Matched filter based iterative adaptive approach
NASA Astrophysics Data System (ADS)
Nepal, Ramesh; Zhang, Yan Rockee; Li, Zhengzheng; Blake, William
2016-05-01
Matched Filter sidelobes from diversified LPI waveform design and sensor resolution are two important considerations in radars and active sensors in general. Matched Filter sidelobes can potentially mask weaker targets, and low sensor resolution not only causes a high margin of error but also limits sensing in target-rich environment/ sector. The improvement in those factors, in part, concern with the transmitted waveform and consequently pulse compression techniques. An adaptive pulse compression algorithm is hence desired that can mitigate the aforementioned limitations. A new Matched Filter based Iterative Adaptive Approach, MF-IAA, as an extension to traditional Iterative Adaptive Approach, IAA, has been developed. MF-IAA takes its input as the Matched Filter output. The motivation here is to facilitate implementation of Iterative Adaptive Approach without disrupting the processing chain of traditional Matched Filter. Similar to IAA, MF-IAA is a user parameter free, iterative, weighted least square based spectral identification algorithm. This work focuses on the implementation of MF-IAA. The feasibility of MF-IAA is studied using a realistic airborne radar simulator as well as actual measured airborne radar data. The performance of MF-IAA is measured with different test waveforms, and different Signal-to-Noise (SNR) levels. In addition, Range-Doppler super-resolution using MF-IAA is investigated. Sidelobe reduction as well as super-resolution enhancement is validated. The robustness of MF-IAA with respect to different LPI waveforms and SNR levels is also demonstrated.
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
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.
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
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
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.
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.
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.
Split quaternion nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2010-04-01
A split quaternion learning algorithm for the training of nonlinear finite impulse response adaptive filters for the processing of three- and four-dimensional signals is proposed. The derivation takes into account the non-commutativity of the quaternion product, an aspect neglected in the derivation of the existing learning algorithms. It is shown that the additional information taken into account by a rigorous treatment of quaternion algebra provides improved performance on hypercomplex processes. A rigorous analysis of the convergence of the proposed algorithms is also provided. Simulations on both benchmark and real-world signals support the approach.
An Adaptive Particle Filtering Approach to Tracking Modes in a Varying Shallow Ocean Environment
Candy, J V
2011-03-22
The shallow ocean environment is ever changing mostly due to temperature variations in its upper layers (< 100m) directly affecting sound propagation throughout. The need to develop processors that are capable of tracking these changes implies a stochastic as well as an 'adaptive' design. The stochastic requirement follows directly from the multitude of variations created by uncertain parameters and noise. Some work has been accomplished in this area, but the stochastic nature was constrained to Gaussian uncertainties. It has been clear for a long time that this constraint was not particularly realistic leading a Bayesian approach that enables the representation of any uncertainty distribution. Sequential Bayesian techniques enable a class of processors capable of performing in an uncertain, nonstationary (varying statistics), non-Gaussian, variable shallow ocean. In this paper adaptive processors providing enhanced signals for acoustic hydrophonemeasurements on a vertical array as well as enhanced modal function estimates are developed. Synthetic data is provided to demonstrate that this approach is viable.
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.
NASA Technical Reports Server (NTRS)
Starks, Scott; Abdel-Hafeez, Saleh; Usevitch, Bryan
1997-01-01
This paper discusses the implementation of a fuzzy logic system using an ASICs design approach. The approach is based upon combining the inherent advantages of symmetric triangular membership functions and fuzzy singleton sets to obtain a novel structure for fuzzy logic system application development. The resulting structure utilizes a fuzzy static RAM to store the rule-base and the end-points of the triangular membership functions. This provides advantages over other approaches in which all sampled values of membership functions for all universes must be stored. The fuzzy coprocessor structure implements the fuzzification and defuzzification processes through a two-stage parallel pipeline architecture which is capable of executing complex fuzzy computations in less than 0.55us with an accuracy of more than 95%, thus making it suitable for a wide range of applications. Using the approach presented in this paper, a fuzzy logic rule-base can be directly downloaded via a host processor to an onchip rule-base memory with a size of 64 words. The fuzzy coprocessor's design supports up to 49 rules for seven fuzzy membership functions associated with each of the chip's two input variables. This feature allows designers to create fuzzy logic systems without the need for additional on-board memory. Finally, the paper reports on simulation studies that were conducted for several adaptive filter applications using the least mean squared adaptive algorithm for adjusting the knowledge rule-base.
Insect-Inspired Self-Motion Estimation with Dense Flow Fields—An Adaptive Matched Filter Approach
Strübbe, Simon; Stürzl, Wolfgang; Egelhaaf, Martin
2015-01-01
The control of self-motion is a basic, but complex task for both technical and biological systems. Various algorithms have been proposed that allow the estimation of self-motion from the optic flow on the eyes. We show that two apparently very different approaches to solve this task, one technically and one biologically inspired, can be transformed into each other under certain conditions. One estimator of self-motion is based on a matched filter approach; it has been developed to describe the function of motion sensitive cells in the fly brain. The other estimator, the Koenderink and van Doorn (KvD) algorithm, was derived analytically with a technical background. If the distances to the objects in the environment can be assumed to be known, the two estimators are linear and equivalent, but are expressed in different mathematical forms. However, for most situations it is unrealistic to assume that the distances are known. Therefore, the depth structure of the environment needs to be determined in parallel to the self-motion parameters and leads to a non-linear problem. It is shown that the standard least mean square approach that is used by the KvD algorithm leads to a biased estimator. We derive a modification of this algorithm in order to remove the bias and demonstrate its improved performance by means of numerical simulations. For self-motion estimation it is beneficial to have a spherical visual field, similar to many flying insects. We show that in this case the representation of the depth structure of the environment derived from the optic flow can be simplified. Based on this result, we develop an adaptive matched filter approach for systems with a nearly spherical visual field. Then only eight parameters about the environment have to be memorized and updated during self-motion. PMID:26308839
Insect-Inspired Self-Motion Estimation with Dense Flow Fields--An Adaptive Matched Filter Approach.
Strübbe, Simon; Stürzl, Wolfgang; Egelhaaf, Martin
2015-01-01
The control of self-motion is a basic, but complex task for both technical and biological systems. Various algorithms have been proposed that allow the estimation of self-motion from the optic flow on the eyes. We show that two apparently very different approaches to solve this task, one technically and one biologically inspired, can be transformed into each other under certain conditions. One estimator of self-motion is based on a matched filter approach; it has been developed to describe the function of motion sensitive cells in the fly brain. The other estimator, the Koenderink and van Doorn (KvD) algorithm, was derived analytically with a technical background. If the distances to the objects in the environment can be assumed to be known, the two estimators are linear and equivalent, but are expressed in different mathematical forms. However, for most situations it is unrealistic to assume that the distances are known. Therefore, the depth structure of the environment needs to be determined in parallel to the self-motion parameters and leads to a non-linear problem. It is shown that the standard least mean square approach that is used by the KvD algorithm leads to a biased estimator. We derive a modification of this algorithm in order to remove the bias and demonstrate its improved performance by means of numerical simulations. For self-motion estimation it is beneficial to have a spherical visual field, similar to many flying insects. We show that in this case the representation of the depth structure of the environment derived from the optic flow can be simplified. Based on this result, we develop an adaptive matched filter approach for systems with a nearly spherical visual field. Then only eight parameters about the environment have to be memorized and updated during self-motion.
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.
Kalman filter based control for Adaptive Optics
NASA Astrophysics Data System (ADS)
Petit, Cyril; Quiros-Pacheco, Fernando; Conan, Jean-Marc; Kulcsár, Caroline; Raynaud, Henri-François; Fusco, Thierry
2004-12-01
Classical Adaptive Optics suffer from a limitation of the corrected Field Of View. This drawback has lead to the development of MultiConjugated Adaptive Optics. While the first MCAO experimental set-ups are presently under construction, little attention has been paid to the control loop. This is however a key element in the optimization process especially for MCAO systems. Different approaches have been proposed in recent articles for astronomical applications : simple integrator, Optimized Modal Gain Integrator and Kalman filtering. We study here Kalman filtering which seems a very promising solution. Following the work of Brice Leroux, we focus on a frequential characterization of kalman filters, computing a transfer matrix. The result brings much information about their behaviour and allows comparisons with classical controllers. It also appears that straightforward improvements of the system models can lead to static aberrations and vibrations filtering. Simulation results are proposed and analysed thanks to our frequential characterization. Related problems such as model errors, aliasing effect reduction or experimental implementation and testing of Kalman filter control loop on a simplified MCAO experimental set-up could be then discussed.
Quaternion-valued nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2011-08-01
A class of nonlinear quaternion-valued adaptive filtering algorithms is proposed based on locally analytic nonlinear activation functions. To circumvent the stringent standard analyticity conditions which are prohibitive to the development of nonlinear adaptive quaternion-valued estimation models, we use the fact that stochastic gradient learning algorithms require only local analyticity at the operating point in the estimation space. It is shown that the quaternion-valued exponential function is locally analytic, and, since local analyticity extends to polynomials, products, and ratios, we show that a class of transcendental nonlinear functions can serve as activation functions in nonlinear and neural adaptive models. This provides a unifying framework for the derivation of gradient-based learning algorithms in the quaternion domain, and the derived algorithms are shown to have the same generic form as their real- and complex-valued counterparts. To make such models second-order optimal for the generality of quaternion signals (both circular and noncircular), we use recent developments in augmented quaternion statistics to introduce widely linear versions of the proposed nonlinear adaptive quaternion valued filters. This allows full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances to cater rigorously for second-order noncircularity (improperness), and the corresponding power mismatch in the signal components. Simulations over a range of circular and noncircular synthetic processes and a real world 3-D noncircular wind signal support the approach. PMID:21712159
Quaternion-valued nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2011-08-01
A class of nonlinear quaternion-valued adaptive filtering algorithms is proposed based on locally analytic nonlinear activation functions. To circumvent the stringent standard analyticity conditions which are prohibitive to the development of nonlinear adaptive quaternion-valued estimation models, we use the fact that stochastic gradient learning algorithms require only local analyticity at the operating point in the estimation space. It is shown that the quaternion-valued exponential function is locally analytic, and, since local analyticity extends to polynomials, products, and ratios, we show that a class of transcendental nonlinear functions can serve as activation functions in nonlinear and neural adaptive models. This provides a unifying framework for the derivation of gradient-based learning algorithms in the quaternion domain, and the derived algorithms are shown to have the same generic form as their real- and complex-valued counterparts. To make such models second-order optimal for the generality of quaternion signals (both circular and noncircular), we use recent developments in augmented quaternion statistics to introduce widely linear versions of the proposed nonlinear adaptive quaternion valued filters. This allows full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances to cater rigorously for second-order noncircularity (improperness), and the corresponding power mismatch in the signal components. Simulations over a range of circular and noncircular synthetic processes and a real world 3-D noncircular wind signal support the approach.
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.
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.
Neural nets for adaptive filtering and adaptive pattern recognition
Widrow, B.; Winter, R.
1988-03-01
The fields of adaptive signal processing and adaptive neural networks have been developing independently but have that adaptive linear combiner (ALC) in common. With its inputs connected to a tapped delay line, the ALC becomes a key component of an adaptive filter. With its output connected to a quantizer, the ALC becomes an adaptive threshold element of adaptive neuron. Adaptive threshold elements, on the other hand, are the building blocks of neural networks. Today neural nets are the focus of widespread research interest. Areas of investigation include pattern recognition and trainable logic. Neural network systems have not yet had the commercial impact of adaptive filtering. The commonality of the ALC to adaptive signal processing and adaptive neural networks suggests the two fields have much to share with each other. This article describes practical applications of the ALC in signal processing and pattern recognition.
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…
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.
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.
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.
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 wavelet Wiener filtering of ECG signals.
Smital, Lukáš; Vítek, Martin; Kozumplík, Jiří; Provazník, Ivo
2013-02-01
In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter.
Adaptive wavelet Wiener filtering of ECG signals.
Smital, Lukáš; Vítek, Martin; Kozumplík, Jiří; Provazník, Ivo
2013-02-01
In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter. PMID:23192472
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.
NASA Astrophysics Data System (ADS)
Seitz, F.; Kirschner, S.; Neubersch, D.
2012-09-01
The geophysical interpretation of observed time series of Earth rotation parameters (ERP) is commonly based on numerical models that describe and balance variations of angular momentum in various subsystems of the Earth. Naturally, models are dependent on geometrical, rheological and physical parameters. Many of these are weakly determined from other models or observations. In our study we present an adaptive Kalman filter approach for the improvement of parameters of the dynamic Earth system model DyMEG which acts as a simulator of ERP. In particular we focus on the improvement of the pole tide Love number k2. In the frame of a sensitivity analysis k2 has been identified as one of the most crucial parameters of DyMEG since it directly influences the modeled Chandler oscillation. At the same time k2 is one of the most uncertain parameters in the model. Our simulations with DyMEG cover a period of 60 years after which a steady state of k2 is reached. The estimate for k2, accounting for the anelastic response of the Earth's mantle and the ocean, is 0.3531 + 0.0030i. We demonstrate that the application of the improved parameter k2 in DyMEG leads to significantly better results for polar motion than the original value taken from the Conventions of the International Earth Rotation and Reference Systems Service (IERS).
NASA Astrophysics Data System (ADS)
Seitz, F.; Kirschner, S.; Neubersch, D.
2012-12-01
Earth rotation has been monitored using space geodetic techniques since many decades. The geophysical interpretation of observed time series of Earth rotation parameters (ERP) polar motion and length-of-day is commonly based on numerical models that describe and balance variations of angular momentum in various subsystems of the Earth. Naturally, models are dependent on geometrical, rheological and physical parameters. Many of these are weakly determined from other models or observations. In our study we present an adaptive Kalman filter approach for the improvement of parameters of the dynamic Earth system model DyMEG which acts as a simulator of ERP. In particular we focus on the improvement of the pole tide Love number k2. In the frame of a sensitivity analysis k2 has been identified as one of the most crucial parameters of DyMEG since it directly influences the modeled Chandler oscillation. At the same time k2 is one of the most uncertain parameters in the model. Our simulations with DyMEG cover a period of 60 years after which a steady state of k2 is reached. The estimate for k2, accounting for the anelastic response of the Earth's mantle and the ocean, is 0.3531 + 0.0030i. We demonstrate that the application of the improved parameter k2 in DyMEG leads to significantly better results for polar motion than the original value taken from the Conventions of the International Earth Rotation and Reference Systems Service (IERS).
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
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.
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.
Simple method for adaptive filtering of motion artifacts in E-textile wearable ECG sensors.
Alkhidir, Tamador; Sluzek, Andrzej; Yapici, Murat Kaya
2015-08-01
In this paper, we have developed a simple method for adaptive out-filtering of the motion artifact from the electrocardiogram (ECG) obtained by using conductive textile electrodes. The textile electrodes were placed on the left and the right wrist to measure ECG through lead-1 configuration. The motion artifact was induced by simple hand movements. The reference signal for adaptive filtering was obtained by placing additional electrodes at one hand to capture the motion of the hand. The adaptive filtering was compared to independent component analysis (ICA) algorithm. The signal-to-noise ratio (SNR) for the adaptive filtering approach was higher than independent component analysis in most cases.
Precise adaptive photonic rf filters realized with adaptive Bragg gratings
NASA Astrophysics Data System (ADS)
Wickham, Michael G.; Upton, Eric L.
2000-09-01
The demand for higher data capacity and reduced levels of interference in the communications arena are driving dtat links toward high carrier frequencies and wider modulation bandwidths. Circuitry for performing intermediate frequency processing over these more demanding ranges is needed to provide complex signal processing. We have demonstrated photonics technologies utilizing Bragg Grating Signal Processing (BGSP), which can be used to perform a variety of RF filter functions. The desirable benefits of multiple-tap adaptive finite impulse response (FIR) filters, infinite impulse response (IIR) filters, and equalizers are well known; however, they are usually the province of digital signal processing and demand preprocessor sample rates that require high system power consumption. BGSPs provide these functions with discrete optical taps and digital controls while only requiring bandwidths easily provided by conventional RF circuitry. This is because the actual signal processing of the large information bandwidths is performed in the optical regime, while control functions are performed at RF frequencies compatible with integrated circuit technologies. To realize the performance benefits of photonic processing, the Bragg grating reflectors must be stabilized against environmental without unduly taxing the RF control circuitry. We have implemented a orthogonally coded tap modulation technique which stabilizes the transfer function of the signal processor and enables significant adaptive IF signal processing to be obtained with very low size, weight, and power. Our demonstration of a photonic proof-of-concept architecture is a reconfigurable, multiple-tap FIR filter that is dynamically controlled to implement low-pass, high-pass, band-pass, band-stop, and tunable filters operating over bandwidths of 3 Ghz.
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
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
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-07-26
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.
Adaptive bilateral filter for sharpness enhancement and noise removal.
Zhang, Buyue; Allebach, Jan P
2008-05-01
In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method-the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images. PMID:18390373
Speckle reduction in ultrasound images using nonisotropic adaptive filtering.
Eom, Kie B
2011-10-01
In this article, a speckle reduction approach for ultrasound imaging that preserves important features such as edges, corners and point targets is presented. Speckle reduction is an important problem in coherent imaging, such as ultrasound imaging or synthetic aperture radar, and many speckle reduction algorithms have been developed. Speckle is a non-additive and non-white process and the reduction of speckle without blurring sharp features is known to be difficult. The new speckle reduction algorithm presented in this article utilizes a nonhomogeneous filter that adapts to the proximity and direction of the nearest important features. To remove speckle without blurring important features, the location and direction of edges in the image are estimated. Then for each pixel in the image, the distance and angle to the nearest edge are efficiently computed by a two-pass algorithm and stored in distance and angle maps. Finally for each pixel, an adaptive directional filter aligned to the nearest edge is applied. The shape and orientation of the adaptive filter are determined from the distance and angle maps. The new speckle reduction algorithm is tested with both synthesized and real ultrasound images. The performance of the new algorithm is also compared with those of other speckle reduction approaches and it is shown that the new algorithm performs favorably in reducing speckle without blurring important features.
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.
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.
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.
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 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.
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
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 (δi) and adaptive disturbance attenuation (γ), 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
Autonomous navigation system using a fuzzy adaptive nonlinear H∞ filter.
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-09-19
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.
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
Diagnostic analysis of vibration signals using adaptive digital filtering techniques
NASA Technical Reports Server (NTRS)
Jewell, R. E.; Jones, J. H.; Paul, J. E.
1983-01-01
Signal enhancement techniques are described using recently developed digital adaptive filtering equipment. Adaptive filtering concepts are not new; however, as a result of recent advances in microprocessor-based electronics, hardware has been developed that has stable characteristics and of a size exceeding 1000th order. Selected data processing examples are presented illustrating spectral line enhancement, adaptive noise cancellation, and transfer function estimation in the presence of corrupting noise.
A Novel Modulation Classification Approach Using Gabor Filter Network
Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed
2014-01-01
A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603
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.
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…
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.…
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.
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.
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.
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
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
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.
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.
Study on GPS attitude determination system aided INS using adaptive Kalman filter
NASA Astrophysics Data System (ADS)
Bian, Hongwei; Jin, Zhihua; Tian, Weifeng
2005-10-01
A marine INS/GPS (inertial navigation system/global positioning system) adaptive navigation system is presented in this paper. The GPS with two antennae providing vessel attitude is selected as the auxiliary system to fuse with INS. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The conventional Kalman filter (CKF) assumes that the statistics of the noise of each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However, the GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce a fuzzy logic control method into innovation-based adaptive estimation Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However, how to design the fuzzy logic controller is a very complicated problem, which is still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested in the developed INS/GPS integrated marine navigation system. Real field test results show that the adaptive Kalman filter outperforms the CKF with higher accuracy and robustness. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise existing in the Kalman filter.
An Exploration of Trainer Filtering Approaches
NASA Technical Reports Server (NTRS)
Hester, Patrick; Tolk, Andreas; Gadi, Sandeep; Carver, Quinn; Roland, Philippe
2011-01-01
Simutator operators face a twofold entity management problem during Live-Virtual-Constructive (LVC) training events. They first must filter potentially hundreds of thousands of simulation entities in order 10 determine which elements are necessary for optimal trainee comprehension. Secondarily, they must manage the number of entities entering the simulation from those present in the object model in order to limit the computational burden on the simulation system and prevent unnecessary entities from entering the simulation, This paper focuses on the first filtering stage and describes a novel approach to entity filtering undertaken to maximize trainee awareness and learning. The feasibility of this novel approach is demonstrated on a case study and limitations to the proposed approach and future work are discussed.
Sidelobe reduction via adaptive FIR filtering in SAR imagery.
Degraaf, S R
1994-01-01
The paper describes a class of adaptive weighting functions that greatly reduce sidelobes, interference, and noise in Fourier transform data. By restricting the class of adaptive weighting functions, the adaptively weighted Fourier transform data can be represented as the convolution of the unweighted Fourier transform with a data adaptive FIR filter where one selects the FIR filter coefficients to maximize signal-to-interference ratio. This adaptive sidelobe reduction (ASR) procedure is analogous to Capon's (1969) minimum variance method (MVM) of adaptive spectral estimation. Unlike MVM, which provides a statistical estimate of the real-valued power spectral density, thereby estimating noise level and improving resolution, ASR provides a single-realization complex-valued estimate of the Fourier transform that suppresses sidelobes and noise. Further, the computational complexity of ASR is dramatically lower than that of MVM, which is critical for large multidimensional problems such as synthetic aperture radar (SAR) image formation. ASR performance characteristics can be varied through the choice of filter order, l(1)- or l(2)-norm filter vector constraints and a separable or nonseparable multidimensional implementation. The author compares simulated point scattering SAR imagery produced by the ASR, MVM, and MUSIC algorithms and illustrates ASR performance on three sets of collected SAR imagery.
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.
Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting
NASA Astrophysics Data System (ADS)
Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.
2014-12-01
Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.
Adaptive data filtering of inertial sensors with variable bandwidth.
Alam, Mushfiqul; Rohac, Jan
2015-02-02
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.
Adaptive filtering for ECG rejection from surface EMG recordings.
Marque, C; Bisch, C; Dantas, R; Elayoubi, S; Brosse, V; Pérot, C
2005-06-01
Surface electromyograms (EMG) of back muscles are often corrupted by electrocardiogram (ECG) signals. This noise in the EMG signals does not allow to appreciate correctly the spectral content of the EMG signals and to follow its evolution during, for example, a fatigue process. Several methods have been proposed to reject the ECG noise from EMG recordings, but seldom taking into account the eventual changes in ECG characteristics during the experiment. In this paper we propose an adaptive filtering algorithm specifically developed for the rejection of the electrocardiogram corrupting surface electromyograms (SEMG). The first step of the study was to choose the ECG electrode position in order to record the ECG with a shape similar to that found in the noised SEMGs. Then, the efficiency of different algorithms were tested on 28 erector spinae SEMG recordings. The best algorithm belongs to the fast recursive least square family (FRLS). More precisely, the best results were obtained with the simplified formulation of a FRLS algorithm. As an application of the adaptive filtering, the paper compares the evolutions of spectral parameters of noised or denoised (after adaptive filtering) surface EMGs recorded on erector spinae muscles during a trunk extension. The fatigue test was analyzed on 16 EMG recordings. After adaptive filtering, mean initial values of energy and of mean power frequency (MPF) were significantly lower and higher respectively. The differences corresponded to the removal of the ECG components. Furthermore, classical fatigue criteria (increase in energy and decrease in MPF values over time during the fatigue test) were better observed on the denoised EMGs. The mean values of the slopes of the energy-time and MPF-time linear relationships differed significantly when established before and after adaptive filtering. These results account for the efficacy of the adaptive filtering method proposed here to denoise electrophysiological signals.
Adaptive texture filtering for defect inspection in ultrasound images
NASA Astrophysics Data System (ADS)
Zmola, Carl; Segal, Andrew C.; Lovewell, Brian; Nash, Charles
1993-05-01
The use of ultrasonic imaging to analyze defects and characterize materials is critical in the development of non-destructive testing and non-destructive evaluation (NDT/NDE) tools for manufacturing. To develop better quality control and reliability in the manufacturing environment advanced image processing techniques are useful. For example, through the use of texture filtering on ultrasound images, we have been able to filter characteristic textures from highly-textured C-scan images of materials. The materials have highly regular characteristic textures which are of the same resolution and dynamic range as other important features within the image. By applying texture filters and adaptively modifying their filter response, we have examined a family of filters for removing these textures.
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.
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.
Streak image denoising and segmentation using adaptive Gaussian guided filter.
Jiang, Zhuocheng; Guo, Baoping
2014-09-10
In streak tube imaging lidar (STIL), streak images are obtained using a CCD camera. However, noise in the captured streak images can greatly affect the quality of reconstructed 3D contrast and range images. The greatest challenge for streak image denoising is reducing the noise while preserving details. In this paper, we propose an adaptive Gaussian guided filter (AGGF) for noise removal and detail enhancement of streak images. The proposed algorithm is based on a guided filter (GF) and part of an adaptive bilateral filter (ABF). In the AGGF, the details are enhanced by optimizing the offset parameter. AGGF-denoised streak images are significantly sharper than those denoised by the GF. Moreover, the AGGF is a fast linear time algorithm achieved by recursively implementing a Gaussian filter kernel. Experimentally, AGGF demonstrates its capacity to preserve edges and thin structures and outperforms the existing bilateral filter and domain transform filter in terms of both visual quality and peak signal-to-noise ratio performance.
Streak image denoising and segmentation using adaptive Gaussian guided filter.
Jiang, Zhuocheng; Guo, Baoping
2014-09-10
In streak tube imaging lidar (STIL), streak images are obtained using a CCD camera. However, noise in the captured streak images can greatly affect the quality of reconstructed 3D contrast and range images. The greatest challenge for streak image denoising is reducing the noise while preserving details. In this paper, we propose an adaptive Gaussian guided filter (AGGF) for noise removal and detail enhancement of streak images. The proposed algorithm is based on a guided filter (GF) and part of an adaptive bilateral filter (ABF). In the AGGF, the details are enhanced by optimizing the offset parameter. AGGF-denoised streak images are significantly sharper than those denoised by the GF. Moreover, the AGGF is a fast linear time algorithm achieved by recursively implementing a Gaussian filter kernel. Experimentally, AGGF demonstrates its capacity to preserve edges and thin structures and outperforms the existing bilateral filter and domain transform filter in terms of both visual quality and peak signal-to-noise ratio performance. PMID:25321679
Hardware implementation of a discrete-time analog adaptive filter
Donohoe, G.W.
1981-01-01
This paper describes a hardware implementation of a discrete-time adaptive filter using a bucket-brigade device (BBD) tapped analog delay line, analog voltage multipliers and operational amplifier integrators and summing circuits. Some design considerations for this class of circuits are discussed.
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.
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.
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.
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.
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.
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.
Combination of Adaptive Feedback Cancellation and Binaural Adaptive Filtering in Hearing Aids
NASA Astrophysics Data System (ADS)
Lombard, Anthony; Reindl, Klaus; Kellermann, Walter
2009-12-01
We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaural scenario is presented, some already existing and commonly used feedback cancellation performance measures for the unilateral case are adapted to the binaural case, and possible interaction effects between the algorithms are identified. For illustration purposes, a blind source separation algorithm has been chosen as an example for adaptive binaural spatial filtering. Experimental results for binaural hearing aids confirm the theoretical findings and the validity of the new measures.
Adaptation and the temporal delay filter of fly motion detectors.
Harris, R A; O'Carroll, D C; Laughlin, S B
1999-08-01
Recent accounts attribute motion adaptation to a shortening of the delay filter in elementary motion detectors (EMDs). Using computer modelling and recordings from HS neurons in the drone-fly Eristalis tenax, we present evidence that challenges this theory. (i) Previous evidence for a change in the delay filter comes from 'image step' (or 'velocity impulse') experiments. We note a large discrepancy between the temporal frequency tuning predicted from these experiments and the observed tuning of motion sensitive cells. (ii) The results of image step experiments are highly sensitive to the experimental method used. (iii) An apparent motion stimulus reveals a much shorter EMD delay than suggested by previous 'image step' experiments. This short delay agrees with the observed temporal frequency sensitivity of the unadapted cell. (iv) A key prediction of a shortening delay filter is that the temporal frequency optimum of the cell should show a large shift to higher temporal frequencies after motion adaptation. We show little change in the temporal or spatial frequency (and hence velocity) optima following adaptation.
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).
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.
A New Adaptive Framework for Collaborative Filtering Prediction
Almosallam, Ibrahim A.; Shang, Yi
2010-01-01
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
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.
The Limits to Adaptation; A Systems Approach
The Limits to Adaptation: A Systems Approach. The ability to adapt to climate change is delineated by capacity thresholds, after which climate damages begin to overwhelm the adaptation response. Such thresholds depend upon physical properties (natural processes and engineering...
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 (
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.
Adaptive filters and internal models: multilevel description of cerebellar function.
Porrill, John; Dean, Paul; Anderson, Sean R
2013-11-01
Cerebellar function is increasingly discussed in terms of engineering schemes for motor control and signal processing that involve internal models. To address the relation between the cerebellum and internal models, we adopt the chip metaphor that has been used to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections. This metaphor indicates that identifying the function of a particular cerebellar chip requires knowledge of both the general microcircuit algorithm and the chip's individual connections. Here we use a popular candidate algorithm as embodied in the adaptive filter, which learns to decorrelate its inputs from a reference ('teaching', 'error') signal. This algorithm is computationally powerful enough to be used in a very wide variety of engineering applications. However, the crucial issue is whether the external connectivity required by such applications can be implemented biologically. We argue that some applications appear to be in principle biologically implausible: these include the Smith predictor and Kalman filter (for state estimation), and the feedback-error-learning scheme for adaptive inverse control. However, even for plausible schemes, such as forward models for noise cancellation and novelty-detection, and the recurrent architecture for adaptive inverse control, there is unlikely to be a simple mapping between microzone function and internal model structure. This initial analysis suggests that cerebellar involvement in particular behaviours is therefore unlikely to have a neat classification into categories such as 'forward model'. It is more likely that cerebellar microzones learn a task-specific adaptive-filter operation which combines a number of signal-processing roles.
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
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.
Adaptive filter for reconstruction of stereo audio signals
NASA Astrophysics Data System (ADS)
Cisowski, Krzysztof
2004-05-01
The paper presents a new approach to reconstruction of impulsively disturbed stereo audio signals. The problems of restoration of large blocks of missing samples are outlined. Present methods of removing of covariance defect are discussed. Model of stereophonic signal is defined and Kalman filter appropriate for this model is introduced. Modifications of the filter directing to the new method of reconstruction of block of missing samples are discussed. Projection based algorithm allows to recover samples of left (or right) stereo channel using additional information included in undistorted samples from the other channel.
Adaptive filtering of Echelle spectra of distant Quasars
NASA Technical Reports Server (NTRS)
Priebe, A.; Liebscher, D.-E.; Lorenz, H.; Richter, G.-M.
1992-01-01
The study of the Ly alpha - forest of distant (approximately greater than 3) Quasars is an important tool in obtaining a more detailed picture of the distribution of matter along the line of sight and thus of the general distribution of matter in the Universe and is therefore of important cosmological significance. Obviously, this is one of the tasks where spectral resolution plays an important role. The spectra used were obtained with the EFOSC at the ESO 3.6m telescope. Applying for the data reduction the standard Echelle procedure, as it is implemented for instance in the MIDAS-package, one uses stationary filters (e.g. median) for noise and cosmic particle event reduction in the 2-dimensional Echelle image. These filters are useful if the spatial spectrum of the noise reaches essentially higher frequencies then the highest resolution features in the image. Otherwise the resolution in the data will be degraded and the spectral lines smoothed. However, in the Echelle spectra the highest resolution is already in the range of one or a few pixels and therefore stationary filtering means always a loss of resolution. An Echelle reduction procedure on the basis of a space variable filter described which recognizes the local resolution in the presence of noise and adapts to it is developed. It was shown that this technique leads to an improvement in resolution by a factor of 2 with respect to standard procedures.
An Adaptive Multipath Mitigation Filter for GNSS Applications
NASA Astrophysics Data System (ADS)
Chang, Chung-Liang; Juang, Jyh-Ching
2008-12-01
Global navigation satellite system (GNSS) is designed to serve both civilian and military applications. However, the GNSS performance suffers from several errors, such as ionosphere delay, troposphere delay, ephemeris error, and receiver noise and multipath. Among these errors, the multipath is one of the most unpredictable error sources in high-accuracy navigation. This paper applies a modified adaptive filter to reduce code and carrier multipath errors in GPS. The filter employs a tap-delay line with an Adaline network to estimate the direction and the delayed-signal parameters. Then, the multipath effect is mitigated by subtracting the estimated multipath effects from the processed correlation function. The hardware complexity of the method is also compared with other existing methods. Simulation results show that the proposed method using field data has a significant reduction in multipath error especially in short-delay multipath scenarios.
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
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.
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 micrometeorological humidity data were used to calibrate the ratio of the lidar gains of the H(2)O and the N(2) photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem.
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
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
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
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.
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
ADAPTIVE LAPLACIAN FILTERING FOR SENSORIMOTOR RHYTHM-BASED BRAIN-COMPUTER INTERFACES
Lu, Jun; McFarland, Dennis J.; Wolpaw, Jonathan R.
2013-01-01
Objective Sensorimotor rhythms (SMRs) are 8–30 Hz oscillations in the 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-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 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 filter. 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 as well as 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. PMID:23220879
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.
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.
Subotić, Miško; Šarić, Zoran; Jovičić, Slobodan T
2012-03-01
Transient otoacoustic emission (TEOAE) is a method widely used in clinical practice for assessment of hearing quality. The main problem in TEOAE detection is its much lower level than the level of environmental and biological noise. While the environmental noise level can be controlled, the biological noise can be only reduced by appropriate signal processing. This paper presents a new two-probe preprocessing TEOAE system for suppression of the biological noise by adaptive filtering. The system records biological noises in both ears and applies a specific adaptive filtering approach for suppression of biological noise in the ear canal with TEOAE. The adaptive filtering approach includes robust sign error LMS algorithm, stimuli response summation according to the derived non-linear response (DNLR) technique, subtraction of the estimated TEOAE signal and residual noise suppression. The proposed TEOAE detection system is tested by three quality measures: signal-to-noise ratio (S/N), reproducibility of TEOAE, and measurement time. The maximal TEOAE detection improvement is dependent on the coherence function between biological noise in left and right ears. The experimental results show maximal improvement of 7 dB in S/N, improvement in reproducibility near 40% and reduction in duration of TEOAE measurement of over 30%.
A Novel Design Approach for Contourlet Filter Banks
NASA Astrophysics Data System (ADS)
Yang, Guoan; van de Wetering, Huub; Hou, Ming; Ikuta, Chihiro; Liu, Yuehu
This letter proposes a novel design approach for optimal contourlet filter banks based on the parametric 9/7 filter family. The Laplacian pyramid decomposition is replaced by optimal 9/7 filter banks with rational coefficients, and directional filter banks are activated using a pkva 12 filter in the contourlets. Moreover, based on this optimal 9/7 filter, we present an image denoising approach using a contourlet domain hidden Markov tree model. Finally, experimental results show that our approach in denoising images with texture detail is only 0.20dB less compared to the method of Po and Do, and the visual quality is as good as for their method. Compared with the method of Po and Do, our approach has lower computational complexity and is more suitable for VLSI hardware implementation.
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 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.
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
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter.
Zhang, Zhen; Ma, Yaopeng
2016-02-06
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively.
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
Understanding the adaptive approach to thermal comfort
Humphreys, M.A.; Nicol, J.F.
1998-10-01
This paper explains the adaptive approach to thermal comfort, and an adaptive model for thermal comfort is presented. The model is an example of a complex adaptive system (Casti 1996) whose equilibria are determined by the restrictions acting upon it. People`s adaptive actions are generally effective in securing comfort, which occurs at a wide variety of indoor temperatures. These comfort temperatures depend upon the circumstances in which people live, such as the climate and the heating or cooling regime. The temperatures may be estimated from the mean outdoor temperature and the availability of a heating or cooling plant. The evaluation of the parameters of the adaptive model requires cross-sectional surveys to establish current norms and sequential surveys (with and without intervention) to evaluate the rapidity of people`s adaptive actions. Standards for thermal comfort will need revision in the light of the adaptive approach. Implications of the adaptive model for the HVAC industry are noted.
Piaggi, Paolo; Menicucci, Danilo; Gentili, Claudio; Handjaras, Giacomo; Gemignani, Angelo; Landi, Alberto
2014-05-01
Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, which need to be filtered before a functional connectivity analysis of brain regions is performed. These noisy components show autocorrelated and nonstationary properties that limit the efficacy of standard techniques (i.e. time filtering and general linear model). Herein we describe a novel approach based on the combination of singular spectrum analysis and adaptive filtering, which allows a greater noise reduction and yields better connectivity estimates between regions at rest, providing a new feasible procedure to analyze fMRI data.
An adaptive signal-processing approach to online adaptive tutoring.
Bergeron, Bryan; Cline, Andrew
2011-01-01
Conventional intelligent or adaptive tutoring online systems rely on domain-specific models of learner behavior based on rules, deep domain knowledge, and other resource-intensive methods. We have developed and studied a domain-independent methodology of adaptive tutoring based on domain-independent signal-processing approaches that obviate the need for the construction of explicit expert and student models. A key advantage of our method over conventional approaches is a lower barrier to entry for educators who want to develop adaptive online learning materials.
Acquiring case adaptation knowledge: A hybrid approach
Leake, D.B.; Kinley, A.; Wilson, D.
1996-12-31
The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very difficult task. This paper describes a hybrid method for performing case adaptation, using a combination of rule-based and case-based reasoning. It shows how this approach provides a framework for acquiring flexible adaptation knowledge from experiences with autonomous adaptation and suggests its potential as a basis for acquisition of adaptation knowledge from interactive user guidance. It also presents initial experimental results examining the benefits of the approach and comparing the relative contributions of case learning and adaptation learning to reasoning performance.
Adaptive hybrid likelihood model for visual tracking based on Gaussian particle filter
NASA Astrophysics Data System (ADS)
Wang, Yong; Tan, Yihua; Tian, Jinwen
2010-07-01
We present a new scheme based on multiple-cue integration for visual tracking within a Gaussian particle filter framework. The proposed method integrates the color, shape, and texture cues of an object to construct a hybrid likelihood model. During the measurement step, the likelihood model can be switched adaptively according to environmental changes, which improves the object representation to deal with the complex disturbances, such as appearance changes, partial occlusions, and significant clutter. Moreover, the confidence weights of the cues are adjusted online through the estimation using a particle filter, which ensures the tracking accuracy and reliability. Experiments are conducted on several real video sequences, and the results demonstrate that the proposed method can effectively track objects in complex scenarios. Compared with previous similar approaches through some quantitative and qualitative evaluations, the proposed method performs better in terms of tracking robustness and precision.
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
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-07-16
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.
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
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.
Adaptive spatial filtering for daytime satellite quantum key distribution
NASA Astrophysics Data System (ADS)
Gruneisen, Mark T.; Sickmiller, Brett A.; Flanagan, Michael B.; Black, James P.; Stoltenberg, Kurt E.; Duchane, Alexander W.
2014-11-01
The rate of secure key generation (SKG) in quantum key distribution (QKD) is adversely affected by optical noise and loss in the quantum channel. In a free-space atmospheric channel, the scattering of sunlight into the channel can lead to quantum bit error ratios (QBERs) sufficiently large to preclude SKG. Furthermore, atmospheric turbulence limits the degree to which spatial filtering can reduce sky noise without introducing signal losses. A system simulation quantifies the potential benefit of tracking and higher-order adaptive optics (AO) technologies to SKG rates in a daytime satellite engagement scenario. The simulations are performed assuming propagation from a low-Earth orbit (LEO) satellite to a terrestrial receiver that includes an AO system comprised of a Shack-Hartmann wave-front sensor (SHWFS) and a continuous-face-sheet deformable mirror (DM). The effects of atmospheric turbulence, tracking, and higher-order AO on the photon capture efficiency are simulated using statistical representations of turbulence and a time-domain waveoptics hardware emulator. Secure key generation rates are then calculated for the decoy state QKD protocol as a function of the receiver field of view (FOV) for various pointing angles. The results show that at FOVs smaller than previously considered, AO technologies can enhance SKG rates in daylight and even enable SKG where it would otherwise be prohibited as a consequence of either background optical noise or signal loss due to turbulence effects.
Locally adaptive regression filter-based infrared focal plane array non-uniformity correction
NASA Astrophysics Data System (ADS)
Li, Jia; Qin, Hanlin; Yan, Xiang; Huang, He; Zhao, Yingjuan; Zhou, Huixin
2015-10-01
Due to the limitations of the manufacturing technology, the response rates to the same infrared radiation intensity in each infrared detector unit are not identical. As a result, the non-uniformity of infrared focal plane array, also known as fixed pattern noise (FPN), is generated. To solve this problem, correcting the non-uniformity in infrared image is a promising approach, and many non-uniformity correction (NUC) methods have been proposed. However, they have some defects such as slow convergence, ghosting and scene degradation. To overcome these defects, a novel non-uniformity correction method based on locally adaptive regression filter is proposed. First, locally adaptive regression method is used to separate the infrared image into base layer containing main scene information and the detail layer containing detailed scene with FPN. Then, the detail layer sequence is filtered by non-linear temporal filter to obtain the non-uniformity. Finally, the high quality infrared image is obtained by subtracting non-uniformity component from original image. The experimental results show that the proposed method can significantly eliminate the ghosting and the scene degradation. The results of correction are superior to the THPF-NUC and NN-NUC in the aspects of subjective visual and objective evaluation index.
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.
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.
Non-adaptive robust filters for speckle noise reduction
NASA Astrophysics Data System (ADS)
Frery, Alejandro C.; Santanna, Sidnei J. S.
1993-06-01
After briefly reviewing some classical filters for speckle removal, five filters with characteristics of robustness, suitable for speckle noise reduction, are derived and implemented. These filters are the ones based on the trimmed maximum likelihood and moments estimators, the ones based on the median, on the inter-quartile range, and on the median absolute deviation. Assuming that observations within a synthetic aperture radar image are outcomes of independent Rayleigh random variables, these filters exhibit a good performance from both the signal-to-noise reduction and from the edge preserving criteria. The problem of filtering in an image is posed as an estimation problem.
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.
Infrared Target Acquisition Using An Adaptive Difference-Of-Boxes Filter
NASA Astrophysics Data System (ADS)
Guissin, Rami
1990-01-01
A variety of spatial filters have been previously proposed as detection filters for automated target acquisition. One class of filters, namely the matched filter, is designed for maximimum signal to noise response at true target locations. The filter design is a function of target dimensions and intensity distributions, and of the corresponding background spectrum. The filter sensitivity to target dimensions may be overcome by adapting the filter's dimensions to the incoming image signal, or by the economical use of (at least) two filters, designed separately for small and large targets. The robustness of the Difference-of-Boxes (DOB) filter is established for a class of targets having smooth, 2nd order intensity distributions, in the presence of both white noise and cluttered backgrounds.
Digital filter design approach for SQUID gradiometers
Bruno, A.C.; Ribeiro, P.C.
1988-04-15
A review of the traditional method for designing gradiometers is made. A nonrecursive digital filter model for the gradiometer is presented, giving a new set of parameters for the gradiometer identification. Some designs are analyzed using the proposed set. As an example, a true differentiator is designed to be used as the SQUID input coil. It is shown that the differentiator has the same noise rejection as the conventional gradiometer but provides more signal sensitivity.
Flight Test Approach to Adaptive Control Research
NASA Technical Reports Server (NTRS)
Pavlock, Kate Maureen; Less, James L.; Larson, David Nils
2011-01-01
The National Aeronautics and Space Administration s Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The validation of adaptive controls has the potential to enhance safety in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research.
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.
A Predictive Analysis Approach to Adaptive Testing.
ERIC Educational Resources Information Center
Kirisci, Levent; Hsu, Tse-Chi
The predictive analysis approach to adaptive testing originated in the idea of statistical predictive analysis suggested by J. Aitchison and I.R. Dunsmore (1975). The adaptive testing model proposed is based on parameter-free predictive distribution. Aitchison and Dunsmore define statistical prediction analysis as the use of data obtained from an…
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.
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
Adaptive filter for mine detection and classification in side-scan sonar imagery
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Antoni, Diana; Fernandez, Manuel F.; Dobeck, Gerald J.
1995-06-01
A need exists to develop robust automatic techniques for discriminating between minelike target and clutter returns in sonar imagery. To address this need, an adaptive clutter suppression linear FIR filtering technique has been developed and applied to side scan sonar imagery data. The adaptive filtering procedure consists of four stages. First, a normalized average target signature (shape) within the filter window is computed using training set data. Second, the background clutter covariance matrix is computed by scanning the filter window over the data. Third, following substitutions of the average target signature and covariance expressions into a set of normal equations, an adaptive filter is computed which simultaneously suppresses the background clutter while preserving the peak of the average target signature. Finally, the data is filtered using the 2D adaptive range-crossrange filter. The overall mine detection processing string includes automatic gain control, data decimation, adaptive clutter filtering (ACF), 2D normalization, thresholding, exceedance clustering, limiting the number of exceedances and secondary thresholding processing blocks. The utility of the ACF processing string was demonstrated with three side scan sonar datasets. The ACF algorithm provided average probability of detection and false alarm rate performance similar to that obtained when utilizing an expert sonar operator.
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.
Argonne National Laboratory - West's approach to filter characterization.
Miller, T. A.
1999-02-10
Like other DOE facilities, ANL-W uses a variety of nuclear grade, industrial grade, or furnace-type particulate filters to control airborne radioactivity and hazardous contaminants in radiological containment structures or processes. As designed, these filters entrain and ultimately concentrate contaminants in the media. Toxic metal contaminants include cadmium, chromium, lead; and mercury present in sufficient concentrations to exhibit the hazardous waste characteristic of toxicity as defined in 40 CFR 261.24. Radionuclide contaminants deposited in the media may at times accumulate in sufficient quantity to classify the filter as transuranic or remote-handled waste. Upon their removal from the ventilation system, these particulate filters become wastes, which must be characterized to determine their hazardous and radioactive classifications. A well defined filter characterization process is essential for the proper/consistent waste characterization and minimization and for maintaining personnel radiological exposures as-low-as-reasonably-achievable (ALARA) (1,2). ANL-W has developed an approach to filter sampling and characterization to meet these needs. The ANL-W filter sampling and characterization process is designed to ensure representative sampling and/or process knowledge is utilized in characterizing the filters. The data obtained through sampling and/or process knowledge is used to show compliance with the Resource Conservation and Recovery Act (3) and Treatment/Storage/Disposal Facility Waste Acceptance Criteria. The ANL-W filter characterization involves the collection of process information, filter handling and sampling, sample analysis, data management filter characterization, and waste handling. Each element of the process is streamlined to ensure proper characterization while minimizing radiological exposure to maintenance workers, samplers, laboratory personnel, and waste handlers.
A tracker based on a CPHD filter approach for infrared applications
NASA Astrophysics Data System (ADS)
Petetin, Y.; Clark, D.; Ristic, B.; Maltese, D.
2011-06-01
Since the derivation of PHD filter, a number of track management schemes have been proposed to adapt the PHD filter for determining the tracks of multiple objects. Nevertheless, the problem remains that such approaches can fail when targets are too close or are crossing. In this paper, we propose to improve the tracking by maintaining a set of locally-based trackers and managing the tracks with an assignment method. Furthermore, the new algorithm is based on a Gaussian mixture implementation of the CPHD filter, by clustering neighbouring Gaussians before the update step and updating each cluster with the CPHD filter update. In order to be computationally efficient, the algorithm includes gating techniques for the local trackers and constructs local cardinality distributions for the targets and clutter within the gated regions. An improvement in multi-object estimation performance has been experienced on both synthetic and real IR data scenarios.
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
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.
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
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.
Charisis, Vasileios S; Hadjileontiadis, Leontios J
2016-01-01
A new feature extraction technique for the detection of lesions created from mucosal inflammations in Crohn’s disease, based on wireless capsule endoscopy (WCE) images processing is presented here. More specifically, a novel filtering process, namely Hybrid Adaptive Filtering (HAF), was developed for efficient extraction of lesion-related structural/textural characteristics from WCE images, by employing Genetic Algorithms to the Curvelet-based representation of images. Additionally, Differential Lacunarity (DLac) analysis was applied for feature extraction from the HAF-filtered images. The resulted scheme, namely HAF-DLac, incorporates support vector machines for robust lesion recognition performance. For the training and testing of HAF-DLac, an 800-image database was used, acquired from 13 patients who undertook WCE examinations, where the abnormal cases were grouped into mild and severe, according to the severity of the depicted lesion, for a more extensive evaluation of the performance. Experimental results, along with comparison with other related efforts, have shown that the HAF-DLac approach evidently outperforms them in the field of WCE image analysis for automated lesion detection, providing higher classification results, up to 93.8% (accuracy), 95.2% (sensitivity), 92.4% (specificity) and 92.6% (precision). The promising performance of HAF-DLac paves the way for a complete computer-aided diagnosis system that could support physicians’ clinical practice.
A waveguide invariant adaptive matched filter for active sonar target depth classification.
Goldhahn, Ryan; Hickman, Granger; Krolik, Jeffrey
2011-04-01
This paper addresses depth discrimination of a water column target from bottom clutter discretes in wideband active sonar. To facilitate classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide invariant spectral density matrix (WI-SDM), which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is achieved using a waveguide invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) derived from signal covariance matrices averaged over the uncertain channel parameters. Simulation and real data results from the SCARAB98 and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach. Receiver operating characteristics (ROC) for robust waveguide invariant depth classification approaches are presented which illustrate performance under uncertain environmental conditions. PMID:21476638
A waveguide invariant adaptive matched filter for active sonar target depth classification.
Goldhahn, Ryan; Hickman, Granger; Krolik, Jeffrey
2011-04-01
This paper addresses depth discrimination of a water column target from bottom clutter discretes in wideband active sonar. To facilitate classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide invariant spectral density matrix (WI-SDM), which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is achieved using a waveguide invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) derived from signal covariance matrices averaged over the uncertain channel parameters. Simulation and real data results from the SCARAB98 and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach. Receiver operating characteristics (ROC) for robust waveguide invariant depth classification approaches are presented which illustrate performance under uncertain environmental conditions.
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.
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.
Johansson, A Torbjorn; White, Paul R
2011-08-01
This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Background noise estimation and compensation is accomplished using order statistics and pre-whitening. Using simulated signals as well as recorded calls of marine mammals and a human whistled speech utterance, it is shown that the proposed method can detect more simultaneous whistles than two competing spectrogram-based methods while not reporting any false alarms on the example datasets. In one example, it extracts complete 1.4 and 1.8 s bottlenose dolphin whistles successfully through frequency crossings. The method performs detection and estimates frequency tracks even at high sweep rates. The algorithm is also shown to be effective on human whistled utterances. PMID:21877804
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 Kalman-Bucy filter for differential absorption lidar time series data.
Warren, R E
1987-11-15
An extension of the Kalman-Bucy algorithm for on-line estimation of multimaterial path-integrated concentration from multiwavelength differential absorption lidar time series data is presented in which the system model covariance is adaptively estimated from the input data. Performance of the filter is compared with that of a nonadaptive Kalman-Bucy filter using synthetic and actual lidar data.
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
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.
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
Adaptive alpha-trimmed mean filters under deviations from assumed noise model.
Oten, Remzi; de Figueiredo, Rui J P
2004-05-01
Alpha-trimmed mean filters are widely used for the restoration of signals and images corrupted by additive non-Gaussian noise. They are especially preferred if the underlying noise deviates from Gaussian with the impulsive noise components. The key design issue of these filters is to select its only parameter, alpha, optimally for a given noise type. In image restoration, adaptive filters utilize the flexibility of selecting alpha according to some local noise statistics. In the present paper, we first review the existing adaptive alpha-trimmed mean filter schemes. We then analyze the performance of these filters when the underlying noise distribution deviates from the Gaussian and does not satisfy the assumptions such as symmetry. Specifically, the clipping effect and the mixed noise cases are analyzed. We also present a new adaptive alpha-trimmed filter implementation that detects the nonsymmetry points locally and applies alpha-trimmed mean filter that trims out the outlier pixels such as edges or impulsive noise according to this local decision. Comparisons of the speed and filtering performances under deviations from symmetry and Gaussian assumptions show that the proposed filter is a very good alternative to the existing schemes. PMID:15376595
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.
Flight Approach to Adaptive Control Research
NASA Technical Reports Server (NTRS)
Pavlock, Kate Maureen; Less, James L.; Larson, David Nils
2011-01-01
The National Aeronautics and Space Administration's Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The testbed served as a full-scale vehicle to test and validate adaptive flight control research addressing technical challenges involved with reducing risk to enable safe flight in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research.
Chaotic satellite attitude control by adaptive approach
NASA Astrophysics Data System (ADS)
Wei, Wei; Wang, Jing; Zuo, Min; Liu, Zaiwen; Du, Junping
2014-06-01
In this article, chaos control of satellite attitude motion is considered. Adaptive control based on dynamic compensation is utilised to suppress the chaotic behaviour. Control approaches with three control inputs and with only one control input are proposed. Since the adaptive control employed is based on dynamic compensation, faithful model of the system is of no necessity. Sinusoidal disturbance and parameter uncertainties are considered to evaluate the robustness of the closed-loop system. Both of the approaches are confirmed by theoretical and numerical results.
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.
A model for radar images and its application to adaptive digital filtering of multiplicative noise.
Frost, V S; Stiles, J A; Shanmugan, K S; Holtzman, J C
1982-02-01
Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.
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.
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.
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…
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.
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.
A modular approach to adaptive structures.
Pagitz, Markus; Pagitz, Manuel; Hühne, Christian
2014-01-01
A remarkable property of nastic, shape changing plants is their complete fusion between actuators and structure. This is achieved by combining a large number of cells whose geometry, internal pressures and material properties are optimized for a given set of target shapes and stiffness requirements. An advantage of such a fusion is that cell walls are prestressed by cell pressures which increases, decreases the overall structural stiffness, weight. Inspired by the nastic movement of plants, Pagitz et al (2012 Bioinspir. Biomim. 7) published a novel concept for pressure actuated cellular structures. This article extends previous work by introducing a modular approach to adaptive structures. An algorithm that breaks down any continuous target shapes into a small number of standardized modules is presented. Furthermore it is shown how cytoskeletons within each cell enhance the properties of adaptive modules. An adaptive passenger seat and an aircrafts leading, trailing edge is used to demonstrate the potential of a modular approach. PMID:25289521
Cross-Cultural Adaptation: Current Approaches.
ERIC Educational Resources Information Center
Kim, Young Yun, Ed.; Gudykunst, William B., Ed.
1988-01-01
Reflecting multidisciplinary and multisocietal approaches, this collection presents 14 theoretical or research-based essays dealing with cross-cultural adaptation of individuals who are born and raised in one culture and find themselves in need of modifying their customary life patterns in a foreign culture. Papers in the collection are:…
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.
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
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.
[A novel denoising approach to SVD filtering based on DCT and PCA in CT image].
Feng, Fuqiang; Wang, Jun
2013-10-01
Because of various effects of the imaging mechanism, noises are inevitably introduced in medical CT imaging process. Noises in the images will greatly degrade the quality of images and bring difficulties to clinical diagnosis. This paper presents a new method to improve singular value decomposition (SVD) filtering performance in CT image. Filter based on SVD can effectively analyze characteristics of the image in horizontal (and/or vertical) directions. According to the features of CT image, we can make use of discrete cosine transform (DCT) to extract the region of interest and to shield uninterested region so as to realize the extraction of structure characteristics of the image. Then we transformed SVD to the image after DCT, constructing weighting function for image reconstruction adaptively weighted. The algorithm for the novel denoising approach in this paper was applied in CT image denoising, and the experimental results showed that the new method could effectively improve the performance of SVD filtering.
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.
A Novel Approach for Adaptive Signal Processing
NASA Technical Reports Server (NTRS)
Chen, Ya-Chin; Juang, Jer-Nan
1998-01-01
Adaptive linear predictors have been used extensively in practice in a wide variety of forms. In the main, their theoretical development is based upon the assumption of stationarity of the signals involved, particularly with respect to the second order statistics. On this basis, the well-known normal equations can be formulated. If high- order statistical stationarity is assumed, then the equivalent normal equations involve high-order signal moments. In either case, the cross moments (second or higher) are needed. This renders the adaptive prediction procedure non-blind. A novel procedure for blind adaptive prediction has been proposed and considerable implementation has been made in our contributions in the past year. The approach is based upon a suitable interpretation of blind equalization methods that satisfy the constant modulus property and offers significant deviations from the standard prediction methods. These blind adaptive algorithms are derived by formulating Lagrange equivalents from mechanisms of constrained optimization. In this report, other new update algorithms are derived from the fundamental concepts of advanced system identification to carry out the proposed blind adaptive prediction. The results of the work can be extended to a number of control-related problems, such as disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. The applications implemented are speech processing, such as coding and synthesis. Simulations are included to verify the novel modelling method.
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.
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
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.
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.
Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network.
Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou
2011-09-01
To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.
NASA Astrophysics Data System (ADS)
Öhberg, Fredrik; Lundström, Ronnie; Grip, Helena
2013-08-01
For all segments and tests, a modified Kalman filter and a quasi-static sensor fusion algorithm were equally accurate (precision and accuracy ˜2-3°) compared to normalized least mean squares filtering, recursive least-squares filtering and standard Kalman filtering. The aims were to: (1) compare adaptive filtering techniques used for sensor fusion and (2) evaluate the precision and accuracy for a chosen adaptive filter. Motion sensors (based on inertial measurement units) are limited by accumulative integration errors arising from sensor bias. This drift can partly be handled with adaptive filtering techniques. To advance the measurement technique in this area, a new modified Kalman filter is developed. Differences in accuracy were observed during different tests especially drift in the internal/external rotation angle. This drift can be minimized if the sensors include magnetometers.
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
NASA Astrophysics Data System (ADS)
Abramovich, Iu. I.; Arov, D. Z.; Kachur, V. G.
1987-12-01
The paper considers the problem of finding the vector of an adaptive filter of stationary-noise compensation which corresponds to a Toeplitz correlation-matrix structure. The existence of a Toeplitz solution is demonstrated. Lower-bound estimates are obtained for the gain in noise-compensation efficiency using a priori information about the Toeplitz matrix structure. Constructive methods for obtaining adaptive solutions corresponding to these estimates are indicated.
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.
Infrared image detail enhancement approach based on improved joint bilateral filter
NASA Astrophysics Data System (ADS)
Liu, Ning; Chen, Xiaohong
2016-07-01
In this paper, we proposed a new infrared image detail enhancement approach. This approach could not only achieve the goal of enhancing the digital detail, but also make the processed image much closer to the real situation. Inspired by the joint-bilateral filter, two adjacent images were utilized to calculate the kernel functions in order to distinguish the detail information from the raw image. We also designed a new kernel function to modify the joint-bilateral filter and to eliminate the gradient reversal artifacts caused by the non-linear filtering. The new kernel is based on an adaptive emerge coefficient to realize the detail layer determination. The detail information was modified by the adaptive emerge coefficient along with two key parameters to realize the detail enhancement. Finally, we combined the processed detail layer with the base layer and rearrange the high dynamic image into monitor-suited low dynamic range to achieve better visual effect. Numerical calculation showed that this new technology has the best value compare to the previous research in detail enhancement. Figures and data flowcharts were demonstrated in the paper.
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
A ROle-Oriented Filtering (ROOF) approach for collaborative recommendation
NASA Astrophysics Data System (ADS)
Ghani, Imran; Ryul Jeong, Seung
2016-09-01
In collaborative filtering (CF) recommender systems, existing techniques frequently focus on determining similarities among users' historical interests. This generally refers to situations in which each user normally plays a single role and his/her taste remains consistent over the long term. However, we note that existing techniques have not been significantly employed in a role-oriented context. This is especially so in situations where users may change their roles over time or play multiple roles simultaneously, while still expecting to access relevant information resources accordingly. Such systems include enterprise architecture management systems, e-commerce sites or journal management systems. In scenarios involving existing techniques, each user needs to build up very different profiles (preferences and interests) based on multiple roles which change over time. Should this not occur to a satisfactory degree, their previous information will either be lost or not utilised at all. To limit the occurrence of such issues, we propose a ROle-Oriented Filtering (ROOF) approach focusing on the manner in which multiple user profiles are obtained and maintained over time. We conducted a number of experiments using an enterprise architecture management scenario. In so doing, we observed that the ROOF approach performs better in comparison with other existing collaborative filtering-based techniques.
Naik, Naren; Vasu, R M; Ananthasayanam, M R
2010-02-20
The problem of reconstruction of a refractive-index distribution (RID) in optical refraction tomography (ORT) with optical path-length difference (OPD) data is solved using two adaptive-estimation-based extended-Kalman-filter (EKF) approaches. First, a basic single-resolution EKF (SR-EKF) is applied to a state variable model describing the tomographic process, to estimate the RID of an optically transparent refracting object from noisy OPD data. The initialization of the biases and covariances corresponding to the state and measurement noise is discussed. The state and measurement noise biases and covariances are adaptively estimated. An EKF is then applied to the wavelet-transformed state variable model to yield a wavelet-based multiresolution EKF (MR-EKF) solution approach. To numerically validate the adaptive EKF approaches, we evaluate them with benchmark studies of standard stationary cases, where comparative results with commonly used efficient deterministic approaches can be obtained. Detailed reconstruction studies for the SR-EKF and two versions of the MR-EKF (with Haar and Daubechies-4 wavelets) compare well with those obtained from a typically used variant of the (deterministic) algebraic reconstruction technique, the average correction per projection method, thus establishing the capability of the EKF for ORT. To the best of our knowledge, the present work contains unique reconstruction studies encompassing the use of EKF for ORT in single-resolution and multiresolution formulations, and also in the use of adaptive estimation of the EKF's noise covariances.
Spatial adaptive upsampling filter for HDR image based on multiple luminance range
NASA Astrophysics Data System (ADS)
Chen, Qian; Su, Guan-ming; Peng, Yin
2014-03-01
In this paper, we propose an adaptive upsampling filter to spatially upscale HDR image based on luminance range of the HDR picture in each color channel. It first searches for the optimal luminance range values to partition an HDR image to three different parts: dark, mid-tone and highlight. Then we derive the optimal set of filter coefficients both vertically and horizontally for each part. When the HDR pixel is within the dark area, we apply one set of filter coefficients to vertically upsample the pixel. If the HDR pixel falls in mid-tone area, we apply another set of filter for vertical upsampling. Otherwise the HDR pixel is in highlight area, another set of filter will be applied for vertical upsampling. Horizontal upsampling will be carried out likewise based on its luminance. The inherent idea to partition HDR image to different luminance areas is based on the fact that most HDR images are created from multiple exposures. Different exposures usually demonstrate slight variation in captured signal statistics, such as noise level, subtle misalignment etc. Hence, to group different regions to three luminance partitions actually helps to eliminate the variation between signals, and to derive optimal filter for each group with signals of lesser variation is certainly more efficient than for the entire HDR image. Experimental results show that the proposed adaptive upsampling filter based on luminance ranges outperforms the optimal upsampling filter around 0.57dB for R channel, 0.44dB for G channel and 0.31dB for B channel.
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
A Filter Bank Approach to Earthquake Early Warning
NASA Astrophysics Data System (ADS)
Meier, Men-Andrin; Heaton, Tom; Clinton, John
2014-05-01
Earthquake Early Warning (EEW) is a race against time. The longer it takes to detect and characterize an ongoing event, the larger is the blind zone - the region where a warning arrives only after the most damaging ground motion has occurred. The problem is most acute during medium size earthquakes, where damaging ground motion is confined to a small zone around the epicenter. An ideal EEW algorithm which is fast enough to provide relevant alerts for such scenario events would have to produce reliable event characterization based on observations of very short snippets of data recorded at only very few stations. For such a scheme to work, without significant numbers of false alarms (which continue to hamper both single-station and network based approaches today), the real-time information that is available for an earthquake has to be exploited in a more optimal way than what is currently done. Our approach is to fully mine the broadband frequency content of incoming waveforms that contains significant information on the size and epicentral distance of the ongoing event. We propose a filter bank approach with minimum phase delay filters which allows us to use frequency information from each frequency band at each triggered station at the earliest possible time. We have compiled and processed an extensive dataset of near-field earthquake waveforms. In an empirical maximum likelihood scheme, we use the filter bank output from the first seconds after the P-wave onset of each waveform to estimate the most likely magnitude and epicentral distance to have caused this waveform. We show how our single station approach can be integrated into an evolutionary and fully probabilistic network EEW system. We demonstrate that our method can allow sufficiently accurate characterization of an ongoing event with two stations, with consistent characterization of the evolving uncertainty of the location and magnitude.
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.
Multiple-filter approach to phase retrieval from modulus data.
Frieden, B R; Oh, C
1992-03-10
A new approach is given for the problem of reconstruction of phase from modulus data. A set of Wiener-filter functions is formed that multiply, in turn, displaced versions of the modulus data (in frequency space) such that the sum is a minimum L(2)-error norm solution for the object. The modulus data are permitted to contain both noise and signal (object) components. The required statistics are power spectra of the signal and noise, and correlations between modulus data at given frequencies and complex object spectral values at adjacent frequencies. In a numerical simulation, a 3 x 3 filter array is used to reconstruct any member of an object class consisting of 16 pictures of space shuttles in various combinations. The 16 pictures are used as a learning set to form the required power spectra and correlations mentioned above. Reconstructions are formed in the presence of data noise, data gaps, and filter-construction noise, in varying amounts. Results are encouraging in that the space shuttle images are always recognizable.
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.
NASA Astrophysics Data System (ADS)
Mohamed, Khaled M.; Hardie, Russell C.
2015-12-01
We present a new patch-based image restoration algorithm using an adaptive Wiener filter (AWF) with a novel spatial-domain multi-patch correlation model. The new filter structure is referred to as a collaborative adaptive Wiener filter (CAWF). The CAWF employs a finite size moving window. At each position, the current observation window represents the reference patch. We identify the most similar patches in the image within a given search window about the reference patch. A single-stage weighted sum of all of the pixels in the similar patches is used to estimate the center pixel in the reference patch. The weights are based on a new multi-patch correlation model that takes into account each pixel's spatial distance to the center of its corresponding patch, as well as the intensity vector distances among the similar patches. One key advantage of the CAWF approach, compared with many other patch-based algorithms, is that it can jointly handle blur and noise. Furthermore, it can also readily treat spatially varying signal and noise statistics. To the best of our knowledge, this is the first multi-patch algorithm to use a single spatial-domain weighted sum of all pixels within multiple similar patches to form its estimate and the first to use a spatial-domain multi-patch correlation model to determine the weights. The experimental results presented show that the proposed method delivers high performance in image restoration in a variety of scenarios.
Absolute phase image reconstruction: a stochastic nonlinear filtering approach.
Leitão, J N; Figueiredo, M A
1998-01-01
This paper formulates and proposes solutions to the problem of estimating/reconstructing the absolute (not simply modulo-2pi) phase of a complex random field from noisy observations of its real and imaginary parts. This problem is representative of a class of important imaging techniques such as interferometric synthetic aperture radar, optical interferometry, magnetic resonance imaging, and diffraction tomography. We follow a Bayesian approach; then, not only a probabilistic model of the observation mechanism, but also prior knowledge concerning the (phase) image to be reconstructed, are needed. We take as prior a nonsymmetrical half plane autoregressive (NSHP AR) Gauss-Markov random field (GMRF). Based on a reduced order state-space formulation of the (linear) NSHP AR model and on the (nonlinear) observation mechanism, a recursive stochastic nonlinear filter is derived, The corresponding estimates are compared with those obtained by the extended Kalman-Bucy filter, a classical linearizing approach to the same problem. A set of examples illustrate the effectiveness of the proposed approach. PMID:18276299
A piezo-shunted kirigami auxetic lattice for adaptive elastic wave filtering
NASA Astrophysics Data System (ADS)
Ouisse, Morvan; Collet, Manuel; Scarpa, Fabrizio
2016-11-01
Tailoring the dynamical behavior of wave-guide structures can provide an efficient and physically elegant approach for optimizing mechanical components with regards to vibroacoustic propagation. Architectured materials as pyramidal core kirigami cells combined with smart systems may represent a promising way to improve the vibroacoustic quality of structural components. This paper describes the design and modeling of a pyramidal core with auxetic (negative Poisson’s ratio) characteristics and distributed shunted piezoelectric patches that allow for wave propagation control. The core is produced using a kirigami technique, inspired by the cutting/folding processes of the ancient Japanese art. The kirigami structure has a pyramidal unit cell shape that creates an in-plane negative Poisson’s ratio macroscopic behavior. This structure exhibits in-plane elastic properties (Young’s and shear modulus) which are higher than the out-of-plane ones, and hence this lattice has very specific properties in terms of wave propagation that are investigated in this work. The short-circuited configuration is first analyzed, before using negative capacitance and resistance as a shunt which provides impressive band gaps in the low frequency range. All configurations are investigated by using a full analysis of the Brillouin zone, rendering possible the deep understanding of the dynamical properties of the smart lattice. The results are presented in terms of dispersion and directivity diagrams, and the smart lattice shows quite interesting properties for the adaptive filtering of elastic waves at low frequencies bandwidths.
An adaptive filter for studying the life cycle of optical rogue waves.
Liu, Chu; Rees, Eric J; Laurila, Toni; Jian, Shuisheng; Kaminski, Clemens F
2010-12-01
We present an adaptive numerical filter for analyzing fiber-length dependent properties of optical rogue waves, which are highly intense and extremely red-shifted solitons that arise during supercontinuum generation in photonic crystal fiber. We use this filter to study a data set of 1000 simulated supercontinuum pulses, produced from 5 ps pump pulses containing random noise. Optical rogue waves arise in different supercontinuum pulses at various positions along the fiber, and exhibit a lifecycle: their intensity peaks over a finite range of fiber length before declining slowly.
Ghatan, Christine E; Ryu, Robert K
2016-06-01
Inferior vena cava (IVC) filtration for thromboembolic protection is not without risks, and there are important differences among commercially available IVC filters. While retrievable filters are approved for permanent implantation, they may be associated with higher device-related complications in the long term when compared with permanent filters. Prospective patient selection in determining which patients might be better served by permanent or retrievable filter devices is central to resource optimization, in addition to improved clinical follow-up and a concerted effort to retrieve filters when no longer needed. This article highlights the differences between permanent and retrievable devices, describes the interplay between these differences and the clinical indications for IVC filtration, advises against a "one-filter-for-all" approach to mechanical thromboembolic prophylaxis, and discusses strategies for optimizing personalized device selection.
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.
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
Simulation of sprays using a Lagrangian filtered density function approach
NASA Astrophysics Data System (ADS)
Liu, Wanjiao; Garrick, Sean
2013-11-01
Sprays and atomization have wide applications in industry, including combustion/engines, pharmaceutics and agricultural spraying. Due to the complexity of the underlying processes, much of the underlying phenomena are not fully understood. Numerical simulation may provide ways to investigate atomization and spray dynamics. Large eddy simulation (LES) is a practical approach to flow simulation as it resolves only the large-scale structures while modeling the sub-grid scale (SGS) effects. We combine a filtered density function (FDF) based approach with a Lagrangian volume-of-fluid method to perform LES. This resulting methodology is advantageous in that it has no diffusive or dissipative numerical errors, and the highly non-linear surface tension force appears in closed form thus the modeling of the SGS surface tension is not needed when simulating turbulent, multiphase flows. We present the methodology and some results for the simulation of multiphase jets.
Rucci, Michael; Hardie, Russell C; Barnard, Kenneth J
2014-05-01
In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.
NASA Astrophysics Data System (ADS)
Makowski, Ryszard; Zimroz, Radoslaw
2013-07-01
A procedure for feature extraction using adaptive Schur filter for damage detection in rolling element bearings is proposed in the paper. Damaged bearings produce impact signals (shocks) related with local change (loss) of stiffness in pairs: inner/outer race-rolling element. If significant disturbances do not occur (i.e. signal to noise ratio is sufficient), diagnostics is not very complicated and usually envelope analysis is used. Unfortunately, in most industrial examples, these impulsive contributions in vibration are completely masked by noise or other high energy sources. Moreover, impulses may have time varying amplitudes caused by transmission path, load and properties of noise changing in time. Thus, in order to extract time varying signal of interest, the solution would be an adaptive one. The proposed approach is based on the normalized exact least-square time-variant lattice filter (adaptive Schur filter). It is characterized by an extremely fast start-up performance, excellent convergence behavior, and fast parameter tracking capability, making this approach interesting. Schur adaptive filter consists of P sections, estimating, among others, time-varying reflection coefficients (RCs). In this paper it is proposed to use RCs and their derivatives as diagnostic features. However, it is not convenient to analyze simultaneously P signals for P sections, so instead of these, weighted sum of derivatives of RCs can be used. The key question is how to find these weight values for summation procedure. An original contributions are: application of Schur filter to bearings vibration processing, proposal of several features that can be used for detection and mentioned procedure of weighted summation of signal from sections of Schur filter. The method of signal processing is well-adapted for analysis of the non-stationary time-series, so it sounds very promising for diagnostics of machines working in time varying load/speed conditions.
Tone reproduction for high-dynamic range imaging based on adaptive filtering
NASA Astrophysics Data System (ADS)
Ha, Changwoo; Lee, Joohyun; Jeong, Jechang
2014-03-01
A tone reproduction algorithm with enhanced contrast of high-dynamic range images on conventional low-dynamic range display devices is presented. The proposed algorithm consists mainly of block-based parameter estimation, a characteristic-based luminance adjustment, and an adaptive Gaussian filter using minimum description length. Instead of relying only on the reduction of the dynamic range, a characteristic-based luminance adjustment process modifies the luminance values. The Gaussian-filtered luminance value is obtained from appropriate value of variance, and the contrast is then enhanced through the use of a relation between the adjusted luminance and Gaussian-filtered luminance values. In the final tone-reproduction process, the proposed algorithm combines color and luminance components in order to preserve the color consistency. The experimental results demonstrate that the proposed algorithm achieves a good subjective quality while enhancing the contrast of the image details.
Multicomponent AM-FM demodulation based on energy separation and adaptive filtering
NASA Astrophysics Data System (ADS)
Qin, Yi
2013-07-01
Multicomponent AM-FM demodulation is an important tool in many engineering applications. To improve the demodulation accuracy of the commonly used methods, such as iterative Hilbert transform (IHT) and Hilbert-Huang transform (HHT), a new multicomponent AM-FM demodulation method is proposed in this paper. The proposed method achieves multicomponent demodulation by using an iteratively energy separation algorithm and adaptive low-pass filtering. Using the frequency spectra of instantaneous amplitude and frequency obtained by the energy separation algorithm at each level, the used filters are adaptively designed. In addition, this proposed method uses symmetric extension to solve the boundary effect in the estimation of instantaneous amplitudes and frequencies. The demodulation process is automatic for an arbitrary signal. Simulation and application results show that the proposed method has high demodulation accuracy than IHT, HHT and other typical methods, and it can be effectively applied to extracting weak fault feature from mechanical vibration signals.
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.
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
A filter bank approach to earthquake early warning
NASA Astrophysics Data System (ADS)
Meier, M.; Heaton, T. H.; Clinton, J. F.
2013-12-01
Earthquake Early Warning (EEW) is a race against time. The longer it takes to detect and characterize an ongoing event, the larger is the blind zone - the region where a warning arrives only after the most damaging ground motion has occurred. The problem is most acute during medium size earthquakes, where damaging ground motion is confined to a small zone around the epicenter. An ideal EEW algorithm which is fast enough to provide relevant alerts for such scenario events would have to produce reliable event characterization based on observations of very short snippets of data recorded at only very few stations. For such a scheme to work, without significant numbers of false alarms (which continue to hamper both single-station and network based approaches today), the real-time information that is available for an earthquake has to be exploited in a more optimal way than what is currently done. Our approach is to fully mine the broadband frequency content of incoming waveforms that contains significant information on the size and epicentral distance of the ongoing event. We propose a filter bank approach with minimum phase delay filters which allows us to use frequency information from each frequency band at each triggered station at the earliest possible time. We have compiled and processed an extensive event dataset of near-field earthquake waveforms. In an empirical maximum likelihood scheme, we use the filter bank output from the first seconds after the P-wave onset of each waveform to estimate a) the likelihood that the waveform corresponds to a local seismic event; and b) the most likely magnitude and epicentral distance to have caused this waveform. We show how our single station approach can be integrated into an evolutionary and fully probabilistic network EEW system. This method could be expanded to a Bayesian framework by considering prior information, as proposed in the Virtual Seismologist (Cua and Heaton, 2007). We demonstrate that our method can allow
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.
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 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 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.
Approaching neuropsychological tasks through adaptive neurorobots
NASA Astrophysics Data System (ADS)
Gigliotta, Onofrio; Bartolomeo, Paolo; Miglino, Orazio
2015-04-01
Neuropsychological phenomena have been modelized mainly, by the mainstream approach, by attempting to reproduce their neural substrate whereas sensory-motor contingencies have attracted less attention. In this work, we introduce a simulator based on the evolutionary robotics platform Evorobot* in order to setting up in silico neuropsychological tasks. Moreover, in this study we trained artificial embodied neurorobotic agents equipped with a pan/tilt camera, provided with different neural and motor capabilities, to solve a well-known neuropsychological test: the cancellation task in which an individual is asked to cancel target stimuli surrounded by distractors. Results showed that embodied agents provided with additional motor capabilities (a zooming/attentional actuator) outperformed simple pan/tilt agents, even those equipped with more complex neural controllers and that the zooming ability is exploited to correctly categorising presented stimuli. We conclude that since the sole neural computational power cannot explain the (artificial) cognition which emerged throughout the adaptive process, such kind of modelling approach can be fruitful in neuropsychological modelling where the importance of having a body is often neglected.
The Limits to Adaptation: A Systems Approach
The ability to adapt to climate change is delineated by capacity thresholds, after which climate damages begin to overwhelm the adaptation response. Such thresholds depend upon physical properties (natural processes and engineering parameters), resource constraints (expressed th...
An outlier filtering approach for machine sourced weak segmentations
NASA Astrophysics Data System (ADS)
Margolin, Elza; Furst, Jacob; Raicu, Daniela
2015-03-01
Analysis of medical images by radiologists is often a time consuming and costly process. Computer Aided diagnosis (CAD) systems can be used to provide second opinion and assist radiologists in diagnosis. Crowdsourcing has been a tool used by many domains in order to generate fast and cheap labels. However, exposing medical images to a large crowd is problematic. Our system takes a machine sourcing approach to CADs - that is, the idea that multiple weak segmentations can be used to create predictions with the same quality as an expert. We propose that the use of a large amount of weak segmentations can simulate crowdsourcing to create accurate predictions of semantic characteristics. In addition, we investigated the idea that outliers filtering will better performance of this CAD. Three segmentation algorithms were employed to create 20 weak segmentations. Low quality segmentations were then removed using outlier filtering method. Segmentations were then classified using an ensemble classifier to create final predictions. It was found that this CAD preformed just as well or better than a radiologist. In contrast, the removal of outliers from the set of segmentations does not improve the result further.
An Adaptive Critic Approach to Reference Model Adaptation
NASA Technical Reports Server (NTRS)
Krishnakumar, K.; Limes, G.; Gundy-Burlet, K.; Bryant, D.
2003-01-01
Neural networks have been successfully used for implementing control architectures for different applications. In this work, we examine a neural network augmented adaptive critic as a Level 2 intelligent controller for a C- 17 aircraft. This intelligent control architecture utilizes an adaptive critic to tune the parameters of a reference model, which is then used to define the angular rate command for a Level 1 intelligent controller. The present architecture is implemented on a high-fidelity non-linear model of a C-17 aircraft. The goal of this research is to improve the performance of the C-17 under degraded conditions such as control failures and battle damage. Pilot ratings using a motion based simulation facility are included in this paper. The benefits of using an adaptive critic are documented using time response comparisons for severe damage situations.
The discrete Kalman filtering approach for seismic signals deconvolution
Kurniadi, Rizal; Nurhandoko, Bagus Endar B.
2012-06-20
Seismic signals are a convolution of reflectivity and seismic wavelet. One of the most important stages in seismic data processing is deconvolution process; the process of deconvolution is inverse filters based on Wiener filter theory. This theory is limited by certain modelling assumptions, which may not always valid. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The main advantage of Kalman filtering is capability of technique to handling continually time varying models and has high resolution capabilities. In this work, we use discrete Kalman filter that it was combined with primitive deconvolution. Filtering process works on reflectivity function, hence the work flow of filtering is started with primitive deconvolution using inverse of wavelet. The seismic signals then are obtained by convoluting of filtered reflectivity function with energy waveform which is referred to as the seismic wavelet. The higher frequency of wavelet gives smaller wave length, the graphs of these results are presented.
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.
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
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.
Anomalous human behavior detection: an adaptive approach
NASA Astrophysics Data System (ADS)
van Leeuwen, Coen; Halma, Arvid; Schutte, Klamer
2013-05-01
Detection of anomalies (outliers or abnormal instances) is an important element in a range of applications such as fault, fraud, suspicious behavior detection and knowledge discovery. In this article we propose a new method for anomaly detection and performed tested its ability to detect anomalous behavior in videos from DARPA's Mind's Eye program, containing a variety of human activities. In this semi-unsupervised task a set of normal instances is provided for training, after which unknown abnormal behavior has to be detected in a test set. The features extracted from the video data have high dimensionality, are sparse and inhomogeneously distributed in the feature space making it a challenging task. Given these characteristics a distance-based method is preferred, but choosing a threshold to classify instances as (ab)normal is non-trivial. Our novel aproach, the Adaptive Outlier Distance (AOD) is able to detect outliers in these conditions based on local distance ratios. The underlying assumption is that the local maximum distance between labeled examples is a good indicator of the variation in that neighborhood, and therefore a local threshold will result in more robust outlier detection. We compare our method to existing state-of-art methods such as the Local Outlier Factor (LOF) and the Local Distance-based Outlier Factor (LDOF). The results of the experiments show that our novel approach improves the quality of the anomaly detection.
NASA Astrophysics Data System (ADS)
Sartori, Pablo; Tu, Yuhai
2011-04-01
Two distinct mechanisms for filtering noise in an input signal are identified in a class of adaptive sensory networks. We find that the high-frequency noise is filtered by the output degradation process through time-averaging; while the low-frequency noise is damped by adaptation through negative feedback. Both filtering processes themselves introduce intrinsic noises, which are found to be unfiltered and can thus amount to a significant internal noise floor even without signaling. These results are applied to E. coli chemotaxis. We show unambiguously that the molecular mechanism for the Berg-Purcell time-averaging scheme is the dephosphorylation of the response regulator CheY-P, not the receptor adaptation process as previously suggested. The high-frequency noise due to the stochastic ligand binding-unbinding events and the random ligand molecule diffusion is averaged by the CheY-P dephosphorylation process to a negligible level in E. coli. We identify a previously unstudied noise source caused by the random motion of the cell in a ligand gradient. We show that this random walk induced signal noise has a divergent low-frequency component, which is only rendered finite by the receptor adaptation process. For gradients within the E. coli sensing range, this dominant external noise can be comparable to the significant intrinsic noise in the system. The dependence of the response and its fluctuations on the key time scales of the system are studied systematically. We show that the chemotaxis pathway may have evolved to optimize gradient sensing, strong response, and noise control in different time scales.
NASA Technical Reports Server (NTRS)
Nishimura, T.
1974-01-01
The worst error performance of the sequential filter is compared with the performance of the batch filter which is still in general use in the deep space tracking. An approach phase of a spacecraft on a typical mission to the planet Mars is considered. The estimated parameters include the position and the speed of the spacecraft, nongravitational acceleration acting on the spacecraft, and the locations of the tracking stations.
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
NASA Technical Reports Server (NTRS)
Lam, Quang; Chipman, Richard; Sunkel, John
1991-01-01
Two algorithms, extended Kalman filter and neuro-filter, are formulated to perform mass property identification for the Space Station Freedom. Control moment gyros that are part of the Station's basic momentum management system are chosen to provide input excitation in the form of applied torques. These torques together with the measured angular body rate responses are supplied to the filters. From these data, both algorithms are shown to accurately identify the station mass properties when excitation levels are high and balanced between axes. The neuro-filter, however, is shown to be more robust and to perform well even with weakly persistent, unbalanced signals contaminated with noise.
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.
Improving the response of accelerometers for automotive applications by using LMS adaptive filters.
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg; Fernández, Eduardo
2010-01-01
In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s(2)) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory. PMID:22315542
Improving the response of accelerometers for automotive applications by using LMS adaptive filters.
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg; Fernández, Eduardo
2010-01-01
In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s(2)) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory.
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
Chen, Xiyuan; Wang, Xiying; Xu, Yuan
2014-12-09
This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.
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.
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.
Russian Loanword Adaptation in Persian; Optimal Approach
ERIC Educational Resources Information Center
Kambuziya, Aliye Kord Zafaranlu; Hashemi, Eftekhar Sadat
2011-01-01
In this paper we analyzed some of the phonological rules of Russian loanword adaptation in Persian, on the view of Optimal Theory (OT) (Prince & Smolensky, 1993/2004). It is the first study of phonological process on Russian loanwords adaptation in Persian. By gathering about 50 current Russian loanwords, we selected some of them to analyze. We…
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.
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.
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.
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.
Liu, Yan; Pecht, Michael G
2006-01-01
The effectiveness of electrocardiogram (ECG) monitors can be significantly impaired by motion artifacts which can cause misdiagnoses, lead to inappropriate treatment decisions, and trigger false alarms. Skin stretch associated with patient motion is a significant source of motion artifacts in current ECG monitoring. In this study, motion artifacts are adaptively filtered by using skin strain as the reference variable. Skin strain is measured non-invasively using a light emitting diode (LED) and an optical sensor incorporated in an ECG electrode. The results demonstrate that this device and method can significantly reduce skin strain induced ECG artifacts.
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.
Forward scattering detection of a submerged moving target based on adaptive filtering technique.
He, Chuanlin; Yang, Kunde; Lei, Bo; Ma, Yuanliang
2015-09-01
Forward scattered waves are always overwhelmed by severely intense direct blasts when a submerged target crosses the source-receiver line. A processing scheme called direct blast suppression based on adaptive filtering (DBS-AF) is proposed to suppress such blasts. A verification experiment was conducted in a lake with a vertical hydrophone array and 10 kHz CW impulses. Processing results show that the direct blast is suppressed in a single channel, and an intruding target is identified by the lobes in the detection curve. The detection performance is improved by adopting a time-delay beam-former on the array as a pre-processing technique. PMID:26428829
Discrete adaptive zone light elements (DAZLE): a new approach to adaptive imaging
NASA Astrophysics Data System (ADS)
Kellogg, Robert L.; Escuti, Michael J.
2007-09-01
New advances in Liquid Crystal Spatial Light Modulators (LCSLM) offer opportunities for large adaptive optics in the midwave infrared spectrum. A light focusing adaptive imaging system, using the zero-order diffraction state of a polarizer-free liquid crystal polarization grating modulator to create millions of high transmittance apertures, is envisioned in a system called DAZLE (Discrete Adaptive Zone Light Elements). DAZLE adaptively selects large sets of LCSLM apertures using the principles of coded masks, embodied in a hybrid Discrete Fresnel Zone Plate (DFZP) design. Issues of system architecture, including factors of LCSLM aperture pattern and adaptive control, image resolution and focal plane array (FPA) matching, and trade-offs between filter bandwidths, background photon noise, and chromatic aberration are discussed.
Filtered density function approach for reactive transport in groundwater
NASA Astrophysics Data System (ADS)
Suciu, Nicolae; Schüler, Lennart; Attinger, Sabine; Knabner, Peter
2016-04-01
Spatial filtering may be used in coarse-grained simulations (CGS) of reactive transport in groundwater, similar to the large eddy simulations (LES) in turbulence. The filtered density function (FDF), stochastically equivalent to a probability density function (PDF), provides a statistical description of the sub-grid, unresolved, variability of the concentration field. Besides closing the chemical source terms in the transport equation for the mean concentration, like in LES-FDF methods, the CGS-FDF approach aims at quantifying the uncertainty over the whole hierarchy of heterogeneity scales exhibited by natural porous media. Practically, that means estimating concentration PDFs on coarse grids, at affordable computational costs. To cope with the high dimensionality of the problem in case of multi-component reactive transport and to reduce the numerical diffusion, FDF equations are solved by particle methods. But, while trajectories of computational particles are modeled as stochastic processes indexed by time, the concentration's heterogeneity is modeled as a random field, with multi-dimensional, spatio-temporal sets of indices. To overcome this conceptual inconsistency, we consider FDFs/PDFs of random species concentrations weighted by conserved scalars and we show that their evolution equations can be formulated as Fokker-Planck equations describing stochastically equivalent processes in concentration-position spaces. Numerical solutions can then be approximated by the density in the concentration-position space of an ensemble of computational particles governed by the associated Itô equations. Instead of sequential particle methods we use a global random walk (GRW) algorithm, which is stable, free of numerical diffusion, and practically insensitive to the increase of the number of particles. We illustrate the general FDF approach and the GRW numerical solution for a reduced complexity problem consisting of the transport of a single scalar in groundwater
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.
Sadjadi, Firooz A; Mahalanobis, Abhijit
2006-05-01
We report the development of a technique for adaptive selection of polarization ellipse tilt and ellipticity angles such that the target separation from clutter is maximized. From the radar scattering matrix [S] and its complex components, in phase and quadrature phase, the elements of the Mueller matrix are obtained. Then, by means of polarization synthesis, the radar cross section of the radar scatters are obtained at different transmitting and receiving polarization states. By designing a maximum average correlation height filter, we derive a target versus clutter distance measure as a function of four transmit and receive polarization state angles. The results of applying this method on real synthetic aperture radar imagery indicate a set of four transmit and receive angles that lead to maximum target versus clutter discrimination. These optimum angles are different for different targets. Hence, by adaptive control of the state of polarization of polarimetric radar, one can noticeably improve the discrimination of targets from clutter.
High dynamic range image rendering with a Retinex-based adaptive filter.
Meylan, Laurence; Süsstrunk, Sabine
2006-09-01
We propose a new method to render high dynamic range images that models global and local adaptation of the human visual system. Our method is based on the center-surround Retinex model. The novelties of our method is first to use an adaptive filter, whose shape follows the image high-contrast edges, thus reducing halo artifacts common to other methods. Second, only the luminance channel is processed, which is defined by the first component of a principal component analysis. Principal component analysis provides orthogonality between channels and thus reduces the chromatic changes caused by the modification of luminance. We show that our method efficiently renders high dynamic range images and we compare our results with the current state of the art. PMID:16948325
An Impact-Based Filtering Approach for Literature Searches
ERIC Educational Resources Information Center
Vista, Alvin
2013-01-01
This paper aims to present an alternative and simple method to improve the filtering of search results so as to increase the efficiency of literature searches, particularly for individual researchers who have limited logistical resources. The method proposed here is scope restriction using an impact-based filter, made possible by the emergence of…
Baresová, E; Grieszbach, G; Schack, B; Vilser, W; Bräuer-Burchardt, C; Senff, I
This study deals with methods focused on estimating blood velocity. The estimation of the linear trend function of a non-stationary signal based on the adaptive recursive estimation of the mean value function is used for the determination of the time delay of two indicator dilution curves. The filter property of this trend operator depends on the choice of a constant parameter c, the so-called adaptation factor. The functional connection between the filter property and the adaptation factor is considered in such a way that an objective calculation of arterial blood velocity in retinal vessels is possible.
Adaptive multi-scale total variation minimization filter for low dose CT imaging
NASA Astrophysics Data System (ADS)
Zamyatin, Alexander; Katsevich, Gene; Krylov, Roman; Shi, Bibo; Yang, Zhi
2014-03-01
In this work we revisit TV filter and propose an improved version that is tailored to diagnostic CT purposes. We revise TV cost function, which results in symmetric gradient function that leads to more natural noise texture. We apply a multi-scale approach to resolve noise grain issue in CT images. We examine noise texture, granularity, and loss of low contrast in the test images. We also discuss potential acceleration by Nesterov and Conjugate Gradient methods.
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
2015-10-23
An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.
Experimental Demonstration of Adaptive Infrared Multispectral Imaging using Plasmonic Filter Array
Jang, Woo-Yong; Ku, Zahyun; Jeon, Jiyeon; Kim, Jun Oh; Lee, Sang Jun; Park, James; Noyola, Michael J.; Urbas, Augustine
2016-01-01
In our previous theoretical study, we performed target detection using a plasmonic sensor array incorporating the data-processing technique termed “algorithmic spectrometry”. We achieved the reconstruction of a target spectrum by extracting intensity at multiple wavelengths with high resolution from the image data obtained from the plasmonic array. The ultimate goal is to develop a full-scale focal plane array with a plasmonic opto-coupler in order to move towards the next generation of versatile infrared cameras. To this end, and as an intermediate step, this paper reports the experimental demonstration of adaptive multispectral imagery using fabricated plasmonic spectral filter arrays and proposed target detection scenarios. Each plasmonic filter was designed using periodic circular holes perforated through a gold layer, and an enhanced target detection strategy was proposed to refine the original spectrometry concept for spatial and spectral computation of the data measured from the plasmonic array. Both the spectrum of blackbody radiation and a metal ring object at multiple wavelengths were successfully reconstructed using the weighted superposition of plasmonic output images as specified in the proposed detection strategy. In addition, plasmonic filter arrays were theoretically tested on a target at extremely high temperature as a challenging scenario for the detection scheme. PMID:27721506
Experimental Demonstration of Adaptive Infrared Multispectral Imaging using Plasmonic Filter Array
NASA Astrophysics Data System (ADS)
Jang, Woo-Yong; Ku, Zahyun; Jeon, Jiyeon; Kim, Jun Oh; Lee, Sang Jun; Park, James; Noyola, Michael J.; Urbas, Augustine
2016-10-01
In our previous theoretical study, we performed target detection using a plasmonic sensor array incorporating the data-processing technique termed “algorithmic spectrometry”. We achieved the reconstruction of a target spectrum by extracting intensity at multiple wavelengths with high resolution from the image data obtained from the plasmonic array. The ultimate goal is to develop a full-scale focal plane array with a plasmonic opto-coupler in order to move towards the next generation of versatile infrared cameras. To this end, and as an intermediate step, this paper reports the experimental demonstration of adaptive multispectral imagery using fabricated plasmonic spectral filter arrays and proposed target detection scenarios. Each plasmonic filter was designed using periodic circular holes perforated through a gold layer, and an enhanced target detection strategy was proposed to refine the original spectrometry concept for spatial and spectral computation of the data measured from the plasmonic array. Both the spectrum of blackbody radiation and a metal ring object at multiple wavelengths were successfully reconstructed using the weighted superposition of plasmonic output images as specified in the proposed detection strategy. In addition, plasmonic filter arrays were theoretically tested on a target at extremely high temperature as a challenging scenario for the detection scheme.
Adaptive filtering of ECG interference on surface EEnGs based on signal averaging.
Garcia-Casado, Javier; Martinez-de-Juan, Jose L; Ponce, Jose L
2006-06-01
An external electroenterogram (EEnG) is the recording of the small bowel myoelectrical signal using contact electrodes placed on the abdominal surface. It is a weak signal affected by possible movements and by the interferences of respiration and, principally, of the cardiac signal. In this paper an adaptive filtering technique was proposed to identify and subsequently cancel ECG interference on canine surface EEnGs by means of a signal averaging process time-locked with the R-wave. Twelve recording sessions were carried out on six conscious dogs in the fasting state. The adaptive filtering technique used increases the signal-to-interference ratio of the raw surface EEnG from 16.7 +/- 6.5 dB up to 31.9 +/- 4.0 dB. In addition to removing ECG interference, this technique has been proven to respect intestinal SB activity, i.e. the EEnG component associated with bowel contractions, despite the fact that they overlap in the frequency domain. In this way, more robust non-invasive intestinal motility indicators can be obtained with correlation coefficients of 0.68 +/- 0.09 with internal intestinal activity. The method proposed here may also be applied to other biological recordings affected by cardiac interference and could be a very helpful tool for future applications of non-invasive recordings of gastrointestinal signals.
Research of fetal ECG extraction using wavelet analysis and adaptive filtering.
Wu, Shuicai; Shen, Yanni; Zhou, Zhuhuang; Lin, Lan; Zeng, Yanjun; Gao, Xiaofeng
2013-10-01
Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals.
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
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.
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
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
Liang, Fan; Yu, Yang; Cui, Shigang; Zhao, Li; Wu, Xingli
2014-05-01
Robot Assisted Coronary Artery Bypass Graft (CABG) allows the heart keep beating in the surgery by actively eliminating the relative motion between point of interest (POI) on the heart surface and surgical tool. The inherited nonlinear and diverse nature of beating heart motion gives a huge obstacle for the robot to meet the demanding tracking control requirements. In this paper, we novelty propose a Master-slave Kalman Filter based on beating heart motion Nonlinear Adaptive Prediction (NAP) algorithm. In the study, we describe the beating heart motion as the combination of nonlinearity relating mathematics part and uncertainty relating non-mathematics part. Specifically, first, we model the nonlinearity of the heart motion via quadratic modulated sinusoids and estimate it by a Master Kalman Filter. Second, we involve the uncertainty heart motion by adaptively change the covariance of the process noise through the slave Kalman Filter. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The results indicate that the new approach reduces prediction errors by at least 30 μm. Moreover, the new approach performs well in robustness test, in which two kinds of arrhythmia datasets from MIT-BIH arrhythmia database are assessed.
NASA Astrophysics Data System (ADS)
Panniello, Umberto; Gorgoglione, Michele; Palmisano, Cosimo
Recent literature predicts that including context in a recommender system may improve its performance. The context-based recommendation approaches are classified as pre-filtering, post-filtering and contextual modeling. Little research has been done on studying whether including context in a recommender system improves the recommendation performance and no research has compared yet the different approaches to contextual RS. The research contribution of this work lies in studying the effect of the context on the recommendation performance and comparing a pre-filtering approach to a post-filtering using a collaborative filtering recommender system.
Passive and active adaptive management: Approaches and an example
Williams, B.K.
2011-01-01
Adaptive management is a framework for resource conservation that promotes iterative learning-based decision making. Yet there remains considerable confusion about what adaptive management entails, and how to actually make resource decisions adaptively. A key but somewhat ambiguous distinction in adaptive management is between active and passive forms of adaptive decision making. The objective of this paper is to illustrate some approaches to active and passive adaptive management with a simple example involving the drawdown of water impoundments on a wildlife refuge. The approaches are illustrated for the drawdown example, and contrasted in terms of objectives, costs, and potential learning rates. Some key challenges to the actual practice of AM are discussed, and tradeoffs between implementation costs and long-term benefits are highlighted. ?? 2010 Elsevier Ltd.
On adaptive robustness approach to Anti-Jam signal processing
NASA Astrophysics Data System (ADS)
Poberezhskiy, Y. S.; Poberezhskiy, G. Y.
An effective approach to exploiting statistical differences between desired and jamming signals named adaptive robustness is proposed and analyzed in this paper. It combines conventional Bayesian, adaptive, and robust approaches that are complementary to each other. This combining strengthens the advantages and mitigates the drawbacks of the conventional approaches. Adaptive robustness is equally applicable to both jammers and their victim systems. The capabilities required for realization of adaptive robustness in jammers and victim systems are determined. The employment of a specific nonlinear robust algorithm for anti-jam (AJ) processing is described and analyzed. Its effectiveness in practical situations has been proven analytically and confirmed by simulation. Since adaptive robustness can be used by both sides in electronic warfare, it is more advantageous for the fastest and most intelligent side. Many results obtained and discussed in this paper are also applicable to commercial applications such as communications in unregulated or poorly regulated frequency ranges and systems with cognitive capabilities.
Nonlinear Statistical Signal Processing: A Particle Filtering Approach
Candy, J
2007-09-19
A introduction to particle filtering is discussed starting with an overview of Bayesian inference from batch to sequential processors. Once the evolving Bayesian paradigm is established, simulation-based methods using sampling theory and Monte Carlo realizations are discussed. Here the usual limitations of nonlinear approximations and non-gaussian processes prevalent in classical nonlinear processing algorithms (e.g. Kalman filters) are no longer a restriction to perform Bayesian inference. It is shown how the underlying hidden or state variables are easily assimilated into this Bayesian construct. Importance sampling methods are then discussed and shown how they can be extended to sequential solutions implemented using Markovian state-space models as a natural evolution. With this in mind, the idea of a particle filter, which is a discrete representation of a probability distribution, is developed and shown how it can be implemented using sequential importance sampling/resampling methods. Finally, an application is briefly discussed comparing the performance of the particle filter designs with classical nonlinear filter implementations.
Approach for reconstructing anisoplanatic adaptive optics images.
Aubailly, Mathieu; Roggemann, Michael C; Schulz, Timothy J
2007-08-20
Atmospheric turbulence corrupts astronomical images formed by ground-based telescopes. Adaptive optics systems allow the effects of turbulence-induced aberrations to be reduced for a narrow field of view corresponding approximately to the isoplanatic angle theta(0). For field angles larger than theta(0), the point spread function (PSF) gradually degrades as the field angle increases. We present a technique to estimate the PSF of an adaptive optics telescope as function of the field angle, and use this information in a space-varying image reconstruction technique. Simulated anisoplanatic intensity images of a star field are reconstructed by means of a block-processing method using the predicted local PSF. Two methods for image recovery are used: matrix inversion with Tikhonov regularization, and the Lucy-Richardson algorithm. Image reconstruction results obtained using the space-varying predicted PSF are compared to space invariant deconvolution results obtained using the on-axis PSF. The anisoplanatic reconstruction technique using the predicted PSF provides a significant improvement of the mean squared error between the reconstructed image and the object compared to the deconvolution performed using the on-axis PSF. PMID:17712366
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.
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
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.
NASA Astrophysics Data System (ADS)
Ibey, Bennett; Subramanian, Hariharan; Ericson, Nance; Xu, Weijian; Wilson, Mark; Cote, Gerard L.
2005-03-01
A blood perfusion and oxygenation sensor has been developed for in situ monitoring of transplanted organs. In processing in situ data, motion artifacts due to increased perfusion can create invalid oxygenation saturation values. In order to remove the unwanted artifacts from the pulsatile signal, adaptive filtering was employed using a third wavelength source centered at 810nm as a reference signal. The 810 nm source resides approximately at the isosbestic point in the hemoglobin absorption curve where the absorbance of light is nearly equal for oxygenated and deoxygenated hemoglobin. Using an autocorrelation based algorithm oxygenation saturation values can be obtained without the need for large sampling data sets allowing for near real-time processing. This technique has been shown to be more reliable than traditional techniques and proven to adequately improve the measurement of oxygenation values in varying perfusion states.
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
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.
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
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.
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 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.
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.
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
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.
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
Brain source localization based on fast fully adaptive approach.
Ravan, Maryam; Reilly, James P
2012-01-01
In the electroencephalogram (EEG) or magnetoencephalogram (MEG) context, brain source localization (beamforming) methods often fail when the number of observations is small. This is particularly true when measuring evoked potentials, especially when the number of electrodes is large. Due to the nonstationarity of the EEG/MEG, an adaptive capability is desirable. Previous work has addressed these issues by reducing the adaptive degrees of freedom (DoFs). This paper develops and tests a new multistage adaptive processing for brain source localization that has been previously used for radar statistical signal processing application with uniform linear antenna array. This processing, referred to as the fast fully adaptive (FFA) approach, could significantly reduce the required sample support and computational complexity, while still processing all available DoFs. The performance improvement offered by the FFA approach in comparison to the fully adaptive minimum variance beamforming (MVB) with limited data is demonstrated by bootstrapping simulated data to evaluate the variability of the source location.
A User-Centered Approach to Adaptive Hypertext Based on an Information Relevance Model
NASA Technical Reports Server (NTRS)
Mathe, Nathalie; Chen, James
1994-01-01
Rapid and effective to information in large electronic documentation systems can be facilitated if information relevant in an individual user's content can be automatically supplied to this user. However most of this knowledge on contextual relevance is not found within the contents of documents, it is rather established incrementally by users during information access. We propose a new model for interactively learning contextual relevance during information retrieval, and incrementally adapting retrieved information to individual user profiles. The model, called a relevance network, records the relevance of references based on user feedback for specific queries and user profiles. It also generalizes such knowledge to later derive relevant references for similar queries and profiles. The relevance network lets users filter information by context of relevance. Compared to other approaches, it does not require any prior knowledge nor training. More importantly, our approach to adaptivity is user-centered. It facilitates acceptance and understanding by users by giving them shared control over the adaptation without disturbing their primary task. Users easily control when to adapt and when to use the adapted system. Lastly, the model is independent of the particular application used to access information, and supports sharing of adaptations among users.
Investigation of Alternative Approaches for Cleaning Mott Porous Metal Filters
Poirier, M.R.
2003-10-30
The Department of Energy selected Caustic Side Solvent Extraction (CSSX) as the preferred cesium removal technology for Savannah River Site (SRS) waste. As a pretreatment step for the CSSX flowsheet, the incoming salt solution that contains entrained sludge is contacted with monosodium titanate (MST) to adsorb strontium and select actinides. The resulting slurry is filtered to remove the sludge and MST. Filter fouling occurs during this process. At times, personnel can increase the filtrate rate by backpulsing or scouring. At other times, the filtrate rate drops significantly and only chemical cleaning will restore filter performance. The current baseline technology for filter cleaning uses 0.5 M oxalic acid. The Salt Processing Project (SPP) at SRS, through the Tanks Focus Area, requested an evaluation of other cleaning agents to determine their effectiveness at removing trapped sludge and MST solids compared with the baseline oxalic acid method. A review of the technical literature identified compounds that appear effective at dissolving solid compounds. Consultation with the SPP management team, engineering personnel, and researchers led to a selection of oxalic acid, nitric acid, citric acid, and ascorbic acid for testing. Tests used simulated waste and actual waste as follows. Personnel placed simulated or actual SRS High Level Waste sludge and MST in a beaker. They added the selected cleaning agents, stirred the beakers, and collected supernate samples periodically analyzing for dissolved metals.
Acoustic cardiac signals analysis: a Kalman filter-based approach.
Salleh, Sheik Hussain; Hussain, Hadrina Sheik; Swee, Tan Tian; Ting, Chee-Ming; Noor, Alias Mohd; Pipatsart, Surasak; Ali, Jalil; Yupapin, Preecha P
2012-01-01
Auscultation of the heart is accompanied by both electrical activity and sound. Heart auscultation provides clues to diagnose many cardiac abnormalities. Unfortunately, detection of relevant symptoms and diagnosis based on heart sound through a stethoscope is difficult. The reason GPs find this difficult is that the heart sounds are of short duration and separated from one another by less than 30 ms. In addition, the cost of false positives constitutes wasted time and emotional anxiety for both patient and GP. Many heart diseases cause changes in heart sound, waveform, and additional murmurs before other signs and symptoms appear. Heart-sound auscultation is the primary test conducted by GPs. These sounds are generated primarily by turbulent flow of blood in the heart. Analysis of heart sounds requires a quiet environment with minimum ambient noise. In order to address such issues, the technique of denoising and estimating the biomedical heart signal is proposed in this investigation. Normally, the performance of the filter naturally depends on prior information related to the statistical properties of the signal and the background noise. This paper proposes Kalman filtering for denoising statistical heart sound. The cycles of heart sounds are certain to follow first-order Gauss-Markov process. These cycles are observed with additional noise for the given measurement. The model is formulated into state-space form to enable use of a Kalman filter to estimate the clean cycles of heart sounds. The estimates obtained by Kalman filtering are optimal in mean squared sense.
NASA Astrophysics Data System (ADS)
Hannequin, Pascal Paul
2015-06-01
Noise reduction in photon-counting images remains challenging, especially at low count levels. We have developed an original procedure which associates two complementary filters using a Wiener-derived approach. This approach combines two statistically adaptive filters into a dual-weighted (DW) filter. The first one, a statistically weighted adaptive (SWA) filter, replaces the central pixel of a sliding window with a statistically weighted sum of its neighbors. The second one, a statistical and heuristic noise extraction (extended) (SHINE-Ext) filter, performs a discrete cosine transformation (DCT) using sliding blocks. Each block is reconstructed using its significant components which are selected using tests derived from multiple linear regression (MLR). The two filters are weighted according to Wiener theory. This approach has been validated using a numerical phantom and a real planar Jaszczak phantom. It has also been illustrated using planar bone scintigraphy and myocardial single-photon emission computed tomography (SPECT) data. Performances of filters have been tested using mean normalized absolute error (MNAE) between the filtered images and the reference noiseless or high-count images. Results show that the proposed filters quantitatively decrease the MNAE in the images and then increase the signal-to-noise Ratio (SNR). This allows one to work with lower count images. The SHINE-Ext filter is well suited to high-size images and low-variance areas. DW filtering is efficient for low-size images and in high-variance areas. The relative proportion of eliminated noise generally decreases when count level increases. In practice, SHINE filtering alone is recommended when pixel spacing is less than one-quarter of the effective resolution of the system and/or the size of the objects of interest. It can also be used when the practical interest of high frequencies is low. In any case, DW filtering will be preferable. The proposed filters have been applied to nuclear
[An adapted relational approach to hospitalised adolescents].
Naville, Lydie
2011-01-01
The treatment of an adolescent hospitalised in paediatrics often poses difficulties. The relational aspect of the nurse's work in this period of development between childhood and adulthood remains delicate in a context of institutionalisation. What is the interaction between the relational approach and the adolescent's experience of hospitalisation in paediatrics?
[An adapted relational approach to hospitalised adolescents].
Naville, Lydie
2011-01-01
The treatment of an adolescent hospitalised in paediatrics often poses difficulties. The relational aspect of the nurse's work in this period of development between childhood and adulthood remains delicate in a context of institutionalisation. What is the interaction between the relational approach and the adolescent's experience of hospitalisation in paediatrics? PMID:21520581
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.
A New Approach to Interference Excision in Radio Astronomy: Real-Time Adaptive Cancellation
NASA Astrophysics Data System (ADS)
Barnbaum, Cecilia; Bradley, Richard F.
1998-11-01
Every year, an increasing amount of radio-frequency (RF) spectrum in the VHF, UHF, and microwave bands is being utilized to support new commercial and military ventures, and all have the potential to interfere with radio astronomy observations. Such services already cause problems for radio astronomy even in very remote observing sites, and the potential for this form of light pollution to grow is alarming. Preventive measures to eliminate interference through FCC legislation and ITU agreements can be effective; however, many times this approach is inadequate and interference excision at the receiver is necessary. Conventional techniques such as RF filters, RF shielding, and postprocessing of data have been only somewhat successful, but none has been sufficient. Adaptive interference cancellation is a real-time approach to interference excision that has not been used before in radio astronomy. We describe here, for the first time, adaptive interference cancellation in the context of radio astronomy instrumentation, and we present initial results for our prototype receiver. In the 1960s, analog adaptive interference cancelers were developed that obtain a high degree of cancellation in problems of radio communications and radar. However, analog systems lack the dynamic range, noised performance, and versatility required by radio astronomy. The concept of digital adaptive interference cancellation was introduced in the mid-1960s 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 compartments of automobiles. These audio-frequency applications require bandwidths of only a few tens of kilohertz. Only recently has high-speed digital filter technology made high dynamic range adaptive canceling possible in a bandwidth as large as a few megahertz, finally opening the door to application in radio astronomy. We have
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.
Lu, Jun; Xie, Kan; McFarland, Dennis J
2014-07-01
Movement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.
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
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
Dong, Feng; Pierpaoli, Elena; Gunn, James E.; Wechsler, Risa H.
2007-10-29
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is {approx} 85% complete and over 90% pure for clusters with masses above 1.0 x 10{sup 14}h{sup -1} M and redshifts up to z = 0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensity of {Delta} = 200, we find the derived cluster richness {Lambda}{sub 200} a roughly linear indicator of its virial mass M{sub 200}, which well recovers the relation between total luminosity and cluster mass of the input simulation.
Staged sacrectomy--an adaptive approach.
Ramamurthy, Rajaraman; Bose, Jagadish Chandra; Muthusamy, Vimalakannan; Natarajan, Mayilvahanan; Kunjithapatham, Deiveegan
2009-09-01
Object Sacral tumors are commonly diagnosed late and therefore present at an advanced stage. The late presentation makes curative surgery technically demanding. Sacrectomy is fraught with a high local recurrence rate and potential complications: deep infection; substantial blood loss; large-bone and soft-tissue defects; bladder, bowel, and sexual dysfunction; spinopelvic nonunion; and gait disturbance. The aim of this study was to analyze the complications and morbidity of sacrectomy and the modifications meant to reduce the morbidity. Methods This is a retrospective study of the patients who underwent sacrectomy between February 1997 and September 2008 in the Department of Surgical Oncology, Government Royapettah Hospital, Kilpauk Medical College, in Chennai, Tamilnadu, India. Sacrectomy was performed using 1 of the following approaches: posterior approach, abdominolateral approach, or abdominosacral approach, either as sequential or staged operations. The morbidity rate after the sequential and staged abdominosacral approaches was analyzed. Functional assessment was made based on the Enneking functional scoring system. The results were analyzed and survival analysis was done using the Kaplan-Meier method (with SPSS software). Results Nineteen patients underwent sacrectomy, of which 12 operations were partial, 3 were subtotal, and 4 were total sacrectomy. Histological diagnosis included giant cell tumor, chordoma, chondroblastoma, adenocarcinoma of rectum, and retroperitoneal sarcoma. The giant cell tumor was the most common tumor in this series, followed by chordoma. The patients' mean age at diagnosis was 32 years. There were 10 male and 9 female patients. Fortyseven percent of patients had bowel and bladder disturbances postoperatively, and 57.89% of patients had wound complications. The median follow-up duration was 24 months (range 2-140 months). The 5-year overall survival rate was 70.4%, and the 5-year disease-free survival rate was 65% (based on the Kaplan
Robust tracking with spatio-velocity snakes: Kalman filtering approach
Peterfreund, N.
1997-06-01
Using results from robust Kalman filtering, the author presents a new Kalman filter-based snake model for tracking of nonrigid objects in combined spatio-velocity space. The proposed model is the stochastic version of the velocity snake, an active contour model for combined tracking of position and velocity of nonrigid boundaries. The proposed model uses image gradient and optical flow measurements along the contour as system measurements. An optical-flow based measurement error is used to detect and reject image measurements which correspond to image clutter or to other objects. The method was applied to object tracking of both rigid and nonrigid objects, resulting in good tracking results and robustness to image clutter, occlusions and numerical noise.
Robust tracking with spatio-velocity snakes: Kalman filtering approach
Peterfreund, N.
1998-12-31
Using results from robust Kalman filtering, we present a new Kalman filter-based snake model for tracking of nonrigid objects in combined spatio-velocity space. The proposed model is the stochastic version of the velocity snake, an active contour model for combined tracking of position and velocity of nonrigid boundaries. The proposed model uses image gradient and optical flow measurements along the contour as system measurements. An optical-flow based measurement error is used to detect and reject image measurements which correspond to image clutter or to other objects. The method was applied to object tracking of both rigid and nonrigid objects, resulting in good tracking results and robustness to image clutter, occlusions and numerical noise. 19 refs., 3 figs.
Adapting to the Digital Age: A Narrative Approach
ERIC Educational Resources Information Center
Cousins, Sarah; Bissar, Dounia
2012-01-01
The article adopts a narrative inquiry approach to foreground informal learning and exposes a collection of stories from tutors about how they adapted comfortably to the digital age.We were concerned that despite substantial evidence that bringing about changes in pedagogic practices can be difficult, there is a gap in convincing approaches to…
A new approach to UTC calculation by means of the Kalman filter
NASA Astrophysics Data System (ADS)
Parisi, Federica; Panfilo, Gianna
2016-10-01
In this paper a new approach to Coordinated Universal Time (UTC) calculation is presented by means of the Kalman filter. An ensemble of atomic clocks participating in UTC is selected for analyzing and testing the potentiality of this new method.
Adaptive variable-fidelity wavelet-based eddy-capturing approaches for compressible turbulence
NASA Astrophysics Data System (ADS)
Brown-Dymkoski, Eric; Vasilyev, Oleg V.
2015-11-01
Multiresolution wavelet methods have been developed for efficient simulation of compressible turbulence. They rely upon a filter to identify dynamically important coherent flow structures and adapt the mesh to resolve them. The filter threshold parameter, which can be specified globally or locally, allows for a continuous tradeoff between computational cost and fidelity, ranging seamlessly between DNS and adaptive LES. There are two main approaches to specifying the adaptive threshold parameter. It can be imposed as a numerical error bound, or alternatively, derived from real-time flow phenomena to ensure correct simulation of desired turbulent physics. As LES relies on often imprecise model formulations that require a high-quality mesh, this variable-fidelity approach offers a further tool for improving simulation by targeting deficiencies and locally increasing the resolution. Simultaneous physical and numerical criteria, derived from compressible flow physics and the governing equations, are used to identify turbulent regions and evaluate the fidelity. Several benchmark cases are considered to demonstrate the ability to capture variable density and thermodynamic effects in compressible turbulence. This work was supported by NSF under grant No. CBET-1236505.
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
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.
Efficient pulse compression for LPI waveforms based on a nonparametric iterative adaptive approach
NASA Astrophysics Data System (ADS)
Li, Zhengzheng; Nepal, Ramesh; Zhang, Yan; Blake, WIlliam
2015-05-01
In order to achieve low probability-of-intercept (LPI), radar waveforms are usually long and randomly generated. Due to the randomized nature, Matched filter responses (autocorrelation) of those waveforms can have high sidelobes which would mask weaker targets near a strong target, limiting radar's ability to distinguish close-by targets. To improve resolution and reduced sidelobe contaminations, a waveform independent pulse compression filter is desired. Furthermore, the pulse compression filter needs to be able to adapt to received signal to achieve optimized performance. As many existing pulse techniques require intensive computation, real-time implementation is infeasible. This paper introduces a new adaptive pulse compression technique for LPI waveforms that is based on a nonparametric iterative adaptive approach (IAA). Due to the nonparametric nature, no parameter tuning is required for different waveforms. IAA can achieve super-resolution and sidelobe suppression in both range and Doppler domains. Also it can be extended to directly handle the matched filter (MF) output (called MF-IAA), which further reduces the computational load. The practical impact of LPI waveform operations on IAA and MF-IAA has not been carefully studied in previous work. Herein the typical LPI waveforms such as random phase coding and other non- PI waveforms are tested with both single-pulse and multi-pulse IAA processing. A realistic airborne radar simulator as well as actual measured radar data are used for the validations. It is validated that in spite of noticeable difference with different test waveforms, the IAA algorithms and its improvement can effectively achieve range-Doppler super-resolution in realistic data.
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
Goodarz Ahmadi
2002-07-01
In this project, a computational modeling approach for analyzing flow and ash transport and deposition in filter vessels was developed. An Eulerian-Lagrangian formulation for studying hot-gas filtration process was established. The approach uses an Eulerian analysis of gas flows in the filter vessel, and makes use of the Lagrangian trajectory analysis for the particle transport and deposition. Particular attention was given to the Siemens-Westinghouse filter vessel at Power System Development Facility in Wilsonville in Alabama. Details of hot-gas flow in this tangential flow filter vessel are evaluated. The simulation results show that the rapidly rotation flow in the spacing between the shroud and the vessel refractory acts as cyclone that leads to the removal of a large fraction of the larger particles from the gas stream. Several alternate designs for the filter vessel are considered. These include a vessel with a short shroud, a filter vessel with no shroud and a vessel with a deflector plate. The hot-gas flow and particle transport and deposition in various vessels are evaluated. The deposition patterns in various vessels are compared. It is shown that certain filter vessel designs allow for the large particles to remain suspended in the gas stream and to deposit on the filters. The presence of the larger particles in the filter cake leads to lower mechanical strength thus allowing for the back-pulse process to more easily remove the filter cake. A laboratory-scale filter vessel for testing the cold flow condition was designed and fabricated. A laser-based flow visualization technique is used and the gas flow condition in the laboratory-scale vessel was experimental studied. A computer model for the experimental vessel was also developed and the gas flow and particle transport patterns are evaluated.
Nasal filters: a novel approach to tackling allergic rhinitis.
Sigsgaard, Torben; Tovey, Euan Roger
2014-09-01
More than 300 million individuals in industrialized countries suffer from allergic rhinitis. Rhinitis is a disease characterized by stuffy or runny nose, followed by red, itchy watering eyes and repeated sneezing. But more common problems for rhinitis patients are the overlooked social difficulties, with the majority reporting tiredness, feeling miserable or irritable. Often, medication is not able to adequately control symptoms and there is a need for other aids against the disease. Here, we describe the current situation after five trials using nasal filters in the remediation of seasonal allergic rhinitis.
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.
A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation
Zhang, Jinghe; Welch, Greg; Bishop, Gary; Huang, Zhenyu
2014-04-01
As electricity demand continues to grow and renewable energy increases its penetration in the power grid, realtime state estimation becomes essential for system monitoring and control. Recent development in phasor technology makes it possible with high-speed time-synchronized data provided by Phasor Measurement Units (PMU). In this paper we present a two-stage Kalman filter approach to estimate the static state of voltage magnitudes and phase angles, as well as the dynamic state of generator rotor angles and speeds. Kalman filters achieve optimal performance only when the system noise characteristics have known statistical properties (zero-mean, Gaussian, and spectrally white). However in practice the process and measurement noise models are usually difficult to obtain. Thus we have developed the Adaptive Kalman Filter with Inflatable Noise Variances (AKF with InNoVa), an algorithm that can efficiently identify and reduce the impact of incorrect system modeling and/or erroneous measurements. In stage one, we estimate the static state from raw PMU measurements using the AKF with InNoVa; then in stage two, the estimated static state is fed into an extended Kalman filter to estimate the dynamic state. Simulations demonstrate its robustness to sudden changes of system dynamics and erroneous measurements.
An analog filter approach to frequency domain fluorescence spectroscopy
Trainham, Clifford P.; O'Neill, Mary D.; McKenna, Ian J.
2015-10-01
The rate equations found in frequency domain fluorescence spectroscopy are the same as those found in electronics under analog filter theory. Laplace transform methods are a natural way to solve the equations, and the methods can provide solutions for arbitrary excitation functions. The fluorescence terms can be modeled as circuit components and cascaded with drive and detection electronics to produce a global transfer function. Electronics design tools such as Spicea can be used to model fluorescence problems. In applications, such as remote sensing, where detection electronics are operated at high gain and limited bandwidth, a global modeling of the entiremore » system is important, since the filter terms of the drive and detection electronics affect the measured response of the fluorescence signals. Furthermore, the techniques described here can be used to separate signals from fast and slow fluorophores emitting into the same spectral band, and data collection can be greatly accelerated by means of a frequency comb driver waveform and appropriate signal processing of the response.« less
An Analog Filter Approach to Frequency Domain Fluorescence Spectroscopy.
Trainham, R; O'Neill, M; McKenna, I J
2015-11-01
The rate equations found in frequency domain fluorescence spectroscopy are the same as those found in electronics under analog filter theory. Laplace transform methods are a natural way to solve the equations, and the methods can provide solutions for arbitrary excitation functions. The fluorescence terms can be modelled as circuit components and cascaded with drive and detection electronics to produce a global transfer function. Electronics design tools such as SPICE can be used to model fluorescence problems. In applications, such as remote sensing, where detection electronics are operated at high gain and limited bandwidth, a global modelling of the entire system is important, since the filter terms of the drive and detection electronics affect the measured response of the fluorescence signals. The techniques described here can be used to separate signals from fast and slow fluorophores emitting into the same spectral band, and data collection can be greatly accelerated by means of a frequency comb driver waveform and appropriate signal processing of the response. The simplification of the analysis mathematics, and the ability to model the entire detection chain, make it possible to develop more compact instruments for remote sensing applications. PMID:26429345
An analog filter approach to frequency domain fluorescence spectroscopy
Trainham, Clifford P.; O'Neill, Mary D.; McKenna, Ian J.
2015-10-01
The rate equations found in frequency domain fluorescence spectroscopy are the same as those found in electronics under analog filter theory. Laplace transform methods are a natural way to solve the equations, and the methods can provide solutions for arbitrary excitation functions. The fluorescence terms can be modeled as circuit components and cascaded with drive and detection electronics to produce a global transfer function. Electronics design tools such as Spicea can be used to model fluorescence problems. In applications, such as remote sensing, where detection electronics are operated at high gain and limited bandwidth, a global modeling of the entire system is important, since the filter terms of the drive and detection electronics affect the measured response of the fluorescence signals. Furthermore, the techniques described here can be used to separate signals from fast and slow fluorophores emitting into the same spectral band, and data collection can be greatly accelerated by means of a frequency comb driver waveform and appropriate signal processing of the response.
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.
The AdaptiV Approach to Verification of Adaptive Systems
Rouff, Christopher; Buskens, Richard; Pullum, Laura L; Cui, Xiaohui; Hinchey, Mike
2012-01-01
Adaptive systems are critical for future space and other unmanned and intelligent systems. Verification of these systems is also critical for their use in systems with potential harm to human life or with large financial investments. Due to their nondeterministic nature and extremely large state space, current methods for verification of software systems are not adequate to provide a high level of assurance. The combination of stabilization science, high performance computing simulations, compositional verification and traditional verification techniques, plus operational monitors, provides a complete approach to verification and deployment of adaptive systems that has not been used before. This paper gives an overview of this approach.
A Decentralized Adaptive Approach to Fault Tolerant Flight Control
NASA Technical Reports Server (NTRS)
Wu, N. Eva; Nikulin, Vladimir; Heimes, Felix; Shormin, Victor
2000-01-01
This paper briefly reports some results of our study on the application of a decentralized adaptive control approach to a 6 DOF nonlinear aircraft model. The simulation results showed the potential of using this approach to achieve fault tolerant control. Based on this observation and some analysis, the paper proposes a multiple channel adaptive control scheme that makes use of the functionally redundant actuating and sensing capabilities in the model, and explains how to implement the scheme to tolerate actuator and sensor failures. The conditions, under which the scheme is applicable, are stated in the paper.
A Statistical Filtering Approach for Gravity Recovery and Climate Experiment (GRACE) Gravity Data
NASA Technical Reports Server (NTRS)
Davis. J. L.; Tamisiea, M. E.; Elosegui, P.; Mitrovica, J. X.; Hill, E. M.
2008-01-01
We describe and analyze a statistical filtering approach for GRACE data that uses a parametrized model for the temporal evolution of the GRACE coefficients. After least-squares adjustment, a statistical test is performed to assess the significance of the estimated parameters. If the test is passed, the parameters are used by the filter in the reconstruction of the field; otherwise they are rejected. The test is performed, and the filter is formed, separately for annual components of the model and the trend. This new approach is distinct from Gaussian smoothing since it uses the data themselves to test for specific components of the time-varying gravity field. The statistical filter appears inherently to remove most of the "stripes" present in the GRACE fields, although destriping the fields prior to filtering seems to help the trend recovery. We demonstrate that the statistical filter produces reasonable maps for the annual components and trend. We furthermore assess the statistical filter for the annual components using ground-based GPS data in South America by assuming that the annual component of the gravity signal is associated only with groundwater storage. The un-destriped, statistically filtered field has a X2 value relative to the GPS data consistent with the best result from smoothing. In the space domain, the statistical filters are qualitatively similar to Gaussian smoothing. Unlike Gaussian smoothing, however, the statistical filter has significant sidelobes, including large negative sidelobes on the north-south axis, potentially revealing information on the errors, and the correlations among the errors, for the GRACE coefficients.
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
NASA Astrophysics Data System (ADS)
Peña, M.
2016-10-01
Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.
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.
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
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.
NASA Astrophysics Data System (ADS)
Wang, Liang; Chen, Tao; Liu, Xin-yue; Lin, Xu-dong; Yang, Xiao-xia; Li, Hong-zhuang
2016-02-01
In this research, investigations on the closed-loop control stability of adaptive optics systems are conducted by using the Lyapunov approach. As an direct metric of the control stability, the error propagator includes the effects of both the integral gain and the influence matrix and is effective for control-stability evaluation. An experimental 97-element AO system is developed for the control-stability investigation, and the Southwell sensor-actuator configuration rather than the Fried geometry is adopted so as to suppress the potential waffle mode. Because filtering out small singular values of the influence matrix can be used to improve the control stability, the effect of the influence matrix and the effect of the integral gain are considered as a whole by using the error propagator. Then, the control stability of the AO system is evaluated for varying the integral gains and the number of filtered-out singular values. Afterwards, an analysis of the evaluations of the error propagator is made, and a conclusion can be drawn that the control stability can be improved by filtering out more singular values of the influence matrix when the integral gain is high. In other words, the error propagator is useful for trading off the bandwidth error and the fitting error of AO systems in a control-stability approach. Finally, a performance measurement of the experimental AO system is conducted when 13 smaller singular values of the influence matrix are filtered out, and the results show that filtering out a small fraction of the singular values has a minor influence on the performance of this AO system.
A new approach to adaptive control of manipulators
NASA Technical Reports Server (NTRS)
Seraji, H.
1987-01-01
An approach in which the manipulator inverse is used as a feedforward controller is employed in the adaptive control of manipulators in order to achieve trajectory tracking by the joint angles. The desired trajectory is applied as an input to the feedforward controller, and the controller output is used as the driving torque for the manipulator. An adaptive algorithm obtained from MRAC theory is used to update the controller gains to cope with variations in the manipulator inverse due to changes of the operating point. An adaptive feedback controller and an auxiliary signal enhance closed-loop stability and achieve faster adaptation. Simulation results demonstrate the effectiveness of the proposed control scheme for different reference trajectories, and despite large variations in the payload.
Adaptive bilateral filter for image denoising and its application to in-vitro Time-of-Flight data
NASA Astrophysics Data System (ADS)
Seitel, Alexander; dos Santos, Thiago R.; Mersmann, Sven; Penne, Jochen; Groch, Anja; Yung, Kwong; Tetzlaff, Ralf; Meinzer, Hans-Peter; Maier-Hein, Lena
2011-03-01
Image-guided therapy systems generally require registration of pre-operative planning data with the patient's anatomy. One common approach to achieve this is to acquire intra-operative surface data and match it to surfaces extracted from the planning image. Although increasingly popular for surface generation in general, the novel Time-of-Flight (ToF) technology has not yet been applied in this context. This may be attributed to the fact that the ToF range images are subject to considerable noise. The contribution of this study is two-fold. Firstly, we present an adaption of the well-known bilateral filter for denoising ToF range images based on the noise characteristics of the camera. Secondly, we assess the quality of organ surfaces generated from ToF range data with and without bilateral smoothing using corresponding high resolution CT data as ground truth. According to an evaluation on five porcine organs, the root mean squared (RMS) distance between the denoised ToF data points and the reference computed tomography (CT) surfaces ranged from 3.0 mm (lung) to 9.0 mm (kidney). This corresponds to an error-reduction of up to 36% compared to the error of the original ToF surfaces.
Ghumare, Eshwar; Schrooten, Maarten; Vandenberghe, Rik; Dupont, Patrick
2015-08-01
Kalman filter approaches are widely applied to derive time varying effective connectivity from electroencephalographic (EEG) data. For multi-trial data, a classical Kalman filter (CKF) designed for the estimation of single trial data, can be implemented by trial-averaging the data or by averaging single trial estimates. A general linear Kalman filter (GLKF) provides an extension for multi-trial data. In this work, we studied the performance of the different Kalman filtering approaches for different values of signal-to-noise ratio (SNR), number of trials and number of EEG channels. We used a simulated model from which we calculated scalp recordings. From these recordings, we estimated cortical sources. Multivariate autoregressive model parameters and partial directed coherence was calculated for these estimated sources and compared with the ground-truth. The results showed an overall superior performance of GLKF except for low levels of SNR and number of trials.
A particle filtering approach for spatial arrival time tracking in ocean acoustics.
Jain, Rashi; Michalopoulou, Zoi-Heleni
2011-06-01
The focus of this work is on arrival time and amplitude estimation from acoustic signals recorded at spatially separated hydrophones in the ocean. A particle filtering approach is developed that treats arrival times as "targets" and tracks their "location" across receivers, also modeling arrival time gradient. The method is evaluated via Monte Carlo simulations and is compared to a maximum likelihood estimator, which does not relate arrivals at neighboring receivers. The comparison demonstrates a significant advantage in using the particle filter. It is also shown that posterior probability density functions of times and amplitudes become readily available with particle filtering. PMID:21682358
A particle filtering approach for spatial arrival time tracking in ocean acoustics.
Jain, Rashi; Michalopoulou, Zoi-Heleni
2011-06-01
The focus of this work is on arrival time and amplitude estimation from acoustic signals recorded at spatially separated hydrophones in the ocean. A particle filtering approach is developed that treats arrival times as "targets" and tracks their "location" across receivers, also modeling arrival time gradient. The method is evaluated via Monte Carlo simulations and is compared to a maximum likelihood estimator, which does not relate arrivals at neighboring receivers. The comparison demonstrates a significant advantage in using the particle filter. It is also shown that posterior probability density functions of times and amplitudes become readily available with particle filtering.
Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach.
Cavagnaro, Daniel R; Gonzalez, Richard; Myung, Jay I; Pitt, Mark A
2013-02-01
Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called Adaptive Design Optimization (ADO), adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate Expected Utility, Weighted Expected Utility, Original Prospect Theory, and Cumulative Prospect Theory models. PMID:24532856
Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach
Cavagnaro, Daniel R.; Gonzalez, Richard; Myung, Jay I.; Pitt, Mark A.
2014-01-01
Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called Adaptive Design Optimization (ADO), adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate Expected Utility, Weighted Expected Utility, Original Prospect Theory, and Cumulative Prospect Theory models. PMID:24532856
Searching for adaptive traits in genetic resources - phenology based approach
NASA Astrophysics Data System (ADS)
Bari, Abdallah
2015-04-01
Searching for adaptive traits in genetic resources - phenology based approach Abdallah Bari, Kenneth Street, Eddy De Pauw, Jalal Eddin Omari, and Chandra M. Biradar International Center for Agricultural Research in the Dry Areas, Rabat Institutes, Rabat, Morocco Phenology is an important plant trait not only for assessing and forecasting food production but also for searching in genebanks for adaptive traits. Among the phenological parameters we have been considering to search for such adaptive and rare traits are the onset (sowing period) and the seasonality (growing period). Currently an application is being developed as part of the focused identification of germplasm strategy (FIGS) approach to use climatic data in order to identify crop growing seasons and characterize them in terms of onset and duration. These approximations of growing period characteristics can then be used to estimate flowering and maturity dates for dryland crops, such as wheat, barley, faba bean, lentils and chickpea, and assess, among others, phenology-related traits such as days to heading [dhe] and grain filling period [gfp]. The approach followed here is based on first calculating long term average daily temperatures by fitting a curve to the monthly data over days from beginning of the year. Prior to the identification of these phenological stages the onset is extracted first from onset integer raster GIS layers developed based on a model of the growing period that considers both moisture and temperature limitations. The paper presents some examples of real applications of the approach to search for rare and adaptive traits.
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
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.
Tian, Ya; Wei, Hongxing; Tan, Jindong
2013-03-01
High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. In order to achieve real-time ambulatory human motion tracking with low-cost MARG (magnetic, angular rate, and gravity) sensors, a computationally efficient and robust algorithm for orientation estimation is critical. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as adaptive-gain orientation filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for earth's magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. The features of this approach include the accurate estimation of the gyroscope bias to correct the instantaneous gyroscope measurements and robust estimation in conditions of fast motions and magnetic distortions. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods.
Wastewater renovation using constructed soil filter (CSF): a novel approach.
Nemade, P D; Kadam, A M; Shankar, H S
2009-10-30
Constructed soil filter (CSF) also known as Soil Biotechnology (SBT) is a process for water renovation which makes use of formulated media with culture of soil macro- and microorganisms. CSF combines sedimentation, infiltration and biodegradation processes to remove oxidizable organics and inorganics of wastewater in a single facility. Operating experience shows hydraulic loading in the range of 0.05-0.25 m(3)/m(2) h and organic loading up to 200-680 g/m(2) d. The results show increase in dissolved oxygen levels, COD removal (from 352 mg/l to 20 mg/l); BOD removal (from 211 mg/l to 7.0 mg/l); suspended solids removal (from 293 mg/l to 16 mg/l); turbidity reduction (from 145 NTU to 5.3 NTU); iron (from 5 mg/l to 0.3 mg/l); arsenic (from 500 microg/l to 10 microg/l); total coliform and fecal coliform removal (from 145 x 10(5) to 55 CFU/100 mL and 150 x 10(8) to 110 CFU/100 mL respectively), with desired pathogen levels as per WHO standards, i.e. < or =10(3) CFU/100 mL. CSF reveals advantages such as low HRT (0.5-2.0 h), low energy requirement (0.04 kWh/m(3)), no pre-treatment, high dissolved oxygen levels in the effluent, no biosludge production, no mechanical aeration and no odor, fish compatible water quality and evergreen ambience. PMID:19501460
Manosueb, Anchalee; Koseeyaporn, Jeerasuda; Wardkein, Paramote
2014-01-01
This paper presents a technique for finding the optimal initial weight for adaptive filter by using difference equation. The obtained analytical response of the system identifies the appropriate weights for the system and shows that the MSE depends on the initial weight. The proposed technique is applied to eliminate the known frequency power line interference (PLI) signal in the electrocardiogram (ECG) signal. The PLI signal is considered as a combination of cosine and sine signals. The adaptive filter, therefore, attempts to adjust the amplitude of cosine and sine signals to synthesize a reference signal very similar to the contaminated PLI signal. To compare the potential of the proposed technique to other techniques, the system is simulated by using the Matlab program and the TMS320C6713 digital board. The simulation results demonstrate that the proposed technique enables the system to eliminate the PLI signal with the fastest time and gains the superior results of the recovered ECG signal.
A special adapted retractor for the mini-sternotomy approach.
Massetti, M; Babatasi, G; Bhoyroo, S; Le Page, O; Khayat, A
1999-07-01
Minimally invasive cardiac operations are now possible through different approaches. To provide the best exposure and sufficient space to manipulate the heart, a special adapted thoracic retractor has been developed for the ministernotomy approach. It is universally adjustable and provides excellent and consistent exposure especially below the incision edges. The retractor has the further advantage of a very low profile on the surgeon's side and at the cephalic and caudal extremes of the operative field, which permits the greatest possible access through a limited access. We have successfully used this retractor in more than 180 patients. A less invasive median sternotomy through a 6-9-cm incision has been our original approach.
Novel Approaches to Adaptive Angular Approximations in Computational Transport
Marvin L. Adams; Igor Carron; Paul Nelson
2006-06-04
The particle-transport equation is notoriously difficult to discretize accurately, largely because the solution can be discontinuous in every variable. At any given spatial position and energy E, for example, the transport solution can be discontinuous at an arbitrary number of arbitrary locations in the direction domain. Even if the solution is continuous it is often devoid of smoothness. This makes the direction variable extremely difficult to discretize accurately. We have attacked this problem with adaptive discretizations in the angle variables, using two distinctly different approaches. The first approach used wavelet function expansions directly and exploited their ability to capture sharp local variations. The second used discrete ordinates with a spatially varying quadrature set that adapts to the local solution. The first approach is very different from that in today’s transport codes, while the second could conceivably be implemented in such codes. Both approaches succeed in reducing angular discretization error to any desired level. The work described and results presented in this report add significantly to the understanding of angular discretization in transport problems and demonstrate that it is possible to solve this important long-standing problem in deterministic transport. Our results show that our adaptive discrete-ordinates (ADO) approach successfully: 1) Reduces angular discretization error to user-selected “tolerance” levels in a variety of difficult test problems; 2) Achieves a given error with significantly fewer unknowns than non-adaptive discrete ordinates methods; 3) Can be implemented within standard discrete-ordinates solution techniques, and thus could generate a significant impact on the field in a relatively short time. Our results show that our adaptive wavelet approach: 1) Successfully reduces the angular discretization error to arbitrarily small levels in a variety of difficult test problems, even when using the
Tankanag, Arina V; Chemeris, Nikolay K
2009-10-01
The paper describes an original method for analysis of the peripheral blood flow oscillations measured with the laser Doppler flowmetry (LDF) technique. The method is based on the continuous wavelet transform and adaptive wavelet theory and applies an adaptive wavelet filtering to the LDF data. The method developed allows one to examine the dynamics of amplitude oscillations in a wide frequency range (from 0.007 to 2 Hz) and to process both stationary and non-stationary short (6 min) signals. The capabilities of the method have been demonstrated by analyzing LDF signals registered in the state of rest and upon humeral occlusion. The paper shows the main advantage of the method proposed, which is the significant reduction of 'border effects', as compared to the traditional wavelet analysis. It was found that the low-frequency amplitudes obtained by adaptive wavelets are significantly higher than those obtained by non-adaptive ones. The method suggested would be useful for the analysis of low-frequency components of the short-living transitional processes under the conditions of functional tests. The method of adaptive wavelet filtering can be used to process stationary and non-stationary biomedical signals (cardiograms, encephalograms, myograms, etc), as well as signals studied in the other fields of science and engineering.
A Comparison of Filter-based Approaches for Model-based Prognostics
NASA Technical Reports Server (NTRS)
Daigle, Matthew John; Saha, Bhaskar; Goebel, Kai
2012-01-01
Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the stateparameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.
Uniform smooth filtering approach for fast template matching
NASA Astrophysics Data System (ADS)
Li, Bing C.
2016-05-01
Sum of square difference (SSD) and normalized cross correlation (NCC) are two different template matching techniques and their fast implementations have been investigated independently. The SSD approach is known to be simple and fast, however it is variant to image intensity change that lead to low performance. On the other hand, the NCC method is invariant to intensity change and has high performance, but its computational cost is high. In this paper, we derive an equation that connects NCC and SSD. From this equation, we propose SSD based partial elimination for the fast implementation of NCC template matching. This new technique takes the advantages of both NCC's high performance and SSD's low computational cost. It is fast and has high performance. Then we propose a uniform smoothing approach that further reduces computational cost for NCC. Experiments show that the proposed method is significantly faster than the techniques reported in literature.
NASA Astrophysics Data System (ADS)
Amian, M.; Setarehdan, S. Kamaledin; Yousefi, H.
2014-09-01
Functional Near infrared spectroscopy (fNIRS) is a newly noninvasive way to measure oxy hemoglobin and deoxy hemoglobin concentration changes of human brain. Relatively safe and affordable than other functional imaging techniques such as fMRI, it is widely used for some special applications such as infant examinations and pilot's brain monitoring. In such applications, fNIRS data sometimes suffer from undesirable movements of subject's head which called motion artifact and lead to a signal corruption. Motion artifact in fNIRS data may result in fallacy of concluding or diagnosis. In this work we try to reduce these artifacts by a novel Kalman filtering algorithm that is based on an autoregressive moving average (ARMA) model for fNIRS system. Our proposed method does not require to any additional hardware and sensor and also it does not need to whole data together that once were of ineluctable necessities in older algorithms such as adaptive filter and Wiener filtering. Results show that our approach is successful in cleaning contaminated fNIRS data.
An Approach to V&V of Embedded Adaptive Systems
NASA Technical Reports Server (NTRS)
Liu, Yan; Yerramalla, Sampath; Fuller, Edgar; Cukic, Bojan; Gururajan, Srikaruth
2004-01-01
Rigorous Verification and Validation (V&V) techniques are essential for high assurance systems. Lately, the performance of some of these systems is enhanced by embedded adaptive components in order to cope with environmental changes. Although the ability of adapting is appealing, it actually poses a problem in terms of V&V. Since uncertainties induced by environmental changes have a significant impact on system behavior, the applicability of conventional V&V techniques is limited. In safety-critical applications such as flight control system, the mechanisms of change must be observed, diagnosed, accommodated and well understood prior to deployment. In this paper, we propose a non-conventional V&V approach suitable for online adaptive systems. We apply our approach to an intelligent flight control system that employs a particular type of Neural Networks (NN) as the adaptive learning paradigm. Presented methodology consists of a novelty detection technique and online stability monitoring tools. The novelty detection technique is based on Support Vector Data Description that detects novel (abnormal) data patterns. The Online Stability Monitoring tools based on Lyapunov's Stability Theory detect unstable learning behavior in neural networks. Cases studies based on a high fidelity simulator of NASA's Intelligent Flight Control System demonstrate a successful application of the presented V&V methodology. ,
Adaptation in flower form: a comparative evodevo approach.
Specht, Chelsea D; Howarth, Dianella G
2015-04-01
Evolutionary developmental biology (evodevo) attempts to explain how the process of organismal development evolves, utilizing a comparative approach to investigate changes in developmental pathways and processes that occur during the evolution of a given lineage. Evolutionary genetics uses a population approach to understand how organismal changes in form or function are linked to underlying genetics, focusing on changes in gene and genotype frequencies within populations and the fixation of genotypic variation into traits that define species or evoke speciation events. Microevolutionary processes, including mutation, genetic drift, natural selection and gene flow, can provide the foundation for macroevolutionary patterns observed as morphological evolution and adaptation. The temporal element linking microevolutionary processes to macroevolutionary patterns is development: an organism's genotype is converted to phenotype by ontogenetic processes. Because selection acts upon the phenotype, the connection between evolutionary genetics and developmental evolution becomes essential to understanding adaptive evolution in organismal form and function. Here, we discuss how developmental genetic studies focused on key developmental processes could be linked within a comparative framework to study the developmental genetics of adaptive evolution, providing examples from research on two key processes of plant evodevo - floral symmetry and organ fusion - and their role in the adaptation of floral form. PMID:25470511
A novel modular approach to active power-line harmonic filtering in distribution systems
NASA Astrophysics Data System (ADS)
El Shatshat, Ramadan A.
The objective of this research is to develop an efficient and reliable modular active harmonic filter system to realize a cost-effective solution to the harmonic problem. The proposed filter system consists of a number CSC modules, each dedicated to filter a specific harmonic of choice (Frequency-Splitting Approach). The power rating of the modules will decrease and their switching frequency will increase as the order of the harmonic to be filtered is increased. The overall switching losses are minimized due to the selected harmonic elimination and balanced a "power rating"-"switching frequency" product. Two ADALINEs are proposed as a part of the filter controller for processing the signals obtained from the power-line. One ADALINE (the Current ADALINE) extracts the fundamental and harmonic components of the distorted current. The other ADALINE (the Voltage ADALINE) estimates the line voltage. The outputs of both ADALINEs are used to construct the modulating signals of the filter modules. The proposed controller decides which CSC filter module(s) is connected to the electric grid. The automated connection of the corresponding filter module(s) is based on decision-making rules in such a way that the IEEE 519-1992 limits are not violated. The information available on the magnitude of each harmonic component allows us to select the active filter bandwidth (i.e., the highest harmonic to be suppressed). This will result in more efficiency and higher performance. The proposed controller adjusts the I dc in each CSC module according to the present magnitude of the corresponding harmonic current. This results in optimum dc-side current value and minimal converter losses. The comparison of the proposed modular active filter scheme and the conventional one converter scheme on practical use in industry is presented. This comparison shows that the proposed solution is more economical, reliable and flexible compared to conventional one. High speed and accuracy of ADALINE, self
Filter multiplexing by use of spatial Code Division Multiple Access approach.
Solomon, Jonathan; Zalevsky, Zeev; Mendlovic, David; Monreal, Javier Garcia
2003-02-10
The increasing popularity of optical communication has also brought a demand for a broader bandwidth. The trend, naturally, was to implement methods from traditional electronic communication. One of the most effective traditional methods is Code Division Multiple Access. In this research, we suggest the use of this approach for spatial coding applied to images. The approach is to multiplex several filters into one plane while keeping their mutual orthogonality. It is shown that if the filters are limited by their bandwidth, the output of all the filters can be sampled in the original image resolution and fully recovered through an all-optical setup. The theoretical analysis of such a setup is verified in an experimental demonstration.
The adaptive, cut-cell Cartesian approach (warts and all)
NASA Astrophysics Data System (ADS)
Powell, Kenneth G.
1995-10-01
Solution-adaptive methods based on cutting bodies out of Cartesian grids are gaining popularity now that the ways of circumventing the accuracy problems associated with small cut cells have been developed. Researchers are applying Cartesian-based schemes to a broad class of problems now, and, although there is still development work to be done, it is becoming clearer which problems are best suited to the approach (and which are not). The purpose of this paper is to give a candid assessment, based on applying Cartesian schemes to a variety of problems, of the strengths and weaknesses of the approach as it is currently implemented.
Hybrid Kalman Filter: A New Approach for Aircraft Engine In-Flight Diagnostics
NASA Technical Reports Server (NTRS)
Kobayashi, Takahisa; Simon, Donald L.
2006-01-01
In this paper, a uniquely structured Kalman filter is developed for its application to in-flight diagnostics of aircraft gas turbine engines. The Kalman filter is a hybrid of a nonlinear on-board engine model (OBEM) and piecewise linear models. The utilization of the nonlinear OBEM allows the reference health baseline of the in-flight diagnostic system to be updated to the degraded health condition of the engines through a relatively simple process. Through this health baseline update, the effectiveness of the in-flight diagnostic algorithm can be maintained as the health of the engine degrades over time. Another significant aspect of the hybrid Kalman filter methodology is its capability to take advantage of conventional linear and nonlinear Kalman filter approaches. Based on the hybrid Kalman filter, an in-flight fault detection system is developed, and its diagnostic capability is evaluated in a simulation environment. Through the evaluation, the suitability of the hybrid Kalman filter technique for aircraft engine in-flight diagnostics is demonstrated.
An Approach for Prioritizing Agile Practices for Adaptation
NASA Astrophysics Data System (ADS)
Mikulenas, Gytenis; Kapocius, Kestutis
Agile software development approaches offer a strong alternative to the traditional plan-driven methodologies that have not been able to warrant successfulness of the software projects. However, the move toward Agile is often hampered by the wealth of alternative practices that are accompanied by numerous success or failure stories. Clearly, the formal methods for choosing most suitable practices are lacking. In this chapter, we present an overview of this problem and propose an approach for prioritization of available practices in accordance to the particular circumstances. The proposal combines ideas from Analytic Hierarchy Process (AHP) decision-making technique, cost-value analysis, and Rule-Description-Practice (RDP) technique. Assumption that such approach could facilitate the Agile adaptation process was supported by the case study of the approach illustrating the process of choosing most suitable Agile practices within a real-life project.
NASA Astrophysics Data System (ADS)
Bostater, Charles R., Jr.
2006-09-01
This paper describes a wavelet based approach to derivative spectroscopy. The approach is utilized to select, through optimization, optimal channels or bands to use as derivative based remote sensing algorithms. The approach is applied to airborne and modeled or synthetic reflectance signatures of environmental media and features or objects within such media, such as benthic submerged vegetation canopies. The technique can also applied to selected pixels identified within a hyperspectral image cube obtained from an board an airborne, ground based, or subsurface mobile imaging system. This wavelet based image processing technique is an extremely fast numerical method to conduct higher order derivative spectroscopy which includes nonlinear filter windows. Essentially, the wavelet filter scans a measured or synthetic signature in an automated sequential manner in order to develop a library of filtered spectra. The library is utilized in real time to select the optimal channels for direct algorithm application. The unique wavelet based derivative filtering technique makes us of a translating, and dilating derivative spectroscopy signal processing (TDDS-SP (R)) approach based upon remote sensing science and radiative transfer processes unlike other signal processing techniques applied to hyperspectral signatures.
urCF: An Approach to Integrating User Reviews into Memory-Based Collaborative Filtering
ERIC Educational Resources Information Center
Zhang, Zhenxue
2013-01-01
Blessed by the Internet age, many online retailers (e.g., Amazon.com) have deployed recommender systems to help their customers identify products that may be of their interest in order to improve cross-selling and enhance customer loyalty. Collaborative Filtering (CF) is the most successful technique among different approaches to generating…
An Unscented Kalman Filter Approach to the Estimation of Nonlinear Dynamical Systems Models
ERIC Educational Resources Information Center
Chow, Sy-Miin; Ferrer, Emilio; Nesselroade, John R.
2007-01-01
In the past several decades, methodologies used to estimate nonlinear relationships among latent variables have been developed almost exclusively to fit cross-sectional models. We present a relatively new estimation approach, the unscented Kalman filter (UKF), and illustrate its potential as a tool for fitting nonlinear dynamic models in two ways:…
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
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-07-12
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.
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.
NASA Astrophysics Data System (ADS)
Wu, Xiaoping; Abbondanza, Claudio; Altamimi, Zuheir; Chin, T. Mike; Collilieux, Xavier; Gross, Richard S.; Heflin, Michael B.; Jiang, Yan; Parker, Jay W.
2015-05-01
The current International Terrestrial Reference Frame is based on a piecewise linear site motion model and realized by reference epoch coordinates and velocities for a global set of stations. Although linear motions due to tectonic plates and glacial isostatic adjustment dominate geodetic signals, at today's millimeter precisions, nonlinear motions due to earthquakes, volcanic activities, ice mass losses, sea level rise, hydrological changes, and other processes become significant. Monitoring these (sometimes rapid) changes desires consistent and precise realization of the terrestrial reference frame (TRF) quasi-instantaneously. Here, we use a Kalman filter and smoother approach to combine time series from four space geodetic techniques to realize an experimental TRF through weekly time series of geocentric coordinates. In addition to secular, periodic, and stochastic components for station coordinates, the Kalman filter state variables also include daily Earth orientation parameters and transformation parameters from input data frames to the combined TRF. Local tie measurements among colocated stations are used at their known or nominal epochs of observation, with comotion constraints applied to almost all colocated stations. The filter/smoother approach unifies different geodetic time series in a single geocentric frame. Fragmented and multitechnique tracking records at colocation sites are bridged together to form longer and coherent motion time series. While the time series approach to TRF reflects the reality of a changing Earth more closely than the linear approximation model, the filter/smoother is computationally powerful and flexible to facilitate incorporation of other data types and more advanced characterization of stochastic behavior of geodetic time series.
Proton computed tomography reconstruction using a backprojection-then-filtering approach
NASA Astrophysics Data System (ADS)
Poludniowski, G.; Allinson, N. M.; Evans, P. M.
2014-12-01
A novel approach to proton CT reconstruction using backprojection-then-filtering (BPF) is proposed. A list-mode algorithm is formulated accommodating non-linear proton paths. The analytical form is derived for the deblurring kernel necessary for the filtering step. Further, a finite matrix correction is derived to correct for the limited size of the backprojection matrix. High quantitative accuracy in relative stopping power is demonstrated (⩽0.1%) using Monte Carlo simulations. This accuracy makes the algorithm a promising candidate for future proton CT systems in proton therapy applications. For the purposes of reconstruction, each proton path in the object-of-interest was estimated based on a cubic spline fit to the proton entry and exit vectors. The superior spatial-resolution of the BPF method over the standard filtering-then-backprojection approach is demonstrated. As the BPF algorithm requires only one backprojection and filtering operation on a scan data set, it also offers computational advantages over an iterative reconstruction approach.
Variable Neural Adaptive Robust Control: A Switched System Approach
Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.
2015-05-01
Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.
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.
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.
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Libera, Peter; Fernandez, Manuel F.; Dobeck, Gerald J.
1996-05-01
An automatic, robust, adaptive clutter suppression, mine detection and classification processing string has been developed and applied to side-scan sonar imagery data. The overall processing string includes data pre-processing, adaptive clutter filtering (ACF), 2D normalization, detection, feature extraction, and classification processing blocks. The data pre-processing block contains automatic gain control and data decimation processing. The ACF technique designs a 2D adaptive range-crossrange linear FIR filter which is optimal in the Least Squares sense, simultaneously suppressing the background clutter while preserving an average peak target signature (normalized shape) computed a priori using training set data. A multiple reference ACF algorithm version was utilized to account for multiple target shapes (due to different mine types, multiple target aspect angles, etc.). The detection block consists of thresholding, clustering of exceedances and limiting their number, and a secondary thresholding process. Following feature extraction, the classification block applies a novel transformation to the data, which orthogonalizes the features and enables an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule. The utility of the overall processing string was demonstrated with two side-scan sonar data sets. The ACF/feature orthogonalization based LLRT mine classification processing string provided average probability of correct mine classification and false alarm rate performance similar to that obtained when utilizing an expert sonar operator.
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Fernandez, Manuel F.; Dobeck, Gerald J.
1997-07-01
An automatic, robust, adaptive clutter suppression, predetection level fusion, sea mine detection and classification processing string has been developed and applied to shallow water side-scan sonar imagery data. The overall processing string includes pre-processing string includes pre-processing, adaptive clutter filtering (ACF), 2D normalization, detection, feature extraction and classification processing blocks. The pre-processing block contains automatic gain control, data decimation and data alignment processing. The ACF is a multi-dimensional adaptive linear FIR filter, optimal in the least squares sense, for simultaneous background clutter suppression and preservation of an average peak target signature. After data alignment, using a 3D ACF enables simultaneous multiple frequency data fusion and clutter suppression in the composite frequency-range-crossrange domain. Following 2D normalization, the detection consists of thresholding, clustering of exceedances and limiting their number. Finally, features are extracted and a orthogonalization transformation is applied to the data, enabling an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule. The utility of the overall processing string was demonstrated with two side-scan sonar data sets. The ACF, feature orthogonalization, LLRT-based classification processing string provided average probability of correct mine classification and false alarm rate performance exceeding the one obtained when utilizing an expert sonar operator. The overall processing string can be easily implemented in real-time using COTS technology.
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.
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.
Meckley, Trevor D.; Holbrook, Christopher M.; Wagner, C. Michael; Binder, Thomas R.
2014-01-01
The use of position precision estimates that reflect the confidence in the positioning process should be considered prior to the use of biological filters that rely on a priori expectations of the subject’s movement capacities and tendencies. Position confidence goals should be determined based upon the needs of the research questions and analysis requirements versus arbitrary selection, in which filters of previous studies are adopted. Data filtering with this approach ensures that data quality is sufficient for the selected analyses and presents the opportunity to adjust or identify a different analysis in the event that the requisite precision was not attained. Ignoring these steps puts a practitioner at risk of reporting errant findings.
A New Method for E-Government Procurement Using Collaborative Filtering and Bayesian Approach
Wang, Yan
2013-01-01
Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach. PMID:24385869
A new method for E-government procurement using collaborative filtering and Bayesian approach.
Zhang, Shuai; Xi, Chengyu; Wang, Yan; Zhang, Wenyu; Chen, Yanhong
2013-01-01
Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach.
A novel approach to achieving modular retrovirus clearance for a parvovirus filter.
Stuckey, Juliana; Strauss, Daniel; Venkiteshwaran, Adith; Gao, Jinxin; Luo, Wen; Quertinmont, Michelle; O'Donnell, Sean; Chen, Dayue
2014-01-01
Viral filtration is routinely incorporated into the downstream purification processes for the production of biologics produced in mammalian cell cultures (MCC) to remove potential viral contaminants. In recent years, the use of retentive filters designed for retaining parvovirus (~20 nm) has become an industry standard in a conscious effort to further improve product safety. Since retentive filters remove viruses primarily by the size exclusion mechanism, it is expected that filters designed for parvovirus removal can effectively clear larger viruses such as retroviruses (~100 nm). In an attempt to reduce the number of viral clearance studies, we have taken a novel approach to demonstrate the feasibility of claiming modular retrovirus clearance for Asahi Planova 20N filters. Porcine parvovirus (PPV) and xenotropic murine leukemia virus (XMuLV) were co-spiked into six different feedstreams and then subjected to laboratory scale Planova 20N filtration. Our results indicate that Planova 20N filters consistently retain retroviruses and no retrovirus has ever been detected in the filtrates even when significant PPV breakthrough is observed. Based on the data from multiple in-house viral validation studies and the results from the co-spiking experiments, we have successfully claimed a modular retrovirus clearance of greater than 6 log10 reduction factors (LRF) to support clinical trial applications in both USA and Europe.
A novel approach to achieving modular retrovirus clearance for a parvovirus filter.
Stuckey, Juliana; Strauss, Daniel; Venkiteshwaran, Adith; Gao, Jinxin; Luo, Wen; Quertinmont, Michelle; O'Donnell, Sean; Chen, Dayue
2014-01-01
Viral filtration is routinely incorporated into the downstream purification processes for the production of biologics produced in mammalian cell cultures (MCC) to remove potential viral contaminants. In recent years, the use of retentive filters designed for retaining parvovirus (~20 nm) has become an industry standard in a conscious effort to further improve product safety. Since retentive filters remove viruses primarily by the size exclusion mechanism, it is expected that filters designed for parvovirus removal can effectively clear larger viruses such as retroviruses (~100 nm). In an attempt to reduce the number of viral clearance studies, we have taken a novel approach to demonstrate the feasibility of claiming modular retrovirus clearance for Asahi Planova 20N filters. Porcine parvovirus (PPV) and xenotropic murine leukemia virus (XMuLV) were co-spiked into six different feedstreams and then subjected to laboratory scale Planova 20N filtration. Our results indicate that Planova 20N filters consistently retain retroviruses and no retrovirus has ever been detected in the filtrates even when significant PPV breakthrough is observed. Based on the data from multiple in-house viral validation studies and the results from the co-spiking experiments, we have successfully claimed a modular retrovirus clearance of greater than 6 log10 reduction factors (LRF) to support clinical trial applications in both USA and Europe. PMID:24123923
Papageorgiou, G T; Mocé-Llivina, L; Christodoulou, C G; Lucena, F; Akkelidou, D; Ioannou, E; Jofre, J
2000-01-01
We identified conditions under which Buffalo green monkey cells grew on the surfaces of cellulose nitrate membrane filters in such a way that they covered the entire surface of each filter and penetrated through the pores. When such conditions were used, poliovirus that had previously been adsorbed on the membranes infected the cells and replicated. A plaque assay method and a quantal method (most probable number of cytopathic units) were used to detect and count the viruses adsorbed on the membrane filters. Polioviruses in aqueous suspensions were then concentrated by adsorption to cellulose membrane filters and were subsequently counted without elution, a step which is necessary when the commonly used methods are employed. The pore size of the membrane filter, the sample contents, and the sample volume were optimized for tap water, seawater, and a 0.25 M glycine buffer solution. The numbers of viruses recovered under the optimized conditions were more than 50% greater than the numbers counted by the standard plaque assay. When ceftazidime was added to the assay medium in addition to the antibiotics which are typically used, the method could be used to study natural samples with low and intermediate levels of microbial pollution without decontamination of the samples. This methodological approach also allowed plaque hybridization either directly on cellulose nitrate membranes or on Hybond N+ membranes after the preparations were transferred.
Adaptive Wing Camber Optimization: A Periodic Perturbation Approach
NASA Technical Reports Server (NTRS)
Espana, Martin; Gilyard, Glenn
1994-01-01
Available redundancy among aircraft control surfaces allows for effective wing camber modifications. As shown in the past, this fact can be used to improve aircraft performance. To date, however, algorithm developments for in-flight camber optimization have been limited. This paper presents a perturbational approach for cruise optimization through in-flight camber adaptation. The method uses, as a performance index, an indirect measurement of the instantaneous net thrust. As such, the actual performance improvement comes from the integrated effects of airframe and engine. The algorithm, whose design and robustness properties are discussed, is demonstrated on the NASA Dryden B-720 flight simulator.
Switching among pulse-generation regimes in passively mode-locked fibre laser by adaptive filtering
NASA Astrophysics Data System (ADS)
Peng, Junsong; Boscolo, Sonia
2016-04-01
We show both numerically and experimentally that dispersion management can be realized by manipulating the dispersion of a filter in a passively mode-locked fibre laser. A programmable filter the dispersion of which can be software configured is employed in the laser. Solitons, stretched-pulses, and dissipative solitons can be targeted reliably by controlling the filter transmission function only, while the length of fibres is fixed in the laser. This technique shows remarkable advantages in controlling operation regimes in ultrafast fibre lasers, in contrast to the traditional technique in which dispersion management is achieved by optimizing the relative length of fibres with opposite-sign dispersion. Our versatile ultrafast fibre laser will be attractive for applications requiring different pulse profiles such as in optical signal processing and optical communications.
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.
The Kalman filter approach to inductively coupled plasma atomic emission spectrometry
NASA Astrophysics Data System (ADS)
Van Veen, E. H.; Bosch, S.; De Loos-Vollebregt, M. T. C.
1994-07-01
This article is an electronic publication in Spectrochimica Acta Electronica (SAE), the electronic section of Spectrochimica Acta Part B (SAB). The hardcopy text, comprising the main article and two appendices, is accompanied by a disk containing the compiled program, a reference manual and data files. The work deals with data handling in inductively coupled plasma atomic emission spectrometry (ICP-AES). With this technique, the analyte signal is superimposed on a background signal. When separating the signals by manual or automated three-point background correction, there are many instances in which the data reduction fails. Based on scans recorded in a fast-scanning mode and on a library of pure-component scans, the Kaiman filter approach models the emission in the spectral window (about 100 pm) of the analyte and mathematically solves the problem of background correction. By using a criterion-based algorithm to correct for optical instability, the uncertainty in the determination of the interferent line signal is eliminated. Therefore, the present filter implementation yields more accurate and precise results, especially in the case of line overlap. The Kalman filter Approach to Atomic Spectrometry (KAAS) software automatically processes Perkin-Elmer Plasma 1000/2000 text files, but can also handle ASCII data files. Practical and comprehensive examples are given to evoke the "Kalman filter feeling" in the crucial step of creating the emission model.
Parallel, grid-adaptive approaches for relativistic hydro and magnetohydrodynamics
NASA Astrophysics Data System (ADS)
Keppens, R.; Meliani, Z.; van Marle, A. J.; Delmont, P.; Vlasis, A.; van der Holst, B.
2012-02-01
Relativistic hydro and magnetohydrodynamics provide continuum fluid descriptions for gas and plasma dynamics throughout the visible universe. We present an overview of state-of-the-art modeling in special relativistic regimes, targeting strong shock-dominated flows with speeds approaching the speed of light. Significant progress in its numerical modeling emerged in the last two decades, and we highlight specifically the need for grid-adaptive, shock-capturing treatments found in several contemporary codes in active use and development. Our discussion highlights one such code, MPI-AMRVAC (Message-Passing Interface-Adaptive Mesh Refinement Versatile Advection Code), but includes generic strategies for allowing massively parallel, block-tree adaptive simulations in any dimensionality. We provide implementation details reflecting the underlying data structures as used in MPI-AMRVAC. Parallelization strategies and scaling efficiencies are discussed for representative applications, along with guidelines for data formats suitable for parallel I/O. Refinement strategies available in MPI-AMRVAC are presented, which cover error estimators in use in many modern AMR frameworks. A test suite for relativistic hydro and magnetohydrodynamics is provided, chosen to cover all aspects encountered in high-resolution, shock-governed astrophysical applications. This test suite provides ample examples highlighting the advantages of AMR in relativistic flow problems.
NASA Astrophysics Data System (ADS)
Lacerda, Márcio J.; Tognetti, Eduardo S.; Oliveira, Ricardo C. L. F.; Peres, Pedro L. D.
2016-04-01
This paper presents a general framework to cope with full-order ? linear parameter-varying (LPV) filter design subject to inexactly measured parameters. The main novelty is the ability of handling additive and multiplicative uncertainties in the measurements, for both continuous and discrete-time LPV systems, in a unified approach. By conveniently modelling scheduling parameters and uncertainties affecting the measurements, the ? filter design problem can be expressed in terms of robust matrix inequalities that become linear when two scalar parameters are fixed. Therefore, the proposed conditions can be efficiently solved through linear matrix inequality relaxations based on polynomial solutions. Numerical examples are presented to illustrate the improved efficiency of the proposed approach when compared to other methods and, more important, its capability to deal with scenarios where the available strategies in the literature cannot be used.
A Kalman-Filter-Based Approach to Combining Independent Earth-Orientation Series
NASA Technical Reports Server (NTRS)
Gross, Richard S.; Eubanks, T. M.; Steppe, J. A.; Freedman, A. P.; Dickey, J. O.; Runge, T. F.
1998-01-01
An approach. based upon the use of a Kalman filter. that is currently employed at the Jet Propulsion Laboratory (JPL) for combining independent measurements of the Earth's orientation, is presented. Since changes in the Earth's orientation can be described is a randomly excited stochastic process, the uncertainty in our knowledge of the Earth's orientation grows rapidly in the absence of measurements. The Kalman-filter methodology allows for an objective accounting of this uncertainty growth, thereby facilitating the intercomparison of measurements taken at different epochs (not necessarily uniformly spaced in time) and with different precision. As an example of this approach to combining Earth-orientation series, a description is given of a combination, SPACE95, that has been generated recently at JPL.
Time-Filtered Navier-Stokes Approach and Emulation of Turbulence-Chemistry Interaction
NASA Technical Reports Server (NTRS)
Liu, Nan-Suey; Wey, Thomas; Shih, Tsan-Hsing
2013-01-01
This paper describes the time-filtered Navier-Stokes approach capable of capturing unsteady flow structures important for turbulent mixing and an accompanying subgrid model directly accounting for the major processes in turbulence-chemistry interaction. They have been applied to the computation of two-phase turbulent combustion occurring in a single-element lean-direct-injection combustor. Some of the preliminary results from this computational effort are presented in this paper.
Bosson, Maël; Grudinin, Sergei; Redon, Stephane
2013-03-01
We present a novel Block-Adaptive Quantum Mechanics (BAQM) approach to interactive quantum chemistry. Although quantum chemistry models are known to be computationally demanding, we achieve interactive rates by focusing computational resources on the most active parts of the system. BAQM is based on a divide-and-conquer technique and constrains some nucleus positions and some electronic degrees of freedom on the fly to simplify the simulation. As a result, each time step may be performed significantly faster, which in turn may accelerate attraction to the neighboring local minima. By applying our approach to the nonself-consistent Atom Superposition and Electron Delocalization Molecular Orbital theory, we demonstrate interactive rates and efficient virtual prototyping for systems containing more than a thousand of atoms on a standard desktop computer.
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.
A Residuals Approach to Filtering, Smoothing and Identification for Static Distributed Systems
NASA Technical Reports Server (NTRS)
Rodriguez, G.
1985-01-01
An approach for state estimation and identification of spatially distributed parameters embedded in static distributed (elliptic) system models is advanced. The method of maximum likelihood is used to find parameter values that maximize a likelihood functional for the system model, or equivalently, that minimize the negative logarithm of this functional. To find the minimum, a Newton-Raphson search is conducted that from an initial estimate generates a convergent sequence of parameter estimates. For simplicity, a Gauss-Markov approach is used to approximate the Hessian in terms of products of first derivatives. The gradient and approximate Hessian are computed by first arranging the negative log likelihood functional into a form based on the square root factorization of the predicted covariance of the measurement process. The resulting data processing approach, referred to here by the new term of predicted data covariance square root filtering, makes the gradient and approximate Hessian calculations very simple. A closely related set of state estimates is also produced by the maximum likelihood method: smoothed estimates that are optimal in a conditional mean sense and filtered estimates that emerge from the predicted data covariance square root filter.
Hwang, Wei-Chin
2010-01-01
How do we culturally adapt psychotherapy for ethnic minorities? Although there has been growing interest in doing so, few therapy adaptation frameworks have been developed. The majority of these frameworks take a top-down theoretical approach to adapting psychotherapy. The purpose of this paper is to introduce a community-based developmental approach to modifying psychotherapy for ethnic minorities. The Formative Method for Adapting Psychotherapy (FMAP) is a bottom-up approach that involves collaborating with consumers to generate and support ideas for therapy adaptation. It involves 5-phases that target developing, testing, and reformulating therapy modifications. These phases include: (a) generating knowledge and collaborating with stakeholders (b) integrating generated information with theory and empirical and clinical knowledge, (c) reviewing the initial culturally adapted clinical intervention with stakeholders and revising the culturally adapted intervention, (d) testing the culturally adapted intervention, and (e) finalizing the culturally adapted intervention. Application of the FMAP is illustrated using examples from a study adapting psychotherapy for Chinese Americans, but can also be readily applied to modify therapy for other ethnic groups. PMID:20625458
NASA Astrophysics Data System (ADS)
Tehsin, Sara; Rehman, Saad; Awan, Ahmad B.; Chaudry, Qaiser; Abbas, Muhammad; Young, Rupert; Asif, Afia
2016-04-01
Sensitivity to the variations in the reference image is a major concern when recognizing target objects. A combinational framework of correlation filters and logarithmic transformation has been previously reported to resolve this issue alongside catering for scale and rotation changes of the object in the presence of distortion and noise. In this paper, we have extended the work to include the influence of different logarithmic bases on the resultant correlation plane. The meaningful changes in correlation parameters along with contraction/expansion in the correlation plane peak have been identified under different scenarios. Based on our research, we propose some specific log bases to be used in logarithmically transformed correlation filters for achieving suitable tolerance to different variations. The study is based upon testing a range of logarithmic bases for different situations and finding an optimal logarithmic base for each particular set of distortions. Our results show improved correlation and target detection accuracies.
Comparison of various schema of filter adaptivity for the tracking of maneuvering targets
NASA Astrophysics Data System (ADS)
Jouan, Alexandre; Bosse, Eloi; Simard, Marc-Alain; Shahbazian, Elisa
1998-09-01
Tracking maneuvering targets is a complex problem which has generated a great deal of effort over the past several years. It has now been well established that in terms of tracking accuracy, the Interacting Multiple Model (IMM) algorithm, where state estimates are mixed, performs significantly better for maneuvering targets than other types of filters. However, the complexity of the IMM algorithm can prohibit its use in these applications of which similar algorithms cannot provide the necessary accuracy and which can ont afford the computational load of IMM algorithm. This paper presents the evaluation of the tracking accuracy of a multiple model track filter using three different constant-velocity models running in parallel and a maneuver detector. The output estimate is defined by selecting the model whose likelihood function is lower than a target maneuver threshold.
Optimal filters - A unified approach for SNR and PCE. [Peak-To-Correlation-Energy
NASA Technical Reports Server (NTRS)
Juday, Richard D.
1993-01-01
A unified approach for a general metric that encompasses both the signal-to-noise ratio (SNR) and the peak-to-correlation (PCE) ratio in optical correlators is described. In this approach, the connection between optimizing SNR and optimizing PCE is achieved by considering a metric in which the central correlation irradiance is divided by the total energy of the correlation plane. The peak-to-total energy (PTE) is shown to be optimized similarly to SNR and PCE. Since PTE is a function of the search values G and beta, the optimal filter is determined with only a two-dimensional search.
Extreme Sea Levels and Approaches to Adaptation in Germany
NASA Astrophysics Data System (ADS)
Weisse, R.; Kappenberg, J.; Sothmann, J.
2014-12-01
Germany's coastal areas are exposed to extra-tropical storms and related marine hazards such as wind waves and storm surges. About 50% of the coast is below 5 m NN and considerable parts are protected by an almost continuous dike line. Rising mean and extreme sea levels provide substantial threat. In this presentation we briefly review the present situation. Storm related sea level changes are characterized by pronounced inter-annual and decadal variability but do not show a long-term trend over the last century. Mean sea level has increased over the past about 100-150 years at a rate roughly comparable to global mean sea level rise. As a consequence extreme sea levels have increased in the area as increasing mean sea level shifts the baseline for storm surges and wind waves towards higher values. Different approaches for adaptation are investigated in a number ongoing research projects. Some case studies for potential adaptation and challenges are presented. Examples range from detailed analyses of retreat and accommodation strategies to multi-purpose strategies such as concepts for sustainable development of tidal estuaries.
Salt stress adaptation of Bacillus subtilis: a physiological proteomics approach.
Höper, Dirk; Bernhardt, Jörg; Hecker, Michael
2006-03-01
The adaptation to osmotic stress is crucial for growth and survival of Bacillus subtilis in its natural ecosystem. Dual channel imaging and warping of 2-D protein gels were used to visualize global changes in the protein synthesis pattern of cells in response to osmotic stress (6% NaCl). Many vegetative enzymes were repressed in response to salt stress and derepressed after resumption of growth. The enzymes catalyzing the metabolic steps from glucose to 2-oxoglutarate, however, were almost constantly synthesized during salt stress despite the growth arrest. This indicates an enhanced need for the proline precursor glutamate. The synthesis of enzymes involved in sulfate assimilation and in the formation of Fe-S clusters was also induced, suggesting an enhanced need for the formation or repair of Fe-S clusters in response to salt stress. One of the most obvious changes in the protein synthesis profile can be followed by the very strong induction of the SigB regulon. Furthermore, members of the SigW regulon and of the PerR regulon, indicating oxidative stress after salt challenge, were also induced. This proteomic approach provides an overview of cell adaptation to an osmotic upshift in B. subtilis visualizing the most dramatic changes in the protein synthesis pattern.
ANALYSIS OF RADIAL VELOCITY DATA BY A NOVEL ADAPTIVE APPROACH
Babu, P.; Stoica, P.; Li, J.; Chen, Z.; Ge, J.
2010-02-15
In this paper, we introduce an estimation technique for analyzing radial velocity data commonly encountered in extrasolar planet detection. We discuss the Keplerian model for radial velocity data measurements and introduce a technique named the iterative adaptive approach (IAA) to estimate the three-dimensional spectrum (power versus eccentricity, orbital period and periastron passage time) of the radial velocity data. We then discuss different ways to regularize the IAA algorithm in the presence of noise and measurement errors. We also discuss briefly the computational aspects of the method and introduce a computationally efficient version of IAA. Finally, we establish the significance of the spectral peaks by using a relaxation maximum likelihood algorithm and a generalized likelihood ratio test. Numerical experiments are carried out on both simulated and real life data sets to evaluate the performance of our method. The real life data sets discussed are radial velocity measurements of the stars HD 63454, HD 208487, and GJ 876.
Adaptive Neuro-fuzzy approach in friction identification
NASA Astrophysics Data System (ADS)
Zaiyad Muda @ Ismail, Muhammad
2016-05-01
Friction is known to affect the performance of motion control system, especially in terms of its accuracy. Therefore, a number of techniques or methods have been explored and implemented to alleviate the effects of friction. In this project, the Artificial Intelligent (AI) approach is used to model the friction which will be then used to compensate the friction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is chosen among several other AI methods because of its reliability and capabilities of solving complex computation. ANFIS is a hybrid AI-paradigm that combines the best features of neural network and fuzzy logic. This AI method (ANFIS) is effective for nonlinear system identification and compensation and thus, being used in this project.
[Spanish adaptation of Hobfoll's Strategic Approach to Coping Scale (SACS)].
Pedrero Pérez, Eduardo J; Santed Germán, Miguel A; Pérez García, Ana M
2012-01-01
The present research adapted the Strategic Approach to Coping Scale (SACS), developed by Hobfoll and colleagues, to the Spanish population. SACS is an instrument derived from Hobfoll's Conservation of Resources Theory, which emphasises the contribution of social factors to coping processes. This instrument assesses coping strategies in 9-subscales, organised in three dimensions: orientation to the problem (active/passive), use of social resources (prosocial/antisocial), and orientation to others involved (direct/indirect). The Spanish version, administered to a non-clinical sample (N= 767), found 7-subscales structured in prosocial/antisocial, active/passive and reflexive/intuitive dimensions, with adequate reliability and construct validity. To conclude, the Spanish SACS is a potentially useful and reliable instrument for research and clinical purposes, mainly in areas in which social components need to be explicitly considered.
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.
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.
Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.
Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram
2014-12-01
Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.
Fluctuations and information filtering in coupled populations of spiking neurons with adaptation
NASA Astrophysics Data System (ADS)
Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram
2014-12-01
Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.
Adaptive Filter for Automatic Identification of Multiple Faults in a Noisy OTDR Profile
NASA Astrophysics Data System (ADS)
von der Weid, Jean Pierre; Souto, Mario H.; Garcia, Joaquim D.; Amaral, Gustavo C.
2016-07-01
We present a novel methodology able to distinguish meaningful level shifts from typical signal fluctuations. A two-stage regularization filtering can accurately identify the location of the significant level-shifts with an efficient parameter-free algorithm. The developed methodology demands low computational effort and can easily be embedded in a dedicated processing unit. Our case studies compare the new methodology with current available ones and show that it is the most adequate technique for fast detection of multiple unknown level-shifts in a noisy OTDR profile.
Mie light-scattering granulometer with adaptive numerical filtering. I. Theory.
Hespel, L; Delfour, A
2000-12-20
A search procedure based on a least-squares method including a regularization scheme constructed from numerical filtering is presented. This method, with the addition of a nephelometer, can be used to determine the particle-size distributions of various scattering media (aerosols, fogs, rocket exhausts, motor plumes) from angular static light-scattering measurements. For retrieval of the distribution function, the experimental data are matched with theoretical patterns derived from Mie theory. The method is numerically investigated with simulated data, and the performance of the inverse procedure is evaluated. The results show that the retrieved distribution function is quite reliable, even for strong levels of noise.
NASA Technical Reports Server (NTRS)
Narasimhan, Sriram; Dearden, Richard; Benazera, Emmanuel
2004-01-01
Fault detection and isolation are critical tasks to ensure correct operation of systems. When we consider stochastic hybrid systems, diagnosis algorithms need to track both the discrete mode and the continuous state of the system in the presence of noise. Deterministic techniques like Livingstone cannot deal with the stochasticity in the system and models. Conversely Bayesian belief update techniques such as particle filters may require many computational resources to get a good approximation of the true belief state. In this paper we propose a fault detection and isolation architecture for stochastic hybrid systems that combines look-ahead Rao-Blackwellized Particle Filters (RBPF) with the Livingstone 3 (L3) diagnosis engine. In this approach RBPF is used to track the nominal behavior, a novel n-step prediction scheme is used for fault detection and L3 is used to generate a set of candidates that are consistent with the discrepant observations which then continue to be tracked by the RBPF scheme.
A novel approach for detail-enhanced exposure fusion using guided filter.
Singh, Harbinder; Kumar, Vinay; Bhooshan, Sunil
2014-01-01
In this paper we propose a novel detail-enhancing exposure fusion approach using nonlinear translation-variant filter (NTF). With the captured Standard Dynamic Range (SDR) images under different exposure settings, first the fine details are extracted based on guided filter. Next, the base layers (i.e., images obtained from NTF) across all input images are fused using multiresolution pyramid. Exposure, contrast, and saturation measures are considered to generate a mask that guides the fusion process of the base layers. Finally, the fused base layer is combined with the extracted fine details to obtain detail-enhanced fused image. The goal is to preserve details in both very dark and extremely bright regions without High Dynamic Range Image (HDRI) representation and tone mapping step. Moreover, we have demonstrated that the proposed method is also suitable for the multifocus image fusion without introducing artifacts. PMID:24683353
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.
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
2015-05-13
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.
NASA Astrophysics Data System (ADS)
Huda, J.; Kauneckis, D. L.
2013-12-01
Climate change adaptation represents a number of unique policy-making challenges. Foremost among these is dealing with the range of future climate impacts to a wide scope of inter-related natural systems, their interaction with social and economic systems, and uncertainty resulting from the variety of downscaled climate model scenarios and climate science projections. These cascades of uncertainty have led to a number of new approaches as well as a reexamination of traditional methods for evaluating risk and uncertainty in policy-making. Policy makers are required to make decisions and formulate policy irrespective of the level of uncertainty involved and while a debate continues regarding the level of scientific certainty required in order to make a decision, incremental change in the climate policy continues at multiple governance levels. This project conducts a comparative analysis of the range of methodological approaches that are evolving to address uncertainty in climate change policy. It defines 'methodologies' to include a variety of quantitative and qualitative approaches involving both top-down and bottom-up policy processes that attempt to enable policymakers to synthesize climate information into the policy process. The analysis examines methodological approaches to decision-making in climate policy based on criteria such as sources of policy choice information, sectors to which the methodology has been applied, sources from which climate projections were derived, quantitative and qualitative methods used to deal with uncertainty, and the benefits and limitations of each. A typology is developed to better categorize the variety of approaches and methods, examine the scope of policy activities they are best suited for, and highlight areas for future research and development.
An efficient and self-adapted approach to the sharpening of color images.
Kau, Lih-Jen; Lee, Tien-Lin
2013-01-01
An efficient approach to the sharpening of color images is proposed in this paper. For this, the image to be sharpened is first transformed to the HSV color model, and then only the channel of Value will be used for the process of sharpening while the other channels are left unchanged. We then apply a proposed edge detector and low-pass filter to the channel of Value to pick out pixels around boundaries. After that, those pixels detected as around edges or boundaries are adjusted so that the boundary can be sharpened, and those nonedge pixels are kept unaltered. The increment or decrement magnitude that is to be added to those edge pixels is determined in an adaptive manner based on global statistics of the image and local statistics of the pixel to be sharpened. With the proposed approach, the discontinuities can be highlighted while most of the original information contained in the image can be retained. Finally, the adjusted channel of Value and that of Hue and Saturation will be integrated to get the sharpened color image. Extensive experiments on natural images will be given in this paper to highlight the effectiveness and efficiency of the proposed approach. PMID:24348136
A Fuzzy Genetic Algorithm Approach to an Adaptive Information Retrieval Agent.
ERIC Educational Resources Information Center
Martin-Bautista, Maria J.; Vila, Maria-Amparo; Larsen, Henrik Legind
1999-01-01
Presents an approach to a Genetic Information Retrieval Agent Filter (GIRAF) that filters and ranks documents retrieved from the Internet according to users' preferences by using a Genetic Algorithm and fuzzy set theory to handle the imprecision of users' preferences and users' evaluation of the retrieved documents. (Author/LRW)
NASA Technical Reports Server (NTRS)
Bailey, R. E.; Smith, R. E.
1982-01-01
An investigation of pilot-induced oscillation suppression (PIOS) filters was performed using the USAF/Flight Dynamics Laboratory variable stability NT-33 aircraft, modified and operated by Calspan. This program examined the effects of PIOS filtering on the longitudinal flying qualities of fighter aircraft during the visual approach and landing task. Forty evaluations were flown to test the effects of different PIOS filters. Although detailed analyses were not undertaken, the results indicate that PIOS filtering can improve the flying qualities of an otherwise unacceptable aircraft configuration (Level 3 flying qualities). However, the ability of the filters to suppress pilot-induced oscillations appears to be dependent upon the aircraft configuration characteristics. Further, the data show that the filters can adversely affect landing flying qualities if improperly designed. The data provide an excellent foundation from which detail analyses can be performed.
Analyzing Hedges in Verbal Communication: An Adaptation-Based Approach
ERIC Educational Resources Information Center
Wang, Yuling
2010-01-01
Based on Adaptation Theory, the article analyzes the production process of hedges. The procedure consists of the continuous making of choices in linguistic forms and communicative strategies. These choices are made just for adaptation to the contextual correlates. Besides, the adaptation process is dynamic, intentional and bidirectional.
A novel conceptual approach to read-filtering in high-throughput amplicon sequencing studies.
Puente-Sánchez, Fernando; Aguirre, Jacobo; Parro, Víctor
2016-02-29
Adequate read filtering is critical when processing high-throughput data in marker-gene-based studies. Sequencing errors can cause the mis-clustering of otherwise similar reads, artificially increasing the number of retrieved Operational Taxonomic Units (OTUs) and therefore leading to the overestimation of microbial diversity. Sequencing errors will also result in OTUs that are not accurate reconstructions of the original biological sequences. Herein we present the Poisson binomial filtering algorithm (PBF), which minimizes both problems by calculating the error-probability distribution of a sequence from its quality scores. In order to validate our method, we quality-filtered 37 publicly available datasets obtained by sequencing mock and environmental microbial communities with the Roche 454, Illumina MiSeq and IonTorrent PGM platforms, and compared our results to those obtained with previous approaches such as the ones included in mothur, QIIME and USEARCH. Our algorithm retained substantially more reads than its predecessors, while resulting in fewer and more accurate OTUs. This improved sensitiveness produced more faithful representations, both quantitatively and qualitatively, of the true microbial diversity present in the studied samples. Furthermore, the method introduced in this work is computationally inexpensive and can be readily applied in conjunction with any existent analysis pipeline.
A novel conceptual approach to read-filtering in high-throughput amplicon sequencing studies
Puente-Sánchez, Fernando; Aguirre, Jacobo; Parro, Víctor
2016-01-01
Adequate read filtering is critical when processing high-throughput data in marker-gene-based studies. Sequencing errors can cause the mis-clustering of otherwise similar reads, artificially increasing the number of retrieved Operational Taxonomic Units (OTUs) and therefore leading to the overestimation of microbial diversity. Sequencing errors will also result in OTUs that are not accurate reconstructions of the original biological sequences. Herein we present the Poisson binomial filtering algorithm (PBF), which minimizes both problems by calculating the error-probability distribution of a sequence from its quality scores. In order to validate our method, we quality-filtered 37 publicly available datasets obtained by sequencing mock and environmental microbial communities with the Roche 454, Illumina MiSeq and IonTorrent PGM platforms, and compared our results to those obtained with previous approaches such as the ones included in mothur, QIIME and USEARCH. Our algorithm retained substantially more reads than its predecessors, while resulting in fewer and more accurate OTUs. This improved sensitiveness produced more faithful representations, both quantitatively and qualitatively, of the true microbial diversity present in the studied samples. Furthermore, the method introduced in this work is computationally inexpensive and can be readily applied in conjunction with any existent analysis pipeline. PMID:26553806
NASA Astrophysics Data System (ADS)
Erdinc, Ozgur; Willett, Peter; Bar-Shalom, Yaakov
2006-05-01
The probability hypothesis density (PHD) filter, an automatically track-managed multi-target tracker, is attracting increasing but cautious attention. Its derivation is elegant and mathematical, and thus of course many engineers fear it; perhaps that is currently limiting the number of researchers working on the subject. In this paper, we explore a physical-space approach - a bin model - which leads us to arrive the same filter equations as the PHD. Unlike the original derivation of the PHD filter, the concepts used are the familiar ones of conditional probability. The original PHD suffers from a "target-death" problem in which even a single missed detection can lead to the apparent disappearance of a target. To obviate this, PHD originator Mahler has recently developed a new "cardinalized" version of PHD (CPHD). We are able to extend our physical-space derivation to the CPHD case as well. We stress that the original derivations are mathematically correct, and need no embellishment from us; our contribution here is to offer an alternative derivation, one that we find appealing.
Boettiger, Alistair N; Wittemyer, George; Starfield, Richard; Volrath, Fritz; Douglas-Hamilton, Iain; Getz, Wayne M
2011-08-01
Understanding the environmental factors influencing animal movements is fundamental to theoretical and applied research in the field of movement ecology. Studies relating fine-scale movement paths to spatiotemporally structured landscape data, such as vegetation productivity or human activity, are particularly lacking despite the obvious importance of such information to understanding drivers of animal movement. In part, this may be because few approaches provide the sophistication to characterize the complexity of movement behavior and relate it to diverse, varying environmental stimuli. We overcame this hurdle by applying, for the first time to an ecological question, a finite impulse-response signal-filtering approach to identify human and natural environmental drivers of movements of 13 free-ranging African elephants (Loxodonta africana) from distinct social groups collected over seven years. A minimum mean-square error (MMSE) estimation criterion allowed comparison of the predictive power of landscape and ecological model inputs. We showed that a filter combining vegetation dynamics, human and physical landscape features, and previous movement outperformed simpler filter structures, indicating the importance of both dynamic and static landscape features, as well as habit, on movement decisions taken by elephants. Elephant responses to vegetation productivity indices were not uniform in time or space, indicating that elephant foraging strategies are more complex than simply gravitation toward areas of high productivity. Predictions were most frequently inaccurate outside protected area boundaries near human settlements, suggesting that human activity disrupts typical elephant movement behavior. Successful management strategies at the human-elephant interface, therefore, are likely to be context specific and dynamic. Signal processing provides a promising approach for elucidating environmental factors that drive animal movements over large time and spatial
Numerical and experimental approaches to study soil transport and clogging in granular filters
NASA Astrophysics Data System (ADS)
Kanarska, Y.; Smith, J. J.; Ezzedine, S. M.; Lomov, I.; Glascoe, L. G.
2012-12-01
Failure of a dam by erosion ranks among the most serious accidents in civil engineering. The best way to prevent internal erosion is using adequate granular filters in the transition areas where important hydraulic gradients can appear. In case of cracking and erosion, if the filter is capable of retaining the eroded particles, the crack will seal and the dam safety will be ensured. Numerical modeling has proved to be a cost-effective tool for improving our understanding of physical processes. Traditionally, the consideration of flow and particle transport in porous media has focused on treating the media as continuum. Practical models typically address flow and transport based on the Darcy's law as a function of a pressure gradient and a medium-dependent permeability parameter. Additional macroscopic constitutes describe porosity, and permeability changes during the migration of a suspension through porous media. However, most of them rely on empirical correlations, which often need to be recalibrated for each application. Grain-scale modeling can be used to gain insight into scale dependence of continuum macroscale parameters. A finite element numerical solution of the Navier-Stokes equations for fluid flow together with Lagrange multiplier technique for solid particles was applied to the simulation of soil filtration in the filter layers of gravity dam. The numerical approach was validated through comparison of numerical simulations with the experimental results of base soil particle clogging in the filter layers performed at ERDC. The numerical simulation correctly predicted flow and pressure decay due to particle clogging. The base soil particle distribution was almost identical to those measured in the laboratory experiment. It is believed that the agreement between simulations and experimental data demonstrates the applicability of the proposed approach for prediction of the soil transport and clogging in embankment dams. To get more precise understanding of
Han, Houzeng; Xu, Tianhe; Wang, Jian
2016-01-01
Precise Point Positioning (PPP) makes use of the undifferenced pseudorange and carrier phase measurements with ionospheric-free (IF) combinations to achieve centimeter-level positioning accuracy. Conventionally, the IF ambiguities are estimated as float values. To improve the PPP positioning accuracy and shorten the convergence time, the integer phase clock model with between-satellites single-difference (BSSD) operation is used to recover the integer property. However, the continuity and availability of stand-alone PPP is largely restricted by the observation environment. The positioning performance will be significantly degraded when GPS operates under challenging environments, if less than five satellites are present. A commonly used approach is integrating a low cost inertial sensor to improve the positioning performance and robustness. In this study, a tightly coupled (TC) algorithm is implemented by integrating PPP with inertial navigation system (INS) using an Extended Kalman filter (EKF). The navigation states, inertial sensor errors and GPS error states are estimated together. The troposphere constrained approach, which utilizes external tropospheric delay as virtual observation, is applied to further improve the ambiguity-fixed height positioning accuracy, and an improved adaptive filtering strategy is implemented to improve the covariance modelling considering the realistic noise effect. A field vehicular test with a geodetic GPS receiver and a low cost inertial sensor was conducted to validate the improvement on positioning performance with the proposed approach. The results show that the positioning accuracy has been improved with inertial aiding. Centimeter-level positioning accuracy is achievable during the test, and the PPP/INS TC integration achieves a fast re-convergence after signal outages. For troposphere constrained solutions, a significant improvement for the height component has been obtained. The overall positioning accuracies of the height
Han, Houzeng; Xu, Tianhe; Wang, Jian
2016-01-01
Precise Point Positioning (PPP) makes use of the undifferenced pseudorange and carrier phase measurements with ionospheric-free (IF) combinations to achieve centimeter-level positioning accuracy. Conventionally, the IF ambiguities are estimated as float values. To improve the PPP positioning accuracy and shorten the convergence time, the integer phase clock model with between-satellites single-difference (BSSD) operation is used to recover the integer property. However, the continuity and availability of stand-alone PPP is largely restricted by the observation environment. The positioning performance will be significantly degraded when GPS operates under challenging environments, if less than five satellites are present. A commonly used approach is integrating a low cost inertial sensor to improve the positioning performance and robustness. In this study, a tightly coupled (TC) algorithm is implemented by integrating PPP with inertial navigation system (INS) using an Extended Kalman filter (EKF). The navigation states, inertial sensor errors and GPS error states are estimated together. The troposphere constrained approach, which utilizes external tropospheric delay as virtual observation, is applied to further improve the ambiguity-fixed height positioning accuracy, and an improved adaptive filtering strategy is implemented to improve the covariance modelling considering the realistic noise effect. A field vehicular test with a geodetic GPS receiver and a low cost inertial sensor was conducted to validate the improvement on positioning performance with the proposed approach. The results show that the positioning accuracy has been improved with inertial aiding. Centimeter-level positioning accuracy is achievable during the test, and the PPP/INS TC integration achieves a fast re-convergence after signal outages. For troposphere constrained solutions, a significant improvement for the height component has been obtained. The overall positioning accuracies of the height
Han, Houzeng; Xu, Tianhe; Wang, Jian
2016-07-08
Precise Point Positioning (PPP) makes use of the undifferenced pseudorange and carrier phase measurements with ionospheric-free (IF) combinations to achieve centimeter-level positioning accuracy. Conventionally, the IF ambiguities are estimated as float values. To improve the PPP positioning accuracy and shorten the convergence time, the integer phase clock model with between-satellites single-difference (BSSD) operation is used to recover the integer property. However, the continuity and availability of stand-alone PPP is largely restricted by the observation environment. The positioning performance will be significantly degraded when GPS operates under challenging environments, if less than five satellites are present. A commonly used approach is integrating a low cost inertial sensor to improve the positioning performance and robustness. In this study, a tightly coupled (TC) algorithm is implemented by integrating PPP with inertial navigation system (INS) using an Extended Kalman filter (EKF). The navigation states, inertial sensor errors and GPS error states are estimated together. The troposphere constrained approach, which utilizes external tropospheric delay as virtual observation, is applied to further improve the ambiguity-fixed height positioning accuracy, and an improved adaptive filtering strategy is implemented to improve the covariance modelling considering the realistic noise effect. A field vehicular test with a geodetic GPS receiver and a low cost inertial sensor was conducted to validate the improvement on positioning performance with the proposed approach. The results show that the positioning accuracy has been improved with inertial aiding. Centimeter-level positioning accuracy is achievable during the test, and the PPP/INS TC integration achieves a fast re-convergence after signal outages. For troposphere constrained solutions, a significant improvement for the height component has been obtained. The overall positioning accuracies of the height
Cornelis, Bram; Moonen, Marc; Wouters, Jan
2012-06-01
This paper evaluates noise reduction techniques in bilateral and binaural hearing aids. Adaptive implementations (on a real-time test platform) of the bilateral and binaural speech distortion weighted multichannel Wiener filter (SDW-MWF) and a competing bilateral fixed beamformer are evaluated. As the SDW-MWF relies on a voice activity detector (VAD), a realistic binaural VAD is also included. The test subjects (both normal hearing subjects and hearing aid users) are tested by an adaptive speech reception threshold (SRT) test in different spatial scenarios, including a realistic cafeteria scenario with nonstationary noise. The main conclusions are: (a) The binaural SDW-MWF can further improve the SRT (up to 2 dB) over the improvements achieved by bilateral algorithms, although a significant difference is only achievable if the binaural SDW-MWF uses a perfect VAD. However, in the cafeteria scenario only the binaural SDW-MWF achieves a significant SRT improvement (2.6 dB with perfect VAD, 2.2 dB with real VAD), for the group of hearing aid users. (b) There is no significant degradation when using a real VAD at the input signal-to-noise ratio (SNR) levels where the hearing aid users reach their SRT. (c) The bilateral SDW-MWF achieves no SRT improvements compared to the bilateral fixed beamformer.
Background adaptive division filtering for hand-held ground penetrating radar
NASA Astrophysics Data System (ADS)
Lee, Matthew A.; Anderson, Derek T.; Ball, John E.; White, Julie L.
2016-05-01
The challenge in detecting explosive hazards is that there are multiple types of targets buried at different depths in a highlycluttered environment. A wide array of target and clutter signatures exist, which makes detection algorithm design difficult. Such explosive hazards are typically deployed in past and present war zones and they pose a grave threat to the safety of civilians and soldiers alike. This paper focuses on a new image enhancement technique for hand-held ground penetrating radar (GPR). Advantages of the proposed technique is it runs in real-time and it does not require the radar to remain at a constant distance from the ground. Herein, we evaluate the performance of the proposed technique using data collected from a U.S. Army test site, which includes targets with varying amounts of metal content, placement depths, clutter and times of day. Receiver operating characteristic (ROC) curve-based results are presented for the detection of shallow, medium and deeply buried targets. Preliminary results are very encouraging and they demonstrate the usefulness of the proposed filtering technique.
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.
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
Use of range dependent empirical filters to simulate seismic waveforms: approach and examples
NASA Astrophysics Data System (ADS)
Salzberg, D. H.; Marshall, M. E.
2006-12-01
We have previously demonstrated a technique which designs empirical filters that, when applied to synthetic seismograms, results in much better agreement between a synthetic waveform and the observed waveform if the two events are nearly co-located by deconvolving a synthetic waveform for a specific event - station pair from the observed data. With greater event separation, the empirical filter is unable to correct the synthetic to match the data, particularly at higher frequencies (Salzberg et al, 2005). By modifying the parameterization of the semi-empirical correction to allow for a range dependant phase spectrum, the empirical filter is able to correct the synthetic to match the data even when the event separation is large (> 50 km), and in particular, in cases where the range invariant phase correction fails. The improved parameterization characterizes the spectrum in terms of wavenumber and range. Since the range is known, we can determine a misfit in wavenumber verses range by dividing the unwrapped phase spectrum by its range. Then, we compute the phase for the new range, and apply it as a phase shift to the synthetic waveform. This approach requires us to isolate individual propagating modes as such. Using the fundamental mode Rayleigh timed relative to the ray-theory P-wave arrival time (as a reference time), we are able to generate synthetic Rayleigh wave at frequencies of 0.1 Hz or higher. Using the time synthetic Rayleigh waves for matched waveform event locations allows us to locate events to within GT5 precision, or to within 5 km error or less using only two stations at local to regional distances. We demonstrate the results using two data sets: Nevada Test Site (and Little skull Mountain) using an NTS explosion for reference, and events within 100 km of Parkfield, California, using a M=4.9 aftershock as reference. We will present comparisons of the results using both the new approach and the old approach by utilizing ground truth for a data set of
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 Technical Reports Server (NTRS)
1974-01-01
Communications equipment for use with the Skylab project is examined to show compliance with contract requirements. The items of equipment considered are: (1) communications carrier assemblies, (2) filter bypass adapter assemblies, and (3) sub-assemblies, parts, and repairs. Additional information is provided concerning contract requirements, test requirements, and failure investigation actions.
Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng
2015-01-01
The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly. PMID:25572661
NASA Technical Reports Server (NTRS)
Venosa, Elettra; Vermeire, Bert; Alakija, Cameron; Harris, Fred; Strobel, David; Sheehe, Charles J.; Krunz, Marwan
2017-01-01
In the last few years, radio technologies for unmanned aircraft vehicle (UAV) have advanced very rapidly. The increasing need to fly unmanned aircraft systems (UAS) in the national airspace system (NAS) to perform missions of vital importance to national security, defense, and science has pushed ahead the design and implementation of new radio platforms. However, a lot still has to be done to improve those radios in terms of performance and capabilities. In addition, an important aspect to account for is hardware cost and the feasibility to implement these radios using commercial off-the-shelf (COTS) components. UAV radios come with numerous technical challenges and their development involves contributions at different levels of the design. Cognitive algorithms need to be developed in order to perform agile communications using appropriate frequency allocation while maintaining safe and efficient operations in the NAS and, digital reconfigurable architectures have to be designed in order to ensure a prompt response to environmental changes. Command and control (C2) communications have to be preserved during "standard" operations while crew operations have to be minimized. It is clear that UAV radios have to be software-defined systems, where size, weight and power consumption (SWaP) are critical parameters. This paper provides preliminary results of the efforts performed to design a fully digital radio architecture as part of a NASA Phase I STTR. In this paper, we will explain the basic idea and technical principles behind our dynamic/adaptive frequency hopping radio for UAVs. We will present our Simulink model of the dynamic FH radio transmitter design for UAV communications and show simulation results and FPGA system analysis.
Approach for Using Learner Satisfaction to Evaluate the Learning Adaptation Policy
ERIC Educational Resources Information Center
Jeghal, Adil; Oughdir, Lahcen; Tairi, Hamid; Radouane, Abdelhay
2016-01-01
The learning adaptation is a very important phase in a learning situation in human learning environments. This paper presents the authors' approach used to evaluate the effectiveness of learning adaptive systems. This approach is based on the analysis of learner satisfaction notices collected by a questionnaire on a learning situation; to analyze…
A novel spatiotemporal muscle activity imaging approach based on the Extended Kalman Filter.
Wang, Jing; Zhang, Yingchun; Zhu, Xiangjun; Zhou, Ping; Liu, Chenguang; Rymer, William Z
2012-01-01
A novel spatiotemporal muscle activity imaging (sMAI) approach has been developed using the Extended Kalman Filter (EKF) to reconstruct internal muscle activities from non-invasive multi-channel surface electromyogram (sEMG) recordings. A distributed bioelectric dipole source model is employed to describe the internal muscle activity space, and a linear relationship between the muscle activity space and the sEMG measurement space is then established. The EKF is employed to recursively solve the ill-posed inverse problem in the sMAI approach, in which the weighted minimum norm (WMN) method is utilized to calculate the initial state and a new nonlinear method is developed based on the propagating features of muscle activities to predict the recursive state. A series of computer simulations was conducted to test the performance of the proposed sMAI approach. Results show that the localization error rapidly decreases over 35% and the overlap ratio rapidly increases over 45% compared to the results achieved using the WMN method only. The present promising results demonstrate the feasibility of utilizing the proposed EKF-based sMAI approach to accurately reconstruct internal muscle activities from non-invasive sEMG recordings.
Favaro, Livio; Gamba, Marco; Alfieri, Chiara; Pessani, Daniela; McElligott, Alan G
2015-01-01
The African penguin is a nesting seabird endemic to southern Africa. In penguins of the genus Spheniscus vocalisations are important for social recognition. However, it is not clear which acoustic features of calls can encode individual identity information. We recorded contact calls and ecstatic display songs of 12 adult birds from a captive colony. For each vocalisation, we measured 31 spectral and temporal acoustic parameters related to both source and filter components of calls. For each parameter, we calculated the Potential of Individual Coding (PIC). The acoustic parameters showing PIC ≥ 1.1 were used to perform a stepwise cross-validated discriminant function analysis (DFA). The DFA correctly classified 66.1% of the contact calls and 62.5% of display songs to the correct individual. The DFA also resulted in the further selection of 10 acoustic features for contact calls and 9 for display songs that were important for vocal individuality. Our results suggest that studying the anatomical constraints that influence nesting penguin vocalisations from a source-filter perspective, can lead to a much better understanding of the acoustic cues of individuality contained in their calls. This approach could be further extended to study and understand vocal communication in other bird species. PMID:26602001
Favaro, Livio; Gamba, Marco; Alfieri, Chiara; Pessani, Daniela; McElligott, Alan G.
2015-01-01
The African penguin is a nesting seabird endemic to southern Africa. In penguins of the genus Spheniscus vocalisations are important for social recognition. However, it is not clear which acoustic features of calls can encode individual identity information. We recorded contact calls and ecstatic display songs of 12 adult birds from a captive colony. For each vocalisation, we measured 31 spectral and temporal acoustic parameters related to both source and filter components of calls. For each parameter, we calculated the Potential of Individual Coding (PIC). The acoustic parameters showing PIC ≥ 1.1 were used to perform a stepwise cross-validated discriminant function analysis (DFA). The DFA correctly classified 66.1% of the contact calls and 62.5% of display songs to the correct individual. The DFA also resulted in the further selection of 10 acoustic features for contact calls and 9 for display songs that were important for vocal individuality. Our results suggest that studying the anatomical constraints that influence nesting penguin vocalisations from a source-filter perspective, can lead to a much better understanding of the acoustic cues of individuality contained in their calls. This approach could be further extended to study and understand vocal communication in other bird species. PMID:26602001
Favaro, Livio; Gamba, Marco; Alfieri, Chiara; Pessani, Daniela; McElligott, Alan G
2015-01-01
The African penguin is a nesting seabird endemic to southern Africa. In penguins of the genus Spheniscus vocalisations are important for social recognition. However, it is not clear which acoustic features of calls can encode individual identity information. We recorded contact calls and ecstatic display songs of 12 adult birds from a captive colony. For each vocalisation, we measured 31 spectral and temporal acoustic parameters related to both source and filter components of calls. For each parameter, we calculated the Potential of Individual Coding (PIC). The acoustic parameters showing PIC ≥ 1.1 were used to perform a stepwise cross-validated discriminant function analysis (DFA). The DFA correctly classified 66.1% of the contact calls and 62.5% of display songs to the correct individual. The DFA also resulted in the further selection of 10 acoustic features for contact calls and 9 for display songs that were important for vocal individuality. Our results suggest that studying the anatomical constraints that influence nesting penguin vocalisations from a source-filter perspective, can lead to a much better understanding of the acoustic cues of individuality contained in their calls. This approach could be further extended to study and understand vocal communication in other bird species.
A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains
NASA Astrophysics Data System (ADS)
Ramezani, Mohsen; Moradi, Parham; Akhlaghian, Fardin
2014-08-01
Recommender systems seek to find the interesting items by filtering out the worthless items. Collaborative filtering is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items and recommends the items which are welcomed by others in the group to the user. But, many challenges like sparsity and computational issues still arise. In this paper, to overcome these challenges, we propose a novel method to find the neighbor users based on the users’ interest patterns. The main idea is that users who are interested in the same set of items share similar interest patterns. Therefore, the non-redundant item subspaces are extracted to indicate the different patterns of interest. Then, a user’s tree structure is created based on the patterns he has in common with the active user. Moreover, a novel recommendation method is presented to predict a new rating value for unseen items. Experimental results on the Movielens and the Jester datasets show that in most cases, the proposed method gains better results than already widely used methods.
NASA Astrophysics Data System (ADS)
Li, S. G.; Shi, L.
2014-10-01
The recommendation system for virtual items in massive multiplayer online role-playing games (MMORPGs) has aroused the interest of researchers. Of the many approaches to construct a recommender system, collaborative filtering (CF) has been the most successful one. However, the traditional CFs just lure customers into the purchasing action and overlook customers' satisfaction, moreover, these techniques always suffer from low accuracy under cold-start conditions. Therefore, a novel collaborative filtering (NCF) method is proposed to identify like-minded customers according to the preference similarity coefficient (PSC), which implies correlation between the similarity of customers' characteristics and the similarity of customers' satisfaction level for the product. Furthermore, the analytic hierarchy process (AHP) is used to determine the relative importance of each characteristic of the customer and the improved ant colony optimisation (IACO) is adopted to generate the expression of the PSC. The IACO creates solutions using the Markov random walk model, which can accelerate the convergence of algorithm and prevent prematurity. For a target customer whose neighbours can be found, the NCF can predict his satisfaction level towards the suggested products and recommend the acceptable ones. Under cold-start conditions, the NCF will generate the recommendation list by excluding items that other customers prefer.
Non-adaptive and adaptive hybrid approaches for enhancing water quality management
NASA Astrophysics Data System (ADS)
Kalwij, Ineke M.; Peralta, Richard C.
2008-09-01
SummaryUsing optimization to help solve groundwater management problems cost-effectively is becoming increasingly important. Hybrid optimization approaches, that combine two or more optimization algorithms, will become valuable and common tools for addressing complex nonlinear hydrologic problems. Hybrid heuristic optimizers have capabilities far beyond those of a simple genetic algorithm (SGA), and are continuously improving. SGAs having only parent selection, crossover, and mutation are inefficient and rarely used for optimizing contaminant transport management. Even an advanced genetic algorithm (AGA) that includes elitism (to emphasize using the best strategies as parents) and healing (to help assure optimal strategy feasibility) is undesirably inefficient. Much more efficient than an AGA is the presented hybrid (AGCT), which adds comprehensive tabu search (TS) features to an AGA. TS mechanisms (TS probability, tabu list size, search coarseness and solution space size, and a TS threshold value) force the optimizer to search portions of the solution space that yield superior pumping strategies, and to avoid reproducing similar or inferior strategies. An AGCT characteristic is that TS control parameters are unchanging during optimization. However, TS parameter values that are ideal for optimization commencement can be undesirable when nearing assumed global optimality. The second presented hybrid, termed global converger (GC), is significantly better than the AGCT. GC includes AGCT plus feedback-driven auto-adaptive control that dynamically changes TS parameters during run-time. Before comparing AGCT and GC, we empirically derived scaled dimensionless TS control parameter guidelines by evaluating 50 sets of parameter values for a hypothetical optimization problem. For the hypothetical area, AGCT optimized both well locations and pumping rates. The parameters are useful starting values because using trial-and-error to identify an ideal combination of control
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
Konrad, Christopher P.
2014-01-01
Marine bivalves such as clams, mussels, and oysters are an important component of the food web, which influence nutrient dynamics and water quality in many estuaries. The role of bivalves in nutrient dynamics and, particularly, the contribution of commercial shellfish activities, are not well understood in Puget Sound, Washington. Numerous approaches have been used in other estuaries to quantify the effects of bivalves on nutrient dynamics, ranging from simple nutrient budgeting to sophisticated numerical models that account for tidal circulation, bioenergetic fluxes through food webs, and biochemical transformations in the water column and sediment. For nutrient management in Puget Sound, it might be possible to integrate basic biophysical indicators (residence time, phytoplankton growth rates, and clearance rates of filter feeders) as a screening tool to identify places where nutrient dynamics and water quality are likely to be sensitive to shellfish density and, then, apply more sophisticated methods involving in-situ measurements and simulation models to quantify those dynamics.
Ye, Tao; Zhou, Fuqiang
2015-04-10
When imaged by detectors, space targets (including satellites and debris) and background stars have similar point-spread functions, and both objects appear to change as detectors track targets. Therefore, traditional tracking methods cannot separate targets from stars and cannot directly recognize targets in 2D images. Consequently, we propose an autonomous space target recognition and tracking approach using a star sensor technique and a Kalman filter (KF). A two-step method for subpixel-scale detection of star objects (including stars and targets) is developed, and the combination of the star sensor technique and a KF is used to track targets. The experimental results show that the proposed method is adequate for autonomously recognizing and tracking space targets.
NASA Technical Reports Server (NTRS)
Deutschmann, Julie; Harman, Rick; Bar-Itzhack, Itzhack
1998-01-01
An innovative approach to autonomous attitude and trajectory estimation is available using only magnetic field data and rate data. The estimation is performed simultaneously using an Extended Kalman Filter, a well known algorithm used extensively in onboard applications. The magnetic field is measured on a satellite by a magnetometer, an inexpensive and reliable sensor flown on virtually all satellites in low earth orbit. Rate data is provided by a gyro, which can be costly. This system has been developed and successfully tested in a post-processing mode using magnetometer and gyro data from 4 satellites supported by the Flight Dynamics Division at Goddard. In order for this system to be truly low cost, an alternative source for rate data must be utilized. An independent system which estimate spacecraft rate has been successfully developed and tested using only magnetometer data or a combination of magnetometer data and sun sensor data, which is less costly than a gyro. This system also uses an Extended Kalman Filter. Merging the two systems will provide an extremely low cost, autonomous approach to attitude and trajectory estimation. In this work we provide the theoretical background of the combined system. The measurement matrix is developed by combining the measurement matrix of the orbit and attitude estimation EKF with the measurement matrix of the rate estimation EKF, which is composed of a pseudo-measurement which makes the effective measurement a function of the angular velocity. Associated with this is the development of the noise covariance matrix associated with the original measurement combined with the new pseudo-measurement. In addition, the combination of the dynamics from the two systems is presented along with preliminary test results.
Luo, Yong; Wu, Wenqi; Babu, Ravindra; Tang, Kanghua; Luo, Bing
2012-01-01
COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System). Since the ultra-tight GPS/INS (Inertial Navigation System) integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF), and INS's accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load. PMID:23012564
Luo, Yong; Wu, Wenqi; Babu, Ravindra; Tang, Kanghua; Luo, Bing
2012-01-01
COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System). Since the ultra-tight GPS/INS (Inertial Navigation System) integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF), and INS's accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load.
An examination of the handheld adapter approach for measuring hand-transmitted vibration exposure
Xu, Xueyan S.; Dong, Ren G.; Welcome, Daniel E.; Warren, Christopher; McDowell, Thomas W.
2015-01-01
The use of a handheld adapter equipped with a tri-axial accelerometer is the most convenient and efficient approach for measuring vibration exposure at the hand-tool interface, especially when the adapter is incorporated into a miniature handheld or wrist-strapped dosimeter. To help optimize the adapter approach, the specific aims of this study are to identify and understand the major sources and mechanisms of measurement errors and uncertainties associated with using these adapters, and to explore their improvements. Five representative adapter models were selected and used in the experiment. Five human subjects served as operators in the experiment on a hand-arm vibration test system. The results of this study confirm that many of the handheld adapters can produce substantial overestimations of vibration exposure, and measurement errors can significantly vary with tool, adapter model, mounting position, mounting orientation, and subject. Major problems with this approach include unavoidable influence of the hand dynamic motion on the adapter, unstable attachment, insufficient attachment contact force, and inappropriate adapter structure. However, the results of this study also suggest that measurement errors can be substantially reduced if the design and use of an adapter can be systematically optimized toward minimizing the combined effects of the identified factors. Some potential methods for improving the design and use of the adapters are also proposed and discussed. PMID:26744580
Liu, Zhi; Wang, Fang; Zhang, Yun; Chen, Xin; Chen, C L Philip
2014-10-01
This paper focuses on an input-to-state practical stability (ISpS) problem of nonlinear systems which possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances. The dynamic disturbances depend on the states and the measured output of the system, and its assumption conditions are relaxed compared with the common restrictions. Based on an input-driven filter, fuzzy logic systems are directly used to approximate the unknown and desired control signals instead of the unknown nonlinear functions, and an integrated backstepping technique is used to design an adaptive output-feedback controller that ensures robustness with respect to unknown parameters and uncertain nonlinearities. This paper, by applying the ISpS theory and the generalized small-gain approach, shows that the proposed adaptive fuzzy controller guarantees the closed-loop system being semi-globally uniformly ultimately bounded. A main advantage of the proposed controller is that it contains only three adaptive parameters that need to be updated online, no matter how many states there are in the systems. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples. PMID:25222716
Hierarchy-Direction Selective Approach for Locally Adaptive Sparse Grids
Stoyanov, Miroslav K
2013-09-01
We consider the problem of multidimensional adaptive hierarchical interpolation. We use sparse grids points and functions that are induced from a one dimensional hierarchical rule via tensor products. The classical locally adaptive sparse grid algorithm uses an isotropic refinement from the coarser to the denser levels of the hierarchy. However, the multidimensional hierarchy provides a more complex structure that allows for various anisotropic and hierarchy selective refinement techniques. We consider the more advanced refinement techniques and apply them to a number of simple test functions chosen to demonstrate the various advantages and disadvantages of each method. While there is no refinement scheme that is optimal for all functions, the fully adaptive family-direction-selective technique is usually more stable and requires fewer samples.
Classification of EEG for Affect Recognition: An Adaptive Approach
NASA Astrophysics Data System (ADS)
Alzoubi, Omar; Calvo, Rafael A.; Stevens, Ronald H.
Research on affective computing is growing rapidly and new applications are being developed more frequently. They use information about the affective/mental states of users to adapt their interfaces or add new functionalities. Face activity, voice, text physiology and other information about the user are used as input to affect recognition modules, which are built as classification algorithms. Brain EEG signals have rarely been used to build such classifiers due to the lack of a clear theoretical framework. We present here an evaluation of three different classification techniques and their adaptive variations of a 10-class emotion recognition experiment. Our results show that affect recognition from EEG signals might be possible and an adaptive algorithm improves the performance of the classification task.
ERIC Educational Resources Information Center
Reinschmidt, Kerstin M.; Teufel-Shone, Nicolette I.; Bradford, Gail; Drummond, Rebecca L.; Torres, Emma; Redondo, Floribella; Elenes, Jo Jean; Sanders, Alicia; Gastelum, Sylvia; Moore-Monroy, Martha; Barajas, Salvador; Fernandez, Lourdes; Alvidrez, Rosy; de Zapien, Jill Guernsey; Staten, Lisa K.
2010-01-01
Diabetes health disparities among Hispanic populations have been countered with federally funded health promotion and disease prevention programs. Dissemination has focused on program adaptation to local cultural contexts for greater acceptability and sustainability. Taking a broader approach and drawing on our experience in Mexican American…
Towards a filtered density function approach for reactive transport in groundwater
NASA Astrophysics Data System (ADS)
Suciu, N.; Schüler, L.; Attinger, S.; Knabner, P.
2016-04-01
Evolution equations for probability density functions (PDFs) and filtered density functions (FDFs) of random species concentrations weighted by conserved scalars are formulated as Fokker-Planck equations describing stochastically equivalent processes in concentration-position spaces. This approach provides consistent numerical PDF/FDF solutions, given by the density in the concentration-position space of an ensemble of computational particles governed by the associated Itô equations. The solutions are obtained by a global random walk (GRW) algorithm, which is stable, free of numerical diffusion, and practically insensitive to the increase of the number of particles. The general FDF approach and the GRW numerical solution are illustrated for a reduced complexity problem consisting of the transport of a single scalar in groundwater. Randomness is induced by the stochastic parameterization of the hydraulic conductivity, characterized by short range correlations and small variance. The objective is to infer the statistics of the random concentration sampled at the plume center of mass, integrated over the transverse dimension of a two-dimensional spatial domain. The PDF/FDF problem can therefore be formulated in a two-dimensional domain as well, a spatial dimension and one in the concentration space. The upscaled drift and diffusion coefficients describing the PDF transport in the physical space are estimated on single-trajectories of diffusion in velocity fields with short-range correlations, owing to their self-averaging property. The mixing coefficients describing the PDF transport in concentration spaces are parameterized by the trend and the noise inferred from the statistical analysis of an ensemble of simulated concentration time series, as well as by classical mixing models. A Gaussian spatial filter applied to a Kraichnan velocity field generator is used to construct coarse-grained simulations (CGS) for FDF problems. The purposes of the CGS simulations are
Zaneveld, Jesse RR.; Parfrey, Laura Wegener; Van Treuren, Will; Lozupone, Catherine; Clemente, Jose C.; Knights, Dan; Stombaugh, Jesse; Kuczynski, Justin; Knight, Rob
2011-01-01
High-throughput sequencing technologies provide new opportunities to address longstanding questions about habitat adaptation in microbial organisms. How have microbes managed to adapt to such a wide range of environments, and what genomic features allow for such adaptation? We review recent large-scale studies of habitat adaptation, with emphasis on those that utilize phylogenetic techniques. On the basis of current trends, we summarize methodological challenges faced by investigators, and the tools, techniques, and analytical approaches available to overcome them. Phylogenetic approaches and detailed information about each environmental sample will be critical as the ability to collect genome sequences continues to expand. PMID:21872475
A Monte Carlo Approach for Adaptive Testing with Content Constraints
ERIC Educational Resources Information Center
Belov, Dmitry I.; Armstrong, Ronald D.; Weissman, Alexander
2008-01-01
This article presents a new algorithm for computerized adaptive testing (CAT) when content constraints are present. The algorithm is based on shadow CAT methodology to meet content constraints but applies Monte Carlo methods and provides the following advantages over shadow CAT: (a) lower maximum item exposure rates, (b) higher utilization of the…
Dissociating Conflict Adaptation from Feature Integration: A Multiple Regression Approach
ERIC Educational Resources Information Center
Notebaert, Wim; Verguts, Tom
2007-01-01
Congruency effects are typically smaller after incongruent than after congruent trials. One explanation is in terms of higher levels of cognitive control after detection of conflict (conflict adaptation; e.g., M. M. Botvinick, T. S. Braver, D. M. Barch, C. S. Carter, & J. D. Cohen, 2001). An alternative explanation for these results is based on…
Adaptive E-Learning Environments: Research Dimensions and Technological Approaches
ERIC Educational Resources Information Center
Di Bitonto, Pierpaolo; Roselli, Teresa; Rossano, Veronica; Sinatra, Maria
2013-01-01
One of the most closely investigated topics in e-learning research has always been the effectiveness of adaptive learning environments. The technological evolutions that have dramatically changed the educational world in the last six decades have allowed ever more advanced and smarter solutions to be proposed. The focus of this paper is to depict…
The Canadian approach to the settlement and adaptation of immigrants.
1986-01-01
Canada has been the host to over 400,000 refugees since World War II. The settlement and adaptation process is supported by the federal government and by the majority of provincial governments. Under the national and regional Employment and Immigration Commission CEIC) settlement organizations the major programs administered to effect the adaptation of newcomers are: 1) the Adjustment Assistance Program, 2) the Immigrant Settlement and Adaptation Program, 3) the Language/Skill Training Program, and 4) the Employment Services Program. Ontario, the recipient of more than 1/2 the newcomers that arrive in Canada each year, pursues active programs in the reception of newcomers through their Welcome House Program which offers a wide range of reception services to the newcomers. The employment and unemployment experiences of refugees is very much influenced by the prevailing labor market conditions, the refugees' proficiency in the country's official languages, the amount of sympathy evoked by the media reports on the plight of refugees, the availability of people of the same ethnic origin already well settled in the country, and the adaptability of the refugees themselves. The vast majority of refugee groups that came to Canada during the last 1/4 century seem to have adjusted well economically, despite having had difficulty in entering the occupations they intended to join. It is calculated that an average of $6607 per arrival is needed to cover the CEIC program costs of 1983-1984.
Manoli, Gabriele; Rossi, Matteo; Pasetto, Damiano; Deiana, Rita; Ferraris, Stefano; Cassiani, Giorgio; Putti, Mario
2015-02-15
The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.
Nonchromatographic "stir and filter approach" (SAFA) for isolating Sc3N@C80 metallofullerenes.
Stevenson, Steven; Harich, Kim; Yu, Hua; Stephen, Ryan R; Heaps, David; Coumbe, Curtis; Phillips, J Paige
2006-07-12
Separation difficulties have led to a paucity of purified metallic nitride fullerenes (MNFs). Fundamental research and application development has been hampered with limited sample availability. Separation techniques designed to remove contaminant empty-cage fullerenes (e.g., C(60), C(70)...C(2)(n)) and classical metallofullerenes (e.g., non-MNFs) traditionally require expensive and tedious chromatographic methods. Our motivation is an alternative purification approach to minimize dependence on HPLC. Herein we report the use of cyclopentadienyl (CPD) and amino functionalized silica to selectively bind contaminant fullerenes. This "Stir and Filter Approach" (SAFA) provides purified MNF samples at ambient and reflux conditions. Under reflux conditions, purified MNFs (80% recovery, 41 h) are obtained using CPD silica. However, at room temperature, there is an equilibrium established between fullerenes and CPD silica, and no purified MNF samples are obtained using SAFA. In contrast, purified MNF samples (99+%) are readily obtained at room temperature using amino, diamino, and triamino silica at recoveries of 93% (11 h), 76% (9 h), and 50% (6 h), respectively.
Multi-frequency Phase Unwrap from Noisy Data: Adaptive Least Squares Approach
NASA Astrophysics Data System (ADS)
Katkovnik, Vladimir; Bioucas-Dias, José
2010-04-01
Multiple frequency interferometry is, basically, a phase acquisition strategy aimed at reducing or eliminating the ambiguity of the wrapped phase observations or, equivalently, reducing or eliminating the fringe ambiguity order. In multiple frequency interferometry, the phase measurements are acquired at different frequencies (or wavelengths) and recorded using the corresponding sensors (measurement channels). Assuming that the absolute phase to be reconstructed is piece-wise smooth, we use a nonparametric regression technique for the phase reconstruction. The nonparametric estimates are derived from a local least squares criterion, which, when applied to the multifrequency data, yields denoised (filtered) phase estimates with extended ambiguity (periodized), compared with the phase ambiguities inherent to each measurement frequency. The filtering algorithm is based on local polynomial (LPA) approximation for design of nonlinear filters (estimators) and adaptation of these filters to unknown smoothness of the spatially varying absolute phase [9]. For phase unwrapping, from filtered periodized data, we apply the recently introduced robust (in the sense of discontinuity preserving) PUMA unwrapping algorithm [1]. Simulations give evidence that the proposed algorithm yields state-of-the-art performance for continuous as well as for discontinues phase surfaces, enabling phase unwrapping in extraordinary difficult situations when all other algorithms fail.
An approach to fabrication of large adaptive optics mirrors
NASA Astrophysics Data System (ADS)
Schwartz, Eric; Rey, Justin; Blaszak, David; Cavaco, Jeffrey
2014-07-01
For more than two decades, Northrop Grumman Xinetics has been the principal supplier of small deformable mirrors that enable adaptive optical (AO) systems for the ground-based astronomical telescope community. With today's drive toward extremely large aperture systems, and the desire of telescope designers to include adaptive optics in the main optical path of the telescope, Xinetics has recognized the need for large active mirrors with the requisite bandwidth and actuator stoke. Presented in this paper is the proposed use of Northrop Grumman Xinetics' large, ultra-lightweight Silicon Carbide substrates with surface parallel actuation of sufficient spatial density and bandwidth to meet the requirements of tomorrow's AO systems, while reducing complexity and cost.
Assessing confidence in management adaptation approaches for climate-sensitive ecosystems
NASA Astrophysics Data System (ADS)
West, J. M.; Julius, S. H.; Weaver, C. P.
2012-03-01
A number of options are available for adapting ecosystem management to improve resilience in the face of climatic changes. However, uncertainty exists as to the effectiveness of these options. A report prepared for the US Climate Change Science Program reviewed adaptation options for a range of federally managed systems in the United States. The report included a qualitative uncertainty analysis of conceptual approaches to adaptation derived from the review. The approaches included reducing anthropogenic stressors, protecting key ecosystem features, maintaining representation, replicating, restoring, identifying refugia and relocating organisms. The results showed that the expert teams had the greatest scientific confidence in adaptation options that reduce anthropogenic stresses. Confidence in other approaches was lower because of gaps in understanding of ecosystem function, climate change impacts on ecosystems, and management effectiveness. This letter discusses insights gained from the confidence exercise and proposes strategies for improving future assessments of confidence for management adaptations to climate change.
Whittemore, Stephen Richard
2013-09-10
Imaging systems include a detector and a spatial light modulator (SLM) that is coupled so as to control image intensity at the detector based on predetermined detector limits. By iteratively adjusting SLM element values, image intensity at one or all detector elements or portions of an imaging detector can be controlled to be within limits. The SLM can be secured to the detector at a spacing such that the SLM is effectively at an image focal plane. In some applications, the SLM can be adjusted to impart visible or hidden watermarks to images or to reduce image intensity at one or a selected set of detector elements so as to reduce detector blooming
Impact of rare earth element added filters on the X-ray beam spectra: a Monte Carlo approach.
Eskandarlou, Amir; Jafari, Amir Abbas; Mohammadi, Mohammad; Zehtabian, Mehdi; Faghihi, Reza; Shokri, Abbas; Pourolajal, Jalal
2014-01-01
The effectiveness of added filters including conventional and rare earth materials for dental radiography tasks was investigated using a simulation approach. Current study focuses on the combination of a range of various filters to investigate the reduction of radiation absorbed dose and improving the quality of a radiography image. To simulate the X-ray beam spectrum, a MCNP5 code was applied. Relative intensity, beam quality, and mean energy were investigated for a typical dental radiography machine. The impact of different rare-earth materials with different thicknesses and tube voltages on the X-ray spectrum was investigated. For Aluminum as a conventional filter, the modeled X-ray spectra and HVL values were in a good agreement with those reported by IPEM. The results showed that for a 70 kVp voltage, with an increase of the thickness and atomic number of a given added filters, an increase of HVL values were observed. However, with the increase of the attenuator thickness, X-ray beam intensity decreases. For mean energy, different results were observed. It was also found that rare earth made filters reduce high energy X-ray radiation due to k-edge absorption. This leads to an ideal beam for intra-oral radiography tasks. However, as a disadvantage of rare earth added filters, the reduction of the tube output levels should also be considered.
Sánchez Rodríguez, A; Rodrigo Sanz, M; Betancort Rodríguez, J R
2015-07-01
Due to the growing concern about human health effects of ultraviolet (UV) radiation, the use of UV filters has increased in recent decades. Unfortunately, some common UV filters are bioaccumulated in aquatic organisms and show a potential for estrogenic activity. The aim of the present study is to determine the presence of some UV filters in the coastal waters of six beaches around Gran Canaria Island as consequence of recreational seaside activities. Eight commonly used UV filters: benzophenone-3 (BP-3), octocrylene (OC), octyl-dimethyl-PABA (OD-PABA), ethylhexyl methoxy cinnamate (EHMC), homosalate (HMS), butyl methoxydibenzoyl methane (BMDBM), 4-methylbenzylidene camphor (4-MBC) and diethylamino hydroxybenzoyl hexyl benzoate (DHHB), were monitored and, with the exception of OD-PABA, all were detected in the samples collected. 99% of the samples showed some UV filters and concentration levels reached up to 3316.7 ng/L for BP-3. Environmental risk assessment (ERA) approach showed risk quotients (RQ) higher than 10, which means that there is a significant potential for adverse effects, for 4-MBC and EHMC for those samples with highest levels of UV filters.
A regional approach to climate adaptation in the Nile Basin
NASA Astrophysics Data System (ADS)
Butts, Michael B.; Buontempo, Carlo; Lørup, Jens K.; Williams, Karina; Mathison, Camilla; Jessen, Oluf Z.; Riegels, Niels D.; Glennie, Paul; McSweeney, Carol; Wilson, Mark; Jones, Richard; Seid, Abdulkarim H.
2016-10-01
The Nile Basin is one of the most important shared basins in Africa. Managing and developing the water resources within the basin must not only address different water uses but also the trade-off between developments upstream and water use downstream, often between different countries. Furthermore, decision-makers in the region need to evaluate and implement climate adaptation measures. Previous work has shown that the Nile flows can be highly sensitive to climate change and that there is considerable uncertainty in climate projections in the region with no clear consensus as to the direction of change. Modelling current and future changes in river runoff must address a number of challenges; including the large size of the basin, the relative scarcity of data, and the corresponding dramatic variety of climatic conditions and diversity in hydrological characteristics. In this paper, we present a methodology, to support climate adaptation on a regional scale, for assessing climate change impacts and adaptation potential for floods, droughts and water scarcity within the basin.
The adaptive significance of adult neurogenesis: an integrative approach
Konefal, Sarah; Elliot, Mick; Crespi, Bernard
2013-01-01
Adult neurogenesis in mammals is predominantly restricted to two brain regions, the dentate gyrus (DG) of the hippocampus and the olfactory bulb (OB), suggesting that these two brain regions uniquely share functions that mediate its adaptive significance. Benefits of adult neurogenesis across these two regions appear to converge on increased neuronal and structural plasticity that subserves coding of novel, complex, and fine-grained information, usually with contextual components that include spatial positioning. By contrast, costs of adult neurogenesis appear to center on potential for dysregulation resulting in higher risk of brain cancer or psychological dysfunctions, but such costs have yet to be quantified directly. The three main hypotheses for the proximate functions and adaptive significance of adult neurogenesis, pattern separation, memory consolidation, and olfactory spatial, are not mutually exclusive and can be reconciled into a simple general model amenable to targeted experimental and comparative tests. Comparative analysis of brain region sizes across two major social-ecological groups of primates, gregarious (mainly diurnal haplorhines, visually-oriented, and in large social groups) and solitary (mainly noctural, territorial, and highly reliant on olfaction, as in most rodents) suggest that solitary species, but not gregarious species, show positive associations of population densities and home range sizes with sizes of both the hippocampus and OB, implicating their functions in social-territorial systems mediated by olfactory cues. Integrated analyses of the adaptive significance of adult neurogenesis will benefit from experimental studies motivated and structured by ecologically and socially relevant selective contexts. PMID:23882188
A Robust Approach for a Filter-Based Monocular Simultaneous Localization and Mapping (SLAM) System
Munguía, Rodrigo; Castillo-Toledo, Bernardino; Grau, Antoni
2013-01-01
Simultaneous localization and mapping (SLAM) is an important problem to solve in robotics theory in order to build truly autonomous mobile robots. This work presents a novel method for implementing a SLAM system based on a single camera sensor. The SLAM with a single camera, or monocular SLAM, is probably one of the most complex SLAM variants. In this case, a single camera, which is freely moving through its environment, represents the sole sensor input to the system. The sensors have a large impact on the algorithm used for SLAM. Cameras are used more frequently, because they provide a lot of information and are well adapted for embedded systems: they are light, cheap and power-saving. Nevertheless, and unlike range sensors, which provide range and angular information, a camera is a projective sensor providing only angular measurements of image features. Therefore, depth information (range) cannot be obtained in a single step. In this case, special techniques for feature system-initialization are needed in order to enable the use of angular sensors (as cameras) in SLAM systems. The main contribution of this work is to present a novel and robust scheme for incorporating and measuring visual features in filtering-based monocular SLAM systems. The proposed method is based in a two-step technique, which is intended to exploit all the information available in angular measurements. Unlike previous schemes, the values of parameters used by the initialization technique are derived directly from the sensor characteristics, thus simplifying the tuning of the system. The experimental results show that the proposed method surpasses the performance of previous schemes. PMID:23823972
A robust approach for a filter-based monocular simultaneous localization and mapping (SLAM) system.
Munguía, Rodrigo; Castillo-Toledo, Bernardino; Grau, Antoni
2013-07-03
Simultaneous localization and mapping (SLAM) is an important problem to solve in robotics theory in order to build truly autonomous mobile robots. This work presents a novel method for implementing a SLAM system based on a single camera sensor. The SLAM with a single camera, or monocular SLAM, is probably one of the most complex SLAM variants. In this case, a single camera, which is freely moving through its environment, represents the sole sensor input to the system. The sensors have a large impact on the algorithm used for SLAM. Cameras are used more frequently, because they provide a lot of information and are well adapted for embedded systems: they are light, cheap and power-saving. Nevertheless, and unlike range sensors, which provide range and angular information, a camera is a projective sensor providing only angular measurements of image features. Therefore, depth information (range) cannot be obtained in a single step. In this case, special techniques for feature system-initialization are needed in order to enable the use of angular sensors (as cameras) in SLAM systems. The main contribution of this work is to present a novel and robust scheme for incorporating and measuring visual features in filtering-based monocular SLAM systems. The proposed method is based in a two-step technique, which is intended to exploit all the information available in angular measurements. Unlike previous schemes, the values of parameters used by the initialization technique are derived directly from the sensor characteristics, thus simplifying the tuning of the system. The experimental results show that the proposed method surpasses the performance of previous schemes.
A robust approach for a filter-based monocular simultaneous localization and mapping (SLAM) system.
Munguía, Rodrigo; Castillo-Toledo, Bernardino; Grau, Antoni
2013-01-01
Simultaneous localization and mapping (SLAM) is an important problem to solve in robotics theory in order to build truly autonomous mobile robots. This work presents a novel method for implementing a SLAM system based on a single camera sensor. The SLAM with a single camera, or monocular SLAM, is probably one of the most complex SLAM variants. In this case, a single camera, which is freely moving through its environment, represents the sole sensor input to the system. The sensors have a large impact on the algorithm used for SLAM. Cameras are used more frequently, because they provide a lot of information and are well adapted for embedded systems: they are light, cheap and power-saving. Nevertheless, and unlike range sensors, which provide range and angular information, a camera is a projective sensor providing only angular measurements of image features. Therefore, depth information (range) cannot be obtained in a single step. In this case, special techniques for feature system-initialization are needed in order to enable the use of angular sensors (as cameras) in SLAM systems. The main contribution of this work is to present a novel and robust scheme for incorporating and measuring visual features in filtering-based monocular SLAM systems. The proposed method is based in a two-step technique, which is intended to exploit all the information available in angular measurements. Unlike previous schemes, the values of parameters used by the initialization technique are derived directly from the sensor characteristics, thus simplifying the tuning of the system. The experimental results show that the proposed method surpasses the performance of previous schemes. PMID:23823972
Signal post processing in frequency domain OCT and OCM using a filter bank approach
NASA Astrophysics Data System (ADS)
Hofer, Bernd; Považay, Boris; Hermann, Boris; Unterhuber, Angelika; Matz, Gerald; Hlawatsch, Franz; Drexler, Wolfgang
2007-02-01
Current signal post processing in spectrally encoded frequency domain (FD) optical coherence microscopy (OCM) and optical coherence tomography (OCT) uses Fourier transforms in combination with non-uniform resampling strategies to map the k-space data acquired by the spectrometer to spatial domain signals which are necessary for tomogram generation. We propose to use a filter bank (FB) framework for the remapping process. With our new approach, the spectrometer is modeled as a critically sampled analysis FB, whose outputs are quantized subband signals that constitute the k-space spectroscopic data. The optimal procedure to map this data to the spatial domain is via a suitably designed synthesis FB which has low complexity. FB theory additionally states that 1) it is possible to find a synthesis FB such that the overall system has the perfect reconstruction (PR) property; 2) any processing on critically sampled subband signals (as done in current schemes) results in aliasing artifacts. These perspectives are evaluated both theoretically and experimentally. We determine the analysis FB corresponding to our FD-OCM system by using a tunable laser and show that for our grating-based spectrometer - employing a CCD-line camera - the non-uniform resampling together with FFT indeed causes aliasing terms and depth dependent signal attenuation. Furthermore, we compute a finite impulse response based synthesis FB and assess the desired PR property by means of layered samples. The resulting images exhibit higher resolution and improved SNR compared to the common FFT-based approach. The potential of the proposed FB approach opens a new perspective also for other spectroscopic applications.
ERIC Educational Resources Information Center
Mao, Xiuzhen; Xin, Tao
2013-01-01
The Monte Carlo approach which has previously been implemented in traditional computerized adaptive testing (CAT) is applied here to cognitive diagnostic CAT to test the ability of this approach to address multiple content constraints. The performance of the Monte Carlo approach is compared with the performance of the modified maximum global…
Evidence of an Adaptive Level Grading Practice through a Causal Approach.
ERIC Educational Resources Information Center
Gallini, Joan
1982-01-01
The adaptation level theory in grading implies that students select major programs whose grading practices are realistic with their ability. A causal approach using a system of multiple equations was used to investigate this theory. The results lent support to occurrence of the adaptive grading practice. (Author/CM)
An Adaptive Approach to Managing Knowledge Development in a Project-Based Learning Environment
ERIC Educational Resources Information Center
Tilchin, Oleg; Kittany, Mohamed
2016-01-01
In this paper we propose an adaptive approach to managing the development of students' knowledge in the comprehensive project-based learning (PBL) environment. Subject study is realized by two-stage PBL. It shapes adaptive knowledge management (KM) process and promotes the correct balance between personalized and collaborative learning. The…
Ahirwal, M K; Kumar, Anil; Singh, G K
2013-01-01
This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.
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.
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.
Making CORBA objects persistent: The object database adapter approach
Reverbel, F.C.R.
1997-05-01
In spite of its remarkable successes in promoting standards for distributed object systems, the Object Management Group (OMG) has not yet settled the issue of object persistence in the Object Request Broker (ORB) environment. The Common Object Request Broker Architecture (CORBA) specification briefly mentions an Object-Oriented Database Adapter that makes objects stored in an object-oriented database accessible through the ORB. This idea is pursued in the Appendix B of the ODMG standard, which identifies a number of issues involved in using an Object Database Management System (ODBMS) in a CORBA environment, and proposes an Object Database Adapter (ODA) to realize the integration of the ORB with the ODBMS. This paper discusses the design and implementation of an ODA that integrates an ORB and an ODBMS with C++ bindings. For the author`s purposes, an ODBMS is a system with programming interfaces. It may be a pure object-oriented DBMS (an OODBMS), or a combination of a relational DBMS and an object-relational mapper.
State-of-charge estimation in lithium-ion batteries: A particle filter approach
NASA Astrophysics Data System (ADS)
Tulsyan, Aditya; Tsai, Yiting; Gopaluni, R. Bhushan; Braatz, Richard D.
2016-11-01
The dynamics of lithium-ion batteries are complex and are often approximated by models consisting of partial differential equations (PDEs) relating the internal ionic concentrations and potentials. The Pseudo two-dimensional model (P2D) is one model that performs sufficiently accurately under various operating conditions and battery chemistries. Despite its widespread use for prediction, this model is too complex for standard estimation and control applications. This article presents an original algorithm for state-of-charge estimation using the P2D model. Partial differential equations are discretized using implicit stable algorithms and reformulated into a nonlinear state-space model. This discrete, high-dimensional model (consisting of tens to hundreds of states) contains implicit, nonlinear algebraic equations. The uncertainty in the model is characterized by additive Gaussian noise. By exploiting the special structure of the pseudo two-dimensional model, a novel particle filter algorithm that sweeps in time and spatial coordinates independently is developed. This algorithm circumvents the degeneracy problems associated with high-dimensional state estimation and avoids the repetitive solution of implicit equations by defining a 'tether' particle. The approach is illustrated through extensive simulations.
Wong, H S; Guan, L
2001-01-01
We address the problem of adaptive regularization in image restoration by adopting a neural-network learning approach. Instead of explicitly specifying the local regularization parameter values, they are regarded as network weights which are then modified through the supply of appropriate training examples. The desired response of the network is in the form of a gray level value estimate of the current pixel using weighted order statistic (WOS) filter. However, instead of replacing the previous value with this estimate, this is used to modify the network weights, or equivalently, the regularization parameters such that the restored gray level value produced by the network is closer to this desired response. In this way, the single WOS estimation scheme can allow appropriate parameter values to emerge under different noise conditions, rather than requiring their explicit selection in each occasion. In addition, we also consider the separate regularization of edges and textures due to their different noise masking capabilities. This in turn requires discriminating between these two feature types. Due to the inability of conventional local variance measures to distinguish these two high variance features, we propose the new edge-texture characterization (ETC) measure which performs this discrimination based on a scalar value only. This is then incorporated into a fuzzified form of the previous neural network which determines the degree of membership of each high variance pixel in two fuzzy sets, the EDGE and TEXTURE fuzzy sets, from the local ETC value, and then evaluates the appropriate regularization parameter by appropriately combining these two membership function values.
Randriamparany, T; Kouakou, K V; Michaud, V; Fernández-Pinero, J; Gallardo, C; Le Potier, M-F; Rabenarivahiny, R; Couacy-Hymann, E; Raherimandimby, M; Albina, E
2016-08-01
The performance of Whatman 3-MM filter papers for the collection, drying, shipment and long-term storage of blood at ambient temperature, and for the detection of African swine fever virus and antibodies was assessed. Conventional and real-time PCR, viral isolation and antibody detection by ELISA were performed on paired samples (blood/tissue versus dried-blood 3-MM filter papers) collected from experimentally infected pigs and from farm pigs in Madagascar and Côte d'Ivoire. 3-MM filter papers were used directly in the conventional and real-time PCR without previous extraction of nucleic acids. Tests that performed better with 3-MM filter papers were in descending order: virus isolation, real-time UPL PCR and conventional PCR. The analytical sensitivity of real-time UPL PCR on filter papers was similar to conventional testing (virus isolation or conventional PCR) on organs or blood. In addition, blood-dried filter papers were tested in ELISA for antibody detection and the observed sensitivity was very close to conventional detection on serum samples and gave comparable results. Filter papers were stored up to 9 months at 20-25°C and for 2 months at 37°C without significant loss of sensitivity for virus genome detection. All tests on 3-MM filter papers had 100% specificity compared to the gold standards. Whatman 3-MM filter papers have the advantage of being cheap and of preserving virus viability for future virus isolation and characterization. In this study, Whatman 3-MM filter papers proved to be a suitable support for the collection, storage and use of blood in remote areas of tropical countries without the need for a cold chain and thus provide new possibilities for antibody testing and virus isolation.
Hiding objects and creating illusions above a carpet filter using a Fourier optics approach.
Wu, Kedi; Wang, Guo Ping
2010-09-13
Invisibility carpet cloaks are usually used to hide an object beneath carpet. In this paper we propose and demonstrate a carpet filter to hide objects and create illusions above the filter by using a Fourier optics method. Instead of using transformation optics, we get electromagnetic parameters of the filter by optical transfer functions, which play the role of modulating the propagation of the scattering angular spectrum directly from an object above the filter. By further adding a functional layer onto the filter, we can even camouflage the object so that it appears to be a different object. The analytical results are confirmed by numerical simulations. Our method is completely different from the current coordinate transfer method and may provide another point of view to more clearly understand the mechanism of invisibility cloaks.
Reduced Complexity HMM Filtering With Stochastic Dominance Bounds: A Convex Optimization Approach
NASA Astrophysics Data System (ADS)
Krishnamurthy, Vikram; Rojas, Cristian R.
2014-12-01
This paper uses stochastic dominance principles to construct upper and lower sample path bounds for Hidden Markov Model (HMM) filters. Given a HMM, by using convex optimization methods for nuclear norm minimization with copositive constraints, we construct low rank stochastic marices so that the optimal filters using these matrices provably lower and upper bound (with respect to a partially ordered set) the true filtered distribution at each time instant. Since these matrices are low rank (say R), the computational cost of evaluating the filtering bounds is O(XR) instead of O(X2). A Monte-Carlo importance sampling filter is presented that exploits these upper and lower bounds to estimate the optimal posterior. Finally, using the Dobrushin coefficient, explicit bounds are given on the variational norm between the true posterior and the upper and lower bounds.
NASA Technical Reports Server (NTRS)
Deutschmann, Julie; Harman, Rick; Bar-Itzhack, Itzhack
1998-01-01
An innovative approach to autonomous attitude and trajectory estimation is available using only magnetic field data and rate data. The estimation is performed simultaneously using an Extended Kalman Filter (EKF), a well known algorithm used extensively in onboard applications. The magnetic field is measured on a satellite by a magnetometer, an inexpensive and reliable sensor flown on virtually all satellites in low earth orbit. Rate data is provided by a gyro, which can be costly. This system has been developed and successfully tested in a post-processing mode using magnetometer and gyro data from 4 satellites supported by the Flight Dynamics Division at Goddard. In order for this system to be truly low cost, an alternative source for rate data must be utilized. An independent system which estimates spacecraft rate has been successfully developed and tested using only magnetometer data or a combination of magnetometer data and sun sensor data, which is less costly than a gyro. This system also uses an EKF. Merging the two systems will provide an extremely low cost, autonomous approach to attitude and trajectory estimation. In this work we provide the theoretical background of the combined system. The measurement matrix is developed by combining the measurement matrix of the orbit and attitude estimation EKF with the measurement matrix of the rate estimation EKF, which is composed of a pseudo-measurement which makes the effective measurement a function of the angular velocity. Associated with this is the development of the noise covariance matrix associated with the original measurement combined with the new pseudo-measurement. In addition, the combination of the dynamics from the two systems is presented along with preliminary test results.
Design of order statistics filters using feedforward neural networks
NASA Astrophysics Data System (ADS)
Maslennikova, Yu. S.; Bochkarev, V. V.
2016-08-01
In recent years significant progress have been made in the development of nonlinear data processing techniques. Such techniques are widely used in digital data filtering and image enhancement. Many of the most effective nonlinear filters based on order statistics. The widely used median filter is the best known order statistic filter. Generalized form of these filters could be presented based on Lloyd's statistics. Filters based on order statistics have excellent robustness properties in the presence of impulsive noise. In this paper, we present special approach for synthesis of order statistics filters using artificial neural networks. Optimal Lloyd's statistics are used for selecting of initial weights for the neural network. Adaptive properties of neural networks provide opportunities to optimize order statistics filters for data with asymmetric distribution function. Different examples demonstrate the properties and performance of presented approach.
[Molecular genetic bases of adaptation processes and approaches to their analysis].
Salmenkova, E A
2013-01-01
Great interest in studying the molecular genetic bases of the adaptation processes is explained by their importance in understanding evolutionary changes, in the development ofintraspecific and interspecific genetic diversity, and in the creation of approaches and programs for maintaining and restoring the population. The article examines the sources and conditions for generating adaptive genetic variability and contribution of neutral and adaptive genetic variability to the population structure of the species; methods for identifying the adaptive genetic variability on the genome level are also described. Considerable attention is paid to the potential of new technologies of genome analysis, including next-generation sequencing and some accompanying methods. In conclusion, the important role of the joint use of genomics and proteomics approaches in understanding the molecular genetic bases of adaptation is emphasized.
Revathi, V M; Balasubramaniam, P
2016-04-01
In this paper, the [Formula: see text] filtering problem is treated for N coupled genetic oscillator networks with time-varying delays and extrinsic molecular noises. Each individual genetic oscillator is a complex dynamical network that represents the genetic oscillations in terms of complicated biological functions with inner or outer couplings denote the biochemical interactions of mRNAs, proteins and other small molecules. Throughout the paper, first, by constructing appropriate delay decomposition dependent Lyapunov-Krasovskii functional combined with reciprocal convex approach, improved delay-dependent sufficient conditions are obtained to ensure the asymptotic stability of the filtering error system with a prescribed [Formula: see text] performance. Second, based on the above analysis, the existence of the designed [Formula: see text] filters are established in terms of linear matrix inequalities with Kronecker product. Finally, numerical examples including a coupled Goodwin oscillator model are inferred to illustrate the effectiveness and less conservatism of the proposed techniques.
An Evidence-Based Public Health Approach to Climate Change Adaptation
Eidson, Millicent; Tlumak, Jennifer E.; Raab, Kristin K.; Luber, George
2014-01-01
Background: Public health is committed to evidence-based practice, yet there has been minimal discussion of how to apply an evidence-based practice framework to climate change adaptation. Objectives: Our goal was to review the literature on evidence-based public health (EBPH), to determine whether it can be applied to climate change adaptation, and to consider how emphasizing evidence-based practice may influence research and practice decisions related to public health adaptation to climate change. Methods: We conducted a substantive review of EBPH, identified a consensus EBPH framework, and modified it to support an EBPH approach to climate change adaptation. We applied the framework to an example and considered implications for stakeholders. Discussion: A modified EBPH framework can accommodate the wide range of exposures, outcomes, and modes of inquiry associated with climate change adaptation and the variety of settings in which adaptation activities will be pursued. Several factors currently limit application of the framework, including a lack of higher-level evidence of intervention efficacy and a lack of guidelines for reporting climate change health impact projections. To enhance the evidence base, there must be increased attention to designing, evaluating, and reporting adaptation interventions; standardized health impact projection reporting; and increased attention to knowledge translation. This approach has implications for funders, researchers, journal editors, practitioners, and policy makers. Conclusions: The current approach to EBPH can, with modifications, support climate change adaptation activities, but there is little evidence regarding interventions and knowledge translation, and guidelines for projecting health impacts are lacking. Realizing the goal of an evidence-based approach will require systematic, coordinated efforts among various stakeholders. Citation: Hess JJ, Eidson M, Tlumak JE, Raab KK, Luber G. 2014. An evidence-based public
Ares-I Bending Filter Design using a Constrained Optimization Approach
NASA Technical Reports Server (NTRS)
Hall, Charles; Jang, Jiann-Woei; Hall, Robert; Bedrossian, Nazareth
2008-01-01
The Ares-I launch vehicle represents a challenging flex-body structural environment for control system design. Software filtering of the inertial sensor output is required to ensure adequate stable response to guidance commands while minimizing trajectory deviations. This paper presents a design methodology employing numerical optimization to develop the Ares-I bending filters. The design objectives include attitude tracking accuracy and robust stability with respect to rigid body dynamics, propellant slosh, and flex. Under the assumption that the Ares-I time-varying dynamics and control system can be frozen over a short period of time, the bending filters are designed to stabilize all the selected frozen-time launch control systems in the presence of parameter uncertainty. To ensure adequate response to guidance command, step response specifications are introduced as constraints in the optimization problem. Imposing these constrains minimizes performance degradation caused by the addition of the bending filters. The first stage bending filter design achieves stability by adding lag to the first structural frequency to phase stabilize the first flex mode while gain stabilizing the higher modes. The upper stage bending filter design gain stabilizes all the flex bending modes. The bending filter designs provided here have been demonstrated to provide stable first and second stage control systems in both Draper Ares Stability Analysis Tool (ASAT) and the MSFC MAVERIC 6DOF nonlinear time domain simulation.
Monochromatic filter with multiple manipulation approaches by the layered all-dielectric patch array
NASA Astrophysics Data System (ADS)
Liu, Xiaoshan; Liu, Guiqiang; Fu, Guolan; Liu, Mulin; Liu, Zhengqi
2016-03-01
Monochromatic filtering with ultra-narrowband and high spectral contrast is desirable for wide applications in display, image, and other optoelectronics. However, owing to the inherent omhic losses in the metallic materials, a broadband spectrum with a low Q-factor down to 10 inevitably limits the device performance. Herein, we for the first time theoretically propose and demonstrate an ultra-narrowband color-filtering platform based on the layered all-dielectric meta-material (LADM), which consists of a triple-layer high/low/high-index dielectrics cavity structure. Owing to the lossless dielectric materials used, sharp resonances with the bandwidth down to sub-10 nm are observed in the sub-wavelength LADM-based filters. A spectral Q-factor of 361.6 is achieved, which is orders of magnitude larger than that of the plasmonic resonators. Moreover, for the other significant factor for evaluation of filtering performance, the spectral contrast reaches 94.5%. These optical properties are the main results of the excitation of the resonant modes in the LADMs. Furthermore, polarization-manipulated light filtering is realized in this LADM. The classical Malus law is also confirmed in the reflective spectrum by tuning the polarization state. More interestingly and importantly, the filtering phenomenon shows novel features of the wavelength-independent and tunable resonant intensity for the reflective spectrum when the LADM-based filter is illuminated under an oblique state. High scalability of the sharp reflective spectrum is obtained by tuning the structural parameters. A single-wavelength reflective filtering window is also achieved in the visible frequencies. These features hold promise for the LADM-based filter with wide applications in color engineering, displaying, imaging, etc.
An Efficient Machine Learning Approach To Low-Complexity Filtering In Biological Sequences
Barber, Christopher A; Oehmen, Christopher S
2012-06-09
Biological sequences contain low-complexity regions (LCRs) which produce superfluous matches in homology searches, and lead to slow execution of database search algorithms such as BLAST. These regions are efficiently identified by low-complexity filtering algorithms such as SDUST and SEG, which are included in the BLAST tool-suite. These algorithms target differing notions of complexity, so an algorithm which combines their sensitivities is pursued. A variety of features are derived from these algorithms, as well as a new filtering algorithm based on Lempel-Ziv complexity. Artificial sequences with known LCRs are used to train and evaluate an SVM classifier, which significantly outperforms the standalone filtering algorithms.
A Novel Approach to Noise-Filtering Based on a Gain-Scheduling Neural Network Architecture
NASA Technical Reports Server (NTRS)
Troudet, T.; Merrill, W.
1994-01-01
A gain-scheduling neural network architecture is proposed to enhance the noise-filtering efficiency of feedforward neural networks, in terms of both nominal performance and robustness. The synergistic benefits of the proposed architecture are demonstrated and discussed in the context of the noise-filtering of signals that are typically encountered in aerospace control systems. The synthesis of such a gain-scheduled neurofiltering provides the robustness of linear filtering, while preserving the nominal performance advantage of conventional nonlinear neurofiltering. Quantitative performance and robustness evaluations are provided for the signal processing of pitch rate responses to typical pilot command inputs for a modern fighter aircraft model.
Quantization noise filtering in ADPCM systems
NASA Astrophysics Data System (ADS)
Gibson, J. D.
1980-08-01
Differential pulse code modulation (DPCM) systems utilizing adaptive quantizers and fixed or adaptive predictors are effective methods for voice encoding at data rates of 9.6 to 40 kbits/s. The principal performance limitation on these systems is the presence of quantization noise in the receiver output and the predictor feedback loop. An approach to reducing the quantization noise using sequential filtering methods based on estimation theory concepts is described. Several different filter structures are presented and the efficacy of the approach is illustrated via system simulations using actual speech. Signal-to-quantization noise ratio, sound spectrograms, and subjective listening tests are used for system performance evaluations.
Adaptation to floods in future climate: a practical approach
NASA Astrophysics Data System (ADS)
Doroszkiewicz, Joanna; Romanowicz, Renata; Radon, Radoslaw; Hisdal, Hege
2016-04-01
In this study some aspects of the application of the 1D hydraulic model are discussed with a focus on its suitability for flood adaptation under future climate conditions. The Biała Tarnowska catchment is used as a case study. A 1D hydraulic model is developed for the evaluation of inundation extent and risk maps in future climatic conditions. We analyse the following flood indices: (i) extent of inundation area; (ii) depth of water on flooded land; (iii) the flood wave duration; (iv) the volume of a flood wave over the threshold value. In this study we derive a model cross-section geometry following the results of primary research based on a 500-year flood inundation extent. We compare two methods of localisation of cross-sections from the point of view of their suitability to the derivation of the most precise inundation outlines. The aim is to specify embankment heights along the river channel that would protect the river valley in the most vulnerable locations under future climatic conditions. We present an experimental design for scenario analysis studies and uncertainty reduction options for future climate projections obtained from the EUROCORDEX project. Acknowledgements: This work was supported by the project CHIHE (Climate Change Impact on Hydrological Extremes), carried out in the Institute of Geophysics Polish Academy of Sciences, funded by Norway Grants (contract No. Pol-Nor/196243/80/2013). The hydro-meteorological observations were provided by the Institute of Meteorology and Water Management (IMGW), Poland.
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
Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping
2014-12-03
High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.
ERIC Educational Resources Information Center
Greenspan, Stanley I.; Lourie, Reginald S.
This paper applies a developmental structuralist approach to the classification of adaptive and pathologic personality organizations and behavior in infancy and early childhood, and it discusses implications of this approach for preventive intervention. In general, as development proceeds, the structural capacity of the developing infant and child…
Adaptation of a weighted regression approach to evaluate water quality trends in anestuary
To improve the description of long-term changes in water quality, a weighted regression approach developed to describe trends in pollutant transport in rivers was adapted to analyze a long-term water quality dataset from Tampa Bay, Florida. The weighted regression approach allows...
Adaptation of a Weighted Regression Approach to Evaluate Water Quality Trends in an Estuary
To improve the description of long-term changes in water quality, we adapted a weighted regression approach to analyze a long-term water quality dataset from Tampa Bay, Florida. The weighted regression approach, originally developed to resolve pollutant transport trends in rivers...
Adaptive Role Playing Games: An Immersive Approach for Problem Based Learning
ERIC Educational Resources Information Center
Sancho, Pilar; Moreno-Ger, Pablo; Fuentes-Fernandez, Ruben; Fernandez-Manjon, Baltasar
2009-01-01
In this paper we present a general framework, called NUCLEO, for the application of socio-constructive educational approaches in higher education. The underlying pedagogical approach relies on an adaptation model in order to improve group dynamics, as this has been identified as one of the key features in the success of collaborative learning…