Technical note: Improving the AWAT filter with interpolation schemes for advanced processing of high resolution data

NASA Astrophysics Data System (ADS)

Peters, Andre; Nehls, Thomas; Wessolek, Gerd

2016-06-01

Weighing lysimeters with appropriate data filtering yield the most precise and unbiased information for precipitation (P) and evapotranspiration (ET). A recently introduced filter scheme for such data is the AWAT (Adaptive Window and Adaptive Threshold) filter (Peters et al., 2014). The filter applies an adaptive threshold to separate significant from insignificant mass changes, guaranteeing that P and ET are not overestimated, and uses a step interpolation between the significant mass changes. In this contribution we show that the step interpolation scheme, which reflects the resolution of the measuring system, can lead to unrealistic prediction of P and ET, especially if they are required in high temporal resolution. We introduce linear and spline interpolation schemes to overcome these problems. To guarantee that medium to strong precipitation events abruptly following low or zero fluxes are not smoothed in an unfavourable way, a simple heuristic selection criterion is used, which attributes such precipitations to the step interpolation. The three interpolation schemes (step, linear and spline) are tested and compared using a data set from a grass-reference lysimeter with 1 min resolution, ranging from 1 January to 5 August 2014. The selected output resolutions for P and ET prediction are 1 day, 1 h and 10 min. As expected, the step scheme yielded reasonable flux rates only for a resolution of 1 day, whereas the other two schemes are well able to yield reasonable results for any resolution. The spline scheme returned slightly better results than the linear scheme concerning the differences between filtered values and raw data. Moreover, this scheme allows continuous differentiability of filtered data so that any output resolution for the fluxes is sound. Since computational burden is not problematic for any of the interpolation schemes, we suggest always using the spline scheme.

Ultra compact spectrometer using linear variable filters

NASA Astrophysics Data System (ADS)

Dami, M.; De Vidi, R.; Aroldi, G.; Belli, F.; Chicarella, L.; Piegari, A.; Sytchkova, A.; Bulir, J.; Lemarquis, F.; Lequime, M.; Abel Tibérini, L.; Harnisch, B.

2017-11-01

The Linearly Variable Filters (LVF) are complex optical devices that, integrated in a CCD, can realize a "single chip spectrometer". In the framework of an ESA Study, a team of industries and institutes led by SELEX-Galileo explored the design principles and manufacturing techniques, realizing and characterizing LVF samples based both on All-Dielectric (AD) and Metal-Dielectric (MD) Coating Structures in the VNIR and SWIR spectral ranges. In particular the achieved performances on spectral gradient, transmission bandwidth and Spectral Attenuation (SA) are presented and critically discussed. Potential improvements will be highlighted. In addition the results of a feasibility study of a SWIR Linear Variable Filter are presented with the comparison of design prediction and measured performances. Finally criticalities related to the filter-CCD packaging are discussed. The main achievements reached during these activities have been: - to evaluate by design, manufacturing and test of LVF samples the achievable performances compared with target requirements; - to evaluate the reliability of the projects by analyzing their repeatability; - to define suitable measurement methodologies

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.

Video-signal improvement using comb filtering techniques.

NASA Technical Reports Server (NTRS)

Arndt, G. D.; Stuber, F. M.; Panneton, R. J.

1973-01-01

Significant improvement in the signal-to-noise performance of television signals has been obtained through the application of comb filtering techniques. This improvement is achieved by removing the inherent redundancy in the television signal through linear prediction and by utilizing the unique noise-rejection characteristics of the receiver comb filter. Theoretical and experimental results describe the signal-to-noise ratio and picture-quality improvement obtained through the use of baseband comb filters and the implementation of a comb network as the loop filter in a phase-lock-loop demodulator. Attention is given to the fact that noise becomes correlated when processed by the receiver comb filter.

Smoothing-based compressed state Kalman filter for joint state-parameter estimation: Applications in reservoir characterization and CO2 storage monitoring

NASA Astrophysics Data System (ADS)

Li, Y. J.; Kokkinaki, Amalia; Darve, Eric F.; Kitanidis, Peter K.

2017-08-01

The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. We present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.

A Sequential Ensemble Prediction System at Convection Permitting Scales

NASA Astrophysics Data System (ADS)

Milan, M.; Simmer, C.

2012-04-01

A Sequential Assimilation Method (SAM) following some aspects of particle filtering with resampling, also called SIR (Sequential Importance Resampling), is introduced and applied in the framework of an Ensemble Prediction System (EPS) for weather forecasting on convection permitting scales, with focus to precipitation forecast. At this scale and beyond, the atmosphere increasingly exhibits chaotic behaviour and non linear state space evolution due to convectively driven processes. One way to take full account of non linear state developments are particle filter methods, their basic idea is the representation of the model probability density function by a number of ensemble members weighted by their likelihood with the observations. In particular particle filter with resampling abandons ensemble members (particles) with low weights restoring the original number of particles adding multiple copies of the members with high weights. In our SIR-like implementation we substitute the likelihood way to define weights and introduce a metric which quantifies the "distance" between the observed atmospheric state and the states simulated by the ensemble members. We also introduce a methodology to counteract filter degeneracy, i.e. the collapse of the simulated state space. To this goal we propose a combination of resampling taking account of simulated state space clustering and nudging. By keeping cluster representatives during resampling and filtering, the method maintains the potential for non linear system state development. We assume that a particle cluster with initially low likelihood may evolve in a state space with higher likelihood in a subsequent filter time thus mimicking non linear system state developments (e.g. sudden convection initiation) and remedies timing errors for convection due to model errors and/or imperfect initial condition. We apply a simplified version of the resampling, the particles with highest weights in each cluster are duplicated; for the model evolution for each particle pair one particle evolves using the forward model; the second particle, however, is nudged to the radar and satellite observation during its evolution based on the forward model.

An optimization of the FPGA/NIOS adaptive FIR filter using linear prediction to reduce narrow band RFI for the next generation ground-based ultra-high energy cosmic-ray experiment

NASA Astrophysics Data System (ADS)

Szadkowski, Zbigniew; Fraenkel, E. D.; Glas, Dariusz; Legumina, Remigiusz

2013-12-01

The electromagnetic part of an extensive air shower developing in the atmosphere provides significant information complementary to that obtained by water Cherenkov detectors which are predominantly sensitive to the muonic content of an air shower at ground. The emissions can be observed in the frequency band between 10 and 100 MHz. However, this frequency range is significantly contaminated by narrow-band RFI and other human-made distortions. The Auger Engineering Radio Array currently suppresses the RFI by multiple time-to-frequency domain conversions using an FFT procedure as well as by a set of manually chosen IIR notch filters in the time-domain. An alternative approach developed in this paper is an adaptive FIR filter based on linear prediction (LP). The coefficients for the linear predictor are dynamically refreshed and calculated in the virtual NIOS processor. The radio detector is an autonomous system installed on the Argentinean pampas and supplied from a solar panel. Powerful calculation capacity inside the FPGA is a factor. Power consumption versus the degree of effectiveness of the calculation inside the FPGA is a figure of merit to be minimized. Results show that the RFI contamination can be significantly suppressed by the LP FIR filter for 64 or less stages.

[Research on engine remaining useful life prediction based on oil spectrum analysis and particle filtering].

PubMed

Sun, Lei; Jia, Yun-xian; Cai, Li-ying; Lin, Guo-yu; Zhao, Jin-song

2013-09-01

The spectrometric oil analysis(SOA) is an important technique for machine state monitoring, fault diagnosis and prognosis, and SOA based remaining useful life(RUL) prediction has an advantage of finding out the optimal maintenance strategy for machine system. Because the complexity of machine system, its health state degradation process can't be simply characterized by linear model, while particle filtering(PF) possesses obvious advantages over traditional Kalman filtering for dealing nonlinear and non-Gaussian system, the PF approach was applied to state forecasting by SOA, and the RUL prediction technique based on SOA and PF algorithm is proposed. In the prediction model, according to the estimating result of system's posterior probability, its prior probability distribution is realized, and the multi-step ahead prediction model based on PF algorithm is established. Finally, the practical SOA data of some engine was analyzed and forecasted by the above method, and the forecasting result was compared with that of traditional Kalman filtering method. The result fully shows the superiority and effectivity of the

Solar wind control of auroral zone geomagnetic activity

NASA Technical Reports Server (NTRS)

Clauer, C. R.; Mcpherron, R. L.; Searls, C.; Kivelson, M. G.

1981-01-01

Solar wind magnetosphere energy coupling functions are analyzed using linear prediction filtering with 2.5 minute data. The relationship of auroral zone geomagnetic activity to solar wind power input functions are examined, and a least squares prediction filter, or impulse response function is designed from the data. Computed impulse response functions are observed to have characteristics of a low pass filter with time delay. The AL index is found well related to solar wind energy functions, although the AU index shows a poor relationship. High frequency variations of auroral indices and substorm expansions are not predictable with solar wind information alone, suggesting influence by internal magnetospheric processes. Finally, the epsilon parameter shows a poorer relationship with auroral geomagnetic activity than a power parameter, having a VBs solar wind dependency.

Unscented Kalman Filter for Brain-Machine Interfaces

PubMed Central

Li, Zheng; O'Doherty, Joseph E.; Hanson, Timothy L.; Lebedev, Mikhail A.; Henriquez, Craig S.; Nicolelis, Miguel A. L.

2009-01-01

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation. PMID:19603074

Microwave photonic filters with negative coefficients based on phase inversion in an electro-optic modulator.

PubMed

Capmany, José; Pastor, Daniel; Martinez, Alfonso; Ortega, Beatriz; Sales, Salvador

2003-08-15

We report on a novel technical approach to the implementation of photonic rf filters that is based on the pi phase inversion that a rf modulating signal suffers in an electro-optic Mach-Zehnder modulator, which depends on whether the positive or the negative linear slope of the signal's modulation transfer function is employed. Experimental evidence is provided of the implementation of filters with negative coefficients that shows excellent agreement with results predicted by the theory.

A new smooth-k space filter approach to calculate halo abundances

NASA Astrophysics Data System (ADS)

Leo, Matteo; Baugh, Carlton M.; Li, Baojiu; Pascoli, Silvia

2018-04-01

We propose a new filter, a smooth-k space filter, to use in the Press-Schechter approach to model the dark matter halo mass function which overcomes shortcomings of other filters. We test this against the mass function measured in N-body simulations. We find that the commonly used sharp-k filter fails to reproduce the behaviour of the halo mass function at low masses measured from simulations of models with a sharp truncation in the linear power spectrum. We show that the predictions with our new filter agree with the simulation results over a wider range of halo masses for both damped and undamped power spectra than is the case with the sharp-k and real-space top-hat filters.

An accurate nonlinear stochastic model for MEMS-based inertial sensor error with wavelet networks

NASA Astrophysics Data System (ADS)

El-Diasty, Mohammed; El-Rabbany, Ahmed; Pagiatakis, Spiros

2007-12-01

The integration of Global Positioning System (GPS) with Inertial Navigation System (INS) has been widely used in many applications for positioning and orientation purposes. Traditionally, random walk (RW), Gauss-Markov (GM), and autoregressive (AR) processes have been used to develop the stochastic model in classical Kalman filters. The main disadvantage of classical Kalman filter is the potentially unstable linearization of the nonlinear dynamic system. Consequently, a nonlinear stochastic model is not optimal in derivative-based filters due to the expected linearization error. With a derivativeless-based filter such as the unscented Kalman filter or the divided difference filter, the filtering process of a complicated highly nonlinear dynamic system is possible without linearization error. This paper develops a novel nonlinear stochastic model for inertial sensor error using a wavelet network (WN). A wavelet network is a highly nonlinear model, which has recently been introduced as a powerful tool for modelling and prediction. Static and kinematic data sets are collected using a MEMS-based IMU (DQI-100) to develop the stochastic model in the static mode and then implement it in the kinematic mode. The derivativeless-based filtering method using GM, AR, and the proposed WN-based processes are used to validate the new model. It is shown that the first-order WN-based nonlinear stochastic model gives superior positioning results to the first-order GM and AR models with an overall improvement of 30% when 30 and 60 seconds GPS outages are introduced.

Time-Dependent Stochastic Inversion in Acoustic Tomography of the Atmosphere with Reciprocal Sound Transmission

DTIC Science & Technology

2008-10-17

R E 1960 A new approach to linear filtering and prediction problems Trans. ASME D 82 35–45 [23] Brown R G and Hwang Y C 1992 Introduction to Random...vector Wiener filter [21]. TDSI is also somewhat similar to the Kalman filter [22, 23] which is applied in many areas including tomography [24–27]. The...453–76 [21] Links J M, Prince J L and Gupta S N 1996 A vector Wiener filter for dual-radionuclide imaging IEEE Trans. Med. Imaging 15 700–9 [22] Kalman

Real-time speech encoding based on Code-Excited Linear Prediction (CELP)

NASA Technical Reports Server (NTRS)

Leblanc, Wilfrid P.; Mahmoud, S. A.

1988-01-01

This paper reports on the work proceeding with regard to the development of a real-time voice codec for the terrestrial and satellite mobile radio environments. The codec is based on a complexity reduced version of code-excited linear prediction (CELP). The codebook search complexity was reduced to only 0.5 million floating point operations per second (MFLOPS) while maintaining excellent speech quality. Novel methods to quantize the residual and the long and short term model filters are presented.

A recursive linear predictive vocoder

NASA Astrophysics Data System (ADS)

Janssen, W. A.

1983-12-01

A non-real time 10 pole recursive autocorrelation linear predictive coding vocoder was created for use in studying effects of recursive autocorrelation on speech. The vocoder is composed of two interchangeable pitch detectors, a speech analyzer, and speech synthesizer. The time between updating filter coefficients is allowed to vary from .125 msec to 20 msec. The best quality was found using .125 msec between each update. The greatest change in quality was noted when changing from 20 msec/update to 10 msec/update. Pitch period plots for the center clipping autocorrelation pitch detector and simplified inverse filtering technique are provided. Plots of speech into and out of the vocoder are given. Formant versus time three dimensional plots are shown. Effects of noise on pitch detection and formants are shown. Noise effects the voiced/unvoiced decision process causing voiced speech to be re-constructed as unvoiced.

Speech coding at low to medium bit rates

NASA Astrophysics Data System (ADS)

Leblanc, Wilfred Paul

1992-09-01

Improved search techniques coupled with improved codebook design methodologies are proposed to improve the performance of conventional code-excited linear predictive coders for speech. Improved methods for quantizing the short term filter are developed by employing a tree search algorithm and joint codebook design to multistage vector quantization. Joint codebook design procedures are developed to design locally optimal multistage codebooks. Weighting during centroid computation is introduced to improve the outlier performance of the multistage vector quantizer. Multistage vector quantization is shown to be both robust against input characteristics and in the presence of channel errors. Spectral distortions of about 1 dB are obtained at rates of 22-28 bits/frame. Structured codebook design procedures for excitation in code-excited linear predictive coders are compared to general codebook design procedures. Little is lost using significant structure in the excitation codebooks while greatly reducing the search complexity. Sparse multistage configurations are proposed for reducing computational complexity and memory size. Improved search procedures are applied to code-excited linear prediction which attempt joint optimization of the short term filter, the adaptive codebook, and the excitation. Improvements in signal to noise ratio of 1-2 dB are realized in practice.

New Evidence That Nonlinear Source-Filter Coupling Affects Harmonic Intensity and fo Stability During Instances of Harmonics Crossing Formants.

PubMed

Maxfield, Lynn; Palaparthi, Anil; Titze, Ingo

2017-03-01

The traditional source-filter theory of voice production describes a linear relationship between the source (glottal flow pulse) and the filter (vocal tract). Such a linear relationship does not allow for nor explain how changes in the filter may impact the stability and regularity of the source. The objective of this experiment was to examine what effect unpredictable changes to vocal tract dimensions could have on fo stability and individual harmonic intensities in situations in which low frequency harmonics cross formants in a fundamental frequency glide. To determine these effects, eight human subjects (five male, three female) were recorded producing fo glides while their vocal tracts were artificially lengthened by a section of vinyl tubing inserted into the mouth. It was hypothesized that if the source and filter operated as a purely linear system, harmonic intensities would increase and decrease at nearly the same rates as they passed through a formant bandwidth, resulting in a relatively symmetric peak on an intensity-time contour. Additionally, fo stability should not be predictably perturbed by formant/harmonic crossings in a linear system. Acoustic analysis of these recordings, however, revealed that harmonic intensity peaks were asymmetric in 76% of cases, and that 85% of fo instabilities aligned with a crossing of one of the first four harmonics with the first three formants. These results provide further evidence that nonlinear dynamics in the source-filter relationship can impact fo stability as well as harmonic intensities as harmonics cross through formant bandwidths. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

An innovations approach to decoupling of multibody dynamics and control

NASA Technical Reports Server (NTRS)

Rodriguez, G.

1989-01-01

The problem of hinged multibody dynamics is solved using an extension of the innovations approach of linear filtering and prediction theory to the problem of mechanical system modeling and control. This approach has been used quite effectively to diagonalize the equations for filtering and prediction for linear state space systems. It has similar advantages in the study of dynamics and control of multibody systems. The innovations approach advanced here consists of expressing the equations of motion in terms of two closely related processes: (1) the innovations process e, a sequence of moments, obtained from the applied moments T by means of a spatially recursive Kalman filter that goes from the tip of the manipulator to its base; (2) a residual process, a sequence of velocities, obtained from the joint-angle velocities by means of an outward smoothing operations. The innovations e and the applied moments T are related by means of the relationships e = (I - L)T and T = (I + K)e. The operation (I - L) is a causal lower triangular matrix which is generated by a spatially recursive Kalman filter and the corresponding discrete-step Riccati equation. Hence, the innovations and the applied moments can be obtained from each other by means of a causal operation which is itself casually invertible.

The Laser Synthesis of Linear Polyynes: The Particle in a Box Revisited

ERIC Educational Resources Information Center

Anderson, Bruce D.; Gordon, Christopher M.

2008-01-01

In this experiment, linear polyynes are synthesized and then the predictions of a one-dimensional, particle in a box are used to calculate the quantum mechanical box length for the polyynes. A solution of graphite in ethanol is irradiated with a Nd:YAG laser (532 nm) and the resulting solution is filtered and analyzed. Data from gas…

Linear and nonlinear trending and prediction for AVHRR time series data

NASA Technical Reports Server (NTRS)

Smid, J.; Volf, P.; Slama, M.; Palus, M.

1995-01-01

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Linear theory for filtering nonlinear multiscale systems with model error

PubMed Central

Berry, Tyrus; Harlim, John

2014-01-01

In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online, as part of a filtering procedure, simultaneously produce accurate filtering and equilibrium statistical prediction. In contrast, an offline estimation technique based on a linear regression, which fits the parameters to a training dataset without using the filter, yields filter estimates which are worse than the observations or even divergent when the slow variables are not fully observed. This finding does not imply that all offline methods are inherently inferior to the online method for nonlinear estimation problems, it only suggests that an ideal estimation technique should estimate all parameters simultaneously whether it is online or offline. PMID:25002829

Robustness of neuroprosthetic decoding algorithms.

PubMed

Serruya, Mijail; Hatsopoulos, Nicholas; Fellows, Matthew; Paninski, Liam; Donoghue, John

2003-03-01

We assessed the ability of two algorithms to predict hand kinematics from neural activity as a function of the amount of data used to determine the algorithm parameters. Using chronically implanted intracortical arrays, single- and multineuron discharge was recorded during trained step tracking and slow continuous tracking tasks in macaque monkeys. The effect of increasing the amount of data used to build a neural decoding model on the ability of that model to predict hand kinematics accurately was examined. We evaluated how well a maximum-likelihood model classified discrete reaching directions and how well a linear filter model reconstructed continuous hand positions over time within and across days. For each of these two models we asked two questions: (1) How does classification performance change as the amount of data the model is built upon increases? (2) How does varying the time interval between the data used to build the model and the data used to test the model affect reconstruction? Less than 1 min of data for the discrete task (8 to 13 neurons) and less than 3 min (8 to 18 neurons) for the continuous task were required to build optimal models. Optimal performance was defined by a cost function we derived that reflects both the ability of the model to predict kinematics accurately and the cost of taking more time to build such models. For both the maximum-likelihood classifier and the linear filter model, increasing the duration between the time of building and testing the model within a day did not cause any significant trend of degradation or improvement in performance. Linear filters built on one day and tested on neural data on a subsequent day generated error-measure distributions that were not significantly different from those generated when the linear filters were tested on neural data from the initial day (p<0.05, Kolmogorov-Smirnov test). These data show that only a small amount of data from a limited number of cortical neurons appears to be necessary to construct robust models to predict kinematic parameters for the subsequent hours. Motor-control signals derived from neurons in motor cortex can be reliably acquired for use in neural prosthetic devices. Adequate decoding models can be built rapidly from small numbers of cells and maintained with daily calibration sessions.

Analytical modeling and tolerance analysis of a linear variable filter for spectral order sorting.

PubMed

Ko, Cheng-Hao; Chang, Kuei-Ying; Huang, You-Min

2015-02-23

This paper proposes an innovative method to overcome the low production rate of current linear variable filter (LVF) fabrication. During the fabrication process, a commercial coater is combined with a local mask on a substrate. The proposed analytical thin film thickness model, which is based on the geometry of the commercial coater, is developed to more effectively calculate the profiles of LVFs. Thickness tolerance, LVF zone width, thin film layer structure, transmission spectrum and the effects of variations in critical parameters of the coater are analyzed. Profile measurements demonstrate the efficacy of local mask theory in the prediction of evaporation profiles with a high degree of accuracy.

Design Techniques for Uniform-DFT, Linear Phase Filter Banks

NASA Technical Reports Server (NTRS)

Sun, Honglin; DeLeon, Phillip

1999-01-01

Uniform-DFT filter banks are an important class of filter banks and their theory is well known. One notable characteristic is their very efficient implementation when using polyphase filters and the FFT. Separately, linear phase filter banks, i.e. filter banks in which the analysis filters have a linear phase are also an important class of filter banks and desired in many applications. Unfortunately, it has been proved that one cannot design critically-sampled, uniform-DFT, linear phase filter banks and achieve perfect reconstruction. In this paper, we present a least-squares solution to this problem and in addition prove that oversampled, uniform-DFT, linear phase filter banks (which are also useful in many applications) can be constructed for perfect reconstruction. Design examples are included illustrate the methods.

Modified linear predictive coding approach for moving target tracking by Doppler radar

NASA Astrophysics Data System (ADS)

Ding, Yipeng; Lin, Xiaoyi; Sun, Ke-Hui; Xu, Xue-Mei; Liu, Xi-Yao

2016-07-01

Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation.

PubMed

Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

2017-09-01

Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.

An algebraic method for constructing stable and consistent autoregressive filters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harlim, John, E-mail: jharlim@psu.edu; Department of Meteorology, the Pennsylvania State University, University Park, PA 16802; Hong, Hoon, E-mail: hong@ncsu.edu

2015-02-15

In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides amore » discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.« less

Lossless Compression of Data into Fixed-Length Packets

NASA Technical Reports Server (NTRS)

Kiely, Aaron B.; Klimesh, Matthew A.

2009-01-01

A computer program effects lossless compression of data samples from a one-dimensional source into fixed-length data packets. The software makes use of adaptive prediction: it exploits the data structure in such a way as to increase the efficiency of compression beyond that otherwise achievable. Adaptive linear filtering is used to predict each sample value based on past sample values. The difference between predicted and actual sample values is encoded using a Golomb code.

A statistical rain attenuation prediction model with application to the advanced communication technology satellite project. 3: A stochastic rain fade control algorithm for satellite link power via non linear Markow filtering theory

NASA Technical Reports Server (NTRS)

Manning, Robert M.

1991-01-01

The dynamic and composite nature of propagation impairments that are incurred on Earth-space communications links at frequencies in and above 30/20 GHz Ka band, i.e., rain attenuation, cloud and/or clear air scintillation, etc., combined with the need to counter such degradations after the small link margins have been exceeded, necessitate the use of dynamic statistical identification and prediction processing of the fading signal in order to optimally estimate and predict the levels of each of the deleterious attenuation components. Such requirements are being met in NASA's Advanced Communications Technology Satellite (ACTS) Project by the implementation of optimal processing schemes derived through the use of the Rain Attenuation Prediction Model and nonlinear Markov filtering theory.

Application of a baseflow filter for evaluating model structure suitability of the IHACRES CMD

NASA Astrophysics Data System (ADS)

Kim, H. S.

2015-02-01

The main objective of this study was to assess the predictive uncertainty from the rainfall-runoff model structure coupling a conceptual module (non-linear module) with a metric transfer function module (linear module). The methodology was primarily based on the comparison between the outputs of the rainfall-runoff model and those from an alternative model approach. An alternative model approach was used to minimise uncertainties arising from data and the model structure. A baseflow filter was adopted to better understand deficiencies in the forms of the rainfall-runoff model by avoiding the uncertainties related to data and the model structure. The predictive uncertainty from the model structure was investigated for representative groups of catchments having similar hydrological response characteristics in the upper Murrumbidgee Catchment. In the assessment of model structure suitability, the consistency (or variability) of catchment response over time and space in model performance and parameter values has been investigated to detect problems related to the temporal and spatial variability of the model accuracy. The predictive error caused by model uncertainty was evaluated through analysis of the variability of the model performance and parameters. A graphical comparison of model residuals, effective rainfall estimates and hydrographs was used to determine a model's ability related to systematic model deviation between simulated and observed behaviours and general behavioural differences in the timing and magnitude of peak flows. The model's predictability was very sensitive to catchment response characteristics. The linear module performs reasonably well in the wetter catchments but has considerable difficulties when applied to the drier catchments where a hydrologic response is dominated by quick flow. The non-linear module has a potential limitation in its capacity to capture non-linear processes for converting observed rainfall into effective rainfall in both the wetter and drier catchments. The comparative study based on a better quantification of the accuracy and precision of hydrological modelling predictions yields a better understanding for the potential improvement of model deficiencies.

On the Use of Linearized Euler Equations in the Prediction of Jet Noise

NASA Technical Reports Server (NTRS)

Mankbadi, Reda R.; Hixon, R.; Shih, S.-H.; Povinelli, L. A.

1995-01-01

Linearized Euler equations are used to simulate supersonic jet noise generation and propagation. Special attention is given to boundary treatment. The resulting solution is stable and nearly free from boundary reflections without the need for artificial dissipation, filtering, or a sponge layer. The computed solution is in good agreement with theory and observation and is much less CPU-intensive as compared to large-eddy simulations.

Factors associated with reduced radiation exposure, cost, and technical difficulty of inferior vena cava filter placement and retrieval.

PubMed

Neill, Matthew; Charles, Hearns W; Pflager, Daniel; Deipolyi, Amy R

2017-01-01

We sought to delineate factors of inferior vena cava filter placement associated with increased radiation and cost and difficult subsequent retrieval. In total, 299 procedures from August 2013 to December 2014, 252 in a fluoroscopy suite (FS) and 47 in the operating room (OR), were reviewed for radiation exposure, fluoroscopy time, filter type, and angulation. The number of retrieval devices and fluoroscopy time needed for retrieval were assessed. Multiple linear regression assessed the impact of filter type, procedure location, and patient and procedural variables on radiation dose, fluoroscopy time, and filter angulation. Logistic regression assessed the impact of filter angulation, type, and filtration duration on retrieval difficulty. Access site and filter type had no impact on radiation exposure. However, placement in the OR, compared to the FS, entailed more radiation (156.3 vs 71.4 mGy; P = 0.001), fluoroscopy time (6.1 vs 2.8 min; P < 0.001), and filter angulation (4.8° vs 2.6°; P < 0.001). Angulation was primarily dependent on filter type ( P = 0.02), with VenaTech and Denali filters associated with decreased angulation (2.2°, 2.4°) and Option filters associated with greater angulation (4.2°). Filter angulation, but not filter type or filtration duration, predicted cases requiring >1 retrieval device ( P < 0.001) and >30 min fluoroscopy time ( P = 0.02). Cost savings for placement in the FS vs OR were estimated at $444.50 per case. In conclusion, increased radiation and cost were associated with placement in the OR. Filter angulation independently predicted difficult filter retrieval; angulation was determined by filter type. Performing filter placement in the FS using specific filters may reduce radiation and cost while enabling future retrieval.

An iterated cubature unscented Kalman filter for large-DoF systems identification with noisy data

NASA Astrophysics Data System (ADS)

Ghorbani, Esmaeil; Cha, Young-Jin

2018-04-01

Structural and mechanical system identification under dynamic loading has been an important research topic over the last three or four decades. Many Kalman-filtering-based approaches have been developed for linear and nonlinear systems. For example, to predict nonlinear systems, an unscented Kalman filter was applied. However, from extensive literature reviews, the unscented Kalman filter still showed weak performance on systems with large degrees of freedom. In this research, a modified unscented Kalman filter is proposed by integration of a cubature Kalman filter to improve the system identification performance of systems with large degrees of freedom. The novelty of this work lies on conjugating the unscented transform with the cubature integration concept to find a more accurate output from the transformation of the state vector and its related covariance matrix. To evaluate the proposed method, three different numerical models (i.e., the single degree-of-freedom Bouc-Wen model, the linear 3-degrees-of-freedom system, and the 10-degrees-of-freedom system) are investigated. To evaluate the robustness of the proposed method, high levels of noise in the measured response data are considered. The results show that the proposed method is significantly superior to the traditional UKF for noisy measured data in systems with large degrees of freedom.

The Elementary Operations of Human Vision Are Not Reducible to Template-Matching

PubMed Central

Neri, Peter

2015-01-01

It is generally acknowledged that biological vision presents nonlinear characteristics, yet linear filtering accounts of visual processing are ubiquitous. The template-matching operation implemented by the linear-nonlinear cascade (linear filter followed by static nonlinearity) is the most widely adopted computational tool in systems neuroscience. This simple model achieves remarkable explanatory power while retaining analytical tractability, potentially extending its reach to a wide range of systems and levels in sensory processing. The extent of its applicability to human behaviour, however, remains unclear. Because sensory stimuli possess multiple attributes (e.g. position, orientation, size), the issue of applicability may be asked by considering each attribute one at a time in relation to a family of linear-nonlinear models, or by considering all attributes collectively in relation to a specified implementation of the linear-nonlinear cascade. We demonstrate that human visual processing can operate under conditions that are indistinguishable from linear-nonlinear transduction with respect to substantially different stimulus attributes of a uniquely specified target signal with associated behavioural task. However, no specific implementation of a linear-nonlinear cascade is able to account for the entire collection of results across attributes; a satisfactory account at this level requires the introduction of a small gain-control circuit, resulting in a model that no longer belongs to the linear-nonlinear family. Our results inform and constrain efforts at obtaining and interpreting comprehensive characterizations of the human sensory process by demonstrating its inescapably nonlinear nature, even under conditions that have been painstakingly fine-tuned to facilitate template-matching behaviour and to produce results that, at some level of inspection, do conform to linear filtering predictions. They also suggest that compliance with linear transduction may be the targeted outcome of carefully crafted nonlinear circuits, rather than default behaviour exhibited by basic components. PMID:26556758

Fibrous filter efficiency and pressure drop in the viscous-inertial transition flow regime.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanchez, Andres L.; Brockmann, John E.; Dellinger, Jennifer Gwynne

2011-10-01

Fibrous filter pressure drop and aerosol collection efficiency were measured at low air pressures (0.2 to 0.8 atm) and high face velocities (5 to 20 meters per second) to give fiber Reynolds numbers in the viscous-inertial transition flow regime (1 to 16). In this regime, contemporary filtration theory based on Kuwabara's viscous flow through an ensemble of fibers under-predicts single fiber impaction by several orders of magnitude. Streamline curvature increases substantially as inertial forces become dominant. Dimensionless pressure drop measurements followed the viscous-inertial theory of Robinson and Franklin rather than Darcy's linear pressure-velocity relationship (1972). Sodium chloride and iron nano-agglomeratemore » test aerosols were used to evaluate the effects of particle density and shape factor. Total filter efficiency collapsed when plotted against the particle Stokes and fiber Reynolds numbers. Efficiencies were then fitted with an impactor type equation where the cutpoint Stokes number and a steepness parameter described data well in the sharply increasing portion of the curve (20% to 80% efficiency). The cutpoint Stokes number was a linearly decreasing function of fiber Reynolds number. Single fiber efficiencies were calculated from total filter efficiencies and compared to contemporary viscous flow impaction theory (Stechkina et al. 1969), and numerical simulations from the literature. Existing theories under-predicted measured single fiber efficiencies although the assumption of uniform flow conditions for each successive layer of fibers is questionable; the common exponential relationship between single fiber efficiency and total filter efficiency may not be appropriate in this regime.« less

Application of a Reduced Order Kalman Filter to Initialize a Coupled Atmosphere-Ocean Model: Impact on the Prediction of El Nino

NASA Technical Reports Server (NTRS)

Ballabrera-Poy, J.; Busalacchi, A.; Murtugudde, R.

2000-01-01

A reduced order Kalman Filter, based on a simplification of the Singular Evolutive Extended Kalman (SEEK) filter equations, is used to assimilate observed fields of the surface wind stress, sea surface temperature and sea level into the nonlinear coupled ocean-atmosphere model of Zebiak and Cane. The SEEK filter projects the Kalman Filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high dimensional systems. The Zebiak and Cane model couples a linear reduced gravity ocean model with a single vertical mode atmospheric model of Zebiak. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the model to represent the simultaneous value of the wind stress, SST and sea level, when the fields are limited to the latitude band 10 deg S - 10 deg N In this first application of the Kalman Filter to a coupled ocean-atmosphere prediction model, the sea level fields are assimilated in terms of the Kelvin and Rossby modes of the thermocline depth anomaly. An estimation of the error of these modes is derived from the projection of an estimation of the sea level error over such modes. This method gives a value of 12 for the error of the Kelvin amplitude, and 6 m of error for the Rossby component of the thermocline depth. The ability of the method to reconstruct the state of the equatorial Pacific and predict its time evolution is demonstrated. The method is shown to be quite robust for predictions up to six months, and able to predict the onset of the 1997 warm event fifteen months before its occurrence.

Application of a Reduced Order Kalman Filter to Initialize a Coupled Atmosphere-Ocean Model: Impact on the Prediction of El Nino

NASA Technical Reports Server (NTRS)

Ballabrera-Poy, Joaquim; Busalacchi, Antonio J.; Murtugudde, Ragu

2000-01-01

A reduced order Kalman Filter, based on a simplification of the Singular Evolutive Extended Kalman (SEEK) filter equations, is used to assimilate observed fields of the surface wind stress, sea surface temperature and sea level into the nonlinear coupled ocean-atmosphere model. The SEEK filter projects the Kalman Filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high dimensional systems. The Zebiak and Cane model couples a linear reduced gravity ocean model with a single vertical mode atmospheric model of Zebiak. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the model to represent the simultaneous value of the wind stress, SST and sea level, when the fields are limited to the latitude band 10 deg S - 10 deg N. In this first application of the Kalman Filter to a coupled ocean-atmosphere prediction model, the sea level fields are assimilated in terms of the Kelvin and Rossby modes of the thermocline depth anomaly. An estimation of the error of these modes is derived from the projection of an estimation of the sea level error over such modes. This method gives a value of 12 for the error of the Kelvin amplitude, and 6 m of error for the Rossby component of the thermocline depth. The ability of the method to reconstruct the state of the equatorial Pacific and predict its time evolution is demonstrated. The method is shown to be quite robust for predictions I up to six months, and able to predict the onset of the 1997 warm event fifteen months before its occurrence.

Further studies using matched filter theory and stochastic simulation for gust loads prediction

NASA Technical Reports Server (NTRS)

Scott, Robert C.; Pototzky, Anthony S.; Perry, Boyd Iii

1993-01-01

This paper describes two analysis methods -- one deterministic, the other stochastic -- for computing maximized and time-correlated gust loads for aircraft with nonlinear control systems. The first method is based on matched filter theory; the second is based on stochastic simulation. The paper summarizes the methods, discusses the selection of gust intensity for each method and presents numerical results. A strong similarity between the results from the two methods is seen to exist for both linear and nonlinear configurations.

Reaction times to weak test lights. [psychophysics biological model

NASA Technical Reports Server (NTRS)

Wandell, B. A.; Ahumada, P.; Welsh, D.

1984-01-01

Maloney and Wandell (1984) describe a model of the response of a single visual channel to weak test lights. The initial channel response is a linearly filtered version of the stimulus. The filter output is randomly sampled over time. Each time a sample occurs there is some probability increasing with the magnitude of the sampled response - that a discrete detection event is generated. Maloney and Wandell derive the statistics of the detection events. In this paper a test is conducted of the hypothesis that the reaction time responses to the presence of a weak test light are initiated at the first detection event. This makes it possible to extend the application of the model to lights that are slightly above threshold, but still within the linear operating range of the visual system. A parameter-free prediction of the model proposed by Maloney and Wandell for lights detected by this statistic is tested. The data are in agreement with the prediction.

Methods of sequential estimation for determining initial data in numerical weather prediction. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Cohn, S. E.

1982-01-01

Numerical weather prediction (NWP) is an initial-value problem for a system of nonlinear differential equations, in which initial values are known incompletely and inaccurately. Observational data available at the initial time must therefore be supplemented by data available prior to the initial time, a problem known as meteorological data assimilation. A further complication in NWP is that solutions of the governing equations evolve on two different time scales, a fast one and a slow one, whereas fast scale motions in the atmosphere are not reliably observed. This leads to the so called initialization problem: initial values must be constrained to result in a slowly evolving forecast. The theory of estimation of stochastic dynamic systems provides a natural approach to such problems. For linear stochastic dynamic models, the Kalman-Bucy (KB) sequential filter is the optimal data assimilation method, for linear models, the optimal combined data assimilation-initialization method is a modified version of the KB filter.

Chameleon's behavior of modulable nonlinear electrical transmission line

NASA Astrophysics Data System (ADS)

Togueu Motcheyo, A. B.; Tchinang Tchameu, J. D.; Fewo, S. I.; Tchawoua, C.; Kofane, T. C.

2017-12-01

We show that modulable discrete nonlinear transmission line can adopt Chameleon's behavior due to the fact that, without changing its appearance structure, it can become alternatively purely right or left handed line which is different to the composite one. Using a quasidiscrete approximation, we derive a nonlinear Schrödinger equation, that predicts accurately the carrier frequency threshold from the linear analysis. It appears that the increasing of the linear capacitor in parallel in the series branch induced the selectivity of the filter in the right-handed region while it increases band pass filter in the left-handed region. Numerical simulations of the nonlinear model confirm the forward wave in the right handed line and the backward wave in the left handed one.

Reducing Conservatism of Analytic Transient Response Bounds via Shaping Filters

NASA Technical Reports Server (NTRS)

Kwan, Aiyueh; Bedrossian, Nazareth; Jan, Jiann-Woei; Grigoriadis, Karolos; Hua, Tuyen (Technical Monitor)

1999-01-01

Recent results show that the peak transient response of a linear system to bounded energy inputs can be computed using the energy-to-peak gain of the system. However, analytically computed peak response bound can be conservative for a class of class bounded energy signals, specifically pulse trains generated from jet firings encountered in space vehicles. In this paper, shaping filters are proposed as a Methodology to reduce the conservatism of peak response analytic bounds. This Methodology was applied to a realistic Space Station assembly operation subject to jet firings. The results indicate that shaping filters indeed reduce the predicted peak response bounds.

Linear phase compressive filter

DOEpatents

McEwan, Thomas E.

1995-01-01

A phase linear filter for soliton suppression is in the form of a laddered series of stages of non-commensurate low pass filters with each low pass filter having a series coupled inductance (L) and a reverse biased, voltage dependent varactor diode, to ground which acts as a variable capacitance (C). L and C values are set to levels which correspond to a linear or conventional phase linear filter. Inductance is mapped directly from that of an equivalent nonlinear transmission line and capacitance is mapped from the linear case using a large signal equivalent of a nonlinear transmission line.

The development of a Kalman filter clock predictor

NASA Technical Reports Server (NTRS)

Davis, John A.; Greenhall, Charles A.; Boudjemaa, Redoane

2005-01-01

A Kalman filter based clock predictor is developed, and its performance evaluated using both simulated and real data. The clock predictor is shown to possess a neat to optimal Prediction Error Variance (PEV) when the underlying noise consists of one of the power law noise processes commonly encountered in time and frequency measurements. The predictor's performance is the presence of multiple noise processes is also examined. The relationship between the PEV obtained in the presence of multiple noise processes and those obtained for the individual component noise processes is examined. Comparisons are made with a simple linear clock predictor. The clock predictor is used to predict future values of the time offset between pairs of NPL's active hydrogen masers.

Dynamic linear models using the Kalman filter for early detection and early warning of malaria outbreaks

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Liu, Y.; DeVos, M.; Wimberly, M. C.

2015-12-01

Malaria early detection and early warning systems are important tools for public health decision makers in regions where malaria transmission is seasonal and varies from year to year with fluctuations in rainfall and temperature. Here we present a new data-driven dynamic linear model based on the Kalman filter with time-varying coefficients that are used to identify malaria outbreaks as they occur (early detection) and predict the location and timing of future outbreaks (early warning). We fit linear models of malaria incidence with trend and Fourier form seasonal components using three years of weekly malaria case data from 30 districts in the Amhara Region of Ethiopia. We identified past outbreaks by comparing the modeled prediction envelopes with observed case data. Preliminary results demonstrated the potential for improved accuracy and timeliness over commonly-used methods in which thresholds are based on simpler summary statistics of historical data. Other benefits of the dynamic linear modeling approach include robustness to missing data and the ability to fit models with relatively few years of training data. To predict future outbreaks, we started with the early detection model for each district and added a regression component based on satellite-derived environmental predictor variables including precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and land surface temperature (LST) and spectral indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). We included lagged environmental predictors in the regression component of the model, with lags chosen based on cross-correlation of the one-step-ahead forecast errors from the first model. Our results suggest that predictions of future malaria outbreaks can be improved by incorporating lagged environmental predictors.

Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

DOE Office of Scientific and Technical Information (OSTI.GOV)

Melin, Alexander M.; Olama, Mohammed M.; Dong, Jin

The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed tomore » estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our lab.« less

Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter

NASA Astrophysics Data System (ADS)

Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai; Sun, Han; Yu, Xiaowei

2017-10-01

To overcome the range anxiety, one of the important strategies is to accurately predict the range or dischargeable time of the battery system. To accurately predict the remaining dischargeable time (RDT) of a battery, a RDT prediction framework based on accurate battery modeling and state estimation is presented in this paper. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery. Then, an online recursive least-square-algorithm method and unscented-Kalman-filter are employed to estimate the system matrices and SOC at every prediction point. Besides, a discrete wavelet transform technique is employed to capture the statistical information of past dynamics of input currents, which are utilized to predict the future battery currents. Finally, the RDT can be predicted based on the battery model, SOC estimation results and predicted future battery currents. The performance of the proposed methodology has been verified by a lithium-ion battery cell. Experimental results indicate that the proposed method can provide an accurate SOC and parameter estimation and the predicted RDT can solve the range anxiety issues.

Linear phase compressive filter

DOEpatents

McEwan, T.E.

1995-06-06

A phase linear filter for soliton suppression is in the form of a laddered series of stages of non-commensurate low pass filters with each low pass filter having a series coupled inductance (L) and a reverse biased, voltage dependent varactor diode, to ground which acts as a variable capacitance (C). L and C values are set to levels which correspond to a linear or conventional phase linear filter. Inductance is mapped directly from that of an equivalent nonlinear transmission line and capacitance is mapped from the linear case using a large signal equivalent of a nonlinear transmission line. 2 figs.

Comparison of field portable measurements of ultrafine TiO2: X-ray fluorescence, laser-induced breakdown spectroscopy, and Fourier-transform infrared spectroscopy.

PubMed

LeBouf, Ryan F; Miller, Arthur L; Stipe, Christopher; Brown, Jonathan; Murphy, Nate; Stefaniak, Aleksandr B

2013-06-01

Laboratory measurements of ultrafine titanium dioxide (TiO2) particulate matter loaded on filters were made using three field portable methods (X-ray fluorescence (XRF), laser-induced breakdown spectroscopy (LIBS), and Fourier-transform infrared (FTIR) spectroscopy) to assess their potential for determining end-of-shift exposure. Ultrafine TiO2 particles were aerosolized and collected onto 37 mm polycarbonate track-etched (PCTE) filters in the range of 3 to 578 μg titanium (Ti). Limit of detection (LOD), limit of quantification (LOQ), and calibration fit were determined for each measurement method. The LOD's were 11.8, 0.032, and 108 μg Ti per filter, for XRF, LIBS, and FTIR, respectively and the LOQ's were 39.2, 0.11, and 361 μg Ti per filter, respectively. The XRF calibration curve was linear over the widest dynamic range, up to the maximum loading tested (578 μg Ti per filter). LIBS was more sensitive but, due to the sample preparation method, the highest loaded filter measurable was 252 μg Ti per filter. XRF and LIBS had good predictability measured by regressing the predicted mass to the gravimetric mass on the filter. XRF and LIBS produced overestimations of 4% and 2%, respectively, with coefficients of determination (R(2)) of 0.995 and 0.998. FTIR measurements were less dependable due to interference from the PCTE filter media and overestimated mass by 2% with an R(2) of 0.831.

Regression of non-linear coupling of noise in LIGO detectors

NASA Astrophysics Data System (ADS)

Da Silva Costa, C. F.; Billman, C.; Effler, A.; Klimenko, S.; Cheng, H.-P.

2018-03-01

In 2015, after their upgrade, the advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) detectors started acquiring data. The effort to improve their sensitivity has never stopped since then. The goal to achieve design sensitivity is challenging. Environmental and instrumental noise couple to the detector output with different, linear and non-linear, coupling mechanisms. The noise regression method we use is based on the Wiener–Kolmogorov filter, which uses witness channels to make noise predictions. We present here how this method helped to determine complex non-linear noise couplings in the output mode cleaner and in the mirror suspension system of the LIGO detector.

Linear Phase Sharp Transition BPF to Detect Noninvasive Maternal and Fetal Heart Rate.

PubMed

Marchon, Niyan; Naik, Gourish; Pai, K R

2018-01-01

Fetal heart rate (FHR) detection can be monitored using either direct fetal scalp electrode recording (invasive) or by indirect noninvasive technique. Weeks before delivery, the invasive method poses a risk factor to the fetus, while the latter provides accurate fetal ECG (FECG) information which can help diagnose fetal's well-being. Our technique employs variable order linear phase sharp transition (LPST) FIR band-pass filter which shows improved stopband attenuation at higher filter orders. The fetal frequency fiduciary edges form the band edges of the filter characterized by varying amounts of overlap of maternal ECG (MECG) spectrum. The one with the minimum maternal spectrum overlap was found to be optimum with no power line interference and maximum fetal heart beats being detected. The improved filtering is reflected in the enhancement of the performance of the fetal QRS detector (FQRS). The improvement has also occurred in fetal heart rate obtained using our algorithm which is in close agreement with the true reference (i.e., invasive fetal scalp ECG). The performance parameters of the FQRS detector such as sensitivity (Se), positive predictive value (PPV), and accuracy (F 1 ) were found to improve even for lower filter order. The same technique was extended to evaluate maternal QRS detector (MQRS) and found to yield satisfactory maternal heart rate (MHR) results.

Fire spread estimation on forest wildfire using ensemble kalman filter

NASA Astrophysics Data System (ADS)

Syarifah, Wardatus; Apriliani, Erna

2018-04-01

Wildfire is one of the most frequent disasters in the world, for example forest wildfire, causing population of forest decrease. Forest wildfire, whether naturally occurring or prescribed, are potential risks for ecosystems and human settlements. These risks can be managed by monitoring the weather, prescribing fires to limit available fuel, and creating firebreaks. With computer simulations we can predict and explore how fires may spread. The model of fire spread on forest wildfire was established to determine the fire properties. The fire spread model is prepared based on the equation of the diffusion reaction model. There are many methods to estimate the spread of fire. The Kalman Filter Ensemble Method is a modified estimation method of the Kalman Filter algorithm that can be used to estimate linear and non-linear system models. In this research will apply Ensemble Kalman Filter (EnKF) method to estimate the spread of fire on forest wildfire. Before applying the EnKF method, the fire spread model will be discreted using finite difference method. At the end, the analysis obtained illustrated by numerical simulation using software. The simulation results show that the Ensemble Kalman Filter method is closer to the system model when the ensemble value is greater, while the covariance value of the system model and the smaller the measurement.

Multi-exponential analysis of magnitude MR images using a quantitative multispectral edge-preserving filter.

PubMed

Bonny, Jean Marie; Boespflug-Tanguly, Odile; Zanca, Michel; Renou, Jean Pierre

2003-03-01

A solution for discrete multi-exponential analysis of T(2) relaxation decay curves obtained in current multi-echo imaging protocol conditions is described. We propose a preprocessing step to improve the signal-to-noise ratio and thus lower the signal-to-noise ratio threshold from which a high percentage of true multi-exponential detection is detected. It consists of a multispectral nonlinear edge-preserving filter that takes into account the signal-dependent Rician distribution of noise affecting magnitude MR images. Discrete multi-exponential decomposition, which requires no a priori knowledge, is performed by a non-linear least-squares procedure initialized with estimates obtained from a total least-squares linear prediction algorithm. This approach was validated and optimized experimentally on simulated data sets of normal human brains.

Predictive Ensemble Decoding of Acoustical Features Explains Context-Dependent Receptive Fields.

PubMed

Yildiz, Izzet B; Mesgarani, Nima; Deneve, Sophie

2016-12-07

A primary goal of auditory neuroscience is to identify the sound features extracted and represented by auditory neurons. Linear encoding models, which describe neural responses as a function of the stimulus, have been primarily used for this purpose. Here, we provide theoretical arguments and experimental evidence in support of an alternative approach, based on decoding the stimulus from the neural response. We used a Bayesian normative approach to predict the responses of neurons detecting relevant auditory features, despite ambiguities and noise. We compared the model predictions to recordings from the primary auditory cortex of ferrets and found that: (1) the decoding filters of auditory neurons resemble the filters learned from the statistics of speech sounds; (2) the decoding model captures the dynamics of responses better than a linear encoding model of similar complexity; and (3) the decoding model accounts for the accuracy with which the stimulus is represented in neural activity, whereas linear encoding model performs very poorly. Most importantly, our model predicts that neuronal responses are fundamentally shaped by "explaining away," a divisive competition between alternative interpretations of the auditory scene. Neural responses in the auditory cortex are dynamic, nonlinear, and hard to predict. Traditionally, encoding models have been used to describe neural responses as a function of the stimulus. However, in addition to external stimulation, neural activity is strongly modulated by the responses of other neurons in the network. We hypothesized that auditory neurons aim to collectively decode their stimulus. In particular, a stimulus feature that is decoded (or explained away) by one neuron is not explained by another. We demonstrated that this novel Bayesian decoding model is better at capturing the dynamic responses of cortical neurons in ferrets. Whereas the linear encoding model poorly reflects selectivity of neurons, the decoding model can account for the strong nonlinearities observed in neural data. Copyright © 2016 Yildiz et al.

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

PubMed

Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

2018-06-12

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

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

NASA Astrophysics Data System (ADS)

Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong

2018-06-01

The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.

From neurons to circuits: linear estimation of local field potentials.

PubMed

Rasch, Malte; Logothetis, Nikos K; Kreiman, Gabriel

2009-11-04

Extracellular physiological recordings are typically separated into two frequency bands: local field potentials (LFPs) (a circuit property) and spiking multiunit activity (MUA). Recently, there has been increased interest in LFPs because of their correlation with functional magnetic resonance imaging blood oxygenation level-dependent measurements and the possibility of studying local processing and neuronal synchrony. To further understand the biophysical origin of LFPs, we asked whether it is possible to estimate their time course based on the spiking activity from the same electrode or nearby electrodes. We used "signal estimation theory" to show that a linear filter operation on the activity of one or a few neurons can explain a significant fraction of the LFP time course in the macaque monkey primary visual cortex. The linear filter used to estimate the LFPs had a stereotypical shape characterized by a sharp downstroke at negative time lags and a slower positive upstroke for positive time lags. The filter was similar across different neocortical regions and behavioral conditions, including spontaneous activity and visual stimulation. The estimations had a spatial resolution of approximately 1 mm and a temporal resolution of approximately 200 ms. By considering a causal filter, we observed a temporal asymmetry such that the positive time lags in the filter contributed more to the LFP estimation than the negative time lags. Additionally, we showed that spikes occurring within approximately 10 ms of spikes from nearby neurons yielded better estimation accuracies than nonsynchronous spikes. In summary, our results suggest that at least some circuit-level local properties of the field potentials can be predicted from the activity of one or a few neurons.

From neurons to circuits: linear estimation of local field potentials

PubMed Central

Rasch, Malte; Logthetis, Nikos K.; Kreiman, Gabriel

2010-01-01

Extracellular physiological recordings are typically separated into two frequency bands: local field potentials (LFPs, a circuit property) and spiking multi-unit activity (MUA). There has been increased interest in LFPs due to their correlation with fMRI measurements and the possibility of studying local processing and neuronal synchrony. To further understand the biophysical origin of LFPs, we asked whether it is possible to estimate their time course based on the spiking activity from the same or nearby electrodes. We used Signal Estimation Theory to show that a linear filter operation on the activity of one/few neurons can explain a significant fraction of the LFP time course in the macaque primary visual cortex. The linear filter used to estimate the LFPs had a stereotypical shape characterized by a sharp downstroke at negative time lags and a slower positive upstroke for positve time lags. The filter was similar across neocortical regions and behavioral conditions including spontaneous activity and visual stimulation. The estimations had a spatial resolution of ~1 mm and a temporal resolution of ~200 ms. By considering a causal filter, we observed a temporal asymmetry such that the positive time lags in the filter contributed more to the LFP estimation than negative time lags. Additionally, we showed that spikes occurring within ~10 ms of spikes from nearby neurons yielded better estimation accuracies than nonsynchronous spikes. In sum, our results suggest that at least some circuit-level local properties of the field potentials can be predicted from the activity of one or a few neurons. PMID:19889990

Computational principles underlying recognition of acoustic signals in grasshoppers and crickets.

PubMed

Ronacher, Bernhard; Hennig, R Matthias; Clemens, Jan

2015-01-01

Grasshoppers and crickets independently evolved hearing organs and acoustic communication. They differ considerably in the organization of their auditory pathways, and the complexity of their songs, which are essential for mate attraction. Recent approaches aimed at describing the behavioral preference functions of females in both taxa by a simple modeling framework. The basic structure of the model consists of three processing steps: (1) feature extraction with a bank of 'LN models'-each containing a linear filter followed by a nonlinearity, (2) temporal integration, and (3) linear combination. The specific properties of the filters and nonlinearities were determined using a genetic learning algorithm trained on a large set of different song features and the corresponding behavioral response scores. The model showed an excellent prediction of the behavioral responses to the tested songs. Most remarkably, in both taxa the genetic algorithm found Gabor-like functions as the optimal filter shapes. By slight modifications of Gabor filters several types of preference functions could be modeled, which are observed in different cricket species. Furthermore, this model was able to explain several so far enigmatic results in grasshoppers. The computational approach offered a remarkably simple framework that can account for phenotypically rather different preference functions across several taxa.

Non-linear identification of a squeeze-film damper

NASA Technical Reports Server (NTRS)

Stanway, Roger; Mottershead, John; Firoozian, Riaz

1987-01-01

Described is an experimental study to identify the damping laws associated with a squeeze-film vibration damper. This is achieved by using a non-linear filtering algorithm to process displacement responses of the damper ring to synchronous excitation and thus to estimate the parameters in an nth-power velocity model. The experimental facility is described in detail and a representative selection of results is included. The identified models are validated through the prediction of damper-ring orbits and comparison with observed responses.

Non-linear Post Processing Image Enhancement

NASA Technical Reports Server (NTRS)

Hunt, Shawn; Lopez, Alex; Torres, Angel

1997-01-01

A non-linear filter for image post processing based on the feedforward Neural Network topology is presented. This study was undertaken to investigate the usefulness of "smart" filters in image post processing. The filter has shown to be useful in recovering high frequencies, such as those lost during the JPEG compression-decompression process. The filtered images have a higher signal to noise ratio, and a higher perceived image quality. Simulation studies comparing the proposed filter with the optimum mean square non-linear filter, showing examples of the high frequency recovery, and the statistical properties of the filter are given,

Novel programmable microwave photonic filter with arbitrary filtering shape and linear phase.

PubMed

Zhu, Xiaoqi; Chen, Feiya; Peng, Huanfa; Chen, Zhangyuan

2017-04-17

We propose and demonstrate a novel optical frequency comb (OFC) based microwave photonic filter which is able to realize arbitrary filtering shape with linear phase response. The shape of filter response is software programmable using finite impulse response (FIR) filter design method. By shaping the OFC spectrum using a programmable waveshaper, we can realize designed amplitude of FIR taps. Positive and negative sign of FIR taps are achieved by balanced photo-detection. The double sideband (DSB) modulation and symmetric distribution of filter taps are used to maintain the linear phase condition. In the experiment, we realize a fully programmable filter in the range from DC to 13.88 GHz. Four basic types of filters (lowpass, highpass, bandpass and bandstop) with different bandwidths, cut-off frequencies and central frequencies are generated. Also a triple-passband filter is realized in our experiment. To the best of our knowledge, it is the first demonstration of a programmable multiple passband MPF with linear phase response. The experiment shows good agreement with the theoretical result.

Relationships between digital signal processing and control and estimation theory

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1978-01-01

Research areas associated with digital signal processing and control and estimation theory are identified. Particular attention is given to image processing, system identification problems (parameter identification, linear prediction, least squares, Kalman filtering), stability analyses (the use of the Liapunov theory, frequency domain criteria, passivity), and multiparameter systems, distributed processes, and random fields.

Predicting perceptual quality of images in realistic scenario using deep filter banks

NASA Astrophysics Data System (ADS)

Zhang, Weixia; Yan, Jia; Hu, Shiyong; Ma, Yang; Deng, Dexiang

2018-03-01

Classical image perceptual quality assessment models usually resort to natural scene statistic methods, which are based on an assumption that certain reliable statistical regularities hold on undistorted images and will be corrupted by introduced distortions. However, these models usually fail to accurately predict degradation severity of images in realistic scenarios since complex, multiple, and interactive authentic distortions usually appear on them. We propose a quality prediction model based on convolutional neural network. Quality-aware features extracted from filter banks of multiple convolutional layers are aggregated into the image representation. Furthermore, an easy-to-implement and effective feature selection strategy is used to further refine the image representation and finally a linear support vector regression model is trained to map image representation into images' subjective perceptual quality scores. The experimental results on benchmark databases present the effectiveness and generalizability of the proposed model.

Comparisons of linear and nonlinear pyramid schemes for signal and image processing

NASA Astrophysics Data System (ADS)

Morales, Aldo W.; Ko, Sung-Jea

1997-04-01

Linear filters banks are being used extensively in image and video applications. New research results in wavelet applications for compression and de-noising are constantly appearing in the technical literature. On the other hand, non-linear filter banks are also being used regularly in image pyramid algorithms. There are some inherent advantages in using non-linear filters instead of linear filters when non-Gaussian processes are present in images. However, a consistent way of comparing performance criteria between these two schemes has not been fully developed yet. In this paper a recently discovered tool, sample selection probabilities, is used to compare the behavior of linear and non-linear filters. In the conversion from weights of order statistics (OS) filters to coefficients of the impulse response is obtained through these probabilities. However, the reverse problem: the conversion from coefficients of the impulse response to the weights of OS filters is not yet fully understood. One of the reasons for this difficulty is the highly non-linear nature of the partitions and generating function used. In the present paper the problem is posed as an optimization of integer linear programming subject to constraints directly obtained from the coefficients of the impulse response. Although the technique to be presented in not completely refined, it certainly appears to be promising. Some results will be shown.

Effects of non-tidal atmospheric loading on a Kalman filter-based terrestrial reference frame

NASA Astrophysics Data System (ADS)

Abbondanza, C.; Altamimi, Z.; Chin, T. M.; Collilieux, X.; Dach, R.; Heflin, M. B.; Gross, R. S.; König, R.; Lemoine, F. G.; MacMillan, D. S.; Parker, J. W.; van Dam, T. M.; Wu, X.

2013-12-01

The International Terrestrial Reference Frame (ITRF) adopts a piece-wise linear model to parameterize regularized station positions and velocities. The space-geodetic (SG) solutions from VLBI, SLR, GPS and DORIS global networks used as input in the ITRF combination process account for tidal loading deformations, but ignore the non-tidal part. As a result, the non-linear signal observed in the time series of SG-derived station positions in part reflects non-tidal loading displacements not introduced in the SG data reduction. In this analysis, the effect of non-tidal atmospheric loading (NTAL) corrections on the TRF is assessed adopting a Remove/Restore approach: (i) Focusing on the a-posteriori approach, the NTAL model derived from the National Center for Environmental Prediction (NCEP) surface pressure is removed from the SINEX files of the SG solutions used as inputs to the TRF determinations. (ii) Adopting a Kalman-filter based approach, a linear TRF is estimated combining the 4 SG solutions free from NTAL displacements. (iii) Linear fits to the NTAL displacements removed at step (i) are restored to the linear reference frame estimated at (ii). The velocity fields of the (standard) linear reference frame in which the NTAL model has not been removed and the one in which the model has been removed/restored are compared and discussed.

Multi-frame linear regressive filter for the measurement of infrared pixel spatial response and MTF from sparse data.

PubMed

Huard, Edouard; Derelle, Sophie; Jaeck, Julien; Nghiem, Jean; Haïdar, Riad; Primot, Jérôme

2018-03-05

A challenging point in the prediction of the image quality of infrared imaging systems is the evaluation of the detector modulation transfer function (MTF). In this paper, we present a linear method to get a 2D continuous MTF from sparse spectral data. Within the method, an object with a predictable sparse spatial spectrum is imaged by the focal plane array. The sparse data is then treated to return the 2D continuous MTF with the hypothesis that all the pixels have an identical spatial response. The linearity of the treatment is a key point to estimate directly the error bars of the resulting detector MTF. The test bench will be presented along with measurement tests on a 25 μm pitch InGaAs detector.

Quadratic correlation filters for optical correlators

NASA Astrophysics Data System (ADS)

Mahalanobis, Abhijit; Muise, Robert R.; Vijaya Kumar, Bhagavatula V. K.

2003-08-01

Linear correlation filters have been implemented in optical correlators and successfully used for a variety of applications. The output of an optical correlator is usually sensed using a square law device (such as a CCD array) which forces the output to be the squared magnitude of the desired correlation. It is however not a traditional practice to factor the effect of the square-law detector in the design of the linear correlation filters. In fact, the input-output relationship of an optical correlator is more accurately modeled as a quadratic operation than a linear operation. Quadratic correlation filters (QCFs) operate directly on the image data without the need for feature extraction or segmentation. In this sense, the QCFs retain the main advantages of conventional linear correlation filters while offering significant improvements in other respects. Not only is more processing required to detect peaks in the outputs of multiple linear filters, but choosing a winner among them is an error prone task. In contrast, all channels in a QCF work together to optimize the same performance metric and produce a combined output that leads to considerable simplification of the post-processing. In this paper, we propose a novel approach to the design of quadratic correlation based on the Fukunaga Koontz transform. Although quadratic filters are known to be optimum when the data is Gaussian, it is expected that they will perform as well as or better than linear filters in general. Preliminary performance results are provided that show that quadratic correlation filters perform better than their linear counterparts.

Broadband microwave photonic fully tunable filter using a single heterogeneously integrated III-V/SOI-microdisk-based phase shifter.

PubMed

Lloret, Juan; Morthier, Geert; Ramos, Francisco; Sales, Salvador; Van Thourhout, Dries; Spuesens, Thijs; Olivier, Nicolas; Fédéli, Jean-Marc; Capmany, José

2012-05-07

A broadband microwave photonic phase shifter based on a single III-V microdisk resonator heterogeneously integrated on and coupled to a nanophotonic silicon-on-insulator waveguide is reported. The phase shift tunability is accomplished by modifying the effective index through carrier injection. A comprehensive semi-analytical model aiming at predicting its behavior is formulated and confirmed by measurements. Quasi-linear and continuously tunable 2π phase shifts at radiofrequencies greater than 18 GHz are experimentally demonstrated. The phase shifter performance is also evaluated when used as a key element in tunable filtering schemes. Distortion-free and wideband filtering responses with a tuning range of ~100% over the free spectral range are obtained.

Cortical Decoding of Individual Finger and Wrist Kinematics for an Upper-Limb Neuroprosthesis

PubMed Central

Aggarwal, Vikram; Tenore, Francesco; Acharya, Soumyadipta; Schieber, Marc H.; Thakor, Nitish V.

2010-01-01

Previous research has shown that neuronal activity can be used to continuously decode the kinematics of gross movements involving arm and hand trajectory. However, decoding the kinematics of fine motor movements, such as the manipulation of individual fingers, has not been demonstrated. In this study, single unit activities were recorded from task-related neurons in M1 of two trained rhesus monkey as they performed individuated movements of the fingers and wrist. The primates’ hand was placed in a manipulandum, and strain gauges at the tips of each finger were used to track the digit’s position. Both linear and non-linear filters were designed to simultaneously predict kinematics of each digit and the wrist, and their performance compared using mean squared error and correlation coefficients. All models had high decoding accuracy, but the feedforward ANN (R=0.76–0.86, MSE=0.04–0.05) and Kalman filter (R=0.68–0.86, MSE=0.04–0.07) performed better than a simple linear regression filter (0.58–0.81, 0.05–0.07). These results suggest that individual finger and wrist kinematics can be decoded with high accuracy, and be used to control a multi-fingered prosthetic hand in real-time. PMID:19964645

Comparison between the TRS-398 code of practice and the TG-51 dosimetry protocol for flattening filter free beams

NASA Astrophysics Data System (ADS)

Lye, J. E.; Butler, D. J.; Oliver, C. P.; Alves, A.; Lehmann, J.; Gibbons, F. P.; Williams, I. M.

2016-07-01

Dosimetry protocols for external beam radiotherapy currently in use, such as the IAEA TRS-398 and AAPM TG-51, were written for conventional linear accelerators. In these accelerators, a flattening filter is used to produce a beam which is uniform at water depths where the ionization chamber is used to measure the absorbed dose. Recently, clinical linacs have been implemented without the flattening filter, and published theoretical analysis suggested that with these beams a dosimetric error of order 0.6% could be expected for IAEA TRS-398, because the TPR20,10 beam quality index does not accurately predict the stopping power ratio (water to air) for the softer flattening-filter-free (FFF) beam spectra. We measured doses on eleven FFF linacs at 6 MV and 10 MV using both dosimetry protocols and found average differences of 0.2% or less. The expected shift due to stopping powers was not observed. We present Monte Carlo k Q calculations which show a much smaller difference between FFF and flattened beams than originally predicted. These results are explained by the inclusion of the added backscatter plates and build-up filters used in modern clinical FFF linacs, compared to a Monte Carlo model of an FFF linac in which the flattening filter is removed and no additional build-up or backscatter plate is added.

A myocontrolled neuroprosthesis integrated with a passive exoskeleton to support upper limb activities.

PubMed

Ambrosini, Emilia; Ferrante, Simona; Schauer, Thomas; Klauer, Christian; Gaffuri, Marina; Ferrigno, Giancarlo; Pedrocchi, Alessandra

2014-04-01

This work aimed at designing a myocontrolled arm neuroprosthesis for both assistive and rehabilitative purposes. The performance of an adaptive linear prediction filter and a high-pass filter to estimate the volitional EMG was evaluated on healthy subjects (N=10) and neurological patients (N=8) during dynamic hybrid biceps contractions. A significant effect of filter (p=0.017 for healthy; p<0.001 for patients) was obtained. The post hoc analysis revealed that for both groups only the adaptive filter was able to reliably detect the presence of a small volitional contribution. An on/off non-linear controller integrated with an exoskeleton for weight support was developed. The controller allowed the patient to activate/deactivate the stimulation intensity based on the residual EMG estimated by the adaptive filter. Two healthy subjects and 3 people with Spinal Cord Injury were asked to flex the elbow while tracking a trapezoidal target with and without myocontrolled-NMES support. Both healthy subjects and patients easily understood how to use the controller in a single session. Two patients reduced their tracking error by more than 60% with NMES support, while the last patient obtained a tracking error always comparable to the healthy subjects performance (<4°). This study proposes a reliable and feasible solution to combine NMES with voluntary effort. Copyright © 2014 Elsevier Ltd. All rights reserved.

Molecular factor computing for predictive spectroscopy.

PubMed

Dai, Bin; Urbas, Aaron; Douglas, Craig C; Lodder, Robert A

2007-08-01

The concept of molecular factor computing (MFC)-based predictive spectroscopy was demonstrated here with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument. Molecular computing of vectors for transformation matrices enabled spectra to be represented in a desired coordinate system. New coordinate systems were selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a new MFC spectrometer employing transmission MFC filters. A library search algorithm was developed to calculate the chemical constituents of the MFC filters. The prototype instrument was used to collect data from 39 ethanol-in-water mixtures (range 0-14%). For each sample, four different voltage outputs from the detector (forming two factor scores) were measured by using four different MFC filters. Twenty samples were used to calibrate the instrument and build a multivariate linear regression prediction model, and the remaining samples were used to validate the predictive ability of the model. In engineering simulations, four MFC filters gave an adequate calibration model (r2 = 0.995, RMSEC = 0.229%, RMSECV = 0.339%, p = 0.05 by f test). This result is slightly better than a corresponding PCR calibration model based on corrected transmission spectra (r2 = 0.993, RMSEC = 0.359%, RMSECV = 0.551%, p = 0.05 by f test). The first actual MFC prototype gave an RMSECV = 0.735%. MFC was a viable alternative to conventional spectrometry with the potential to be more simply implemented and more rapid and accurate.

Computing local edge probability in natural scenes from a population of oriented simple cells

PubMed Central

Ramachandra, Chaithanya A.; Mel, Bartlett W.

2013-01-01

A key computation in visual cortex is the extraction of object contours, where the first stage of processing is commonly attributed to V1 simple cells. The standard model of a simple cell—an oriented linear filter followed by a divisive normalization—fits a wide variety of physiological data, but is a poor performing local edge detector when applied to natural images. The brain's ability to finely discriminate edges from nonedges therefore likely depends on information encoded by local simple cell populations. To gain insight into the corresponding decoding problem, we used Bayes's rule to calculate edge probability at a given location/orientation in an image based on a surrounding filter population. Beginning with a set of ∼ 100 filters, we culled out a subset that were maximally informative about edges, and minimally correlated to allow factorization of the joint on- and off-edge likelihood functions. Key features of our approach include a new, efficient method for ground-truth edge labeling, an emphasis on achieving filter independence, including a focus on filters in the region orthogonal rather than tangential to an edge, and the use of a customized parametric model to represent the individual filter likelihood functions. The resulting population-based edge detector has zero parameters, calculates edge probability based on a sum of surrounding filter influences, is much more sharply tuned than the underlying linear filters, and effectively captures fine-scale edge structure in natural scenes. Our findings predict nonmonotonic interactions between cells in visual cortex, wherein a cell may for certain stimuli excite and for other stimuli inhibit the same neighboring cell, depending on the two cells' relative offsets in position and orientation, and their relative activation levels. PMID:24381295

Linear spectral response of a Fano-resonant graded-stub filter based on pillar-photonic-crystal waveguides.

PubMed

Tokushima, Masatoshi

2018-02-01

To achieve high spectral linearity, we developed a Fano-resonant graded-stub filter on the basis of a pillar-photonic-crystal (PhC) waveguide. In a numerical simulation, the availability of a linear region within a peak-to-bottom wavelength span was nearly doubled compared to that of a sinusoidal spectrum, which was experimentally demonstrated with a fabricated silicon-pillar PhC stub filter. The high linearity of this filter is suitable for optical modulators used in multilevel amplitude modulation.

Vectorization of linear discrete filtering algorithms

NASA Technical Reports Server (NTRS)

Schiess, J. R.

1977-01-01

Linear filters, including the conventional Kalman filter and versions of square root filters devised by Potter and Carlson, are studied for potential application on streaming computers. The square root filters are known to maintain a positive definite covariance matrix in cases in which the Kalman filter diverges due to ill-conditioning of the matrix. Vectorization of the filters is discussed, and comparisons are made of the number of operations and storage locations required by each filter. The Carlson filter is shown to be the most efficient of the filters on the Control Data STAR-100 computer.

Mean-square state and parameter estimation for stochastic linear systems with Gaussian and Poisson noises

NASA Astrophysics Data System (ADS)

Basin, M.; Maldonado, J. J.; Zendejo, O.

2016-07-01

This paper proposes new mean-square filter and parameter estimator design for linear stochastic systems with unknown parameters over linear observations, where unknown parameters are considered as combinations of Gaussian and Poisson white noises. The problem is treated by reducing the original problem to a filtering problem for an extended state vector that includes parameters as additional states, modelled as combinations of independent Gaussian and Poisson processes. The solution to this filtering problem is based on the mean-square filtering equations for incompletely polynomial states confused with Gaussian and Poisson noises over linear observations. The resulting mean-square filter serves as an identifier for the unknown parameters. Finally, a simulation example shows effectiveness of the proposed mean-square filter and parameter estimator.

Integration of Cold Atom Interferometry INS with Other Sensors

DTIC Science & Technology

2012-03-22

Kalman filtering 2.6.1 Linear Kalman filtering . Kalman filtering is used to estimate the solution to a linear... Kalman Filter . This filter will estimate the errors in the navigation grade measurement. Whenever an outage occurs the mechanization must be done using ...navigation solution, with periodic GPS measurements being brought into a Kalman Filter to estimate the errors in the INS solution. The results of

Automatic Data Filter Customization Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Mandrake, Lukas

2013-01-01

This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.

A biased filter for linear discrete dynamic systems.

NASA Technical Reports Server (NTRS)

Chang, J. W.; Hoerl, A. E.; Leathrum, J. F.

1972-01-01

A recursive estimator, the ridge filter, was developed for the linear discrete dynamic estimation problem. Theorems were established to show that the ridge filter can be, on the average, closer to the expected value of the system state than the Kalman filter. On the other hand, Kalman filter, on the average, is closer to the instantaneous system state than the ridge filter. The ridge filter has been formulated in such a way that the computational features of the Kalman filter are preserved.

Kalman/Map filtering-aided fast normalized cross correlation-based Wi-Fi fingerprinting location sensing.

PubMed

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-11-13

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

PubMed Central

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-01-01

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results. PMID:24233027

Predictive accuracy of particle filtering in dynamic models supporting outbreak projections.

PubMed

Safarishahrbijari, Anahita; Teyhouee, Aydin; Waldner, Cheryl; Liu, Juxin; Osgood, Nathaniel D

2017-09-26

While a new generation of computational statistics algorithms and availability of data streams raises the potential for recurrently regrounding dynamic models with incoming observations, the effectiveness of such arrangements can be highly subject to specifics of the configuration (e.g., frequency of sampling and representation of behaviour change), and there has been little attempt to identify effective configurations. Combining dynamic models with particle filtering, we explored a solution focusing on creating quickly formulated models regrounded automatically and recurrently as new data becomes available. Given a latent underlying case count, we assumed that observed incident case counts followed a negative binomial distribution. In accordance with the condensation algorithm, each such observation led to updating of particle weights. We evaluated the effectiveness of various particle filtering configurations against each other and against an approach without particle filtering according to the accuracy of the model in predicting future prevalence, given data to a certain point and a norm-based discrepancy metric. We examined the effectiveness of particle filtering under varying times between observations, negative binomial dispersion parameters, and rates with which the contact rate could evolve. We observed that more frequent observations of empirical data yielded super-linearly improved accuracy in model predictions. We further found that for the data studied here, the most favourable assumptions to make regarding the parameters associated with the negative binomial distribution and changes in contact rate were robust across observation frequency and the observation point in the outbreak. Combining dynamic models with particle filtering can perform well in projecting future evolution of an outbreak. Most importantly, the remarkable improvements in predictive accuracy resulting from more frequent sampling suggest that investments to achieve efficient reporting mechanisms may be more than paid back by improved planning capacity. The robustness of the results on particle filter configuration in this case study suggests that it may be possible to formulate effective standard guidelines and regularized approaches for such techniques in particular epidemiological contexts. Most importantly, the work tentatively suggests potential for health decision makers to secure strong guidance when anticipating outbreak evolution for emerging infectious diseases by combining even very rough models with particle filtering method.

LLSURE: local linear SURE-based edge-preserving image filtering.

PubMed

Qiu, Tianshuang; Wang, Aiqi; Yu, Nannan; Song, Aimin

2013-01-01

In this paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.

Optical ranked-order filtering using threshold decomposition

DOEpatents

Allebach, Jan P.; Ochoa, Ellen; Sweeney, Donald W.

1990-01-01

A hybrid optical/electronic system performs median filtering and related ranked-order operations using threshold decomposition to encode the image. Threshold decomposition transforms the nonlinear neighborhood ranking operation into a linear space-invariant filtering step followed by a point-to-point threshold comparison step. Spatial multiplexing allows parallel processing of all the threshold components as well as recombination by a second linear, space-invariant filtering step. An incoherent optical correlation system performs the linear filtering, using a magneto-optic spatial light modulator as the input device and a computer-generated hologram in the filter plane. Thresholding is done electronically. By adjusting the value of the threshold, the same architecture is used to perform median, minimum, and maximum filtering of images. A totally optical system is also disclosed.

DUAL STATE-PARAMETER UPDATING SCHEME ON A CONCEPTUAL HYDROLOGIC MODEL USING SEQUENTIAL MONTE CARLO FILTERS

NASA Astrophysics Data System (ADS)

Noh, Seong Jin; Tachikawa, Yasuto; Shiiba, Michiharu; Kim, Sunmin

Applications of data assimilation techniques have been widely used to improve upon the predictability of hydrologic modeling. Among various data assimilation techniques, sequential Monte Carlo (SMC) filters, known as "particle filters" provide the capability to handle non-linear and non-Gaussian state-space models. This paper proposes a dual state-parameter updating scheme (DUS) based on SMC methods to estimate both state and parameter variables of a hydrologic model. We introduce a kernel smoothing method for the robust estimation of uncertain model parameters in the DUS. The applicability of the dual updating scheme is illustrated using the implementation of the storage function model on a middle-sized Japanese catchment. We also compare performance results of DUS combined with various SMC methods, such as SIR, ASIR and RPF.

Relationships between digital signal processing and control and estimation theory

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1978-01-01

Research directions in the fields of digital signal processing and modern control and estimation theory are discussed. Stability theory, linear prediction and parameter identification, system synthesis and implementation, two-dimensional filtering, decentralized control and estimation, and image processing are considered in order to uncover some of the basic similarities and differences in the goals, techniques, and philosophy of the disciplines.

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

PubMed Central

Li, Xiaoqing; Wang, Yu

2018-01-01

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology. PMID:29351254

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model.

PubMed

Xin, Jingzhou; Zhou, Jianting; Yang, Simon X; Li, Xiaoqing; Wang, Yu

2018-01-19

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology.

A motion-constraint logic for moving-base simulators based on variable filter parameters

NASA Technical Reports Server (NTRS)

Miller, G. K., Jr.

1974-01-01

A motion-constraint logic for moving-base simulators has been developed that is a modification to the linear second-order filters generally employed in conventional constraints. In the modified constraint logic, the filter parameters are not constant but vary with the instantaneous motion-base position to increase the constraint as the system approaches the positional limits. With the modified constraint logic, accelerations larger than originally expected are limited while conventional linear filters would result in automatic shutdown of the motion base. In addition, the modified washout logic has frequency-response characteristics that are an improvement over conventional linear filters with braking for low-frequency pilot inputs. During simulated landing approaches of an externally blown flap short take-off and landing (STOL) transport using decoupled longitudinal controls, the pilots were unable to detect much difference between the modified constraint logic and the logic based on linear filters with braking.

Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions.

PubMed

Vosmeer, C Ruben; Kooi, Derk P; Capoferri, Luigi; Terpstra, Margreet M; Vermeulen, Nico P E; Geerke, Daan P

2016-01-01

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.

Data assimilation for groundwater flow modelling using Unbiased Ensemble Square Root Filter: Case study in Guantao, North China Plain

NASA Astrophysics Data System (ADS)

Li, N.; Kinzelbach, W.; Li, H.; Li, W.; Chen, F.; Wang, L.

2017-12-01

Data assimilation techniques are widely used in hydrology to improve the reliability of hydrological models and to reduce model predictive uncertainties. This provides critical information for decision makers in water resources management. This study aims to evaluate a data assimilation system for the Guantao groundwater flow model coupled with a one-dimensional soil column simulation (Hydrus 1D) using an Unbiased Ensemble Square Root Filter (UnEnSRF) originating from the Ensemble Kalman Filter (EnKF) to update parameters and states, separately or simultaneously. To simplify the coupling between unsaturated and saturated zone, a linear relationship obtained from analyzing inputs to and outputs from Hydrus 1D is applied in the data assimilation process. Unlike EnKF, the UnEnSRF updates parameter ensemble mean and ensemble perturbations separately. In order to keep the ensemble filter working well during the data assimilation, two factors are introduced in the study. One is called damping factor to dampen the update amplitude of the posterior ensemble mean to avoid nonrealistic values. The other is called inflation factor to relax the posterior ensemble perturbations close to prior to avoid filter inbreeding problems. The sensitivities of the two factors are studied and their favorable values for the Guantao model are determined. The appropriate observation error and ensemble size were also determined to facilitate the further analysis. This study demonstrated that the data assimilation of both model parameters and states gives a smaller model prediction error but with larger uncertainty while the data assimilation of only model states provides a smaller predictive uncertainty but with a larger model prediction error. Data assimilation in a groundwater flow model will improve model prediction and at the same time make the model converge to the true parameters, which provides a successful base for applications in real time modelling or real time controlling strategies in groundwater resources management.

Optical ranked-order filtering using threshold decomposition

DOEpatents

Allebach, J.P.; Ochoa, E.; Sweeney, D.W.

1987-10-09

A hybrid optical/electronic system performs median filtering and related ranked-order operations using threshold decomposition to encode the image. Threshold decomposition transforms the nonlinear neighborhood ranking operation into a linear space-invariant filtering step followed by a point-to-point threshold comparison step. Spatial multiplexing allows parallel processing of all the threshold components as well as recombination by a second linear, space-invariant filtering step. An incoherent optical correlation system performs the linear filtering, using a magneto-optic spatial light modulator as the input device and a computer-generated hologram in the filter plane. Thresholding is done electronically. By adjusting the value of the threshold, the same architecture is used to perform median, minimum, and maximum filtering of images. A totally optical system is also disclosed. 3 figs.

System and Method for Generating a Frequency Modulated Linear Laser Waveform

NASA Technical Reports Server (NTRS)

Pierrottet, Diego F. (Inventor); Petway, Larry B. (Inventor); Amzajerdian, Farzin (Inventor); Barnes, Bruce W. (Inventor); Lockard, George E. (Inventor); Hines, Glenn D. (Inventor)

2017-01-01

A system for generating a frequency modulated linear laser waveform includes a single frequency laser generator to produce a laser output signal. An electro-optical modulator modulates the frequency of the laser output signal to define a linear triangular waveform. An optical circulator passes the linear triangular waveform to a band-pass optical filter to filter out harmonic frequencies created in the waveform during modulation of the laser output signal, to define a pure filtered modulated waveform having a very narrow bandwidth. The optical circulator receives the pure filtered modulated laser waveform and transmits the modulated laser waveform to a target.

System and Method for Generating a Frequency Modulated Linear Laser Waveform

NASA Technical Reports Server (NTRS)

Pierrottet, Diego F. (Inventor); Petway, Larry B. (Inventor); Amzajerdian, Farzin (Inventor); Barnes, Bruce W. (Inventor); Lockard, George E. (Inventor); Hines, Glenn D. (Inventor)

2014-01-01

A system for generating a frequency modulated linear laser waveform includes a single frequency laser generator to produce a laser output signal. An electro-optical modulator modulates the frequency of the laser output signal to define a linear triangular waveform. An optical circulator passes the linear triangular waveform to a band-pass optical filter to filter out harmonic frequencies created in the waveform during modulation of the laser output signal, to define a pure filtered modulated waveform having a very narrow bandwidth. The optical circulator receives the pure filtered modulated laser waveform and transmits the modulated laser waveform to a target.

Input-output characterization of an ultrasonic testing system by digital signal analysis

NASA Technical Reports Server (NTRS)

Williams, J. H., Jr.; Lee, S. S.; Karagulle, H.

1986-01-01

Ultrasonic test system input-output characteristics were investigated by directly coupling the transmitting and receiving transducers face to face without a test specimen. Some of the fundamentals of digital signal processing were summarized. Input and output signals were digitized by using a digital oscilloscope, and the digitized data were processed in a microcomputer by using digital signal-processing techniques. The continuous-time test system was modeled as a discrete-time, linear, shift-invariant system. In estimating the unit-sample response and frequency response of the discrete-time system, it was necessary to use digital filtering to remove low-amplitude noise, which interfered with deconvolution calculations. A digital bandpass filter constructed with the assistance of a Blackman window and a rectangular time window were used. Approximations of the impulse response and the frequency response of the continuous-time test system were obtained by linearly interpolating the defining points of the unit-sample response and the frequency response of the discrete-time system. The test system behaved as a linear-phase bandpass filter in the frequency range 0.6 to 2.3 MHz. These frequencies were selected in accordance with the criterion that they were 6 dB below the maximum peak of the amplitude of the frequency response. The output of the system to various inputs was predicted and the results were compared with the corresponding measurements on the system.

HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.

PubMed

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2017-08-01

Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Ocean Predictability and Uncertainty Forecasts Using Local Ensemble Transfer Kalman Filter (LETKF)

NASA Astrophysics Data System (ADS)

Wei, M.; Hogan, P. J.; Rowley, C. D.; Smedstad, O. M.; Wallcraft, A. J.; Penny, S. G.

2017-12-01

Ocean predictability and uncertainty are studied with an ensemble system that has been developed based on the US Navy's operational HYCOM using the Local Ensemble Transfer Kalman Filter (LETKF) technology. One of the advantages of this method is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates operational observations using ensemble method. The background covariance during this assimilation process is implicitly supplied with the ensemble avoiding the difficult task of developing tangent linear and adjoint models out of HYCOM with the complicated hybrid isopycnal vertical coordinate for 4D-VAR. The flow-dependent background covariance from the ensemble will be an indispensable part in the next generation hybrid 4D-Var/ensemble data assimilation system. The predictability and uncertainty for the ocean forecasts are studied initially for the Gulf of Mexico. The results are compared with another ensemble system using Ensemble Transfer (ET) method which has been used in the Navy's operational center. The advantages and disadvantages are discussed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yue; Reeves, Geoffrey D.; Cunningham, Gregory S.

Our study demonstrates the feasibility and reliability of using observations from low Earth orbit (LEO) to forecast and nowcast relativistic electrons in the outer radiation belt. Furthermore, we first report a high cross-energy, cross-pitch-angle coherence discovered between the trapped MeV electrons and precipitating approximately hundreds (~100s) of keV electrons—observed by satellites with very different altitudes—with correlation coefficients as high as ≳ 0.85. We then tested the feasibility of applying linear prediction filters to LEO data to predict the arrival of new MeV electrons during geomagnetic storms, as well as their evolving distributions afterward, based on the coherence. Reliability of thesemore » predictive filters is quantified by the performance efficiency with values as high as 0.74 when driven merely by LEO observations (or up to 0.94 with the inclusion of in situ MeV electron measurements). Finally, a hypothesis based upon the wave-particle resonance theory is proposed to explain the coherence, and a first-principle electron tracing model yields supporting evidence.« less

A Zonal Approach for Prediction of Jet Noise

NASA Technical Reports Server (NTRS)

Shih, S. H.; Hixon, D. R.; Mankbadi, Reda R.

1995-01-01

A zonal approach for direct computation of sound generation and propagation from a supersonic jet is investigated. The present work splits the computational domain into a nonlinear, acoustic-source regime and a linear acoustic wave propagation regime. In the nonlinear regime, the unsteady flow is governed by the large-scale equations, which are the filtered compressible Navier-Stokes equations. In the linear acoustic regime, the sound wave propagation is described by the linearized Euler equations. Computational results are presented for a supersonic jet at M = 2. 1. It is demonstrated that no spurious modes are generated in the matching region and the computational expense is reduced substantially as opposed to fully large-scale simulation.

Dense grid sibling frames with linear phase filters

NASA Astrophysics Data System (ADS)

Abdelnour, Farras

2013-09-01

We introduce new 5-band dyadic sibling frames with dense time-frequency grid. Given a lowpass filter satisfying certain conditions, the remaining filters are obtained using spectral factorization. The analysis and synthesis filterbanks share the same lowpass and bandpass filters but have different and oversampled highpass filters. This leads to wavelets approximating shift-invariance. The filters are FIR, have linear phase, and the resulting wavelets have vanishing moments. The filters are designed using spectral factorization method. The proposed method leads to smooth limit functions with higher approximation order, and computationally stable filterbanks.

Spatiotemporal Filter for Visual Motion Integration from Pursuit Eye Movements in Humans and Monkeys

PubMed Central

Liu, Bing

2017-01-01

Despite the enduring interest in motion integration, a direct measure of the space–time filter that the brain imposes on a visual scene has been elusive. This is perhaps because of the challenge of estimating a 3D function from perceptual reports in psychophysical tasks. We take a different approach. We exploit the close connection between visual motion estimates and smooth pursuit eye movements to measure stimulus–response correlations across space and time, computing the linear space–time filter for global motion direction in humans and monkeys. Although derived from eye movements, we find that the filter predicts perceptual motion estimates quite well. To distinguish visual from motor contributions to the temporal duration of the pursuit motion filter, we recorded single-unit responses in the monkey middle temporal cortical area (MT). We find that pursuit response delays are consistent with the distribution of cortical neuron latencies and that temporal motion integration for pursuit is consistent with a short integration MT subpopulation. Remarkably, the visual system appears to preferentially weight motion signals across a narrow range of foveal eccentricities rather than uniformly over the whole visual field, with a transiently enhanced contribution from locations along the direction of motion. We find that the visual system is most sensitive to motion falling at approximately one-third the radius of the stimulus aperture. Hypothesizing that the visual drive for pursuit is related to the filtered motion energy in a motion stimulus, we compare measured and predicted eye acceleration across several other target forms. SIGNIFICANCE STATEMENT A compact model of the spatial and temporal processing underlying global motion perception has been elusive. We used visually driven smooth eye movements to find the 3D space–time function that best predicts both eye movements and perception of translating dot patterns. We found that the visual system does not appear to use all available motion signals uniformly, but rather weights motion preferentially in a narrow band at approximately one-third the radius of the stimulus. Although not universal, the filter predicts responses to other types of stimuli, demonstrating a remarkable degree of generalization that may lead to a deeper understanding of visual motion processing. PMID:28003348

The research of radar target tracking observed information linear filter method

NASA Astrophysics Data System (ADS)

Chen, Zheng; Zhao, Xuanzhi; Zhang, Wen

2018-05-01

Aiming at the problems of low precision or even precision divergent is caused by nonlinear observation equation in radar target tracking, a new filtering algorithm is proposed in this paper. In this algorithm, local linearization is carried out on the observed data of the distance and angle respectively. Then the kalman filter is performed on the linearized data. After getting filtered data, a mapping operation will provide the posteriori estimation of target state. A large number of simulation results show that this algorithm can solve above problems effectively, and performance is better than the traditional filtering algorithm for nonlinear dynamic systems.

Implicit filtered P{sub N} for high-energy density thermal radiation transport using discontinuous Galerkin finite elements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Laboure, Vincent M., E-mail: vincent.laboure@tamu.edu; McClarren, Ryan G., E-mail: rgm@tamu.edu; Hauck, Cory D., E-mail: hauckc@ornl.gov

2016-09-15

In this work, we provide a fully-implicit implementation of the time-dependent, filtered spherical harmonics (FP{sub N}) equations for non-linear, thermal radiative transfer. We investigate local filtering strategies and analyze the effect of the filter on the conditioning of the system, showing in particular that the filter improves the convergence properties of the iterative solver. We also investigate numerically the rigorous error estimates derived in the linear setting, to determine whether they hold also for the non-linear case. Finally, we simulate a standard test problem on an unstructured mesh and make comparisons with implicit Monte Carlo (IMC) calculations.

Nonlinear-drifted Brownian motion with multiple hidden states for remaining useful life prediction of rechargeable batteries

NASA Astrophysics Data System (ADS)

Wang, Dong; Zhao, Yang; Yang, Fangfang; Tsui, Kwok-Leung

2017-09-01

Brownian motion with adaptive drift has attracted much attention in prognostics because its first hitting time is highly relevant to remaining useful life prediction and it follows the inverse Gaussian distribution. Besides linear degradation modeling, nonlinear-drifted Brownian motion has been developed to model nonlinear degradation. Moreover, the first hitting time distribution of the nonlinear-drifted Brownian motion has been approximated by time-space transformation. In the previous studies, the drift coefficient is the only hidden state used in state space modeling of the nonlinear-drifted Brownian motion. Besides the drift coefficient, parameters of a nonlinear function used in the nonlinear-drifted Brownian motion should be treated as additional hidden states of state space modeling to make the nonlinear-drifted Brownian motion more flexible. In this paper, a prognostic method based on nonlinear-drifted Brownian motion with multiple hidden states is proposed and then it is applied to predict remaining useful life of rechargeable batteries. 26 sets of rechargeable battery degradation samples are analyzed to validate the effectiveness of the proposed prognostic method. Moreover, some comparisons with a standard particle filter based prognostic method, a spherical cubature particle filter based prognostic method and two classic Bayesian prognostic methods are conducted to highlight the superiority of the proposed prognostic method. Results show that the proposed prognostic method has lower average prediction errors than the particle filter based prognostic methods and the classic Bayesian prognostic methods for battery remaining useful life prediction.

MLS (Microwave Landing System) Multipath Studies, Phase 3. Volume 3. Application of Models to MLS Assessment Issues. Part 1. Chapters 1 through 4.

DTIC Science & Technology

1981-06-08

generated by the AWOP panel members from the FRG, Netherlands, U.K., and U.S. 1-3 Airport-specific simulations, intended to predict MLS performance at a...05. C.𔃼 C. 00 0,5 r, !,. . lSTM FIN I l "i Fig. 2-89. Elevation rate error filter output for scenario 4. 2-116...Report No. 1, Texas Instruments Incorporated, Dallas, Texas (25 June 1975), ALEX(03)-TR-75-01. 50. J. Makhoul, "Linear Prediction ; A Tutorial Review

Seismoelectric data processing for surface surveys of shallow targets

USGS Publications Warehouse

Haines, S.S.; Guitton, A.; Biondi, B.

2007-01-01

The utility of the seismoelectric method relies on the development of methods to extract the signal of interest from background and source-generated coherent noise that may be several orders-of-magnitude stronger. We compare data processing approaches to develop a sequence of preprocessing and signal/noise separation and to quantify the noise level from which we can extract signal events. Our preferred sequence begins with the removal of power line harmonic noise and the use of frequency filters to minimize random and source-generated noise. Mapping to the linear Radon domain with an inverse process incorporating a sparseness constraint provides good separation of signal from noise, though it is ineffective on noise that shows the same dip as the signal. Similarly, the seismoelectric signal and noise do not separate cleanly in the Fourier domain, so f-k filtering can not remove all of the source-generated noise and it also disrupts signal amplitude patterns. We find that prediction-error filters provide the most effective method to separate signal and noise, while also preserving amplitude information, assuming that adequate pattern models can be determined for the signal and noise. These Radon-domain and prediction-error-filter methods successfully separate signal from <33 dB stronger noise in our test data. ?? 2007 Society of Exploration Geophysicists.

Linear Quantum Systems: Non-Classical States and Robust Stability

DTIC Science & Technology

2016-06-29

quantum linear systems subject to non-classical quantum fields. The major outcomes of this project are (i) derivation of quantum filtering equations for...derivation of quantum filtering equations for systems non-classical input states including single photon states, (ii) determination of how linear...history going back some 50 years, to the birth of modern control theory with Kalman’s foundational work on filtering and LQG optimal control

Coding tools investigation for next generation video coding based on HEVC

NASA Astrophysics Data System (ADS)

Chen, Jianle; Chen, Ying; Karczewicz, Marta; Li, Xiang; Liu, Hongbin; Zhang, Li; Zhao, Xin

2015-09-01

The new state-of-the-art video coding standard, H.265/HEVC, has been finalized in 2013 and it achieves roughly 50% bit rate saving compared to its predecessor, H.264/MPEG-4 AVC. This paper provides the evidence that there is still potential for further coding efficiency improvements. A brief overview of HEVC is firstly given in the paper. Then, our improvements on each main module of HEVC are presented. For instance, the recursive quadtree block structure is extended to support larger coding unit and transform unit. The motion information prediction scheme is improved by advanced temporal motion vector prediction, which inherits the motion information of each small block within a large block from a temporal reference picture. Cross component prediction with linear prediction model improves intra prediction and overlapped block motion compensation improves the efficiency of inter prediction. Furthermore, coding of both intra and inter prediction residual is improved by adaptive multiple transform technique. Finally, in addition to deblocking filter and SAO, adaptive loop filter is applied to further enhance the reconstructed picture quality. This paper describes above-mentioned techniques in detail and evaluates their coding performance benefits based on the common test condition during HEVC development. The simulation results show that significant performance improvement over HEVC standard can be achieved, especially for the high resolution video materials.

A highly linear baseband Gm—C filter for WLAN application

NASA Astrophysics Data System (ADS)

Lijun, Yang; Zheng, Gong; Yin, Shi; Zhiming, Chen

2011-09-01

A low voltage, highly linear transconductan—C (Gm—C) low-pass filter for wireless local area network (WLAN) transceiver application is proposed. This transmitter (Tx) filter adopts a 9.8 MHz 3rd-order Chebyshev low pass prototype and achieves 35 dB stop-band attenuation at 30 MHz frequency. By utilizing pseudo-differential linear-region MOS transconductors, the filter IIP3 is measured to be as high as 9.5 dBm. Fabricated in a 0.35 μm standard CMOS technology, the proposed filter chip occupies a 0.41 × 0.17 mm2 die area and consumes 3.36 mA from a 3.3-V power supply.

A real-time recursive filter for the attitude determination of the Spacelab instrument pointing subsystem

NASA Technical Reports Server (NTRS)

West, M. E.

1992-01-01

A real-time estimation filter which reduces sensitivity to system variations and reduces the amount of preflight computation is developed for the instrument pointing subsystem (IPS). The IPS is a three-axis stabilized platform developed to point various astronomical observation instruments aboard the shuttle. Currently, the IPS utilizes a linearized Kalman filter (LKF), with premission defined gains, to compensate for system drifts and accumulated attitude errors. Since the a priori gains are generated for an expected system, variations result in a suboptimal estimation process. This report compares the performance of three real-time estimation filters with the current LKF implementation. An extended Kalman filter and a second-order Kalman filter are developed to account for the system nonlinearities, while a linear Kalman filter implementation assumes that the nonlinearities are negligible. The performance of each of the four estimation filters are compared with respect to accuracy, stability, settling time, robustness, and computational requirements. It is shown, that for the current IPS pointing requirements, the linear Kalman filter provides improved robustness over the LKF with less computational requirements than the two real-time nonlinear estimation filters.

Finite-time H∞ filtering for non-linear stochastic systems

NASA Astrophysics Data System (ADS)

Hou, Mingzhe; Deng, Zongquan; Duan, Guangren

2016-09-01

This paper describes the robust H∞ filtering analysis and the synthesis of general non-linear stochastic systems with finite settling time. We assume that the system dynamic is modelled by Itô-type stochastic differential equations of which the state and the measurement are corrupted by state-dependent noises and exogenous disturbances. A sufficient condition for non-linear stochastic systems to have the finite-time H∞ performance with gain less than or equal to a prescribed positive number is established in terms of a certain Hamilton-Jacobi inequality. Based on this result, the existence of a finite-time H∞ filter is given for the general non-linear stochastic system by a second-order non-linear partial differential inequality, and the filter can be obtained by solving this inequality. The effectiveness of the obtained result is illustrated by a numerical example.

Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation.

PubMed

Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng; Wang, Meng

2016-09-20

A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.

Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

PubMed Central

Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng; Wang, Meng

2016-01-01

A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm. PMID:27657069

Cubature/ Unscented/ Sigma Point Kalman Filtering with Angular Measurement Models

DTIC Science & Technology

2015-07-06

Cubature/ Unscented/ Sigma Point Kalman Filtering with Angular Measurement Models David Frederic Crouse Naval Research Laboratory 4555 Overlook Ave...measurement and process non- linearities, such as the cubature Kalman filter , can perform ex- tremely poorly in many applications involving angular... Kalman filtering is a realization of the best linear unbiased estimator (BLUE) that evaluates certain integrals for expected values using different forms

An improved push-pull voltage fed converter using a tapped output-filter inductor

NASA Technical Reports Server (NTRS)

Wester, G. W.

1983-01-01

A new concept of using a tapped output-filter inductor and an auxiliary commutating diode to reduce the likelihood of transformer core saturation in a push-pull, voltage-fed converter is presented. The linearized circuit model and transfer functions are derived with a hybrid approach using both state-space and circuit averaging. Operation of the new converter - including parasitic effects - is discussed, and a design equation for inductor tap ratio is established. It is predicted and experimentally confirmed that the new converter has more symmetrical transformer core operation, and the potential exits for lower transistor turnon current and reduced transistor voltage stress. These benefits reduce switching loss and enhance transistor reliability.

Rapid 3D NMR using the filter diagonalization method: application to oligosaccharides derivatized with 13C-labeled acetyl groups

NASA Astrophysics Data System (ADS)

Armstrong, Geoffrey S.; Cano, Kristin E.; Mandelshtam, Vladimir A.; Shaka, A. J.; Bendiak, Brad

2004-09-01

Rapid 3D NMR spectroscopy of oligosaccharides having isotopically labeled acetyl "isotags" was made possible with high resolution in the indirect dimensions using the filter diagonalization method (FDM). A pulse sequence was designed for the optimal correlation of acetyl methyl protons, methyl carbons, and carbonyl carbons. The multi-dimensional nature of the FDM, coupled with the advantages of constant-time evolution periods, resulted in marked improvements over Fourier transform (FT) and mirror-image linear prediction (MI-LP) processing methods. The three methods were directly compared using identical data sets. A highly resolved 3D spectrum was achieved with the FDM using a very short experimental time (28 min).

Rapid 3D NMR using the filter diagonalization method: application to oligosaccharides derivatized with 13C-labeled acetyl groups.

PubMed

Armstrong, Geoffrey S; Cano, Kristin E; Mandelshtam, Vladimir A; Shaka, A J; Bendiak, Brad

2004-09-01

Rapid 3D NMR spectroscopy of oligosaccharides having isotopically labeled acetyl "isotags" was made possible with high resolution in the indirect dimensions using the filter diagonalization method (FDM). A pulse sequence was designed for the optimal correlation of acetyl methyl protons, methyl carbons, and carbonyl carbons. The multi-dimensional nature of the FDM, coupled with the advantages of constant-time evolution periods, resulted in marked improvements over Fourier transform (FT) and mirror-image linear prediction (MI-LP) processing methods. The three methods were directly compared using identical data sets. A highly resolved 3D spectrum was achieved with the FDM using a very short experimental time (28 min).

A highly linear fully integrated powerline filter for biopotential acquisition systems.

PubMed

Alzaher, Hussain A; Tasadduq, Noman; Mahnashi, Yaqub

2013-10-01

Powerline interference is one of the most dominant problems in detection and processing of biopotential signals. This work presents a new fully integrated notch filter exhibiting high linearity and low power consumption. High filter linearity is preserved utilizing active-RC approach while IC implementation is achieved through replacing passive resistors by R-2R ladders achieving area saving of approximately 120 times. The filter design is optimized for low power operation using an efficient circuit topology and an ultra-low power operational amplifier. Fully differential implementation of the proposed filter shows notch depth of 43 dB (78 dB for 4th-order) with THD of better than -70 dB while consuming about 150 nW from 1.5 V supply.

A State-Space Approach to Optimal Level-Crossing Prediction for Linear Gaussian Processes

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2009-01-01

In many complex engineered systems, the ability to give an alarm prior to impending critical events is of great importance. These critical events may have varying degrees of severity, and in fact they may occur during normal system operation. In this article, we investigate approximations to theoretically optimal methods of designing alarm systems for the prediction of level-crossings by a zero-mean stationary linear dynamic system driven by Gaussian noise. An optimal alarm system is designed to elicit the fewest false alarms for a fixed detection probability. This work introduces the use of Kalman filtering in tandem with the optimal level-crossing problem. It is shown that there is a negligible loss in overall accuracy when using approximations to the theoretically optimal predictor, at the advantage of greatly reduced computational complexity. I

Linear-Quadratic Control of a MEMS Micromirror using Kalman Filtering

DTIC Science & Technology

2011-12-01

LINEAR-QUADRATIC CONTROL OF A MEMS MICROMIRROR USING KALMAN FILTERING THESIS Jamie P...A MEMS MICROMIRROR USING KALMAN FILTERING THESIS Presented to the Faculty Department of Electrical Engineering Graduate School of...actuated micromirrors fabricated by PolyMUMPs. Successful application of these techniques enables demonstration of smooth, stable deflections of 50% and

Power-ratio tunable dual-wavelength laser using linearly variable Fabry-Perot filter as output coupler.

PubMed

Wang, Xiaozhong; Wang, Zhongfa; Bu, Yikun; Chen, Lujian; Cai, Guoxiong; Huang, Wencai; Cai, Zhiping; Chen, Nan

2016-02-01

For a linearly variable Fabry-Perot filter, the peak transmission wavelengths change linearly with the transverse position shift of the substrate. Such a Fabry-Perot filter is designed and fabricated and used as an output coupler of a c-cut Nd:YVO₄ laser experimentally in this paper to obtain a 1062 and 1083 nm dual-wavelength laser. The peak transmission wavelengths are gradually shifted from 1040.8 to 1070.8 nm. The peak transmission wavelength of the Fabry-Perot filter used as the output coupler for the dual-wavelength laser is 1068 nm and resides between 1062 and 1083 nm, which makes the transmissions of the desired dual wavelengths change in opposite slopes with the transverse shift of the filter. Consequently, powers of the two wavelengths change in opposite directions. A branch power, oppositely tunable 1062 and 1083 nm dual-wavelength laser is successfully demonstrated. Design principles of the linear variable Fabry-Perot filter used as an output coupler are discussed. Advantages of the method are summarized.

Performance of compost filtration practice for green infrastructure stormwater applications.

PubMed

Faucette, Britt; Cardoso, Fatima; Mulbry, Walter; Millner, Pat

2013-09-01

Urban storm water runoff poses a substantial threat of pollution to receiving surface waters. Green infrastructure, low impact development, green building ordinances, National Pollutant Discharge Elimination System (NPDES) storm water permit compliance, and Total Maximum Daily Load (TMDL) implementation strategies have become national priorities; however, designers need more sustainable, low-cost solutions to meet these goals and guidelines. The objective of this study was to determine the multiple-event removal efficiency and capacity of compost filter socks (FS) and filter socks with natural sorbents (NS) to remove soluble phosphorus, ammonium-nitrogen, nitrate-nitrogen, E. coli, Enterococcus, and oil from urban storm water runoff. Treatments were exposed to simulated storm water pollutant concentrations consistent with urban runoff originating from impervious surfaces, such as parking lots and roadways. Treatments were exposed to a maximum of 25 runoff events, or when removal efficiencies were < or = 25%, whichever occurred first. Experiments were conducted in triplicate. The filter socks with natural sorbents removed significantly greater soluble phosphorus than the filter socks alone, removing a total of 237 mg/linear m over eight runoff events, or an average of 34%. The filter socks with natural sorbents removed 54% of ammonium-nitrogen over 25 runoff events, or 533 mg/linear m, and only 11% of nitrate-nitrogen, or 228 mg/linear m. The filter socks and filter socks with natural sorbents both removed 99% of oil over 25 runoff events, or a total load of 38,486 mg/linear m. Over 25 runoff events the filter socks with natural sorbents removed E. coli and Enteroccocus at 85% and 65%, or a total load of 3.14 CFUs x 10(8)/ linear m and 1.5 CFUs x 10(9)/linear m, respectively; both were significantly greater than treatment by filter socks alone. Based on these experiments, this technique can be used to reduce soluble pollutants from storm water over multiple runoff events.

Morphodynamic data assimilation used to understand changing coasts

USGS Publications Warehouse

Plant, Nathaniel G.; Long, Joseph W.

2015-01-01

Morphodynamic data assimilation blends observations with model predictions and comes in many forms, including linear regression, Kalman filter, brute-force parameter estimation, variational assimilation, and Bayesian analysis. Importantly, data assimilation can be used to identify sources of prediction errors that lead to improved fundamental understanding. Overall, models incorporating data assimilation yield better information to the people who must make decisions impacting safety and wellbeing in coastal regions that experience hazards due to storms, sea-level rise, and erosion. We present examples of data assimilation associated with morphologic change. We conclude that enough morphodynamic predictive capability is available now to be useful to people, and that we will increase our understanding and the level of detail of our predictions through assimilation of observations and numerical-statistical models.

State-Dependent Pseudo-Linear Filter for Spacecraft Attitude and Rate Estimation

NASA Technical Reports Server (NTRS)

Bar-Itzhack, Itzhack Y.; Harman, Richard R.

2001-01-01

This paper presents the development and performance of a special algorithm for estimating the attitude and angular rate of a spacecraft. The algorithm is a pseudo-linear Kalman filter, which is an ordinary linear Kalman filter that operates on a linear model whose matrices are current state estimate dependent. The nonlinear rotational dynamics equation of the spacecraft is presented in the state space as a state-dependent linear system. Two types of measurements are considered. One type is a measurement of the quaternion of rotation, which is obtained from a newly introduced star tracker based apparatus. The other type of measurement is that of vectors, which permits the use of a variety of vector measuring sensors like sun sensors and magnetometers. While quaternion measurements are related linearly to the state vector, vector measurements constitute a nonlinear function of the state vector. Therefore, in this paper, a state-dependent linear measurement equation is developed for the vector measurement case. The state-dependent pseudo linear filter is applied to simulated spacecraft rotations and adequate estimates of the spacecraft attitude and rate are obtained for the case of quaternion measurements as well as of vector measurements.

LROC assessment of non-linear filtering methods in Ga-67 SPECT imaging

NASA Astrophysics Data System (ADS)

De Clercq, Stijn; Staelens, Steven; De Beenhouwer, Jan; D'Asseler, Yves; Lemahieu, Ignace

2006-03-01

In emission tomography, iterative reconstruction is usually followed by a linear smoothing filter to make such images more appropriate for visual inspection and diagnosis by a physician. This will result in a global blurring of the images, smoothing across edges and possibly discarding valuable image information for detection tasks. The purpose of this study is to investigate which possible advantages a non-linear, edge-preserving postfilter could have on lesion detection in Ga-67 SPECT imaging. Image quality can be defined based on the task that has to be performed on the image. This study used LROC observer studies based on a dataset created by CPU-intensive Gate Monte Carlo simulations of a voxelized digital phantom. The filters considered in this study were a linear Gaussian filter, a bilateral filter, the Perona-Malik anisotropic diffusion filter and the Catte filtering scheme. The 3D MCAT software phantom was used to simulate the distribution of Ga-67 citrate in the abdomen. Tumor-present cases had a 1-cm diameter tumor randomly placed near the edges of the anatomical boundaries of the kidneys, bone, liver and spleen. Our data set was generated out of a single noisy background simulation using the bootstrap method, to significantly reduce the simulation time and to allow for a larger observer data set. Lesions were simulated separately and added to the background afterwards. These were then reconstructed with an iterative approach, using a sufficiently large number of MLEM iterations to establish convergence. The output of a numerical observer was used in a simplex optimization method to estimate an optimal set of parameters for each postfilter. No significant improvement was found for using edge-preserving filtering techniques over standard linear Gaussian filtering.

Development, characterization, and modeling of a tunable filter camera

NASA Astrophysics Data System (ADS)

Sartor, Mark Alan

1999-10-01

This paper describes the development, characterization, and modeling of a Tunable Filter Camera (TFC). The TFC is a new multispectral instrument with electronically tuned spectral filtering and low-light-level sensitivity. It represents a hybrid between hyperspectral and multispectral imaging spectrometers that incorporates advantages from each, addressing issues such as complexity, cost, lack of sensitivity, and adaptability. These capabilities allow the TFC to be applied to low- altitude video surveillance for real-time spectral and spatial target detection and image exploitation. Described herein are the theory and principles of operation for the TFC, which includes a liquid crystal tunable filter, an intensified CCD, and a custom apochromatic lens. The results of proof-of-concept testing, and characterization of two prototype cameras are included, along with a summary of the design analyses for the development of a multiple-channel system. A significant result of this effort was the creation of a system-level model, which was used to facilitate development and predict performance. It includes models for the liquid crystal tunable filter and intensified CCD. Such modeling was necessary in the design of the system and is useful for evaluation of the system in remote-sensing applications. Also presented are characterization data from component testing, which included quantitative results for linearity, signal to noise ratio (SNR), linearity, and radiometric response. These data were used to help refine and validate the model. For a pre-defined source, the spatial and spectral response, and the noise of the camera, system can now be predicted. The innovation that sets this development apart is the fact that this instrument has been designed for integrated, multi-channel operation for the express purpose of real-time detection/identification in low- light-level conditions. Many of the requirements for the TFC were derived from this mission. In order to provide background for the design requirements for the TFC development, the mission and principles of operation behind the multi-channel system will be reviewed. Given the combination of the flexibility, simplicity, and sensitivity, the TFC and its multiple-channel extension can play a significant role in the next generation of remote-sensing instruments.

Forecasting and remote sensing outer belt relativistic electrons from low Earth orbit

DOE PAGES

Chen, Yue; Reeves, Geoffrey D.; Cunningham, Gregory S.; ...

2016-02-15

Our study demonstrates the feasibility and reliability of using observations from low Earth orbit (LEO) to forecast and nowcast relativistic electrons in the outer radiation belt. Furthermore, we first report a high cross-energy, cross-pitch-angle coherence discovered between the trapped MeV electrons and precipitating approximately hundreds (~100s) of keV electrons—observed by satellites with very different altitudes—with correlation coefficients as high as ≳ 0.85. We then tested the feasibility of applying linear prediction filters to LEO data to predict the arrival of new MeV electrons during geomagnetic storms, as well as their evolving distributions afterward, based on the coherence. Reliability of thesemore » predictive filters is quantified by the performance efficiency with values as high as 0.74 when driven merely by LEO observations (or up to 0.94 with the inclusion of in situ MeV electron measurements). Finally, a hypothesis based upon the wave-particle resonance theory is proposed to explain the coherence, and a first-principle electron tracing model yields supporting evidence.« less

Model-Based Engine Control Architecture with an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2016-01-01

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The non-linear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

Filtering of the Radon transform to enhance linear signal features via wavelet pyramid decomposition

NASA Astrophysics Data System (ADS)

Meckley, John R.

1995-09-01

The information content in many signal processing applications can be reduced to a set of linear features in a 2D signal transform. Examples include the narrowband lines in a spectrogram, ship wakes in a synthetic aperture radar image, and blood vessels in a medical computer-aided tomography scan. The line integrals that generate the values of the projections of the Radon transform can be characterized as a bank of matched filters for linear features. This localization of energy in the Radon transform for linear features can be exploited to enhance these features and to reduce noise by filtering the Radon transform with a filter explicitly designed to pass only linear features, and then reconstructing a new 2D signal by inverting the new filtered Radon transform (i.e., via filtered backprojection). Previously used methods for filtering the Radon transform include Fourier based filtering (a 2D elliptical Gaussian linear filter) and a nonlinear filter ((Radon xfrm)**y with y >= 2.0). Both of these techniques suffer from the mismatch of the filter response to the true functional form of the Radon transform of a line. The Radon transform of a line is not a point but is a function of the Radon variables (rho, theta) and the total line energy. This mismatch leads to artifacts in the reconstructed image and a reduction in achievable processing gain. The Radon transform for a line is computed as a function of angle and offset (rho, theta) and the line length. The 2D wavelet coefficients are then compared for the Haar wavelets and the Daubechies wavelets. These filter responses are used as frequency filters for the Radon transform. The filtering is performed on the wavelet pyramid decomposition of the Radon transform by detecting the most likely positions of lines in the transform and then by convolving the local area with the appropriate response and zeroing the pyramid coefficients outside of the response area. The response area is defined to contain 95% of the total wavelet coefficient energy. The detection algorithm provides an estimate of the line offset, orientation, and length that is then used to index the appropriate filter shape. Additional wavelet pyramid decomposition is performed in areas of high energy to refine the line position estimate. After filtering, the new Radon transform is generated by inverting the wavelet pyramid. The Radon transform is then inverted by filtered backprojection to produce the final 2D signal estimate with the enhanced linear features. The wavelet-based method is compared to both the Fourier and the nonlinear filtering with examples of sparse and dense shapes in imaging, acoustics and medical tomography with test images of noisy concentric lines, a real spectrogram of a blow fish (a very nonstationary spectrum), and the Shepp Logan Computer Tomography phantom image. Both qualitative and derived quantitative measures demonstrate the improvement of wavelet-based filtering. Additional research is suggested based on these results. Open questions include what level(s) to use for detection and filtering because multiple-level representations exist. The lower levels are smoother at reduced spatial resolution, while the higher levels provide better response to edges. Several examples are discussed based on analytical and phenomenological arguments.

3D Wavelet-Based Filter and Method

DOEpatents

Moss, William C.; Haase, Sebastian; Sedat, John W.

2008-08-12

A 3D wavelet-based filter for visualizing and locating structural features of a user-specified linear size in 2D or 3D image data. The only input parameter is a characteristic linear size of the feature of interest, and the filter output contains only those regions that are correlated with the characteristic size, thus denoising the image.

Non-linear feedback control of the p53 protein-mdm2 inhibitor system using the derivative-free non-linear Kalman filter.

PubMed

Rigatos, Gerasimos G

2016-06-01

It is proven that the model of the p53-mdm2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p53 protein at the desirable levels. To estimate the non-measurable elements of the state vector describing the p53-mdm2 system dynamics, the derivative-free non-linear Kalman filter is used. Moreover, to compensate for modelling uncertainties and external disturbances that affect the p53-mdm2 system, the derivative-free non-linear Kalman filter is re-designed as a disturbance observer. The derivative-free non-linear Kalman filter consists of the Kalman filter recursion applied on the linearised equivalent of the protein synthesis model together with an inverse transformation based on differential flatness theory that enables to retrieve estimates for the state variables of the initial non-linear model. The proposed non-linear feedback control and perturbations compensation method for the p53-mdm2 system can result in more efficient chemotherapy schemes where the infusion of medication will be better administered.

The attitude inversion method of geostationary satellites based on unscented particle filter

NASA Astrophysics Data System (ADS)

Du, Xiaoping; Wang, Yang; Hu, Heng; Gou, Ruixin; Liu, Hao

2018-04-01

The attitude information of geostationary satellites is difficult to be obtained since they are presented in non-resolved images on the ground observation equipment in space object surveillance. In this paper, an attitude inversion method for geostationary satellite based on Unscented Particle Filter (UPF) and ground photometric data is presented. The inversion algorithm based on UPF is proposed aiming at the strong non-linear feature in the photometric data inversion for satellite attitude, which combines the advantage of Unscented Kalman Filter (UKF) and Particle Filter (PF). This update method improves the particle selection based on the idea of UKF to redesign the importance density function. Moreover, it uses the RMS-UKF to partially correct the prediction covariance matrix, which improves the applicability of the attitude inversion method in view of UKF and the particle degradation and dilution of the attitude inversion method based on PF. This paper describes the main principles and steps of algorithm in detail, correctness, accuracy, stability and applicability of the method are verified by simulation experiment and scaling experiment in the end. The results show that the proposed method can effectively solve the problem of particle degradation and depletion in the attitude inversion method on account of PF, and the problem that UKF is not suitable for the strong non-linear attitude inversion. However, the inversion accuracy is obviously superior to UKF and PF, in addition, in the case of the inversion with large attitude error that can inverse the attitude with small particles and high precision.

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.

Non-linear feeding functional responses in the Greater Flamingo (Phoenicopterus roseus) predict immediate negative impact of wetland degradation on this flagship species

PubMed Central

Deville, Anne-Sophie; Grémillet, David; Gauthier-Clerc, Michel; Guillemain, Matthieu; Von Houwald, Friederike; Gardelli, Bruno; Béchet, Arnaud

2013-01-01

Accurate knowledge of the functional response of predators to prey density is essential for understanding food web dynamics, to parameterize mechanistic models of animal responses to environmental change, and for designing appropriate conservation measures. Greater flamingos (Phoenicopterus roseus), a flagship species of Mediterranean wetlands, primarily feed on Artemias (Artemia spp.) in commercial salt pans, an industry which may collapse for economic reasons. Flamingos also feed on alternative prey such as Chironomid larvae (e.g., Chironomid spp.) and rice seeds (Oryza sativa). However, the profitability of these food items for flamingos remains unknown. We determined the functional responses of flamingos feeding on Artemias, Chironomids, or rice. Experiments were conducted on 11 captive flamingos. For each food item, we offered different ranges of food densities, up to 13 times natural abundance. Video footage allowed estimating intake rates. Contrary to theoretical predictions for filter feeders, intake rates did not increase linearly with increasing food density (type I). Intake rates rather increased asymptotically with increasing food density (type II) or followed a sigmoid shape (type III). Hence, flamingos were not able to ingest food in direct proportion to their abundance, possibly because of unique bill structure resulting in limited filtering capabilities. Overall, flamingos foraged more efficiently on Artemias. When feeding on Chironomids, birds had lower instantaneous rates of food discovery and required more time to extract food from the sediment and ingest it, than when filtering Artemias from the water column. However, feeding on rice was energetically more profitable for flamingos than feeding on Artemias or Chironomids, explaining their attraction for rice fields. Crucially, we found that food densities required for flamingos to reach asymptotic intake rates are rarely met under natural conditions. This allows us to predict an immediate negative effect of any decrease in prey density upon flamingo foraging performance. PMID:23762525

Formulation of Efficient Finite Element Prediction Models.

DTIC Science & Technology

1980-01-01

vorticity-divergence FEM formulation. This paper will compare these FEM formulations by considering the Vgeostrophic adjustment process with the linearized...by Fourier transforming the terms that are independent of t in (2.12)-(2.14) or (2.19)-(2.21). However, in this paper the final state will be...filtering in a baroclinic primitive equation model. 17 L . , 5. Conclusions The objective of this paper is to determine the response of various finite

Sensory Information Processing and Symbolic Computation

DTIC Science & Technology

1973-12-31

plague all image deblurring methods when working with high signal to noise ratios, is that of a ringing or ghost image phenomenon which surrounds high...Figure 11 The Impulse Response of an All-Pass Random Phase Filter 24 Figure 12 (a) Unsmoothed Log Spectra of the Sentence "The pipe began to...of automatic deblurring of images, linear predictive coding of speech and the refinement and application of mathematical models of human vision and

An efficient implementation of a high-order filter for a cubed-sphere spectral element model

NASA Astrophysics Data System (ADS)

Kang, Hyun-Gyu; Cheong, Hyeong-Bin

2017-03-01

A parallel-scalable, isotropic, scale-selective spatial filter was developed for the cubed-sphere spectral element model on the sphere. The filter equation is a high-order elliptic (Helmholtz) equation based on the spherical Laplacian operator, which is transformed into cubed-sphere local coordinates. The Laplacian operator is discretized on the computational domain, i.e., on each cell, by the spectral element method with Gauss-Lobatto Lagrange interpolating polynomials (GLLIPs) as the orthogonal basis functions. On the global domain, the discrete filter equation yielded a linear system represented by a highly sparse matrix. The density of this matrix increases quadratically (linearly) with the order of GLLIP (order of the filter), and the linear system is solved in only O (Ng) operations, where Ng is the total number of grid points. The solution, obtained by a row reduction method, demonstrated the typical accuracy and convergence rate of the cubed-sphere spectral element method. To achieve computational efficiency on parallel computers, the linear system was treated by an inverse matrix method (a sparse matrix-vector multiplication). The density of the inverse matrix was lowered to only a few times of the original sparse matrix without degrading the accuracy of the solution. For better computational efficiency, a local-domain high-order filter was introduced: The filter equation is applied to multiple cells, and then the central cell was only used to reconstruct the filtered field. The parallel efficiency of applying the inverse matrix method to the global- and local-domain filter was evaluated by the scalability on a distributed-memory parallel computer. The scale-selective performance of the filter was demonstrated on Earth topography. The usefulness of the filter as a hyper-viscosity for the vorticity equation was also demonstrated.

Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation

NASA Astrophysics Data System (ADS)

Leisenring, Marc; Moradkhani, Hamid

2012-10-01

SummaryA first step in understanding the impacts of sediment and controlling the sources of sediment is to quantify the mass loading. Since mass loading is the product of flow and concentration, the quantification of loads first requires the quantification of runoff volume. Using the National Weather Service's SNOW-17 and the Sacramento Soil Moisture Accounting (SAC-SMA) models, this study employed particle filter based Bayesian data assimilation methods to predict seasonal snow water equivalent (SWE) and runoff within a small watershed in the Lake Tahoe Basin located in California, USA. A procedure was developed to scale the variance multipliers (a.k.a hyperparameters) for model parameters and predictions based on the accuracy of the mean predictions relative to the ensemble spread. In addition, an online bias correction algorithm based on the lagged average bias was implemented to detect and correct for systematic bias in model forecasts prior to updating with the particle filter. Both of these methods significantly improved the performance of the particle filter without requiring excessively wide prediction bounds. The flow ensemble was linked to a non-linear regression model that was used to predict suspended sediment concentrations (SSCs) based on runoff rate and time of year. Runoff volumes and SSC were then combined to produce an ensemble of suspended sediment load estimates. Annual suspended sediment loads for the 5 years of simulation were finally computed along with 95% prediction intervals that account for uncertainty in both the SSC regression model and flow rate estimates. Understanding the uncertainty associated with annual suspended sediment load predictions is critical for making sound watershed management decisions aimed at maintaining the exceptional clarity of Lake Tahoe. The computational methods developed and applied in this research could assist with similar studies where it is important to quantify the predictive uncertainty of pollutant load estimates.

Integrating the ECG power-line interference removal methods with rule-based system.

PubMed

Kumaravel, N; Senthil, A; Sridhar, K S; Nithiyanandam, N

1995-01-01

The power-line frequency interference in electrocardiographic signals is eliminated to enhance the signal characteristics for diagnosis. The power-line frequency normally varies +/- 1.5 Hz from its standard value of 50 Hz. In the present work, the performances of the linear FIR filter, Wave digital filter (WDF) and adaptive filter for the power-line frequency variations from 48.5 to 51.5 Hz in steps of 0.5 Hz are studied. The advantage of the LMS adaptive filter in the removal of power-line frequency interference even if the frequency of interference varies by +/- 1.5 Hz from its normal value of 50 Hz over other fixed frequency filters is very well justified. A novel method of integrating rule-based system approach with linear FIR filter and also with Wave digital filter are proposed. The performances of Rule-based FIR filter and Rule-based Wave digital filter are compared with the LMS adaptive filter.

An accelerated non-Gaussianity based multichannel predictive deconvolution method with the limited supporting region of filters

NASA Astrophysics Data System (ADS)

Li, Zhong-xiao; Li, Zhen-chun

2016-09-01

The multichannel predictive deconvolution can be conducted in overlapping temporal and spatial data windows to solve the 2D predictive filter for multiple removal. Generally, the 2D predictive filter can better remove multiples at the cost of more computation time compared with the 1D predictive filter. In this paper we first use the cross-correlation strategy to determine the limited supporting region of filters where the coefficients play a major role for multiple removal in the filter coefficient space. To solve the 2D predictive filter the traditional multichannel predictive deconvolution uses the least squares (LS) algorithm, which requires primaries and multiples are orthogonal. To relax the orthogonality assumption the iterative reweighted least squares (IRLS) algorithm and the fast iterative shrinkage thresholding (FIST) algorithm have been used to solve the 2D predictive filter in the multichannel predictive deconvolution with the non-Gaussian maximization (L1 norm minimization) constraint of primaries. The FIST algorithm has been demonstrated as a faster alternative to the IRLS algorithm. In this paper we introduce the FIST algorithm to solve the filter coefficients in the limited supporting region of filters. Compared with the FIST based multichannel predictive deconvolution without the limited supporting region of filters the proposed method can reduce the computation burden effectively while achieving a similar accuracy. Additionally, the proposed method can better balance multiple removal and primary preservation than the traditional LS based multichannel predictive deconvolution and FIST based single channel predictive deconvolution. Synthetic and field data sets demonstrate the effectiveness of the proposed method.

External Aiding Methods for IMU-Based Navigation

DTIC Science & Technology

2016-11-26

Carlo simulation and particle filtering . This approach allows for the utilization of highly complex systems in a black box configuration with minimal...alternative method, which has the advantage of being less computationally demanding, is to use a Kalman filtering -based approach. The particular...Kalman filtering -based approach used here is known as linear covariance analysis. In linear covariance analysis, the nonlinear systems describing the

Identification of linear system models and state estimators for controls

NASA Technical Reports Server (NTRS)

Chen, Chung-Wen

1992-01-01

The following paper is presented in viewgraph format and covers topics including: (1) linear state feedback control system; (2) Kalman filter state estimation; (3) relation between residual and stochastic part of output; (4) obtaining Kalman filter gain; (5) state estimation under unknown system model and unknown noises; and (6) relationship between filter Markov parameters and system Markov parameters.

Silicon photonic filters with high rejection of both TE and TM modes for on-chip four wave mixing applications.

PubMed

Cantarella, Giuseppe; Klitis, Charalambos; Sorel, Marc; Strain, Michael J

2017-08-21

Wavelength selective filters represent one of the key elements for photonic integrated circuits (PIC) and many of their applications in linear and non-linear optics. In devices optimised for single polarisation operation, cross-polarisation scattering can significantly limit the achievable filter rejection. An on-chip filter consisting of elements to filter both TE and TM polarisations is demonstrated, based on a cascaded ring resonator geometry, which exhibits a high total optical rejection of over 60 dB. Monolithic integration of a cascaded ring filter with a four-wave mixing micro-ring device is also experimentally demonstrated with a FWM efficiency of -22dB and pump filter extinction of 62dB.

Computer simulation results of attitude estimation of earth orbiting satellites

NASA Technical Reports Server (NTRS)

Kou, S. R.

1976-01-01

Computer simulation results of attitude estimation of Earth-orbiting satellites (including Space Telescope) subjected to environmental disturbances and noises are presented. Decomposed linear recursive filter and Kalman filter were used as estimation tools. Six programs were developed for this simulation, and all were written in the basic language and were run on HP 9830A and HP 9866A computers. Simulation results show that a decomposed linear recursive filter is accurate in estimation and fast in response time. Furthermore, for higher order systems, this filter has computational advantages (i.e., less integration errors and roundoff errors) over a Kalman filter.

Modeling error analysis of stationary linear discrete-time filters

NASA Technical Reports Server (NTRS)

Patel, R.; Toda, M.

1977-01-01

The performance of Kalman-type, linear, discrete-time filters in the presence of modeling errors is considered. The discussion is limited to stationary performance, and bounds are obtained for the performance index, the mean-squared error of estimates for suboptimal and optimal (Kalman) filters. The computation of these bounds requires information on only the model matrices and the range of errors for these matrices. Consequently, a design can easily compare the performance of a suboptimal filter with that of the optimal filter, when only the range of errors in the elements of the model matrices is available.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, Y.M.; DiSante, C.J.; Lion, L.W.

Toxic trace metals have been implicated as a potential cause of recent flamingo kills at Lake Nakuru, Kenya. Chromium (Cr), copper (Cu), lead (Pb), and zinc (Zn) have accumulated in the lake sediments as a result of unregulated discharges and because this alkaline lake has no natural outlet. Lesser flamingos (Phoeniconaias minor) at Lake Nakuru feed predominantly on the cyanobacterium Spirulina platensis, and because of their filter-feeding mechanism, they are susceptible to exposure to particle-bound metals. Trace metal adsorption isotherms to lake sediments and S. platensis were obtained under simulated lake conditions, and a mathematical model was developed to predictmore » metal exposure via filter feeding based on predicted trace metal phase distribution. Metal adsorption to suspended solids followed the trend Pb {much_gt} Zn > Cr > Cu, and isotherms were linear up to 60 {micro}g/L. Adsorption to S. platensis cells followed the trend Pb {much_gt} Zn > Cu > Cr and fit Langmuir isotherms for Cr, Cu and Zn and a linear isotherm for Pb. Predicted phase distributions indicated that Cr and Pb in Lake Nakuru are predominantly associated with suspended solids, whereas Cu and Zn are distributed more evenly between the dissolved phase and particulate phases of both S. platensis and suspended solids. Based on established flamingo feeding rates and particle size selection, predicted Cr and Pb exposure occurs predominantly through ingestion of suspended solids, whereas Cu and Zn exposure occurs through ingestion of both suspended solids and S. platensis. For the lake conditions at the time of sampling, predicted ingestion rates based on measured metal concentrations in lake suspended solids were 0.71, 6.2, 0.81, and 13 mg/kg-d for Cr, Cu, Pb, and Zn, respectively.« less

The role of model dynamics in ensemble Kalman filter performance for chaotic systems

USGS Publications Warehouse

Ng, G.-H.C.; McLaughlin, D.; Entekhabi, D.; Ahanin, A.

2011-01-01

The ensemble Kalman filter (EnKF) is susceptible to losing track of observations, or 'diverging', when applied to large chaotic systems such as atmospheric and ocean models. Past studies have demonstrated the adverse impact of sampling error during the filter's update step. We examine how system dynamics affect EnKF performance, and whether the absence of certain dynamic features in the ensemble may lead to divergence. The EnKF is applied to a simple chaotic model, and ensembles are checked against singular vectors of the tangent linear model, corresponding to short-term growth and Lyapunov vectors, corresponding to long-term growth. Results show that the ensemble strongly aligns itself with the subspace spanned by unstable Lyapunov vectors. Furthermore, the filter avoids divergence only if the full linearized long-term unstable subspace is spanned. However, short-term dynamics also become important as non-linearity in the system increases. Non-linear movement prevents errors in the long-term stable subspace from decaying indefinitely. If these errors then undergo linear intermittent growth, a small ensemble may fail to properly represent all important modes, causing filter divergence. A combination of long and short-term growth dynamics are thus critical to EnKF performance. These findings can help in developing practical robust filters based on model dynamics. ?? 2011 The Authors Tellus A ?? 2011 John Wiley & Sons A/S.

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2010-01-01

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy

A quantum extended Kalman filter

NASA Astrophysics Data System (ADS)

Emzir, Muhammad F.; Woolley, Matthew J.; Petersen, Ian R.

2017-06-01

In quantum physics, a stochastic master equation (SME) estimates the state (density operator) of a quantum system in the Schrödinger picture based on a record of measurements made on the system. In the Heisenberg picture, the SME is a quantum filter. For a linear quantum system subject to linear measurements and Gaussian noise, the dynamics may be described by quantum stochastic differential equations (QSDEs), also known as quantum Langevin equations, and the quantum filter reduces to a so-called quantum Kalman filter. In this article, we introduce a quantum extended Kalman filter (quantum EKF), which applies a commutative approximation and a time-varying linearization to systems of nonlinear QSDEs. We will show that there are conditions under which a filter similar to a classical EKF can be implemented for quantum systems. The boundedness of estimation errors and the filtering problem with ‘state-dependent’ covariances for process and measurement noises are also discussed. We demonstrate the effectiveness of the quantum EKF by applying it to systems that involve multiple modes, nonlinear Hamiltonians, and simultaneous jump-diffusive measurements.

40 CFR 86.884-11 - Instrument checks.

Code of Federal Regulations, 2010 CFR

2010-07-01

... collection equipment response of zero; (3) Calibrated neutral density filters having approximately 10, 20, and 40 percent opacity shall be employed to check the linearity of the instrument. The filter(s) shall.... Filters with exposed filtering media should be checked for opacity every six months; all other filters...

40 CFR 92.122 - Smoke meter calibration.

Code of Federal Regulations, 2010 CFR

2010-07-01

... collection equipment response of zero; (b) Calibrated neutral density filters having approximately 10, 20, and 40 percent opacity shall be employed to check the linearity of the instrument. The filter(s) shall.... Filters with exposed filtering media should be checked for opacity every six months; all other filters...

40 CFR 86.884-11 - Instrument checks.

Code of Federal Regulations, 2012 CFR

2012-07-01

... collection equipment response of zero; (3) Calibrated neutral density filters having approximately 10, 20, and 40 percent opacity shall be employed to check the linearity of the instrument. The filter(s) shall.... Filters with exposed filtering media should be checked for opacity every six months; all other filters...

40 CFR 92.122 - Smoke meter calibration.

Code of Federal Regulations, 2013 CFR

2013-07-01

... collection equipment response of zero; (b) Calibrated neutral density filters having approximately 10, 20, and 40 percent opacity shall be employed to check the linearity of the instrument. The filter(s) shall.... Filters with exposed filtering media should be checked for opacity every six months; all other filters...

40 CFR 92.122 - Smoke meter calibration.

Code of Federal Regulations, 2012 CFR

2012-07-01

... collection equipment response of zero; (b) Calibrated neutral density filters having approximately 10, 20, and 40 percent opacity shall be employed to check the linearity of the instrument. The filter(s) shall.... Filters with exposed filtering media should be checked for opacity every six months; all other filters...

40 CFR 92.122 - Smoke meter calibration.

Code of Federal Regulations, 2011 CFR

2011-07-01

... collection equipment response of zero; (b) Calibrated neutral density filters having approximately 10, 20, and 40 percent opacity shall be employed to check the linearity of the instrument. The filter(s) shall.... Filters with exposed filtering media should be checked for opacity every six months; all other filters...

40 CFR 86.884-11 - Instrument checks.

Code of Federal Regulations, 2011 CFR

2011-07-01

... collection equipment response of zero; (3) Calibrated neutral density filters having approximately 10, 20, and 40 percent opacity shall be employed to check the linearity of the instrument. The filter(s) shall.... Filters with exposed filtering media should be checked for opacity every six months; all other filters...

40 CFR 86.884-11 - Instrument checks.

Code of Federal Regulations, 2013 CFR

2013-07-01

... collection equipment response of zero; (3) Calibrated neutral density filters having approximately 10, 20, and 40 percent opacity shall be employed to check the linearity of the instrument. The filter(s) shall.... Filters with exposed filtering media should be checked for opacity every six months; all other filters...

A comparison of washout filters using a human dynamic orientation model. M.S. Thesis

NASA Technical Reports Server (NTRS)

Riedel, S. A.

1977-01-01

The Ormsby model of human dynamic orientation, a discrete time computer program, was used to provide a vestibular explanation for observed differences between two washout schemes. These washout schemes, a linear washout and a nonlinear washout, were subjectively evaluated. It was found that the linear washout presented false rate cues, causing pilots to rate the simulation fidelity of the linear scheme much lower than the nonlinear scheme. By inputting these motion histories into the Ormsby model, it was shown that the linear filter causes discontinuities in the pilot's perceived angular velocity, resulting in the sensation of an anomalous rate cue. This phenomenon does not occur with the use of the nonlinear filter.

A Multi Directional Perfect Reconstruction Filter Bank Designed with 2-D Eigenfilter Approach: Application to Ultrasound Speckle Reduction.

PubMed

Nagare, Mukund B; Patil, Bhushan D; Holambe, Raghunath S

2017-02-01

B-Mode ultrasound images are degraded by inherent noise called Speckle, which creates a considerable impact on image quality. This noise reduces the accuracy of image analysis and interpretation. Therefore, reduction of speckle noise is an essential task which improves the accuracy of the clinical diagnostics. In this paper, a Multi-directional perfect-reconstruction (PR) filter bank is proposed based on 2-D eigenfilter approach. The proposed method used for the design of two-dimensional (2-D) two-channel linear-phase FIR perfect-reconstruction filter bank. In this method, the fan shaped, diamond shaped and checkerboard shaped filters are designed. The quadratic measure of the error function between the passband and stopband of the filter has been used an objective function. First, the low-pass analysis filter is designed and then the PR condition has been expressed as a set of linear constraints on the corresponding synthesis low-pass filter. Subsequently, the corresponding synthesis filter is designed using the eigenfilter design method with linear constraints. The newly designed 2-D filters are used in translation invariant pyramidal directional filter bank (TIPDFB) for reduction of speckle noise in ultrasound images. The proposed 2-D filters give better symmetry, regularity and frequency selectivity of the filters in comparison to existing design methods. The proposed method is validated on synthetic and real ultrasound data which ensures improvement in the quality of ultrasound images and efficiently suppresses the speckle noise compared to existing methods.

Study of the use of a nonlinear, rate limited, filter on pilot control signals

NASA Technical Reports Server (NTRS)

Adams, J. J.

1977-01-01

The use of a filter on the pilot's control output could improve the performance of the pilot-aircraft system. What is needed is a filter with a sharp high frequency cut-off, no resonance peak, and a minimum of lag at low frequencies. The present investigation studies the usefulness of a nonlinear, rate limited, filter in performing the needed function. The nonlinear filter is compared with a linear, first order filter, and no filter. An analytical study using pilot models and a simulation study using experienced test pilots was performed. The results showed that the nonlinear filter does promote quick, steady maneuvering. It is shown that the nonlinear filter attenuates the high frequency remnant and adds less phase lag to the low frequency signal than does the linear filter. It is also shown that the rate limit in the nonlinear filter can be set to be too restrictive, causing an unstable pilot-aircraft system response.

Third-Order Elliptic Lowpass Filter for Multi-Standard Baseband Chain Using Highly Linear Digitally Programmable OTA

NASA Astrophysics Data System (ADS)

Elamien, Mohamed B.; Mahmoud, Soliman A.

2018-03-01

In this paper, a third-order elliptic lowpass filter is designed using highly linear digital programmable balanced OTA. The filter exhibits a cutoff frequency tuning range from 2.2 MHz to 7.1 MHz, thus, it covers W-CDMA, UMTS, and DVB-H standards. The programmability concept in the filter is achieved by using digitally programmable operational transconductors amplifier (DPOTA). The DPOTA employs three linearization techniques which are the source degeneration, double differential pair and the adaptive biasing. Two current division networks (CDNs) are used to control the value of the transconductance. For the DPOTA, the third-order harmonic distortion (HD3) remains below -65 dB up to 0.4 V differential input voltage at 1.2 V supply voltage. The DPOTA and the filter are designed and simulated in 90 nm CMOS technology with LTspice simulator.

A state-based probabilistic model for tumor respiratory motion prediction

NASA Astrophysics Data System (ADS)

Kalet, Alan; Sandison, George; Wu, Huanmei; Schmitz, Ruth

2010-12-01

This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more general HMM-type predictive models. RMS errors for the time average model approach the theoretical limit of the HMM, and predicted state sequences are well correlated with sequences known to fit the data.

Multidimensional deconvolution of optical microscope and ultrasound imaging using adaptive least-mean-square (LMS) inverse filtering

NASA Astrophysics Data System (ADS)

Sapia, Mark Angelo

2000-11-01

Three-dimensional microscope images typically suffer from reduced resolution due to the effects of convolution, optical aberrations and out-of-focus blurring. Two- dimensional ultrasound images are also degraded by convolutional bluffing and various sources of noise. Speckle noise is a major problem in ultrasound images. In microscopy and ultrasound, various methods of digital filtering have been used to improve image quality. Several methods of deconvolution filtering have been used to improve resolution by reversing the convolutional effects, many of which are based on regularization techniques and non-linear constraints. The technique discussed here is a unique linear filter for deconvolving 3D fluorescence microscopy or 2D ultrasound images. The process is to solve for the filter completely in the spatial-domain using an adaptive algorithm to converge to an optimum solution for de-blurring and resolution improvement. There are two key advantages of using an adaptive solution: (1)it efficiently solves for the filter coefficients by taking into account all sources of noise and degraded resolution at the same time, and (2)achieves near-perfect convergence to the ideal linear deconvolution filter. This linear adaptive technique has other advantages such as avoiding artifacts of frequency-domain transformations and concurrent adaptation to suppress noise. Ultimately, this approach results in better signal-to-noise characteristics with virtually no edge-ringing. Many researchers have not adopted linear techniques because of poor convergence, noise instability and negative valued data in the results. The methods presented here overcome many of these well-documented disadvantages and provide results that clearly out-perform other linear methods and may also out-perform regularization and constrained algorithms. In particular, the adaptive solution is most responsible for overcoming the poor performance associated with linear techniques. This linear adaptive approach to deconvolution is demonstrated with results of restoring blurred phantoms for both microscopy and ultrasound and restoring 3D microscope images of biological cells and 2D ultrasound images of human subjects (courtesy of General Electric and Diasonics, Inc.).

An Integrity Framework for Image-Based Navigation Systems

DTIC Science & Technology

2010-06-01

Anton H. and Rorres C. Elementary Linear Algebra . New York, NY: John Wiley & Sons, Inc., 2000. 4. Arthur T. “The Disparity of Parity, Determining...107. Spilker , James J.J. Digital Communications by Satellite. Englewood Cliffs NJ: Prentice Hall, 1977. 108. Strang G. Linear Algebra and its...2.3 The Linearized and Extended Kalman Filters . . . . . . 22 2.3.1 State and Measurement Model Equations . . . 23 2.3.2 The Linearized Kalman Filter

Constructing an Efficient Self-Tuning Aircraft Engine Model for Control and Health Management Applications

NASA Technical Reports Server (NTRS)

Armstrong, Jeffrey B.; Simon, Donald L.

2012-01-01

Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulatns.

Generic Kalman Filter Software

NASA Technical Reports Server (NTRS)

Lisano, Michael E., II; Crues, Edwin Z.

2005-01-01

The Generic Kalman Filter (GKF) software provides a standard basis for the development of application-specific Kalman-filter programs. Historically, Kalman filters have been implemented by customized programs that must be written, coded, and debugged anew for each unique application, then tested and tuned with simulated or actual measurement data. Total development times for typical Kalman-filter application programs have ranged from months to weeks. The GKF software can simplify the development process and reduce the development time by eliminating the need to re-create the fundamental implementation of the Kalman filter for each new application. The GKF software is written in the ANSI C programming language. It contains a generic Kalman-filter-development directory that, in turn, contains a code for a generic Kalman filter function; more specifically, it contains a generically designed and generically coded implementation of linear, linearized, and extended Kalman filtering algorithms, including algorithms for state- and covariance-update and -propagation functions. The mathematical theory that underlies the algorithms is well known and has been reported extensively in the open technical literature. Also contained in the directory are a header file that defines generic Kalman-filter data structures and prototype functions and template versions of application-specific subfunction and calling navigation/estimation routine code and headers. Once the user has provided a calling routine and the required application-specific subfunctions, the application-specific Kalman-filter software can be compiled and executed immediately. During execution, the generic Kalman-filter function is called from a higher-level navigation or estimation routine that preprocesses measurement data and post-processes output data. The generic Kalman-filter function uses the aforementioned data structures and five implementation- specific subfunctions, which have been developed by the user on the basis of the aforementioned templates. The GKF software can be used to develop many different types of unfactorized Kalman filters. A developer can choose to implement either a linearized or an extended Kalman filter algorithm, without having to modify the GKF software. Control dynamics can be taken into account or neglected in the filter-dynamics model. Filter programs developed by use of the GKF software can be made to propagate equations of motion for linear or nonlinear dynamical systems that are deterministic or stochastic. In addition, filter programs can be made to operate in user-selectable "covariance analysis" and "propagation-only" modes that are useful in design and development stages.

EXPLICIT LEAST-DEGREE BOUNDARY FILTERS FOR DISCONTINUOUS GALERKIN.

PubMed

Nguyen, Dang-Manh; Peters, Jörg

2017-01-01

Convolving the output of Discontinuous Galerkin (DG) computations using spline filters can improve both smoothness and accuracy of the output. At domain boundaries, these filters have to be one-sided for non-periodic boundary conditions. Recently, position-dependent smoothness-increasing accuracy-preserving (PSIAC) filters were shown to be a superset of the well-known one-sided RLKV and SRV filters. Since PSIAC filters can be formulated symbolically, PSIAC filtering amounts to forming linear products with local DG output and so offers a more stable and efficient implementation. The paper introduces a new class of PSIAC filters NP 0 that have small support and are piecewise constant. Extensive numerical experiments for the canonical hyperbolic test equation show NP 0 filters outperform the more complex known boundary filters. NP 0 filters typically reduce the L ∞ error in the boundary region below that of the interior where optimally superconvergent symmetric filters of the same support are applied. NP 0 filtering can be implemented as forming linear combinations of the data with short rational weights. Exact derivatives of the convolved output are easy to compute.

EXPLICIT LEAST-DEGREE BOUNDARY FILTERS FOR DISCONTINUOUS GALERKIN*

PubMed Central

Nguyen, Dang-Manh; Peters, Jörg

2017-01-01

Convolving the output of Discontinuous Galerkin (DG) computations using spline filters can improve both smoothness and accuracy of the output. At domain boundaries, these filters have to be one-sided for non-periodic boundary conditions. Recently, position-dependent smoothness-increasing accuracy-preserving (PSIAC) filters were shown to be a superset of the well-known one-sided RLKV and SRV filters. Since PSIAC filters can be formulated symbolically, PSIAC filtering amounts to forming linear products with local DG output and so offers a more stable and efficient implementation. The paper introduces a new class of PSIAC filters NP0 that have small support and are piecewise constant. Extensive numerical experiments for the canonical hyperbolic test equation show NP0 filters outperform the more complex known boundary filters. NP0 filters typically reduce the L∞ error in the boundary region below that of the interior where optimally superconvergent symmetric filters of the same support are applied. NP0 filtering can be implemented as forming linear combinations of the data with short rational weights. Exact derivatives of the convolved output are easy to compute. PMID:29081643

Linear variable narrow bandpass optical filters in the far infrared (Conference Presentation)

NASA Astrophysics Data System (ADS)

Rahmlow, Thomas D.

2017-06-01

We are currently developing linear variable filters (LVF) with very high wavelength gradients. In the visible, these filters have a wavelength gradient of 50 to 100 nm/mm. In the infrared, the wavelength gradient covers the range of 500 to 900 microns/mm. Filter designs include band pass, long pass and ulta-high performance anti-reflection coatings. The active area of the filters is on the order of 5 to 30 mm along the wavelength gradient and up to 30 mm in the orthogonal, constant wavelength direction. Variation in performance along the constant direction is less than 1%. Repeatable performance from filter to filter, absolute placement of the filter relative to a substrate fiducial and, high in-band transmission across the full spectral band is demonstrated. Applications include order sorting filters, direct replacement of the spectrometer and hyper-spectral imaging. Off-band rejection with an optical density of greater than 3 allows use of the filter as an order sorting filter. The linear variable order sorting filters replaces other filter types such as block filters. The disadvantage of block filters is the loss of pixels due to the transition between filter blocks. The LVF is a continuous gradient without a discrete transition between filter wavelength regions. If the LVF is designed as a narrow band pass filter, it can be used in place of a spectrometer thus reducing overall sensor weight and cost while improving the robustness of the sensor. By controlling the orthogonal performance (smile) the LVF can be sized to the dimensions of the detector. When imaging on to a 2 dimensional array and operating the sensor in a push broom configuration, the LVF spectrometer performs as a hyper-spectral imager. This paper presents performance of LVF fabricated in the far infrared on substrates sized to available detectors. The impact of spot size, F-number and filter characterization are presented. Results are also compared to extended visible LVF filters.

Desired Accuracy Estimation of Noise Function from ECG Signal by Fuzzy Approach

PubMed Central

Vahabi, Zahra; Kermani, Saeed

2012-01-01

Unknown noise and artifacts present in medical signals with non-linear fuzzy filter will be estimated and then removed. An adaptive neuro-fuzzy interference system which has a non-linear structure presented for the noise function prediction by before Samples. This paper is about a neuro-fuzzy method to estimate unknown noise of Electrocardiogram signal. Adaptive neural combined with Fuzzy System to construct a fuzzy Predictor. For this system setting parameters such as the number of Membership Functions for each input and output, training epochs, type of MFs for each input and output, learning algorithm and etc. is determined by learning data. At the end simulated experimental results are presented for proper validation. PMID:23717810

SU-G-TeP1-03: Beam Quality Correction Factors for Linear Accelerator with and Without Flattening Filter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Czarnecki, D; Voigts-Rhetz, P von; Zink, K

2016-06-15

Purpose: The impact of removing the flattening filter on absolute dosimetry based on IAEA’s TPR-398 and AAPM’s TG-51 was investigated in this study using Monte Carlo simulations. Methods: The EGSnrc software package was used for all Monte Carlo simulations performed in this work. Five different ionization chambers and nine linear accelerator heads have been modeled according to technical drawings. To generate a flattening filter free radiation field the flattening filter was replaced by a 2 mm thick aluminum layer. Dose calculation in a water phantom were performed to calculate the beam quality correction factor k{sub Q} as a function ofmore » the beam quality specifiers %dd(10){sub x}, TPR{sub 20,10} and mean photon and electron energies at the point of measurement in photon fields with (WFF) and without flattening filter (FFF). Results: The beam quality correction factor as a function of %dd(10){sub x} differs systematically between FFF and WFF beams for all investigated ionization chambers. The largest difference of 1.8% was observed for the largest investigated Farmer-type ionization chamber with a sensitive volume of 0.69 cm{sup 3}. For ionization chambers with a smaller nominal sensitive volume (0.015 – 0.3 cm{sup 3}) the deviation was less than 0.4% between WFF and FFF beams for %dd(10){sub x} > 62%. The specifier TPR{sub 20,10} revealed only a good correlation between WFF and FFF beams (< 0.3%) for low energies. Conclusion: The results confirm that %dd(10){sub x} is a suitable beam quality specifier for FFF beams with an acceptable bias. The deviation depends on the volume of the ionization chamber. Using %dd(10){sub x} to predict k{sub Q} for a large volume chamber in a FFF photon field may lead to not acceptable errors according to the results of this study. This bias may be caused by the volume effect due to the inhomogeneous photon fields of FFF linear accelerators.« less

Small field detector correction factors: effects of the flattening filter for Elekta and Varian linear accelerators

PubMed Central

Liu, Paul Z.Y.; Lee, Christopher; McKenzie, David R.; Suchowerska, Natalka

2016-01-01

Flattening filter‐free (FFF) beams are becoming the preferred beam type for stereotactic radiosurgery (SRS) and stereotactic ablative radiation therapy (SABR), as they enable an increase in dose rate and a decrease in treatment time. This work assesses the effects of the flattening filter on small field output factors for 6 MV beams generated by both Elekta and Varian linear accelerators, and determines differences between detector response in flattened (FF) and FFF beams. Relative output factors were measured with a range of detectors (diodes, ionization chambers, radiochromic film, and microDiamond) and referenced to the relative output factors measured with an air core fiber optic dosimeter (FOD), a scintillation dosimeter developed at Chris O'Brien Lifehouse, Sydney. Small field correction factors were generated for both FF and FFF beams. Diode measured detector response was compared with a recently published mathematical relation to predict diode response corrections in small fields. The effect of flattening filter removal on detector response was quantified using a ratio of relative detector responses in FFF and FF fields for the same field size. The removal of the flattening filter was found to have a small but measurable effect on ionization chamber response with maximum deviations of less than ±0.9% across all field sizes measured. Solid‐state detectors showed an increased dependence on the flattening filter of up to ±1.6%. Measured diode response was within ±1.1% of the published mathematical relation for all fields up to 30 mm, independent of linac type and presence or absence of a flattening filter. For 6 MV beams, detector correction factors between FFF and FF beams are interchangeable for a linac between FF and FFF modes, providing that an additional uncertainty of up to ±1.6% is accepted. PACS number(s): 87.55.km, 87.56.bd, 87.56.Da PMID:27167280

Discrete integration of continuous Kalman filtering equations for time invariant second-order structural systems

NASA Technical Reports Server (NTRS)

Park, K. C.; Belvin, W. Keith

1990-01-01

A general form for the first-order representation of the continuous second-order linear structural-dynamics equations is introduced to derive a corresponding form of first-order continuous Kalman filtering equations. Time integration of the resulting equations is carried out via a set of linear multistep integration formulas. It is shown that a judicious combined selection of computational paths and the undetermined matrices introduced in the general form of the first-order linear structural systems leads to a class of second-order discrete Kalman filtering equations involving only symmetric sparse N x N solution matrices.

Minimotif Miner 3.0: database expansion and significantly improved reduction of false-positive predictions from consensus sequences.

PubMed

Mi, Tian; Merlin, Jerlin Camilus; Deverasetty, Sandeep; Gryk, Michael R; Bill, Travis J; Brooks, Andrew W; Lee, Logan Y; Rathnayake, Viraj; Ross, Christian A; Sargeant, David P; Strong, Christy L; Watts, Paula; Rajasekaran, Sanguthevar; Schiller, Martin R

2012-01-01

Minimotif Miner (MnM available at http://minimotifminer.org or http://mnm.engr.uconn.edu) is an online database for identifying new minimotifs in protein queries. Minimotifs are short contiguous peptide sequences that have a known function in at least one protein. Here we report the third release of the MnM database which has now grown 60-fold to approximately 300,000 minimotifs. Since short minimotifs are by their nature not very complex we also summarize a new set of false-positive filters and linear regression scoring that vastly enhance minimotif prediction accuracy on a test data set. This online database can be used to predict new functions in proteins and causes of disease.

The use of linear programming techniques to design optimal digital filters for pulse shaping and channel equalization

NASA Technical Reports Server (NTRS)

Houts, R. C.; Burlage, D. W.

1972-01-01

A time domain technique is developed to design finite-duration impulse response digital filters using linear programming. Two related applications of this technique in data transmission systems are considered. The first is the design of pulse shaping digital filters to generate or detect signaling waveforms transmitted over bandlimited channels that are assumed to have ideal low pass or bandpass characteristics. The second is the design of digital filters to be used as preset equalizers in cascade with channels that have known impulse response characteristics. Example designs are presented which illustrate that excellent waveforms can be generated with frequency-sampling filters and the ease with which digital transversal filters can be designed for preset equalization.

Reconfigurable Analog PDE computation for Baseband and RFComputation

DTIC Science & Technology

2017-03-01

waveguiding PDEs. One-dimensional ladder topologies enable linear delays, linear-phase analog filters , as well as analog beamforming, potentially at RF...performance. This discussion focuses on ODE / PDE analog computation available in SoC FPAA structures. One such computation is a ladder filter (Fig...Implementation of a one-dimensional ladder filter for computing inductor (L) and capacitor (C) lines. These components can be implemented in CABs or as

Improvement of PM concentration predictability using WRF-CMAQ-DLM coupled system and its applications

NASA Astrophysics Data System (ADS)

Lee, Soon Hwan; Kim, Ji Sun; Lee, Kang Yeol; Shon, Keon Tae

2017-04-01

Air quality due to increasing Particulate Matter(PM) in Korea in Asia is getting worse. At present, the PM forecast is announced based on the PM concentration predicted from the air quality prediction numerical model. However, forecast accuracy is not as high as expected due to various uncertainties for PM physical and chemical characteristics. The purpose of this study was to develop a numerical-statistically ensemble models to improve the accuracy of prediction of PM10 concentration. Numerical models used in this study are the three dimensional atmospheric model Weather Research and Forecasting(WRF) and the community multiscale air quality model (CMAQ). The target areas for the PM forecast are Seoul, Busan, Daegu, and Daejeon metropolitan areas in Korea. The data used in the model development are PM concentration and CMAQ predictions and the data period is 3 months (March 1 - May 31, 2014). The dynamic-statistical technics for reducing the systematic error of the CMAQ predictions was applied to the dynamic linear model(DLM) based on the Baysian Kalman filter technic. As a result of applying the metrics generated from the dynamic linear model to the forecasting of PM concentrations accuracy was improved. Especially, at the high PM concentration where the damage is relatively large, excellent improvement results are shown.

Comparing Consider-Covariance Analysis with Sigma-Point Consider Filter and Linear-Theory Consider Filter Formulations

NASA Technical Reports Server (NTRS)

Lisano, Michael E.

2007-01-01

Recent literature in applied estimation theory reflects growing interest in the sigma-point (also called unscented ) formulation for optimal sequential state estimation, often describing performance comparisons with extended Kalman filters as applied to specific dynamical problems [c.f. 1, 2, 3]. Favorable attributes of sigma-point filters are described as including a lower expected error for nonlinear even non-differentiable dynamical systems, and a straightforward formulation not requiring derivation or implementation of any partial derivative Jacobian matrices. These attributes are particularly attractive, e.g. in terms of enabling simplified code architecture and streamlined testing, in the formulation of estimators for nonlinear spaceflight mechanics systems, such as filter software onboard deep-space robotic spacecraft. As presented in [4], the Sigma-Point Consider Filter (SPCF) algorithm extends the sigma-point filter algorithm to the problem of consider covariance analysis. Considering parameters in a dynamical system, while estimating its state, provides an upper bound on the estimated state covariance, which is viewed as a conservative approach to designing estimators for problems of general guidance, navigation and control. This is because, whether a parameter in the system model is observable or not, error in the knowledge of the value of a non-estimated parameter will increase the actual uncertainty of the estimated state of the system beyond the level formally indicated by the covariance of an estimator that neglects errors or uncertainty in that parameter. The equations for SPCF covariance evolution are obtained in a fashion similar to the derivation approach taken with standard (i.e. linearized or extended) consider parameterized Kalman filters (c.f. [5]). While in [4] the SPCF and linear-theory consider filter (LTCF) were applied to an illustrative linear dynamics/linear measurement problem, in the present work examines the SPCF as applied to nonlinear sequential consider covariance analysis, i.e. in the presence of nonlinear dynamics and nonlinear measurements. A simple SPCF for orbit determination, exemplifying an algorithm hosted in the guidance, navigation and control (GN&C) computer processor of a hypothetical robotic spacecraft, was implemented, and compared with an identically-parameterized (standard) extended, consider-parameterized Kalman filter. The onboard filtering scenario examined is a hypothetical spacecraft orbit about a small natural body with imperfectly-known mass. The formulations, relative complexities, and performances of the filters are compared and discussed.

Total and methylmercury partitioning between colloids and true solution: From case studies in sediment overlying and porewaters to a generalized model.

PubMed

Guédron, Stéphane; Devin, Simon; Vignati, Davide A L

2016-02-01

Tangential flow ultrafiltration was used to determine the partitioning of total mercury (THg) and monomethylmercury (MMHg) between colloids and true solution in sediment overlying and porewaters collected in Lake Geneva (Switzerland and France), Venice Lagoon (Italy), and Baihua Reservoir (China). Overlying water and porewater spanned different ranges of THg and MMHg concentrations, redox conditions, and salinity. Total Hg, MMHg, and dissolved organic carbon (DOC) concentrations were measured in filter-passing (<0.45 μm), colloidal (3 kDa-0.45 μm), and truly dissolved (<3 kDa) fractions. The percentages of filterable Hg and MMHg associated with colloids (arithmetic means ±1 standard deviation [SD]) were 29 ± 11% for THg (range, 4-60%) and 44 ± 17% for MMHg (range, 15-65%). Ultrafiltration DOC mass balances were often not satisfactory. However, this was apparently without consequences on THg/MMHg fractionation, suggesting that only a part of total DOC controlled THg/MMHg partitioning in overlying water and porewater. Linear relationships existed between filter passing and truly dissolved concentrations of THg and MMHg, suggesting that mechanisms controlling their partitioning are, at least partly, similar across aquatic systems. These linear relationships could be extended to data from published studies and ultrafilterable concentrations often could be predicted, within a factor of 2, from the measurement of filter-passing ones. The possibility to easily model THg/MMHg partitioning across aquatic systems will facilitate its consideration in general biogeochemical THg/MMHg models. © 2015 SETAC.

Near-infrared spectral image analysis of pork marbling based on Gabor filter and wide line detector techniques.

PubMed

Huang, Hui; Liu, Li; Ngadi, Michael O; Gariépy, Claude; Prasher, Shiv O

2014-01-01

Marbling is an important quality attribute of pork. Detection of pork marbling usually involves subjective scoring, which raises the efficiency costs to the processor. In this study, the ability to predict pork marbling using near-infrared (NIR) hyperspectral imaging (900-1700 nm) and the proper image processing techniques were studied. Near-infrared images were collected from pork after marbling evaluation according to current standard chart from the National Pork Producers Council. Image analysis techniques-Gabor filter, wide line detector, and spectral averaging-were applied to extract texture, line, and spectral features, respectively, from NIR images of pork. Samples were grouped into calibration and validation sets. Wavelength selection was performed on calibration set by stepwise regression procedure. Prediction models of pork marbling scores were built using multiple linear regressions based on derivatives of mean spectra and line features at key wavelengths. The results showed that the derivatives of both texture and spectral features produced good results, with correlation coefficients of validation of 0.90 and 0.86, respectively, using wavelengths of 961, 1186, and 1220 nm. The results revealed the great potential of the Gabor filter for analyzing NIR images of pork for the effective and efficient objective evaluation of pork marbling.

Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit

PubMed Central

Bharioke, Arjun; Chklovskii, Dmitri B.

2015-01-01

Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs. PMID:26247884

Assessment of the non-Gaussianity and non-linearity levels of simulated sEMG signals on stationary segments.

PubMed

Messaoudi, Noureddine; Bekka, Raïs El'hadi; Ravier, Philippe; Harba, Rachid

2017-02-01

The purpose of this paper was to evaluate the effects of the longitudinal single differential (LSD), the longitudinal double differential (LDD) and the normal double differential (NDD) spatial filters, the electrode shape, the inter-electrode distance (IED) on non-Gaussianity and non-linearity levels of simulated surface EMG (sEMG) signals when the maximum voluntary contraction (MVC) varied from 10% to 100% by a step of 10%. The effects of recruitment range thresholds (RR), the firing rate (FR) strategy and the peak firing rate (PFR) of motor units were also considered. A cylindrical multilayer model of the volume conductor and a model of motor unit (MU) recruitment and firing rate were used to simulate sEMG signals in a pool of 120 MUs for 5s. Firstly, the stationarity of sEMG signals was tested by the runs, the reverse arrangements (RA) and the modified reverse arrangements (MRA) tests. Then the non-Gaussianity was characterised with bicoherence and kurtosis, and non-linearity levels was evaluated with linearity test. The kurtosis analysis showed that the sEMG signals detected by the LSD filter were the most Gaussian and those detected by the NDD filter were the least Gaussian. In addition, the sEMG signals detected by the LSD filter were the most linear. For a given filter, the sEMG signals detected by using rectangular electrodes were more Gaussian and more linear than that detected with circular electrodes. Moreover, the sEMG signals are less non-Gaussian and more linear with reverse onion-skin firing rate strategy than those with onion-skin strategy. The levels of sEMG signal Gaussianity and linearity increased with the increase of the IED, RR and PFR. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bias Reduction and Filter Convergence for Long Range Stereo

NASA Technical Reports Server (NTRS)

Sibley, Gabe; Matthies, Larry; Sukhatme, Gaurav

2005-01-01

We are concerned here with improving long range stereo by filtering image sequences. Traditionally, measurement errors from stereo camera systems have been approximated as 3-D Gaussians, where the mean is derived by triangulation and the covariance by linearized error propagation. However, there are two problems that arise when filtering such 3-D measurements. First, stereo triangulation suffers from a range dependent statistical bias; when filtering this leads to over-estimating the true range. Second, filtering 3-D measurements derived via linearized error propagation leads to apparent filter divergence; the estimator is biased to under-estimate range. To address the first issue, we examine the statistical behavior of stereo triangulation and show how to remove the bias by series expansion. The solution to the second problem is to filter with image coordinates as measurements instead of triangulated 3-D coordinates.

Development of a new linearly variable edge filter (LVEF)-based compact slit-less mini-spectrometer

NASA Astrophysics Data System (ADS)

Mahmoud, Khaled; Park, Seongchong; Lee, Dong-Hoon

2018-02-01

This paper presents the development of a compact charge-coupled detector (CCD) spectrometer. We describe the design, concept and characterization of VNIR linear variable edge filter (LVEF)- based mini-spectrometer. The new instrument has been realized for operation in the 300 nm to 850 nm wavelength range. The instrument consists of a linear variable edge filter in front of CCD array. Low-size, light-weight and low-cost could be achieved using the linearly variable filters with no need to use any moving parts for wavelength selection as in the case of commercial spectrometers available in the market. This overview discusses the main components characteristics, the main concept with the main advantages and limitations reported. Experimental characteristics of the LVEFs are described. The mathematical approach to get the position-dependent slit function of the presented prototype spectrometer and its numerical de-convolution solution for a spectrum reconstruction is described. The performance of our prototype instrument is demonstrated by measuring the spectrum of a reference light source.

DVD-COOP: Innovative Conjunction Prediction Using Voronoi-filter based on the Dynamic Voronoi Diagram of 3D Spheres

NASA Astrophysics Data System (ADS)

Cha, J.; Ryu, J.; Lee, M.; Song, C.; Cho, Y.; Schumacher, P.; Mah, M.; Kim, D.

Conjunction prediction is one of the critical operations in space situational awareness (SSA). For geospace objects, common algorithms for conjunction prediction are usually based on all-pairwise check, spatial hash, or kd-tree. Computational load is usually reduced through some filters. However, there exists a good chance of missing potential collisions between space objects. We present a novel algorithm which both guarantees no missing conjunction and is efficient to answer to a variety of spatial queries including pairwise conjunction prediction. The algorithm takes only O(k log N) time for N objects in the worst case to answer conjunctions where k is a constant which is linear to prediction time length. The proposed algorithm, named DVD-COOP (Dynamic Voronoi Diagram-based Conjunctive Orbital Object Predictor), is based on the dynamic Voronoi diagram of moving spherical balls in 3D space. The algorithm has a preprocessing which consists of two steps: The construction of an initial Voronoi diagram (taking O(N) time on average) and the construction of a priority queue for the events of topology changes in the Voronoi diagram (taking O(N log N) time in the worst case). The scalability of the proposed algorithm is also discussed. We hope that the proposed Voronoi-approach will change the computational paradigm in spatial reasoning among space objects.

Design of Linear Quadratic Regulators and Kalman Filters

NASA Technical Reports Server (NTRS)

Lehtinen, B.; Geyser, L.

1986-01-01

AESOP solves problems associated with design of controls and state estimators for linear time-invariant systems. Systems considered are modeled in state-variable form by set of linear differential and algebraic equations with constant coefficients. Two key problems solved by AESOP are linear quadratic regulator (LQR) design problem and steady-state Kalman filter design problem. AESOP is interactive. User solves design problems and analyzes solutions in single interactive session. Both numerical and graphical information available to user during the session.

Ortho-Babinet polarization-interrogating filter: an interferometric approach to polarization measurement.

PubMed

Van Delden, Jay S

2003-07-15

A novel, interferometric, polarization-interrogating filter assembly and method for the simultaneous measurement of all four Stokes parameters across a partially polarized irradiance image in a no-moving-parts, instantaneous, highly sensitive manner is described. In the reported embodiment of the filter, two spatially varying linear retarders and a linear polarizer comprise an ortho-Babinet, polarization-interrogating (OBPI) filter. The OBPI filter uniquely encodes the incident ensemble of electromagnetic wave fronts comprising a partially polarized irradiance image in a controlled, deterministic, spatially varying manner to map the complete state of polarization across the image to local variations in a superposed interference pattern. Experimental interferograms are reported along with a numerical simulation of the method.

Digital signal processing and control and estimation theory -- Points of tangency, area of intersection, and parallel directions

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1976-01-01

A number of current research directions in the fields of digital signal processing and modern control and estimation theory were studied. Topics such as stability theory, linear prediction and parameter identification, system analysis and implementation, two-dimensional filtering, decentralized control and estimation, image processing, and nonlinear system theory were examined in order to uncover some of the basic similarities and differences in the goals, techniques, and philosophy of the two disciplines. An extensive bibliography is included.

Fast estimate of Hartley entropy in image sharpening

NASA Astrophysics Data System (ADS)

Krbcová, Zuzana; Kukal, Jaromír.; Svihlik, Jan; Fliegel, Karel

2016-09-01

Two classes of linear IIR filters: Laplacian of Gaussian (LoG) and Difference of Gaussians (DoG) are frequently used as high pass filters for contextual vision and edge detection. They are also used for image sharpening when linearly combined with the original image. Resulting sharpening filters are radially symmetric in spatial and frequency domains. Our approach is based on the radial approximation of unknown optimal filter, which is designed as a weighted sum of Gaussian filters with various radii. The novel filter is designed for MRI image enhancement where the image intensity represents anatomical structure plus additive noise. We prefer the gradient norm of Hartley entropy of whole image intensity as a measure which has to be maximized for the best sharpening. The entropy estimation procedure is as fast as FFT included in the filter but this estimate is a continuous function of enhanced image intensities. Physically motivated heuristic is used for optimum sharpening filter design by its parameter tuning. Our approach is compared with Wiener filter on MRI images.

Accurate and scalable social recommendation using mixed-membership stochastic block models.

PubMed

Godoy-Lorite, Antonia; Guimerà, Roger; Moore, Cristopher; Sales-Pardo, Marta

2016-12-13

With increasing amounts of information available, modeling and predicting user preferences-for books or articles, for example-are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.

Accurate and scalable social recommendation using mixed-membership stochastic block models

PubMed Central

Godoy-Lorite, Antonia; Moore, Cristopher

2016-01-01

With increasing amounts of information available, modeling and predicting user preferences—for books or articles, for example—are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users’ ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user’s and item’s groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets. PMID:27911773

Filter Selection for Optimizing the Spectral Sensitivity of Broadband Multispectral Cameras Based on Maximum Linear Independence.

PubMed

Li, Sui-Xian

2018-05-07

Previous research has shown that the effectiveness of selecting filter sets from among a large set of commercial broadband filters by a vector analysis method based on maximum linear independence (MLI). However, the traditional MLI approach is suboptimal due to the need to predefine the first filter of the selected filter set to be the maximum ℓ₂ norm among all available filters. An exhaustive imaging simulation with every single filter serving as the first filter is conducted to investigate the features of the most competent filter set. From the simulation, the characteristics of the most competent filter set are discovered. Besides minimization of the condition number, the geometric features of the best-performed filter set comprise a distinct transmittance peak along the wavelength axis of the first filter, a generally uniform distribution for the peaks of the filters and substantial overlaps of the transmittance curves of the adjacent filters. Therefore, the best-performed filter sets can be recognized intuitively by simple vector analysis and just a few experimental verifications. A practical two-step framework for selecting optimal filter set is recommended, which guarantees a significant enhancement of the performance of the systems. This work should be useful for optimizing the spectral sensitivity of broadband multispectral imaging sensors.

A circuit model for nonlinear simulation of radio-frequency filters using bulk acoustic wave resonators.

PubMed

Ueda, Masanori; Iwaki, Masafumi; Nishihara, Tokihiro; Satoh, Yoshio; Hashimoto, Ken-ya

2008-04-01

This paper describes a circuit model for the analysis of nonlinearity in the filters based on radiofrequency (RF) bulk acoustic wave (BAW) resonators. The nonlinear output is expressed by a current source connected parallel to the linear resonator. Amplitude of the nonlinear current source is programmed proportional to the product of linear currents flowing in the resonator. Thus, the nonlinear analysis is performed by the common linear analysis, even for complex device structures. The analysis is applied to a ladder-type RF BAW filter, and frequency dependence of the nonlinear output is discussed. Furthermore, this analysis is verified through comparison with experiments.

Distance estimation and collision prediction for on-line robotic motion planning

NASA Technical Reports Server (NTRS)

Kyriakopoulos, K. J.; Saridis, G. N.

1992-01-01

An efficient method for computing the minimum distance and predicting collisions between moving objects is presented. This problem is incorporated into the framework of an in-line motion-planning algorithm to satisfy collision avoidance between a robot and moving objects modeled as convex polyhedra. In the beginning, the deterministic problem where the information about the objects is assumed to be certain is examined. L(1) or L(infinity) norms are used to represent distance and the problem becomes a linear programming problem. The stochastic problem is formulated where the uncertainty is induced by sensing and the unknown dynamics of the moving obstacles. Two problems are considered: First, filtering of the distance between the robot and the moving object at the present time. Second, prediction of the minimum distance in the future in order to predict the collision time.

A comparison of optimal MIMO linear and nonlinear models for brain machine interfaces

NASA Astrophysics Data System (ADS)

Kim, S.-P.; Sanchez, J. C.; Rao, Y. N.; Erdogmus, D.; Carmena, J. M.; Lebedev, M. A.; Nicolelis, M. A. L.; Principe, J. C.

2006-06-01

The field of brain-machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100-200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

Model-Based Engine Control Architecture with an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2016-01-01

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces.

PubMed

Kim, S-P; Sanchez, J C; Rao, Y N; Erdogmus, D; Carmena, J M; Lebedev, M A; Nicolelis, M A L; Principe, J C

2006-06-01

The field of brain-machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100-200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

Kalman filter with a linear state model for PDR+WLAN positioning and its application to assisting a particle filter

NASA Astrophysics Data System (ADS)

Raitoharju, Matti; Nurminen, Henri; Piché, Robert

2015-12-01

Indoor positioning based on wireless local area network (WLAN) signals is often enhanced using pedestrian dead reckoning (PDR) based on an inertial measurement unit. The state evolution model in PDR is usually nonlinear. We present a new linear state evolution model for PDR. In simulated-data and real-data tests of tightly coupled WLAN-PDR positioning, the positioning accuracy with this linear model is better than with the traditional models when the initial heading is not known, which is a common situation. The proposed method is computationally light and is also suitable for smoothing. Furthermore, we present modifications to WLAN positioning based on Gaussian coverage areas and show how a Kalman filter using the proposed model can be used for integrity monitoring and (re)initialization of a particle filter.

Color Sparse Representations for Image Processing: Review, Models, and Prospects.

PubMed

Barthélemy, Quentin; Larue, Anthony; Mars, Jérôme I

2015-11-01

Sparse representations have been extended to deal with color images composed of three channels. A review of dictionary-learning-based sparse representations for color images is made here, detailing the differences between the models, and comparing their results on the real and simulated data. These models are considered in a unifying framework that is based on the degrees of freedom of the linear filtering/transformation of the color channels. Moreover, this allows it to be shown that the scalar quaternionic linear model is equivalent to constrained matrix-based color filtering, which highlights the filtering implicitly applied through this model. Based on this reformulation, the new color filtering model is introduced, using unconstrained filters. In this model, spatial morphologies of color images are encoded by atoms, and colors are encoded by color filters. Color variability is no longer captured in increasing the dictionary size, but with color filters, this gives an efficient color representation.

From Spiking Neuron Models to Linear-Nonlinear Models

PubMed Central

Ostojic, Srdjan; Brunel, Nicolas

2011-01-01

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates. PMID:21283777

From spiking neuron models to linear-nonlinear models.

PubMed

Ostojic, Srdjan; Brunel, Nicolas

2011-01-20

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

Assessing the impact of non-tidal atmospheric loading on a Kalman filter-based terrestrial reference frame

NASA Astrophysics Data System (ADS)

Abbondanza, Claudio; Altamimi, Zuheir; Chin, Toshio; Collilieux, Xavier; Dach, Rolf; Gross, Richard; Heflin, Michael; König, Rolf; Lemoine, Frank; Macmillan, Dan; Parker, Jay; van Dam, Tonie; Wu, Xiaoping

2014-05-01

The International Terrestrial Reference Frame (ITRF) adopts a piece-wise linear model to parameterize regularized station positions and velocities. The space-geodetic (SG) solutions from VLBI, SLR, GPS and DORIS used as input in the ITRF combination process account for tidal loading deformations, but ignore the non-tidal part. As a result, the non-linear signal observed in the time series of SG-derived station positions in part reflects non-tidal loading displacements not introduced in the SG data reduction. In this analysis, we assess the impact of non-tidal atmospheric loading (NTAL) corrections on the TRF computation. Focusing on the a-posteriori approach, (i) the NTAL model derived from the National Centre for Environmental Prediction (NCEP) surface pressure is removed from the SINEX files of the SG solutions used as inputs to the TRF determinations; (ii) adopting a Kalman-filter based approach, two distinct linear TRFs are estimated combining the 4 SG solutions with (corrected TRF solution) and without the NTAL displacements (standard TRF solution). Linear fits (offset and atmospheric velocity) of the NTAL displacements removed during step (i) are estimated accounting for the station position discontinuities introduced in the SG solutions and adopting different weighting strategies. The NTAL-derived (atmospheric) velocity fields are compared to those obtained from the TRF reductions during step (ii). The consistency between the atmospheric and the TRF-derived velocity fields is examined. We show how the presence of station position discontinuities in SG solutions degrades the agreement between the velocity fields and compare the effect of different weighting structure adopted while estimating the linear fits to the NTAL displacements. Finally, we evaluate the effect of restoring the atmospheric velocities determined through the linear fits of the NTAL displacements to the single-technique linear reference frames obtained by stacking the standard SG SINEX files. Differences between the velocity fields obtained restoring the NTAL displacements and the standard stacked linear reference frames are discussed.

SU-E-T-525: Ionization Chamber Perturbation in Flattening Filter Free Beams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Czarnecki, D; Voigts-Rhetz, P von; Zink, K

2015-06-15

Purpose: Changing the characteristic of a photon beam by mechanically removing the flattening filter may impact the dose response of ionization chambers. Thus, perturbation factors of cylindrical ionization chambers in conventional and flattening filter free photon beams were calculated by Monte Carlo simulations. Methods: The EGSnrc/BEAMnrc code system was used for all Monte Carlo calculations. BEAMnrc models of nine different linear accelerators with and without flattening filter were used to create realistic photon sources. Monte Carlo based calculations to determine the fluence perturbations due to the presens of the chambers components, the different material of the sensitive volume (air insteadmore » of water) as well as the volume effect were performed by the user code egs-chamber. Results: Stem, central electrode, wall, density and volume perturbation factors for linear accelerators with and without flattening filter were calculated as a function of the beam quality specifier TPR{sub 20/10}. A bias between the perturbation factors as a function of TPR{sub 20/10} for flattening filter free beams and conventional linear accelerators could not be observed for the perturbations caused by the components of the ionization chamber and the sensitive volume. Conclusion: The results indicate that the well-known small bias between the beam quality correction factor as a function of TPR20/10 for the flattening filter free and conventional linear accelerators is not caused by the geometry of the detector but rather by the material of the sensitive volume. This suggest that the bias for flattening filter free photon fields is only caused by the different material of the sensitive volume (air instead of water)« less

Comparison of different filter methods for data assimilation in the unsaturated zone

NASA Astrophysics Data System (ADS)

Lange, Natascha; Berkhahn, Simon; Erdal, Daniel; Neuweiler, Insa

2016-04-01

The unsaturated zone is an important compartment, which plays a role for the division of terrestrial water fluxes into surface runoff, groundwater recharge and evapotranspiration. For data assimilation in coupled systems it is therefore important to have a good representation of the unsaturated zone in the model. Flow processes in the unsaturated zone have all the typical features of flow in porous media: Processes can have long memory and as observations are scarce, hydraulic model parameters cannot be determined easily. However, they are important for the quality of model predictions. On top of that, the established flow models are highly non-linear. For these reasons, the use of the popular Ensemble Kalman filter as a data assimilation method to estimate state and parameters in unsaturated zone models could be questioned. With respect to the long process memory in the subsurface, it has been suggested that iterative filters and smoothers may be more suitable for parameter estimation in unsaturated media. We test the performance of different iterative filters and smoothers for data assimilation with a focus on parameter updates in the unsaturated zone. In particular we compare the Iterative Ensemble Kalman Filter and Smoother as introduced by Bocquet and Sakov (2013) as well as the Confirming Ensemble Kalman Filter and the modified Restart Ensemble Kalman Filter proposed by Song et al. (2014) to the original Ensemble Kalman Filter (Evensen, 2009). This is done with simple test cases generated numerically. We consider also test examples with layering structure, as a layering structure is often found in natural soils. We assume that observations are water content, obtained from TDR probes or other observation methods sampling relatively small volumes. Particularly in larger data assimilation frameworks, a reasonable balance between computational effort and quality of results has to be found. Therefore, we compare computational costs of the different methods as well as the quality of open loop model predictions and the estimated parameters. Bocquet, M. and P. Sakov, 2013: Joint state and parameter estimation with an iterative ensemble Kalman smoother, Nonlinear Processes in Geophysics 20(5): 803-818. Evensen, G., 2009: Data assimilation: The ensemble Kalman filter. Springer Science & Business Media. Song, X.H., L.S. Shi, M. Ye, J.Z. Yang and I.M. Navon, 2014: Numerical comparison of iterative ensemble Kalman filters for unsaturated flow inverse modeling. Vadose Zone Journal 13(2), 10.2136/vzj2013.05.0083.

A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings

PubMed Central

Okorokova, Elizaveta; Lebedev, Mikhail; Linderman, Michael; Ossadtchi, Alex

2015-01-01

In recent years, several assistive devices have been proposed to reconstruct arm and hand movements from electromyographic (EMG) activity. Although simple to implement and potentially useful to augment many functions, such myoelectric devices still need improvement before they become practical. Here we considered the problem of reconstruction of handwriting from multichannel EMG activity. Previously, linear regression methods (e.g., the Wiener filter) have been utilized for this purpose with some success. To improve reconstruction accuracy, we implemented the Kalman filter, which allows to fuse two information sources: the physical characteristics of handwriting and the activity of the leading hand muscles, registered by the EMG. Applying the Kalman filter, we were able to convert eight channels of EMG activity recorded from the forearm and the hand muscles into smooth reconstructions of handwritten traces. The filter operates in a causal manner and acts as a true predictor utilizing the EMGs from the past only, which makes the approach suitable for real-time operations. Our algorithm is appropriate for clinical neuroprosthetic applications and computer peripherals. Moreover, it is applicable to a broader class of tasks where predictive myoelectric control is needed. PMID:26578856

Predicting BCI subject performance using probabilistic spatio-temporal filters.

PubMed

Suk, Heung-Il; Fazli, Siamac; Mehnert, Jan; Müller, Klaus-Robert; Lee, Seong-Whan

2014-01-01

Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian-and thereby probabilistic-framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO framework allows to analyze which spatio-spectral parameters are common and which ones differ across the subject population. As expected, large variability of brain rhythms is observed between subjects. We have clustered subjects according to similarities in their corresponding spectral characteristics from the BSSFO model, which is found to reflect their BCI performances well. In BCI, a considerable percentage of subjects is unable to use a BCI for communication, due to their missing ability to modulate their brain rhythms-a phenomenon sometimes denoted as BCI-illiteracy or inability. Predicting individual subjects' performance preceding the actual, time-consuming BCI-experiment enhances the usage of BCIs, e.g., by detecting users with BCI inability. This work additionally contributes by using the novel BSSFO method to predict the BCI-performance using only 2 minutes and 3 channels of resting-state EEG data recorded before the actual BCI-experiment. Specifically, by grouping the individual frequency characteristics we have nicely classified them into the subject 'prototypes' (like μ - or β -rhythm type subjects) or users without ability to communicate with a BCI, and then by further building a linear regression model based on the grouping we could predict subjects' performance with the maximum correlation coefficient of 0.581 with the performance later seen in the actual BCI session.

Optimal design of FIR triplet halfband filter bank and application in image coding.

PubMed

Kha, H H; Tuan, H D; Nguyen, T Q

2011-02-01

This correspondence proposes an efficient semidefinite programming (SDP) method for the design of a class of linear phase finite impulse response triplet halfband filter banks whose filters have optimal frequency selectivity for a prescribed regularity order. The design problem is formulated as the minimization of the least square error subject to peak error constraints and regularity constraints. By using the linear matrix inequality characterization of the trigonometric semi-infinite constraints, it can then be exactly cast as a SDP problem with a small number of variables and, hence, can be solved efficiently. Several design examples of the triplet halfband filter bank are provided for illustration and comparison with previous works. Finally, the image coding performance of the filter bank is presented.

Electro-optical tunable birefringent filter

DOEpatents

Levinton, Fred M [Princeton, NJ

2012-01-31

An electrically tunable Lyot type filter is a Lyot that include one or more filter elements. Each filter element may have a planar, solid crystal comprised of a material that exhibits birefringence and is electro-optically active. Transparent electrodes may be coated on each face of the crystal. An input linear light polarizer may be located on one side of the crystal and oriented at 45 degrees to the optical axis of the birefringent crystal. An output linear light polarizer may be located on the other side of the crystal and oriented at -45 degrees with respect to the optical axis of the birefringent crystal. When an electric voltage is applied between the electrodes, the retardation of the crystal changes and so does the spectral transmission of the optical filter.

Bio-knowledge based filters improve residue-residue contact prediction accuracy.

PubMed

Wozniak, P P; Pelc, J; Skrzypecki, M; Vriend, G; Kotulska, M

2018-05-29

Residue-residue contact prediction through direct coupling analysis has reached impressive accuracy, but yet higher accuracy will be needed to allow for routine modelling of protein structures. One way to improve the prediction accuracy is to filter predicted contacts using knowledge about the particular protein of interest or knowledge about protein structures in general. We focus on the latter and discuss a set of filters that can be used to remove false positive contact predictions. Each filter depends on one or a few cut-off parameters for which the filter performance was investigated. Combining all filters while using default parameters resulted for a test-set of 851 protein domains in the removal of 29% of the predictions of which 92% were indeed false positives. All data and scripts are available from http://comprec-lin.iiar.pwr.edu.pl/FPfilter/. malgorzata.kotulska@pwr.edu.pl. Supplementary data are available at Bioinformatics online.

An OTA-C filter for ECG acquisition systems with highly linear range and less passband attenuation

NASA Astrophysics Data System (ADS)

Jihai, Duan; Chuang, Lan; Weilin, Xu; Baolin, Wei

2015-05-01

A fifth order operational transconductance amplifier-C (OTA-C) Butterworth type low-pass filter with highly linear range and less passband attenuation is presented for wearable bio-telemetry monitoring applications in a UWB wireless body area network. The source degeneration structure applied in typical small transconductance circuit is improved to provide a highly linear range for the OTA-C filter. Moreover, to reduce the passband attenuation of the filter, a cascode structure is employed as the output stage of the OTA. The OTA-based circuit is operated in weak inversion due to strict power limitation in the biomedical chip. The filter is fabricated in a SMIC 0.18-μm CMOS process. The measured results for the filter have shown a passband gain of -6.2 dB, while the -3-dB frequency is around 276 Hz. For the 0.8 VPP sinusoidal input at 100 Hz, a total harmonic distortion (THD) of -56.8 dB is obtained. An electrocardiogram signal with noise interference is fed into this chip to validate the function of the designed filter. Project supported by the National Natural Science Foundation of China (Nos. 61161003, 61264001, 61166004) and the Guangxi Natural Science Foundation (No. 2013GXNSFAA019333).

Linear estimation of coherent structures in wall-bounded turbulence at Re τ = 2000

NASA Astrophysics Data System (ADS)

Oehler, S.; Garcia–Gutiérrez, A.; Illingworth, S.

2018-04-01

The estimation problem for a fully-developed turbulent channel flow at Re τ = 2000 is considered. Specifically, a Kalman filter is designed using a Navier–Stokes-based linear model. The estimator uses time-resolved velocity measurements at a single wall-normal location (provided by DNS) to estimate the time-resolved velocity field at other wall-normal locations. The estimator is able to reproduce the largest scales with reasonable accuracy for a range of wavenumber pairs, measurement locations and estimation locations. Importantly, the linear model is also able to predict with reasonable accuracy the performance that will be achieved by the estimator when applied to the DNS. A more practical estimation scheme using the shear stress at the wall as measurement is also considered. The estimator is still able to estimate the largest scales with reasonable accuracy, although the estimator’s performance is reduced.

Sampled-Data Kalman Filtering and Multiple Model Adaptive Estimation for Infinite-Dimensional Continuous-Time Systems

DTIC Science & Technology

2007-03-01

mathematical frame- 1-6 work of linear algebra and functional analysis [122, 33], while Kalman-Bucy filtering [96, 32] is an especially important...Engineering, Air Force Institute of Technology (AU), Wright- Patterson AFB, Ohio, March 2002. 85. Hoffman, Kenneth and Ray Kunze. Linear Algebra (Second Edition...Engineering, Air Force Institute of Technology (AU), Wright- Patterson AFB, Ohio, December 1989. 189. Strang, Gilbert. Linear Algebra and Its Applications

Trajectory-based visual localization in underwater surveying missions.

PubMed

Burguera, Antoni; Bonin-Font, Francisco; Oliver, Gabriel

2015-01-14

We present a new vision-based localization system applied to an autonomous underwater vehicle (AUV) with limited sensing and computation capabilities. The traditional EKF-SLAM approaches are usually expensive in terms of execution time; the approach presented in this paper strengthens this method by adopting a trajectory-based schema that reduces the computational requirements. The pose of the vehicle is estimated using an extended Kalman filter (EKF), which predicts the vehicle motion by means of a visual odometer and corrects these predictions using the data associations (loop closures) between the current frame and the previous ones. One of the most important steps in this procedure is the image registration method, as it reinforces the data association and, thus, makes it possible to close loops reliably. Since the use of standard EKFs entail linearization errors that can distort the vehicle pose estimations, the approach has also been tested using an iterated Kalman filter (IEKF). Experiments have been conducted using a real underwater vehicle in controlled scenarios and in shallow sea waters, showing an excellent performance with very small errors, both in the vehicle pose and in the overall trajectory estimates.

Prediction of rainfall anomalies during the dry to wet transition season over the Southern Amazonia using machine learning tools

NASA Astrophysics Data System (ADS)

Shan, X.; Zhang, K.; Zhuang, Y.; Fu, R.; Hong, Y.

2017-12-01

Seasonal prediction of rainfall during the dry-to-wet transition season in austral spring (September-November) over southern Amazonia is central for improving planting crops and fire mitigation in that region. Previous studies have identified the key large-scale atmospheric dynamic and thermodynamics pre-conditions during the dry season (June-August) that influence the rainfall anomalies during the dry to wet transition season over Southern Amazonia. Based on these key pre-conditions during dry season, we have evaluated several statistical models and developed a Neural Network based statistical prediction system to predict rainfall during the dry to wet transition for Southern Amazonia (5-15°S, 50-70°W). Multivariate Empirical Orthogonal Function (EOF) Analysis is applied to the following four fields during JJA from the ECMWF Reanalysis (ERA-Interim) spanning from year 1979 to 2015: geopotential height at 200 hPa, surface relative humidity, convective inhibition energy (CIN) index and convective available potential energy (CAPE), to filter out noise and highlight the most coherent spatial and temporal variations. The first 10 EOF modes are retained for inputs to the statistical models, accounting for at least 70% of the total variance in the predictor fields. We have tested several linear and non-linear statistical methods. While the regularized Ridge Regression and Lasso Regression can generally capture the spatial pattern and magnitude of rainfall anomalies, we found that that Neural Network performs best with an accuracy greater than 80%, as expected from the non-linear dependence of the rainfall on the large-scale atmospheric thermodynamic conditions and circulation. Further tests of various prediction skill metrics and hindcasts also suggest this Neural Network prediction approach can significantly improve seasonal prediction skill than the dynamic predictions and regression based statistical predictions. Thus, this statistical prediction system could have shown potential to improve real-time seasonal rainfall predictions in the future.

Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces

PubMed Central

Kamrunnahar, M.; Dias, N. S.; Schiff, S. J.

2013-01-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain–computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8–23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications. PMID:21267657

Toward a model-based predictive controller design in brain-computer interfaces.

PubMed

Kamrunnahar, M; Dias, N S; Schiff, S J

2011-05-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.

Time-frequency filtering and synthesis from convex projections

NASA Astrophysics Data System (ADS)

White, Langford B.

1990-11-01

This paper describes the application of the theory of projections onto convex sets to time-frequency filtering and synthesis problems. We show that the class of Wigner-Ville Distributions (WVD) of L2 signals form the boundary of a closed convex subset of L2(R2). This result is obtained by considering the convex set of states on the Heisenberg group of which the ambiguity functions form the extreme points. The form of the projection onto the set of WVDs is deduced. Various linear and non-linear filtering operations are incorporated by formulation as convex projections. An example algorithm for simultaneous time-frequency filtering and synthesis is suggested.

A nonlinear Kalman filtering approach to embedded control of turbocharged diesel engines

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan

2014-10-01

The development of efficient embedded control for turbocharged Diesel engines, requires the programming of elaborated nonlinear control and filtering methods. To this end, in this paper nonlinear control for turbocharged Diesel engines is developed with the use of Differential flatness theory and the Derivative-free nonlinear Kalman Filter. It is shown that the dynamic model of the turbocharged Diesel engine is differentially flat and admits dynamic feedback linearization. It is also shown that the dynamic model can be written in the linear Brunovsky canonical form for which a state feedback controller can be easily designed. To compensate for modeling errors and external disturbances the Derivative-free nonlinear Kalman Filter is used and redesigned as a disturbance observer. The filter consists of the Kalman Filter recursion on the linearized equivalent of the Diesel engine model and of an inverse transformation based on differential flatness theory which enables to obtain estimates for the state variables of the initial nonlinear model. Once the disturbances variables are identified it is possible to compensate them by including an additional control term in the feedback loop. The efficiency of the proposed control method is tested through simulation experiments.

RB Particle Filter Time Synchronization Algorithm Based on the DPM Model.

PubMed

Guo, Chunsheng; Shen, Jia; Sun, Yao; Ying, Na

2015-09-03

Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN) applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB) particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM) model. In a state-space equation with a linear substructure, state variables are divided into linear and non-linear variables by the RB particle filter algorithm. These two variables can be estimated using Kalman filter and particle filter, respectively, which improves the computational efficiency more so than if only the particle filter was used. In addition, the DPM model is used to describe the distribution of non-deterministic delays and to automatically adjust the number of Gaussian mixture model components based on the observational data. This improves the estimation accuracy of clock offset and skew, which allows achieving the time synchronization. The time synchronization performance of this algorithm is also validated by computer simulations and experimental measurements. The results show that the proposed algorithm has a higher time synchronization precision than traditional time synchronization algorithms.

Wavelength interrogation of fiber Bragg grating sensors based on crossed optical Gaussian filters.

PubMed

Cheng, Rui; Xia, Li; Zhou, Jiaao; Liu, Deming

2015-04-15

Conventional intensity-modulated measurements require to be operated in linear range of filter or interferometric response to ensure a linear detection. Here, we present a wavelength interrogation system for fiber Bragg grating sensors where the linear transition is achieved with crossed Gaussian transmissions. This unique filtering characteristic makes the responses of the two branch detections follow Gaussian functions with the same parameters except for a delay. The substraction of these two delayed Gaussian responses (in dB) ultimately leads to a linear behavior, which is exploited for the sensor wavelength determination. Beside its flexibility and inherently power insensitivity, the proposal also shows a potential of a much wider operational range. Interrogation of a strain-tuned grating was accomplished, with a wide sensitivity tuning range from 2.56 to 8.7 dB/nm achieved.

Multiscale morphological filtering for analysis of noisy and complex images

NASA Astrophysics Data System (ADS)

Kher, A.; Mitra, S.

Images acquired with passive sensing techniques suffer from illumination variations and poor local contrasts that create major difficulties in interpretation and identification tasks. On the other hand, images acquired with active sensing techniques based on monochromatic illumination are degraded with speckle noise. Mathematical morphology offers elegant techniques to handle a wide range of image degradation problems. Unlike linear filters, morphological filters do not blur the edges and hence maintain higher image resolution. Their rich mathematical framework facilitates the design and analysis of these filters as well as their hardware implementation. Morphological filters are easier to implement and are more cost effective and efficient than several conventional linear filters. Morphological filters to remove speckle noise while maintaining high resolution and preserving thin image regions that are particularly vulnerable to speckle noise were developed and applied to SAR imagery. These filters used combination of linear (one-dimensional) structuring elements in different (typically four) orientations. Although this approach preserves more details than the simple morphological filters using two-dimensional structuring elements, the limited orientations of one-dimensional elements approximate the fine details of the region boundaries. A more robust filter designed recently overcomes the limitation of the fixed orientations. This filter uses a combination of concave and convex structuring elements. Morphological operators are also useful in extracting features from visible and infrared imagery. A multiresolution image pyramid obtained with successive filtering and a subsampling process aids in the removal of the illumination variations and enhances local contrasts. A morphology-based interpolation scheme was also introduced to reduce intensity discontinuities created in any morphological filtering task. The generality of morphological filtering techniques in extracting information from a wide variety of images obtained with active and passive sensing techniques is discussed. Such techniques are particularly useful in obtaining more information from fusion of complex images by different sensors such as SAR, visible, and infrared.

Multiscale Morphological Filtering for Analysis of Noisy and Complex Images

NASA Technical Reports Server (NTRS)

Kher, A.; Mitra, S.

1993-01-01

Images acquired with passive sensing techniques suffer from illumination variations and poor local contrasts that create major difficulties in interpretation and identification tasks. On the other hand, images acquired with active sensing techniques based on monochromatic illumination are degraded with speckle noise. Mathematical morphology offers elegant techniques to handle a wide range of image degradation problems. Unlike linear filters, morphological filters do not blur the edges and hence maintain higher image resolution. Their rich mathematical framework facilitates the design and analysis of these filters as well as their hardware implementation. Morphological filters are easier to implement and are more cost effective and efficient than several conventional linear filters. Morphological filters to remove speckle noise while maintaining high resolution and preserving thin image regions that are particularly vulnerable to speckle noise were developed and applied to SAR imagery. These filters used combination of linear (one-dimensional) structuring elements in different (typically four) orientations. Although this approach preserves more details than the simple morphological filters using two-dimensional structuring elements, the limited orientations of one-dimensional elements approximate the fine details of the region boundaries. A more robust filter designed recently overcomes the limitation of the fixed orientations. This filter uses a combination of concave and convex structuring elements. Morphological operators are also useful in extracting features from visible and infrared imagery. A multiresolution image pyramid obtained with successive filtering and a subsampling process aids in the removal of the illumination variations and enhances local contrasts. A morphology-based interpolation scheme was also introduced to reduce intensity discontinuities created in any morphological filtering task. The generality of morphological filtering techniques in extracting information from a wide variety of images obtained with active and passive sensing techniques is discussed. Such techniques are particularly useful in obtaining more information from fusion of complex images by different sensors such as SAR, visible, and infrared.

Applications of charge-coupled device transversal filters to communication

NASA Technical Reports Server (NTRS)

Buss, D. D.; Bailey, W. H.; Brodersen, R. W.; Hewes, C. R.; Tasch, A. F., Jr.

1975-01-01

The paper discusses the computational power of state-of-the-art charged-coupled device (CCD) transversal filters in communications applications. Some of the performance limitations of CCD transversal filters are discussed, with attention given to time delay and bandwidth, imperfect charge transfer efficiency, weighting coefficient error, noise, and linearity. The application of CCD transversal filters to matched filtering, spectral filtering, and Fourier analysis is examined. Techniques for making programmable transversal filters are briefly outlined.

Fault detection and isolation for complex system

NASA Astrophysics Data System (ADS)

Jing, Chan Shi; Bayuaji, Luhur; Samad, R.; Mustafa, M.; Abdullah, N. R. H.; Zain, Z. M.; Pebrianti, Dwi

2017-07-01

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

Convis: A Toolbox to Fit and Simulate Filter-Based Models of Early Visual Processing

PubMed Central

Huth, Jacob; Masquelier, Timothée; Arleo, Angelo

2018-01-01

We developed Convis, a Python simulation toolbox for large scale neural populations which offers arbitrary receptive fields by 3D convolutions executed on a graphics card. The resulting software proves to be flexible and easily extensible in Python, while building on the PyTorch library (The Pytorch Project, 2017), which was previously used successfully in deep learning applications, for just-in-time optimization and compilation of the model onto CPU or GPU architectures. An alternative implementation based on Theano (Theano Development Team, 2016) is also available, although not fully supported. Through automatic differentiation, any parameter of a specified model can be optimized to approach a desired output which is a significant improvement over e.g., Monte Carlo or particle optimizations without gradients. We show that a number of models including even complex non-linearities such as contrast gain control and spiking mechanisms can be implemented easily. We show in this paper that we can in particular recreate the simulation results of a popular retina simulation software VirtualRetina (Wohrer and Kornprobst, 2009), with the added benefit of providing (1) arbitrary linear filters instead of the product of Gaussian and exponential filters and (2) optimization routines utilizing the gradients of the model. We demonstrate the utility of 3d convolution filters with a simple direction selective filter. Also we show that it is possible to optimize the input for a certain goal, rather than the parameters, which can aid the design of experiments as well as closed-loop online stimulus generation. Yet, Convis is more than a retina simulator. For instance it can also predict the response of V1 orientation selective cells. Convis is open source under the GPL-3.0 license and available from https://github.com/jahuth/convis/ with documentation at https://jahuth.github.io/convis/. PMID:29563867

40 CFR 86.884-11 - Instrument checks.

Code of Federal Regulations, 2014 CFR

2014-07-01

... equipment response of zero; (3) Calibrated neutral density filters having approximately 10, 20, and 40 percent opacity shall be employed to check the linearity of the instrument. The filter(s) shall be... beam of light from the light source emanates, and the recorder response shall be noted. Filters with...

40 CFR 92.122 - Smoke meter calibration.

Code of Federal Regulations, 2014 CFR

2014-07-01

... equipment response of zero; (b) Calibrated neutral density filters having approximately 10, 20, and 40 percent opacity shall be employed to check the linearity of the instrument. The filter(s) shall be... beam of light from the light source emanates, and the recorder response shall be noted. Filters with...

Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design

NASA Astrophysics Data System (ADS)

Leube, P. C.; Geiges, A.; Nowak, W.

2012-02-01

Incorporating hydro(geo)logical data, such as head and tracer data, into stochastic models of (subsurface) flow and transport helps to reduce prediction uncertainty. Because of financial limitations for investigation campaigns, information needs toward modeling or prediction goals should be satisfied efficiently and rationally. Optimal design techniques find the best one among a set of investigation strategies. They optimize the expected impact of data on prediction confidence or related objectives prior to data collection. We introduce a new optimal design method, called PreDIA(gnosis) (Preposterior Data Impact Assessor). PreDIA derives the relevant probability distributions and measures of data utility within a fully Bayesian, generalized, flexible, and accurate framework. It extends the bootstrap filter (BF) and related frameworks to optimal design by marginalizing utility measures over the yet unknown data values. PreDIA is a strictly formal information-processing scheme free of linearizations. It works with arbitrary simulation tools, provides full flexibility concerning measurement types (linear, nonlinear, direct, indirect), allows for any desired task-driven formulations, and can account for various sources of uncertainty (e.g., heterogeneity, geostatistical assumptions, boundary conditions, measurement values, model structure uncertainty, a large class of model errors) via Bayesian geostatistics and model averaging. Existing methods fail to simultaneously provide these crucial advantages, which our method buys at relatively higher-computational costs. We demonstrate the applicability and advantages of PreDIA over conventional linearized methods in a synthetic example of subsurface transport. In the example, we show that informative data is often invisible for linearized methods that confuse zero correlation with statistical independence. Hence, PreDIA will often lead to substantially better sampling designs. Finally, we extend our example to specifically highlight the consideration of conceptual model uncertainty.

Distance estimation and collision prediction for on-line robotic motion planning

NASA Technical Reports Server (NTRS)

Kyriakopoulos, K. J.; Saridis, G. N.

1991-01-01

An efficient method for computing the minimum distance and predicting collisions between moving objects is presented. This problem has been incorporated in the framework of an in-line motion planning algorithm to satisfy collision avoidance between a robot and moving objects modeled as convex polyhedra. In the beginning the deterministic problem, where the information about the objects is assumed to be certain is examined. If instead of the Euclidean norm, L(sub 1) or L(sub infinity) norms are used to represent distance, the problem becomes a linear programming problem. The stochastic problem is formulated, where the uncertainty is induced by sensing and the unknown dynamics of the moving obstacles. Two problems are considered: (1) filtering of the minimum distance between the robot and the moving object, at the present time; and (2) prediction of the minimum distance in the future, in order to predict possible collisions with the moving obstacles and estimate the collision time.

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

PubMed

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

2014-01-01

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

Highway traffic estimation of improved precision using the derivative-free nonlinear Kalman Filter

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Siano, Pierluigi; Zervos, Nikolaos; Melkikh, Alexey

2015-12-01

The paper proves that the PDE dynamic model of the highway traffic is a differentially flat one and by applying spatial discretization its shows that the model's transformation into an equivalent linear canonical state-space form is possible. For the latter representation of the traffic's dynamics, state estimation is performed with the use of the Derivative-free nonlinear Kalman Filter. The proposed filter consists of the Kalman Filter recursion applied on the transformed state-space model of the highway traffic. Moreover, it makes use of an inverse transformation, based again on differential flatness theory which enables to obtain estimates of the state variables of the initial nonlinear PDE model. By avoiding approximate linearizations and the truncation of nonlinear terms from the PDE model of the traffic's dynamics the proposed filtering methods outperforms, in terms of accuracy, other nonlinear estimators such as the Extended Kalman Filter. The article's theoretical findings are confirmed through simulation experiments.

Pseudo-Linear Attitude Determination of Spinning Spacecraft

NASA Technical Reports Server (NTRS)

Bar-Itzhack, Itzhack Y.; Harman, Richard R.

2004-01-01

This paper presents the overall mathematical model and results from pseudo linear recursive estimators of attitude and rate for a spinning spacecraft. The measurements considered are vector measurements obtained by sun-sensors, fixed head star trackers, horizon sensors, and three axis magnetometers. Two filters are proposed for estimating the attitude as well as the angular rate vector. One filter, called the q-Filter, yields the attitude estimate as a quaternion estimate, and the other filter, called the D-Filter, yields the estimated direction cosine matrix. Because the spacecraft is gyro-less, Euler s equation of angular motion of rigid bodies is used to enable the estimation of the angular velocity. A simpler Markov model is suggested as a replacement for Euler's equation in the case where the vector measurements are obtained at high rates relative to the spacecraft angular rate. The performance of the two filters is examined using simulated data.

Regularized iterative integration combined with non-linear diffusion filtering for phase-contrast x-ray computed tomography.

PubMed

Burger, Karin; Koehler, Thomas; Chabior, Michael; Allner, Sebastian; Marschner, Mathias; Fehringer, Andreas; Willner, Marian; Pfeiffer, Franz; Noël, Peter

2014-12-29

Phase-contrast x-ray computed tomography has a high potential to become clinically implemented because of its complementarity to conventional absorption-contrast.In this study, we investigate noise-reducing but resolution-preserving analytical reconstruction methods to improve differential phase-contrast imaging. We apply the non-linear Perona-Malik filter on phase-contrast data prior or post filtered backprojected reconstruction. Secondly, the Hilbert kernel is replaced by regularized iterative integration followed by ramp filtered backprojection as used for absorption-contrast imaging. Combining the Perona-Malik filter with this integration algorithm allows to successfully reveal relevant sample features, quantitatively confirmed by significantly increased structural similarity indices and contrast-to-noise ratios. With this concept, phase-contrast imaging can be performed at considerably lower dose.

Morphology filter bank for extracting nodular and linear patterns in medical images.

PubMed

Hashimoto, Ryutaro; Uchiyama, Yoshikazu; Uchimura, Keiichi; Koutaki, Gou; Inoue, Tomoki

2017-04-01

Using image processing to extract nodular or linear shadows is a key technique of computer-aided diagnosis schemes. This study proposes a new method for extracting nodular and linear patterns of various sizes in medical images. We have developed a morphology filter bank that creates multiresolution representations of an image. Analysis bank of this filter bank produces nodular and linear patterns at each resolution level. Synthesis bank can then be used to perfectly reconstruct the original image from these decomposed patterns. Our proposed method shows better performance based on a quantitative evaluation using a synthesized image compared with a conventional method based on a Hessian matrix, often used to enhance nodular and linear patterns. In addition, experiments show that our method can be applied to the followings: (1) microcalcifications of various sizes in mammograms can be extracted, (2) blood vessels of various sizes in retinal fundus images can be extracted, and (3) thoracic CT images can be reconstructed while removing normal vessels. Our proposed method is useful for extracting nodular and linear shadows or removing normal structures in medical images.

Change Detection via Selective Guided Contrasting Filters

NASA Astrophysics Data System (ADS)

Vizilter, Y. V.; Rubis, A. Y.; Zheltov, S. Y.

2017-05-01

Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented filters provide the robustness relative to weak geometrical discrepancy of compared images. Selective guided contrasting based on morphological opening/closing and thresholded morphological correlation demonstrates the best change detection result.

Nonlinear tuning techniques of plasmonic nano-filters

NASA Astrophysics Data System (ADS)

Kotb, Rehab; Ismail, Yehea; Swillam, Mohamed A.

2015-02-01

In this paper, a fitting model to the propagation constant and the losses of Metal-Insulator-Metal (MIM) plasmonic waveguide is proposed. Using this model, the modal characteristics of MIM plasmonic waveguide can be solved directly without solving Maxwell's equations from scratch. As a consequence, the simulation time and the computational cost that are needed to predict the response of different plasmonic structures can be reduced significantly. This fitting model is used to develop a closed form model that describes the behavior of a plasmonic nano-filter. Easy and accurate mechanisms to tune the filter are investigated and analyzed. The filter tunability is based on using a nonlinear dielectric material with Pockels or Kerr effect. The tunability is achieved by applying an external voltage or through controlling the input light intensity. The proposed nano-filter supports both red and blue shift in the resonance response depending on the type of the used non-linear material. A new approach to control the input light intensity by applying an external voltage to a previous stage is investigated. Therefore, the filter tunability to a stage that has Kerr material can be achieved by applying voltage to a previous stage that has Pockels material. Using this method, the Kerr effect can be achieved electrically instead of varying the intensity of the input source. This technique enhances the ability of the device integration for on-chip applications. Tuning the resonance wavelength with high accuracy, minimum insertion loss and high quality factor is obtained using these approaches.

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.

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.

Improved determination of particulate absorption from combined filter pad and PSICAM measurements.

PubMed

Lefering, Ina; Röttgers, Rüdiger; Weeks, Rebecca; Connor, Derek; Utschig, Christian; Heymann, Kerstin; McKee, David

2016-10-31

Filter pad light absorption measurements are subject to two major sources of experimental uncertainty: the so-called pathlength amplification factor, β, and scattering offsets, o, for which previous null-correction approaches are limited by recent observations of non-zero absorption in the near infrared (NIR). A new filter pad absorption correction method is presented here which uses linear regression against point-source integrating cavity absorption meter (PSICAM) absorption data to simultaneously resolve both β and the scattering offset. The PSICAM has previously been shown to provide accurate absorption data, even in highly scattering waters. Comparisons of PSICAM and filter pad particulate absorption data reveal linear relationships that vary on a sample by sample basis. This regression approach provides significantly improved agreement with PSICAM data (3.2% RMS%E) than previously published filter pad absorption corrections. Results show that direct transmittance (T-method) filter pad absorption measurements perform effectively at the same level as more complex geometrical configurations based on integrating cavity measurements (IS-method and QFT-ICAM) because the linear regression correction compensates for the sensitivity to scattering errors in the T-method. This approach produces accurate filter pad particulate absorption data for wavelengths in the blue/UV and in the NIR where sensitivity issues with PSICAM measurements limit performance. The combination of the filter pad absorption and PSICAM is therefore recommended for generating full spectral, best quality particulate absorption data as it enables correction of multiple errors sources across both measurements.

System and method for 100% moisture and basis weight measurement of moving paper

DOEpatents

Hernandez, Jose E.; Koo, Jackson C.

2002-01-01

A system for characterizing a set of properties for a moving substance are disclosed. The system includes: a first near-infrared linear array; a second near-infrared linear array; a first filter transparent to a first absorption wavelength emitted by the moving substance and juxtaposed between the substance and the first array; a second filter blocking the first absorption wavelength emitted by the moving substance and juxtaposed between the substance and the second array; and a computational device for characterizing data from the arrays into information on a property of the substance. The method includes the steps of: filtering out a first absorption wavelength emitted by a substance; monitoring the first absorption wavelength with a first near-infrared linear array; blocking the first wavelength from reaching a second near-infrared linear array; and characterizing data from the arrays into information on a property of the substance.

Noise removal in extended depth of field microscope images through nonlinear signal processing.

PubMed

Zahreddine, Ramzi N; Cormack, Robert H; Cogswell, Carol J

2013-04-01

Extended depth of field (EDF) microscopy, achieved through computational optics, allows for real-time 3D imaging of live cell dynamics. EDF is achieved through a combination of point spread function engineering and digital image processing. A linear Wiener filter has been conventionally used to deconvolve the image, but it suffers from high frequency noise amplification and processing artifacts. A nonlinear processing scheme is proposed which extends the depth of field while minimizing background noise. The nonlinear filter is generated via a training algorithm and an iterative optimizer. Biological microscope images processed with the nonlinear filter show a significant improvement in image quality and signal-to-noise ratio over the conventional linear filter.

An ultra-low-power filtering technique for biomedical applications.

PubMed

Zhang, Tan-Tan; Mak, Pui-In; Vai, Mang-I; Mak, Peng-Un; Wan, Feng; Martins, R P

2011-01-01

This paper describes an ultra-low-power filtering technique for biomedical applications designated as T-wave sensing in heart-activities detection systems. The topology is based on a source-follower-based Biquad operating in the sub-threshold region. With the intrinsic advantages of simplicity and high linearity of the source-follower, ultra-low-cutoff filtering can be achieved, simultaneously with ultra low power and good linearity. An 8(th)-order 2.4-Hz lowpass filter design example optimized in a 0.35-μm CMOS process was designed achieving over 85-dB dynamic range, 74-dB stopband attenuation and consuming only 0.36 nW at a 3-V supply.

Changes in solar wind-magnetosphere coupling with solar cycle, season, and time relative to stream interfaces

NASA Astrophysics Data System (ADS)

McPherron, Robert L.; Baker, Daniel N.; Pulkkinen, T. I.; Hsu, T.-S.; Kissinger, J.; Chu, X.

2013-07-01

Geomagnetic activity depends on a variety of factors including solar zenith angle, solar UV, strength of the interplanetary magnetic field, speed and density of the solar wind, orientation of the Earth’s dipole, distance of the Earth from Sun, occurrence of CMEs and CIRs, and possibly other parameters. We have investigated some of these using state-dependant linear prediction filters. For a given state a prediction filter transforms a coupling function such as rectified solar wind electric field (VBs) to an output like the auroral electrojet index (AL). The area of this filter calculated from the sum of the filter coefficients measures the strength of the coupling. When the input and output are steady for a time longer than the duration of the filter the ratio of output to input is equal to this area. We find coupling strength defined in this way for Es=VBs to AL (and AU) is weakest at solar maximum and strongest at solar minimum. AL coupling displays a semiannual variation being weakest at the solstices and strongest at the equinoxes. AU coupling has only an annual variation being strongest at summer solstice. AL and AU coupling also vary with time relative to a stream interface. Es coupling is weaker after the interface, but ULF coupling is stronger. Total prediction efficiency remains about constant at the interface. The change in coupling strength with the solar cycle can be explained as an effect of more frequent saturation of the polar cap potential causing a smaller ratio of AL to Es. Stronger AL coupling at the equinoxes possibly indicates some process that makes magnetic reconnection less efficient when the dipole axis is tilted along the Earth-Sun line. Strong AU coupling at summer solstice is likely due to high conductivity in northern summer. Coupling changes at a stream interface are correlated with the presence of strong wave activity in ground and satellite measurements and may be an artifact of the method by which solar wind data are propagated.

Filter-based multiscale entropy analysis of complex physiological time series.

PubMed

Xu, Yuesheng; Zhao, Liang

2013-08-01

Multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiological time series. We reinterpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce filter-based multiscale entropy (FME), which filters a time series to generate multiple frequency components, and then we compute the blockwise entropy of the resulting components. By choosing filters adapted to the feature of a given time series, FME is able to better capture its multiscale information and to provide more flexibility for studying its complexity. Motivated by the heart rate turbulence theory, which suggests that the human heartbeat interval time series can be described in piecewise linear patterns, we propose piecewise linear filter multiscale entropy (PLFME) for the complexity analysis of the time series. Numerical results from PLFME are more robust to data of various lengths than those from MSE. The numerical performance of the adaptive piecewise constant filter multiscale entropy without prior information is comparable to that of PLFME, whose design takes prior information into account.

Linear-phase delay filters for ultra-low-power signal processing in neural recording implants.

PubMed

Gosselin, Benoit; Sawan, Mohamad; Kerherve, Eric

2010-06-01

We present the design and implementation of linear-phase delay filters for ultra-low-power signal processing in neural recording implants. We use these filters as low-distortion delay elements along with an automatic biopotential detector to perform integral waveform extraction and efficient power management. The presented delay elements are realized employing continuous-time OTA-C filters featuring 9th-order equiripple transfer functions with constant group delay. Such analog delay enables processing neural waveforms with reduced overhead compared to a digital delay since it does not requires sampling and digitization. It uses an allpass transfer function for achieving wider constant-delay bandwidth than all-pole does. Two filters realizations are compared for implementing the delay element: the Cascaded structure and the Inverse follow-the-leader feedback filter. Their respective strengths and drawbacks are assessed by modeling parasitics and non-idealities of OTAs, and by transistor-level simulations. A budget of 200 nA is used in both filters. Experimental measurements with the chosen filter topology are presented and discussed.

The influence of surface roughness on the contact stiffness and the contact filter effect in nonlinear wheel-track interaction

NASA Astrophysics Data System (ADS)

Lundberg, Oskar E.; Nordborg, Anders; Lopez Arteaga, Ines

2016-03-01

A state-dependent contact model including nonlinear contact stiffness and nonlinear contact filtering is used to calculate contact forces and rail vibrations with a time-domain wheel-track interaction model. In the proposed method, the full three-dimensional contact geometry is reduced to a point contact in order to lower the computational cost and to reduce the amount of required input roughness-data. Green's functions including the linear dynamics of the wheel and the track are coupled with a point contact model, leading to a numerically efficient model for the wheel-track interaction. Nonlinear effects due to the shape and roughness of the wheel and the rail surfaces are included in the point contact model by pre-calculation of functions for the contact stiffness and contact filters. Numerical results are compared to field measurements of rail vibrations for passenger trains running at 200 kph on a ballast track. Moreover, the influence of vehicle pre-load and different degrees of roughness excitation on the resulting wheel-track interaction is studied by means of numerical predictions.

A Study Into the Effects of Kalman Filtered Noise in Advanced Guidance Laws of Missile Navigation

DTIC Science & Technology

2014-03-01

Kalman filtering algorithm is a highly effective linear state estimator . Known as the workhorse of estimation , the discrete time Kalman filter uses ...15]. At any discrete time 1k  the state estimate can be determined by (3.7). A Kalman filter estimates the state using the process described in...acceleration is calculated using Kalman filter outputs. It is not available to the Kalman filter for

Design of multiplier-less sharp transition width non-uniform filter banks using gravitational search algorithm

NASA Astrophysics Data System (ADS)

Bindiya T., S.; Elias, Elizabeth

2015-01-01

In this paper, multiplier-less near-perfect reconstruction tree-structured filter banks are proposed. Filters with sharp transition width are preferred in filter banks in order to reduce the aliasing between adjacent channels. When sharp transition width filters are designed as conventional finite impulse response filters, the order of the filters will become very high leading to increased complexity. The frequency response masking (FRM) method is known to result in linear-phase sharp transition width filters with low complexity. It is found that the proposed design method, which is based on FRM, gives better results compared to the earlier reported results, in terms of the number of multipliers when sharp transition width filter banks are needed. To further reduce the complexity and power consumption, the tree-structured filter bank is made totally multiplier-less by converting the continuous filter bank coefficients to finite precision coefficients in the signed power of two space. This may lead to performance degradation and calls for the use of a suitable optimisation technique. In this paper, gravitational search algorithm is proposed to be used in the design of the multiplier-less tree-structured uniform as well as non-uniform filter banks. This design method results in uniform and non-uniform filter banks which are simple, alias-free, linear phase and multiplier-less and have sharp transition width.

Filter replacement lifetime prediction

DOEpatents

Hamann, Hendrik F.; Klein, Levente I.; Manzer, Dennis G.; Marianno, Fernando J.

2017-10-25

Methods and systems for predicting a filter lifetime include building a filter effectiveness history based on contaminant sensor information associated with a filter; determining a rate of filter consumption with a processor based on the filter effectiveness history; and determining a remaining filter lifetime based on the determined rate of filter consumption. Methods and systems for increasing filter economy include measuring contaminants in an internal and an external environment; determining a cost of a corrosion rate increase if unfiltered external air intake is increased for cooling; determining a cost of increased air pressure to filter external air; and if the cost of filtering external air exceeds the cost of the corrosion rate increase, increasing an intake of unfiltered external air.

Stimulus and recording variables and their effects on mammalian vestibular evoked potentials

NASA Technical Reports Server (NTRS)

Jones, Sherri M.; Subramanian, Geetha; Avniel, Wilma; Guo, Yuqing; Burkard, Robert F.; Jones, Timothy A.

2002-01-01

Linear vestibular evoked potentials (VsEPs) measure the collective neural activity of the gravity receptor organs in the inner ear that respond to linear acceleration transients. The present study examined the effects of electrode placement, analog filtering, stimulus polarity and stimulus rate on linear VsEP thresholds, latencies and amplitudes recorded from mice. Two electrode-recording montages were evaluated, rostral (forebrain) to 'mastoid' and caudal (cerebellum) to 'mastoid'. VsEP thresholds and peak latencies were identical between the two recording sites; however, peak amplitudes were larger for the caudal recording montage. VsEPs were also affected by filtering. Results suggest optimum high pass filter cutoff at 100-300 Hz, and low pass filter cutoff at 10,000 Hz. To evaluate stimulus rate, linear jerk pulses were presented at 9.2, 16, 25, 40 and 80 Hz. At 80 Hz, mean latencies were longer (0.350-0.450 ms) and mean amplitudes reduced (0.8-1.8 microV) for all response peaks. In 50% of animals, late peaks (P3, N3) disappeared at 80 Hz. The results offer options for VsEP recording protocols. Copyright 2002 Elsevier Science B.V.

Identification of Linear and Nonlinear Sensory Processing Circuits from Spiking Neuron Data.

PubMed

Florescu, Dorian; Coca, Daniel

2018-03-01

Inferring mathematical models of sensory processing systems directly from input-output observations, while making the fewest assumptions about the model equations and the types of measurements available, is still a major issue in computational neuroscience. This letter introduces two new approaches for identifying sensory circuit models consisting of linear and nonlinear filters in series with spiking neuron models, based only on the sampled analog input to the filter and the recorded spike train output of the spiking neuron. For an ideal integrate-and-fire neuron model, the first algorithm can identify the spiking neuron parameters as well as the structure and parameters of an arbitrary nonlinear filter connected to it. The second algorithm can identify the parameters of the more general leaky integrate-and-fire spiking neuron model, as well as the parameters of an arbitrary linear filter connected to it. Numerical studies involving simulated and real experimental recordings are used to demonstrate the applicability and evaluate the performance of the proposed algorithms.

Investigating the Use of the Intel Xeon Phi for Event Reconstruction

NASA Astrophysics Data System (ADS)

Sherman, Keegan; Gilfoyle, Gerard

2014-09-01

The physics goal of Jefferson Lab is to understand how quarks and gluons form nuclei and it is being upgraded to a higher, 12-GeV beam energy. The new CLAS12 detector in Hall B will collect 5-10 terabytes of data per day and will require considerable computing resources. We are investigating tools, such as the Intel Xeon Phi, to speed up the event reconstruction. The Kalman Filter is one of the methods being studied. It is a linear algebra algorithm that estimates the state of a system by combining existing data and predictions of those measurements. The tools required to apply this technique (i.e. matrix multiplication, matrix inversion) are being written using C++ intrinsics for Intel's Xeon Phi Coprocessor, which uses the Many Integrated Cores (MIC) architecture. The Intel MIC is a new high-performance chip that connects to a host machine through the PCIe bus and is built to run highly vectorized and parallelized code making it a well-suited device for applications such as the Kalman Filter. Our tests of the MIC optimized algorithms needed for the filter show significant increases in speed. For example, matrix multiplication of 5x5 matrices on the MIC was able to run up to 69 times faster than the host core. The physics goal of Jefferson Lab is to understand how quarks and gluons form nuclei and it is being upgraded to a higher, 12-GeV beam energy. The new CLAS12 detector in Hall B will collect 5-10 terabytes of data per day and will require considerable computing resources. We are investigating tools, such as the Intel Xeon Phi, to speed up the event reconstruction. The Kalman Filter is one of the methods being studied. It is a linear algebra algorithm that estimates the state of a system by combining existing data and predictions of those measurements. The tools required to apply this technique (i.e. matrix multiplication, matrix inversion) are being written using C++ intrinsics for Intel's Xeon Phi Coprocessor, which uses the Many Integrated Cores (MIC) architecture. The Intel MIC is a new high-performance chip that connects to a host machine through the PCIe bus and is built to run highly vectorized and parallelized code making it a well-suited device for applications such as the Kalman Filter. Our tests of the MIC optimized algorithms needed for the filter show significant increases in speed. For example, matrix multiplication of 5x5 matrices on the MIC was able to run up to 69 times faster than the host core. Work supported by the University of Richmond and the US Department of Energy.

A Reconfiguration Scheme for Accommodating Actuator Failures in Multi-Input, Multi-Output Flight Control Systems

NASA Technical Reports Server (NTRS)

Siwakosit, W.; Hess, R. A.; Bacon, Bart (Technical Monitor); Burken, John (Technical Monitor)

2000-01-01

A multi-input, multi-output reconfigurable flight control system design utilizing a robust controller and an adaptive filter is presented. The robust control design consists of a reduced-order, linear dynamic inversion controller with an outer-loop compensation matrix derived from Quantitative Feedback Theory (QFT). A principle feature of the scheme is placement of the adaptive filter in series with the QFT compensator thus exploiting the inherent robustness of the nominal flight control system in the presence of plant uncertainties. An example of the scheme is presented in a pilot-in-the-loop computer simulation using a simplified model of the lateral-directional dynamics of the NASA F18 High Angle of Attack Research Vehicle (HARV) that included nonlinear anti-wind up logic and actuator limitations. Prediction of handling qualities and pilot-induced oscillation tendencies in the presence of these nonlinearities is included in the example.

Dual Control of Giant Field-like Spin Torque in Spin Filter Tunnel Junctions

PubMed Central

Tang, Y. -H.; Chu, F. -C.; Kioussis, Nicholas

2015-01-01

We predict a giant field-like spin torque, , in spin-filter (SF) barrier tunnel junctions in sharp contrast to existing junctions based on nonmagnetic passive barriers. We demonstrate that has linear bias behavior, is independent of the SF thickness, and has odd parity with respect to the SF’s exchange splitting. Thus, it can be selectively controlled via external bias or external magnetic field which gives rise to sign reversal of via magnetic field switching. The underlying mechanism is the interlayer exchange coupling between the noncollinear magnetizations of the SF and free ferromagnetic electrode via the nonmagnetic insulating (I) spacer giving rise to giant spin-dependent reflection at the SF/I interface. These findings suggest that the proposed field-like-spin-torque MRAM may provide promising dual functionalities for both ‘reading’ and ‘writing’ processes which require lower critical current densities and faster writing and reading speeds. PMID:26095146

High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.

PubMed

Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong

2018-08-01

This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

A resolved two-way coupled CFD/6-DOF approach for predicting embolus transport and the embolus-trapping efficiency of IVC filters.

PubMed

Aycock, Kenneth I; Campbell, Robert L; Manning, Keefe B; Craven, Brent A

2017-06-01

Inferior vena cava (IVC) filters are medical devices designed to provide a mechanical barrier to the passage of emboli from the deep veins of the legs to the heart and lungs. Despite decades of development and clinical use, IVC filters still fail to prevent the passage of all hazardous emboli. The objective of this study is to (1) develop a resolved two-way computational model of embolus transport, (2) provide verification and validation evidence for the model, and (3) demonstrate the ability of the model to predict the embolus-trapping efficiency of an IVC filter. Our model couples computational fluid dynamics simulations of blood flow to six-degree-of-freedom simulations of embolus transport and resolves the interactions between rigid, spherical emboli and the blood flow using an immersed boundary method. Following model development and numerical verification and validation of the computational approach against benchmark data from the literature, embolus transport simulations are performed in an idealized IVC geometry. Centered and tilted filter orientations are considered using a nonlinear finite element-based virtual filter placement procedure. A total of 2048 coupled CFD/6-DOF simulations are performed to predict the embolus-trapping statistics of the filter. The simulations predict that the embolus-trapping efficiency of the IVC filter increases with increasing embolus diameter and increasing embolus-to-blood density ratio. Tilted filter placement is found to decrease the embolus-trapping efficiency compared with centered filter placement. Multiple embolus-trapping locations are predicted for the IVC filter, and the trapping locations are predicted to shift upstream and toward the vessel wall with increasing embolus diameter. Simulations of the injection of successive emboli into the IVC are also performed and reveal that the embolus-trapping efficiency decreases with increasing thrombus load in the IVC filter. In future work, the computational tool could be used to investigate IVC filter design improvements, the effect of patient anatomy on embolus transport and IVC filter embolus-trapping efficiency, and, with further development and validation, optimal filter selection and placement on a patient-specific basis.

Visual Tracking Using 3D Data and Region-Based Active Contours

DTIC Science & Technology

2016-09-28

adaptive control strategies which explicitly take uncertainty into account. Filtering methods ranging from the classical Kalman filters valid for...linear systems to the much more general particle filters also fit into this framework in a very natural manner. In particular, the particle filtering ...the number of samples required for accurate filtering increases with the dimension of the system noise. In our approach, we approximate curve

Comparison of sEMG processing methods during whole-body vibration exercise.

PubMed

Lienhard, Karin; Cabasson, Aline; Meste, Olivier; Colson, Serge S

2015-12-01

The objective was to investigate the influence of surface electromyography (sEMG) processing methods on the quantification of muscle activity during whole-body vibration (WBV) exercises. sEMG activity was recorded while the participants performed squats on the platform with and without WBV. The spikes observed in the sEMG spectrum at the vibration frequency and its harmonics were deleted using state-of-the-art methods, i.e. (1) a band-stop filter, (2) a band-pass filter, and (3) spectral linear interpolation. The same filtering methods were applied on the sEMG during the no-vibration trial. The linear interpolation method showed the highest intraclass correlation coefficients (no vibration: 0.999, WBV: 0.757-0.979) with the comparison measure (unfiltered sEMG during the no-vibration trial), followed by the band-stop filter (no vibration: 0.929-0.975, WBV: 0.661-0.938). While both methods introduced a systematic bias (P < 0.001), the error increased with increasing mean values to a higher degree for the band-stop filter. After adjusting the sEMG(RMS) during WBV for the bias, the performance of the interpolation method and the band-stop filter was comparable. The band-pass filter was in poor agreement with the other methods (ICC: 0.207-0.697), unless the sEMG(RMS) was corrected for the bias (ICC ⩾ 0.931, %LOA ⩽ 32.3). In conclusion, spectral linear interpolation or a band-stop filter centered at the vibration frequency and its multiple harmonics should be applied to delete the artifacts in the sEMG signals during WBV. With the use of a band-stop filter it is recommended to correct the sEMG(RMS) for the bias as this procedure improved its performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optical Fourier filtering for whole lens assessment of progressive power lenses.

PubMed

Spiers, T; Hull, C C

2000-07-01

Four binary filter designs for use in an optical Fourier filtering set-up were evaluated when taking quantitative measurements and when qualitatively mapping the power variation of progressive power lenses (PPLs). The binary filters tested were concentric ring, linear grating, grid and "chevron" designs. The chevron filter was considered best for quantitative measurements since it permitted a vernier acuity task to be used for measuring the fringe spacing, significantly reducing errors, and it also gave information on the polarity of the lens power. The linear grating filter was considered best for qualitatively evaluating the power variation. Optical Fourier filtering and a Nidek automatic focimeter were then used to measure the powers in the distance and near portions of five PPLs of differing design. Mean measurement error was 0.04 D with a maximum value of 0.13 D. Good qualitative agreement was found between the iso-cylinder plots provided by the manufacturer and the Fourier filter fringe patterns for the PPLs indicating that optical Fourier filtering provides the ability to map the power distribution across the entire lens aperture without the need for multiple point measurements. Arguments are presented that demonstrate that it should be possible to derive both iso-sphere and iso-cylinder plots from the binary filter patterns.

Reconstruction of three-dimensional ultrasound images based on cyclic Savitzky-Golay filters

NASA Astrophysics Data System (ADS)

Toonkum, Pollakrit; Suwanwela, Nijasri C.; Chinrungrueng, Chedsada

2011-01-01

We present a new algorithm for reconstructing a three-dimensional (3-D) ultrasound image from a series of two-dimensional B-scan ultrasound slices acquired in the mechanical linear scanning framework. Unlike most existing 3-D ultrasound reconstruction algorithms, which have been developed and evaluated in the freehand scanning framework, the new algorithm has been designed to capitalize the regularity pattern of the mechanical linear scanning, where all the B-scan slices are precisely parallel and evenly spaced. The new reconstruction algorithm, referred to as the cyclic Savitzky-Golay (CSG) reconstruction filter, is an improvement on the original Savitzky-Golay filter in two respects: First, it is extended to accept a 3-D array of data as the filter input instead of a one-dimensional data sequence. Second, it incorporates the cyclic indicator function in its least-squares objective function so that the CSG algorithm can simultaneously perform both smoothing and interpolating tasks. The performance of the CSG reconstruction filter compared to that of most existing reconstruction algorithms in generating a 3-D synthetic test image and a clinical 3-D carotid artery bifurcation in the mechanical linear scanning framework are also reported.

Optimized method for atmospheric signal reduction in irregular sampled InSAR time series assisted by external atmospheric information

NASA Astrophysics Data System (ADS)

Gong, W.; Meyer, F. J.

2013-12-01

It is well known that spatio-temporal the tropospheric phase signatures complicate the interpretation and detection of smaller magnitude deformation signals or unstudied motion fields. Several advanced time-series InSAR techniques were developed in the last decade that make assumptions about the stochastic properties of the signal components in interferometric phases to reduce atmospheric delay effects on surface deformation estimates. However, their need for large datasets to successfully separate the different phase contributions limits their performance if data is scarce and irregularly sampled. Limited SAR data coverage is true for many areas affected by geophysical deformation. This is either due to their low priority in mission programming, unfavorable ground coverage condition, or turbulent seasonal weather effects. In this paper, we present new adaptive atmospheric phase filtering algorithms that are specifically designed to reconstruct surface deformation signals from atmosphere-affected and irregularly sampled InSAR time series. The filters take advantage of auxiliary atmospheric delay information that is extracted from various sources, e.g. atmospheric weather models. They are embedded into a model-free Persistent Scatterer Interferometry (PSI) approach that was selected to accommodate non-linear deformation patterns that are often observed near volcanoes and earthquake zones. Two types of adaptive phase filters were developed that operate in the time dimension and separate atmosphere from deformation based on their different temporal correlation properties. Both filter types use the fact that atmospheric models can reliably predict the spatial statistics and signal power of atmospheric phase delay fields in order to automatically optimize the filter's shape parameters. In essence, both filter types will attempt to maximize the linear correlation between a-priori and the extracted atmospheric phase information. Topography-related phase components, orbit errors and the master atmospheric delays are first removed in a pre-processing step before the atmospheric filters are applied. The first adaptive filter type is using a filter kernel of Gaussian shape and is adaptively adjusting the width (defined in days) of this filter until the correlation of extracted and modeled atmospheric signal power is maximized. If atmospheric properties vary along the time series, this approach will lead to filter setting that are adapted to best reproduce atmospheric conditions at a certain observation epoch. Despite the superior performance of this first filter design, its Gaussian shape imposes non-physical relative weights onto acquisitions that ignore the known atmospheric noise in the data. Hence, in our second approach we are using atmospheric a-priori information to adaptively define the full shape of the atmospheric filter. For this process, we use a so-called normalized convolution (NC) approach that is often used in image reconstruction. Several NC designs will be presented in this paper and studied for relative performance. A cross-validation of all developed algorithms was done using both synthetic and real data. This validation showed designed filters are outperforming conventional filter methods that particularly useful for regions with limited data coverage or lack of a deformation field prior.

NMR spectroscopy of filtered serum of prostate cancer: A new frontier in metabolomics.

PubMed

Kumar, Deepak; Gupta, Ashish; Mandhani, Anil; Sankhwar, Satya Narain

2016-09-01

To address the shortcomings of digital rectal examinations (DRE), serum prostate-specific antigen (PSA), and trans-rectal ultrasound (TRUS) for precise determination of prostate cancer (PC) and differentiation from benign prostatic hyperplasia (BPH), we applied (1) H-nuclear magnetic resonance (NMR) spectroscopy as a surrogate tactic for probing and prediction of PC and BPH. The study comprises 210 filtered sera from suspected PC, BPH, and a healthy subjects' cohort (HC). The filtered serum approach delineates to identify and quantify 52 metabolites using (1) H NMR spectroscopy. All subjects had undergone clinical evaluations (DRE, PSA, and TRUS) followed by biopsy for Gleason score, if needed. NMR-measured metabolites and clinical evaluation data were examined separately using linear multivariate discriminant function analysis (DFA) to probe the signature descriptors for each cohort. DFA indicated that glycine, sarcosine, alanine, creatine, xanthine, and hypoxanthine were able to determine abnormal prostate (BPH + PC). DFA-based classification presented high precision (86.2% by NMR and 68.1% by clinical laboratory method) in discriminating HC from BPH + PC. DFA reveals that alanine, sarcosine, creatinine, glycine, and citrate were able to discriminate PC from BPH. DFA-based categorization exhibited high accuracy (88.3% by NMR and 75.2% by clinical laboratory method) to differentiate PC from BPH. (1) H NMR-based metabolic profiling of filtered-serum sample appears to be assuring, swift, and least-invasive for probing and prediction of PC and BPH with its signature metabolic profile. This novel technique is not only on a par with histopathological evaluation of PC determination but is also comparable to liquid chromatography-based mass spectrometry to identify the metabolites. Prostate 76:1106-1119, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A New Quaternion-Based Kalman Filter for Real-Time Attitude Estimation Using the Two-Step Geometrically-Intuitive Correction Algorithm.

PubMed

Feng, Kaiqiang; Li, Jie; Zhang, Xiaoming; Shen, Chong; Bi, Yu; Zheng, Tao; Liu, Jun

2017-09-19

In order to reduce the computational complexity, and improve the pitch/roll estimation accuracy of the low-cost attitude heading reference system (AHRS) under conditions of magnetic-distortion, a novel linear Kalman filter, suitable for nonlinear attitude estimation, is proposed in this paper. The new algorithm is the combination of two-step geometrically-intuitive correction (TGIC) and the Kalman filter. In the proposed algorithm, the sequential two-step geometrically-intuitive correction scheme is used to make the current estimation of pitch/roll immune to magnetic distortion. Meanwhile, the TGIC produces a computed quaternion input for the Kalman filter, which avoids the linearization error of measurement equations and reduces the computational complexity. Several experiments have been carried out to validate the performance of the filter design. The results demonstrate that the mean time consumption and the root mean square error (RMSE) of pitch/roll estimation under magnetic disturbances are reduced by 45.9% and 33.8%, respectively, when compared with a standard filter. In addition, the proposed filter is applicable for attitude estimation under various dynamic conditions.

A New Quaternion-Based Kalman Filter for Real-Time Attitude Estimation Using the Two-Step Geometrically-Intuitive Correction Algorithm

PubMed Central

Feng, Kaiqiang; Li, Jie; Zhang, Xiaoming; Shen, Chong; Bi, Yu; Zheng, Tao; Liu, Jun

2017-01-01

In order to reduce the computational complexity, and improve the pitch/roll estimation accuracy of the low-cost attitude heading reference system (AHRS) under conditions of magnetic-distortion, a novel linear Kalman filter, suitable for nonlinear attitude estimation, is proposed in this paper. The new algorithm is the combination of two-step geometrically-intuitive correction (TGIC) and the Kalman filter. In the proposed algorithm, the sequential two-step geometrically-intuitive correction scheme is used to make the current estimation of pitch/roll immune to magnetic distortion. Meanwhile, the TGIC produces a computed quaternion input for the Kalman filter, which avoids the linearization error of measurement equations and reduces the computational complexity. Several experiments have been carried out to validate the performance of the filter design. The results demonstrate that the mean time consumption and the root mean square error (RMSE) of pitch/roll estimation under magnetic disturbances are reduced by 45.9% and 33.8%, respectively, when compared with a standard filter. In addition, the proposed filter is applicable for attitude estimation under various dynamic conditions. PMID:28925979

The effects of the Asselin time filter on numerical solutions to the linearized shallow-water wave equations

NASA Technical Reports Server (NTRS)

Schlesinger, R. E.; Johnson, D. R.; Uccellini, L. W.

1983-01-01

In the present investigation, a one-dimensional linearized analysis is used to determine the effect of Asselin's (1972) time filter on both the computational stability and phase error of numerical solutions for the shallow water wave equations, in cases with diffusion but without rotation. An attempt has been made to establish the approximate optimal values of the filtering parameter nu for each of the 'lagged', Dufort-Frankel, and Crank-Nicholson diffusion schemes, suppressing the computational wave mode without materially altering the physical wave mode. It is determined that in the presence of diffusion, the optimum filter length depends on whether waves are undergoing significant propagation. When moderate propagation is present, with or without diffusion, the Asselin filter has little effect on the spatial phase lag of the physical mode for the leapfrog advection scheme of the three diffusion schemes considered.

A Kalman filter for a two-dimensional shallow-water model

NASA Technical Reports Server (NTRS)

Parrish, D. F.; Cohn, S. E.

1985-01-01

A two-dimensional Kalman filter is described for data assimilation for making weather forecasts. The filter is regarded as superior to the optimal interpolation method because the filter determines the forecast error covariance matrix exactly instead of using an approximation. A generalized time step is defined which includes expressions for one time step of the forecast model, the error covariance matrix, the gain matrix, and the evolution of the covariance matrix. Subsequent time steps are achieved by quantifying the forecast variables or employing a linear extrapolation from a current variable set, assuming the forecast dynamics are linear. Calculations for the evolution of the error covariance matrix are banded, i.e., are performed only with the elements significantly different from zero. Experimental results are provided from an application of the filter to a shallow-water simulation covering a 6000 x 6000 km grid.

Separation of man-made and natural patterns in high-altitude imagery of agricultural areas

NASA Technical Reports Server (NTRS)

Samulon, A. S.

1975-01-01

A nonstationary linear digital filter is designed and implemented which extracts the natural features from high-altitude imagery of agricultural areas. Essentially, from an original image a new image is created which displays information related to soil properties, drainage patterns, crop disease, and other natural phenomena, and contains no information about crop type or row spacing. A model is developed to express the recorded brightness in a narrow-band image in terms of man-made and natural contributions and which describes statistically the spatial properties of each. The form of the minimum mean-square error linear filter for estimation of the natural component of the scene is derived and a suboptimal filter is implemented. Nonstationarity of the two-dimensional random processes contained in the model requires a unique technique for deriving the optimum filter. Finally, the filter depends on knowledge of field boundaries. An algorithm for boundary location is proposed, discussed, and implemented.

Influence of linearly polarized near-infrared irradiation on deformability of human stored erythrocytes.

PubMed

Yokoyama, Kozo; Sugiyama, Kazuna

2003-02-01

To investigate the influence of linearly polarized near-infrared irradiation using the Super Lizer trade mark on deformability of human erythrocytes. Not only low-powered laser but also linearly polarized near-infrared beams have some biostimulation effects on various tissues. There were some reports of erythrocyte deformability improved by low-powered He-Ne laser irradiation. Human erythrocyte samples stored for three weeks were adjusted to 30% hematocrit. Erythrocyte deformability presented as the filter filtration rate was measured. There was no difference of the filter filtration rate between control group without irradiation and the group of 125 mJ/cm(2) exposure level at a wavelength of 830 nm. However, the groups of 625 and 1,250 mJ/cm(2) exposure levels at a wavelength of 830 nm showed higher filter filtration rates compared to the control group. Linearly polarized near-infrared irradiation in a range of 625-1,250 mJ/cm(2) exposure level at a wavelength of 830 nm improved deformability of human stored erythrocytes.

Bayesian Approach to the Joint Inversion of Gravity and Magnetic Data, with Application to the Ismenius Area of Mars

NASA Technical Reports Server (NTRS)

Jewell, Jeffrey B.; Raymond, C.; Smrekar, S.; Millbury, C.

2004-01-01

This viewgraph presentation reviews a Bayesian approach to the inversion of gravity and magnetic data with specific application to the Ismenius Area of Mars. Many inverse problems encountered in geophysics and planetary science are well known to be non-unique (i.e. inversion of gravity the density structure of a body). In hopes of reducing the non-uniqueness of solutions, there has been interest in the joint analysis of data. An example is the joint inversion of gravity and magnetic data, with the assumption that the same physical anomalies generate both the observed magnetic and gravitational anomalies. In this talk, we formulate the joint analysis of different types of data in a Bayesian framework and apply the formalism to the inference of the density and remanent magnetization structure for a local region in the Ismenius area of Mars. The Bayesian approach allows prior information or constraints in the solutions to be incorporated in the inversion, with the "best" solutions those whose forward predictions most closely match the data while remaining consistent with assumed constraints. The application of this framework to the inversion of gravity and magnetic data on Mars reveals two typical challenges - the forward predictions of the data have a linear dependence on some of the quantities of interest, and non-linear dependence on others (termed the "linear" and "non-linear" variables, respectively). For observations with Gaussian noise, a Bayesian approach to inversion for "linear" variables reduces to a linear filtering problem, with an explicitly computable "error" matrix. However, for models whose forward predictions have non-linear dependencies, inference is no longer given by such a simple linear problem, and moreover, the uncertainty in the solution is no longer completely specified by a computable "error matrix". It is therefore important to develop methods for sampling from the full Bayesian posterior to provide a complete and statistically consistent picture of model uncertainty, and what has been learned from observations. We will discuss advanced numerical techniques, including Monte Carlo Markov

Perception of global image contrast involves transparent spatial filtering and the integration and suppression of local contrasts (not RMS contrast)

PubMed Central

2017-01-01

When adjusting the contrast setting on a television set, we experience a perceptual change in the global image contrast. But how is that statistic computed? We addressed this using a contrast-matching task for checkerboard configurations of micro-patterns in which the contrasts and spatial spreads of two interdigitated components were controlled independently. When the patterns differed greatly in contrast, the higher contrast determined the perceived global contrast. Crucially, however, low contrast additions of one pattern to intermediate contrasts of the other caused a paradoxical reduction in the perceived global contrast. None of the following metrics/models predicted this: max, linear sum, average, energy, root mean squared (RMS), Legge and Foley. However, a nonlinear gain control model, derived from contrast detection and discrimination experiments, incorporating wide-field summation and suppression, did predict the results with no free parameters, but only when spatial filtering was removed. We conclude that our model describes fundamental processes in human contrast vision (the pattern of results was the same for expert and naive observers), but that above threshold—when contrast pedestals are clearly visible—vision's spatial filtering characteristics become transparent, tending towards those of a delta function prior to spatial summation. The global contrast statistic from our model is as easily derived as the RMS contrast of an image, and since it more closely relates to human perception, we suggest it be used as an image contrast metric in practical applications. PMID:28989735

Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles

PubMed Central

Meyer, Arne F.; Diepenbrock, Jan-Philipp; Happel, Max F. K.; Ohl, Frank W.; Anemüller, Jörn

2014-01-01

Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design. PMID:24699631

Discriminative learning of receptive fields from responses to non-Gaussian stimulus ensembles.

PubMed

Meyer, Arne F; Diepenbrock, Jan-Philipp; Happel, Max F K; Ohl, Frank W; Anemüller, Jörn

2014-01-01

Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.

Analysis of JPSS J1 VIIRS Polarization Sensitivity Using the NIST T-SIRCUS

NASA Technical Reports Server (NTRS)

McIntire, Jeffrey W.; Young, James B.; Moyer, David; Waluschka, Eugene; Oudrari, Hassan; Xiong, Xiaoxiong

2015-01-01

The polarization sensitivity of the Joint Polar Satellite System (JPSS) J1 Visible Infrared Imaging Radiometer Suite (VIIRS) measured pre-launch using a broadband source was observed to be larger than expected for many reflective bands. Ray trace modeling predicted that the observed polarization sensitivity was the result of larger diattenuation at the edges of the focal plane filter spectral bandpass. Additional ground measurements were performed using a monochromatic source (the NIST T-SIRCUS) to input linearly polarized light at a number of wavelengths across the bandpass of two VIIRS spectral bands and two scan angles. This work describes the data processing, analysis, and results derived from the T-SIRCUS measurements, comparing them with broadband measurements. Results have shown that the observed degree of linear polarization, when weighted by the sensor's spectral response function, is generally larger on the edges and smaller in the center of the spectral bandpass, as predicted. However, phase angle changes in the center of the bandpass differ between model and measurement. Integration of the monochromatic polarization sensitivity over wavelength produced results consistent with the broadband source measurements, for all cases considered.

Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

PubMed Central

Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

2015-01-01

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature. PMID:25799141

Elimination of fuel pressure fluctuation and multi-injection fuel mass deviation of high pressure common-rail fuel injection system

NASA Astrophysics Data System (ADS)

Li, Pimao; Zhang, Youtong; Li, Tieshuan; Xie, Lizhe

2015-03-01

The influence of fuel pressure fluctuation on multi-injection fuel mass deviation has been studied a lot, but the fuel pressure fluctuation at injector inlet is still not eliminated efficiently. In this paper, a new type of hydraulic filter consisting of a damping hole and a chamber is developed for elimination of fuel pressure fluctuation and multi-injection fuel mass deviation. Linear model of the improved high pressure common-rail system(HPCRS) including injector, the pipe connecting common-rail with injector and the hydraulic filter is built. Fuel pressure fluctuation at injector inlet, on which frequency domain analysis is conducted through fast Fourier transformation, is acquired at different target pressure and different damping hole diameter experimentally. The linear model is validated and can predict the natural frequencies of the system. Influence of damping hole diameter on fuel pressure fluctuation is analyzed qualitatively based on the linear model, and it can be inferred that an optimal diameter of the damping hole for elimination of fuel pressure fluctuation exists. Fuel pressure fluctuation and fuel mass deviation under different damping hole diameters are measured experimentally, and it is testified that the amplitude of both fuel pressure fluctuation and fuel mass deviation decreases first and then increases with the increasing of damping hole diameter. The amplitude of main injection fuel mass deviation can be reduced by 73% at most under pilot-main injection mode, and the amplitude of post injection fuel mass deviation can be reduced by 92% at most under main-post injection mode. Fuel mass of a single injection increases with the increasing of the damping hole diameter. The hydraulic filter proposed by this research can be potentially used to eliminate fuel pressure fluctuation at injector inlet and improve the stability of HPCRS fuel injection.

Control, Filtering and Prediction for Phased Arrays in Directed Energy Systems

DTIC Science & Technology

2016-04-30

adaptive optics. 15. SUBJECT TERMS control, filtering, prediction, system identification, adaptive optics, laser beam pointing, target tracking, phase... laser beam control; furthermore, wavefront sensors are plagued by the difficulty of maintaining the required alignment and focusing in dynamic mission...developed new methods for filtering, prediction and system identification in adaptive optics for high energy laser systems including phased arrays. The

Modelling the spatio-temporal modulation response of ganglion cells with difference-of-Gaussians receptive fields: relation to photoreceptor response kinetics.

PubMed

Donner, K; Hemilä, S

1996-01-01

Difference-of-Gaussians (DOG) models for the receptive fields of retinal ganglion cells accurately predict linear responses to both periodic stimuli (typically moving sinusoidal gratings) and aperiodic stimuli (typically circular fields presented as square-wave pulses). While the relation of spatial organization to retinal anatomy has received considerable attention, temporal characteristics have been only loosely connected to retinal physiology. Here we integrate realistic photoreceptor response waveforms into the DOG model to clarify how far a single set of physiological parameters predict temporal aspects of linear responses to both periodic and aperiodic stimuli. Traditional filter-cascade models provide a useful first-order approximation of the single-photon response in photoreceptors. The absolute time scale of these, plus a time for retinal transmission, here construed as a fixed delay, are obtained from flash/step data. Using these values, we find that the DOG model predicts the main features of both the amplitude and phase response of linear cat ganglion cells to sinusoidal flicker. Where the simplest model formulation fails, it serves to reveal additional mechanisms. Unforeseen facts are the attenuation of low temporal frequencies even in pure center-type responses, and the phase advance of the response relative to the stimulus at low frequencies. Neither can be explained by any experimentally documented cone response waveform, but both would be explained by signal differentiation, e.g. in the retinal transmission pathway, as demonstrated at least in turtle retina.

Improved optical filter

NASA Technical Reports Server (NTRS)

Title, A. M.

1978-01-01

Filter includes partial polarizer between birefrigent elements. Plastic film on partial polarizer compensates for any polarization rotation by partial polarizer. Two quarter-wave plates change incident, linearly polarized light into elliptically polarized light.

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

NASA Astrophysics Data System (ADS)

Correia, Carlos M.; Bond, Charlotte Z.; Sauvage, Jean-François; Fusco, Thierry; Conan, Rodolphe; Wizinowich, Peter L.

2017-10-01

We build on a long-standing tradition in astronomical adaptive optics (AO) of specifying performance metrics and error budgets using linear systems modeling in the spatial-frequency domain. Our goal is to provide a comprehensive tool for the calculation of error budgets in terms of residual temporally filtered phase power spectral densities and variances. In addition, the fast simulation of AO-corrected point spread functions (PSFs) provided by this method can be used as inputs for simulations of science observations with next-generation instruments and telescopes, in particular to predict post-coronagraphic contrast improvements for planet finder systems. We extend the previous results and propose the synthesis of a distributed Kalman filter to mitigate both aniso-servo-lag and aliasing errors whilst minimizing the overall residual variance. We discuss applications to (i) analytic AO-corrected PSF modeling in the spatial-frequency domain, (ii) post-coronagraphic contrast enhancement, (iii) filter optimization for real-time wavefront reconstruction, and (iv) PSF reconstruction from system telemetry. Under perfect knowledge of wind velocities, we show that $\\sim$60 nm rms error reduction can be achieved with the distributed Kalman filter embodying anti- aliasing reconstructors on 10 m class high-order AO systems, leading to contrast improvement factors of up to three orders of magnitude at few ${\\lambda}/D$ separations ($\\sim1-5{\\lambda}/D$) for a 0 magnitude star and reaching close to one order of magnitude for a 12 magnitude star.

Assessing FRET using Spectral Techniques

PubMed Central

Leavesley, Silas J.; Britain, Andrea L.; Cichon, Lauren K.; Nikolaev, Viacheslav O.; Rich, Thomas C.

2015-01-01

Förster resonance energy transfer (FRET) techniques have proven invaluable for probing the complex nature of protein–protein interactions, protein folding, and intracellular signaling events. These techniques have traditionally been implemented with the use of one or more fluorescence band-pass filters, either as fluorescence microscopy filter cubes, or as dichroic mirrors and band-pass filters in flow cytometry. In addition, new approaches for measuring FRET, such as fluorescence lifetime and acceptor photobleaching, have been developed. Hyperspectral techniques for imaging and flow cytometry have also shown to be promising for performing FRET measurements. In this study, we have compared traditional (filter-based) FRET approaches to three spectral-based approaches: the ratio of acceptor-to-donor peak emission, linear spectral unmixing, and linear spectral unmixing with a correction for direct acceptor excitation. All methods are estimates of FRET efficiency, except for one-filter set and three-filter set FRET indices, which are included for consistency with prior literature. In the first part of this study, spectrofluorimetric data were collected from a CFP–Epac–YFP FRET probe that has been used for intracellular cAMP measurements. All comparisons were performed using the same spectrofluorimetric datasets as input data, to provide a relevant comparison. Linear spectral unmixing resulted in measurements with the lowest coefficient of variation (0.10) as well as accurate fits using the Hill equation. FRET efficiency methods produced coefficients of variation of less than 0.20, while FRET indices produced coefficients of variation greater than 8.00. These results demonstrate that spectral FRET measurements provide improved response over standard, filter-based measurements. Using spectral approaches, single-cell measurements were conducted through hyperspectral confocal microscopy, linear unmixing, and cell segmentation with quantitative image analysis. Results from these studies confirmed that spectral imaging is effective for measuring subcellular, time-dependent FRET dynamics and that additional fluorescent signals can be readily separated from FRET signals, enabling multilabel studies of molecular interactions. PMID:23929684

Assessing FRET using spectral techniques.

PubMed

Leavesley, Silas J; Britain, Andrea L; Cichon, Lauren K; Nikolaev, Viacheslav O; Rich, Thomas C

2013-10-01

Förster resonance energy transfer (FRET) techniques have proven invaluable for probing the complex nature of protein-protein interactions, protein folding, and intracellular signaling events. These techniques have traditionally been implemented with the use of one or more fluorescence band-pass filters, either as fluorescence microscopy filter cubes, or as dichroic mirrors and band-pass filters in flow cytometry. In addition, new approaches for measuring FRET, such as fluorescence lifetime and acceptor photobleaching, have been developed. Hyperspectral techniques for imaging and flow cytometry have also shown to be promising for performing FRET measurements. In this study, we have compared traditional (filter-based) FRET approaches to three spectral-based approaches: the ratio of acceptor-to-donor peak emission, linear spectral unmixing, and linear spectral unmixing with a correction for direct acceptor excitation. All methods are estimates of FRET efficiency, except for one-filter set and three-filter set FRET indices, which are included for consistency with prior literature. In the first part of this study, spectrofluorimetric data were collected from a CFP-Epac-YFP FRET probe that has been used for intracellular cAMP measurements. All comparisons were performed using the same spectrofluorimetric datasets as input data, to provide a relevant comparison. Linear spectral unmixing resulted in measurements with the lowest coefficient of variation (0.10) as well as accurate fits using the Hill equation. FRET efficiency methods produced coefficients of variation of less than 0.20, while FRET indices produced coefficients of variation greater than 8.00. These results demonstrate that spectral FRET measurements provide improved response over standard, filter-based measurements. Using spectral approaches, single-cell measurements were conducted through hyperspectral confocal microscopy, linear unmixing, and cell segmentation with quantitative image analysis. Results from these studies confirmed that spectral imaging is effective for measuring subcellular, time-dependent FRET dynamics and that additional fluorescent signals can be readily separated from FRET signals, enabling multilabel studies of molecular interactions. © 2013 International Society for Advancement of Cytometry. Copyright © 2013 International Society for Advancement of Cytometry.

Kalman filter for onboard state of charge estimation and peak power capability analysis of lithium-ion batteries

NASA Astrophysics Data System (ADS)

Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai

2016-10-01

To evaluate the continuous and instantaneous load capability of a battery, this paper describes a joint estimator for state-of-charge (SOC) and state-of-function (SOF) of lithium-ion batteries (LIB) based on Kalman filter (KF). The SOC is a widely used index for remain useful capacity left in a battery. The SOF represents the peak power capability of the battery. It can be determined by real-time SOC estimation and terminal voltage prediction, which can be derived from impedance parameters. However, the open-circuit-voltage (OCV) of LiFePO4 is highly nonlinear with SOC, which leads to the difficulties in SOC estimation. To solve these problems, this paper proposed an onboard SOC estimation method. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery, where the OCV is regarded as a linearized function of SOC. Then, the system states are estimated based on the KF. Besides, the factors that influence peak power capability are analyzed according to statistical data. Finally, the performance of the proposed methodology is demonstrated by experiments conducted on a LiFePO4 LIBs under different operating currents and temperatures. Experimental results indicate that the proposed approach is suitable for battery onboard SOC and SOF estimation.

Application of optimal control theory to the design of the NASA/JPL 70-meter antenna servos

NASA Technical Reports Server (NTRS)

Alvarez, L. S.; Nickerson, J.

1989-01-01

The application of Linear Quadratic Gaussian (LQG) techniques to the design of the 70-m axis servos is described. Linear quadratic optimal control and Kalman filter theory are reviewed, and model development and verification are discussed. Families of optimal controller and Kalman filter gain vectors were generated by varying weight parameters. Performance specifications were used to select final gain vectors.

A Reduced Dimension Static, Linearized Kalman Filter and Smoother

NASA Technical Reports Server (NTRS)

Fukumori, I.

1995-01-01

An approximate Kalman filter and smoother, based on approximations of the state estimation error covariance matrix, is described. Approximations include a reduction of the effective state dimension, use of a static asymptotic error limit, and a time-invariant linearization of the dynamic model for error integration. The approximations lead to dramatic computational savings in applying estimation theory to large complex systems. Examples of use come from TOPEX/POSEIDON.

Catchments as non-linear filters: evaluating data-driven approaches for spatio-temporal predictions in ungauged basins

NASA Astrophysics Data System (ADS)

Bellugi, D. G.; Tennant, C.; Larsen, L.

2016-12-01

Catchment and climate heterogeneity complicate prediction of runoff across time and space, and resulting parameter uncertainty can lead to large accumulated errors in hydrologic models, particularly in ungauged basins. Recently, data-driven modeling approaches have been shown to avoid the accumulated uncertainty associated with many physically-based models, providing an appealing alternative for hydrologic prediction. However, the effectiveness of different methods in hydrologically and geomorphically distinct catchments, and the robustness of these methods to changing climate and changing hydrologic processes remain to be tested. Here, we evaluate the use of machine learning techniques to predict daily runoff across time and space using only essential climatic forcing (e.g. precipitation, temperature, and potential evapotranspiration) time series as model input. Model training and testing was done using a high quality dataset of daily runoff and climate forcing data for 25+ years for 600+ minimally-disturbed catchments (drainage area range 5-25,000 km2, median size 336 km2) that cover a wide range of climatic and physical characteristics. Preliminary results using Support Vector Regression (SVR) suggest that in some catchments this nonlinear-based regression technique can accurately predict daily runoff, while the same approach fails in other catchments, indicating that the representation of climate inputs and/or catchment filter characteristics in the model structure need further refinement to increase performance. We bolster this analysis by using Sparse Identification of Nonlinear Dynamics (a sparse symbolic regression technique) to uncover the governing equations that describe runoff processes in catchments where SVR performed well and for ones where it performed poorly, thereby enabling inference about governing processes. This provides a robust means of examining how catchment complexity influences runoff prediction skill, and represents a contribution towards the integration of data-driven inference and physically-based models.

Nonlinear Motion Cueing Algorithm: Filtering at Pilot Station and Development of the Nonlinear Optimal Filters for Pitch and Roll

NASA Technical Reports Server (NTRS)

Zaychik, Kirill B.; Cardullo, Frank M.

2012-01-01

Telban and Cardullo have developed and successfully implemented the non-linear optimal motion cueing algorithm at the Visual Motion Simulator (VMS) at the NASA Langley Research Center in 2005. The latest version of the non-linear algorithm performed filtering of motion cues in all degrees-of-freedom except for pitch and roll. This manuscript describes the development and implementation of the non-linear optimal motion cueing algorithm for the pitch and roll degrees of freedom. Presented results indicate improved cues in the specified channels as compared to the original design. To further advance motion cueing in general, this manuscript describes modifications to the existing algorithm, which allow for filtering at the location of the pilot's head as opposed to the centroid of the motion platform. The rational for such modification to the cueing algorithms is that the location of the pilot's vestibular system must be taken into account as opposed to the off-set of the centroid of the cockpit relative to the center of rotation alone. Results provided in this report suggest improved performance of the motion cueing algorithm.

Bilinear modeling and nonlinear estimation

NASA Technical Reports Server (NTRS)

Dwyer, Thomas A. W., III; Karray, Fakhreddine; Bennett, William H.

1989-01-01

New methods are illustrated for online nonlinear estimation applied to the lateral deflection of an elastic beam on board measurements of angular rates and angular accelerations. The development of the filter equations, together with practical issues of their numerical solution as developed from global linearization by nonlinear output injection are contrasted with the usual method of the extended Kalman filter (EKF). It is shown how nonlinear estimation due to gyroscopic coupling can be implemented as an adaptive covariance filter using off-the-shelf Kalman filter algorithms. The effect of the global linearization by nonlinear output injection is to introduce a change of coordinates in which only the process noise covariance is to be updated in online implementation. This is in contrast to the computational approach which arises in EKF methods arising by local linearization with respect to the current conditional mean. Processing refinements for nonlinear estimation based on optimal, nonlinear interpolation between observations are also highlighted. In these methods the extrapolation of the process dynamics between measurement updates is obtained by replacing a transition matrix with an operator spline that is optimized off-line from responses to selected test inputs.

Evaluation of the effect of vibration nonlinearity on convergence behavior of adaptive higher harmonic controllers

NASA Technical Reports Server (NTRS)

Molusis, J. A.; Mookerjee, P.; Bar-Shalom, Y.

1983-01-01

Effect of nonlinearity on convergence of the local linear and global linear adaptive controllers is evaluated. A nonlinear helicopter vibration model is selected for the evaluation which has sufficient nonlinearity, including multiple minimum, to assess the vibration reduction capability of the adaptive controllers. The adaptive control algorithms are based upon a linear transfer matrix assumption and the presence of nonlinearity has a significant effect on algorithm behavior. Simulation results are presented which demonstrate the importance of the caution property in the global linear controller. Caution is represented by a time varying rate weighting term in the local linear controller and this improves the algorithm convergence. Nonlinearity in some cases causes Kalman filter divergence. Two forms of the Kalman filter covariance equation are investigated.

A linear 180 nm SOI CMOS antenna switch module using integrated passive device filters for cellular applications

NASA Astrophysics Data System (ADS)

Jie, Cui; Lei, Chen; Peng, Zhao; Xu, Niu; Yi, Liu

2014-06-01

A broadband monolithic linear single pole, eight throw (SP8T) switch has been fabricated in 180 nm thin film silicon-on-insulator (SOI) CMOS technology with a quad-band GSM harmonic filter in integrated passive devices (IPD) technology, which is developed for cellular applications. The antenna switch module (ASM) features 1.2 dB insertion loss with filter on 2G bands and 0.4 dB insertion loss in 3G bands, less than -45 dB isolation and maximum -103 dB intermodulation distortion for mobile front ends by applying distributed architecture and adaptive supply voltage generator.

A variable capacitance based modeling and power capability predicting method for ultracapacitor

NASA Astrophysics Data System (ADS)

Liu, Chang; Wang, Yujie; Chen, Zonghai; Ling, Qiang

2018-01-01

Methods of accurate modeling and power capability predicting for ultracapacitors are of great significance in management and application of lithium-ion battery/ultracapacitor hybrid energy storage system. To overcome the simulation error coming from constant capacitance model, an improved ultracapacitor model based on variable capacitance is proposed, where the main capacitance varies with voltage according to a piecewise linear function. A novel state-of-charge calculation approach is developed accordingly. After that, a multi-constraint power capability prediction is developed for ultracapacitor, in which a Kalman-filter-based state observer is designed for tracking ultracapacitor's real-time behavior. Finally, experimental results verify the proposed methods. The accuracy of the proposed model is verified by terminal voltage simulating results under different temperatures, and the effectiveness of the designed observer is proved by various test conditions. Additionally, the power capability prediction results of different time scales and temperatures are compared, to study their effects on ultracapacitor's power capability.

Efficacy of predictive wavefront control for compensating aero-optical aberrations

NASA Astrophysics Data System (ADS)

Goorskey, David J.; Schmidt, Jason; Whiteley, Matthew R.

2013-07-01

Imaging and laser beam propagation from airborne platforms are degraded by dynamic aberrations due to air flow around the aircraft, aero-mechanical distortions and jitter, and free atmospheric turbulence. For certain applications, like dim-object imaging, free-space optical communications, and laser weapons, adaptive optics (AO) is necessary to compensate for the aberrations in real time. Aero-optical flow is a particularly interesting source of aberrations whose flowing structures can be exploited by adaptive and predictive AO controllers, thereby realizing significant performance gains. We analyze dynamic aero-optical wavefronts to determine the pointing angles at which predictive wavefront control is more effective than conventional, fixed-gain, linear-filter control. It was found that properties of the spatial decompositions and temporal statistics of the wavefronts are directly traceable to specific features in the air flow. Furthermore, the aero-optical wavefront aberrations at the side- and aft-looking angles were the most severe, but they also benefited the most from predictive AO.

Second-order discrete Kalman filtering equations for control-structure interaction simulations

NASA Technical Reports Server (NTRS)

Park, K. C.; Belvin, W. Keith; Alvin, Kenneth F.

1991-01-01

A general form for the first-order representation of the continuous, second-order linear structural dynamics equations is introduced in order to derive a corresponding form of first-order Kalman filtering equations (KFE). Time integration of the resulting first-order KFE is carried out via a set of linear multistep integration formulas. It is shown that a judicious combined selection of computational paths and the undetermined matrices introduced in the general form of the first-order linear structural systems leads to a class of second-order discrete KFE involving only symmetric, N x N solution matrix.

WFPC2 CYCLE 15 Intflat Linearity Check and Filter Rotation Anomaly Monitor

NASA Astrophysics Data System (ADS)

Gonzaga, Shireen

2006-07-01

Intflat observations will be taken to provide a linearity check: the linearity test consists of a series of intflats in F555W, in each gain and each shutter. A combination of intflats, visflats, and earthflats will be used to check the repeatability of filter wheel motions. {Intflat sequences tied to decons, visits 1-18 in prop 10363, have been moved to the cycle 15 decon proposal xxxx for easier scheduling.} Note: long-exposure WFPC2 intflats must be scheduled during ACS anneals to prevent stray light from the WFPC2 lamps from contaminating long ACS external exposures.

A comparison of linear and non-linear data assimilation methods using the NEMO ocean model

NASA Astrophysics Data System (ADS)

Kirchgessner, Paul; Tödter, Julian; Nerger, Lars

2015-04-01

The assimilation behavior of the widely used LETKF is compared with the Equivalent Weight Particle Filter (EWPF) in a data assimilation application with an idealized configuration of the NEMO ocean model. The experiments show how the different filter methods behave when they are applied to a realistic ocean test case. The LETKF is an ensemble-based Kalman filter, which assumes Gaussian error distributions and hence implicitly requires model linearity. In contrast, the EWPF is a fully nonlinear data assimilation method that does not rely on a particular error distribution. The EWPF has been demonstrated to work well in highly nonlinear situations, like in a model solving a barotropic vorticity equation, but it is still unknown how the assimilation performance compares to ensemble Kalman filters in realistic situations. For the experiments, twin assimilation experiments with a square basin configuration of the NEMO model are performed. The configuration simulates a double gyre, which exhibits significant nonlinearity. The LETKF and EWPF are both implemented in PDAF (Parallel Data Assimilation Framework, http://pdaf.awi.de), which ensures identical experimental conditions for both filters. To account for the nonlinearity, the assimilation skill of the two methods is assessed by using different statistical metrics, like CRPS and Histograms.

Response of integrate-and-fire neurons to noisy inputs filtered by synapses with arbitrary timescales: firing rate and correlations.

PubMed

Moreno-Bote, Rubén; Parga, Néstor

2010-06-01

Delivery of neurotransmitter produces on a synapse a current that flows through the membrane and gets transmitted into the soma of the neuron, where it is integrated. The decay time of the current depends on the synaptic receptor's type and ranges from a few (e.g., AMPA receptors) to a few hundred milliseconds (e.g., NMDA receptors). The role of the variety of synaptic timescales, several of them coexisting in the same neuron, is at present not understood. A prime question to answer is which is the effect of temporal filtering at different timescales of the incoming spike trains on the neuron's response. Here, based on our previous work on linear synaptic filtering, we build a general theory for the stationary firing response of integrate-and-fire (IF) neurons receiving stochastic inputs filtered by one, two, or multiple synaptic channels, each characterized by an arbitrary timescale. The formalism applies to arbitrary IF model neurons and arbitrary forms of input noise (i.e., not required to be gaussian or to have small amplitude), as well as to any form of synaptic filtering (linear or nonlinear). The theory determines with exact analytical expressions the firing rate of an IF neuron for long synaptic time constants using the adiabatic approach. The correlated spiking (cross-correlations function) of two neurons receiving common as well as independent sources of noise is also described. The theory is illustrated using leaky, quadratic, and noise-thresholded IF neurons. Although the adiabatic approach is exact when at least one of the synaptic timescales is long, it provides a good prediction of the firing rate even when the timescales of the synapses are comparable to that of the leak of the neuron; it is not required that the synaptic time constants are longer than the mean interspike intervals or that the noise has small variance. The distribution of the potential for general IF neurons is also characterized. Our results provide powerful analytical tools that can allow a quantitative description of the dynamics of neuronal networks with realistic synaptic dynamics.

Consequences of broad auditory filters for identification of multichannel-compressed vowels

PubMed Central

Souza, Pamela; Wright, Richard; Bor, Stephanie

2012-01-01

Purpose In view of previous findings (Bor, Souza & Wright, 2008) that some listeners are more susceptible to spectral changes from multichannel compression (MCC) than others, this study addressed the extent to which differences in effects of MCC were related to differences in auditory filter width. Method Listeners were recruited in three groups: listeners with flat sensorineural loss, listeners with sloping sensorineural loss, and a control group of listeners with normal hearing. Individual auditory filter measurements were obtained at 500 and 2000 Hz. The filter widths were related to identification of vowels processed with 16-channel MCC and with a control (linear) condition. Results Listeners with flat loss had broader filters at 500 Hz but not at 2000 Hz, compared to listeners with sloping loss. Vowel identification was poorer for MCC compared to linear amplification. Listeners with flat loss made more errors than listeners with sloping loss, and there was a significant relationship between filter width and the effects of MCC. Conclusions Broadened auditory filters can reduce the ability to process amplitude-compressed vowel spectra. This suggests that individual frequency selectivity is one factor which influences benefit of MCC, when a high number of compression channels are used. PMID:22207696

The effect of environmental uncertainty on morphological design and fluid balance in Sarracenia purpurea L.

PubMed

Kingsolver, Joel

1981-03-01

To explore principles of organismic design in fluctuating environments, morphological design of the leaf of the pitcher-plant, Sarracenia purpurea, was studied for a population in northern Michigan. The design criterion focused upon the leaf shape and minimum size which effectively avoids leaf desiccation (complete loss of fluid from the leaf cavity) in the face of fluctuating rainfall and meteorological conditions. Bowl- and pitcher-shaped leaves were considered. Simulations show that the pitcher geometry experiences less frequent desiccation than bowls of the same size. Desiccation frequency is inversely related to leaf size; the size distribution of pitcher leaves in the field shows that the majority of pitchers desiccate only 1-3 times per season on average, while smaller pitchers may average up to 8 times per season. A linear filter model of an organism in a fluctuating environment is presented, in which the organism selectively filters the temporal patterns of environmental input. General measures of rainfall predictability based upon information theory and spectral analysis are consistent with the model of a pitcher leaf as a low-pass (frequency) filter which avoids desiccation by eliminating high-frequency rainfall variability.

Identifying Bearing Rotodynamic Coefficients Using an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Miller, Brad A.; Howard, Samuel A.

2008-01-01

An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter's performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.

The Effect of Pulse Shaping QPSK on Bandwidth Efficiency

NASA Technical Reports Server (NTRS)

Purba, Josua Bisuk Mubyarto; Horan, Shelia

1997-01-01

This research investigates the effect of pulse shaping QPSK on bandwidth efficiency over a non-linear channel. This investigation will include software simulations and the hardware implementation. Three kinds of filters: the 5th order Butterworth filter, the 3rd order Bessel filter and the Square Root Raised Cosine filter with a roll off factor (alpha) of 0.25,0.5 and 1, have been investigated as pulse shaping filters. Two different high power amplifiers, one a Traveling Wave Tube Amplifier (TWTA) and the other a Solid State Power Amplifier (SSPA) have been investigated in the hardware implementation. A significant improvement in the bandwidth utilization (rho) for the filtered data compared to unfiltered data through the non-linear channel is shown in the results. This method promises strong performance gains in a bandlimited channel when compared to unfiltered systems. This work was conducted at NMSU in the Center for Space Telemetering, and Telecommunications Systems in the Klipsch School of Electrical and Computer Engineering Department and is supported by a grant from the National Aeronautics and Space Administration (NASA) NAG5-1491.

An Algebraic Approach to Inference in Complex Networked Structures

DTIC Science & Technology

2015-07-09

44], [45],[46] where the shift is the elementary non-trivial filter that generates, under an appropriate notion of shift invariance, all linear ... elementary filter, and its output is a graph signal with the value at vertex n of the graph given approximately by a weighted linear combination of...AFRL-AFOSR-VA-TR-2015-0265 An Algebraic Approach to Inference in Complex Networked Structures Jose Moura CARNEGIE MELLON UNIVERSITY Final Report 07

Studies in astronomical time series analysis. IV - Modeling chaotic and random processes with linear filters

NASA Technical Reports Server (NTRS)

Scargle, Jeffrey D.

1990-01-01

While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.

Fault detection for singular switched linear systems with multiple time-varying delay in finite frequency domain

NASA Astrophysics Data System (ADS)

Zhai, Ding; Lu, Anyang; Li, Jinghao; Zhang, Qingling

2016-10-01

This paper deals with the problem of the fault detection (FD) for continuous-time singular switched linear systems with multiple time-varying delay. In this paper, the actuator fault is considered. Besides, the systems faults and unknown disturbances are assumed in known frequency domains. Some finite frequency performance indices are initially introduced to design the switched FD filters which ensure that the filtering augmented systems under switching signal with average dwell time are exponentially admissible and guarantee the fault input sensitivity and disturbance robustness. By developing generalised Kalman-Yakubovic-Popov lemma and using Parseval's theorem and Fourier transform, finite frequency delay-dependent sufficient conditions for the existence of such a filter which can guarantee the finite-frequency H- and H∞ performance are derived and formulated in terms of linear matrix inequalities. Four examples are provided to illustrate the effectiveness of the proposed finite frequency method.

Flatness-based control and Kalman filtering for a continuous-time macroeconomic model

NASA Astrophysics Data System (ADS)

Rigatos, G.; Siano, P.; Ghosh, T.; Busawon, K.; Binns, R.

2017-11-01

The article proposes flatness-based control for a nonlinear macro-economic model of the UK economy. The differential flatness properties of the model are proven. This enables to introduce a transformation (diffeomorphism) of the system's state variables and to express the state-space description of the model in the linear canonical (Brunowsky) form in which both the feedback control and the state estimation problem can be solved. For the linearized equivalent model of the macroeconomic system, stabilizing feedback control can be achieved using pole placement methods. Moreover, to implement stabilizing feedback control of the system by measuring only a subset of its state vector elements the Derivative-free nonlinear Kalman Filter is used. This consists of the Kalman Filter recursion applied on the linearized equivalent model of the financial system and of an inverse transformation that is based again on differential flatness theory. The asymptotic stability properties of the control scheme are confirmed.

H∞ filtering for discrete-time systems subject to stochastic missing measurements: a decomposition approach

NASA Astrophysics Data System (ADS)

Gu, Zhou; Fei, Shumin; Yue, Dong; Tian, Engang

2014-07-01

This paper deals with the problem of H∞ filtering for discrete-time systems with stochastic missing measurements. A new missing measurement model is developed by decomposing the interval of the missing rate into several segments. The probability of the missing rate in each subsegment is governed by its corresponding random variables. We aim to design a linear full-order filter such that the estimation error converges to zero exponentially in the mean square with a less conservatism while the disturbance rejection attenuation is constrained to a given level by means of an H∞ performance index. Based on Lyapunov theory, the reliable filter parameters are characterised in terms of the feasibility of a set of linear matrix inequalities. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach.

Applying Kalman filtering to investigate tropospheric effects in VLBI

NASA Astrophysics Data System (ADS)

Soja, Benedikt; Nilsson, Tobias; Karbon, Maria; Heinkelmann, Robert; Liu, Li; Lu, Cuixian; Andres Mora-Diaz, Julian; Raposo-Pulido, Virginia; Xu, Minghui; Schuh, Harald

2014-05-01

Very Long Baseline Interferometry (VLBI) currently provides results, e.g., estimates of the tropospheric delays, with a delay of more than two weeks. In the future, with the coming VLBI2010 Global Observing System (VGOS) and increased usage of electronic data transfer, it is planned that the time between observations and results is decreased. This may, for instance, allow the integration of VLBI-derived tropospheric delays into numerical weather prediction models. Therefore, future VLBI analysis software packages need to be able to process the observational data autonomously in near real-time. For this purpose, we have extended the Vienna VLBI Software (VieVS) by a Kalman filter module. This presentation describes the filter and discusses its application for tropospheric studies. Instead of estimating zenith wet delays as piece-wise linear functions in a least-squares adjustment, the Kalman filter allows for more sophisticated stochastic modeling. We start with a random walk process to model the time-dependent behavior of the zenith wet delays. Other possible approaches include the stochastic model described by turbulence theory, e.g. the model by Treuhaft and Lanyi (1987). Different variance-covariance matrices of the prediction error, depending on the time of the year and the geographic latitude, have been tested. In winter and closer to the poles, lower variances and covariances are appropriate. The horizontal variations in tropospheric delays have been investigated by comparing three different strategies: assumption of a horizontally stratified troposphere, using north and south gradients modeled, e.g., as Gauss-Markov processes, and applying a turbulence model assuming correlations between observations in different azimuths. By conducting Monte-Carlo simulations of current standard VLBI networks and of future VGOS networks, the different tropospheric modeling strategies are investigated. For this purpose, we use the simulator module of VieVS which takes into account the errors due to the atomic clocks at the stations, the troposphere, and white noise processes. The simulated data as well as actual observational data from the two-week CONT11 campaign are analyzed using the Kalman filter, focusing on the tropospheric effects. The results of the different strategies are compared with solutions applying the classical least-squares method. An advantage of the Kalman filter is the possibility of easily integrating additional external information. It is expected that by including tropospheric delays from GNSS, water vapor radiometers, or ray-traced delays from numerical weather prediction models, the accuracy of the VLBI solution could be improved.

Filtering Non-Linear Transfer Functions on Surfaces.

PubMed

Heitz, Eric; Nowrouzezahrai, Derek; Poulin, Pierre; Neyret, Fabrice

2014-07-01

Applying non-linear transfer functions and look-up tables to procedural functions (such as noise), surface attributes, or even surface geometry are common strategies used to enhance visual detail. Their simplicity and ability to mimic a wide range of realistic appearances have led to their adoption in many rendering problems. As with any textured or geometric detail, proper filtering is needed to reduce aliasing when viewed across a range of distances, but accurate and efficient transfer function filtering remains an open problem for several reasons: transfer functions are complex and non-linear, especially when mapped through procedural noise and/or geometry-dependent functions, and the effects of perspective and masking further complicate the filtering over a pixel's footprint. We accurately solve this problem by computing and sampling from specialized filtering distributions on the fly, yielding very fast performance. We investigate the case where the transfer function to filter is a color map applied to (macroscale) surface textures (like noise), as well as color maps applied according to (microscale) geometric details. We introduce a novel representation of a (potentially modulated) color map's distribution over pixel footprints using Gaussian statistics and, in the more complex case of high-resolution color mapped microsurface details, our filtering is view- and light-dependent, and capable of correctly handling masking and occlusion effects. Our approach can be generalized to filter other physical-based rendering quantities. We propose an application to shading with irradiance environment maps over large terrains. Our framework is also compatible with the case of transfer functions used to warp surface geometry, as long as the transformations can be represented with Gaussian statistics, leading to proper view- and light-dependent filtering results. Our results match ground truth and our solution is well suited to real-time applications, requires only a few lines of shader code (provided in supplemental material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TVCG.2013.102), is high performance, and has a negligible memory footprint.

Hydraulic modeling of clay ceramic water filters for point-of-use water treatment.

PubMed

Schweitzer, Ryan W; Cunningham, Jeffrey A; Mihelcic, James R

2013-01-02

The acceptability of ceramic filters for point-of-use water treatment depends not only on the quality of the filtered water, but also on the quantity of water the filters can produce. This paper presents two mathematical models for the hydraulic performance of ceramic water filters under typical usage. A model is developed for two common filter geometries: paraboloid- and frustum-shaped. Both models are calibrated and evaluated by comparison to experimental data. The hydraulic models are able to predict the following parameters as functions of time: water level in the filter (h), instantaneous volumetric flow rate of filtrate (Q), and cumulative volume of water produced (V). The models' utility is demonstrated by applying them to estimate how the volume of water produced depends on factors such as the filter shape and the frequency of filling. Both models predict that the volume of water produced can be increased by about 45% if users refill the filter three times per day versus only once per day. Also, the models predict that filter geometry affects the volume of water produced: for two filters with equal volume, equal wall thickness, and equal hydraulic conductivity, a filter that is tall and thin will produce as much as 25% more water than one which is shallow and wide. We suggest that the models can be used as tools to help optimize filter performance.

The nonstationary strain filter in elastography: Part I. Frequency dependent attenuation.

PubMed

Varghese, T; Ophir, J

1997-01-01

The accuracy and precision of the strain estimates in elastography depend on a myriad number of factors. A clear understanding of the various factors (noise sources) that plague strain estimation is essential to obtain quality elastograms. The nonstationary variation in the performance of the strain filter due to frequency-dependent attenuation and lateral and elevational signal decorrelation are analyzed in this and the companion paper for the cross-correlation-based strain estimator. In this paper, we focus on the role of frequency-dependent attenuation in the performance of the strain estimator. The reduction in the signal-to-noise ratio (SNRs) in the RF signal, and the center frequency and bandwidth downshift with frequency-dependent attenuation are incorporated into the strain filter formulation. Both linear and nonlinear frequency dependence of attenuation are theoretically analyzed. Monte-Carlo simulations are used to corroborate the theoretically predicted results. Experimental results illustrate the deterioration in the precision of the strain estimates with depth in a uniformly elastic phantom. Theoretical, simulation and experimental results indicate the importance of high SNRs values in the RF signals, because the strain estimation sensitivity, elastographic SNRe and dynamic range deteriorate rapidly with a decrease in the SNRs. In addition, a shift in the strain filter toward higher strains is observed at large depths in tissue due to the center frequency downshift.

Face identification with frequency domain matched filtering in mobile environments

NASA Astrophysics Data System (ADS)

Lee, Dong-Su; Woo, Yong-Hyun; Yeom, Seokwon; Kim, Shin-Hwan

2012-06-01

Face identification at a distance is very challenging since captured images are often degraded by blur and noise. Furthermore, the computational resources and memory are often limited in the mobile environments. Thus, it is very challenging to develop a real-time face identification system on the mobile device. This paper discusses face identification based on frequency domain matched filtering in the mobile environments. Face identification is performed by the linear or phase-only matched filter and sequential verification stages. The candidate window regions are decided by the major peaks of the linear or phase-only matched filtering outputs. The sequential stages comprise a skin-color test and an edge mask filtering test, which verify color and shape information of the candidate regions in order to remove false alarms. All algorithms are built on the mobile device using Android platform. The preliminary results show that face identification of East Asian people can be performed successfully in the mobile environments.

Dynamic State Estimation of Power Systems With Quantization Effects: A Recursive Filter Approach.

PubMed

Hu, Liang; Wang, Zidong; Liu, Xiaohui

2016-08-01

In this paper, a recursive filter algorithm is developed to deal with the state estimation problem for power systems with quantized nonlinear measurements. The measurements from both the remote terminal units and the phasor measurement unit are subject to quantizations described by a logarithmic quantizer. Attention is focused on the design of a recursive filter such that, in the simultaneous presence of nonlinear measurements and quantization effects, an upper bound for the estimation error covariance is guaranteed and subsequently minimized. Instead of using the traditional approximation methods in nonlinear estimation that simply ignore the linearization errors, we treat both the linearization and quantization errors as norm-bounded uncertainties in the algorithm development so as to improve the performance of the estimator. For the power system with such kind of introduced uncertainties, a filter is designed in the framework of robust recursive estimation, and the developed filter algorithm is tested on the IEEE benchmark power system to demonstrate its effectiveness.

Singular value decomposition based impulsive noise reduction in multi-frequency phase-sensitive demodulation of electrical impedance tomography

NASA Astrophysics Data System (ADS)

Hao, Zhenhua; Cui, Ziqiang; Yue, Shihong; Wang, Huaxiang

2018-06-01

As an important means in electrical impedance tomography (EIT), multi-frequency phase-sensitive demodulation (PSD) can be viewed as a matched filter for measurement signals and as an optimal linear filter in the case of Gaussian-type noise. However, the additive noise usually possesses impulsive noise characteristics, so it is a challenging task to reduce the impulsive noise in multi-frequency PSD effectively. In this paper, an approach for impulsive noise reduction in multi-frequency PSD of EIT is presented. Instead of linear filters, a singular value decomposition filter is employed as the pre-stage filtering module prior to PSD, which has advantages of zero phase shift, little distortion, and a high signal-to-noise ratio (SNR) in digital signal processing. Simulation and experimental results demonstrated that the proposed method can effectively eliminate the influence of impulsive noise in multi-frequency PSD, and it was capable of achieving a higher SNR and smaller demodulation error.

Full-range k-domain linearization in spectral-domain optical coherence tomography.

PubMed

Jeon, Mansik; Kim, Jeehyun; Jung, Unsang; Lee, Changho; Jung, Woonggyu; Boppart, Stephen A

2011-03-10

A full-bandwidth k-domain linearization method for spectral-domain optical coherence tomography (SD-OCT) is demonstrated. The method uses information of the wavenumber-pixel-position provided by a translating-slit-based wavelength filter. For calibration purposes, the filter is placed either after a broadband source or at the end of the sample path, and the filtered spectrum with a narrowed line width (∼0.5 nm) is incident on a line-scan camera in the detection path. The wavelength-swept spectra are co-registered with the pixel positions according to their central wavelengths, which can be automatically measured with an optical spectrum analyzer. For imaging, the method does not require a filter or a software recalibration algorithm; it simply resamples the OCT signal from the detector array without employing rescaling or interpolation methods. The accuracy of k-linearization is maximized by increasing the k-linearization order, which is known to be a crucial parameter for maintaining a narrow point-spread function (PSF) width at increasing depths. The broadening effect is studied by changing the k-linearization order by undersampling to search for the optimal value. The system provides more position information, surpassing the optimum without compromising the imaging speed. The proposed full-range k-domain linearization method can be applied to SD-OCT systems to simplify their hardware/software, increase their speed, and improve the axial image resolution. The experimentally measured width of PSF in air has an FWHM of 8 μm at the edge of the axial measurement range. At an imaging depth of 2.5 mm, the sensitivity of the full-range calibration case drops less than 10 dB compared with the uncompensated case.

The effect of sampling rate and anti-aliasing filters on high-frequency response spectra

USGS Publications Warehouse

Boore, David M.; Goulet, Christine

2013-01-01

The most commonly used intensity measure in ground-motion prediction equations is the pseudo-absolute response spectral acceleration (PSA), for response periods from 0.01 to 10 s (or frequencies from 0.1 to 100 Hz). PSAs are often derived from recorded ground motions, and these motions are usually filtered to remove high and low frequencies before the PSAs are computed. In this article we are only concerned with the removal of high frequencies. In modern digital recordings, this filtering corresponds at least to an anti-aliasing filter applied before conversion to digital values. Additional high-cut filtering is sometimes applied both to digital and to analog records to reduce high-frequency noise. Potential errors on the short-period (high-frequency) response spectral values are expected if the true ground motion has significant energy at frequencies above that of the anti-aliasing filter. This is especially important for areas where the instrumental sample rate and the associated anti-aliasing filter corner frequency (above which significant energy in the time series is removed) are low relative to the frequencies contained in the true ground motions. A ground-motion simulation study was conducted to investigate these effects and to develop guidance for defining the usable bandwidth for high-frequency PSA. The primary conclusion is that if the ratio of the maximum Fourier acceleration spectrum (FAS) to the FAS at a frequency fsaa corresponding to the start of the anti-aliasing filter is more than about 10, then PSA for frequencies above fsaa should be little affected by the recording process, because the ground-motion frequencies that control the response spectra will be less than fsaa . A second topic of this article concerns the resampling of the digital acceleration time series to a higher sample rate often used in the computation of short-period PSA. We confirm previous findings that sinc-function interpolation is preferred to the standard practice of using linear time interpolation for the resamplin

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space

NASA Astrophysics Data System (ADS)

Hong, S.-M.; Jung, B.-H.; Ruan, D.

2011-03-01

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

Real-time prediction of respiratory motion based on a local dynamic model in an augmented space.

PubMed

Hong, S-M; Jung, B-H; Ruan, D

2011-03-21

Motion-adaptive radiotherapy aims to deliver ablative radiation dose to the tumor target with minimal normal tissue exposure, by accounting for real-time target movement. In practice, prediction is usually necessary to compensate for system latency induced by measurement, communication and control. This work focuses on predicting respiratory motion, which is most dominant for thoracic and abdominal tumors. We develop and investigate the use of a local dynamic model in an augmented space, motivated by the observation that respiratory movement exhibits a locally circular pattern in a plane augmented with a delayed axis. By including the angular velocity as part of the system state, the proposed dynamic model effectively captures the natural evolution of respiratory motion. The first-order extended Kalman filter is used to propagate and update the state estimate. The target location is predicted by evaluating the local dynamic model equations at the required prediction length. This method is complementary to existing work in that (1) the local circular motion model characterizes 'turning', overcoming the limitation of linear motion models; (2) it uses a natural state representation including the local angular velocity and updates the state estimate systematically, offering explicit physical interpretations; (3) it relies on a parametric model and is much less data-satiate than the typical adaptive semiparametric or nonparametric method. We tested the performance of the proposed method with ten RPM traces, using the normalized root mean squared difference between the predicted value and the retrospective observation as the error metric. Its performance was compared with predictors based on the linear model, the interacting multiple linear models and the kernel density estimator for various combinations of prediction lengths and observation rates. The local dynamic model based approach provides the best performance for short to medium prediction lengths under relatively low observation rate. Sensitivity analysis indicates its robustness toward the choice of parameters. Its simplicity, robustness and low computation cost makes the proposed local dynamic model an attractive tool for real-time prediction with system latencies below 0.4 s.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wolfram, Phillip J.; Ringler, Todd D.

Meridional diffusivity is assessed in this paper for a baroclinically unstable jet in a high-latitudeIdealized Circumpolar Current (ICC) using the Model for Prediction Across Scales-Ocean (MPAS-O) and the online Lagrangian In-situ Global High-performance particle Tracking (LIGHT) diagnostic via space-time dispersion of particle clusters over 120 monthly realizations of O(10 6) particles on 11 potential density surfaces. Diffusivity in the jet reaches values of O(6000 m 2 s -1) and is largest near the critical layer supporting mixing suppression and critical layer theory. Values in the vicinity of the shelf break are suppressed to O(100 m 2 s -1) due tomore » the presence of westward slope front currents. Diffusivity attenuates less rapidly with depth in the jet than both eddy velocity and kinetic energy scalings would suggest. Removal of the mean flow via high-pass filtering shifts the nonlinear parameter (ratio of the eddy velocity to eddy phase speed) into the linear wave regime by increasing the eddy phase speed via the depth-mean flow. Low-pass filtering, in contrast, quantifies the effect of mean shear. Diffusivity is decomposed into mean flow shear, linear waves, and the residual nonhomogeneous turbulence components, where turbulence dominates and eddy-produced filamentation strained by background mean shear enhances mixing, accounting for ≥ 80% of the total diffusivity relative to mean shear [O(100 m 2 s -1)], linear waves [O(1000 m 2 s -1)], and undecomposed full diffusivity [O(6000 m 2 s -1)]. Finally, diffusivity parameterizations accounting for both the nonhomogeneous turbulence residual and depth variability are needed.« less

Recent results of nonlinear estimators applied to hereditary systems.

NASA Technical Reports Server (NTRS)

Schiess, J. R.; Roland, V. R.; Wells, W. R.

1972-01-01

An application of the extended Kalman filter to delayed systems to estimate the state and time delay is presented. Two nonlinear estimators are discussed and the results compared with those of the Kalman filter. For all the filters considered, the hereditary system was treated with the delay in the pure form and by using Pade approximations of the delay. A summary of the convergence properties of the filters studied is given. The results indicate that the linear filter applied to the delayed system performs inadequately while the nonlinear filters provide reasonable estimates of both the state and the parameters.

RADC Multi-Dimensional Signal-Processing Research Program.

DTIC Science & Technology

1980-09-30

Formulation 7 3.2.2 Methods of Accelerating Convergence 8 3.2.3 Application to Image Deblurring 8 3.2.4 Extensions 11 3.3 Convergence of Iterative Signal... noise -driven linear filters, permit development of the joint probability density function oz " kelihood function for the image. With an expression...spatial linear filter driven by white noise (see Fig. i). If the probability density function for the white noise is known, Fig. t. Model for image

Direct-on-Filter α-Quartz Estimation in Respirable Coal Mine Dust Using Transmission Fourier Transform Infrared Spectrometry and Partial Least Squares Regression

PubMed Central

Miller, Arthur L.; Weakley, Andrew Todd; Griffiths, Peter R.; Cauda, Emanuele G.; Bayman, Sean

2017-01-01

In order to help reduce silicosis in miners, the National Institute for Occupational Health and Safety (NIOSH) is developing field-portable methods for measuring airborne respirable crystalline silica (RCS), specifically the polymorph α-quartz, in mine dusts. In this study we demonstrate the feasibility of end-of-shift measurement of α-quartz using a direct-on-filter (DoF) method to analyze coal mine dust samples deposited onto polyvinyl chloride filters. The DoF method is potentially amenable for on-site analyses, but deviates from the current regulatory determination of RCS for coal mines by eliminating two sample preparation steps: ashing the sampling filter and redepositing the ash prior to quantification by Fourier transform infrared (FT-IR) spectrometry. In this study, the FT-IR spectra of 66 coal dust samples from active mines were used, and the RCS was quantified by using: (1) an ordinary least squares (OLS) calibration approach that utilizes standard silica material as done in the Mine Safety and Health Administration's P7 method; and (2) a partial least squares (PLS) regression approach. Both were capable of accounting for kaolinite, which can confound the IR analysis of silica. The OLS method utilized analytical standards for silica calibration and kaolin correction, resulting in a good linear correlation with P7 results and minimal bias but with the accuracy limited by the presence of kaolinite. The PLS approach also produced predictions well-correlated to the P7 method, as well as better accuracy in RCS prediction, and no bias due to variable kaolinite mass. Besides decreased sensitivity to mineral or substrate confounders, PLS has the advantage that the analyst is not required to correct for the presence of kaolinite or background interferences related to the substrate, making the method potentially viable for automated RCS prediction in the field. This study demonstrated the efficacy of FT-IR transmission spectrometry for silica determination in coal mine dusts, using both OLS and PLS analyses, when kaolinite was present. PMID:27645724

Direct-on-Filter α-Quartz Estimation in Respirable Coal Mine Dust Using Transmission Fourier Transform Infrared Spectrometry and Partial Least Squares Regression.

PubMed

Miller, Arthur L; Weakley, Andrew Todd; Griffiths, Peter R; Cauda, Emanuele G; Bayman, Sean

2017-05-01

In order to help reduce silicosis in miners, the National Institute for Occupational Health and Safety (NIOSH) is developing field-portable methods for measuring airborne respirable crystalline silica (RCS), specifically the polymorph α-quartz, in mine dusts. In this study we demonstrate the feasibility of end-of-shift measurement of α-quartz using a direct-on-filter (DoF) method to analyze coal mine dust samples deposited onto polyvinyl chloride filters. The DoF method is potentially amenable for on-site analyses, but deviates from the current regulatory determination of RCS for coal mines by eliminating two sample preparation steps: ashing the sampling filter and redepositing the ash prior to quantification by Fourier transform infrared (FT-IR) spectrometry. In this study, the FT-IR spectra of 66 coal dust samples from active mines were used, and the RCS was quantified by using: (1) an ordinary least squares (OLS) calibration approach that utilizes standard silica material as done in the Mine Safety and Health Administration's P7 method; and (2) a partial least squares (PLS) regression approach. Both were capable of accounting for kaolinite, which can confound the IR analysis of silica. The OLS method utilized analytical standards for silica calibration and kaolin correction, resulting in a good linear correlation with P7 results and minimal bias but with the accuracy limited by the presence of kaolinite. The PLS approach also produced predictions well-correlated to the P7 method, as well as better accuracy in RCS prediction, and no bias due to variable kaolinite mass. Besides decreased sensitivity to mineral or substrate confounders, PLS has the advantage that the analyst is not required to correct for the presence of kaolinite or background interferences related to the substrate, making the method potentially viable for automated RCS prediction in the field. This study demonstrated the efficacy of FT-IR transmission spectrometry for silica determination in coal mine dusts, using both OLS and PLS analyses, when kaolinite was present.

? filtering for stochastic systems driven by Poisson processes

NASA Astrophysics Data System (ADS)

Song, Bo; Wu, Zheng-Guang; Park, Ju H.; Shi, Guodong; Zhang, Ya

2015-01-01

This paper investigates the ? filtering problem for stochastic systems driven by Poisson processes. By utilising the martingale theory such as the predictable projection operator and the dual predictable projection operator, this paper transforms the expectation of stochastic integral with respect to the Poisson process into the expectation of Lebesgue integral. Then, based on this, this paper designs an ? filter such that the filtering error system is mean-square asymptotically stable and satisfies a prescribed ? performance level. Finally, a simulation example is given to illustrate the effectiveness of the proposed filtering scheme.

Development of a predictive model for 6 month survival in patients with venous thromboembolism and solid malignancy requiring IVC filter placement.

PubMed

Huang, Steven Y; Odisio, Bruno C; Sabir, Sharjeel H; Ensor, Joe E; Niekamp, Andrew S; Huynh, Tam T; Kroll, Michael; Gupta, Sanjay

2017-07-01

Our purpose was to develop a predictive model for short-term survival (i.e. <6 months) following inferior vena cava filter placement in patients with venous thromboembolism (VTE) and solid malignancy. Clinical and laboratory parameters were retrospectively reviewed for patients with solid malignancy who received a filter between January 2009 and December 2011 at a tertiary care cancer center. Multivariate Cox proportional hazards modeling was used to assess variables associated with 6 month survival following filter placement in patients with VTE and solid malignancy. Significant variables were used to generate a predictive model. 397 patients with solid malignancy received a filter during the study period. Three variables were associated with 6 month survival: (1) serum albumin [hazard ratio (HR) 0.496, P < 0.0001], (2) recent or planned surgery (<30 days) (HR 0.409, P < 0.0001), (3) TNM staging (stage 1 or 2 vs. stage 4, HR 0.177, P = 0.0001; stage 3 vs. stage 4, HR 0.367, P = 0.0002). These variables were used to develop a predictive model to estimate 6 month survival with an area under the receiver operating characteristic curve of 0.815, sensitivity of 0.782, and specificity of 0.715. Six month survival in patients with VTE and solid malignancy requiring filter placement can be predicted from three patient variables. Our predictive model could be used to help physicians decide whether a permanent or retrievable filter may be more appropriate as well as to assess the risks and benefits for filter retrieval within the context of survival longevity in patients with cancer.

Model-based estimation and control for off-axis parabolic mirror alignment

NASA Astrophysics Data System (ADS)

Fang, Joyce; Savransky, Dmitry

2018-02-01

This paper propose an model-based estimation and control method for an off-axis parabolic mirror (OAP) alignment. Current studies in automated optical alignment systems typically require additional wavefront sensors. We propose a self-aligning method using only focal plane images captured by the existing camera. Image processing methods and Karhunen-Loève (K-L) decomposition are used to extract measurements for the observer in closed-loop control system. Our system has linear dynamic in state transition, and a nonlinear mapping from the state to the measurement. An iterative extended Kalman filter (IEKF) is shown to accurately predict the unknown states, and nonlinear observability is discussed. Linear-quadratic regulator (LQR) is applied to correct the misalignments. The method is validated experimentally on the optical bench with a commercial OAP. We conduct 100 tests in the experiment to demonstrate the consistency in between runs.

Railway crossing risk area detection using linear regression and terrain drop compensation techniques.

PubMed

Chen, Wen-Yuan; Wang, Mei; Fu, Zhou-Xing

2014-06-16

Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1) we use a terrain drop compensation (TDC) technique to solve the problem of the concavity of railway crossings; (2) we use a linear regression technique to predict the position and length of an object from image processing; (3) we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP) to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas.

Railway Crossing Risk Area Detection Using Linear Regression and Terrain Drop Compensation Techniques

PubMed Central

Chen, Wen-Yuan; Wang, Mei; Fu, Zhou-Xing

2014-01-01

Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1) we use a terrain drop compensation (TDC) technique to solve the problem of the concavity of railway crossings; (2) we use a linear regression technique to predict the position and length of an object from image processing; (3) we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP) to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas. PMID:24936948

Some estimation formulae for continuous time-invariant linear systems

NASA Technical Reports Server (NTRS)

Bierman, G. J.; Sidhu, G. S.

1975-01-01

In this brief paper we examine a Riccati equation decomposition due to Reid and Lainiotis and apply the result to the continuous time-invariant linear filtering problem. Exploitation of the time-invariant structure leads to integration-free covariance recursions which are of use in covariance analyses and in filter implementations. A super-linearly convergent iterative solution to the algebraic Riccati equation (ARE) is developed. The resulting algorithm, arranged in a square-root form, is thought to be numerically stable and competitive with other ARE solution methods. Certain covariance relations that are relevant to the fixed-point and fixed-lag smoothing problems are also discussed.

Improving the transparency of a rehabilitation robot by exploiting the cyclic behaviour of walking.

PubMed

van Dijk, W; van der Kooij, H; Koopman, B; van Asseldonk, E H F; van der Kooij, H

2013-06-01

To promote active participation of neurological patients during robotic gait training, controllers, such as "assist as needed" or "cooperative control", are suggested. Apart from providing support, these controllers also require that the robot should be capable of resembling natural, unsupported, walking. This means that they should have a transparent mode, where the interaction forces between the human and the robot are minimal. Traditional feedback-control algorithms do not exploit the cyclic nature of walking to improve the transparency of the robot. The purpose of this study was to improve the transparent mode of robotic devices, by developing two controllers that use the rhythmic behavior of gait. Both controllers use adaptive frequency oscillators and kernel-based non-linear filters. Kernelbased non-linear filters can be used to estimate signals and their time derivatives, as a function of the gait phase. The first controller learns the motor angle, associated with a certain joint angle pattern, and acts as a feed-forward controller to improve the torque tracking (including the zero-torque mode). The second controller learns the state of the mechanical system and compensates for the dynamical effects (e.g. the acceleration of robot masses). Both controllers have been tested separately and in combination on a small subject population. Using the feedforward controller resulted in an improved torque tracking of at least 52 percent at the hip joint, and 61 percent at the knee joint. When both controllers were active simultaneously, the interaction power between the robot and the human leg was reduced by at least 40 percent at the thigh, and 43 percent at the shank. These results indicate that: if a robotic task is cyclic, the torque tracking and transparency can be improved by exploiting the predictions of adaptive frequency oscillator and kernel-based nonlinear filters.

Evaluation of dosimetric properties of 6 MV & 10 MV photon beams from a linear accelerator with no flattening filter

NASA Astrophysics Data System (ADS)

Pearson, David

A linear accelerator manufactured by Elekta, equipped with a multi leaf collimation (MLC) system has been modelled using Monte Carlo simulations with the photon flattening filter removed. The purpose of this investigation was to show that more efficient and more accurate Intensity Modulated Radiation Therapy (IMRT) treatments can be delivered from a standard linear accelerator with the flattening filter removed from the beam. A range of simulations of 6 MV and 10 MV photon were studied and compared to a model of a standard accelerator which included the flattening filter for those beams. Measurements using a scanning water phantom were also performed after the flattening filter had been removed. We show here that with the flattening filter removed, an increase to the dose on the central axis by a factor of 2.35 and 4.18 is achieved for 6 MV and 10 MV photon beams respectively using a standard 10x 10cm2 field size. A comparison of the dose at points at the field edges led to the result that, removal of the flattening filter reduced the dose at these points by approximately 10% for the 6 MV beam over the clinical range of field sizes. A further consequence of removing the flattening filter was the softening of the photon energy spectrum leading to a steeper reduction in dose at depths greater than dmax. Also studied was the electron contamination brought about by the removal of the filter. To reduce this electron contamination and thus reduce the skin dose to the patient we consider the use of an electron scattering foil in the beam path. The electron scattering foil had very little effect on dmax. From simulations of a standard 6MV beam, a filter-free beam and a filter-free beam with electron scattering foil, we deduce that the proportion of electrons in the photon beam is 0.35%, 0.28% and 0.27%, consecutively. In short, higher dose rates will result in decreased treatment times and the reduced dose outside of the field is indicative of reducing the dose to the surrounding tissue. Electron contamination was found to be comparable with conventional IMRT treatments carried out with a flattening filter.

An experimental study of the fluid mechanics associated with porous walls

NASA Technical Reports Server (NTRS)

Ramachandran, N.; Heaman, J.; Smith, A.

1992-01-01

The fluid mechanics associated with the blowing phenomenon from porous walls is measured and characterized. The measurements indicate that the flow exiting a porous wall exhibits a lumpy velocity profile caused by the coalescence effects of smaller jets emerging from the surface. The velocity variations are spatially stable and prevail even at low flow rates. The intensity of this pseudoturbulence is found to be directly proportional to the filter rating of the porous wall and to increase linearly with the mean velocity. Beyond a critical mean velocity, the pseudoturbulence intensity shows a leveling trend with increase in the mean velocity. This critical velocity varies inversely as the filter rating and represents the onset of fully developed jetting action in the flow field. Based on the data, a more appropriate length scale for the flow field is proposed and a correlation is developed that can be used to predict the onset of fully developed jets in the flow emerging from a porous wall.

Magneto-ballistic transport in GaN nanowires

DOE Office of Scientific and Technical Information (OSTI.GOV)

Santoruvo, Giovanni, E-mail: giovanni.santoruvo@epfl.ch; Allain, Adrien; Ovchinnikov, Dmitry

2016-09-05

The ballistic filtering property of nanoscale crosses was used to investigate the effect of perpendicular magnetic fields on the ballistic transport of electrons on wide band-gap GaN heterostructures. The straight scattering-less trajectory of electrons was modified by a perpendicular magnetic field which produced a strong non-linear behavior in the measured output voltage of the ballistic filters and allowed the observation of semi-classical and quantum effects, such as quenching of the Hall resistance and manifestation of the last plateau, in excellent agreement with the theoretical predictions. A large measured phase coherence length of 190 nm allowed the observation of universal quantum fluctuationsmore » and weak localization of electrons due to quantum interference up to ∼25 K. This work also reveals the prospect of wide band-gap GaN semiconductors as a platform for basic transport and quantum studies, whose properties allow the investigation of ballistic transport and quantum phenomena at much larger voltages and temperatures than in other semiconductors.« less

Multistage degradation modeling for BLDC motor based on Wiener process

NASA Astrophysics Data System (ADS)

Yuan, Qingyang; Li, Xiaogang; Gao, Yuankai

2018-05-01

Brushless DC motors are widely used, and their working temperatures, regarding as degradation processes, are nonlinear and multistage. It is necessary to establish a nonlinear degradation model. In this research, our study was based on accelerated degradation data of motors, which are their working temperatures. A multistage Wiener model was established by using the transition function to modify linear model. The normal weighted average filter (Gauss filter) was used to improve the results of estimation for the model parameters. Then, to maximize likelihood function for parameter estimation, we used numerical optimization method- the simplex method for cycle calculation. Finally, the modeling results show that the degradation mechanism changes during the degradation of the motor with high speed. The effectiveness and rationality of model are verified by comparison of the life distribution with widely used nonlinear Wiener model, as well as a comparison of QQ plots for residual. Finally, predictions for motor life are gained by life distributions in different times calculated by multistage model.

Constrained State Estimation for Individual Localization in Wireless Body Sensor Networks

PubMed Central

Feng, Xiaoxue; Snoussi, Hichem; Liang, Yan; Jiao, Lianmeng

2014-01-01

Wireless body sensor networks based on ultra-wideband radio have recently received much research attention due to its wide applications in health-care, security, sports and entertainment. Accurate localization is a fundamental problem to realize the development of effective location-aware applications above. In this paper the problem of constrained state estimation for individual localization in wireless body sensor networks is addressed. Priori knowledge about geometry among the on-body nodes as additional constraint is incorporated into the traditional filtering system. The analytical expression of state estimation with linear constraint to exploit the additional information is derived. Furthermore, for nonlinear constraint, first-order and second-order linearizations via Taylor series expansion are proposed to transform the nonlinear constraint to the linear case. Examples between the first-order and second-order nonlinear constrained filters based on interacting multiple model extended kalman filter (IMM-EKF) show that the second-order solution for higher order nonlinearity as present in this paper outperforms the first-order solution, and constrained IMM-EKF obtains superior estimation than IMM-EKF without constraint. Another brownian motion individual localization example also illustrates the effectiveness of constrained nonlinear iterative least square (NILS), which gets better filtering performance than NILS without constraint. PMID:25390408

On the matrix Fourier filtering problem for a class of models of nonlinear optical systems with a feedback

NASA Astrophysics Data System (ADS)

Razgulin, A. V.; Sazonova, S. V.

2017-09-01

A novel statement of the Fourier filtering problem based on the use of matrix Fourier filters instead of conventional multiplier filters is considered. The basic properties of the matrix Fourier filtering for the filters in the Hilbert-Schmidt class are established. It is proved that the solutions with a finite energy to the periodic initial boundary value problem for the quasi-linear functional differential diffusion equation with the matrix Fourier filtering Lipschitz continuously depend on the filter. The problem of optimal matrix Fourier filtering is formulated, and its solvability for various classes of matrix Fourier filters is proved. It is proved that the objective functional is differentiable with respect to the matrix Fourier filter, and the convergence of a version of the gradient projection method is also proved.

In-vivo detectability index: development and validation of an automated methodology

NASA Astrophysics Data System (ADS)

Smith, Taylor Brunton; Solomon, Justin; Samei, Ehsan

2017-03-01

The purpose of this study was to develop and validate a method to estimate patient-specific detectability indices directly from patients' CT images (i.e., "in vivo"). The method works by automatically extracting noise (NPS) and resolution (MTF) properties from each patient's CT series based on previously validated techniques. Patient images are thresholded into skin-air interfaces to form edge-spread functions, which are further binned, differentiated, and Fourier transformed to form the MTF. The NPS is likewise estimated from uniform areas of the image. These are combined with assumed task functions (reference function: 10 mm disk lesion with contrast of -15 HU) to compute detectability indices for a non-prewhitening matched filter model observer predicting observer performance. The results were compared to those from a previous human detection study on 105 subtle, hypo-attenuating liver lesions, using a two-alternative-forcedchoice (2AFC) method, over 6 dose levels using 16 readers. The in vivo detectability indices estimated for all patient images were compared to binary 2AFC outcomes with a generalized linear mixed-effects statistical model (Probit link function, linear terms only, no interactions, random term for readers). The model showed that the in vivo detectability indices were strongly predictive of 2AFC outcomes (P < 0.05). A linear comparison between the human detection accuracy and model-predicted detection accuracy (for like conditions) resulted in Pearson and Spearman correlations coefficients of 0.86 and 0.87, respectively. These data provide evidence that the in vivo detectability index could potentially be used to automatically estimate and track image quality in a clinical operation.

Learning to Estimate Dynamical State with Probabilistic Population Codes.

PubMed

Makin, Joseph G; Dichter, Benjamin K; Sabes, Philip N

2015-11-01

Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH)-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Learning to Estimate Dynamical State with Probabilistic Population Codes

PubMed Central

Sabes, Philip N.

2015-01-01

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states. PMID:26540152

Effects of changing hydraulic and organic loading rates on pollutant reduction in bark, charcoal and sand filters treating greywater.

PubMed

Dalahmeh, Sahar S; Pell, Mikael; Hylander, Lars D; Lalander, Cecilia; Vinnerås, Björn; Jönsson, Håkan

2014-01-01

Greywater flows and concentrations vary greatly, thus evaluation and prediction of the response of on-site treatment filters to variable loading regimes is challenging. The performance of 0.6 m × 0.2 m (height × diameter) filters of bark, activated charcoal and sand in reduction of biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total nitrogen (Tot-N) and total phosphorus (Tot-P) under variable loading regimes was investigated and modelled. During seven runs, the filters were fed with synthetic greywater at hydraulic loading rates (HLR) of 32-128 L m(-2) day(-1) and organic loading rates (OLR) of 13-76 g BOD5 m(-2) day(-1). Based on the changes in HLR and OLR, the reduction in pollutants was modelled using multiple linear regression. The models showed that increasing the HLR from 32 to 128 L m(-2) day(-1) decreased COD reduction in the bark filters from 74 to 40%, but increased COD reduction in the charcoal and sand filters from 76 to 90% and 65 to 83%, respectively. Moreover, the models showed that increasing the OLR from 13 to 76 g BOD5 m(-2) day(-1) enhanced the pollutant reduction in all filters except for Tot-P in the bark filters, which decreased slightly from 81 to 73%. Decreasing the HLR from 128 to 32 L m(-2) day(-1) enhanced the pollutant reduction in all filters, but decreasing the OLR from 76 to 14 g BOD5 m(-2) day(-1) detached biofilm and decreased the Tot-N and Tot-P reduction in the bark and sand filters. Overall, the bark filters had the capacity to treat high OLR, while the charcoal filters had the capacity to treat high HLR and high OLR. Both bark and charcoal filters had higher capacity than sand filters in dealing with high and variable loads. Bark seems to be an attractive substitute for sand filters in settings short in water and its effluent would be valuable for irrigation, while charcoal filters should be an attractive alternative for settings both rich and short in water supply and when environmental eutrophication has to be considered. Copyright © 2013 Elsevier Ltd. All rights reserved.

Dimension reduction: additional benefit of an optimal filter for independent component analysis to extract event-related potentials.

PubMed

Cong, Fengyu; Leppänen, Paavo H T; Astikainen, Piia; Hämäläinen, Jarmo; Hietanen, Jari K; Ristaniemi, Tapani

2011-09-30

The present study addresses benefits of a linear optimal filter (OF) for independent component analysis (ICA) in extracting brain event-related potentials (ERPs). A filter such as the digital filter is usually considered as a denoising tool. Actually, in filtering ERP recordings by an OF, the ERP' topography should not be changed by the filter, and the output should also be able to be modeled by the linear transformation. Moreover, an OF designed for a specific ERP source or component may remove noise, as well as reduce the overlap of sources and even reject some non-targeted sources in the ERP recordings. The OF can thus accomplish both the denoising and dimension reduction (reducing the number of sources) simultaneously. We demonstrated these effects using two datasets, one containing visual and the other auditory ERPs. The results showed that the method including OF and ICA extracted much more reliable components than the sole ICA without OF did, and that OF removed some non-targeted sources and made the underdetermined model of EEG recordings approach to the determined one. Thus, we suggest designing an OF based on the properties of an ERP to filter recordings before using ICA decomposition to extract the targeted ERP component. Copyright © 2011 Elsevier B.V. All rights reserved.

Least squares restoration of multi-channel images

NASA Technical Reports Server (NTRS)

Chin, Roland T.; Galatsanos, Nikolas P.

1989-01-01

In this paper, a least squares filter for the restoration of multichannel imagery is presented. The restoration filter is based on a linear, space-invariant imaging model and makes use of an iterative matrix inversion algorithm. The restoration utilizes both within-channel (spatial) and cross-channel information as constraints. Experiments using color images (three-channel imagery with red, green, and blue components) were performed to evaluate the filter's performance and to compare it with other monochrome and multichannel filters.

An information-theoretic approach to motor action decoding with a reconfigurable parallel architecture.

PubMed

Craciun, Stefan; Brockmeier, Austin J; George, Alan D; Lam, Herman; Príncipe, José C

2011-01-01

Methods for decoding movements from neural spike counts using adaptive filters often rely on minimizing the mean-squared error. However, for non-Gaussian distribution of errors, this approach is not optimal for performance. Therefore, rather than using probabilistic modeling, we propose an alternate non-parametric approach. In order to extract more structure from the input signal (neuronal spike counts) we propose using minimum error entropy (MEE), an information-theoretic approach that minimizes the error entropy as part of an iterative cost function. However, the disadvantage of using MEE as the cost function for adaptive filters is the increase in computational complexity. In this paper we present a comparison between the decoding performance of the analytic Wiener filter and a linear filter trained with MEE, which is then mapped to a parallel architecture in reconfigurable hardware tailored to the computational needs of the MEE filter. We observe considerable speedup from the hardware design. The adaptation of filter weights for the multiple-input, multiple-output linear filters, necessary in motor decoding, is a highly parallelizable algorithm. It can be decomposed into many independent computational blocks with a parallel architecture readily mapped to a field-programmable gate array (FPGA) and scales to large numbers of neurons. By pipelining and parallelizing independent computations in the algorithm, the proposed parallel architecture has sublinear increases in execution time with respect to both window size and filter order.

Comparing auditory filter bandwidths, spectral ripple modulation detection, spectral ripple discrimination, and speech recognition: Normal and impaired hearinga)

PubMed Central

Davies-Venn, Evelyn; Nelson, Peggy; Souza, Pamela

2015-01-01

Some listeners with hearing loss show poor speech recognition scores in spite of using amplification that optimizes audibility. Beyond audibility, studies have suggested that suprathreshold abilities such as spectral and temporal processing may explain differences in amplified speech recognition scores. A variety of different methods has been used to measure spectral processing. However, the relationship between spectral processing and speech recognition is still inconclusive. This study evaluated the relationship between spectral processing and speech recognition in listeners with normal hearing and with hearing loss. Narrowband spectral resolution was assessed using auditory filter bandwidths estimated from simultaneous notched-noise masking. Broadband spectral processing was measured using the spectral ripple discrimination (SRD) task and the spectral ripple depth detection (SMD) task. Three different measures were used to assess unamplified and amplified speech recognition in quiet and noise. Stepwise multiple linear regression revealed that SMD at 2.0 cycles per octave (cpo) significantly predicted speech scores for amplified and unamplified speech in quiet and noise. Commonality analyses revealed that SMD at 2.0 cpo combined with SRD and equivalent rectangular bandwidth measures to explain most of the variance captured by the regression model. Results suggest that SMD and SRD may be promising clinical tools for diagnostic evaluation and predicting amplification outcomes. PMID:26233047

Comparing auditory filter bandwidths, spectral ripple modulation detection, spectral ripple discrimination, and speech recognition: Normal and impaired hearing.

PubMed

Davies-Venn, Evelyn; Nelson, Peggy; Souza, Pamela

2015-07-01

Some listeners with hearing loss show poor speech recognition scores in spite of using amplification that optimizes audibility. Beyond audibility, studies have suggested that suprathreshold abilities such as spectral and temporal processing may explain differences in amplified speech recognition scores. A variety of different methods has been used to measure spectral processing. However, the relationship between spectral processing and speech recognition is still inconclusive. This study evaluated the relationship between spectral processing and speech recognition in listeners with normal hearing and with hearing loss. Narrowband spectral resolution was assessed using auditory filter bandwidths estimated from simultaneous notched-noise masking. Broadband spectral processing was measured using the spectral ripple discrimination (SRD) task and the spectral ripple depth detection (SMD) task. Three different measures were used to assess unamplified and amplified speech recognition in quiet and noise. Stepwise multiple linear regression revealed that SMD at 2.0 cycles per octave (cpo) significantly predicted speech scores for amplified and unamplified speech in quiet and noise. Commonality analyses revealed that SMD at 2.0 cpo combined with SRD and equivalent rectangular bandwidth measures to explain most of the variance captured by the regression model. Results suggest that SMD and SRD may be promising clinical tools for diagnostic evaluation and predicting amplification outcomes.

Attitude estimation of earth orbiting satellites by decomposed linear recursive filters

NASA Technical Reports Server (NTRS)

Kou, S. R.

1975-01-01

Attitude estimation of earth orbiting satellites (including Large Space Telescope) subjected to environmental disturbances and noises was investigated. Modern control and estimation theory is used as a tool to design an efficient estimator for attitude estimation. Decomposed linear recursive filters for both continuous-time systems and discrete-time systems are derived. By using this accurate estimation of the attitude of spacecrafts, state variable feedback controller may be designed to achieve (or satisfy) high requirements of system performance.

ORACLS: A system for linear-quadratic-Gaussian control law design

NASA Technical Reports Server (NTRS)

Armstrong, E. S.

1978-01-01

A modern control theory design package (ORACLS) for constructing controllers and optimal filters for systems modeled by linear time-invariant differential or difference equations is described. Numerical linear-algebra procedures are used to implement the linear-quadratic-Gaussian (LQG) methodology of modern control theory. Algorithms are included for computing eigensystems of real matrices, the relative stability of a matrix, factored forms for nonnegative definite matrices, the solutions and least squares approximations to the solutions of certain linear matrix algebraic equations, the controllability properties of a linear time-invariant system, and the steady state covariance matrix of an open-loop stable system forced by white noise. Subroutines are provided for solving both the continuous and discrete optimal linear regulator problems with noise free measurements and the sampled-data optimal linear regulator problem. For measurement noise, duality theory and the optimal regulator algorithms are used to solve the continuous and discrete Kalman-Bucy filter problems. Subroutines are also included which give control laws causing the output of a system to track the output of a prescribed model.

A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer.

PubMed

Ligorio, Gabriele; Sabatini, Angelo M

2015-08-01

Design and development of a linear Kalman filter to create an inertial-based inclinometer targeted to dynamic conditions of motion. The estimation of the body attitude (i.e., the inclination with respect to the vertical) was treated as a source separation problem to discriminate the gravity and the body acceleration from the specific force measured by a triaxial accelerometer. The sensor fusion between triaxial gyroscope and triaxial accelerometer data was performed using a linear Kalman filter. Wrist-worn inertial measurement unit data from ten participants were acquired while performing two dynamic tasks: 60-s sequence of seven manual activities and 90 s of walking at natural speed. Stereophotogrammetric data were used as a reference. A statistical analysis was performed to assess the significance of the accuracy improvement over state-of-the-art approaches. The proposed method achieved, on an average, a root mean square attitude error of 3.6° and 1.8° in manual activities and locomotion tasks (respectively). The statistical analysis showed that, when compared to few competing methods, the proposed method improved the attitude estimation accuracy. A novel Kalman filter for inertial-based attitude estimation was presented in this study. A significant accuracy improvement was achieved over state-of-the-art approaches, due to a filter design that better matched the basic optimality assumptions of Kalman filtering. Human motion tracking is the main application field of the proposed method. Accurately discriminating the two components present in the triaxial accelerometer signal is well suited for studying both the rotational and the linear body kinematics.

The linear stability of vertical mixture seepage into the close porous filter with clogging

NASA Astrophysics Data System (ADS)

Maryshev, Boris S.

2017-02-01

In the present paper, filtration of a mixture through a close porous filter is considered. A heavy solute penetrates from the upper side of the filter into the filter body due to seepage flow and diffusion. In the presence of heavy solute a domain with a heavy fluid is formed near the upper boundary of the filter. The stratification, at which the heavy fluid is located above the light, is unstable. When the mass of the heavy solute exceeds the critical value, one can observe the onset of instability. As a result, two regimes of vertical filtration can occur: (1) homogeneous seepage and (2) convective filtration. Filtration of a mixture in porous media is a complex process. It is necessary to take into account the solute immobilization (or sorption) and clogging of porous medium. We consider the case of low solute concentrations, in which the immobilization is described by the linear MIM (mobile/immobile media) model. The clogging is described by the dependence of permeability on porosity in terms of the Carman-Kozeny formula. The presence of immobile (or adsorbed) particles of the solute decreases the porosity of media and porous media becomes less permeable. The purpose of the paper is to find the stability conditions for the homogeneous vertical seepage of the mixture into the close porous filter. The linear stability problem is solved using the quasi-static approach. The critical times of instability are estimated. The stability maps have been plotted in the space of system parameters. The applicability of quasi-static approach is substantiated by direct numerical simulation.

A Dynamic Compressive Gammachirp Auditory Filterbank

PubMed Central

Irino, Toshio; Patterson, Roy D.

2008-01-01

It is now common to use knowledge about human auditory processing in the development of audio signal processors. Until recently, however, such systems were limited by their linearity. The auditory filter system is known to be level-dependent as evidenced by psychophysical data on masking, compression, and two-tone suppression. However, there were no analysis/synthesis schemes with nonlinear filterbanks. This paper describe18300060s such a scheme based on the compressive gammachirp (cGC) auditory filter. It was developed to extend the gammatone filter concept to accommodate the changes in psychophysical filter shape that are observed to occur with changes in stimulus level in simultaneous, tone-in-noise masking. In models of simultaneous noise masking, the temporal dynamics of the filtering can be ignored. Analysis/synthesis systems, however, are intended for use with speech sounds where the glottal cycle can be long with respect to auditory time constants, and so they require specification of the temporal dynamics of auditory filter. In this paper, we describe a fast-acting level control circuit for the cGC filter and show how psychophysical data involving two-tone suppression and compression can be used to estimate the parameter values for this dynamic version of the cGC filter (referred to as the “dcGC” filter). One important advantage of analysis/synthesis systems with a dcGC filterbank is that they can inherit previously refined signal processing algorithms developed with conventional short-time Fourier transforms (STFTs) and linear filterbanks. PMID:19330044

Fundamentals of digital filtering with applications in geophysical prospecting for oil

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mesko, A.

This book is a comprehensive work bringing together the important mathematical foundations and computing techniques for numerical filtering methods. The first two parts of the book introduce the techniques, fundamental theory and applications, while the third part treats specific applications in geophysical prospecting. Discussion is limited to linear filters, but takes in related fields such as correlational and spectral analysis.

Identifying Bearing Rotordynamic Coefficients using an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Miller, Brad A.; Howard, Samuel A.

2008-01-01

An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor-dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter s performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor-bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.

Wavelength-Filter Based Spectral Calibrated Wave number - Linearization in 1.3 mm Spectral Domain Optical Coherence.

PubMed

Wijeisnghe, Ruchire Eranga Henry; Cho, Nam Hyun; Park, Kibeom; Shin, Yongseung; Kim, Jeehyun

2013-12-01

In this study, we demonstrate the enhanced spectral calibration method for 1.3 μm spectral-domain optical coherence tomography (SD-OCT). The calibration method using wavelength-filter simplifies the SD-OCT system, and also the axial resolution and the entire speed of the OCT system can be dramatically improved as well. An externally connected wavelength-filter is utilized to obtain the information of the wavenumber and the pixel position. During the calibration process the wavelength-filter is placed after a broadband source by connecting through an optical circulator. The filtered spectrum with a narrow line width of 0.5 nm is detected by using a line-scan camera. The method does not require a filter or a software recalibration algorithm for imaging as it simply resamples the OCT signal from the detector array without employing rescaling or interpolation methods. One of the main drawbacks of SD-OCT is the broadened point spread functions (PSFs) with increasing imaging depth can be compensated by increasing the wavenumber-linearization order. The sensitivity of our system was measured at 99.8 dB at an imaging depth of 2.1 mm compared with the uncompensated case.

Multilevel Mixture Kalman Filter

NASA Astrophysics Data System (ADS)

Guo, Dong; Wang, Xiaodong; Chen, Rong

2004-12-01

The mixture Kalman filter is a general sequential Monte Carlo technique for conditional linear dynamic systems. It generates samples of some indicator variables recursively based on sequential importance sampling (SIS) and integrates out the linear and Gaussian state variables conditioned on these indicators. Due to the marginalization process, the complexity of the mixture Kalman filter is quite high if the dimension of the indicator sampling space is high. In this paper, we address this difficulty by developing a new Monte Carlo sampling scheme, namely, the multilevel mixture Kalman filter. The basic idea is to make use of the multilevel or hierarchical structure of the space from which the indicator variables take values. That is, we draw samples in a multilevel fashion, beginning with sampling from the highest-level sampling space and then draw samples from the associate subspace of the newly drawn samples in a lower-level sampling space, until reaching the desired sampling space. Such a multilevel sampling scheme can be used in conjunction with the delayed estimation method, such as the delayed-sample method, resulting in delayed multilevel mixture Kalman filter. Examples in wireless communication, specifically the coherent and noncoherent 16-QAM over flat-fading channels, are provided to demonstrate the performance of the proposed multilevel mixture Kalman filter.

On optimal infinite impulse response edge detection filters

NASA Technical Reports Server (NTRS)

Sarkar, Sudeep; Boyer, Kim L.

1991-01-01

The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter. The optimal filter is computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise. An expression for the width of the filter, which is appropriate for infinite-length filters, is incorporated directly in the expression for spurious responses. The three criteria are maximized using the variational method and nonlinear constrained optimization. The optimal filter parameters are tabulated for various values of the filter performance criteria. A complete methodology for implementing the optimal filter using approximating recursive digital filtering is presented. The approximating recursive digital filter is separable into two linear filters operating in two orthogonal directions. The implementation is very simple and computationally efficient, has a constant time of execution for different sizes of the operator, and is readily amenable to real-time hardware implementation.

A multiscale filter for noise reduction of low-dose cone beam projections.

PubMed

Yao, Weiguang; Farr, Jonathan B

2015-08-21

The Poisson or compound Poisson process governs the randomness of photon fluence in cone beam computed tomography (CBCT) imaging systems. The probability density function depends on the mean (noiseless) of the fluence at a certain detector. This dependence indicates the natural requirement of multiscale filters to smooth noise while preserving structures of the imaged object on the low-dose cone beam projection. In this work, we used a Gaussian filter, exp(-x2/2σ(2)(f)) as the multiscale filter to de-noise the low-dose cone beam projections. We analytically obtained the expression of σ(f), which represents the scale of the filter, by minimizing local noise-to-signal ratio. We analytically derived the variance of residual noise from the Poisson or compound Poisson processes after Gaussian filtering. From the derived analytical form of the variance of residual noise, optimal σ(2)(f)) is proved to be proportional to the noiseless fluence and modulated by local structure strength expressed as the linear fitting error of the structure. A strategy was used to obtain the reliable linear fitting error: smoothing the projection along the longitudinal direction to calculate the linear fitting error along the lateral direction and vice versa. The performance of our multiscale filter was examined on low-dose cone beam projections of a Catphan phantom and a head-and-neck patient. After performing the filter on the Catphan phantom projections scanned with pulse time 4 ms, the number of visible line pairs was similar to that scanned with 16 ms, and the contrast-to-noise ratio of the inserts was higher than that scanned with 16 ms about 64% in average. For the simulated head-and-neck patient projections with pulse time 4 ms, the visibility of soft tissue structures in the patient was comparable to that scanned with 20 ms. The image processing took less than 0.5 s per projection with 1024   ×   768 pixels.

An Empirical Comparison between Two Recursive Filters for Attitude and Rate Estimation of Spinning Spacecraft

NASA Technical Reports Server (NTRS)

Harman, Richard R.

2006-01-01

The advantages of inducing a constant spin rate on a spacecraft are well known. A variety of science missions have used this technique as a relatively low cost method for conducting science. Starting in the late 1970s, NASA focused on building spacecraft using 3-axis control as opposed to the single-axis control mentioned above. Considerable effort was expended toward sensor and control system development, as well as the development of ground systems to independently process the data. As a result, spinning spacecraft development and their resulting ground system development stagnated. In the 1990s, shrinking budgets made spinning spacecraft an attractive option for science. The attitude requirements for recent spinning spacecraft are more stringent and the ground systems must be enhanced in order to provide the necessary attitude estimation accuracy. Since spinning spacecraft (SC) typically have no gyroscopes for measuring attitude rate, any new estimator would need to rely on the spacecraft dynamics equations. One estimation technique that utilized the SC dynamics and has been used successfully in 3-axis gyro-less spacecraft ground systems is the pseudo-linear Kalman filter algorithm. Consequently, a pseudo-linear Kalman filter has been developed which directly estimates the spacecraft attitude quaternion and rate for a spinning SC. Recently, a filter using Markley variables was developed specifically for spinning spacecraft. The pseudo-linear Kalman filter has the advantage of being easier to implement but estimates the quaternion which, due to the relatively high spinning rate, changes rapidly for a spinning spacecraft. The Markley variable filter is more complicated to implement but, being based on the SC angular momentum, estimates parameters which vary slowly. This paper presents a comparison of the performance of these two filters. Monte-Carlo simulation runs will be presented which demonstrate the advantages and disadvantages of both filters.

Dynamic Filtering Improves Attentional State Prediction with fNIRS

NASA Technical Reports Server (NTRS)

Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

2016-01-01

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

Algorithms for spacecraft formation flying navigation based on wireless positioning system measurements

NASA Astrophysics Data System (ADS)

Goh, Shu Ting

Spacecraft formation flying navigation continues to receive a great deal of interest. The research presented in this dissertation focuses on developing methods for estimating spacecraft absolute and relative positions, assuming measurements of only relative positions using wireless sensors. The implementation of the extended Kalman filter to the spacecraft formation navigation problem results in high estimation errors and instabilities in state estimation at times. This is due to the high nonlinearities in the system dynamic model. Several approaches are attempted in this dissertation aiming at increasing the estimation stability and improving the estimation accuracy. A differential geometric filter is implemented for spacecraft positions estimation. The differential geometric filter avoids the linearization step (which is always carried out in the extended Kalman filter) through a mathematical transformation that converts the nonlinear system into a linear system. A linear estimator is designed in the linear domain, and then transformed back to the physical domain. This approach demonstrated better estimation stability for spacecraft formation positions estimation, as detailed in this dissertation. The constrained Kalman filter is also implemented for spacecraft formation flying absolute positions estimation. The orbital motion of a spacecraft is characterized by two range extrema (perigee and apogee). At the extremum, the rate of change of a spacecraft's range vanishes. This motion constraint can be used to improve the position estimation accuracy. The application of the constrained Kalman filter at only two points in the orbit causes filter instability. Two variables are introduced into the constrained Kalman filter to maintain the stability and improve the estimation accuracy. An extended Kalman filter is implemented as a benchmark for comparison with the constrained Kalman filter. Simulation results show that the constrained Kalman filter provides better estimation accuracy as compared with the extended Kalman filter. A Weighted Measurement Fusion Kalman Filter (WMFKF) is proposed in this dissertation. In wireless localizing sensors, a measurement error is proportional to the distance of the signal travels and sensor noise. In this proposed Weighted Measurement Fusion Kalman Filter, the signal traveling time delay is not modeled; however, each measurement is weighted based on the measured signal travel distance. The obtained estimation performance is compared to the standard Kalman filter in two scenarios. The first scenario assumes using a wireless local positioning system in a GPS denied environment. The second scenario assumes the availability of both the wireless local positioning system and GPS measurements. The simulation results show that the WMFKF has similar accuracy performance as the standard Kalman Filter (KF) in the GPS denied environment. However, the WMFKF maintains the position estimation error within its expected error boundary when the WLPS detection range limit is above 30km. In addition, the WMFKF has a better accuracy and stability performance when GPS is available. Also, the computational cost analysis shows that the WMFKF has less computational cost than the standard KF, and the WMFKF has higher ellipsoid error probable percentage than the standard Measurement Fusion method. A method to determine the relative attitudes between three spacecraft is developed. The method requires four direction measurements between the three spacecraft. The simulation results and covariance analysis show that the method's error falls within a three sigma boundary without exhibiting any singularity issues. A study of the accuracy of the proposed method with respect to the shape of the spacecraft formation is also presented.

Optimization of nonlinear, non-Gaussian Bayesian filtering for diagnosis and prognosis of monotonic degradation processes

NASA Astrophysics Data System (ADS)

Corbetta, Matteo; Sbarufatti, Claudio; Giglio, Marco; Todd, Michael D.

2018-05-01

The present work critically analyzes the probabilistic definition of dynamic state-space models subject to Bayesian filters used for monitoring and predicting monotonic degradation processes. The study focuses on the selection of the random process, often called process noise, which is a key perturbation source in the evolution equation of particle filtering. Despite the large number of applications of particle filtering predicting structural degradation, the adequacy of the picked process noise has not been investigated. This paper reviews existing process noise models that are typically embedded in particle filters dedicated to monitoring and predicting structural damage caused by fatigue, which is monotonic in nature. The analysis emphasizes that existing formulations of the process noise can jeopardize the performance of the filter in terms of state estimation and remaining life prediction (i.e., damage prognosis). This paper subsequently proposes an optimal and unbiased process noise model and a list of requirements that the stochastic model must satisfy to guarantee high prognostic performance. These requirements are useful for future and further implementations of particle filtering for monotonic system dynamics. The validity of the new process noise formulation is assessed against experimental fatigue crack growth data from a full-scale aeronautical structure using dedicated performance metrics.

Rotational response of suspended particles to turbulent flow: laboratory and numerical synthesis

NASA Astrophysics Data System (ADS)

Variano, Evan; Zhao, Lihao; Byron, Margaret; Bellani, Gabriele; Tao, Yiheng; Andersson, Helge

2014-11-01

Using laboratory and DNS measurements, we consider how aspherical and inertial particles suspended in a turbulent flow act to ``filter'' the fluid-phase vorticity. We use three approaches to predict the magnitude and structure of this filter. The first approach is based on Buckingham's Pi theorem, which shows a clear result for the relationship between filter strength and particle aspect ratio. Results are less clear for the dependence of filter strength on Stokes number; we briefly discuss some issues in the proper definition of Stokes number for use in this context. The second approach to predicting filter strength is based on a consideration of vorticity and enstrophy spectra in the fluid phase. This method has a useful feature: it can be used to predict the filter a priori, without need for measurements as input. We compare the results of this approach to measurements as a method of validation. The third and final approach to predicting filter strength is from the consideration of torques experienced by particles, and how the ``angular slip'' or ``spin slip'' evolves in an unsteady flow. We show results from our DNS that indicate different flow conditions in which the spin slip is more or less important in setting the particle rotation dynamics. Collaboration made possible by the Peder Sather Center.

Evapotranspiration and favorable growing degree-days are key to tree height growth and ecosystem functioning: Meta-analyses of Pacific Northwest historical data.

PubMed

Liu, Yang; El-Kassaby, Yousry A

2018-05-29

While temperature and precipitation comprise important ecological filtering for native ranges of forest trees and are predisposing factors underlying forest ecosystem dynamics, the extent and severity of drought raises reasonable concerns for carbon storage and species diversity. Based on historical data from common garden experiments across the Pacific Northwest region, we developed non-linear niche models for height-growth trajectories of conifer trees at the sapling stage using annual or seasonal climatic variables. The correlations between virtual tree height for each locality and ecosystem functions were respectively assessed. Best-fitted models were composed of two distinct components: evapotranspiration and the degree-days disparity for temperature regimes between 5 °C and 18 °C (effective temperature sum and growth temperature, respectively). Tree height prediction for adaptive generalists (e.g., Pinus monticola, Thuja plicata) had smaller residuals than for specialists (e.g., Pinus contorta, Pseudotsuga menziesii), albeit a potential confounding factor - tree age. Discernably, there were linearly positive patterns between tree height growth and ecosystem functions (productivity, biomass and species diversity). Additionally, there was a minor effect of tree diversity on height growth in coniferous forests. This study uncovers the implication of key ecological filtering and increases our integrated understanding of how environmental cues affect tree stand growth, species dominance and ecosystem functions.

Silicon cross-connect filters using microring resonator coupled multimode-interference-based waveguide crossings.

PubMed

Xu, Fang; Poon, Andrew W

2008-06-09

We report silicon cross-connect filters using microring resonator coupled multimode-interference (MMI) based waveguide crossings. Our experiments reveal that the MMI-based cross-connect filters impose lower crosstalk at the crossing than the conventional cross-connect filters using plain crossings, while offering a nearly symmetric resonance line shape in the drop-port transmission. As a proof-of-concept for cross-connection applications, we demonstrate on a silicon-on-insulator substrate (i) a 4-channel 1 x 4 linear-cascaded MMI-based cross-connect filter, and (ii) a 2-channel 2 x 2 array-cascaded MMI-based cross-connect filter.

Dual linear structured support vector machine tracking method via scale correlation filter

NASA Astrophysics Data System (ADS)

Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

2018-01-01

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

Joint polarization tracking and channel equalization based on radius-directed linear Kalman filter

NASA Astrophysics Data System (ADS)

Zhang, Qun; Yang, Yanfu; Zhong, Kangping; Liu, Jie; Wu, Xiong; Yao, Yong

2018-01-01

We propose a joint polarization tracking and channel equalization scheme based on radius-directed linear Kalman filter (RD-LKF) by introducing the butterfly finite-impulse-response (FIR) filter in our previously proposed RD-LKF method. Along with the fast polarization tracking, it can also simultaneously compensate the inter-symbol interference (ISI) effects including residual chromatic dispersion and polarization mode dispersion. Compared with the conventional radius-directed equalizer (RDE) algorithm, it is demonstrated experimentally that three times faster convergence speed, one order of magnitude better tracking capability, and better BER performance is obtained in polarization division multiplexing 16 quadrature amplitude modulation system. Besides, the influences of the algorithm parameters on the convergence and the tracking performance are investigated by numerical simulation.

Linear motor drive system for continuous-path closed-loop position control of an object

DOEpatents

Barkman, William E.

1980-01-01

A precision numerical controlled servo-positioning system is provided for continuous closed-loop position control of a machine slide or platform driven by a linear-induction motor. The system utilizes filtered velocity feedback to provide system stability required to operate with a system gain of 100 inches/minute/0.001 inch of following error. The filtered velocity feedback signal is derived from the position output signals of a laser interferometer utilized to monitor the movement of the slide. Air-bearing slides mounted to a stable support are utilized to minimize friction and small irregularities in the slideway which would tend to introduce positioning errors. A microprocessor is programmed to read command and feedback information and converts this information into the system following error signal. This error signal is summed with the negative filtered velocity feedback signal at the input of a servo amplifier whose output serves as the drive power signal to the linear motor position control coil.

Identification of a Class of Filtered Poisson Processes.

DTIC Science & Technology

1981-01-01

LD-A135 371 IDENTIFICATION OF A CLASS OF FILERED POISSON PROCESSES I AU) NORTH CAROLINA UNIV AT CHAPEL HIL DEPT 0F STATISTICS D DE RRUC ET AL 1981...STNO&IO$ !tt ~ 4.s " . , ".7" -L N ~ TITLE :IDENTIFICATION OF A CLASS OF FILTERED POISSON PROCESSES Authors : DE BRUCQ Denis - GUALTIEROTTI Antonio...filtered Poisson processes is intro- duced : the amplitude has a law which is spherically invariant and the filter is real, linear and causal. It is shown

Jammed-array wideband sawtooth filter.

PubMed

Tan, Zhongwei; Wang, Chao; Goda, Keisuke; Malik, Omer; Jalali, Bahram

2011-11-21

We present an all-optical passive low-cost spectral filter that exhibits a high-resolution periodic sawtooth spectral pattern without the need for active optoelectronic components. The principle of the filter is the partial masking of a phased array of virtual light sources with multiply jammed diffraction orders. We utilize the filter's periodic linear map between frequency and intensity to demonstrate fast sensitive interrogation of fiber Bragg grating sensor arrays and ultrahigh-frequency electrical sawtooth waveform generation. © 2011 Optical Society of America

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

PubMed Central

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Gu, Chengfan

2018-01-01

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation. PMID:29415509

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

PubMed

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

2018-02-06

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

Optical filters for linearly polarized light using sculptured nematic thin flim of TiO2

NASA Astrophysics Data System (ADS)

Muhammad, Zahir; Wali, Faiz; Rehman, Zia ur

2018-05-01

A study of optical filters using sculptured nematic thin films is presented in this article. A central 90◦ twist-defect between two sculptured nematic thin films (SNTFs) sections transmit light of same polarization state and reflect other in the spectral Bragg regime. The SNTFs reflect light of both linearly polarized states in the Bragg regime if the amplitude of modulation of vapor incident angle is increased. A twist-defect in a tilt-modulated sculptured nematic thin films as a result produces bandpass or ultra-narrow bandpass filter depending upon the thickness of the SNTFs. However, both the bandpass or/and ultra-narrow bandpass filters can make polarization-insensitive Bragg mirrors by the appropriate modulation of the tilted 2D nanostructures of a given sculptured nematic thin films. Moreover, it is also observed that the sculptured nematic thin films are very tolerant of the structural defects if the amplitude of modulating vapor incident angle of the structural nano-materials is sufficiently large. Similarly, we observed the affect of incident angles on Bragg filters.

Hydrodynamics of microbial filter feeding

PubMed Central

Asadzadeh, Seyed Saeed; Dölger, Julia; Walther, Jens H.; Andersen, Anders

2017-01-01

Microbial filter feeders are an important group of grazers, significant to the microbial loop, aquatic food webs, and biogeochemical cycling. Our understanding of microbial filter feeding is poor, and, importantly, it is unknown what force microbial filter feeders must generate to process adequate amounts of water. Also, the trade-off in the filter spacing remains unexplored, despite its simple formulation: A filter too coarse will allow suitably sized prey to pass unintercepted, whereas a filter too fine will cause strong flow resistance. We quantify the feeding flow of the filter-feeding choanoflagellate Diaphanoeca grandis using particle tracking, and demonstrate that the current understanding of microbial filter feeding is inconsistent with computational fluid dynamics (CFD) and analytical estimates. Both approaches underestimate observed filtration rates by more than an order of magnitude; the beating flagellum is simply unable to draw enough water through the fine filter. We find similar discrepancies for other choanoflagellate species, highlighting an apparent paradox. Our observations motivate us to suggest a radically different filtration mechanism that requires a flagellar vane (sheet), something notoriously difficult to visualize but sporadically observed in the related choanocytes (sponges). A CFD model with a flagellar vane correctly predicts the filtration rate of D. grandis, and using a simple model we can account for the filtration rates of other microbial filter feeders. We finally predict how optimum filter mesh size increases with cell size in microbial filter feeders, a prediction that accords very well with observations. We expect our results to be of significance for small-scale biophysics and trait-based ecological modeling. PMID:28808016

Hydrodynamics of microbial filter feeding.

PubMed

Nielsen, Lasse Tor; Asadzadeh, Seyed Saeed; Dölger, Julia; Walther, Jens H; Kiørboe, Thomas; Andersen, Anders

2017-08-29

Microbial filter feeders are an important group of grazers, significant to the microbial loop, aquatic food webs, and biogeochemical cycling. Our understanding of microbial filter feeding is poor, and, importantly, it is unknown what force microbial filter feeders must generate to process adequate amounts of water. Also, the trade-off in the filter spacing remains unexplored, despite its simple formulation: A filter too coarse will allow suitably sized prey to pass unintercepted, whereas a filter too fine will cause strong flow resistance. We quantify the feeding flow of the filter-feeding choanoflagellate Diaphanoeca grandis using particle tracking, and demonstrate that the current understanding of microbial filter feeding is inconsistent with computational fluid dynamics (CFD) and analytical estimates. Both approaches underestimate observed filtration rates by more than an order of magnitude; the beating flagellum is simply unable to draw enough water through the fine filter. We find similar discrepancies for other choanoflagellate species, highlighting an apparent paradox. Our observations motivate us to suggest a radically different filtration mechanism that requires a flagellar vane (sheet), something notoriously difficult to visualize but sporadically observed in the related choanocytes (sponges). A CFD model with a flagellar vane correctly predicts the filtration rate of D. grandis , and using a simple model we can account for the filtration rates of other microbial filter feeders. We finally predict how optimum filter mesh size increases with cell size in microbial filter feeders, a prediction that accords very well with observations. We expect our results to be of significance for small-scale biophysics and trait-based ecological modeling.

Application of biological filters in water treatment systems

NASA Technical Reports Server (NTRS)

Hurley, T. L.; Bambenek, R. A.

1973-01-01

Silver chloride placed on or close to barrier kills bacteria as they arrive. Dead bacteria accumulate linearly, whereas previously, live bacteria accumulated exponentially. During continuous 30-day tests, no bacteriological contamination was found downstream of filters with silver chloride added.

Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation.

PubMed

Ławryńczuk, Maciej

2017-03-01

This paper details development of a Model Predictive Control (MPC) algorithm for a boiler-turbine unit, which is a nonlinear multiple-input multiple-output process. The control objective is to follow set-point changes imposed on two state (output) variables and to satisfy constraints imposed on three inputs and one output. In order to obtain a computationally efficient control scheme, the state-space model is successively linearised on-line for the current operating point and used for prediction. In consequence, the future control policy is easily calculated from a quadratic optimisation problem. For state estimation the extended Kalman filter is used. It is demonstrated that the MPC strategy based on constant linear models does not work satisfactorily for the boiler-turbine unit whereas the discussed algorithm with on-line successive model linearisation gives practically the same trajectories as the truly nonlinear MPC controller with nonlinear optimisation repeated at each sampling instant. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Signal detection in power-law noise: effect of spectrum exponents.

PubMed

Burgess, Arthur E; Judy, Philip F

2007-12-01

Many natural backgrounds have approximately isotropic power spectra of the power-law form, P(f)=K/f(beta), where f is radial frequency. For natural scenes and mammograms, the values of the exponent, beta, range from 1.5 to 3.5. The ideal observer model predicts that for signals with certain properties and backgrounds that can be treated as random noise, a plot of log (contrast threshold) versus log (signal size) will be linear with slope, m, given by: m=(beta-2)/2. This plot is referred to as a contrast-detail (CD) diagram. It is interesting that this predicts a detection threshold that is independent of signal size for beta equal to 2. We present two-alternative forced-choice (2AFC) detection results for human and channelized model observers of a simple signal in filtered noise with exponents from 1.5 to 3.5. The CD diagram results are in good agreement with the prediction of this equation.

Improving Fermi Orbit Determination and Prediction in an Uncertain Atmospheric Drag Environment

NASA Technical Reports Server (NTRS)

Vavrina, Matthew A.; Newman, Clark P.; Slojkowski, Steven E.; Carpenter, J. Russell

2014-01-01

Orbit determination and prediction of the Fermi Gamma-ray Space Telescope trajectory is strongly impacted by the unpredictability and variability of atmospheric density and the spacecraft's ballistic coefficient. Operationally, Global Positioning System point solutions are processed with an extended Kalman filter for orbit determination, and predictions are generated for conjunction assessment with secondary objects. When these predictions are compared to Joint Space Operations Center radar-based solutions, the close approach distance between the two predictions can greatly differ ahead of the conjunction. This work explores strategies for improving prediction accuracy and helps to explain the prediction disparities. Namely, a tuning analysis is performed to determine atmospheric drag modeling and filter parameters that can improve orbit determination as well as prediction accuracy. A 45% improvement in three-day prediction accuracy is realized by tuning the ballistic coefficient and atmospheric density stochastic models, measurement frequency, and other modeling and filter parameters.

Harmonic distortion in microwave photonic filters.

PubMed

Rius, Manuel; Mora, José; Bolea, Mario; Capmany, José

2012-04-09

We present a theoretical and experimental analysis of nonlinear microwave photonic filters. Far from the conventional condition of low modulation index commonly used to neglect high-order terms, we have analyzed the harmonic distortion involved in microwave photonic structures with periodic and non-periodic frequency responses. We show that it is possible to design microwave photonic filters with reduced harmonic distortion and high linearity even under large signal operation.

Spectral imagery with an acousto-optic tunable filter

NASA Technical Reports Server (NTRS)

Smith, W. Hayden; Schempp, W. V.; Conner, C. P.; Katzka, P.

1987-01-01

.A spectral imager for astronomy and aeronomy has been fabricated using collinear or non-collinear acoustooptic tunable filters (AOTFs). The AOTF provides high transparency, rapid tunability over a wide wavelength range, a capability of varying the bandwidth by more than an order of magnitude, high etendue, and linearly polarized output. Some typical observational applications of acoustooptic tunable filters used in several configurations at astronomical telescopes are demonstrated.

Tunable multimode-interference bandpass fiber filter.

PubMed

Antonio-Lopez, J E; Castillo-Guzman, A; May-Arrioja, D A; Selvas-Aguilar, R; Likamwa, P

2010-02-01

We report on a wavelength-tunable filter based on multimode interference (MMI) effects. A typical MMI filter consists of a multimode fiber (MMF) spliced between two single-mode fibers (SMF). The peak wavelength response of the filter exhibits a linear dependence when the length of the MMF is modified. Therefore a capillary tube filled with refractive-index-matching liquid is used to effectively increase the length of the MMF, and thus wavelength tuning is achieved. Using this filter a ring-based tunable erbium-doped fiber laser is demonstrated with a tunability of 30 nm, covering the full C-band.

Monte Carlo-based investigations on the impact of removing the flattening filter on beam quality specifiers for photon beam dosimetry.

PubMed

Czarnecki, Damian; Poppe, Björn; Zink, Klemens

2017-06-01

The impact of removing the flattening filter in clinical electron accelerators on the relationship between dosimetric quantities such as beam quality specifiers and the mean photon and electron energies of the photon radiation field was investigated by Monte Carlo simulations. The purpose of this work was to determine the uncertainties when using the well-known beam quality specifiers or energy-based beam specifiers as predictors of dosimetric photon field properties when removing the flattening filter. Monte Carlo simulations applying eight different linear accelerator head models with and without flattening filter were performed in order to generate realistic radiation sources and calculate field properties such as restricted mass collision stopping power ratios (L¯/ρ)airwater, mean photon and secondary electron energies. To study the impact of removing the flattening filter on the beam quality correction factors k Q , this factor for detailed ionization chamber models was calculated by Monte Carlo simulations. Stopping power ratios (L¯/ρ)airwater and k Q values for different ionization chambers as a function of TPR1020 and %dd(10) x were calculated. Moreover, mean photon energies in air and at the point of measurement in water as well as mean secondary electron energies at the point of measurement were calculated. The results revealed that removing the flattening filter led to a change within 0.3% in the relationship between %dd(10) x and (L¯/ρ)airwater, whereby the relationship between TPR1020 and (L¯/ρ)airwater changed up to 0.8% for high energy photon beams. However, TPR1020 was a good predictor of (L¯/ρ)airwater for both types of linear accelerator with energies < 10 MeV with a maximal deviation between both types of accelerators of 0.23%. According to the results, the mean photon energy below the linear accelerators head as well as at the point of measurement may not be suitable as a predictor of (L¯/ρ)airwater and k Q to merge the dosimetry of both linear accelerator types. It was possible to derive (L¯/ρ)airwater using the mean secondary electron energy at the point of measurement as a predictor with an accuracy of 0.17%. A bias between k Q for linear accelerators with and without flattening filter within 1.1% and 1.6% was observed for TPR1020 and %dd(10) x respectively. The results of this study have shown that removing the flattening filter led to a change in the relationship between the well-known beam quality specifiers and dosimetric quantities at the point of measurement, namely (L¯/ρ)airwater, mean photon and electron energy. Furthermore, the results show that a beam profile correction is important for dose measurements with large ionization chambers in flattening filter free beams. © 2017 American Association of Physicists in Medicine.

Hydrologic Remote Sensing and Land Surface Data Assimilation.

PubMed

Moradkhani, Hamid

2008-05-06

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring.

PubMed

Martinek, Radek; Nedoma, Jan; Fajkus, Marcel; Kahankova, Radana; Konecny, Jaromir; Janku, Petr; Kepak, Stanislav; Bilik, Petr; Nazeran, Homer

2017-04-18

This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.

A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring

PubMed Central

Martinek, Radek; Nedoma, Jan; Fajkus, Marcel; Kahankova, Radana; Konecny, Jaromir; Janku, Petr; Kepak, Stanislav; Bilik, Petr; Nazeran, Homer

2017-01-01

This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV. PMID:28420215

Selection of optimal oligonucleotide probes for microarrays usingmultiple criteria, global alignment and parameter estimation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Xingyuan; He, Zhili; Zhou, Jizhong

2005-10-30

The oligonucleotide specificity for microarray hybridizationcan be predicted by its sequence identity to non-targets, continuousstretch to non-targets, and/or binding free energy to non-targets. Mostcurrently available programs only use one or two of these criteria, whichmay choose 'false' specific oligonucleotides or miss 'true' optimalprobes in a considerable proportion. We have developed a software tool,called CommOligo using new algorithms and all three criteria forselection of optimal oligonucleotide probes. A series of filters,including sequence identity, free energy, continuous stretch, GC content,self-annealing, distance to the 3'-untranslated region (3'-UTR) andmelting temperature (Tm), are used to check each possibleoligonucleotide. A sequence identity is calculated based onmore » gapped globalalignments. A traversal algorithm is used to generate alignments for freeenergy calculation. The optimal Tm interval is determined based on probecandidates that have passed all other filters. Final probes are pickedusing a combination of user-configurable piece-wise linear functions andan iterative process. The thresholds for identity, stretch and freeenergy filters are automatically determined from experimental data by anaccessory software tool, CommOligo_PE (CommOligo Parameter Estimator).The program was used to design probes for both whole-genome and highlyhomologous sequence data. CommOligo and CommOligo_PE are freely availableto academic users upon request.« less

Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.

PubMed

Agarwal, Harshit; Rathore, Anurag S; Hadpe, Sandeep Ramesh; Alva, Solomon J

2016-11-01

This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R 2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016. © 2016 American Institute of Chemical Engineers.

Suitability of adsorption isotherms for predicting the retention capacity of active slag filters removing phosphorus from wastewater.

PubMed

Pratt, C; Shilton, A

2009-01-01

Active slag filters are an emerging technology for removing phosphorus (P) from wastewater. A number of researchers have suggested that adsorption isotherms are a useful tool for predicting P retention capacity. However, to date the appropriateness of using isotherms for slag filter design remains unverified due to the absence of benchmark data from a full-scale, field filter operated to exhaustion. This investigation compared the isotherm-predicted P retention capacity of a melter slag with the P adsorption capacity determined from a full-scale, melter slag filter which had reached exhaustion after five years of successfully removing P from waste stabilization pond effluent. Results from the standard laboratory batch test showed that P adsorption correlated more strongly with the Freundlich Isotherm (R(2)=0.97, P<0.01) than the Langmuir Isotherm, a similar finding to previous studies. However, at a P concentration of 10 mg/L, typical of domestic effluent, the Freundlich equation predicted a retention capacity of 0.014 gP/kg slag; markedly lower than the 1.23 gP/kg slag adsorbed by the field filter. Clearly, the result generated by the isotherm bears no resemblance to actual field capacity. Scanning electron microscopy analysis revealed porous, reactive secondary minerals on the slag granule surfaces from the field filter which were likely created by weathering. This slow weathering effect, which generates substantial new adsorption sites, is not accounted for by adsorption isotherms rendering them ineffective in slag filter design.

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

NASA Astrophysics Data System (ADS)

Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.

2012-04-01

Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.

Dynamic filtering improves attentional state prediction with fNIRS

PubMed Central

Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

2016-01-01

Brain activity can predict a person’s level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise – thereby increasing such state prediction accuracy – remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%). PMID:27231602

New Predictive Filters for Compensating the Transport Delay on a Flight Simulator

NASA Technical Reports Server (NTRS)

Guo, Liwen; Cardullo, Frank M.; Houck, Jacob A.; Kelly, Lon C.; Wolters, Thomas E.

2004-01-01

The problems of transport delay in a flight simulator, such as its sources and effects, are reviewed. Then their effects on a pilot-in-the-loop control system are investigated with simulations. Three current prominent delay compensators the lead/lag filter, McFarland filter, and the Sobiski/Cardullo filter were analyzed and compared. This paper introduces two novel delay compensation techniques an adaptive predictor using the Kalman estimator and a state space predictive filter using a reference aerodynamic model. Applications of these two new compensators on recorded data from the NASA Langley Research Center Visual Motion Simulator show that they achieve better compensation over the current ones.

Verification of sub-grid filtered drag models for gas-particle fluidized beds with immersed cylinder arrays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sarkar, Avik; Sun, Xin; Sundaresan, Sankaran

2014-04-23

The accuracy of coarse-grid multiphase CFD simulations of fluidized beds may be improved via the inclusion of filtered constitutive models. In our previous study (Sarkar et al., Chem. Eng. Sci., 104, 399-412), we developed such a set of filtered drag relationships for beds with immersed arrays of cooling tubes. Verification of these filtered drag models is addressed in this work. Predictions from coarse-grid simulations with the sub-grid filtered corrections are compared against accurate, highly-resolved simulations of full-scale turbulent and bubbling fluidized beds. The filtered drag models offer a computationally efficient yet accurate alternative for obtaining macroscopic predictions, but the spatialmore » resolution of meso-scale clustering heterogeneities is sacrificed.« less

HARDI denoising using nonlocal means on S2

NASA Astrophysics Data System (ADS)

Kuurstra, Alan; Dolui, Sudipto; Michailovich, Oleg

2012-02-01

Diffusion MRI (dMRI) is a unique imaging modality for in vivo delineation of the anatomical structure of white matter in the brain. In particular, high angular resolution diffusion imaging (HARDI) is a specific instance of dMRI which is known to excel in detection of multiple neural fibers within a single voxel. Unfortunately, the angular resolution of HARDI is known to be inversely proportional to SNR, which makes the problem of denoising of HARDI data be of particular practical importance. Since HARDI signals are effectively band-limited, denoising can be accomplished by means of linear filtering. However, the spatial dependency of diffusivity in brain tissue makes it impossible to find a single set of linear filter parameters which is optimal for all types of diffusion signals. Hence, adaptive filtering is required. In this paper, we propose a new type of non-local means (NLM) filtering which possesses the required adaptivity property. As opposed to similar methods in the field, however, the proposed NLM filtering is applied in the spherical domain of spatial orientations. Moreover, the filter uses an original definition of adaptive weights, which are designed to be invariant to both spatial rotations as well as to a particular sampling scheme in use. As well, we provide a detailed description of the proposed filtering procedure, its efficient implementation, as well as experimental results with synthetic data. We demonstrate that our filter has substantially better adaptivity as compared to a number of alternative methods.

The influence of filtering and downsampling on the estimation of transfer entropy

PubMed Central

Florin, Esther; von Papen, Michael; Timmermann, Lars

2017-01-01

Transfer entropy (TE) provides a generalized and model-free framework to study Wiener-Granger causality between brain regions. Because of its nonparametric character, TE can infer directed information flow also from nonlinear systems. Despite its increasing number of applications in neuroscience, not much is known regarding the influence of common electrophysiological preprocessing on its estimation. We test the influence of filtering and downsampling on a recently proposed nearest neighborhood based TE estimator. Different filter settings and downsampling factors were tested in a simulation framework using a model with a linear coupling function and two nonlinear models with sigmoid and logistic coupling functions. For nonlinear coupling and progressively lower low-pass filter cut-off frequencies up to 72% false negative direct connections and up to 26% false positive connections were identified. In contrast, for the linear model, a monotonic increase was only observed for missed indirect connections (up to 86%). High-pass filtering (1 Hz, 2 Hz) had no impact on TE estimation. After low-pass filtering interaction delays were significantly underestimated. Downsampling the data by a factor greater than the assumed interaction delay erased most of the transmitted information and thus led to a very high percentage (67–100%) of false negative direct connections. Low-pass filtering increases the number of missed connections depending on the filters cut-off frequency. Downsampling should only be done if the sampling factor is smaller than the smallest assumed interaction delay of the analyzed network. PMID:29149201

Practice patterns of retrievable inferior vena cava filters and predictors of filter retrieval in patients with pulmonary embolism.

PubMed

Kang, Jieun; Ko, Heung-Kyu; Shin, Ji Hoon; Ko, Gi-Young; Jo, Kyung-Wook; Huh, Jin Won; Oh, Yeon-Mok; Lee, Sang-Do; Lee, Jae Seung

2017-12-01

Retrievable inferior vena cava (IVC) filters are increasingly used in patients with venous thromboembolism (VTE) who have contraindications to anticoagulant therapy. However, previous studies have shown that many retrievable filters are left permanently in patients. This study aimed to identify the common indications for IVC filter insertion, the filter retrieval rate, and the predictive factors for filter retrieval attempts. To this end, a retrospective cohort study was performed at a tertiary care center in South Korea between January 2010 and May 2016. Electronic medical charts were reviewed for patients with pulmonary embolism (PE) who underwent IVC filter insertion. A total of 439 cases were reviewed. The most common indication for filter insertion was a preoperative/procedural aim, followed by extensive iliofemoral deep vein thrombosis (DVT). Retrieval of the IVC filter was attempted in 44.9% of patients. The retrieval success rate was 93.9%. History of cerebral hemorrhage, malignancy, and admission to a nonsurgical department were the significant predictive factors of a lower retrieval attempt rate in multivariate analysis. With the increased use of IVC filters, more issues should be addressed before placing a filter and physicians should attempt to improve the filter retrieval rate.

Control of AUVs using differential flatness theory and the derivative-free nonlinear Kalman Filter

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Raffo, Guilerme

2015-12-01

The paper proposes nonlinear control and filtering for Autonomous Underwater Vessels (AUVs) based on differential flatness theory and on the use of the Derivative-free nonlinear Kalman Filter. First, it is shown that the 6-DOF dynamic model of the AUV is a differentially flat one. This enables its transformation into the linear canonical (Brunovsky) form and facilitates the design of a state feedback controller. A problem that has to be dealt with is the uncertainty about the parameters of the AUV's dynamic model, as well the external perturbations which affect its motion. To cope with this, it is proposed to use a disturbance observer which is based on the Derivative-free nonlinear Kalman Filter. The considered filtering method consists of the standard Kalman Filter recursion applied on the linearized model of the vessel and of an inverse transformation based on differential flatness theory, which enables to obtain estimates of the state variables of the initial nonlinear model of the vessel. The Kalman Filter-based disturbance observer performs simultaneous estimation of the non-measurable state variables of the AUV and of the perturbation terms that affect its dynamics. By estimating such disturbances, their compensation is also succeeded through suitable modification of the feedback control input. The efficiency of the proposed AUV control and estimation scheme is confirmed through simulation experiments.

Particle rejuvenation of Rao-Blackwellized sequential Monte Carlo smoothers for conditionally linear and Gaussian models

NASA Astrophysics Data System (ADS)

Nguyen, Ngoc Minh; Corff, Sylvain Le; Moulines, Éric

2017-12-01

This paper focuses on sequential Monte Carlo approximations of smoothing distributions in conditionally linear and Gaussian state spaces. To reduce Monte Carlo variance of smoothers, it is typical in these models to use Rao-Blackwellization: particle approximation is used to sample sequences of hidden regimes while the Gaussian states are explicitly integrated conditional on the sequence of regimes and observations, using variants of the Kalman filter/smoother. The first successful attempt to use Rao-Blackwellization for smoothing extends the Bryson-Frazier smoother for Gaussian linear state space models using the generalized two-filter formula together with Kalman filters/smoothers. More recently, a forward-backward decomposition of smoothing distributions mimicking the Rauch-Tung-Striebel smoother for the regimes combined with backward Kalman updates has been introduced. This paper investigates the benefit of introducing additional rejuvenation steps in all these algorithms to sample at each time instant new regimes conditional on the forward and backward particles. This defines particle-based approximations of the smoothing distributions whose support is not restricted to the set of particles sampled in the forward or backward filter. These procedures are applied to commodity markets which are described using a two-factor model based on the spot price and a convenience yield for crude oil data.

Quantifying residual, eddy, and mean flow effects on mixing in an idealized circumpolar current

DOE PAGES

Wolfram, Phillip J.; Ringler, Todd D.

2017-07-13

Meridional diffusivity is assessed in this paper for a baroclinically unstable jet in a high-latitudeIdealized Circumpolar Current (ICC) using the Model for Prediction Across Scales-Ocean (MPAS-O) and the online Lagrangian In-situ Global High-performance particle Tracking (LIGHT) diagnostic via space-time dispersion of particle clusters over 120 monthly realizations of O(10 6) particles on 11 potential density surfaces. Diffusivity in the jet reaches values of O(6000 m 2 s -1) and is largest near the critical layer supporting mixing suppression and critical layer theory. Values in the vicinity of the shelf break are suppressed to O(100 m 2 s -1) due tomore » the presence of westward slope front currents. Diffusivity attenuates less rapidly with depth in the jet than both eddy velocity and kinetic energy scalings would suggest. Removal of the mean flow via high-pass filtering shifts the nonlinear parameter (ratio of the eddy velocity to eddy phase speed) into the linear wave regime by increasing the eddy phase speed via the depth-mean flow. Low-pass filtering, in contrast, quantifies the effect of mean shear. Diffusivity is decomposed into mean flow shear, linear waves, and the residual nonhomogeneous turbulence components, where turbulence dominates and eddy-produced filamentation strained by background mean shear enhances mixing, accounting for ≥ 80% of the total diffusivity relative to mean shear [O(100 m 2 s -1)], linear waves [O(1000 m 2 s -1)], and undecomposed full diffusivity [O(6000 m 2 s -1)]. Finally, diffusivity parameterizations accounting for both the nonhomogeneous turbulence residual and depth variability are needed.« less

Prediction of the thickness of the compensator filter in radiation therapy using computational intelligence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dehlaghi, Vahab; Taghipour, Mostafa; Haghparast, Abbas

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are investigated to predict the thickness of the compensator filter in radiation therapy. In the proposed models, the input parameters are field size (S), off-axis distance, and relative dose (D/D{sub 0}), and the output is the thickness of the compensator. The obtained results show that the proposed ANN and ANFIS models are useful, reliable, and cheap tools to predict the thickness of the compensator filter in intensity-modulated radiation therapy.

On Markov parameters in system identification

NASA Technical Reports Server (NTRS)

Phan, Minh; Juang, Jer-Nan; Longman, Richard W.

1991-01-01

A detailed discussion of Markov parameters in system identification is given. Different forms of input-output representation of linear discrete-time systems are reviewed and discussed. Interpretation of sampled response data as Markov parameters is presented. Relations between the state-space model and particular linear difference models via the Markov parameters are formulated. A generalization of Markov parameters to observer and Kalman filter Markov parameters for system identification is explained. These extended Markov parameters play an important role in providing not only a state-space realization, but also an observer/Kalman filter for the system of interest.

Synthesizing folded band chaos.

PubMed

Corron, Ned J; Hayes, Scott T; Pethel, Shawn D; Blakely, Jonathan N

2007-04-01

A randomly driven linear filter that synthesizes Lorenz-like, reverse-time chaos is shown also to produce Rössler-like folded band wave forms when driven using a different encoding of the random source. The relationship between the topological entropy of the random source, dissipation in the linear filter, and the positive Lyapunov exponent for the reverse-time wave form is exposed. The two drive encodings are viewed as grammar restrictions on a more general encoding that produces a chaotic superset encompassing both the Lorenz butterfly and Rössler folded band paradigms of nonlinear dynamics.

A digital matched filter for reverse time chaos.

PubMed

Bailey, J Phillip; Beal, Aubrey N; Dean, Robert N; Hamilton, Michael C

2016-07-01

The use of reverse time chaos allows the realization of hardware chaotic systems that can operate at speeds equivalent to existing state of the art while requiring significantly less complex circuitry. Matched filter decoding is possible for the reverse time system since it exhibits a closed form solution formed partially by a linear basis pulse. Coefficients have been calculated and are used to realize the matched filter digitally as a finite impulse response filter. Numerical simulations confirm that this correctly implements a matched filter that can be used for detection of the chaotic signal. In addition, the direct form of the filter has been implemented in hardware description language and demonstrates performance in agreement with numerical results.

A digital matched filter for reverse time chaos

NASA Astrophysics Data System (ADS)

Bailey, J. Phillip; Beal, Aubrey N.; Dean, Robert N.; Hamilton, Michael C.

2016-07-01

The use of reverse time chaos allows the realization of hardware chaotic systems that can operate at speeds equivalent to existing state of the art while requiring significantly less complex circuitry. Matched filter decoding is possible for the reverse time system since it exhibits a closed form solution formed partially by a linear basis pulse. Coefficients have been calculated and are used to realize the matched filter digitally as a finite impulse response filter. Numerical simulations confirm that this correctly implements a matched filter that can be used for detection of the chaotic signal. In addition, the direct form of the filter has been implemented in hardware description language and demonstrates performance in agreement with numerical results.

Dynamics of short-pulse generation via spectral filtering from intensely excited gain-switched 1.55-μm distributed-feedback laser diodes.

PubMed

Chen, Shaoqiang; Yoshita, Masahiro; Sato, Aya; Ito, Takashi; Akiyama, Hidefumi; Yokoyama, Hiroyuki

2013-05-06

Picosecond-pulse-generation dynamics and pulse-width limiting factors via spectral filtering from intensely pulse-excited gain-switched 1.55-μm distributed-feedback laser diodes were studied. The spectral and temporal characteristics of the spectrally filtered pulses indicated that the short-wavelength component stems from the initial part of the gain-switched main pulse and has a nearly linear down-chirp of 5.2 ps/nm, whereas long-wavelength components include chirped pulse-lasing components and steady-state-lasing components. Rate-equation calculations with a model of linear change in refractive index with carrier density explained the major features of the experimental results. The analysis of the expected pulse widths with optimum spectral widths was also consistent with the experimental data.

On detection of median filtering in digital images

NASA Astrophysics Data System (ADS)

Kirchner, Matthias; Fridrich, Jessica

2010-01-01

In digital image forensics, it is generally accepted that intentional manipulations of the image content are most critical and hence numerous forensic methods focus on the detection of such 'malicious' post-processing. However, it is also beneficial to know as much as possible about the general processing history of an image, including content-preserving operations, since they can affect the reliability of forensic methods in various ways. In this paper, we present a simple yet effective technique to detect median filtering in digital images-a widely used denoising and smoothing operator. As a great variety of forensic methods relies on some kind of a linearity assumption, a detection of non-linear median filtering is of particular interest. The effectiveness of our method is backed with experimental evidence on a large image database.

Orbit Determination Using Vinti’s Solution

DTIC Science & Technology

2016-09-15

Surveillance Network STK Systems Tool Kit TBP Two Body Problem TLE Two-line Element Set xv Acronym Definition UKF Unscented Kalman Filter WPAFB Wright...simplicity, stability, and speed. On the other hand, Kalman filters would be best suited for sequential estimation of stochastic or random components of a...be likened to how an Unscented Kalman Filter samples a system’s nonlinearities directly, avoiding linearizing the dynamics in the partials matrices

Computer-Based Algorithmic Determination of Muscle Movement Onset Using M-Mode Ultrasonography

DTIC Science & Technology

2017-05-01

contraction images were analyzed visually and with three different classes of algorithms: pixel standard deviation (SD), high-pass filter and Teager Kaiser...Linear relationships and agreements between computed and visual muscle onset were calculated. The top algorithms were high-pass filtered with a 30 Hz...suggest that computer automated determination using high-pass filtering is a potential objective alternative to visual determination in human

Wavelet-Based Blind Superresolution from Video Sequence and in MRI

DTIC Science & Technology

2005-12-31

in Fig. 4(e) and (f), respectively. The PSNR- based optimal threshold gives better noise filtering but poor deblurring [see Fig. 4(c) and (e)] while...that ultimately produces the deblurred , noise filtered, superresolved image. Finite support linear shift invariant blurs are reasonable to assume... Deblurred and Noise Filtered HR Image Cameras with different PSFs Figure 1: Multichannel Blind Superresolution Model condition [11] on the zeros of the

Identification d’une Classe de Processus de Poisson Filtres (Identification of a Class of Filtered Poisson Processes).

DTIC Science & Technology

1983-05-20

Poisson processes is introduced: the amplitude has a law which is spherically invariant and the filter is real, linear and causal. It is shown how such a model can be identified from experimental data. (Author)

Single-step fabrication of thin-film linear variable bandpass filters based on metal-insulator-metal geometry.

PubMed

Williams, Calum; Rughoobur, Girish; Flewitt, Andrew J; Wilkinson, Timothy D

2016-11-10

A single-step fabrication method is presented for ultra-thin, linearly variable optical bandpass filters (LVBFs) based on a metal-insulator-metal arrangement using modified evaporation deposition techniques. This alternate process methodology offers reduced complexity and cost in comparison to conventional techniques for fabricating LVBFs. We are able to achieve linear variation of insulator thickness across a sample, by adjusting the geometrical parameters of a typical physical vapor deposition process. We demonstrate LVBFs with spectral selectivity from 400 to 850 nm based on Ag (25 nm) and MgF₂ (75-250 nm). Maximum spectral transmittance is measured at ∼70% with a Q-factor of ∼20.

Resolution enhancement of pump-probe microscope with an inverse-annular filter

NASA Astrophysics Data System (ADS)

Kobayashi, Takayoshi; Kawasumi, Koshi; Miyazaki, Jun; Nakata, Kazuaki

2018-04-01

Optical pump-probe microscopy can provide images by detecting changes in probe light intensity induced by stimulated emission, photoinduced absorbance change, or photothermal-induced refractive index change in either transmission or reflection mode. Photothermal microscopy, which is one type of optical pump-probe microscopy, has intrinsically super resolution capability due to the bilinear dependence of signal intensity of pump and probe. We introduce new techniques for further resolution enhancement and fast imaging in photothermal microscope. First, we introduce a new pupil filter, an inverse-annular pupil filter in a pump-probe photothermal microscope, which provides resolution enhancement in three dimensions. The resolutions are proved to be improved in lateral and axial directions by imaging experiment using 20-nm gold nanoparticles. The improvement in X (perpendicular to the common pump and probe polarization direction), Y (parallel to the polarization direction), and Z (axial direction) are by 15 ± 6, 8 ± 8, and 21 ± 2% from the resolution without a pupil filter. The resolution enhancement is even better than the calculation using vector field, which predicts the corresponding enhancement of 11, 8, and 6%. The discussion is made to explain the unexpected results. We also demonstrate the photothermal imaging of thick biological samples (cells from rabbit intestine and kidney) stained with hematoxylin and eosin dye with the inverse-annular filter. Second, a fast, high-sensitivity photothermal microscope is developed by implementing a spatially segmented balanced detection scheme into a laser scanning microscope using a Galvano mirror. We confirm a 4.9 times improvement in signal-to-noise ratio in the spatially segmented balanced detection compared with that of conventional detection. The system demonstrates simultaneous bi-modal photothermal and confocal fluorescence imaging of transgenic mouse brain tissue with a pixel dwell time of 20 µs. The fluorescence image visualizes neurons expressing yellow fluorescence proteins, while the photothermal signal detected endogenous chromophores in the mouse brain, allowing 3D visualization of the distribution of various features such as blood cells and fine structures most probably due to lipids. This imaging modality was constructed using compact and cost-effective laser diodes, and will thus be widely useful in the life and medical sciences. Third, we have made further resolution improvement of high-sensitivity laser scanning photothermal microscopy by applying non-linear detection. By this, the new method has super resolution with 61 and 42% enhancement from the diffraction limit values of the probe and pump wavelengths, respectively, by a second-order non-linear scheme and a high-frame rate in a laser scanning microscope. The maximum resolution is determined to be 160 nm in the second-order non-linear detection mode and 270 nm in the linear detection mode by the PT signal of GNPs. The pixel rate and frame rate for 300 × 300 pixel image are 50 µs and 4.5 s, respectively. The pixel and frame rate are shorter than the rates, those are 1 ms and 100 s, using the piezo-driven stage system.

Adaptive filtering with the self-organizing map: a performance comparison.

PubMed

Barreto, Guilherme A; Souza, Luís Gustavo M

2006-01-01

In this paper we provide an in-depth evaluation of the SOM as a feasible tool for nonlinear adaptive filtering. A comprehensive survey of existing SOM-based and related architectures for learning input-output mappings is carried out and the application of these architectures to nonlinear adaptive filtering is formulated. Then, we introduce two simple procedures for building RBF-based nonlinear filters using the Vector-Quantized Temporal Associative Memory (VQTAM), a recently proposed method for learning dynamical input-output mappings using the SOM. The aforementioned SOM-based adaptive filters are compared with standard FIR/LMS and FIR/LMS-Newton linear transversal filters, as well as with powerful MLP-based filters in nonlinear channel equalization and inverse modeling tasks. The obtained results in both tasks indicate that SOM-based filters can consistently outperform powerful MLP-based ones.

Real time microcontroller implementation of an adaptive myoelectric filter.

PubMed

Bagwell, P J; Chappell, P H

1995-03-01

This paper describes a real time digital adaptive filter for processing myoelectric signals. The filter time constant is automatically selected by the adaptation algorithm, giving a significant improvement over linear filters for estimating the muscle force and controlling a prosthetic device. Interference from mains sources often produces problems for myoelectric processing, and so 50 Hz and all harmonic frequencies are reduced by an averaging filter and differential process. This makes practical electrode placement and contact less critical and time consuming. An economic real time implementation is essential for a prosthetic controller, and this is achieved using an Intel 80C196KC microcontroller.

Amplitude- and rise-time-compensated filters

DOEpatents

Nowlin, Charles H.

1984-01-01

An amplitude-compensated rise-time-compensated filter for a pulse time-of-occurrence (TOOC) measurement system is disclosed. The filter converts an input pulse, having the characteristics of random amplitudes and random, non-zero rise times, to a bipolar output pulse wherein the output pulse has a zero-crossing time that is independent of the rise time and amplitude of the input pulse. The filter differentiates the input pulse, along the linear leading edge of the input pulse, and subtracts therefrom a pulse fractionally proportional to the input pulse. The filter of the present invention can use discrete circuit components and avoids the use of delay lines.

Three filters for visualization of phase objects with large variations of phase gradients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sagan, Arkadiusz; Antosiewicz, Tomasz J.; Szoplik, Tomasz

2009-02-20

We propose three amplitude filters for visualization of phase objects. They interact with the spectra of pure-phase objects in the frequency plane and are based on tangent and error functions as well as antisymmetric combination of square roots. The error function is a normalized form of the Gaussian function. The antisymmetric square-root filter is composed of two square-root filters to widen its spatial frequency spectral range. Their advantage over other known amplitude frequency-domain filters, such as linear or square-root graded ones, is that they allow high-contrast visualization of objects with large variations of phase gradients.

Comparison of VLBI TRF solutions based on Kalman filtering and recent ITRS realizations

NASA Astrophysics Data System (ADS)

Soja, Benedikt; Nilsson, Tobias; Glaser, Susanne; Balidakis, Kyriakos; Karbon, Maria; Heinkelmann, Robert; Gross, Richard; Schuh, Harald

2016-04-01

Compared to previous prominent global terrestrial reference frames (TRF) solutions, such as the ITRF2008 or DTRF2008, the current accuracy requirements demand among other things extended parameterization to account for various non-linear signals present in the time series of station coordinates. The next generation of TRFs, built upon geodetic data until the end of 2014, employs different approaches to tackle in particular seasonal variations and post-seismic deformations. The ITRF2014, developed at the International Earth Rotation and Reference Systems Service (IERS) Combination Center (CC) at Institut Géographique National, introduces harmonic, exponential and logarithmic functions to take into account aforementioned effects. In contrast, the ITRS realization of the IERS CC at Jet Propulsion Laboratory is based on Kalman filtering, which allows coordinate variations to be modeled in a stochastic sense besides the parameterized linear and seasonal signals. In our study, we compare these multi-technique TRFs with solutions solely based on VLBI data, including 104 radio telescopes and 4239 VLBI sessions, covering a time span of 34 years. We calculated a VLBI TRF based on the traditional least-squares adjustment of session-wise normal equations, and an ensemble of Kalman filter and smoother solutions with different parameterizations and stochastic models. In particular, we investigate the impact of different process noise levels for station coordinates, the choice of stochastic processes, e.g. random walks, and the application of time- and station-dependent noise models. For instance, we find that the estimation of seasonal signals, while important for predictions, does not affect the filtered coordinate time series when observational data is available. Furthermore, post-seismic deformations after major earthquakes require the process noise to be scaled accordingly. For instance, we detected coordinate differences of up to 5 cm immediately after the Chile 2010 earthquake when changing the process noise by a factor of 10. Finally, we investigated velocity differences and found the RMS of the differences between the VLBI solutions reaching 0.3 mm/yr for stations with good observational history.

Predicting ambient aerosol Thermal Optical Reflectance (TOR) measurements from infrared spectra: organic carbon

NASA Astrophysics Data System (ADS)

Dillner, A. M.; Takahama, S.

2014-11-01

Organic carbon (OC) can constitute 50% or more of the mass of atmospheric particulate matter. Typically, the organic carbon concentration is measured using thermal methods such as Thermal-Optical Reflectance (TOR) from quartz fiber filters. Here, methods are presented whereby Fourier Transform Infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE or Teflon) filters are used to accurately predict TOR OC. Transmittance FT-IR analysis is rapid, inexpensive, and non-destructive to the PTFE filters. To develop and test the method, FT-IR absorbance spectra are obtained from 794 samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites sampled during 2011. Partial least squares regression is used to calibrate sample FT-IR absorbance spectra to artifact-corrected TOR OC. The FTIR spectra are divided into calibration and test sets by sampling site and date which leads to precise and accurate OC predictions by FT-IR as indicated by high coefficient of determination (R2; 0.96), low bias (0.02 μg m-3, all μg m-3 values based on the nominal IMPROVE sample volume of 32.8 m-3), low error (0.08 μg m-3) and low normalized error (11%). These performance metrics can be achieved with various degrees of spectral pretreatment (e.g., including or excluding substrate contributions to the absorbances) and are comparable in precision and accuracy to collocated TOR measurements. FT-IR spectra are also divided into calibration and test sets by OC mass and by OM / OC which reflects the organic composition of the particulate matter and is obtained from organic functional group composition; this division also leads to precise and accurate OC predictions. Low OC concentrations have higher bias and normalized error due to TOR analytical errors and artifact correction errors, not due to the range of OC mass of the samples in the calibration set. However, samples with low OC mass can be used to predict samples with high OC mass indicating that the calibration is linear. Using samples in the calibration set that have a different OM / OC or ammonium / OC distributions than the test set leads to only a modest increase in bias and normalized error in the predicted samples. We conclude that FT-IR analysis with partial least squares regression is a robust method for accurately predicting TOR OC in IMPROVE network samples; providing complementary information to the organic functional group composition and organic aerosol mass estimated previously from the same set of sample spectra (Ruthenburg et al., 2014).

Compressive Detection of Highly Overlapped Spectra Using Walsh-Hadamard-Based Filter Functions.

PubMed

Corcoran, Timothy C

2018-03-01

In the chemometric context in which spectral loadings of the analytes are already known, spectral filter functions may be constructed which allow the scores of mixtures of analytes to be determined in on-the-fly fashion directly, by applying a compressive detection strategy. Rather than collecting the entire spectrum over the relevant region for the mixture, a filter function may be applied within the spectrometer itself so that only the scores are recorded. Consequently, compressive detection shrinks data sets tremendously. The Walsh functions, the binary basis used in Walsh-Hadamard transform spectroscopy, form a complete orthonormal set well suited to compressive detection. A method for constructing filter functions using binary fourfold linear combinations of Walsh functions is detailed using mathematics borrowed from genetic algorithm work, as a means of optimizing said functions for a specific set of analytes. These filter functions can be constructed to automatically strip the baseline from analysis. Monte Carlo simulations were performed with a mixture of four highly overlapped Raman loadings and with ten excitation-emission matrix loadings; both sets showed a very high degree of spectral overlap. Reasonable estimates of the true scores were obtained in both simulations using noisy data sets, proving the linearity of the method.

Using Kalman Filters to Reduce Noise from RFID Location System

PubMed Central

Xavier, José; Reis, Luís Paulo; Petry, Marcelo

2014-01-01

Nowadays, there are many technologies that support location systems involving intrusive and nonintrusive equipment and also varying in terms of precision, range, and cost. However, the developers some time neglect the noise introduced by these systems, which prevents these systems from reaching their full potential. Focused on this problem, in this research work a comparison study between three different filters was performed in order to reduce the noise introduced by a location system based on RFID UWB technology with an associated error of approximately 18 cm. To achieve this goal, a set of experiments was devised and executed using a miniature train moving at constant velocity in a scenario with two distinct shapes—linear and oval. Also, this train was equipped with a varying number of active tags. The obtained results proved that the Kalman Filter achieved better results when compared to the other two filters. Also, this filter increases the performance of the location system by 15% and 12% for the linear and oval paths respectively, when using one tag. For a multiple tags and oval shape similar results were obtained (11–13% of improvement). PMID:24592186

Efficient and Accurate Optimal Linear Phase FIR Filter Design Using Opposition-Based Harmony Search Algorithm

PubMed Central

Saha, S. K.; Dutta, R.; Choudhury, R.; Kar, R.; Mandal, D.; Ghoshal, S. P.

2013-01-01

In this paper, opposition-based harmony search has been applied for the optimal design of linear phase FIR filters. RGA, PSO, and DE have also been adopted for the sake of comparison. The original harmony search algorithm is chosen as the parent one, and opposition-based approach is applied. During the initialization, randomly generated population of solutions is chosen, opposite solutions are also considered, and the fitter one is selected as a priori guess. In harmony memory, each such solution passes through memory consideration rule, pitch adjustment rule, and then opposition-based reinitialization generation jumping, which gives the optimum result corresponding to the least error fitness in multidimensional search space of FIR filter design. Incorporation of different control parameters in the basic HS algorithm results in the balancing of exploration and exploitation of search space. Low pass, high pass, band pass, and band stop FIR filters are designed with the proposed OHS and other aforementioned algorithms individually for comparative optimization performance. A comparison of simulation results reveals the optimization efficacy of the OHS over the other optimization techniques for the solution of the multimodal, nondifferentiable, nonlinear, and constrained FIR filter design problems. PMID:23844390

Efficient and accurate optimal linear phase FIR filter design using opposition-based harmony search algorithm.

PubMed

Saha, S K; Dutta, R; Choudhury, R; Kar, R; Mandal, D; Ghoshal, S P

2013-01-01

In this paper, opposition-based harmony search has been applied for the optimal design of linear phase FIR filters. RGA, PSO, and DE have also been adopted for the sake of comparison. The original harmony search algorithm is chosen as the parent one, and opposition-based approach is applied. During the initialization, randomly generated population of solutions is chosen, opposite solutions are also considered, and the fitter one is selected as a priori guess. In harmony memory, each such solution passes through memory consideration rule, pitch adjustment rule, and then opposition-based reinitialization generation jumping, which gives the optimum result corresponding to the least error fitness in multidimensional search space of FIR filter design. Incorporation of different control parameters in the basic HS algorithm results in the balancing of exploration and exploitation of search space. Low pass, high pass, band pass, and band stop FIR filters are designed with the proposed OHS and other aforementioned algorithms individually for comparative optimization performance. A comparison of simulation results reveals the optimization efficacy of the OHS over the other optimization techniques for the solution of the multimodal, nondifferentiable, nonlinear, and constrained FIR filter design problems.

Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.

PubMed

Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing

2011-01-01

In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.

A multiscale filter for noise reduction of low-dose cone beam projections

NASA Astrophysics Data System (ADS)

Yao, Weiguang; Farr, Jonathan B.

2015-08-01

The Poisson or compound Poisson process governs the randomness of photon fluence in cone beam computed tomography (CBCT) imaging systems. The probability density function depends on the mean (noiseless) of the fluence at a certain detector. This dependence indicates the natural requirement of multiscale filters to smooth noise while preserving structures of the imaged object on the low-dose cone beam projection. In this work, we used a Gaussian filter, \\text{exp}≤ft(-{{x}2}/2σ f2\\right) as the multiscale filter to de-noise the low-dose cone beam projections. We analytically obtained the expression of {σf} , which represents the scale of the filter, by minimizing local noise-to-signal ratio. We analytically derived the variance of residual noise from the Poisson or compound Poisson processes after Gaussian filtering. From the derived analytical form of the variance of residual noise, optimal σ f2 is proved to be proportional to the noiseless fluence and modulated by local structure strength expressed as the linear fitting error of the structure. A strategy was used to obtain the reliable linear fitting error: smoothing the projection along the longitudinal direction to calculate the linear fitting error along the lateral direction and vice versa. The performance of our multiscale filter was examined on low-dose cone beam projections of a Catphan phantom and a head-and-neck patient. After performing the filter on the Catphan phantom projections scanned with pulse time 4 ms, the number of visible line pairs was similar to that scanned with 16 ms, and the contrast-to-noise ratio of the inserts was higher than that scanned with 16 ms about 64% in average. For the simulated head-and-neck patient projections with pulse time 4 ms, the visibility of soft tissue structures in the patient was comparable to that scanned with 20 ms. The image processing took less than 0.5 s per projection with 1024   ×   768 pixels.

A preliminary evaluation of a failure detection filter for detecting and identifying control element failures in a transport aircraft

NASA Technical Reports Server (NTRS)

Bundick, W. T.

1985-01-01

The application of the failure detection filter to the detection and identification of aircraft control element failures was evaluated in a linear digital simulation of the longitudinal dynamics of a B-737 Aircraft. Simulation results show that with a simple correlator and threshold detector used to process the filter residuals, the failure detection performance is seriously degraded by the effects of turbulence.

Optical Flow Analysis and Kalman Filter Tracking in Video Surveillance Algorithms

DTIC Science & Technology

2007-06-01

Grover Brown and Patrick Y.C. Hwang , Introduction to Random Signals and Applied Kalman Filtering, Third edition, John Wiley & Sons, New York, 1997...noise. Brown and Hwang [6] achieve this improvement by linearly blending the prior estimate, 1kx ∧ − , with the noisy measurement, kz , in the equation...AND KALMAN FILTER TRACKING IN VIDEO SURVEILLANCE ALGORITHMS by David A. Semko June 2007 Thesis Advisor: Monique P. Fargues Second

Radar Measurements of Ocean Surface Waves using Proper Orthogonal Decomposition

DTIC Science & Technology

2017-03-30

rely on use of Fourier transforms (FFT) and filtering spectra on the linear dispersion relationship for ocean surface waves. This report discusses...the measured signal (e.g., Young et al., 1985). In addition, the methods often rely on filtering the FFT of radar backscatter or Doppler velocities...to those obtained with conventional FFT and dispersion curve filtering techniques (iv) Compare both results of(iii) to ground truth sensors (i .e

Nonlinear filter based decision feedback equalizer for optical communication systems.

PubMed

Han, Xiaoqi; Cheng, Chi-Hao

2014-04-07

Nonlinear impairments in optical communication system have become a major concern of optical engineers. In this paper, we demonstrate that utilizing a nonlinear filter based Decision Feedback Equalizer (DFE) with error detection capability can deliver a better performance compared with the conventional linear filter based DFE. The proposed algorithms are tested in simulation using a coherent 100 Gb/sec 16-QAM optical communication system in a legacy optical network setting.

Study of Interpolated Timing Recovery Phase-Locked Loop with Linearly Constrained Adaptive Prefilter for Higher-Density Optical Disc

NASA Astrophysics Data System (ADS)

Kajiwara, Yoshiyuki; Shiraishi, Junya; Kobayashi, Shoei; Yamagami, Tamotsu

2009-03-01

A digital phase-locked loop (PLL) with a linearly constrained adaptive filter (LCAF) has been studied for higher-linear-density optical discs. LCAF has been implemented before an interpolated timing recovery (ITR) PLL unit in order to improve the quality of phase error calculation by using an adaptively equalized partial response (PR) signal. Coefficient update of an asynchronous sampled adaptive FIR filter with a least-mean-square (LMS) algorithm has been constrained by a projection matrix in order to suppress the phase shift of the tap coefficients of the adaptive filter. We have developed projection matrices that are suitable for Blu-ray disc (BD) drive systems by numerical simulation. Results have shown the properties of the projection matrices. Then, we have designed the read channel system of the ITR PLL with an LCAF model on the FPGA board for experiments. Results have shown that the LCAF improves the tilt margins of 30 gigabytes (GB) recordable BD (BD-R) and 33 GB BD read-only memory (BD-ROM) with a sufficient LMS adaptation stability.

A coupling method for a cardiovascular simulation model which includes the Kalman filter.

PubMed

Hasegawa, Yuki; Shimayoshi, Takao; Amano, Akira; Matsuda, Tetsuya

2012-01-01

Multi-scale models of the cardiovascular system provide new insight that was unavailable with in vivo and in vitro experiments. For the cardiovascular system, multi-scale simulations provide a valuable perspective in analyzing the interaction of three phenomenons occurring at different spatial scales: circulatory hemodynamics, ventricular structural dynamics, and myocardial excitation-contraction. In order to simulate these interactions, multiscale cardiovascular simulation systems couple models that simulate different phenomena. However, coupling methods require a significant amount of calculation, since a system of non-linear equations must be solved for each timestep. Therefore, we proposed a coupling method which decreases the amount of calculation by using the Kalman filter. In our method, the Kalman filter calculates approximations for the solution to the system of non-linear equations at each timestep. The approximations are then used as initial values for solving the system of non-linear equations. The proposed method decreases the number of iterations required by 94.0% compared to the conventional strong coupling method. When compared with a smoothing spline predictor, the proposed method required 49.4% fewer iterations.

Method validation using weighted linear regression models for quantification of UV filters in water samples.

PubMed

da Silva, Claudia Pereira; Emídio, Elissandro Soares; de Marchi, Mary Rosa Rodrigues

2015-01-01

This paper describes the validation of a method consisting of solid-phase extraction followed by gas chromatography-tandem mass spectrometry for the analysis of the ultraviolet (UV) filters benzophenone-3, ethylhexyl salicylate, ethylhexyl methoxycinnamate and octocrylene. The method validation criteria included evaluation of selectivity, analytical curve, trueness, precision, limits of detection and limits of quantification. The non-weighted linear regression model has traditionally been used for calibration, but it is not necessarily the optimal model in all cases. Because the assumption of homoscedasticity was not met for the analytical data in this work, a weighted least squares linear regression was used for the calibration method. The evaluated analytical parameters were satisfactory for the analytes and showed recoveries at four fortification levels between 62% and 107%, with relative standard deviations less than 14%. The detection limits ranged from 7.6 to 24.1 ng L(-1). The proposed method was used to determine the amount of UV filters in water samples from water treatment plants in Araraquara and Jau in São Paulo, Brazil. Copyright © 2014 Elsevier B.V. All rights reserved.

A digital matched filter for reverse time chaos

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bailey, J. Phillip, E-mail: mchamilton@auburn.edu; Beal, Aubrey N.; Dean, Robert N.

2016-07-15

The use of reverse time chaos allows the realization of hardware chaotic systems that can operate at speeds equivalent to existing state of the art while requiring significantly less complex circuitry. Matched filter decoding is possible for the reverse time system since it exhibits a closed form solution formed partially by a linear basis pulse. Coefficients have been calculated and are used to realize the matched filter digitally as a finite impulse response filter. Numerical simulations confirm that this correctly implements a matched filter that can be used for detection of the chaotic signal. In addition, the direct form ofmore » the filter has been implemented in hardware description language and demonstrates performance in agreement with numerical results.« less

Bandwidth-variable tunable optical filter unit for illumination and spectral imaging systems using thin-film optical band-pass filters.

PubMed

Hennig, Georg; Brittenham, Gary M; Sroka, Ronald; Kniebühler, Gesa; Vogeser, Michael; Stepp, Herbert

2013-04-01

An optical filter unit is demonstrated, which uses two successively arranged tunable thin-film optical band-pass filters and allows for simultaneous adjustment of the central wavelength in the spectral range 522-555 nm and of the spectral bandwidth in the range 3-16 nm with a wavelength switching time of 8 ms∕nm. Different spectral filter combinations can cover the complete visible spectral range. The transmitted intensity was found to decrease only linearly with the spectral bandwidth for bandwidths >6 nm, allowing a high maximum transmission efficiency of >75%. The image of a fiber bundle was spectrally filtered and analyzed in terms of position-dependency of the transmitted bandwidth and central wavelength.

A simple filter circuit for denoising biomechanical impact signals.

PubMed

Subramaniam, Suba R; Georgakis, Apostolos

2009-01-01

We present a simple scheme for denoising non-stationary biomechanical signals with the aim of accurately estimating their second derivative (acceleration). The method is based on filtering in fractional Fourier domains using well-known low-pass filters in a way that amounts to a time-varying cut-off threshold. The resulting algorithm is linear and its design is facilitated by the relationship between the fractional Fourier transform and joint time-frequency representations. The implemented filter circuit employs only three low-order filters while its efficiency is further supported by the low computational complexity of the fractional Fourier transform. The results demonstrate that the proposed method can denoise the signals effectively and is more robust against noise as compared to conventional low-pass filters.

Method and system for determining induction motor speed

DOEpatents

Parlos, Alexander G.; Bharadwaj, Raj M.

2004-03-30

A non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer, is disclosed. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed calculator derived from the actual current and voltage measurements. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations.

KALREF—A Kalman filter and time series approach to the International Terrestrial Reference Frame realization

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.

Visual communication with retinex coding.

PubMed

Huck, F O; Fales, C L; Davis, R E; Alter-Gartenberg, R

2000-04-10

Visual communication with retinex coding seeks to suppress the spatial variation of the irradiance (e.g., shadows) across natural scenes and preserve only the spatial detail and the reflectance (or the lightness) of the surface itself. The separation of reflectance from irradiance begins with nonlinear retinex coding that sharply and clearly enhances edges and preserves their contrast, and it ends with a Wiener filter that restores images from this edge and contrast information. An approximate small-signal model of image gathering with retinex coding is found to consist of the familiar difference-of-Gaussian bandpass filter and a locally adaptive automatic-gain control. A linear representation of this model is used to develop expressions within the small-signal constraint for the information rate and the theoretical minimum data rate of the retinex-coded signal and for the maximum-realizable fidelity of the images restored from this signal. Extensive computations and simulations demonstrate that predictions based on these figures of merit correlate closely with perceptual and measured performance. Hence these predictions can serve as a general guide for the design of visual communication channels that produce images with a visual quality that consistently approaches the best possible sharpness, clarity, and reflectance constancy, even for nonuniform irradiances. The suppression of shadows in the restored image is found to be constrained inherently more by the sharpness of their penumbra than by their depth.

Visual Communication with Retinex Coding

NASA Astrophysics Data System (ADS)

Huck, Friedrich O.; Fales, Carl L.; Davis, Richard E.; Alter-Gartenberg, Rachel

2000-04-01

Visual communication with retinex coding seeks to suppress the spatial variation of the irradiance (e.g., shadows) across natural scenes and preserve only the spatial detail and the reflectance (or the lightness) of the surface itself. The separation of reflectance from irradiance begins with nonlinear retinex coding that sharply and clearly enhances edges and preserves their contrast, and it ends with a Wiener filter that restores images from this edge and contrast information. An approximate small-signal model of image gathering with retinex coding is found to consist of the familiar difference-of-Gaussian bandpass filter and a locally adaptive automatic-gain control. A linear representation of this model is used to develop expressions within the small-signal constraint for the information rate and the theoretical minimum data rate of the retinex-coded signal and for the maximum-realizable fidelity of the images restored from this signal. Extensive computations and simulations demonstrate that predictions based on these figures of merit correlate closely with perceptual and measured performance. Hence these predictions can serve as a general guide for the design of visual communication channels that produce images with a visual quality that consistently approaches the best possible sharpness, clarity, and reflectance constancy, even for nonuniform irradiances. The suppression of shadows in the restored image is found to be constrained inherently more by the sharpness of their penumbra than by their depth.

Application of near-infrared spectroscopy for the rapid quality assessment of Radix Paeoniae Rubra

NASA Astrophysics Data System (ADS)

Zhan, Hao; Fang, Jing; Tang, Liying; Yang, Hongjun; Li, Hua; Wang, Zhuju; Yang, Bin; Wu, Hongwei; Fu, Meihong

2017-08-01

Near-infrared (NIR) spectroscopy with multivariate analysis was used to quantify gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra, and the feasibility to classify the samples originating from different areas was investigated. A new high-performance liquid chromatography method was developed and validated to analyze gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra as the reference. Partial least squares (PLS), principal component regression (PCR), and stepwise multivariate linear regression (SMLR) were performed to calibrate the regression model. Different data pretreatments such as derivatives (1st and 2nd), multiplicative scatter correction, standard normal variate, Savitzky-Golay filter, and Norris derivative filter were applied to remove the systematic errors. The performance of the model was evaluated according to the root mean square of calibration (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and correlation coefficient (r). The results show that compared to PCR and SMLR, PLS had a lower RMSEC, RMSECV, and RMSEP and higher r for all the four analytes. PLS coupled with proper pretreatments showed good performance in both the fitting and predicting results. Furthermore, the original areas of Radix Paeoniae Rubra samples were partly distinguished by principal component analysis. This study shows that NIR with PLS is a reliable, inexpensive, and rapid tool for the quality assessment of Radix Paeoniae Rubra.

Design and Analysis of a Navigation System Using the Federated Filter

DTIC Science & Technology

1995-12-01

There are a number of different sizes for INS states in each Kalman filter. In DKFSIM 3.3, the largest available is the so-called ABIAS model, which...REPRESENTATION PARAMETERS INS States - ABIAS Model 3 Position drifts Linearized propagation driven by ECEF velocity drifts 3 Velocity drifts

Online vegetation parameter estimation using passive microwave remote sensing observations

USDA-ARS?s Scientific Manuscript database

In adaptive system identification the Kalman filter can be used to identify the coefficient of the observation operator of a linear system. Here the ensemble Kalman filter is tested for adaptive online estimation of the vegetation opacity parameter of a radiative transfer model. A state augmentatio...

The analysis of decimation and interpolation in the linear canonical transform domain.

PubMed

Xu, Shuiqing; Chai, Yi; Hu, Youqiang; Huang, Lei; Feng, Li

2016-01-01

Decimation and interpolation are the two basic building blocks in the multirate digital signal processing systems. As the linear canonical transform (LCT) has been shown to be a powerful tool for optics and signal processing, it is worthwhile and interesting to analyze the decimation and interpolation in the LCT domain. In this paper, the definition of equivalent filter in the LCT domain have been given at first. Then, by applying the definition, the direct implementation structure and polyphase networks for decimator and interpolator in the LCT domain have been proposed. Finally, the perfect reconstruction expressions for differential filters in the LCT domain have been presented as an application. The proposed theorems in this study are the bases for generalizations of the multirate signal processing in the LCT domain, which can advance the filter banks theorems in the LCT domain.

Balancing Vibrations at Harmonic Frequencies by Injecting Harmonic Balancing Signals into the Armature of a Linear Motor/Alternator Coupled to a Stirling Machine

NASA Technical Reports Server (NTRS)

Holliday, Ezekiel S. (Inventor)

2014-01-01

Vibrations at harmonic frequencies are reduced by injecting harmonic balancing signals into the armature of a linear motor/alternator coupled to a Stirling machine. The vibrations are sensed to provide a signal representing the mechanical vibrations. A harmonic balancing signal is generated for selected harmonics of the operating frequency by processing the sensed vibration signal with adaptive filter algorithms of adaptive filters for each harmonic. Reference inputs for each harmonic are applied to the adaptive filter algorithms at the frequency of the selected harmonic. The harmonic balancing signals for all of the harmonics are summed with a principal control signal. The harmonic balancing signals modify the principal electrical drive voltage and drive the motor/alternator with a drive voltage component in opposition to the vibration at each harmonic.

Investigation, development, and application of optimal output feedback theory. Volume 3: The relationship between dynamic compensators and observers and Kalman filters

NASA Technical Reports Server (NTRS)

Broussard, John R.

1987-01-01

Relationships between observers, Kalman Filters and dynamic compensators using feedforward control theory are investigated. In particular, the relationship, if any, between the dynamic compensator state and linear functions of a discrete plane state are investigated. It is shown that, in steady state, a dynamic compensator driven by the plant output can be expressed as the sum of two terms. The first term is a linear combination of the plant state. The second term depends on plant and measurement noise, and the plant control. Thus, the state of the dynamic compensator can be expressed as an estimator of the first term with additive error given by the second term. Conditions under which a dynamic compensator is a Kalman filter are presented, and reduced-order optimal estimaters are investigated.

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.

PubMed

Bai, Ou; Lin, Peter; Vorbach, Sherry; Li, Jiang; Furlani, Steve; Hallett, Mark

2007-12-01

To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

Sonoluminescence at Carthage: Sound into Light

NASA Astrophysics Data System (ADS)

Swanson, Lukas K.; Arion, D.; Crosby, K.

2006-12-01

Single bubble sonoluminescence is a phenomenon in which acoustic energy traps and compresses a bubble resulting in the emission of light through an, as of yet, unidentified mechanism. Mathematical modeling of the single bubble system allows for theoretical predictions of the bubbles interior atmosphere such as radius, pressure and temperature as a function of time. Profiling of the light through polarization measurements, wavelength specific filter imaging as well as raw image analysis may give further insight as to the dynamics of the trapped bubble and a possible mechanism. Results of the linear polarization measurements indicate that the light emitted is not linearly polarized. Long exposures of the light clearly reproduce previously reported data of the high energy, short wavelength end of the visible spectrum by the bluish-violet glow emanating from the bubble. The procedure and design improvements of the apparatus that were made make the phenomenon of sonoluminescence more accessible to study as an undergraduate. My AAPT sponsors are Prof. Douglas Arion and Prof. Kevin Crosby.

Space time neural networks for tether operations in space

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Villarreal, James A.; Jani, Yashvant; Copeland, Charles

1993-01-01

A space shuttle flight scheduled for 1992 will attempt to prove the feasibility of operating tethered payloads in earth orbit. due to the interaction between the Earth's magnetic field and current pulsing through the tether, the tethered system may exhibit a circular transverse oscillation referred to as the 'skiprope' phenomenon. Effective damping of skiprope motion depends on rapid and accurate detection of skiprope magnitude and phase. Because of non-linear dynamic coupling, the satellite attitude behavior has characteristic oscillations during the skiprope motion. Since the satellite attitude motion has many other perturbations, the relationship between the skiprope parameters and attitude time history is very involved and non-linear. We propose a Space-Time Neural Network implementation for filtering satellite rate gyro data to rapidly detect and predict skiprope magnitude and phase. Training and testing of the skiprope detection system will be performed using a validated Orbital Operations Simulator and Space-Time Neural Network software developed in the Software Technology Branch at NASA's Lyndon B. Johnson Space Center.

Photonic crystal enhanced silicon cell based thermophotovoltaic systems

DOE PAGES

Yeng, Yi Xiang; Chan, Walker R.; Rinnerbauer, Veronika; ...

2015-01-30

We report the design, optimization, and experimental results of large area commercial silicon solar cell based thermophotovoltaic (TPV) energy conversion systems. Using global non-linear optimization tools, we demonstrate theoretically a maximum radiative heat-to-electricity efficiency of 6.4% and a corresponding output electrical power density of 0.39 W cm⁻² at temperature T = 1660 K when implementing both the optimized two-dimensional (2D) tantalum photonic crystal (PhC) selective emitter, and the optimized 1D tantalum pentoxide – silicon dioxide PhC cold-side selective filter. In addition, we have developed an experimental large area TPV test setup that enables accurate measurement of radiative heat-to-electricity efficiency formore » any emitter-filter-TPV cell combination of interest. In fact, the experimental results match extremely well with predictions of our numerical models. Our experimental setup achieved a maximum output electrical power density of 0.10W cm⁻² and radiative heat-to-electricity efficiency of 1.18% at T = 1380 K using commercial wafer size back-contacted silicon solar cells.« less

ECG artifact cancellation in surface EMG signals by fractional order calculus application.

PubMed

Miljković, Nadica; Popović, Nenad; Djordjević, Olivera; Konstantinović, Ljubica; Šekara, Tomislav B

2017-03-01

New aspects for automatic electrocardiography artifact removal from surface electromyography signals by application of fractional order calculus in combination with linear and nonlinear moving window filters are explored. Surface electromyography recordings of skeletal trunk muscles are commonly contaminated with spike shaped artifacts. This artifact originates from electrical heart activity, recorded by electrocardiography, commonly present in the surface electromyography signals recorded in heart proximity. For appropriate assessment of neuromuscular changes by means of surface electromyography, application of a proper filtering technique of electrocardiography artifact is crucial. A novel method for automatic artifact cancellation in surface electromyography signals by applying fractional order calculus and nonlinear median filter is introduced. The proposed method is compared with the linear moving average filter, with and without prior application of fractional order calculus. 3D graphs for assessment of window lengths of the filters, crest factors, root mean square differences, and fractional calculus orders (called WFC and WRC graphs) have been introduced. For an appropriate quantitative filtering evaluation, the synthetic electrocardiography signal and analogous semi-synthetic dataset have been generated. The examples of noise removal in 10 able-bodied subjects and in one patient with muscle dystrophy are presented for qualitative analysis. The crest factors, correlation coefficients, and root mean square differences of the recorded and semi-synthetic electromyography datasets showed that the most successful method was the median filter in combination with fractional order calculus of the order 0.9. Statistically more significant (p < 0.001) ECG peak reduction was obtained by the median filter application compared to the moving average filter in the cases of low level amplitude of muscle contraction compared to ECG spikes. The presented results suggest that the novel method combining a median filter and fractional order calculus can be used for automatic filtering of electrocardiography artifacts in the surface electromyography signal envelopes recorded in trunk muscles. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

An exponential filter model predicts lightness illusions

PubMed Central

Zeman, Astrid; Brooks, Kevin R.; Ghebreab, Sennay

2015-01-01

Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects. PMID:26157381

Linear FBG Temperature Sensor Interrogation with Fabry-Perot ITU Multi-wavelength Reference.

PubMed

Park, Hyoung-Jun; Song, Minho

2008-10-29

The equidistantly spaced multi-passbands of a Fabry-Perot ITU filter are used as an efficient multi-wavelength reference for fiber Bragg grating sensor demodulation. To compensate for the nonlinear wavelength tuning effect in the FBG sensor demodulator, a polynomial fitting algorithm was applied to the temporal peaks of the wavelength-scanned ITU filter. The fitted wavelength values are assigned to the peak locations of the FBG sensor reflections, obtaining constant accuracy, regardless of the wavelength scan range and frequency. A linearity error of about 0.18% against a reference thermocouple thermometer was obtained with the suggested method.

Stochastic stability of sigma-point Unscented Predictive Filter.

PubMed

Cao, Lu; Tang, Yu; Chen, Xiaoqian; Zhao, Yong

2015-07-01

In this paper, the Unscented Predictive Filter (UPF) is derived based on unscented transformation for nonlinear estimation, which breaks the confine of conventional sigma-point filters by employing Kalman filter as subject investigated merely. In order to facilitate the new method, the algorithm flow of UPF is given firstly. Then, the theoretical analyses demonstrate that the estimate accuracy of the model error and system for the UPF is higher than that of the conventional PF. Moreover, the authors analyze the stochastic boundedness and the error behavior of Unscented Predictive Filter (UPF) for general nonlinear systems in a stochastic framework. In particular, the theoretical results present that the estimation error remains bounded and the covariance keeps stable if the system׳s initial estimation error, disturbing noise terms as well as the model error are small enough, which is the core part of the UPF theory. All of the results have been demonstrated by numerical simulations for a nonlinear example system. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

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.

Investigation of x-ray spectra for iodinated contrast-enhanced dedicated breast CT

PubMed Central

Glick, Stephen J.; Makeev, Andrey

2017-01-01

Abstract. Screening for breast cancer with mammography has been very successful, resulting in part to a reduction of breast cancer mortality by approximately 39% since 1990. However, mammography still has limitations in performance, especially for women with dense breast tissue. Iodinated contrast-enhanced, dedicated breast CT (BCT) has been proposed to improve lesion analysis and the accuracy of diagnostic workup for patients suspected of having breast cancer. A mathematical analysis to explore the use of various x-ray filters for iodinated contrast-enhanced BCT is presented. To assess task-based performance, the ideal linear observer signal-to-noise ratio (SNR) is used as a figure-of-merit under the assumptions of a linear, shift-invariant imaging system. To estimate signal and noise propagation through the BCT detector, a parallel-cascade model was used. The lesion model was embedded into a structured background and included a realistic level of iodine uptake. SNR was computed for 84,000 different exposure settings by varying the kV setting, x-ray filter materials and thickness, breast size, and composition and radiation dose. It is shown that some x-ray filter material/thickness combinations can provide up to 75% improvement in the linear ideal observer SNR over a conventionally used x-ray filter for BCT. This improvement in SNR can be traded off for substantial reductions in mean glandular dose. PMID:28149923

Are Local Filters Blind to Provenance? Ant Seed Predation Suppresses Exotic Plants More than Natives

PubMed Central

Pearson, Dean E.; Icasatti, Nadia S.; Hierro, Jose L.; Bird, Benjamin J.

2014-01-01

The question of whether species’ origins influence invasion outcomes has been a point of substantial debate in invasion ecology. Theoretically, colonization outcomes can be predicted based on how species’ traits interact with community filters, a process presumably blind to species’ origins. Yet, exotic plant introductions commonly result in monospecific plant densities not commonly seen in native assemblages, suggesting that exotic species may respond to community filters differently than natives. Here, we tested whether exotic and native species differed in their responses to a local community filter by examining how ant seed predation affected recruitment of eighteen native and exotic plant species in central Argentina. Ant seed predation proved to be an important local filter that strongly suppressed plant recruitment, but ants suppressed exotic recruitment far more than natives (89% of exotic species vs. 22% of natives). Seed size predicted ant impacts on recruitment independent of origins, with ant preference for smaller seeds resulting in smaller seeded plant species being heavily suppressed. The disproportionate effects of provenance arose because exotics had generally smaller seeds than natives. Exotics also exhibited greater emergence and earlier peak emergence than natives in the absence of ants. However, when ants had access to seeds, these potential advantages of exotics were negated due to the filtering bias against exotics. The differences in traits we observed between exotics and natives suggest that higher-order introduction filters or regional processes preselected for certain exotic traits that then interacted with the local seed predation filter. Our results suggest that the interactions between local filters and species traits can predict invasion outcomes, but understanding the role of provenance will require quantifying filtering processes at multiple hierarchical scales and evaluating interactions between filters. PMID:25099535

Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording

PubMed Central

Eliseyev, Andrey; Aksenova, Tetiana

2016-01-01

In the current paper the decoding algorithms for motor-related BCI systems for continuous upper limb trajectory prediction are considered. Two methods for the smooth prediction, namely Sobolev and Polynomial Penalized Multi-Way Partial Least Squares (PLS) regressions, are proposed. The methods are compared to the Multi-Way Partial Least Squares and Kalman Filter approaches. The comparison demonstrated that the proposed methods combined the prediction accuracy of the algorithms of the PLS family and trajectory smoothness of the Kalman Filter. In addition, the prediction delay is significantly lower for the proposed algorithms than for the Kalman Filter approach. The proposed methods could be applied in a wide range of applications beyond neuroscience. PMID:27196417

Chaos without nonlinear dynamics.

PubMed

Corron, Ned J; Hayes, Scott T; Pethel, Shawn D; Blakely, Jonathan N

2006-07-14

A linear, second-order filter driven by randomly polarized pulses is shown to generate a waveform that is chaotic under time reversal. That is, the filter output exhibits determinism and a positive Lyapunov exponent when viewed backward in time. The filter is demonstrated experimentally using a passive electronic circuit, and the resulting waveform exhibits a Lorenz-like butterfly structure. This phenomenon suggests that chaos may be connected to physical theories whose underlying framework is not that of a traditional deterministic nonlinear dynamical system.

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.

Calculation of selective filters of a device for primary analysis of speech signals

NASA Astrophysics Data System (ADS)

Chudnovskii, L. S.; Ageev, V. M.

2014-07-01

The amplitude-frequency responses of filters for primary analysis of speech signals, which have a low quality factor and a high rolloff factor in the high-frequency range, are calculated using the linear theory of speech production and psychoacoustic measurement data. The frequency resolution of the filter system for a sinusoidal signal is 40-200 Hz. The modulation-frequency resolution of amplitude- and frequency-modulated signals is 3-6 Hz. The aforementioned features of the calculated filters are close to the amplitudefrequency responses of biological auditory systems at the level of the eighth nerve.

Gain-Scheduled Complementary Filter Design for a MEMS Based Attitude and Heading Reference System

PubMed Central

Yoo, Tae Suk; Hong, Sung Kyung; Yoon, Hyok Min; Park, Sungsu

2011-01-01

This paper describes a robust and simple algorithm for an attitude and heading reference system (AHRS) based on low-cost MEMS inertial and magnetic sensors. The proposed approach relies on a gain-scheduled complementary filter, augmented by an acceleration-based switching architecture to yield robust performance, even when the vehicle is subject to strong accelerations. Experimental results are provided for a road captive test during which the vehicle dynamics are in high-acceleration mode and the performance of the proposed filter is evaluated against the output from a conventional linear complementary filter. PMID:22163824

Wilkinson Microwave Anisotropy Probe (WMAP) Attitude Estimation Filter Comparison

NASA Technical Reports Server (NTRS)

Harman, Richard R.

2005-01-01

The Wilkinson Microwave Anisotropy Probe (WMAP) spacecraft was launched in June of 2001. The sensor complement of WMAP consists of two Autonomous Star Trackers (ASTs), two Fine Sun Sensors (FSSs), and a gyro package which contains redundancy about one of the WMAP body axes. The onboard attitude estimation filter consists of an extended Kalman filter (EKF) solving for attitude and gyro bias errors which are then resolved into a spacecraft attitude quaternion and gyro bias. A pseudo-linear Kalman filter has been developed which directly estimates the spacecraft attitude quaternion, rate, and gyro bias. In this paper, the performance of the two filters is compared for the two major control modes of WMAP: inertial mode and observation mode.

Simplification of the Kalman filter for meteorological data assimilation

NASA Technical Reports Server (NTRS)

Dee, Dick P.

1991-01-01

The paper proposes a new statistical method of data assimilation that is based on a simplification of the Kalman filter equations. The forecast error covariance evolution is approximated simply by advecting the mass-error covariance field, deriving the remaining covariances geostrophically, and accounting for external model-error forcing only at the end of each forecast cycle. This greatly reduces the cost of computation of the forecast error covariance. In simulations with a linear, one-dimensional shallow-water model and data generated artificially, the performance of the simplified filter is compared with that of the Kalman filter and the optimal interpolation (OI) method. The simplified filter produces analyses that are nearly optimal, and represents a significant improvement over OI.

Active optimal control strategies for increasing the efficiency of photovoltaic cells

NASA Astrophysics Data System (ADS)

Aljoaba, Sharif Zidan Ahmad

Energy consumption has increased drastically during the last century. Currently, the worldwide energy consumption is about 17.4 TW and is predicted to reach 25 TW by 2035. Solar energy has emerged as one of the potential renewable energy sources. Since its first physical recognition in 1887 by Adams and Day till nowadays, research in solar energy is continuously developing. This has lead to many achievements and milestones that introduced it as one of the most reliable and sustainable energy sources. Recently, the International Energy Agency declared that solar energy is predicted to be one of the major electricity production energy sources by 2035. Enhancing the efficiency and lifecycle of photovoltaic (PV) modules leads to significant cost reduction. Reducing the temperature of the PV module improves its efficiency and enhances its lifecycle. To better understand the PV module performance, it is important to study the interaction between the output power and the temperature. A model that is capable of predicting the PV module temperature and its effects on the output power considering the individual contribution of the solar spectrum wavelengths significantly advances the PV module edsigns toward higher efficiency. In this work, a thermoelectrical model is developed to predict the effects of the solar spectrum wavelengths on the PV module performance. The model is characterized and validated under real meteorological conditions where experimental temperature and output power of the PV module measurements are shown to agree with the predicted results. The model is used to validate the concept of active optical filtering. Since this model is wavelength-based, it is used to design an active optical filter for PV applications. Applying this filter to the PV module is expected to increase the output power of the module by filtering the spectrum wavelengths. The active filter performance is optimized, where different cutoff wavelengths are used to maximize the module output power. It is predicted that if the optimized active optical filter is applied to the PV module, the module efficiency is predicted to increase by about 1%. Different technologies are considered for physical implementation of the active optical filter.

International Space Station Bacteria Filter Element Post-Flight Testing and Service Life Prediction

NASA Technical Reports Server (NTRS)

Perry, J. L.; von Jouanne, R. G.; Turner, E. H.

2003-01-01

The International Space Station uses high efficiency particulate air (HEPA) filters to remove particulate matter from the cabin atmosphere. Known as Bacteria Filter Elements (BFEs), there are 13 elements deployed on board the ISS's U.S. Segment. The pre-flight service life prediction of 1 year for the BFEs is based upon performance engineering analysis of data collected during developmental testing that used a synthetic dust challenge. While this challenge is considered reasonable and conservative from a design perspective, an understanding of the actual filter loading is required to best manage the critical ISS Program resources. Thus testing was conducted on BFEs returned from the ISS to refine the service life prediction. Results from this testing and implications to ISS resource management are discussed. Recommendations for realizing significant savings to the ISS Program are presented.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, K; Li, X; Liu, B

Purpose: To accurately measure CT bow-tie profiles from various manufacturers and to provide non-proprietary information for CT system modeling. Methods: A GOS-based linear detector (0.8 mm per pixel and 51.2 cm in length) with a fast data sampling speed (0.24 ms/sample) was used to measure the relative profiles of bow-tie filters from a collection of eight CT scanners by three different vendors, GE (LS Xtra, LS VCT, Discovery HD750), Siemens (Sensation 64, Edge, Flash, Force), and Philips (iBrilliance 256). The linear detector was first calibrated for its energy response within typical CT beam quality ranges and compared with an ionmore » chamber and analytical modeling (SPECTRA and TASMIP). A geometrical calibration process was developed to determine key parameters including the distance from the focal spot to the linear detector, the angular increment of the gantry at each data sampling, the location of the central x-ray on the linear detector, and the angular response of the detector pixel. Measurements were performed under axial-scan modes for most representative bow-tie filters and kV selections from each scanner. Bow-tie profiles were determined by re-binning the measured rotational data with an angular accuracy of 0.1 degree using the calibrated geometrical parameters. Results: The linear detector demonstrated an energy response as a solid state detector, which is close to the CT imaging detector. The geometrical calibration was proven to be sufficiently accurate (< 1mm in error for distances >550 mm) and the bow-tie profiles measured from rotational mode matched closely to those from the gantry-stationary mode. Accurate profiles were determined for a total of 21 bow-tie filters and 83 filter/kV combinations from the abovementioned scanner models. Conclusion: A new improved approach of CT bow-tie measurement was proposed and accurate bow-tie profiles were provided for a broad list of CT scanner models.« less

A low computation cost method for seizure prediction.

PubMed

Zhang, Yanli; Zhou, Weidong; Yuan, Qi; Wu, Qi

2014-10-01

The dynamic changes of electroencephalograph (EEG) signals in the period prior to epileptic seizures play a major role in the seizure prediction. This paper proposes a low computation seizure prediction algorithm that combines a fractal dimension with a machine learning algorithm. The presented seizure prediction algorithm extracts the Higuchi fractal dimension (HFD) of EEG signals as features to classify the patient's preictal or interictal state with Bayesian linear discriminant analysis (BLDA) as a classifier. The outputs of BLDA are smoothed by a Kalman filter for reducing possible sporadic and isolated false alarms and then the final prediction results are produced using a thresholding procedure. The algorithm was evaluated on the intracranial EEG recordings of 21 patients in the Freiburg EEG database. For seizure occurrence period of 30 min and 50 min, our algorithm obtained an average sensitivity of 86.95% and 89.33%, an average false prediction rate of 0.20/h, and an average prediction time of 24.47 min and 39.39 min, respectively. The results confirm that the changes of HFD can serve as a precursor of ictal activities and be used for distinguishing between interictal and preictal epochs. Both HFD and BLDA classifier have a low computational complexity. All of these make the proposed algorithm suitable for real-time seizure prediction. Copyright © 2014 Elsevier B.V. All rights reserved.

Microcomputer-based artificial vision support system for real-time image processing for camera-driven visual prostheses

NASA Astrophysics Data System (ADS)

Fink, Wolfgang; You, Cindy X.; Tarbell, Mark A.

2010-01-01

It is difficult to predict exactly what blind subjects with camera-driven visual prostheses (e.g., retinal implants) can perceive. Thus, it is prudent to offer them a wide variety of image processing filters and the capability to engage these filters repeatedly in any user-defined order to enhance their visual perception. To attain true portability, we employ a commercial off-the-shelf battery-powered general purpose Linux microprocessor platform to create the microcomputer-based artificial vision support system (μAVS2) for real-time image processing. Truly standalone, μAVS2 is smaller than a deck of playing cards, lightweight, fast, and equipped with USB, RS-232 and Ethernet interfaces. Image processing filters on μAVS2 operate in a user-defined linear sequential-loop fashion, resulting in vastly reduced memory and CPU requirements during execution. μAVS2 imports raw video frames from a USB or IP camera, performs image processing, and issues the processed data over an outbound Internet TCP/IP or RS-232 connection to the visual prosthesis system. Hence, μAVS2 affords users of current and future visual prostheses independent mobility and the capability to customize the visual perception generated. Additionally, μAVS2 can easily be reconfigured for other prosthetic systems. Testing of μAVS2 with actual retinal implant carriers is envisioned in the near future.

Microcomputer-based artificial vision support system for real-time image processing for camera-driven visual prostheses.

PubMed

Fink, Wolfgang; You, Cindy X; Tarbell, Mark A

2010-01-01

It is difficult to predict exactly what blind subjects with camera-driven visual prostheses (e.g., retinal implants) can perceive. Thus, it is prudent to offer them a wide variety of image processing filters and the capability to engage these filters repeatedly in any user-defined order to enhance their visual perception. To attain true portability, we employ a commercial off-the-shelf battery-powered general purpose Linux microprocessor platform to create the microcomputer-based artificial vision support system (microAVS(2)) for real-time image processing. Truly standalone, microAVS(2) is smaller than a deck of playing cards, lightweight, fast, and equipped with USB, RS-232 and Ethernet interfaces. Image processing filters on microAVS(2) operate in a user-defined linear sequential-loop fashion, resulting in vastly reduced memory and CPU requirements during execution. MiccroAVS(2) imports raw video frames from a USB or IP camera, performs image processing, and issues the processed data over an outbound Internet TCP/IP or RS-232 connection to the visual prosthesis system. Hence, microAVS(2) affords users of current and future visual prostheses independent mobility and the capability to customize the visual perception generated. Additionally, microAVS(2) can easily be reconfigured for other prosthetic systems. Testing of microAVS(2) with actual retinal implant carriers is envisioned in the near future.

Q-Method Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Zanetti, Renato; Ainscough, Thomas; Christian, John; Spanos, Pol D.

2012-01-01

A new algorithm is proposed that smoothly integrates non-linear estimation of the attitude quaternion using Davenport s q-method and estimation of non-attitude states through an extended Kalman filter. The new method is compared to a similar existing algorithm showing its similarities and differences. The validity of the proposed approach is confirmed through numerical simulations.

Air-Gapped Structures as Magnetic Elements for Use in Power Processing Systems. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Ohri, A. K.

1977-01-01

Methodical approaches to the design of inductors for use in LC filters and dc-to-dc converters using air gapped magnetic structures are presented. Methods for the analysis and design of full wave rectifier LC filter circuits operating with the inductor current in both the continuous conduction and the discontinuous conduction modes are also described. In the continuous conduction mode, linear circuit analysis techniques are employed, while in the case of the discontinuous mode, the method of analysis requires computer solutions of the piecewise linear differential equations which describe the filter in the time domain. Procedures for designing filter inductors using air gapped cores are presented. The first procedure requires digital computation to yield a design which is optimized in the sense of minimum core volume and minimum number of turns. The second procedure does not yield an optimized design as defined above, but the design can be obtained by hand calculations or with a small calculator. The third procedure is based on the use of specially prepared magnetic core data and provides an easy way to quickly reach a workable design.

Low-sensitivity H ∞ filter design for linear delta operator systems with sampling time jitter

NASA Astrophysics Data System (ADS)

Guo, Xiang-Gui; Yang, Guang-Hong

2012-04-01

This article is concerned with the problem of designing H ∞ filters for a class of linear discrete-time systems with low-sensitivity to sampling time jitter via delta operator approach. Delta-domain model is used to avoid the inherent numerical ill-condition resulting from the use of the standard shift-domain model at high sampling rates. Based on projection lemma in combination with the descriptor system approach often used to solve problems related to delay, a novel bounded real lemma with three slack variables for delta operator systems is presented. A sensitivity approach based on this novel lemma is proposed to mitigate the effects of sampling time jitter on system performance. Then, the problem of designing a low-sensitivity filter can be reduced to a convex optimisation problem. An important consideration in the design of correlation filters is the optimal trade-off between the standard H ∞ criterion and the sensitivity of the transfer function with respect to sampling time jitter. Finally, a numerical example demonstrating the validity of the proposed design method is given.

Evolution of an interfacial crack on the concrete-embankment boundary

DOE Office of Scientific and Technical Information (OSTI.GOV)

Glascoe, Lee; Antoun, Tarabay; Kanarska, Yuliya

2013-07-10

Failure of a dam can have subtle beginnings. A small crack or dislocation at the interface of the concrete dam and the surrounding embankment soil initiated by, for example, a seismic or an explosive event can lead to a catastrophic failure of the dam. The dam may ‘self-rehabilitate’ if a properly designed granular filter is engineered around the embankment. Currently, the design criteria for such filters have only been based on experimental studies. We demonstrate the numerical prediction of filter effectiveness at the soil grain scale. This joint LLNL-ERDC basic research project, funded by the Department of Homeland Security’s Sciencemore » and Technology Directorate (DHS S&T), consists of validating advanced high performance computer simulations of soil erosion and transport of grain- and dam-scale models to detailed centrifuge and soil erosion tests. Validated computer predictions highlight that a resilient filter is consistent with the current design specifications for dam filters. These predictive simulations, unlike the design specifications, can be used to assess filter success or failure under different soil or loading conditions and can lead to meaningful estimates of the timing and nature of full-scale dam failure.« less

A high resolution model of linear trend in mass variations from DMT-2: Added value of accounting for coloured noise in GRACE data

NASA Astrophysics Data System (ADS)

Farahani, Hassan H.; Ditmar, Pavel; Inácio, Pedro; Didova, Olga; Gunter, Brian; Klees, Roland; Guo, Xiang; Guo, Jing; Sun, Yu; Liu, Xianglin; Zhao, Qile; Riva, Riccardo

2017-01-01

We present a high resolution model of the linear trend in the Earth's mass variations based on DMT-2 (Delft Mass Transport model, release 2). DMT-2 was produced primarily from K-Band Ranging (KBR) data of the Gravity Recovery And Climate Experiment (GRACE). It comprises a time series of monthly solutions complete to spherical harmonic degree 120. A novel feature in its production was the accurate computation and incorporation of stochastic properties of coloured noise when processing KBR data. The unconstrained DMT-2 monthly solutions are used to estimate the linear trend together with a bias, as well as annual and semi-annual sinusoidal terms. The linear term is further processed with an anisotropic Wiener filter, which uses full noise and signal covariance matrices. Given the fact that noise in an unconstrained model of the trend is reduced substantially as compared to monthly solutions, the Wiener filter associated with the trend is much less aggressive compared to a Wiener filter applied to monthly solutions. Consequently, the trend estimate shows an enhanced spatial resolution. It allows signals in relatively small water bodies, such as Aral sea and Ladoga lake, to be detected. Over the ice sheets, it allows for a clear identification of signals associated with some outlet glaciers or their groups. We compare the obtained trend estimate with the ones from the CSR-RL05 model using (i) the same approach based on monthly noise covariance matrices and (ii) a commonly-used approach based on the DDK-filtered monthly solutions. We use satellite altimetry data as independent control data. The comparison demonstrates a high spatial resolution of the DMT-2 linear trend. We link this to the usage of high-accuracy monthly noise covariance matrices, which is due to an accurate computation and incorporation of coloured noise when processing KBR data. A preliminary comparison of the linear trend based on DMT-2 with that computed from GSFC_global_mascons_v01 reveals, among other, a high concentration of the signal along the coast for both models in areas like the ice sheets, Gulf of Alaska, and Iceland.

Recursive least squares estimation and its application to shallow trench isolation

NASA Astrophysics Data System (ADS)

Wang, Jin; Qin, S. Joe; Bode, Christopher A.; Purdy, Matthew A.

2003-06-01

In recent years, run-to-run (R2R) control technology has received tremendous interest in semiconductor manufacturing. One class of widely used run-to-run controllers is based on the exponentially weighted moving average (EWMA) statistics to estimate process deviations. Using an EWMA filter to smooth the control action on a linear process has been shown to provide good results in a number of applications. However, for a process with severe drifts, the EWMA controller is insufficient even when large weights are used. This problem becomes more severe when there is measurement delay, which is almost inevitable in semiconductor industry. In order to control drifting processes, a predictor-corrector controller (PCC) and a double EWMA controller have been developed. Chen and Guo (2001) show that both PCC and double-EWMA controller are in effect Integral-double-Integral (I-II) controllers, which are able to control drifting processes. However, since offset is often within the noise of the process, the second integrator can actually cause jittering. Besides, tuning the second filter is not as intuitive as a single EWMA filter. In this work, we look at an alternative way Recursive Least Squares (RLS), to estimate and control the drifting process. EWMA and double-EWMA are shown to be the least squares estimate for locally constant mean model and locally constant linear trend model. Then the recursive least squares with exponential factor is applied to shallow trench isolation etch process to predict the future etch rate. The etch process, which is a critical process in the flash memory manufacturing, is known to suffer from significant etch rate drift due to chamber seasoning. In order to handle the metrology delay, we propose a new time update scheme. RLS with the new time update method gives very good result. The estimate error variance is smaller than that from EWMA, and mean square error decrease more than 10% compared to that from EWMA.

Kalman Filter Constraint Tuning for Turbofan Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2005-01-01

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints are often neglected because they do not fit easily into the structure of the Kalman filter. Recently published work has shown a new method for incorporating state variable inequality constraints in the Kalman filter, which has been shown to generally improve the filter s estimation accuracy. However, the incorporation of inequality constraints poses some risk to the estimation accuracy as the Kalman filter is theoretically optimal. This paper proposes a way to tune the filter constraints so that the state estimates follow the unconstrained (theoretically optimal) filter when the confidence in the unconstrained filter is high. When confidence in the unconstrained filter is not so high, then we use our heuristic knowledge to constrain the state estimates. The confidence measure is based on the agreement of measurement residuals with their theoretical values. The algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate engine health.

Generation of global VTEC maps from low latency GNSS observations based on B-spline modelling and Kalman filtering

NASA Astrophysics Data System (ADS)

Erdogan, Eren; Dettmering, Denise; Limberger, Marco; Schmidt, Michael; Seitz, Florian; Börger, Klaus; Brandert, Sylvia; Görres, Barbara; Kersten, Wilhelm F.; Bothmer, Volker; Hinrichs, Johannes; Venzmer, Malte

2015-04-01

In May 2014 DGFI-TUM (the former DGFI) and the German Space Situational Awareness Centre (GSSAC) started to develop an OPerational Tool for Ionospheric Mapping And Prediction (OPTIMAP); since November 2014 the Institute of Astrophysics at the University of Göttingen (IAG) joined the group as the third partner. This project aims on the computation and prediction of maps of the vertical total electron content (VTEC) and the electron density distribution of the ionosphere on a global scale from both various space-geodetic observation techniques such as GNSS and satellite altimetry as well as Sun observations. In this contribution we present first results, i.e. a near-real time processing framework for generating VTEC maps by assimilating GNSS (GPS, GLONASS) based ionospheric data into a two-dimensional global B-spline approach. To be more specific, the spatial variations of VTEC are modelled by trigonometric B-spline functions in longitude and by endpoint-interpolating polynomial B-spline functions in latitude, respectively. Since B-spline functions are compactly supported and highly localizing our approach can handle large data gaps appropriately and, thus, provides a better approximation of data with heterogeneous density and quality compared to the commonly used spherical harmonics. The presented method models temporal variations of VTEC inside a Kalman filter. The unknown parameters of the filter state vector are composed of the B-spline coefficients as well as the satellite and receiver DCBs. To approximate the temporal variation of these state vector components as part of the filter the dynamical model has to be set up. The current implementation of the filter allows to select between a random walk process, a Gauss-Markov process and a dynamic process driven by an empirical ionosphere model, e.g. the International Reference Ionosphere (IRI). For running the model ionospheric input data is acquired from terrestrial GNSS networks through online archive systems (such as IGS) with approximately one hour latency. Before feeding the filter with new hourly data, the raw GNSS observations are downloaded and pre-processed via geometry free linear combinations to provide signal delay information including the ionospheric effects and the differential code biases. Next steps will implement further space geodetic techniques and will introduce the Sun observations into the procedure. The final destination is to develop a time dependent model of the electron density based on different geodetic and solar observations.

Orbital State Uncertainty Realism

NASA Astrophysics Data System (ADS)

Horwood, J.; Poore, A. B.

2012-09-01

Fundamental to the success of the space situational awareness (SSA) mission is the rigorous inclusion of uncertainty in the space surveillance network. The *proper characterization of uncertainty* in the orbital state of a space object is a common requirement to many SSA functions including tracking and data association, resolution of uncorrelated tracks (UCTs), conjunction analysis and probability of collision, sensor resource management, and anomaly detection. While tracking environments, such as air and missile defense, make extensive use of Gaussian and local linearity assumptions within algorithms for uncertainty management, space surveillance is inherently different due to long time gaps between updates, high misdetection rates, nonlinear and non-conservative dynamics, and non-Gaussian phenomena. The latter implies that "covariance realism" is not always sufficient. SSA also requires "uncertainty realism"; the proper characterization of both the state and covariance and all non-zero higher-order cumulants. In other words, a proper characterization of a space object's full state *probability density function (PDF)* is required. In order to provide a more statistically rigorous treatment of uncertainty in the space surveillance tracking environment and to better support the aforementioned SSA functions, a new class of multivariate PDFs are formulated which more accurately characterize the uncertainty of a space object's state or orbit. The new distribution contains a parameter set controlling the higher-order cumulants which gives the level sets a distinctive "banana" or "boomerang" shape and degenerates to a Gaussian in a suitable limit. Using the new class of PDFs within the general Bayesian nonlinear filter, the resulting filter prediction step (i.e., uncertainty propagation) is shown to have the *same computational cost as the traditional unscented Kalman filter* with the former able to maintain a proper characterization of the uncertainty for up to *ten times as long* as the latter. The filter correction step also furnishes a statistically rigorous *prediction error* which appears in the likelihood ratios for scoring the association of one report or observation to another. Thus, the new filter can be used to support multi-target tracking within a general multiple hypothesis tracking framework. Additionally, the new distribution admits a distance metric which extends the classical Mahalanobis distance (chi^2 statistic). This metric provides a test for statistical significance and facilitates single-frame data association methods with the potential to easily extend the covariance-based track association algorithm of Hill, Sabol, and Alfriend. The filtering, data fusion, and association methods using the new class of orbital state PDFs are shown to be mathematically tractable and operationally viable.

Development of Computational Aeroacoustics Code for Jet Noise and Flow Prediction

NASA Astrophysics Data System (ADS)

Keith, Theo G., Jr.; Hixon, Duane R.

2002-07-01

Accurate prediction of jet fan and exhaust plume flow and noise generation and propagation is very important in developing advanced aircraft engines that will pass current and future noise regulations. In jet fan flows as well as exhaust plumes, two major sources of noise are present: large-scale, coherent instabilities and small-scale turbulent eddies. In previous work for the NASA Glenn Research Center, three strategies have been explored in an effort to computationally predict the noise radiation from supersonic jet exhaust plumes. In order from the least expensive computationally to the most expensive computationally, these are: 1) Linearized Euler equations (LEE). 2) Very Large Eddy Simulations (VLES). 3) Large Eddy Simulations (LES). The first method solves the linearized Euler equations (LEE). These equations are obtained by linearizing about a given mean flow and the neglecting viscous effects. In this way, the noise from large-scale instabilities can be found for a given mean flow. The linearized Euler equations are computationally inexpensive, and have produced good noise results for supersonic jets where the large-scale instability noise dominates, as well as for the tone noise from a jet engine blade row. However, these linear equations do not predict the absolute magnitude of the noise; instead, only the relative magnitude is predicted. Also, the predicted disturbances do not modify the mean flow, removing a physical mechanism by which the amplitude of the disturbance may be controlled. Recent research for isolated airfoils' indicates that this may not affect the solution greatly at low frequencies. The second method addresses some of the concerns raised by the LEE method. In this approach, called Very Large Eddy Simulation (VLES), the unsteady Reynolds averaged Navier-Stokes equations are solved directly using a high-accuracy computational aeroacoustics numerical scheme. With the addition of a two-equation turbulence model and the use of a relatively coarse grid, the numerical solution is effectively filtered into a directly calculated mean flow with the small-scale turbulence being modeled, and an unsteady large-scale component that is also being directly calculated. In this way, the unsteady disturbances are calculated in a nonlinear way, with a direct effect on the mean flow. This method is not as fast as the LEE approach, but does have many advantages to recommend it; however, like the LEE approach, only the effect of the largest unsteady structures will be captured. An initial calculation was performed on a supersonic jet exhaust plume, with promising results, but the calculation was hampered by the explicit time marching scheme that was employed. This explicit scheme required a very small time step to resolve the nozzle boundary layer, which caused a long run time. Current work is focused on testing a lower-order implicit time marching method to combat this problem.

Measurement, analysis and prediction of topical UV filter bioavailability.

PubMed

Roussel, L; Gilbert, E; Salmon, D; Serre, C; Gabard, B; Haftek, M; Maibach, H I; Pirot, F

2015-01-30

The aim of the present study was to objectively quantify and predict bioavailability of three sunscreen agents (i.e., benzophenone-3, 2-ethylhexylsalicylate, and 2 ethylhexyl-4-methoxycinnamate) in epidermis treated by petrolatum and emulsion-based formulations for 7 and 30min on four human volunteers. Profiles of sunscreen agents through stratum corneum (SC), derived from the assessment of chemical amounts in SC layers collected by successive adhesive tape-stripping, were successfully fitted to Fick's second law of diffusion. Therefore, permeability coefficients of sunscreen agents were found lower with petrolatum than with emulsion based formulations confirming the crucial role of vehicle in topical delivery. Furthermore, the robustness of that methodology was confirmed by the linear relationship between the chemical absorption measured after 30min and that predicted from the 7-min exposure experiment. Interestingly, in this dermatopharmacokinetic method, the deconvolution of permeability coefficients in their respective partition coefficients and absorption constants allowed a better understanding of vehicle effects upon topical bioavailability mechanisms and bioequivalence of skin products. Copyright © 2014 Elsevier B.V. All rights reserved.

High efficiency and broadband acoustic diodes

NASA Astrophysics Data System (ADS)

Fu, Congyi; Wang, Bohan; Zhao, Tianfei; Chen, C. Q.

2018-01-01

Energy transmission efficiency and working bandwidth are the two major factors limiting the application of current acoustic diodes (ADs). This letter presents a design of high efficiency and broadband acoustic diodes composed of a nonlinear frequency converter and a linear wave filter. The converter consists of two masses connected by a bilinear spring with asymmetric tension and compression stiffness. The wave filter is a linear mass-spring lattice (sonic crystal). Both numerical simulation and experiment show that the energy transmission efficiency of the acoustic diode can be improved by as much as two orders of magnitude, reaching about 61%. Moreover, the primary working band width of the AD is about two times of the cut-off frequency of the sonic crystal filter. The cut-off frequency dependent working band of the AD implies that the developed AD can be scaled up or down from macro-scale to micro- and nano-scale.

Decentralized Observer with a Consensus Filter for Distributed Discrete-Time Linear Systems

NASA Technical Reports Server (NTRS)

Acikmese, Behcet; Mandic, Milan

2011-01-01

This paper presents a decentralized observer with a consensus filter for the state observation of a discrete-time linear distributed systems. In this setup, each agent in the distributed system has an observer with a model of the plant that utilizes the set of locally available measurements, which may not make the full plant state detectable. This lack of detectability is overcome by utilizing a consensus filter that blends the state estimate of each agent with its neighbors' estimates. We assume that the communication graph is connected for all times as well as the sensing graph. It is proven that the state estimates of the proposed observer asymptotically converge to the actual plant states under arbitrarily changing, but connected, communication and sensing topologies. As a byproduct of this research, we also obtained a result on the location of eigenvalues, the spectrum, of the Laplacian for a family of graphs with self-loops.

Bessel smoothing filter for spectral-element mesh

NASA Astrophysics Data System (ADS)

Trinh, P. T.; Brossier, R.; Métivier, L.; Virieux, J.; Wellington, P.

2017-06-01

Smoothing filters are extremely important tools in seismic imaging and inversion, such as for traveltime tomography, migration and waveform inversion. For efficiency, and as they can be used a number of times during inversion, it is important that these filters can easily incorporate prior information on the geological structure of the investigated medium, through variable coherent lengths and orientation. In this study, we promote the use of the Bessel filter to achieve these purposes. Instead of considering the direct application of the filter, we demonstrate that we can rely on the equation associated with its inverse filter, which amounts to the solution of an elliptic partial differential equation. This enhances the efficiency of the filter application, and also its flexibility. We apply this strategy within a spectral-element-based elastic full waveform inversion framework. Taking advantage of this formulation, we apply the Bessel filter by solving the associated partial differential equation directly on the spectral-element mesh through the standard weak formulation. This avoids cumbersome projection operators between the spectral-element mesh and a regular Cartesian grid, or expensive explicit windowed convolution on the finite-element mesh, which is often used for applying smoothing operators. The associated linear system is solved efficiently through a parallel conjugate gradient algorithm, in which the matrix vector product is factorized and highly optimized with vectorized computation. Significant scaling behaviour is obtained when comparing this strategy with the explicit convolution method. The theoretical numerical complexity of this approach increases linearly with the coherent length, whereas a sublinear relationship is observed practically. Numerical illustrations are provided here for schematic examples, and for a more realistic elastic full waveform inversion gradient smoothing on the SEAM II benchmark model. These examples illustrate well the efficiency and flexibility of the approach proposed.

Power prediction in mobile communication systems using an optimal neural-network structure.

PubMed

Gao, X M; Gao, X Z; Tanskanen, J A; Ovaska, S J

1997-01-01

Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.