed method can achieve comparable or better performance than the state-of-the-art methods in terms of accuracy. PMID:27404627
Adaptive filtering image preprocessing for smart FPA technology
NASA Astrophysics Data System (ADS)
Brooks, Geoffrey W.
1995-05-01
This paper discusses two applications of adaptive filters for image processing on parallel architectures. The first, based on the results of previously accomplished work, summarizes the analyses of various adaptive filters implemented for pixel-level image prediction. FIR filters, fixed and adaptive IIR filters, and various variable step size algorithms were compared with a focus on algorithm complexity against the ability to predict future pixel values. A gaussian smoothing operation with varying spatial and temporal constants were also applied for comparisons of random noise reductions. The second application is a suggestion to use memory-adaptive IIR filters for detecting and tracking motion within an image. Objects within an image are made of edges, or segments, with varying degrees of motion. An application has been previously published that describes FIR filters connecting pixels and using correlations to determine motion and direction. This implementation seems limited to detecting motion coinciding with FIR filter operation rate and the associated harmonics. Upgrading the FIR structures with adaptive IIR structures can eliminate these limitations. These and any other pixel-level adaptive filtering application require data memory for filter parameters and some basic computational capability. Tradeoffs have to be made between chip real estate and these desired features. System tradeoffs will also have to be made as to where it makes the most sense to do which level of processing. Although smart pixels may not be ready to implement adaptive filters, applications such as these should give the smart pixel designer some long range goals.
Adaptive control of large space structures using recursive lattice filters
NASA Technical Reports Server (NTRS)
Goglia, G. L.
1985-01-01
The use of recursive lattice filters for identification and adaptive control of large space structures was studied. Lattice filters are used widely in the areas of speech and signal processing. Herein, they are used to identify the structural dynamics model of the flexible structures. This identified model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures control is engaged. This type of validation scheme prevents instability when the overall loop is closed. The results obtained from simulation were compared to those obtained from experiments. In this regard, the flexible beam and grid apparatus at the Aerospace Control Research Lab (ACRL) of NASA Langley Research Center were used as the principal candidates for carrying out the above tasks. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods.
Turbo LMS algorithm: supercharger meets adaptive filter
NASA Astrophysics Data System (ADS)
Meyer-Baese, Uwe
2006-04-01
Adaptive digital filters (ADFs) are, in general, the most sophisticated and resource intensive components of modern digital signal processing (DSP) and communication systems. Improvements in performance or the complexity of ADFs can have a significant impact on the overall size, speed, and power properties of a complete system. The least mean square (LMS) algorithm is a popular algorithm for coefficient adaptation in ADF because it is robust, easy to implement, and a close approximation to the optimal Wiener-Hopf least mean square solution. The main weakness of the LMS algorithm is the slow convergence, especially for non Markov-1 colored noise input signals with high eigenvalue ratios (EVRs). Since its introduction in 1993, the turbo (supercharge) principle has been successfully applied in error correction decoding and has become very popular because it reaches the theoretical limits of communication capacity predicted 5 decades ago by Shannon. The turbo principle applied to LMS ADF is analogous to the turbo principle used for error correction decoders: First, an "interleaver" is used to minimize crosscorrelation, secondly, an iterative improvement which uses the same data set several times is implemented using the standard LMS algorithm. Results for 6 different interleaver schemes for EVR in the range 1-100 are presented.
Frequency-shift low-pass filtering and least mean square adaptive filtering for ultrasound imaging
NASA Astrophysics Data System (ADS)
Wang, Shanshan; Li, Chunyu; Ding, Mingyue; Yuchi, Ming
2016-04-01
Ultrasound image quality enhancement is a problem of considerable interest in medical imaging modality and an ongoing challenge to date. This paper investigates a method based on frequency-shift low-pass filtering (FSLF) and least mean square adaptive filtering (LMSAF) for ultrasound image quality enhancement. FSLF is used for processing the ultrasound signal in the frequency domain, while LMSAPF in the time domain. Firstly, FSLF shifts the center frequency of the focused signal to zero. Then the real and imaginary part of the complex data are filtered respectively by finite impulse response (FIR) low-pass filter. Thus the information around the center frequency are retained while the undesired ones, especially background noises are filtered. Secondly, LMSAF multiplies the signals with an automatically adjusted weight vector to further eliminate the noises and artifacts. Through the combination of the two filters, the ultrasound image is expected to have less noises and artifacts and higher resolution, and contrast. The proposed method was verified with the RF data of the CIRS phantom 055A captured by SonixTouch DAQ system. Experimental results show that the background noises and artifacts can be efficiently restrained, the wire object has a higher resolution and the contrast ratio (CR) can be enhanced for about 12dB to 15dB at different image depth comparing to delay-and-sum (DAS).
Autonomous navigation system using a fuzzy adaptive nonlinear H∞ filter.
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-01-01
Although nonlinear H∞ (NH∞) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter. PMID:25244587
DEMONSTRATION BULLETIN: COLLOID POLISHING FILTER METHOD - FILTER FLOW TECHNOLOGY, INC.
The Filter Flow Technology, Inc. (FFT) Colloid Polishing Filter Method (CPFM) was tested as a transportable, trailer mounted, system that uses sorption and chemical complexing phenomena to remove heavy metals and nontritium radionuclides from water. Contaminated waters can be pro...
An Adaptive Fourier Filter for Relaxing Time Stepping Constraints for Explicit Solvers
Gelb, Anne; Archibald, Richard K
2015-01-01
Filtering is necessary to stabilize piecewise smooth solutions. The resulting diffusion stabilizes the method, but may fail to resolve the solution near discontinuities. Moreover, high order filtering still requires cost prohibitive time stepping. This paper introduces an adaptive filter that controls spurious modes of the solution, but is not unnecessarily diffusive. Consequently we are able to stabilize the solution with larger time steps, but also take advantage of the accuracy of a high order filter.
Multimodal Medical Image Fusion by Adaptive Manifold Filter.
Geng, Peng; Liu, Shuaiqi; Zhuang, Shanna
2015-01-01
Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images. PMID:26664494
Essentially nonoscillatory postprocessing filtering methods
NASA Technical Reports Server (NTRS)
Lafon, F.; Osher, S.
1992-01-01
High order accurate centered flux approximations used in the computation of numerical solutions to nonlinear partial differential equations produce large oscillations in regions of sharp transitions. Here, we present a new class of filtering methods denoted by Essentially Nonoscillatory Least Squares (ENOLS), which constructs an upgraded filtered solution that is close to the physically correct weak solution of the original evolution equation. Our method relies on the evaluation of a least squares polynomial approximation to oscillatory data using a set of points which is determined via the ENO network. Numerical results are given in one and two space dimensions for both scalar and systems of hyperbolic conservation laws. Computational running time, efficiency, and robustness of method are illustrated in various examples such as Riemann initial data for both Burgers' and Euler's equations of gas dynamics. In all standard cases, the filtered solution appears to converge numerically to the correct solution of the original problem. Some interesting results based on nonstandard central difference schemes, which exactly preserve entropy, and have been recently shown generally not to be weakly convergent to a solution of the conservation law, are also obtained using our filters.
NASA Astrophysics Data System (ADS)
Meng, Yang; Gao, Shesheng; Zhong, Yongmin; Hu, Gaoge; Subic, Aleksandar
2016-03-01
The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performance of the standard UKF is dependent on the accurate statistical characterizations of system noise. If the noise distributions of inertial instruments and GNSS receivers are not appropriately described, the standard UKF will produce deteriorated or even divergent navigation solutions. This paper presents an adaptive UKF with noise statistic estimator to overcome the limitation of the standard UKF. According to the covariance matching technique, the innovation and residual sequences are used to determine the covariance matrices of the process and measurement noises. The proposed algorithm can estimate and adjust the system noise statistics online, and thus enhance the adaptive capability of the standard UKF. Simulation and experimental results demonstrate that the performance of the proposed algorithm is significantly superior to that of the standard UKF and adaptive-robust UKF under the condition without accurate knowledge on system noise, leading to improved navigation precision.
Adaptive multidirectional frequency domain filter for noise removal in wrapped phase patterns.
Liu, Guixiong; Chen, Dongxue; Peng, Yanhua; Zeng, Qilin
2016-08-01
In order to avoid the detrimental effects of excessive noise in the phase fringe patterns of a laser digital interferometer over the accuracy of phase unwrapping and the successful detection of mechanical fatigue defects, an effective method of adaptive multidirectional frequency domain filtering is introduced based on the characteristics of the energy spectrum of localized wrapped phase patterns. Not only can this method automatically set the cutoff frequency, but it can also effectively filter out noise while preserving the image edge information. Compared with the sine and cosine transform filtering and the multidirectional frequency domain filtering, the experimental results demonstrate that the image filtered by our method has the fewest number of residues and is the closest to the noise-free image, compared to the two aforementioned methods, demonstrating the effectiveness of this adaptive multidirectional frequency domain filter. PMID:27505376
Adaptive mean filtering for noise reduction in CT polymer gel dosimetry
Hilts, Michelle; Jirasek, Andrew
2008-01-15
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 {approx}20), the filtered results are excellent and use of adaptive mean filtering is recommended as a standard processing tool. For high noise images (SNR {approx}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.
Burst noise reduction of image by decimation and adaptive weighted median filter
NASA Astrophysics Data System (ADS)
Nakayama, Fumitaka; Meguro, Mitsuhiko; Hamada, Nozomu
2000-12-01
The removal of noise in image is one of the important issues, and useful as a preprocessing for edge detection, motion estimation and so on. Recently, many studies on the nonlinear digital filter for impulsive noise reduction have been reported. The median filter, the representative of the nonlinear filters, is very effective for removing impulsive noise and preserving sharp edge. In some cases, burst (i.e., successive) impulsive noise is added to image, and this type of noise is difficult to remove by using the median filter. In this paper, we propose an Adaptive Weighted Median (AWM) filter with Decimation (AWM-D filter) for burst noise reduction. This method can also be applied to recover large destructive regions, such as blotch and scratch. The proposed filter is an extension of the Decimated Median (DM) filter, which is useful for reducing successive impulsive noise. The DM filter can split long impulsive noise sequences into short ones, and remove burst noise in spite of the short filter window. Nevertheless, the DM filter also has two disadvantages. One is that the signals without added noise is unnecessary filtered. The other is that the position information in the window is not considered in the weight determinative process, as common in the median type filter. To improve detail-preserving property of the DM filter, we use the noise detection procedure and the AWM-D filter, which can be tuned by Least Mean Absolute (LMA) algorithm. The AWM-D filter preserves details more precisely than the median-type filter, because the AWM-D filter has the weights that can control the filter output. Through some simulations, the higher performance of the proposed filter is shown compared with the simple median, the WM filter, and the DM filter.
Reduced-Rank Adaptive Filtering Using Krylov Subspace
NASA Astrophysics Data System (ADS)
Burykh, Sergueï; Abed-Meraim, Karim
2003-12-01
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.
Filter. Remix. Make.: Cultivating Adaptability through Multimodality
ERIC Educational Resources Information Center
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…
Adaptive Control of Flexible Structures Using Residual Mode Filters
NASA Technical Reports Server (NTRS)
Balas, Mark J.; Frost, Susan
2010-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling 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 our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter. We have proposed a modified adaptive controller with a residual mode filter. The RMF is used to accommodate troublesome modes in the system that might otherwise inhibit the adaptive controller, in particular the ASPR condition. This new theory accounts for leakage of the disturbance term into the Q modes. A simple three-mode example shows that the RMF can restore stability to an otherwise unstable adaptively controlled system. This is done without modifying the adaptive controller design.
Adaptive median filtering for preprocessing of time series measurements
NASA Technical Reports Server (NTRS)
Paunonen, Matti
1993-01-01
A median (L1-norm) filtering program using polynomials was developed. This program was used in automatic recycling data screening. Additionally, a special adaptive program to work with asymmetric distributions was developed. Examples of adaptive median filtering of satellite laser range observations and TV satellite time measurements are given. The program proved to be versatile and time saving in data screening of time series measurements.
A Windowing Frequency Domain Adaptive Filter for Acoustic Echo Cancellation
NASA Astrophysics Data System (ADS)
Wu, Sheng; Qiu, Xiaojun
This letter proposes a windowing frequency domain adaptive algorithm, which reuses the filtering error to apply window function in the filter updating symmetrically. By using a proper window function to reduce the negative influence of the spectral leakage, the proposed algorithm can significantly improve the performance of the acoustic echo cancellation for speech signals.
Dynamic data filtering system and method
Bickford, Randall L; Palnitkar, Rahul M
2014-04-29
A computer-implemented dynamic data filtering system and method for selectively choosing operating data of a monitored asset that modifies or expands a learned scope of an empirical model of normal operation of the monitored asset while simultaneously rejecting operating data of the monitored asset that is indicative of excessive degradation or impending failure of the monitored asset, and utilizing the selectively chosen data for adaptively recalibrating the empirical model to more accurately monitor asset aging changes or operating condition changes of the monitored asset.
Lossless compression of weight vectors from an adaptive filter
Bredemann, M.V.; Elliott, G.R.; Stearns, S.D.
1994-08-01
Techniques for lossless waveform compression can be applied to the transmission of weight vectors from an orbiting satellite. The vectors, which are a part of a hybrid analog/digital adaptive filter, are a representation of the radio frequency background seen by the satellite. An approach is used which treats each adaptive weight as a time-varying waveform.
Analysis on Influence Factors of Adaptive Filter Acting on ANC
NASA Astrophysics Data System (ADS)
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.
An adaptive filtered back-projection for photoacoustic image reconstruction
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong
2015-01-01
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
An adaptive filtered back-projection for photoacoustic image reconstruction
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong
2015-05-15
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
Adaptive Algebraic Multigrid Methods
Brezina, M; Falgout, R; MacLachlan, S; Manteuffel, T; McCormick, S; Ruge, J
2004-04-09
Our ability to simulate physical processes numerically is constrained by our ability to solve the resulting linear systems, prompting substantial research into the development of multiscale iterative methods capable of solving these linear systems with an optimal amount of effort. Overcoming the limitations of geometric multigrid methods to simple geometries and differential equations, algebraic multigrid methods construct the multigrid hierarchy based only on the given matrix. While this allows for efficient black-box solution of the linear systems associated with discretizations of many elliptic differential equations, it also results in a lack of robustness due to assumptions made on the near-null spaces of these matrices. This paper introduces an extension to algebraic multigrid methods that removes the need to make such assumptions by utilizing an adaptive process. The principles which guide the adaptivity are highlighted, as well as their application to algebraic multigrid solution of certain symmetric positive-definite linear systems.
A Nonlinear Adaptive Filter for Gyro Thermal Bias Error Cancellation
NASA Technical Reports Server (NTRS)
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.
A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations.
Gur, M Berke; Niezrecki, Christopher
2011-04-01
Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation. PMID:21476661
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. PMID:25983690
Improving nonlinear modeling capabilities of functional link adaptive filters.
Comminiello, Danilo; Scarpiniti, Michele; Scardapane, Simone; Parisi, Raffaele; Uncini, Aurelio
2015-09-01
The functional link adaptive filter (FLAF) represents an effective solution for online nonlinear modeling problems. In this paper, we take into account a FLAF-based architecture, which separates the adaptation of linear and nonlinear elements, and we focus on the nonlinear branch to improve the modeling performance. In particular, we propose a new model that involves an adaptive combination of filters downstream of the nonlinear expansion. Such combination leads to a cooperative behavior of the whole architecture, thus yielding a performance improvement, particularly in the presence of strong nonlinearities. An advanced architecture is also proposed involving the adaptive combination of multiple filters on the nonlinear branch. The proposed models are assessed in different nonlinear modeling problems, in which their effectiveness and capabilities are shown. PMID:26057613
An Efficient Adaptive Weighted Switching Median Filter for Removing High Density Impulse Noise
NASA Astrophysics Data System (ADS)
Nair, Madhu S.; Ameera Mol, P. M.
2014-09-01
Restoration of images corrupted by impulse noise is a very active research area in image processing. In this paper, an Efficient Adaptive Weighted Switching Median filter for restoration of images that are corrupted by high density impulse noise is proposed. The filtering is performed as a two phase process—a detection phase followed by a filtering phase. In the proposed method, noise detection is done by HEIND algorithm proposed by Duan et al. The filtering algorithm is then applied to the pixels which are detected as noisy by the detection algorithm. All uncorrupted pixels in the image are left unchanged. The filtering window size is chosen adaptively depending on the local noise distribution around each corrupted pixels. Noisy pixels are replaced by a weighted median value of uncorrupted pixels in the filtering window. The weight value assigned to each uncorrupted pixels depends on its closeness to the central pixel.
2014-01-01
Background 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. Methods 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. Results 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
NASA Astrophysics Data System (ADS)
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
Dynamic analysis of neural encoding by point process adaptive filtering.
Eden, Uri T; Frank, Loren M; Barbieri, Riccardo; Solo, Victor; Brown, Emery N
2004-05-01
Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt their representations of biological information, analyses of receptive field plasticity from experimental measurements are crucial. Adaptive signal processing, the well-established engineering discipline for characterizing the temporal evolution of system parameters, suggests a framework for studying the plasticity of receptive fields. We use the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains. We derive point process filter analogues of the Kalman filter, recursive least squares, and steepest-descent algorithms and describe the properties of these new filters. We illustrate our algorithms in two simulated data examples. The first is a study of slow and rapid evolution of spatial receptive fields in hippocampal neurons. The second is an adaptive decoding study in which a signal is decoded from ensemble neural spiking activity as the receptive fields of the neurons in the ensemble evolve. Our results provide a paradigm for adaptive estimation for point process observations and suggest a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale. PMID:15070506
Adaptive Control Using Residual Mode Filters Applied to Wind Turbines
NASA Technical Reports Server (NTRS)
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.
Controller-structure interaction compensation using adaptive residual mode filters
NASA Technical Reports Server (NTRS)
Davidson, Roger A.; Balas, Mark J.
1990-01-01
It is not feasible to construct controllers for large space structures or large scale systems (LSS's) which are of the same order as the structures. The complexity of the dynamics of these systems is such that full knowledge of its behavior cannot by processed by today's controller design methods. The controller for system performance of such a system is therefore based on a much smaller reduced-order model (ROM). Unfortunately, the interaction between the LSS and the ROM-based controller can produce instabilities in the closed-loop system due to the unmodeled dynamics of the LSS. Residual mode filters (RMF's) allow the systematic removal of these instabilities in a matter which does not require a redesign of the controller. In addition RMF's have a strong theoretical basis. As simple first- or second-order filters, the RMF CSI compensation technique is at once modular, simple and highly effective. RMF compensation requires knowledge of the dynamics of the system modes which resulted in the previous closed-loop instabilities (the residual modes), but this information is sometimes known imperfectly. An adaptive, self-tuning RMF design, which compensates for uncertainty in the frequency of the residual mode, has been simulated using continuous-time and discrete-time models of a flexible robot manipulator. Work has also been completed on the discrete-time experimental implementation on the Martin Marietta flexible robot manipulator experiment. This paper will present the results of that work on adaptive, self-tuning RMF's, and will clearly show the advantage of this adaptive compensation technique for controller-structure interaction (CSI) instabilities in actively-controlled LSS's.
Subsurface characterization with localized ensemble Kalman filter employing adaptive thresholding
NASA Astrophysics Data System (ADS)
Delijani, Ebrahim Biniaz; Pishvaie, Mahmoud Reza; Boozarjomehry, Ramin Bozorgmehry
2014-07-01
Ensemble Kalman filter, EnKF, as a Monte Carlo sequential data assimilation method has emerged promisingly for subsurface media characterization during past decade. Due to high computational cost of large ensemble size, EnKF is limited to small ensemble set in practice. This results in appearance of spurious correlation in covariance structure leading to incorrect or probable divergence of updated realizations. In this paper, a universal/adaptive thresholding method is presented to remove and/or mitigate spurious correlation problem in the forecast covariance matrix. This method is, then, extended to regularize Kalman gain directly. Four different thresholding functions have been considered to threshold forecast covariance and gain matrices. These include hard, soft, lasso and Smoothly Clipped Absolute Deviation (SCAD) functions. Three benchmarks are used to evaluate the performances of these methods. These benchmarks include a small 1D linear model and two 2D water flooding (in petroleum reservoirs) cases whose levels of heterogeneity/nonlinearity are different. It should be noted that beside the adaptive thresholding, the standard distance dependant localization and bootstrap Kalman gain are also implemented for comparison purposes. We assessed each setup with different ensemble sets to investigate the sensitivity of each method on ensemble size. The results indicate that thresholding of forecast covariance yields more reliable performance than Kalman gain. Among thresholding function, SCAD is more robust for both covariance and gain estimation. Our analyses emphasize that not all assimilation cycles do require thresholding and it should be performed wisely during the early assimilation cycles. The proposed scheme of adaptive thresholding outperforms other methods for subsurface characterization of underlying benchmarks.
Local adaptive filtering of images corrupted by nonstationary noise
NASA Astrophysics Data System (ADS)
Lukin, Vladimir V.; Fevralev, Dmitriy V.; Ponomarenko, Nikolay N.; Pogrebnyak, Oleksiy B.; Egiazarian, Karen O.; Astola, Jaakko T.
2009-02-01
In various practical situations of remote sensing image processing it is assumed that noise is nonstationary and no a priory information on noise dependence on local mean or about local properties of noise statistics is available. It is shown that in such situations it is difficult to find a proper filter for effective image processing, i.e., for noise removal with simultaneous edge/detail preservation. To deal with such images, a local adaptive filter based on discrete cosine transform in overlapping blocks is proposed. A threshold is set locally based on a noise standard deviation estimate obtained for each block. Several other operations to improve performance of the locally adaptive filter are proposed and studied. The designed filter effectiveness is demonstrated for simulated data as well as for real life radar remote sensing and marine polarimetric radar images.
Accelerated adaptive integration method.
Kaus, Joseph W; Arrar, Mehrnoosh; McCammon, J Andrew
2014-05-15
Conformational changes that occur upon ligand binding may be too slow to observe on the time scales routinely accessible using molecular dynamics simulations. The adaptive integration method (AIM) leverages the notion that when a ligand is either fully coupled or decoupled, according to λ, barrier heights may change, making some conformational transitions more accessible at certain λ values. AIM adaptively changes the value of λ in a single simulation so that conformations sampled at one value of λ seed the conformational space sampled at another λ value. Adapting the value of λ throughout a simulation, however, does not resolve issues in sampling when barriers remain high regardless of the λ value. In this work, we introduce a new method, called Accelerated AIM (AcclAIM), in which the potential energy function is flattened at intermediate values of λ, promoting the exploration of conformational space as the ligand is decoupled from its receptor. We show, with both a simple model system (Bromocyclohexane) and the more complex biomolecule Thrombin, that AcclAIM is a promising approach to overcome high barriers in the calculation of free energies, without the need for any statistical reweighting or additional processors. PMID:24780083
Accelerated Adaptive Integration Method
2015-01-01
Conformational changes that occur upon ligand binding may be too slow to observe on the time scales routinely accessible using molecular dynamics simulations. The adaptive integration method (AIM) leverages the notion that when a ligand is either fully coupled or decoupled, according to λ, barrier heights may change, making some conformational transitions more accessible at certain λ values. AIM adaptively changes the value of λ in a single simulation so that conformations sampled at one value of λ seed the conformational space sampled at another λ value. Adapting the value of λ throughout a simulation, however, does not resolve issues in sampling when barriers remain high regardless of the λ value. In this work, we introduce a new method, called Accelerated AIM (AcclAIM), in which the potential energy function is flattened at intermediate values of λ, promoting the exploration of conformational space as the ligand is decoupled from its receptor. We show, with both a simple model system (Bromocyclohexane) and the more complex biomolecule Thrombin, that AcclAIM is a promising approach to overcome high barriers in the calculation of free energies, without the need for any statistical reweighting or additional processors. PMID:24780083
Adaptive noise cancellation based on beehive pattern evolutionary digital filter
NASA Astrophysics Data System (ADS)
Zhou, Xiaojun; Shao, Yimin
2014-01-01
Evolutionary digital filtering (EDF) exhibits the advantage of avoiding the local optimum problem by using cloning and mating searching rules in an adaptive noise cancellation system. However, convergence performance is restricted by the large population of individuals and the low level of information communication among them. The special beehive structure enables the individuals on neighbour beehive nodes to communicate with each other and thus enhance the information spread and random search ability of the algorithm. By introducing the beehive pattern evolutionary rules into the original EDF, this paper proposes an improved beehive pattern evolutionary digital filter (BP-EDF) to overcome the defects of the original EDF. In the proposed algorithm, a new evolutionary rule which combines competing cloning, complete cloning and assistance mating methods is constructed to enable the individuals distributed on the beehive to communicate with their neighbours. Simulation results are used to demonstrate the improved performance of the proposed algorithm in terms of convergence speed to the global optimum compared with the original methods. Experimental results also verify the effectiveness of the proposed algorithm in extracting feature signals that are contaminated by significant amounts of noise during the fault diagnosis task.
A rigid porous filter and filtration method
Chiang, Ta-Kuan; Straub, Douglas, Straub L.; Dennis, Richard A.
1998-12-01
The present invention involves a porous rigid filter comprising a plurality of concentric filtration elements having internal flow passages and forming external flow passages there between. The present invention also involves a pressure vessel containing the filter for the removal of particulate from high pressure particulate containing gases, and further involves a method for using the filter to remove such particulate. The present filter has the advantage of requiring fewer filter elements due to the high surface area- to-volume ratio provided by the filter, requires a reduced pressure vessel size, and exhibits enhanced mechanical design properties, improved cleaning properties, configuration options, modularity and ease of fabrication.
Acoustic Echo Cancellation Using Sub-Adaptive Filter
NASA Astrophysics Data System (ADS)
Ohta, Satoshi; Kajikawa, Yoshinobu; Nomura, Yasuo
In the acoustic echo canceller (AEC), the step-size parameter of the adaptive filter must be varied according to the situation if double talk occurs and/or the echo path changes. We propose an AEC that uses a sub-adaptive filter. The proposed AEC can control the step-size parameter according to the situation. Moreover, it offers superior convergence compared to the conventional AEC even when the double talk and the echo path change occur simultaneously. Simulations demonstrate that the proposed AEC can achieve higher ERLE and faster convergence than the conventional AEC. The computational complexity of the proposed AEC can be reduced by reducing the number of taps of the sub-adaptive filter.
Robust Wiener filtering for Adaptive Optics
Poyneer, L A
2004-06-17
In many applications of optical systems, the observed field in the pupil plane has a non-uniform phase component. This deviation of the phase of the field from uniform is called a phase aberration. In imaging systems this aberration will degrade the quality of the images. In the case of a large astronomical telescope, random fluctuations in the atmosphere lead to significant distortion. These time-varying distortions can be corrected using an Adaptive Optics (AO) system, which is a real-time control system composed of optical, mechanical and computational parts. Adaptive optics is also applicable to problems in vision science, laser propagation and communication. For a high-level overview, consult this web site. For an in-depth treatment of the astronomical case, consult these books.
An information theoretic approach of designing sparse kernel adaptive filters.
Liu, Weifeng; Park, Il; Principe, José C
2009-12-01
This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented. PMID:19923047
Theory and experimental study on low-light-level images by adaptive mode filter
NASA Astrophysics Data System (ADS)
Bai, Lianfa; Zhang, Baomin; Liu, Yunfen; Chen, Qian
1996-09-01
Real-time low light level (LLL) image processing technology is the important developmental subject in the area of LLL night vision. But there is an essential distinction between the LLL TV image and ordinary TV image, so the conventional digital image processing technique aren't suitable for LLL image. In this paper, the noise theoretical model of LLL imaging system is described and the LLL image processing system is set up. With regard to the characteristics of LLL image and its noise, a novel noise suppression method, adaptive mode filter, is presented. The experimental results show that the adaptive mode filter can suppress the sharp noise of LLL image effectively, and as for the protection of the image edge, the property of adaptive mode filter is better that of median filter. Finally, the processing results and the conclusions are given.
Adaptive Wiener filter super-resolution of color filter array images.
Karch, Barry K; Hardie, Russell C
2013-08-12
Digital color cameras using a single detector array with a Bayer color filter array (CFA) require interpolation or demosaicing to estimate missing color information and provide full-color images. However, demosaicing does not specifically address fundamental undersampling and aliasing inherent in typical camera designs. Fast non-uniform interpolation based super-resolution (SR) is an attractive approach to reduce or eliminate aliasing and its relatively low computational load is amenable to real-time applications. The adaptive Wiener filter (AWF) SR algorithm was initially developed for grayscale imaging and has not previously been applied to color SR demosaicing. Here, we develop a novel fast SR method for CFA cameras that is based on the AWF SR algorithm and uses global channel-to-channel statistical models. We apply this new method as a stand-alone algorithm and also as an initialization image for a variational SR algorithm. This paper presents the theoretical development of the color AWF SR approach and applies it in performance comparisons to other SR techniques for both simulated and real data. PMID:23938797
Adaptive identification and control of structural dynamics systems using recursive lattice filters
NASA Technical Reports Server (NTRS)
Sundararajan, N.; Montgomery, R. C.; Williams, J. P.
1985-01-01
A new approach for adaptive identification and control of structural dynamic systems by using least squares lattice filters thar are widely used in the signal processing area is presented. Testing procedures for interfacing the lattice filter identification methods and modal control method for stable closed loop adaptive control are presented. The methods are illustrated for a free-free beam and for a complex flexible grid, with the basic control objective being vibration suppression. The approach is validated by using both simulations and experimental facilities available at the Langley Research Center.
Enhancing Adaptive Filtering Approaches for Land Data Assimilation Systems
Technology Transfer Automated Retrieval System (TEKTRAN)
Recent work has presented the initial application of adaptive filtering techniques to land surface data assimilation systems. Such techniques are motivated by our current lack of knowledge concerning the structure of large-scale error in either land surface modeling output or remotely-sensed estima...
An improved adaptive deblocking filter for MPEG video decoder
NASA Astrophysics Data System (ADS)
Kwon, Do-Kyoung; Shen, Mei-Yin; Kuo, C.-C. Jay
2005-03-01
A highly adaptive deblocking algorithm is proposed for MPEG video in this research. In comparison with previous work in this area, the proposed deblocking filter improves in three aspects. First, the proposed algorithm is adaptive to the change of the quantization parameter (QP). Since blocking artifacts between two blocks encoded with different QPs tend to be more visible due to quality difference, filters should be able to adapt dynamically to the QP change between blocks. Second, the proposed algorithm classifies the block boundary into three different region modes based on local region characteristics. The three modes are active, smooth and dormant regions. The active region represents a complex region with details and high activities while the smooth and the dormant regions refer to moderately flat and extremely flat regions, respectively. By applying different filters of different strengths to each region mode, the proposed algorithm can minimize the undesirable blur so that both subjective and objective qualities improve for various types of sequences at a wide range of bitrates. Finally, the proposed algorithm also provides a way to determine the threshold values. The proposed adaptive deblocking algorithms require several thresholds in determining proper region modes and filters. Since the quality of image sequences after filtering depends largely on the threshold values, they have to be determined carefully. In the proposed algorithm, thresholds are determined adaptively to the strength of the blocking artifact and, as a result, to various encoding parameters such as QP, absolute difference between QPs, the coding type, and motion vectors. It is shown by experimental results that the proposed algorithm can achieve 0.2-0.4 dB gains for I- and P-frames, and 0.1-0.3 dB gains for the B-frame when bit streams are encoded using the TM5 rate control algorithm.
An adaptive filter bank for motor imagery based Brain Computer Interface.
Thomas, Kavitha P; Guan, Cuntai; Tong, Lau Chiew; Prasad, Vinod A
2008-01-01
Brain Computer Interface (BCI) provides an alternative communication and control method for people with severe motor disabilities. Motor imagery patterns are widely used in Electroencephalogram (EEG) based BCIs. These motor imagery activities are associated with variation in alpha and beta band power of EEG signals called Event Related Desynchronization/synchronization (ERD/ERS). The dominant frequency bands are subject-specific and therefore performance of motor imagery based BCIs are sensitive to both temporal filtering and spatial filtering. As the optimum filter is strongly subject-dependent, we propose a method that selects the subject-specific discriminative frequency components using time-frequency plots of Fisher ratio of two-class motor imagery patterns. We also propose a low complexity adaptive Finite Impulse Response (FIR) filter bank system based on coefficient decimation technique which can realize the subject-specific bandpass filters adaptively depending on the information of Fisher ratio map. Features are extracted only from the selected frequency components. The proposed adaptive filter bank based system offers average classification accuracy of about 90%, which is slightly better than the existing fixed filter bank system. PMID:19162856
An Adaptive Filter for the Removal of Drifting Sinusoidal Noise Without a Reference.
Kelly, John W; Siewiorek, Daniel P; Smailagic, Asim; Wang, Wei
2016-01-01
This paper presents a method for filtering sinusoidal noise with a variable bandwidth filter that is capable of tracking a sinusoid's drifting frequency. The method, which is based on the adaptive noise canceling (ANC) technique, will be referred to here as the adaptive sinusoid canceler (ASC). The ASC eliminates sinusoidal contamination by tracking its frequency and achieving a narrower bandwidth than typical notch filters. The detected frequency is used to digitally generate an internal reference instead of relying on an external one as ANC filters typically do. The filter's bandwidth adjusts to achieve faster and more accurate convergence. In this paper, the focus of the discussion and the data is physiological signals, specifically electrocorticographic (ECoG) neural data contaminated with power line noise, but the presented technique could be applicable to other recordings as well. On simulated data, the ASC was able to reliably track the noise's frequency, properly adjust its bandwidth, and outperform comparative methods including standard notch filters and an adaptive line enhancer. These results were reinforced by visual results obtained from real ECoG data. The ASC showed that it could be an effective method for increasing signal to noise ratio in the presence of drifting sinusoidal noise, which is of significant interest for biomedical applications. PMID:25474814
Extended adaptive filtering for wide-angle SAR image formation
NASA Astrophysics Data System (ADS)
Wang, Yanwei; Roberts, William; Li, Jian
2005-05-01
For two-dimensional (2-D) spectral analysis, the adaptive filtering based technologies, such as CAPON and APES (Amplitude and Phase EStimation), are developed under the implicit assumption that the data sets are rectangular. However, in real SAR applications, especially for the wide-angle cases, the collected data sets are always non-rectangular. This raises the problem of how to extend the original adaptive filtering based algorithms for such kind of scenarios. In this paper, we propose an extended adaptive filtering (EAF) approach, which includes Extended APES (E-APES) and Extended CAPON (E-CAPON), for arbitrarily shaped 2-D data. The EAF algorithms adopt a missing-data approach where the unavailable data samples close to the collected data set are assumed missing. Using a group of filter-banks with varying sizes, these algorithms are non-iterative and do not require the estimation of the unavailable samples. The improved imaging results of the proposed algorithms are demonstrated by applying them to two different SAR data sets.
Prototype adaptive bow-tie filter based on spatial exposure time modulation
NASA Astrophysics Data System (ADS)
Badal, Andreu
2016-03-01
In recent years, there has been an increased interest in the development of dynamic bow-tie filters that are able to provide patient-specific x-ray beam shaping. We introduce the first physical prototype of a new adaptive bow-tie filter design based on the concept of "spatial exposure time modulation." While most existing bow-tie filters operate by attenuating the radiation beam differently in different locations using partially attenuating objects, the presented filter shapes the radiation field using two movable completely radio-opaque collimators. The aperture and speed of the collimators is modulated in synchrony with the x-ray exposure to selectively block the radiation emitted to different parts of the object. This mode of operation does not allow the reproduction of every possible attenuation profile, but it can reproduce the profile of any object with an attenuation profile monotonically decreasing from the center to the periphery, such as an object with an elliptical cross section. Therefore, the new adaptive filter provides the same advantages as the currently existing static bow-tie filters, which are typically designed to work for a pre-determined cylindrical object at a fixed distance from the source, and provides the additional capability to adapt its performance at image acquisition time to better compensate for the actual diameter and location of the imaged object. A detailed description of the prototype filter, the implemented control methods, and a preliminary experimental validation of its performance are presented.
Fuel-flow filter for internal combustion engine, adaptable for use with a by-pass filter
Schmidt, R.
1987-06-16
This patent describes a filter apparatus for an internal combustion engine to replace a spin-on, full-flow oil filter threadably connected to an oil filter bushing. The engine has an oil system with an oil pump, an oil pan, and an oil cap at a low pressure side of the oil system. The apparatus comprises: a full-flow filter to be connected to the oil filter bushing to permit oil within the oil system to flow into the full-flow filter. The full-flow filter is of such density and filtering capacity that the oil flows from the oil pump through the full-flow filter with a minimum pressure drop; adapter means to permit use of the full-flow filter either with or without a by-pass filter. The adapter means is a nut located at the forward end of the full-flow filter opposite the oil filter bushing and extending outwardly. The nut defines an area that can be either left intact, permitting all of the oil flow outward from the full-flow filter after filtering, or punctured, permitting most of the oil to flow outward from the full-flow filter after filtering. A small portion of the oil to flows outward therefrom prior to filtering. The nut is within a specific range of depth and circumference so as to provide a means for controlling the size of the hole. The nut is inwardly threaded.
Predicting Hyper-Chaotic Time Series Using Adaptive Higher-Order Nonlinear Filter
NASA Astrophysics Data System (ADS)
Zhang, Jia-Shu; Xiao, Xian-Ci
2001-03-01
A newly proposed method, i.e. the adaptive higher-order nonlinear finite impulse response (HONFIR) filter based on higher-order sparse Volterra series expansions, is introduced to predict hyper-chaotic time series. The effectiveness of using the adaptive HONFIR filter for making one-step and multi-step predictions is tested based on very few data points by computer-generated hyper-chaotic time series including the Mackey-Glass equation and four-dimensional nonlinear dynamical system. A comparison is made with some neural networks for predicting the Mackey-Glass hyper-chaotic time series. Numerical simulation results show that the adaptive HONFIR filter proposed here is a very powerful tool for making prediction of hyper-chaotic time series.
A study of infrared spectroscopy de-noising based on LMS adaptive filter
NASA Astrophysics Data System (ADS)
Mo, Jia-qing; Lv, Xiao-yi; Yu, Xiao
2015-12-01
Infrared spectroscopy has been widely used, but which often contains a lot of noise, so the spectral characteristic of the sample is seriously affected. Therefore the de-noising is very important in the spectrum analysis and processing. In the study of infrared spectroscopy, the least mean square (LMS) adaptive filter was applied in the field firstly. LMS adaptive filter algorithm can reserve the detail and envelope of the effective signal when the method was applied to infrared spectroscopy of breast cancer which signal-to-noise ratio (SNR) is lower than 10 dB, contrast and analysis the result with result of wavelet transform and ensemble empirical mode decomposition (EEMD). The three evaluation standards (SNR, root mean square error (RMSE) and the correlation coefficient (ρ)) fully proved de-noising advantages of LMS adaptive filter in infrared spectroscopy of breast cancer.
Adaptive gain and filtering circuit for a sound reproduction system
NASA Technical Reports Server (NTRS)
Engebretson, A. Maynard (Inventor); O'Connell, Michael P. (Inventor)
1998-01-01
Adaptive compressive gain and level dependent spectral shaping circuitry for a hearing aid include a microphone to produce an input signal and a plurality of channels connected to a common circuit output. Each channel has a preset frequency response. Each channel includes a filter with a preset frequency response to receive the input signal and to produce a filtered signal, a channel amplifier to amplify the filtered signal to produce a channel output signal, a threshold register to establish a channel threshold level, and a gain circuit. The gain circuit increases the gain of the channel amplifier when the channel output signal falls below the channel threshold level and decreases the gain of the channel amplifier when the channel output signal rises above the channel threshold level. A transducer produces sound in response to the signal passed by the common circuit output.
Kalman filtering to suppress spurious signals in Adaptive Optics control
Poyneer, L; Veran, J P
2010-03-29
In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.
Model Adaptation for Prognostics in a Particle Filtering Framework
NASA Technical Reports Server (NTRS)
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.
Adaptive distributed Kalman filtering with wind estimation for astronomical adaptive optics.
Massioni, Paolo; Gilles, Luc; Ellerbroek, Brent
2015-12-01
In the framework of adaptive optics (AO) for astronomy, it is a common assumption to consider the atmospheric turbulent layers as "frozen flows" sliding according to the wind velocity profile. For this reason, having knowledge of such a velocity profile is beneficial in terms of AO control system performance. In this paper we show that it is possible to exploit the phase estimate from a Kalman filter running on an AO system in order to estimate wind velocity. This allows the update of the Kalman filter itself with such knowledge, making it adaptive. We have implemented such an adaptive controller based on the distributed version of the Kalman filter, for a realistic simulation of a multi-conjugate AO system with laser guide stars on a 30 m telescope. Simulation results show that this approach is effective and promising and the additional computational cost with respect to the distributed filter is negligible. Comparisons with a previously published slope detection and ranging wind profiler are made and the impact of turbulence profile quantization is assessed. One of the main findings of the paper is that all flavors of the adaptive distributed Kalman filter are impacted more significantly by turbulence profile quantization than the static minimum mean square estimator which does not incorporate wind profile information. PMID:26831389
Switched Band-Pass Filters for Adaptive Transceivers
NASA Technical Reports Server (NTRS)
Wang, Ray
2007-01-01
Switched band-pass filters are key components of proposed adaptive, software- defined radio transceivers that would be parts of envisioned digital-data-communication networks that would enable real-time acquisition and monitoring of data from geographically distributed sensors. Examples of sensors to be connected to such networks include security cameras, radio-frequency identification units, and geolocation units based on the Global Positioning System. Through suitable software configuration and without changing hardware, these transceivers could be made to operate according to any of a number of complex wireless-communication standards that could be characterized by diverse modulation schemes, bandwidths, and data-handling protocols. The adaptive transceivers would include field-programmable gate arrays (FPGAs) and digital signal-processing hardware. In the receiving path of a transceiver, the incoming signal would be amplified by a low-noise amplifier (LNA). The output spectrum of the LNA would be processed by a band-pass filter operating in the frequency range between 900 MHz and 2.4 GHz. Then a down-converter would translate the signal to a lower frequency range to facilitate analog-to-digital conversion, which would be followed by baseband processing by one or more FPGAs. In the transmitting path, a digital stream would first be converted to an analog signal, which would then be up-converted to a selected frequency band before being applied to a transmitting power amplifier. The aforementioned band-pass filter in the receiving path would be a combination of resonant inductor-and-capacitor filters and switched band-pass filters. The overall combination would implement a switch function designed mathematically to exhibit desired frequency responses and to switch the signal in each frequency band to an analog-to-digital converter appropriate for that band to produce a digital intermediate-frequency signal for digital signal processing.
NASA Astrophysics Data System (ADS)
Hayes, Charles E.; McClellan, James H.; Scott, Waymond R.; Kerr, Andrew J.
2016-05-01
This work introduces two advances in wide-band electromagnetic induction (EMI) processing: a novel adaptive matched filter (AMF) and matched subspace detection methods. Both advances make use of recent work with a subspace SVD approach to separating the signal, soil, and noise subspaces of the frequency measurements The proposed AMF provides a direct approach to removing the EMI self-response while improving the signal to noise ratio of the data. Unlike previous EMI adaptive downtrack filters, this new filter will not erroneously optimize the EMI soil response instead of the EMI target response because these two responses are projected into separate frequency subspaces. The EMI detection methods in this work elaborate on how the signal and noise subspaces in the frequency measurements are ideal for creating the matched subspace detection (MSD) and constant false alarm rate matched subspace detection (CFAR) metrics developed by Scharf The CFAR detection metric has been shown to be the uniformly most powerful invariant detector.
Zurbenko, I.; Chen, J.; Rao, S.T.
1997-11-01
The issue of global climate change due to increased anthropogenic emissions of greenhouse gases in the atmosphere has gained considerable attention and importance. Climate change studies require the interpretation of weather data collected in numerous locations and/or over the span of several decades. Unfortunately, these data contain biases caused by changes in instruments and data acquisition procedures. It is essential that biases are identified and/or removed before these data can be used confidently in the context of climate change research. The purpose of this paper is to illustrate the use of an adaptive moving average filter and compare it with traditional parametric methods. The advantage of the adaptive filter over traditional parametric methods is that it is less effected by seasonal patterns and trends. The filter has been applied to upper air relative humidity and temperature data. Applied to generated data, the filter has a root mean squared error accuracy of about 600 days when locating changes of 0.1 standard deviations and about 20 days for changes of 0.5 standard deviations. In some circumstances, the accuracy of location estimation can be improved through parametric techniques used in conjunction with the adaptive filter.
Candle filter fabrication using combustion synthesis method
Hanus, G.J.; Uznanski, K.T.; DeCoursin, D.G.; Hickel, S.D.; Shtessel, E.
1996-12-31
Using self-propagating, high-temperature synthesis fabrication techniques, unique combinations of materials were studied for use in candle filter development. Materials suitable for corrosive atmospheres with improved filtration and mechanical properties were identified within the context of manufacturing simplicity, versatility and cost-effectiveness. Material combinations suitable for combustion synthesis were identified which match the temperature, strength and corrosion resistance requirements of operation in pressurized, fluidized-bed combustion environments. Tests on porosity, permeability and strength indicated the ability of the method to process porous filter material which matches or surpasses the performance levels of current, production candle filter material samples.
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. PMID:25619991
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. PMID:25619991
Statistical-uncertainty-based adaptive filtering of lidar signals
Fuehrer, P. L.; Friehe, C. A.; Hristov, T. S.; Cooper, D. I.; Eichinger, W. E.
2000-02-10
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{sub 2}O and the N{sub 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.
Applying well flow adapted filtering to transient pumping tests
NASA Astrophysics Data System (ADS)
Zech, Alraune; Attinger, Sabine
2014-05-01
Transient pumping tests are often used to estimate porous medium characteristics like hydraulic conductivity and storativity. The interpretation of pumping test drawdowns is based on methods which are normally developed under the assumption of homogeneous porous media. However aquifer heterogeneity strongly impacts on well flow pattern, in particular in the vicinity of the pumping well. The purpose of this work is to present a method to interpret drawdowns of transient pumping tests in heterogeneous porous media. With this method we are able to describe the effects that statistical quantities like variance and correlation length have on pumping test drawdowns. Furthermore it allows inferring on the statistical parameters of aquifer heterogeneity from drawdown data by invers estimation, which is not possible using methods for homogeneous media like Theis' solution. The method is based on a representative description of hydraulic conductivity for radial flow regimes. It is derived from a well flow adapted filtering procedure (Coarse Graining), where the heterogeneity of hydraulic conductivity is assumed to be log-normal distributed with a Gaussian correlation structure. applying the up scaled hydraulic conductivity to the groundwater flow equation results in a hydraulic head which depends on the statistical parameters of the porous medium. It describes the drawdown of a transient pumping test in heterogeneous media. We used an ensemble of transient pumping test simulations to verify the up scaled drawdown solution. We generated transient pumping tests in heterogeneous media for various values of the statistical parameters variance and correlation length and evaluated their impact on the drawdown behavior as well as on the temporal evolution. We further examined the impact of several aspects like the location of an observation well or the local conductivity at the pumping well on the drawdown behavior. This work can be understood as an expansion of the work of Zech et
Object tracking with adaptive HOG detector and adaptive Rao-Blackwellised particle filter
NASA Astrophysics Data System (ADS)
Rosa, Stefano; Paleari, Marco; Ariano, Paolo; Bona, Basilio
2012-01-01
Scenarios for a manned mission to the Moon or Mars call for astronaut teams to be accompanied by semiautonomous robots. A prerequisite for human-robot interaction is the capability of successfully tracking humans and objects in the environment. In this paper we present a system for real-time visual object tracking in 2D images for mobile robotic systems. The proposed algorithm is able to specialize to individual objects and to adapt to substantial changes in illumination and object appearance during tracking. The algorithm is composed by two main blocks: a detector based on Histogram of Oriented Gradient (HOG) descriptors and linear Support Vector Machines (SVM), and a tracker which is implemented by an adaptive Rao-Blackwellised particle filter (RBPF). The SVM is re-trained online on new samples taken from previous predicted positions. We use the effective sample size to decide when the classifier needs to be re-trained. Position hypotheses for the tracked object are the result of a clustering procedure applied on the set of particles. The algorithm has been tested on challenging video sequences presenting strong changes in object appearance, illumination, and occlusion. Experimental tests show that the presented method is able to achieve near real-time performances with a precision of about 7 pixels on standard video sequences of dimensions 320 × 240.
NASA Astrophysics Data System (ADS)
Yushkov, Konstantin B.; Molchanov, Vladimir Y.; Belousov, Pavel V.; Abrosimov, Aleksander Y.
2016-01-01
We report a method for edge enhancement in the images of transparent samples using analog image processing in coherent light. The experimental technique is based on adaptive spatial filtering with an acousto-optic tunable filter in a telecentric optical system. We demonstrate processing of microscopic images of unstained and stained histological sections of human thyroid tumor with improved contrast.
A multi-stage noise adaptive switching filter for extremely corrupted images
NASA Astrophysics Data System (ADS)
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.
Analysis of dynamic deformation processes with adaptive KALMAN-filtering
NASA Astrophysics Data System (ADS)
Eichhorn, Andreas
2007-05-01
In this paper the approach of a full system analysis is shown quantifying a dynamic structural ("white-box"-) model for the calculation of thermal deformations of bar-shaped machine elements. The task was motivated from mechanical engineering searching new methods for the precise prediction and computational compensation of thermal influences in the heating and cooling phases of machine tools (i.e. robot arms, etc.). The quantification of thermal deformations under variable dynamic loads requires the modelling of the non-stationary spatial temperature distribution inside the object. Based upon FOURIERS law of heat flow the high-grade non-linear temperature gradient is represented by a system of partial differential equations within the framework of a dynamic Finite Element topology. It is shown that adaptive KALMAN-filtering is suitable to quantify relevant disturbance influences and to identify thermal parameters (i.e. thermal diffusivity) with a deviation of only 0,2%. As result an identified (and verified) parametric model for the realistic prediction respectively simulation of dynamic temperature processes is presented. Classifying the thermal bend as the main deformation quantity of bar-shaped machine tools, the temperature model is extended to a temperature deformation model. In lab tests thermal load steps are applied to an aluminum column. Independent control measurements show that the identified model can be used to predict the columns bend with a mean deviation (
Attitude determination using an adaptive multiple model filtering Scheme
NASA Technical Reports Server (NTRS)
Lam, Quang; Ray, Surendra N.
1995-01-01
Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown
Attitude determination using an adaptive multiple model filtering Scheme
NASA Astrophysics Data System (ADS)
Lam, Quang; Ray, Surendra N.
1995-05-01
Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown
NASA Astrophysics Data System (ADS)
Campos Trujillo, Oliver G.; Díaz Blancas, Gerardo
2014-09-01
In recent years, many proposals that consider an adaptive perspective had been developed to solve some drawbacks, such as geometric distortions, background noise and target discrimination. The metrics are based only in the correlation peak output for the filter synthesis. In this paper, the correlation shape is studied to implement adaptive correlation filters guided by the peak and shape of the correlation output. Furthermore, the shape of correlation output is studied to improve the search in the filters bank. In addition, parallel algorithms are developed for accelerated the search in the filters bank. Some results are shown, such as time of synthesis, filter performance and comparisons with other adaptive correlation filter proposals.
An adaptive selective frequency damping method
NASA Astrophysics Data System (ADS)
Jordi, Bastien; Cotter, Colin; Sherwin, Spencer
2015-03-01
The selective frequency damping (SFD) method is used to obtain unstable steady-state solutions of dynamical systems. The stability of this method is governed by two parameters that are the control coefficient and the filter width. Convergence is not guaranteed for arbitrary choice of these parameters. Even when the method does converge, the time necessary to reach a steady-state solution may be very long. We present an adaptive SFD method. We show that by modifying the control coefficient and the filter width all along the solver execution, we can reach an optimum convergence rate. This method is based on successive approximations of the dominant eigenvalue of the flow studied. We design a one-dimensional model to select SFD parameters that enable us to control the evolution of the least stable eigenvalue of the system. These parameters are then used for the application of the SFD method to the multi-dimensional flow problem. We apply this adaptive method to a set of classical test cases of computational fluid dynamics and show that the steady-state solutions obtained are similar to what can be found in the literature. Then we apply it to a specific vortex dominated flow (of interest for the automotive industry) whose stability had never been studied before. Seventh Framework Programme of the European Commission - ANADE project under Grant Contract PITN-GA-289428.
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm. PMID:26920086
Neural Network Aided Adaptive Extended Kalman Filtering Approach for DGPS Positioning
NASA Astrophysics Data System (ADS)
Jwo, Dah-Jing; Huang, Hung-Chih
2004-09-01
The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.
Residual mode filters and adaptive control in large space structures
NASA Technical Reports Server (NTRS)
Davidson, Roger A.; Balas, Mark J.
1989-01-01
One of the most difficult problems in controlling large systems and structures is compensating for the destructive interaction which can occur between the reduced-order model (ROM) of the plant, which is used by the controller, and the unmodeled dynamics of the plant, often called the residual modes. The problem is more significant in the case of large space structures because their naturally light damping and high performance requirements lead to more frequent, destructive residual mode interaction (RMI). Using the design/compensation technique of residual mode filters (RMF's), effective compensation of RMI can be accomplished in a straightforward manner when using linear controllers. The use of RMF's has been shown to be effective for a variety of large structures, including a space-based laser and infinite dimensional systems. However, the dynamics of space structures is often uncertain and may even change over time due to on-orbit erosion from space debris and corrosive chemicals in the upper atmosphere. In this case, adaptive control can be extremely beneficial in meeting the performance requirements of the structure. Adaptive control for large structures is also based on ROM's and so destructive RMI may occur. Unfortunately, adaptive control is inherently nonlinear, and therefore the known results of RMF's cannot be applied. The purpose is to present the results of new research showing the effects of RMI when using adaptive control and the work which will hopefully lead to RMF compensation of this problem.
Maier, Andreas; Wigström, 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
Maier, Andreas; Wigstroem, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu Lei; Strobel, Norbert; Fahrig, Rebecca
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 8.9-fold
Adaptive filtering of radar images for autofocus applications
NASA Technical Reports Server (NTRS)
Stiles, J. A.; Frost, V. S.; Gardner, J. S.; Eland, D. R.; Shanmugam, K. S.; Holtzman, J. C.
1981-01-01
Autofocus techniques are being designed at the Jet Propulsion Laboratory to automatically choose the filter parameters (i.e., the focus) for the digital synthetic aperture radar correlator; currently, processing relies upon interaction with a human operator who uses his subjective assessment of the quality of the processed SAR data. Algorithms were devised applying image cross-correlation to aid in the choice of filter parameters, but this method also has its drawbacks in that the cross-correlation result may not be readily interpretable. Enhanced performance of the cross-correlation techniques of JPL was hypothesized given that the images to be cross-correlated were first filtered to improve the signal-to-noise ratio for the pair of scenes. The results of experiments are described and images are shown.
Microwave Photonic Filters for Interference Cancellation and Adaptive Beamforming
NASA Astrophysics Data System (ADS)
Chang, John
Wireless communication has experienced an explosion of growth, especially in the past half- decade, due to the ubiquity of wireless devices, such as tablets, WiFi-enabled devices, and especially smartphones. Proliferation of smartphones with powerful processors and graphic chips have given an increasing amount of people the ability to access anything from anywhere. Unfortunately, this ease of access has greatly increased mobile wireless bandwidth and have begun to stress carrier networks and spectra. Wireless interference cancellation will play a big role alongside the popularity of wire- less communication. In this thesis, we will investigate optical signal processing methods for wireless interference cancellation methods. Optics provide the perfect backdrop for interference cancellation. Mobile wireless data is already aggregated and transported through fiber backhaul networks in practice. By sandwiching the signal processing stage between the receiver and the fiber backhaul, processing can easily be done locally in one location. Further, optics offers the advantages of being instantaneously broadband and size, weight, and power (SWAP). We are primarily concerned with two methods for interference cancellation, based on microwave photonic filters, in this thesis. The first application is for a co-channel situation, in which a transmitter and receiver are co-located and transmitting at the same frequency. A novel analog optical technique extended for multipath interference cancellation of broadband signals is proposed and experimentally demonstrated in this thesis. The proposed architecture was able to achieve a maximum of 40 dB of cancellation over 200 MHz and 50 dB of cancellation over 10 MHz. The broadband nature of the cancellation, along with its depth, demonstrates both the precision of the optical components and the validity of the architecture. Next, we are interested in a scenario with dynamically changing interference, which requires an adaptive photonic
Q-Method Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Zanetti, Renato; Ainscough, Thomas; Christian, John; Spanos, Pol D.
2012-01-01
A new algorithm is proposed that smoothly integrates non-linear estimation of the attitude quaternion using Davenport s q-method and estimation of non-attitude states through an extended Kalman filter. The new method is compared to a similar existing algorithm showing its similarities and differences. The validity of the proposed approach is confirmed through numerical simulations.
On application of adaptive decorrelation filtering to assistive listening
NASA Astrophysics Data System (ADS)
Zhao, Yunxin; Yen, Kuan-Chieh; Soli, Sig; Gao, Shawn; Vermiglio, Andy
2002-02-01
This paper describes an application of the multichannel signal processing technique of adaptive decorrelation filtering to the design of an assistive listening system. A simulated ``dinner table'' scenario was studied. The speech signal of a desired talker was corrupted by three simultaneous speech jammers and by a speech-shaped diffusive noise. The technique of adaptive decorrelation filtering processing was used to extract the desired speech from the interference speech and noise. The effectiveness of the assistive listening system was evaluated by observing improvements in A-weighted signal-to-noise ratio (SNR) and in sentence intelligibility, where the latter was evaluated in a listening test with eight normal hearing subjects and three subjects with hearing impairments. Significant improvements in SNR and sentence intelligibility were achieved with the use of the assistive listening system. For subjects with normal hearing, the speech reception threshold was improved by 3 to 5 dBA, and for subjects with hearing impairments, the threshold was improved by 4 to 8 dBA.
Particle filter based visual tracking with multi-cue adaptive fusion
NASA Astrophysics Data System (ADS)
Li, Anping; Jing, Zhongliang; Hu, Shiqiang
2005-06-01
To improve the robustness of visual tracking in complex environments such as: cluttered backgrounds, partial occlusions, similar distraction and pose variations, a novel tracking method based on adaptive fusion and particle filter is proposed in this paper. In this method, the image color and shape cues are adaptively fused to represent the target observation; fuzzy logic is applied to dynamically adjust each cue weight according to its associated reliability in the past frame; particle filter is adopted to deal with non-linear and non-Gaussian problems in visual tracking. The method is demonstrated to be robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion for a test image sequence.
A unified set-based test with adaptive filtering for gene-environment interaction analyses.
Liu, Qianying; Chen, Lin S; Nicolae, Dan L; Pierce, Brandon L
2016-06-01
In genome-wide gene-environment interaction (GxE) studies, a common strategy to improve power is to first conduct a filtering test and retain only the SNPs that pass the filtering in the subsequent GxE analyses. Inspired by two-stage tests and gene-based tests in GxE analysis, we consider the general problem of jointly testing a set of parameters when only a few are truly from the alternative hypothesis and when filtering information is available. We propose a unified set-based test that simultaneously considers filtering on individual parameters and testing on the set. We derive the exact distribution and approximate the power function of the proposed unified statistic in simplified settings, and use them to adaptively calculate the optimal filtering threshold for each set. In the context of gene-based GxE analysis, we show that although the empirical power function may be affected by many factors, the optimal filtering threshold corresponding to the peak of the power curve primarily depends on the size of the gene. We further propose a resampling algorithm to calculate P-values for each gene given the estimated optimal filtering threshold. The performance of the method is evaluated in simulation studies and illustrated via a genome-wide gene-gender interaction analysis using pancreatic cancer genome-wide association data. PMID:26496228
A unified set-based test with adaptive filtering for gene-environment interaction analyses
Liu, Qianying; Chen, Lin S.; Nicolae, Dan L.; Pierce, Brandon L.
2015-01-01
Summary In genome-wide gene-environment interaction (GxE) studies, a common strategy to improve power is to first conduct a filtering test and retain only the SNPs that pass the filtering in the subsequent GxE analyses. Inspired by two-stage tests and gene-based tests in GxE analysis, we consider the general problem of jointly testing a set of parameters when only a few are truly from the alternative hypothesis and when filtering information is available. We propose a unified set-based test that simultaneously considers filtering on individual parameters and testing on the set. We derive the exact distribution and approximate the power function of the proposed unified statistic in simplified settings, and use them to adaptively calculate the optimal filtering threshold for each set. In the context of gene-based GxE analysis, we show that although the empirical power function may be affected by many factors, the optimal filtering threshold corresponding to the peak of the power curve primarily depends on the size of the gene. We further propose a resampling algorithm to calculate p-values for each gene given the estimated optimal filtering threshold. The performance of the method is evaluated in simulation studies and illustrated via a genome-wide gene-gender interaction analysis using pancreatic cancer genome-wide association data. PMID:26496228
Mesh-free adjoint methods for nonlinear filters
NASA Astrophysics Data System (ADS)
Daum, Fred
2005-09-01
We apply a new industrial strength numerical approximation, called the "mesh-free adjoint method", to solve the nonlinear filtering problem. This algorithm exploits the smoothness of the problem, unlike particle filters, and hence we expect that mesh-free adjoints are superior to particle filters for many practical applications. The nonlinear filter problem is equivalent to solving the Fokker-Planck equation in real time. The key idea is to use a good adaptive non-uniform quantization of state space to approximate the solution of the Fokker-Planck equation. In particular, the adjoint method computes the location of the nodes in state space to minimize errors in the final answer. This use of an adjoint is analogous to optimal control algorithms, but it is more interesting. The adjoint method is also analogous to importance sampling in particle filters, but it is better for four reasons: (1) it exploits the smoothness of the problem; (2) it explicitly minimizes the errors in the relevant functional; (3) it explicitly models the dynamics in state space; and (4) it can be used to compute a corrected value for the desired functional using the residuals. We will attempt to make this paper accessible to normal engineers who do not have PDEs for breakfast.
Li, Yongxiao; Wang, Zinan; Peng, Chao; Li, Zhengbin
2014-10-10
Conventional signal processing methods for improving the random walk coefficient and the bias stability of interferometric fiber-optic gyroscopes are usually implemented in one-dimension sequence. In this paper, as a comparison, we allocated synchronous adaptive filters with the calculations of correlations of multidimensional signals in the perspective of the signal subspace. First, two synchronous independent channels are obtained through quadrature demodulation. Next, synchronous adaptive filters were carried out in order to project the original channels to the high related error channels and the approximation channels. The error channel signals were then processed by principal component analysis for suppressing coherent noises. Finally, an optimal state estimation of these error channels and approximation channels based on the Kalman gain coefficient was operated. Experimental results show that this signal processing method improved the raw measurements' variance from 0.0630 [(°/h)2] to 0.0103 [(°/h)2]. PMID:25322393
A New Adaptive Image Denoising Method Based on Neighboring Coefficients
NASA Astrophysics Data System (ADS)
Biswas, Mantosh; Om, Hari
2016-03-01
Many good techniques have been discussed for image denoising that include NeighShrink, improved adaptive wavelet denoising method based on neighboring coefficients (IAWDMBNC), improved wavelet shrinkage technique for image denoising (IWST), local adaptive wiener filter (LAWF), wavelet packet thresholding using median and wiener filters (WPTMWF), adaptive image denoising method based on thresholding (AIDMT). These techniques are based on local statistical description of the neighboring coefficients in a window. These methods however do not give good quality of the images since they cannot modify and remove too many small wavelet coefficients simultaneously due to the threshold. In this paper, a new image denoising method is proposed that shrinks the noisy coefficients using an adaptive threshold. Our method overcomes these drawbacks and it has better performance than the NeighShrink, IAWDMBNC, IWST, LAWF, WPTMWF, and AIDMT denoising methods.
A Filtering Method For Gravitationally Stratified Flows
Gatti-Bono, Caroline; Colella, Phillip
2005-04-25
Gravity waves arise in gravitationally stratified compressible flows at low Mach and Froude numbers. These waves can have a negligible influence on the overall dynamics of the fluid but, for numerical methods where the acoustic waves are treated implicitly, they impose a significant restriction on the time step. A way to alleviate this restriction is to filter out the modes corresponding to the fastest gravity waves so that a larger time step can be used. This paper presents a filtering strategy of the fully compressible equations based on normal mode analysis that is used throughout the simulation to compute the fast dynamics and that is able to damp only fast gravity modes.
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback-Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
Filtered cathodic arc deposition apparatus and method
Krauss, Alan R.
1999-01-01
A filtered cathodic arc deposition method and apparatus for the production of highly dense, wear resistant coatings which are free from macro particles. The filtered cathodic arc deposition apparatus includes a cross shaped vacuum chamber which houses a cathode target having an evaporable surface comprised of the coating material, means for generating a stream of plasma, means for generating a transverse magnetic field, and a macro particle deflector. The transverse magnetic field bends the generated stream of plasma in the direction of a substrate. Macro particles are effectively filtered from the stream of plasma by traveling, unaffected by the transverse magnetic field, along the initial path of the plasma stream to a macro particle deflector. The macro particle deflector has a preformed surface which deflects macro particles away from the substrate.
Gas image enhancement based on adaptive time-domain filtering and morphology
NASA Astrophysics Data System (ADS)
Zhang, Changxing; Wang, Lingxue; Li, Jiakun; Long, Yunting; Zhang, Bei
2011-05-01
The fingerprint region of most gases is within 3 to 14μm. A mid-wave or long-wave infrared thermal imager is therefore commonly applied in gas detection. With further influence of low gas concentration and heterogeneity of infrared focal plane arrays, the image has numerous drawbacks. These include loud noise, weak gas signal, gridding, and dead points, all of which are particularly evident in sequential images. In order to solve these problems, we take into account the characteristics of the leaking gas image and propose an enhancement method based on adaptive time-domain filtering with morphology. The adaptive time-domain filtering which operates on time sequence images is a hybrid method combining the recursive filtering and mean filtering. It segments gas and background according to a selected threshold; removes speckle noise according to the median; and removes background domain using weighted difference image. The morphology method can not only dilate the gas region along the direction of gas diffusion to greatly enhance the visibility of the leakage area, but also effectively remove the noise, and smooth the contour. Finally, the false color is added to the gas domain. Results show that the gas infrared region is effectively enhanced.
Ship detection for high resolution optical imagery with adaptive target filter
NASA Astrophysics Data System (ADS)
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.
Method For Model-Reference Adaptive Control
NASA Technical Reports Server (NTRS)
Seraji, Homayoun
1990-01-01
Relatively simple method of model-reference adaptive control (MRAC) developed from two prior classes of MRAC techniques: signal-synthesis method and parameter-adaption method. Incorporated into unified theory, which yields more general adaptation scheme.
NASA Astrophysics Data System (ADS)
Ma, Shaokang; Wu, Peijun; Ji, Jinhu; Li, Xuchun
2016-02-01
This article presents a sensorless control approach of salient PMSM with an online parameter identifier. Adaptive Integrator is proposed and utilised for the estimation of active flux and rotor position. As a result, integrator overflow caused by DC offset is avoided. Meanwhile, an online stator resistance identification algorithm using strong tracking filter is employed, and the identified stator resistance is fed back to the estimating algorithm. Thus, the estimating algorithm can calculate the rotor position correctly. Simulations and experimental results validate the feasibility of both adaptive integrator and the parameter identification method.
Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth
Alam, Mushfiqul; Rohac, Jan
2015-01-01
MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing. PMID:25648711
Adaptive data filtering of inertial sensors with variable bandwidth.
Alam, Mushfiqul; Rohac, Jan
2015-01-01
MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing. PMID:25648711
NASA Astrophysics Data System (ADS)
Mahmood, Muhammad Tariq; Chu, Yeon-Ho; Choi, Young-Kyu
2016-05-01
This paper proposes a Rician noise reduction method for magnetic resonance (MR) images. The proposed method is based on adaptive non-local mean and guided image filtering techniques. In the first phase, a guidance image is obtained from the noisy image through an adaptive non-local mean filter. Sobel operators are applied to compute the strength of edges which is further used to control the spread of the kernel in non-local mean filtering. In the second phase, the noisy and the guidance images are provided to the guided image filter as input to restore the noise-free image. The improved performance of the proposed method is investigated using the simulated and real data sets of MR images. Its performance is also compared with the previously proposed state-of-the art methods. Comparative analysis demonstrates the superiority of the proposed scheme over the existing approaches.
NASA Astrophysics Data System (ADS)
Mahmood, Muhammad Tariq; Chu, Yeon-Ho; Choi, Young-Kyu
2016-06-01
This paper proposes a Rician noise reduction method for magnetic resonance (MR) images. The proposed method is based on adaptive non-local mean and guided image filtering techniques. In the first phase, a guidance image is obtained from the noisy image through an adaptive non-local mean filter. Sobel operators are applied to compute the strength of edges which is further used to control the spread of the kernel in non-local mean filtering. In the second phase, the noisy and the guidance images are provided to the guided image filter as input to restore the noise-free image. The improved performance of the proposed method is investigated using the simulated and real data sets of MR images. Its performance is also compared with the previously proposed state-of-the art methods. Comparative analysis demonstrates the superiority of the proposed scheme over the existing approaches.
Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces
NASA Astrophysics Data System (ADS)
Lu, Jun; McFarland, Dennis J.; Wolpaw, Jonathan R.
2013-02-01
Objective. Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an ‘adaptive Laplacian (ALAP) filter’, can provide better performance for SMR-based BCIs. Approach. An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible. Main results. Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP. Significance. Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.
Adaptive nonlocal means filtering based on local noise level for CT denoising
Li, Zhoubo; Trzasko, Joshua D.; Lake, David S.; Blezek, Daniel J.; Manduca, Armando; Yu, Lifeng; Fletcher, Joel G.; McCollough, Cynthia H.
2014-01-15
Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the
Parameter estimation with an iterative version of the adaptive Gaussian mixture filter
NASA Astrophysics Data System (ADS)
Stordal, A.; Lorentzen, R.
2012-04-01
The adaptive Gaussian mixture filter (AGM) was introduced in Stordal et. al. (ECMOR 2010) as a robust filter technique for large scale applications and an alternative to the well known ensemble Kalman filter (EnKF). It consists of two analysis steps, one linear update and one weighting/resampling step. The bias of AGM is determined by two parameters, one adaptive weight parameter (forcing the weights to be more uniform to avoid filter collapse) and one pre-determined bandwidth parameter which decides the size of the linear update. It has been shown that if the adaptive parameter approaches one and the bandwidth parameter decrease with increasing sample size, the filter can achieve asymptotic optimality. For large scale applications with a limited sample size the filter solution may be far from optimal as the adaptive parameter gets close to zero depending on how well the samples from the prior distribution match the data. The bandwidth parameter must often be selected significantly different from zero in order to make large enough linear updates to match the data, at the expense of bias in the estimates. In the iterative AGM we take advantage of the fact that the history matching problem is usually estimation of parameters and initial conditions. If the prior distribution of initial conditions and parameters is close to the posterior distribution, it is possible to match the historical data with a small bandwidth parameter and an adaptive weight parameter that gets close to one. Hence the bias of the filter solution is small. In order to obtain this scenario we iteratively run the AGM throughout the data history with a very small bandwidth to create a new prior distribution from the updated samples after each iteration. After a few iterations, nearly all samples from the previous iteration match the data and the above scenario is achieved. A simple toy problem shows that it is possible to reconstruct the true posterior distribution using the iterative version of
NASA Astrophysics Data System (ADS)
Koga, Takanori; Suetake, Noriaki
2015-02-01
This paper describes the detail-preserving impulse noise removal performance of a one-dimensional (1-D) switching median filter (SMF) applied along an adaptive space-filling curve. Usually, a SMF with a two-dimensional (2-D) filter window is widely used for impulse noise removal while still preserving detailed parts in an input image. However, the noise detector of the 2-D filter does not always distinguish between the original pixels and the noise-corrupted ones perfectly. In particular, pixels constituting thin lines in an input image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered regardless of the necessity of the filtering. To cope with this problem, we propose a new impulse noise removal method based on a 1-D SMF and a space-filling curve which is adaptively drawn using a minimum spanning tree reflecting structural context of an input image.
NASA Technical Reports Server (NTRS)
Penland, Cecile; Ghil, Michael; Weickmann, Klaus M.
1991-01-01
The spectral resolution and statistical significance of a harmonic analysis obtained by low-order MEM can be improved by subjecting the data to an adaptive filter. This adaptive filter consists of projecting the data onto the leading temporal empirical orthogonal functions obtained from singular spectrum analysis (SSA). The combined SSA-MEM method is applied both to a synthetic time series and a time series of AAM data. The procedure is very effective when the background noise is white and less so when the background noise is red. The latter case obtains in the AAM data. Nevertheless, reliable evidence for intraseasonal and interannual oscillations in AAM is detected. The interannual periods include a quasi-biennial one and an LF one, of 5 years, both related to the El Nino/Southern Oscillation. In the intraseasonal band, separate oscillations of about 48.5 and 51 days are ascertained.
ROI extraction of chest CT images using adaptive opening filter
NASA Astrophysics Data System (ADS)
Yamada, Nobuhiro; Kubo, Mitsuru; Kawata, Yoshiki; Niki, Noboru; Eguchi, Kenji; Omatsu, Hironobu; Kakinuma, Ryutaro; Kaneko, Masahiro; Kusumoto, Masahiko; Nishiyama, Hiroyuki; Moriyama, Noriyuki
2003-05-01
We have already developed a prototype of computer-aided diagnosis (CAD) system that can automatically detect suspicious shadows from Chest CT images. But the CAD system cannot detect Ground-Grass-Attenuation perfectly. In many cases, this reason depends on the inaccurate extraction of the region of interests (ROI) that CAD system analyzes, so we need to improve it. In this paper, we propose a method of an accurate extraction of the ROI, and compare proposed method to ordinary method that have used in CAD system. Proposed Method is performed by application of the three steps. Firstly we extract lung area using threshold. Secondly we remove the slowly varying bias field using flexible Opening Filter. This Opening Filter is calculated by the combination of the ordinary opening value and the distribution which CT value and contrast follow. Finally we extract Region of Interest using fuzzy clustering. When we applied proposal method to Chest CT images, we got a good result in which ordinary method cannot achieve. In this study we used the Helical CT images that are obtained under the following measurement: 10mm beam width; 20mm/sec table speed; 120kV tube voltage; 50mA tube current; 10mm reconstruction interval.
Independent motion detection with a rival penalized adaptive particle filter
NASA Astrophysics Data System (ADS)
Becker, Stefan; Hübner, Wolfgang; Arens, Michael
2014-10-01
Aggregation of pixel based motion detection into regions of interest, which include views of single moving objects in a scene is an essential pre-processing step in many vision systems. Motion events of this type provide significant information about the object type or build the basis for action recognition. Further, motion is an essential saliency measure, which is able to effectively support high level image analysis. When applied to static cameras, background subtraction methods achieve good results. On the other hand, motion aggregation on freely moving cameras is still a widely unsolved problem. The image flow, measured on a freely moving camera is the result from two major motion types. First the ego-motion of the camera and second object motion, that is independent from the camera motion. When capturing a scene with a camera these two motion types are adverse blended together. In this paper, we propose an approach to detect multiple moving objects from a mobile monocular camera system in an outdoor environment. The overall processing pipeline consists of a fast ego-motion compensation algorithm in the preprocessing stage. Real-time performance is achieved by using a sparse optical flow algorithm as an initial processing stage and a densely applied probabilistic filter in the post-processing stage. Thereby, we follow the idea proposed by Jung and Sukhatme. Normalized intensity differences originating from a sequence of ego-motion compensated difference images represent the probability of moving objects. Noise and registration artefacts are filtered out, using a Bayesian formulation. The resulting a posteriori distribution is located on image regions, showing strong amplitudes in the difference image which are in accordance with the motion prediction. In order to effectively estimate the a posteriori distribution, a particle filter is used. In addition to the fast ego-motion compensation, the main contribution of this paper is the design of the probabilistic
Milne, R.B.
1995-12-01
This thesis describes a new method for the numerical solution of partial differential equations of the parabolic type on an adaptively refined mesh in two or more spatial dimensions. The method is motivated and developed in the context of the level set formulation for the curvature dependent propagation of surfaces in three dimensions. In that setting, it realizes the multiple advantages of decreased computational effort, localized accuracy enhancement, and compatibility with problems containing a range of length scales.
Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators.
Hooshmand, Rahmat Allah; Torabian Esfahani, Mahdi
2011-04-01
Thyristor controlled reactor with fixed capacitor (TCR/FC) compensators have the capability of compensating reactive power and improving power quality phenomena. Delay in the response of such compensators degrades their performance. In this paper, a new method based on adaptive filters (AF) is proposed in order to eliminate delay and increase the response of the TCR compensator. The algorithm designed for the adaptive filters is performed based on the least mean square (LMS) algorithm. In this design, instead of fixed capacitors, band-pass LC filters are used. To evaluate the filter, a TCR/FC compensator was used for nonlinear and time varying loads of electric arc furnaces (EAFs). These loads caused occurrence of power quality phenomena in the supplying system, such as voltage fluctuation and flicker, odd and even harmonics and unbalancing in voltage and current. The above design was implemented in a realistic system model of a steel complex. The simulation results show that applying the proposed control in the TCR/FC compensator efficiently eliminated delay in the response and improved the performance of the compensator in the power system. PMID:21193194
Adaptive filtering and prediction of the Southern Oscillation index
NASA Astrophysics Data System (ADS)
Keppenne, Christian L.; Ghil, Michael
1992-12-01
Singular spectrum analysis (SSA), a variant of principal component analysis, is applied to a time series of the Southern Oscillation index (SOI). The analysis filters out variability unrelated to the Southern Oscillation and separates the high-frequency, 2- to 3-year variability, including the quasi-biennial oscillation, from the lower-frequency 4- to 6-year El Niño cycle. The maximum entropy method (MEM) is applied to forecasting the prefiltered SOI. Prediction based on MEM-associated autoregressive models has useful skill for 30-36 months. A 1993-1994 La Niña event is predicted based on data through February 1992.
Adaptive filtering and prediction of the Southern Oscillation index
NASA Technical Reports Server (NTRS)
Keppenne, Christian L.; Ghil, Michael
1992-01-01
Singular spectrum analysis (SSA), a variant of principal component analysis, is applied to a time series of the Southern Oscillation index (SOI). The analysis filters out variability unrelated to the Southern Oscillation and separates the high-frequency, 2- to 3-year variability, including the quasi-biennial oscillation, from the lower-frequency 4- to 6-year El Nino cycle. The maximum entropy method (MEM) is applied to forecasting the prefiltered SOI. Prediction based on MEM-associated autoregresive models has useful skill for 30-36 months. A 1993-1994 La Nina event is predicted based on data through February 1992.
Adaptive filtering and prediction of the Southern Oscillation index
Keppenne, C.L. California Inst. of Technology, Pasadena ); Ghil, M. )
1992-12-20
Singular spectrum analysis (SSA), a variant of principal component analysis, is applied to a time series of the Southern Oscillation index (SOI). The analysis filters out variability unrelated to the Southern Oscillation and separates the high-frequency, 2- to 3-year variability, including the quasi-biennial oscillation, from the lower-frequency 4- to 6-year El Nino cycle. The maximum entropy method (MEM) is applied to forecasting the prefiltered SOI. Prediction based on MEM-associated autoregressive models has useful skill for 30-36 months. A 1993-1994 La Nina event is predicted based on data through February 1992. 52 refs., 4 figs.
NASA Astrophysics Data System (ADS)
Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng
2016-06-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a sufficiently 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 expansion (PCE) to represent and propagate the uncertainties in parameters and states. However, PCKF suffers from the so-called "curse of dimensionality". Its computational cost increases drastically with the increasing number of parameters and system nonlinearity. Furthermore, PCKF may fail to provide accurate estimations due to the joint updating scheme for strongly nonlinear models. Motivated by recent developments in uncertainty quantification and EnKF, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected at each assimilation step; the "restart" scheme is utilized to eliminate the inconsistency between updated model parameters and states variables. The performance of RAPCKF is systematically tested with numerical cases of unsaturated flow models. It is shown that the adaptive approach and restart scheme can significantly improve the performance of PCKF. Moreover, RAPCKF has been demonstrated to be more efficient than EnKF with the same computational cost.
Cardiac fiber tracking using adaptive particle filtering based on tensor rotation invariant in MRI
NASA Astrophysics Data System (ADS)
Kong, Fanhui; Liu, Wanyu; Magnin, Isabelle E.; Zhu, Yuemin
2016-03-01
Diffusion magnetic resonance imaging (dMRI) is a non-invasive method currently available for cardiac fiber tracking. However, accurate and efficient cardiac fiber tracking is still a challenge. This paper presents a probabilistic cardiac fiber tracking method based on particle filtering. In this framework, an adaptive sampling technique is presented to describe the posterior distribution of fiber orientations by adjusting the number and status of particles according to the fractional anisotropy of diffusion. An observation model is then proposed to update the weight of particles by rotating diffusion tensor from the primary eigenvector to a given fiber orientation while keeping the shape of the tensor invariant. The results on human cardiac dMRI show that the proposed method is robust to noise and outperforms conventional streamline and particle filtering techniques.
Method and apparatus for filtering visual documents
NASA Technical Reports Server (NTRS)
Rorvig, Mark E. (Inventor); Shelton, Robert O. (Inventor)
1993-01-01
A method and apparatus for producing an abstract or condensed version of a visual document is presented. The frames comprising the visual document are first sampled to reduce the number of frames required for processing. The frames are then subjected to a structural decomposition process that reduces all information in each frame to a set of values. These values are in turn normalized and further combined to produce only one information content value per frame. The information content values of these frames are then compared to a selected distribution cutoff point. This effectively selects those values at the tails of a normal distribution, thus filtering key frames from their surrounding frames. The value for each frame is then compared with the value from the previous frame, and the respective frame is finally stored only if the values are significantly different. The method filters or compresses a visual document with a reduction in digital storage on the ratio of up to 700 to 1 or more, depending on the content of the visual document being filtered.
Filtering apparatus and method of use
NASA Technical Reports Server (NTRS)
Gavalas, Lillian Susan (Inventor)
2011-01-01
A filtering apparatus comprises a microporous membrane and an actuator. The membrane is positioned to traverse across the hollow interior of a conduit used for the transport of molecules in bulk. In one example, the pores of the membrane comprise a plurality of open-ended carbon nanotubes. The actuator comprises a transducing material such as a polyvinyledene fluoride film that is operatively positioned in contact with the membrane and is capable of propagating acoustic vibration onto the membrane at a particular frequency so as to hasten the movement of the molecules through the membrane. Similarly, a method of filtering water comprises the steps of: (a) sifting molecules of water through the membrane, the pores of the membrane comprising a plurality of carbon nanotubes; and (b) propagating acoustic vibration onto the microporous membrane at a libration frequency of ice so as to hasten movement of the water molecules within the carbon nanotubes.
Membrane filter enumeration method for Clostridium perfringens.
Bisson, J W; Cabelli, V J
1979-01-01
A membrane filter procedure has been developed for the rapid quantitation of C. perfringens in the aquatic environment. Background growth is inhibited by the use of D-cycloserine, polymyxin B sulfate, and incubation at 45 degrees C. Differential characteristics include the fermentation of sucrose, production of acid phosphatase, and the absence of beta-D-glucosidase activity. The medium is prepared as follows (in grams per 100 ml of distilled water): tryptose, 3.0; yeast extract, 2.0; sucrose, 0.5; L-cysteine, 0.1; MgSO4. 7H2O, 0.01; bromocresol purple, 0.004; and agar, 1.5. The ingredients are dissolved, and the pH is adjusted to 7.6. After autoclaving at 121 degrees C for 15 min, the medium is allowed to cool at 50 degrees C, and the following are added per 100 ml: D-cycloserine, 40 mg; polymyxin B sulfate, 2.5 mg; indoxyl-beta-D-glucoside, 60 mg; 2.0 ml of a filter-sterilized 0.5% phenolpthalein diphosphate solution; and 0.2 ml of a filter-sterilized 4.5% FeCl3.6H2O solution. Enumeration of C. perfringens in a water sample is completed within 18 to 24 h. The verification of typical colonies was 93%. The average recovery from peptone-water spore suspensions of five strains was 79%, and that from filter-sterilized seawater suspensions was 90%. The precision of the method was approximately equal to that expected from random error alone. Confirmed recoveries of C. perfringens from water and sewage samples generally were greater than those by the Bonde pour tube method. PMID:216310
Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter
NASA Astrophysics Data System (ADS)
Yoshikawa, Ruriha; Teramoto, Atsushi; Matsubara, Tomoko; Fujita, Hiroshi
2013-03-01
Breast cancer is a serious health concern for all women. Computer-aided detection for mammography has been used for detecting mass and micro-calcification. However, there are challenges regarding the automated detection of the architectural distortion about the sensitivity. In this study, we propose a novel automated method for detecting architectural distortion. Our method consists of the analysis of the mammary gland structure, detection of the distorted region, and reduction of false positive results. We developed the adaptive Gabor filter for analyzing the mammary gland structure that decides filter parameters depending on the thickness of the gland structure. As for post-processing, healthy mammary glands that run from the nipple to the chest wall are eliminated by angle analysis. Moreover, background mammary glands are removed based on the intensity output image obtained from adaptive Gabor filter. The distorted region of the mammary gland is then detected as an initial candidate using a concentration index followed by binarization and labeling. False positives in the initial candidate are eliminated using 23 types of characteristic features and a support vector machine. In the experiments, we compared the automated detection results with interpretations by a radiologist using 50 cases (200 images) from the Digital Database of Screening Mammography (DDSM). As a result, true positive rate was 82.72%, and the number of false positive per image was 1.39. There results indicate that the proposed method may be useful for detecting architectural distortion in mammograms.
Chen, Xiyuan; Wang, Xiying; Xu, Yuan
2014-01-01
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. PMID:25502124
NASA Astrophysics Data System (ADS)
Hu, Hongtao; Jing, Zhongliang; Hu, Shiqiang
2006-12-01
A novel adaptive algorithm for tracking maneuvering targets is proposed. The algorithm is implemented with fuzzy-controlled current statistic model adaptive filtering and unscented transformation. A fuzzy system allows the filter to tune the magnitude of maximum accelerations to adapt to different target maneuvers, and unscented transformation can effectively handle nonlinear system. A bearing-only tracking scenario simulation results show the proposed algorithm has a robust advantage over a wide range of maneuvers and overcomes the shortcoming of the traditional current statistic model and adaptive filtering algorithm.
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
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. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
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. PMID:27438835
An online novel adaptive filter for denoising time series measurements.
Willis, Andrew J
2006-04-01
A nonstationary form of the Wiener filter based on a principal components analysis is described for filtering time series data possibly derived from noisy instrumentation. The theory of the filter is developed, implementation details are presented and two examples are given. The filter operates online, approximating the maximum a posteriori optimal Bayes reconstruction of a signal with arbitrarily distributed and non stationary statistics. PMID:16649562
A method and apparatus for signal filtering
NASA Astrophysics Data System (ADS)
Alexander, Edward M.; Spezio, Anthony E.
1994-08-01
A signal filter, and method of signal filtering, in which an optical signal is spatially dispersed according to frequency, and undesired frequencies blocked out, is introduced. In an embodiment, a Bragg cell receives a (typically microwave) signal, and transduces it to an acoustic signal. A light source, e.g. a laser diode, directs light through the acoustic signal in a known manner so as to produce an optical output which is spatially dispersed according to frequency. A programmable spatial light modulator blocks out unwanted frequency components. Upon removal of the optical carrier, e.g. by heterodyning the spatial light modulator's output with a reference signal from the light source, the components can be recombined into a resultant filtered signal. The invention can be used as a repeater, a military electronic countermeasure, in environments which have large amounts of electromagnetic clutter but in which one does not want the clutter repeated. The spatial light modulator can be programmed to pass only frequency components in which one is interested, thus blocking the clutter from the repeated signal.
Electronic filters, signal conversion apparatus, hearing aids and methods
NASA Technical Reports Server (NTRS)
Morley, Jr., Robert E. (Inventor); Engebretson, A. Maynard (Inventor); Engel, George L. (Inventor); Sullivan, Thomas J. (Inventor)
1994-01-01
An electronic filter for filtering an electrical signal. Signal processing circuitry therein includes a logarithmic filter having a series of filter stages with inputs and outputs in cascade and respective circuits associated with the filter stages for storing electrical representations of filter parameters. The filter stages include circuits for respectively adding the electrical representations of the filter parameters to the electrical signal to be filtered thereby producing a set of filter sum signals. At least one of the filter stages includes circuitry for producing a filter signal in substantially logarithmic form at its output by combining a filter sum signal for that filter stage with a signal from an output of another filter stage. The signal processing circuitry produces an intermediate output signal, and a multiplexer connected to the signal processing circuit multiplexes the intermediate output signal with the electrical signal to be filtered so that the logarithmic filter operates as both a logarithmic prefilter and a logarithmic postfilter. Other electronic filters, signal conversion apparatus, electroacoustic systems, hearing aids and methods are also disclosed.
Method and apparatus for a self-cleaning filter
Diebold, James P.; Lilley, Arthur; Browne, III, Kingsbury; Walt, Robb Ray; Duncan, Dustin; Walker, Michael; Steele, John; Fields, Michael
2010-11-16
A method and apparatus for removing fine particulate matter from a fluid stream without interrupting the overall process or flow. The flowing fluid inflates and expands the flexible filter, and particulate is deposited on the filter media while clean fluid is permitted to pass through the filter. This filter is cleaned when the fluid flow is stopped, the filter collapses, and a force is applied to distort the flexible filter media to dislodge the built-up filter cake. The dislodged filter cake falls to a location that allows undisrupted flow of the fluid after flow is restored. The shed particulate is removed to a bin for periodic collection. A plurality of filter cells can operate independently or in concert, in parallel, or in series to permit cleaning the filters without shutting off the overall fluid flow. The self-cleaning filter is low cost, has low power consumption, and exhibits low differential pressures.
Automatic artifact suppression in simultaneous tDCS-EEG using adaptive filtering.
Mancini, Matteo; Pellicciari, Maria Concetta; Brignani, Debora; Mauri, Piercarlo; De Marchis, Cristiano; Miniussi, Carlo; Conforto, Silvia
2015-08-01
Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method that can be used in cognitive and clinical protocols in order to modulate neural activity. Although some macro effects are known, the underlying mechanisms are still not clear. tDCS in combination with electroencephalography (EEG) could help to understand these mechanisms from a neural point of view. However, simultaneous tDCS-EEG still remains challenging because of the artifacts that affect the recorded signals. In this paper, an automated artifact cancellation method based on adaptive filtering is proposed. Using independent component analysis (ICA), the artifacts were characterized using data from both a phantom and a group of healthy subjects. The resulting filter can successfully remove tDCS-related artifacts during anodal and cathodal stimulations. PMID:26736856
Automatic balancing of AMB systems using plural notch filter and adaptive synchronous compensation
NASA Astrophysics Data System (ADS)
Xu, Xiangbo; Chen, Shao; Zhang, Yanan
2016-07-01
To achieve automatic balancing in active magnetic bearing (AMB) system, a control method with notch filters and synchronous compensators is widely employed. However, the control precision is significantly affected by the synchronous compensation error, which is caused by parameter errors and variations of the power amplifiers. Furthermore, the computation effort may become intolerable if a 4-degree-of-freedom (dof) AMB system is studied. To solve these problems, an adaptive automatic balancing control method in the AMB system is presented in this study. Firstly, a 4-dof radial AMB system is described and analyzed. To simplify the controller design, the 4-dof dynamic equations are transferred into two plural functions related to translation and rotation, respectively. Next, to achieve automatic balancing of the AMB system, two synchronous equations are formed. Solution of them leads to a control strategy based on notch filters and feedforward controllers with an inverse function of the power amplifier. The feedforward controllers can be simplified as synchronous phases and amplitudes. Then, a plural phase-shift notch filter which can identify the synchronous components in 2-dof motions is formulated, and an adaptive compensation method that can form two closed-loop systems to tune the synchronous amplitude of the feedforward controller and the phase of the plural notch filter is proposed. Finally, the proposed control strategy is verified by both simulations and experiments on a test rig of magnetically suspended control moment gyro. The results indicate that this method can fulfill the automatic balancing of the AMB system with a light computational load.
Adaptive RSOV filter using the FELMS algorithm for nonlinear active noise control systems
NASA Astrophysics Data System (ADS)
Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou; Li, Tianrui
2013-01-01
This paper presents a recursive second-order Volterra (RSOV) filter to solve the problems of signal saturation and other nonlinear distortions that occur in nonlinear active noise control systems (NANC) used for actual applications. Since this nonlinear filter based on an infinite impulse response (IIR) filter structure can model higher than second-order and third-order nonlinearities for systems where the nonlinearities are harmonically related, the RSOV filter is more effective in NANC systems with either a linear secondary path (LSP) or a nonlinear secondary path (NSP). Simulation results clearly show that the RSOV adaptive filter using the multichannel structure filtered-error least mean square (FELMS) algorithm can further greatly reduce the computational burdens and is more suitable to eliminate nonlinear distortions in NANC systems than a SOV filter, a bilinear filter and a third-order Volterra (TOV) filter.
Discrete cosine transform-based local adaptive filtering of images corrupted by nonstationary noise
NASA Astrophysics Data System (ADS)
Lukin, Vladimir V.; Fevralev, Dmitriy V.; Ponomarenko, Nikolay N.; Abramov, Sergey K.; Pogrebnyak, Oleksiy; Egiazarian, Karen O.; Astola, Jaakko T.
2010-04-01
In many image-processing applications, observed images are contaminated by a nonstationary noise and no a priori information on noise dependence on local mean or about local properties of noise statistics is available. In order to remove such a noise, a locally adaptive filter has to be applied. We study a locally adaptive filter based on evaluation of image local activity in a ``blind'' manner and on discrete cosine transform computed in overlapping blocks. Two mechanisms of local adaptation are proposed and applied. The first mechanism takes into account local estimates of noise standard deviation while the second one exploits discrimination of homogeneous and heterogeneous image regions by adaptive threshold setting. The designed filter performance is tested for simulated data as well as for real-life remote-sensing and maritime radar images. Recommendations concerning filter parameter setting are provided. An area of applicability of the proposed filter is defined.
Geometric-Algebra LMS Adaptive Filter and Its Application to Rotation Estimation
NASA Astrophysics Data System (ADS)
Lopes, Wilder B.; Al-Nuaimi, Anas; Lopes, Cassio G.
2016-06-01
This paper exploits Geometric (Clifford) Algebra (GA) theory in order to devise and introduce a new adaptive filtering strategy. From a least-squares cost function, the gradient is calculated following results from Geometric Calculus (GC), the extension of GA to handle differential and integral calculus. The novel GA least-mean-squares (GA-LMS) adaptive filter, which inherits properties from standard adaptive filters and from GA, is developed to recursively estimate a rotor (multivector), a hypercomplex quantity able to describe rotations in any dimension. The adaptive filter (AF) performance is assessed via a 3D point-clouds registration problem, which contains a rotation estimation step. Calculating the AF computational complexity suggests that it can contribute to reduce the cost of a full-blown 3D registration algorithm, especially when the number of points to be processed grows. Moreover, the employed GA/GC framework allows for easily applying the resulting filter to estimating rotors in higher dimensions.
NASA Technical Reports Server (NTRS)
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.
Method and apparatus for filtering gas with a moving granular filter bed
Brown, Robert C.; Wistrom, Corey; Smeenk, Jerod L.
2007-12-18
A method and apparatus for filtering gas (58) with a moving granular filter bed (48) involves moving a mass of particulate filter material (48) downwardly through a filter compartment (35); tangentially introducing gas into the compartment (54) to move in a cyclonic path downwardly around the moving filter material (48); diverting the cyclonic path (58) to a vertical path (62) to cause the gas to directly interface with the particulate filter material (48); thence causing the gas to move upwardly through the filter material (48) through a screened partition (24, 32) into a static upper compartment (22) of a filter compartment for exodus (56) of the gas which has passed through the particulate filter material (48).
Method of and apparatus for testing the integrity of filters
Herman, Raymond L [Richland, WA
1985-01-01
A method of and apparatus for testing the integrity of individual filters or filter stages of a multistage filtering system including a diffuser permanently mounted upstream and/or downstream of the filter stage to be tested for generating pressure differentials to create sufficient turbulence for uniformly dispersing trace agent particles within the airstream upstream and downstream of such filter stage. Samples of the particle concentration are taken upstream and downstream of the filter stage for comparison to determine the extent of particle leakage past the filter stage.
Methods of and apparatus for testing the integrity of filters
Herman, R.L.
1984-01-01
A method of and apparatus for testing the integrity of individual filters or filter stages of a multistage filtering system including a diffuser permanently mounted upstream and/or downstream of the filter stage to be tested for generating pressure differentials to create sufficient turbulence for uniformly dispersing trace agent particles within the airstram upstream and downstream of such filter stage. Samples of the particel concentration are taken upstream and downstream of the filter stage for comparison to determine the extent of particle leakage past the filter stage.
Method of and apparatus for testing the integrity of filters
Herman, R.L.
1985-05-07
A method of and apparatus are disclosed for testing the integrity of individual filters or filter stages of a multistage filtering system including a diffuser permanently mounted upstream and/or downstream of the filter stage to be tested for generating pressure differentials to create sufficient turbulence for uniformly dispersing trace agent particles within the airstream upstream and downstream of such filter stage. Samples of the particle concentration are taken upstream and downstream of the filter stage for comparison to determine the extent of particle leakage past the filter stage. 5 figs.
Adaptive model training system and method
Bickford, Randall L; Palnitkar, Rahul M; Lee, Vo
2014-04-15
An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.
Adaptive model training system and method
Bickford, Randall L; Palnitkar, Rahul M
2014-11-18
An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.
Adaptive Spatial Filtering of Interferometric Data Stack Oriented to Distributed Scatterers
NASA Astrophysics Data System (ADS)
Zhang, Y.; Xie, C.; Shao, Y.; Yuan, M.
2013-07-01
Standard interferometry poses a challenge in non-urban areas due to temporal and spatial decorrelation of the radar signal, where there is high signal noise. Techniques such as Small Baseline Subset Algorithm (SBAS) have been proposed to make use of multiple interferometric combinations to alleviate the problem. However, the interferograms used in SBAS are multilooked with a boxcar (rectangle) filter to reduce phase noise, resulting in a loss of resolution and signal superstition from different objects. In this paper, we proposed a modified adaptive spatial filtering algorithm for accurate estimation of interferogram and coherence without resolution loss even in rural areas, to better support the deformation monitoring with time series interferometric synthetic aperture radar (InSAR) technique. The implemented method identifies the statistically homogenous pixels in a neighbourhood based on the goodness-of-fit test, and then applies an adaptive spatial filtering of interferograms. Three statistical tests for the identification of distributed targets will be presented, applied to real data. PALSAR data of the yellow river delta in China is used for demonstrating the effectiveness of this algorithm in rural areas.
Robust Optimal Adaptive Control Method with Large Adaptive Gain
NASA Technical Reports Server (NTRS)
Nguyen, Nhan T.
2009-01-01
In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.
Method for filtering solvent and tar sand mixtures
Kelterborn, J. C.; Stone, R. A.
1985-09-03
A method for filtering spent tar sands from a bitumen and organic solvent solution comprises separating the solution into two streams wherein the bulk of the coarser spent tar sand is in a first stream and has an average particle size of about 10 to about 100 mesh and the bulk of the finer spent tar sand is in a second stream; producing a filter cake by filtering the coarser spent tar sand from the first stream; and filtering the finer spent tar sand from the second stream with the filter cake. The method is particularly useful for filtering solutions of bitumen extracted from bitumen containing diatomite, spent diatomite and organic solvent.
Echo motion imaging with adaptive clutter filter for assessment of cardiac blood flow
NASA Astrophysics Data System (ADS)
Takahashi, Hiroki; Hasegawa, Hideyuki; Kanai, Hiroshi
2015-07-01
Visualization of the vortex blood flow in the cardiac chamber is a potential diagnostic tool for the evaluation of cardiac function. In the present study, a method for automatic selection of the desirable cutoff frequency of a moving target indicator filter, namely, a clutter filter, was proposed in order to visualize complex blood flows by the ultrahigh-frame-rate imaging of echoes from blood particles while suppressing clutter echoes. In this method, the cutoff frequency was adaptively changed as a function of the velocity of the heart wall (clutter source) in each frame. The feasibility of the proposed method was examined through the measurement of a healthy volunteer using parallel receive beamforming with a single transmission of a non-steered diverging beam. Using the moving target indicator filter as above with the cutoff frequency determined by the proposed method, the vortex-like blood flow in the cardiac chamber was visualized as movements of echoes from blood particles at a very high frame rate of 6024 Hz while suppressing clutter echoes.
Color filter array demosaicking: new method and performance measures.
Lu, Wenmiao; Tan, Yap-Peng
2003-01-01
Single-sensor digital cameras capture imagery by covering the sensor surface with a color filter array (CFA) such that each sensor pixel only samples one of three primary color values. To render a full-color image, an interpolation process, commonly referred to as CFA demosaicking, is required to estimate the other two missing color values at each pixel. In this paper, we present two contributions to the CFA demosaicking: a new and improved CFA demosaicking method for producing high quality color images and new image measures for quantifying the performance of demosaicking methods. The proposed demosaicking method consists of two successive steps: an interpolation step that estimates missing color values by exploiting spatial and spectral correlations among neighboring pixels, and a post-processing step that suppresses noticeable demosaicking artifacts by adaptive median filtering. Moreover, in recognition of the limitations of current image measures, we propose two types of image measures to quantify the performance of different demosaicking methods; the first type evaluates the fidelity of demosaicked images by computing the peak signal-to-noise ratio and CIELAB DeltaE(*)(ab) for edge and smooth regions separately, and the second type accounts for one major demosaicking artifact-zipper effect. We gauge the proposed demosaicking method and image measures using several existing methods as benchmarks, and demonstrate their efficacy using a variety of test images. PMID:18237887
Singh, Omkar; Sunkaria, Ramesh Kumar
2015-01-01
Separating an information-bearing signal from the background noise is a general problem in signal processing. In a clinical environment during acquisition of an electrocardiogram (ECG) signal, The ECG signal is corrupted by various noise sources such as powerline interference (PLI), baseline wander and muscle artifacts. This paper presents novel methods for reduction of powerline interference in ECG signals using empirical wavelet transform (EWT) and adaptive filtering. The proposed methods are compared with the empirical mode decomposition (EMD) based PLI cancellation methods. A total of six methods for PLI reduction based on EMD and EWT are analysed and their results are presented in this paper. The EWT-based de-noising methods have less computational complexity and are more efficient as compared with the EMD-based de-noising methods. PMID:25412942
Real data assimilation experiments using filtering methods
NASA Astrophysics Data System (ADS)
Jakubiak, B.; Szpindler, M.
2009-04-01
ICM runs few NWP models in quasi operational regime, using the multivariate optimal interpolation (MVOI) method for data assimilation. Recently we are working on implementation of more advanced methods. The goal of this work is to demonstrate that the assimilation of radiosondes and amdar observations using the ensemble Kalman filter gives an estimation of the initial state of the atmosphere with a quality and performance comparable to another methods. Upper air conventional radiosonde data taken at land stations and from ocean observing systems include information on geopotential height, temperature, humidity and wind on mandatory and significant levels. This information is of primary importance to NWP models. To present mesoscale models this information is to coarse, as the average distance between radiosondes is about 100 km. Automated weather reports from commercial aircraft are also an important data source for numerical weather prediction (NWP) models, and have improved the forecasts from these models. Commercial aircraft now provide over 130,000 meteorological observations per day, including temperature, winds, and in some cases humidity, vertical wind gust, or eddy dissipation rate (turbulence). The temperature and wind data are used in most operational numerical weather prediction models at meteorological centers worldwide. At non-synoptic times, these data are often the primary source of upper air information over the U.S. Even at synoptic times, these data are critical in depicting the atmosphere along oceanic air routes. In Europe, for example in U. K. Meteorological Office models, ACARS/AMDAR are most important data source for winds over the Atlantic Ocean, and the second most important data source after radiosondes over North America. All our experiments with assimilation of radiosondes and commercial aircraft observations are performed using ensemble Kalman filter (ENKF) technique in version proposed by Data Assimilation Research Testbed (DART) of
Method of treating contaminated HEPA filter media in pulp process
Hu, Jian S.; Argyle, Mark D.; Demmer, Ricky L.; Mondok, Emilio P.
2003-07-29
A method for reducing contamination of HEPA filters with radioactive and/or hazardous materials is described. The method includes pre-processing of the filter for removing loose particles. Next, the filter medium is removed from the housing, and the housing is decontaminated. Finally, the filter medium is processed as pulp for removing contaminated particles by physical and/or chemical methods, including gravity, flotation, and dissolution of the particles. The decontaminated filter medium is then disposed of as non-RCRA waste; the particles are collected, stabilized, and disposed of according to well known methods of handling such materials; and the liquid medium in which the pulp was processed is recycled.
Adaptive Filtering for Large Space Structures: A Closed-Form Solution
NASA Technical Reports Server (NTRS)
Rauch, H. E.; Schaechter, D. B.
1985-01-01
In a previous paper Schaechter proposes using an extended Kalman filter to estimate adaptively the (slowly varying) frequencies and damping ratios of a large space structure. The time varying gains for estimating the frequencies and damping ratios can be determined in closed form so it is not necessary to integrate the matrix Riccati equations. After certain approximations, the time varying adaptive gain can be written as the product of a constant matrix times a matrix derived from the components of the estimated state vector. This is an important savings of computer resources and allows the adaptive filter to be implemented with approximately the same effort as the nonadaptive filter. The success of this new approach for adaptive filtering was demonstrated using synthetic data from a two mode system.
Impulse radar imaging for dispersive concrete using inverse adaptive filtering techniques
Arellano, J.; Hernandez, J.M.; Brase, J.
1993-05-01
This publication addresses applications of a delayed inverse model adaptive filter for modeled data obtained from short-pulse radar reflectometry. To determine the integrity of concrete, a digital adaptive filter was used, which allows compensation of dispersion and clutter generated by the concrete. A standard set of weights produced by an adaptive filter are used on modeled data to obtain the inverse-impulse response of the concrete. The data for this report include: Multiple target, nondispersive data; single-target, variable-size dispersive data; single-target, variable-depth dispersive data; and single-target, variable transmitted-pulse-width dispersive data. Results of this simulation indicate that data generated by the weights of the adaptive filter, coupled with a two-dimensional, synthetic-aperture focusing technique, successfully generate two-dimensional images of targets within the concrete from modeled data.
Adaptive box filters for removal of random noise from digital images
Eliason, E.M.; McEwen, A.S.
1990-01-01
We have developed adaptive box-filtering algorithms to (1) remove random bit errors (pixel values with no relation to the image scene) and (2) smooth noisy data (pixels related to the image scene but with an additive or multiplicative component of noise). For both procedures, we use the standard deviation (??) of those pixels within a local box surrounding each pixel, hence they are adaptive filters. This technique effectively reduces speckle in radar images without eliminating fine details. -from Authors
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg Y; Fernández, Eduardo
2010-01-01
In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate. PMID:22315579
Adaptive Spatial Filtering with Principal Component Analysis for Biomedical Photoacoustic Imaging
NASA Astrophysics Data System (ADS)
Nagaoka, Ryo; Yamazaki, Rena; Saijo, Yoshifumi
Photoacoustic (PA) signal is very sensitive to noise generated by peripheral equipment such as power supply, stepping motor or semiconductor laser. Band-pass filter is not effective because the frequency bandwidth of the PA signal also covers the noise frequency. The objective of the present study is to reduce the noise by using an adaptive spatial filter with principal component analysis (PCA).
Adaptive high temperature superconducting filters for interference rejection
Raihn, K.F.; Fenzi, N.O.; Hey-Shipton, G.L.; Saito, E.R.; Loung, P.V.; Aidnik, D.L.
1996-07-01
An optically switched high temperature superconducting (HTS) band-reject filter bank is presented. Fast low loss switching of high quality (Q) factor HTS filter elements enables digital selection of arbitrary pass-bands and stop-bands. Patterned pieces of GaAs and silicon are used in the manufacture of the photosensitive switches. Fiber optic cabling is used to transfer the optical energy from an LED to the switch. The fiber optic cable minimizes the thermal loading of the filter package and de-couples the switch`s power source from the RF circuit. This paper will discuss the development of a computer-controlled HTS bank of optically switchable, narrow band, high Q bandstop filters which incorporates a cryocooler to maintain the 77 K operating temperature of the HTS microwave circuit.
NASA Astrophysics Data System (ADS)
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.
Shih, Cheng-Ting; Lin, Hsin-Hon; Chuang, Keh-Shih; Wu, Jay; Chang, Shu-Jun
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.
Method for reducing pressure drop through filters, and filter exhibiting reduced pressure drop
Sappok, Alexander; Wong, Victor
2014-11-18
Methods for generating and applying coatings to filters with porous material in order to reduce large pressure drop increases as material accumulates in a filter, as well as the filter exhibiting reduced and/or more uniform pressure drop. The filter can be a diesel particulate trap for removing particulate matter such as soot from the exhaust of a diesel engine. Porous material such as ash is loaded on the surface of the substrate or filter walls, such as by coating, depositing, distributing or layering the porous material along the channel walls of the filter in an amount effective for minimizing or preventing depth filtration during use of the filter. Efficient filtration at acceptable flow rates is achieved.
Investigation of the Multiple Method Adaptive Control (MMAC) method for flight control systems
NASA Technical Reports Server (NTRS)
Athans, M.; Baram, Y.; Castanon, D.; Dunn, K. P.; Green, C. S.; Lee, W. H.; Sandell, N. R., Jr.; Willsky, A. S.
1979-01-01
The stochastic adaptive control of the NASA F-8C digital-fly-by-wire aircraft using the multiple model adaptive control (MMAC) method is presented. The selection of the performance criteria for the lateral and the longitudinal dynamics, the design of the Kalman filters for different operating conditions, the identification algorithm associated with the MMAC method, the control system design, and simulation results obtained using the real time simulator of the F-8 aircraft at the NASA Langley Research Center are discussed.
Simple method for model reference adaptive control
NASA Technical Reports Server (NTRS)
Seraji, H.
1989-01-01
A simple method is presented for combined signal synthesis and parameter adaptation within the framework of model reference adaptive control theory. The results are obtained using a simple derivation based on an improved Liapunov function.
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.
Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang
2015-01-01
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. PMID:26512665
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal
Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang
2015-01-01
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. PMID:26512665
NASA Astrophysics Data System (ADS)
Wu, Chunyan; Liu, Jian; Peng, Fuqiang; Yu, Dejie; Li, Rong
2013-07-01
When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.
Learning Motivation and Adaptive Video Caption Filtering for EFL Learners Using Handheld Devices
ERIC Educational Resources Information Center
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…
Low-Complexity Lossless Compression of Hyperspectral Imagery Via Adaptive Filtering
NASA Technical Reports Server (NTRS)
Klimesh, Matthew A.
2005-01-01
A low-complexity, adaptive predictive technique for lossless compression of hyperspectral data is presented. The technique relies on the sign algorithm from the repertoire of adaptive filtering. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.
Low-Complexity Lossless Compression of Hyperspectral Imagery via Adaptive Filtering
NASA Technical Reports Server (NTRS)
Klimesh, M.
2005-01-01
A low-complexity, adaptive predictive technique for lossless compression of hyperspectral data is presented. The technique relies on the sign algorithm from the repertoire of adaptive filtering. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.
Block-adaptive filtering and its application to seismic-event detection
Clark, G.A.
1981-04-01
Block digital filtering involves the calculation of a block or finite set of filter output samples from a block of input samples. The motivation for block processing arises from computational advantages of the technique. Block filters take good advantage of parallel processing architectures, which are becoming more and more attractive with the advent of very large scale integrated (VLSI) circuits. This thesis extends the block technique to Wiener and adaptive filters, both of which are statistical filters. The key ingredient to this extension turns out to be the definition of a new performance index, block mean square error (BMSE), which combines the well known sum square error (SSE) and mean square error (MSE). A block adaptive filtering procedure is derived in which the filter coefficients are adjusted once per each output block in accordance with a generalized block least mean-square (BLMS) algorithm. Convergence properties of the BLMS algorithm are studied, including conditions for guaranteed convergence, convergence speed, and convergence accuracy. Simulation examples are given for clarity. Convergence properties of the BLMS and LMS algorithms are analyzed and compared. They are shown to be analogous, and under the proper circumstances, equivalent. The block adaptive filter was applied to the problem of detecting small seismic events in microseismic background noise. The predictor outperformed the world-wide standardized seismograph network (WWSSN) seismometers in improving signal-to-noise ratio (SNR).
Real-time 3D adaptive filtering for portable imaging systems
NASA Astrophysics Data System (ADS)
Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark
2015-03-01
Portable 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. 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 not able to run with sufficient performance on a portable platform. In recent years, advanced multicore DSPs have been introduced that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms like 3D adaptive filtering, improving the image quality of portable medical imaging devices. In this study, the performance of a 3D adaptive filtering algorithm on a digital signal processor (DSP) is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec.
Adaptive box filters for removal of random noise from digital images
NASA Technical Reports Server (NTRS)
Eliason, Eric M.; Mcewen, Alfred S.
1990-01-01
Adaptive box-filtering algorithms to remove random bit errors and to smooth noisy data have been developed. For both procedures, the standard deviation of those pixels within a local box surrounding each pixel is used. A series of two or three filters with decreasing box sizes can be run to clean up extremely noisy images and to remove bit errors near sharp edges. The second filter, for noise smoothing, is similar to the 'sigma filter' of Lee (1983). The technique effectively reduces speckle in radar images without eliminating fine details.
Adaptive Bloom Filter: A Space-Efficient Counting Algorithm for Unpredictable Network Traffic
NASA Astrophysics Data System (ADS)
Matsumoto, Yoshihide; Hazeyama, Hiroaki; Kadobayashi, Youki
The Bloom Filter (BF), a space-and-time-efficient hashcoding method, is used as one of the fundamental modules in several network processing algorithms and applications such as route lookups, cache hits, packet classification, per-flow state management or network monitoring. BF is a simple space-efficient randomized data structure used to represent a data set in order to support membership queries. However, BF generates false positives, and cannot count the number of distinct elements. A counting Bloom Filter (CBF) can count the number of distinct elements, but CBF needs more space than BF. We propose an alternative data structure of CBF, and we called this structure an Adaptive Bloom Filter (ABF). Although ABF uses the same-sized bit-vector used in BF, the number of hash functions employed by ABF is dynamically changed to record the number of appearances of a each key element. Considering the hash collisions, the multiplicity of a each key element on ABF can be estimated from the number of hash functions used to decode the membership of the each key element. Although ABF can realize the same functionality as CBF, ABF requires the same memory size as BF. We describe the construction of ABF and IABF (Improved ABF), and provide a mathematical analysis and simulation using Zipf's distribution. Finally, we show that ABF can be used for an unpredictable data set such as real network traffic.
Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery
NASA Astrophysics Data System (ADS)
Zheng, Hong; Liu, Xu; Wei, Min
2015-09-01
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. Project supported by the National Natural Science Foundation of China (Grant Nos. 61004048 and 61201010).
Reduction of EEG artifacts in simultaneous EEG-fMRI: Reference layer adaptive filtering (RLAF).
Steyrl, David; Patz, Franz; Krausz, Gunther; Edlinger, Günter; Müller-Putz, Gernot R
2015-08-01
Although simultaneous measurement of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is one of the most valuable methods for studying human brain activity non-invasively, it remains challenging to measure high quality EEG inside the MRI scanner. Recently, a new approach for minimizing residual MRI scanner artifacts in the EEG was presented: reference layer artifact subtraction (RLAS). Here, reference electrodes capture only the artifacts, which are subsequently subtracted from the measurement electrodes. With the present work we demonstrate that replacing the subtraction by adaptive filtering statistically significantly outperforms RLAS. Reference layer adaptive filtering (RLAF) attenuates the average artifact root-mean-square (RMS) voltage of the passive MRI scanner to 0.7 μV (-14.4 dB). RLAS achieves 0.78 μV (-13.5 dB). The combination of average artifact subtraction (AAS) and RLAF reduces the residual average gradient artifact RMS voltage to 2.3 μV (-49.2 dB). AAS alone achieves 5.7 μV (-39.0 dB). All measurements were conducted with an MRI phantom, as the reference layer cap available to us was a prototype. PMID:26737122
Carmena, Jose M.
2016-01-01
Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to
Shanechi, Maryam M; Orsborn, Amy L; Carmena, Jose M
2016-04-01
Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter
NASA Astrophysics Data System (ADS)
Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar
2011-12-01
This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.
New Approach for IIR Adaptive Lattice Filter Structure Using Simultaneous Perturbation Algorithm
NASA Astrophysics Data System (ADS)
Martinez, Jorge Ivan Medina; Nakano, Kazushi; Higuchi, Kohji
Adaptive infinite impulse response (IIR), or recursive, filters are less attractive mainly because of the stability and the difficulties associated with their adaptive algorithms. Therefore, in this paper the adaptive IIR lattice filters are studied in order to devise algorithms that preserve the stability of the corresponding direct-form schemes. We analyze the local properties of stationary points, a transformation achieving this goal is suggested, which gives algorithms that can be efficiently implemented. Application to the Steiglitz-McBride (SM) and Simple Hyperstable Adaptive Recursive Filter (SHARF) algorithms is presented. Also a modified version of Simultaneous Perturbation Stochastic Approximation (SPSA) is presented in order to get the coefficients in a lattice form more efficiently and with a lower computational cost and complexity. The results are compared with previous lattice versions of these algorithms. These previous lattice versions may fail to preserve the stability of stationary points.
Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance. PMID:23012559
Adaptive UAV attitude estimation employing unscented Kalman Filter, FOAM and low-cost MEMS sensors.
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance. PMID:23012559
NASA Astrophysics Data System (ADS)
Kiani, Maryam; Pourtakdoust, Seid H.
2014-12-01
A novel algorithm is presented in this study for estimation of spacecraft's attitudes and angular rates from vector observations. In this regard, a new cubature-quadrature particle filter (CQPF) is initially developed that uses the Square-Root Cubature-Quadrature Kalman Filter (SR-CQKF) to generate the importance proposal distribution. The developed CQPF scheme avoids the basic limitation of particle filter (PF) with regards to counting the new measurements. Subsequently, CQPF is enhanced to adjust the sample size at every time step utilizing the idea of confidence intervals, thus improving the efficiency and accuracy of the newly proposed adaptive CQPF (ACQPF). In addition, application of the q-method for filter initialization has intensified the computation burden as well. The current study also applies ACQPF to the problem of attitude estimation of a low Earth orbit (LEO) satellite. For this purpose, the undertaken satellite is equipped with a three-axis magnetometer (TAM) as well as a sun sensor pack that provide noisy geomagnetic field data and Sun direction measurements, respectively. The results and performance of the proposed filter are investigated and compared with those of the extended Kalman filter (EKF) and the standard particle filter (PF) utilizing a Monte Carlo simulation. The comparison demonstrates the viability and the accuracy of the proposed nonlinear estimator.
SITE TECHNOLOGY CAPSULE: FILTER FLOW TECHNOLOGY, INC. - COLLOID POLISHING FILTER METHOD
The Filter Flow Technology, Inc. (FFT) Coloid Polishing Filter Method (CPFM) was demonstrated at the U.S Department of Energy's (DOE) Rock Flats Plant (RFP) as part of the U.S. Environmental Protection Agency's (EPA) Superfund and Innovative Technology Evaluation (SITE) program. ...
Linearly-Constrained Adaptive Signal Processing Methods
NASA Astrophysics Data System (ADS)
Griffiths, Lloyd J.
1988-01-01
In adaptive least-squares estimation problems, a desired signal d(n) is estimated using a linear combination of L observation values samples xi (n), x2(n), . . . , xL-1(n) and denoted by the vector X(n). The estimate is formed as the inner product of this vector with a corresponding L-dimensional weight vector W. One particular weight vector of interest is Wopt which minimizes the mean-square between d(n) and the estimate. In this context, the term `mean-square difference' is a quadratic measure such as statistical expectation or time average. The specific value of W which achieves the minimum is given by the prod-uct of the inverse data covariance matrix and the cross-correlation between the data vector and the desired signal. The latter is often referred to as the P-vector. For those cases in which time samples of both the desired and data vector signals are available, a variety of adaptive methods have been proposed which will guarantee that an iterative weight vector Wa(n) converges (in some sense) to the op-timal solution. Two which have been extensively studied are the recursive least-squares (RLS) method and the LMS gradient approximation approach. There are several problems of interest in the communication and radar environment in which the optimal least-squares weight set is of interest and in which time samples of the desired signal are not available. Examples can be found in array processing in which only the direction of arrival of the desired signal is known and in single channel filtering where the spectrum of the desired response is known a priori. One approach to these problems which has been suggested is the P-vector algorithm which is an LMS-like approximate gradient method. Although it is easy to derive the mean and variance of the weights which result with this algorithm, there has never been an identification of the corresponding underlying error surface which the procedure searches. The purpose of this paper is to suggest an alternative
Bilateral filtering and adaptive tone-mapping for qualified edge and image enhancement
NASA Astrophysics Data System (ADS)
Hu, Kuo-Jui; Chang, Ting-Ting; Lu, Min-Yao; Li, Wu-Jeng; Huang, Jih-Fon
2009-01-01
Most of high-contrast images are common with dark and bright area. It is difficult to present the detail on both dark and high light areas on display devices. In order to resolve this problem, we proposed a method of image enhancement to improve this image quality and used bilateral filter to keep the detail. In paper, we applied an appropriate algorithm to process images. At first, we use bilateral filter to separate image. One is large scale image and the other is detail image. Second, we made large scale image which was translated into histogram. In order to make the images divided into three stairs, such as lightness, middle-tone and darkness region. We decided two optimal threshold parameters. Finally, according to three images we use different tone-mapping method to process each stair. The tone-mapping method includes adaptive s-curve and gamma curve algorithms. The experiment results of this study revealed image detail and enhancement. To avoid contour phenomenon is in lightness region.
Stent enhancement in digital x-ray fluoroscopy using an adaptive feature enhancement filter
NASA Astrophysics Data System (ADS)
Jiang, Yuhao; Zachary, Josey
2016-03-01
Fluoroscopic images belong to the classes of low contrast and high noise. Simply lowering radiation dose will render the images unreadable. Feature enhancement filters can reduce patient dose by acquiring images at low dose settings and then digitally restoring them to the original quality. In this study, a stent contrast enhancement filter is developed to selectively improve the contrast of stent contour without dramatically boosting the image noise including quantum noise and clinical background noise. Gabor directional filter banks are implemented to detect the edges and orientations of the stent. A high orientation resolution of 9° is used. To optimize the use of the information obtained from Gabor filters, a computerized Monte Carlo simulation followed by ROC study is used to find the best nonlinear operator. The next stage of filtering process is to extract symmetrical parts in the stent. The global and local symmetry measures are used. The information gathered from previous two filter stages are used to generate a stent contour map. The contour map is then scaled and added back to the original image to get a contrast enhanced stent image. We also apply a spatio-temporal channelized Hotelling observer model and other numerical measures to characterize the response of the filters and contour map to optimize the selections of parameters for image quality. The results are compared to those filtered by an adaptive unsharp masking filter previously developed. It is shown that stent enhancement filter can effectively improve the stent detection and differentiation in the interventional fluoroscopy.
NASA Technical Reports Server (NTRS)
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.
Adaptive filters for suppressing irregular hostile jamming in direct sequence spread-spectrum system
NASA Astrophysics Data System (ADS)
Lee, Jung Hoon; Lee, Choong Woong
A stable and high-performance adaptive filter for suppressing irregular hostile jamming in direct-sequence (DS) spread-spectrum systems is designed. A gradient-search fast converging algorithm (GFC) is suggested. For the case of a sudden parameter jump or incoming of an interference, the transient behaviors of the receiver using a GFC adaptive filter are investigated and compared with those of the receiver using a least-mean-square (LMS) or a lattice adaptive filter. The results are shown in the response graphs of the simulated receiver during the short period when the characteristic of a jammer is suddenly changed. Steady-state performances of those receivers are also evaluated in the sense of the excess mean-square error over that of an optimum receiver for suppressing stationary interferences.
Adaptation of Gabor filters for simulation of human preattentive mechanism for a mobile robot
NASA Astrophysics Data System (ADS)
Kulkarni, Naren; Naghdy, Golshah A.
1993-08-01
Vision guided mobile robot navigation is complex and requires analysis of tremendous amounts of information in real time. In order to simplify the task and reduce the amount of information, human preattentive mechanism can be adapted [Nag90]. During the preattentive search the scene is analyzed rapidly but in sufficient detail for the attention to be focused on the `area of interest.' The `area of interest' can further be scrutinized in more detail for recognition purposes. This `area of interest' can be a text message to facilitate navigation. Gabor filters and an automated turning mechanism are used to isolate the `area of interest.' These regions are subsequently processed with optimal spatial resolution for perception tasks. This method has clear advantages over the global operators in that, after an initial search, it scans each region of interest with optimum resolution. This reduces the volume of information for recognition stages and ensures that no region is over or under estimated.
Yoon, Paul K; Zihajehzadeh, Shaghayegh; Bong-Soo Kang; Park, Edward J
2015-08-01
This paper proposes a novel indoor localization method using the Bluetooth Low Energy (BLE) and an inertial measurement unit (IMU). The multipath and non-line-of-sight errors from low-power wireless localization systems commonly result in outliers, affecting the positioning accuracy. We address this problem by adaptively weighting the estimates from the IMU and BLE in our proposed cascaded Kalman filter (KF). The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The performance of the proposed algorithm is compared against that of the standard KF experimentally. The results show that the proposed algorithm can maintain high accuracy for position tracking the sensor in the presence of the outliers. PMID:26736389
Ko, Byung-hoon; Lee, Takhyung; Choi, Changmok; Kim, Youn-ho; Park, Gunguk; Kang, KyoungHo; Bae, Sang Kon; Shin, Kunsoo
2012-01-01
The electrocardiogram (ECG) is the main measurement parameter for effectively diagnosing chronic disease and guiding cardio-fitness therapy. ECGs contaminated by noise or artifacts disrupt the normal functioning of the automatic analysis algorithm. The objective of this study is to evaluate a method of measuring the HCP variation in motion artifacts through direct monitoring. The proposed wearable sensing device has two channels. One channel is used to measure the ECG through a differential amplifier. The other is for monitoring motion artifacts using the modified electrode and the same differential amplifier. Noise reduction was performed using adaptive filtering, based on a reference signal highly correlated with it. Direct measurement of HCP variations can eliminate the need for additional sensors. PMID:23366209
Color filter array demosaicing: an adaptive progressive interpolation based on the edge type
NASA Astrophysics Data System (ADS)
Dong, Qiqi; Liu, Zhaohui
2015-10-01
Color filter array (CFA) is one of the key points for single-sensor digital cameras to produce color images. Bayer CFA is the most commonly used pattern. In this array structure, the sampling frequency of green is two times of red or blue, which is consistent with the sensitivity of human eyes to colors. However, each sensor pixel only samples one of three primary color values. To render a full-color image, an interpolation process, commonly referred to CFA demosaicing, is required to estimate the other two missing color values at each pixel. In this paper, we explore an adaptive progressive interpolation based on the edge type algorithm. The proposed demosaicing method consists of two successive steps: an interpolation step that estimates missing color values according to various edges and a post-processing step by iterative interpolation.
A hybrid filtering method based on a novel empirical mode decomposition for friction signals
NASA Astrophysics Data System (ADS)
Li, Chengwei; Zhan, Liwei
2015-12-01
During a measurement, the measured signal usually contains noise. To remove the noise and preserve the important feature of the signal, we introduce a hybrid filtering method that uses a new intrinsic mode function (NIMF) and a modified Hausdorff distance. The NIMF is defined as the difference between the noisy signal and each intrinsic mode function (IMF), which is obtained by empirical mode decomposition (EMD), ensemble EMD, complementary ensemble EMD, or complete ensemble EMD with adaptive noise (CEEMDAN). The relevant mode selecting is based on the similarity between the first NIMF and the rest of the NIMFs. With this filtering method, the EMD and improved versions are used to filter the simulation and friction signals. The friction signal between an airplane tire and the runaway is recorded during a simulated airplane touchdown and features spikes of various amplitudes and noise. The filtering effectiveness of the four hybrid filtering methods are compared and discussed. The results show that the filtering method based on CEEMDAN outperforms other signal filtering methods.
Conductivity image enhancement in MREIT using adaptively weighted spatial averaging filter
2014-01-01
Background In magnetic resonance electrical impedance tomography (MREIT), we reconstruct conductivity images using magnetic flux density data induced by externally injected currents. Since we extract magnetic flux density data from acquired MR phase images, the amount of measurement noise increases in regions of weak MR signals. Especially for local regions of MR signal void, there may occur excessive amounts of noise to deteriorate the quality of reconstructed conductivity images. In this paper, we propose a new conductivity image enhancement method as a postprocessing technique to improve the image quality. Methods Within a magnetic flux density image, the amount of noise varies depending on the position-dependent MR signal intensity. Using the MR magnitude image which is always available in MREIT, we estimate noise levels of measured magnetic flux density data in local regions. Based on the noise estimates, we adjust the window size and weights of a spatial averaging filter, which is applied to reconstructed conductivity images. Without relying on a partial differential equation, the new method is fast and can be easily implemented. Results Applying the novel conductivity image enhancement method to experimental data, we could improve the image quality to better distinguish local regions with different conductivity contrasts. From phantom experiments, the estimated conductivity values had 80% less variations inside regions of homogeneous objects. Reconstructed conductivity images from upper and lower abdominal regions of animals showed much less artifacts in local regions of weak MR signals. Conclusion We developed the fast and simple method to enhance the conductivity image quality by adaptively adjusting the weights and window size of the spatial averaging filter using MR magnitude images. Since the new method is implemented as a postprocessing step, we suggest adopting it without or with other preprocessing methods for application studies where conductivity
Seasonal signal capturing in time series of up coordinates by means of adaptive filters
NASA Astrophysics Data System (ADS)
Yalvac, S.; Ustun, A.
2013-12-01
Digital filters, is a system that performs mathematical operations on a sampled or discrete time signals. Adaptive filters designed for noise canceling are capable tools of decomposing correlated parts of data sets. This kind of filters which optimize itself using Least Mean Square (LMS) algorithm is a powerful tool for understand the truth hidden into the complex data sets like time series in Geosciences. The complex data sets such as CGPS (Continuously operating reference station) station's time series can be understood better with adaptive noise canceling by means of decompose coherent (seasonal effect, tectonic plate motion) and incoherent (noise; site-specific effects) parts of data. In this study, it is aimed to model the subsidence caused by groundwater withdrawal based on the seasonal correlation between consecutive years of CGPS time series. For this purpose, two stations where located into subsidence area of 3 year time series have analyzed with adaptive noise canceling filter. According to the results, the annual movement of these two stations have strong relationship. Also, subsidence behavior are correlated with annual rainfall data. BELD station one year filtered movement KAMN station one year filtered movements
NASA Astrophysics Data System (ADS)
Li, Wei; Haese-Coat, Veronique; Ronsin, Joseph
1996-03-01
An adaptive GA scheme is adopted for the optimal morphological filter design problem. The adaptive crossover and mutation rate which make the GA avoid premature and at the same time assure convergence of the program are successfully used in optimal morphological filter design procedure. In the string coding step, each string (chromosome) is composed of a structuring element coding chain concatenated with a filter sequence coding chain. In decoding step, each string is divided into 3 chains which then are decoded respectively into one structuring element with a size inferior to 5 by 5 and two concatenating morphological filter operators. The fitness function in GA is based on the mean-square-error (MSE) criterion. In string selection step, a stochastic tournament procedure is used to replace the simple roulette wheel program in order to accelerate the convergence. The final convergence of our algorithm is reached by a two step converging strategy. In presented applications of noise removal from texture images, it is found that with the optimized morphological filter sequences, the obtained MSE values are smaller than those using corresponding non-adaptive morphological filters, and the optimized shapes and orientations of structuring elements take approximately the same shapes and orientations as those of the image textons.
An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics.
Xin, Yao; Li, Will X Y; Zhang, Zhaorui; Cheung, Ray C C; Song, Dong; Berger, Theodore W
2015-01-01
Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance. PMID:26451817
Sudeep, P V; Issac Niwas, S; Palanisamy, P; Rajan, Jeny; Xiaojun, Yu; Wang, Xianghong; Luo, Yuemei; Liu, Linbo
2016-04-01
Optical coherence tomography (OCT) has continually evolved and expanded as one of the most valuable routine tests in ophthalmology. However, noise (speckle) in the acquired images causes quality degradation of OCT images and makes it difficult to analyze the acquired images. In this paper, an iterative approach based on bilateral filtering is proposed for speckle reduction in multiframe OCT data. Gamma noise model is assumed for the observed OCT image. First, the adaptive version of the conventional bilateral filter is applied to enhance the multiframe OCT data and then the bias due to noise is reduced from each of the filtered frames. These unbiased filtered frames are then refined using an iterative approach. Finally, these refined frames are averaged to produce the denoised OCT image. Experimental results on phantom images and real OCT retinal images demonstrate the effectiveness of the proposed filter. PMID:26907572
Impact of data assimilation filtering methods on the mesosphere
NASA Astrophysics Data System (ADS)
Sankey, David; Ren, Shuzhan; Polavarapu, Saroja; Rochon, Yves J.; Nezlin, Yulia; Beagley, Stephen
2007-12-01
Three-dimensional data assimilation schemes typically produce analyses that are not in balance. This is evidenced by the generation of spurious high-frequency waves during the first 2 d of forecasts which start from analyses. To remove these spurious waves, assimilation systems frequently filter analyses before using them in models. This work examines the behavior of various spurious wave filtering methods in the context of a model with a mesosphere. Since gravity waves comprise a significant portion of the mesospheric energy spectrum, it is necessary to retain naturally occurring high-frequency waves while filtering spurious waves. The results show that filtering the full analysis state can remove many important high-frequency oscillations from the mesosphere. On the other hand, filtering analysis increments preserves much more of the natural variability of the model. The incremental analysis updating scheme and the incremental digital filter, which are equivalent for linear models and identical coefficients, are shown to give very similar results in the context of a realistic nonlinear model. Results also show a nonlocal response to the insertion of analysis increments in the troposphere and stratosphere. The global mean temperature in the vicinity of the model lid and the diurnal tidal amplitudes are sensitive to the choice of filtering schemes because the filters reduce the amount of resolved waves available to propagate upward into mesosphere. This sensitivity of the mesosphere to the filtering of the lower atmosphere is exploited to choose an optimal filter for our system using measurements of the mesosphere.
Stent enhancement using a locally adaptive unsharp masking filter in digital x-ray fluoroscopy
NASA Astrophysics Data System (ADS)
Jiang, Yuhao; Ekanayake, Eranda
2014-03-01
Low exposure X-ray fluoroscopy is used to guide some complicate interventional procedures. Due to the inherent high levels of noise, improving the visibility of some interventional devices such as stent will greatly benefit those interventional procedures. Stent, which is made up of tiny steel wires, is also suffered from contrast dilutions of large flat panel detector pixels. A novel adaptive unsharp masking filter has been developed to improve stent contrast in real-time applications. In unsharp masking processing, the background is estimated and subtracted from the original input image to create a foreground image containing objects of interest. A background estimator is therefore critical in the unsharp masking processing. In this specific study, orientation filter kernels are used as the background estimator. To make the process simple and fast, the kernels average along a line of pixels. A high orientation resolution of 18° is used. A nonlinear operator is then used to combine the information from the images generated from convolving the original background and noise only images with orientation filters. A computerized Monte Carlo simulation followed by ROC study is used to identify the best nonlinear operator. We then apply the unsharp masking filter to the images with stents present. It is shown that the locally adaptive unsharp making filter is an effective filter for improving stent visibility in the interventional fluoroscopy. We also apply a spatio-temporal channelized human observer model to quantitatively optimize and evaluate the filter.
An improved filtered spherical harmonic method for transport calculations
Ahrens, C.; Merton, S.
2013-07-01
Motivated by the work of R. G. McClarren, C. D. Hauck, and R. B. Lowrie on a filtered spherical harmonic method, we present a new filter for such numerical approximations to the multi-dimensional transport equation. In several test problems, we demonstrate that the new filter produces results with significantly less Gibbs phenomena than the filter used by McClarren, Hauck and Lowrie. This reduction in Gibbs phenomena translates into propagation speeds that more closely match the correct propagation speed and solutions that have fewer regions where the scalar flux is negative. (authors)
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. PMID:26089975
Multiframe adaptive Wiener filter super-resolution with JPEG2000-compressed images
NASA Astrophysics Data System (ADS)
Narayanan, Barath Narayanan; Hardie, Russell C.; Balster, Eric J.
2014-12-01
Historically, Joint Photographic Experts Group 2000 (JPEG2000) image compression and multiframe super-resolution (SR) image processing techniques have evolved separately. In this paper, we propose and compare novel processing architectures for applying multiframe SR with JPEG2000 compression. We propose a modified adaptive Wiener filter (AWF) SR method and study its performance as JPEG2000 is incorporated in different ways. In particular, we perform compression prior to SR and compare this to compression after SR. We also compare both independent-frame compression and difference-frame compression approaches. We find that some of the SR artifacts that result from compression can be reduced by decreasing the assumed global signal-to-noise ratio (SNR) for the AWF SR method. We also propose a novel spatially adaptive SNR estimate for the AWF designed to compensate for the spatially varying compression artifacts in the input frames. The experimental results include the use of simulated imagery for quantitative analysis. We also include real-video results for subjective analysis.
Track and vertex reconstruction: From classical to adaptive methods
Strandlie, Are; Fruehwirth, Rudolf
2010-04-15
This paper reviews classical and adaptive methods of track and vertex reconstruction in particle physics experiments. Adaptive methods have been developed to meet the experimental challenges at high-energy colliders, in particular, the CERN Large Hadron Collider. They can be characterized by the obliteration of the traditional boundaries between pattern recognition and statistical estimation, by the competition between different hypotheses about what constitutes a track or a vertex, and by a high level of flexibility and robustness achieved with a minimum of assumptions about the data. The theoretical background of some of the adaptive methods is described, and it is shown that there is a close connection between the two main branches of adaptive methods: neural networks and deformable templates, on the one hand, and robust stochastic filters with annealing, on the other hand. As both classical and adaptive methods of track and vertex reconstruction presuppose precise knowledge of the positions of the sensitive detector elements, the paper includes an overview of detector alignment methods and a survey of the alignment strategies employed by past and current experiments.
Adaptive Filter-bank Approach to Restoration and Spectral Analysis of Gapped Data
NASA Astrophysics Data System (ADS)
Stoica, Petre; Larsson, Erik G.; Li, Jian
2000-10-01
The main topic of this paper is the nonparametric estimation of complex (both amplitude and phase) spectra from gapped data, as well as the restoration of such data. The focus is on the extension of the APES (amplitude and phase estimation) approach to data sequences with gaps. APES, which is one of the most successful existing nonparametric approaches to the spectral analysis of full data sequences, uses a bank of narrowband adaptive (both frequency and data dependent) filters to estimate the spectrum. A recent interpretation of this approach showed that the filterbank used by APES and the resulting spectrum minimize a least-squares (LS) fitting criterion between the filtered sequence and its spectral decomposition. The extended approach, which is called GAPES for somewhat obvious reasons, capitalizes on the aforementioned interpretation: it minimizes the APES-LS fitting criterion with respect to the missing data as well. This should be a sensible thing to do whenever the full data sequence is stationary, and hence the missing data have the same spectral content as the available data. We use both simulated and real data examples to show that GAPES estimated spectra and interpolated data sequences have excellent accuracy. We also show the performance gain achieved by GAPES over two of the most commonly used approaches for gapped-data spectral analysis, viz., the periodogram and the parametric CLEAN method. This work was partly supported by the Swedish Foundation for Strategic Research.
Robust flicker evaluation method for low power adaptive dimming LCDs
NASA Astrophysics Data System (ADS)
Kim, Seul-Ki; Song, Seok-Jeong; Nam, Hyoungsik
2015-05-01
This paper describes a robust dimming flicker evaluation method of adaptive dimming algorithms for low power liquid crystal displays (LCDs). While the previous methods use sum of square difference (SSD) values without excluding the image sequence information, the proposed modified SSD (mSSD) values are obtained only with the dimming flicker effects by making use of differential images. The proposed scheme is verified for eight dimming configurations of two dimming level selection methods and four temporal filters over three test videos. Furthermore, a new figure of merit is introduced to cover the dimming flicker as well as image qualities and power consumption.
NASA Astrophysics Data System (ADS)
Wang, Xudong; Syrmos, Vassilis L.
2004-07-01
In this paper, an adaptive reconfigurable control system based on extended Kalman filter approach and eigenstructure assignments is proposed. System identification is carried out using an extended Kalman filter (EKF) approach. An eigenstructure assignment (EA) technique is applied for reconfigurable feedback control law design to recover the system dynamic performance. The reconfigurable feedforward controllers are designed to achieve the steady-state tracking using input weighting approach. The proposed scheme can identify not only actuator and sensor variations, but also changes in the system structures using the extended Kalman filtering method. The overall design is robust with respect to uncertainties in the state-space matrices of the reconfigured system. To illustrate the effectiveness of the proposed reconfigurable control system design technique, an aircraft longitudinal vertical takeoff and landing (VTOL) control system is used to demonstrate the reconfiguration procedure.
Lei, Xusheng; Li, Jingjing
2012-01-01
This paper presents an adaptive information fusion method to improve the accuracy and reliability of the altitude measurement information for small unmanned aerial rotorcraft during the landing process. Focusing on the low measurement performance of sensors mounted on small unmanned aerial rotorcraft, a wavelet filter is applied as a pre-filter to attenuate the high frequency noises in the sensor output. Furthermore, to improve altitude information, an adaptive extended Kalman filter based on a maximum a posteriori criterion is proposed to estimate measurement noise covariance matrix in real time. Finally, the effectiveness of the proposed method is proved by static tests, hovering flight and autonomous landing flight tests. PMID:23201993
A novel method of drift-scanning stars suppression based on the standardized linear filter
NASA Astrophysics Data System (ADS)
Lin, Jianlin; Ping, Xijian; Hou, Guanghua; Ma, Debao
2011-11-01
A large number of stars in the drift-scanning star image have interfered with the detection of small target, this paper proposes an adaptive linear filtering method to achieve the small target detection by suppressing the stars. Firstly, the characteristics of stars, interest target and noise three different representative objects in the star image are analyzed, then the standardized linear filter is constructed to suppress the stars. For the purpose of decreasing the influence region of stars filtering uniformly, a gradient linear filter is constructed to modify the stars suppression method with the standardized linear filter. Then the filter parameter selection method is given. Finally, a multi-frame target track experiment on the real drift-scanning data is made to testify the validity of the proposed method. With the processing results of different methods, it has been showed that the proposed method for suppressing stars with different length and lean angle has a better effect, higher robustness and easier application than the others.
NASA Technical Reports Server (NTRS)
Balas, Mark; Frost, Susan
2012-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling 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 our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter.
Development of NDE methods for hot gas filters.
Deemer, C.; Ellingson, W. A.; Koehl, E. R.; Lee, H.; Spohnholtz, T.; Sun, J. G.
1999-07-21
Ceramic hot gas candle filters are currently under development for hot gas particulate cleanup in advanced coal-based power systems. The ceramic materials for these filters include nonoxide monolithic, nonoxide-fiber-reinforced composites, and nonoxide reticulated foam. A concern is the lack of reliable data on which to base decisions for reusing or replacing hot gas filters during plant shutdowns. The work in this project is aimed at developing nondestructive evaluation (FIDE) technology to allow detection, and determination of extent, of life-limiting characteristics such as thermal fatigue, oxidation, damage from ash bridging such as localized cracking, damage from local burning, and elongation at elevated temperature. Although in-situ NDE methods are desirable in order to avoid disassembly of the candle filter vessels, the current vessel designs, the presence of filter cakes and possible ash bridging, and the state of NDE technology prevent this. Candle filter producers use a variety of NDE methods to ensure as-produced quality. While impact acoustic resonance offers initial promise for examining new as-produced filters and for detecting damage in some monolithic filters when removed from service, it presents difficulties in data interpretation, it lacks localization capability, and its applicability to composites has yet to be demonstrated. Additional NDE technologies being developed and evaluated in this program and whose applicability to both monolithics and composites has been demonstrated include (a) full-scale thermal imaging for analyzing thermal property variations; (b) fret, high-spatial-resolution X-ray imaging for detecting density variations and dimensional changes; (c) air-coupled ultrasonic methods for determining through-thickness compositional variations; and (d) acoustic emission technology with mechanical loading for detecting localized bulk damage. New and exposed clay-bonded SiC filters and CVI-SiC composite filters have been tested with
NASA Astrophysics Data System (ADS)
Boz, Utku; Basdogan, Ipek
2015-12-01
Structural vibrations is a major cause for noise problems, discomfort and mechanical failures in aerospace, automotive and marine systems, which are mainly composed of plate-like structures. In order to reduce structural vibrations on these structures, active vibration control (AVC) is an effective approach. Adaptive filtering methodologies are preferred in AVC due to their ability to adjust themselves for varying dynamics of the structure during the operation. The filtered-X LMS (FXLMS) algorithm is a simple adaptive filtering algorithm widely implemented in active control applications. Proper implementation of FXLMS requires availability of a reference signal to mimic the disturbance and model of the dynamics between the control actuator and the error sensor, namely the secondary path. However, the controller output could interfere with the reference signal and the secondary path dynamics may change during the operation. This interference problem can be resolved by using an infinite impulse response (IIR) filter which considers feedback of the one or more previous control signals to the controller output and the changing secondary path dynamics can be updated using an online modeling technique. In this paper, IIR filtering based filtered-U LMS (FULMS) controller is combined with online secondary path modeling algorithm to suppress the vibrations of a plate-like structure. The results are validated through numerical and experimental studies. The results show that the FULMS with online secondary path modeling approach has more vibration rejection capabilities with higher convergence rate than the FXLMS counterpart.
A new orientation-adaptive interpolation method.
Wang, Qing; Ward, Rabab Kreidieh
2007-04-01
We propose an isophote-oriented, orientation-adaptive interpolation method. The proposed method employs an interpolation kernel that adapts to the local orientation of isophotes, and the pixel values are obtained through an oriented, bilinear interpolation. We show that, by doing so, the curvature of the interpolated isophotes is reduced, and, thus, zigzagging artifacts are largely suppressed. Analysis and experiments show that images interpolated using the proposed method are visually pleasing and almost artifact free. PMID:17405424
The Method of Adaptive Comparative Judgement
ERIC Educational Resources Information Center
Pollitt, Alastair
2012-01-01
Adaptive Comparative Judgement (ACJ) is a modification of Thurstone's method of comparative judgement that exploits the power of adaptivity, but in scoring rather than testing. Professional judgement by teachers replaces the marking of tests; a judge is asked to compare the work of two students and simply to decide which of them is the better.…
NASA Astrophysics Data System (ADS)
Liu, Delian; Li, Zhaohui; Wang, Xiaorui; Zhang, Jianqi
2015-11-01
Target detection is of great importance both in civil and military fields. Here a new moving target detection approach is proposed, which employs a nonlinear adaptive filter to remove large fluctuations on temporal profiles that are produced by evolving clutters. Initially, this paper discusses the temporal behaviors of different pixels in infrared sequences. Then, the new nonlinear adaptive filter that is a variation of the median-modified Wiener filter is given to extract pulse signals on temporal profiles that relate to moving targets. Next, the variance of each temporal profile is estimated by segmenting each temporal profile into several segments to normalize the amplitude of the pulse signals. Finally, the proposed approach is tested via two infrared image sequences and compared with several conventional target detection algorithms. The results show our approach has a high effectiveness in extracting target temporal profiles amidst heavy and slowly evolving clutters.
Damping filter method for obtaining spatially localized solutions.
Teramura, Toshiki; Toh, Sadayoshi
2014-05-01
Spatially localized structures are key components of turbulence and other spatiotemporally chaotic systems. From a dynamical systems viewpoint, it is desirable to obtain corresponding exact solutions, though their existence is not guaranteed. A damping filter method is introduced to obtain variously localized solutions and adapted in two typical cases. This method introduces a spatially selective damping effect to make a good guess at the exact solution, and we can obtain an exact solution through a continuation with the damping amplitude. The first target is a steady solution to the Swift-Hohenberg equation, which is a representative of bistable systems in which localized solutions coexist and a model for spanwise-localized cases. Not only solutions belonging to the well-known snaking branches but also those belonging to isolated branches known as "isolas" are found with continuation paths between them in phase space extended with the damping amplitude. This indicates that this spatially selective excitation mechanism has an advantage in searching spatially localized solutions. The second target is a spatially localized traveling-wave solution to the Kuramoto-Sivashinsky equation, which is a model for streamwise-localized cases. Since the spatially selective damping effect breaks Galilean and translational invariances, the propagation velocity cannot be determined uniquely while the damping is active, and a singularity arises when these invariances are recovered. We demonstrate that this singularity can be avoided by imposing a simple condition, and a localized traveling-wave solution is obtained with a specific propagation speed. PMID:25353864
Mechanisms and Methods for Selective Wavelength Filtering
NASA Technical Reports Server (NTRS)
Tuma, Margaret (Inventor); Brown, Thomas G. (Inventor); Gruhlke, Russell (Inventor)
2007-01-01
An optical filter includes a dielectric waveguide layer, supporting waveguide modes at specific wavelengths and receiving incident light, a corrugated film layer, composed of one of a metal and a semiconductor and positioned adjacent to a second surface of the waveguide layer and a sensor layer, wherein the sensor layer is capable of absorbing optical energy and generating a corresponding electrical signal. The metal film layer supports a plurality of plasmons, the plurality of plasmons producing a first field and is excited by a transverse mode of the waveguide modes at a wavelength interval. The first field penetrates the sensor layer and the sensor layer generates an electrical signal corresponding to an intensity of received incident light within the wavelength interval.
NASA Astrophysics Data System (ADS)
Ushaq, Muhammad; Fang, Jiancheng
2013-10-01
Integrated navigation systems for various applications, generally employs the centralized Kalman filter (CKF) wherein all measured sensor data are communicated to a single central Kalman filter. The advantage of CKF is that there is a minimal loss of information and high precision under benign conditions. But CKF may suffer computational overloading, and poor fault tolerance. The alternative is the federated Kalman filter (FKF) wherein the local estimates can deliver optimal or suboptimal state estimate as per certain information fusion criterion. FKF has enhanced throughput and multiple level fault detection capability. The Standard CKF or FKF require that the system noise and the measurement noise are zero-mean and Gaussian. Moreover it is assumed that covariance of system and measurement noises remain constant. But if the theoretical and actual statistical features employed in Kalman filter are not compatible, the Kalman filter does not render satisfactory solutions and divergence problems also occur. To resolve such problems, in this paper, an adaptive Kalman filter scheme strengthened with fuzzy inference system (FIS) is employed to adapt the statistical features of contributing sensors, online, in the light of real system dynamics and varying measurement noises. The excessive faults are detected and isolated by employing Chi Square test method. As a case study, the presented scheme has been implemented on Strapdown Inertial Navigation System (SINS) integrated with the Celestial Navigation System (CNS), GPS and Doppler radar using FKF. Collectively the overall system can be termed as SINS/CNS/GPS/Doppler integrated navigation system. The simulation results have validated the effectiveness of the presented scheme with significantly enhanced precision, reliability and fault tolerance. Effectiveness of the scheme has been tested against simulated abnormal errors/noises during different time segments of flight. It is believed that the presented scheme can be
Ham, Bumsub; Min, Dongbo; Sohn, Kwanghoon
2013-03-01
Anisotropic diffusion has been known to be closely related to adaptive smoothing and discretized in a similar manner. This paper revisits a fundamental relationship between two approaches. It is shown that adaptive smoothing and anisotropic diffusion have different theoretical backgrounds by exploring their characteristics with the perspective of normalization, evolution step size, and energy flow. Based on this principle, adaptive smoothing is derived from a second order partial differential equation (PDE), not a conventional anisotropic diffusion, via the coupling of Fick's law with a generalized continuity equation where a "source" or "sink" exists, which has not been extensively exploited. We show that the source or sink is closely related to the asymmetry of energy flow as well as the normalization term of adaptive smoothing. It enables us to analyze behaviors of adaptive smoothing, such as the maximum principle and stability with a perspective of a PDE. Ultimately, this relationship provides new insights into application-specific filtering algorithm design. By modeling the source or sink in the PDE, we introduce two specific diffusion filters, the robust anisotropic diffusion and the robust coherence enhancing diffusion, as novel instantiations which are more robust against the outliers than the conventional filters. PMID:23193236
Variational method for adaptive grid generation
Brackbill, J.U.
1983-01-01
A variational method for generating adaptive meshes is described. Functionals measuring smoothness, skewness, orientation, and the Jacobian are minimized to generate a mapping from a rectilinear domain in natural coordinate to an arbitrary domain in physical coordinates. From the mapping, a mesh is easily constructed. In using the method to adaptively zone computational problems, as few as one third the number of mesh points are required in each coordinate direction compared with a uniformly zoned mesh.
Seismic random noise attenuation based on adaptive time-frequency peak filtering
NASA Astrophysics Data System (ADS)
Deng, Xinhuan; Ma, Haitao; Li, Yue; Zeng, Qian
2015-02-01
Time-frequency peak filtering (TFPF) method uses a specific window with fixed length to recover band-limited signal in stationary random noise. However, the derivatives of signal such as seismic wavelets may change rapidly in some short time intervals. In this case, TFPF equipped with fixed window length will not provide an optimal solution. In this letter, we present an adaptive version of TFPF for seismic random noise attenuation. In our version, the improved intersection of confidence intervals combined with short-time energy criterion is used to preprocess the noisy signal. And then, we choose an appropriate threshold to divide the noisy signal into signal, buffer and noise. Different optimal window lengths are used in each type of segments. We test the proposed method on both synthetic and field seismic data. The experimental results illustrate that the proposed method makes the degree of amplitude preservation raise more than 10% and signal-to-noise (SNR) improve 2-4 dB compared with the original algorithm.
Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators
NASA Astrophysics Data System (ADS)
Law, K. J. H.; Sanz-Alonso, D.; Shukla, A.; Stuart, A. M.
2016-06-01
In the context of filtering chaotic dynamical systems it is well-known that partial observations, if sufficiently informative, can be used to control the inherent uncertainty due to chaos. The purpose of this paper is to investigate, both theoretically and numerically, conditions on the observations of chaotic systems under which they can be accurately filtered. In particular, we highlight the advantage of adaptive observation operators over fixed ones. The Lorenz '96 model is used to exemplify our findings. We consider discrete-time and continuous-time observations in our theoretical developments. We prove that, for fixed observation operator, the 3DVAR filter can recover the system state within a neighbourhood determined by the size of the observational noise. It is required that a sufficiently large proportion of the state vector is observed, and an explicit form for such sufficient fixed observation operator is given. Numerical experiments, where the data is incorporated by use of the 3DVAR and extended Kalman filters, suggest that less informative fixed operators than given by our theory can still lead to accurate signal reconstruction. Adaptive observation operators are then studied numerically; we show that, for carefully chosen adaptive observation operators, the proportion of the state vector that needs to be observed is drastically smaller than with a fixed observation operator. Indeed, we show that the number of state coordinates that need to be observed may even be significantly smaller than the total number of positive Lyapunov exponents of the underlying system.
NASA Astrophysics Data System (ADS)
Yang, Jianfei; Poot, Dirk H. J.; Arkesteijn, Georgius A. M.; Caan, Matthan W.; van Vliet, Lucas J.; Vos, Frans M.
2015-03-01
Conventionally, a single rank-2 tensor is used to assess the white matter integrity in diffusion imaging of the human brain. However, a single tensor fails to describe the diffusion in fiber crossings. Although a dual tensor model is able to do so, the low signal-to-noise ratio hampers reliable parameter estimation as the number of parameters is doubled. We present a framework for structure-adaptive tensor field filtering to enhance the statistical analysis in complex fiber structures. In our framework, a tensor model will be fitted based on an automated relevance determination method. Particularly, a single tensor model is applied to voxels in which the data seems to represent a single fiber and a dualtensor model to voxels appearing to contain crossing fibers. To improve the estimation of the model parameters we propose a structure-adaptive tensor filter that is applied to tensors belonging to the same fiber compartment only. It is demonstrated that the structure-adaptive tensor-field filter improves the continuity and regularity of the estimated tensor field. It outperforms an existing denoising approach called LMMSE, which is applied to the diffusion-weighted images. Track-based spatial statistics analysis of fiber-specific FA maps show that the method sustains the detection of more subtle changes in white matter tracts than the classical single-tensor-based analysis. Thus, the filter enhances the applicability of the dual-tensor model in diffusion imaging research. Specifically, the reliable estimation of two tensor diffusion properties facilitates fiber-specific extraction of diffusion features.
NASA Technical Reports Server (NTRS)
Keel, Byron M.
1989-01-01
An optimum adaptive clutter rejection filter for use with airborne Doppler weather radar is presented. The radar system is being designed to operate at low-altitudes for the detection of windshear in an airport terminal area where ground clutter returns may mask the weather return. The coefficients of the adaptive clutter rejection filter are obtained using a complex form of a square root normalized recursive least squares lattice estimation algorithm which models the clutter return data as an autoregressive process. The normalized lattice structure implementation of the adaptive modeling process for determining the filter coefficients assures that the resulting coefficients will yield a stable filter and offers possible fixed point implementation. A 10th order FIR clutter rejection filter indexed by geographical location is designed through autoregressive modeling of simulated clutter data. Filtered data, containing simulated dry microburst and clutter return, are analyzed using pulse-pair estimation techniques. To measure the ability of the clutter rejection filters to remove the clutter, results are compared to pulse-pair estimates of windspeed within a simulated dry microburst without clutter. In the filter evaluation process, post-filtered pulse-pair width estimates and power levels are also used to measure the effectiveness of the filters. The results support the use of an adaptive clutter rejection filter for reducing the clutter induced bias in pulse-pair estimates of windspeed.
An adaptive additive inflation scheme for Ensemble Kalman Filters
NASA Astrophysics Data System (ADS)
Sommer, Matthias; Janjic, Tijana
2016-04-01
Data assimilation for atmospheric dynamics requires an accurate estimate for the uncertainty of the forecast in order to obtain an optimal combination with available observations. This uncertainty has two components, firstly the uncertainty which originates in the the initial condition of that forecast itself and secondly the error of the numerical model used. While the former can be approximated quite successfully with an ensemble of forecasts (an additional sampling error will occur), little is known about the latter. For ensemble data assimilation, ad-hoc methods to address model error include multiplicative and additive inflation schemes, possibly also flow-dependent. The additive schemes rely on samples for the model error e.g. from short-term forecast tendencies or differences of forecasts with varying resolutions. However since these methods work in ensemble space (i.e. act directly on the ensemble perturbations) the sampling error is fixed and can be expected to affect the skill substiantially. In this contribution we show how inflation can be generalized to take into account more degrees of freedom and what improvements for future operational ensemble data assimilation can be expected from this, also in comparison with other inflation schemes.
Kikuchi, Kazuro
2011-03-14
We analyze the clock-recovery process based on adaptive finite-impulse-response (FIR) filtering in digital coherent optical receivers. When the clock frequency is synchronized between the transmitter and the receiver, only five taps in half-symbol-spaced FIR filters can adjust the sampling phase of analog-to-digital conversion optimally, enabling bit-error rate performance independent of the initial sampling phase. Even if the clock frequency is not synchronized between them, the clock-frequency misalignment can be adjusted within an appropriate block interval; thus, we can achieve an asynchronous clock mode of operation of digital coherent receivers with block processing of the symbol sequence. PMID:21445201
Method and System for Temporal Filtering in Video Compression Systems
NASA Technical Reports Server (NTRS)
Lu, Ligang; He, Drake; Jagmohan, Ashish; Sheinin, Vadim
2011-01-01
Three related innovations combine improved non-linear motion estimation, video coding, and video compression. The first system comprises a method in which side information is generated using an adaptive, non-linear motion model. This method enables extrapolating and interpolating a visual signal, including determining the first motion vector between the first pixel position in a first image to a second pixel position in a second image; determining a second motion vector between the second pixel position in the second image and a third pixel position in a third image; determining a third motion vector between the first pixel position in the first image and the second pixel position in the second image, the second pixel position in the second image, and the third pixel position in the third image using a non-linear model; and determining a position of the fourth pixel in a fourth image based upon the third motion vector. For the video compression element, the video encoder has low computational complexity and high compression efficiency. The disclosed system comprises a video encoder and a decoder. The encoder converts the source frame into a space-frequency representation, estimates the conditional statistics of at least one vector of space-frequency coefficients with similar frequencies, and is conditioned on previously encoded data. It estimates an encoding rate based on the conditional statistics and applies a Slepian-Wolf code with the computed encoding rate. The method for decoding includes generating a side-information vector of frequency coefficients based on previously decoded source data and encoder statistics and previous reconstructions of the source frequency vector. It also performs Slepian-Wolf decoding of a source frequency vector based on the generated side-information and the Slepian-Wolf code bits. The video coding element includes receiving a first reference frame having a first pixel value at a first pixel position, a second reference frame
NASA Astrophysics Data System (ADS)
Lee, Seunghee; Bae, Kwanghyuk; Kyung, Kyu-min; Kim, Tae-Chan
2012-03-01
In this work, we present an adaptive switching filter for noise reduction and sharpness preservation in depth maps provided by Time-of-Flight (ToF) image sensors. Median filter and bilateral filter are commonly used in cost-sensitive applications where low computational complexity is needed. However, median filter blurs fine details and edges in depth map while bilateral filter works poorly with impulse noise present in the image. Since the variance of depth is inversely proportional to amplitude, we suggest an adaptive filter that switches between median filter and bilateral filter based on the level of amplitude. If a region of interest has low amplitude indicating low confidence level of measured depth data, then median filter is applied on the depth at the position while regions with high level of amplitude is processed with bilateral filter using Gaussian kernel with adaptive weights. Results show that the suggested algorithm performs surface smoothing and detail preservation as well as median filter and bilateral filter, respectively. By using the suggested algorithm, significant gain in visual quality is obtained in depth maps while low computational cost is maintained.
Mihajlovic, Vojkan; Patki, Shrishail; Grundlehner, Bernard
2014-01-01
Designing and developing a comfortable and convenient EEG system for daily usage that can provide reliable and robust EEG signal, encompasses a number of challenges. Among them, the most ambitious is the reduction of artifacts due to body movements. This paper studies the effect of head movement artifacts on the EEG signal and on the dry electrode-tissue impedance (ETI), monitored continuously using the imec's wireless EEG headset. We have shown that motion artifacts have huge impact on the EEG spectral content in the frequency range lower than 20 Hz. Coherence and spectral analysis revealed that ETI is not capable of describing disturbances at very low frequencies (below 2 Hz). Therefore, we devised a motion artifact reduction (MAR) method that uses a combination of a band-pass filtering and multi-channel adaptive filtering (AF), suitable for real-time MAR. This method was capable of substantially reducing artifacts produced by head movements. PMID:25571131
Optimal and adaptive methods of processing hydroacoustic signals (review)
NASA Astrophysics Data System (ADS)
Malyshkin, G. S.; Sidel'nikov, G. B.
2014-09-01
Different methods of optimal and adaptive processing of hydroacoustic signals for multipath propagation and scattering are considered. Advantages and drawbacks of the classical adaptive (Capon, MUSIC, and Johnson) algorithms and "fast" projection algorithms are analyzed for the case of multipath propagation and scattering of strong signals. The classical optimal approaches to detecting multipath signals are presented. A mechanism of controlled normalization of strong signals is proposed to automatically detect weak signals. The results of simulating the operation of different detection algorithms for a linear equidistant array under multipath propagation and scattering are presented. An automatic detector is analyzed, which is based on classical or fast projection algorithms, which estimates the background proceeding from median filtering or the method of bilateral spatial contrast.
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-01-01
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. PMID:27420062
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-01-01
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. PMID:27420062
Application of global phase filtering method in multi frequency measurement.
Song, Limei; Chang, Yulan; Li, Zongyan; Wang, Pengqiang; Xing, Guangxin; Xi, Jiangtao
2014-06-01
In reverse engineering, reconstruction of 3D point cloud data is the key step to acquire the final profile of the object. However, the quality of 3D reconstruction is influenced by noise in the three-dimensional measurement. This paper aims to tackle the issue of removing the noisy data from the complex point cloud data. The 3D-GPF (Three Dimensional Global Phase Filtering) global phase filtering method is proposed based on the study of phase filtering method, consisting of the steps below. Firstly, the six-step phase shift profilometry is used to obtain the local phase information, and encoding the obtained phase information. Through the global phase unwrapping method, the global phase can be acquired. Secondly, 3D-GPF method is used for the obtained global phase. Finally, the effect of 3D reconstruction is analyzed after the global phase filtering. Experimental results indicate that the noisy points of three-dimensional graphics is reduced 98.02%, the speed of 3D reconstruction is raised 12%.The effect of the proposed global phase filtering method is better than DCT and GSM methods. It is high precision and fast speed, and can be widely used in other 3D reconstruction application. PMID:24921558
System and method for recycling used oil filters
McCarty, M.W.; Taylor, J.M.; Baillie, L.A.
1993-08-17
A system is described for recycling oil filters comprising: (a) means for shredding used oil filters, said filters comprising ferrous metal parts and nonmetal parts, and containing trapped oil, into smaller metal parts and nonmetal parts, and for releasing at least a portion of the trapped oil; (b) means for separating the released oil from the metal parts and nonmetal parts; (c) means for separating the metal parts from the nonmetal parts without incineration using gravitational and magnetic means; (d) means for draining oil from the separated metal parts and nonmetal parts; (e) means for separately recovering the oil, the metal parts, and the nonmetal parts; and (f) means for compacting the separated nonmetal parts. A method is also described for recycling used oil filters using this system.
A 3D approach for object recognition in illuminated scenes with adaptive correlation filters
NASA Astrophysics Data System (ADS)
Picos, Kenia; Díaz-Ramírez, Víctor H.
2015-09-01
In this paper we solve the problem of pose recognition of a 3D object in non-uniformly illuminated and noisy scenes. The recognition system employs a bank of space-variant correlation filters constructed with an adaptive approach based on local statistical parameters of the input scene. The position and orientation of the target are estimated with the help of the filter bank. For an observed input frame, the algorithm computes the correlation process between the observed image and the bank of filters using a combination of data and task parallelism by taking advantage of a graphics processing unit (GPU) architecture. The pose of the target is estimated by finding the template that better matches the current view of target within the scene. The performance of the proposed system is evaluated in terms of recognition accuracy, location and orientation errors, and computational performance.
Adaptive filtering for reduction of speckle in ultrasonic pulse-echo images.
Bamber, J C; Daft, C
1986-01-01
Current medical ultrasonic scanning instrumentation permits the display of fine image detail (speckle) which does not transfer useful information but degrades the apparent low contrast resolution in the image. An adaptive two-dimensional filter has been developed which uses local features of image texture to recognize and maximally low-pass filter those parts of the image which correspond to fully developed speckle, while substantially preserving information associated with resolved-object structure. A first implementation of the filter is described which uses the ratio of the local variance and the local mean as the speckle recognition feature. Preliminary results of applying this form of display processing to medical ultrasound images are very encouraging; it appears that the visual perception of features such as small discrete structures, subtle fluctuations in mean echo level and changes in image texture may be enhanced relative to that for unprocessed images. PMID:3510500
NASA Technical Reports Server (NTRS)
Smith, J. W.; Edwards, J. W.
1980-01-01
Analysis of a longitudinal pilot-induced oscillation (PIO) experienced just prior to touchdown on the final flight of the space shuttle's approach landing tests indicated that the source of the problem was a combination of poor basic handling qualities aggravated by time delays through the digital flight control computer and rate limiting of the elevator actuators due to high pilot gain. A nonlinear PIO suppression (PIOS) filter was designed and developed to alleviate the vehicle's PIO tendencies by reducing the gain in the command path. From analytical and simulator studies it was shown that the PIOS filter, in an adaptive fashion, can attenuate the command path gain without adding phase lag to the system. With the pitch attitude loop of a simulated shuttle model closed, the PIOS filter increased the gain margin by a factor of about two.
NASA Astrophysics Data System (ADS)
Sheng-Hui, Rong; Hui-Xin, Zhou; Han-Lin, Qin; Rui, Lai; Kun, Qian
2016-05-01
Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.
Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum
Wilson, Emma D.; Assaf, Tareq; Pearson, Martin J.; Rossiter, Jonathan M.; Dean, Paul; Anderson, Sean R.; Porrill, John
2015-01-01
The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks. PMID:26257638
Denoising preterm EEG by signal decomposition and adaptive filtering: a comparative study.
Navarro, X; Porée, F; Beuchée, A; Carrault, G
2015-03-01
Electroencephalography (EEG) from preterm infant monitoring systems is usually contaminated by several sources of noise that have to be removed in order to correctly interpret signals and perform automated analysis reliably. Band-pass and adaptive filters (AF) continue to be systematically applied, but their efficacy may be decreased facing preterm EEG patterns such as the tracé alternant and slow delta-waves. In this paper, we propose the combination of EEG decomposition with AF to improve the overall denoising process. Using artificially contaminated signals from real EEGs, we compared the quality of filtered signals applying different decomposition techniques: the discrete wavelet transform, the empirical mode decomposition (EMD) and a recent improved version, the complete ensemble EMD with adaptive noise. Simulations demonstrate that introducing EMD-based techniques prior to AF can reduce up to 30% the root mean squared errors in denoised EEGs. PMID:25659233
Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
NASA Astrophysics Data System (ADS)
Sannelli, Claudia; Vidaurre, Carmen; Müller, Klaus-Robert; Blankertz, Benjamin
2016-08-01
Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain–computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI
Adaptation of filtered back-projection to compton imaging with non-uniform azimuthal geometry
NASA Astrophysics Data System (ADS)
Lee, Hyounggun; Lee, Taewoong; Lee, Wonho
2016-05-01
For Compton image reconstruction, analytic reconstruction methods such as filtered backprojection have been used for real-time imaging. The conventional filtered back-projection method assumes a uniformly distributed azimuthal response in the detector system. In this study, we applied filtered back-projection to the experimental data from detector systems with limited azimuthal angle coverage ranges and estimated the limitations of the analytic reconstruction methods when applied to these systems. For the system with a uniform azimuthal response, the images reconstructed by using filtered back-projection showed better angular resolutions than the images obtained by using simple back-projection did. However, when filtered back-projection was applied to reconstruct Compton images based on measurements performed by using Compton cameras with limited response geometries, the reconstructed images exhibited artifacts caused by the geometrical limitations. Our proposed method employs the Compton camera's rotation to overcome the angular response limitations; when the rotation method was applied in this study, the artifacts in the reconstructed images caused by angular response limitations were minimized. With this method, filtered back-projection can be applied to reconstruct real-time Compton images even when the radiation measurements are performed by using Compton cameras with non-uniform azimuthal response geometries.
Design of adaptive control systems by means of self-adjusting transversal filters
NASA Technical Reports Server (NTRS)
Merhav, S. J.
1986-01-01
The design of closed-loop adaptive control systems based on nonparametric identification was addressed. Implementation is by self-adjusting Least Mean Square (LMS) transversal filters. The design concept is Model Reference Adaptive Control (MRAC). Major issues are to preserve the linearity of the error equations of each LMS filter, and to prevent estimation bias that is due to process or measurement noise, thus providing necessary conditions for the convergence and stability of the control system. The controlled element is assumed to be asymptotically stable and minimum phase. Because of the nonparametric Finite Impulse Response (FIR) estimates provided by the LMS filters, a-priori information on the plant model is needed only in broad terms. Following a survey of control system configurations and filter design considerations, system implementation is shown here in Single Input Single Output (SISO) format which is readily extendable to multivariable forms. In extensive computer simulation studies the controlled element is represented by a second-order system with widely varying damping, natural frequency, and relative degree.
Adaptive non-local means filtering based on local noise level for CT denoising
NASA Astrophysics Data System (ADS)
Li, Zhoubo; Yu, Lifeng; Trzasko, Joshua D.; Fletcher, Joel G.; McCollough, Cynthia H.; Manduca, Armando
2012-03-01
Radiation dose from CT scans is an increasing health concern in the practice of radiology. Higher dose scans can produce clearer images with high diagnostic quality, but may increase the potential risk of radiation-induced cancer or other side effects. Lowering radiation dose alone generally produces a noisier image and may degrade diagnostic performance. Recently, CT dose reduction based on non-local means (NLM) filtering for noise reduction has yielded promising results. However, traditional NLM denoising operates under the assumption that image noise is spatially uniform noise, while in CT images the noise level varies significantly within and across slices. Therefore, applying NLM filtering to CT data using a global filtering strength cannot achieve optimal denoising performance. In this work, we have developed a technique for efficiently estimating the local noise level for CT images, and have modified the NLM algorithm to adapt to local variations in noise level. The local noise level estimation technique matches the true noise distribution determined from multiple repetitive scans of a phantom object very well. The modified NLM algorithm provides more effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with the clinical workflow.
Wang, Pengchong; Zhang, Zhonghua
2016-05-01
A hyperspectral imaging system was demonstrated based on two acousto-optic tunable filters (AOTFs). Efficient regulation of the incoherent beam was executed by means of the wide-angular regime of Bragg diffraction in the birefringent materials. A double-filtering process was achieved when these two AOTFs operated with a central wavelength difference. In comparison with the single-filtering method, the spectral bandwidth was greatly compressed, giving an increment of 42.02% in spectral resolution at the wavelength of 651.62 nm. Experimental results and theoretical calculations are basically identical. Furthermore, the sidelobe was found to be suppressed by the double-filtering process with the first order maximum decreased from -9.25 dB to -22.35 dB. The results indicated high spectral resolution and high spectral purity were obtained simultaneously from this method. The basic spectral resolution performance was examined with a didymium glass by this configuration. We present our experimental methods and the detailed results obtained. PMID:27137600
Adaptive Finite Element Methods in Geodynamics
NASA Astrophysics Data System (ADS)
Davies, R.; Davies, H.; Hassan, O.; Morgan, K.; Nithiarasu, P.
2006-12-01
Adaptive finite element methods are presented for improving the quality of solutions to two-dimensional (2D) and three-dimensional (3D) convection dominated problems in geodynamics. The methods demonstrate the application of existing technology in the engineering community to problems within the `solid' Earth sciences. Two-Dimensional `Adaptive Remeshing': The `remeshing' strategy introduced in 2D adapts the mesh automatically around regions of high solution gradient, yielding enhanced resolution of the associated flow features. The approach requires the coupling of an automatic mesh generator, a finite element flow solver and an error estimator. In this study, the procedure is implemented in conjunction with the well-known geodynamical finite element code `ConMan'. An unstructured quadrilateral mesh generator is utilised, with mesh adaptation accomplished through regeneration. This regeneration employs information provided by an interpolation based local error estimator, obtained from the computed solution on an existing mesh. The technique is validated by solving thermal and thermo-chemical problems with known benchmark solutions. In a purely thermal context, results illustrate that the method is highly successful, improving solution accuracy whilst increasing computational efficiency. For thermo-chemical simulations the same conclusions can be drawn. However, results also demonstrate that the grid based methods employed for simulating the compositional field are not competitive with the other methods (tracer particle and marker chain) currently employed in this field, even at the higher spatial resolutions allowed by the adaptive grid strategies. Three-Dimensional Adaptive Multigrid: We extend the ideas from our 2D work into the 3D realm in the context of a pre-existing 3D-spherical mantle dynamics code, `TERRA'. In its original format, `TERRA' is computationally highly efficient since it employs a multigrid solver that depends upon a grid utilizing a clever
Anantrasirichai, N; Nicholson, Lindsay; Morgan, James E; Erchova, Irina; Mortlock, Katie; North, Rachel V; Albon, Julie; Achim, Alin
2014-09-01
This paper presents novel pre-processing image enhancement algorithms for retinal optical coherence tomography (OCT). These images contain a large amount of speckle causing them to be grainy and of very low contrast. To make these images valuable for clinical interpretation, we propose a novel method to remove speckle, while preserving useful information contained in each retinal layer. The process starts with multi-scale despeckling based on a dual-tree complex wavelet transform (DT-CWT). We further enhance the OCT image through a smoothing process that uses a novel adaptive-weighted bilateral filter (AWBF). This offers the desirable property of preserving texture within the OCT image layers. The enhanced OCT image is then segmented to extract inner retinal layers that contain useful information for eye research. Our layer segmentation technique is also performed in the DT-CWT domain. Finally we describe an OCT/fundus image registration algorithm which is helpful when two modalities are used together for diagnosis and for information fusion. PMID:25034317
A comparison of methods for DPLL loop filter design
NASA Technical Reports Server (NTRS)
Aguirre, S.; Hurd, W. J.; Kumar, R.; Statman, J.
1986-01-01
Four design methodologies for loop filters for a class of digital phase-locked loops (DPLLs) are presented. The first design maps an optimum analog filter into the digital domain; the second approach designs a filter that minimizes in discrete time weighted combination of the variance of the phase error due to noise and the sum square of the deterministic phase error component; the third method uses Kalman filter estimation theory to design a filter composed of a least squares fading memory estimator and a predictor. The last design relies on classical theory, including rules for the design of compensators. Linear analysis is used throughout the article to compare different designs, and includes stability, steady state performance and transient behavior of the loops. Design methodology is not critical when the loop update rate can be made high relative to loop bandwidth, as the performance approaches that of continuous time. For low update rates, however, the miminization method is significantly superior to the other methods.
Adaptive non-local means method for speckle reduction in ultrasound images
NASA Astrophysics Data System (ADS)
Ai, Ling; Ding, Mingyue; Zhang, Xuming
2016-03-01
Noise removal is a crucial step to enhance the quality of ultrasound images. However, some existing despeckling methods cannot ensure satisfactory restoration performance. In this paper, an adaptive non-local means (ANLM) filter is proposed for speckle noise reduction in ultrasound images. The distinctive property of the proposed method lies in that the decay parameter will not take the fixed value for the whole image but adapt itself to the variation of the local features in the ultrasound images. In the proposed method, the pre-filtered image will be obtained using the traditional NLM method. Based on the pre-filtered result, the local gradient will be computed and it will be utilized to determine the decay parameter adaptively for each image pixel. The final restored image will be produced by the ANLM method using the obtained decay parameters. Simulations on the synthetic image show that the proposed method can deliver sufficient speckle reduction while preserving image details very well and it outperforms the state-of-the-art despeckling filters in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Experiments on the clinical ultrasound image further demonstrate the practicality and advantage of the proposed method over the compared filtering methods.
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. PMID:24802943
Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators
Stuart, Andrew M.; Shukla, Abhishek; Sanz-Alonso, Daniel; Law, K. J. H.
2016-02-23
In the context of filtering chaotic dynamical systems it is well-known that partial observations, if sufficiently informative, can be used to control the inherent uncertainty due to chaos. The purpose of this paper is to investigate, both theoretically and numerically, conditions on the observations of chaotic systems under which they can be accurately filtered. In particular, we highlight the advantage of adaptive observation operators over fixed ones. The Lorenz ’96 model is used to exemplify our findings. Here, we consider discrete-time and continuous-time observations in our theoretical developments. We prove that, for fixed observation operator, the 3DVAR filter can recovermore » the system state within a neighbourhood determined by the size of the observational noise. It is required that a sufficiently large proportion of the state vector is observed, and an explicit form for such sufficient fixed observation operator is given. Numerical experiments, where the data is incorporated by use of the 3DVAR and extended Kalman filters, suggest that less informative fixed operators than given by our theory can still lead to accurate signal reconstruction. Adaptive observation operators are then studied numerically; we show that, for carefully chosen adaptive observation operators, the proportion of the state vector that needs to be observed is drastically smaller than with a fixed observation operator. Indeed, we show that the number of state coordinates that need to be observed may even be significantly smaller than the total number of positive Lyapunov exponents of the underlying system.« less
Nonlinear filtering and limiting in high order methods for ideal and non-ideal MHD
NASA Technical Reports Server (NTRS)
Yee,H. C.; Sjogreen, B.
2004-01-01
The various filtering mechanisms and base scheme options of the newly developed adaptive numerical dissipation control in spatially high order filter schemes for the ideal and non-ideal magnetohydrodynamics (MHD) equations are investigated. These filter schemes are applicable to complex unsteady MHD high-speed shock/shear/turbulence problems. They also provide a natural and efficient way for the minimization of Div(B) numerical error. The type of spatial base scheme to be used in conjunction with our filter idea is very general. For example, spectral, compact and non-compact spatially central finite difference schemes are possible candidates. The adaptive numerical dissipation mechanism consists of automatic detection of different flow features as distinct sensors to signal the appropriate type and amount of numerical dissipation/filter where needed and to leave the rest of the region free from numerical dissipation contamination. The numerical dissipation considered consists of high order linear dissipation for the suppression of high frequency oscillation and the nonlinear dissipative portion of high-resolution shock-capturing methods for discontinuity capturing. The applicable nonlinear dissipative portion of high-resolution shock-capturing methods is also very general. The objective of this paper is to investigate the performance of using compact and non-compact central base schemes in conjunction with three commonly used types of nonlinear numerical dissipation for both the ideal and non-ideal MHD. This extended abstract shows the performance of three nonlinear filters in conjunction with a sixth-order non-compact spatial central base scheme. In the final paper, the high order compact spatial central base scheme will be illustrated and compared with the non-compact base scheme. The reason for the investigation of the high order compact spatial central base scheme over the non-compact base scheme is to evaluate if additional accuracy can be gained in regions of
Simplified Method for Groundwater Treatment Using Dilution and Ceramic Filter
NASA Astrophysics Data System (ADS)
Musa, S.; Ariff, N. A.; Kadir, M. N. Abdul; Denan, F.
2016-07-01
Groundwater is one of the natural resources that is not susceptible to pollutants. However, increasing activities of municipal, industrial, agricultural or extreme land use activities have resulted in groundwater contamination as occured at the Research Centre for Soft Soil Malaysia (RECESS), Universiti Tun Hussein Onn Malaysia (UTHM). Thus, aims of this study is to treat groundwater by using rainwater and simple ceramic filter as a treatment agent. The treatment uses rain water dilution, ceramic filters and combined method of dilute and filtering as an alternate treatment which are simple and more practical compared to modern or chemical methods. The water went through dilution treatment processes able to get rid of 57% reduction compared to initial condition. Meanwhile, the water that passes through the filtering process successfully get rid of as much as 86% groundwater parameters where only chloride does not pass the standard. Favorable results for the combination methods of dilution and filtration methods that can succesfully eliminate 100% parameters that donot pass the standards of the Ministry of Health and the Interim National Drinking Water Quality Standard such as those found in groundwater in RECESS, UTHM especially sulfate and chloride. As a result, it allows the raw water that will use clean drinking water and safe. It also proves that the method used in this study is very effective in improving the quality of groundwater.
NASA Astrophysics Data System (ADS)
Yao, Jianjun; Di, Duotao; Jiang, Guilin; Gao, Shuang
2012-10-01
Electro-hydraulic servo shaking table usually requires good control performance for acceleration replication. The poles of the electro-hydraulic servo shaking table are placed by three-variable control method using pole placement theory. The system frequency band is thus extended and the system stability is also enhanced. The phase delay and amplitude attenuation phenomenon occurs in electro-hydraulic servo shaking table corresponding to an acceleration sinusoidal input. The method for phase delay and amplitude attenuation elimination based on LMS adaptive filtering algorithm is proposed here. The task is accomplished by adjusting the weights using LMS adaptive filtering algorithm when there exits phase delay and amplitude attenuation between the input and its corresponding acceleration response. The reference input is weighted in such a way that it makes the system output track the input efficiently. The weighted input signal is inputted to the control system such that the output phase delay and amplitude attenuation are all cancelled. The above concept is used as a basis for the development of amplitude-phase regulation (APR) algorithm. The method does not need to estimate the system model and has good real-time performance. Experimental results demonstrate the efficiency and validity of the proposed APR control scheme.
Modified patch-based locally optimal Wiener method for interferometric SAR phase filtering
NASA Astrophysics Data System (ADS)
Wang, Yang; Huang, Haifeng; Dong, Zhen; Wu, Manqing
2016-04-01
This paper presents a modified patch-based locally optimal Wiener (PLOW) method for interferometric synthetic aperture radar (InSAR) phase filtering. PLOW is a linear minimum mean squared error (LMMSE) estimator based on a Gaussian additive noise condition. It jointly estimates moments, including mean and covariance, using a non-local technique. By using similarities between image patches, this method can effectively filter noise while preserving details. When applied to InSAR phase filtering, three modifications are proposed based on spatial variant noise. First, pixels are adaptively clustered according to their coherence magnitudes. Second, rather than a global estimator, a locally adaptive estimator is used to estimate noise covariance. Third, using the coherence magnitudes as weights, the mean of each cluster is estimated, using a weighted mean to further reduce noise. The performance of the proposed method is experimentally verified using simulated and real data. The results of our study demonstrate that the proposed method is on par or better than the non-local interferometric SAR (NL-InSAR) method.
Evaluation of Five Decontamination Methods for Filtering Facepiece Respirators
Bergman, Michael S.; Eimer, Benjamin C.; Shaffer, Ronald E.
2009-01-01
Concerns have been raised regarding the availability of National Institute for Occupational Safety and Health (NIOSH)-certified N95 filtering facepiece respirators (FFRs) during an influenza pandemic. One possible strategy to mitigate a respirator shortage is to reuse FFRs following a biological decontamination process to render infectious material on the FFR inactive. However, little data exist on the effects of decontamination methods on respirator integrity and performance. This study evaluated five decontamination methods [ultraviolet germicidal irradiation (UVGI), ethylene oxide, vaporized hydrogen peroxide (VHP), microwave oven irradiation, and bleach] using nine models of NIOSH-certified respirators (three models each of N95 FFRs, surgical N95 respirators, and P100 FFRs) to determine which methods should be considered for future research studies. Following treatment by each decontamination method, the FFRs were evaluated for changes in physical appearance, odor, and laboratory performance (filter aerosol penetration and filter airflow resistance). Additional experiments (dry heat laboratory oven exposures, off-gassing, and FFR hydrophobicity) were subsequently conducted to better understand material properties and possible health risks to the respirator user following decontamination. However, this study did not assess the efficiency of the decontamination methods to inactivate viable microorganisms. Microwave oven irradiation melted samples from two FFR models. The remainder of the FFR samples that had been decontaminated had expected levels of filter aerosol penetration and filter airflow resistance. The scent of bleach remained noticeable following overnight drying and low levels of chlorine gas were found to off-gas from bleach-decontaminated FFRs when rehydrated with deionized water. UVGI, ethylene oxide (EtO), and VHP were found to be the most promising decontamination methods; however, concerns remain about the throughput capabilities for EtO and VHP
A New Adaptive Image Denoising Method
NASA Astrophysics Data System (ADS)
Biswas, Mantosh; Om, Hari
2016-03-01
In this paper, a new adaptive image denoising method is proposed that follows the soft-thresholding technique. In our method, a new threshold function is also proposed, which is determined by taking the various combinations of noise level, noise-free signal variance, subband size, and decomposition level. It is simple and adaptive as it depends on the data-driven parameters estimation in each subband. The state-of-the-art denoising methods viz. VisuShrink, SureShrink, BayesShrink, WIDNTF and IDTVWT are not able to modify the coefficients in an efficient manner to provide the good quality of image. Our method removes the noise from the noisy image significantly and provides better visual quality of an image.
RAPID SEPARATION METHOD FOR ACTINIDES IN EMERGENCY AIR FILTER SAMPLES
Maxwell, S.; Noyes, G.; Culligan, B.
2010-02-03
A new rapid method for the determination of actinides and strontium in air filter samples has been developed at the Savannah River Site Environmental Lab (Aiken, SC, USA) that can be used in emergency response situations. The actinides and strontium in air filter method utilizes a rapid acid digestion method and a streamlined column separation process with stacked TEVA, TRU and Sr Resin cartridges. Vacuum box technology and rapid flow rates are used to reduce analytical time. Alpha emitters are prepared using cerium fluoride microprecipitation for counting by alpha spectrometry. The purified {sup 90}Sr fractions are mounted directly on planchets and counted by gas flow proportional counting. The method showed high chemical recoveries and effective removal of interferences. This new procedure was applied to emergency air filter samples received in the NRIP Emergency Response exercise administered by the National Institute for Standards and Technology (NIST) in April, 2009. The actinide and {sup 90}Sr in air filter results were reported in {approx}4 hours with excellent quality.
Matched filtering method for separating magnetic anomaly using fractal model
NASA Astrophysics Data System (ADS)
Chen, Guoxiong; Cheng, Qiuming; Zhang, Henglei
2016-05-01
Fractal/scaling distribution of magnetization in the crust has found with growing body of evidences from spectral analysis of borehole susceptibility logs and magnetic field data, and fractal properties of magnetic sources have already been considered in processing magnetic data such as the Spector and Grant method for depth determination. In this study, the fractal-based matched filtering method is presented for separating magnetic anomalies caused by fractal sources. We argue the benefits of considering fractal natures of source distribution for data processing in magnetic exploration: the first is that the depth determination can be improved by using multiscaling model to interpret the magnetic data power spectrum; the second is that the matched filtering can be reconstructed by employing the difference in scaling exponent together with the corrected depth and amplitude estimates. In the application of synthetic data obtained from fractal modeling and real aeromagnetic data from the Qikou district of China, the proposed fractal-based matched filtering method obtains more reliable depth estimations as well as improved separation between local anomalies (caused by volcanic rocks) and regional field (crystalline basement) in comparison with the conventional matched filtering method.
Method and apparatus for measuring flow velocity using matched filters
Raptis, Apostolos C.
1983-01-01
An apparatus and method for measuring the flow velocities of individual phase flow components of a multiphase flow utilizes matched filters. Signals arising from flow noise disturbance are extracted from the flow, at upstream and downstream locations. The signals are processed through pairs of matched filters which are matched to the flow disturbance frequency characteristics of the phase flow component to be measured. The processed signals are then cross-correlated to determine the transit delay time of the phase flow component between sensing positions.
Method and apparatus for measuring flow velocity using matched filters
Raptis, A.C.
1983-09-06
An apparatus and method for measuring the flow velocities of individual phase flow components of a multiphase flow utilizes matched filters. Signals arising from flow noise disturbance are extracted from the flow, at upstream and downstream locations. The signals are processed through pairs of matched filters which are matched to the flow disturbance frequency characteristics of the phase flow component to be measured. The processed signals are then cross-correlated to determine the transit delay time of the phase flow component between sensing positions. 8 figs.
Domain adaptive boosting method and its applications
NASA Astrophysics Data System (ADS)
Geng, Jie; Miao, Zhenjiang
2015-03-01
Differences of data distributions widely exist among datasets, i.e., domains. For many pattern recognition, nature language processing, and content-based analysis systems, a decrease in performance caused by the domain differences between the training and testing datasets is still a notable problem. We propose a domain adaptation method called domain adaptive boosting (DAB). It is based on the AdaBoost approach with extensions to cover the domain differences between the source and target domains. Two main stages are contained in this approach: source-domain clustering and source-domain sample selection. By iteratively adding the selected training samples from the source domain, the discrimination model is able to achieve better domain adaptation performance based on a small validation set. The DAB algorithm is suitable for the domains with large scale samples and easy to extend for multisource adaptation. We implement this method on three computer vision systems: the skin detection model in single images, the video concept detection model, and the object classification model. In the experiments, we compare the performances of several commonly used methods and the proposed DAB. Under most situations, the DAB is superior.
Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg; Fernández, Eduardo
2010-01-01
In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s2) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory. PMID:22315542
High performance 3D adaptive filtering for DSP based portable medical imaging systems
NASA Astrophysics Data System (ADS)
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.
A filtered cathodic arc deposition apparatus and method
Krauss, Alan R.
1997-12-01
A filtered cathodic arc deposition method and apparatus are described for the production of highly dense, wear resistant coatings which are free from macro particles. The filtered cathodic arc deposition apparatus includes a cross shaped vacuum chamber which houses a cathode target having an evaporable surface comprised of the coating material, means for generating a stream of plasma, means for generating a transverse magnetic field, and a macro particle deflector. The transverse magnetic field bends the generated stream of plasma in the direction of a substrate. Macro particles are effectively filtered from the stream of plasma by traveling, unaffected by the transverse magnetic field, along the initial path of the plasma stream to a macro particle deflector. The macro particle deflector has a preformed surface which deflects macro particles away from the substrate.
Structured adaptive grid generation using algebraic methods
NASA Technical Reports Server (NTRS)
Yang, Jiann-Cherng; Soni, Bharat K.; Roger, R. P.; Chan, Stephen C.
1993-01-01
The accuracy of the numerical algorithm depends not only on the formal order of approximation but also on the distribution of grid points in the computational domain. Grid adaptation is a procedure which allows optimal grid redistribution as the solution progresses. It offers the prospect of accurate flow field simulations without the use of an excessively timely, computationally expensive, grid. Grid adaptive schemes are divided into two basic categories: differential and algebraic. The differential method is based on a variational approach where a function which contains a measure of grid smoothness, orthogonality and volume variation is minimized by using a variational principle. This approach provided a solid mathematical basis for the adaptive method, but the Euler-Lagrange equations must be solved in addition to the original governing equations. On the other hand, the algebraic method requires much less computational effort, but the grid may not be smooth. The algebraic techniques are based on devising an algorithm where the grid movement is governed by estimates of the local error in the numerical solution. This is achieved by requiring the points in the large error regions to attract other points and points in the low error region to repel other points. The development of a fast, efficient, and robust algebraic adaptive algorithm for structured flow simulation applications is presented. This development is accomplished in a three step process. The first step is to define an adaptive weighting mesh (distribution mesh) on the basis of the equidistribution law applied to the flow field solution. The second, and probably the most crucial step, is to redistribute grid points in the computational domain according to the aforementioned weighting mesh. The third and the last step is to reevaluate the flow property by an appropriate search/interpolate scheme at the new grid locations. The adaptive weighting mesh provides the information on the desired concentration
NASA Astrophysics Data System (ADS)
Dai, Haifeng; Zhu, Letao; Zhu, Jiangong; Wei, Xuezhe; Sun, Zechang
2015-10-01
The accurate monitoring of battery cell temperature is indispensible to the design of battery thermal management system. To obtain the internal temperature of a battery cell online, an adaptive temperature estimation method based on Kalman filtering and an equivalent time-variant electrical network thermal (EENT) model is proposed. The EENT model uses electrical components to simulate the battery thermodynamics, and the model parameters are obtained with a least square algorithm. With a discrete state-space description of the EENT model, a Kalman filtering (KF) based internal temperature estimator is developed. Moreover, considering the possible time-varying external heat exchange coefficient, a joint Kalman filtering (JKF) based estimator is designed to simultaneously estimate the internal temperature and the external thermal resistance. Several experiments using the hard-cased LiFePO4 cells with embedded temperature sensors have been conducted to validate the proposed method. Validation results show that, the EENT model expresses the battery thermodynamics well, the KF based temperature estimator tracks the real central temperature accurately even with a poor initialization, and the JKF based estimator can simultaneously estimate both central temperature and external thermal resistance precisely. The maximum estimation errors of the KF- and JKF-based estimators are less than 1.8 °C and 1 °C respectively.
NASA Astrophysics Data System (ADS)
Fang, Hao; Li, Qian; Huang, Zhenghua
2015-12-01
Denoising algorithms based on gradient dependent energy functionals, such as Perona-Malik, total variation and adaptive total variation denoising, modify images towards piecewise constant functions. Although edge sharpness and location is well preserved, important information, encoded in image features like textures or certain details, is often compromised in the process of denoising. In this paper, We propose a novel Spatially Adaptive Guide-Filtering Total Variation (SAGFTV) regularization with image restoration algorithm for denoising images. The guide-filter is extended to the variational formulations of imaging problem, and the spatially adaptive operator can easily distinguish flat areas from texture areas. Our simulating experiments show the improvement of peak signal noise ratio (PSNR), root mean square error (RMSE) and structure similarity increment measurement (SSIM) over other prior algorithms. The results of both simulating and practical experiments are more appealing visually. This type of processing can be used for a variety of tasks in PDE-based image processing and computer vision, and is stable and meaningful from a mathematical viewpoint.
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF. PMID:27475606
NASA Astrophysics Data System (ADS)
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.
A DSP-Based Beam Current Monitoring System for Machine Protection Using Adaptive Filtering
J. Musson; H. Dong; R. Flood; C. Hovater; J. Hereford
2001-06-01
The CEBAF accelerator at Jefferson Lab is currently using an analog beam current monitoring (BCM) system for its machine protection system (MPS), which has a loss accuracy of 2 micro-amps. Recent burn-through simulations predict catastrophic beam line component failures below 1 micro-amp of loss, resulting in a blind spot for the MPS. Revised MPS requirements target an ultimate beam loss accuracy of 250 nA. A new beam current monitoring system has been developed which utilizes modern digital receiver technology and digital signal processing concepts. The receiver employs a direct-digital down converter integrated circuit, mated with a Jefferson Lab digital signal processor VME card. Adaptive filtering is used to take advantage of current-dependent burn-through rates. Benefits of such a system include elimination of DC offsets, generic algorithm development, extensive filter options, and interfaces to UNIX-based control systems.
A 2-D orientation-adaptive prediction filter in lifting structures for image coding.
Gerek, Omer N; Cetin, A Enis
2006-01-01
Lifting-style implementations of wavelets are widely used in image coders. A two-dimensional (2-D) edge adaptive lifting structure, which is similar to Daubechies 5/3 wavelet, is presented. The 2-D prediction filter predicts the value of the next polyphase component according to an edge orientation estimator of the image. Consequently, the prediction domain is allowed to rotate +/-45 degrees in regions with diagonal gradient. The gradient estimator is computationally inexpensive with additional costs of only six subtractions per lifting instruction, and no multiplications are required. PMID:16435541
Evaluation of an adaptive filtering algorithm for CT cardiac imaging with EKG modulated tube current
NASA Astrophysics Data System (ADS)
Li, Jianying; Hsieh, Jiang; Mohr, Kelly; Okerlund, Darin
2005-04-01
We have developed an adaptive filtering algorithm for cardiac CT scans with EKG-modulated tube current to optimize resolution and noise for different cardiac phases and to provide safety net for cases where end-systole phase is used for coronary imaging. This algorithm has been evaluated using patient cardiac CT scans where lower tube currents are used for the systolic phases. In this paper, we present the evaluation results. The results demonstrated that with the use of the proposed algorithm, we could improve image quality for all cardiac phases, while providing greater noise and streak artifact reduction for systole phases where lower CT dose were used.
Sun, W Y
1993-04-01
This thesis solves the problem of finding the optimal linear noise-reduction filter for linear tomographic image reconstruction. The optimization is data dependent and results in minimizing the mean-square error of the reconstructed image. The error is defined as the difference between the result and the best possible reconstruction. Applications for the optimal filter include reconstructions of positron emission tomographic (PET), X-ray computed tomographic, single-photon emission tomographic, and nuclear magnetic resonance imaging. Using high resolution PET as an example, the optimal filter is derived and presented for the convolution backprojection, Moore-Penrose pseudoinverse, and the natural-pixel basis set reconstruction methods. Simulations and experimental results are presented for the convolution backprojection method.
Method of synthesized phase objects for pattern recognition: matched filtering.
Yezhov, Pavel V; Kuzmenko, Alexander V; Kim, Jin-Tae; Smirnova, Tatiana N
2012-12-31
To solve the pattern recognition problem, a method of synthesized phase objects is suggested. The essence of the suggested method is that synthesized phase objects are used instead of real amplitude objects. The former is object-dependent phase distributions calculated using the iterative Fourier-transform (IFT) algorithm. The method is experimentally studied with a Vander Lugt optical-digital 4F-correlator. We present the comparative analysis of recognition results using conventional and proposed methods, estimate the sensitivity of the latter to distortions of the structure of objects, and determine the applicability limits. It is demonstrated that the proposed method allows one: (а) to simplify the procedure of choice of recognition signs (criteria); (b) to obtain one-type δ-like recognition signals irrespective of the type of objects; (с) to improve signal-to-noise ratio (SNR) for correlation signals by 20 - 30 dB on average. The spatial separation of the Fourier-spectra of objects and optical noises of the correlator by means of the superposition of the phase grating on recognition objects at the recording of holographic filters and at the matched filtering has additionally improved SNR (>10 dB) for correlation signals. To introduce recognition objects in the correlator, we use a SLM LC-R 2500 device. Matched filters are recorded on a self-developing photopolymer. PMID:23388812
Parallel adaptive wavelet collocation method for PDEs
Nejadmalayeri, Alireza; Vezolainen, Alexei; Brown-Dymkoski, Eric; Vasilyev, Oleg V.
2015-10-01
A parallel adaptive wavelet collocation method for solving a large class of Partial Differential Equations is presented. The parallelization is achieved by developing an asynchronous parallel wavelet transform, which allows one to perform parallel wavelet transform and derivative calculations with only one data synchronization at the highest level of resolution. The data are stored using tree-like structure with tree roots starting at a priori defined level of resolution. Both static and dynamic domain partitioning approaches are developed. For the dynamic domain partitioning, trees are considered to be the minimum quanta of data to be migrated between the processes. This allows fully automated and efficient handling of non-simply connected partitioning of a computational domain. Dynamic load balancing is achieved via domain repartitioning during the grid adaptation step and reassigning trees to the appropriate processes to ensure approximately the same number of grid points on each process. The parallel efficiency of the approach is discussed based on parallel adaptive wavelet-based Coherent Vortex Simulations of homogeneous turbulence with linear forcing at effective non-adaptive resolutions up to 2048{sup 3} using as many as 2048 CPU cores.
Adaptive Fuzzy Hysteresis Band Current Controller for Four-Wire Shunt Active Filter
NASA Astrophysics Data System (ADS)
Hamoudi, F.; Chaghi, A.; Amimeur, H.; Merabet, E.
2008-06-01
This paper presents an adaptive fuzzy hysteresis band current controller for four-wire shunt active power filters to eliminate harmonics and to compensate reactive power in distribution systems in order to keep currents at the point of common coupling sinusoidal and in phase with the corresponding voltage and the cancel neutral current. The conventional hysteresis band known for its robustness and its advantage in current controlled applications is adapted with a fuzzy logic controller to change the bandwidth according to the operating point in order to keep the frequency modulation at tolerable limits. The algorithm used to identify the reference currents is based on the synchronous reference frame theory (dqγ). Finally, simulation results using Matlab/Simulink are given to validate the proposed control.
NASA Astrophysics Data System (ADS)
Zhang, Weige; Shi, Wei; Ma, Zeyu
2015-09-01
Accurate estimations of battery energy and available power capability are of great of importance for realizing an efficient and reliable operation of electric vehicles. To improve the estimation accuracy and reliability for battery state of energy and power capability, a novel model-based joint estimation approach has been proposed against uncertain external operating conditions and internal degradation status of battery cells. Firstly, it proposes a three-dimensional response surface open circuit voltage model to calibrate the estimation inaccuracies of battery state of energy. Secondly, the adaptive unscented Kalman filter (AUKF) is employed to develop a novel model-based joint state estimator for battery state of energy and power capability. The AUKF algorithm utilizes the well-known features of the Kalman filter but employs the method of unscented transform (UT) and adaptive error covariance matching technology to improve the state estimation accuracy. Thirdly, the proposed joint estimator has been verified by a LiFePO4 lithium-ion battery cell under different operating temperatures and aging levels. The result indicates that the estimation errors of battery voltage and state-of-energy are less than 2% even if given a large erroneous initial value, which makes the state of available power capability predict more accurate and reliable for the electric vehicles application.
Fingerprint image enhancement method using directional median filter
NASA Astrophysics Data System (ADS)
Wu, Chaohong; Shi, Zhixin; Govindaraju, Venu
2004-08-01
The performance of any fingerprint recognizer highly depends on the fingerprint image quality. Different types of noises in the fingerprint images pose greater difficulty for recognizers. Most Automatic Fingerprint Identification Systems (AFIS) use some form of image enhancement. Although several methods have been described in the literature, there is still scope for improvement. In particular, effective methodology of cleaning the valleys between the ridge contours are lacking. We observe that noisy valley pixels and the pixels in the interrupted ridge flow gap are "impulse noises". Therefore, this paper describes a new approach to fingerprint image enhancement, which is based on integration of Anisotropic Filter and directional median filter(DMF). Gaussian-distributed noises are reduced effectively by Anisotropic Filter, "impulse noises" are reduced efficiently by DMF. Usually, traditional median filter is the most effective method to remove pepper-and-salt noise and other small artifacts, the proposed DMF can not only finish its original tasks, it can also join broken fingerprint ridges, fill out the holes of fingerprint images, smooth irregular ridges as well as remove some annoying small artifacts between ridges. The enhancement algorithm has been implemented and tested on fingerprint images from FVC2002. Images of varying quality have been used to evaluate the performance of our approach. We have compared our method with other methods described in the literature in terms of matched minutiae, missed minutiae, spurious minutiae, and flipped minutiae(between end points and bifurcation points). Experimental results show our method to be superior to those described in the literature.
On controlling nonlinear dissipation in high order filter methods for ideal and non-ideal MHD
NASA Technical Reports Server (NTRS)
Yee, H. C.; Sjogreen, B.
2004-01-01
The newly developed adaptive numerical dissipation control in spatially high order filter schemes for the compressible Euler and Navier-Stokes equations has been recently extended to the ideal and non-ideal magnetohydrodynamics (MHD) equations. These filter schemes are applicable to complex unsteady MHD high-speed shock/shear/turbulence problems. They also provide a natural and efficient way for the minimization of Div(B) numerical error. The adaptive numerical dissipation mechanism consists of automatic detection of different flow features as distinct sensors to signal the appropriate type and amount of numerical dissipation/filter where needed and leave the rest of the region free from numerical dissipation contamination. The numerical dissipation considered consists of high order linear dissipation for the suppression of high frequency oscillation and the nonlinear dissipative portion of high-resolution shock-capturing methods for discontinuity capturing. The applicable nonlinear dissipative portion of high-resolution shock-capturing methods is very general. The objective of this paper is to investigate the performance of three commonly used types of nonlinear numerical dissipation for both the ideal and non-ideal MHD.
A characterization of a single-trial adaptive filter and its implementation in the frequency domain.
Arpaia, J P; Isenhart, R; Sandman, C A
1989-10-01
A single-trial adaptive filter (SAF) was implemented in the frequency domain (FDAF) by using the Fast Fourier Transform. The FDAF is significantly more efficient than the SAF. In the data presented the FDAF ran approximately 2 times faster than the SAF. For time series containing larger numbers of data points (n) the efficiency of the calculation will increase on the order of N/Ln(N). The FDAF was tested under a variety of conditions to determine the limits of its usefulness. Pre-filtering the data was found to be necessary to prevent the FDAF from lining up on high frequency activity not related to the signal. The importance of minimizing the amount of low frequency noise was emphasized since it adversely affected the performance of the FDAF and was difficult to filter. The single-trial latencies predicted by the FDAF were much more sensitive to increasing noise than the final wave form. In the absence of excessive low frequency noise a negative exponential relationship was found between the mean error in latency prediction and the SNR estimate. Since the SAF technique is also used to determine signal latency in single sweep data the SNR estimate can be a useful test to determine if the FDAF is locating the signal correctly or merely amplifying chance regularities in noisy data. PMID:2477222
NASA Astrophysics Data System (ADS)
Steeb, P.; Krause, S.; Linke, P.; Hensen, C.; Dale, A. W.; Nuzzo, M.; Treude, T.
2014-11-01
Large amounts of methane are delivered by fluids through the erosive forearc of the convergent margin offshore Costa Rica and lead to the formation of cold seeps at the sediment surface. Besides mud extrusion, numerous cold seeps are created by landslides induced by seamount subduction or fluid migration along major faults. Most of the dissolved methane reaching the seafloor at cold seeps is oxidized within the benthic microbial methane filter by anaerobic oxidation of methane (AOM). Measurements of AOM and sulfate reduction as well as numerical modeling of porewater profiles revealed a highly active and efficient benthic methane filter at Quepos Slide site; a landslide on the continental slope between the Nicoya and Osa Peninsula. Integrated areal rates of AOM ranged from 12.9 ± 6.0 to 45.2 ± 11.5 mmol m-2 d-1, with only 1 to 2.5% of the upward methane flux being released into the water column. Additionally, two parallel sediment cores from Quepos Slide were used for in vitro experiments in a recently developed Sediment-F low-Through (SLOT) system to simulate an increased fluid and methane flux from the bottom of the sediment core. The benthic methane filter revealed a high adaptability whereby the methane oxidation efficiency responded to the increased fluid flow within 150-170 days. To our knowledge, this study provides the first estimation of the natural biogeochemical response of seep sediments to changes in fluid flow.
Bai, Mingsian R; Chi, Li-Wen; Liang, Li-Huang; Lo, Yi-Yang
2016-02-01
In this paper, an evolutionary exposition is given in regard to the enhancing strategies for acoustic echo cancellers (AECs). A fixed beamformer (FBF) is utilized to focus on the near-end speaker while suppressing the echo from the far end. In reality, the array steering vector could differ considerably from the ideal freefield plane wave model. Therefore, an experimental procedure is developed to interpolate a practical array model from the measured frequency responses. Subband (SB) filtering with polyphase implementation is exploited to accelerate the cancellation process. Generalized sidelobe canceller (GSC) composed of an FBF and an adaptive blocking module is combined with AEC to maximize cancellation performance. Another enhancement is an internal iteration (IIT) procedure that enables efficient convergence in the adaptive SB filters within a sample time. Objective tests in terms of echo return loss enhancement (ERLE), perceptual evaluation of speech quality (PESQ), word recognition rate for automatic speech recognition (ASR), and subjective listening tests are conducted to validate the proposed AEC approaches. The results show that the GSC-SB-AEC-IIT approach has attained the highest ERLE without speech quality degradation, even in double-talk scenarios. PMID:26936567
NASA Astrophysics Data System (ADS)
Dong, Gangqi; Zhu, Zheng H.
2016-05-01
This paper presents a real-time, vision-based algorithm for the pose and motion estimation of non-cooperative targets and its application in visual servo robotic manipulator to perform autonomous capture. A hybrid approach of adaptive extended Kalman filter and photogrammetry is developed for the real-time pose and motion estimation of non-cooperative targets. Based on the pose and motion estimates, the desired pose and trajectory of end-effector is defined and the corresponding desired joint angles of the robotic manipulator are derived by inverse kinematics. A close-loop visual servo control scheme is then developed for the robotic manipulator to track, approach and capture the target. Validating experiments are designed and performed on a custom-built six degrees of freedom robotic manipulator with an eye-in-hand configuration. The experimental results demonstrate the feasibility, effectiveness and robustness of the proposed adaptive extended Kalman filter enabled pose and motion estimation and visual servo strategy.
CROWDER, STEPHEN V.
1999-09-01
In many manufacturing environments such as the nuclear weapons complex, emphasis has shifted from the regular production and delivery of large orders to infrequent small orders. However, the challenge to maintain the same high quality and reliability standards while building much smaller lot sizes remains. To meet this challenge, specific areas need more attention, including fast and on-target process start-up, low volume statistical process control, process characterization with small experiments, and estimating reliability given few actual performance tests of the product. In this paper we address the issue of low volume statistical process control. We investigate an adaptive filtering approach to process monitoring with a relatively short time series of autocorrelated data. The emphasis is on estimation and minimization of mean squared error rather than the traditional hypothesis testing and run length analyses associated with process control charting. We develop an adaptive filtering technique that assumes initial process parameters are unknown, and updates the parameters as more data become available. Using simulation techniques, we study the data requirements (the length of a time series of autocorrelated data) necessary to adequately estimate process parameters. We show that far fewer data values are needed than is typically recommended for process control applications. We also demonstrate the techniques with a case study from the nuclear weapons manufacturing complex.
Crowder, S.V.; Eshleman, L.
1998-08-01
In many manufacturing environments such as the nuclear weapons complex, emphasis has shifted from the regular production and delivery of large orders to infrequent small orders. However, the challenge to maintain the same high quality and reliability standards white building much smaller lot sizes remains. To meet this challenge, specific areas need more attention, including fast and on-target process start-up, low volume statistical process control, process characterization with small experiments, and estimating reliability given few actual performance tests of the product. In this paper the authors address the issue of low volume statistical process control. They investigate an adaptive filtering approach to process monitoring with a relatively short time series of autocorrelated data. The emphasis is on estimation and minimization of mean squared error rather than the traditional hypothesis testing and run length analyses associated with process control charting. The authors develop an adaptive filtering technique that assumes initial process parameters are unknown, and updates the parameters as more data become available. Using simulation techniques, they study the data requirements (the length of a time series of autocorrelated data) necessary to adequately estimate process parameters. They show that far fewer data values are needed than is typically recommended for process control applications. And they demonstrate the techniques with a case study from the nuclear weapons manufacturing complex.
Tunnel Point Cloud Filtering Method Based on Elliptic Cylindrical Model
NASA Astrophysics Data System (ADS)
Zhua, Ningning; Jiaa, Yonghong; Luo, Lun
2016-06-01
The large number of bolts and screws that attached to the subway shield ring plates, along with the great amount of accessories of metal stents and electrical equipments mounted on the tunnel walls, make the laser point cloud data include lots of non-tunnel section points (hereinafter referred to as non-points), therefore affecting the accuracy for modeling and deformation monitoring. This paper proposed a filtering method for the point cloud based on the elliptic cylindrical model. The original laser point cloud data was firstly projected onto a horizontal plane, and a searching algorithm was given to extract the edging points of both sides, which were used further to fit the tunnel central axis. Along the axis the point cloud was segmented regionally, and then fitted as smooth elliptic cylindrical surface by means of iteration. This processing enabled the automatic filtering of those inner wall non-points. Experiments of two groups showed coincident results, that the elliptic cylindrical model based method could effectively filter out the non-points, and meet the accuracy requirements for subway deformation monitoring. The method provides a new mode for the periodic monitoring of tunnel sections all-around deformation in subways routine operation and maintenance.
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. PMID:26451391
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. PMID:26451391
Integrated adaptive filtering and design for control experiments of flexible structures
NASA Technical Reports Server (NTRS)
Huang, Jen-Kuang
1991-01-01
A novel method is presented of identifying a state space model and a state estimator for linear stochastic systems from input and output data. The method is primarily based on the relations between the state space model and the finite difference model for linear stochastic systems derived through projection filters. It is proven that least squares identification of a finite difference model converges to the model derived from the projection filters. System pulse response samples are computed from the coefficients of the finite difference model. In estimating the corresponding state estimator gain, a z-domain method is used. First the deterministic component of the output is subtracted out, and then the state estimator gain is obtained by whitening the remaining signal. Experimental example is used to illustrate the feasibility of the method.
Taboada-Serrano, Patricia; Tsouris, Constantino; Contescu, Cristian I; McFarlane, Joanna
2013-10-08
The present invention provides magnetically responsive activated carbon, and a method of forming magnetically responsive activated carbon. The method of forming magnetically responsive activated carbon typically includes providing activated carbon in a solution containing ions of ferrite forming elements, wherein at least one of the ferrite forming elements has an oxidation state of +3 and at least a second of the ferrite forming elements has an oxidation state of +2, and increasing pH of the solution to precipitate particles of ferrite that bond to the activated carbon, wherein the activated carbon having the ferrite particles bonded thereto have a positive magnetic susceptibility. The present invention also provides a method of filtering waste water using magnetic activated carbon.
Mazumder, Ria; Clymer, Bradley D; Mo, Xiaokui; White, Richard D; Kolipaka, Arunark
2016-06-01
Diffusion tensor imaging (DTI) is used to quantify myocardial fiber orientation based on helical angles (HA). Accurate HA measurements require multiple excitations (NEX) and/or several diffusion encoding directions (DED). However, increasing NEX and/or DED increases acquisition time (TA). Therefore, in this study, we propose to reduce TA by implementing a 3D adaptive anisotropic Gaussian filter (AAGF) on the DTI data acquired from ex-vivo healthy and infarcted porcine hearts. DTI was performed on ex-vivo hearts [9-healthy, 3-myocardial infarction (MI)] with several combinations of DED and NEX. AAGF, mean (AVF) and median filters (MF) were applied on the primary eigenvectors of the diffusion tensor prior to HA estimation. The performance of AAGF was compared against AVF and MF. Root mean square error (RMSE), concordance correlation-coefficients and Bland-Altman's technique was used to determine optimal combination of DED and NEX that generated the best HA maps in the least possible TA. Lastly, the effect of implementing AAGF on the infarcted porcine hearts was also investigated. RMSE in HA estimation for AAGF was lower compared to AVF or MF. Post-filtering (AAGF) fewer DED and NEX were required to achieve HA maps with similar integrity as those obtained from higher NEX and/or DED. Pathological alterations caused in HA orientation in the MI model were preserved post-filtering (AAGF). Our results demonstrate that AAGF reduces TA without affecting the integrity of the myocardial microstructure. PMID:26843150
Filtering method for 3D laser scanning point cloud
NASA Astrophysics Data System (ADS)
Liu, Da; Wang, Li; Hao, Yuncai; Zhang, Jun
2015-10-01
In recent years, with the rapid development of the hardware and software of the three-dimensional model acquisition, three-dimensional laser scanning technology is utilized in various aspects, especially in space exploration. The point cloud filter is very important before using the data. In the paper, considering both the processing quality and computing speed, an improved mean-shift point cloud filter method is proposed. Firstly, by analyze the relevance of the normal vector between the upcoming processing point and the near points, the iterative neighborhood of the mean-shift is selected dynamically, then the high frequency noise is constrained. Secondly, considering the normal vector of the processing point, the normal vector is updated. Finally, updated position is calculated for each point, then each point is moved in the normal vector according to the updated position. The experimental results show that the large features are retained, at the same time, the small sharp features are also existed for different size and shape of objects, so the target feature information is protected precisely. The computational complexity of the proposed method is not high, it can bring high precision results with fast speed, so it is very suitable for space application. It can also be utilized in civil, such as large object measurement, industrial measurement, car navigation etc. In the future, filter with the help of point strength will be further exploited.
An Adaptive VOF Method on Unstructured Grid
NASA Astrophysics Data System (ADS)
Wu, L. L.; Huang, M.; Chen, B.
2011-09-01
In order to improve the accuracy of interface capturing and keeping the computational efficiency, an adaptive VOF method on unstructured grid is proposed in this paper. The volume fraction in each cell is regarded as the criterion to locally refine the interface cell. With the movement of interface, new interface cells (0 ≤ f ≤ 1) are subdivided into child cells, while those child cells that no longer contain interface will be merged back into the original parent cell. In order to avoid the complicated redistribution of volume fraction during the subdivision and amalgamation procedure, a predictor-corrector algorithm is proposed to implement the subdivision and amalgamation procedures only in empty or full cell ( f = 0 or 1). Thus volume fraction in the new cell can take the value from the original cell directly, and the interpolation of the interface is avoided. The advantage of this method is that the re-generation of the whole grid system is not necessary, so its implementation is very efficient. Moreover, an advection flow test of a hollow square was performed, and the relative shape error of the result obtained by adaptive mesh is smaller than those by non-refined grid, which verifies the validation of our method.
Ensemble transform sensitivity method for adaptive observations
NASA Astrophysics Data System (ADS)
Zhang, Yu; Xie, Yuanfu; Wang, Hongli; Chen, Dehui; Toth, Zoltan
2016-01-01
The Ensemble Transform (ET) method has been shown to be useful in providing guidance for adaptive observation deployment. It predicts forecast error variance reduction for each possible deployment using its corresponding transformation matrix in an ensemble subspace. In this paper, a new ET-based sensitivity (ETS) method, which calculates the gradient of forecast error variance reduction in terms of analysis error variance reduction, is proposed to specify regions for possible adaptive observations. ETS is a first order approximation of the ET; it requires just one calculation of a transformation matrix, increasing computational efficiency (60%-80% reduction in computational cost). An explicit mathematical formulation of the ETS gradient is derived and described. Both the ET and ETS methods are applied to the Hurricane Irene (2011) case and a heavy rainfall case for comparison. The numerical results imply that the sensitive areas estimated by the ETS and ET are similar. However, ETS is much more efficient, particularly when the resolution is higher and the number of ensemble members is larger.
An algebraic method for constructing stable and consistent autoregressive filters
NASA Astrophysics Data System (ADS)
Harlim, John; Hong, Hoon; Robbins, Jacob L.
2015-02-01
In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams-Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden-Julian Oscillation, a dominant tropical atmospheric wave pattern.
An algebraic method for constructing stable and consistent autoregressive filters
Harlim, John; Hong, Hoon; Robbins, Jacob L.
2015-02-15
In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.
Adaptive spatial carrier frequency method for fast monitoring optical properties of fibres
NASA Astrophysics Data System (ADS)
Sokkar, T. Z. N.; El-Farahaty, K. A.; El-Bakary, M. A.; Omar, E. Z.; Agour, M.; Hamza, A. A.
2016-05-01
We present an extension of the adaptive spatial carrier frequency method which is proposed for fast measuring optical properties of fibrous materials. The method can be considered as a two complementary steps. In the first step, the support of the adaptive filter shall be defined. In the second step, the angle between the sample under test and the interference fringe system generated by the utilized interferometer has to be determined. Thus, the support of the optical filter associated with the implementation of the adaptive spatial carrier frequency method is accordingly rotated. This method is experimentally verified by measuring optical properties of polypropylene (PP) fibre with the help of a Mach-Zehnder interferometer. The results show that errors resulting from rotating the fibre with respect to the interference fringes of the interferometer are reduced compared with the traditional band pass filter method. This conclusion was driven by comparing results of the mean refractive index of drown PP fibre at parallel polarization direction obtained from the new and adaptive spatial carrier frequency method.
Linear multistep methods, particle filtering and sequential Monte Carlo
NASA Astrophysics Data System (ADS)
Arnold, Andrea; Calvetti, Daniela; Somersalo, Erkki
2013-08-01
Numerical integration is the main bottleneck in particle filter methodologies for dynamic inverse problems to estimate model parameters, initial values, and non-observable components of an ordinary differential equation (ODE) system from partial, noisy observations, because proposals may result in stiff systems which first slow down or paralyze the time integration process, then end up being discarded. The immediate advantage of formulating the problem in a sequential manner is that the integration is carried out on shorter intervals, thus reducing the risk of long integration processes followed by rejections. We propose to solve the ODE systems within a particle filter framework with higher order numerical integrators which can handle stiffness and to base the choice of the variance of the innovation on estimates of the discretization errors. The application of linear multistep methods to particle filters gives a handle on the stability and accuracy of the propagation, and linking the innovation variance to the accuracy estimate helps keep the variance of the estimate as low as possible. The effectiveness of the methodology is demonstrated with a simple ODE system similar to those arising in biochemical applications.
Electronic filters, repeated signal charge conversion apparatus, hearing aids and methods
NASA Technical Reports Server (NTRS)
Morley, Jr., Robert E. (Inventor); Engebretson, A. Maynard (Inventor); Engel, George L. (Inventor); Sullivan, Thomas J. (Inventor)
1993-01-01
An electronic filter for filtering an electrical signal. Signal processing circuitry therein includes a logarithmic filter having a series of filter stages with inputs and outputs in cascade and respective circuits associated with the filter stages for storing electrical representations of filter parameters. The filter stages include circuits for respectively adding the electrical representations of the filter parameters to the electrical signal to be filtered thereby producing a set of filter sum signals. At least one of the filter stages includes circuitry for producing a filter signal in substantially logarithmic form at its output by combining a filter sum signal for that filter stage with a signal from an output of another filter stage. The signal processing circuitry produces an intermediate output signal, and a multiplexer connected to the signal processing circuit multiplexes the intermediate output signal with the electrical signal to be filtered so that the logarithmic filter operates as both a logarithmic prefilter and a logarithmic postfilter. Other electronic filters, signal conversion apparatus, electroacoustic systems, hearing aids and methods are also disclosed.
Adaptive characterization method for desktop color printers
NASA Astrophysics Data System (ADS)
Shen, Hui-Liang; Zheng, Zhi-Huan; Jin, Chong-Chao; Du, Xin; Shao, Si-Jie; Xin, John H.
2013-04-01
With the rapid development of multispectral imaging technique, it is desired that the spectral color can be accurately reproduced using desktop color printers. However, due to the specific spectral gamuts determined by printer inks, it is almost impossible to exactly replicate the reflectance spectra in other media. In addition, as ink densities can not be individually controlled, desktop printers can only be regarded as red-green-blue devices, making physical models unfeasible. We propose a locally adaptive method, which consists of both forward and inverse models, for desktop printer characterization. In the forward model, we establish the adaptive transform between control values and reflectance spectrum on individual cellular subsets by using weighted polynomial regression. In the inverse model, we first determine the candidate space of the control values based on global inverse regression and then compute the optimal control values by minimizing the color difference between the actual spectrum and the predicted spectrum via forward transform. Experimental results show that the proposed method can reproduce colors accurately for different media under multiple illuminants.
Radiation dose reduction with application of non-linear adaptive filters for abdominal CT
Singh, Sarabjeet; Kalra, Mannudeep K; Sung, Mi Kim; Back, Anni; Blake, Michael A
2012-01-01
AIM: To evaluate the effect of non-linear adaptive filters (NLAF) on abdominal computed tomography (CT) images acquired at different radiation dose levels. METHODS: Nineteen patients (mean age 61.6 ± 7.9 years, M:F = 8:11) gave informed consent for an Institutional Review Board approved prospective study involving acquisition of 4 additional image series (200, 150, 100, 50 mAs and 120 kVp) on a 64 slice multidetector row CT scanner over an identical 10 cm length in the abdomen. The CT images acquired at 150, 100 and 50 mAs were processed with the NLAF. Two radiologists reviewed unprocessed and processed images for image quality in a blinded randomized manner. CT dose index volume, dose length product, patient weight, transverse diameters, objective noise and CT numbers were recorded. Data were analyzed using Analysis of Variance and Wilcoxon signed rank test. RESULTS: Of the 31 lesions detected in abdominal CT images, 28 lesions were less than 1 cm in size. Subjective image noise was graded as unacceptable in unprocessed images at 50 and 100 mAs, and in NLAF processed images at 50 mAs only. In NLAF processed images, objective image noise was decreased by 21% (14.4 ± 4/18.2 ± 4.9) at 150 mAs, 28.3% (15.7 ± 5.6/21.9 ± 4) at 100 mAs and by 39.4% (18.8 ± 9/30.4 ± 9.2) at 50 mAs compared to unprocessed images acquired at respective radiation dose levels. At 100 mAs the visibility of smaller structures improved from suboptimal in unprocessed images to excellent in NLAF processed images, whereas diagnostic confidence was respectively improved from probably confident to fully confident. CONCLUSION: NLAF lowers image noise, improves the visibility of small structures and maintains lesion conspicuity at down to 100 mAs for abdominal CT. PMID:22328968
Adaptive method with intercessory feedback control for an intelligent agent
Goldsmith, Steven Y.
2004-06-22
An adaptive architecture method with feedback control for an intelligent agent provides for adaptively integrating reflexive and deliberative responses to a stimulus according to a goal. An adaptive architecture method with feedback control for multiple intelligent agents provides for coordinating and adaptively integrating reflexive and deliberative responses to a stimulus according to a goal. Re-programming of the adaptive architecture is through a nexus which coordinates reflexive and deliberator components.
Berset, Torfinn; Geng, Di; Romero, Iñaki
2012-01-01
Noise from motion artifacts is currently one of the main challenges in the field of ambulatory ECG recording. To address this problem, we propose the use of two different approaches. First, an adaptive filter with electrode-skin impedance as a reference signal is described. Secondly, a multi-channel ECG algorithm based on Independent Component Analysis is introduced. Both algorithms have been designed and further optimized for real-time work embedded in a dedicated Digital Signal Processor. We show that both algorithms improve the performance of a beat detection algorithm when applied in high noise conditions. In addition, an efficient way of choosing this methods is suggested with the aim of reduce the overall total system power consumption. PMID:23367417
Aelterman, Jan; Goossens, Bart; De Vylder, Jonas; Pižurica, Aleksandra; Philips, Wilfried
2013-01-01
Most digital cameras use an array of alternating color filters to capture the varied colors in a scene with a single sensor chip. Reconstruction of a full color image from such a color mosaic is what constitutes demosaicing. In this paper, a technique is proposed that performs this demosaicing in a way that incurs a very low computational cost. This is done through a (dual-tree complex) wavelet interpretation of the demosaicing problem. By using a novel locally adaptive approach for demosaicing (complex) wavelet coefficients, we show that many of the common demosaicing artifacts can be avoided in an efficient way. Results demonstrate that the proposed method is competitive with respect to the current state of the art, but incurs a lower computational cost. The wavelet approach also allows for computationally effective denoising or deblurring approaches. PMID:23671575
Adaptive Accommodation Control Method for Complex Assembly
NASA Astrophysics Data System (ADS)
Kang, Sungchul; Kim, Munsang; Park, Shinsuk
Robotic systems have been used to automate assembly tasks in manufacturing and in teleoperation. Conventional robotic systems, however, have been ineffective in controlling contact force in multiple contact states of complex assemblythat involves interactions between complex-shaped parts. Unlike robots, humans excel at complex assembly tasks by utilizing their intrinsic impedance, forces and torque sensation, and tactile contact clues. By examining the human behavior in assembling complex parts, this study proposes a novel geometry-independent control method for robotic assembly using adaptive accommodation (or damping) algorithm. Two important conditions for complex assembly, target approachability and bounded contact force, can be met by the proposed control scheme. It generates target approachable motion that leads the object to move closer to a desired target position, while contact force is kept under a predetermined value. Experimental results from complex assembly tests have confirmed the feasibility and applicability of the proposed method.
An adaptive Kalman filter technique for context-aware heart rate monitoring.
Xu, Min; Goldfain, Albert; Dellostritto, Jim; Iyengar, Satish
2012-01-01
Traditional physiological monitoring systems convert a person's vital sign waveforms, such as heart rate, respiration rate and blood pressure, into meaningful information by comparing the instant reading with a preset threshold or a baseline without considering the contextual information of the person. It would be beneficial to incorporate the contextual data such as activity status of the person to the physiological data in order to obtain a more accurate representation of a person's physiological status. In this paper, we proposed an algorithm based on adaptive Kalman filter that describes the heart rate response with respect to different activity levels. It is towards our final goal of intelligent detection of any abnormality in the person's vital signs. Experimental results are provided to demonstrate the feasibility of the algorithm. PMID:23367423
Adaptive filters for monitoring localized brain activity from surface potential time series
Spencer, M.E. |; Leahy, R.M.; Mosher, J.C. |; Lewis, P.S.
1992-12-01
We address the problem of processing electroencephalographic (EEG) data to monitor the time series of the components of a current dipole source vector at a given location in the head. This is the spatial filtering problem for vector sources in a lossy, three dimensional, zero delay medium. Dipolar and distributed sources at other than the desired location are canceled or attenuated with an adaptive linearly constrained minimum variance (LCMV) beamformer. Actual EEG data acquired from a human subject serves as the interference in a case where the desired source is simulated and superimposed on the actual data. It is shown that the LCMV beamformer extracts the desired dipole time series while effectively canceling the subjects interference.
Adaptive filters for monitoring localized brain activity from surface potential time series
Spencer, M.E. . Signal and Image Processing Inst. TRW, Inc., Redondo Beach, CA ); Leahy, R.M. . Signal and Image Processing Inst.); Mosher, J.C. . Signal and Image Processing Inst. Lo
1992-01-01
We address the problem of processing electroencephalographic (EEG) data to monitor the time series of the components of a current dipole source vector at a given location in the head. This is the spatial filtering problem for vector sources in a lossy, three dimensional, zero delay medium. Dipolar and distributed sources at other than the desired location are canceled or attenuated with an adaptive linearly constrained minimum variance (LCMV) beamformer. Actual EEG data acquired from a human subject serves as the interference in a case where the desired source is simulated and superimposed on the actual data. It is shown that the LCMV beamformer extracts the desired dipole time series while effectively canceling the subjects interference.
Adapting implicit methods to parallel processors
Reeves, L.; McMillin, B.; Okunbor, D.; Riggins, D.
1994-12-31
When numerically solving many types of partial differential equations, it is advantageous to use implicit methods because of their better stability and more flexible parameter choice, (e.g. larger time steps). However, since implicit methods usually require simultaneous knowledge of the entire computational domain, these methods axe difficult to implement directly on distributed memory parallel processors. This leads to infrequent use of implicit methods on parallel/distributed systems. The usual implementation of implicit methods is inefficient due to the nature of parallel systems where it is common to take the computational domain and distribute the grid points over the processors so as to maintain a relatively even workload per processor. This creates a problem at the locations in the domain where adjacent points are not on the same processor. In order for the values at these points to be calculated, messages have to be exchanged between the corresponding processors. Without special adaptation, this will result in idle processors during part of the computation, and as the number of idle processors increases, the lower the effective speed improvement by using a parallel processor.
Adaptive Kalman filtering for real-time mapping of the visual field
Ward, B. Douglas; Janik, John; Mazaheri, Yousef; Ma, Yan; DeYoe, Edgar A.
2013-01-01
This paper demonstrates the feasibility of real-time mapping of the visual field for clinical applications. Specifically, three aspects of this problem were considered: (1) experimental design, (2) statistical analysis, and (3) display of results. Proper experimental design is essential to achieving a successful outcome, particularly for real-time applications. A random-block experimental design was shown to have less sensitivity to measurement noise, as well as greater robustness to error in modeling of the hemodynamic impulse response function (IRF) and greater flexibility than common alternatives. In addition, random encoding of the visual field allows for the detection of voxels that are responsive to multiple, not necessarily contiguous, regions of the visual field. Due to its recursive nature, the Kalman filter is ideally suited for real-time statistical analysis of visual field mapping data. An important feature of the Kalman filter is that it can be used for nonstationary time series analysis. The capability of the Kalman filter to adapt, in real time, to abrupt changes in the baseline arising from subject motion inside the scanner and other external system disturbances is important for the success of clinical applications. The clinician needs real-time information to evaluate the success or failure of the imaging run and to decide whether to extend, modify, or terminate the run. Accordingly, the analytical software provides real-time displays of (1) brain activation maps for each stimulus segment, (2) voxel-wise spatial tuning profiles, (3) time plots of the variability of response parameters, and (4) time plots of activated volume. PMID:22100663
A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.
Zhao, Haiquan; Zhang, Jiashu
2009-12-01
To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module. PMID:19523787
Charisis, Vasileios S; Hadjileontiadis, Leontios J
2016-03-01
The aim of this Letter is to present a new capsule endoscopy (CE) image analysis scheme for the detection of small bowel ulcers that relate to Crohn's disease. More specifically, this scheme is based on: (i) a hybrid adaptive filtering (HAF) process, that utilises genetic algorithms to the curvelet-based representation of images for efficient extraction of the lesion-related morphological characteristics, (ii) differential lacunarity (DL) analysis for texture feature extraction from the HAF-filtered images and (iii) support vector machines for robust classification performance. For the training of the proposed scheme, namely HAF-DL, an 800-image database was used and the evaluation was based on ten 30-second long endoscopic videos. Experimental results, along with comparison with other related efforts, have shown that the HAF-DL approach evidently outperforms the latter in the field of CE image analysis for automated lesion detection, providing higher classification results. The promising performance of HAF-DL paves the way for a complete computer-aided diagnosis system that could support the physicians' clinical practice. PMID:27222730
NASA Astrophysics Data System (ADS)
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.
Local stimulus disambiguation with global motion filters predicts adaptive surround modulation.
Dellen, Babette; Torras, Carme
2013-10-01
Humans have no problem segmenting different motion stimuli despite the ambiguity of local motion signals. Adaptive surround modulation, i.e., the apparent switching between integrative and antagonistic modes, is assumed to play a crucial role in this process. However, so far motion processing models based on local integration have not been able to provide a unifying explanation for this phenomenon. This motivated us to investigate the problem of local stimulus disambiguation in an alternative and fundamentally distinct motion-processing model which uses global motion filters for velocity computation. Local information is reconstructed at the end of the processing stream through the constructive interference of global signals, i.e., inverse transformations. We show that in this model local stimulus disambiguation can be achieved by means of a novel filter embedded in this architecture. This gives rise to both integrative and antagonistic effects which are in agreement with those observed in psychophysical experiments with humans, providing a functional explanation for effects of motion repulsion. PMID:23685285
Speckle reduction in optical coherence tomography by adaptive total variation method
NASA Astrophysics Data System (ADS)
Wu, Tong; Shi, Yaoyao; Liu, Youwen; He, Chongjun
2015-12-01
An adaptive total variation method based on the combination of speckle statistics and total variation restoration is proposed and developed for reducing speckle noise in optical coherence tomography (OCT) images. The statistical distribution of the speckle noise in OCT image is investigated and measured. With the measured parameters such as the mean value and variance of the speckle noise, the OCT image is restored by the adaptive total variation restoration method. The adaptive total variation restoration algorithm was applied to the OCT images of a volunteer's hand skin, which showed effective speckle noise reduction and image quality improvement. For image quality comparison, the commonly used median filtering method was also applied to the same images to reduce the speckle noise. The measured results demonstrate the superior performance of the adaptive total variation restoration method in terms of image signal-to-noise ratio, equivalent number of looks, contrast-to-noise ratio, and mean square error.
The stochastic control of the F-8C aircraft using the Multiple Model Adaptive Control (MMAC) method
NASA Technical Reports Server (NTRS)
Athans, M.; Dunn, K. P.; Greene, E. S.; Lee, W. H.; Sandel, N. R., Jr.
1975-01-01
The purpose of this paper is to summarize results obtained for the adaptive control of the F-8C aircraft using the so-called Multiple Model Adaptive Control method. The discussion includes the selection of the performance criteria for both the lateral and the longitudinal dynamics, the design of the Kalman filters for different flight conditions, the 'identification' aspects of the design using hypothesis testing ideas, and the performance of the closed loop adaptive system.
An adaptive SPH method for strong shocks
NASA Astrophysics Data System (ADS)
Sigalotti, Leonardo Di G.; López, Hender; Trujillo, Leonardo
2009-09-01
We propose an alternative SPH scheme to usual SPH Godunov-type methods for simulating supersonic compressible flows with sharp discontinuities. The method relies on an adaptive density kernel estimation (ADKE) algorithm, which allows the width of the kernel interpolant to vary locally in space and time so that the minimum necessary smoothing is applied in regions of low density. We have performed a von Neumann stability analysis of the SPH equations for an ideal gas and derived the corresponding dispersion relation in terms of the local width of the kernel. Solution of the dispersion relation in the short wavelength limit shows that stability is achieved for a wide range of the ADKE parameters. Application of the method to high Mach number shocks confirms the predictions of the linear analysis. Examples of the resolving power of the method are given for a set of difficult problems, involving the collision of two strong shocks, the strong shock-tube test, and the interaction of two blast waves.
Method and system of filtering and recommending documents
Patton, Robert M.; Potok, Thomas E.
2016-02-09
Disclosed is a method and system for discovering documents using a computer and providing a small set of the most relevant documents to the attention of a human observer. Using the method, the computer obtains a seed document from the user and generates a seed document vector using term frequency-inverse corpus frequency weighting. A keyword index for a plurality of source documents can be compared with the weighted terms of the seed document vector. The comparison is then filtered to reduce the number of documents, which define an initial subset of the source documents. Initial subset vectors are generated and compared to the seed document vector to obtain a similarity value for each comparison. Based on the similarity value, the method then recommends one or more of the source documents.
Adaptive wavelet methods - Matrix-vector multiplication
NASA Astrophysics Data System (ADS)
Černá, Dana; Finěk, Václav
2012-12-01
The design of most adaptive wavelet methods for elliptic partial differential equations follows a general concept proposed by A. Cohen, W. Dahmen and R. DeVore in [3, 4]. The essential steps are: transformation of the variational formulation into the well-conditioned infinite-dimensional l2 problem, finding of the convergent iteration process for the l2 problem and finally derivation of its finite dimensional version which works with an inexact right hand side and approximate matrix-vector multiplications. In our contribution, we shortly review all these parts and wemainly pay attention to approximate matrix-vector multiplications. Effective approximation of matrix-vector multiplications is enabled by an off-diagonal decay of entries of the wavelet stiffness matrix. We propose here a new approach which better utilize actual decay of matrix entries.
Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus.
Fontaine, Bertrand; MacLeod, Katrina M; Lubejko, Susan T; Steinberg, Louisa J; Köppl, Christine; Peña, Jose L
2014-07-15
In the visual, auditory, and electrosensory modalities, stimuli are defined by first- and second-order attributes. The fast time-pressure signal of a sound, a first-order attribute, is important, for instance, in sound localization and pitch perception, while its slow amplitude-modulated envelope, a second-order attribute, can be used for sound recognition. Ascending the auditory pathway from ear to midbrain, neurons increasingly show a preference for the envelope and are most sensitive to particular envelope modulation frequencies, a tuning considered important for encoding sound identity. The level at which this tuning property emerges along the pathway varies across species, and the mechanism of how this occurs is a matter of debate. In this paper, we target the transition between auditory nerve fibers and the cochlear nucleus angularis (NA). While the owl's auditory nerve fibers simultaneously encode the fast and slow attributes of a sound, one synapse further, NA neurons encode the envelope more efficiently than the auditory nerve. Using in vivo and in vitro electrophysiology and computational analysis, we show that a single-cell mechanism inducing spike threshold adaptation can explain the difference in neural filtering between the two areas. We show that spike threshold adaptation can explain the increased selectivity to modulation frequency, as input level increases in NA. These results demonstrate that a spike generation nonlinearity can modulate the tuning to second-order stimulus features, without invoking network or synaptic mechanisms. PMID:24790170
Iterative version of the QRD for adaptive recursive least squares (RLS) filtering
NASA Astrophysics Data System (ADS)
Goetze, Juergen
1994-10-01
A modified version of the QR-decomposition (QRD) is presented. It uses approximate Givens rotations instead of exact Givens rotations, i.e., a matrix entry usually annihilated with an exact rotation by an angle (sigma) is only reduced by using an approximate rotation by an angle (sigma) . The approximation of the rotations is based on the idea of CORDIC. Evaluating a CORDIC-based approximate rotation is to determine the angle (sigma) equals (sigma) t equals arctan 2-t, which is closest to the exact rotation angle (sigma) . This angle (sigma) t is applied instead of (sigma) . Using approximate rotations for computing the QRD results in an iterative version of the original QRD. A recursive version of this QRD using CORDIC-based approximate rotations is applied to adaptive RLS filtering. Only a few angles of the CORDIC sequence, r say (r << b, where b is the word length), work as well as using exact rotations (r equals b, original CORDIC). The misadjustment error decreases as r increases. The convergence of the QRD-RLS algorithm, however, is insensitive to the value of r. Adapting the approximation accuracy during the course of the QRD-RLS algorithm is also discussed. Simulations (channel equalization) confirm the results.
Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus
MacLeod, Katrina M.; Lubejko, Susan T.; Steinberg, Louisa J.; Köppl, Christine; Peña, Jose L.
2014-01-01
In the visual, auditory, and electrosensory modalities, stimuli are defined by first- and second-order attributes. The fast time-pressure signal of a sound, a first-order attribute, is important, for instance, in sound localization and pitch perception, while its slow amplitude-modulated envelope, a second-order attribute, can be used for sound recognition. Ascending the auditory pathway from ear to midbrain, neurons increasingly show a preference for the envelope and are most sensitive to particular envelope modulation frequencies, a tuning considered important for encoding sound identity. The level at which this tuning property emerges along the pathway varies across species, and the mechanism of how this occurs is a matter of debate. In this paper, we target the transition between auditory nerve fibers and the cochlear nucleus angularis (NA). While the owl's auditory nerve fibers simultaneously encode the fast and slow attributes of a sound, one synapse further, NA neurons encode the envelope more efficiently than the auditory nerve. Using in vivo and in vitro electrophysiology and computational analysis, we show that a single-cell mechanism inducing spike threshold adaptation can explain the difference in neural filtering between the two areas. We show that spike threshold adaptation can explain the increased selectivity to modulation frequency, as input level increases in NA. These results demonstrate that a spike generation nonlinearity can modulate the tuning to second-order stimulus features, without invoking network or synaptic mechanisms. PMID:24790170
Adaptive Resampling Particle Filters for GPS Carrier-Phase Navigation and Collision Avoidance System
NASA Astrophysics Data System (ADS)
Hwang, Soon Sik
This dissertation addresses three problems: 1) adaptive resampling technique (ART) for Particle Filters, 2) precise relative positioning using Global Positioning System (GPS) Carrier-Phase (CP) measurements applied to nonlinear integer resolution problem for GPS CP navigation using Particle Filters, and 3) collision detection system based on GPS CP broadcasts. First, Monte Carlo filters, called Particle Filters (PF), are widely used where the system is non-linear and non-Gaussian. In real-time applications, their estimation accuracies and efficiencies are significantly affected by the number of particles and the scheduling of relocating weights and samples, the so-called resampling step. In this dissertation, the appropriate number of particles is estimated adaptively such that the error of the sample mean and variance stay in bounds. These bounds are given by the confidence interval of a normal probability distribution for a multi-variate state. Two required number of samples maintaining the mean and variance error within the bounds are derived. The time of resampling is determined when the required sample number for the variance error crosses the required sample number for the mean error. Second, the PF using GPS CP measurements with adaptive resampling is applied to precise relative navigation between two GPS antennas. In order to make use of CP measurements for navigation, the unknown number of cycles between GPS antennas, the so called integer ambiguity, should be resolved. The PF is applied to this integer ambiguity resolution problem where the relative navigation states estimation involves nonlinear observations and nonlinear dynamics equation. Using the PF, the probability density function of the states is estimated by sampling from the position and velocity space and the integer ambiguities are resolved without using the usual hypothesis tests to search for the integer ambiguity. The ART manages the number of position samples and the frequency of the
Online Adaptive Replanning Method for Prostate Radiotherapy
Ahunbay, Ergun E.; Peng Cheng; Holmes, Shannon; Godley, Andrew; Lawton, Colleen; Li, X. Allen
2010-08-01
Purpose: To report the application of an adaptive replanning technique for prostate cancer radiotherapy (RT), consisting of two steps: (1) segment aperture morphing (SAM), and (2) segment weight optimization (SWO), to account for interfraction variations. Methods and Materials: The new 'SAM+SWO' scheme was retroactively applied to the daily CT images acquired for 10 prostate cancer patients on a linear accelerator and CT-on-Rails combination during the course of RT. Doses generated by the SAM+SWO scheme based on the daily CT images were compared with doses generated after patient repositioning using the current planning target volume (PTV) margin (5 mm, 3 mm toward rectum) and a reduced margin (2 mm), along with full reoptimization scans based on the daily CT images to evaluate dosimetry benefits. Results: For all cases studied, the online replanning method provided significantly better target coverage when compared with repositioning with reduced PTV (13% increase in minimum prostate dose) and improved organ sparing when compared with repositioning with regular PTV (13% decrease in the generalized equivalent uniform dose of rectum). The time required to complete the online replanning process was 6 {+-} 2 minutes. Conclusion: The proposed online replanning method can be used to account for interfraction variations for prostate RT with a practically acceptable time frame (5-10 min) and with significant dosimetric benefits. On the basis of this study, the developed online replanning scheme is being implemented in the clinic for prostate RT.
Electronic filters, signal conversion apparatus, hearing aids and methods
NASA Technical Reports Server (NTRS)
Morley, Jr., Robert E. (Inventor); Engebretson, A. Maynard (Inventor); Engel, George L. (Inventor); Sullivan, Thomas J. (Inventor)
1992-01-01
An electronic filter for filtering an electrical signal. Signal processing circuitry therein includes a logarithmic filter having a series of filter stages with inputs and outputs in cascade and respective circuits as GOVERNMENT SUPPORT This invention was made with U.S. Government support under Veterans Administration Contract VA KV 674P857 and National Aeronautics and Space Administration (NASA) Research Grant No. NAG10-0040. The U.S. Government has certain rights in this invention.
Electrically heated particulate filter propagation support methods and systems
Gonze, Eugene V [Pinckney, MI; Ament, Frank [Troy, MI
2011-06-07
A control system that controls regeneration of a particulate filter is provided. The system generally includes a regeneration module that controls current to the particulate filter to initiate combustion of particulate matter in the particulate filter. A propagation module estimates a propagation status of the combustion of the particulate matter based on a combustion temperature. A temperature adjustment module controls the combustion temperature by selectively increasing a temperature of exhaust that passes through the particulate filter.
Electrically heated particulate filter preparation methods and systems
Gonze, Eugene V
2012-01-31
A control system that controls regeneration of a particulate filter is provided. The system generally includes a fuel control module that controls injection of fuel into exhaust that passes through the particulate filter. A regeneration module controls current to the particulate filter to initiate regeneration after the fuel has been injected into the exhaust.
Adaptive Control of Non-Minimum Phase Modal Systems Using Residual Mode Filters2. Parts 1 and 2
NASA Technical Reports Server (NTRS)
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.
Method for enhanced longevity of in situ microbial filter used for bioremediation
Carman, M. Leslie; Taylor, Robert T.
1999-01-01
An improved method for in situ microbial filter bioremediation having increasingly operational longevity of an in situ microbial filter emplaced into an aquifer. A method for generating a microbial filter of sufficient catalytic density and thickness, which has increased replenishment interval, improved bacteria attachment and detachment characteristics and the endogenous stability under in situ conditions. A system for in situ field water remediation.
Adaptive clutter filter in 2-D color flow imaging based on in vivo I/Q signal.
Zhou, Xiaoming; Zhang, Congyao; Liu, Dong C
2014-01-01
Color flow imaging has been well applied in clinical diagnosis. For the high quality color flow images, clutter filter is important to separate the Doppler signals from blood and tissue. Traditional clutter filters, such as finite impulse response, infinite impulse response and regression filters, were applied, which are based on the hypothesis that the clutter signal is stationary or tissue moves slowly. However, in realistic clinic color flow imaging, the signals are non-stationary signals because of accelerated moving tissue. For most related papers, simulated RF signals are widely used without in vivo I/Q signal. Hence, in this paper, adaptive polynomial regression filter, which is down mixing with instantaneous clutter frequency, was proposed based on in vivo carotid I/Q signal in realistic color flow imaging. To get the best performance, the optimal polynomial order of polynomial regression filter and the optimal polynomial order for estimation of instantaneous clutter frequency respectively were confirmed. Finally, compared with the mean blood velocity and quality of 2-D color flow image, the experiment results show that adaptive polynomial regression filter, which is down mixing with instantaneous clutter frequency, can significantly enhance the mean blood velocity and get high quality 2-D color flow image. PMID:24211911
A geometric method for optimal design of color filter arrays.
Hao, Pengwei; Li, Yan; Lin, Zhouchen; Dubois, Eric
2011-03-01
A color filter array (CFA) used in a digital camera is a mosaic of spectrally selective filters, which allows only one color component to be sensed at each pixel. The missing two components of each pixel have to be estimated by methods known as demosaicking. The demosaicking algorithm and the CFA design are crucial for the quality of the output images. In this paper, we present a CFA design methodology in the frequency domain. The frequency structure, which is shown to be just the symbolic DFT of the CFA pattern (one period of the CFA), is introduced to represent images sampled with any rectangular CFAs in the frequency domain. Based on the frequency structure, the CFA design involves the solution of a constrained optimization problem that aims at minimizing the demosaicking error. To decrease the number of parameters and speed up the parameter searching, the optimization problem is reformulated as the selection of geometric points on the boundary of a convex polygon or the surface of a convex polyhedron. Using our methodology, several new CFA patterns are found, which outperform the currently commercialized and published ones. Experiments demonstrate the effectiveness of our CFA design methodology and the superiority of our new CFA patterns. PMID:20858581
NASA Astrophysics Data System (ADS)
Wang, Xin; Wu, Linhui; Yi, Xi; Zhang, Limin; Gao, Feng; Zhao, Huijuan
2014-03-01
According to the morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic-rate images of fluorophore can provide diagnostic information for tumor differentiation, and especially have the potential for staging of tumors. In this paper, fluorescence diffuse optical tomography method is firstly used to acquire metabolism-related time-course images of the fluorophore concentration. Based on a two-compartment model comprised of plasma and extracelluar-extravascular space, we next propose an adaptive-EKF framework to estimate the pharmacokinetic-rate images. With the aid of a forgetting factor, the adaptive-EKF compensate the inaccuracy initial values and emphasize the effect of the current data in order to realize a better online estimation compared with the conventional EKF. We use simulate data to evaluate the performance of the proposed methodology. The results suggest that the adaptive-EKF can obtain preferable pharmacokinetic-rate images than the conventional EKF with higher quantitativeness and noise robustness.
Longmire, M S; Milton, A F; Takken, E H
1982-11-01
Several 1-D signal processing techniques have been evaluated by simulation with a digital computer using high-spatial-resolution (0.15 mrad) noise data gathered from back-lit clouds and uniform sky with a scanning data collection system operating in the 4.0-4.8-microm spectral band. Two ordinary bandpass filters and a least-mean-square (LMS) spatial filter were evaluated in combination with a fixed or adaptive threshold algorithm. The combination of a 1-D LMS filter and a 1-D adaptive threshold sensor was shown to reject extreme cloud clutter effectively and to provide nearly equal signal detection in a clear and cluttered sky, at least in systems whose NEI (noise equivalent irradiance) exceeds 1.5 x 10(-13) W/cm(2) and whose spatial resolution is better than 0.15 x 0.36 mrad. A summary gives highlights of the work, key numerical results, and conclusions. PMID:20396326
NASA Technical Reports Server (NTRS)
Toldalagi, P. M.
1980-01-01
A review is made of recursive statistical regression techniques incorporating past or past and future observations through smoothing and Kalman filtering, respectively; with results for the cases of the Tiros-N/MSU and Nimbus-6/Scams remote sensing satellite experiments. In response to the lack of a satisfactory model for the medium sounded, which is presently a major limitation on retrieval technique performance, a novel, global approach is proposed which casts the retrieval problem into the framework of adaptive filtering. A numerical implementation of such an adaptive system is presented, with a multilayer, semi-spectral general circulation model for the atmosphere being used to fine-tune the sensor as well as the dynamical equations of a Kalman filter. It is shown that the assimilation of radiometric data becomes a straightforward subproblem.
Tsanas, Athanasios; Zañartu, Matías; Little, Max A.; Fox, Cynthia; Ramig, Lorraine O.; Clifford, Gari D.
2014-01-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F0) of speech signals. This study examines ten F0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F0 estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F0 estimation is required. PMID:24815269
Tsanas, Athanasios; Zañartu, Matías; Little, Max A; Fox, Cynthia; Ramig, Lorraine O; Clifford, Gari D
2014-05-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F(0)) of speech signals. This study examines ten F(0) estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F(0) in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F(0) estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F(0) estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F(0) estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F(0) estimation is required. PMID:24815269
Emergency sacrificial sealing method in filters, equipment, or systems
Brown, Erik P
2014-09-30
A system seals a filter or equipment component to a base and will continue to seal the filter or equipment component to the base in the event of hot air or fire. The system includes a first sealing material between the filter or equipment component and the base; and a second sealing material between the filter or equipment component and the base and proximate the first sealing material. The first sealing material and the second seal material are positioned relative to each other and relative to the filter or equipment component and the base to seal the filter or equipment component to the base and upon the event of fire the second sealing material will be activated and expand to continue to seal the filter or equipment component to the base in the event of hot air or fire.
Particle Filtering Equalization Method for a Satellite Communication Channel
NASA Astrophysics Data System (ADS)
Sénécal, Stéphane; Amblard, Pierre-Olivier; Cavazzana, Laurent
2004-12-01
We propose the use of particle filtering techniques and Monte Carlo methods to tackle the in-line and blind equalization of a satellite communication channel. The main difficulties encountered are the nonlinear distortions caused by the amplifier stage in the satellite. Several processing methods manage to take into account these nonlinearities but they require the knowledge of a training input sequence for updating the equalizer parameters. Blind equalization methods also exist but they require a Volterra modelization of the system which is not suited for equalization purpose for the present model. The aim of the method proposed in the paper is also to blindly restore the emitted message. To reach this goal, a Bayesian point of view is adopted. Prior knowledge of the emitted symbols and of the nonlinear amplification model, as well as the information available from the received signal, is jointly used by considering the posterior distribution of the input sequence. Such a probability distribution is very difficult to study and thus motivates the implementation of Monte Carlo simulation methods. The presentation of the equalization method is cut into two parts. The first part solves the problem for a simplified model, focusing on the nonlinearities of the model. The second part deals with the complete model, using sampling approaches previously developed. The algorithms are illustrated and their performance is evaluated using bit error rate versus signal-to-noise ratio curves.
Gearbox coupling modulation separation method based on match pursuit and correlation filtering
NASA Astrophysics Data System (ADS)
He, Guolin; Ding, Kang; Lin, Huibin
2016-01-01
The vibration signal of faulty gearbox commonly involves complex coupling modulation components. The method of sparse representation has been successfully used for gearbox fault diagnosis, but most of the literatures only focus on the extraction of impact modulation and always neglect the steady modulation representing the distributed faults. This paper presents a new method for separating coupling modulation from vibration signal of gearbox based on match pursuit and correlation filtering. To separate the steady modulation caused by distributed fault and the impact modulation caused by impact fault, two sub-dictionaries are specially designed according to the gearbox operating and structural parameters and the characteristics of vibration signal. The new dictionaries have clear physical meaning and good adaptability. In addition, an amplitude recovery step is conducted to improve the matching accuracy in the match pursuit. Simulation and experimental results show that the proposed method can separate the coupling components of gearbox vibration signal effectively under intensive background noise.
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
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
Improved characterization of slow-moving landslides by means of adaptive NL-InSAR filtering
NASA Astrophysics Data System (ADS)
Albiol, David; Iglesias, Rubén.; Sánchez, Francisco; Duro, Javier
2014-10-01
Advanced remote sensing techniques based on space-borne Synthetic Aperture Radar (SAR) have been developed during the last decade showing their applicability for the monitoring of surface displacements in landslide areas. This paper presents an advanced Persistent Scatterer Interferometry (PSI) processing based on the Stable Point Network (SPN) technique, developed by the company Altamira-Information, for the monitoring of an active slowmoving landslide in the mountainous environment of El Portalet, Central Spanish Pyrenees. For this purpose, two TerraSAR-X data sets acquired in ascending mode corresponding to the period from April to November 2011, and from August to November 2013, respectively, are employed. The objective of this work is twofold. On the one hand, the benefits of employing Nonlocal Interferomtric SAR (NL-InSAR) adaptive filtering techniques over vegetated scenarios to maximize the chances of detecting natural distributed scatterers, such as bare or rocky areas, and deterministic point-like scatterers, such as man-made structures or poles, is put forward. In this context, the final PSI displacement maps retrieved with the proposed filtering technique are compared in terms of pixels' density and quality with classical PSI, showing a significant improvement. On the other hand, since SAR systems are only sensitive to detect displacements in the line-of-sight (LOS) direction, the importance of projecting the PSI displacement results retrieved along the steepest gradient of the terrain slope is discussed. The improvements presented in this paper are particularly interesting in these type of applications since they clearly allow to better determine the extension and dynamics of complex landslide phenomena.
NASA Technical Reports Server (NTRS)
Lai, Jonathan Y.
1994-01-01
This dissertation focuses on the signal processing problems associated with the detection of hazardous windshears using airborne Doppler radar when weak weather returns are in the presence of strong clutter returns. In light of the frequent inadequacy of spectral-processing oriented clutter suppression methods, we model a clutter signal as multiple sinusoids plus Gaussian noise, and propose adaptive filtering approaches that better capture the temporal characteristics of the signal process. This idea leads to two research topics in signal processing: (1) signal modeling and parameter estimation, and (2) adaptive filtering in this particular signal environment. A high-resolution, low SNR threshold maximum likelihood (ML) frequency estimation and signal modeling algorithm is devised and proves capable of delineating both the spectral and temporal nature of the clutter return. Furthermore, the Least Mean Square (LMS) -based adaptive filter's performance for the proposed signal model is investigated, and promising simulation results have testified to its potential for clutter rejection leading to more accurate estimation of windspeed thus obtaining a better assessment of the windshear hazard.
NASA Astrophysics Data System (ADS)
Schneider, Martin; Kellermann, Walter
2016-01-01
Acoustic echo cancellation (AEC) is a well-known application of adaptive filters in communication acoustics. To implement AEC for multichannel reproduction systems, powerful adaptation algorithms like the generalized frequency-domain adaptive filtering (GFDAF) algorithm are required for satisfactory convergence behavior. In this paper, the GFDAF algorithm is rigorously derived as an approximation of the block recursive least-squares (RLS) algorithm. Thereby, the original formulation of the GFDAF algorithm is generalized while avoiding an error that has been in the original derivation. The presented algorithm formulation is applied to pruned transform-domain loudspeaker-enclosure-microphone models in a mathematically consistent manner. Such pruned models have recently been proposed to cope with the tremendous computational demands of massive multichannel AEC. Beyond its generalization, a regularization of the GFDAF is shown to have a close relation to the well-known block least-mean-squares algorithm.
An Adaptive De-Aliasing Strategy for Discontinuous Galerkin methods
NASA Astrophysics Data System (ADS)
Beck, Andrea; Flad, David; Frank, Hannes; Munz, Claus-Dieter
2015-11-01
Discontinuous Galerkin methods combine the accuracy of a local polynomial representation with the geometrical flexibility of an element-based discretization. In combination with their excellent parallel scalability, these methods are currently of great interest for DNS and LES. For high order schemes, the dissipation error approaches a cut-off behavior, which allows an efficient wave resolution per degree of freedom, but also reduces robustness against numerical errors. One important source of numerical error is the inconsistent discretization of the non-linear convective terms, which results in aliasing of kinetic energy and solver instability. Consistent evaluation of the inner products prevents this form of error, but is computationally very expensive. In this talk, we discuss the need for a consistent de-aliasing to achieve a neutrally stable scheme, and present a novel strategy for recovering a part of the incurred computational costs. By implementing the de-aliasing operation through a cell-local projection filter, we can perform adaptive de-aliasing in space and time, based on physically motivated indicators. We will present results for a homogeneous isotropic turbulence and the Taylor-Green vortex flow, and discuss implementation details, accuracy and efficiency.
Method of producing monolithic ceramic cross-flow filter
Larsen, David A.; Bacchi, David P.; Connors, Timothy F.; Collins, III, Edwin L.
1998-01-01
Ceramic filter of various configuration have been used to filter particulates from hot gases exhausted from coal-fired systems. Prior ceramic cross-flow filters have been favored over other types, but those previously horn have been assemblies of parts somehow fastened together and consequently subject often to distortion or delamination on exposure hot gas in normal use. The present new monolithic, seamless, cross-flow ceramic filters, being of one-piece construction, are not prone to such failure. Further, these new products are made by novel casting process which involves the key steps of demolding the ceramic filter green body so that none of the fragile inner walls of the filter is cracked or broken.
Method of producing monolithic ceramic cross-flow filter
Larsen, D.A.; Bacchi, D.P.; Connors, T.F.; Collins, E.L. III
1998-02-10
Ceramic filter of various configuration have been used to filter particulates from hot gases exhausted from coal-fired systems. Prior ceramic cross-flow filters have been favored over other types, but those previously have been assemblies of parts somehow fastened together and consequently subject often to distortion or delamination on exposure hot gas in normal use. The present new monolithic, seamless, cross-flow ceramic filters, being of one-piece construction, are not prone to such failure. Further, these new products are made by a novel casting process which involves the key steps of demolding the ceramic filter green body so that none of the fragile inner walls of the filter is cracked or broken. 2 figs.
NASA Astrophysics Data System (ADS)
Wu, Shang-Teh; Lian, Sing-Han; Chen, Sheng-Han
2015-07-01
For a low-stiffness beam driven by a ball-screw stage, the lateral vibrations cannot be adequately controlled by a collocated compensator based on rotary-encoder feedback alone. Acceleration signals at the tip of the flexible beam are measured for active vibration control in addition to the collocated compensator. A second-order bandpass filter (a line enhancer) and two notch filters are included in the acceleration-feedback loop to raise modal dampings for the first and the second flexible modes without exciting higher-frequency resonances. A novel adaptation algorithm is devised to tune the center frequencies of the notch filters in real time. It consists of a second-order low-pass filter, a second-order bandpass filter and a phase detector. Improvement of the control system is elaborated progressively with the root-locus and bode-plot analyses, along with a physical interpretation. Extensive testings are conducted on an experimental device to verify the effectiveness of the control method.
Method of recovering hazardous waste from phenolic resin filters
Meikrantz, David H.; Bourne, Gary L.; McFee, John N.; Burdge, Bradley G.; McConnell, Jr., John W.
1991-01-01
The invention is a process for the recovery of hazardous wastes such as heavy metals and radioactive elements from phenolic resin filter by a circulating a solution of 8 to 16 molar nitric acid at a temperature of 110 to 190 degrees F. through the filter. The hot solution dissolves the filter material and releases the hazardous material so that it can be recovered or treated for long term storage in an environmentally safe manner.
A tunable electrochromic fabry-perot filter for adaptive optics applications.
Blaich, Jonathan David; Kammler, Daniel R.; Ambrosini, Andrea; Sweatt, William C.; Verley, Jason C.; Heller, Edwin J.; Yelton, William Graham
2006-10-01
The potential for electrochromic (EC) materials to be incorporated into a Fabry-Perot (FP) filter to allow modest amounts of tuning was evaluated by both experimental methods and modeling. A combination of chemical vapor deposition (CVD), physical vapor deposition (PVD), and electrochemical methods was used to produce an ECFP film stack consisting of an EC WO{sub 3}/Ta{sub 2}O{sub 5}/NiO{sub x}H{sub y} film stack (with indium-tin-oxide electrodes) sandwiched between two Si{sub 3}N{sub 4}/SiO{sub 2} dielectric reflector stacks. A process to produce a NiO{sub x}H{sub y} charge storage layer that freed the EC stack from dependence on atmospheric humidity and allowed construction of this complex EC-FP stack was developed. The refractive index (n) and extinction coefficient (k) for each layer in the EC-FP film stack was measured between 300 and 1700 nm. A prototype EC-FP filter was produced that had a transmission at 500 nm of 36%, and a FWHM of 10 nm. A general modeling approach that takes into account the desired pass band location, pass band width, required transmission and EC optical constants in order to estimate the maximum tuning from an EC-FP filter was developed. Modeling shows that minor thickness changes in the prototype stack developed in this project should yield a filter with a transmission at 600 nm of 33% and a FWHM of 9.6 nm, which could be tuned to 598 nm with a FWHM of 12.1 nm and a transmission of 16%. Additional modeling shows that if the EC WO{sub 3} absorption centers were optimized, then a shift from 600 nm to 598 nm could be made with a FWHM of 11.3 nm and a transmission of 20%. If (at 600 nm) the FWHM is decreased to 1 nm and transmission maintained at a reasonable level (e.g. 30%), only fractions of a nm of tuning would be possible with the film stack considered in this study. These tradeoffs may improve at other wavelengths or with EC materials different than those considered here. Finally, based on our limited investigation and material set
Adaptive numerical methods for partial differential equations
Cololla, P.
1995-07-01
This review describes a structured approach to adaptivity. The Automated Mesh Refinement (ARM) algorithms developed by M Berger are described, touching on hyperbolic and parabolic applications. Adaptivity is achieved by overlaying finer grids only in areas flagged by a generalized error criterion. The author discusses some of the issues involved in abutting disparate-resolution grids, and demonstrates that suitable algorithms exist for dissipative as well as hyperbolic systems.
NASA Astrophysics Data System (ADS)
Pipa, Daniel; Morikawa, Sérgio; Pires, Gustavo; Camerini, Claudio; Santos, JoãoMárcio
2010-12-01
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.
Signal quality improvement of holographic data storage using adaptive two-dimensional filter
NASA Astrophysics Data System (ADS)
Takahata, Yosuke; Kondo, Yo; Yoshida, Shuhei; Yamamoto, Manabu
2010-05-01
Holographic data storage is being widely studied for the purpose of developing next-generation large optical memories. A prospective use of this type of memory is in building image archives in large-scale data centers. In particular, demand for energy conservation at data centers, and therefore for holographic data storage, is growing. In holographic data storage, interference between bits occurs owing to wave aberration in the optical system, shrinkage of the medium, and crosstalk noise from neighboring holograms during multiplex recording; as a result of the interference, the reproduced image deteriorates and the bit error rate (BER) increases. In this study, to reduce the BER in both off-axis-type recording and coaxial-type recording, a two-dimensional finite impulse response (FIR) filter is applied to a reproduced image that has been recorded by angle multiplex recording and shift multiplex recording. First, for the optimization of the FIR filter coefficients, the linear minimum mean square error (LMMSE) method is applied; this method optimizes the coefficients by reducing the BER. Furthermore, for evaluating the optimization performance of the LMMSE method, the optimization performance is compared with that of the real-coded genetic algorithm (RCGA), which has the capability to search a wide range of coefficients. The optimization by the LMMSE method has been found to be excellent for off-axis-type recording but not for coaxial-type recording. It is speculated that this is because of the brightness irregularity in the reproduced image, resulting from crosstalk. On the other hand, a marked reduction in the BER is observed using the RCGA, despite the brightness irregularity. In this study, the effectiveness of the LMMSE method for signals recorded by coaxial-type recording, in which large brightness irregularity is expected, is examined using automatic gain control (AGC). It is found that the application of AGC reduces the BER even in the case of coaxial
Correia, Carlos M; Teixeira, Joel
2014-12-01
Computationally efficient wave-front reconstruction techniques for astronomical adaptive-optics (AO) systems have seen great development in the past decade. Algorithms developed in the spatial-frequency (Fourier) domain have gathered much attention, especially for high-contrast imaging systems. In this paper we present the Wiener filter (resulting in the maximization of the Strehl ratio) and further develop formulae for the anti-aliasing (AA) Wiener filter that optimally takes into account high-order wave-front terms folded in-band during the sensing (i.e., discrete sampling) process. We employ a continuous spatial-frequency representation for the forward measurement operators and derive the Wiener filter when aliasing is explicitly taken into account. We further investigate and compare to classical estimates using least-squares filters the reconstructed wave-front, measurement noise, and aliasing propagation coefficients as a function of the system order. Regarding high-contrast systems, we provide achievable performance results as a function of an ensemble of forward models for the Shack-Hartmann wave-front sensor (using sparse and nonsparse representations) and compute point-spread-function raw intensities. We find that for a 32×32 single-conjugated AOs system the aliasing propagation coefficient is roughly 60% of the least-squares filters, whereas the noise propagation is around 80%. Contrast improvements of factors of up to 2 are achievable across the field in the H band. For current and next-generation high-contrast imagers, despite better aliasing mitigation, AA Wiener filtering cannot be used as a standalone method and must therefore be used in combination with optical spatial filters deployed before image formation actually takes place. PMID:25606767
An improved peer group method to filter impulsive noise for color images
NASA Astrophysics Data System (ADS)
Zhu, Yuhong; Jiang, Huageng; Feng, Jinting
2015-07-01
This paper presents an improved filter method based on the lately proposed method of IFPGF[1] which is peer group-based. The IFPGF method improves the trade-off between computational efficiency and filtering quality of previous peer group-based methods and gains a good filtering quality at relatively low density of noisy pixels. But when the noisy density goes high(≥20%), the IFPGF method cannot work well. So in this paper, we propose an improved method to fix the drawbacks on filtering the salt-and-pepper impulsive noise. Experimental results suggest that the proposed method is able to outperform the classical vector filters and the recent proposed p eer group-based filters, including IFPGF.
A simple tuning method for high temperature superconducting microstrip filters
NASA Astrophysics Data System (ADS)
Pal, Srikanta; Stevens, Chris; Edwards, David
2005-07-01
In this paper we report a simple tuning methodology of high temperature superconducting (HTS) microstrip filters. In order to establish this new tuning approach a 5 pole lumped element HTS bandpass filter, centre frequency 800 MHz, narrowband (bandwidth, 7.6 MHz), is designed and fabricated on a 2 inch (diameter) Y Ba2Cu3O6.94 thin film of thickness 700 nm and lanthanum aluminate (LaAlO3) substrate of 0.5 mm thickness. The filter was etched using conventional photolithography and a wet chemical etching process. The 5 pole lumped element filter is tested in an integrated RF-cryocooler measurement system at 65 K and also in liquid nitrogen (77 K). We demonstrate a 19.3 MHz downward shift of the centre frequency of the 5 pole filter response. This is achieved by overlaying two layers of thin dielectric tape (PTFE) (50.0 µm thickness, permittivity 3.2) over the conducting patterned part of the filter surface. A full wave electromagnetic analysis of the dielectric environment of this tuning arrangement matches the measured response closely. To improve the passband response, the filter is tuned in liquid nitrogen, placing pieces of sapphire rods over the resonators of the filter layout.
3D Wavelet-Based Filter and Method
Moss, William C.; Haase, Sebastian; Sedat, John W.
2008-08-12
A 3D wavelet-based filter for visualizing and locating structural features of a user-specified linear size in 2D or 3D image data. The only input parameter is a characteristic linear size of the feature of interest, and the filter output contains only those regions that are correlated with the characteristic size, thus denoising the image.
Method of concurrently filtering particles and collecting gases
Mitchell, Mark A; Meike, Annemarie; Anderson, Brian L
2015-04-28
A system for concurrently filtering particles and collecting gases. Materials are be added (e.g., via coating the ceramic substrate, use of loose powder(s), or other means) to a HEPA filter (ceramic, metal, or otherwise) to collect gases (e.g., radioactive gases such as iodine). The gases could be radioactive, hazardous, or valuable gases.
NASA Astrophysics Data System (ADS)
Wells, Gregg B.; Ricci, Anthony J.
2011-11-01
In the auditory system, mechanotransduction occurs in the hair cell sensory hair bundle and is the first major step in the translation of mechanical energy into electrical. Tonotopic variations in the activation kinetics of this process are posited to provide a low pass filter to the input. An adaptation process, also associated with mechanotransduction, is postulated to provide a high pass filter to the input in a tonotopic manner. Together a bandpass filter is created at the hair cell input. Corresponding mechanical components to both activation and adaptation are also suggested to be involved in generating cochlear amplification. A paradox to this story is that hair cells where the mechanotransduction properties are most robust possess an intrinsic electrical resonance mechanism proposed to account for all required tuning and amplification. A simple Hodgkin-Huxley type model is presented to attempt to determine the role of the activation and adaptation kinetics in further tuning hair cells that exhibit electrical resonance. Results further support that steady state mechanotransduction properties are critical for setting the resting potential of the hair cell while the kinetics of activation and adaptation are important for sharpening tuning around the characteristic frequency of the hair cell.
Method and apparatus for filtering a stream of gas while drying waste lignocellulosic material
Potter, G.R.
1983-09-27
A method is disclosed for using comminuted lignocellulosic waste material such as flake-like wood and bark chips which have been stored outside includes placing the material in a filter bed by movement in a first direction, and thereafter passing polluted effluent gas from a boiler furnace, for example through said filter bed in a direction generally perpendicular to said first direction to filter pollutants from said effluent gas and simultaneously dry the comminuted lignocellulosic waste material. During drying of the material a portion of the material on the gas outlet side of the filter bed, toward which the effluent gas proceeds, is maintained at a moisture content which is greater than the average moisture content of the lignocellulosic material at the time it is placed into the filter bed. The lignocellulosic waste material is thereafter used as furnace fuel or as a furnace for an industrial process. A filter/dryer apparatus includes a vertically oriented filter bed for holding a quantity of filter material, with downwardly sloping, upwardly convex, horizontally extending louvers on a gas inlet side to retain the filter material. On a gas outlet side, a grid of vertical triangular bars defines a slotted surface to retain the filter material. The grid is reciprocatingly movable up and down and a horizontally movable shuttle is located beneath the filter bed. Means coordinate the movement of the grid and shuttle to move filter material downward through the filter bed of the apparatus.
Optimal filtering methods to structural damage estimation under ground excitation.
Hsieh, Chien-Shu; Liaw, Der-Cherng; Lin, Tzu-Hsuan
2013-01-01
This paper considers the problem of shear building damage estimation subject to earthquake ground excitation using the Kalman filtering approach. The structural damage is assumed to take the form of reduced elemental stiffness. Two damage estimation algorithms are proposed: one is the multiple model approach via the optimal two-stage Kalman estimator (OTSKE), and the other is the robust two-stage Kalman filter (RTSKF), an unbiased minimum-variance filtering approach to determine the locations and extents of the damage stiffness. A numerical example of a six-storey shear plane frame structure subject to base excitation is used to illustrate the usefulness of the proposed results. PMID:24453869
Principles and Methods of Adapted Physical Education.
ERIC Educational Resources Information Center
Arnheim, Daniel D.; And Others
Programs in adapted physical education are presented preceded by a background of services for the handicapped, by the psychosocial implications of disability, and by the growth and development of the handicapped. Elements of conducting programs discussed are organization and administration, class organization, facilities, exercise programs…
QUEST - A Bayesian adaptive psychometric method
NASA Technical Reports Server (NTRS)
Watson, A. B.; Pelli, D. G.
1983-01-01
An adaptive psychometric procedure that places each trial at the current most probable Bayesian estimate of threshold is described. The procedure takes advantage of the common finding that the human psychometric function is invariant in form when expressed as a function of log intensity. The procedure is simple, fast, and efficient, and may be easily implemented on any computer.
Adaptive method of realizing natural gradient learning for multilayer perceptrons.
Amari, S; Park, H; Fukumizu, K
2000-06-01
The natural gradient learning method is known to have ideal performances for on-line training of multilayer perceptrons. It avoids plateaus, which give rise to slow convergence of the backpropagation method. It is Fisher efficient, whereas the conventional method is not. However, for implementing the method, it is necessary to calculate the Fisher information matrix and its inverse, which is practically very difficult. This article proposes an adaptive method of directly obtaining the inverse of the Fisher information matrix. It generalizes the adaptive Gauss-Newton algorithms and provides a solid theoretical justification of them. Simulations show that the proposed adaptive method works very well for realizing natural gradient learning. PMID:10935719
Dolfus, Claire; Piton, Nicolas; Toure, Emmanuel
2015-01-01
Circulating tumor cells (CTCs) arise from primary or secondary tumors and enter the bloodstream by active or passive intravasation. Given the low number of CTCs, enrichment is necessary for detection. Filtration methods are based on selection of CTCs by size using a filter with 6.5 to 8 µm pores. After coloration, collected CTCs are evaluated according to morphological criteria. Immunophenotyping and fluorescence in situ hybridization techniques may be used. Selected CTCs can also be cultivated in vitro to provide more material. Analysis of genomic mutations is difficult because it requires adapted techniques due to limited DNA materials. Filtration-selected CTCs have shown prognostic value in many studies but multicentric validating trials are mandatory to strengthen this assessment. Other clinical applications are promising such as follow-up, therapy response prediction and diagnosis. Microfluidic emerging systems could optimize filtration-selected CTCs by increasing selection accuracy. PMID:26543334
Sequential bearings-only-tracking initiation with particle filtering method.
Liu, Bin; Hao, Chengpeng
2013-01-01
The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement (a.k.a the data association problem). In addition, the nonlinear measurements lead to a non-Gaussian posterior probability density function (pdf) in the optimal Bayesian sequential estimation framework. The consequence of this nonlinear/non-Gaussian context is the absence of a closed-form solution. This paper models the linkage uncertainty and the nonlinear/non-Gaussian estimation problem jointly with solid Bayesian formalism. A particle filtering (PF) algorithm is derived for estimating the model's parameters in a sequential manner. Numerical results show that the proposed solution provides a significant benefit over the most commonly used methods, IPDA and IMMPDA. The posterior Cramér-Rao bounds are also involved for performance evaluation. PMID:24453865
Sequential Bearings-Only-Tracking Initiation with Particle Filtering Method
Hao, Chengpeng
2013-01-01
The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement (a.k.a the data association problem). In addition, the nonlinear measurements lead to a non-Gaussian posterior probability density function (pdf) in the optimal Bayesian sequential estimation framework. The consequence of this nonlinear/non-Gaussian context is the absence of a closed-form solution. This paper models the linkage uncertainty and the nonlinear/non-Gaussian estimation problem jointly with solid Bayesian formalism. A particle filtering (PF) algorithm is derived for estimating the model's parameters in a sequential manner. Numerical results show that the proposed solution provides a significant benefit over the most commonly used methods, IPDA and IMMPDA. The posterior Cramér-Rao bounds are also involved for performance evaluation. PMID:24453865
Solution-adaptive finite element method in computational fracture mechanics
NASA Technical Reports Server (NTRS)
Min, J. B.; Bass, J. M.; Spradley, L. W.
1993-01-01
Some recent results obtained using solution-adaptive finite element method in linear elastic two-dimensional fracture mechanics problems are presented. The focus is on the basic issue of adaptive finite element method for validating the applications of new methodology to fracture mechanics problems by computing demonstration problems and comparing the stress intensity factors to analytical results.
Standardized methods for in-place filter testing
Dykes, M.; Fretthold, J.K.; Slawski, J.
1997-08-01
The conference minutes of a US DOE meeting held on in-place filter testing are presented. The purpose of the conference was to transfer technical in-place testing knowledge throughout the DOE complex. Major items discussed included purchase requisitions, in-place testing, instrumentation, and in-place test personnel qualifications and training. Future actions identified by conference attendees centered on establishing complex-wide DOE policies on training, inspection and testing, and filter specifications.
Nikolic, Nina; Böcker, Reinhard; Kostic-Kravljanac, Ljiljana; Nikolic, Miroslav
2014-01-01
Questions Effects of soil on vegetation patterns are commonly obscured by other environmental factors; clear and general relationships are difficult to find. How would community assembly processes be affected by a substantial change in soil characteristics when all other relevant factors are held constant? In particular, can we identify some functional adaptations which would underpin such soil-induced vegetation response? Location Eastern Serbia: fields partially damaged by long-term and large-scale fluvial deposition of sulphidic waste from a Cu mine; subcontinental/submediterranean climate. Methods We analysed the multivariate response of cereal weed assemblages (including biomass and foliar analyses) to a strong man-made soil gradient (from highly calcareous to highly acidic, nutrient-poor soils) over short distances (field scale). Results The soil gradient favoured a substitution of calcicoles by calcifuges, and an increase in abundance of pseudometallophytes, with preferences for Atlantic climate, broad geographical distribution, hemicryptophytic life form, adapted to low-nutrient and acidic soils, with lower concentrations of Ca, and very narrow range of Cu concentrations in leaves. The trends of abundance of the different ecological groups of indicator species along the soil gradient were systematically reflected in the maintenance of leaf P concentrations, and strong homeostasis in biomass N:P ratio. Conclusion Using annual weed vegetation at the field scale as a fairly simple model, we demonstrated links between gradients in soil properties (pH, nutrient availability) and floristic composition that are normally encountered over large geographic distances. We showed that leaf nutrient status, in particular the maintenance of leaf P concentrations and strong homeostasis of biomass N:P ratio, underpinned a clear functional response of vegetation to mineral stress. These findings can help to understand assembly processes leading to unusual, novel combinations
Method and apparatus for selective filtering of ions
Page, Jason S.; Tang, Keqi; Smith, Richard D.
2009-04-07
An adjustable, low mass-to-charge (m/z) filter is disclosed employing electrospray ionization to block ions associated with unwanted low m/z species from entering the mass spectrometer and contributing their space charge to down-stream ion accumulation steps. The low-mass filter is made by using an adjustable potential energy barrier from the conductance limiting terminal electrode of an electrodynamic ion funnel, which prohibits species with higher ion mobilities from being transmitted. The filter provides a linear voltage adjustment of low-mass filtering from m/z values from about 50 to about 500. Mass filtering above m/z 500 can also be performed; however, higher m/z species are attenuated. The mass filter was evaluated with a liquid chromatography-mass spectrometry analysis of an albumin tryptic digest and resulted in the ability to block low-mass, "background" ions which account for 40-70% of the total ion current from the ESI source during peak elution.
Adaptive method for electron bunch profile prediction
NASA Astrophysics Data System (ADS)
Scheinker, Alexander; Gessner, Spencer
2015-10-01
We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial control system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET.
Adaptive method for electron bunch profile prediction
Scheinker, Alexander; Gessner, Spencer
2015-10-01
We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial control system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET. © 2015 authors. Published by the American Physical Society.
NASA Technical Reports Server (NTRS)
Rourke, K. H.; Jordan, J. F.
1972-01-01
This paper presents the results of the applications of advanced filtering methods to the determination of the interplanetary orbit of the Mariner '71 spacecraft. The advanced techniques are specific extensions of the Kalman filter. The special problems associated with applying these techniques are discussed and the particular algorithmic implementations are outlined. The advanced methods are compared against the weighted least squares filters of conventional application. The results reveal that relatively simple advanced filter configurations yield solutions superior to those of the conventional methods when applied to the Mariner '71 radio measurements.
Method for enhanced longevity of in situ microbial filter used for bioremediation
Carman, M.L.; Taylor, R.T.
1999-03-30
An improved method is disclosed for in situ microbial filter bioremediation having increasingly operational longevity of an in situ microbial filter emplaced into an aquifer. A method is presented for generating a microbial filter of sufficient catalytic density and thickness, which has increased replenishment interval, improved bacteria attachment and detachment characteristics and the endogenous stability under in situ conditions. A system is also disclosed for in situ field water remediation. 31 figs.
NASA Technical Reports Server (NTRS)
Gevargiz, J. M.; Holmes, J. K.
1991-01-01
The next generation of digital receivers for NASA's Deep Space Network is composed of in-phase and quadrature-phase channels. The authors have modeled and simulated a quadrature-phase baseband channel that includes a low-pass filter and a digital matched filter. The simulation is used to study the performance of the three schemes of digital matched filtering that use digital weighted integrate-and-dump filters. Using three methods for calculating the near-optimum matched filter weight coefficients, the simulation results are analyzed for the NRZ and Manchester data formats. The performances of the digital matched filters are studied in the presence of a timing error between the demodulated symbols and the integrate-and-dump filters.
High-performance RC bandpass filter is adapted to miniaturized construction
NASA Technical Reports Server (NTRS)
1966-01-01
Miniaturized bandpass filter with RC networks is suitable for use in integrated circuits. The circuit consists of three stages of amplification with additional resistive and capacitive components to obtain the desired characteristics. The advantages of the active RC filter network are the reduction in size and weight and elimination of magnetic materials.
The algorithm analysis on non-uniformity correction based on LMS adaptive filtering
NASA Astrophysics Data System (ADS)
Zhan, Dongjun; Wang, Qun; Wang, Chensheng; Chen, Huawang
2010-11-01
The traditional least mean square (LMS) algorithm has the performance of good adaptivity to noise, but there are several disadvantages in the traditional LMS algorithm, such as the defect in desired value of pending pixels, undetermined original coefficients, which result in slow convergence speed and long convergence period. Method to solve the desired value of pending pixel has improved based on these problems, also, the correction gain and offset coefficients worked out by the method of two-point temperature non-uniformity correction (NUC) as the original coefficients, which has improved the convergence speed. The simulation with real infrared images has proved that the new LMS algorithm has the advantages of better correction effect. Finally, the algorithm is implemented on the hardware structure of FPGA+DSP.
Chen, Xiyuan; Li, Qinghua
2014-01-01
As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF. PMID:24693225
Xu, Yuan; Chen, Xiyuan; Li, Qinghua
2014-01-01
As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF. PMID:24693225
Assessing Adaptive Instructional Design Tools and Methods in ADAPT[IT].
ERIC Educational Resources Information Center
Eseryel, Deniz; Spector, J. Michael
ADAPT[IT] (Advanced Design Approach for Personalized Training - Interactive Tools) is a European project within the Information Society Technologies program that is providing design methods and tools to guide a training designer according to the latest cognitive science and standardization principles. ADAPT[IT] addresses users in two significantly…
A Comparative Study of Different Deblurring Methods Using Filters
NASA Astrophysics Data System (ADS)
Srimani, P. K.; Kavitha, S.
2011-12-01
This paper attempts to undertake the study of Restored Gaussian Blurred Images by using four types of techniques of deblurring image viz., Wiener filter, Regularized filter, Lucy Richardson deconvolution algorithm and Blind deconvolution algorithm with an information of the Point Spread Function (PSF) corrupted blurred image. The same is applied to the scanned image of seven months baby in the womb and they are compared with one another, so as to choose the best technique for restored or deblurring image. This paper also attempts to undertake the study of restored blurred image using Regualr Filter(RF) with no information about the Point Spread Function (PSF) by using the same four techniques after executing the guess of the PSF. The number of iterations and the weight threshold of it to choose the best guesses for restored or deblurring image of these techniques are determined.
Extended Kalman filter sensor failure detection method for pressurizer monitoring
Filho, E.O.A.; Nakata, H. )
1992-01-01
This work presents the development of the sensor failure detection and isolation system (FDIS) methodology, which is suitable for implementation in nuclear plant control systems. The methodology is based on the extended Kalman filter applied to a pressurized water reactor pressurizer. The utilization of the Kalman filter follows the standard procedure: First, an estimate of the state variables and the corresponding covariances are obtained; then, based on the state equations, the estimated state variables are propagated until the next measurements for the new estimate.
Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images
Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi
2016-01-01
Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms. PMID:27399704
Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images.
Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi
2016-01-01
Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms. PMID:27399704
Moving and adaptive grid methods for compressible flows
NASA Technical Reports Server (NTRS)
Trepanier, Jean-Yves; Camarero, Ricardo
1995-01-01
This paper describes adaptive grid methods developed specifically for compressible flow computations. The basic flow solver is a finite-volume implementation of Roe's flux difference splitting scheme or arbitrarily moving unstructured triangular meshes. The grid adaptation is performed according to geometric and flow requirements. Some results are included to illustrate the potential of the methodology.
An adaptive pseudospectral method for discontinuous problems
NASA Technical Reports Server (NTRS)
Augenbaum, Jeffrey M.
1988-01-01
The accuracy of adaptively chosen, mapped polynomial approximations is studied for functions with steep gradients or discontinuities. It is shown that, for steep gradient functions, one can obtain spectral accuracy in the original coordinate system by using polynomial approximations in a transformed coordinate system with substantially fewer collocation points than are necessary using polynomial expansion directly in the original, physical, coordinate system. It is also shown that one can avoid the usual Gibbs oscillation associated with steep gradient solutions of hyperbolic pde's by approximation in suitably chosen coordinate systems. Continuous, high gradient solutions are computed with spectral accuracy (as measured in the physical coordinate system). Discontinuous solutions associated with nonlinear hyperbolic equations can be accurately computed by using an artificial viscosity chosen to smooth out the solution in the mapped, computational domain. Thus, shocks can be effectively resolved on a scale that is subgrid to the resolution available with collocation only in the physical domain. Examples with Fourier and Chebyshev collocation are given.
Adaptable radiation monitoring system and method
Archer, Daniel E.; Beauchamp, Brock R.; Mauger, G. Joseph; Nelson, Karl E.; Mercer, Michael B.; Pletcher, David C.; Riot, Vincent J.; Schek, James L.; Knapp, David A.
2006-06-20
A portable radioactive-material detection system capable of detecting radioactive sources moving at high speeds. The system has at least one radiation detector capable of detecting gamma-radiation and coupled to an MCA capable of collecting spectral data in very small time bins of less than about 150 msec. A computer processor is connected to the MCA for determining from the spectral data if a triggering event has occurred. Spectral data is stored on a data storage device, and a power source supplies power to the detection system. Various configurations of the detection system may be adaptably arranged for various radiation detection scenarios. In a preferred embodiment, the computer processor operates as a server which receives spectral data from other networked detection systems, and communicates the collected data to a central data reporting system.
Adaptive computational methods for aerothermal heating analysis
NASA Technical Reports Server (NTRS)
Price, John M.; Oden, J. Tinsley
1988-01-01
The development of adaptive gridding techniques for finite-element analysis of fluid dynamics equations is described. The developmental work was done with the Euler equations with concentration on shock and inviscid flow field capturing. Ultimately this methodology is to be applied to a viscous analysis for the purpose of predicting accurate aerothermal loads on complex shapes subjected to high speed flow environments. The development of local error estimate strategies as a basis for refinement strategies is discussed, as well as the refinement strategies themselves. The application of the strategies to triangular elements and a finite-element flux-corrected-transport numerical scheme are presented. The implementation of these strategies in the GIM/PAGE code for 2-D and 3-D applications is documented and demonstrated.
A novel timestamp based adaptive clock method for circuit emulation service over packet network
NASA Astrophysics Data System (ADS)
Dai, Jin-you; Yu, Shao-hua
2007-11-01
It is necessary to transport TDM (time division multiplexing) over packet network such as IP and Ethernet, and synchronization is a problem when carrying TDM over the packet network. Clock methods for TDM over packet network are introduced. A new adaptive clock method is presented. The method is a kind of timestamp based adaptive method, but no timestamp needs transporting over packet network. By using the local oscillator and a counter, the timestamp information (local timestamp) related to the service clock of the remote PE (provide edge) and the near PE can be attained. By using D-EWMA filter algorithm, the noise caused by packet network can be filtered and the useful timestamp can be extracted out. With the timestamp and a voltage-controlled oscillator, clock frequency of near PE can be adjusted the same as clock frequency of the remote PE. A kind of simulation device is designed and a test network topology is set up to test and verify the method. The experiment result shows that synthetical performance of the new method is better than ordinary buffer based method and ordinary timestamp based method.
Adaptive mesh strategies for the spectral element method
NASA Technical Reports Server (NTRS)
Mavriplis, Catherine
1992-01-01
An adaptive spectral method was developed for the efficient solution of time dependent partial differential equations. Adaptive mesh strategies that include resolution refinement and coarsening by three different methods are illustrated on solutions to the 1-D viscous Burger equation and the 2-D Navier-Stokes equations for driven flow in a cavity. Sharp gradients, singularities, and regions of poor resolution are resolved optimally as they develop in time using error estimators which indicate the choice of refinement to be used. The adaptive formulation presents significant increases in efficiency, flexibility, and general capabilities for high order spectral methods.
Comparing Anisotropic Output-Based Grid Adaptation Methods by Decomposition
NASA Technical Reports Server (NTRS)
Park, Michael A.; Loseille, Adrien; Krakos, Joshua A.; Michal, Todd
2015-01-01
Anisotropic grid adaptation is examined by decomposing the steps of flow solution, ad- joint solution, error estimation, metric construction, and simplex grid adaptation. Multiple implementations of each of these steps are evaluated by comparison to each other and expected analytic results when available. For example, grids are adapted to analytic metric fields and grid measures are computed to illustrate the properties of multiple independent implementations of grid adaptation mechanics. Different implementations of each step in the adaptation process can be evaluated in a system where the other components of the adaptive cycle are fixed. Detailed examination of these properties allows comparison of different methods to identify the current state of the art and where further development should be targeted.
Electrically heated particulate filter diagnostic systems and methods
Gonze, Eugene V [Pinckney, MI
2009-09-29
A system that diagnoses regeneration of an electrically heated particulate filter is provided. The system generally includes a grid module that diagnoses a fault of the grid based on at least one of a current signal and a voltage signal. A diagnostic module at least one of sets a fault status and generates a warning signal based on the fault of the grid.
NASA Astrophysics Data System (ADS)
Seddik, Hassene
2014-12-01
Noise can occur during image capture, transmission, or processing phases. Image de-noising is a very important step in image processing, and many approaches are developed in order to achieve this goal such as the Gaussian filter which is efficient in noise removal. Its smoothing efficiency depends on the value of its standard deviation. The mask representing the filter presents generally static weights with invariant lobe. In this paper, an adaptive de-noising approach is proposed. The proposed approach uses a Gaussian kernel with variable width and direction called adaptive Gaussian kernel (AGK). In each processed window of the image, the smoothing strength changes according to the image content, noise kind, and intensity. In addition, the location of its lobe changes in eight different directions over the processed window. This directional variability avoids averaging details by the highest mask weights in order to preserve the edges and the borders. The recovered data is de-noised efficiently without introducing blur or losing details. A comparative study with the static Gaussian filter and other recent techniques is presented to prove the efficiency of the proposed approach.
Adaptive filtering in spatial vision: evidence from feature marking in plaids.
Georgeson, M A; Meese, T S
1999-01-01
Much evidence shows that early vision employs an array of spatial filters tuned for different spatial frequencies and orientations. We suggest that for moderately low spatial frequencies these preliminary filters are not treated independently, but are used to perform grouping and segmentation in the patchwise Fourier domain. For example, consider a stationary plaid made from two superimposed sinusoidal gratings of the same contrast and spatial frequency oriented +/- 45 degrees from vertical. Most of the energy in a wavelet-like (e.g. simple-cell) transform of this stimulus is in the oblique orientations, but typically it looks like a compound structure containing blurred vertical and horizontal edges. This checkerboard structure corresponds with the locations of zero crossings in the output of an isotropic (circular) filter, synthesised from the linear sum of a set of oriented basis-filters (Georgeson, 1992 Proceedings of the Royal Society of London, Series B 249 235-245). However, the addition of a third harmonic in square-wave phase causes almost complete perceptual segmentation of the plaid into two overlapping oblique gratings. Here we confirm this result psychophysically using a feature-marking technique, and argue that this perceptual segmentation cannot be understood in terms of the zero crossings marked in the output of any static linear filter that is sensitive to all of the plaid's components. If it is assumed that zero crossings or similar are an appropriate feature-primitive in human vision, our results require a flexible process that combines and segments early basis-filters according to prevailing image conditions. Thus, we suggest that combination and segmentation of spatial filters in the patchwise Fourier domain underpins the perceptual segmentation observed in our experiments. Under this kind of image-processing scheme, registration across spatial scales occurs at the level of spatial filters, before features are extracted. This contrasts with
Tan, Ge; Muffato, Matthieu; Ledergerber, Christian; Herrero, Javier; Goldman, Nick; Gil, Manuel; Dessimoz, Christophe
2015-01-01
Phylogenetic inference is generally performed on the basis of multiple sequence alignments (MSA). Because errors in an alignment can lead to errors in tree estimation, there is a strong interest in identifying and removing unreliable parts of the alignment. In recent years several automated filtering approaches have been proposed, but despite their popularity, a systematic and comprehensive comparison of different alignment filtering methods on real data has been lacking. Here, we extend and apply recently introduced phylogenetic tests of alignment accuracy on a large number of gene families and contrast the performance of unfiltered versus filtered alignments in the context of single-gene phylogeny reconstruction. Based on multiple genome-wide empirical and simulated data sets, we show that the trees obtained from filtered MSAs are on average worse than those obtained from unfiltered MSAs. Furthermore, alignment filtering often leads to an increase in the proportion of well-supported branches that are actually wrong. We confirm that our findings hold for a wide range of parameters and methods. Although our results suggest that light filtering (up to 20% of alignment positions) has little impact on tree accuracy and may save some computation time, contrary to widespread practice, we do not generally recommend the use of current alignment filtering methods for phylogenetic inference. By providing a way to rigorously and systematically measure the impact of filtering on alignments, the methodology set forth here will guide the development of better filtering algorithms. PMID:26031838
Tan, Ge; Muffato, Matthieu; Ledergerber, Christian; Herrero, Javier; Goldman, Nick; Gil, Manuel; Dessimoz, Christophe
2015-09-01
Phylogenetic inference is generally performed on the basis of multiple sequence alignments (MSA). Because errors in an alignment can lead to errors in tree estimation, there is a strong interest in identifying and removing unreliable parts of the alignment. In recent years several automated filtering approaches have been proposed, but despite their popularity, a systematic and comprehensive comparison of different alignment filtering methods on real data has been lacking. Here, we extend and apply recently introduced phylogenetic tests of alignment accuracy on a large number of gene families and contrast the performance of unfiltered versus filtered alignments in the context of single-gene phylogeny reconstruction. Based on multiple genome-wide empirical and simulated data sets, we show that the trees obtained from filtered MSAs are on average worse than those obtained from unfiltered MSAs. Furthermore, alignment filtering often leads to an increase in the proportion of well-supported branches that are actually wrong. We confirm that our findings hold for a wide range of parameters and methods. Although our results suggest that light filtering (up to 20% of alignment positions) has little impact on tree accuracy and may save some computation time, contrary to widespread practice, we do not generally recommend the use of current alignment filtering methods for phylogenetic inference. By providing a way to rigorously and systematically measure the impact of filtering on alignments, the methodology set forth here will guide the development of better filtering algorithms. PMID:26031838
NASA Astrophysics Data System (ADS)
Ikeshita, Kazuki; Hasegawa, Hideyuki; Kanai, Hiroshi
2012-07-01
In our previous study, the stress-strain relationship of the radial arterial wall was measured and the viscoelasticity of the intima-media region was estimated from the stress-strain relationship. Furthermore, the transient change in viscoelasticity due to flow-mediated dilation (FMD) was estimated by the automated detection of wall boundaries. In the present study, the strain rate was adaptively filtered to improve the accuracy of viscoelasticity estimation by decreasing the high-frequency noise. Additionally, in a basic experiment, this method was validated using a silicone tube (simulating artery). In the basic experiment, the elasticity was estimated with a mean error of 1.2%. The elasticity measured at each beam position was highly reproducible among measurements, whereas there was a slight variation in measured elasticity among beams. Consequently, in in vivo measurements, the normalized mean square error (MSE) was clearly decreased. Additionally, the stress-strain relationship of the radial arterial wall was obtained and the viscoelasticity was estimated accurately. The inner small loop, which corresponds to the negative pressure wave caused by the closure of the aortic valve, can be observed using the adaptive low-pass filtering (LPF). Moreover, the transient changes in these parameters were similar to those in the previous study. These results show the potential of the proposed method for the thorough analysis of the transient change in viscoelasticity due to FMD.
Adaptive sequential methods for detecting network intrusions
NASA Astrophysics Data System (ADS)
Chen, Xinjia; Walker, Ernest
2013-06-01
In this paper, we propose new sequential methods for detecting port-scan attackers which routinely perform random "portscans" of IP addresses to find vulnerable servers to compromise. In addition to rigorously control the probability of falsely implicating benign remote hosts as malicious, our method performs significantly faster than other current solutions. Moreover, our method guarantees that the maximum amount of observational time is bounded. In contrast to the previous most effective method, Threshold Random Walk Algorithm, which is explicit and analytical in nature, our proposed algorithm involve parameters to be determined by numerical methods. We have introduced computational techniques such as iterative minimax optimization for quick determination of the parameters of the new detection algorithm. A framework of multi-valued decision for detecting portscanners and DoS attacks is also proposed.
A robust infrared dim target detection method based on template filtering and saliency extraction
NASA Astrophysics Data System (ADS)
Wang, Wenguang; Li, Chenming; Shi, Jianing
2015-11-01
Dim target detection in infrared image with complex background and low signal-clutter ratio (SCR) is a significant and difficult task in the infrared target tracking system. A robust infrared dim target detection method based on template filtering and saliency extraction is proposed in this paper. The weighted gray map is obtained from the infrared image to highlight the target which is brighter than its neighbors and has weak correlation with its background. The target saliency map is then calculated by phase spectrum of Fourier Transform, so that the dim target detection could be converted to salient region extraction. The potential targets are finally extracted by combining the two maps. Moreover, position discrimination between targets in the two maps is used to exclude the false alarms and extract the targets. Experimental results on measured images indicate that our method is feasible, adaptable and robust in different backgrounds. The ROC (Receiver Operating Characteristic) curves obtained from the simulated images demonstrate the proposed method outperforms some existing typical methods in both detection rate and false alarm rate, for target detection with low SCR.
NASA Astrophysics Data System (ADS)
Fayadh, Rashid A.; Malek, F.; Fadhil, Hilal A.; Aldhaibani, Jaafar A.; Salman, M. K.; Abdullah, Farah Salwani
2015-05-01
For high data rate propagation in wireless ultra-wideband (UWB) communication systems, the inter-symbol interference (ISI), multiple-access interference (MAI), and multiple-users interference (MUI) are influencing the performance of the wireless systems. In this paper, the rake-receiver was presented with the spread signal by direct sequence spread spectrum (DS-SS) technique. The adaptive rake-receiver structure was shown with adjusting the receiver tap weights using least mean squares (LMS), normalized least mean squares (NLMS), and affine projection algorithms (APA) to support the weak signals by noise cancellation and mitigate the interferences. To minimize the data convergence speed and to reduce the computational complexity by the previous algorithms, a well-known approach of partial-updates (PU) adaptive filters were employed with algorithms, such as sequential-partial, periodic-partial, M-max-partial, and selective-partial updates (SPU) in the proposed system. The simulation results of bit error rate (BER) versus signal-to-noise ratio (SNR) are illustrated to show the performance of partial-update algorithms that have nearly comparable performance with the full update adaptive filters. Furthermore, the SPU-partial has closed performance to the full-NLMS and full-APA while the M-max-partial has closed performance to the full-LMS updates algorithms.
NASA Astrophysics Data System (ADS)
Gray, Morgan; Petit, Cyril; Rodionov, Sergey; Bertino, Laurent; Bocquet, Marc; Fusco, Thierry
2013-12-01
We propose a new algorithm for an AO control law which allows to reduce the computation burden in the case of an Extremely Large Telescope and to deal with a non stationary behavior of the atmospheric turbulence. This approach uses Ensemble Transform Kalman Filter (ETKF) and localizations by domains decomposition: the assimilation is split into local domains on the pupil of the telescope and each of the update data assimilation for each domain is performed independently. This kind of assimilation enables parallel computation of much less data during the update stage. This is a Kalman Filter adaptation for large scale systems with a non stationary turbulence when the explicit storage and manipulation of extremely large covariance matrices are impossible. This distributed parallel environment implementation is highlighted and studied in the context of an ELT application. First simulation results are proposed to assess our theoretical analysis and to demonstrate the potentiality of this new approach for an AO control law on ELTs.
NASA Astrophysics Data System (ADS)
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.
Adaptive finite-element method for diffraction gratings
NASA Astrophysics Data System (ADS)
Bao, Gang; Chen, Zhiming; Wu, Haijun
2005-06-01
A second-order finite-element adaptive strategy with error control for one-dimensional grating problems is developed. The unbounded computational domain is truncated to a bounded one by a perfectly-matched-layer (PML) technique. The PML parameters, such as the thickness of the layer and the medium properties, are determined through sharp a posteriori error estimates. The adaptive finite-element method is expected to increase significantly the accuracy and efficiency of the discretization as well as reduce the computation cost. Numerical experiments are included to illustrate the competitiveness of the proposed adaptive method.
Adaptive multiscale method for two-dimensional nanoscale adhesive contacts
NASA Astrophysics Data System (ADS)
Tong, Ruiting; Liu, Geng; Liu, Lan; Wu, Liyan
2013-05-01
There are two separate traditional approaches to model contact problems: continuum and atomistic theory. Continuum theory is successfully used in many domains, but when the scale of the model comes to nanometer, continuum approximation meets challenges. Atomistic theory can catch the detailed behaviors of an individual atom by using molecular dynamics (MD) or quantum mechanics, although accurately, it is usually time-consuming. A multiscale method coupled MD and finite element (FE) is presented. To mesh the FE region automatically, an adaptive method based on the strain energy gradient is introduced to the multiscale method to constitute an adaptive multiscale method. Utilizing the proposed method, adhesive contacts between a rigid cylinder and an elastic substrate are studied, and the results are compared with full MD simulations. The process of FE meshes refinement shows that adaptive multiscale method can make FE mesh generation more flexible. Comparison of the displacements of boundary atoms in the overlap region with the results from full MD simulations indicates that adaptive multiscale method can transfer displacements effectively. Displacements of atoms and FE nodes on the center line of the multiscale model agree well with that of atoms in full MD simulations, which shows the continuity in the overlap region. Furthermore, the Von Mises stress contours and contact force distributions in the contact region are almost same as full MD simulations. The method presented combines multiscale method and adaptive technique, and can provide a more effective way to multiscale method and to the investigation on nanoscale contact problems.
Fast adaptive composite grid methods on distributed parallel architectures
NASA Technical Reports Server (NTRS)
Lemke, Max; Quinlan, Daniel
1992-01-01
The fast adaptive composite (FAC) grid method is compared with the adaptive composite method (AFAC) under variety of conditions including vectorization and parallelization. Results are given for distributed memory multiprocessor architectures (SUPRENUM, Intel iPSC/2 and iPSC/860). It is shown that the good performance of AFAC and its superiority over FAC in a parallel environment is a property of the algorithm and not dependent on peculiarities of any machine.
Organic and Elemental Carbon Filter Sets: Preparation Method and Interlaboratory Results
Chai, Ming; Birch, M. Eileen; Deye, Greg
2012-01-01
Carbonaceous aerosols play an important role in climate, visibility, air quality, and human health effects, and they have been routinely monitored in workplace and environmental settings. Different thermal analysis methods have been applied to determine the carbon content of carbonaceous aerosols. Good agreement between results for total carbon (TC) generally has been found, but the organic and elemental carbon (OC and EC) fractions determined by different methods often disagree. Measurement uncertainty is mainly due to pyrolysis and charring of OC sample components. Lack of reference materials has impeded progress on method standardization and understanding method biases. A relatively simple method for generating matched filter sets having known OC–EC contents is reported. After generation and analysis of each set to confirm agreement between filters, the filter sets were distributed to six laboratories for an interlaboratory comparison. Analytical results indicate a uniform carbon distribution for the filter sets and good agreement between the participating laboratories. Relative standard deviations (RSDs) for mean TC (OC + EC), OC, and EC results for seven laboratories were <10, 11, and 12% (respectively). Except for one EC result (RSD = 16%), RSDs reported by individual laboratories for TC, OC, and EC were <12%. The method of filter generation is generally applicable and reproducible. Depending on the application, different filter loadings and types of OC materials can be employed. Matched filter sets prepared by the described approach can be used for determining the accuracy of OC–EC methods and thereby contribute to method standardization. PMID:22459320
Svenson, Björn; Larsson, Lars; Båth, Magnus
2016-01-01
Objective The purpose of the present study was to investigate the potential of using advanced external adaptive image processing for maintaining image quality while reducing exposure in dental panoramic storage phosphor plate (SPP) radiography. Materials and methods Thirty-seven SPP radiographs of a skull phantom were acquired using a Scanora panoramic X-ray machine with various tube load, tube voltage, SPP sensitivity and filtration settings. The radiographs were processed using General Operator Processor (GOP) technology. Fifteen dentists, all within the dental radiology field, compared the structural image quality of each radiograph with a reference image on a 5-point rating scale in a visual grading characteristics (VGC) study. The reference image was acquired with the acquisition parameters commonly used in daily operation (70 kVp, 150 mAs and sensitivity class 200) and processed using the standard process parameters supplied by the modality vendor. Results All GOP-processed images with similar (or higher) dose as the reference image resulted in higher image quality than the reference. All GOP-processed images with similar image quality as the reference image were acquired at a lower dose than the reference. This indicates that the external image processing improved the image quality compared with the standard processing. Regarding acquisition parameters, no strong dependency of the image quality on the radiation quality was seen and the image quality was mainly affected by the dose. Conclusions The present study indicates that advanced external adaptive image processing may be beneficial in panoramic radiography for increasing the image quality of SPP radiographs or for reducing the exposure while maintaining image quality. PMID:26478956
NASA Astrophysics Data System (ADS)
Pierce, A.; Gustin, M. S.; Huang, J.; Heidecorn, K.
2014-12-01
Three active mercury (Hg) measurement methods were operated side by side at an urban site (University of Nevada, Reno College of Agriculture Greenhouse facility, elev. 1370 m) in Reno, and at a high elevation site (Peavine Peak, elev. 2515 m) adjacent to Reno from December 2013 to October 2014. A model 602 BetaPlus Teledyne Advanced Pollution Instrumentation (TAPI, San Diego, CA USA) particulate measurement system was used to collect particulate matter on a 47 mm diameter cation exchange membrane (CEM, PN# MSTGS3R Mustang S, Pall Corp. Port Washington, NY) at a rate of 16.7 lpm for 24 hours to four days. Particulate concentrations were calculated using beta attenuation across the filters (non-destructive to filter material); the CEM filters were then analyzed for total Hg on a Tekran Total Hg Analysis system (model 2600, Tekran Instruments Corp. Knoxville, TN, USA). Concurrently, samples were collected on an active Hg membrane system. The active Hg membrane system consisted of 3 CEM filters sampling at a rate of 1 lpm for one to two weeks. CEM filters were then analyzed on the Tekran 2600. A Tekran speciation unit (model 1130, 1135, 2537) was also in operation and ambient air samples were analyzed for gaseous elemental Hg (GEM), gaseous oxidized Hg (GOM), and particulate bound Hg (PBM). Both the 602 BetaPlus system and the active Hg membrane system should collect RM on the CEM filters. The active Hg membrane system most likely captures mainly GOM based on previous tests with the Teflon inlet setup that indicated there was high static electricity effective in removing particulate matter. Flow rate and length of measurement (24 hours vs. four days) affected the Hg concentrations on the 602 BetaPlus system. Based on these measurements we hypothesize that, due to the high flow rate, and therefore short retention time, the 602 BetaPlus only captured PBM. It is also possible that there was loss of Hg to inlet walls due to the longer inlet on the 602 BetaPlus system
Filterability of blood cells: methods and clinical applications.
Nash, G B
1990-01-01
It could be claimed that expansion in clinical aspects of haemorheology has largely been fueled by the development of a simple test of blood flow properties, i.e., analysis of filterability. With time, the level of sophistication in equipment, theory and sample preparation has increased. Theories for the development of flow in the filter have been described and these enable cellular parameters, such as transit times, to be calculated from experimental data. These theories can be quite general, and applied to filtration of red or white cells. Ideally, experimental design requires an understanding of cell behaviour at the filter and of the effects of factors such as the sample concentration and volume, and the presence of different types of cells or subpopulations. Otherwise, results are susceptible to misinterpretation, particularly if impurities or mixed populations of cells are present. It is thus very important to know accurately the constituents of the test suspension. In clinical applications the trend has been to move away from whole blood filtration, toward use of relatively pure suspension of separated red cells and white cells. In the area of red cell filtration this has led to reappraisal of some previously reported abnormalities. The relatively new study of white cell filtration should benefit from previous experience, but there is the added problem of the reactivity of the cells under test, and their mixed nature. In any case, critical evaluation of the meaning and clinical relevance of results is necessary. PMID:2093395
Improved Kalman Filter Method for Measurement Noise Reduction in Multi Sensor RFID Systems
Eom, Ki Hwan; Lee, Seung Joon; Kyung, Yeo Sun; Lee, Chang Won; Kim, Min Chul; Jung, Kyung Kwon
2011-01-01
Recently, the range of available Radio Frequency Identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less Mean Squared Error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments. PMID:22346641
In order to determine if suspended solids interfere with enteric virus recovery from water by microporous filter methods, the effects of bentonite clay solids at a concentration of 10 NTU on the recovery of poliovirus type 1 from seeded, activated carbon-treated, filtered tap wat...
Adaptive upscaling with the dual mesh method
Guerillot, D.; Verdiere, S.
1997-08-01
The objective of this paper is to demonstrate that upscaling should be calculated during the flow simulation instead of trying to enhance the a priori upscaling methods. Hence, counter-examples are given to motivate our approach, the so-called Dual Mesh Method. The main steps of this numerical algorithm are recalled. Applications illustrate the necessity to consider different average relative permeability values depending on the direction in space. Moreover, these values could be different for the same average saturation. This proves that an a priori upscaling cannot be the answer even in homogeneous cases because of the {open_quotes}dynamical heterogeneity{close_quotes} created by the saturation profile. Other examples show the efficiency of the Dual Mesh Method applied to heterogeneous medium and to an actual field case in South America.
Adaptive Finite Element Methods for Continuum Damage Modeling
NASA Technical Reports Server (NTRS)
Min, J. B.; Tworzydlo, W. W.; Xiques, K. E.
1995-01-01
The paper presents an application of adaptive finite element methods to the modeling of low-cycle continuum damage and life prediction of high-temperature components. The major objective is to provide automated and accurate modeling of damaged zones through adaptive mesh refinement and adaptive time-stepping methods. The damage modeling methodology is implemented in an usual way by embedding damage evolution in the transient nonlinear solution of elasto-viscoplastic deformation problems. This nonlinear boundary-value problem is discretized by adaptive finite element methods. The automated h-adaptive mesh refinements are driven by error indicators, based on selected principal variables in the problem (stresses, non-elastic strains, damage, etc.). In the time domain, adaptive time-stepping is used, combined with a predictor-corrector time marching algorithm. The time selection is controlled by required time accuracy. In order to take into account strong temperature dependency of material parameters, the nonlinear structural solution a coupled with thermal analyses (one-way coupling). Several test examples illustrate the importance and benefits of adaptive mesh refinements in accurate prediction of damage levels and failure time.
Optimal-adaptive filters for modelling spectral shape, site amplification, and source scaling
Safak, Erdal
1989-01-01
This paper introduces some applications of optimal filtering techniques to earthquake engineering by using the so-called ARMAX models. Three applications are presented: (a) spectral modelling of ground accelerations, (b) site amplification (i.e., the relationship between two records obtained at different sites during an earthquake), and (c) source scaling (i.e., the relationship between two records obtained at a site during two different earthquakes). A numerical example for each application is presented by using recorded ground motions. The results show that the optimal filtering techniques provide elegant solutions to above problems, and can be a useful tool in earthquake engineering.
The filter of choice: filtration method preference among injecting drug users
2011-01-01
Background Injection drug use syringe filters (IDUSF) are designed to prevent several complications related to the injection of drugs. Due to their small pore size, their use can reduce the solution's insoluble particle content and thus diminish the prevalence of phlebitis, talcosis.... Their low drug retention discourages from filter reuse and sharing and can thus prevent viral and microbial infections. In France, drug users have access to sterile cotton filters for 15 years and to an IDUSF (the Sterifilt®) for 5 years. This study was set up to explore the factors influencing filter preference amongst injecting drug users. Methods Quantitative and qualitative data were gathered through 241 questionnaires and the participation of 23 people in focus groups. Results Factors found to significantly influence filter preference were duration and frequency of injecting drug use, the type of drugs injected and subculture. Furthermore, IDU's rationale for the preference of one type of filter over others was explored. It was found that filter preference depends on perceived health benefits (reduced harms, prevention of vein damage, protection of injection sites), drug retention (low retention: better high, protective mechanism against the reuse of filters; high retention: filter reuse as a protective mechanism against withdrawal), technical and practical issues (filter clogging, ease of use, time needed to prepare an injection) and believes (the conviction that a clear solution contains less active compound). Conclusion It was concluded that the factors influencing filter preference are in favour of change; a shift towards the use of more efficient filters can be made through increased availability, information and demonstrations. PMID:21859488
Iterative method for the design of SLM realizable minimum average correlation energy (MACE) filters
NASA Astrophysics Data System (ADS)
Rajan, P. Karivaratha; Ramakrishnan, R.
1995-03-01
Design of optical pattern recognition filters taking into account the nonideal characteristics of the spatial light modulators on which the filters are implemented is an important research problem. In this paper, an iterative method is developed for the design of SLM constrained minimum average correlation energy (MACE) filters. The algorithm uses a relaxation algorithm in conjunction with Juday's minimum euclidean distance (MED) mapping technique in an iterative manner. The performance of the filter designed using this method was evaluated using computer simulations and the results are compared with a constrained MACE filter designed using a software based on a simulated annealing technique. The new software requires much less computer time than the simulated annealing based software providing comparable response. The time taken by the new algorithm is more than that for the MED mapped design; but, the new algorithm provides less deviation from the specified response for training images than the MED mapped design.
Method for improving line flux and redshift measurements with narrowband filters
NASA Astrophysics Data System (ADS)
Zabl, J.; Freudling, W.; Møller, P.; Milvang-Jensen, B.; Nilsson, K. K.; Fynbo, J. P. U.; Le Fèvre, O.; Tasca, L. A. M.
2016-05-01
Context. High redshift star-forming galaxies are discovered routinely because of a flux excess in narrowband filters caused by an emission line. In most cases, the width of such filters is broader than typical line widths, and the throughput of the filters varies substantially within the bandpass. This leads to substantial uncertainties in redshifts and fluxes that are derived from the observations with one specific narrowband filter. Aims: The uncertainty in measured line parameters can be sharply reduced by using repeated observations of the same target field with filters that have overlapping passbands but differ slightly in central wavelength or wavelength dependence of the effective filter curve. Such data are routinely collected with some large field imaging cameras that use multiple detectors and a separate filter for each of the detectors. An example is the European Southern Observatory's VISTA InfraRed CAMera (VIRCAM). Methods: We developed a method of determining more accurate redshift and line flux estimates from the ratio of apparent fluxes measured from observations in different narrowband filters and several matching broadband filters. A parameterized model of the line and continuum flux is used to predict the flux ratios as a function of redshift based on the known filter curves. These model predictions are then used to determine the most likely redshift and line flux. Results: We tested the obtainable quality of parameter estimation for the example of Hα in the VIRCAM NB118 filters both on simulated and actual observations where the latter were based on the UltraVISTA DR2 data set. We combined the narrowband data with deep broadband data in Y, J, and H. We find that with this method, the errors in the measured lines fluxes can be reduced up to almost an order of magnitude. Conclusions: We conclude that existing narrowband data can be used to derive accurate line fluxes if the observations include images taken with sufficiently different filter
An auto-adaptive background subtraction method for Raman spectra.
Xie, Yi; Yang, Lidong; Sun, Xilong; Wu, Dewen; Chen, Qizhen; Zeng, Yongming; Liu, Guokun
2016-05-15
Background subtraction is a crucial step in the preprocessing of Raman spectrum. Usually, parameter manipulating of the background subtraction method is necessary for the efficient removal of the background, which makes the quality of the spectrum empirically dependent. In order to avoid artificial bias, we proposed an auto-adaptive background subtraction method without parameter adjustment. The main procedure is: (1) select the local minima of spectrum while preserving major peaks, (2) apply an interpolation scheme to estimate background, (3) and design an iteration scheme to improve the adaptability of background subtraction. Both simulated data and Raman spectra have been used to evaluate the proposed method. By comparing the backgrounds obtained from three widely applied methods: the polynomial, the Baek's and the airPLS, the auto-adaptive method meets the demand of practical applications in terms of efficiency and accuracy. PMID:26950502
An auto-adaptive background subtraction method for Raman spectra
NASA Astrophysics Data System (ADS)
Xie, Yi; Yang, Lidong; Sun, Xilong; Wu, Dewen; Chen, Qizhen; Zeng, Yongming; Liu, Guokun
2016-05-01
Background subtraction is a crucial step in the preprocessing of Raman spectrum. Usually, parameter manipulating of the background subtraction method is necessary for the efficient removal of the background, which makes the quality of the spectrum empirically dependent. In order to avoid artificial bias, we proposed an auto-adaptive background subtraction method without parameter adjustment. The main procedure is: (1) select the local minima of spectrum while preserving major peaks, (2) apply an interpolation scheme to estimate background, (3) and design an iteration scheme to improve the adaptability of background subtraction. Both simulated data and Raman spectra have been used to evaluate the proposed method. By comparing the backgrounds obtained from three widely applied methods: the polynomial, the Baek's and the airPLS, the auto-adaptive method meets the demand of practical applications in terms of efficiency and accuracy.
Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals.
Dadashi, Farzin; Crettenand, Florent; Millet, Grégoire P; Seifert, Ludovic; Komar, John; Aminian, Kamiar
2013-01-01
This study introduces a novel approach for automatic temporal phase detection and inter-arm coordination estimation in front-crawl swimming using inertial measurement units (IMUs). We examined the validity of our method by comparison against a video-based system. Three waterproofed IMUs (composed of 3D accelerometer, 3D gyroscope) were placed on both forearms and the sacrum of the swimmer. We used two underwater video cameras in side and frontal views as our reference system. Two independent operators performed the video analysis. To test our methodology, seven well-trained swimmers performed three 300 m trials in a 50 m indoor pool. Each trial was in a different coordination mode quantified by the index of coordination. We detected different phases of the arm stroke by employing orientation estimation techniques and a new adaptive change detection algorithm on inertial signals. The difference of 0.2 ± 3.9% between our estimation and video-based system in assessment of the index of coordination was comparable to experienced operators' difference (1.1 ± 3.6%). The 95% limits of agreement of the difference between the two systems in estimation of the temporal phases were always less than 7.9% of the cycle duration. The inertial system offers an automatic easy-to-use system with timely feedback for the study of swimming. PMID:23560703
High-temperature gas stream filter and method
Notestein, J.E.
1994-12-31
The present invention relates generally to the removal of solid particulate material from high-temperature gas streams, and more particularly the removal of such particulate material by employing a barrier filter formed of a carbon-carbon composite provided by a porous carbon fiber substrate with open interstitial regions between adjacently disposed carbon fibers selectively restricted by carbon integrally attached to the carbon fibers of the substrate. In a typical utilization of a particulate-bearing hot gas stream, the particulate loading of the gas stream after cleaning is normally less than about 50 ppm and with essentially no particulates larger than about 10 microns. This carbon-carbon filter for removing particulate material of a particle size larger than a preselected particle size from a gas stream at a temperature greater than about 800 F, is produced by the steps which comprise: providing a substrate of carbonaceous fibers with pore-forming open interstitial regions between adjacently disposed fibers; and, sufficiently filling these open interstitial regions with carbon integrally attached to and supported by the fibers for providing the interstitial regions with throughgoing passage-ways of a pore size sufficient to provide for the passage of the gas stream while preventing the passage of particulate material larger than a preselected particle size.
Introduction to Adaptive Methods for Differential Equations
NASA Astrophysics Data System (ADS)
Eriksson, Kenneth; Estep, Don; Hansbo, Peter; Johnson, Claes
Knowing thus the Algorithm of this calculus, which I call Differential Calculus, all differential equations can be solved by a common method (Gottfried Wilhelm von Leibniz, 1646-1719).When, several years ago, I saw for the first time an instrument which, when carried, automatically records the number of steps taken by a pedestrian, it occurred to me at once that the entire arithmetic could be subjected to a similar kind of machinery so that not only addition and subtraction, but also multiplication and division, could be accomplished by a suitably arranged machine easily, promptly and with sure results. For it is unworthy of excellent men to lose hours like slaves in the labour of calculations, which could safely be left to anyone else if the machine was used. And now that we may give final praise to the machine, we may say that it will be desirable to all who are engaged in computations which, as is well known, are the managers of financial affairs, the administrators of others estates, merchants, surveyors, navigators, astronomers, and those connected with any of the crafts that use mathematics (Leibniz).
NASA Astrophysics Data System (ADS)
Steeb, P.; Krause, S.; Linke, P.; Hensen, C.; Dale, A. W.; Nuzzo, M.; Treude, T.
2015-11-01
Large amounts of methane are delivered by fluids through the erosive forearc of the convergent margin offshore of Costa Rica and lead to the formation of cold seeps at the sediment surface. Besides mud extrusion, numerous cold seeps are created by landslides induced by seamount subduction or fluid migration along major faults. Most of the dissolved methane migrating through the sediments of cold seeps is oxidized within the benthic microbial methane filter by anaerobic oxidation of methane (AOM). Measurements of AOM and sulfate reduction as well as numerical modeling of porewater profiles revealed a highly active and efficient benthic methane filter at the Quepos Slide site, a landslide on the continental slope between the Nicoya and Osa Peninsula. Integrated areal rates of AOM ranged from 12.9 ± 6.0 to 45.2 ± 11.5 mmol m-2 d-1, with only 1 to 2.5 % of the upward methane flux being released into the water column. Additionally, two parallel sediment cores from Quepos Slide were used for in vitro experiments in a recently developed sediment-flow-through (SLOT) system to simulate an increased fluid and methane flux from the bottom of the sediment core. The benthic methane filter revealed a high adaptability whereby the methane oxidation efficiency responded to the increased fluid flow within ca. 170 d. To our knowledge, this study provides the first estimation of the natural biogeochemical response of seep sediments to changes in fluid flow.
NASA Astrophysics Data System (ADS)
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.
Stability and error estimation for Component Adaptive Grid methods
NASA Technical Reports Server (NTRS)
Oliger, Joseph; Zhu, Xiaolei
1994-01-01
Component adaptive grid (CAG) methods for solving hyperbolic partial differential equations (PDE's) are discussed in this paper. Applying recent stability results for a class of numerical methods on uniform grids. The convergence of these methods for linear problems on component adaptive grids is established here. Furthermore, the computational error can be estimated on CAG's using the stability results. Using these estimates, the error can be controlled on CAG's. Thus, the solution can be computed efficiently on CAG's within a given error tolerance. Computational results for time dependent linear problems in one and two space dimensions are presented.
NASA Astrophysics Data System (ADS)
Borodachev, S. M.
2016-06-01
The simple derivation of recursive least squares (RLS) method equations is given as special case of Kalman filter estimation of a constant system state under changing observation conditions. A numerical example illustrates application of RLS to multicollinearity problem.
METHODS FOR IMPROVEMENT OF TRICKLING FILTER PLANT PERFORMANCE. PART II. CHEMICAL ADDITION
An experimental program to explore potential methods for removing phosphorus and generally enhancing trickling filter plant performance was conducted at the Mason Farm Wastewater Treatment Plant, Chapel Hill, North Carolina. Preliminary investigations included jar testing with se...
Tang, Chen; Wang, Linlin; Yan, Haiqing
2012-07-10
In this paper, we first present the general description for partial differential equations (PDEs) based image processing methods, including the basic idea, the main advantages and disadvantages, a few representative PDE models, and the derivation of PDE models. Then we review our contributions on PDE-based anisotropic filtering methods for electronic speckle pattern interferometry, including the second-order, fourth-order, and coupled nonoriented PDE filtering models and the second-order and coupled nonlinear oriented PDE filtering models. We have summarized the features of each model. PMID:22781274
Method for HEPA filter leak scanning with differentiating aerosol detector
Kovach, B.J.; Banks, E.M.; Wikoff, W.O.
1997-08-01
While scanning HEPA filters for leaks with {open_quotes}Off the Shelf{close_quote} aerosol detection equipment, the operator`s scanning speed is limited by the time constant and threshold sensitivity of the detector. This is based on detection of the aerosol density, where the maximum signal is achieved when the scanning probe resides over the pinhole longer than several detector time-constants. Since the differential value of the changing signal can be determined by observing only the first small fraction of the rising signal, using a differentiating amplifier will speed up the locating process. The other advantage of differentiation is that slow signal drift or zero offset will not interfere with the process of locating the leak, since they are not detected. A scanning hand-probe attachable to any NUCON{reg_sign} Aerosol Detector displaying the combination of both aerosol density and differentiated signal was designed. 3 refs., 1 fig.
Online signal filtering based on the algebraic method and its experimental validation
NASA Astrophysics Data System (ADS)
Morales, R.; Segura, E.; Somolinos, J. A.; Núñez, L. R.; Sira-Ramírez, H.
2016-01-01
An on-line algebraic filtering scheme, based on the recently introduced algebraic approach to parameter and state estimation, is presented along with successful experimental results. The proposed filtering algorithm is based on the connections between a time derivative estimator and an algebraically based signal filtering option. The main advantages of the proposed approach are: (i) there are no appreciable delays in the filtered signal; (ii) the method does not require any statistical assessment of the noises corrupting the signal; (iii) high attenuation of the noise effects is achieved; (iv) the on-line computations are carried out in real time; and (v) high versatility and ease of implementation. Several experiments related to real depth measurements were conducted to show the effectiveness of the proposed algorithm. Comparisons are performed with different filtering alternatives.
Backus, Sterling J.; Kapteyn, Henry C.
2007-07-10
A method for optimizing multipass laser amplifier output utilizes a spectral filter in early passes but not in later passes. The pulses shift position slightly for each pass through the amplifier, and the filter is placed such that early passes intersect the filter while later passes bypass it. The filter position may be adjust offline in order to adjust the number of passes in each category. The filter may be optimized for use in a cryogenic amplifier.
Advanced testing method to evaluate the performance of respirator filter media.
Wang, Qiang; Golshahi, Laleh; Chen, Da-Ren
2016-10-01
Filter media for respirator applications are typically exposed to the cyclic flow condition, which is different from the constant flow condition adopted in filter testing standards. To understand the real performance of respirator filter media in the field it is required to investigate the penetration of particles through respirator filters under cyclic flow conditions representing breathing flow patterns of human beings. This article reports a new testing method for studying the individual effect of breathing frequency (BF) and peak inhalation flow rate (PIFR) on the particle penetration through respirator filter media. The new method includes the use of DMA (Differential Mobility Analyzer)-classified particles having the most penetrating particle size, MPPS (at the constant flowrate of equivalent mean inhalation flow rate, MIFR) as test aerosol. Two condensation particle counters (CPCs) are applied to measure the particle concentrations at the upstream and downstream of test filter media at the same time. Given the 10 Hz sampling time of CPCs, close-to-instantaneous particle penetration could be measured. A pilot study was performed to demonstrate the new testing method. It is found that the effect of BF on the particle penetration of test respirator filter media is of importance at all the tested peak inhalation flow rates (PIFRs), which is different from those reported in the previous work. PMID:27104915
Adaptive Kalman filter implementation by a neural network scheme for tracking maneuvering targets
NASA Astrophysics Data System (ADS)
Amoozegar, Farid; Sundareshan, Malur K.
1995-07-01
Conventional target tracking algorithms based on linear estimation techniques perform quite efficiently when the target motion does not involve maneuvers. Target maneuvers involving short term accelerations, however, cause a bias (e.g. jump) in the measurement sequence, which unless compensated, results in divergence of the Kalman filter that provides estimates of target position and velocity, in turn leading to a loss of track. Accurate compensation for the bias requires processing more samples of the input signals which adds to the computational complexity. The waiting time for more samples can also result in a total loss of track since the target can begin a new maneuver and if the target begins a new maneuver before the first one is compensated for, the filter would never converge. Most of the proposed algorithms in the current literature hence have the disadvantage of losing the target in short term accelerations, i.e., when the duration of acceleration is comparable to the time period between the measurements. The time lag for maneuver modelings, which have been based on Bayesian probability calculations and linear estimation shall propose a neural network scheme for the modeling of target maneuvers. The primary motivation for employing compensation. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates and hence can take the burden off the primary Kalman filter which still provides the target position and velocity estimates.
Adaptive DCT-based filtering of images corrupted by spatially correlated noise
NASA Astrophysics Data System (ADS)
Ponomarenko, Nikolay N.; Lukin, Vladimir V.; Zelensky, Aleksandr A.; Astola, Jaakko T.; Egiazarian, Karen O.
2008-02-01
Majority of image filtering techniques are designed under assumption that noise is of special, a priori known type and it is i.i.d., i.e. spatially uncorrelated. However, in many practical situations the latter assumption is not true due to several reasons. Moreover, spatial correlation properties of noise might be rather different and a priori unknown. Then the assumption that noise is i.i.d. under real conditions of spatially correlated noise commonly leads to considerable decrease of a used filter effectiveness in comparison to a case if this spatial correlation is taken into account. Our paper deals with two basic aspects. The first one is how to modify a denoising algorithm, in particular, a discrete cosine transform (DCT) based filter in order to incorporate a priori or preliminarily obtained knowledge of spatial correlation characteristics of noise. The second aspect is how to estimate spatial correlation characteristics of noise for a given image with appropriate accuracy and robustness under condition that there is some a priori information about, at least, noise type and statistics like variance (for additive noise case) or relative variance (for multiplicative noise). We also present simulation results showing the effectiveness (the benefit) of taking into consideration noise correlation properties.
An Innovative Method for Dynamic Characterization of Fan FilterUnit Operation.
Xu, Tengfang
2006-12-21
Fan filter units (FFU) are widely used to deliver re-circulated air while providing filtration control of particle concentration in controlled environments such as cleanrooms, minienvironments, and operating rooms in hospitals. The objective of this paper is to document an innovative method for characterizing operation and control of an individual fan filter unit within its operable conditions. Built upon the draft laboratory method previously published [1] , this paper presents an updated method including a testing procedure to characterize dynamic operation of fan filter units, i.e., steady-state operation conditions determined by varied control schemes, airflow rates, and pressure differential across the units. The parameters for dynamic characterization include total electric power demand, total pressure efficiency, airflow rate, pressure differential across fan filter units, and airflow uniformity.
Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising
Lentini, Marianela; Paluszny, Marco
2014-01-01
The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T2 weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek's algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T2 MR images, and the filter is applied to each image before using the variant of the Prony method. PMID:24834108
NASA Astrophysics Data System (ADS)
Schneider, Kai; Roussel, Olivier; Farge, Marie
2007-11-01
Coherent Vortex Simulation is based on the wavelet decomposition of the flow into coherent and incoherent components. An adaptive multiresolution method using second order finite volumes with explicit time discretization, a 2-4 Mac Cormack scheme, allows an efficient computation of the coherent flow on a dynamically adapted grid. Neglecting the influence of the incoherent background models turbulent dissipation. We present CVS computation of three dimensional compressible time developing mixing layer. We show the speed up in CPU time with respect to DNS and the obtained memory reduction thanks to dynamical octree data structures. The impact of different filtering strategies is discussed and it is found that isotropic wavelet thresholding of the Favre averaged gradient of the momentum yields the most effective results.
Adaptive multiscale model reduction with Generalized Multiscale Finite Element Methods
NASA Astrophysics Data System (ADS)
Chung, Eric; Efendiev, Yalchin; Hou, Thomas Y.
2016-09-01
In this paper, we discuss a general multiscale model reduction framework based on multiscale finite element methods. We give a brief overview of related multiscale methods. Due to page limitations, the overview focuses on a few related methods and is not intended to be comprehensive. We present a general adaptive multiscale model reduction framework, the Generalized Multiscale Finite Element Method. Besides the method's basic outline, we discuss some important ingredients needed for the method's success. We also discuss several applications. The proposed method allows performing local model reduction in the presence of high contrast and no scale separation.
NASA Astrophysics Data System (ADS)
Songer, Jocelyn E.; Eatock, Ruth Anne
2011-11-01
The mammalian saccule detects head tilt and low-frequency head accelerations as well as higher-frequency bone vibrations and sounds. It has two different hair cell types, I and II, dispersed throughout two morphologically distinct regions, the striola and extrastriola. Afferents from the two zones have distinct response dynamics which may arise partly from zonal differences in hair cell properties. We find that type II hair cells in the rat saccular epithelium adapt with a time course appropriate for influencing afferent responses to head motions. Moreover, striolar type II hair cells adapted by a greater extent than extrastriolar type II hair cells and had greater phase leads in the mid-frequency range (5-50 Hz). These differences suggest that hair cell transduction may contribute to zonal differences in the adaptation of vestibular afferents to head motions.
Hybrid method for designing digital FIR filters based on fractional derivative constraints.
Baderia, Kuldeep; Kumar, Anil; Kumar Singh, Girish
2015-09-01
In this manuscript, a hybrid approach based on Lagrange multiplier method and cuckoo search (CS) optimization technique is proposed for the design of linear phase finite impulse response (FIR) filters using fractional derivative constraints. In the proposed method, FIR filter is designed by optimizing the integral squares in passband and stopband from ideal response such that the fractional derivatives of designed filter response become zero at a given frequency point. Lagrange multiplier method is exploited for finding the optimized filter coefficients. Optimal value of fractional derivative constraints for optimized filter coefficients are determined by minimizing the objective function constructed using a sum of maximum passband ripple and maximum stopband ripple in frequency domain using CS algorithm. Performance of the proposed method is evaluated by passband error (ϕ(p)), stopband error (ϕ(s)), stopband attenuation (A(s)), maximum passband ripple (MPR), maximum stopband ripple (MSR) and CPU time. A comparative study of the performance of particle swarm optimization (PSO) and artificial bee colony (ABC) for designing FIR filters using the proposed method is also made. PMID:26142984
Final Report: Symposium on Adaptive Methods for Partial Differential Equations
Pernice, M.; Johnson, C.R.; Smith, P.J.; Fogelson, A.
1998-12-10
OAK-B135 Final Report: Symposium on Adaptive Methods for Partial Differential Equations. Complex physical phenomena often include features that span a wide range of spatial and temporal scales. Accurate simulation of such phenomena can be difficult to obtain, and computations that are under-resolved can even exhibit spurious features. While it is possible to resolve small scale features by increasing the number of grid points, global grid refinement can quickly lead to problems that are intractable, even on the largest available computing facilities. These constraints are particularly severe for three dimensional problems that involve complex physics. One way to achieve the needed resolution is to refine the computational mesh locally, in only those regions where enhanced resolution is required. Adaptive solution methods concentrate computational effort in regions where it is most needed. These methods have been successfully applied to a wide variety of problems in computational science and engineering. Adaptive methods can be difficult to implement, prompting the development of tools and environments to facilitate their use. To ensure that the results of their efforts are useful, algorithm and tool developers must maintain close communication with application specialists. Conversely it remains difficult for application specialists who are unfamiliar with the methods to evaluate the trade-offs between the benefits of enhanced local resolution and the effort needed to implement an adaptive solution method.
2013-01-01
Background When mathematical modelling is applied to many different application areas, a common task is the estimation of states and parameters based on measurements. With this kind of inference making, uncertainties in the time when the measurements have been taken are often neglected, but especially in applications taken from the life sciences, this kind of errors can considerably influence the estimation results. As an example in the context of personalized medicine, the model-based assessment of the effectiveness of drugs is becoming to play an important role. Systems biology may help here by providing good pharmacokinetic and pharmacodynamic (PK/PD) models. Inference on these systems based on data gained from clinical studies with several patient groups becomes a major challenge. Particle filters are a promising approach to tackle these difficulties but are by itself not ready to handle uncertainties in measurement times. Results In this article, we describe a variant of the standard particle filter (PF) algorithm which allows state and parameter estimation with the inclusion of measurement time uncertainties (MTU). The modified particle filter, which we call MTU-PF, also allows the application of an adaptive stepsize choice in the time-continuous case to avoid degeneracy problems. The modification is based on the model assumption of uncertain measurement times. While the assumption of randomness in the measurements themselves is common, the corresponding measurement times are generally taken as deterministic and exactly known. Especially in cases where the data are gained from measurements on blood or tissue samples, a relatively high uncertainty in the true measurement time seems to be a natural assumption. Our method is appropriate in cases where relatively few data are used from a relatively large number of groups or individuals, which introduce mixed effects in the model. This is a typical setting of clinical studies. We demonstrate the method on a small
Design of cross-coupled planar microstrip band-pass filters using a novel adjustment method
NASA Astrophysics Data System (ADS)
Alkafaji, Muhammed S. S.
2015-02-01
In this paper the design of a cross-coupled planar microwave band-pass filter using open-loop square microstrip resonators, with two symmetrically placed attenuation poles, is presented. The design starts from the following specifications: central frequency 3GHz, 150MHz bandwidth, 1dB ripple in the pass-band corresponding to return loss of 6.8dB.The adjustment method using additional ports placed on each resonator of the filter is used, allowing the interconnection of the filter structure with external lumped elements. Connecting a reactive element (capacitor) from such an additional port to ground allows a fine tuning of the resonator. Connecting a reactive element between two such additional ports allows a fine change in the coupling coefficient between these resonators. After adding four extra ports and connecting the external elements (capacitors), it is possible to use fast circuit simulation software to optimize the filter's response. Then all these capacitors have to be gradually eliminated, by compensating their effects through fine changes back in the layouts. If some specific issues are properly handled, this procedure improves considerably the quality of the final design of the filter. After a thorough optimization of the layout, the filter was fabricated and measured. The results of measurements are in good agreement with the specifications of the filter, showing this way the efficiency of the applied optimization method.
Automated nondestructive evaluation method for characterizing ceramic and metallic hot gas filters.
Ellingson, W. A.; Koehl, E. R.; Deemer, C.; Pastilla, P.; Wheeler, B.; Forster, G. A.
2002-06-03
In advanced coal-fired power generation, one technology under development to clean up hot gases before their use as fuel for gas turbines is rigid ceramic candle filters. These porous filters are typically 1.5 m long and 60 mm in diameter and are made of various ceramic materials, including clay-bonded SiC. The high costs of downtime in a large utility demands that nondestructive evaluation/characterization (NDE/C) methods be available. At shutdowns, data from such analysis are needed to decide which filters are still usable and which need to be replaced, and if possible, to estimate the remaining lifetimes. Thus our objective was to develop reliable low-cost NDE technology for these filters. Our approach was to develop NDE/C technology, referred to as acousto-ultrasonics (AU), for application to hot gas filters. Lamb waves generated by the AU method were analyzed to derive a stress wave factor (SWF). This technology was tested by comparing SWF data with the measured strength for a variety of rigid ceramic filters and was shown to work on iron-aluminide filters as well but no strength data have been obtained on the iron-aluminides at this time.
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-01-01
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. PMID:25985165
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-01-01
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. PMID:25985165
NASA Astrophysics Data System (ADS)
Neuhäuser, Markus; Krackow, Sven
2007-02-01
The neonatal incidence rate of Down syndrome (DS) is well-known to accelerate strongly with maternal age. This non-linearity renders mere accumulation of defects at recombination during prolonged first meiotic prophase implausible as an explanation for DS rate increase with maternal age, but might be anticipated from chromosomal drive (CD) for trisomy 21. Alternatively, as there is selection against genetically disadvantaged embryos, the screening system that eliminates embryos with trisomy 21 might decay with maternal age. In this paper, we provide the first evidence for relaxed filtering stringency (RFS) to represent an adaptive maternal response that could explain accelerating DS rates with maternal age. Using historical data, we show that the proportion of aberrant live births decrease with increased family size in older mothers, that inter-birth intervals are longer before affected neonates than before normal ones, and that primiparae exhibit elevated levels of DS incidence at higher age. These findings are predicted by adaptive RFS but cannot be explained by the currently available alternative non-adaptive hypotheses, including CD. The identification of the relaxation control mechanism and therapeutic restoration of a stringent screen may have considerable medical implications.
A Dynamically Adaptive Arbitrary Lagrangian-Eulerian Method for Hydrodynamics
Anderson, R W; Pember, R B; Elliott, N S
2004-01-28
A new method that combines staggered grid Arbitrary Lagrangian-Eulerian (ALE) techniques with structured local adaptive mesh refinement (AMR) has been developed for solution of the Euler equations. The novel components of the combined ALE-AMR method hinge upon the integration of traditional AMR techniques with both staggered grid Lagrangian operators as well as elliptic relaxation operators on moving, deforming mesh hierarchies. Numerical examples demonstrate the utility of the method in performing detailed three-dimensional shock-driven instability calculations.
A Dynamically Adaptive Arbitrary Lagrangian-Eulerian Method for Hydrodynamics
Anderson, R W; Pember, R B; Elliott, N S
2002-10-19
A new method that combines staggered grid Arbitrary Lagrangian-Eulerian (ALE) techniques with structured local adaptive mesh refinement (AMR) has been developed for solution of the Euler equations. The novel components of the combined ALE-AMR method hinge upon the integration of traditional AMR techniques with both staggered grid Lagrangian operators as well as elliptic relaxation operators on moving, deforming mesh hierarchies. Numerical examples demonstrate the utility of the method in performing detailed three-dimensional shock-driven instability calculations.