Adaptive spectral estimators for fast flow-profile detection.
Ricci, Stefano
2013-02-01
In multigate spectral Doppler (MSD) analysis, hundreds of small sample volumes (SVs) aligned along a pulse wave-line can be simultaneously investigated. The so-called spectral profile, reporting the scatterers' velocity distribution in a vessel, is obtained by estimating the frequency content of the echoes detected from each SV. The preferred frequency estimator is the Welch method, which is robust and fast, but requires an observation window (OW) of at least 64 to 128 samples to guarantee adequate spectral resolution. The blood amplitude and phase estimator (BAPES) and the blood iterative adaptive approach (BIAA) are alternative methods which were recently proven to be capable of producing good spectrograms from one SV using shorter OWs. This paper shows that BAPES and BIAA can be successfully applied to MSD estimations. The use of short OWs can be exploited to produce spectral profiles with high temporal resolution and/or to perform simultaneous investigations at multiple sites. Two in vivo examples of application are reported: in the first, the blood velocity distribution during the fast systolic acceleration in a carotid artery is detailed with high temporal resolution; in the second, four spectral profiles are simultaneously detected at different sites of the carotid bifurcation.
Power spectral estimation algorithms
NASA Technical Reports Server (NTRS)
Bhatia, Manjit S.
1989-01-01
Algorithms to estimate the power spectrum using Maximum Entropy Methods were developed. These algorithms were coded in FORTRAN 77 and were implemented on the VAX 780. The important considerations in this analysis are: (1) resolution, i.e., how close in frequency two spectral components can be spaced and still be identified; (2) dynamic range, i.e., how small a spectral peak can be, relative to the largest, and still be observed in the spectra; and (3) variance, i.e., how accurate the estimate of the spectra is to the actual spectra. The application of the algorithms based on Maximum Entropy Methods to a variety of data shows that these criteria are met quite well. Additional work in this direction would help confirm the findings. All of the software developed was turned over to the technical monitor. A copy of a typical program is included. Some of the actual data and graphs used on this data are also included.
NASA Technical Reports Server (NTRS)
Lai, Jonathan Y.
1994-01-01
This dissertation focuses on the signal processing problems associated with the detection of hazardous windshears using airborne Doppler radar when weak weather returns are in the presence of strong clutter returns. In light of the frequent inadequacy of spectral-processing oriented clutter suppression methods, we model a clutter signal as multiple sinusoids plus Gaussian noise, and propose adaptive filtering approaches that better capture the temporal characteristics of the signal process. This idea leads to two research topics in signal processing: (1) signal modeling and parameter estimation, and (2) adaptive filtering in this particular signal environment. A high-resolution, low SNR threshold maximum likelihood (ML) frequency estimation and signal modeling algorithm is devised and proves capable of delineating both the spectral and temporal nature of the clutter return. Furthermore, the Least Mean Square (LMS) -based adaptive filter's performance for the proposed signal model is investigated, and promising simulation results have testified to its potential for clutter rejection leading to more accurate estimation of windspeed thus obtaining a better assessment of the windshear hazard.
Adaptive mesh strategies for the spectral element method
NASA Technical Reports Server (NTRS)
Mavriplis, Catherine
1992-01-01
An adaptive spectral method was developed for the efficient solution of time dependent partial differential equations. Adaptive mesh strategies that include resolution refinement and coarsening by three different methods are illustrated on solutions to the 1-D viscous Burger equation and the 2-D Navier-Stokes equations for driven flow in a cavity. Sharp gradients, singularities, and regions of poor resolution are resolved optimally as they develop in time using error estimators which indicate the choice of refinement to be used. The adaptive formulation presents significant increases in efficiency, flexibility, and general capabilities for high order spectral methods.
On the Estimation of Photometric Spectral Types
NASA Astrophysics Data System (ADS)
Oblak, E.; Chareton, M.
1981-09-01
We have estimated a photometric spectral type based on indices of the uvbyβ photometry for the normal stars of the Hauck and Mermilliod (1975) compilation. In this sample 1563 stars have no MK spectral types for 440 stars it is difficult or impossible to estimate a spectral type from the photometry for 436 stars having an estimated photometric spectral type we have found an MK spectral type on the literature which allowed a comparative study. We give the absolute magnitudes for the MK and photometric spectral types.
A quadtree-adaptive spectral wave model
NASA Astrophysics Data System (ADS)
Popinet, Stéphane; Gorman, Richard M.; Rickard, Graham J.; Tolman, Hendrik L.
A spectral wave model coupling a quadtree-adaptive discretisation of the two spatial dimensions with a standard discretisation of the two spectral dimensions is described. The implementation is greatly simplified by reusing components of the Gerris solver (for spatial advection on quadtrees) and WAVEWATCH III (for spectral advection and source terms). Strict equivalence between the anisotropic diffusion and spatial filtering methods for alleviation of the Garden Sprinkler Effect (GSE) is demonstrated. This equivalence facilitates the generalisation of GSE alleviation techniques to quadtree grids. For the case of a cyclone-generated wave field, the cost of the adaptive method increases linearly with spatial resolution compared to quadratically for constant-resolution methods. This leads to decrease in runtimes of one to two orders of magnitude for practical spatial resolutions. Similar efficiency gains are shown to be possible for global spectral wave forecasting.
Adaptation to spectrally-rotated speech.
Green, Tim; Rosen, Stuart; Faulkner, Andrew; Paterson, Ruth
2013-08-01
Much recent interest surrounds listeners' abilities to adapt to various transformations that distort speech. An extreme example is spectral rotation, in which the spectrum of low-pass filtered speech is inverted around a center frequency (2 kHz here). Spectral shape and its dynamics are completely altered, rendering speech virtually unintelligible initially. However, intonation, rhythm, and contrasts in periodicity and aperiodicity are largely unaffected. Four normal hearing adults underwent 6 h of training with spectrally-rotated speech using Continuous Discourse Tracking. They and an untrained control group completed pre- and post-training speech perception tests, for which talkers differed from the training talker. Significantly improved recognition of spectrally-rotated sentences was observed for trained, but not untrained, participants. However, there were no significant improvements in the identification of medial vowels in /bVd/ syllables or intervocalic consonants. Additional tests were performed with speech materials manipulated so as to isolate the contribution of various speech features. These showed that preserving intonational contrasts did not contribute to the comprehension of spectrally-rotated speech after training, and suggested that improvements involved adaptation to altered spectral shape and dynamics, rather than just learning to focus on speech features relatively unaffected by the transformation.
SAR imaging via modern 2-D spectral estimation methods.
DeGraaf, S R
1998-01-01
This paper discusses the use of modern 2D spectral estimation algorithms for synthetic aperture radar (SAR) imaging. The motivation for applying power spectrum estimation methods to SAR imaging is to improve resolution, remove sidelobe artifacts, and reduce speckle compared to what is possible with conventional Fourier transform SAR imaging techniques. This paper makes two principal contributions to the field of adaptive SAR imaging. First, it is a comprehensive comparison of 2D spectral estimation methods for SAR imaging. It provides a synopsis of the algorithms available, discusses their relative merits for SAR imaging, and illustrates their performance on simulated and collected SAR imagery. Some of the algorithms presented or their derivations are new, as are some of the insights into or analyses of the algorithms. Second, this work develops multichannel variants of four related algorithms, minimum variance method (MVM), reduced-rank MVM (RRMVM), adaptive sidelobe reduction (ASR) and space variant apodization (SVA) to estimate both reflectivity intensity and interferometric height from polarimetric displaced-aperture interferometric data. All of these interferometric variants are new. In the interferometric contest, adaptive spectral estimation can improve the height estimates through a combination of adaptive nulling and averaging. Examples illustrate that MVM, ASR, and SVA offer significant advantages over Fourier methods for estimating both scattering intensity and interferometric height, and allow empirical comparison of the accuracies of Fourier, MVM, ASR, and SVA interferometric height estimates.
Spectral procedures for estimating crop biomass
Wanjura, D.F.; Hatfield, J.L.
1985-05-01
Spectral reflectance was measured semi-weekly and used to estimate leaf area and plant dry weight accumulation in cotton, soybeans, and sunflower. Integration of spectral crop growth cycle curves explained up to 95 and 91%, respectively, of the variation in cotton lint yield and dry weight. A theoretical relationship for dry weight accumulation, in which only intercepted radiation or intercepted radiation and solar energy to biomass conversion efficiency were spectrally estimated, explained 99 and 96%, respectively, of the observed plant dry weight variation of the three crops. These results demonstrate the feasibility of predicting crop biomass from spectral measurements collected frequently during the growing season. 15 references.
Adaptable Multivariate Calibration Models for Spectral Applications
THOMAS,EDWARD V.
1999-12-20
Multivariate calibration techniques have been used in a wide variety of spectroscopic situations. In many of these situations spectral variation can be partitioned into meaningful classes. For example, suppose that multiple spectra are obtained from each of a number of different objects wherein the level of the analyte of interest varies within each object over time. In such situations the total spectral variation observed across all measurements has two distinct general sources of variation: intra-object and inter-object. One might want to develop a global multivariate calibration model that predicts the analyte of interest accurately both within and across objects, including new objects not involved in developing the calibration model. However, this goal might be hard to realize if the inter-object spectral variation is complex and difficult to model. If the intra-object spectral variation is consistent across objects, an effective alternative approach might be to develop a generic intra-object model that can be adapted to each object separately. This paper contains recommendations for experimental protocols and data analysis in such situations. The approach is illustrated with an example involving the noninvasive measurement of glucose using near-infrared reflectance spectroscopy. Extensions to calibration maintenance and calibration transfer are discussed.
Spectral moment estimation in MST radars
NASA Technical Reports Server (NTRS)
Woodman, R. F.
1983-01-01
Signal processing techniques used in Mesosphere-Stratosphere-Troposphere (MST) radars are reviewed. Techniques which produce good estimates of the total power, frequency shift, and spectral width of the radar power spectra are considered. Non-linear curve fitting, autocovariance, autocorrelation, covariance, and maximum likelihood estimators are discussed.
Rank-based camera spectral sensitivity estimation.
Finlayson, Graham; Darrodi, Maryam Mohammadzadeh; Mackiewicz, Michal
2016-04-01
In order to accurately predict a digital camera response to spectral stimuli, the spectral sensitivity functions of its sensor need to be known. These functions can be determined by direct measurement in the lab-a difficult and lengthy procedure-or through simple statistical inference. Statistical inference methods are based on the observation that when a camera responds linearly to spectral stimuli, the device spectral sensitivities are linearly related to the camera rgb response values, and so can be found through regression. However, for rendered images, such as the JPEG images taken by a mobile phone, this assumption of linearity is violated. Even small departures from linearity can negatively impact the accuracy of the recovered spectral sensitivities, when a regression method is used. In our work, we develop a novel camera spectral sensitivity estimation technique that can recover the linear device spectral sensitivities from linear images and the effective linear sensitivities from rendered images. According to our method, the rank order of a pair of responses imposes a constraint on the shape of the underlying spectral sensitivity curve (of the sensor). Technically, each rank-pair splits the space where the underlying sensor might lie in two parts (a feasible region and an infeasible region). By intersecting the feasible regions from all the ranked-pairs, we can find a feasible region of sensor space. Experiments demonstrate that using rank orders delivers equal estimation to the prior art. However, the Rank-based method delivers a step-change in estimation performance when the data is not linear and, for the first time, allows for the estimation of the effective sensitivities of devices that may not even have "raw mode." Experiments validate our method. PMID:27140768
Rank-based camera spectral sensitivity estimation.
Finlayson, Graham; Darrodi, Maryam Mohammadzadeh; Mackiewicz, Michal
2016-04-01
In order to accurately predict a digital camera response to spectral stimuli, the spectral sensitivity functions of its sensor need to be known. These functions can be determined by direct measurement in the lab-a difficult and lengthy procedure-or through simple statistical inference. Statistical inference methods are based on the observation that when a camera responds linearly to spectral stimuli, the device spectral sensitivities are linearly related to the camera rgb response values, and so can be found through regression. However, for rendered images, such as the JPEG images taken by a mobile phone, this assumption of linearity is violated. Even small departures from linearity can negatively impact the accuracy of the recovered spectral sensitivities, when a regression method is used. In our work, we develop a novel camera spectral sensitivity estimation technique that can recover the linear device spectral sensitivities from linear images and the effective linear sensitivities from rendered images. According to our method, the rank order of a pair of responses imposes a constraint on the shape of the underlying spectral sensitivity curve (of the sensor). Technically, each rank-pair splits the space where the underlying sensor might lie in two parts (a feasible region and an infeasible region). By intersecting the feasible regions from all the ranked-pairs, we can find a feasible region of sensor space. Experiments demonstrate that using rank orders delivers equal estimation to the prior art. However, the Rank-based method delivers a step-change in estimation performance when the data is not linear and, for the first time, allows for the estimation of the effective sensitivities of devices that may not even have "raw mode." Experiments validate our method.
Simultaneous Spectral Temporal Adaptive Raman Spectrometer - SSTARS
NASA Technical Reports Server (NTRS)
Blacksberg, Jordana
2010-01-01
Raman spectroscopy is a prime candidate for the next generation of planetary instruments, as it addresses the primary goal of mineralogical analysis, which is structure and composition. However, large fluorescence return from many mineral samples under visible light excitation can render Raman spectra unattainable. Using the described approach, Raman and fluorescence, which occur on different time scales, can be simultaneously obtained from mineral samples using a compact instrument in a planetary environment. This new approach is taken based on the use of time-resolved spectroscopy for removing the fluorescence background from Raman spectra in the laboratory. In the SSTARS instrument, a visible excitation source (a green, pulsed laser) is used to generate Raman and fluorescence signals in a mineral sample. A spectral notch filter eliminates the directly reflected beam. A grating then disperses the signal spectrally, and a streak camera provides temporal resolution. The output of the streak camera is imaged on the CCD (charge-coupled device), and the data are read out electronically. By adjusting the sweep speed of the streak camera, anywhere from picoseconds to milliseconds, it is possible to resolve Raman spectra from numerous fluorescence spectra in the same sample. The key features of SSTARS include a compact streak tube capable of picosecond time resolution for collection of simultaneous spectral and temporal information, adaptive streak tube electronics that can rapidly change from one sweep rate to another over ranges of picoseconds to milliseconds, enabling collection of both Raman and fluorescence signatures versus time and wavelength, and Synchroscan integration that allows for a compact, low-power laser without compromising ultimate sensitivity.
Optimized spectral estimation for nonlinear synchronizing systems
NASA Astrophysics Data System (ADS)
Sommerlade, Linda; Mader, Malenka; Mader, Wolfgang; Timmer, Jens; Thiel, Marco; Grebogi, Celso; Schelter, Björn
2014-03-01
In many fields of research nonlinear dynamical systems are investigated. When more than one process is measured, besides the distinct properties of the individual processes, their interactions are of interest. Often linear methods such as coherence are used for the analysis. The estimation of coherence can lead to false conclusions when applied without fulfilling several key assumptions. We introduce a data driven method to optimize the choice of the parameters for spectral estimation. Its applicability is demonstrated based on analytical calculations and exemplified in a simulation study. We complete our investigation with an application to nonlinear tremor signals in Parkinson's disease. In particular, we analyze electroencephalogram and electromyogram data.
Perceptual Adaptation of Voice Gender Discrimination with Spectrally Shifted Vowels
ERIC Educational Resources Information Center
Li, Tianhao; Fu, Qian-Jie
2011-01-01
Purpose: To determine whether perceptual adaptation improves voice gender discrimination of spectrally shifted vowels and, if so, which acoustic cues contribute to the improvement. Method: Voice gender discrimination was measured for 10 normal-hearing subjects, during 5 days of adaptation to spectrally shifted vowels, produced by processing the…
Estimator reduction and convergence of adaptive BEM.
Aurada, Markus; Ferraz-Leite, Samuel; Praetorius, Dirk
2012-06-01
A posteriori error estimation and related adaptive mesh-refining algorithms have themselves proven to be powerful tools in nowadays scientific computing. Contrary to adaptive finite element methods, convergence of adaptive boundary element schemes is, however, widely open. We propose a relaxed notion of convergence of adaptive boundary element schemes. Instead of asking for convergence of the error to zero, we only aim to prove estimator convergence in the sense that the adaptive algorithm drives the underlying error estimator to zero. We observe that certain error estimators satisfy an estimator reduction property which is sufficient for estimator convergence. The elementary analysis is only based on Dörfler marking and inverse estimates, but not on reliability and efficiency of the error estimator at hand. In particular, our approach gives a first mathematical justification for the proposed steering of anisotropic mesh-refinements, which is mandatory for optimal convergence behavior in 3D boundary element computations.
Estimator reduction and convergence of adaptive BEM
Aurada, Markus; Ferraz-Leite, Samuel; Praetorius, Dirk
2012-01-01
A posteriori error estimation and related adaptive mesh-refining algorithms have themselves proven to be powerful tools in nowadays scientific computing. Contrary to adaptive finite element methods, convergence of adaptive boundary element schemes is, however, widely open. We propose a relaxed notion of convergence of adaptive boundary element schemes. Instead of asking for convergence of the error to zero, we only aim to prove estimator convergence in the sense that the adaptive algorithm drives the underlying error estimator to zero. We observe that certain error estimators satisfy an estimator reduction property which is sufficient for estimator convergence. The elementary analysis is only based on Dörfler marking and inverse estimates, but not on reliability and efficiency of the error estimator at hand. In particular, our approach gives a first mathematical justification for the proposed steering of anisotropic mesh-refinements, which is mandatory for optimal convergence behavior in 3D boundary element computations. PMID:23482248
Adaptive vehicle motion estimation and prediction
NASA Astrophysics Data System (ADS)
Zhao, Liang; Thorpe, Chuck E.
1999-01-01
Accurate motion estimation and reliable maneuver prediction enable an automated car to react quickly and correctly to the rapid maneuvers of the other vehicles, and so allow safe and efficient navigation. In this paper, we present a car tracking system which provides motion estimation, maneuver prediction and detection of the tracked car. The three strategies employed - adaptive motion modeling, adaptive data sampling, and adaptive model switching probabilities - result in an adaptive interacting multiple model algorithm (AIMM). The experimental results on simulated and real data demonstrate that our tracking system is reliable, flexible, and robust. The adaptive tracking makes the system intelligent and useful in various autonomous driving tasks.
Spectral abundance fraction estimation of materials using Kalman filters
NASA Astrophysics Data System (ADS)
Wang, Su; Chang, Chein; Jensen, Janet L.; Jensen, James O.
2004-12-01
Kalman filter has been widely used in statistical signal processing for parameter estimation. Although a Kalman filter approach has been recently developed for spectral unmixing, referred to as Kalman filter-based linear unmixing (KFLU), its applicability to spectral characterization within a single pixel vector has not been explored. This paper presents a new application of Kalman filtering in spectral estimation and quantification. It develops a Kalman filter-based spectral signature esimator (KFSSE) which is different from the KFLU in the sense that the former performs a Kalman filter wavelength by wavelength across a spectral signature as opposed to the latter which implements a Kalman filter pixel vector by pixel vector in an image cube. The idea of the KFSSE is to implement the state equation to characterize the true spectral signature, while the measurement equation is being used to describe the spectral signature to be processed. Additionally, since a Kalman filter can accurately estimate spectral abundance fraction of a signature, our proposed KFSSE can further used for spectral quantification for subpixel targets and mixed pixel vectors, called Kalman filter-based spectral quantifier (KFSQ). Such spectral quantification is particularly important for chemical/biological defense which requires quantification of detected agents for damage control assessment. Several different types of hyperspectral data are used for experiments to demonstrate the ability of the KFSSE in estimation of spectral signature and the utility of the KFSQ in spectral quantification.
Some Reliability Estimates for Computerized Adaptive Tests.
ERIC Educational Resources Information Center
Nicewander, W. Alan; Thomasson, Gary L.
1999-01-01
Derives three reliability estimates for the Bayes modal estimate (BME) and the maximum-likelihood estimate (MLE) of theta in computerized adaptive tests (CATs). Computes the three reliability estimates and the true reliabilities of both BME and MLE for seven simulated CATs. Results show the true reliabilities for BME and MLE to be nearly identical…
Applications of adaptive state estimation theory
NASA Technical Reports Server (NTRS)
Moose, R. L.; Vanlandingham, H. F.; Mccabe, D. H.
1980-01-01
Two main areas of application of adaptive state estimation theory are presented. Following a review of the basic estimation approach, its application to both the control of nonlinear plants and to the problem of tracking maneuvering targets is presented. Results are brought together from these two areas of investigation to provide insight into the wide range of possible applications of the general estimation method.
NASA Astrophysics Data System (ADS)
Goldstein, Neil; Fox, Marsha; Adler-Golden, Steven; Gregor, Brian
2013-03-01
Field test results are presented for a prototype long-wave adaptive imager that provides both hyperspectral imagery and contrast imagery based on the direct application of hyperspectral detection algorithms in hardware. Programmable spatial light modulators are used to provide both spectral and spatial resolution using a single element detector. Programmable spectral and spatial detection filters can be used to superimpose any possible analog spectral detection filter on the image. In this work, we demonstrate three modes of operation, including hyperspectral imagery, and one and two-dimensional imagery using a generalized matched filter for detection of a specific target gas within the scene.
Adaptive density estimator for galaxy surveys
NASA Astrophysics Data System (ADS)
Saar, Enn
2016-10-01
Galaxy number or luminosity density serves as a basis for many structure classification algorithms. Several methods are used to estimate this density. Among them kernel methods have probably the best statistical properties and allow also to estimate the local sample errors of the estimate. We introduce a kernel density estimator with an adaptive data-driven anisotropic kernel, describe its properties and demonstrate the wealth of additional information it gives us about the local properties of the galaxy distribution.
Murakami, Yuri; Ietomi, Kunihiko; Yamaguchi, Masahiro; Ohyama, Nagaaki
2007-10-01
Accurate color image reproduction under arbitrary illumination can be realized if the spectral reflectance functions in a scene are obtained. Although multispectral imaging is one of the promising methods to obtain the reflectance of a scene, it is expected to reduce the number of color channels without significant loss of accuracy. This paper presents what we believe to be a new method for estimating spectral reflectance functions from color image and multipoint spectral measurements based on maximum a posteriori (MAP) estimation. Multipoint spectral measurements are utilized as auxiliary information to improve the accuracy of spectral reflectance estimated from image data. Through simulations, it is confirmed that the proposed method improves the estimation accuracy, particularly when a scene includes subjects that belong to various categories.
An adaptive pseudo-spectral method for reaction diffusion problems
NASA Technical Reports Server (NTRS)
Bayliss, A.; Matkowsky, B. J.; Gottlieb, D.; Minkoff, M.
1989-01-01
The spectral interpolation error was considered for both the Chebyshev pseudo-spectral and Galerkin approximations. A family of functionals I sub r (u), with the property that the maximum norm of the error is bounded by I sub r (u)/J sub r, where r is an integer and J is the degree of the polynomial approximation, was developed. These functionals are used in the adaptive procedure whereby the problem is dynamically transformed to minimize I sub r (u). The number of collocation points is then chosen to maintain a prescribed error bound. The method is illustrated by various examples from combustion problems in one and two dimensions.
An adaptive pseudo-spectral method for reaction diffusion problems
NASA Technical Reports Server (NTRS)
Bayliss, A.; Gottlieb, D.; Matkowsky, B. J.; Minkoff, M.
1987-01-01
The spectral interpolation error was considered for both the Chebyshev pseudo-spectral and Galerkin approximations. A family of functionals I sub r (u), with the property that the maximum norm of the error is bounded by I sub r (u)/J sub r, where r is an integer and J is the degree of the polynomial approximation, was developed. These functionals are used in the adaptive procedure whereby the problem is dynamically transformed to minimize I sub r (u). The number of collocation points is then chosen to maintain a prescribed error bound. The method is illustrated by various examples from combustion problems in one and two dimensions.
NASA Astrophysics Data System (ADS)
Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang
2016-02-01
Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses
Estimation of agronomic variables using spectral signatures
NASA Technical Reports Server (NTRS)
Goel, N. S.; Thompson, R. L.
1984-01-01
Techniques for the determination of leaf area index or leaf angle distribution from remote-sensing canopy-reflectance (CR) measurements are developed on the basis of empirical models relating CR to parameters such as soil and vegetation spectral properties, solar flux, and viewing angle. A general procedure for inverting CR models is presented and applied to the models of Suits (1972), Verhoef and Bunnik (1981), and Norman (1979) in the IR range. Numerical results for a soybean canopy are compared in a table, and the error sensitivity of the inverted models is shown to be relatively high, requiring the use of ancillary data such as soil reflectance, leaf reflectance, and leaf transmittance.
Xu, Haojie; Lu, Yunfeng; Zhu, Shanan
2014-01-01
It is of significance to assess the dynamic spectral causality among physiological signals. Several practical estimators adapted from spectral Granger causality have been exploited to track dynamic causality based on the framework of time-varying multivariate autoregressive (tvMVAR) models. The non-zero covariance of the model’s residuals has been used to describe the instantaneous effect phenomenon in some causality estimators. However, for the situations with Gaussian residuals in some autoregressive models, it is challenging to distinguish the directed instantaneous causality if the sufficient prior information about the “causal ordering” is missing. Here, we propose a new algorithm to assess the time-varying causal ordering of tvMVAR model under the assumption that the signals follow the same acyclic causal ordering for all time lags and to estimate the instantaneous effect factor (IEF) value in order to track the dynamic directed instantaneous connectivity. The time-lagged adaptive directed transfer function (ADTF) is also estimated to assess the lagged causality after removing the instantaneous effect. In the present study, we firstly investigated the performance of the causal-ordering estimation algorithm and the accuracy of IEF value. Then, we presented the results of IEF and time-lagged ADTF method by comparing with the conventional ADTF method through simulations of various propagation models. Statistical analysis results suggest that the new algorithm could accurately estimate the causal ordering and give a good estimation of the IEF values in the Gaussian residual conditions. Meanwhile, the time-lagged ADTF approach is also more accurate in estimating the time-lagged dynamic interactions in a complex nervous system after extracting the instantaneous effect. In addition to the simulation studies, we applied the proposed method to estimate the dynamic spectral causality on real visual evoked potential (VEP) data in a human subject. Its usefulness in
Spectral reflectance estimation using a six-color scanner
NASA Astrophysics Data System (ADS)
Tominaga, Shoji; Kohno, Satoshi; Kakinuma, Hirokazu; Nohara, Fuminori; Horiuchi, Takahiko
2009-01-01
A method is proposed for estimating the spectral reflectance function of an object surface by using a six-color scanner. The scanner is regarded as a six-band spectral imaging system, since it captures six color channels in total from two separate scans using two difference lamps. First, we describe the basic characteristics of the imaging systems for a HP color scanner and a multiband camera used for comparison. Second, we describe a computational method for recovering surface-spectral reflectances from the noisy sensor outputs. A LMMSE estimator is presented as an optimal estimator. We discuss the reflectance estimation for non-flat surfaces with shading effect. A solution method is presented for the reliable reflectance estimation. Finally, the performance of the proposed method is examined in detail on experiments using the Macbeth Color Checker and non-flat objects.
A two-dimensional adaptive spectral element method for the direct simulation of incompressible flow
NASA Astrophysics Data System (ADS)
Hsu, Li-Chieh
The spectral element method is a high order discretization scheme for the solution of nonlinear partial differential equations. The method draws its strengths from the finite element method for geometrical flexibility and spectral methods for high accuracy. Although the method is, in theory, very powerful for complex phenomena such as transitional flows, its practical implementation is limited by the arbitrary choice of domain discretization. For instance, it is hard to estimate the appropriate number of elements for a specific case. Selection of regions to be refined or coarsened is difficult especially as the flow becomes more complex and memory limits of the computer are stressed. We present an adaptive spectral element method in which the grid is automatically refined or coarsened in order to capture underresolved regions of the domain and to follow regions requiring high resolution as they develop in time. The objective is to provide the best and most efficient solution to a time-dependent nonlinear problem by continually optimizing resource allocation. The adaptivity is based on an error estimator which determines which regions need more resolution. The solution strategy is as follows: compute an initial solution with a suitable initial mesh, estimate errors in the solution locally in each element, modify the mesh according to the error estimators, interpolate old mesh solutions onto the new elements, and resume the numerical solution process. A two-dimensional adaptive spectral element method for the direct simulation of incompressible flows has been developed. The adaptive algorithm effectively diagnoses and refines regions of the flow where complexity of the solution requires increased resolution. The method has been demonstrated on two-dimensional examples in heat conduction, Stokes and Navier-Stokes flows.
Adaptive compressed sensing for spectral-domain optical coherence tomography
NASA Astrophysics Data System (ADS)
Wang, Yi; Chen, Xiaodong; Wang, Ting; Li, Hongxiao; Yu, Daoyin
2014-03-01
Spectral-domain optical coherence tomography (SD-OCT) is a non-contact and non-invasive method for measuring the change of biological tissues caused by pathological changes of body. CCD with huge number of pixels is usually used in SD-OCT to increase the detecting depth, thus enhancing the hardness of data transmission and storage. The usage of compressed sensing (CS) in SD-OCT is able to reduce the trouble of large data transfer and storage, thus eliminating the complexity of processing system. The traditional CS uses the same sampling model for SD-OCT images of different tissue, leading to reconstruction images with different quality. We proposed a CS with adaptive sampling model. The new model is based on uniform sampling model, and the interference spectral of SD-OCT is considered to adjust the local sampling ratio. Compared with traditional CS, adaptive CS can modify the sampling model for images of different tissue according to different interference spectral, getting reconstruction images with high quality without changing sampling model.
Accurate Biomass Estimation via Bayesian Adaptive Sampling
NASA Technical Reports Server (NTRS)
Wheeler, Kevin R.; Knuth, Kevin H.; Castle, Joseph P.; Lvov, Nikolay
2005-01-01
The following concepts were introduced: a) Bayesian adaptive sampling for solving biomass estimation; b) Characterization of MISR Rahman model parameters conditioned upon MODIS landcover. c) Rigorous non-parametric Bayesian approach to analytic mixture model determination. d) Unique U.S. asset for science product validation and verification.
A parametric estimation approach to instantaneous spectral imaging.
Oktem, Figen S; Kamalabadi, Farzad; Davila, Joseph M
2014-12-01
Spectral imaging, the simultaneous imaging and spectroscopy of a radiating scene, is a fundamental diagnostic technique in the physical sciences with widespread application. Due to the intrinsic limitation of two-dimensional (2D) detectors in capturing inherently three-dimensional (3D) data, spectral imaging techniques conventionally rely on a spatial or spectral scanning process, which renders them unsuitable for dynamic scenes. In this paper, we present a nonscanning (instantaneous) spectral imaging technique that estimates the physical parameters of interest by combining measurements with a parametric model and solving the resultant inverse problem computationally. The associated inverse problem, which can be viewed as a multiframe semiblind deblurring problem (with shift-variant blur), is formulated as a maximum a posteriori (MAP) estimation problem since in many such experiments prior statistical knowledge of the physical parameters can be well estimated. Subsequently, an efficient dynamic programming algorithm is developed to find the global optimum of the nonconvex MAP problem. Finally, the algorithm and the effectiveness of the spectral imaging technique are illustrated for an application in solar spectral imaging. Numerical simulation results indicate that the physical parameters can be estimated with the same order of accuracy as state-of-the-art slit spectroscopy but with the added benefit of an instantaneous, 2D field-of-view. This technique will be particularly useful for studying the spectra of dynamic scenes encountered in space remote sensing. PMID:25347878
Adaptive link selection algorithms for distributed estimation
NASA Astrophysics Data System (ADS)
Xu, Songcen; de Lamare, Rodrigo C.; Poor, H. Vincent
2015-12-01
This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search-based least mean squares (LMS) / recursive least squares (RLS) link selection algorithms and sparsity-inspired LMS / RLS link selection algorithms that can exploit the topology of networks with poor-quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady-state, and tracking performance and computational complexity. In comparison with the existing centralized or distributed estimation strategies, the key features of the proposed algorithms are as follows: (1) more accurate estimates and faster convergence speed can be obtained and (2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.
Comparison of spectral estimators for characterizing fractionated atrial electrograms
2013-01-01
Background Complex fractionated atrial electrograms (CFAE) acquired during atrial fibrillation (AF) are commonly assessed using the discrete Fourier transform (DFT), but this can lead to inaccuracy. In this study, spectral estimators derived by averaging the autocorrelation function at lags were compared to the DFT. Method Bipolar CFAE of at least 16 s duration were obtained from pulmonary vein ostia and left atrial free wall sites (9 paroxysmal and 10 persistent AF patients). Power spectra were computed using the DFT and three other methods: 1. a novel spectral estimator based on signal averaging (NSE), 2. the NSE with harmonic removal (NSH), and 3. the autocorrelation function average at lags (AFA). Three spectral parameters were calculated: 1. the largest fundamental spectral peak, known as the dominant frequency (DF), 2. the DF amplitude (DA), and 3. the mean spectral profile (MP), which quantifies noise floor level. For each spectral estimator and parameter, the significance of the difference between paroxysmal and persistent AF was determined. Results For all estimators, mean DA and mean DF values were higher in persistent AF, while the mean MP value was higher in paroxysmal AF. The differences in means between paroxysmals and persistents were highly significant for 3/3 NSE and NSH measurements and for 2/3 DFT and AFA measurements (p<0.001). For all estimators, the standard deviation in DA and MP values were higher in persistent AF, while the standard deviation in DF value was higher in paroxysmal AF. Differences in standard deviations between paroxysmals and persistents were highly significant in 2/3 NSE and NSH measurements, in 1/3 AFA measurements, and in 0/3 DFT measurements. Conclusions Measurements made from all four spectral estimators were in agreement as to whether the means and standard deviations in three spectral parameters were greater in CFAEs acquired from paroxysmal or in persistent AF patients. Since the measurements were consistent, use of
Spectral estimates of solar radiation intercepted by corn canopies
NASA Technical Reports Server (NTRS)
Bauer, M. E. (Principal Investigator); Daughtry, C. S. T.; Gallo, K. P.
1982-01-01
Reflectance factor data were acquired with a Landsat band radiometer throughout two growing seasons for corn (Zea mays L.) canopies differing in planting dates, populations, and soil types. Agronomic data collected included leaf area index (LAI), biomass, development stage, and final grain yields. The spectral variable, greenness, was associated with 78 percent of the variation in LAI over all treatments. Single observations of LAI or greenness have limited value in predicting corn yields. The proportions of solar radiation intercepted (SRI) by these canopies were estimated using either measured LAI or greenness. Both SRI estimates, when accumulated over the growing season, accounted for approximately 65 percent of the variation in yields. Models which simulated the daily effects of weather and intercepted solar radiation on growth had the highest correlations to grain yields. This concept of estimating intercepted solar radiation using spectral data represents a viable approach for merging spectral and meteorological data for crop yield models.
Adaptive Filter-bank Approach to Restoration and Spectral Analysis of Gapped Data
NASA Astrophysics Data System (ADS)
Stoica, Petre; Larsson, Erik G.; Li, Jian
2000-10-01
The main topic of this paper is the nonparametric estimation of complex (both amplitude and phase) spectra from gapped data, as well as the restoration of such data. The focus is on the extension of the APES (amplitude and phase estimation) approach to data sequences with gaps. APES, which is one of the most successful existing nonparametric approaches to the spectral analysis of full data sequences, uses a bank of narrowband adaptive (both frequency and data dependent) filters to estimate the spectrum. A recent interpretation of this approach showed that the filterbank used by APES and the resulting spectrum minimize a least-squares (LS) fitting criterion between the filtered sequence and its spectral decomposition. The extended approach, which is called GAPES for somewhat obvious reasons, capitalizes on the aforementioned interpretation: it minimizes the APES-LS fitting criterion with respect to the missing data as well. This should be a sensible thing to do whenever the full data sequence is stationary, and hence the missing data have the same spectral content as the available data. We use both simulated and real data examples to show that GAPES estimated spectra and interpolated data sequences have excellent accuracy. We also show the performance gain achieved by GAPES over two of the most commonly used approaches for gapped-data spectral analysis, viz., the periodogram and the parametric CLEAN method. This work was partly supported by the Swedish Foundation for Strategic Research.
Parameter adaptive estimation of random processes
NASA Technical Reports Server (NTRS)
Caglayan, A. K.; Vanlandingham, H. F.
1975-01-01
This paper is concerned with the parameter adaptive least squares estimation of random processes. The main result is a general representation theorem for the conditional expectation of a random variable on a product probability space. Using this theorem along with the general likelihood ratio expression, the least squares estimate of the process is found in terms of the parameter conditioned estimates. The stochastic differential for the a posteriori probability and the stochastic differential equation for the a posteriori density are found by using simple stochastic calculus on the representations obtained. The results are specialized to the case when the parameter has a discrete distribution. The results can be used to construct an implementable recursive estimator for certain types of nonlinear filtering problems. This is illustrated by some simple examples.
A Spectral Adaptive Mesh Refinement Method for the Burgers equation
NASA Astrophysics Data System (ADS)
Nasr Azadani, Leila; Staples, Anne
2013-03-01
Adaptive mesh refinement (AMR) is a powerful technique in computational fluid dynamics (CFD). Many CFD problems have a wide range of scales which vary with time and space. In order to resolve all the scales numerically, high grid resolutions are required. The smaller the scales the higher the resolutions should be. However, small scales are usually formed in a small portion of the domain or in a special period of time. AMR is an efficient method to solve these types of problems, allowing high grid resolutions where and when they are needed and minimizing memory and CPU time. Here we formulate a spectral version of AMR in order to accelerate simulations of a 1D model for isotropic homogenous turbulence, the Burgers equation, as a first test of this method. Using pseudo spectral methods, we applied AMR in Fourier space. The spectral AMR (SAMR) method we present here is applied to the Burgers equation and the results are compared with the results obtained using standard solution methods performed using a fine mesh.
Optimal estimation of spectral reflectance based on metamerism
NASA Astrophysics Data System (ADS)
Chou, Tzren-Ru; Lin, Wei-Ju
2012-01-01
In this paper, we proposed an accurate estimation method for spectral reflectance of objects captured in an image. The spectral reflectance is simply modeled by a linear combination of three basic spectrums of R, G, and B colors respectively, named as spectral reflective bases of objects, which are acquired by solving a linear system based on the principle of color metamerism. Some experiments were performed to evaluate the accuracy of the estimated spectral reflectance of objects. The average mean square error of 24 colors in Macbeth checker between we simulated and the measured is 0.0866, and the maximum is 0.310. In addition, the average color difference of the 24 colors is less than 1.5 under the D65 illuminant. There are 13 colors having their color difference values less than 1, and other 8 colors having the values during the range of 1 and 2. Only three colors are relatively larger, with the differences of 2.558, 4.130 and 2.569, from the colors of No. 2, No. 13, and No. 18 in Macbeth checker respectively. Furthermore, the computational cost of this spectral estimation is very low and suitable for many practical applications in real time.
[Optimized Spectral Indices Based Estimation of Forage Grass Biomass].
An, Hai-bo; Li, Fei; Zhao, Meng-li; Liu, Ya-jun
2015-11-01
As an important indicator of forage production, aboveground biomass will directly illustrate the growth of forage grass. Therefore, Real-time monitoring biomass of forage grass play a crucial role in performing suitable grazing and management in artificial and natural grassland. However, traditional sampling and measuring are time-consuming and labor-intensive. Recently, development of hyperspectral remote sensing provides the feasibility in timely and nondestructive deriving biomass of forage grass. In the present study, the main objectives were to explore the robustness of published and optimized spectral indices in estimating biomass of forage grass in natural and artificial pasture. The natural pasture with four grazing density (control, light grazing, moderate grazing and high grazing) was designed in desert steppe, and different forage cultivars with different N rate were conducted in artificial forage fields in Inner Mongolia. The canopy reflectance and biomass in each plot were measured during critical stages. The result showed that, due to the influence in canopy structure and biomass, the canopy reflectance have a great difference in different type of forage grass. The best performing spectral index varied in different species of forage grass with different treatments (R² = 0.00-0.69). The predictive ability of spectral indices decreased under low biomass of desert steppe, while red band based spectral indices lost sensitivity under moderate-high biomass of forage maize. When band combinations of simple ratio and normalized difference spectral indices were optimized in combined datasets of natural and artificial grassland, optimized spectral indices significant increased predictive ability and the model between biomass and optimized spectral indices had the highest R² (R² = 0.72) compared to published spectral indices. Sensitive analysis further confirmed that the optimized index had the lowest noise equivalent and were the best performing index in
[Optimized Spectral Indices Based Estimation of Forage Grass Biomass].
An, Hai-bo; Li, Fei; Zhao, Meng-li; Liu, Ya-jun
2015-11-01
As an important indicator of forage production, aboveground biomass will directly illustrate the growth of forage grass. Therefore, Real-time monitoring biomass of forage grass play a crucial role in performing suitable grazing and management in artificial and natural grassland. However, traditional sampling and measuring are time-consuming and labor-intensive. Recently, development of hyperspectral remote sensing provides the feasibility in timely and nondestructive deriving biomass of forage grass. In the present study, the main objectives were to explore the robustness of published and optimized spectral indices in estimating biomass of forage grass in natural and artificial pasture. The natural pasture with four grazing density (control, light grazing, moderate grazing and high grazing) was designed in desert steppe, and different forage cultivars with different N rate were conducted in artificial forage fields in Inner Mongolia. The canopy reflectance and biomass in each plot were measured during critical stages. The result showed that, due to the influence in canopy structure and biomass, the canopy reflectance have a great difference in different type of forage grass. The best performing spectral index varied in different species of forage grass with different treatments (R² = 0.00-0.69). The predictive ability of spectral indices decreased under low biomass of desert steppe, while red band based spectral indices lost sensitivity under moderate-high biomass of forage maize. When band combinations of simple ratio and normalized difference spectral indices were optimized in combined datasets of natural and artificial grassland, optimized spectral indices significant increased predictive ability and the model between biomass and optimized spectral indices had the highest R² (R² = 0.72) compared to published spectral indices. Sensitive analysis further confirmed that the optimized index had the lowest noise equivalent and were the best performing index in
Spectral estimation optical coherence tomography for axial super-resolution.
Liu, Xinyu; Chen, Si; Cui, Dongyao; Yu, Xiaojun; Liu, Linbo
2015-10-01
The depth reflectivity profile of Fourier domain optical coherence tomography (FD-OCT) is estimated from the inverse Fourier transform of the spectral interference signals (interferograms). As a result, the axial resolution is fundamentally limited by the coherence length of the light source. We demonstrate that using the autoregressive spectral estimation technique instead of the inverse Fourier transform, to analyze the spectral interferograms can improve the axial resolution. We name this method spectral estimation OCT (SE-OCT). SE-OCT breaks the coherence length limitation and improves the axial resolution by a factor of up to 4.7 compared with FD-OCT. Furthermore, SE-OCT provides complete sidelobe suppression in the depth point-spread function, further improving the image quality. We demonstrate that these technical advances enables clear identification of corneal endothelium anatomical details ex vivo that cannot be identified using the corresponding FD-OCT. Given that SE-OCT can be implemented in the FD-OCT devices without any hardware changes, the new capabilities provided by SE-OCT are likely to offer immediate improvements to the diagnosis and management of diseases based on OCT imaging.
Nonlinear and adaptive estimation in reentry.
NASA Technical Reports Server (NTRS)
Jazwinski, A. H.
1972-01-01
The problem of real-time estimation of a lifting reentry vehicle trajectory of the shuttle orbiter type is considered. Simulations feature large position and velocity uncertainties at radar acquisition and realistic model errors in lift, drag and other model parameters. Radar tracking and accelerometer data are simulated. Significant nonlinearities are found to exist on spacecraft acquisition. An iterated nonlinear filter is shown to perform optimally during the radar acquisition phase. An adaptive filter is shown to track time-varying model errors, such as errors in the lift and drag coefficients, down to the noise level. Such real-time model tracking (identification) is frequently required for guidance and control implementation.
Fast adaptive estimation of multidimensional psychometric functions.
DiMattina, Christopher
2015-01-01
Recently in vision science there has been great interest in understanding the perceptual representations of complex multidimensional stimuli. Therefore, it is becoming very important to develop methods for performing psychophysical experiments with multidimensional stimuli and efficiently estimating psychometric models that have multiple free parameters. In this methodological study, I analyze three efficient implementations of the popular Ψ method for adaptive data collection, two of which are novel approaches to psychophysical experiments. Although the standard implementation of the Ψ procedure is intractable in higher dimensions, I demonstrate that my implementations generalize well to complex psychometric models defined in multidimensional stimulus spaces and can be implemented very efficiently on standard laboratory computers. I show that my implementations may be of particular use for experiments studying how subjects combine multiple cues to estimate sensory quantities. I discuss strategies for speeding up experiments and suggest directions for future research in this rapidly growing area at the intersection of cognitive science, neuroscience, and machine learning.
Fast adaptive estimation of multidimensional psychometric functions.
DiMattina, Christopher
2015-01-01
Recently in vision science there has been great interest in understanding the perceptual representations of complex multidimensional stimuli. Therefore, it is becoming very important to develop methods for performing psychophysical experiments with multidimensional stimuli and efficiently estimating psychometric models that have multiple free parameters. In this methodological study, I analyze three efficient implementations of the popular Ψ method for adaptive data collection, two of which are novel approaches to psychophysical experiments. Although the standard implementation of the Ψ procedure is intractable in higher dimensions, I demonstrate that my implementations generalize well to complex psychometric models defined in multidimensional stimulus spaces and can be implemented very efficiently on standard laboratory computers. I show that my implementations may be of particular use for experiments studying how subjects combine multiple cues to estimate sensory quantities. I discuss strategies for speeding up experiments and suggest directions for future research in this rapidly growing area at the intersection of cognitive science, neuroscience, and machine learning. PMID:26200886
Yield estimation of sugarcane based on agrometeorological-spectral models
NASA Technical Reports Server (NTRS)
Rudorff, Bernardo Friedrich Theodor; Batista, Getulio Teixeira
1990-01-01
This work has the objective to assess the performance of a yield estimation model for sugarcane (Succharum officinarum). The model uses orbital gathered spectral data along with yield estimated from an agrometeorological model. The test site includes the sugarcane plantations of the Barra Grande Plant located in Lencois Paulista municipality in Sao Paulo State. Production data of four crop years were analyzed. Yield data observed in the first crop year (1983/84) were regressed against spectral and agrometeorological data of that same year. This provided the model to predict the yield for the following crop year i.e., 1984/85. The model to predict the yield of subsequent years (up to 1987/88) were developed similarly, incorporating all previous years data. The yield estimations obtained from these models explained 69, 54, and 50 percent of the yield variation in the 1984/85, 1985/86, and 1986/87 crop years, respectively. The accuracy of yield estimations based on spectral data only (vegetation index model) and on agrometeorological data only (agrometeorological model) were also investigated.
Adaptive Estimation with Partially Overlapping Models
Shin, Sunyoung; Fine, Jason; Liu, Yufeng
2015-01-01
In many problems, one has several models of interest that capture key parameters describing the distribution of the data. Partially overlapping models are taken as models in which at least one covariate effect is common to the models. A priori knowledge of such structure enables efficient estimation of all model parameters. However, in practice, this structure may be unknown. We propose adaptive composite M-estimation (ACME) for partially overlapping models using a composite loss function, which is a linear combination of loss functions defining the individual models. Penalization is applied to pairwise differences of parameters across models, resulting in data driven identification of the overlap structure. Further penalization is imposed on the individual parameters, enabling sparse estimation in the regression setting. The recovery of the overlap structure enables more efficient parameter estimation. An oracle result is established. Simulation studies illustrate the advantages of ACME over existing methods that fit individual models separately or make strong a priori assumption about the overlap structure. PMID:26917931
Spectral estimation of green leaf area index of oats
NASA Technical Reports Server (NTRS)
Best, R. G.; Harlan, J. C.
1985-01-01
Green leaf area index (LAI) is a measure of vegetative growth and development and is frequently used as an input parameter in yield estimation and evapotranspiration models. Extensive destructive sampling is usually required to achieve accurate estimates of green LAI in natural situations. In this investigation, a statistical modeling approach was used to predict the green LAI of oats from bidirectional reflectance data collected with multiband radiometers. Stepwise multiple regression models based on two sets of spectral reflectance factors accounted for 73 percent and 65 percent of the variance in green LAI of oats. Exponential models of spectral data transformations of greenness, normalized difference, and near-infrared/red ratio accounted for more of the variance in green LAI than the multiple regression models.
Constrained Spectral Conditioning for spatial sound level estimation
NASA Astrophysics Data System (ADS)
Spalt, Taylor B.; Brooks, Thomas F.; Fuller, Christopher R.
2016-11-01
Microphone arrays are utilized in aeroacoustic testing to spatially map the sound emitted from an article under study. Whereas a single microphone allows only the total sound level to be estimated at the measurement location, an array permits differentiation between the contributions of distinct components. The accuracy of these spatial sound estimates produced by post-processing the array outputs is continuously being improved. One way of increasing the estimation accuracy is to filter the array outputs before they become inputs to a post-processor. This work presents a constrained method of linear filtering for microphone arrays which minimizes the total signal present on the array channels while preserving the signal from a targeted spatial location. Thus, each single-channel, filtered output for a given targeted location estimates only the signal from that location, even when multiple and/or distributed sources have been measured simultaneously. The method is based on Conditioned Spectral Analysis and modifies the Wiener-Hopf equation in a manner similar to the Generalized Sidelobe Canceller. This modified form of Conditioned Spectral Analysis is embedded within an iterative loop and termed Constrained Spectral Conditioning. Linear constraints are derived which prevent the cancellation of targeted signal due to random statistical error as well as location error in the sensor and/or source positions. The increased spatial mapping accuracy of Constrained Spectral Conditioning is shown for a simulated dataset of point sources which vary in strength. An experimental point source is used to validate the efficacy of the constraints which yield preservation of the targeted signal at the expense of reduced filtering ability. The beamforming results of a cold, supersonic jet demonstrate the qualitative and quantitative improvement obtained when using this technique to map a spatially-distributed, complex, and possibly coherent sound source.
Respiratory impedance spectral estimation for digitally created random noise.
Davis, K A; Lutchen, K R
1991-01-01
Measurement of respiratory input mechanical impedance (Zrs) is noninvasive, requires minimal subject cooperation, and contains information related to mechanical lung function. A common approach to measure Zrs is to apply random noise pressure signals at the airway opening, measure the resulting flow variations, and then estimate Zrs using Fast-Fourier Transform (FFT) techniques. The goal of this study was to quantify how several signal processing issues affect the quality of a Zrs spectral estimate when the input pressure sequence is created digitally. Random noise driven pressure and flow time domain data were simulated for three models, which permitted predictions of Zrs characteristics previously reported from 0-4, 4-32, and 4-200 Hz. Then, the quality of the Zrs estimate was evaluated as a function of the number of runs ensemble averaged, windowing, flow signal-to-noise ratio (SNR), and pressure spectral magnitude shape magnitude of P(j omega). For a magnitude of P(j omega) with uniform power distribution and a SNR less than 100, the 0-4 Hz and 4-200 Hz Zrs estimates for 10 runs were poor (minimum coherence gamma 2 less than 0.75) particularly where Zrs is high. When the SNR greater than 200 and 10 runs were averaged, the minimum gamma 2 greater than 0.95. However, when magnitude of P(j omega) was matched to magnitude of Zrs, gamma 2 greater than 0.91 even for 5 runs and a SNR of 20. For data created digitally with equally spaced spectral content, the rectangular window was superior to the Hanning. Finally, coherence alone may not be a reliable measure of Zrs quality because coherence is only an estimate itself. We conclude that an accurate estimate of Zrs is best obtained by matching magnitude of P(j omega) to magnitude of Zin (subject and speaker) and using rectangular windowing. PMID:2048776
Estimation of spectral emissivity in the thermal infrared
NASA Technical Reports Server (NTRS)
Kryskowski, David; Maxwell, J. R.
1993-01-01
A number of algorithms are available in the literature that attempt to remove most of the effects of temperature from thermal multispectral data where the final goal is to extract emissivity differences. Early approaches include adjacent spectral band ratioing, broad band radiance normalization and the use of one band where emissivities are generally high (e.g., 11 to 12 micrometers) to determine the temperature. More recent work has produced two techniques that use data averaging to extract temperature to leave a quantity related to emissivity changes. These two techniques have been investigated and compared and appear to provide reasonable results. The analysis presented in this paper develops a thermal IR multispectral temperature/emissivity estimation procedure based on formal estimation theory, Gaussian statistics, and a stochastic radiance signal model including the effects of both temperature and emissivity. The importance of this work is that this is an optimal estimation procedure which will provide minimum variance estimates of temperature and emissivity changes directly. Section 2 discusses optimal linear spectral emissivity estimation and Section 3 is a summary.
Spectral estimators of absorbed photosynthetically active radiation in corn canopies
NASA Technical Reports Server (NTRS)
Gallo, K. P.; Daughtry, C. S. T.; Bauer, M. E.
1984-01-01
Most models of crop growth and yield require an estimate of canopy leaf area index (LAI) or absorption of radiation. Relationships between photosynthetically active radiation (PAR) absorbed by corn canopies and the spectral reflectance of the canopies were investigated. Reflectance factor data were acquired with a LANDSAT MSS band radiometer. From planting to silking, the three spectrally predicted vegetation indices examined were associated with more than 95% of the variability in absorbed PAR. The relationships developed between absorbed PAR and the three indices were evaluated with reflectance factor data acquired from corn canopies planted in 1979 through 1982. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50% of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR were associated with up to 73% of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index. Absorbed PAR may be estimated reliably from spectral reflectance data of crop canopies.
Spectral estimators of absorbed photosynthetically active radiation in corn canopies
NASA Technical Reports Server (NTRS)
Gallo, K. P.; Daughtry, C. S. T.; Bauer, M. E.
1985-01-01
Most models of crop growth and yield require an estimate of canopy leaf area index (LAI) or absorption of radiation. Relationships between photosynthetically active radiation (PAR) absorbed by corn canopies and the spectral reflectance of the canopies were investigated. Reflectance factor data were acquired with a Landsat MSS band radiometer. From planting to silking, the three spectrally predicted vegetation indices examined were associated with more than 95 percent of the variability in absorbed PAR. The relationships developed between absorbed PAR and the three indices were evaluated with reflectance factor data acquired from corn canopies planted in 1979 through 1982. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50 percent of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR were associated with up to 73 percent of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index. Absorbed PAR may be estimated reliably from spectral reflectance data of crop canopies.
Model-based spectral estimation of Doppler signals using parallel genetic algorithms.
Solano González, J; Rodríguez Vázquez, K; García Nocetti, D F
2000-05-01
Conventional spectral analysis methods use a fast Fourier transform (FFT) on consecutive or overlapping windowed data segments. For Doppler ultrasound signals, this approach suffers from an inadequate frequency resolution due to the time segment duration and the non-stationarity characteristics of the signals. Parametric or model-based estimators can give significant improvements in the time-frequency resolution at the expense of a higher computational complexity. This work describes an approach which implements in real-time a parametric spectral estimator method using genetic algorithms (GAs) in order to find the optimum set of parameters for the adaptive filter that minimises the error function. The aim is to reduce the computational complexity of the conventional algorithm by using the simplicity associated to GAs and exploiting its parallel characteristics. This will allow the implementation of higher order filters, increasing the spectrum resolution, and opening a greater scope for using more complex methods. PMID:10767617
Model-based spectral estimation of Doppler signals using parallel genetic algorithms.
Solano González, J; Rodríguez Vázquez, K; García Nocetti, D F
2000-05-01
Conventional spectral analysis methods use a fast Fourier transform (FFT) on consecutive or overlapping windowed data segments. For Doppler ultrasound signals, this approach suffers from an inadequate frequency resolution due to the time segment duration and the non-stationarity characteristics of the signals. Parametric or model-based estimators can give significant improvements in the time-frequency resolution at the expense of a higher computational complexity. This work describes an approach which implements in real-time a parametric spectral estimator method using genetic algorithms (GAs) in order to find the optimum set of parameters for the adaptive filter that minimises the error function. The aim is to reduce the computational complexity of the conventional algorithm by using the simplicity associated to GAs and exploiting its parallel characteristics. This will allow the implementation of higher order filters, increasing the spectrum resolution, and opening a greater scope for using more complex methods.
Alternative techniques for high-resolution spectral estimation of spectrally encoded endoscopy
NASA Astrophysics Data System (ADS)
Mousavi, Mahta; Duan, Lian; Javidi, Tara; Ellerbee, Audrey K.
2015-09-01
Spectrally encoded endoscopy (SEE) is a minimally invasive optical imaging modality capable of fast confocal imaging of internal tissue structures. Modern SEE systems use coherent sources to image deep within the tissue and data are processed similar to optical coherence tomography (OCT); however, standard processing of SEE data via the Fast Fourier Transform (FFT) leads to degradation of the axial resolution as the bandwidth of the source shrinks, resulting in a well-known trade-off between speed and axial resolution. Recognizing the limitation of FFT as a general spectral estimation algorithm to only take into account samples collected by the detector, in this work we investigate alternative high-resolution spectral estimation algorithms that exploit information such as sparsity and the general region position of the bulk sample to improve the axial resolution of processed SEE data. We validate the performance of these algorithms using bothMATLAB simulations and analysis of experimental results generated from a home-built OCT system to simulate an SEE system with variable scan rates. Our results open a new door towards using non-FFT algorithms to generate higher quality (i.e., higher resolution) SEE images at correspondingly fast scan rates, resulting in systems that are more accurate and more comfortable for patients due to the reduced image time.
NASA Astrophysics Data System (ADS)
Dat, Tran Huy; Takeda, Kazuya; Itakura, Fumitada
We present a multichannel speech enhancement method based on MAP speech spectral magnitude estimation using a generalized gamma model of speech prior distribution, where the model parameters are adapted from actual noisy speech in a frame-by-frame manner. The utilization of a more general prior distribution with its online adaptive estimation is shown to be effective for speech spectral estimation in noisy environments. Furthermore, the multi-channel information in terms of cross-channel statistics are shown to be useful to better adapt the prior distribution parameters to the actual observation, resulting in better performance of speech enhancement algorithm. We tested the proposed algorithm in an in-car speech database and obtained significant improvements of the speech recognition performance, particularly under non-stationary noise conditions such as music, air-conditioner and open window.
Adaptive camouflage in the VIS and IR spectral range: main principles and mechanisms
NASA Astrophysics Data System (ADS)
Schwarz, Alexander
2015-10-01
This paper presents a survey of main applicable technical principles and mechanisms for adaptive camouflage in the visible (VIS) and infrared (IR) spectral ranges. All principles are described by their operation method and technical data such as the active spectral range, the degree and speed of adaptation, weight, power consumption, robustness, usability, lifetime, technology readiness level (TRL) etc.. The paper allows to compare the different principles and to assess them with regard to an application to an adaptive camouflage system.
Adaptive uniform grayscale coded aperture design for high dynamic range compressive spectral imaging
NASA Astrophysics Data System (ADS)
Diaz, Nelson; Rueda, Hoover; Arguello, Henry
2016-05-01
Imaging spectroscopy is an important area with many applications in surveillance, agriculture and medicine. The disadvantage of conventional spectroscopy techniques is that they collect the whole datacube. In contrast, compressive spectral imaging systems capture snapshot compressive projections, which are the input of reconstruction algorithms to yield the underlying datacube. Common compressive spectral imagers use coded apertures to perform the coded projections. The coded apertures are the key elements in these imagers since they define the sensing matrix of the system. The proper design of the coded aperture entries leads to a good quality in the reconstruction. In addition, the compressive measurements are prone to saturation due to the limited dynamic range of the sensor, hence the design of coded apertures must consider saturation. The saturation errors in compressive measurements are unbounded and compressive sensing recovery algorithms only provide solutions for bounded noise or bounded with high probability. In this paper it is proposed the design of uniform adaptive grayscale coded apertures (UAGCA) to improve the dynamic range of the estimated spectral images by reducing the saturation levels. The saturation is attenuated between snapshots using an adaptive filter which updates the entries of the grayscale coded aperture based on the previous snapshots. The coded apertures are optimized in terms of transmittance and number of grayscale levels. The advantage of the proposed method is the efficient use of the dynamic range of the image sensor. Extensive simulations show improvements in the image reconstruction of the proposed method compared with grayscale coded apertures (UGCA) and adaptive block-unblock coded apertures (ABCA) in up to 10 dB.
Spectral estimates of net radiation and soil heat flux
Daughtry, C.S.T.; Kustas, W.P.; Moran, M.S.; Pinter, P. J.; Jackson, R. D.; Brown, P.W.; Nichols, W.D.; Gay, L.W.
1990-01-01
Conventional methods of measuring surface energy balance are point measurements and represent only a small area. Remote sensing offers a potential means of measuring outgoing fluxes over large areas at the spatial resolution of the sensor. The objective of this study was to estimate net radiation (Rn) and soil heat flux (G) using remotely sensed multispectral data acquired from an aircraft over large agricultural fields. Ground-based instruments measured Rn and G at nine locations along the flight lines. Incoming fluxes were also measured by ground-based instruments. Outgoing fluxes were estimated using remotely sensed data. Remote Rn, estimated as the algebraic sum of incoming and outgoing fluxes, slightly underestimated Rn measured by the ground-based net radiometers. The mean absolute errors for remote Rn minus measured Rn were less than 7%. Remote G, estimated as a function of a spectral vegetation index and remote Rn, slightly overestimated measured G; however, the mean absolute error for remote G was 13%. Some of the differences between measured and remote values of Rn and G are associated with differences in instrument designs and measurement techniques. The root mean square error for available energy (Rn - G) was 12%. Thus, methods using both ground-based and remotely sensed data can provide reliable estimates of the available energy which can be partitioned into sensible and latent heat under nonadvective conditions. ?? 1990.
Optimizing spectral wave estimates with adjoint-based sensitivity maps
NASA Astrophysics Data System (ADS)
Orzech, Mark; Veeramony, Jay; Flampouris, Stylianos
2014-04-01
A discrete numerical adjoint has recently been developed for the stochastic wave model SWAN. In the present study, this adjoint code is used to construct spectral sensitivity maps for two nearshore domains. The maps display the correlations of spectral energy levels throughout the domain with the observed energy levels at a selected location or region of interest (LOI/ROI), providing a full spectrum of values at all locations in the domain. We investigate the effectiveness of sensitivity maps based on significant wave height ( H s ) in determining alternate offshore instrument deployment sites when a chosen nearshore location or region is inaccessible. Wave and bathymetry datasets are employed from one shallower, small-scale domain (Duck, NC) and one deeper, larger-scale domain (San Diego, CA). The effects of seasonal changes in wave climate, errors in bathymetry, and multiple assimilation points on sensitivity map shapes and model performance are investigated. Model accuracy is evaluated by comparing spectral statistics as well as with an RMS skill score, which estimates a mean model-data error across all spectral bins. Results indicate that data assimilation from identified high-sensitivity alternate locations consistently improves model performance at nearshore LOIs, while assimilation from low-sensitivity locations results in lesser or no improvement. Use of sub-sampled or alongshore-averaged bathymetry has a domain-specific effect on model performance when assimilating from a high-sensitivity alternate location. When multiple alternate assimilation locations are used from areas of lower sensitivity, model performance may be worse than with a single, high-sensitivity assimilation point.
A comparison of spectral estimation methods for the analysis of sibilant fricatives
Reidy, Patrick F.
2015-01-01
It has been argued that, to ensure accurate spectral feature estimates for sibilants, the spectral estimation method should include a low-variance spectral estimator; however, no empirical evaluation of estimation methods in terms of feature estimates has been given. The spectra of /s/ and /ʃ/ were estimated with different methods that varied the pre-emphasis filter and estimator. These methods were evaluated in terms of effects on two features (centroid and degree of sibilance) and on the detection of four linguistic contrasts within these features. Estimation method affected the spectral features but none of the tested linguistic contrasts. PMID:25920873
Experimental demonstration of an adaptive architecture for direct spectral imaging classification.
Dunlop-Gray, Matthew; Poon, Phillip K; Golish, Dathon; Vera, Esteban; Gehm, Michael E
2016-08-01
Spectral imaging is a powerful tool for providing in situ material classification across a spatial scene. Typically, spectral imaging analyses are interested in classification, though often the classification is performed only after reconstruction of the spectral datacube. We present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator, the AFSSI-C measures specific projections of the spectral datacube which are generated by an adaptive Bayesian classification and feature design framework. We experimentally demonstrate multiple order-of-magnitude improvement of classification accuracy in low signal-to-noise (SNR) environments when compared to legacy spectral imaging systems.
Speech Enhancement, Gain, and Noise Spectrum Adaptation Using Approximate Bayesian Estimation.
Hao, Jiucang; Attias, Hagai; Nagarajan, Srikantan; Lee, Te-Won; Sejnowski, Terrence J
2009-01-01
This paper presents a new approximate Bayesian estimator for enhancing a noisy speech signal. The speech model is assumed to be a Gaussian mixture model (GMM) in the log-spectral domain. This is in contrast to most current models in frequency domain. Exact signal estimation is a computationally intractable problem. We derive three approximations to enhance the efficiency of signal estimation. The Gaussian approximation transforms the log-spectral domain GMM into the frequency domain using minimal Kullback-Leiber (KL)-divergency criterion. The frequency domain Laplace method computes the maximum a posteriori (MAP) estimator for the spectral amplitude. Correspondingly, the log-spectral domain Laplace method computes the MAP estimator for the log-spectral amplitude. Further, the gain and noise spectrum adaptation are implemented using the expectation-maximization (EM) algorithm within the GMM under Gaussian approximation. The proposed algorithms are evaluated by applying them to enhance the speeches corrupted by the speech-shaped noise (SSN). The experimental results demonstrate that the proposed algorithms offer improved signal-to-noise ratio, lower word recognition error rate, and less spectral distortion. PMID:20428253
Smallwood, D. O.
1996-01-01
It is shown that the usual method for estimating the coherence functions (ordinary, partial, and multiple) for a general multiple-input! multiple-output problem can be expressed as a modified form of Cholesky decomposition of the cross-spectral density matrix of the input and output records. The results can be equivalently obtained using singular value decomposition (SVD) of the cross-spectral density matrix. Using SVD suggests a new form of fractional coherence. The formulation as a SVD problem also suggests a way to order the inputs when a natural physical order of the inputs is absent.
Tracking closely spaced multiple sources via spectral-estimation techniques
NASA Astrophysics Data System (ADS)
Gabriel, W. F.
1982-06-01
Modern spectral-estimation techniques have achieved a level of performance that attracts interest in applications area such as the tracking of multiple spatial sources. In addition to the original "superresolution' capability, these techniques offer an apparent 'absence of sidelobes' characteristic and some reasonable solutions to the difficult radar coherent-source problem that involves a phase-dependent SNR (signal-to-noise ratio) penalty. This report reviews the situation briefly, and it discusses a few of the techniques that have been found useful, including natural or synthetic doppler shifts, non-Toeplitz forward-backward subaperture-shift processing, and recent eigenvalue/eigenvector analysis algorithms. The techniques are applied to multiple-source situations that include mixtures of coherent and noncoherent sources of unequal strengths, with either an 8-or a 12-element linear-array sampling aperture. The first test case involves the estimation of six sources, two of which are 95% correlated. The second test case involves a tracking-simulation display example of four moving sources: three are -10dB coherent sources 95% correlated, and the other is a strong 20-dB noncoherent source. These test cases demonstrate the remarkable improvements obtained with the recent estimation techniques, and they point to the possibilities for real-world applications.
Bayesian parameter estimation in spectral quantitative photoacoustic tomography
NASA Astrophysics Data System (ADS)
Pulkkinen, Aki; Cox, Ben T.; Arridge, Simon R.; Kaipio, Jari P.; Tarvainen, Tanja
2016-03-01
Photoacoustic tomography (PAT) is an imaging technique combining strong contrast of optical imaging to high spatial resolution of ultrasound imaging. These strengths are achieved via photoacoustic effect, where a spatial absorption of light pulse is converted into a measurable propagating ultrasound wave. The method is seen as a potential tool for small animal imaging, pre-clinical investigations, study of blood vessels and vasculature, as well as for cancer imaging. The goal in PAT is to form an image of the absorbed optical energy density field via acoustic inverse problem approaches from the measured ultrasound data. Quantitative PAT (QPAT) proceeds from these images and forms quantitative estimates of the optical properties of the target. This optical inverse problem of QPAT is illposed. To alleviate the issue, spectral QPAT (SQPAT) utilizes PAT data formed at multiple optical wavelengths simultaneously with optical parameter models of tissue to form quantitative estimates of the parameters of interest. In this work, the inverse problem of SQPAT is investigated. Light propagation is modelled using the diffusion equation. Optical absorption is described with chromophore concentration weighted sum of known chromophore absorption spectra. Scattering is described by Mie scattering theory with an exponential power law. In the inverse problem, the spatially varying unknown parameters of interest are the chromophore concentrations, the Mie scattering parameters (power law factor and the exponent), and Gruneisen parameter. The inverse problem is approached with a Bayesian method. It is numerically demonstrated, that estimation of all parameters of interest is possible with the approach.
Informed spectral analysis: audio signal parameter estimation using side information
NASA Astrophysics Data System (ADS)
Fourer, Dominique; Marchand, Sylvain
2013-12-01
Parametric models are of great interest for representing and manipulating sounds. However, the quality of the resulting signals depends on the precision of the parameters. When the signals are available, these parameters can be estimated, but the presence of noise decreases the resulting precision of the estimation. Furthermore, the Cramér-Rao bound shows the minimal error reachable with the best estimator, which can be insufficient for demanding applications. These limitations can be overcome by using the coding approach which consists in directly transmitting the parameters with the best precision using the minimal bitrate. However, this approach does not take advantage of the information provided by the estimation from the signal and may require a larger bitrate and a loss of compatibility with existing file formats. The purpose of this article is to propose a compromised approach, called the 'informed approach,' which combines analysis with (coded) side information in order to increase the precision of parameter estimation using a lower bitrate than pure coding approaches, the audio signal being known. Thus, the analysis problem is presented in a coder/decoder configuration where the side information is computed and inaudibly embedded into the mixture signal at the coder. At the decoder, the extra information is extracted and is used to assist the analysis process. This study proposes applying this approach to audio spectral analysis using sinusoidal modeling which is a well-known model with practical applications and where theoretical bounds have been calculated. This work aims at uncovering new approaches for audio quality-based applications. It provides a solution for challenging problems like active listening of music, source separation, and realistic sound transformations.
SPECTRAL data-based estimation of soil heat flux
Singh, R.K.; Irmak, A.; Walter-Shea, Elizabeth; Verma, S.B.; Suyker, A.E.
2011-01-01
Numerous existing spectral-based soil heat flux (G) models have shown wide variation in performance for maize and soybean cropping systems in Nebraska, indicating the need for localized calibration and model development. The objectives of this article are to develop a semi-empirical model to estimate G from a normalized difference vegetation index (NDVI) and net radiation (R n) for maize (Zea mays L.) and soybean (Glycine max L.) fields in the Great Plains, and present the suitability of the developed model to estimate G under similar and different soil and management conditions. Soil heat fluxes measured in both irrigated and rainfed fields in eastern and south-central Nebraska were used for model development and validation. An exponential model that uses NDVI and Rn was found to be the best to estimate G based on r2 values. The effect of geographic location, crop, and water management practices were used to develop semi-empirical models under four case studies. Each case study has the same exponential model structure but a different set of coefficients and exponents to represent the crop, soil, and management practices. Results showed that the semi-empirical models can be used effectively for G estimation for nearby fields with similar soil properties for independent years, regardless of differences in crop type, crop rotation, and irrigation practices, provided that the crop residue from the previous year is more than 4000 kg ha-1. The coefficients calibrated from particular fields can be used at nearby fields in order to capture temporal variation in G. However, there is a need for further investigation of the models to account for the interaction effects of crop rotation and irrigation. Validation at an independent site having different soil and crop management practices showed the limitation of the semi-empirical model in estimating G under different soil and environment conditions. ?? 2011 American Society of Agricultural and Biological Engineers ISSN 2151-0032.
Application of Sequential Interval Estimation to Adaptive Mastery Testing
ERIC Educational Resources Information Center
Chang, Yuan-chin Ivan
2005-01-01
In this paper, we apply sequential one-sided confidence interval estimation procedures with beta-protection to adaptive mastery testing. The procedures of fixed-width and fixed proportional accuracy confidence interval estimation can be viewed as extensions of one-sided confidence interval procedures. It can be shown that the adaptive mastery…
NASA Technical Reports Server (NTRS)
Barnard, Stephen T.; Simon, Horst; Lasinski, T. A. (Technical Monitor)
1994-01-01
The design of a parallel implementation of multilevel recursive spectral bisection is described. The goal is to implement a code that is fast enough to enable dynamic repartitioning of adaptive meshes.
Lam, Henry; Deutsch, Eric W; Aebersold, Ruedi
2010-01-01
The challenge of estimating false discovery rates (FDR) in peptide identification from MS/MS spectra has received increased attention in proteomics. The simple approach of target-decoy searching has become popular with traditional sequence (database) searching methods, but has yet to be practiced in spectral (library) searching, an emerging alternative to sequence searching. We extended this target-decoy searching approach to spectral searching by developing and validating a robust method to generate realistic, but unnatural, decoy spectra. Our method involves randomly shuffling the peptide identification of each reference spectrum in the library, and repositioning each fragment ion peak along the m/z axis to match the fragment ions expected from the shuffled sequence. We show that this method produces decoy spectra that are sufficiently realistic, such that incorrect identifications are equally likely to match real and decoy spectra, a key assumption necessary for decoy counting. This approach has been implemented in the open-source library building software, SpectraST.
Multi-element stochastic spectral projection for high quantile estimation
NASA Astrophysics Data System (ADS)
Ko, Jordan; Garnier, Josselin
2013-06-01
We investigate quantile estimation by multi-element generalized Polynomial Chaos (gPC) metamodel where the exact numerical model is approximated by complementary metamodels in overlapping domains that mimic the model's exact response. The gPC metamodel is constructed by the non-intrusive stochastic spectral projection approach and function evaluation on the gPC metamodel can be considered as essentially free. Thus, large number of Monte Carlo samples from the metamodel can be used to estimate α-quantile, for moderate values of α. As the gPC metamodel is an expansion about the means of the inputs, its accuracy may worsen away from these mean values where the extreme events may occur. By increasing the approximation accuracy of the metamodel, we may eventually improve accuracy of quantile estimation but it is very expensive. A multi-element approach is therefore proposed by combining a global metamodel in the standard normal space with supplementary local metamodels constructed in bounded domains about the design points corresponding to the extreme events. To improve the accuracy and to minimize the sampling cost, sparse-tensor and anisotropic-tensor quadratures are tested in addition to the full-tensor Gauss quadrature in the construction of local metamodels; different bounds of the gPC expansion are also examined. The global and local metamodels are combined in the multi-element gPC (MEgPC) approach and it is shown that MEgPC can be more accurate than Monte Carlo or importance sampling methods for high quantile estimations for input dimensions roughly below N=8, a limit that is very much case- and α-dependent.
Canonical correlation analysis for assessing the performance of adaptive spectral imagers
NASA Astrophysics Data System (ADS)
Wang, Zhipeng; Paskalova, Biliana; Tyo, J. Scott; Hayat, M. M.
2005-06-01
A new class of spectrally adaptive infrared detectors has been reported recently that has a spectral response function that can be altered electronically by controlling the bias voltage of the photodetector. Unlike conventional sensors, these new sensors have ``bands'' that have highly correlated spectral responses. The potential benefit of these sensors is that the number of bands (and their spectral features) used can be adapted to a specific task. The drawback is that there might not be enough spectral diversity to perform detection and classification operations. In this paper we present a new theory that describes the suitability of an arbitrary spectral sensor to perform a specific spectral detection/classification task. This theory is based on the geometric relationships between the sensor space that describes the spectral characteristics of the detector and a scene space that contains the spectra to be observed. We adapt the theory of canonical correlation analysis to provide a rigorous framework for assessing the utility of spectral detectors. We also show that this general theory encompasses traditional band selection methods, but provides much greater flexibility and a more transparent and intuitive explanation of the phenomenology.
Smoothed aggregation adaptive spectral element-based algebraic multigrid
2015-01-20
SAAMGE provides parallel methods for building multilevel hierarchies and solvers that can be used for elliptic equations with highly heterogeneous coefficients. Additionally, hierarchy adaptation is implemented allowing solving multiple problems with close coefficients without rebuilding the hierarchy.
NASA Technical Reports Server (NTRS)
Scaife, Bradley James
1999-01-01
In any satellite communication, the Doppler shift associated with the satellite's position and velocity must be calculated in order to determine the carrier frequency. If the satellite state vector is unknown then some estimate must be formed of the Doppler-shifted carrier frequency. One elementary technique is to examine the signal spectrum and base the estimate on the dominant spectral component. If, however, the carrier is spread (as in most satellite communications) this technique may fail unless the chip rate-to-data rate ratio (processing gain) associated with the carrier is small. In this case, there may be enough spectral energy to allow peak detection against a noise background. In this thesis, we present a method to estimate the frequency (without knowledge of the Doppler shift) of a spread-spectrum carrier assuming a small processing gain and binary-phase shift keying (BPSK) modulation. Our method relies on an averaged discrete Fourier transform along with peak detection on spectral match filtered data. We provide theory and simulation results indicating the accuracy of this method. In addition, we will describe an all-digital hardware design based around a Motorola DSP56303 and high-speed A/D which implements this technique in real-time. The hardware design is to be used in NMSU's implementation of NASA's demand assignment, multiple access (DAMA) service.
MUSIC for Multidimensional Spectral Estimation: Stability and Super-Resolution
NASA Astrophysics Data System (ADS)
Liao, Wenjing
2015-12-01
This paper presents a performance analysis of the MUltiple SIgnal Classification (MUSIC) algorithm applied on $D$ dimensional single-snapshot spectral estimation while $s$ true frequencies are located on the continuum of a bounded domain. Inspired by the matrix pencil form, we construct a D-fold Hankel matrix from the measurements and exploit its Vandermonde decomposition in the noiseless case. MUSIC amounts to identifying a noise subspace, evaluating a noise-space correlation function, and localizing frequencies by searching the $s$ smallest local minima of the noise-space correlation function. In the noiseless case, $(2s)^D$ measurements guarantee an exact reconstruction by MUSIC as the noise-space correlation function vanishes exactly at true frequencies. When noise exists, we provide an explicit estimate on the perturbation of the noise-space correlation function in terms of noise level, dimension $D$, the minimum separation among frequencies, the maximum and minimum amplitudes while frequencies are separated by two Rayleigh Length (RL) at each direction. As a by-product the maximum and minimum non-zero singular values of the multidimensional Vandermonde matrix whose nodes are on the unit sphere are estimated under a gap condition of the nodes. Under the 2-RL separation condition, if noise is i.i.d. gaussian, we show that perturbation of the noise-space correlation function decays like $\\sqrt{\\log(\\#(\\mathbf{N}))/\\#(\\mathbf{N})}$ as the sample size $\\#(\\mathbf{N})$ increases. When the separation among frequencies drops below 2 RL, our numerical experiments show that the noise tolerance of MUSIC obeys a power law with the minimum separation of frequencies.
Skorupski, Peter; Chittka, Lars
2011-01-01
Colour vision depends on comparison of signals from photoreceptors with different spectral sensitivities. However, response properties of photoreceptor cells may differ in ways other than spectral tuning. In insects, for example, broadband photoreceptors, with a major sensitivity peak in the green region of the spectrum (>500 nm), drive fast visual processes, which are largely blind to chromatic signals from more narrowly-tuned photoreceptors with peak sensitivities in the blue and UV regions of the spectrum. In addition, electrophysiological properties of the photoreceptor membrane may result in differences in response dynamics of photoreceptors of similar spectral class between species, and different spectral classes within a species. We used intracellular electrophysiological techniques to investigate response dynamics of the three spectral classes of photoreceptor underlying trichromatic colour vision in the bumblebee, Bombus impatiens, and we compare these with previously published data from a related species, Bombus terrestris. In both species, we found significantly faster responses in green, compared with blue- or UV-sensitive photoreceptors, although all 3 photoreceptor types are slower in B. impatiens than in B. terrestris. Integration times for light-adapted B. impatiens photoreceptors (estimated from impulse response half-width) were 11.3±1.6 ms for green photoreceptors compared with 18.6±4.4 ms and 15.6±4.4 for blue and UV, respectively. We also measured photoreceptor input resistance in dark- and light-adapted conditions. All photoreceptors showed a decrease in input resistance during light adaptation, but this decrease was considerably larger (declining to about 22% of the dark value) in green photoreceptors, compared to blue and UV (41% and 49%, respectively). Our results suggest that the conductances associated with light adaptation are largest in green photoreceptors, contributing to their greater temporal processing speed. We suggest that the
Spectral estimation of artist oil paints using multi-filter trichromatic imaging
NASA Astrophysics Data System (ADS)
Imai, Francisco H.; Berns, Roy S.
2002-06-01
A practical and easy way to capture images of oil-paintings and estimate their spectral reflectance as a function of position was tested. For the image acquisition, a trichromatic digital camera was used in conjunction with an absorption filter producing six channels. From an a priori statistical analysis of common artist oil paints, spectral reflectance was estimated. These experiments showed that it is possible to estimate the spectral reflectance with an accuracy of average ΔE*94 of 1.7 and spectral reflectance rms error of 2.2%. Of particular interest is guidance towards the design of a universal calibration target for imaging paintings.
Spectral saliency via automatic adaptive amplitude spectrum analysis
NASA Astrophysics Data System (ADS)
Wang, Xiaodong; Dai, Jialun; Zhu, Yafei; Zheng, Haiyong; Qiao, Xiaoyan
2016-03-01
Suppressing nonsalient patterns by smoothing the amplitude spectrum at an appropriate scale has been shown to effectively detect the visual saliency in the frequency domain. Different filter scales are required for different types of salient objects. We observe that the optimal scale for smoothing amplitude spectrum shares a specific relation with the size of the salient region. Based on this observation and the bottom-up saliency detection characterized by spectrum scale-space analysis for natural images, we propose to detect visual saliency, especially with salient objects of different sizes and locations via automatic adaptive amplitude spectrum analysis. We not only provide a new criterion for automatic optimal scale selection but also reserve the saliency maps corresponding to different salient objects with meaningful saliency information by adaptive weighted combination. The performance of quantitative and qualitative comparisons is evaluated by three different kinds of metrics on the four most widely used datasets and one up-to-date large-scale dataset. The experimental results validate that our method outperforms the existing state-of-the-art saliency models for predicting human eye fixations in terms of accuracy and robustness.
NASA Astrophysics Data System (ADS)
Smith-Boughner, Lindsay
Many Earth systems cannot be studied directly. One cannot measure the velocities of convecting fluid in the Earth's core but can measure the magnetic field generated by these motions on the surface. Examining how the magnetic field changes over long periods of time, using power spectral density estimation provides insight into the dynamics driving the system. The changes in the magnetic field can also be used to study Earth properties - variations in magnetic fields outside of Earth like the ring-current induce currents to flow in the Earth, generating magnetic fields. Estimating the transfer function between the external changes and the induced response characterizes the electromagnetic response of the Earth. From this response inferences can be made about the electrical conductivity of the Earth. However, these types of time series, and many others have long breaks in the record with no samples available and limit the analysis. Standard methods require interpolation or section averaging, with associated problems of introducing bias or reducing the frequency resolution. Extending the methods of Fodor and Stark (2000), who adapt a set of orthogonal multi-tapers to compensate for breaks in sampling- an algorithm and software package for applying these techniques is developed. Methods of empirically estimating the average transfer function of a set of tapers and confidence intervals are also tested. These methods are extended for cross-spectral, coherence and transfer function estimation in the presence of noise. With these methods, new analysis of a highly interrupted ocean sediment core from the Oligocene (Hartl et al., 1993) reveals a quasi-periodic signal in the calibrated paleointensity time series at 2.5 cpMy. The power in the magnetic field during this period appears to be dominated by reversal rate processes with less overall power than the early Oligocene. Previous analysis of the early Oligocene by Constable et al. (1998) detected a signal near 8 cp
NASA Astrophysics Data System (ADS)
Pedersen, Mads Møller; Pihl, Michael Johannes; Haugaard, Per; Hansen, Jens Munk; Lindskov Hansen, Kristoffer; Bachmann Nielsen, Michael; Jensen, Jørgen Arendt
2011-03-01
Spectral velocity estimation is considered the gold standard in medical ultrasound. Peak systole (PS), end diastole (ED), and resistive index (RI) are used clinically. Angle correction is performed using a flow angle set manually. With Transverse Oscillation (TO) velocity estimates the flow angle, peak systole (PSTO), end diastole (EDTO), and resistive index (RITO) are estimated. This study investigates if these clinical parameters are estimated equally good using spectral and TO data. The right common carotid arteries of three healthy volunteers were scanned longitudinally. Average TO flow angles and std were calculated { 52+/-18 ; 55+/-23 ; 60+/-16 }°. Spectral angles { 52 ; 56 ; 52 }° were obtained from the B-mode images. Obtained values are: PSTO { 76+/-15 ; 89+/-28 ; 77+/-7 } cm/s, spectral PS { 77 ; 110 ; 76 } cm/s, EDTO { 10+/-3 ; 14+/-8 ; 15+/-3 } cm/s, spectral ED { 18 ; 13 ; 20 } cm/s, RITO { 0.87+/-0.05 ; 0.79+/-0.21 ; 0.79+/-0.06 }, and spectral RI { 0.77 ; 0.88 ; 0.73 }. Vector angles are within +/-two std of the spectral angle. TO velocity estimates are within +/-three std of the spectral estimates. RITO are within +/-two std of the spectral estimates. Preliminary data indicates that the TO and spectral velocity estimates are equally good. With TO there is no manual angle setting and no flow angle limitation. TO velocity estimation can also automatically handle situations where the angle varies over the cardiac cycle. More detailed temporal and spatial vector estimates with diagnostic potential are available with the TO velocity estimation.
Adaptive pumping for spectral control of random lasers
NASA Astrophysics Data System (ADS)
Bachelard, Nicolas; Gigan, Sylvain; Noblin, Xavier; Sebbah, Patrick
2014-06-01
A laser is not necessarily a sophisticated device: pumping an amplifying medium randomly filled with scatterers makes a perfectly viable `random laser'. The absence of mirrors greatly simplifies laser design, but control over the emission wavelength and directionality is lost, seriously hindering prospects for this otherwise simple laser. Recently, we proposed an approach to tame random lasers, inspired by coherent light control in complex media. Here, we implement this method in an optofluidic random laser where modes are spatially extended and overlap, making individual mode selection impossible, a priori. We show experimentally that control over laser emission can be regained even in this extreme case. By actively shaping the optical pump within the random laser, single-mode operation at any selected wavelength is achieved with spectral selectivity down to 0.06 nm and more than 10 dB side-lobe rejection. This method paves the way towards versatile tunable and controlled random lasers as well as the taming of other laser sources.
Adaptive position estimation for an automated guided vehicle
NASA Astrophysics Data System (ADS)
Lapin, Brett D.
1993-05-01
In a mobile robotic system, complexities in positioning arise due to the motion. An adaptive position estimation scheme has been developed for an automated guide vehicle (AGV) to overcome these complexities. The scheme's purpose is to minimize the position error--the difference between the estimated position and the actual position. The method to achieve this is to adapt the system model by incorporating a parameter vector and using a maximum likelihood algorithm to estimate the parameters after an accurate position determination is made. A simulation of the vehicle's guidance system was developed and the estimator tested on an oval-shaped path. Upon injecting biases into the system, initial position errors were 10 centimeters or more. After the estimator converged, the maximum final errors were on the order of 1 to 2 centimeters (prior to measurement update). After each measurement update, after the estimator had converged, errors were on the order of 1 to 2 millimeters.
Biomass estimator for NIR image with a few additional spectral band images taken from light UAS
NASA Astrophysics Data System (ADS)
Pölönen, Ilkka; Salo, Heikki; Saari, Heikki; Kaivosoja, Jere; Pesonen, Liisa; Honkavaara, Eija
2012-05-01
A novel way to produce biomass estimation will offer possibilities for precision farming. Fertilizer prediction maps can be made based on accurate biomass estimation generated by a novel biomass estimator. By using this knowledge, a variable rate amount of fertilizers can be applied during the growing season. The innovation consists of light UAS, a high spatial resolution camera, and VTT's novel spectral camera. A few properly selected spectral wavelengths with NIR images and point clouds extracted by automatic image matching have been used in the estimation. The spectral wavelengths were chosen from green, red, and NIR channels.
Robust time and frequency domain estimation methods in adaptive control
NASA Technical Reports Server (NTRS)
Lamaire, Richard Orville
1987-01-01
A robust identification method was developed for use in an adaptive control system. The type of estimator is called the robust estimator, since it is robust to the effects of both unmodeled dynamics and an unmeasurable disturbance. The development of the robust estimator was motivated by a need to provide guarantees in the identification part of an adaptive controller. To enable the design of a robust control system, a nominal model as well as a frequency-domain bounding function on the modeling uncertainty associated with this nominal model must be provided. Two estimation methods are presented for finding parameter estimates, and, hence, a nominal model. One of these methods is based on the well developed field of time-domain parameter estimation. In a second method of finding parameter estimates, a type of weighted least-squares fitting to a frequency-domain estimated model is used. The frequency-domain estimator is shown to perform better, in general, than the time-domain parameter estimator. In addition, a methodology for finding a frequency-domain bounding function on the disturbance is used to compute a frequency-domain bounding function on the additive modeling error due to the effects of the disturbance and the use of finite-length data. The performance of the robust estimator in both open-loop and closed-loop situations is examined through the use of simulations.
Estimation of spectral distribution of sky radiance using a commercial digital camera.
Saito, Masanori; Iwabuchi, Hironobu; Murata, Isao
2016-01-10
Methods for estimating spectral distribution of sky radiance from images captured by a digital camera and for accurately estimating spectral responses of the camera are proposed. Spectral distribution of sky radiance is represented as a polynomial of the wavelength, with coefficients obtained from digital RGB counts by linear transformation. The spectral distribution of radiance as measured is consistent with that obtained by spectrometer and radiative transfer simulation for wavelengths of 430-680 nm, with standard deviation below 1%. Preliminary applications suggest this method is useful for detecting clouds and studying the relation between irradiance at the ground and cloud distribution.
Estimation of spectral distribution of sky radiance using a commercial digital camera.
Saito, Masanori; Iwabuchi, Hironobu; Murata, Isao
2016-01-10
Methods for estimating spectral distribution of sky radiance from images captured by a digital camera and for accurately estimating spectral responses of the camera are proposed. Spectral distribution of sky radiance is represented as a polynomial of the wavelength, with coefficients obtained from digital RGB counts by linear transformation. The spectral distribution of radiance as measured is consistent with that obtained by spectrometer and radiative transfer simulation for wavelengths of 430-680 nm, with standard deviation below 1%. Preliminary applications suggest this method is useful for detecting clouds and studying the relation between irradiance at the ground and cloud distribution. PMID:26835780
Stability and error estimation for Component Adaptive Grid methods
NASA Technical Reports Server (NTRS)
Oliger, Joseph; Zhu, Xiaolei
1994-01-01
Component adaptive grid (CAG) methods for solving hyperbolic partial differential equations (PDE's) are discussed in this paper. Applying recent stability results for a class of numerical methods on uniform grids. The convergence of these methods for linear problems on component adaptive grids is established here. Furthermore, the computational error can be estimated on CAG's using the stability results. Using these estimates, the error can be controlled on CAG's. Thus, the solution can be computed efficiently on CAG's within a given error tolerance. Computational results for time dependent linear problems in one and two space dimensions are presented.
Adaptive spectral window sizes for extraction of diagnostic features from optical spectra
NASA Astrophysics Data System (ADS)
Kan, Chih-Wen; Lee, Andy Y.; Nieman, Linda T.; Sokolov, Konstantin; Markey, Mia K.
2010-07-01
We present an approach to adaptively adjust the spectral window sizes for optical spectra feature extraction. Previous studies extracted features from spectral windows of a fixed width. In our algorithm, piecewise linear regression is used to adaptively adjust the window sizes to find the maximum window size with reasonable linear fit with the spectrum. This adaptive windowing technique ensures the signal linearity in defined windows; hence, the adaptive windowing technique retains more diagnostic information while using fewer windows. This method was tested on a data set of diffuse reflectance spectra of oral mucosa lesions. Eight features were extracted from each window. We performed classifications using linear discriminant analysis with cross-validation. Using windowing techniques results in better classification performance than not using windowing. The area under the receiver-operating-characteristics curve for windowing techniques was greater than a nonwindowing technique for both normal versus mild dysplasia (MD) plus severe high-grade dysplasia or carcinama (SD) (MD+SD) and benign versus MD+SD. Although adaptive and fixed-size windowing perform similarly, adaptive windowing utilizes significantly fewer windows than fixed-size windows (number of windows per spectrum: 8 versus 16). Because adaptive windows retain most diagnostic information while reducing the number of windows needed for feature extraction, our results suggest that it isolates unique diagnostic features in optical spectra.
Adaptive Estimation of Intravascular Shear Rate Based on Parameter Optimization
NASA Astrophysics Data System (ADS)
Nitta, Naotaka; Takeda, Naoto
2008-05-01
The relationships between the intravascular wall shear stress, controlled by flow dynamics, and the progress of arteriosclerosis plaque have been clarified by various studies. Since the shear stress is determined by the viscosity coefficient and shear rate, both factors must be estimated accurately. In this paper, an adaptive method for improving the accuracy of quantitative shear rate estimation was investigated. First, the parameter dependence of the estimated shear rate was investigated in terms of the differential window width and the number of averaged velocity profiles based on simulation and experimental data, and then the shear rate calculation was optimized. The optimized result revealed that the proposed adaptive method of shear rate estimation was effective for improving the accuracy of shear rate calculation.
Distributed estimation for adaptive sensor selection in wireless sensor networks
NASA Astrophysics Data System (ADS)
Mahmoud, Magdi S.; Hassan Hamid, Matasm M.
2014-05-01
Wireless sensor networks (WSNs) are usually deployed for monitoring systems with the distributed detection and estimation of sensors. Sensor selection in WSNs is considered for target tracking. A distributed estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision measure is derived for active sensor selection. A consensus-based estimation method is proposed in this paper for heterogeneous WSNs with two types of sensors. The convergence properties of the proposed estimators are analyzed under time-varying inputs. Accordingly, a new adaptive sensor selection (ASS) algorithm is presented in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms.
Airborne spectral radiometry for crop health and yield estimation
NASA Astrophysics Data System (ADS)
O'Mongain, Eon; Green, S. E.; Walsh, James E.; Burke, J.
1995-01-01
Spectral reflectance measurements have been made over sugar beet crops from a helicopter during 1991, 1992, and 1993 using a portable multichannel spectrometer system. In 1994 the studies were extended to demonstrate the potential for the measurement of stress in other crops. The observations are made from an altitude of about 150 m over the spectral range 420 nm to 810 nm, with a bandwidth of 5 nm. Downwelling solar irradiance and upwelling reflected irradiance are monitored by the multichannel spectrometer simultaneously. Both the absolute values of the reflectance at each wavelength and the variance of these reflectance values across each plot are shown to be related to the state of the crop. Concurrent agricultural ground truth consisting of fresh leaf weight and dry matter accumulation, is used in defining the crop yield models. The study aims to determine the appropriate radiometrically derived parameters which could be used as alternative model inputs. Although significant spectral differences exist and can be extracted by conventional band ratio or singular value decomposition techniques, the variance in the samples of ground truth data constrain the ability to define meaningful radiometric parameters. Improved experimental procedures are proposed.
On Using Exponential Parameter Estimators with an Adaptive Controller
NASA Technical Reports Server (NTRS)
Patre, Parag; Joshi, Suresh M.
2011-01-01
Typical adaptive controllers are restricted to using a specific update law to generate parameter estimates. This paper investigates the possibility of using any exponential parameter estimator with an adaptive controller such that the system tracks a desired trajectory. The goal is to provide flexibility in choosing any update law suitable for a given application. The development relies on a previously developed concept of controller/update law modularity in the adaptive control literature, and the use of a converse Lyapunov-like theorem. Stability analysis is presented to derive gain conditions under which this is possible, and inferences are made about the tracking error performance. The development is based on a class of Euler-Lagrange systems that are used to model various engineering systems including space robots and manipulators.
The Role of Parametric Assumptions in Adaptive Bayesian Estimation
ERIC Educational Resources Information Center
Alcala-Quintana, Rocio; Garcia-Perez, Miguel A.
2004-01-01
Variants of adaptive Bayesian procedures for estimating the 5% point on a psychometric function were studied by simulation. Bias and standard error were the criteria to evaluate performance. The results indicated a superiority of (a) uniform priors, (b) model likelihood functions that are odd symmetric about threshold and that have parameter…
Adaptive Channel Estimation for MIMO-Constant Envelope Modulation
NASA Astrophysics Data System (ADS)
Mahmoud Mohamed, Ehab; Muta, Osamu; Furukawa, Hiroshi
The authors have proposed Multi-Input Multi-Output (MIMO)-Constant Envelope Modulation, (MIMO-CEM), as a power and complexity efficient alternative to MIMO-OFDM, suitable for wireless backhaul networks in which relay nodes are fixed in their positions. One of the major problems hindering the real application of MIMO-CEM is to estimate MIMO channel characteristics. MIMO-CEM is based upon two contrary schemes; one is nonlinear equalization such as maximum likelihood sequence estimator, which needs accurate channel information to replicate the received signal passing through it. The other is a low resolution analog-to-digital converter (ADC), e.g., 1-bit in the default operation that removes the received signal amplitude fluctuation. In this paper, as a solution to the channel estimation problem in MIMO-CEM with low resolution ADC receiver, we propose an adaptive MIMO-CEM channel estimation scheme where iterative adaptive channel estimation is carried out to minimize the error between the received preamble signal and the replicated one. We also prove that Code Division Multiplexing (CDM) preamble transmission is effective in estimating MIMO channel parameters in the presence of large quantization noise. Computer simulation results show that MIMO-CEM with the proposed channel estimator using CDM preambles achieves identical BER performance to that with the ideal channel estimation even in presence of severe quantization noise caused by a low resolution ADC.
NASA Astrophysics Data System (ADS)
Rosenberg, Duane; Fournier, Aimé; Fischer, Paul; Pouquet, Annick
2006-06-01
An object-oriented geophysical and astrophysical spectral-element adaptive refinement (GASpAR) code is introduced. Like most spectral-element codes, GASpAR combines finite-element efficiency with spectral-method accuracy. It is also designed to be flexible enough for a range of geophysics and astrophysics applications where turbulence or other complex multiscale problems arise. The formalism accommodates both conforming and non-conforming elements. Several aspects of this code derive from existing methods, but here are synthesized into a new formulation of dynamic adaptive refinement (DARe) of non-conforming h-type. As a demonstration of the code, several new 2D test cases are introduced that have time-dependent analytic solutions and exhibit localized flow features, including the 2D Burgers equation with straight, curved-radial and oblique-colliding fronts. These are proposed as standard test problems for comparable DARe codes. Quantitative errors are reported for 2D spatial and temporal convergence of DARe.
Xia, Peng; Shimozato, Yuki; Ito, Yasunori; Tahara, Tatsuki; Kakue, Takashi; Awatsuji, Yasuhiro; Nishio, Kenzo; Ura, Shogo; Kubota, Toshihiro; Matoba, Osamu
2011-12-01
We propose a color digital holography by using spectral estimation technique to improve the color reproduction of objects. In conventional color digital holography, there is insufficient spectral information in holograms, and the color of the reconstructed images depend on only reflectances at three discrete wavelengths used in the recording of holograms. Therefore the color-composite image of the three reconstructed images is not accurate in color reproduction. However, in our proposed method, the spectral estimation technique was applied, which has been reported in multispectral imaging. According to the spectral estimation technique, the continuous spectrum of object can be estimated and the color reproduction is improved. The effectiveness of the proposed method was confirmed by a numerical simulation and an experiment, and, in the results, the average color differences are decreased from 35.81 to 7.88 and from 43.60 to 25.28, respectively. PMID:22193005
Methodologies for Adaptive Flight Envelope Estimation and Protection
NASA Technical Reports Server (NTRS)
Tang, Liang; Roemer, Michael; Ge, Jianhua; Crassidis, Agamemnon; Prasad, J. V. R.; Belcastro, Christine
2009-01-01
This paper reports the latest development of several techniques for adaptive flight envelope estimation and protection system for aircraft under damage upset conditions. Through the integration of advanced fault detection algorithms, real-time system identification of the damage/faulted aircraft and flight envelop estimation, real-time decision support can be executed autonomously for improving damage tolerance and flight recoverability. Particularly, a bank of adaptive nonlinear fault detection and isolation estimators were developed for flight control actuator faults; a real-time system identification method was developed for assessing the dynamics and performance limitation of impaired aircraft; online learning neural networks were used to approximate selected aircraft dynamics which were then inverted to estimate command margins. As off-line training of network weights is not required, the method has the advantage of adapting to varying flight conditions and different vehicle configurations. The key benefit of the envelope estimation and protection system is that it allows the aircraft to fly close to its limit boundary by constantly updating the controller command limits during flight. The developed techniques were demonstrated on NASA s Generic Transport Model (GTM) simulation environments with simulated actuator faults. Simulation results and remarks on future work are presented.
Online Parameter Estimation and Adaptive Control of Magnetic Wire Actuators
NASA Astrophysics Data System (ADS)
Karve, Harshwardhan
Cantilevered magnetic wires and fibers can be used as actuators in microfluidic applications. The actuator may be unstable in some range of displacements. Precise position control is required for actuation. The goal of this work is to develop position controllers for cantilevered magnetic wires. A simple exact model knowledge (EMK) controller can be used for position control, but the actuator needs to be modeled accurately for the EMK controller to work. Continuum models have been proposed for magnetic wires in literature. Reduced order models have also been proposed. A one degree of freedom model sufficiently describes the dynamics of a cantilevered wire in the field of one magnet over small displacements. This reduced order model is used to develop the EMK controller here. The EMK controller assumes that model parameters are known accurately. Some model parameters depend on the magnetic field. However, the effect of the magnetic field on the wire is difficult to measure in practice. Stability analysis shows that an inaccurate estimate of the magnetic field introduces parametric perturbations in the closed loop system. This makes the system less robust to disturbances. Therefore, the model parameters need to be estimated accurately for the EMK controller to work. An adaptive observer that can estimate system parameters on-line and reduce parametric perturbations is designed here. The adaptive observer only works if the system is stable. The EMK controller is not guaranteed to stabilize the system under perturbations. Precise tuning of parameters is required to stabilize the system using the EMK controller. Therefore, a controller that stabilizes the system using imprecise model parameters is required for the observer to work as intended. The adaptive observer estimates system states and parameters. These states and parameters are used here to implement an indirect adaptive controller. This indirect controller can stabilize the system using imprecise initial
Estimating Skin Cancer Risk: Evaluating Mobile Computer-Adaptive Testing
Djaja, Ngadiman; Janda, Monika; Olsen, Catherine M; Whiteman, David C
2016-01-01
Background Response burden is a major detriment to questionnaire completion rates. Computer adaptive testing may offer advantages over non-adaptive testing, including reduction of numbers of items required for precise measurement. Objective Our aim was to compare the efficiency of non-adaptive (NAT) and computer adaptive testing (CAT) facilitated by Partial Credit Model (PCM)-derived calibration to estimate skin cancer risk. Methods We used a random sample from a population-based Australian cohort study of skin cancer risk (N=43,794). All 30 items of the skin cancer risk scale were calibrated with the Rasch PCM. A total of 1000 cases generated following a normal distribution (mean [SD] 0 [1]) were simulated using three Rasch models with three fixed-item (dichotomous, rating scale, and partial credit) scenarios, respectively. We calculated the comparative efficiency and precision of CAT and NAT (shortening of questionnaire length and the count difference number ratio less than 5% using independent t tests). Results We found that use of CAT led to smaller person standard error of the estimated measure than NAT, with substantially higher efficiency but no loss of precision, reducing response burden by 48%, 66%, and 66% for dichotomous, Rating Scale Model, and PCM models, respectively. Conclusions CAT-based administrations of the skin cancer risk scale could substantially reduce participant burden without compromising measurement precision. A mobile computer adaptive test was developed to help people efficiently assess their skin cancer risk. PMID:26800642
Parameter Estimation Analysis for Hybrid Adaptive Fault Tolerant Control
NASA Astrophysics Data System (ADS)
Eshak, Peter B.
Research efforts have increased in recent years toward the development of intelligent fault tolerant control laws, which are capable of helping the pilot to safely maintain aircraft control at post failure conditions. Researchers at West Virginia University (WVU) have been actively involved in the development of fault tolerant adaptive control laws in all three major categories: direct, indirect, and hybrid. The first implemented design to provide adaptation was a direct adaptive controller, which used artificial neural networks to generate augmentation commands in order to reduce the modeling error. Indirect adaptive laws were implemented in another controller, which utilized online PID to estimate and update the controller parameter. Finally, a new controller design was introduced, which integrated both direct and indirect control laws. This controller is known as hybrid adaptive controller. This last control design outperformed the two earlier designs in terms of less NNs effort and better tracking quality. The performance of online PID has an important role in the quality of the hybrid controller; therefore, the quality of the estimation will be of a great importance. Unfortunately, PID is not perfect and the online estimation process has some inherited issues; the online PID estimates are primarily affected by delays and biases. In order to ensure updating reliable estimates to the controller, the estimator consumes some time to converge. Moreover, the estimator will often converge to a biased value. This thesis conducts a sensitivity analysis for the estimation issues, delay and bias, and their effect on the tracking quality. In addition, the performance of the hybrid controller as compared to direct adaptive controller is explored. In order to serve this purpose, a simulation environment in MATLAB/SIMULINK has been created. The simulation environment is customized to provide the user with the flexibility to add different combinations of biases and delays to
Estimated spectrum adaptive postfilter and the iterative prepost filtering algirighms
NASA Technical Reports Server (NTRS)
Linares, Irving (Inventor)
2004-01-01
The invention presents The Estimated Spectrum Adaptive Postfilter (ESAP) and the Iterative Prepost Filter (IPF) algorithms. These algorithms model a number of image-adaptive post-filtering and pre-post filtering methods. They are designed to minimize Discrete Cosine Transform (DCT) blocking distortion caused when images are highly compressed with the Joint Photographic Expert Group (JPEG) standard. The ESAP and the IPF techniques of the present invention minimize the mean square error (MSE) to improve the objective and subjective quality of low-bit-rate JPEG gray-scale images while simultaneously enhancing perceptual visual quality with respect to baseline JPEG images.
Li, Tianhao; Galvin, John J.; Fu, Qian-Jie
2009-01-01
Objectives Cochlear implant listeners are able to at least partially adapt to the spectral mismatch associated with the implant device and speech processor via daily exposure and/or explicit training. The overall goal of this study was to investigate interactions between short-term unsupervised learning (i.e., passive adaptation) and the degree of spectral mismatch in normal-hearing listeners’ adaptation to spectrally-shifted vowels. Methods Normal-hearing subjects were tested while listening to acoustic cochlear implant simulations. Unsupervised learning was measured by testing vowel recognition repeatedly over a five-day period; no feedback or explicit training was provided. In Experiment 1, subjects listened to 8-channel, sine-wave vocoded speech. The spectral envelope was compressed to simulate a 16 mm cochlear implant electrode array. The analysis bands were fixed and the compressed spectral envelope was linearly shifted toward the base by 3.6, 6 or 8.3 mm to simulate different insertion depths of the electrode array, resulting in a slight, moderate, or severe spectral shift. In Experiment 2, half the subjects were exclusively exposed to a severe shift with 8 or 16 channels (“exclusive groups”), and half the subjects were exposed to 8-channel severely-shifted speech, 16-channel severely-shifted speech and 8-channel moderately-shifted speech, alternately presented within each test session (“mixed group”). The region of stimulation in the cochlea was fixed (16 mm in extent, 15 mm from the apex) and the analysis bands were manipulated to create the spectral shift conditions. To determine whether increased spectral resolution would improve adaptation, subjects were exposed to 8- or 16-channel severely-shifted speech. Results In Experiment 1, at the end of the adaptation period, there was no significant difference between 8-channel speech that was spectrally-matched or shifted by 3.6 mm. There was a significant, but less-complete adaptation to the 6 mm
Rainfall Estimation over the Nile Basin using Multi-Spectral, Multi- Instrument Satellite Techniques
NASA Astrophysics Data System (ADS)
Habib, E.; Kuligowski, R.; Sazib, N.; Elshamy, M.; Amin, D.; Ahmed, M.
2012-04-01
Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite- derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared (IR) algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). In this study, the authors report on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self- Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application by NFC over the Nile Basin. The algorithm uses a set of rainfall predictors that come from multi-spectral Infrared cloud top observations and self-calibrate them to a set of predictands that come from the more accurate, but less frequent, Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels that have become recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as the Special Sensor Microwave/Imager (SSM/I), the Special Sensor Microwave Imager and Sounder (SSMIS), the Advanced Microwave Sounding Unit (AMSU), the Advanced Microwave Scanning Radiometer on EOS (AMSR-E), and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real- time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static
Efficient integration of spectral features for vehicle tracking utilizing an adaptive sensor
NASA Astrophysics Data System (ADS)
Uzkent, Burak; Hoffman, Matthew J.; Vodacek, Anthony
2015-03-01
Object tracking in urban environments is an important and challenging problem that is traditionally tackled using visible and near infrared wavelengths. By inserting extended data such as spectral features of the objects one can improve the reliability of the identification process. However, huge increase in data created by hyperspectral imaging is usually prohibitive. To overcome the complexity problem, we propose a persistent air-to-ground target tracking system inspired by a state-of-the-art, adaptive, multi-modal sensor. The adaptive sensor is capable of providing panchromatic images as well as the spectra of desired pixels. This addresses the data challenge of hyperspectral tracking by only recording spectral data as needed. Spectral likelihoods are integrated into a data association algorithm in a Bayesian fashion to minimize the likelihood of misidentification. A framework for controlling spectral data collection is developed by incorporating motion segmentation information and prior information from a Gaussian Sum filter (GSF) movement predictions from a multi-model forecasting set. An intersection mask of the surveillance area is extracted from OpenStreetMap source and incorporated into the tracking algorithm to perform online refinement of multiple model set. The proposed system is tested using challenging and realistic scenarios generated in an adverse environment.
Wavelet-Based Speech Enhancement Using Time-Adapted Noise Estimation
NASA Astrophysics Data System (ADS)
Lei, Sheau-Fang; Tung, Ying-Kai
Spectral subtraction is commonly used for speech enhancement in a single channel system because of the simplicity of its implementation. However, this algorithm introduces perceptually musical noise while suppressing the background noise. We propose a wavelet-based approach in this paper for suppressing the background noise for speech enhancement in a single channel system. The wavelet packet transform, which emulates the human auditory system, is used to decompose the noisy signal into critical bands. Wavelet thresholding is then temporally adjusted with the noise power by time-adapted noise estimation. The proposed algorithm can efficiently suppress the noise while reducing speech distortion. Experimental results, including several objective measurements, show that the proposed wavelet-based algorithm outperforms spectral subtraction and other wavelet-based denoising approaches for speech enhancement for nonstationary noise environments.
Adaptive whitening of the electromyogram to improve amplitude estimation.
Clancy, E A; Farry, K A
2000-06-01
Previous research showed that whitening the surface electromyogram (EMG) can improve EMG amplitude estimation (where EMG amplitude is defined as the time-varying standard deviation of the EMG). However, conventional whitening via a linear filter seems to fail at low EMG amplitude levels, perhaps due to additive background noise in the measured EMG. This paper describes an adaptive whitening technique that overcomes this problem by cascading a nonadaptive whitening filter, an adaptive Wiener filter, and an adaptive gain correction. These stages can be calibrated from two, five second duration, constant-angle, constant-force contractions, one at a reference level [e.g., 50% maximum voluntary contraction (MVC)] and one at 0% MVC. In experimental studies, subjects used real-time EMG amplitude estimates to track a uniform-density, band-limited random target. With a 0.25-Hz bandwidth target, either adaptive whitening or multiple-channel processing reduced the tracking error roughly half-way to the error achieved using the dynamometer signal as the feedback. At the 1.00-Hz bandwidth, all of the EMG processors had errors equivalent to that of the dynamometer signal, reflecting that errors in this task were dominated by subjects' inability to track targets at this bandwidth. Increases in the additive noise level, smoothing window length, and tracking bandwidth diminish the advantages of whitening. PMID:10833845
Bayesian Adaptive Estimation of Psychometric Functions in Noisy Environments
NASA Astrophysics Data System (ADS)
Aihara, Takatsugu; Kitajo, Keiichi; Nozaki, Daichi; Yamamoto, Yoshiharu
2007-07-01
We propose a new psychometric model incorporating noise as well as stimulus effects, based on recent findings that noise can improve human perception via a mechanism of stochastic resonance (SR). This model assumes that the psychometric function can be regarded as a bivariate function of noise and stimulus intensities. The algorithm of the Ψ Bayesian adaptive estimation method is modified so that it is applicable to our new model. In computer simulations, our new procedure successfully estimates the bivariate psychometric function within a few hundred trials. We also demonstrate several examples in which the procedure is applied to actual psychophysical experiments.
Long-wavelength adaptation reveals slow, spectrally opponent inputs to the human luminance pathway.
Stockman, Andrew; Plummer, Daniel J
2005-01-01
In addition to its expected fast, additive L- and M-cone inputs (L + M), the luminance pathway has slow, spectrally opponent inputs. We have previously shown that on long-wavelength fields, the dominant slow signals change from L-M at moderate intensity levels to M-L signals at high. Here, we focus on the transition between them, which we find is marked by substantial changes in temporal phase delay, and by large and unexpected shifts in flicker spectral sensitivity. At moderate temporal frequencies, counter to the selective adaptation caused by the field, spectral sensitivity changes from being M-cone-like to more L-cone-like. These changes can be accounted for by a change in the relative strengths of the slow spectrally opponent cone signals from L-M exceeding M-L below the transition to M-L exceeding L-M above it, and by the resulting changes in constructive and destructive interference between the dominant signal components. We speculate that the transition is caused by the deep-red field becoming equivalent, postreceptorally, to a green field at high bleaching levels. These results further challenge the dogma that there are separable psychophysical channels for the transmission and processing of color and luminance information. Although its output generates an achromatic percept, the luminance channel has spectrally opponent inputs.
NASA Technical Reports Server (NTRS)
Freedman, Ellis; Ryan, Robert; Pagnutti, Mary; Holekamp, Kara; Gasser, Gerald; Carver, David; Greer, Randy
2007-01-01
Spectral Dark Subtraction (SDS) provides good ground reflectance estimates across a variety of atmospheric conditions with no knowledge of those conditions. The algorithm may be sensitive to errors from stray light, calibration, and excessive haze/water vapor. SDS seems to provide better estimates than traditional algorithms using on-site atmospheric measurements much of the time.
NASA Astrophysics Data System (ADS)
Funamizu, Hideki; Tokuno, Yuta; Aizu, Yoshihisa
2016-06-01
We investigate the estimation of spectral transmittance curves in color digital holographic microscopy using speckle illuminations. In color digital holography, it has the disadvantage in that the color-composite image gives poor color information due to the use of lasers with the two or three wavelengths. To overcome this disadvantage, the Wiener estimation method and an averaging process using multiple holograms are applied to color digital holographic microscopy. Estimated spectral transmittance and color-composite images are shown to indicate the usefulness of the proposed method.
[Estimation of Hunan forest carbon density based on spectral mixture analysis of MODIS data].
Yan, En-ping; Lin, Hui; Wang, Guang-xing; Chen, Zhen-xiong
2015-11-01
With the fast development of remote sensing technology, combining forest inventory sample plot data and remotely sensed images has become a widely used method to map forest carbon density. However, the existence of mixed pixels often impedes the improvement of forest carbon density mapping, especially when low spatial resolution images such as MODIS are used. In this study, MODIS images and national forest inventory sample plot data were used to conduct the study of estimation for forest carbon density. Linear spectral mixture analysis with and without constraint, and nonlinear spectral mixture analysis were compared to derive the fractions of different land use and land cover (LULC) types. Then sequential Gaussian co-simulation algorithm with and without the fraction images from spectral mixture analyses were employed to estimate forest carbon density of Hunan Province. Results showed that 1) Linear spectral mixture analysis with constraint, leading to a mean RMSE of 0.002, more accurately estimated the fractions of LULC types than linear spectral and nonlinear spectral mixture analyses; 2) Integrating spectral mixture analysis model and sequential Gaussian co-simulation algorithm increased the estimation accuracy of forest carbon density to 81.5% from 74.1%, and decreased the RMSE to 5.18 from 7.26; and 3) The mean value of forest carbon density for the province was 30.06 t · hm(-2), ranging from 0.00 to 67.35 t · hm(-2). This implied that the spectral mixture analysis provided a great potential to increase the estimation accuracy of forest carbon density on regional and global level.
[Estimation of Hunan forest carbon density based on spectral mixture analysis of MODIS data].
Yan, En-ping; Lin, Hui; Wang, Guang-xing; Chen, Zhen-xiong
2015-11-01
With the fast development of remote sensing technology, combining forest inventory sample plot data and remotely sensed images has become a widely used method to map forest carbon density. However, the existence of mixed pixels often impedes the improvement of forest carbon density mapping, especially when low spatial resolution images such as MODIS are used. In this study, MODIS images and national forest inventory sample plot data were used to conduct the study of estimation for forest carbon density. Linear spectral mixture analysis with and without constraint, and nonlinear spectral mixture analysis were compared to derive the fractions of different land use and land cover (LULC) types. Then sequential Gaussian co-simulation algorithm with and without the fraction images from spectral mixture analyses were employed to estimate forest carbon density of Hunan Province. Results showed that 1) Linear spectral mixture analysis with constraint, leading to a mean RMSE of 0.002, more accurately estimated the fractions of LULC types than linear spectral and nonlinear spectral mixture analyses; 2) Integrating spectral mixture analysis model and sequential Gaussian co-simulation algorithm increased the estimation accuracy of forest carbon density to 81.5% from 74.1%, and decreased the RMSE to 5.18 from 7.26; and 3) The mean value of forest carbon density for the province was 30.06 t · hm(-2), ranging from 0.00 to 67.35 t · hm(-2). This implied that the spectral mixture analysis provided a great potential to increase the estimation accuracy of forest carbon density on regional and global level. PMID:26915200
NASA Technical Reports Server (NTRS)
Linares, Irving; Mersereau, Russell M.; Smith, Mark J. T.
1994-01-01
Two representative sample images of Band 4 of the Landsat Thematic Mapper are compressed with the JPEG algorithm at 8:1, 16:1 and 24:1 Compression Ratios for experimental browsing purposes. We then apply the Optimal PSNR Estimated Spectra Adaptive Postfiltering (ESAP) algorithm to reduce the DCT blocking distortion. ESAP reduces the blocking distortion while preserving most of the image's edge information by adaptively postfiltering the decoded image using the block's spectral information already obtainable from each block's DCT coefficients. The algorithm iteratively applied a one dimensional log-sigmoid weighting function to the separable interpolated local block estimated spectra of the decoded image until it converges to the optimal PSNR with respect to the original using a 2-D steepest ascent search. Convergence is obtained in a few iterations for integer parameters. The optimal logsig parameters are transmitted to the decoder as a negligible byte of overhead data. A unique maxima is guaranteed due to the 2-D asymptotic exponential overshoot shape of the surface generated by the algorithm. ESAP is based on a DFT analysis of the DCT basis functions. It is implemented with pixel-by-pixel spatially adaptive separable FIR postfilters. PSNR objective improvements between 0.4 to 0.8 dB are shown together with their corresponding optimal PSNR adaptive postfiltered images.
Effect of Spectral Index Distribution on Estimating the AGN Radio Luminosity Function
NASA Astrophysics Data System (ADS)
Yuan, Zunli; Wang, Jiancheng; Zhou, Ming; Mao, Jirong
2016-10-01
In this paper, we scrutinize the effect of spectral index distribution on estimating the active galactic nucleus radio luminosity function (RLF) by a Monte Carlo method. We find that the traditional bivariate RLF estimators can cause bias in varying degrees. The bias is especially pronounced for the flat-spectrum radio sources whose spectral index distribution is more scattered. We believe that the bias is caused because the K-corrections complicate the truncation boundary on the L-z plane of the sample, but the traditional bivariate RLF estimators have difficulty dealing with this boundary condition properly. We suggest that the spectral index distribution should be incorporated into the RLF analysis process to obtain a robust estimation. This drives the need for a trivariate function of the form Φ(α, z, L), which we show provides an accurate basis for measuring the RLF.
Digital spectral estimation and modeling of Space Shuttle flight data
NASA Technical Reports Server (NTRS)
Spanos, P. D.; Mushung, L. J.; Nelson, D. A. R., Jr.; Hamilton, D. A.
1988-01-01
Application of the digital signal processing technique of autoregressive-moving-average (ARMA) modeling to the estimation of power spectra and shock spectra from Space Shuttle lift-off flight accelerograms is described in this paper. The background for application to ARMA of lift-off accelerograms which are non-stationary in nature is exemplified through a step-by-step discussion of actual numerical results. Included is a discussion of pertinent mathematical background for the ARMA approximations. Potential areas for application of ARMA modeling in payload integration activities are suggested.
An Adaptive Motion Estimation Scheme for Video Coding
Gao, Yuan; Jia, Kebin
2014-01-01
The unsymmetrical-cross multihexagon-grid search (UMHexagonS) is one of the best fast Motion Estimation (ME) algorithms in video encoding software. It achieves an excellent coding performance by using hybrid block matching search pattern and multiple initial search point predictors at the cost of the computational complexity of ME increased. Reducing time consuming of ME is one of the key factors to improve video coding efficiency. In this paper, we propose an adaptive motion estimation scheme to further reduce the calculation redundancy of UMHexagonS. Firstly, new motion estimation search patterns have been designed according to the statistical results of motion vector (MV) distribution information. Then, design a MV distribution prediction method, including prediction of the size of MV and the direction of MV. At last, according to the MV distribution prediction results, achieve self-adaptive subregional searching by the new estimation search patterns. Experimental results show that more than 50% of total search points are dramatically reduced compared to the UMHexagonS algorithm in JM 18.4 of H.264/AVC. As a result, the proposed algorithm scheme can save the ME time up to 20.86% while the rate-distortion performance is not compromised. PMID:24672313
An adaptive motion estimation scheme for video coding.
Liu, Pengyu; Gao, Yuan; Jia, Kebin
2014-01-01
The unsymmetrical-cross multihexagon-grid search (UMHexagonS) is one of the best fast Motion Estimation (ME) algorithms in video encoding software. It achieves an excellent coding performance by using hybrid block matching search pattern and multiple initial search point predictors at the cost of the computational complexity of ME increased. Reducing time consuming of ME is one of the key factors to improve video coding efficiency. In this paper, we propose an adaptive motion estimation scheme to further reduce the calculation redundancy of UMHexagonS. Firstly, new motion estimation search patterns have been designed according to the statistical results of motion vector (MV) distribution information. Then, design a MV distribution prediction method, including prediction of the size of MV and the direction of MV. At last, according to the MV distribution prediction results, achieve self-adaptive subregional searching by the new estimation search patterns. Experimental results show that more than 50% of total search points are dramatically reduced compared to the UMHexagonS algorithm in JM 18.4 of H.264/AVC. As a result, the proposed algorithm scheme can save the ME time up to 20.86% while the rate-distortion performance is not compromised.
Spectral estimates of intercepted solar radiation by corn and soybean canopies
NASA Technical Reports Server (NTRS)
Gallo, K. P.; Brooks, C. C.; Daughtry, C. S. T.; Bauer, M. E.; Vanderbilt, V. C.
1982-01-01
Attention is given to the development of methods for combining spectral and meteorological data in crop yield models which are capable of providing accurate estimates of crop condition and yields throughout the growing season. The present investigation is concerned with initial tests of these concepts using spectral and agronomic data acquired in controlled experiments. The data were acquired at the Purdue University Agronomy Farm, 10 km northwest of West Lafayette, Indiana. Data were obtained throughout several growing seasons for corn and soybeans. Five methods or models for predicting yields were examined. On the basis of the obtained results, it is concluded that estimating intercepted solar radiation using spectral data is a viable approach for merging spectral and meteorological data in crop yield models.
Intercepted photosynthetically active radiation estimated by spectral reflectance
NASA Technical Reports Server (NTRS)
Hatfield, J. L.; Asrar, G.; Kanemasu, E. T.
1984-01-01
Interception of photosynthetically active radiation (PAR) was evaluated relative to greenness and normalized difference (MSS (7-5)/(7+5) for five planting dates of wheat for 1978-79 and 1979-80 at Phoenix, Arizona. Intercepted PAR was calculated from leaf area index and stage of growth. Linear relatinships were found with greeness and normalized difference with separate relatinships describing growth and senescence of the crop. Normalized difference was significantly better than greenness for all planting dates. For the leaf area growth portion of the season the relation between PAR interception and normalized difference was the same over years and planting dates. For the leaf senescence phase the relationships showed more variability due to the lack of data on light interception in sparse and senescing canopies. Normalized difference could be used to estimate PAR interception throughout a growing season.
Adaptive distributed Kalman filtering with wind estimation for astronomical adaptive optics.
Massioni, Paolo; Gilles, Luc; Ellerbroek, Brent
2015-12-01
In the framework of adaptive optics (AO) for astronomy, it is a common assumption to consider the atmospheric turbulent layers as "frozen flows" sliding according to the wind velocity profile. For this reason, having knowledge of such a velocity profile is beneficial in terms of AO control system performance. In this paper we show that it is possible to exploit the phase estimate from a Kalman filter running on an AO system in order to estimate wind velocity. This allows the update of the Kalman filter itself with such knowledge, making it adaptive. We have implemented such an adaptive controller based on the distributed version of the Kalman filter, for a realistic simulation of a multi-conjugate AO system with laser guide stars on a 30 m telescope. Simulation results show that this approach is effective and promising and the additional computational cost with respect to the distributed filter is negligible. Comparisons with a previously published slope detection and ranging wind profiler are made and the impact of turbulence profile quantization is assessed. One of the main findings of the paper is that all flavors of the adaptive distributed Kalman filter are impacted more significantly by turbulence profile quantization than the static minimum mean square estimator which does not incorporate wind profile information.
A spectral reflectance estimation technique using multispectral data from the Viking lander camera
NASA Technical Reports Server (NTRS)
Park, S. K.; Huck, F. O.
1976-01-01
A technique is formulated for constructing spectral reflectance curve estimates from multispectral data obtained with the Viking lander camera. The multispectral data are limited to six spectral channels in the wavelength range from 0.4 to 1.1 micrometers and most of these channels exhibit appreciable out-of-band response. The output of each channel is expressed as a linear (integral) function of the (known) solar irradiance, atmospheric transmittance, and camera spectral responsivity and the (unknown) spectral responsivity and the (unknown) spectral reflectance. This produces six equations which are used to determine the coefficients in a representation of the spectral reflectance as a linear combination of known basis functions. Natural cubic spline reflectance estimates are produced for a variety of materials that can be reasonably expected to occur on Mars. In each case the dominant reflectance features are accurately reproduced, but small period features are lost due to the limited number of channels. This technique may be a valuable aid in selecting the number of spectral channels and their responsivity shapes when designing a multispectral imaging system.
NASA Astrophysics Data System (ADS)
Kira, Oz; Linker, Raphael; Gitelson, Anatoly
2015-06-01
Leaf pigment content provides valuable insight into the productivity, physiological and phenological status of vegetation. Measurement of spectral reflectance offers a fast, nondestructive method for pigment estimation. A number of methods were used previously for estimation of leaf pigment content, however, spectral bands employed varied widely among the models and data used. Our objective was to find informative spectral bands in three types of models, vegetation indices (VI), neural network (NN) and partial least squares (PLS) regression, for estimating leaf chlorophyll (Chl) and carotenoids (Car) contents of three unrelated tree species and to assess the accuracy of the models using a minimal number of bands. The bands selected by PLS, NN and VIs were in close agreement and did not depend on the data used. The results of the uninformative variable elimination PLS approach, where the reliability parameter was used as an indicator of the information contained in the spectral bands, confirmed the bands selected by the VIs, NN, and PLS models. All three types of models were able to accurately estimate Chl content with coefficient of variation below 12% for all three species with VI showing the best performance. NN and PLS using reflectance in four spectral bands were able to estimate accurately Car content with coefficient of variation below 14%. The quantitative framework presented here offers a new way of estimating foliar pigment content not requiring model re-parameterization for different species. The approach was tested using the spectral bands of the future Sentinel-2 satellite and the results of these simulations showed that accurate pigment estimation from satellite would be possible.
Adaptive distributed video coding with correlation estimation using expectation propagation
NASA Astrophysics Data System (ADS)
Cui, Lijuan; Wang, Shuang; Jiang, Xiaoqian; Cheng, Samuel
2012-10-01
Distributed video coding (DVC) is rapidly increasing in popularity by the way of shifting the complexity from encoder to decoder, whereas no compression performance degrades, at least in theory. In contrast with conventional video codecs, the inter-frame correlation in DVC is explored at decoder based on the received syndromes of Wyner-Ziv (WZ) frame and side information (SI) frame generated from other frames available only at decoder. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations. Generally, the existing correlation estimation methods in DVC can be classified into two main types: pre-estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms pre-estimation techniques with the cost of increased decoding complexity (e.g., sampling methods). In this paper, we propose a low complexity adaptive DVC scheme using expectation propagation (EP), where correlation estimation is performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code. Among different approximate inference methods, EP generally offers better tradeoff between accuracy and complexity. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method.
Adaptive Distributed Video Coding with Correlation Estimation using Expectation Propagation.
Cui, Lijuan; Wang, Shuang; Jiang, Xiaoqian; Cheng, Samuel
2012-10-15
Distributed video coding (DVC) is rapidly increasing in popularity by the way of shifting the complexity from encoder to decoder, whereas no compression performance degrades, at least in theory. In contrast with conventional video codecs, the inter-frame correlation in DVC is explored at decoder based on the received syndromes of Wyner-Ziv (WZ) frame and side information (SI) frame generated from other frames available only at decoder. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations. Generally, the existing correlation estimation methods in DVC can be classified into two main types: pre-estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms pre-estimation techniques with the cost of increased decoding complexity (e.g., sampling methods). In this paper, we propose a low complexity adaptive DVC scheme using expectation propagation (EP), where correlation estimation is performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code. Among different approximate inference methods, EP generally offers better tradeoff between accuracy and complexity. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method.
NASA Astrophysics Data System (ADS)
D'Amato, Anthony M.
Input reconstruction is the process of using the output of a system to estimate its input. In some cases, input reconstruction can be accomplished by determining the output of the inverse of a model of the system whose input is the output of the original system. Inversion, however, requires an exact and fully known analytical model, and is limited by instabilities arising from nonminimum-phase zeros. The main contribution of this work is a novel technique for input reconstruction that does not require model inversion. This technique is based on a retrospective cost, which requires a limited number of Markov parameters. Retrospective cost input reconstruction (RCIR) does not require knowledge of nonminimum-phase zero locations or an analytical model of the system. RCIR provides a technique that can be used for model refinement, state estimation, and adaptive control. In the model refinement application, data are used to refine or improve a model of a system. It is assumed that the difference between the model output and the data is due to an unmodeled subsystem whose interconnection with the modeled system is inaccessible, that is, the interconnection signals cannot be measured and thus standard system identification techniques cannot be used. Using input reconstruction, these inaccessible signals can be estimated, and the inaccessible subsystem can be fitted. We demonstrate input reconstruction in a model refinement framework by identifying unknown physics in a space weather model and by estimating an unknown film growth in a lithium ion battery. The same technique can be used to obtain estimates of states that cannot be directly measured. Adaptive control can be formulated as a model-refinement problem, where the unknown subsystem is the idealized controller that minimizes a measured performance variable. Minimal modeling input reconstruction for adaptive control is useful for applications where modeling information may be difficult to obtain. We demonstrate
Preliminary evaluation of spectral, normal and meteorological crop stage estimation approaches
NASA Technical Reports Server (NTRS)
Cate, R. B.; Artley, J. A.; Doraiswamy, P. C.; Hodges, T.; Kinsler, M. C.; Phinney, D. E.; Sestak, M. L. (Principal Investigator)
1980-01-01
Several of the projects in the AgRISTARS program require crop phenology information, including classification, acreage and yield estimation, and detection of episodal events. This study evaluates several crop calendar estimation techniques for their potential use in the program. The techniques, although generic in approach, were developed and tested on spring wheat data collected in 1978. There are three basic approaches to crop stage estimation: historical averages for an area (normal crop calendars), agrometeorological modeling of known crop-weather relationships agrometeorological (agromet) crop calendars, and interpretation of spectral signatures (spectral crop calendars). In all, 10 combinations of planting and biostage estimation models were evaluated. Dates of stage occurrence are estimated with biases between -4 and +4 days while root mean square errors range from 10 to 15 days. Results are inconclusive as to the superiority of any of the models and further evaluation of the models with the 1979 data set is recommended.
Cao, Dingcai; Barrionuevo, Pablo A
2015-03-01
The intrinsic circadian clock requires photoentrainment to synchronize the 24-hour solar day. Therefore, light stimulation is an important component of chronobiological research. Currently, the chronobiological research field overwhelmingly uses photopic illuminance that is based on the luminous efficiency function, V(λ), to quantify light levels. However, recent discovery of intrinsically photosensitive retinal ganglion cells (ipRGCs), which are activated by self-contained melanopsin photopigment and also by inputs from rods and cones, makes light specification using a one-dimensional unit inadequate. Since the current understanding of how different photoreceptor inputs contribute to the circadian system through ipRGCs is limited, it is recommended to specify light in terms of the excitations of five photoreceptors (S-, M-, L-cones, rods and ipRGCs; Lucas et al., 2014). In the current study, we assessed whether the spectral outputs from a commercially available spectral watch (i.e. Actiwatch Spectrum) could be used to estimate photoreceptor excitations. Based on the color sensor spectral sensitivity functions from a previously published work, as well as from our measurements, we computed spectral outputs in the long-wavelength range (R), middle-wavelength range (G), short-wavelength range (B) and broadband range (W) under 52 CIE illuminants (25 daylight illuminants, 27 fluorescent lights). We also computed the photoreceptor excitations for each illuminant using human photoreceptor spectral sensitivity functions. Linear regression analyses indicated that the Actiwatch spectral outputs could predict photoreceptor excitations reliably, under the assumption of linear responses of the Actiwatch color sensors. In addition, R, G, B outputs could classify illuminant types (fluorescent versus daylight illuminants) satisfactorily. However, the assessment of actual Actiwatch recording under several testing light sources showed that the spectral outputs were subject to
Extended depth of focus adaptive optics spectral domain optical coherence tomography
Sasaki, Kazuhiro; Kurokawa, Kazuhiro; Makita, Shuichi; Yasuno, Yoshiaki
2012-01-01
We present an adaptive optics spectral domain optical coherence tomography (AO-SDOCT) with a long focal range by active phase modulation of the pupil. A long focal range is achieved by introducing AO-controlled third-order spherical aberration (SA). The property of SA and its effects on focal range are investigated in detail using the Huygens-Fresnel principle, beam profile measurement and OCT imaging of a phantom. The results indicate that the focal range is extended by applying SA, and the direction of extension can be controlled by the sign of applied SA. Finally, we demonstrated in vivo human retinal imaging by altering the applied SA. PMID:23082278
[Research on Spectral Scale Effect in the Estimation of Vegetation Leaf Chlorophyll Content].
Jiang, Hai-ling; Zhang, Li-fu; Yang, Hang; Chen, Xiao-pine; Tong, Qing-xi
2016-01-01
Spectral indices (SIs) method has been widely applied in the prediction of vegetation biochemical parameters. Take the diversity of spectral response of different sensors into consideration, this study aimed at researching spectral scale effect of SIs for estimating vegetation chlorophyll content (VCC). The 5 nm leaf reflectance data under 16 levels of chlorophyll content was got by the radiation transfer model PROSPECT and then simulated to multiple bandwidths spectrum (10-35 nm), using Gaussian spectral response function. Firstly, the correlation between SIs and VCC was studied. And then the sensitivity of SIs to VCC and bandwidth were analyzed and compared. Lastly, 112 samples were selected to verify the results above mentioned. The results show that Vegetation Index Based on Universal Pattern Decomposition Method (VIUPD) is the best spectral index due to its high sensitivity to VCC but low sensitivity to bandwidth, and can be successfully used to estimate VCC with coefficient of determination R2 of 0.99 and RMSE of 3.52 μg x cm(-2). Followed by VIUPD, Normalized Difference Vegetation Index (NDVI) and Simple Ratio Index (SRI) presented a comparatively good performance for VCC estimation (R2 > 0.89) with their prediction value of chlorophyll content was lower than the true value. The worse accuracy of other indices were also tested. Results demonstrate that spectral scale effect must be well-considered when estimating chlorophyll content, using SIs method. VIUPD introduced in the present study has the best performance, which reaffirms its special feature of comparatively sensor-independent and illustrates its potential ability in the area of estimating vegetation biochemical parameters based on multiple satellite data. PMID:27228762
[Research on Spectral Scale Effect in the Estimation of Vegetation Leaf Chlorophyll Content].
Jiang, Hai-ling; Zhang, Li-fu; Yang, Hang; Chen, Xiao-pine; Tong, Qing-xi
2016-01-01
Spectral indices (SIs) method has been widely applied in the prediction of vegetation biochemical parameters. Take the diversity of spectral response of different sensors into consideration, this study aimed at researching spectral scale effect of SIs for estimating vegetation chlorophyll content (VCC). The 5 nm leaf reflectance data under 16 levels of chlorophyll content was got by the radiation transfer model PROSPECT and then simulated to multiple bandwidths spectrum (10-35 nm), using Gaussian spectral response function. Firstly, the correlation between SIs and VCC was studied. And then the sensitivity of SIs to VCC and bandwidth were analyzed and compared. Lastly, 112 samples were selected to verify the results above mentioned. The results show that Vegetation Index Based on Universal Pattern Decomposition Method (VIUPD) is the best spectral index due to its high sensitivity to VCC but low sensitivity to bandwidth, and can be successfully used to estimate VCC with coefficient of determination R2 of 0.99 and RMSE of 3.52 μg x cm(-2). Followed by VIUPD, Normalized Difference Vegetation Index (NDVI) and Simple Ratio Index (SRI) presented a comparatively good performance for VCC estimation (R2 > 0.89) with their prediction value of chlorophyll content was lower than the true value. The worse accuracy of other indices were also tested. Results demonstrate that spectral scale effect must be well-considered when estimating chlorophyll content, using SIs method. VIUPD introduced in the present study has the best performance, which reaffirms its special feature of comparatively sensor-independent and illustrates its potential ability in the area of estimating vegetation biochemical parameters based on multiple satellite data.
Spectral estimation of gapped data and SAR imaging with angular diversity
NASA Astrophysics Data System (ADS)
Larsson, Erik G.; Li, Jian; Stoica, Peter; Liu, Guoqing; Williams, Robert L.
2001-08-01
The Amplitude and Phase EStimation (APES) approach to amplitude spectrum estimation has been receiving considerably attention recently. We develop an extension of APES for the spectral estimation of gapped (incomplete) data and apply it to synthetic aperture radar (SAR) imaging with angular diversity. It has recently been shown that APES minimizes a certain least-squares criterion with respect to the estimate of the spectrum. Our new algorithm is called gapped-data APES and is based on minimizing this criterion with respect to the missing data as well. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm and its applicability to SAR imaging with angular diversity.
Power spectral density estimation by spline smoothing in the frequency domain
NASA Technical Reports Server (NTRS)
Defigueiredo, R. J. P.; Thompson, J. R.
1972-01-01
An approach, based on a global averaging procedure, is presented for estimating the power spectrum of a second order stationary zero-mean ergodic stochastic process from a finite length record. This estimate is derived by smoothing, with a cubic smoothing spline, the naive estimate of the spectrum obtained by applying FFT techniques to the raw data. By means of digital computer simulated results, a comparison is made between the features of the present approach and those of more classical techniques of spectral estimation.
Power spectral density estimation by spline smoothing in the frequency domain.
NASA Technical Reports Server (NTRS)
De Figueiredo, R. J. P.; Thompson, J. R.
1972-01-01
An approach, based on a global averaging procedure, is presented for estimating the power spectrum of a second order stationary zero-mean ergodic stochastic process from a finite length record. This estimate is derived by smoothing, with a cubic smoothing spline, the naive estimate of the spectrum obtained by applying Fast Fourier Transform techniques to the raw data. By means of digital computer simulated results, a comparison is made between the features of the present approach and those of more classical techniques of spectral estimation.-
NASA Astrophysics Data System (ADS)
Howell, L. W.
2001-04-01
A simple power law model consisting of a single spectral index (alpha-1) is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 1013 eV, with a transition at knee energy (Ek) to a steeper spectral index alpha-2 > alpha-1 above Ek. The maximum likelihood procedure is developed for estimating these three spectral parameters of the broken power law energy spectrum from simulated detector responses. These estimates and their surrounding statistical uncertainty are being used to derive the requirements in energy resolution, calorimeter size, and energy response of a proposed sampling calorimeter for the Advanced Cosmic-ray Composition Experiment for the Space Station (ACCESS). This study thereby permits instrument developers to make important trade studies in design parameters as a function of the science objectives, which is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose rigorous practical limits to the design envelope.
Daniell method for power spectral density estimation in atomic force microscopy.
Labuda, Aleksander
2016-03-01
An alternative method for power spectral density (PSD) estimation--the Daniell method--is revisited and compared to the most prevalent method used in the field of atomic force microscopy for quantifying cantilever thermal motion--the Bartlett method. Both methods are shown to underestimate the Q factor of a simple harmonic oscillator (SHO) by a predictable, and therefore correctable, amount in the absence of spurious deterministic noise sources. However, the Bartlett method is much more prone to spectral leakage which can obscure the thermal spectrum in the presence of deterministic noise. By the significant reduction in spectral leakage, the Daniell method leads to a more accurate representation of the true PSD and enables clear identification and rejection of deterministic noise peaks. This benefit is especially valuable for the development of automated PSD fitting algorithms for robust and accurate estimation of SHO parameters from a thermal spectrum. PMID:27036781
Estimating the potential for adaptation of corals to climate warming.
Császár, Nikolaus B M; Ralph, Peter J; Frankham, Richard; Berkelmans, Ray; van Oppen, Madeleine J H
2010-03-18
The persistence of tropical coral reefs is threatened by rapidly increasing climate warming, causing a functional breakdown of the obligate symbiosis between corals and their algal photosymbionts (Symbiodinium) through a process known as coral bleaching. Yet the potential of the coral-algal symbiosis to genetically adapt in an evolutionary sense to warming oceans is unknown. Using a quantitative genetics approach, we estimated the proportion of the variance in thermal tolerance traits that has a genetic basis (i.e. heritability) as a proxy for their adaptive potential in the widespread Indo-Pacific reef-building coral Acropora millepora. We chose two physiologically different populations that associate respectively with one thermo-tolerant (Symbiodinium clade D) and one less tolerant symbiont type (Symbiodinium C2). In both symbiont types, pulse amplitude modulated (PAM) fluorometry and high performance liquid chromatography (HPLC) analysis revealed significant heritabilities for traits related to both photosynthesis and photoprotective pigment profile. However, quantitative real-time polymerase chain reaction (qRT-PCR) assays showed a lack of heritability in both coral host populations for their own expression of fundamental stress genes. Coral colony growth, contributed to by both symbiotic partners, displayed heritability. High heritabilities for functional key traits of algal symbionts, along with their short clonal generation time and high population sizes allow for their rapid thermal adaptation. However, the low overall heritability of coral host traits, along with the corals' long generation time, raise concern about the timely adaptation of the coral-algal symbiosis in the face of continued rapid climate warming.
Jorgensen, B B; Cohen, Y; Des Marais, D J
1987-01-01
We studied adaptation to spectral light distribution in undisturbed benthic communities of cyanobacterial mats growing in hypersaline ponds at Guerrero Negro, Baja California, Mexico. Microscale measurements of oxygen photosynthesis and action spectra were performed with microelectrodes; spectral radiance was measured with fiber-optic microprobes. The spatial resolution of all measurements was 0.1 mm, and the spectral resolution was 10 to 15 nm. Light attenuation spectra showed absorption predominantly by chlorophyll a (Chl a) (430 and 670 nm), phycocyanin (620 nm), and carotenoids (440 to 500 nm). Blue light (450 nm) was attenuated 10-fold more strongly than red light (600 nm). The action spectra of the surface film of diatoms accordingly showed activity over the whole spectrum, with maxima for Chl a and carotenoids. The underlying dense Microcoleus population showed almost exclusively activity dependent upon light harvesting by phycobilins at 550 to 660 nm. Maximum activity was at 580 and 650 nm, indicating absorption by phycoerythrin and phycocyanin as well as by allophycocyanin. Very little Chl a-dependent activity could be detected in the cyanobacterial action spectrum, even with additional 600-nm light to excite photosystem II. The depth distribution of photosynthesis showed detectable activity down to a depth of 0.8 to 2.5 mm, where the downwelling radiant flux at 600 nm was reduced to 0.2 to 0.6% of the surface flux. PMID:11536572
The use of the spectral method within the fast adaptive composite grid method
McKay, S.M.
1994-12-31
The use of efficient algorithms for the solution of partial differential equations has been sought for many years. The fast adaptive composite grid (FAC) method combines an efficient algorithm with high accuracy to obtain low cost solutions to partial differential equations. The FAC method achieves fast solution by combining solutions on different grids with varying discretizations and using multigrid like techniques to find fast solution. Recently, the continuous FAC (CFAC) method has been developed which utilizes an analytic solution within a subdomain to iterate to a solution of the problem. This has been shown to achieve excellent results when the analytic solution can be found. The CFAC method will be extended to allow solvers which construct a function for the solution, e.g., spectral and finite element methods. In this discussion, the spectral methods will be used to provide a fast, accurate solution to the partial differential equation. As spectral methods are more accurate than finite difference methods, the ensuing accuracy from this hybrid method outside of the subdomain will be investigated.
Jorgensen, B B; Cohen, Y; Des Marais, D J
1987-04-01
We studied adaptation to spectral light distribution in undisturbed benthic communities of cyanobacterial mats growing in hypersaline ponds at Guerrero Negro, Baja California, Mexico. Microscale measurements of oxygen photosynthesis and action spectra were performed with microelectrodes; spectral radiance was measured with fiber-optic microprobes. The spatial resolution of all measurements was 0.1 mm, and the spectral resolution was 10 to 15 nm. Light attenuation spectra showed absorption predominantly by chlorophyll a (Chl a) (430 and 670 nm), phycocyanin (620 nm), and carotenoids (440 to 500 nm). Blue light (450 nm) was attenuated 10-fold more strongly than red light (600 nm). The action spectra of the surface film of diatoms accordingly showed activity over the whole spectrum, with maxima for Chl a and carotenoids. The underlying dense Microcoleus population showed almost exclusively activity dependent upon light harvesting by phycobilins at 550 to 660 nm. Maximum activity was at 580 and 650 nm, indicating absorption by phycoerythrin and phycocyanin as well as by allophycocyanin. Very little Chl a-dependent activity could be detected in the cyanobacterial action spectrum, even with additional 600-nm light to excite photosystem II. The depth distribution of photosynthesis showed detectable activity down to a depth of 0.8 to 2.5 mm, where the downwelling radiant flux at 600 nm was reduced to 0.2 to 0.6% of the surface flux.
NASA Technical Reports Server (NTRS)
Jorgensen, B. B.; Cohen, Y.; Des Marais, D. J.
1987-01-01
We studied adaptation to spectral light distribution in undisturbed benthic communities of cyanobacterial mats growing in hypersaline ponds at Guerrero Negro, Baja California, Mexico. Microscale measurements of oxygen photosynthesis and action spectra were performed with microelectrodes; spectral radiance was measured with fiber-optic microprobes. The spatial resolution of all measurements was 0.1 mm, and the spectral resolution was 10 to 15 nm. Light attenuation spectra showed absorption predominantly by chlorophyll a (Chl a) (430 and 670 nm), phycocyanin (620 nm), and carotenoids (440 to 500 nm). Blue light (450 nm) was attenuated 10-fold more strongly than red light (600 nm). The action spectra of the surface film of diatoms accordingly showed activity over the whole spectrum, with maxima for Chl a and carotenoids. The underlying dense Microcoleus population showed almost exclusively activity dependent upon light harvesting by phycobilins at 550 to 660 nm. Maximum activity was at 580 and 650 nm, indicating absorption by phycoerythrin and phycocyanin as well as by allophycocyanin. Very little Chl a-dependent activity could be detected in the cyanobacterial action spectrum, even with additional 600-nm light to excite photosystem II. The depth distribution of photosynthesis showed detectable activity down to a depth of 0.8 to 2.5 mm, where the downwelling radiant flux at 600 nm was reduced to 0.2 to 0.6% of the surface flux.
Technology Transfer Automated Retrieval System (TEKTRAN)
This study investigated the potential of point scan Raman spectral imaging method for estimation of different ingredients and chemical contaminant concentration in food powder. Food powder sample was prepared by mixing sugar, vanillin, melamine and non-dairy cream at 5 different concentrations in a ...
Energy Detection Based Estimation of Channel Occupancy Rate with Adaptive Noise Estimation
NASA Astrophysics Data System (ADS)
Lehtomäki, Janne J.; Vuohtoniemi, Risto; Umebayashi, Kenta; Mäkelä, Juha-Pekka
Recently, there has been growing interest in opportunistically utilizing the 2.4GHz ISM-band. Numerous spectrum occupancy measurements covering the ISM-band have been performed to analyze the spectrum usage. However, in these campaigns the verification of the correctness of the obtained occupancy values for the highly dynamic ISM-band has not been presented. In this paper, we propose and verify channel occupancy rate (COR) estimation utilizing energy detection mechanism with a novel adaptive energy detection threshold setting method. The results are compared with the true reference COR values. Several different types of verification measurements showed that our setup can estimate the COR values of 802.11 traffic well, with negligible overestimation. The results from real-time real-life measurements also confirm that the proposed adaptive threshold setting method enables accurate thresholds even in the situations where multiple interferers are present in the received signal.
Spectral Estimation Model Construction of Heavy Metals in Mining Reclamation Areas
Dong, Jihong; Dai, Wenting; Xu, Jiren; Li, Songnian
2016-01-01
The study reported here examined, as the research subject, surface soils in the Liuxin mining area of Xuzhou, and explored the heavy metal content and spectral data by establishing quantitative models with Multivariable Linear Regression (MLR), Generalized Regression Neural Network (GRNN) and Sequential Minimal Optimization for Support Vector Machine (SMO-SVM) methods. The study results are as follows: (1) the estimations of the spectral inversion models established based on MLR, GRNN and SMO-SVM are satisfactory, and the MLR model provides the worst estimation, with R2 of more than 0.46. This result suggests that the stress sensitive bands of heavy metal pollution contain enough effective spectral information; (2) the GRNN model can simulate the data from small samples more effectively than the MLR model, and the R2 between the contents of the five heavy metals estimated by the GRNN model and the measured values are approximately 0.7; (3) the stability and accuracy of the spectral estimation using the SMO-SVM model are obviously better than that of the GRNN and MLR models. Among all five types of heavy metals, the estimation for cadmium (Cd) is the best when using the SMO-SVM model, and its R2 value reaches 0.8628; (4) using the optimal model to invert the Cd content in wheat that are planted on mine reclamation soil, the R2 and RMSE between the measured and the estimated values are 0.6683 and 0.0489, respectively. This result suggests that the method using the SMO-SVM model to estimate the contents of heavy metals in wheat samples is feasible. PMID:27367708
Spectral Estimation Model Construction of Heavy Metals in Mining Reclamation Areas.
Dong, Jihong; Dai, Wenting; Xu, Jiren; Li, Songnian
2016-01-01
The study reported here examined, as the research subject, surface soils in the Liuxin mining area of Xuzhou, and explored the heavy metal content and spectral data by establishing quantitative models with Multivariable Linear Regression (MLR), Generalized Regression Neural Network (GRNN) and Sequential Minimal Optimization for Support Vector Machine (SMO-SVM) methods. The study results are as follows: (1) the estimations of the spectral inversion models established based on MLR, GRNN and SMO-SVM are satisfactory, and the MLR model provides the worst estimation, with R² of more than 0.46. This result suggests that the stress sensitive bands of heavy metal pollution contain enough effective spectral information; (2) the GRNN model can simulate the data from small samples more effectively than the MLR model, and the R² between the contents of the five heavy metals estimated by the GRNN model and the measured values are approximately 0.7; (3) the stability and accuracy of the spectral estimation using the SMO-SVM model are obviously better than that of the GRNN and MLR models. Among all five types of heavy metals, the estimation for cadmium (Cd) is the best when using the SMO-SVM model, and its R² value reaches 0.8628; (4) using the optimal model to invert the Cd content in wheat that are planted on mine reclamation soil, the R² and RMSE between the measured and the estimated values are 0.6683 and 0.0489, respectively. This result suggests that the method using the SMO-SVM model to estimate the contents of heavy metals in wheat samples is feasible.
Spectral Estimation Model Construction of Heavy Metals in Mining Reclamation Areas.
Dong, Jihong; Dai, Wenting; Xu, Jiren; Li, Songnian
2016-01-01
The study reported here examined, as the research subject, surface soils in the Liuxin mining area of Xuzhou, and explored the heavy metal content and spectral data by establishing quantitative models with Multivariable Linear Regression (MLR), Generalized Regression Neural Network (GRNN) and Sequential Minimal Optimization for Support Vector Machine (SMO-SVM) methods. The study results are as follows: (1) the estimations of the spectral inversion models established based on MLR, GRNN and SMO-SVM are satisfactory, and the MLR model provides the worst estimation, with R² of more than 0.46. This result suggests that the stress sensitive bands of heavy metal pollution contain enough effective spectral information; (2) the GRNN model can simulate the data from small samples more effectively than the MLR model, and the R² between the contents of the five heavy metals estimated by the GRNN model and the measured values are approximately 0.7; (3) the stability and accuracy of the spectral estimation using the SMO-SVM model are obviously better than that of the GRNN and MLR models. Among all five types of heavy metals, the estimation for cadmium (Cd) is the best when using the SMO-SVM model, and its R² value reaches 0.8628; (4) using the optimal model to invert the Cd content in wheat that are planted on mine reclamation soil, the R² and RMSE between the measured and the estimated values are 0.6683 and 0.0489, respectively. This result suggests that the method using the SMO-SVM model to estimate the contents of heavy metals in wheat samples is feasible. PMID:27367708
NASA Astrophysics Data System (ADS)
Li, Zuchuan; Li, Lin; Song, Kaishan; Cassar, Nicolas
2013-03-01
Through its influence on the structure of pelagic ecosystems, phytoplankton size distribution (pico-, nano-, and micro-plankton) is believed to play a key role in "the biological pump." In this paper, an algorithm is proposed to estimate phytoplankton size fractions (PSF) for micro-, nano-, and pico-plankton (fm, fn, and fp, respectively) from the spectral features of remote-sensing data. From remote-sensing reflectance spectrum (Rrs(λ)), the algorithm constructs four types of spectral features: a normalized Rrs(λ), band ratios, continuum-removed spectra, and spectral curvatures. Using support vector machine recursive feature elimination, the algorithm ranks the constructed spectral features and Rrs(λ) according to their sensitivities to PSF which is then regressed against the sensitive spectral features through support vector regression. The algorithm is validated with (1) simulated Rrs(λ) and PSF, and (2) Rrs(λ) obtained by Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and PSF determined from High-Performance Liquid Chromatography (HPLC) pigments. The validation results show the overall effectiveness of the algorithm in estimating PSF, with R2 of (1) 0.938 (fm) for the simulated SeaWiFS data set; and (2) 0.617 (fm), 0.475 (fn), and 0.587 (fp) for the SeaWiFS satellite data set. The validation results also indicate that continuum-removed spectra and spectral curvatures are the dominant spectral features sensitive to PSF with their wavelengths mainly centered on the pigment-absorption domain. Global spatial distributions of fm, fn, and fp were mapped with monthly SeaWiFS images. Overall, their biogeographical distributions are consistent with our current understanding that pico-plankton account for a large proportion of total phytoplankton biomass in oligotrophic regions, nano-plankton in transitional areas, and micro-plankton in high-productivity regions.
An Overdetermined System for Improved Autocorrelation Based Spectral Moment Estimator Performance
NASA Technical Reports Server (NTRS)
Keel, Byron M.
1996-01-01
Autocorrelation based spectral moment estimators are typically derived using the Fourier transform relationship between the power spectrum and the autocorrelation function along with using either an assumed form of the autocorrelation function, e.g., Gaussian, or a generic complex form and applying properties of the characteristic function. Passarelli has used a series expansion of the general complex autocorrelation function and has expressed the coefficients in terms of central moments of the power spectrum. A truncation of this series will produce a closed system of equations which can be solved for the central moments of interest. The autocorrelation function at various lags is estimated from samples of the random process under observation. These estimates themselves are random variables and exhibit a bias and variance that is a function of the number of samples used in the estimates and the operational signal-to-noise ratio. This contributes to a degradation in performance of the moment estimators. This dissertation investigates the use autocorrelation function estimates at higher order lags to reduce the bias and standard deviation in spectral moment estimates. In particular, Passarelli's series expansion is cast in terms of an overdetermined system to form a framework under which the application of additional autocorrelation function estimates at higher order lags can be defined and assessed. The solution of the overdetermined system is the least squares solution. Furthermore, an overdetermined system can be solved for any moment or moments of interest and is not tied to a particular form of the power spectrum or corresponding autocorrelation function. As an application of this approach, autocorrelation based variance estimators are defined by a truncation of Passarelli's series expansion and applied to simulated Doppler weather radar returns which are characterized by a Gaussian shaped power spectrum. The performance of the variance estimators determined
NASA Technical Reports Server (NTRS)
Howell, Leonard W.; Whitaker, Ann F. (Technical Monitor)
2001-01-01
The maximum likelihood procedure is developed for estimating the three spectral parameters of an assumed broken power law energy spectrum from simulated detector responses and their statistical properties investigated. The estimation procedure is then generalized for application to real cosmic-ray data. To illustrate the procedure and its utility, analytical methods were developed in conjunction with a Monte Carlo simulation to explore the combination of the expected cosmic-ray environment with a generic space-based detector and its planned life cycle, allowing us to explore various detector features and their subsequent influence on estimating the spectral parameters. This study permits instrument developers to make important trade studies in design parameters as a function of the science objectives, which is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose rigorous practical limits to the design envelope.
NASA Astrophysics Data System (ADS)
Shafian, S.; Maas, S. J.; Rajan, N.
2014-12-01
Water resources and agricultural applications require knowledge of crop water use (CWU) over a range of spatial and temporal scales. Due to the spatial density of meteorological stations, the resolution of CWU estimates based on these data is fairly coarse and not particularly suitable or reliable for water resources planning, irrigation scheduling and decision making. Various methods have been developed for quantifying CWU of agricultural crops. In this study, an improved version of the spectral crop coefficient which includes the effects of stomatal closure is applied. Raw digital count (DC) data in the red, near-infrared, and thermal infrared (TIR) spectral bands of Landsat-7 and Landsat-8 imaging sensors are used to construct the TIR-ground cover (GC) pixel data distribution and estimate the effects of stomatal closure. CWU is then estimated by combining results of the spectral crop coefficient approach and the stomatal closer effect. To test this approach, evapotranspiration was measured in 5 agricultural fields in the semi-arid Texas High Plains during the 2013 and 2014 growing seasons and compared to corresponding estimated values of CWU determined using this approach. The results showed that the estimated CWU from this approach was strongly correlated (R2 = 0.79) with observed evapotranspiration. In addition, the results showed that considering the stomatal closer effect in the proposed approach can improve the accuracy of the spectral crop coefficient method. These results suggest that the proposed approach is suitable for operational estimation of evapotranspiration and irrigation scheduling where irrigation is used to replace the daily CWU of a crop.
Trapeau, Régis; Aubrais, Valérie; Schönwiesner, Marc
2016-08-01
The adult human auditory system can adapt to changes in spectral cues for sound localization. This plasticity was demonstrated by changing the shape of the pinna with earmolds. Previous results indicate that participants regain localization accuracy after several weeks of adaptation and that the adapted state is retained for at least one week without earmolds. No aftereffect was observed after mold removal, but any aftereffect may be too short to be observed when responses are averaged over many trials. This work investigated the lack of aftereffect by analyzing single-trial responses and modifying visual, auditory, and tactile information during the localization task. Results showed that participants localized accurately immediately after mold removal, even at the first stimulus presentation. Knowledge of the stimulus spectrum, tactile information about the absence of the earmolds, and visual feedback were not necessary to localize accurately after adaptation. Part of the adaptation persisted for one month without molds. The results are consistent with the hypothesis of a many-to-one mapping of the spectral cues, in which several spectral profiles are simultaneously associated with one sound location. Additionally, participants with acoustically more informative spectral cues localized sounds more accurately, and larger acoustical disturbances by the molds reduced adaptation success. PMID:27586720
Daniell method for power spectral density estimation in atomic force microscopy
NASA Astrophysics Data System (ADS)
Labuda, Aleksander
2016-03-01
An alternative method for power spectral density (PSD) estimation—the Daniell method—is revisited and compared to the most prevalent method used in the field of atomic force microscopy for quantifying cantilever thermal motion—the Bartlett method. Both methods are shown to underestimate the Q factor of a simple harmonic oscillator (SHO) by a predictable, and therefore correctable, amount in the absence of spurious deterministic noise sources. However, the Bartlett method is much more prone to spectral leakage which can obscure the thermal spectrum in the presence of deterministic noise. By the significant reduction in spectral leakage, the Daniell method leads to a more accurate representation of the true PSD and enables clear identification and rejection of deterministic noise peaks. This benefit is especially valuable for the development of automated PSD fitting algorithms for robust and accurate estimation of SHO parameters from a thermal spectrum.
Adaptive Error Estimation in Linearized Ocean General Circulation Models
NASA Technical Reports Server (NTRS)
Chechelnitsky, Michael Y.
1999-01-01
Data assimilation methods are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. This study addresses the problem of estimating model and measurement error statistics from observations. We start by testing innovation based methods of adaptive error estimation with low-dimensional models in the North Pacific (5-60 deg N, 132-252 deg E) to TOPEX/POSEIDON (TIP) sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The methods are shown to be sensitive to the initial guess for the error statistics and the type of observations. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. The CMA is then applied to T/P sea level anomaly data and a linearization of a global GFDL GCM which uses two vertical modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCM-T/P residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large
Fabre, Sophie; Briottet, Xavier; Lesaignoux, Audrey
2015-02-02
the sensitivity of these methods to the sensor spectral resolution and the water vapour content knowledge. The spectral signatures of the database are then used to simulate the signal at the top of atmosphere with a radiative transfer model and to compute the integrated incident signal representing the spectral radiance measurements of the HYMAP airborne hyperspectral instrument. The sensor radiances are then corrected from the atmosphere by an atmospheric compensation tool to retrieve the surface reflectances. The SMC estimation methods are then applied on the retrieve spectral reflectances. The adaptation of the spectral index wavelengths to the HyMap sensor spectral bands and the application of the convex envelope and ISER models to boarder spectral bands lead to an error on the SMC estimation. The best performance is then obtained with the ISER model (RMSE of 2.9% and R2 of 0.96) while the four other methods lead to quite similar RMSE (from 6.4% to 7.8%) and R² (between 0.79 and 0.83) values. In the atmosphere compensation processing, an error on the water vapour content is introduced. The most robust methods to water vapour content variations are WISOIL, NINSON, NINSOL and ISER model. The convex envelope model and NSMI index require an accurate estimation of the water vapour content in the atmosphere.
Fabre, Sophie; Briottet, Xavier; Lesaignoux, Audrey
2015-01-01
the sensitivity of these methods to the sensor spectral resolution and the water vapour content knowledge. The spectral signatures of the database are then used to simulate the signal at the top of atmosphere with a radiative transfer model and to compute the integrated incident signal representing the spectral radiance measurements of the HYMAP airborne hyperspectral instrument. The sensor radiances are then corrected from the atmosphere by an atmospheric compensation tool to retrieve the surface reflectances. The SMC estimation methods are then applied on the retrieve spectral reflectances. The adaptation of the spectral index wavelengths to the HyMap sensor spectral bands and the application of the convex envelope and ISER models to boarder spectral bands lead to an error on the SMC estimation. The best performance is then obtained with the ISER model (RMSE of 2.9% and R2 of 0.96) while the four other methods lead to quite similar RMSE (from 6.4% to 7.8%) and R² (between 0.79 and 0.83) values. In the atmosphere compensation processing, an error on the water vapour content is introduced. The most robust methods to water vapour content variations are WISOIL, NINSON, NINSOL and ISER model. The convex envelope model and NSMI index require an accurate estimation of the water vapour content in the atmosphere. PMID:25648710
Fabre, Sophie; Briottet, Xavier; Lesaignoux, Audrey
2015-01-01
analyse the sensitivity of these methods to the sensor spectral resolution and the water vapour content knowledge. The spectral signatures of the database are then used to simulate the signal at the top of atmosphere with a radiative transfer model and to compute the integrated incident signal representing the spectral radiance measurements of the HYMAP airborne hyperspectral instrument. The sensor radiances are then corrected from the atmosphere by an atmospheric compensation tool to retrieve the surface reflectances. The SMC estimation methods are then applied on the retrieve spectral reflectances. The adaptation of the spectral index wavelengths to the HyMap sensor spectral bands and the application of the convex envelope and ISER models to boarder spectral bands lead to an error on the SMC estimation. The best performance is then obtained with the ISER model (RMSE of 2.9% and R2 of 0.96) while the four other methods lead to quite similar RMSE (from 6.4% to 7.8%) and R2 (between 0.79 and 0.83) values. In the atmosphere compensation processing, an error on the water vapour content is introduced. The most robust methods to water vapour content variations are WISOIL, NINSON, NINSOL and ISER model. The convex envelope model and NSMI index require an accurate estimation of the water vapour content in the atmosphere. PMID:25648710
NASA Technical Reports Server (NTRS)
Garber, Donald P.
1993-01-01
A probability density function for the variability of ensemble averaged spectral estimates from helicopter acoustic signals in Gaussian background noise was evaluated. Numerical methods for calculating the density function and for determining confidence limits were explored. Density functions were predicted for both synthesized and experimental data and compared with observed spectral estimate variability.
Vencels, Juris; Delzanno, Gian Luca; Johnson, Alec; Peng, Ivy Bo; Laure, Erwin; Markidis, Stefano
2015-06-01
A spectral method for kinetic plasma simulations based on the expansion of the velocity distribution function in a variable number of Hermite polynomials is presented. The method is based on a set of non-linear equations that is solved to determine the coefficients of the Hermite expansion satisfying the Vlasov and Poisson equations. In this paper, we first show that this technique combines the fluid and kinetic approaches into one framework. Second, we present an adaptive strategy to increase and decrease the number of Hermite functions dynamically during the simulation. The technique is applied to the Landau damping and two-stream instabilitymore » test problems. Performance results show 21% and 47% saving of total simulation time in the Landau and two-stream instability test cases, respectively.« less
Piazza, C; Cantiani, C; Tacchino, G; Molteni, M; Reni, G; Bianchi, A M
2014-01-01
The ability to process rapidly-occurring auditory stimuli plays an important role in the mechanisms of language acquisition. For this reason, the research community has begun to investigate infant auditory processing, particularly using the Event Related Potentials (ERP) technique. In this paper we approach this issue by means of time domain and time-frequency domain analysis. For the latter, we propose the use of Adaptive Autoregressive (AAR) identification with spectral power decomposition. Results show EEG delta-theta oscillation enhancement related to the processing of acoustic frequency and duration changes, suggesting that, as expected, power modulation encodes rapid auditory processing (RAP) in infants and that the time-frequency analysis method proposed is able to identify this modulation.
Vencels, Juris; Delzanno, Gian Luca; Johnson, Alec; Peng, Ivy Bo; Laure, Erwin; Markidis, Stefano
2015-06-01
A spectral method for kinetic plasma simulations based on the expansion of the velocity distribution function in a variable number of Hermite polynomials is presented. The method is based on a set of non-linear equations that is solved to determine the coefficients of the Hermite expansion satisfying the Vlasov and Poisson equations. In this paper, we first show that this technique combines the fluid and kinetic approaches into one framework. Second, we present an adaptive strategy to increase and decrease the number of Hermite functions dynamically during the simulation. The technique is applied to the Landau damping and two-stream instability test problems. Performance results show 21% and 47% saving of total simulation time in the Landau and two-stream instability test cases, respectively.
[The Study of the Spectral Model for Estimating Pigment Contents of Tobacco Leaves in Field].
Ren, Xiao; Lao, Cai-lian; Xu, Zhao-li; Jin, Yan; Guo, Yan; Li, Jun-hui; Yang, Yu-hong
2015-06-01
Fast and non-destructive measurements of tobacco leaf pigment contents by spectroscopy in situ in the field has great significance in production guidance for nutrient diagnosis and growth monitoring of tobacco in vegetative growth stage, and it is also very important for the quality evaluation of tobacco leaves in mature stage. The purpose of this study is to estimate the chlorophyll and carotenoid contents of tobacco leaves using tobacco leaf spectrum collected in the field. Reflectance spectrum of tobacco leaves in vegetative growth stage and mature stage were collected in situ in the field and the pigment contents of tobacco leaf samples were measured in this study, taking the tobacco leaf samples collected in each and both stages as modeling sets respectively, and using the methods of support vector machine (SVM) and spectral indice to establish the pigment content estimation models, and then compare the prediction performance of the models built by different methods. The study results indicated that the difference of estimation performance by each stage or mixed stages is not significant. For chlorophyll content, SVM and spectral indice modeling methods can both have a well estimation performance, while for carotenoid content, SVM modeling method has a better estimation performance than spectral indice. The coefficient of determination and the root mean square error of SVM model for estimating tobacco leaf chlorophyll content by each stage were 0.867 6 and 0.014 7, while the coefficient of determination and the root mean square error of SVM model for estimating tobacco leaf chlorophyll content by mixed stages were 0.898 6 and 0.012 3; The coefficient of determination and the root mean square error for estimating tobacco leaf carotenoid content by each stage were 0.861 4 and 0.002 5, while the coefficient of determination and the root mean square error of SVM model for estimating tobacco leaf carotenoid content by mixed stages were 0.839 9 and 0.002 5. The
Performance Bounds on Micro-Doppler Estimation and Adaptive Waveform Design Using OFDM Signals
Sen, Satyabrata; Barhen, Jacob; Glover, Charles Wayne
2014-01-01
We analyze the performance of a wideband orthogonal frequency division multiplexing (OFDM) signal in estimating the micro-Doppler frequency of a target having multiple rotating scatterers (e.g., rotor blades of a helicopter, propellers of a submarine). The presence of rotating scatterers introduces Doppler frequency modulation in the received signal by generating sidebands about the transmitted frequencies. This is called the micro-Doppler effects. The use of a frequency-diverse OFDM signal in this context enables us to independently analyze the micro-Doppler characteristics with respect to a set of orthogonal subcarrier frequencies. Therefore, to characterize the accuracy of micro-Doppler frequency estimation, we compute the Cram er-Rao Bound (CRB) on the angular-velocity estimate of the target while considering the scatterer responses as deterministic but unknown nuisance parameters. Additionally, to improve the accuracy of the estimation procedure, we formulate and solve an optimization problem by minimizing the CRB on the angular-velocity estimate with respect to the transmitting OFDM spectral coefficients. We present several numerical examples to demonstrate the CRB variations at different values of the signal-to-noise ratio (SNR) and the number of OFDM subcarriers. The CRB values not only decrease with the increase in the SNR values, but also reduce as we increase the number of subcarriers implying the significance of frequency-diverse OFDM waveforms. The improvement in estimation accuracy due to the adaptive waveform design is also numerically analyzed. Interestingly, we find that the relative decrease in the CRBs on the angular-velocity estimate is more pronounced for larger number of OFDM subcarriers.
NASA Astrophysics Data System (ADS)
Rao, Roshan
2016-04-01
Aerosol radiative forcing estimates with high certainty are required in climate change studies. The approach in estimating the aerosol radiative forcing by using the chemical composition of aerosols is not effective as the chemical composition data with radiative properties are not widely available. We look into the approach where ground based spectral radiation flux measurement is made and along with an Radtiative transfer (RT) model, radiative forcing is estimated. Measurements of spectral flux were made using an ASD spectroradiometer with 350 - 1050 nm wavelength range and a 3nm resolution during around 54 clear-sky days during which AOD range was around 0.01 to 0.7. Simultaneous measurements of black carbon were also made using Aethalometer (Magee Scientific) which ranged from around 1.5 ug/m3 to 8 ug/m3. The primary study involved in understanding the sensitivity of spectral flux due to change in individual aerosol species (Optical properties of Aerosols and Clouds (OPAC) classified aerosol species) using the SBDART RT model. This made us clearly distinguish the influence of different aerosol species on the spectral flux. Following this, a new technique has been introduced to estimate an optically equivalent mixture of aerosol species for the given location. The new method involves matching different combinations of aerosol species in OPAC model and RT model as long as the combination which gives the minimum root mean squared deviation from measured spectral flux is obtained. Using the optically equivalent aerosol mixture and RT model, aerosol radiative forcing is estimated. Also an alternate method to estimate the spectral SSA is discussed. Here, the RT model, the observed spectral flux and spectral AOD is used. Spectral AOD is input to RT model and SSA is varied till the minimum root mean squared difference between observed and simulated spectral flux from RT model is obtained. The methods discussed are limited to clear sky scenes and its accuracy to derive
A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield
NASA Astrophysics Data System (ADS)
Kazama, Yoriko; Kujirai, Toshihiro
2014-10-01
A method called a "spectral-spatial-dynamic hierarchical-Bayesian (SSD-HB) model," which can deal with many parameters (such as spectral and weather information all together) by reducing the occurrence of multicollinearity, is proposed. Experiments conducted on soybean yields in Brazil fields with a RapidEye satellite image indicate that the proposed SSD-HB model can predict soybean yield with a higher degree of accuracy than other estimation methods commonly used in remote-sensing applications. In the case of the SSD-HB model, the mean absolute error between estimated yield of the target area and actual yield is 0.28 t/ha, compared to 0.34 t/ha when conventional PLS regression was applied, showing the potential effectiveness of the proposed model.
Methods of spectral estimation in local nuclear quadrupole resonance with a dispersion
NASA Astrophysics Data System (ADS)
Grechishkin, V. S.; Grechishkina, R. V.; Persichkin, A. A.; Shpilevoi, A. A.
2002-10-01
The spectral estimation in local nuclear quadrupole resonance at a high noise level is performed for the first time using the modern techniques of linear prediction (LPSVD) and matrix pencil (ITMPM). The fast Fourier transform with signal accumulation does not ensure the required sensitivity in the case of weak signals when the object and the receiver of the spectrometer are spaced widely apart or when there is an effect of adverse factors (screening, interference, random disturbance, etc.), which is typical of remote monitoring in actual practice. It is demonstrated that the use of the proposed techniques considerably increases the efficiency of spectral estimation in this field of solid-state spectroscopy and, in particular, avoids the phase errors arising in usual experiments at a signal-to-noise ratio of less than 0.5.
Navigating sensory conflict in dynamic environments using adaptive state estimation.
Klein, Theresa J; Jeka, John; Kiemel, Tim; Lewis, M Anthony
2011-12-01
Most conventional robots rely on controlling the location of the center of pressure to maintain balance, relying mainly on foot pressure sensors for information. By contrast,humans rely on sensory data from multiple sources, including proprioceptive, visual, and vestibular sources. Several models have been developed to explain how humans reconcile information from disparate sources to form a stable sense of balance. These models may be useful for developing robots that are able to maintain dynamic balance more readily using multiple sensory sources. Since these information sources may conflict, reliance by the nervous system on any one channel can lead to ambiguity in the system state. In humans, experiments that create conflicts between different sensory channels by moving the visual field or the support surface indicate that sensory information is adaptively reweighted. Unreliable information is rapidly down-weighted,then gradually up-weighted when it becomes valid again.Human balance can also be studied by building robots that model features of human bodies and testing them under similar experimental conditions. We implement a sensory reweighting model based on an adaptive Kalman filter in abipedal robot, and subject it to sensory tests similar to those used on human subjects. Unlike other implementations of sensory reweighting in robots, our implementation includes vision, by using optic flow to calculate forward rotation using a camera (visual modality), as well as a three-axis gyro to represent the vestibular system (non-visual modality), and foot pressure sensors (proprioceptive modality). Our model estimates measurement noise in real time, which is then used to recompute the Kalman gain on each iteration, improving the ability of the robot to dynamically balance. We observe that we can duplicate many important features of postural sw ay in humans, including automatic sensory reweighting,effects, constant phase with respect to amplitude, and a temporal
NASA Astrophysics Data System (ADS)
Melnikov, Yuri B.
2016-09-01
Quadratic form approach allows for new results in the analysis of a class of integral-difference operators in finite domains: non-negativity, spectral estimations, a new property of Legendre polynomials, and establishing links with weighted mean-square deviation functionals and with infinite Jacobi matrices with not-bounded coefficients. Generalisation of integral-difference operators to higher dimensions is provided and application to matter relaxation in a field is considered. A new class of special functions naturally appears.
Li, Zenghui; Xu, Bin; Yang, Jian; Song, Jianshe
2015-01-01
This paper focuses on suppressing spectral overlap for sub-band spectral estimation, with which we can greatly decrease the computational complexity of existing spectral estimation algorithms, such as nonlinear least squares spectral analysis and non-quadratic regularized sparse representation. Firstly, our study shows that the nominal ability of the high-order analysis filter to suppress spectral overlap is greatly weakened when filtering a finite-length sequence, because many meaningless zeros are used as samples in convolution operations. Next, an extrapolation-based filtering strategy is proposed to produce a series of estimates as the substitutions of the zeros and to recover the suppression ability. Meanwhile, a steady-state Kalman predictor is applied to perform a linearly-optimal extrapolation. Finally, several typical methods for spectral analysis are applied to demonstrate the effectiveness of the proposed strategy. PMID:25609038
Li, Ying; Wang, Hong; Li, Xiao Bing
2015-01-01
Vegetation is an important part of ecosystem and estimation of fractional vegetation cover is of significant meaning to monitoring of vegetation growth in a certain region. With Landsat TM images and HJ-1B images as data source, an improved selective endmember linear spectral mixture model (SELSMM) was put forward in this research to estimate the fractional vegetation cover in Huangfuchuan watershed in China. We compared the result with the vegetation coverage estimated with linear spectral mixture model (LSMM) and conducted accuracy test on the two results with field survey data to study the effectiveness of different models in estimation of vegetation coverage. Results indicated that: (1) the RMSE of the estimation result of SELSMM based on TM images is the lowest, which is 0.044. The RMSEs of the estimation results of LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.052, 0.077 and 0.082, which are all higher than that of SELSMM based on TM images; (2) the R2 of SELSMM based on TM images, LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.668, 0.531, 0.342 and 0.336. Among these models, SELSMM based on TM images has the highest estimation accuracy and also the highest correlation with measured vegetation coverage. Of the two methods tested, SELSMM is superior to LSMM in estimation of vegetation coverage and it is also better at unmixing mixed pixels of TM images than pixels of HJ-1B images. So, the SELSMM based on TM images is comparatively accurate and reliable in the research of regional fractional vegetation cover estimation. PMID:25905772
Li, Ying; Wang, Hong; Li, Xiao Bing
2015-01-01
Vegetation is an important part of ecosystem and estimation of fractional vegetation cover is of significant meaning to monitoring of vegetation growth in a certain region. With Landsat TM images and HJ-1B images as data source, an improved selective endmember linear spectral mixture model (SELSMM) was put forward in this research to estimate the fractional vegetation cover in Huangfuchuan watershed in China. We compared the result with the vegetation coverage estimated with linear spectral mixture model (LSMM) and conducted accuracy test on the two results with field survey data to study the effectiveness of different models in estimation of vegetation coverage. Results indicated that: (1) the RMSE of the estimation result of SELSMM based on TM images is the lowest, which is 0.044. The RMSEs of the estimation results of LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.052, 0.077 and 0.082, which are all higher than that of SELSMM based on TM images; (2) the R2 of SELSMM based on TM images, LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.668, 0.531, 0.342 and 0.336. Among these models, SELSMM based on TM images has the highest estimation accuracy and also the highest correlation with measured vegetation coverage. Of the two methods tested, SELSMM is superior to LSMM in estimation of vegetation coverage and it is also better at unmixing mixed pixels of TM images than pixels of HJ-1B images. So, the SELSMM based on TM images is comparatively accurate and reliable in the research of regional fractional vegetation cover estimation.
NASA Astrophysics Data System (ADS)
Liu, Xinyu; Chen, Si; Luo, Yuemei; Bo, En; Wang, Nanshuo; Yu, Xiaojun; Liu, Linbo
2016-02-01
The evaluation of the endothelium coverage on the vessel wall is most wanted by cardiologists. Arterial endothelial cells play a crucial role in keeping low-density lipoprotein and leukocytes from entering into the intima. The damage of endothelial cells is considered as the first step of atherosclerosis development and the presence of endothelial cells is an indicator of arterial healing after stent implantation. Intravascular OCT (IVOCT) is the highest-resolution coronary imaging modality, but it is still limited by an axial resolution of 10-15 µm. This limitation in axial resolution hinders our ability to visualize cellular level details associated with coronary atherosclerosis. Spectral estimation optical coherence tomography (SE-OCT) uses modern spectral estimation techniques and may help reveal the microstructures underlying the resolution limit. In this presentation, we conduct an ex vivo study using SE-OCT to image the endothelium cells on the fresh swine aorta. We find that in OCT images with an axial resolution of 10 µm, we may gain the visibility of individual endothelium cells by applying the autoregressive spectral estimation techniques to enhance the axial resolution. We believe the SE-OCT can provide a potential to evaluate the coverage of endothelium cells using current IVOCT with a 10-µm axial resolution.
Proper orthogonal decomposition-based spectral higher-order stochastic estimation
Baars, Woutijn J.; Tinney, Charles E.
2014-05-15
A unique routine, capable of identifying both linear and higher-order coherence in multiple-input/output systems, is presented. The technique combines two well-established methods: Proper Orthogonal Decomposition (POD) and Higher-Order Spectra Analysis. The latter of these is based on known methods for characterizing nonlinear systems by way of Volterra series. In that, both linear and higher-order kernels are formed to quantify the spectral (nonlinear) transfer of energy between the system's input and output. This reduces essentially to spectral Linear Stochastic Estimation when only first-order terms are considered, and is therefore presented in the context of stochastic estimation as spectral Higher-Order Stochastic Estimation (HOSE). The trade-off to seeking higher-order transfer kernels is that the increased complexity restricts the analysis to single-input/output systems. Low-dimensional (POD-based) analysis techniques are inserted to alleviate this void as POD coefficients represent the dynamics of the spatial structures (modes) of a multi-degree-of-freedom system. The mathematical framework behind this POD-based HOSE method is first described. The method is then tested in the context of jet aeroacoustics by modeling acoustically efficient large-scale instabilities as combinations of wave packets. The growth, saturation, and decay of these spatially convecting wave packets are shown to couple both linearly and nonlinearly in the near-field to produce waveforms that propagate acoustically to the far-field for different frequency combinations.
Spectral estimation from laser scanner data for accurate color rendering of objects
NASA Astrophysics Data System (ADS)
Baribeau, Rejean
2002-06-01
Estimation methods are studied for the recovery of the spectral reflectance across the visible range from the sensing at just three discrete laser wavelengths. Methods based on principal component analysis and on spline interpolation are judged based on the CIE94 color differences for some reference data sets. These include the Macbeth color checker, the OSA-UCS color charts, some artist pigments, and a collection of miscellaneous surface colors. The optimal three sampling wavelengths are also investigated. It is found that color can be estimated with average accuracy ΔE94 = 2.3 when optimal wavelengths 455 nm, 540 n, and 610 nm are used.
NASA Astrophysics Data System (ADS)
Weng, Q.
2007-12-01
Impervious surface is a key indicator of urban environmental quality and urbanization degree. Therefore, estimation and mapping of impervious surfaces in urban areas has attracted more and more attention recently by using remote sensing digital images. In this paper, satellite images with various spectral, spatial, and temporal resolutions are employed to examine the effects of these remote sensing data characteristics on mapping accuracy of urban impervious surfaces. The study area was the city proper of Indianapolis (Marion County), Indiana, United States. Linear spectral mixture analysis was applied to generate high albedo, low albedo, vegetation, and soil fraction images (endmembers) from the satellite images, and impervious surfaces were then estimated by adding high albedo and low albedo fraction images. A comparison of EO-1 ALI (multispectral) and Hyperion (hyperspectral) images indicates that the Hyperion image was more effective in discerning low albedo surface materials, especially the spectral bands in the mid-infrared region. Linear spectral mixing modeling was found more useful for medium spatial resolution images, such as Landsat TM/ETM+ and ASTER images, due to the existence of a large amount of mixed pixels in the urban areas. The model, however, may not be suitable for high spatial resolution images, such as IKONOS images, because of less influence from the mixing pixel. The shadow problem in the high spatial resolution images, caused by tall buildings and large tree crowns, is a challenge in impervious surface extraction. Alternative image processing algorithms such as decision tree classifier may be more appropriate to achieve high mapping accuracy. For mid-latitude cities, seasonal vegetation phenology has a significant effect on the spectral response of terrestrial features, and therefore, image analysis must take into account of this environmental characteristic. Three ASTER images, acquired on April 5, 2004, June 16, 2001, and October 3, 2000
NASA Technical Reports Server (NTRS)
Vukovich, Fred M.; Toll, David L.; Kennard, Ruth L.
1989-01-01
Surface biophysical estimates were derived from analysis of NOAA Advanced Very High Spectral Resolution (AVHRR) spectral data of the Senegalese area of west Africa. The parameters derived were of solar albedo, spectral visible and near-infrared band reflectance, spectral vegetative index, and ground temperature. Wet and dry linked AVHRR scenes from 1981 through 1985 in Senegal were analyzed for a semi-wet southerly site near Tambacounda and a predominantly dry northerly site near Podor. Related problems were studied to convert satellite derived radiance to biophysical estimates of the land surface. Problems studied were associated with sensor miscalibration, atmospheric and aerosol spatial variability, surface anisotropy of reflected radiation, narrow satellite band reflectance to broad solar band conversion, and ground emissivity correction. The middle-infrared reflectance was approximated with a visible AVHRR reflectance for improving solar albedo estimates. In addition, the spectral composition of solar irradiance (direct and diffuse radiation) between major spectral regions (i.e., ultraviolet, visible, near-infrared, and middle-infrared) was found to be insensitive to changes in the clear sky atmospheric optical depth in the narrow band to solar band conversion procedure. Solar albedo derived estimates for both sites were not found to change markedly with significant antecedent precipitation events or correspondingly from increases in green leaf vegetation density. The bright soil/substrate contributed to a high albedo for the dry related scenes, whereas the high internal leaf reflectance in green vegetation canopies in the near-infrared contributed to high solar albedo for the wet related scenes. The relationship between solar albedo and ground temperature was poor, indicating the solar albedo has little control of the ground temperature. The normalized difference vegetation index (NDVI) and the derived visible reflectance were more sensitive to antecedent
[Vegetation index estimation by chlorophyll content of grassland based on spectral analysis].
Xiao, Han; Chen, Xiu-Wan; Yang, Zhen-Yu; Li, Huai-Yu; Zhu, Han
2014-11-01
Comparing the methods of existing remote sensing research on the estimation of chlorophyll content, the present paper confirms that the vegetation index is one of the most practical and popular research methods. In recent years, the increasingly serious problem of grassland degradation. This paper, firstly, analyzes the measured reflectance spectral curve and its first derivative curve in the grasslands of Songpan, Sichuan and Gongger, Inner Mongolia, conducts correlation analysis between these two spectral curves and chlorophyll content, and finds out the regulation between REP (red edge position) and grassland chlorophyll content, that is, the higher the chlorophyll content is, the higher the REIP (red-edge inflection point) value would be. Then, this paper constructs GCI (grassland chlorophyll index) and selects the most suitable band for retrieval. Finally, this paper calculates the GCI by the use of satellite hyperspectral image, conducts the verification and accuracy analysis of the calculation results compared with chlorophyll content data collected from field of twice experiments. The result shows that for grassland chlorophyll content, GCI has stronger sensitivity than other indices of chlorophyll, and has higher estimation accuracy. GCI is the first proposed to estimate the grassland chlorophyll content, and has wide application potential for the remote sensing retrieval of grassland chlorophyll content. In addition, the grassland chlorophyll content estimation method based on remote sensing retrieval in this paper provides new research ideas for other vegetation biochemical parameters' estimation, vegetation growth status' evaluation and grassland ecological environment change's monitoring.
White dwarf mass estimation with a new comprehensive X-ray spectral model of intermediate polars
NASA Astrophysics Data System (ADS)
Hayashi, Takayuki; Ishida, Manabu
A white dwarf (WD) mass is important astrophysical quantity because the WD explodes as a type Ia supernova when its mass reaches the Chandrasekhar mass limit of 1.4 solar mass. Many WD masses in intermediate polars (IPs) were measured with their X-ray spectra emitted from plasma flows channeled by strong magnetic fields of the WDs. For the WD mass estimation, multi-temperature X-ray spectral models have been used which made by summing up X-ray spectra emitted from the top to the bottom of the plasma flow. However, in previous studies, distributions of physical quantities such as temperature and density etc., which are base of the X-ray spectral model, were calculated with assumptions of accretion rate per unit area (call "specific accretion rate") a = 1 g cm(-2) s(-1) and cylindrical geometry for the plasma flows. In fact, a part of the WD masses estimated with the X-ray spectral model is not consistent with that dynamically measured. Therefore, we calculated the physical quantity distributions with the dipolar geometry and the wide range of the specific accretion rate a = 0.0001 - 100 g cm(-2) s(-1) . The calculations showed that the geometrical difference changes the physical quantity distributions and the lower specific accretion rate leads softer X-ray spectrum under a critical specific accretion rate. These results clearly indicate that the previous assumptions are not good approximation for low accretion IPs. We made a new spectral model of the plasma flow with our physical quantity distributions and applied that to Suzaku observations of high and low accretion rate IPs V1223 Sagittarii and EX Hydrae. As a results, our WD masses are almost consistent with the those dynamically measured. We will present the summary of our theoretical calculation and X-ray spectral model, and application to the {¥it Suzaku} observations.
Adaptive neuro-fuzzy estimation of optimal lens system parameters
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Pavlović, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani
2014-04-01
Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.
Seevers, P.M.; Sadowski, F.C.; Lauer, D.T.
1990-01-01
Retrospective satellite image data were evaluated for their ability to demonstrate the influence of center-pivot irrigation development in western Nebraska on spectral change and climate-related factors for the region. Periodic images of an albedo index and a normalized difference vegetation index (NDVI) were generated from calibrated Landsat multispectral scanner (MSS) data and used to monitor spectral changes associated with irrigation development from 1972 through 1986. The albedo index was not useful for monitoring irrigation development. For the NDVI, it was found that proportions of counties in irrigated agriculture, as discriminated by a threshold, were more highly correlated with reported ground estimates of irrigated agriculture than were county mean greenness values. A similar result was achieved when using coarse resolution Advanced Very High Resolution Radiometer (AVHRR) image data for estimating irrigated agriculture. The NDVI images were used to evaluate a procedure for making areal estimates of actual evapotranspiration (ET) volumes. Estimates of ET volumes for test counties, using reported ground acreages and corresponding standard crop coefficients, were correlated with the estimates of ET volume using crop coefficients scaled to NDVI values and pixel counts of crop areas. These county estimates were made under the assumption that soil water availability was unlimited. For nonirrigated vegetation, this may result in over-estimation of ET volumes. Ground information regarding crop types and acreages are required to derive the NDVI scaling factor. Potential ET, estimated with the Jensen-Haise model, is common to both methods. These results, achieved with both MSS and AVHRR data, show promise for providing climatologically important land surface information for regional and global climate models. ?? 1990 Kluwer Academic Publishers.
NASA Astrophysics Data System (ADS)
Liu, Xinyu; Yu, Xiaojun; Wang, Nanshuo; Bo, En; Luo, Yuemei; Chen, Si; Cui, Dongyao; Liu, Linbo
2016-03-01
The sample depth reflectivity profile of Fourier domain optical coherence tomography (FD-OCT) is estimated from the inverse Fourier transform of the spectral interference signals (interferograms). As a result, the axial resolution is fundamentally limited by the coherence length of the light source. We demonstrate an axial resolution improvement method by using the autoregressive spectral estimation technique to instead of the inverse Fourier transform to analyze the spectral interferograms, which is named as spectral estimation OCT (SE-OCT). SE-OCT improves the axial resolution by a factor of up to 4.7 compared with the corresponding FD-OCT. Furthermore, SE-OCT provides a complete sidelobe suppression in the point-spread function. Using phantoms such as an air wedge and micro particles, we prove the ability of resolution improvement. To test SE-OCT for real biological tissue, we image the rat cornea and demonstrate that SE-OCT enables clear identification of corneal endothelium anatomical details ex vivo. We also find that the performance of SE-OCT is depended on SNR of the feature object. To evaluate the potential usage and define the application scope of SE-OCT, we further investigate the property of SNR dependence and the artifacts that may be caused. We find SE-OCT may be uniquely suited for viewing high SNR layer structures, such as the epithelium and endothelium in cornea, retina and aorta. Given that SE-OCT can be implemented in the FD-OCT devices easily, the new capabilities provided by SE-OCT are likely to offer immediate improvements to the diagnosis and management of diseases based on OCT imaging.
Moisture estimation in power transformer oil using acoustic signals and spectral kurtosis
NASA Astrophysics Data System (ADS)
Leite, Valéria C. M. N.; Veloso, Giscard F. C.; Borges da Silva, Luiz Eduardo; Lambert-Torres, Germano; Borges da Silva, Jonas G.; Onofre Pereira Pinto, João
2016-03-01
The aim of this paper is to present a new technique for estimating the contamination by moisture in power transformer insulating oil based on the spectral kurtosis analysis of the acoustic signals of partial discharges (PDs). Basically, in this approach, the spectral kurtosis of the PD acoustic signal is calculated and the correlation between its maximum value and the moisture percentage is explored to find a function that calculates the moisture percentage. The function can be easily implemented in DSP, FPGA, or any other type of embedded system for online moisture monitoring. To evaluate the proposed approach, an experiment is assembled with a piezoelectric sensor attached to a tank, which is filled with insulating oil samples contaminated by different levels of moisture. A device generating electrical discharges is submerged into the oil to simulate the occurrence of PDs. Detected acoustic signals are processed using fast kurtogram algorithm to extract spectral kurtosis values. The obtained data are used to find the fitting function that relates the water contamination to the maximum value of the spectral kurtosis. Experimental results show that the proposed method is suitable for online monitoring system of power transformers.
Nishidate, Izumi; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa
2013-01-01
A multi-spectral diffuse reflectance imaging method based on a single snap shot of Red-Green-Blue images acquired with the exposure time of 65 ms (15 fps) was investigated for estimating melanin concentration, blood concentration, and oxygen saturation in human skin tissue. The technique utilizes the Wiener estimation method to deduce spectral reflectance images instantaneously from an RGB image. Using the resultant absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are numerically deduced in advance by the Monte Carlo simulations for light transport in skin. Oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments on fingers during upper limb occlusion demonstrated the ability of the method to evaluate physiological reactions of human skin. PMID:23783740
NASA Technical Reports Server (NTRS)
Howell, L. W.
2001-01-01
A simple power law model consisting of a single spectral index (alpha-1) is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at knee energy (E(sub k)) to a steeper spectral index alpha-2 > alpha-1 above E(sub k). The maximum likelihood procedure is developed for estimating these three spectral parameters of the broken power law energy spectrum from simulated detector responses. These estimates and their surrounding statistical uncertainty are being used to derive the requirements in energy resolution, calorimeter size, and energy response of a proposed sampling calorimeter for the Advanced Cosmic-ray Composition Experiment for the Space Station (ACCESS). This study thereby permits instrument developers to make important trade studies in design parameters as a function of the science objectives, which is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose rigorous practical limits to the design envelope.
NASA Technical Reports Server (NTRS)
Howell, L. W.; Rose, M. Franklin (Technical Monitor)
2000-01-01
A simple power law model consisting of a single spectral index alpha (sub 1), is believed to be an adequate description of the galactic cosmic ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at knee energy E(sub k) to a steeper spectral index alpha(sub 2) greater than alpha(sub 1) above E(sub k). The maximum likelihood procedure is developed for estimating these three spectral parameters of the broken power law energy spectrum from simulated detector responses. These estimates and their surrounding statistical uncertainty are being used to derive the requirements in energy resolution, calorimeter size, and energy response of a proposed sampling calorimeter for the Advanced Cosmic ray Composition Experiment for the Space Station (ACCESS). This study thereby permits instrument developers to make important trade studies in design parameters as a function of the science objectives, which is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose rigorous practical limits to the design envelope.
Using dark current data to estimate AVIRIS noise covariance and improve spectral analyses
NASA Technical Reports Server (NTRS)
Boardman, Joseph W.
1995-01-01
Starting in 1994, all AVIRIS data distributions include a new product useful for quantification and modeling of the noise in the reported radiance data. The 'postcal' file contains approximately 100 lines of dark current data collected at the end of each data acquisition run. In essence this is a regular spectral-image cube, with 614 samples, 100 lines and 224 channels, collected with a closed shutter. Since there is no incident radiance signal, the recorded DN measure only the DC signal level and the noise in the system. Similar dark current measurements, made at the end of each line are used, with a 100 line moving average, to remove the DC signal offset. Therefore, the pixel-by-pixel fluctuations about the mean of this dark current image provide an excellent model for the additive noise that is present in AVIRIS reported radiance data. The 61,400 dark current spectra can be used to calculate the noise levels in each channel and the noise covariance matrix. Both of these noise parameters should be used to improve spectral processing techniques. Some processing techniques, such as spectral curve fitting, will benefit from a robust estimate of the channel-dependent noise levels. Other techniques, such as automated unmixing and classification, will be improved by the stable and scene-independence noise covariance estimate. Future imaging spectrometry systems should have a similar ability to record dark current data, permitting this noise characterization and modeling.
Hu, Zhen-Zhu; Pan, Cun-De; Wang, Shi-Wei; Guo, Zhi-Chao; Wang, Qing-Tao; Ding, Fan; Li, Yuan
2014-09-01
Aimed at providing technology for a rapid nutrition diagnosis system of micronutrients in Armeniaca vulgaris cv. Luntaibaixing, we established an element concentration estimation model for its foliar ferrum (Fe) and manganese (Mn) concentration based on spectrum analysis. The foliar spectrum reflectance at various phenological periods of fruit development under different soil fertility conditions was measured by Unispec-SC spectrometer. By analyzing the correlation of foliar Fe, Mn concentration at various phenological periods of fruit development, the spectrum reflectance Rλ and its first-order differential f' (Rλ), we filtered out its sensitive bands. And we established an element concentration estimation model for its foliar Fe and Mn at various phenological periods of fruit development with the linear regression model. The results showed that the spectral sensitive bands of foliar Fe in fruit setting period were 873 and 874 nm, 375 and 437 nm in fruit core-hardening period, 836 and 837 nm in maturity period and 325 and 1 054 nm in post-harvest period. However, the spectral sensitive bands of Mn were 913 and 1 129 nm, 425 and 970 nm, 390 and 466 nm, 423 and 424 nm, respectively. The Fe and Mn concentration of A. vulgaris cv. Luntaibaixing leaves were the most relevant to the first-order differential f' (RD) of its spectrum reflectance, whose linear spectrum estimation model fitting degree was the highest and reached to a significant or highly significant level. It showed that the spectral sensitive bands of Fe and Mn element varied with different phenological periods of fruit development. The spectrum estimation models for its foliar Fe and Mn concentration could be established with linear model according to its first-order differential f' (Rλ). PMID:25532350
Ambient noise H/V spectral ratio in site effects estimation in Fateh jang area, Pakistan
NASA Astrophysics Data System (ADS)
Talha Qadri, S. M.; Nawaz, Bushra; Sajjad, S. H.; Sheikh, Riaz Ahmad
2015-02-01
Local geology or local site effect is a crucial component while conducting seismic risk assessment studies. Investigations made by utilization of ambient noise are an effective tool for local site estimation. The present study is conducted to perform site response analysis at 13 different sites within urban settlements of Fateh jang area (Pakistan). The aim of this study was achieved by utilizing Nakamura method or H/V spectral ratio method. Some important local site parameters, e.g., the fundamental frequencies f 0 of soft sediments, amplitudes A 0 of corresponding H/V spectral ratios, and alluvium thicknesses over 13 sites within the study area, were measured and analyzed. The results show that the study area reflects low fundamental frequency f 0. The fundamental frequencies of the sediments are highly variable and lie in a range of 0.6-13.0 Hz. Similarly, amplification factors at these sites are in the range of 2.0-4.0.
Cloud discrimination and spectral radiance estimation from a digital sky images
NASA Astrophysics Data System (ADS)
Saito, M.; Iwabuchi, H.; Murata, I.
2015-12-01
Clouds cover more than 60% of the globe with high impacts on incoming solar irradiance on the ground as well as the radiative energy transfer in the Earth-atmosphere system. Several method for detecting clouds from sky images have been developed, and digital signals available from the JPEG image have nonlinear relationship with the corresponding spectral radiances, which may lead to cloud misclassifications. In this work, a method for cloud discrimination from sky images in RAW format taken from a commercial digital camera is developed. The method uses the clear sky index (CSI). In order to take into account the spectral response in red-green-blue (RGB) channels of the camera as well as lens characteristics, these characteristics are first inferred very accurately with a laboratory experiment. Spectral radiance is represented in a simple form with spectra of incoming solar radiation at the top of atmosphere and ozone transmittance and a polynominal with three coefficients that include the intensity index, the molecular index (MI) and the small particle index (SPI). These coefficients can be obtained from the digital RGB RAW counts by linear transformation. The MI and the SPI can be converted to the CSI, which takes different value from that at clear sky and cloudy pixels. Simultaneous observations with the lidar and the digital camera at Tohoku University show that the CSI can discriminate cloud and clear sky at every pixel with correct discrimination rate more than 90%. Furthermore, spectral distribution of sky radiance can also be estimated at every pixel, and estimated ones are consistent with those from spectrometer and those from radiative transfer simulations under various sky conditions in a wavelength range of 430-680 nm with mean biases lower than 3% and bias standard deviations smaller than 1%.
Sabehi, Gazalah; Kirkup, Benjamin C; Rozenberg, Mira; Stambler, Noga; Polz, Martin F; Béjà, Oded
2007-05-01
Proteorhodopsins (PRs) phototrophy was recently discovered in oceanic surface waters. PRs have been observed in different marine environments and in diverse taxa, including the ubiquitous marine alphaproteobacterial SAR11 group and the uncultured gammaproteobacterial SAR86 group. Previously, two SAR86 PR subgroups, discovered in the Pacific Ocean, were shown to absorb light with different maxima, lambda max 527 nm (green) and lambda max 490 nm (blue) and their distribution was explained by prevailing light conditions - green pigments at the surface and blue in deeper waters. Here, we show that PRs display high diversity in geographically distinct patterns despite similar physical water column properties such as mixing and light penetration. We compared summer and winter samples representing stratified and mixed conditions from both the Mediterranean and Sargasso Sea. As expected, in the Mediterranean Sea, green pigments were mainly confined to the surface and the percentage of blue pigments increased toward deeper samples; in the Sargasso Sea, unexpectedly, all PRs were of the blue type. As an additional result, both locations show seasonal dependence in the distribution of different PR families. Finally, spectral tuning was not restricted to a single PR family as previously reported but occurs across the sampled PR families from various microbial taxa. The distribution of tunable PRs across the PR tree suggests that ready adaptability has been distributed widely among microorganisms, and may be a reason that PRs are abundant and taxonomically widely dispersed.
Tang, Guo; Huang, Yue; Tian, Kuangda; Song, Xiangzhong; Yan, Hong; Hu, Jing; Xiong, Yanmei; Min, Shungeng
2014-10-01
The competitive adaptive reweighted sampling-successive projections algorithm (CARS-SPA) method was proposed as a novel variable selection approach to process multivariate calibration. The CARS was first used to select informative variables, and then SPA to refine the variables with minimum redundant information. The proposed method was applied to near-infrared (NIR) reflectance data of nicotine in tobacco lamina and NIR transmission data of active ingredient in pesticide formulation. As a result, fewer but more informative variables were selected by CARS-SPA than by direct CARS. In the system of pesticide formulation, a multiple linear regression (MLR) model using variables selected by CARS-SPA provided a better prediction than the full-range partial least-squares (PLS) model, successive projections algorithm (SPA) model and uninformative variables elimination-successive projections algorithm (UVE-SPA) processed model. The variable subsets selected by CARS-SPA included the spectral ranges with sufficient chemical information, whereas the uninformative variables were hardly selected.
Heasler, Patrick G.; Posse, Christian; Hylden, Jeff L.; Anderson, Kevin K.
2007-06-13
This paper presents a nonlinear Bayesian regression algorithm for the purpose of detecting and estimating gas plume content from hyper-spectral data. Remote sensing data, by its very nature, is collected under less controlled conditions than laboratory data. As a result, the physics-based model that is used to describe the relationship between the observed remotesensing spectra, and the terrestrial (or atmospheric) parameters that we desire to estimate, is typically littered with many unknown "nuisance" parameters (parameters that we are not interested in estimating, but also appear in the model). Bayesian methods are well-suited for this context as they automatically incorporate the uncertainties associated with all nuisance parameters into the error estimates of the parameters of interest. The nonlinear Bayesian regression methodology is illustrated on realistic simulated data from a three-layer model for longwave infrared (LWIR) measurements from a passive instrument. This shows that this approach should permit more accurate estimation as well as a more reasonable description of estimate uncertainty.
An initial model for estimating soybean development stages from spectral data
NASA Technical Reports Server (NTRS)
Henderson, K. E.; Badhwar, G. D.
1982-01-01
A model, utilizing a direct relationship between remotely sensed spectral data and soybean development stage, has been proposed. The model is based upon transforming the spectral data in Landsat bands to greenness values over time and relating the area of this curve to soybean development stage. Soybean development stages were estimated from data acquired in 1978 from research plots at the Purdue University Agronomy Farm as well as Landsat data acquired over sample areas of the U.S. Corn Belt in 1978 and 1979. Analysis of spectral data from research plots revealed that the model works well with reasonable variation in planting date, row spacing, and soil background. The R-squared of calculated U.S. observed development stage exceeded 0.91 for all treatment variables. Using Landsat data the calculated U.S. observed development stage gave an R-squared of 0.89 in 1978 and 0.87 in 1979. No difference in the models performance could be detected between early and late planted fields, small and large fields, or high and low yielding fields.
ZZ-Type a posteriori error estimators for adaptive boundary element methods on a curve.
Feischl, Michael; Führer, Thomas; Karkulik, Michael; Praetorius, Dirk
2014-01-01
In the context of the adaptive finite element method (FEM), ZZ-error estimators named after Zienkiewicz and Zhu (1987) [52] are mathematically well-established and widely used in practice. In this work, we propose and analyze ZZ-type error estimators for the adaptive boundary element method (BEM). We consider weakly singular and hyper-singular integral equations and prove, in particular, convergence of the related adaptive mesh-refining algorithms. Throughout, the theoretical findings are underlined by numerical experiments.
Eldred, Michael Scott; Subia, Samuel Ramirez; Neckels, David; Hopkins, Matthew Morgan; Notz, Patrick K.; Adams, Brian M.; Carnes, Brian; Wittwer, Jonathan W.; Bichon, Barron J.; Copps, Kevin D.
2006-10-01
This report documents the results for an FY06 ASC Algorithms Level 2 milestone combining error estimation and adaptivity, uncertainty quantification, and probabilistic design capabilities applied to the analysis and design of bistable MEMS. Through the use of error estimation and adaptive mesh refinement, solution verification can be performed in an automated and parameter-adaptive manner. The resulting uncertainty analysis and probabilistic design studies are shown to be more accurate, efficient, reliable, and convenient.
NASA Astrophysics Data System (ADS)
Jina, A.; Hsiang, S. M.; Kopp, R. E., III; Rasmussen, D.; Rising, J.
2014-12-01
The American Climate Prospectus (ACP), the technical analysis underlying the Risky Business project, quantitatively assessed the climate risks posed to the United States' economy in a number of economic sectors [1]. The main analysis presents projections of climate impacts with an assumption of "no adaptation". Yet, historically, when the climate imposed an economic cost upon society, adaptive responses were taken to minimise these costs. These adaptive behaviours, both autonomous and planned, can be expected to occur as climate impacts increase in the future. To understand the extent to which adaptation might decrease some of the worst impacts of climate change, we empirically estimate adaptive responses. We do this in three sectors considered in the analysis - crop yield, crime, and mortality - and estimate adaptive capacity in two steps. First, looking at changes in climate impacts through time, we identify a historical rate of adaptation. Second, spatial differences in climate impacts are then used to stratify regions into more adapted or less adapted based on climate averages. As these averages change across counties in the US, we allow each to become more adapted at the rate identified in step one. We are then able to estimate the residual damages, assuming that only the historical adaptive behaviours have taken place (fig 1). Importantly, we are unable to estimate any costs associated with these adaptations, nor are we able to estimate more novel (for example, new technological discoveries) or more disruptive (for example, migration) adaptive behaviours. However, an important insight is that historical adaptive behaviours may not be capable of reducing the worst impacts of climate change. The persistence of impacts in even the most exposed areas indicates that there are non-trivial costs associated with adaptation that will need to be met from other sources or through novel behavioural changes. References: [1] T. Houser et al. (2014), American Climate
Ebel, J.E.; Wald, D.J.
2003-01-01
We describe a new probabilistic method that uses observations of modified Mercalli intensity (MMI) from past earthquakes to make quantitative estimates of ground shaking parameters (i.e., peak ground acceleration, peak ground velocity, 5% damped spectral acceleration values, etc.). The method uses a Bayesian approach to make quantitative estimates of the probabilities of different levels of ground motions from intensity data given an earthquake of known location and magnitude. The method utilizes probability distributions from an intensity/ground motion data set along with a ground motion attenuation relation to estimate the ground motion from intensity. The ground motions with the highest probabilities are the ones most likely experienced at the site of the MMI observation. We test the method using MMI/ground motion data from California and published ground motion attenuation relations to estimate the ground motions for several earthquakes: 1999 Hector Mine, California (M7.1); 1988 Saguenay, Quebec (M5.9); and 1982 Gaza, New Hampshire (M4.4). In an example where the method is applied to a historic earthquake, we estimate that the peak ground accelerations associated with the 1727 (M???5.2) earthquake at Newbury, Massachusetts, ranged from 0.23 g at Newbury to 0.06 g at Boston.
Rainfall Estimation over the Nile Basin using an Adapted Version of the SCaMPR Algorithm
NASA Astrophysics Data System (ADS)
Habib, E. H.; Kuligowski, R. J.; Elshamy, M. E.; Ali, M. A.; Haile, A.; Amin, D.; Eldin, A.
2011-12-01
Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite-derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). This study reports on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self-Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application over the Nile Basin. The algorithm uses a set of rainfall predictors from multi-spectral Infrared cloud top observations and self-calibrates them to a set of predictands from Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as SSM/I, SSMIS, AMSU, AMSR-E, and TMI. The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real-time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static coefficients that are derived from IR-MW data from past observations. We also compare the SCaMPR algorithm to other global-scale satellite rainfall algorithms (e.g., 'Tropical Rainfall Measuring Mission (TRMM) and other sources' (TRMM-3B42) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA
MEG source estimation in the presence of low-rank interference using cross-spectral metrics.
Gutierrez, David; Nehorai, Arye; Dogandzić, Aleksandar
2004-01-01
We estimate a source current dipole at a known location in the presence of low-rank interference using magnetoencephalography (MEG). We present a space-time processor for MEG data based on the generalized sidelobe canceler (GSC). We extend the classical vector beamformer to a matrix structure without making any assumptions on the rank of the covariance matrix of noise and interference, or constraint matrices. Furthermore, we define the cross-spectral metrics (CSM) in their most general form. The CSM method is known to approximate the performance of the matched filter for the case of unknown covariance matrix. In our case, the CSM also allows to reduce the complexity of the filtering problem without significant loss of performance in the signal-to-interference-plus-noise ratio (SINR). Our results show that good estimates of the dipole sources can be achieved by only using a few eigenvalues, namely, those corresponding to the largest CSM.
NASA Astrophysics Data System (ADS)
Edwards, Matthew C.; Meyer, Renate; Christensen, Nelson
2015-09-01
The standard noise model in gravitational wave (GW) data analysis assumes detector noise is stationary and Gaussian distributed, with a known power spectral density (PSD) that is usually estimated using clean off-source data. Real GW data often depart from these assumptions, and misspecified parametric models of the PSD could result in misleading inferences. We propose a Bayesian semiparametric approach to improve this. We use a nonparametric Bernstein polynomial prior on the PSD, with weights attained via a Dirichlet process distribution, and update this using the Whittle likelihood. Posterior samples are obtained using a blocked Metropolis-within-Gibbs sampler. We simultaneously estimate the reconstruction parameters of a rotating core collapse supernova GW burst that has been embedded in simulated Advanced LIGO noise. We also discuss an approach to deal with nonstationary data by breaking longer data streams into smaller and locally stationary components.
NASA Astrophysics Data System (ADS)
Kiuchi, R.; Mori, J. J.
2015-12-01
As a way to understand the characteristics of the earthquake source, studies of source parameters (such as radiated energy and stress drop) and their scaling are important. In order to estimate source parameters reliably, often we must use appropriate source spectrum models and the omega-square model is most frequently used. In this model, the spectrum is flat in lower frequencies and the falloff is proportional to the angular frequency squared. However, Some studies (e.g. Allmann and Shearer, 2009; Yagi et al., 2012) reported that the exponent of the high frequency falloff is other than -2. Therefore, in this study we estimate the source parameters using a spectral model for which the falloff exponent is not fixed. We analyze the mainshock and larger aftershocks of the 2008 Iwate-Miyagi Nairiku earthquake. Firstly, we calculate the P wave and SH wave spectra using empirical Green functions (EGF) to remove the path effect (such as attenuation) and site effect. For the EGF event, we select a smaller earthquake that is highly-correlated with the target event. In order to obtain the stable results, we calculate the spectral ratios using a multitaper spectrum analysis (Prieto et al., 2009). Then we take a geometric mean from multiple stations. Finally, using the obtained spectra ratios, we perform a grid search to determine the high frequency falloffs, as well as corner frequency of both of events. Our results indicate the high frequency falloff exponent is often less than 2.0. We do not observe any regional, focal mechanism, or depth dependencies for the falloff exponent. In addition, our estimated corner frequencies and falloff exponents are consistent between the P wave and SH wave analysis. In our presentation, we show differences in estimated source parameters using a fixed omega-square model and a model allowing variable high-frequency falloff.
Tropospheric Response to Estimated Spectrally Discriminated Solar Forcing Over the Past 500 Years
NASA Technical Reports Server (NTRS)
Rind, David; Hansen, James E. (Technical Monitor)
2000-01-01
The GISS Global Climate Middle Atmosphere Model (GCMAM) is used to investigate the effect of estimated solar irradiance changes on climate for the past 500 years. This model is employed to allow the impact of UV variations on the stratosphere to affect the troposphere via wave-mean flow interactions. Multiple experiments are done with only a total solar irradiance change (peaking at 0.2 percent from the Maunder Minimum to today); with estimated spectrally-varying irradiance changes (i.e., peak changes of 0.7 percent in the UV, 0.2 percent in the visible and near IR; and 0.07 percent in the IR greater than 1 micron); and the spectrally-varying changes in conjunction with model calculated ozone responses in the stratosphere. Results of the varying temperature patterns and radiation response will be discussed. Of interest is whether the different methods of forcing the solar-induced climate change produce different spatial surface temperature signatures, particularly ones that can be differentiated from greenhouse gas warming. In preliminary tests, spectrally-varying solar forcing with induced ozone changes for solar maximum minus solar minimum conditions results in a temperature signal that is primarily at high latitudes.The high latitude response arises due to solar/ozone-induced alterations in the stratospheric wind field that affect planetary wave propagation from the troposphere, and alter tropospheric advection patterns. In contrast, forcing by total solar irradiance changes produces significant response at low and subtropical latitudes as well, driven by water vapor and cloud feedbacks to the radiative perturbation.
NASA Technical Reports Server (NTRS)
Howell, Leonard W.
2002-01-01
The method of Maximum Likelihood (ML) is used to estimate the spectral parameters of an assumed broken power law energy spectrum from simulated detector responses. This methodology, which requires the complete specificity of all cosmic-ray detector design parameters, is shown to provide approximately unbiased, minimum variance, and normally distributed spectra information for events detected by an instrument having a wide range of commonly used detector response functions. The ML procedure, coupled with the simulated performance of a proposed space-based detector and its planned life cycle, has proved to be of significant value in the design phase of a new science instrument. The procedure helped make important trade studies in design parameters as a function of the science objectives, which is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose rigorous practical limits to the design envelope. This ML methodology is then generalized to estimate broken power law spectral parameters from real cosmic-ray data sets.
Spectral reflectance of Kelantan Estuary with ALOS data to estimate transparency
NASA Astrophysics Data System (ADS)
Syahreza, S.; MatJafri, M. Z.; Lim, H. S.
2012-09-01
The Kelantan estuary, located in the northeastern part of Peninsular Malaysia, is characterized by high levels of suspended sediments. Kuala Besar is the estuary of the river directly opposite South China Sea. Spectral reflectance (Rr) and transparency measurements were carried out in the Kelantan estuary. The objective in this study is to establish empirical relationships between spectral remote sensing reflectance in ALOS satellite imagery and water column transparency, i.e. nephelometric turbidity unit (NTU) and Secchi disc depth (SDD) through these numerous in situ measurements. We detected that remote sensing reflectance are linear and power regression functions against NTU and SDD. The results of this sampling show that the wavelengths range from 500-620 nm is the most suitable band for measuring water column transparency. The calibrated reflectance of ALOS AVNIR-2 bands was also regressed against NTU and SDD field data to derive two empirical equations for water transparency estimation. These equations were calculated using ALOS images data on June 12, 2010. The result obtained indicated that reliable estimates of turbidity and transparency values for the Kelantan Estuary, Malaysia, could be retrieved using this method.
NASA Technical Reports Server (NTRS)
Reginato, R. J.; Vedder, J. F.; Idso, S. B.; Jackson, R. D.; Blanchard, M. B.; Goettelman, R.
1977-01-01
For several days in March of 1975, reflected solar radiation measurements were obtained from smooth and rough surfaces of wet, drying, and continually dry Avondale loam at Phoenix, Arizona, with pyranometers located 50 cm above the ground surface and a multispectral scanner flown at a 300-m height. The simple summation of the different band radiances measured by the multispectral scanner proved equally as good as the pyranometer data for estimating surface soil water content if the multispectral scanner data were standardized with respect to the intensity of incoming solar radiation or the reflected radiance from a reference surface, such as the continually dry soil. Without this means of standardization, multispectral scanner data are most useful in a spectral band ratioing context. Our results indicated that, for the bands used, no significant information on soil water content could be obtained by band ratioing. Thus the variability in soil water content should insignificantly affect soil-type discrimination based on identification of type-specific spectral signatures. Therefore remote sensing, conducted in the 0.4- to 1.0-micron wavelength region of the solar spectrum, would seem to be much More suited to identifying crop and soil types than to estimating of soil water content.
Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices
Ji, Lei; Zhang, Li; Rover, Jennifer R.; Wylie, Bruce K.; Chen, Xuexia
2014-01-01
In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.
Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices
NASA Astrophysics Data System (ADS)
Ji, Lei; Zhang, Li; Rover, Jennifer; Wylie, Bruce K.; Chen, Xuexia
2014-10-01
In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.
NASA Astrophysics Data System (ADS)
Rao, R. R.
2015-12-01
Aerosol radiative forcing estimates with high certainty are required in climate change studies. The approach in estimating the aerosol radiative forcing by using the chemical composition of aerosols is not effective as the chemical composition data with radiative properties are not widely available. In this study we look into the approach where ground based spectral radiation flux measurements along with an RT model is used to estimate radiative forcing. Measurements of spectral flux were made using an ASD spectroradiometer with 350 - 1050 nm wavelength range and 3nm resolution for around 54 clear-sky days during which AOD range was around 0.1 to 0.7. Simultaneous measurements of black carbon were also made using Aethalometer (Magee Scientific) which ranged from around 1.5 ug/m3 to 8 ug/m3. All the measurements were made in the campus of Indian Institute of Science which is in the heart of Bangalore city. The primary study involved in understanding the sensitivity of spectral flux to change in the mass concentration of individual aerosol species (Optical properties of Aerosols and Clouds -OPAC classified aerosol species) using the SBDART RT model. This made us clearly distinguish the region of influence of different aerosol species on the spectral flux. Following this, a new technique has been introduced to estimate an optically equivalent mixture of aerosol species for the given location. The new method involves an iterative process where the mixture of aerosol species are changed in OPAC model and RT model is run as long as the mixture which mimics the measured spectral flux within 2-3% deviation from measured spectral flux is obtained. Using the optically equivalent aerosol mixture and RT model aerosol radiative forcing is estimated. The new method is limited to clear sky scenes and its accuracy to derive an optically equivalent aerosol mixture reduces when diffuse component of flux increases. Our analysis also showed that direct component of spectral flux is
Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system
NASA Astrophysics Data System (ADS)
Robinson, Neethu; Guan, Cuntai; Vinod, A. P.
2015-12-01
Objective. The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings. Approach. EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables. Main results. The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p {\\lt }0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational
Estimating workload using EEG spectral power and ERPs in the n-back task
NASA Astrophysics Data System (ADS)
Brouwer, Anne-Marie; Hogervorst, Maarten A.; van Erp, Jan B. F.; Heffelaar, Tobias; Zimmerman, Patrick H.; Oostenveld, Robert
2012-08-01
Previous studies indicate that both electroencephalogram (EEG) spectral power (in particular the alpha and theta band) and event-related potentials (ERPs) (in particular the P300) can be used as a measure of mental work or memory load. We compare their ability to estimate workload level in a well-controlled task. In addition, we combine both types of measures in a single classification model to examine whether this results in higher classification accuracy than either one alone. Participants watched a sequence of visually presented letters and indicated whether or not the current letter was the same as the one (n instances) before. Workload was varied by varying n. We developed different classification models using ERP features, frequency power features or a combination (fusion). Training and testing of the models simulated an online workload estimation situation. All our ERP, power and fusion models provide classification accuracies between 80% and 90% when distinguishing between the highest and the lowest workload condition after 2 min. For 32 out of 35 participants, classification was significantly higher than chance level after 2.5 s (or one letter) as estimated by the fusion model. Differences between the models are rather small, though the fusion model performs better than the other models when only short data segments are available for estimating workload.
NASA Astrophysics Data System (ADS)
Leng, Kuangdai; Nissen-Meyer, Tarje; van Driel, Martin
2016-09-01
We present a new, computationally efficient numerical method to simulate global seismic wave propagation in realistic 3-D Earth models. We characterize the azimuthal dependence of 3-D wavefields in terms of Fourier series, such that the 3-D equations of motion reduce to an algebraic system of coupled 2-D meridian equations, which is then solved by a 2-D spectral element method (SEM). Computational efficiency of such a hybrid method stems from lateral smoothness of 3-D Earth models and axial singularity of seismic point sources, which jointly confine the Fourier modes of wavefields to a few lower orders. We show novel benchmarks for global wave solutions in 3-D structures between our method and an independent, fully discretized 3-D SEM with remarkable agreement. Performance comparisons are carried out on three state-of-the-art tomography models, with seismic period ranging from 34s down to 11s. It turns out that our method has run up to two orders of magnitude faster than the 3-D SEM, featured by a computational advantage expanding with seismic frequency.
[Research on Oil Sands Spectral Characteristics and Oil Content by Remote Sensing Estimation].
You, Jin-feng; Xing, Li-xin; Pan, Jun; Shan, Xuan-long; Liang, Li-heng; Fan, Rui-xue
2015-04-01
Visible and near infrared spectroscopy is a proven technology to be widely used in identification and exploration of hydrocarbon energy sources with high spectral resolution for detail diagnostic absorption characteristics of hydrocarbon groups. The most prominent regions for hydrocarbon absorption bands are 1,740-1,780, 2,300-2,340 and 2,340-2,360 nm by the reflectance of oil sands samples. These spectral ranges are dominated by various C-H overlapping overtones and combination bands. Meanwhile, there is relatively weak even or no absorption characteristics in the region from 1,700 to 1,730 nm in the spectra of oil sands samples with low bitumen content. With the increase in oil content, in the spectral range of 1,700-1,730 nm the obvious hydrocarbon absorption begins to appear. The bitumen content is the critical parameter for oil sands reserves estimation. The absorption depth was used to depict the response intensity of the absorption bands controlled by first-order overtones and combinations of the various C-H stretching and bending fundamentals. According to the Pearson and partial correlation relationships of oil content and absorption depth dominated by hydrocarbon groups in 1,740-1,780, 2,300-2,340 and 2,340-2,360 nm wavelength range, the scheme of association mode was established between the intensity of spectral response and bitumen content, and then unary linear regression(ULR) and partial least squares regression (PLSR) methods were employed to model the equation between absorption depth attributed to various C-H bond and bitumen content. There were two calibration equations in which ULR method was employed to model the relationship between absorption depth near 2,350 nm region and bitumen content and PLSR method was developed to model the relationship between absorption depth of 1,758, 2,310, 2,350 nm regions and oil content. It turned out that the calibration models had good predictive ability and high robustness and they could provide the scientific
Shiklomanov, Alexey N.; Dietze, Michael C.; Viskari, Toni; Townsend, Philip A.; Serbin, Shawn P.
2016-06-09
The remote monitoring of plant canopies is critically needed for understanding of terrestrial ecosystem mechanics and biodiversity as well as capturing the short- to long-term responses of vegetation to disturbance and climate change. A variety of orbital, sub-orbital, and field instruments have been used to retrieve optical spectral signals and to study different vegetation properties such as plant biochemistry, nutrient cycling, physiology, water status, and stress. Radiative transfer models (RTMs) provide a mechanistic link between vegetation properties and observed spectral features, and RTM spectral inversion is a useful framework for estimating these properties from spectral data. However, existing approaches tomore » RTM spectral inversion are typically limited by the inability to characterize uncertainty in parameter estimates. Here, we introduce a Bayesian algorithm for the spectral inversion of the PROSPECT 5 leaf RTM that is distinct from past approaches in two important ways: First, the algorithm only uses reflectance and does not require transmittance observations, which have been plagued by a variety of measurement and equipment challenges. Second, the output is not a point estimate for each parameter but rather the joint probability distribution that includes estimates of parameter uncertainties and covariance structure. We validated our inversion approach using a database of leaf spectra together with measurements of equivalent water thickness (EWT) and leaf dry mass per unit area (LMA). The parameters estimated by our inversion were able to accurately reproduce the observed reflectance (RMSEVIS = 0.0063, RMSENIR-SWIR = 0.0098) and transmittance (RMSEVIS = 0.0404, RMSENIR-SWIR = 0.0551) for both broadleaved and conifer species. Inversion estimates of EWT and LMA for broadleaved species agreed well with direct measurements (CVEWT = 18.8%, CVLMA = 24.5%), while estimates for conifer species were less accurate (CVEWT = 53.2%, CVLMA = 63.3%). To
NASA Astrophysics Data System (ADS)
Shi, C.; Wang, L.
2015-12-01
Spectral unmixing is the process of decomposing the measured spectrum of a mixed pixel into a set of pure spectral signatures called endmembers and their corresponding abundances indicating the fractional area coverage of each endmember present in the pixel. A substantial number of spectral unmixing studies rely on a spectral mixture model which assumes that spectral mixing only occurs within the extent of a pixel. However, due to adjacency effect, the spectral measurement of the pixel may be contaminated by spatial interactions from materials that are present in its spatial neighborhood. In this paper, a linear spatial spectral mixture model is developed to improve the accuracy of the estimated abundance of invasive saltcedar along the Forgotten River reach of the Rio Grande. A spatial weights matrix which specifies for each pixel the locations and the weights of its neighborhood set is used to summarize the spatial relationships among pixels in the Landsat data. A spatial lag operator, defined as a weighted average of the values at neighboring locations, is adopted as an expression of spectral contribution from nearby pixels and added to the classic linear mixture model. The fractional abundances are iteratively estimated using the alternating direction method of multipliers (ADMM) algorithm. With the incorporation of adjacency effect, RMSEs of the fractional cover of ground classes were reduced. The derived sub-pixel abundances of saltcedar are beneficial for ecological management.
Functional error estimators for the adaptive discretization of inverse problems
NASA Astrophysics Data System (ADS)
Clason, Christian; Kaltenbacher, Barbara; Wachsmuth, Daniel
2016-10-01
So-called functional error estimators provide a valuable tool for reliably estimating the discretization error for a sum of two convex functions. We apply this concept to Tikhonov regularization for the solution of inverse problems for partial differential equations, not only for quadratic Hilbert space regularization terms but also for nonsmooth Banach space penalties. Examples include the measure-space norm (i.e., sparsity regularization) or the indicator function of an {L}∞ ball (i.e., Ivanov regularization). The error estimators can be written in terms of residuals in the optimality system that can then be estimated by conventional techniques, thus leading to explicit estimators. This is illustrated by means of an elliptic inverse source problem with the above-mentioned penalties, and numerical results are provided for the case of sparsity regularization.
Taylor, Brian A; Hwang, Ken-Pin; Hazle, John D; Stafford, R Jason
2009-03-01
The authors investigated the performance of the iterative Steiglitz-McBride (SM) algorithm on an autoregressive moving average (ARMA) model of signals from a fast, sparsely sampled, multiecho, chemical shift imaging (CSI) acquisition using simulation, phantom, ex vivo, and in vivo experiments with a focus on its potential usage in magnetic resonance (MR)-guided interventions. The ARMA signal model facilitated a rapid calculation of the chemical shift, apparent spin-spin relaxation time (T2*), and complex amplitudes of a multipeak system from a limited number of echoes (< or equal 16). Numerical simulations of one- and two-peak systems were used to assess the accuracy and uncertainty in the calculated spectral parameters as a function of acquisition and tissue parameters. The measured uncertainties from simulation were compared to the theoretical Cramer-Rao lower bound (CRLB) for the acquisition. Measurements made in phantoms were used to validate the T2* estimates and to validate uncertainty estimates made from the CRLB. We demonstrated application to real-time MR-guided interventions ex vivo by using the technique to monitor a percutaneous ethanol injection into a bovine liver and in vivo to monitor a laser-induced thermal therapy treatment in a canine brain. Simulation results showed that the chemical shift and amplitude uncertainties reached their respective CRLB at a signal-to-noise ratio (SNR) > or =5 for echo train lengths (ETLs) > or =4 using a fixed echo spacing of 3.3 ms. T2* estimates from the signal model possessed higher uncertainties but reached the CRLB at larger SNRs and/or ETLs. Highly accurate estimates for the chemical shift (<0.01 ppm) and amplitude (<1.0%) were obtained with > or =4 echoes and for T2*(<1.0%) with > or =7 echoes. We conclude that, over a reasonable range of SNR, the SM algorithm is a robust estimator of spectral parameters from fast CSI acquisitions that acquire < or =16 echoes for one- and two-peak systems. Preliminary ex vivo
Taylor, Brian A.; Hwang, Ken-Pin; Hazle, John D.; Stafford, R. Jason
2009-01-01
The authors investigated the performance of the iterative Steiglitz–McBride (SM) algorithm on an autoregressive moving average (ARMA) model of signals from a fast, sparsely sampled, multiecho, chemical shift imaging (CSI) acquisition using simulation, phantom, ex vivo, and in vivo experiments with a focus on its potential usage in magnetic resonance (MR)-guided interventions. The ARMA signal model facilitated a rapid calculation of the chemical shift, apparent spin-spin relaxation time (T2*), and complex amplitudes of a multipeak system from a limited number of echoes (≤16). Numerical simulations of one- and two-peak systems were used to assess the accuracy and uncertainty in the calculated spectral parameters as a function of acquisition and tissue parameters. The measured uncertainties from simulation were compared to the theoretical Cramer–Rao lower bound (CRLB) for the acquisition. Measurements made in phantoms were used to validate the T2* estimates and to validate uncertainty estimates made from the CRLB. We demonstrated application to real-time MR-guided interventions ex vivo by using the technique to monitor a percutaneous ethanol injection into a bovine liver and in vivo to monitor a laser-induced thermal therapy treatment in a canine brain. Simulation results showed that the chemical shift and amplitude uncertainties reached their respective CRLB at a signal-to-noise ratio (SNR)≥5 for echo train lengths (ETLs)≥4 using a fixed echo spacing of 3.3 ms. T2* estimates from the signal model possessed higher uncertainties but reached the CRLB at larger SNRs and∕or ETLs. Highly accurate estimates for the chemical shift (<0.01 ppm) and amplitude (<1.0%) were obtained with ≥4 echoes and for T2* (<1.0%) with ≥7 echoes. We conclude that, over a reasonable range of SNR, the SM algorithm is a robust estimator of spectral parameters from fast CSI acquisitions that acquire ≤16 echoes for one- and two-peak systems. Preliminary ex vivo and in vivo
NASA Astrophysics Data System (ADS)
Cui, Qian; Shi, Jiancheng; Xu, Yuanliu
2011-12-01
Water is the basic needs for human society, and the determining factor of stability of ecosystem as well. There are lots of lakes on Tibet Plateau, which will lead to flood and mudslide when the water expands sharply. At present, water area is extracted from TM or SPOT data for their high spatial resolution; however, their temporal resolution is insufficient. MODIS data have high temporal resolution and broad coverage. So it is valuable resource for detecting the change of water area. Because of its low spatial resolution, mixed-pixels are common. In this paper, four spectral libraries are built using MOD09A1 product, based on that, water body is extracted in sub-pixels utilizing Multiple Endmembers Spectral Mixture Analysis (MESMA) using MODIS daily reflectance data MOD09GA. The unmixed result is comparing with contemporaneous TM data and it is proved that this method has high accuracy.
A Recommended Procedure for Estimating the Cosmic Ray Spectral Parameter of a Simple Power Law
NASA Technical Reports Server (NTRS)
Howell, Leonard W.; Rose, M. Franklin (Technical Monitor)
2000-01-01
A simple power law model consisting of a single spectral index a(f(sub i)) is believed to be an adequate description of the galactic cosmic ray (GQ proton flux at energies below 1013 eV. Two procedures for estimating a(f(sub i)), referred as (1) the method of moments, and (2) maximum likelihood, are developed and their statistical performance compared. I concluded that the maximum likelihood procedure attains the most desirable statistical properties and is hence the recommended statistic estimation procedure for estimating a1. The maximum likelihood procedure is then generalized for application to a set of real cosmic ray data and thereby makes this approach applicable to existing cosmic ray data sets. Several other important results, such as the relationship between collecting power and detector energy resolution, as well as inclusion of a non-Gaussian detector response function, are presented. These results have many practical benefits in the design phase of a cosmic ray detector because they permit instrument developers to make important trade studies in design parameters as a function of one of the science objectives, which is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose practical limits to the design envelope.
Assessing a learning process with functional ANOVA estimators of EEG power spectral densities.
Gutiérrez, David; Ramírez-Moreno, Mauricio A
2016-04-01
We propose to assess the process of learning a task using electroencephalographic (EEG) measurements. In particular, we quantify changes in brain activity associated to the progression of the learning experience through the functional analysis-of-variances (FANOVA) estimators of the EEG power spectral density (PSD). Such functional estimators provide a sense of the effect of training in the EEG dynamics. For that purpose, we implemented an experiment to monitor the process of learning to type using the Colemak keyboard layout during a twelve-lessons training. Hence, our aim is to identify statistically significant changes in PSD of various EEG rhythms at different stages and difficulty levels of the learning process. Those changes are taken into account only when a probabilistic measure of the cognitive state ensures the high engagement of the volunteer to the training. Based on this, a series of statistical tests are performed in order to determine the personalized frequencies and sensors at which changes in PSD occur, then the FANOVA estimates are computed and analyzed. Our experimental results showed a significant decrease in the power of [Formula: see text] and [Formula: see text] rhythms for ten volunteers during the learning process, and such decrease happens regardless of the difficulty of the lesson. These results are in agreement with previous reports of changes in PSD being associated to feature binding and memory encoding.
An adaptive displacement estimation algorithm for improved reconstruction of thermal strain.
Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M; Tillman, Bryan; Leers, Steven A; Kim, Kang
2015-01-01
Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas' estimator and time-shift estimators such as normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas' estimator is limited by phase-wrapping and NXcorr performs poorly when the SNR is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas' estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas' estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas' estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI showed that the adaptive displacement estimator was less biased than either Loupas' estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7 to 350% and the spatial accuracy by 1.2 to 23.0% (P < 0.001). An ex vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and resulted in improved strain reconstruction.
An Adaptive Displacement Estimation Algorithm for Improved Reconstruction of Thermal Strain
Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M.; Tillman, Bryan; Leers, Steven A.; Kim, Kang
2014-01-01
Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas’ estimator and time-shift estimators like normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas’ estimator is limited by phase-wrapping and NXcorr performs poorly when the signal-to-noise ratio (SNR) is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas’ estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex-vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas’ estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas’ estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI using Field II showed that the adaptive displacement estimator was less biased than either Loupas’ estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7–350% and the spatial accuracy by 1.2–23.0% (p < 0.001). An ex-vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and results in improved strain reconstruction. PMID:25585398
Spectral parameter estimation of CAT radar echoes in the presence of fading clutter
NASA Technical Reports Server (NTRS)
Sato, T.; Woodman, R. F.
1980-01-01
The analysis technique and a part of the results obtained from CAT radar echoes from higher troposphere and lower stratosphere are presented. First, the effect of processing distortion caused by the periodogram method using FFT algorithm on the slowly fading ground clutter echo is discussed. It is shown that an extremely narrow clutter spectrum can spill over the entire frequency range if the data are truncated at a tie sorter than their correlation time affecting largely the estimation of the CAT spectrum contribution, especially when the latter is a few tens of dB weaker than the former. A nonlinear least squares fitting procedure is used to parameterize the observed power spectrum in terms of CAT echo power, Doppler shift, spectral width, and the parameters which specify the shape of the clutter component.
NASA Technical Reports Server (NTRS)
Eren, K.
1980-01-01
The mathematical background in spectral analysis as applied to geodetic applications is summarized. The resolution (cut-off frequency) of the GEOS 3 altimeter data is examined by determining the shortest wavelength (corresponding to the cut-off frequency) recoverable. The data from some 18 profiles are used. The total power (variance) in the sea surface topography with respect to the reference ellipsoid as well as with respect to the GEM-9 surface is computed. A fast inversion algorithm for matrices of simple and block Toeplitz matrices and its application to least squares collocation is explained. This algorithm yields a considerable gain in computer time and storage in comparison with conventional least squares collocation. Frequency domain least squares collocation techniques are also introduced and applied to estimating gravity anomalies from GEOS 3 altimeter data. These techniques substantially reduce the computer time and requirements in storage associated with the conventional least squares collocation. Numerical examples given demonstrate the efficiency and speed of these techniques.
Volumetric imaging of inner retina with adaptive optics spectral-domain optical coherence tomography
NASA Astrophysics Data System (ADS)
Zhang, Yan, II; Cense, Barry; Jonnal, Ravi S.; Gao, Weihua; Jones, Steve; Olivier, Scot; Miller, Donald T.
2007-02-01
Adaptive optics (AO) coupled with ultra-fast spectral-domain optical coherence tomography (SD-OCT) has achieved the necessary 3D resolution, sensitivity, and speed for imaging the microscopic retina at the cellular level. While this technology has been rigorously applied to evaluating the 3D morphology of cone photoreceptors, similar detailed studies of cell-sized structures in the inner retina have yet to be undertaken. In this paper, we improve the technical performance of our AO ultrafast SD-OCT and investigate its use for imaging the microscopic inner retina, in particular the nerve fiber layer (NFL) and retinal capillary network. To maximize lateral resolution within the inner retina, focus was controlled with a high stroke, 37-actuator bimorph mirror (AOptix) that also served as the wavefront corrector of the AO. The AO system operated at a closed-loop rate of 25 Hz. The SD-OCT sub-system consisted of a superluminescent diode (λ= 842 nm, Δλ= 50 nm) and a 512 pixel line scan charge-coupled device (CCD) that acquired 72,000 A-scans/sec. Three different B-scan lengths (36, 60, and 120 A-scans/B-scan), which correspond to B-scan exposure durations of 0.5, 0.83, and 1.67 ms, were evaluated to determine the maximum B-scan length that could be tolerated without noticeable loss in image quality due to eye motion in the well fixated eye. Additional technical improvements included sub-pixel registration to remove instrument error and axial registration of the volume images. Small volume images were acquired at 2 and 7 degrees retinal eccentricity with focus systematically shifted through the retina. Small capillaries, some approaching the smallest in the human eye, were readily detected with AO SD-OCT. Appearance of the nerve fiber layer varied noticeably with depth. The most inner portion (presumably the inner limiting membrane) appeared as a thin irregular surface with little characteristic speckle noise. Within the NFL, complex striation patterns (presumably NFL
Dungan, Sarah Z; Kosyakov, Alexander; Chang, Belinda S W
2016-02-01
Cetaceans have undergone a remarkable evolutionary transition that was accompanied by many sensory adaptations, including modification of the visual system for underwater environments. Recent sequencing of cetacean genomes has made it possible to begin exploring the molecular basis of these adaptations. In this study we use in vitro expression methods to experimentally characterize the first step of the visual transduction cascade, the light activation of rhodopsin, for the killer whale. To investigate the spectral effects of amino acid substitutions thought to correspond with absorbance shifts relative to terrestrial mammals, we used the orca gene as a background for the first site-directed mutagenesis experiments in a cetacean rhodopsin. The S292A mutation had the largest effect, and was responsible for the majority of the spectral difference between killer whale and bovine (terrestrial) rhodopsin. Using codon-based likelihood models, we also found significant evidence for positive selection in cetacean rhodopsin sequences, including on spectral tuning sites we experimentally mutated. We then investigated patterns of ecological divergence that may be correlated with rhodopsin functional variation by using a series of clade models that partitioned the data set according to phylogeny, habitat, and foraging depth zone. Only the model partitioning according to depth was significant. This suggests that foraging dives might be a selective regime influencing cetacean rhodopsin divergence, and our experimental results indicate that spectral tuning may be playing an adaptive role in this process. Our study demonstrates that combining computational and experimental methods is crucial for gaining insight into the selection pressures underlying molecular evolution. PMID:26486871
Dungan, Sarah Z; Kosyakov, Alexander; Chang, Belinda S W
2016-02-01
Cetaceans have undergone a remarkable evolutionary transition that was accompanied by many sensory adaptations, including modification of the visual system for underwater environments. Recent sequencing of cetacean genomes has made it possible to begin exploring the molecular basis of these adaptations. In this study we use in vitro expression methods to experimentally characterize the first step of the visual transduction cascade, the light activation of rhodopsin, for the killer whale. To investigate the spectral effects of amino acid substitutions thought to correspond with absorbance shifts relative to terrestrial mammals, we used the orca gene as a background for the first site-directed mutagenesis experiments in a cetacean rhodopsin. The S292A mutation had the largest effect, and was responsible for the majority of the spectral difference between killer whale and bovine (terrestrial) rhodopsin. Using codon-based likelihood models, we also found significant evidence for positive selection in cetacean rhodopsin sequences, including on spectral tuning sites we experimentally mutated. We then investigated patterns of ecological divergence that may be correlated with rhodopsin functional variation by using a series of clade models that partitioned the data set according to phylogeny, habitat, and foraging depth zone. Only the model partitioning according to depth was significant. This suggests that foraging dives might be a selective regime influencing cetacean rhodopsin divergence, and our experimental results indicate that spectral tuning may be playing an adaptive role in this process. Our study demonstrates that combining computational and experimental methods is crucial for gaining insight into the selection pressures underlying molecular evolution.
Magnitude Estimation with Noisy Integrators Linked by an Adaptive Reference
Thurley, Kay
2016-01-01
Judgments of physical stimuli show characteristic biases; relatively small stimuli are overestimated whereas relatively large stimuli are underestimated (regression effect). Such biases likely result from a strategy that seeks to minimize errors given noisy estimates about stimuli that itself are drawn from a distribution, i.e., the statistics of the environment. While being conceptually well described, it is unclear how such a strategy could be implemented neurally. The present paper aims toward answering this question. A theoretical approach is introduced that describes magnitude estimation as two successive stages of noisy (neural) integration. Both stages are linked by a reference memory that is updated with every new stimulus. The model reproduces the behavioral characteristics of magnitude estimation and makes several experimentally testable predictions. Moreover, the model identifies the regression effect as a means of minimizing estimation errors and explains how this optimality strategy depends on the subject's discrimination abilities and on the stimulus statistics. The latter influence predicts another property of magnitude estimation, the so-called range effect. Beyond being successful in describing decision-making, the present work suggests that noisy integration may also be important in processing magnitudes. PMID:26909028
NASA Astrophysics Data System (ADS)
Krezhova, Dora D.; Yanev, Tony K.
2007-04-01
Results from a remote sensing study of the influence of stress factors on the leaf spectral reflectance of wheat and tomato plants contaminated by viruses and pea plants treated with herbicides are presented and discussed. The changes arising in the spectral reflectance characteristics of control and treated plants are estimated through statistical methods as well as through derivative analysis to determine specific reflectance features in the red edge region.
Spectral Feature Analysis for Quantitative Estimation of Cyanobacteria Chlorophyll-A
NASA Astrophysics Data System (ADS)
Lin, Yi; Ye, Zhanglin; Zhang, Yugan; Yu, Jie
2016-06-01
In recent years, lake eutrophication caused a large of Cyanobacteria bloom which not only brought serious ecological disaster but also restricted the sustainable development of regional economy in our country. Chlorophyll-a is a very important environmental factor to monitor water quality, especially for lake eutrophication. Remote sensed technique has been widely utilized in estimating the concentration of chlorophyll-a by different kind of vegetation indices and monitoring its distribution in lakes, rivers or along coastline. For each vegetation index, its quantitative estimation accuracy for different satellite data might change since there might be a discrepancy of spectral resolution and channel center between different satellites. The purpose this paper is to analyze the spectral feature of chlorophyll-a with hyperspectral data (totally 651 bands) and use the result to choose the optimal band combination for different satellites. The analysis method developed here in this study could be useful to recognize and monitor cyanobacteria bloom automatically and accrately. In our experiment, the reflectance (from 350nm to 1000nm) of wild cyanobacteria in different consistency (from 0 to 1362.11ug/L) and the corresponding chlorophyll-a concentration were measured simultaneously. Two kinds of hyperspectral vegetation indices were applied in this study: simple ratio (SR) and narrow band normalized difference vegetation index (NDVI), both of which consists of any two bands in the entire 651 narrow bands. Then multivariate statistical analysis was used to construct the linear, power and exponential models. After analyzing the correlation between chlorophyll-a and single band reflectance, SR, NDVI respetively, the optimal spectral index for quantitative estimation of cyanobacteria chlorophyll-a, as well corresponding central wavelength and band width were extracted. Results show that: Under the condition of water disturbance, SR and NDVI are both suitable for quantitative
Model reference adaptive control, estimation and identification using only input and output signals
NASA Technical Reports Server (NTRS)
Carroll, R. L.; Monopoli, R. V.
1975-01-01
Significant recent advances in the application of stability theory to the adaptive control and identification of systems, and adaptive state estimation, are considered. Emphasis is on those methods which utilize only input and output measurements of the system, and do not require derivatives of the output signal.
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm. PMID:26920086
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm.
An hp-adaptivity and error estimation for hyperbolic conservation laws
NASA Technical Reports Server (NTRS)
Bey, Kim S.
1995-01-01
This paper presents an hp-adaptive discontinuous Galerkin method for linear hyperbolic conservation laws. A priori and a posteriori error estimates are derived in mesh-dependent norms which reflect the dependence of the approximate solution on the element size (h) and the degree (p) of the local polynomial approximation. The a posteriori error estimate, based on the element residual method, provides bounds on the actual global error in the approximate solution. The adaptive strategy is designed to deliver an approximate solution with the specified level of error in three steps. The a posteriori estimate is used to assess the accuracy of a given approximate solution and the a priori estimate is used to predict the mesh refinements and polynomial enrichment needed to deliver the desired solution. Numerical examples demonstrate the reliability of the a posteriori error estimates and the effectiveness of the hp-adaptive strategy.
Adaptive beat-to-beat heart rate estimation in ballistocardiograms.
Brüser, Christoph; Stadlthanner, Kurt; de Waele, Stijn; Leonhardt, Steffen
2011-09-01
A ballistocardiograph records the mechanical activity of the heart. We present a novel algorithm for the detection of individual heart beats and beat-to-beat interval lengths in ballistocardiograms (BCGs) from healthy subjects. An automatic training step based on unsupervised learning techniques is used to extract the shape of a single heart beat from the BCG. Using the learned parameters, the occurrence of individual heart beats in the signal is detected. A final refinement step improves the accuracy of the estimated beat-to-beat interval lengths. Compared to many existing algorithms, the new approach offers heart rate estimates on a beat-to-beat basis. The agreement of the proposed algorithm with an ECG reference has been evaluated. A relative beat-to-beat interval error of 1.79% with a coverage of 95.94% was achieved on recordings from 16 subjects.
Adaptive noise estimation and suppression for improving microseismic event detection
NASA Astrophysics Data System (ADS)
Mousavi, S. Mostafa; Langston, Charles A.
2016-09-01
Microseismic data recorded by surface arrays are often strongly contaminated by unwanted noise. This background noise makes the detection of small magnitude events difficult. A noise level estimation and noise reduction algorithm is presented for microseismic data analysis based upon minimally controlled recursive averaging and neighborhood shrinkage estimators. The method might not be compared with more sophisticated and computationally expensive denoising algorithm in terms of preserving detailed features of seismic signal. However, it is fast and data-driven and can be applied in real-time processing of continuous data for event detection purposes. Results from application of this algorithm to synthetic and real seismic data show that it holds a great promise for improving microseismic event detection.
ZZ-Type a posteriori error estimators for adaptive boundary element methods on a curve☆
Feischl, Michael; Führer, Thomas; Karkulik, Michael; Praetorius, Dirk
2014-01-01
In the context of the adaptive finite element method (FEM), ZZ-error estimators named after Zienkiewicz and Zhu (1987) [52] are mathematically well-established and widely used in practice. In this work, we propose and analyze ZZ-type error estimators for the adaptive boundary element method (BEM). We consider weakly singular and hyper-singular integral equations and prove, in particular, convergence of the related adaptive mesh-refining algorithms. Throughout, the theoretical findings are underlined by numerical experiments. PMID:24748725
Source spectral variation and yield estimation for small, near-source explosions
NASA Astrophysics Data System (ADS)
Yoo, S.; Mayeda, K. M.
2012-12-01
Significant S-wave generation is always observed from explosion sources which can lead to difficulty in discriminating explosions from natural earthquakes. While there are numerous S-wave generation mechanisms that are currently the topic of significant research, the mechanisms all remain controversial and appear to be dependent upon the near-source emplacement conditions of that particular explosion. To better understand the generation and partitioning of the P and S waves from explosion sources and to enhance the identification and discrimination capability of explosions, we investigate near-source explosion data sets from the 2008 New England Damage Experiment (NEDE), the Humble-Redwood (HR) series of explosions, and a Massachusetts quarry explosion experiment. We estimate source spectra and characteristic source parameters using moment tensor inversions, direct P and S waves multi-taper analysis, and improved coda spectral analysis using high quality waveform records from explosions from a variety of emplacement conditions (e.g., slow/fast burning explosive, fully tamped, partially tamped, single/ripple-fired, and below/above ground explosions). The results from direct and coda waves are compared to theoretical explosion source model predictions. These well-instrumented experiments provide us with excellent data from which to document the characteristic spectral shape, relative partitioning between P and S-waves, and amplitude/yield dependence as a function of HOB/DOB. The final goal of this study is to populate a comprehensive seismic source reference database for small yield explosions based on the results and to improve nuclear explosion monitoring capability.
NASA Astrophysics Data System (ADS)
Ward, A. L.; Draper, K.; Hasan, N.
2010-12-01
Knowledge of spatially variable aquifer hydraulic and sorption parameters is a pre-requisite for an improved understanding of the transport and spreading of sorbing solutes and for the development of effective strategies for remediation. Local-scale estimates of these parameters are often derived from core measurements but are typically not representative of field values. Fields-scale estimates are typically derived from pump and tracer tests but often lack the spatial resolution necessary to deconvolve the effects of fine-scale heterogeneities. Geophysical methods have the potential to bridge this gap both in terms of coverage and resolution, provided meaningful petrophysical relationships can be developed. The objective of this study was to develop a petrophysical relationship between soil textural attributes and the gamma-energy response of natural sediments. Measurements from Hanford’s 300 Area show the best model to be a linear relationship between 232Th concentration and clay content (R2 = 94%). This relationship was used to generate a 3-D distribution of clay mass fraction based on borehole spectral gamma logs. The distribution of clay was then used to predict distributions of permeability, porosity, bubbling pressure, and the pore-size distribution index, all of which are required for predicting variably saturated flow, as well as the specific surface area and cation exchange capacity needed for reactive transport predictions. With this approach, it is possible to obtain reliable estimates of hydraulic properties in zones that could not be characterized by field or laboratory measurements. The spatial distribution of flow properties is consistent with lithologic transitions inferred from geologist’s logs. A preferential flow path, identified from solute and heat tracer experiments and attributed to an erosional incision in the low-permeability Ringold Formation, is also evident. The resulting distributions can be used as a starting model for the
Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.
Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu
2015-08-01
This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm. PMID:25265622
Bounded Linear Stability Analysis - A Time Delay Margin Estimation Approach for Adaptive Control
NASA Technical Reports Server (NTRS)
Nguyen, Nhan T.; Ishihara, Abraham K.; Krishnakumar, Kalmanje Srinlvas; Bakhtiari-Nejad, Maryam
2009-01-01
This paper presents a method for estimating time delay margin for model-reference adaptive control of systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent the conventional model-reference adaptive law by a locally bounded linear approximation within a small time window using the comparison lemma. The locally bounded linear approximation of the combined adaptive system is cast in a form of an input-time-delay differential equation over a small time window. The time delay margin of this system represents a local stability measure and is computed analytically by a matrix measure method, which provides a simple analytical technique for estimating an upper bound of time delay margin. Based on simulation results for a scalar model-reference adaptive control system, both the bounded linear stability method and the matrix measure method are seen to provide a reasonably accurate and yet not too conservative time delay margin estimation.
Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.
Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu
2015-08-01
This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.
Spectral modelling near the 1.6 μm window for satellite based estimation of CO2.
Prasad, Prabhunath; Rastogi, Shantanu; Singh, R P; Panigrahy, S
2014-01-01
Measurements of inter annual CO2 variability are important inputs for modelling global carbon cycle. Satellite observations play important role in quantification and modelling of CO2 fluxes in the atmosphere, where observed radiances in narrow spectral channels are used to estimate the trace gas concentration using spectroscopic principles. The 1.6 μm spectral window is important for CO2 detection and study of the two CO2 bands in this region is performed at different spectral resolutions. In order to select the optimum spectral resolution and wavelength positions, suitable for CO2 estimation from satellite platform, sensitivities of different spectral lines to changes in CO2 concentration are studied. Analysis is carried out using a line by line FASCOD radiative transfer model in tropical atmospheric and rural aerosol conditions. The CO2 concentration is varied from 200 to 1000 ppmv and spectral resolution is varied from 0.025 nm to 10 nm. It is observed that atmospheric transmittances reduce sharply with increase in CO2 concentration. With decrease in resolution initially the sensitivity steeply reduces but at resolutions lower than 0.15 nm the sensitivity remains nearly constant. The Continuum Interpolated Band Ratio method is used for inverse concentration retrieval. Based on the study it is evaluated that 0.2 nm is the optimum limit for resolution.
Adaptive error covariances estimation methods for ensemble Kalman filters
Zhen, Yicun; Harlim, John
2015-08-01
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.
The Problem of Bias in Person Parameter Estimation in Adaptive Testing
ERIC Educational Resources Information Center
Doebler, Anna
2012-01-01
It is shown that deviations of estimated from true values of item difficulty parameters, caused for example by item calibration errors, the neglect of randomness of item difficulty parameters, testlet effects, or rule-based item generation, can lead to systematic bias in point estimation of person parameters in the context of adaptive testing.…
Estimating unbiased phenological trends by adapting site-occupancy models.
Roth, Tobias; Strebel, Nicolas; Amrhein, Valentin
2014-08-01
As a response to climate warming, many animals and plants have been found to shift phenologies, such as appearance in spring or timing of reproduction. However, traditional measures for shifts in phenology that are based on observational data likely are biased due to a large influence of population size, observational effort, starting date of a survey, or other causes that may affect the probability of detecting a species. Understanding phenological responses of species to climate change, however, requires a robust measure that could be compared among studies and study years. Here, we developed a new method for estimating arrival and departure dates based on site-occupancy models. Using simulated data, we show that our method provided virtually unbiased estimates of phenological events even if detection probability or the number of sites occupied by the species is changing over time. To illustrate the flexibility of our method, we analyzed spring arrival of two long-distance migrant songbirds and the length of the flight period of two butterfly species, using data from a long-term biodiversity monitoring program in Switzerland. In contrast to many birds that migrate short distances, the two long-distance migrant songbirds tended to postpone average spring arrival by -0.5 days per year between 1995 and 2012. Furthermore, the flight period of the short-distance-flying butterfly species apparently became even shorter over the study period, while the flight period of the longer-distance-flying butterfly species remained relatively stable. Our method could be applied to temporally and spatially extensive data from a wide range of monitoring programs and citizen science projects, to help unravel how species and communities respond to global warming.
Toomey, Matthew B; Lind, Olle; Frederiksen, Rikard; Curley, Robert W; Riedl, Ken M; Wilby, David; Schwartz, Steven J; Witt, Christopher C; Harrison, Earl H; Roberts, Nicholas W; Vorobyev, Misha; McGraw, Kevin J; Cornwall, M Carter; Kelber, Almut; Corbo, Joseph C
2016-01-01
Color vision in birds is mediated by four types of cone photoreceptors whose maximal sensitivities (λmax) are evenly spaced across the light spectrum. In the course of avian evolution, the λmax of the most shortwave-sensitive cone, SWS1, has switched between violet (λmax > 400 nm) and ultraviolet (λmax < 380 nm) multiple times. This shift of the SWS1 opsin is accompanied by a corresponding short-wavelength shift in the spectrally adjacent SWS2 cone. Here, we show that SWS2 cone spectral tuning is mediated by modulating the ratio of two apocarotenoids, galloxanthin and 11’,12’-dihydrogalloxanthin, which act as intracellular spectral filters in this cell type. We propose an enzymatic pathway that mediates the differential production of these apocarotenoids in the avian retina, and we use color vision modeling to demonstrate how correlated evolution of spectral tuning is necessary to achieve even sampling of the light spectrum and thereby maintain near-optimal color discrimination. DOI: http://dx.doi.org/10.7554/eLife.15675.001 PMID:27402384
Peng, Wei-Ren; Wu, Xiaoxia; Feng, Kai-Ming; Arbab, Vahid R; Shamee, Bishara; Yang, Jeng-Yuan; Christen, Louis C; Willner, Alan E; Chi, Sien
2009-05-25
We demonstrate a linearly field-modulated, direct-detected virtual SSB-OFDM (VSSB-OFDM) transmission with an RF tone placed at the edge of the signal band. By employing the iterative estimation and cancellation technique for the signal-signal beat interference (SSBI) at the receiver, our approach alleviates the need of the frequency gap, which is typically reserved for isolating the SSBI, and saves half the electrical bandwidth, thus being very spectrally efficient. We derive the theoretical model for the VSSB-OFDM system and detail the signal processing for the iterative approach conducted at the receiver. Possible limitations for this iterative approach are also given and discussed. We successfully transmit a 10 Gbps, 4-quadrature-amplitude-modulation (QAM) VSSB-OFDM signal through 340 km of uncompensated standard single mode fiber (SSMF) with almost no penalty. In addition, the simulated results show that the proposed scheme has an approximately 2 dB optical-signal-to-noise-ratio (OSNR) gain and has a better chromatic dispersion (CD) tolerance compared with the previous intensity-modulated SSB-OFDM system.
Applications methods of spectral ratios in the estimation of site effects: Case Damien (Haiti)
NASA Astrophysics Data System (ADS)
Jean, B. J.; ST Fleur, S.
2014-12-01
Measurements of H/V type were carried out on the Damien site with Tromino hardware an « all in one » station which includes both the sensor and the integrated digitizer. A total of 32 measurements of seismic noise have been completed on this site in order to see if lithological site effects are detectable with this H/V method. After checking the H/V curve reliability criteria (length of the window to be analyzed, the number of windows analyzed, standard deviation) and the criteria for clear peaks in H/V (conditions for the amplitude, conditions for stability) found in the SESAME project in 2004, the results of the H/V spectra obtained are generally very consistent and clearly indicate site effects with peak resonance frequencies between 3 and 14 Hz. The presence of these well defined frequency peaks in the H/V spectral ratio indicates that the ground motion can be amplified by geomorphological site effects. Comparative analyzes of these H/V measurements with Grilla and Geopsy software were made in this paper to estimate the amplification magnitude of these effects. Graphical comparisons between the Grilla and Geopsy H/V maps were completed in this study and allow us to identify typical areas and their associated fundamental resonance frequencies.
Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO
NASA Astrophysics Data System (ADS)
Gao, Zhen; Dai, Linglong; Wang, Zhaocheng; Chen, Sheng
2015-12-01
This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a non-orthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. Additionally, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the non-orthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramer-Rao lower bound of the proposed scheme, which enlightens us to design the non-orthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.
Rath, J J; Veluvolu, K C; Defoort, M
2014-01-01
The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system. PMID:24683321
Rath, J J; Veluvolu, K C; Defoort, M
2014-01-01
The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system.
Rath, J. J.; Veluvolu, K. C.; Defoort, M.
2014-01-01
The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system. PMID:24683321
Merzlyak, Mark N; Solovchenko, Alexei E; Smagin, Alexei I; Gitelson, Anatoly A
2005-02-01
Spectral properties of flavonols of three varieties (Golden Delicious, Antonovka, and Renet Simirenko) of anthocyanin-free apple fruit were investigated with reflectance spectroscopy. The results of spectral and biochemical analyses suggested that fruit reflectance in a broad spectral range 365-430 nm is strongly dependent on and, in sunlit fruit surfaces, governed by flavonols. The build up of peel flavonols (mainly rutin and other quercetin glycosides) resulted in a dramatic decrease of fruit reflectance in this range, flattening of the spectrum, and extending the region with low reflectance (4-5%) to ca. 410 nm. The spectral features observed suggest that flavonols contribute significantly to screening of excessive radiation, not only UV-A, but in the short-wave bands of chlorophyll and carotenoid absorption in the visible part of the spectrum as well. To retrieve quantitatively flavonol content from reflectance spectra, we tested the applicability of an inversion technique developed for non-destructive leaf pigment assessment. The model for flavonol content assessment was suggested in the form (R(-1)410 - R(-1)460)R800, where Rlambda is reflectance at wavelength lambda. The model was linearly related to flavonol content between 8 and 220nmol/cm2 with the coefficient of determination r2=0.92 and root mean square error of flavonol estimation of 20 nmol/ cm2 regardless of cultivar, chlorophyll, and carotenoid content.
NASA Astrophysics Data System (ADS)
Baumann, Sean M.; Keenan, Cameron; Marciniak, Michael A.; Perram, Glen P.
2014-10-01
A database of spectral and temperature-dependent emissivities was created for painted Al-alloy laser-damage-testing targets for the purpose of improving the uncertainty to which temperature on the front and back target surfaces may be estimated during laser-damage testing. Previous temperature estimates had been made by fitting an assumed gray-body radiance curve to the calibrated spectral radiance data collected from the back surface using a Telops Imaging Fourier Transform Spectrometer (IFTS). In this work, temperature-dependent spectral emissivity measurements of the samples were made from room temperature to 500 °C using a Surface Optics Corp. SOC-100 Hemispherical Directional Reflectometer (HDR) with Nicolet FTS. Of particular interest was a high-temperature matte-black enamel paint used to coat the rear surfaces of the Al-alloy samples. The paint had been assumed to have a spectrally flat and temperatureinvariant emissivity. However, the data collected using the HDR showed both spectral variation and temperature dependence. The uncertainty in back-surface temperature estimation during laser-damage testing made using the measured emissivities was improved from greater than +10 °C to less than +5 °C for IFTS pixels away from the laser burn-through hole, where temperatures never exceeded those used in the SOC-100 HDR measurements. At beam center, where temperatures exceeded those used in the SOC-100 HDR, uncertainty in temperature estimates grew beyond those made assuming gray-body emissivity. Accurate temperature estimations during laser-damage testing are useful in informing a predictive model for future high-energy-laser weapon applications.
Error estimation and adaptive order nodal method for solving multidimensional transport problems
Zamonsky, O.M.; Gho, C.J.; Azmy, Y.Y.
1998-01-01
The authors propose a modification of the Arbitrarily High Order Transport Nodal method whereby they solve each node and each direction using different expansion order. With this feature and a previously proposed a posteriori error estimator they develop an adaptive order scheme to automatically improve the accuracy of the solution of the transport equation. They implemented the modified nodal method, the error estimator and the adaptive order scheme into a discrete-ordinates code for solving monoenergetic, fixed source, isotropic scattering problems in two-dimensional Cartesian geometry. They solve two test problems with large homogeneous regions to test the adaptive order scheme. The results show that using the adaptive process the storage requirements are reduced while preserving the accuracy of the results.
Brandt, A B; Kiseleva, M I
1980-01-01
Dependence of cell photosynthesis in different spectral regions on illumination conditions of chlorella culture during growing (blue, green and red light) was studied. Change in the shape of action spectra of photosynthesis (measured with interference light filters) was discovered. This change is mostly pronounced in relation to the red and blue maxima. The largest relative value of the blue maximum was observed for the cells grown in the red light.
Mode estimation and adaptive feedforward control for stabilization of a flexible gun tube
NASA Astrophysics Data System (ADS)
Vandegrift, Mark W.; DiRenzo, Michael T.
1998-07-01
In this paper we describe an approach for designing a pointing and stabilization system for an unbalanced, flexible gun. Our approach is based upon classical control techniques as well as system identification and adaptive feedforward techniques. Adaptive algorithms identify the flexible modes of the system and estimate the dynamics unbalance. This information is used to update the control law in order to improve the stabilization accuracy of the system.
Sava, H; Durand, L G; Cloutier, G
1999-05-01
To achieve an accurate estimation of the instantaneous turbulent velocity fluctuations downstream of prosthetic heart valves in vivo, the variability of the spectral method used to measure the mean frequency shift of the Doppler signal (i.e. the Doppler velocity) should be minimised. This paper investigates the performance of various short-time spectral parametric methods such as the short-time Fourier transform, autoregressive modelling based on two different approaches, autoregressive moving average modelling based on the Steiglitz-McBride method, and Prony's spectral method. A simulated Doppler signal was used to evaluate the performance of the above mentioned spectral methods and Gaussian noise was added to obtain a set of signals with various signal-to-noise ratios. Two different parameters were used to evaluate the performance of each method in terms of variability and accurate matching of the theoretical Doppler mean instantaneous frequency variation within the cardiac cycle. Results show that autoregressive modelling outperforms the other investigated spectral techniques for window lengths varying between 1 and 10 ms. Among the autoregressive algorithms implemented, it is shown that the maximum entropy method based on a block data processing technique gives the best results for a signal-to-noise ratio of 20 dB. However, at 10 and 0 dB, the Levinson-Durbin algorithm surpasses the performance of the maximum entropy method. It is expected that the intrinsic variance of the spectral methods can be an important source of error for the estimation of the turbulence intensity. The range of this error varies from 0.38% to 24% depending on the parameters of the spectral method and the signal-to-noise ratio. PMID:10505377
NASA Astrophysics Data System (ADS)
Hu, Chaofang; Gao, Zhifei; Ren, Yanli; Liu, Yunbing
2016-11-01
In this paper, a reusable launch vehicle (RLV) attitude control problem with actuator faults is addressed via the robust adaptive nonlinear fault-tolerant control (FTC) with norm estimation. Firstly, the accurate tracking task of attitude angles in the presence of parameter uncertainties and external disturbances is considered. A fault-free controller is proposed using dynamic surface control (DSC) combined with fuzzy adaptive approach. Furthermore, the minimal learning parameter strategy via norm estimation technique is introduced to reduce the multi-parameter adaptive computation burden of fuzzy approximation of the lump uncertainties. Secondly, a compensation controller is designed to handle the partial loss fault of actuator effectiveness. The unknown maximum eigenvalue of actuator efficiency loss factors is estimated online. Moreover, stability analysis guarantees that all signals of the closed-loop control system are semi-global uniformly ultimately bounded. Finally, illustrative simulations show the effectiveness of the proposed method.
Masud, Muhammad Mehedi; Junsheng, Ha; Akhtar, Rulia; Al-Amin, Abul Quasem; Kari, Fatimah Binti
2015-02-01
This paper estimates Malaysian farmers' willingness to pay (WTP) for a planned adaptation programme for addressing climate issues in the Malaysian agricultural sector. We used the contingent valuation method (CVM) for a monetary valuation of farmers' preferences for a planned adaptation programme by ascertaining the value attached to address climatic issues in the Malaysian agricultural sector. Structured questionnaires were distributed among the sampled farmers. The study found that 74 % of respondents were willing to pay for a planned adaptation programme and that several socioeconomic and motivation factors have greater influence on their WTP. This paper clearly specifies the steps needed for all institutional bodies to better address issues in climate change. The outcomes of this paper will support policy makers to better design an efficient adaptation framework for adapting to the adverse impacts of climate change. PMID:25632900
Masud, Muhammad Mehedi; Junsheng, Ha; Akhtar, Rulia; Al-Amin, Abul Quasem; Kari, Fatimah Binti
2015-02-01
This paper estimates Malaysian farmers' willingness to pay (WTP) for a planned adaptation programme for addressing climate issues in the Malaysian agricultural sector. We used the contingent valuation method (CVM) for a monetary valuation of farmers' preferences for a planned adaptation programme by ascertaining the value attached to address climatic issues in the Malaysian agricultural sector. Structured questionnaires were distributed among the sampled farmers. The study found that 74 % of respondents were willing to pay for a planned adaptation programme and that several socioeconomic and motivation factors have greater influence on their WTP. This paper clearly specifies the steps needed for all institutional bodies to better address issues in climate change. The outcomes of this paper will support policy makers to better design an efficient adaptation framework for adapting to the adverse impacts of climate change.
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback-Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
NASA Technical Reports Server (NTRS)
Tittle, R. A.
1988-01-01
The primary purpose of many in-situ airborne light scattering experiments in natural waters is to spectrally characterize the subsurface fluorescent organics and estimate their relative concentrations. This is often done by shining a laser beam into the water and monitoring its subsurface return signal. To do this with the proper interpretation, depth must be taken into account. If one disregards depth dependence when taking such estimates, both their spectral characteristics and their concentrations estimates can be rather ambiguous. A simple airborne lidar configuration is used to detect the subsurface return signal from a particular depth and wavelength. Underwater scatterometer were employed to show that in-situ subsurface organics are very sensitive to depth, but they also require the use of slow moving boats to cover large sample areas. Also, their very entry into the water disturbs the sample it is measuring. The method described is superior and simplest to any employed thus far.
Spectral indices of cardiovascular adaptations to short-term simulated microgravity exposure
NASA Technical Reports Server (NTRS)
Patwardhan, A. R.; Evans, J. M.; Berk, M.; Grande, K. J.; Charles, J. B.; Knapp, C. F.
1995-01-01
We investigated the effects of exposure to microgravity on the baseline autonomic balance in cardiovascular regulation using spectral analysis of cardiovascular variables measured during supine rest. Heart rate, arterial pressure, radial flow, thoracic fluid impedance and central venous pressure were recorded from nine volunteers before and after simulated microgravity, produced by 20 hours of 6 degrees head down bedrest plus furosemide. Spectral powers increased after simulated microgravity in the low frequency region (centered at about 0.03 Hz) in arterial pressure, heart rate and radial flow, and decreased in the respiratory frequency region (centered at about 0.25 Hz) in heart rate. Reduced heart rate power in the respiratory frequency region indicates reduced parasympathetic influence on the heart. A concurrent increase in the low frequency power in arterial pressure, heart rate, and radial flow indicates increased sympathetic influence. These results suggest that the baseline autonomic balance in cardiovascular regulation is shifted towards increased sympathetic and decreased parasympathetic influence after exposure to short-term simulated microgravity.
Adaptive force generation for precision-grip lifting by a spectral timing model of the cerebellum.
Ulloa, Antonio; Bullock, Daniel; Rhodes, Bradley J
2003-01-01
We modeled adaptive generation of precision grip forces during object lifting. The model presented adjusts reactive and anticipatory grip forces to a level just above that needed to stabilize lifted objects in the hand. The model obeys principles of cerebellar structure and function by using slip sensations as error signals to adapt phasic motor commands to tonic force generators associated with output synergies controlling grip aperture. The learned phasic commands are weight- and texture-dependent. Simulations of the new circuit model reproduce key aspects of experimental observations of force application. Over learning trials, the onset of grip force buildup comes to lead the load force buildup, and the rate-of-rise of grip force, but not load force, scales inversely with the friction of the object.
Schaffer, Thorsten; Hensel, Bernhard; Weigand, Christian; Schüttler, Jürgen; Jeleazcov, Christian
2014-10-01
Heart rate variability (HRV) analysis is increasingly used in anaesthesia and intensive care monitoring of spontaneous breathing and mechanical ventilated patients. In the frequency domain, different estimation methods of the power spectral density (PSD) of RR-intervals lead to different results. Therefore, we investigated the PSD estimates of fast Fourier transform (FFT), autoregressive modeling (AR) and Lomb-Scargle periodogram (LSP) for 25 young healthy subjects subjected to metronomic breathing. The optimum method for determination of HRV spectral parameters under paced respiration was identified by evaluating the relative error (RE) and the root mean square relative error (RMSRE) for each breathing frequency (BF) and spectral estimation method. Additionally, the sympathovagal balance was investigated by calculating the low frequency/high frequency (LF/HF) ratio. Above 7 breaths per minute, all methods showed a significant increase in LF/HF ratio with increasing BF. On average, the RMSRE of FFT was lower than for LSP and AR. Therefore, under paced respiration conditions, estimating RR-interval PSD using FFT is recommend. PMID:23508826
Adaptive algorithm for cloud cover estimation from all-sky images over the sea
NASA Astrophysics Data System (ADS)
Krinitskiy, M. A.; Sinitsyn, A. V.
2016-05-01
A new algorithm for cloud cover estimation has been formulated and developed based on the synthetic control index, called the grayness rate index, and an additional algorithm step of adaptive filtering of the Mie scattering contribution. A setup for automated cloud cover estimation has been designed, assembled, and tested under field conditions. The results shows a significant advantage of the new algorithm over currently commonly used procedures.
NASA Astrophysics Data System (ADS)
Bo, Yizhou; Shifa, Naima
2013-09-01
An estimator for finding the abundance of a rare, clustered and mobile population has been introduced. This model is based on adaptive cluster sampling (ACS) to identify the location of the population and negative binomial distribution to estimate the total in each site. To identify the location of the population we consider both sampling with replacement (WR) and sampling without replacement (WOR). Some mathematical properties of the model are also developed.
Eckhard, Timo; Valero, Eva M; Hernández-Andrés, Javier; Heikkinen, Ville
2014-03-01
In this work, we evaluate the conditionally positive definite logarithmic kernel in kernel-based estimation of reflectance spectra. Reflectance spectra are estimated from responses of a 12-channel multispectral imaging system. We demonstrate the performance of the logarithmic kernel in comparison with the linear and Gaussian kernel using simulated and measured camera responses for the Pantone and HKS color charts. Especially, we focus on the estimation model evaluations in case the selection of model parameters is optimized using a cross-validation technique. In experiments, it was found that the Gaussian and logarithmic kernel outperformed the linear kernel in almost all evaluation cases (training set size, response channel number) for both sets. Furthermore, the spectral and color estimation accuracies of the Gaussian and logarithmic kernel were found to be similar in several evaluation cases for real and simulated responses. However, results suggest that for a relatively small training set size, the accuracy of the logarithmic kernel can be markedly lower when compared to the Gaussian kernel. Further it was found from our data that the parameter of the logarithmic kernel could be fixed, which simplified the use of this kernel when compared with the Gaussian kernel. PMID:24690652
NASA Technical Reports Server (NTRS)
Eloranta, E. W.; Piironen, P. K.
1996-01-01
Quantitative lidar measurements of aerosol scattering are hampered by the need for calibrations and the problem of correcting observed backscatter profiles for the effects of attenuation. The University of Wisconsin High Spectral Resolution Lidar (HSRL) addresses these problems by separating molecular scattering contributions from the aerosol scattering; the molecular scattering is then used as a calibration target that is available at each point in the observed profiles. While the HSRl approach has intrinsic advantages over competing techniques, realization of these advantages requires implementation of a technically demanding system which is potentially very sensitive to changes in temperature and mechanical alignments. This paper describes a new implementation of the HSRL in an instrumented van which allows measurements during field experiments. The HSRL was modified to measure depolarization. In addition, both the signal amplitude and depolarization variations with receiver field of view are simultaneously measured. This allows for discrimination of ice clouds from water clouds and observation of multiple scattering contributions to the lidar return.
A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops
NASA Astrophysics Data System (ADS)
Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping
2015-01-01
A self-adaptive genetic algorithm for estimating Jiles-Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet's hysteresis loops, and the results are in good agreement with published data.
Contributions to Adaptive Educational Hypermedia Systems via On-Line Learning Style Estimation
ERIC Educational Resources Information Center
Botsios, Sotiris; Georgiou, Demetrius; Safouris, Nikolaos
2008-01-01
In order to establish an online diagnostic system for Learning Style Estimation that contributes to the adaptation of learning objects, we propose an easily applicable expert system founded on Bayesian Networks. The proposed system makes use of Learning Style theories and associated diagnostic techniques, simultaneously avoiding certain error…
ERIC Educational Resources Information Center
Sahin, Alper; Weiss, David J.
2015-01-01
This study aimed to investigate the effects of calibration sample size and item bank size on examinee ability estimation in computerized adaptive testing (CAT). For this purpose, a 500-item bank pre-calibrated using the three-parameter logistic model with 10,000 examinees was simulated. Calibration samples of varying sizes (150, 250, 350, 500,…
NASA Astrophysics Data System (ADS)
Xia, Xiang-Gen; Wang, Genyuan; Chen, Victor C.
2001-03-01
This paper first reviews some basic properties of the discrete chirp-Fourier transform and then present an adaptive chirp- Fourier transform, a generalization of the amplitude and phase estimation of sinusoids (APES) algorithm proposed by Li and Stoica for sinusoidal signals. We finally applied it to the ISAR imaging of maneuvering targets.
NASA Technical Reports Server (NTRS)
Beyon, Jeffrey Y.; Koch, Grady J.
2006-01-01
The signal processing aspect of a 2-m wavelength coherent Doppler lidar system under development at NASA Langley Research Center in Virginia is investigated in this paper. The lidar system is named VALIDAR (validation lidar) and its signal processing program estimates and displays various wind parameters in real-time as data acquisition occurs. The goal is to improve the quality of the current estimates such as power, Doppler shift, wind speed, and wind direction, especially in low signal-to-noise-ratio (SNR) regime. A novel Nonlinear Adaptive Doppler Shift Estimation Technique (NADSET) is developed on such behalf and its performance is analyzed using the wind data acquired over a long period of time by VALIDAR. The quality of Doppler shift and power estimations by conventional Fourier-transform-based spectrum estimation methods deteriorates rapidly as SNR decreases. NADSET compensates such deterioration in the quality of wind parameter estimates by adaptively utilizing the statistics of Doppler shift estimate in a strong SNR range and identifying sporadic range bins where good Doppler shift estimates are found. The authenticity of NADSET is established by comparing the trend of wind parameters with and without NADSET applied to the long-period lidar return data.
Adaptive technique for matching the spectral response in skin lesions' images
NASA Astrophysics Data System (ADS)
Pavlova, P.; Borisova, E.; Pavlova, E.; Avramov, L.
2015-03-01
The suggested technique is a subsequent stage for data obtaining from diffuse reflectance spectra and images of diseased tissue with a final aim of skin cancer diagnostics. Our previous work allows us to extract patterns for some types of skin cancer, as a ratio between spectra, obtained from healthy and diseased tissue in the range of 380 - 780 nm region. The authenticity of the patterns depends on the tested point into the area of lesion, and the resulting diagnose could also be fixed with some probability. In this work, two adaptations are implemented to localize pixels of the image lesion, where the reflectance spectrum corresponds to pattern. First adapts the standard to the personal patient and second - translates the spectrum white point basis to the relative white point of the image. Since the reflectance spectra and the image pixels are regarding to different white points, a correction of the compared colours is needed. The latest is done using a standard method for chromatic adaptation. The technique follows the steps below: -Calculation the colorimetric XYZ parameters for the initial white point, fixed by reflectance spectrum from healthy tissue; -Calculation the XYZ parameters for the distant white point on the base of image of nondiseased tissue; -Transformation the XYZ parameters for the test-spectrum by obtained matrix; -Finding the RGB values of the XYZ parameters for the test-spectrum according sRGB; Finally, the pixels of the lesion's image, corresponding to colour from the test-spectrum and particular diagnostic pattern are marked with a specific colour.
NASA Astrophysics Data System (ADS)
Zhang, Fangliu; He, Jing; Deng, Rui; Cheng, Yun; Xiao, Minlei; Chen, Lin
2016-08-01
In this paper, an adaptive pulse amplitude modulation (APAM) scheme is proposed and experimentally demonstrated in the intensity-modulation and direct-detection (IMDD) optical communications system. In the proposed scheme, the channel is divided into two sub-channels, and different PAM mapping can be chosen for different sub-channel according to the fading conditions. In addition, the 20-km standard single mode fiber (SSMF) transmission of 24 Gbit/s 16/4-APAM signal with the spectral efficiency (SE) up to 6 bit/s/Hz is experimentally demonstrated. The experiment results show that the bit error rate (BER) of the 16/4-APAM signal can be achieved less than 2.4e-2.
Estimation of site-dependent spectral decay parameter from seismic array data
NASA Astrophysics Data System (ADS)
Park, Seon Jeong; Lee, Jung Mo; Baag, Chang-Eob; Choi, Hoseon; Noh, Myunghyun
2016-04-01
The kappa (κ), attenuation of acceleration amplitude at high frequencies, is one of the most important parameters in ground motion evaluation and seismic hazard analysis at sites. κ simply indicates the high frequency decay of the acceleration spectrum in log-linear space. The decay trend can be considered as linear for frequencies higher than a specific frequency, fe which is starting point of the linear regression at the acceleration spectrum. The κ has been investigated using the data from seismic arrays in the south-eastern part of Korea in which nuclear facilities such as power plant and radiological waste depository are located. The seismic array consists of 20 seismic stations and it was operated from October in 2010 through March in 2013. A classical method by Anderson and Hough (1984) and a standard procedure recently suggested by Ktenidou et al. (2013) were applied for computation of κ. There have been just a few studies on spectral attenuation characteristics for Korean Peninsula so far and even those studies utilized small amount of earthquake events whose frequency range was lower than 25 Hz. In this study, the available frequency range is up to 60 Hz based on the sampling rate of 200 and instrument response. This allows us to use a large range of frequencies for κ computations. It is outstanding advantage that we couldn't obtain from earlier κ studies in Korea. In addition, we investigate the regional κ characteristics through calculating the κ using data of 20 seismic stations which are highly extensive seismic array. It allows us to find the more specific attenuation characteristics of high frequencies in study area. Distance and magnitude dependence of κ has also been investigated. Before calculating the κ, the corner frequency (f_c) has been checked so that the fe can lie to the right of fc to exclude source effects in the computation. Manually picked fe is generally in the range of 10 to 25 Hz. The resulting κR is 9.2e-06 and κ0 is 0
NASA Astrophysics Data System (ADS)
Hirose, Misa; Akaho, Rina; Maita, Chikashi; Sugawara, Mai; Tsumura, Norimichi
2016-06-01
In this paper, the spectral sensitivities of a mosaic five-band camera were optimized using a numerical skin phantom to perform the separation of chromophore densities, shading and surface reflection. To simulate the numerical skin phantom, the spectral reflectance of skin was first calculated by Monte Carlo simulation of photon migration for different concentrations of melanin, blood and oxygen saturation levels. The melanin and hemoglobin concentration distributions used in the numerical skin phantom were obtained from actual skin images by independent component analysis. The calculated components were assigned as concentration distributions. The spectral sensitivities of the camera were then optimized using a nonlinear technique to estimate the spectral reflectance for skin separation. In this optimization, the spectral sensitivities were assumed to be normally distributed, and the sensor arrangement was identical to that of a conventional mosaic five-band camera. Our findings demonstrated that spectral estimation could be significantly improved by optimizing the spectral sensitivities.
NASA Astrophysics Data System (ADS)
Alvey, Brendan; Zare, Alina; Cook, Matthew; Ho, Dominic K. C.
2016-05-01
The adaptive coherence estimator (ACE) estimates the squared cosine of the angle between a known target vector and a sample vector in a transformed coordinate space. The space is transformed according to an estimation of the background statistics, which directly effects the performance of the statistic as a target detector. In this paper, the ACE detection statistic is used to detect buried explosive hazards with data from a Wideband Electromagnetic Induction (WEMI) sensor. Target signatures are based on a dictionary defined using a Discrete Spectrum of Relaxation Frequencies (DSRF) model. Results are summarized as a receiver operator curve (ROC) and compared to other leading methods.
F-8C adaptive flight control extensions. [for maximum likelihood estimation
NASA Technical Reports Server (NTRS)
Stein, G.; Hartmann, G. L.
1977-01-01
An adaptive concept which combines gain-scheduled control laws with explicit maximum likelihood estimation (MLE) identification to provide the scheduling values is described. The MLE algorithm was improved by incorporating attitude data, estimating gust statistics for setting filter gains, and improving parameter tracking during changing flight conditions. A lateral MLE algorithm was designed to improve true air speed and angle of attack estimates during lateral maneuvers. Relationships between the pitch axis sensors inherent in the MLE design were examined and used for sensor failure detection. Design details and simulation performance are presented for each of the three areas investigated.
Kwon, Oh-Sang; Knill, David C
2013-03-12
Because of uncertainty and noise, the brain should use accurate internal models of the statistics of objects in scenes to interpret sensory signals. Moreover, the brain should adapt its internal models to the statistics within local stimulus contexts. Consider the problem of hitting a baseball. The impoverished nature of the visual information available makes it imperative that batters use knowledge of the temporal statistics and history of previous pitches to accurately estimate pitch speed. Using a laboratory analog of hitting a baseball, we tested the hypothesis that the brain uses adaptive internal models of the statistics of object speeds to plan hand movements to intercept moving objects. We fit Bayesian observer models to subjects' performance to estimate the statistical environments in which subjects' performance would be ideal and compared the estimated statistics with the true statistics of stimuli in an experiment. A first experiment showed that subjects accurately estimated and used the variance of object speeds in a stimulus set to time hitting behavior but also showed serial biases that are suboptimal for stimuli that were uncorrelated over time. A second experiment showed that the strength of the serial biases depended on the temporal correlations within a stimulus set, even when the biases were estimated from uncorrelated stimulus pairs subsampled from the larger set. Taken together, the results show that subjects adapted their internal models of the variance and covariance of object speeds within a stimulus set to plan interceptive movements but retained a bias to positive correlations.
Estimating Model Parameters of Adaptive Software Systems in Real-Time
NASA Astrophysics Data System (ADS)
Kumar, Dinesh; Tantawi, Asser; Zhang, Li
Adaptive software systems have the ability to adapt to changes in workload and execution environment. In order to perform resource management through model based control in such systems, an accurate mechanism for estimating the software system's model parameters is required. This paper deals with real-time estimation of a performance model for adaptive software systems that process multiple classes of transactional workload. First, insights in to the static performance model estimation problem are provided. Then an Extended Kalman Filter (EKF) design is combined with an open queueing network model to dynamically estimate the model parameters in real-time. Specific problems that are encountered in the case of multiple classes of workload are analyzed. These problems arise mainly due to the under-deterministic nature of the estimation problem. This motivates us to propose a modified design of the filter. Insights for choosing tuning parameters of the modified design, i.e., number of constraints and sampling intervals are provided. The modified filter design is shown to effectively tackle problems with multiple classes of workload through experiments.
Kwon, Oh-Sang; Knill, David C
2013-03-12
Because of uncertainty and noise, the brain should use accurate internal models of the statistics of objects in scenes to interpret sensory signals. Moreover, the brain should adapt its internal models to the statistics within local stimulus contexts. Consider the problem of hitting a baseball. The impoverished nature of the visual information available makes it imperative that batters use knowledge of the temporal statistics and history of previous pitches to accurately estimate pitch speed. Using a laboratory analog of hitting a baseball, we tested the hypothesis that the brain uses adaptive internal models of the statistics of object speeds to plan hand movements to intercept moving objects. We fit Bayesian observer models to subjects' performance to estimate the statistical environments in which subjects' performance would be ideal and compared the estimated statistics with the true statistics of stimuli in an experiment. A first experiment showed that subjects accurately estimated and used the variance of object speeds in a stimulus set to time hitting behavior but also showed serial biases that are suboptimal for stimuli that were uncorrelated over time. A second experiment showed that the strength of the serial biases depended on the temporal correlations within a stimulus set, even when the biases were estimated from uncorrelated stimulus pairs subsampled from the larger set. Taken together, the results show that subjects adapted their internal models of the variance and covariance of object speeds within a stimulus set to plan interceptive movements but retained a bias to positive correlations. PMID:23440185
NASA Astrophysics Data System (ADS)
Homolová, L.; Janoutová, R.; Malenovský, Z.
2016-06-01
In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning method - support vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 - 710 nm, c) CR 705 - 780 nm, and d) CR 680 - 800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab < 10 μg cm-2 and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 - 710 nm, whereas CR bands in range of 680 - 800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view.
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix ‘R’ and the system noise V-C matrix ‘Q’. Then, the global filter uses R to calculate the information allocation factor ‘β’ for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-07-16
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.
Development of the One-Sided Nonlinear Adaptive Doppler Shift Estimation
NASA Technical Reports Server (NTRS)
Beyon, Jeffrey Y.; Koch, Grady J.; Singh, Upendra N.; Kavaya, Michael J.; Serror, Judith A.
2009-01-01
The new development of a one-sided nonlinear adaptive shift estimation technique (NADSET) is introduced. The background of the algorithm and a brief overview of NADSET are presented. The new technique is applied to the wind parameter estimates from a 2-micron wavelength coherent Doppler lidar system called VALIDAR located in NASA Langley Research Center in Virginia. The new technique enhances wind parameters such as Doppler shift and power estimates in low Signal-To-Noise-Ratio (SNR) regimes using the estimates in high SNR regimes as the algorithm scans the range bins from low to high altitude. The original NADSET utilizes the statistics in both the lower and the higher range bins to refine the wind parameter estimates in between. The results of the two different approaches of NADSET are compared.
Adaptive on-line estimation and control of overlay tool bias
NASA Astrophysics Data System (ADS)
Martinez, Victor M.; Finn, Karen; Edgar, Thomas F.
2003-06-01
Modern lithographic manufacturing processes rely on various types of exposure tools, used in a mix-and-match fashion. The motivation to use older tools alongside state-of-the-art tools is lower cost and one of the tradeoffs is a degradation in overlay performance. While average prices of semiconductor products continue to fall, the cost of manufacturing equipment rises with every product generation. Lithography processing, including the cost of ownership for tools, accounts for roughly 30% of the wafer processing costs, thus the importance of mix-and-match strategies. Exponentially Weighted Moving Average (EWMA) run-by-run controllers are widely used in the semiconductor manufacturing industry. This type of controller has been implemented successfully in volume manufacturing, improving Cpk values dramatically in processes like photolithography and chemical mechanical planarization. This simple, but powerful control scheme is well suited for adding corrections to compensate for Overlay Tool Bias (OTB). We have developed an adaptive estimation technique to compensate for overlay variability due to differences in the processing tools. The OTB can be dynamically calculated for each tool, based on the most recent measurements available, and used to correct the control variables. One approach to tracking the effect of different tools is adaptive modeling and control. The basic premise of an adaptive system is to change or adapt the controller as the operating conditions of the system change. Using closed-loop data, the adaptive control algorithm estimates the controller parameters using a recursive estimation technique. Once an updated model of the system is available, modelbased control becomes feasible. In the simplest scenario, the control law can be reformulated to include the current state of the tool (or its estimate) to compensate dynamically for OTB. We have performed simulation studies to predict the impact of deploying this strategy in production. The results
Seasonal Patterns and Remote Spectral Estimation of Canopy Chemistry Across the Oregon Transect
NASA Technical Reports Server (NTRS)
Matson, Pamela; Johnson, Lee; Billow, Christine; Miller, John; Pu, Ruiliang
1994-01-01
We examined seasonal changes in canopy chemical concentrations and content in conifer forests growing along a climate gradient in western Oregon, as part of the Oregon Transect Ecosystem Research (OTTER) study. The chemical variables were related to seasonal patterns of growth and production. Statistical comparisons of chemical variables with data collected from two different airborne remote-sensing platforms were also carried out. Total nitrogen (N) concentrations in foliage varied significantly both seasonally and among sites; when expressed as content in the forest canopy, nitrogen varied to a much greater extent and was significantly related to aboveground net primary production (r = 0.99). Chlorophyll and free amino acid concentrations varied more strongly than did total N and may have reflected changes in physiological demands for N. Large variations in starch concentrations were measured from pre- to post-budbreak in all conifer sites. Examination of remote-sensing data from two different airborne instruments suggests the potential for remote measurement of some canopy chemicals. Multivariate analysis of high-resolution spectral data in the near infrared region indicated significant correlations between spectral signals and N concentration and canopy N content; the correlation with canopy N content was stronger and was probably associated in part with water absorption features of the forest canopy. The spectral bands that were significantly correlated with lignin concentration and content were similar to bands selected in the other laboratory and airborne studies; starch concentrations were not significantly related to spectral reflectance data. Strong relationships between the spectral position of specific reflectance features in the visible region and chlorophyll were also found.
NASA Technical Reports Server (NTRS)
Rutledge, Charles K.
1988-01-01
The validity of applying chi-square based confidence intervals to far-field acoustic flyover spectral estimates was investigated. Simulated data, using a Kendall series and experimental acoustic data from the NASA/McDonnell Douglas 500E acoustics test, were analyzed. Statistical significance tests to determine the equality of distributions of the simulated and experimental data relative to theoretical chi-square distributions were performed. Bias and uncertainty errors associated with the spectral estimates were easily identified from the data sets. A model relating the uncertainty and bias errors to the estimates resulted, which aided in determining the appropriateness of the chi-square distribution based confidence intervals. Such confidence intervals were appropriate for nontonally associated frequencies of the experimental data but were inappropriate for tonally associated estimate distributions. The appropriateness at the tonally associated frequencies was indicated by the presence of bias error and noncomformity of the distributions to the theoretical chi-square distribution. A technique for determining appropriate confidence intervals at the tonally associated frequencies was suggested.
NASA Astrophysics Data System (ADS)
Gusev, A. A.; Guseva, E. M.
2016-07-01
The parameters of S-wave attenuation (the total effect of absorption and scattering) near the Petropavlovsk (PET) station in Kamchatka were estimated by means of the spectral method through an original procedure. The spectral method typically analyzes the changes with distance of the shape of spectra of the acceleration records assuming that the acceleration spectrum at the earthquake source is flat. In reality, this assumption is violated: the source acceleration spectra often have a high-frequency cutoff (the source-controlled f max) which limits the spectral working bandwidth. Ignoring this phenomenon not only leads to a broad scatter of the individual estimates but also causes systematic errors in the form of overestimation of losses. In the approach applied in the present study, we primarily estimated the frequency of the mentioned high-frequency cutoff and then constructed the loss estimates only within the frequency range where the source spectrum is approximately flat. The shape of the source spectrum was preliminarily assessed by the approximate loss compensation technique. For this purpose, we used the tentative attenuation estimates which are close to the final ones. The difference in the logarithms of the spectral amplitudes at the edges of the working bandwidth is the input for calculating the attenuation. We used the digital accelerograms from the PET station, with 80 samples per second digitization rate, and based on them, we calculated the averaged spectrum of the S-waves as the root mean square along two horizontal components. Our analysis incorporates 384 spectra from the local earthquakes with M = 4-6.5 at the hypocentral distances ranging from 80 to 220 km. By applying the nonlinear least-square method, we found the following parameters of the loss model: the Q-factor Q 0 = 156 ± 33 at frequency f = 1 Hz for the distance interval r = 0-100 km; the exponent in the power-law relationship describing the growth of the Q-factor with frequency,
NASA Astrophysics Data System (ADS)
Bousi, Evgenia; Pitris, Costas
2015-03-01
Fourier Domain (FD) Optical Coherence Tomography (OCT) interferograms require a Fourier transformation in order to be converted to A-Scans representing the backscattering intensity from the different depths of the tissue microstructure. Most often, this transformation is performed using a discrete Fourier transform, i.e. the well-known Fast Fourier Transform (FFT). However, there are many alternatives for performing the necessary spectral conversion. Autoregressive (AR) spectral estimation techniques are one such example. The parametric nature of AR techniques offers several advantages, compared to the commonly-used FFT, including better convergence and less susceptibility to noise. They can also be adjusted to represent more or less of the signal detail depending on the order of the autoregression. These features make them uniquely suited for processing the FD OCT data. The advantages of the proposed methodology are illustrated on in vivo skin imaging data and the resolution is verified on single back-reflections from a glass surface. AR spectral estimation can be used to convert the interferograms to A-Scans while at the same time reducing the artifacts caused by high intensity back-reflections (by -20 dB) and diminishing the speckle (by -12 dB) all without the degradation in the resolution associated with other techniques.
ERIC Educational Resources Information Center
Yang, Xiangdong; Poggio, John C.; Glasnapp, Douglas R.
2006-01-01
The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following…
Regularized estimate of the weight vector of an adaptive antenna array
NASA Astrophysics Data System (ADS)
Ermolayev, V. T.; Flaksman, A. G.; Sorokin, I. S.
2013-02-01
We consider an adaptive antenna array (AAA) with the maximum signal-to-noise ratio (SNR) at the output. The antenna configuration is assumed to be arbitrary. A rigorous analytical solution for the optimal weight vector of the AAA is obtained if the input process is defined by the noise correlation matrix and the useful-signal vector. On the basis of this solution, the regularized estimate of the weight vector is derived by using a limited number of input noise samples, which can be either greater or smaller than the number of array elements. Computer simulation results of adaptive signal processing indicate small losses in the SNR compared with the optimal SNR value. It is shown that the computing complexity of the proposed estimate is proportional to the number of noise samples, the number of external noise sources, and the squared number of array elements.
NASA Technical Reports Server (NTRS)
Giono, G.; Katsukawa, Y.; Ishikawa, R.; Narukage, N.; Bando, T.; Kano, R.; Suematsu, Y.; Winebarger, A.; Kobayashi, K.; Auchere, F.
2015-01-01
The Chromospheric Lyman-Alpha SpectroPolarimeter is a sounding rocket experiment design to measure for the first time the polarization signal of the Lyman-Alpha line (121.6nm), emitted in the solar upper-chromosphere and transition region. This instrument aims to detect the Hanle effect's signature hidden in the Ly-alpha polarization, as a tool to probe the chromospheric magnetic field. Hence, an unprecedented polarization accuracy is needed ((is) less than 10 (exp -3). Nevertheless, spatial and spectral resolutions are also crucial to observe chhromospheric feature such as spicules, and to have precise measurement of the Ly-alpha line core and wings. Hence, this poster will present how the telescope and the spectrograph were separately aligned, and their combined spatial and spectral resolutions.
Adaptation strategies for high order discontinuous Galerkin methods based on Tau-estimation
NASA Astrophysics Data System (ADS)
Kompenhans, Moritz; Rubio, Gonzalo; Ferrer, Esteban; Valero, Eusebio
2016-02-01
In this paper three p-adaptation strategies based on the minimization of the truncation error are presented for high order discontinuous Galerkin methods. The truncation error is approximated by means of a τ-estimation procedure and enables the identification of mesh regions that require adaptation. Three adaptation strategies are developed and termed a posteriori, quasi-a priori and quasi-a priori corrected. All strategies require fine solutions, which are obtained by enriching the polynomial order, but while the former needs time converged solutions, the last two rely on non-converged solutions, which lead to faster computations. In addition, the high order method permits the spatial decoupling for the estimated errors and enables anisotropic p-adaptation. These strategies are verified and compared in terms of accuracy and computational cost for the Euler and the compressible Navier-Stokes equations. It is shown that the two quasi-a priori methods achieve a significant reduction in computational cost when compared to a uniform polynomial enrichment. Namely, for a viscous boundary layer flow, we obtain a speedup of 6.6 and 7.6 for the quasi-a priori and quasi-a priori corrected approaches, respectively.
NASA Astrophysics Data System (ADS)
Kirby, Jon F.
2014-09-01
The effective elastic thickness (Te) is a geometric measure of the flexural rigidity of the lithosphere, which describes the resistance to bending under the application of applied, vertical loads. As such, it is likely that its magnitude has a major role in governing the tectonic evolution of both continental and oceanic plates. Of the several ways to estimate Te, one has gained popularity in the 40 years since its development because it only requires gravity and topography data, both of which are now readily available and provide excellent coverage over the Earth and even the rocky planets and moons of the solar system. This method, the ‘inverse spectral method’, develops measures of the relationship between observed gravity and topography data in the spatial frequency (wavenumber) domain, namely the admittance and coherence. The observed measures are subsequently inverted against the predictions of thin, elastic plate models, giving estimates of Te and other lithospheric parameters. This article provides a review of inverse spectral methodology and the studies that have used it. It is not, however, concerned with the geological or geodynamic significance or interpretation of Te, nor does it discuss and compare Te results from different methods in different provinces. Since the three main aspects of the subject are thin elastic plate flexure, spectral analysis, and inversion methods, the article broadly follows developments in these. The review also covers synthetic plate modelling, and concludes with a summary of the controversy currently surrounding inverse spectral methods, whether or not the large Te values returned in cratonic regions are artefacts of the method, or genuine observations.
Adaptive feedforward of estimated ripple improves the closed loop system performance significantly
Kwon, S.; Regan, A.; Wang, Y.M.; Rohlev, T.
1998-12-31
The Low Energy Demonstration Accelerator (LEDA) being constructed at Los Alamos National Laboratory will serve as the prototype for the low energy section of Acceleration Production of Tritium (APT) accelerator. This paper addresses the problem of LLRF control system for LEDA. The authors propose an estimator of the ripple and its time derivative and a control law which is based on PID control and adaptive feedforward of estimated ripple. The control law reduces the effect of the deterministic cathode ripple that is due to high voltage power supply and achieves tracking of desired set points.
Direct estimate of methane radiative forcing by use of nadir spectral radiances.
Chazette, P; Clerbaux, C; Mégie, G
1998-05-20
Direct determination of the radiative forcing of trace gases will be made possible by use of the next generation of nadir-looking spaceborne instruments that provide measurements of atmospheric radiances in the infrared spectral range with improved spectral and spatial resolution. An inversion statistical method has thus been developed and applied to the direct determination of the radiative forcing of methane, based on such instruments as the Fourier-transform Interferometric Monitor for Greenhouse Gases launched onboard the Japanese Advanced Earth Observing Satellite in 1996 and the Infrared Atmospheric Sounding Interferometer planned for the European polar platform Meteorological Operational Satellite in 2000. The method is based on simple statistical laws that directly relate the measured radiances to the radiative forcing by use of an a priori selection of appropriate spectral intervals and global modeling of methane spatial variations. This procedure avoids the use of an indirect determination based on an inversion process that requires precise knowledge of the methane vertical profiles throughout the troposphere. The overall accuracy and precision of this new algorithm are studied, and interfering gases and instrumental characteristics are taken into account. It is shown that radiative forcing can be determined at high horizontal spatial resolution with a precision better than 7% in cloud-free conditions and with well-known surface properties.
Mouget, Jean-Luc; Rosa, Philippe; Tremblin, Gérard
2004-07-19
The marine diatom Haslea ostrearia was cultured under light of different qualities, white (WL), blue (BL), green (GL), yellow (YL), red (RL), and far-red (FRL) and at two irradiance levels, low and high (20 and 100 micromolphotonsm(-2)s(-1), respectively). The effects of the different light regimes were studied on growth, pigment content, and photosynthesis, estimated by the modulated fluorescence of chlorophyll, as relative electron transport rate (rETR). For all the light qualities studied, growth rates were higher at high irradiance. Compared to the corresponding WL controls, growth was higher in BL and lower in YL at low irradiance, and lower in YL and GL at high irradiance. Except for YL, almost all the pigment contents of the cells were lower at high irradiance. At low irradiance, cell pigment contents (chlorophyll a and c, fucoxanthin) and pigment ratios (in function of chlorophyll a) were lower in YL, RL, and FRL. Whatever the irradiance level, the maximum PSII quantum efficiency (F(v)/F(m) remained almost constant for WL, BL, and GL. Other fluorescence parameters (photochemical quenching, rETR(max), and alpha, the maximum light utilization coefficient) were lower in GL, YL, RL, and FRL, at low irradiance. Although not statistically significant, BL caused an increase in these fluorescence parameters. These findings are interpreted as evidence that inverse chromatic acclimation occurs in diatoms.
Pittman, Jeremy Joshua; Arnall, Daryl Brian; Interrante, Sindy M; Moffet, Corey A; Butler, Twain J
2015-01-28
Non-destructive biomass estimation of vegetation has been performed via remote sensing as well as physical measurements. An effective method for estimating biomass must have accuracy comparable to the accepted standard of destructive removal. Estimation or measurement of height is commonly employed to create a relationship between height and mass. This study examined several types of ground-based mobile sensing strategies for forage biomass estimation. Forage production experiments consisting of alfalfa (Medicago sativa L.), bermudagrass [Cynodon dactylon (L.) Pers.], and wheat (Triticum aestivum L.) were employed to examine sensor biomass estimation (laser, ultrasonic, and spectral) as compared to physical measurements (plate meter and meter stick) and the traditional harvest method (clipping). Predictive models were constructed via partial least squares regression and modeled estimates were compared to the physically measured biomass. Least significant difference separated mean estimates were examined to evaluate differences in the physical measurements and sensor estimates for canopy height and biomass. Differences between methods were minimal (average percent error of 11.2% for difference between predicted values versus machine and quadrat harvested biomass values (1.64 and 4.91 t·ha(-1), respectively), except at the lowest measured biomass (average percent error of 89% for harvester and quad harvested biomass < 0.79 t·ha(-1)) and greatest measured biomass (average percent error of 18% for harvester and quad harvested biomass >6.4 t·ha(-1)). These data suggest that using mobile sensor-based biomass estimation models could be an effective alternative to the traditional clipping method for rapid, accurate in-field biomass estimation.
Pittman, Jeremy Joshua; Arnall, Daryl Brian; Interrante, Sindy M.; Moffet, Corey A.; Butler, Twain J.
2015-01-01
Non-destructive biomass estimation of vegetation has been performed via remote sensing as well as physical measurements. An effective method for estimating biomass must have accuracy comparable to the accepted standard of destructive removal. Estimation or measurement of height is commonly employed to create a relationship between height and mass. This study examined several types of ground-based mobile sensing strategies for forage biomass estimation. Forage production experiments consisting of alfalfa (Medicago sativa L.), bermudagrass [Cynodon dactylon (L.) Pers.], and wheat (Triticum aestivum L.) were employed to examine sensor biomass estimation (laser, ultrasonic, and spectral) as compared to physical measurements (plate meter and meter stick) and the traditional harvest method (clipping). Predictive models were constructed via partial least squares regression and modeled estimates were compared to the physically measured biomass. Least significant difference separated mean estimates were examined to evaluate differences in the physical measurements and sensor estimates for canopy height and biomass. Differences between methods were minimal (average percent error of 11.2% for difference between predicted values versus machine and quadrat harvested biomass values (1.64 and 4.91 t·ha−1, respectively), except at the lowest measured biomass (average percent error of 89% for harvester and quad harvested biomass < 0.79 t·ha−1) and greatest measured biomass (average percent error of 18% for harvester and quad harvested biomass >6.4 t·ha−1). These data suggest that using mobile sensor-based biomass estimation models could be an effective alternative to the traditional clipping method for rapid, accurate in-field biomass estimation. PMID:25635415
Multilevel Error Estimation and Adaptive h-Refinement for Cartesian Meshes with Embedded Boundaries
NASA Technical Reports Server (NTRS)
Aftosmis, M. J.; Berger, M. J.; Kwak, Dochan (Technical Monitor)
2002-01-01
This paper presents the development of a mesh adaptation module for a multilevel Cartesian solver. While the module allows mesh refinement to be driven by a variety of different refinement parameters, a central feature in its design is the incorporation of a multilevel error estimator based upon direct estimates of the local truncation error using tau-extrapolation. This error indicator exploits the fact that in regions of uniform Cartesian mesh, the spatial operator is exactly the same on the fine and coarse grids, and local truncation error estimates can be constructed by evaluating the residual on the coarse grid of the restricted solution from the fine grid. A new strategy for adaptive h-refinement is also developed to prevent errors in smooth regions of the flow from being masked by shocks and other discontinuous features. For certain classes of error histograms, this strategy is optimal for achieving equidistribution of the refinement parameters on hierarchical meshes, and therefore ensures grid converged solutions will be achieved for appropriately chosen refinement parameters. The robustness and accuracy of the adaptation module is demonstrated using both simple model problems and complex three dimensional examples using meshes with from 10(exp 6), to 10(exp 7) cells.
NASA Astrophysics Data System (ADS)
Aranha dos Santos, Valentin; Schmetterer, Leopold; Gröschl, Martin; Garhofer, Gerhard; Werkmeister, René M.
2016-03-01
Dry eye syndrome is a highly prevalent disease of the ocular surface characterized by an instability of the tear film. Traditional methods used for the evaluation of tear film stability are invasive or show limited repeatability. Here we propose a new noninvasive approach to measure tear film thickness using an efficient delay estimator and ultrahigh resolution spectral domain OCT. Silicon wafer phantoms with layers of known thickness and group index were used to validate the estimator-based thickness measurement. A theoretical analysis of the fundamental limit of the precision of the estimator is presented and the analytical expression of the Cramér-Rao lower bound (CRLB), which is the minimum variance that may be achieved by any unbiased estimator, is derived. The performance of the estimator against noise was investigated using simulations. We found that the proposed estimator reaches the CRLB associated with the OCT amplitude signal. The technique was applied in vivo in healthy subjects and dry eye patients. Series of tear film thickness maps were generated, allowing for the visualization of tear film dynamics. Our results show that the central tear film thickness precisely measured in vivo with a coefficient of variation of about 0.65% and that repeatable tear film dynamics can be observed. The presented method has the potential of being an alternative to breakup time measurements (BUT) and could be used in clinical setting to study patients with dry eye disease and monitor their treatments.
Osborne, S. L.; Schepers, J. S.; Francis, D. D.; Schlemmer, M. R.
2002-01-01
Current technologies for measuring plant water status are limited, while recently remote sensing techniques for estimating N status have increased with limited research on the interaction between the two stresses. Because plant water status methods are time-consuming and require numerous observations to characterize a field, managers could benefit from remote sensing techniques to assist in irrigation and N management decisions. A 2-yr experiment was initiated to determine specific wavelengths and/or combinations of wavelengths indicative of water stress and N deficiencies, and to evaluate these wavelengths for estimating in-season biomass and corn (Zea mays L.) grain yield. The experiment was a split-plot design with three replications. The treatment structure had five N rates (0, 45, 90, 134, and 269 kg N ha(-1)) and three water treatments [dryland, 0.5 evapotranspiration (ET), and full ET]. Canopy spectral radiance measurements (350-2500 nm) were taken at various growth stages (V6-V7, V13-V16, and V14-R1). Specific wavelengths for estimating crop biomass, N concentration, grain yield, and chlorophyll meter readings changed with growth stage and sampling date. Changes in total N and biomass in the presence of a water stress were estimated using near-infrared (NIR) reflectance and the water absorption bands. Reflectance in the green and NIR regions were used to estimate total N and biomass without water stress. Reflectance at 510, 705, and 1135 nm were found for estimating chlorophyll meter readings regardless of year or sampling date.
NASA Technical Reports Server (NTRS)
Vasquez, R. P.; Klein, J. D.; Barton, J. J.; Grunthaner, F. J.
1981-01-01
A comparison is made between maximum-entropy spectral estimation and traditional methods of deconvolution used in electron spectroscopy. The maximum-entropy method is found to have higher resolution-enhancement capabilities and, if the broadening function is known, can be used with no adjustable parameters with a high degree of reliability. The method and its use in practice are briefly described, and a criterion is given for choosing the optimal order for the prediction filter based on the prediction-error power sequence. The method is demonstrated on a test case and applied to X-ray photoelectron spectra.
Estimation of tissue optical parameters with hyperspectral imaging and spectral unmixing
NASA Astrophysics Data System (ADS)
Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Chen, Zhuo G.; Fei, Baowei
2015-03-01
Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization has been applied for decomposing hyperspectral images into physiologically meaningful chromophore concentration maps. The approach has been validated by computer-simulated hyperspectral images and in vivo tumor hyperspectral images from a head and neck cancer animal model.
Estimation of Tissue Optical Parameters with Hyperspectral Imaging and Spectral Unmixing
Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei
2015-01-01
Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization has been applied for decomposing hyperspectral images into physiologically meaningful chromophore concentration maps. The approach has been validated by computer-simulated hyperspectral images and in vivo tumor hyperspectral images from a head and neck cancer animal model. PMID:26855467
Melbourne, J.; Matthews, K.; Soifer, B. T. E-mail: bts@submm.caltech.edu
2009-06-15
A simple optical to mid-IR color selection, R - [24]>14, i.e., f {sub {nu}}(24 {mu}m)/f {sub {nu}}(R) {approx}> 1000, identifies highly dust obscured galaxies (DOGs) with typical redshifts of z {approx} 2 {+-} 0.5. Extreme mid-IR luminosities (L {sub IR} > 10{sup 12-14}) suggest that DOGs are powered by a combination of active galactic nuclei (AGNs) and star formation, possibly driven by mergers. In an effort to compare their photometric properties with their rest-frame optical morphologies, we obtained high-spatial resolution (0.''05-0.''1) Keck Adaptive Optics K'-band images of 15 DOGs. The images reveal a wide range of morphologies, including small exponential disks (eight of 15), small ellipticals (four of 15), and unresolved sources (two of 15). One particularly diffuse source could not be classified because of low signal-to-noise ratio. We find a statistically significant correlation between galaxy concentration and mid-IR luminosity, with the most luminous DOGs exhibiting higher concentration and smaller physical size. DOGs with high concentration also tend to have spectral energy distributions (SEDs) suggestive of AGN activity. Thus, central AGN light may be biasing the morphologies of the more luminous DOGs to higher concentration. Conversely, more diffuse DOGs tend to show an SED shape suggestive of star formation. Two of 15 in the sample show multiple resolved components with separations of {approx}1 kpc, circumstantial evidence for ongoing mergers.
NASA Astrophysics Data System (ADS)
Pedretti, Daniele; Fernàndez-Garcia, Daniel
2013-09-01
Particle tracking methods to simulate solute transport deal with the issue of having to reconstruct smooth concentrations from a limited number of particles. This is an error-prone process that typically leads to large fluctuations in the determined late-time behavior of breakthrough curves (BTCs). Kernel density estimators (KDE) can be used to automatically reconstruct smooth BTCs from a small number of particles. The kernel approach incorporates the uncertainty associated with subsampling a large population by equipping each particle with a probability density function. Two broad classes of KDE methods can be distinguished depending on the parametrization of this function: global and adaptive methods. This paper shows that each method is likely to estimate a specific portion of the BTCs. Although global methods offer a valid approach to estimate early-time behavior and peak of BTCs, they exhibit important fluctuations at the tails where fewer particles exist. In contrast, locally adaptive methods improve tail estimation while oversmoothing both early-time and peak concentrations. Therefore a new method is proposed combining the strength of both KDE approaches. The proposed approach is universal and only needs one parameter (α) which slightly depends on the shape of the BTCs. Results show that, for the tested cases, heavily-tailed BTCs are properly reconstructed with α ≈ 0.5 .
Chakraborty, Somsubhra; Das, Bhabani S; Ali, Md Nasim; Li, Bin; Sarathjith, M C; Majumdar, K; Ray, D P
2014-03-01
The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation=3.2, validation r(2)=0.91 and RMSE=13.38 μg g(-1) h(-1)) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky-Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.
Chakraborty, Somsubhra; Das, Bhabani S; Ali, Md Nasim; Li, Bin; Sarathjith, M C; Majumdar, K; Ray, D P
2014-03-01
The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation=3.2, validation r(2)=0.91 and RMSE=13.38 μg g(-1) h(-1)) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky-Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity. PMID:24398221
Ferraioli, Luigi; Hueller, Mauro; Vitale, Stefano; Heinzel, Gerhard; Hewitson, Martin; Monsky, Anneke; Nofrarias, Miquel
2010-08-15
The scientific objectives of the LISA Technology Package experiment on board of the LISA Pathfinder mission demand accurate calibration and validation of the data analysis tools in advance of the mission launch. The level of confidence required in the mission outcomes can be reached only by intensively testing the tools on synthetically generated data. A flexible procedure allowing the generation of a cross-correlated stationary noise time series was set up. A multichannel time series with the desired cross-correlation behavior can be generated once a model for a multichannel cross-spectral matrix is provided. The core of the procedure comprises a noise coloring, multichannel filter designed via a frequency-by-frequency eigendecomposition of the model cross-spectral matrix and a subsequent fit in the Z domain. The common problem of initial transients in a filtered time series is solved with a proper initialization of the filter recursion equations. The noise generator performance was tested in a two-dimensional case study of the closed-loop LISA Technology Package dynamics along the two principal degrees of freedom.
Sadek, H.S.; Rashad, S.M.; Blank, H.R.
1984-01-01
If proper account is taken of the constraints of the method, it is capable of providing depth estimates to within an accuracy of about 10 percent under suitable circumstances. The estimates are unaffected by source magnetization and are relatively insensitive to assumptions as to source shape or distribution. The validity of the method is demonstrated by analyses of synthetic profiles and profiles recorded over Harrat Rahat, Saudi Arabia, and Diyur, Egypt, where source depths have been proved by drilling.
NASA Astrophysics Data System (ADS)
Baghi, Quentin; Métris, Gilles; Bergé, Joël; Christophe, Bruno; Touboul, Pierre; Rodrigues, Manuel
2016-06-01
We present a Gaussian regression method for time series with missing data and stationary residuals of unknown power spectral density (PSD). The missing data are efficiently estimated by their conditional expectation as in universal Kriging based on the circulant approximation of the complete data covariance. After initialization with an autoregressive fit of the noise, a few iterations of estimation/reconstruction steps are performed until convergence of the regression and PSD estimates, in a way similar to the expectation-conditional-maximization algorithm. The estimation can be performed for an arbitrary PSD provided that it is sufficiently smooth. The algorithm is developed in the framework of the MICROSCOPE space mission whose goal is to test the weak equivalence principle (WEP) with a precision of 10-15. We show by numerical simulations that the developed method allows us to meet three major requirements: to maintain the targeted precision of the WEP test in spite of the loss of data, to calculate a reliable estimate of this precision and of the noise level, and finally to provide consistent and faithful reconstructed data to the scientific community.
Hellander, Andreas; Lawson, Michael J; Drawert, Brian; Petzold, Linda
2015-01-01
The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps are adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the Diffusive Finite-State Projection (DFSP) method, to incorporate temporal adaptivity. PMID:26865735
NASA Astrophysics Data System (ADS)
Hellander, Andreas; Lawson, Michael J.; Drawert, Brian; Petzold, Linda
2014-06-01
The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if diffusion constants are large. Furthermore, inherent properties of exact kinetic-Monte Carlo simulation methods limit the efficiency of parallel implementations. Several approximate and hybrid methods have appeared that enable more efficient simulation of the RDME. A common feature to most of them is that they rely on splitting the system into its reaction and diffusion parts and updating them sequentially over a discrete timestep. This use of operator splitting enables more efficient simulation but it comes at the price of a temporal discretization error that depends on the size of the timestep. So far, existing methods have not attempted to estimate or control this error in a systematic manner. This makes the solvers hard to use for practitioners since they must guess an appropriate timestep. It also makes the solvers potentially less efficient than if the timesteps were adapted to control the error. Here, we derive estimates of the local error and propose a strategy to adaptively select the timestep when the RDME is simulated via a first order operator splitting. While the strategy is general and applicable to a wide range of approximate and hybrid methods, we exemplify it here by extending a previously published approximate method, the diffusive finite-state projection (DFSP) method, to incorporate temporal adaptivity.
NASA Astrophysics Data System (ADS)
Shi, Lei; Wang, Z. J.
2015-08-01
Adjoint-based mesh adaptive methods are capable of distributing computational resources to areas which are important for predicting an engineering output. In this paper, we develop an adjoint-based h-adaptation approach based on the high-order correction procedure via reconstruction formulation (CPR) to minimize the output or functional error. A dual-consistent CPR formulation of hyperbolic conservation laws is developed and its dual consistency is analyzed. Super-convergent functional and error estimate for the output with the CPR method are obtained. Factors affecting the dual consistency, such as the solution point distribution, correction functions, boundary conditions and the discretization approach for the non-linear flux divergence term, are studied. The presented method is then used to perform simulations for the 2D Euler and Navier-Stokes equations with mesh adaptation driven by the adjoint-based error estimate. Several numerical examples demonstrate the ability of the presented method to dramatically reduce the computational cost comparing with uniform grid refinement.
On the Estimation of T-Wave Alternans Using the Spectral Fast Fourier Transform Method
Armoundas, Antonis A; Mela, Theofanie; Merchant, Faisal M
2012-01-01
BACKGROUND T-wave alternans (TWA), has been associated with increased vulnerability to ventricular tachyarrhythmias and sudden cardiac death (SCD). However, both random (white) noise and (patho)physiologic processes (i.e. premature ventricular contractions [PVCs], heart and respiration rates) may hamper TWA estimation and therefore, lessen its clinical utility for risk stratification. OBJECTIVE To investigate the effect of random noise and certain (patho)physiologic processes on the estimation of TWA using the Fast Fourier Transform (FFT) method and to develop methods to overcome these potential sources of error. METHODS We used a combination of human electrocardiogram data and computer simulations to assess the effects of a PVC, random and colored noise on the accuracy of TWA estimation. RESULTS We quantitatively demonstrate that replacing a “bad” beat with an odd/even median beat is a more accurate approach than replacing it with the overall average or the overall median beat. We also show that phase resetting may have a significant effect on alternans estimation and that estimation of alternans using frequencies greater than 0.4922 cycles/beat in a 128-point FFT provides the most accurate approach for estimating the alternans when phase resetting is likely to occur. Additionally, our data demonstrate that the number of indeterminate TWA tests due to high levels of noise can be reduced when the alternans voltage exceeds a new higher threshold. Also, the amplitude of random noise has a significant effect on alternans estimation and should be considered to adjust the alternans voltage threshold for noise levels greater than 1.8 μV. Finally, we quantitatively demonstrate that colored noise may lead to a false positive or a false negative result. We propose methods to estimate the effect of these (patho)physiologic processes on the alternans estimation in order to determine whether a TWA test is likely to be a true positive or a true negative. CONCLUSION This
Chu, Kai Chuan
2012-01-01
Objectives Noise reduction using wavelet thresholding of multitaper estimators (WTME) and geometric approach to spectral subtraction (GASS) can improve speech quality of noisy sound for speech coding strategy. This study used Perceptual Evaluation of Speech Quality (PESQ) to assess the performance of the WTME and GASS for speech coding strategy. Methods This study included 25 Mandarin sentences as test materials. Environmental noises including the air-conditioner, cafeteria and multi-talker were artificially added to test materials at signal to noise ratio (SNR) of -5, 0, 5, and 10 dB. HiRes 120 vocoder WTME and GASS noise reduction process were used in this study to generate sound outputs. The sound outputs were measured by the PESQ to evaluate sound quality. Results Two figures and three tables were used to assess the speech quality of the sound output of the WTME and GASS. Conclusion There is no significant difference between the overall performance of sound quality in both methods, but the geometric approach to spectral subtraction method is slightly better than the wavelet thresholding of multitaper estimators. PMID:22701151
The use of large-area spectral data in wheat yield estimation
NASA Technical Reports Server (NTRS)
Barnett, T. L.; Thompson, D. R.
1982-01-01
Large-area relations between satellite spectral data and end-of-season crop yield were investigated. Green Index Number (GIN) values from Landsat MSS data of sample segments throughout the U.S. Great Plains winter wheat belt in 1978 were correlated to county USDA-SRS reported yields. A linear relation between GIN and yield appeared to exist up to GIN values of 40 or 50, covering cases of severe to moderate stress. In a test on 1978 Texas winter wheat at the county level, GIN values for sample segments in the counties were used in conjunction with an agronomic-meteorological yield model. The combined fit explained significantly more of the observed yield variation at the county level than the agromet model alone.
NASA Astrophysics Data System (ADS)
Zhang, Xiangwen; Xu, Yong; Pan, Ming; Ren, Fenghua
2014-04-01
A sliding-mode observer is designed to estimate the vehicle velocity with the measured vehicle acceleration, the wheel speeds and the braking torques. Based on the Burckhardt tyre model, the extended Kalman filter is designed to estimate the parameters of the Burckhardt model with the estimated vehicle velocity, the measured wheel speeds and the vehicle acceleration. According to the estimated parameters of the Burckhardt tyre model, the tyre/road friction coefficients and the optimal slip ratios are calculated. A vehicle adaptive sliding-mode control (SMC) algorithm is presented with the estimated vehicle velocity, the tyre/road friction coefficients and the optimal slip ratios. And the adjustment method of the sliding-mode gain factors is discussed. Based on the adaptive SMC algorithm, a vehicle's antilock braking system (ABS) control system model is built with the Simulink Toolbox. Under the single-road condition as well as the different road conditions, the performance of the vehicle ABS system is simulated with the vehicle velocity observer, the tyre/road friction coefficient estimator and the adaptive SMC algorithm. The results indicate that the estimated errors of the vehicle velocity and the tyre/road friction coefficients are acceptable and the vehicle ABS adaptive SMC algorithm is effective. So the proposed adaptive SMC algorithm can be used to control the vehicle ABS without the information of the vehicle velocity and the road conditions.
Adaptive RBF network for parameter estimation and stable air-fuel ratio control.
Wang, Shiwei; Yu, D L
2008-01-01
In the application of variable structure control to engine air-fuel ratio, the ratio is subjected to chattering due to system uncertainty, such as unknown parameters or time varying dynamics. This paper proposes an adaptive neural network method to estimate two immeasurable physical parameters on-line and to compensate for the model uncertainty and engine time varying dynamics, so that the chattering is substantially reduced and the air-fuel ratio is regulated within the desired range of the stoichiometric value. The adaptive law of the neural network is derived using the Lyapunov method, so that the stability of the whole system and the convergence of the networks are guaranteed. Computer simulations based on a mean value engine model demonstrate the effectiveness of the technique. PMID:18166378
Koofigar, Hamid Reza
2016-01-01
The problem of maximum power point tracking (MPPT) in photovoltaic (PV) systems, despite the model uncertainties and the variations in environmental circumstances, is addressed. Introducing a mathematical description, an adaptive sliding mode control (ASMC) algorithm is first developed. Unlike many previous investigations, the output voltage is not required to be sensed and the upper bound of system uncertainties and the variations of irradiance and temperature are not required to be known. Estimating the output voltage by an update law, an adaptive-based H∞ tracking algorithm is then developed for the case the perturbations are energy-bounded. The stability analysis is presented for the proposed tracking control schemes, based on the Lyapunov stability theorem. From a comparison viewpoint, some numerical and experimental studies are also presented and discussed. PMID:26606851
Real-time Adaptive Kinematic Model Estimation of Concentric Tube Robots
Kim, Chunwoo; Ryu, Seok Chang; Dupont, Pierre E.
2016-01-01
Kinematic models of concentric tube robots have matured from considering only tube bending to considering tube twisting as well as external loading. While these models have been demonstrated to approximate actual behavior, modeling error can be significant for medical applications that often call for positioning accuracy of 1–2mm. As an alternative to moving to more complex models, this paper proposes using sensing to adaptively update model parameters during robot operation. Advantages of this method are that the model is constantly tuning itself to provide high accuracy in the region of the workspace where it is currently operating. It also adapts automatically to changes in robot shape and compliance associated with the insertion and removal of tools through its lumen. As an initial exploration of this approach, a recursive on-line estimator is proposed and evaluated experimentally. PMID:27175307
Adaptive Pre-FFT Equalizer with High-Precision Channel Estimator for ISI Channels
NASA Astrophysics Data System (ADS)
Yoshida, Makoto
We present an attractive approach for OFDM transmission using an adaptive pre-FFT equalizer, which can select ICI reduction mode according to channel condition, and a degenerated-inverse-matrix-based channel estimator (DIME), which uses a cyclic sinc-function matrix uniquely determined by transmitted subcarriers. In addition to simulation results, the proposed system with an adaptive pre-FFT equalizer and DIME has been laboratory tested by using a software defined radio (SDR)-based test bed. The simulation and experimental results demonstrated that the system at a rate of more than 100Mbps can provide a bit error rate of less than 10-3 for a fast multi-path fading channel that has a moving velocity of more than 200km/h with a delay spread of 1.9µs (a maximum delay path of 7.3µs) in the 5-GHz band.
Koofigar, Hamid Reza
2016-01-01
The problem of maximum power point tracking (MPPT) in photovoltaic (PV) systems, despite the model uncertainties and the variations in environmental circumstances, is addressed. Introducing a mathematical description, an adaptive sliding mode control (ASMC) algorithm is first developed. Unlike many previous investigations, the output voltage is not required to be sensed and the upper bound of system uncertainties and the variations of irradiance and temperature are not required to be known. Estimating the output voltage by an update law, an adaptive-based H∞ tracking algorithm is then developed for the case the perturbations are energy-bounded. The stability analysis is presented for the proposed tracking control schemes, based on the Lyapunov stability theorem. From a comparison viewpoint, some numerical and experimental studies are also presented and discussed.
Heat kernel estimates and spectral properties of a pseudorelativistic operator with magnetic field
NASA Astrophysics Data System (ADS)
Jakubassa-Amundsen, D. H.
2008-03-01
Based on the Mehler heat kernel of the Schrödinger operator for a free electron in a constant magnetic field, an estimate for the kernel of EA=∣α(p-eA)+βm∣ is derived, where EA represents the kinetic energy of a Dirac electron within the pseudorelativistic no-pair Brown-Ravenhall model. This estimate is used to provide the bottom of the essential spectrum for the two-particle Brown-Ravenhall operator, describing the motion of the electrons in a central Coulomb field and a constant magnetic field, if the central charge is restricted to Z ⩽86.
Optimization of an adaptive SPECT system with the scanning linear estimator
NASA Astrophysics Data System (ADS)
Ghanbari, Nasrin; Clarkson, Eric; Kupinski, Matthew A.; Li, Xin
2015-08-01
The adaptive single-photon emission computed tomography (SPECT) system studied here acquires an initial scout image to obtain preliminary information about the object. Then the configuration is adjusted by selecting the size of the pinhole and the magnification that optimize system performance on an ensemble of virtual objects generated to be consistent with the scout data. In this study the object is a lumpy background that contains a Gaussian signal with a variable width and amplitude. The virtual objects in the ensemble are imaged by all of the available configurations and the subsequent images are evaluated with the scanning linear estimator to obtain an estimate of the signal width and amplitude. The ensemble mean squared error (EMSE) on the virtual ensemble between the estimated and the true parameters serves as the performance figure of merit for selecting the optimum configuration. The results indicate that variability in the original object background, noise and signal parameters leads to a specific optimum configuration in each case. A statistical study carried out for a number of objects show that the adaptive system on average performs better than its nonadaptive counterpart.
Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Shamshirband, Shahaboddin; Pavlović, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat
2014-07-01
The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
Johansson, A Torbjorn; White, Paul R
2011-08-01
This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Background noise estimation and compensation is accomplished using order statistics and pre-whitening. Using simulated signals as well as recorded calls of marine mammals and a human whistled speech utterance, it is shown that the proposed method can detect more simultaneous whistles than two competing spectrogram-based methods while not reporting any false alarms on the example datasets. In one example, it extracts complete 1.4 and 1.8 s bottlenose dolphin whistles successfully through frequency crossings. The method performs detection and estimates frequency tracks even at high sweep rates. The algorithm is also shown to be effective on human whistled utterances. PMID:21877804
Technology Transfer Automated Retrieval System (TEKTRAN)
Hyperspectral forage canopy absorbance was estimated on eight random plots in each of three 1.2 ha common bermudagrass pastures weekly over a period of 9 weeks from June through early August, 2005 using spectroradiometers measuring light reflectance from 410 nm to 1010 nm. Forage in each plot was ...
Fusion of spectral and electrochemical sensor data for estimating soil macronutrients
Technology Transfer Automated Retrieval System (TEKTRAN)
Rapid and efficient quantification of plant-available soil phosphorus (P) and potassium (K) is needed to support variable-rate fertilization strategies. Two methods that have been used for estimating these soil macronutrients are diffuse reflectance spectroscopy in visible and near-infrared (VNIR) w...
SDSS/SEGUE spectral feature analysis for stellar atmospheric parameter estimation
Li, Xiangru; Lu, Yu; Yang, Tan; Wang, Yongjun; Wu, Q. M. Jonathan; Luo, Ali; Zhao, Yongheng; Zuo, Fang
2014-08-01
Large-scale and deep sky survey missions are rapidly collecting a large amount of stellar spectra, which necessitate the estimation of atmospheric parameters directly from spectra and make it feasible to statistically investigate latent principles in a large data set. We present a technique for estimating parameters T{sub eff}, log g, and [Fe/H] from stellar spectra. With this technique, we first extract features from stellar spectra using the LASSO algorithm; then, the parameters are estimated from the extracted features using the support vector regression. On a subsample of 20,000 stellar spectra from the Sloan Digital Sky Survey (SDSS) with reference parameters provided by the SDSS/SEGUE Spectroscopic Parameter Pipeline, estimation consistency are 0.007458 dex for log T{sub eff} (101.609921 K for T{sub eff}), 0.189557 dex for log g, and 0.182060 for [Fe/H], where the consistency is evaluated by mean absolute error. Prominent characteristics of the proposed scheme are sparseness, locality, and physical interpretability. In this work, each spectrum consists of 3821 fluxes, and 10, 19, and 14 typical wavelength positions are detected, respectively, for estimating T{sub eff}, log g, and [Fe/H]. It is shown that the positions are related to typical lines of stellar spectra. This characteristic is important in investigating physical indications from analysis results. Then, stellar spectra can be described by the individual fluxes on the detected positions (PD) or local integration of fluxes near them (LI). The aforementioned consistency is the result based on features described by LI. If features are described by PD, consistency is 0.009092 dex for log T{sub eff} (124.545075 K for T{sub eff}), 0.198928 dex for log g, and 0.206814 dex for [Fe/H].
SDSS/SEGUE Spectral Feature Analysis for Stellar Atmospheric Parameter Estimation
NASA Astrophysics Data System (ADS)
Li, Xiangru; Wu, Q. M. Jonathan; Luo, Ali; Zhao, Yongheng; Lu, Yu; Zuo, Fang; Yang, Tan; Wang, Yongjun
2014-08-01
Large-scale and deep sky survey missions are rapidly collecting a large amount of stellar spectra, which necessitate the estimation of atmospheric parameters directly from spectra and make it feasible to statistically investigate latent principles in a large data set. We present a technique for estimating parameters T eff, log g, and [Fe/H] from stellar spectra. With this technique, we first extract features from stellar spectra using the LASSO algorithm; then, the parameters are estimated from the extracted features using the support vector regression. On a subsample of 20,000 stellar spectra from the Sloan Digital Sky Survey (SDSS) with reference parameters provided by the SDSS/SEGUE Spectroscopic Parameter Pipeline, estimation consistency are 0.007458 dex for log T eff (101.609921 K for T eff), 0.189557 dex for log g, and 0.182060 for [Fe/H], where the consistency is evaluated by mean absolute error. Prominent characteristics of the proposed scheme are sparseness, locality, and physical interpretability. In this work, each spectrum consists of 3821 fluxes, and 10, 19, and 14 typical wavelength positions are detected, respectively, for estimating T eff, log g, and [Fe/H]. It is shown that the positions are related to typical lines of stellar spectra. This characteristic is important in investigating physical indications from analysis results. Then, stellar spectra can be described by the individual fluxes on the detected positions (PD) or local integration of fluxes near them (LI). The aforementioned consistency is the result based on features described by LI. If features are described by PD, consistency is 0.009092 dex for log T eff (124.545075 K for T eff), 0.198928 dex for log g, and 0.206814 dex for [Fe/H].
Kholodtsova, Maria N; Daul, Christian; Loschenov, Victor B; Blondel, Walter C P M
2016-06-13
This paper presents a new approach to estimate optical properties (absorption and scattering coefficients µa and µs) of biological tissues from spatially-resolved spectroscopy measurements. A Particle Swarm Optimization (PSO)-based algorithm was implemented and firstly modified to deal with spatial and spectral resolutions of the data, and to solve the corresponding inverse problem. Secondly, the optimization was improved by fitting exponential decays to the two best points among all clusters of the "particles" randomly distributed all over the parameter space (µs, µa) of possible solutions. The consequent acceleration of all the groups of particles to the "best" curve leads to significant error decrease in the optical property estimation. The study analyzes the estimated optical property error as a function of the various PSO parameter combinations, and several performance criteria such as the cost-function error and the number of iterations in the algorithms proposed. The final one led to error values between ground truth and estimated values of µs and µa less than 6%. PMID:27410289
NASA Astrophysics Data System (ADS)
Rausch, J.; Bennartz, R.; Puygrenier, V.; Brenguier, J. L.
2014-12-01
We attempt to infer cloud vertical structure and improve estimates of cloud microphysical properties through the application of an Adiabatic Spectrally Consistent Retrieval (ASCR) to Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The MODIS Cloud Product provides estimates of cloud optical thickness and droplet effective radius for three near-infrared absorption wavelengths (1.6, 2.1 and 3.7 mm) under the assumption of a plane-parallel, vertically homogeneous (VH) cloud. This is not a physically realistic assumption for boundary layer clouds, where an adiabatically stratified liquid water content profile conforms better. ASCR transforms VH retrievals of optical thickness and droplet effective radii into adiabatically stratified retrievals, exploiting the varying photon penetration depth of each absorption channel. Taking advantage of the data screening and quality controls applied to the MODIS Cloud Product, existing retrievals of optical thickness and droplet effective radii are inverted to obtain equivalent scene reflectances from which two-channel and four-channel adiabatically stratified retrievals of cloud geometrical thickness (H) and cloud droplet number concentration (N) are performed using an optimal estimation framework. Through a comparison of the 2-channel and 4-channel N and H retrievals, we attempt to estimate the degree to which a cloud conforms to an adiabatically stratified model, near cloud-top. Results will be presented, demonstrating ASCR's performance relative to VH retrievals from the cloud product through an analysis of one year's observations of marine stratocumulus from MODIS near the South American and African Continents.
Ignatova, Irina; French, Andrew S; Immonen, Esa-Ville; Frolov, Roman; Weckström, Matti
2014-06-01
Shannon's seminal approach to estimating information capacity is widely used to quantify information processing by biological systems. However, the Shannon information theory, which is based on power spectrum estimation, necessarily contains two sources of error: time delay bias error and random error. These errors are particularly important for systems with relatively large time delay values and for responses of limited duration, as is often the case in experimental work. The window function type and size chosen, as well as the values of inherent delays cause changes in both the delay bias and random errors, with possibly strong effect on the estimates of system properties. Here, we investigated the properties of these errors using white-noise simulations and analysis of experimental photoreceptor responses to naturalistic and white-noise light contrasts. Photoreceptors were used from several insect species, each characterized by different visual performance, behavior, and ecology. We show that the effect of random error on the spectral estimates of photoreceptor performance (gain, coherence, signal-to-noise ratio, Shannon information rate) is opposite to that of the time delay bias error: the former overestimates information rate, while the latter underestimates it. We propose a new algorithm for reducing the impact of time delay bias error and random error, based on discovering, and then using that size of window, at which the absolute values of these errors are equal and opposite, thus cancelling each other, allowing minimally biased measurement of neural coding.
Estimation of response-spectral values as functions of magnitude, distance, and site conditions
Joyner, W.B.; Boore, David M.
1982-01-01
We have developed empirical predictive equations for the horizontal pseudo-velocity response at 5-percent damping for 12 different periods from 0.1 to 4.0 s. Using a multiple linear-regression method similar to the one we used previously for peak horizontal acceleration and velocity, we analyzed response spectra period by period for 64 records of 12 shallow earthquakes in western North America, including the recent Coyote Lake and Imperial Valley, California, earthquakes. The resulting predictive equations show amplification of the response values at soil sites for periods greater than or equal to 0.5 s, with maximum amplification exceeding a factor of 2 at 1.5 s. For periods less than 0.5 s there is no statistically significant difference between rock sites and the soil sites represented in the data set. These results are consistent with those of several earlier studies. A particularly significant aspect of the predictive equations is that the response values at different periods are different functions of magnitude (confirming earlier results by McGuire and by Trifunac and Anderson). The slope of the least-squares straight line relating log response to moment magnitude ranges from 0.21 at a period of 0.1 s to greater than 0.5 at periods of 1 s and longer. This result indicates that the conventional practice of scaling a constant spectral shape by peak acceleration will not give accurate answers. The Newmark and Hall method of spectral scaling, using both peak acceleration and peak velocity, largely avoids this error. Comparison of our spectra with the Regulatory Guide 1.60 spectrum anchored at the same value at 0.1 s shows that the Regulatory Guide 1.60 spectrum is exceeded at soil sites for a magnitude of 7.5 at all distances for periods greater than about 0.5 s. Comparison of our spectra for soil sites with the corresponding ATC-3 curve of lateral design force coefficients for the highest seismic zone indicates that the ATC-3 curve is exceeded within about 5 km
An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications
NASA Technical Reports Server (NTRS)
Schuet, Stefan R.; Lombaerts, Thomas Jan; Acosta, Diana; Wheeler, Kevin; Kaneshige, John
2014-01-01
A nonlinear aircraft model is presented and used to develop an overall unified robust and adaptive approach to passive trim and maneuverability envelope estimation with uncertainty quantification. The concept of time scale separation makes this method suitable for the online characterization of altered safe maneuvering limitations after impairment. The results can be used to provide pilot feedback and/or be combined with flight planning, trajectory generation, and guidance algorithms to help maintain safe aircraft operations in both nominal and off-nominal scenarios.
Estimating high mosquito-producing rice fields using spectral and spatial data
NASA Technical Reports Server (NTRS)
Wood, B. L.; Beck, L. R.; Washino, R. K.; Hibbard, K. A.; Salute, J. S.
1992-01-01
The cultivation of irrigated rice provides ideal larval habitat for a number of anopheline vectors of malaria throughout the world. Anopheles freeborni, a potential vector of human malaria, is associated with the nearly 240,000 hectares of irrigated rice grown annually in Northern and Central California; therefore, this species can serve as a model for the study of rice field anopheline population dynamics. Analysis of field data revealed that rice fields with early season canopy development, that are located near bloodmeal sources (i.e., pastures with livestock) were more likely to produce anopheline larvae than fields with less developed canopies located further from pastures. Remote sensing reflectance measurements of early-season canopy development and geographic information system (GIS) measurements of distanes between rice fields and pastures with livestock were combined to distinguish between high and low mosquito-producing rice fields. Using spectral and distance measures in either a discriminant or Bayesian analysis, the identification of high mosquito-producing fields was made with 85 percent accuracy nearly two months before anopheline larval populations peaked. Since omission errors were also minimized by these approaches, they could provide a new basis for directing abatement techniques for the control of malaria vectors.
Speaker height estimation from speech: Fusing spectral regression and statistical acoustic models.
Hansen, John H L; Williams, Keri; Bořil, Hynek
2015-08-01
Estimating speaker height can assist in voice forensic analysis and provide additional side knowledge to benefit automatic speaker identification or acoustic model selection for automatic speech recognition. In this study, a statistical approach to height estimation that incorporates acoustic models within a non-uniform height bin width Gaussian mixture model structure as well as a formant analysis approach that employs linear regression on selected phones are presented. The accuracy and trade-offs of these systems are explored by examining the consistency of the results, location, and causes of error as well a combined fusion of the two systems using data from the TIMIT corpus. Open set testing is also presented using the Multi-session Audio Research Project corpus and publicly available YouTube audio to examine the effect of channel mismatch between training and testing data and provide a realistic open domain testing scenario. The proposed algorithms achieve a highly competitive performance to previously published literature. Although the different data partitioning in the literature and this study may prevent performance comparisons in absolute terms, the mean average error of 4.89 cm for males and 4.55 cm for females provided by the proposed algorithm on TIMIT utterances containing selected phones suggest a considerable estimation error decrease compared to past efforts.
[Estimation of Winter Wheat Biomass Using Visible Spectral and BP Based Artificial Neural Networks].
Cui, Ri-xian; Liu, Ya-dong; Fu, Jin-dong
2015-09-01
The objective of this study was to evaluate the feasibility of using color digital image analysis and back propagation (BP) based artificial neural networks (ANN) method to estimate above ground biomass at the canopy level of winter wheat field. Digital color images of winter wheat canopies grown under six levels of nitrogen treatments were taken with a digital camera for four times during the elongation stage and at the same time wheat plants were sampled to measure above ground biomass. Canopy cover (CC) and 10 color indices were calculated from winter wheat canopy images by using image analysis program (developed in Microsoft Visual Basic). Correlation analysis was carried out to identify the relationship between CC, 10 color indices and winter wheat above ground biomass. Stepwise multiple linear regression and BP based ANN methods were used to establish the models to estimate winter wheat above ground biomass. The results showed that CC, and two color indices had a significant cor- relation with above ground biomass. CC revealed the highest correlation with winter wheat above ground biomass. Stepwise multiple linear regression model constituting CC and color indices of NDI and b, and BP based ANN model with four variables (CC, g, b and NDI) for input was constructed to estimate winter wheat above ground biomass. The validation results indicate that the model using BP based ANN method has a better performance with higher R2 (0.903) and lower RMSE (61.706) and RRMSE (18.876) in comparation with the stepwise regression model.
NASA Technical Reports Server (NTRS)
Bell, Thomas L.; Kundu, Prasun K.; Lau, William K. M. (Technical Monitor)
2002-01-01
Validation of satellite remote-sensing methods for estimating rainfall against rain-gauge data is attractive because of the direct nature of the rain-gauge measurements. Comparisons of satellite estimates to rain-gauge data are difficult, however, because of the extreme variability of rain and the fact that satellites view large areas over a short time while rain gauges monitor small areas continuously. In this paper, a statistical model of rainfall variability developed for studies of sampling error in averages of satellite data is used to examine the impact of spatial and temporal averaging of satellite and gauge data on intercomparison results. The model parameters were derived from radar observations of rain, but the model appears to capture many of the characteristics of rain-gauge data as well. The model predicts that many months of data from areas containing a few gauges are required to validate satellite estimates over the areas, and that the areas should be of the order of several hundred km in diameter. Over gauge arrays of sufficiently high density, the optimal areas and averaging times are reduced. The possibility of using time-weighted averages of gauge data is explored.
Speaker height estimation from speech: Fusing spectral regression and statistical acoustic models.
Hansen, John H L; Williams, Keri; Bořil, Hynek
2015-08-01
Estimating speaker height can assist in voice forensic analysis and provide additional side knowledge to benefit automatic speaker identification or acoustic model selection for automatic speech recognition. In this study, a statistical approach to height estimation that incorporates acoustic models within a non-uniform height bin width Gaussian mixture model structure as well as a formant analysis approach that employs linear regression on selected phones are presented. The accuracy and trade-offs of these systems are explored by examining the consistency of the results, location, and causes of error as well a combined fusion of the two systems using data from the TIMIT corpus. Open set testing is also presented using the Multi-session Audio Research Project corpus and publicly available YouTube audio to examine the effect of channel mismatch between training and testing data and provide a realistic open domain testing scenario. The proposed algorithms achieve a highly competitive performance to previously published literature. Although the different data partitioning in the literature and this study may prevent performance comparisons in absolute terms, the mean average error of 4.89 cm for males and 4.55 cm for females provided by the proposed algorithm on TIMIT utterances containing selected phones suggest a considerable estimation error decrease compared to past efforts. PMID:26328721
Adaptive UAV attitude estimation employing unscented Kalman Filter, FOAM and low-cost MEMS sensors.
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.
Hensen, Ulf; Grubmüller, Helmut; Lange, Oliver F
2009-07-01
The quasiharmonic approximation is the most widely used estimate for the configurational entropy of macromolecules from configurational ensembles generated from atomistic simulations. This method, however, rests on two assumptions that severely limit its applicability, (i) that a principal component analysis yields sufficiently uncorrelated modes and (ii) that configurational densities can be well approximated by Gaussian functions. In this paper we introduce a nonparametric density estimation method which rests on adaptive anisotropic kernels. It is shown that this method provides accurate configurational entropies for up to 45 dimensions thus improving on the quasiharmonic approximation. When embedded in the minimally coupled subspace framework, large macromolecules of biological interest become accessible, as demonstrated for the 67-residue coldshock protein. PMID:19658735
Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance. PMID:23012559
Motion Estimation Based on Mutual Information and Adaptive Multi-Scale Thresholding.
Xu, Rui; Taubman, David; Naman, Aous Thabit
2016-03-01
This paper proposes a new method of calculating a matching metric for motion estimation. The proposed method splits the information in the source images into multiple scale and orientation subbands, reduces the subband values to a binary representation via an adaptive thresholding algorithm, and uses mutual information to model the similarity of corresponding square windows in each image. A moving window strategy is applied to recover a dense estimated motion field whose properties are explored. The proposed matching metric is a sum of mutual information scores across space, scale, and orientation. This facilitates the exploitation of information diversity in the source images. Experimental comparisons are performed amongst several related approaches, revealing that the proposed matching metric is better able to exploit information diversity, generating more accurate motion fields.
Stockman, Andrew; Plummer, Daniel J
2005-07-01
The luminance pathway has slow (s), spectrally opponent cone inputs in addition to the expected fast (f), non-opponent inputs. The nature of these inputs to luminance flicker perception was further explored psychophysically by measuring middle- (M-) and long-wavelength-sensitive (L-) cone modulation sensitivities, M- and L-cone phase delays, and flicker spectral sensitivities under three conditions of low to moderate long-wavelength adaptation. Under these conditions we find that the luminance channel has fast M- and L-cone input signals (+fM and +fL), and slow, spectrally opponent cone input signals (+sL and -sM). The slow signals found under these conditions are therefore of the opposite polarity to those (+sM and -sL) found under more intense long-wavelength adaptation. At these less intense levels, fast and slow M-cone signals of opposite polarity (-sM and +fM) cancel at low frequencies, but then constructively interfere at intermediate frequencies (ca 12.5-22.5 Hz, depending on adapting level) because of the delay between them. In contrast, fast and slow L-cone signals of the same polarity (+sL and +fL) sum at low frequencies, but then destructively interfere at intermediate frequencies. Importantly, the spectrally opponent signals (+sL and -sM) contribute to flicker nulls without producing visible colour variation. Although its output generates an achromatic percept, the luminance channel has slow spectrally opponent as well as fast non-opponent inputs.
NASA Astrophysics Data System (ADS)
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm. PMID:24697395
Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao
2014-03-15
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
NASA Astrophysics Data System (ADS)
Jäkel, E.; Mey, B.; Levy, R.; Gu, X.; Yu, T.; Li, Z.; Althausen, D.; Heese, B.; Wendisch, M.
2015-12-01
MODIS (MOderate-resolution Imaging Spectroradiometer) retrievals of aerosol optical depth (AOD) are biased over urban areas, primarily because the reflectance characteristics of urban surfaces are different than that assumed by the retrieval algorithm. Specifically, the operational "dark-target" retrieval is tuned towards vegetated (dark) surfaces and assumes a spectral relationship to estimate the surface reflectance in blue and red wavelengths. From airborne measurements of surface reflectance over the city of Zhongshan, China, were collected that could replace the assumptions within the MODIS retrieval algorithm. The subsequent impact was tested upon two versions of the operational algorithm, Collections 5 and 6 (C5 and C6). AOD retrieval results of the operational and modified algorithms were compared for a specific case study over Zhongshan to show minor differences between them all. However, the Zhongshan-based spectral surface relationship was applied to a much larger urban sample, specifically to the MODIS data taken over Beijing between 2010 and 2014. These results were compared directly to ground-based AERONET (AErosol RObotic NETwork) measurements of AOD. A significant reduction of the differences between the AOD retrieved by the modified algorithms and AERONET was found, whereby the mean difference decreased from 0.27±0.14 for the operational C5 and 0.19±0.12 for the operational C6 to 0.10±0.15 and -0.02±0.17 by using the modified C5 and C6 retrievals. Since the modified algorithms assume a higher contribution by the surface to the total measured reflectance from MODIS, consequently the overestimation of AOD by the operational methods is reduced. Furthermore, the sensitivity of the MODIS AOD retrieval with respect to different surface types was investigated. Radiative transfer simulations were performed to model reflectances at top of atmosphere for predefined aerosol properties. The reflectance data were used as input for the retrieval methods. It
Satellite Estimation of Spectral Surface UV Irradiance. 2; Effect of Horizontally Homogeneous Clouds
NASA Technical Reports Server (NTRS)
Krothov, N.; Herman, J. R.; Bhartia, P. K.; Ahmad, Z.a; Fioletov, V.
1998-01-01
The local variability of UV irradiance at the Earth's surface is mostly caused by clouds in addition to the seasonal variability. Parametric representations of radiative transfer RT calculations are presented for the convenient solution of the transmission T of ultraviolet radiation through plane parallel clouds over a surface with reflectivity R(sub s). The calculations are intended for use with the Total Ozone Mapping Spectrometer (TOMS) measured radiances to obtain the calculated Lambert equivalent scene reflectivity R for scenes with and without clouds. The purpose is to extend the theoretical analysis of the estimation of UV irradiance from satellite data for a cloudy atmosphere. Results are presented for a range of cloud optical depths and solar zenith angles for the cases of clouds over a low reflectivity surface R(sub s) less than 0.1, over a snow or ice surface R(sub s) greater than 0.3, and for transmission through a non-conservative scattering cloud with single scattering albedo omega(sub 0) = 0.999. The key finding for conservative scattering is that the cloud-transmission function C(sub T), the ratio of cloudy-to clear-sky transmission, is roughly C(sub T) = 1 - R(sub c) with an error of less than 20% for nearly overhead sun and snow-free surfaces. For TOMS estimates of UV irradiance in the presence of both snow and clouds, independent information about snow albedo is needed for conservative cloud scattering. For non-conservative scattering with R(sub s) greater than 0.5 (snow) the satellite measured scene reflectance cannot be used to estimate surface irradiance. The cloud transmission function has been applied to the calculation of UV irradiance at the Earth's surface and compared with ground-based measurements.
Kang, Jin Ah; Kim, Hong Kook
2011-01-01
An adaptive redundant speech transmission (ARST) approach to improve the perceived speech quality (PSQ) of speech streaming applications over wireless multimedia sensor networks (WMSNs) is proposed in this paper. The proposed approach estimates the PSQ as well as the packet loss rate (PLR) from the received speech data. Subsequently, it decides whether the transmission of redundant speech data (RSD) is required in order to assist a speech decoder to reconstruct lost speech signals for high PLRs. According to the decision, the proposed ARST approach controls the RSD transmission, then it optimizes the bitrate of speech coding to encode the current speech data (CSD) and RSD bitstream in order to maintain the speech quality under packet loss conditions. The effectiveness of the proposed ARST approach is then demonstrated using the adaptive multirate-narrowband (AMR-NB) speech codec and ITU-T Recommendation P.563 as a scalable speech codec and the PSQ estimation, respectively. It is shown from the experiments that a speech streaming application employing the proposed ARST approach significantly improves speech quality under packet loss conditions in WMSNs. PMID:22164086
Error estimation and adaptive mesh refinement for parallel analysis of shell structures
NASA Technical Reports Server (NTRS)
Keating, Scott C.; Felippa, Carlos A.; Park, K. C.
1994-01-01
The formulation and application of element-level, element-independent error indicators is investigated. This research culminates in the development of an error indicator formulation which is derived based on the projection of element deformation onto the intrinsic element displacement modes. The qualifier 'element-level' means that no information from adjacent elements is used for error estimation. This property is ideally suited for obtaining error values and driving adaptive mesh refinements on parallel computers where access to neighboring elements residing on different processors may incur significant overhead. In addition such estimators are insensitive to the presence of physical interfaces and junctures. An error indicator qualifies as 'element-independent' when only visible quantities such as element stiffness and nodal displacements are used to quantify error. Error evaluation at the element level and element independence for the error indicator are highly desired properties for computing error in production-level finite element codes. Four element-level error indicators have been constructed. Two of the indicators are based on variational formulation of the element stiffness and are element-dependent. Their derivations are retained for developmental purposes. The second two indicators mimic and exceed the first two in performance but require no special formulation of the element stiffness mesh refinement which we demonstrate for two dimensional plane stress problems. The parallelizing of substructures and adaptive mesh refinement is discussed and the final error indicator using two-dimensional plane-stress and three-dimensional shell problems is demonstrated.
Error estimation and adaptive mesh refinement for parallel analysis of shell structures
NASA Astrophysics Data System (ADS)
Keating, Scott C.; Felippa, Carlos A.; Park, K. C.
1994-11-01
The formulation and application of element-level, element-independent error indicators is investigated. This research culminates in the development of an error indicator formulation which is derived based on the projection of element deformation onto the intrinsic element displacement modes. The qualifier 'element-level' means that no information from adjacent elements is used for error estimation. This property is ideally suited for obtaining error values and driving adaptive mesh refinements on parallel computers where access to neighboring elements residing on different processors may incur significant overhead. In addition such estimators are insensitive to the presence of physical interfaces and junctures. An error indicator qualifies as 'element-independent' when only visible quantities such as element stiffness and nodal displacements are used to quantify error. Error evaluation at the element level and element independence for the error indicator are highly desired properties for computing error in production-level finite element codes. Four element-level error indicators have been constructed. Two of the indicators are based on variational formulation of the element stiffness and are element-dependent. Their derivations are retained for developmental purposes. The second two indicators mimic and exceed the first two in performance but require no special formulation of the element stiffness mesh refinement which we demonstrate for two dimensional plane stress problems. The parallelizing of substructures and adaptive mesh refinement is discussed and the final error indicator using two-dimensional plane-stress and three-dimensional shell problems is demonstrated.
Bayesian adaptive estimation of the contrast sensitivity function: The quick CSF method
Lesmes, Luis Andres; Lu, Zhong-Lin; Baek, Jongsoo; Albright, Thomas D.
2015-01-01
The contrast sensitivity function (CSF) predicts functional vision better than acuity, but long testing times prevent its psychophysical assessment in clinical and practical applications. This study presents the quick CSF (qCSF) method, a Bayesian adaptive procedure that applies a strategy developed to estimate multiple parameters of the psychometric function (A. B. Cobo-Lewis, 1996; L. L. Kontsevich & C. W. Tyler, 1999). Before each trial, a one-step-ahead search finds the grating stimulus (defined by frequency and contrast) that maximizes the expected information gain (J. V. Kujala & T. J. Lukka, 2006; L. A. Lesmes et al., 2006), about four CSF parameters. By directly estimating CSF parameters, data collected at one spatial frequency improves sensitivity estimates across all frequencies. A psychophysical study validated that CSFs obtained with 100 qCSF trials (~10 min) exhibited good precision across spatial frequencies (SD < 2–3 dB) and excellent agreement with CSFs obtained independently (mean RMSE = 0.86 dB). To estimate the broad sensitivity metric provided by the area under the log CSF (AULCSF), only 25 trials were needed to achieve a coefficient of variation of 15–20%. The current study demonstrates the method’s value for basic and clinical investigations. Further studies, applying the qCSF to measure wider ranges of normal and abnormal vision, will determine how its efficiency translates to clinical assessment. PMID:20377294
Chen, Zeng-Ping; Morris, Julian; Martin, Elaine
2006-11-15
When analyzing complex mixtures that exhibit sample-to-sample variability using spectroscopic instrumentation, the variation in the optical path length, resulting from the physical variations inherent within the individual samples, will result in significant multiplicative light scattering perturbations. Although a number of algorithms have been proposed to address the effect of multiplicative light scattering, each has associated with it a number of underlying assumptions, which necessitates additional information relating to the spectra being attained. This information is difficult to obtain in practice and frequently is not available. Thus, with a view to removing the need for the attainment of additional information, a new algorithm, optical path-length estimation and correction (OPLEC), is proposed. The methodology is applied to two near-infrared transmittance spectral data sets (powder mixture data and wheat kernel data), and the results are compared with the extended multiplicative signal correction (EMSC) and extended inverted signal correction (EISC) algorithms. Within the study, it is concluded that the EMSC algorithm cannot be applied to the wheat kernel data set due to core information for the implementation of the algorithm not being available, while the analysis of the powder mixture data using EISC resulted in incorrect conclusions being drawn and hence a calibration model whose performance was unacceptable. In contrast, OPLEC was observed to effectively mitigate the detrimental effects of physical light scattering and significantly improve the prediction accuracy of the calibration models for the two spectral data sets investigated without any additional information pertaining to the calibration samples being required.
NASA Astrophysics Data System (ADS)
Picard, Ghislain; Libois, Quentin; Arnaud, Laurent; Verin, Gauthier; Dumont, Marie
2016-06-01
Spectral albedo of the snow surface in the visible/near-infrared range has been measured for 3 years by an automatic spectral radiometer installed at Dome C (75° S, 123° E) in Antarctica in order to retrieve the specific surface area (SSA) of superficial snow. This study focuses on the uncertainties of the SSA retrieval due to instrumental and data processing limitations. We find that when the solar zenith angle is high, the main source of uncertainties is the imperfect angular response of the light collectors. This imperfection introduces a small spurious wavelength-dependent trend in the albedo spectra which greatly affects the SSA retrieval. By modeling this effect, we show that for typical snow and illumination conditions encountered at Dome C, retrieving SSA with an accuracy better than 15 % (our target) requires the difference of response between 400 and 1100 nm to not exceed 2 %. Such a small difference can be achieved only by (i) a careful design of the collectors, (ii) an ad hoc correction of the spectra using the actual measured angular response of the collectors, and (iii) for solar zenith angles less than 75°. The 3-year time series of retrieved SSA features a 3-fold decrease every summer which is significantly larger than the estimated uncertainties. This highlights the high dynamics of near-surface SSA at Dome C.
NASA Astrophysics Data System (ADS)
Belghith, Akram; Bowd, Christopher; Weinreb, Robert N.; Zangwill, Linda M.
2014-03-01
Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the "non-progressing" and "progressing" glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection.
Lee, Jae Hoon; Joshi, Amit; Sevick-Muraca, Eva M
2008-01-01
A variety of biomedical imaging techniques such as optical and fluorescence tomography, electrical impedance tomography, and ultrasound imaging can be cast as inverse problems, wherein image reconstruction involves the estimation of spatially distributed parameter(s) of the PDE system describing the physics of the imaging process. Finite element discretization of imaged domain with tetrahedral elements is a popular way of solving the forward and inverse imaging problems on complicated geometries. A dual-adaptive mesh-based approach wherein, one mesh is used for solving the forward imaging problem and the other mesh used for iteratively estimating the unknown distributed parameter, can result in high resolution image reconstruction at minimum computation effort, if both the meshes are allowed to adapt independently. Till date, no efficient method has been reported to identify and resolve intersection between tetrahedrons in independently refined or coarsened dual meshes. Herein, we report a fast and robust algorithm to identify and resolve intersection of tetrahedrons within nested dual meshes generated by 8-similar subtetrahedron subdivision scheme. The algorithm exploits finite element weight functions and gives rise to a set of weight functions on each vertex of disjoint tetrahedron pieces that completely cover up the intersection region of two tetrahedrons. The procedure enables fully adaptive tetrahedral finite elements by supporting independent refinement and coarsening of each individual mesh while preserving fast identification and resolution of intersection. The computational efficiency of the algorithm is demonstrated by diffuse photon density wave solutions obtained from a single- and a dual-mesh, and by reconstructing a fluorescent inclusion in simulated phantom from boundary frequency domain fluorescence measurements.
X-ray dual energy spectral parameter optimization for bone Calcium/Phosphorus mass ratio estimation
NASA Astrophysics Data System (ADS)
Sotiropoulou, P. I.; Fountos, G. P.; Martini, N. D.; Koukou, V. N.; Michail, C. M.; Valais, I. G.; Kandarakis, I. S.; Nikiforidis, G. C.
2015-09-01
Calcium (Ca) and Phosphorus (P) bone mass ratio has been identified as an important, yet underutilized, risk factor in osteoporosis diagnosis. The purpose of this simulation study is to investigate the use of effective or mean mass attenuation coefficient in Ca/P mass ratio estimation with the use of a dual-energy method. The investigation was based on the minimization of the accuracy of Ca/P ratio, with respect to the Coefficient of Variation of the ratio. Different set-ups were examined, based on the K-edge filtering technique and single X-ray exposure. The modified X-ray output was attenuated by various Ca/P mass ratios resulting in nine calibration points, while keeping constant the total bone thickness. The simulated data were obtained considering a photon counting energy discriminating detector. The standard deviation of the residuals was used to compare and evaluate the accuracy between the different dual energy set-ups. The optimum mass attenuation coefficient for the Ca/P mass ratio estimation was the effective coefficient in all the examined set-ups. The variation of the residuals between the different set-ups was not significant.
NASA Astrophysics Data System (ADS)
Zhang, T.; Lin, X.; Yang, X.
2014-12-01
More serious drought has been projected due to the climate change in the Kansas State of the U.S., which might threaten the local agriculture and thus require effective adaptation responses to drought, e.g. better irrigation. But the irrigation adaptation on drought at the current technology-level is poorly quantified, therefore challenges to figure out how much additional efforts are required under more aridity of climate. Here, we collect the irrigation application data for maize, soybean, sorghum and wheat in Kansas, and establish a two-stage model to quantify the crop-specific irrigation application responses to changes in climatic drivers, and further estimate the existing effectiveness of the irrigation to adapt future drought based on the IPCC AR5 ensemble PDSI prediction under RCP4.5 scenario. We find that the three summer season crops (maize, soybean and sorghum) would experience 0 - 20% yield losses depending on county due to more serious drought since 2030s, even though increased irrigation application as the response of drought had saved 0 - 10% yields. At the state level, maize receives most benefits from irrigation, whereas the beneficial effects are least for sorghum among the three crops. To wheat, irrigation adaptation is very weak since irrigation water applied is much less than the above three crops. But wheat yields were projected to have a slight increase in central and eastern regions because climate would become more moisture over the growing season of winter wheat in future. Our results highlight that the existing beneficial effects from irrigation would be surpassed by the negative impact of drought in future, which would cause overall yield reduction in Kansas especially for those summer season crops.
NASA Technical Reports Server (NTRS)
Hatfield, J. L.; Asrar, G.; Kanemasu, E. T.
1982-01-01
The interception of photosynthetically active radiation (PAR) was evaluated relative to greenness and normalized difference (MSS 7-5/7+5) for five planting dates of wheat for 1978-79 and 1979-80 in Phoenix. Intercepted PAR was calculated from a model driven by leaf area index and stage of growth. Linear relationships were found between greenness and normalized difference with a separate model representing growth and senescence of the crop. Normalized difference was a significantly better model and would be easier to apply than the empirically derived greenness parameter. For the leaf area growth portion of the season the model between PAR interception and normalized difference was the same over years, however, for the leaf senescence the models showed more variability due to the lack of data on measured interception in sparse canopies. Normalized difference could be used to estimate PAR interception directly for crop growth models.
NASA Astrophysics Data System (ADS)
Zygielbaum, A. I.; Arkebauer, T. J.; Walter-Shea, E.
2014-12-01
Previously, we reported that reflectance increased across the whole PAR spectrum when plants were subjected to water stress. This effect was shown to exist in maize grown under greenhouse conditions and under field conditions. Greenhouse experiments showed that, in addition to leaf water content, the effect was strongly correlated with incident light intensity. Further, through the use of an integrating sphere, we demonstrated that the change in reflectance was due to a change in absorption rather than in a change scattering or other optical path effect. Time lapse microscopy showed lightening between leaf veins analogous to effects measured by researchers observing cross sections of stressed C4 plants. To further refine our study, additional leaf level and canopy level studies were undertaken. Excised leaf sections were separately exposed to red and white light in the laboratory as the leaf dried. Increasing reflectance and transmittance were observed for the section exposed to white light, while little change was observed under red light. Each of these observations can be explained by chloroplast avoidance movement, a photoprotective response causing chloroplasts to aggregate along cell walls effectively hiding chlorophyll from observation. Chloroplast movement, for example, is driven by blue light; explaining the lack of observed change under red light. Estimation of biophysical parameters, such as chlorophyll content and greenness, are affected by the difference between the "apparent" chlorophyll content and the actual chlorophyll content of leaves and canopies. Up to 30% changes in the VARI remote sensing index have been observed morning to afternoon in field-grown maize. Ten percent changes in chlorophyll estimates have been observed in greenhouse maize. We will report on further research and on the extension of our work to include the impact of chloroplast avoidance on remote sensing of C3 plants, specifically soybean, at leaf and canopy levels.
Strauss, Rupert W; Muñoz, Beatriz; Wolfson, Yulia; Sophie, Raafay; Fletcher, Emily; Bittencourt, Millena G; Scholl, Hendrik P N
2016-01-01
Aims To estimate disease progression based on analysis of macular volume measured by spectral-domain optical coherence tomography (SD-OCT) in patients affected by Stargardt macular dystrophy (STGD1) and to evaluate the influence of software errors on these measurements. Methods 58 eyes of 29 STGD1 patients were included. Numbers and types of algorithm errors were recorded and manually corrected. In a subgroup of 36 eyes of 18 patients with at least two examinations over time, total macular volume (TMV) and volumes of all nine Early Treatment of Diabetic Retinopathy Study (ETDRS) subfields were obtained. Random effects models were used to estimate the rate of change per year for the population, and empirical Bayes slopes were used to estimate yearly decline in TMV for individual eyes. Results 6958 single B-scans from 190 macular cube scans were analysed. 2360 (33.9%) showed algorithm errors. Mean observation period for follow-up data was 15 months (range 3–40). The median (IQR) change in TMV using the empirical Bayes estimates for the individual eyes was −0.103 (−0.145, −0.059) mm3 per year. The mean (±SD) TMV was 6.321±1.000 mm3 at baseline, and rate of decline was −0.118 mm3 per year (p=0.003). Yearly mean volume change was −0.004 mm3 in the central subfield (mean baseline=0.128 mm3), −0.032 mm3 in the inner (mean baseline=1.484 mm3) and −0.079 mm3 in the outer ETDRS subfields (mean baseline=5.206 mm3). Conclusions SD-OCT measurements allow monitoring the decline in retinal volume in STGD1; however, they require significant manual correction of software errors. PMID:26568636
ERIC Educational Resources Information Center
Blais, Jean-Guy; Raiche, Gilles
This paper examines some characteristics of the statistics associated with the sampling distribution of the proficiency level estimate when the Rasch model is used. These characteristics allow the judgment of the meaning to be given to the proficiency level estimate obtained in adaptive testing, and as a consequence, they can illustrate the…
ERIC Educational Resources Information Center
Kim, Sooyeon; Moses, Tim; Yoo, Hanwook Henry
2015-01-01
The purpose of this inquiry was to investigate the effectiveness of item response theory (IRT) proficiency estimators in terms of estimation bias and error under multistage testing (MST). We chose a 2-stage MST design in which 1 adaptation to the examinees' ability levels takes place. It includes 4 modules (1 at Stage 1, 3 at Stage 2) and 3 paths…
Depth Estimation from the Scaling Power Spectral Density of Nonstationary Gravity Profile
NASA Astrophysics Data System (ADS)
Bansal, A. R.; Dimri, V. P.
A technique to estimate the depth to anomalous sources from the scaling power spectra of long nonstationary gravity profiles is presented. The nonstationary profile is divided into piecewise stationary segments based on the criterion of optimum gate length in which the time-varying and time-invariant autocorrelation functions are similar. The division of a nonstationary into piecewise stationary allows identification of the portion of the crust with different geological histories, and using the stationary portion of the gravity profiles, more consistent depths to the anomalous sources have been obtained. The technique is tested with the synthetic gravity profile and applied along the Jaipur-Raipur geotransect in western and central India. The geotransect has been divided into four stationary parts: Vindhyan low, Bundelkhand low, Narmada rift and Chhattisgarh basin; each section corresponding to a different geological formation. Forward modeling of gravity data using results of each stationary section is carried out to propose the subsurface structure along the Jaipur-Raipur transect.
Conroy, M.J.; Runge, J.P.; Barker, R.J.; Schofield, M.R.; Fonnesbeck, C.J.
2008-01-01
Many organisms are patchily distributed, with some patches occupied at high density, others at lower densities, and others not occupied. Estimation of overall abundance can be difficult and is inefficient via intensive approaches such as capture-mark-recapture (CMR) or distance sampling. We propose a two-phase sampling scheme and model in a Bayesian framework to estimate abundance for patchily distributed populations. In the first phase, occupancy is estimated by binomial detection samples taken on all selected sites, where selection may be of all sites available, or a random sample of sites. Detection can be by visual surveys, detection of sign, physical captures, or other approach. At the second phase, if a detection threshold is achieved, CMR or other intensive sampling is conducted via standard procedures (grids or webs) to estimate abundance. Detection and CMR data are then used in a joint likelihood to model probability of detection in the occupancy sample via an abundance-detection model. CMR modeling is used to estimate abundance for the abundance-detection relationship, which in turn is used to predict abundance at the remaining sites, where only detection data are collected. We present a full Bayesian modeling treatment of this problem, in which posterior inference on abundance and other parameters (detection, capture probability) is obtained under a variety of assumptions about spatial and individual sources of heterogeneity. We apply the approach to abundance estimation for two species of voles (Microtus spp.) in Montana, USA. We also use a simulation study to evaluate the frequentist properties of our procedure given known patterns in abundance and detection among sites as well as design criteria. For most population characteristics and designs considered, bias and mean-square error (MSE) were low, and coverage of true parameter values by Bayesian credibility intervals was near nominal. Our two-phase, adaptive approach allows efficient estimation of
Adaptive nonlinear observer for state and unknown parameter estimation in noisy systems
NASA Astrophysics Data System (ADS)
Vijayaraghavan, Krishna; Valibeygi, Amir
2016-01-01
This paper proposes a novel adaptive observer for Lipschitz nonlinear systems and dissipative nonlinear systems in the presence of disturbances and sensor noise. The observer is based on an H∞ observer that can estimate both the system states and unknown parameters by minimising a cost function consisting of the sum of the square integrals of the estimation errors in the states and unknown parameters. The paper presents necessary and sufficient conditions for the existence of the observer, and the equations for determining observer gains are formulated as linear matrix inequalities (LMIs) that can be solved offline using commercially available LMI solvers. The observer design has also been extended to the case of time-varying unknown parameters. The use of the observer is demonstrated through illustrative examples and the performance is compared with extended Kalman filtering. Compared to previous results on nonlinear observers, the proposed observer is more computationally efficient, and guarantees state and parameter estimation for two very broad classes of nonlinear systems (Lipschitz and dissipative nonlinear systems) in the presence of input disturbances and sensor noise. In addition, the proposed observer does not require online computation of the observer gain.
Kolus, Ahmet; Dubé, Philippe-Antoine; Imbeau, Daniel; Labib, Richard; Dubeau, Denise
2014-11-01
In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field. PMID:24793823
NASA Astrophysics Data System (ADS)
Chen, Jinsong; Hubbard, Susan S.; Williams, Kenneth H.; Flores Orozco, AdriáN.; Kemna, Andreas
2012-05-01
We developed a hierarchical Bayesian model to estimate the spatiotemporal distribution of aqueous geochemical parameters associated with in-situ bioremediation using surface spectral induced polarization (SIP) data and borehole geochemical measurements collected during a bioremediation experiment at a uranium-contaminated site near Rifle, Colorado (USA). The SIP data were first inverted for Cole-Cole parameters, including chargeability, time constant, resistivity at the DC frequency, and dependence factor, at each pixel of two-dimensional grids using a previously developed stochastic method. Correlations between the inverted Cole-Cole parameters and the wellbore-based groundwater chemistry measurements indicative of key metabolic processes within the aquifer (e.g., ferrous iron, sulfate, uranium) were established and used as a basis for petrophysical model development. The developed Bayesian model consists of three levels of statistical submodels: (1) data model, providing links between geochemical and geophysical attributes, (2) process model, describing the spatial and temporal variability of geochemical properties in the subsurface system, and (3) parameter model, describing prior distributions of various parameters and initial conditions. The unknown parameters were estimated using Markov chain Monte Carlo methods. By combining the temporally distributed geochemical data with the spatially distributed geophysical data, we obtained the spatiotemporal distribution of ferrous iron, sulfate, and sulfide, and their associated uncertainty information. The obtained results can be used to assess the efficacy of the bioremediation treatment over space and time and to constrain reactive transport models.
NASA Astrophysics Data System (ADS)
Baars, Woutijn J.; Hutchins, Nicholas; Marusic, Ivan
2016-09-01
For wall-bounded flows, the model of Marusic et al. [Science 329, 193 (2010), 10.1126/science.1188765] allows one to predict the statistics of the streamwise fluctuating velocity in the inner region, from a measured input signal in the logarithmic region. Normally, a user-defined large-scale portion of the input forms the large-scale content in the prediction by scaling its amplitude, as well as temporally shifting the signal to account for the physical inclination of these scales. Incoherent smaller scales are then fused to the prediction via universally expressed fluctuations that are subject to an amplitude modulation. Here we present a refined version of the model using spectral linear stochastic estimation, which eliminates a user-defined scale separation of the input. Now, an empirically derived transfer kernel comprises an implicit filtering via a scale-dependent gain and phase; this kernel captures the coherent portion in the prediction. An additional refinement of the model embodies a relative shift between the stochastically estimated scales in the prediction and the modulation envelope of the universal small scales. Predictions over a three-decade span of Reynolds numbers, Reτ˜O (103) to O (106) , highlight promising applications of the refined model to high-Reynolds-number flows, in which coherent scales become the primary contributor to the fluctuating energy.
Improved Estimation of Earth Rotation Parameters Using the Adaptive Ridge Regression
NASA Astrophysics Data System (ADS)
Huang, Chengli; Jin, Wenjing
1998-05-01
The multicollinearity among regression variables is a common phenomenon in the reduction of astronomical data. The phenomenon of multicollinearity and the diagnostic factors are introduced first. As a remedy, a new method, called adaptive ridge regression (ARR), which is an improved method of choosing the departure constant θ in ridge regression, is suggested and applied in a case that the Earth orientation parameters (EOP) are determined by lunar laser ranging (LLR). It is pointed out, via a diagnosis, the variance inflation factors (VIFs), that there exists serious multicollinearity among the regression variables. It is shown that the ARR method is effective in reducing the multicollinearity and makes the regression coefficients more stable than that of using ordinary least squares estimation (LS), especially when there is serious multicollinearity.
Halloran, Jason P.; Erdemir, Ahmet
2011-01-01
Simulation-based prediction of specimen-specific biomechanical behavior commonly requires inverse analysis using geometrically consistent finite element (FE) models. Optimization drives such analyses but previous studies have highlighted a large computational cost dictated by iterative use of nonlinear FE models. The goal of this study was to evaluate the performance of a local regression-based adaptive surrogate modeling approach to decrease computational cost for both global and local optimization approaches using an inverse FE application. Nonlinear elastic material parameters for patient-specific heel-pad tissue were found, both with and without the surrogate model. Surrogate prediction replaced a FE simulation using local regression of previous simulations when the corresponding error estimate was less than a given tolerance. Performance depended on optimization type and tolerance value. The surrogate reduced local optimization expense up to 68%, but achieved accurate results for only 1 of 20 initial conditions. Conversely, up to a tolerance value of 20 N2, global optimization with the surrogate yielded consistent parameter predictions with a concurrent decrease in computational cost (up to 77%). However, the local optimization method without the surrogate, although sensitive to the initial conditions, was still on average seven times faster than the global approach. Our results help establish guide-lines for setting acceptable tolerance values while using an adaptive surrogate model for inverse FE analysis. Most important, the study demonstrates the benefits of a surrogate modeling approach for intensive FE-based iterative analysis. PMID:21544674
NASA Astrophysics Data System (ADS)
Bu, Guochao; Wang, Pei
2016-04-01
Terrestrial laser scanning (TLS) has been used to extract accurate forest biophysical parameters for inventory purposes. The diameter at breast height (DBH) is a key parameter for individual trees because it has the potential for modeling the height, volume, biomass, and carbon sequestration potential of the tree based on empirical allometric scaling equations. In order to extract the DBH from the single-scan data of TLS automatically and accurately within a certain range, we proposed an adaptive circle-ellipse fitting method based on the point cloud transect. This proposed method can correct the error caused by the simple circle fitting method when a tree is slanted. A slanted tree was detected by the circle-ellipse fitting analysis, then the corresponding slant angle was found based on the ellipse fitting result. With this information, the DBH of the trees could be recalculated based on reslicing the point cloud data at breast height. Artificial stem data simulated by a cylindrical model of leaning trees and the scanning data acquired with the RIEGL VZ-400 were used to test the proposed adaptive fitting method. The results shown that the proposed method can detect the trees and accurately estimate the DBH for leaning trees.
Lombardo, Marco; Serrao, Sebastiano; Lombardo, Giuseppe
2014-01-01
Purpose To investigate the influence of various technical factors on the variation of cone packing density estimates in adaptive optics flood illuminated retinal images. Methods Adaptive optics images of the photoreceptor mosaic were obtained in fifteen healthy subjects. The cone density and Voronoi diagrams were assessed in sampling windows of 320×320 µm, 160×160 µm and 64×64 µm at 1.5 degree temporal and superior eccentricity from the preferred locus of fixation (PRL). The technical factors that have been analyzed included the sampling window size, the corrected retinal magnification factor (RMFcorr), the conversion from radial to linear distance from the PRL, the displacement between the PRL and foveal center and the manual checking of cone identification algorithm. Bland-Altman analysis was used to assess the agreement between cone density estimated within the different sampling window conditions. Results The cone density declined with decreasing sampling area and data between areas of different size showed low agreement. A high agreement was found between sampling areas of the same size when comparing density calculated with or without using individual RMFcorr. The agreement between cone density measured at radial and linear distances from the PRL and between data referred to the PRL or the foveal center was moderate. The percentage of Voronoi tiles with hexagonal packing arrangement was comparable between sampling areas of different size. The boundary effect, presence of any retinal vessels, and the manual selection of cones missed by the automated identification algorithm were identified as the factors influencing variation of cone packing arrangements in Voronoi diagrams. Conclusions The sampling window size is the main technical factor that influences variation of cone density. Clear identification of each cone in the image and the use of a large buffer zone are necessary to minimize factors influencing variation of Voronoi diagrams of the cone
Adaptive data-driven models for estimating carbon fluxes in the Northern Great Plains
Wylie, B.K.; Fosnight, E.A.; Gilmanov, T.G.; Frank, A.B.; Morgan, J.A.; Haferkamp, Marshall R.; Meyers, T.P.
2007-01-01
Rangeland carbon fluxes are highly variable in both space and time. Given the expansive areas of rangelands, how rangelands respond to climatic variation, management, and soil potential is important to understanding carbon dynamics. Rangeland carbon fluxes associated with Net Ecosystem Exchange (NEE) were measured from multiple year data sets at five flux tower locations in the Northern Great Plains. These flux tower measurements were combined with 1-km2 spatial data sets of Photosynthetically Active Radiation (PAR), Normalized Difference Vegetation Index (NDVI), temperature, precipitation, seasonal NDVI metrics, and soil characteristics. Flux tower measurements were used to train and select variables for a rule-based piece-wise regression model. The accuracy and stability of the model were assessed through random cross-validation and cross-validation by site and year. Estimates of NEE were produced for each 10-day period during each growing season from 1998 to 2001. Growing season carbon flux estimates were combined with winter flux estimates to derive and map annual estimates of NEE. The rule-based piece-wise regression model is a dynamic, adaptive model that captures the relationships of the spatial data to NEE as conditions evolve throughout the growing season. The carbon dynamics in the Northern Great Plains proved to be in near equilibrium, serving as a small carbon sink in 1999 and as a small carbon source in 1998, 2000, and 2001. Patterns of carbon sinks and sources are very complex, with the carbon dynamics tilting toward sources in the drier west and toward sinks in the east and near the mountains in the extreme west. Significant local variability exists, which initial investigations suggest are likely related to local climate variability, soil properties, and management.
NASA Astrophysics Data System (ADS)
Elmi, Omid; Javad Tourian, Mohammad; Sneeuw, Nico
2015-04-01
The importance of river discharge monitoring is critical for e.g., water resource planning, climate change, hazard monitoring. River discharge has been measured at in situ gauges for more than a century. Despite various attempts, some basins are still ungauged. Moreover, a reduction in the number of worldwide gauging stations increases the interest to employ remote sensing data for river discharge monitoring. Finding an empirical relationship between simultaneous in situ measurements of discharge and river widths derived from satellite imagery has been introduced as a straightforward remote sensing alternative. Classifying water and land in an image is the primary task for defining the river width. Water appears dark in the near infrared and infrared bands in satellite images. As a result low values in the histogram usually represent the water content. In this way, applying a threshold on the image histogram and separating into two different classes is one of the most efficient techniques to build a water mask. Beside its simple definition, finding the appropriate threshold value in each image is the most critical issue. The threshold is variable due to changes in the water level, river extent, atmosphere, sunlight radiation, onboard calibration of the satellite over time. These complexities in water body classification are the main source of error in river width estimation. In this study, we are looking for the most efficient adaptive threshold algorithm to estimate the river discharge. To do this, all cloud free MODIS images coincident with the in situ measurement are collected. Next a number of automatic threshold selection techniques are employed to generate different dynamic water masks. Then, for each of them a separate empirical relationship between river widths and discharge measurements are determined. Through these empirical relationships, we estimate river discharge at the gauge and then validate our results against in situ measurements and also
NASA Astrophysics Data System (ADS)
Zhao, Fengsheng; Li, Zhanqing
2007-11-01
Aerosol single scattering albedo (ωo) is a primary factor dictating aerosol radiative effect. Ground-based remote sensing of ωo has been employed most widely using spectral sky radiance measurements made from a scanning Sun photometer. Reliable results can be achieved for high aerosol loadings and for solar zenith angle >50°. This study presents an alternative method using spectral direct radiance measurements or aerosol optical depths together with total sky irradiance to retrieve ωo. The method does not require sky radiance data that can only be acquired by the expensive scanning Sun photometer. The method is evaluated using extensive measurements by a suite of instruments deployed in northern China under the East Asian Study of Tropospheric Aerosols: An International Regional Experiment (EAST-AIRE) project. The sensitivities of the retrieval to various uncertain factors were first examined by means of radiative transfer simulations. It was found the retrieval is most sensitive to cloud screening, total irradiance and the Angstrom Exponent (AE), but only weakly depends on surface albedo and the fine structure of aerosol size distribution. Using 1 year of rigorously screened clear-sky measurements made at the Xianghe site, the retrieved ωo values were found to agree with those retrieved from the Cimel Sun photometer by the AERONET method to within ˜0.03 (RMS), and ˜0.003 (mean bias). As part of the differences originate from different sky views seen by the Sun photometers and pyranometer under comparison, a further test was conducted by using total sky irradiances simulated with the retrieved aerosol properties from the AERONET. The resulting estimates of ωo agree to within 0.01-0.02 (RMS differences) and 0.002-0.003 (mean bias). These values are better measure of the true retrieval uncertainties, as they are free from any data mismatch. The characteristics of ωo retrievals were discussed.
NASA Astrophysics Data System (ADS)
Coluzzi, Rosa; Lasaponara, Rosa; Montesano, Tiziana; Lanorte, Antonio; de Santis, Fortunato
2010-05-01
Satellite data can help monitoring the dynamics of vegetation in burned and unburned areas. Several methods can be used to perform such kind of analysis. This paper is focused on the use of different satellite-based parameters for fire recovery monitoring. In particular, time series of single spectral channels and vegetation indices from SPOT-VEGETATION have investigated. The test areas is the Mediterranean ecosystems of Southern Italy. For this study we considered: 1) the most widely used index to follow the process of recovery after fire: normalized difference vegetation index (NDVI) obtained from the visible (Red) and near infrared (NIR) by using the following formula NDVI = (NIR_Red)/(NIR + Red), 2) moisture index MSI obtained from the near infrared and Mir for characterization of leaf and canopy water content. 3) NDWI obtained from the near infrared and Mir as in the case of MSI, but with the normalization (as the NDVI) to reduce the atmospheric effects. All analysis for this work was performed on ten-daily normalized difference vegetation index (NDVI) image composites (S10) from the SPOT- VEGETATION (VGT) sensor. The final data set consisted of 279 ten-daily, 1 km resolution NDVI S1O composites for the period 1 April 1998 to 31 December 2005 with additional surface reflectance values in the blue (B; 0.43-0.47,um), red (R; 0.61-0.68,um), near-infrared (NIR; 0.78-0.89,um) and shortwave-infrared (SWIR; 1.58-1.75,um) spectral bands, and information on the viewing geometry and pixel status. Preprocessing of the data was performed by the Vlaamse Instelling voor Technologisch Onderzoek (VITO) in the framework of the Global Vegetation Monitoring (GLOVEG) preprocessing chain. It consisted of the Simplified Method for Atmospheric Correction (SMAC) and compositing at ten-day intervals based on the Maximum Value Compositing (MVC) criterion. All the satellite time series were analysed using the Detrended Fluctuation Analysis (DFA) to estimate post fire vegetation recovery
NASA Technical Reports Server (NTRS)
Lee-Rausch, E. M.; Park, M. A.; Jones, W. T.; Hammond, D. P.; Nielsen, E. J.
2005-01-01
This paper demonstrates the extension of error estimation and adaptation methods to parallel computations enabling larger, more realistic aerospace applications and the quantification of discretization errors for complex 3-D solutions. Results were shown for an inviscid sonic-boom prediction about a double-cone configuration and a wing/body segmented leading edge (SLE) configuration where the output function of the adjoint was pressure integrated over a part of the cylinder in the near field. After multiple cycles of error estimation and surface/field adaptation, a significant improvement in the inviscid solution for the sonic boom signature of the double cone was observed. Although the double-cone adaptation was initiated from a very coarse mesh, the near-field pressure signature from the final adapted mesh compared very well with the wind-tunnel data which illustrates that the adjoint-based error estimation and adaptation process requires no a priori refinement of the mesh. Similarly, the near-field pressure signature for the SLE wing/body sonic boom configuration showed a significant improvement from the initial coarse mesh to the final adapted mesh in comparison with the wind tunnel results. Error estimation and field adaptation results were also presented for the viscous transonic drag prediction of the DLR-F6 wing/body configuration, and results were compared to a series of globally refined meshes. Two of these globally refined meshes were used as a starting point for the error estimation and field-adaptation process where the output function for the adjoint was the total drag. The field-adapted results showed an improvement in the prediction of the drag in comparison with the finest globally refined mesh and a reduction in the estimate of the remaining drag error. The adjoint-based adaptation parameter showed a need for increased resolution in the surface of the wing/body as well as a need for wake resolution downstream of the fuselage and wing trailing edge
Stockman, Andrew; Plummer, Daniel J; Montag, Ethan D
2005-07-01
The nature of the inputs to achromatic luminance flicker perception was explored psychophysically by measuring middle- (M-) and long-wavelength-sensitive (L-) cone modulation sensitivities, M- and L-cone phase delays, and spectral sensitivities as a function of temporal frequency. Under intense long-wavelength adaptation, the existence of multiple luminance inputs was revealed by substantial frequency-dependent changes in all three types of measure. Fast (f) and slow (s) M-cone input signals of the same polarity (+sM and +fM) sum at low frequencies, but then destructively interfere near 16 Hz because of the delay between them. In contrast, fast and slow L-cone input signals of opposite polarity (-sL and +fL) cancel at low frequencies, but then constructively interfere near 16 Hz. Although these slow, spectrally opponent luminance inputs (+sM and -sL) would usually be characterized as chromatic, and the fast, non-opponent inputs (+fM and +fL) as achromatic, both contribute to flicker photometric nulls without producing visible colour variation. Although its output produces an achromatic percept, the luminance channel has slow, spectrally opponent inputs in addition to the expected non-opponent ones. Consequently, it is not possible in general to silence this channel with pairs of 'equiluminant' alternating stimuli, since stimuli equated for the non-opponent luminance mechanism (+fM and +fL) may still generate spectrally opponent signals (+sM and +sL).
NASA Astrophysics Data System (ADS)
Cerasoli, S.; Silva, J. M.; Carvalhais, N.; Correia, A.; Costa e Silva, F.; Pereira, J. S.
2013-12-01
The Light Use Efficiency (LUE) concept is usually applied to retrieve Gross Primary Productivity (GPP) estimates in models integrating spectral indexes, namely Normalized Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI), considered proxies of biophysical properties of vegetation. The integration of spectral measurements into LUE models can increase the robustness of GPP estimates by optimizing particular parameters of the model. NDVI and PRI are frequently obtained by broad band sensors on remote platforms at low spatial resolution (e.g. MODIS). In highly heterogeneous ecosystems such spectral information may not be representative of the dynamic response of the ecosystem to climate variables. In Mediterranean oak woodlands different plant functional types (PFT): trees canopy, shrubs and herbaceous layer, contribute to the overall Gross Primary Productivity (GPP). In situ spectral measurements can provide useful information on each PFT and its temporal variability. The objectives of this study were: i) to analyze the temporal variability of NDVI, PRI and others spectral indices for the three PFT, their response to climate variables and their relationship with biophysical properties of vegetation; ii) to optimize a LUE model integrating selected spectral indexes in which the contribution of each PFT to the overall GPP is estimated individually; iii) to compare the performance of disaggregated GPP estimates and lumped GPP estimates, evaluated against eddy covariance measurements. Ground measurements of vegetation reflectance were performed in a cork oak woodland located in Coruche, Portugal (39°8'N, 8°19'W) where carbon and water fluxes are continuously measured by eddy covariance. Between April 2011 and June 2013 reflectance measurements of the herbaceous layer, shrubs and trees canopy were acquired with a FieldSpec3 spectroradiometer (ASD Inc.) which provided data in the range of 350-2500nm. Measurements were repeated approximately on
Vidaurre, C; Schlögl, A; Cabeza, R; Scherer, R; Pfurtscheller, G
2005-11-01
We present the result of on-line feedback Brain Computer Interface experiments using adaptive and non-adaptive feature extraction methods with an on-line adaptive classifier based on Quadratic Discriminant Analysis. Experiments were performed with 12 naïve subjects, feedback was provided from the first moment and no training sessions were needed. Experiments run in three different days with each subject. Six of them received feedback with Adaptive Autoregressive parameters and the rest with logarithmic Band Power estimates. The study was done using single trial analysis of each of the sessions and the value of the Error Rate and the Mutual Information of the classification were used to discuss the results. Finally, it was shown that even subjects starting with a low performance were able to control the system in a few hours: and contrary to previous results no differences between AAR and BP estimates were found.
Tuna, E. Erdem; Franke, Timothy J.; Bebek, Özkan; Shiose, Akira; Fukamachi, Kiyotaka; Çavuşoğlu, M. Cenk
2013-01-01
Robotic assisted beating heart surgery aims to allow surgeons to operate on a beating heart without stabilizers as if the heart is stationary. The robot actively cancels heart motion by closely following a point of interest (POI) on the heart surface—a process called Active Relative Motion Canceling (ARMC). Due to the high bandwidth of the POI motion, it is necessary to supply the controller with an estimate of the immediate future of the POI motion over a prediction horizon in order to achieve sufficient tracking accuracy. In this paper, two least-square based prediction algorithms, using an adaptive filter to generate future position estimates, are implemented and studied. The first method assumes a linear system relation between the consecutive samples in the prediction horizon. On the contrary, the second method performs this parametrization independently for each point over the whole the horizon. The effects of predictor parameters and variations in heart rate on tracking performance are studied with constant and varying heart rate data. The predictors are evaluated using a 3 degrees of freedom test-bed and prerecorded in-vivo motion data. Then, the one-step prediction and tracking performances of the presented approaches are compared with an Extended Kalman Filter predictor. Finally, the essential features of the proposed prediction algorithms are summarized. PMID:23976889
Goodness-of-Fit Tests and Nonparametric Adaptive Estimation for Spike Train Analysis
2014-01-01
When dealing with classical spike train analysis, the practitioner often performs goodness-of-fit tests to test whether the observed process is a Poisson process, for instance, or if it obeys another type of probabilistic model (Yana et al. in Biophys. J. 46(3):323–330, 1984; Brown et al. in Neural Comput. 14(2):325–346, 2002; Pouzat and Chaffiol in Technical report, http://arxiv.org/abs/arXiv:0909.2785, 2009). In doing so, there is a fundamental plug-in step, where the parameters of the supposed underlying model are estimated. The aim of this article is to show that plug-in has sometimes very undesirable effects. We propose a new method based on subsampling to deal with those plug-in issues in the case of the Kolmogorov–Smirnov test of uniformity. The method relies on the plug-in of good estimates of the underlying model that have to be consistent with a controlled rate of convergence. Some nonparametric estimates satisfying those constraints in the Poisson or in the Hawkes framework are highlighted. Moreover, they share adaptive properties that are useful from a practical point of view. We show the performance of those methods on simulated data. We also provide a complete analysis with these tools on single unit activity recorded on a monkey during a sensory-motor task. Electronic Supplementary Material The online version of this article (doi:10.1186/2190-8567-4-3) contains supplementary material. PMID:24742008
NASA Astrophysics Data System (ADS)
Zisser, N.; Kemna, A.; Nover, G.
2010-09-01
The possibility to estimate permeability from the electrical spectral induced polarization (SIP) response might be the most important benefit offered by SIP measurements. It can thus be deduced that, in the future, SIP measurements will be carried out more frequently at the field scale or in a well-logging context to estimate permeability. In the shallow subsurface, however, the temperature generally exhibits seasonal variability, and in the deeper subsurface, it usually increases with depth. Hence, knowledge about the dependence of the SIP response on temperature is necessary in order to avoid possible misinterpretation of datasets impacted by thermal effects. In our study, we present a semiempirical framework to describe the temperature dependence of the SIP response. We briefly introduce the SIP response and its relation to permeability in terms of an electrochemical polarization mechanism and combine this formulation with relationships for the dependence of ionic mobility on temperature. We compare the predictions of our formulation with the experimental data from SIP measurements performed on sandstone at temperatures from 0°C to 80°C. The measured SIP response was transformed into a relaxation time distribution, using the empirical Cole-Cole model and a regularized Debye decomposition procedure. The SIP response was found to be in good agreement with the theoretical model. The temperature dependence of both direct current conductivity and relaxation time is controlled mainly by the dependence of ionic mobility on temperature, and the shape of the relaxation time distribution of the investigated sandstone is almost independent of temperature. The temperature effect on the SIP response can therefore be easily corrected.
NASA Astrophysics Data System (ADS)
Myrhaug, Dag; Wang, Hong; Holmedal, Lars Erik
2016-04-01
The Stokes drift represents an important transport component of ocean circulation models. Locally it is responsible for transport of e.g. contaminated ballast water from ships, oil spills, plankton and larvae. It also plays an important role in mixing processes across the interphase between the atmosphere and the ocean. The Stokes drift is the mean Lagrangian velocity obtained from the water particle trajectory in the wave propagation direction; it is maximum at the surface, decreasing rapidly with the depth below the surface. The total mean mass transport is obtained by integrating the Stokes drift over the water depth; this is also referred to as the volume Stokes transport. The paper provides a simple analytical method which can be used to give estimates of the Stokes drift in moderate intermediate water depth based on short-term variation of wave conditions. This is achieved by using a joint distribution of individual wave heights and wave periods together with an explicit solution of the wave dispersion equation. The mean values of the surface Stokes drift and the volume Stokes transport for individual random waves within a sea state are presented, and the effects of water depth and spectral bandwidth parameter are discussed. Furthermore, example of results corresponding to typical field conditions are presented to demonstrate the application of the method, including the Stokes drift profile in the water column beneath the surface. Thus, the present analytical method can be used to estimate the Stokes drift in moderate intermediate water depth for random waves within a sea state based on available wave statistics.
Miles, Jeffrey Hilton
2011-05-01
Combustion noise from turbofan engines has become important, as the noise from sources like the fan and jet are reduced. An aligned and un-aligned coherence technique has been developed to determine a threshold level for the coherence and thereby help to separate the coherent combustion noise source from other noise sources measured with far-field microphones. This method is compared with a statistics based coherence threshold estimation method. In addition, the un-aligned coherence procedure at the same time also reveals periodicities, spectral lines, and undamped sinusoids hidden by broadband turbofan engine noise. In calculating the coherence threshold using a statistical method, one may use either the number of independent records or a larger number corresponding to the number of overlapped records used to create the average. Using data from a turbofan engine and a simulation this paper shows that applying the Fisher z-transform to the un-aligned coherence can aid in making the proper selection of samples and produce a reasonable statistics based coherence threshold. Examples are presented showing that the underlying tonal and coherent broad band structure which is buried under random broadband noise and jet noise can be determined. The method also shows the possible presence of indirect combustion noise. PMID:21568410
NASA Technical Reports Server (NTRS)
Miles, Jeffrey Hilton
2010-01-01
Combustion noise from turbofan engines has become important, as the noise from sources like the fan and jet are reduced. An aligned and un-aligned coherence technique has been developed to determine a threshold level for the coherence and thereby help to separate the coherent combustion noise source from other noise sources measured with far-field microphones. This method is compared with a statistics based coherence threshold estimation method. In addition, the un-aligned coherence procedure at the same time also reveals periodicities, spectral lines, and undamped sinusoids hidden by broadband turbofan engine noise. In calculating the coherence threshold using a statistical method, one may use either the number of independent records or a larger number corresponding to the number of overlapped records used to create the average. Using data from a turbofan engine and a simulation this paper shows that applying the Fisher z-transform to the un-aligned coherence can aid in making the proper selection of samples and produce a reasonable statistics based coherence threshold. Examples are presented showing that the underlying tonal and coherent broad band structure which is buried under random broadband noise and jet noise can be determined. The method also shows the possible presence of indirect combustion noise. Copyright 2011 Acoustical Society of America. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the Acoustical Society of America.
NASA Astrophysics Data System (ADS)
Ahn, Myoung-Hwan; Lee, Su Jeong; Kim, Dohyeong
2015-06-01
The five channel meteorological imager (MI) on-board the geostationary Communication, Ocean, and Meteorological Satellite (COMS) of Korea has been operationally used since April 2011. For a better utilization of the MI data, a rigorous characterization of the four infrared channel data has been conducted using the GSICS (Global Space-based Inter-Calibration System) approach with the IASI (Infrared Atmospheric Sounding Interferometer) on-board the European Metop satellite as the reference instrument. Although all four channels show the uncertainty characteristics that are in line with the results from both the ground tests and the in-orbit-test, there shows an unexpected systematic bias in the water vapor channel of MI, showing a cold bias at the warm target temperature and a warm bias with the cold target temperature. It has been shown that this kind of systematic bias could be introduced by the uncertainties in the spectral response function (SRF) of the specific channel which is similar to the heritage instruments on-board GOES series satellite. An extensive radiative transfer simulation using a radiative transfer model has confirmed that the SRF uncertainty could indeed introduce such a systematic bias. By using the collocated data set consisting of the MI data and the hyperspectral IASI data, the first order correction value for the SRF uncertainty is estimated to be about 2.79 cm-1 shift of the central position of the current SRF.
NASA Astrophysics Data System (ADS)
Bargatze, L. F.
2015-12-01
Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted
NASA Technical Reports Server (NTRS)
Balas, Mark J.; Thapa Magar, Kaman S.; Frost, Susan A.
2013-01-01
A theory called Adaptive Disturbance Tracking Control (ADTC) is introduced and used to track the Tip Speed Ratio (TSR) of 5 MW Horizontal Axis Wind Turbine (HAWT). Since ADTC theory requires wind speed information, a wind disturbance generator model is combined with lower order plant model to estimate the wind speed as well as partial states of the wind turbine. In this paper, we present a proof of stability and convergence of ADTC theory with lower order estimator and show that the state feedback can be adaptive.
Technology Transfer Automated Retrieval System (TEKTRAN)
Eggplant fruit is ranked amongst the top ten vegetables in terms of oxygen radical absorbance capacity due to its high phenolic acid content. The main objective of this study was to determine if a simple UV spectral analysis method can be used as a screening tool to estimate the amount of phenolic ...
Reflectivity Reconstruction Based on Adaptive Speed of Sound Estimation from Reflection Information
NASA Astrophysics Data System (ADS)
Yuan, Weiquan
The reflectivity image reconstructed by the synthetic focus algorithm has been verified to be one of the highest in resolution of any ultrasound imaging systems. However present imaging systems with the synthetic focus algorithm suffer from signal registration problems due to diffraction and refraction effects. Recent research efforts on refraction correction for reflection imaging are based on speed of sound estimation. One approach is to determine the speed of the sound map from transmission information. In this technique, both reflection and transmission data are required. Unfortunately, it is difficult to acquire both transmission and reflection in many practical medical environments. In this dissertation, a new reflectivity reconstruction technique is investigated in which the speed of sound estimation is determined only from the reflection data. This method of speed of sound estimation is based on an optimization approach. The speed of sound of the imaging region is updated iterately based one optimization. The criterion employed is the minimization of an objective functional. In particular, the alignment error functional, correlation functional or brightness functional are defined and then investigated. These objective functionals are all dependent on the speed of sound. During the process of minimizing the objective functional with respect to the speed of sound, an approximation to the true speed of sound is determined adaptively. To minimize these specific objective functionals, the conjugate gradient method with line search and with quadratic approximation is investigated. For such a large optimization problem, an undesired local minimum is often found. The penalty function technique is suggested to overcome this local minimum problem. Due to the limitation of available computer capability, all the methods developed are first investigated by a 8 x 8 grid reconstruction of speed of sound. Finally, the simulation results for a lager 64 x 64 reflectivity
Tsanas, Athanasios; Zañartu, Matías; Little, Max A.; Fox, Cynthia; Ramig, Lorraine O.; Clifford, Gari D.
2014-01-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F0) of speech signals. This study examines ten F0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F0 estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F0 estimation is required. PMID:24815269
Tsanas, Athanasios; Zañartu, Matías; Little, Max A; Fox, Cynthia; Ramig, Lorraine O; Clifford, Gari D
2014-05-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F(0)) of speech signals. This study examines ten F(0) estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F(0) in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F(0) estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F(0) estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F(0) estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F(0) estimation is required. PMID:24815269
2013-01-01
Background Elective patient admission and assignment planning is an important task of the strategic and operational management of a hospital and early on became a central topic of clinical operations research. The management of hospital beds is an important subtask. Various approaches have been proposed, involving the computation of efficient assignments with regard to the patients’ condition, the necessity of the treatment, and the patients’ preferences. However, these approaches are mostly based on static, unadaptable estimates of the length of stay and, thus, do not take into account the uncertainty of the patient’s recovery. Furthermore, the effect of aggregated bed capacities have not been investigated in this context. Computer supported bed management, combining an adaptable length of stay estimation with the treatment of shared resources (aggregated bed capacities) has not yet been sufficiently investigated. The aim of our work is: 1) to define a cost function for patient admission taking into account adaptable length of stay estimations and aggregated resources, 2) to define a mathematical program formally modeling the assignment problem and an architecture for decision support, 3) to investigate four algorithmic methodologies addressing the assignment problem and one base-line approach, and 4) to evaluate these methodologies w.r.t. cost outcome, performance, and dismissal ratio. Methods The expected free ward capacity is calculated based on individual length of stay estimates, introducing Bernoulli distributed random variables for the ward occupation states and approximating the probability densities. The assignment problem is represented as a binary integer program. Four strategies for solving the problem are applied and compared: an exact approach, using the mixed integer programming solver SCIP; and three heuristic strategies, namely the longest expected processing time, the shortest expected processing time, and random choice. A baseline approach
Miyano, Takuya; Kano, Manabu; Tanabe, Hideaki; Nakagawa, Hiroshi; Watanabe, Tomoyuki; Minami, Hidemi
2014-11-20
In process analytical technology (PAT) based on near infrared (NIR) spectroscopy, wavenumber selection is crucial to develop an accurate and robust calibration model. The present research proposes new efficient spectral dividing and wavenumber selection methods to significantly reduce the computational load required by conventional wavenumber selection methods such as interval partial least squares (iPLS). The proposed method, named spectral fluctuation dividing (SFD), divides a whole spectrum into multiple spectral intervals at local minimum points of the spectral fluctuation profile, which consists of the standard deviation of absorbance at each wavenumber in a calibration set. SFD is combined with PLS (SFD-PLS) to select the spectral intervals at which input variables have significant influence on a target response. The usefulness of SFD-PLS was demonstrated through its application to the problems of estimating water and drug content in granules. PLS models based on SFD-PLS achieved higher estimation accuracy than those based on conventional methods including iPLS, PLS-beta, and variable influence on projection (VIP). In addition, SFD-PLS was more than 10 times faster than the conventional variable selection methods including PLS-beta and VIP; in particular, SFD-PLS was more than 25 times faster than iPLS. Consequently, the proposed SFD-PLS is a promising wavenumber selection method.
Lesmes, Luis A.; Lu, Zhong-Lin; Baek, Jongsoo; Tran, Nina; Dosher, Barbara A.; Albright, Thomas D.
2015-01-01
Motivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold—the signal intensity corresponding to a pre-defined sensitivity level (d′ = 1)—in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimulus sampling to estimate a single level of %Yes or %Correct, the current methods sample psychometric functions more broadly, to concurrently estimate sensitivity and decision factors, and thereby estimate thresholds that are independent of decision confounds. Developed for four tasks—(1) simple YN detection, (2) cued YN detection, which cues the observer's response state before each trial, (3) rated YN detection, which incorporates a Not Sure response, and (4) FC detection—the qYN and qFC methods yield sensitivity thresholds that are independent of the task's decision structure (YN or FC) and/or the observer's subjective response state. Results from simulation and psychophysics suggest that 25 trials (and sometimes less) are sufficient to estimate YN thresholds with reasonable precision (s.d. = 0.10–0.15 decimal log units), but more trials are needed for FC thresholds. When the same subjects were tested across tasks of simple, cued, rated, and FC detection, adaptive threshold estimates exhibited excellent agreement with the method of constant stimuli (MCS), and with each other. These YN adaptive methods deliver criterion-free thresholds that have previously been exclusive to FC methods. PMID:26300798
Lesmes, Luis A; Lu, Zhong-Lin; Baek, Jongsoo; Tran, Nina; Dosher, Barbara A; Albright, Thomas D
2015-01-01
Motivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold-the signal intensity corresponding to a pre-defined sensitivity level (d' = 1)-in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimulus sampling to estimate a single level of %Yes or %Correct, the current methods sample psychometric functions more broadly, to concurrently estimate sensitivity and decision factors, and thereby estimate thresholds that are independent of decision confounds. Developed for four tasks-(1) simple YN detection, (2) cued YN detection, which cues the observer's response state before each trial, (3) rated YN detection, which incorporates a Not Sure response, and (4) FC detection-the qYN and qFC methods yield sensitivity thresholds that are independent of the task's decision structure (YN or FC) and/or the observer's subjective response state. Results from simulation and psychophysics suggest that 25 trials (and sometimes less) are sufficient to estimate YN thresholds with reasonable precision (s.d. = 0.10-0.15 decimal log units), but more trials are needed for FC thresholds. When the same subjects were tested across tasks of simple, cued, rated, and FC detection, adaptive threshold estimates exhibited excellent agreement with the method of constant stimuli (MCS), and with each other. These YN adaptive methods deliver criterion-free thresholds that have previously been exclusive to FC methods.
NASA Astrophysics Data System (ADS)
Vadivel, R.; Bhaskaran, V. Murali
2010-10-01
The main reason for packet loss in ad hoc networks is the link failure or node failure. In order to increase the path stability, it is essential to distinguish and moderate the failures. By knowing individual link stability along a path, path stability can be identified. In this paper, we develop an adaptive reliable routing protocol using combined link stability estimation for mobile ad hoc networks. The main objective of this protocol is to determine a Quality of Service (QoS) path along with prolonging the network life time and to reduce the packet loss. We calculate a combined metric for a path based on the parameters Link Expiration Time, Node Remaining Energy and Node Velocity and received signal strength to predict the link stability or lifetime. Then, a bypass route is established to retransmit the lost data, when a link failure occurs. By simulation results, we show that the proposed reliable routing protocol achieves high delivery ratio with reduced delay and packet drop.
NASA Astrophysics Data System (ADS)
Teal, Paul D.; Eccles, Craig
2015-04-01
The two most successful methods of estimating the distribution of nuclear magnetic resonance relaxation times from two dimensional data are data compression followed by application of the Butler-Reeds-Dawson algorithm, and a primal-dual interior point method using preconditioned conjugate gradient. Both of these methods have previously been presented using a truncated singular value decomposition of matrices representing the exponential kernel. In this paper it is shown that other matrix factorizations are applicable to each of these algorithms, and that these illustrate the different fundamental principles behind the operation of the algorithms. These are the rank-revealing QR (RRQR) factorization and the LDL factorization with diagonal pivoting, also known as the Bunch-Kaufman-Parlett factorization. It is shown that both algorithms can be improved by adaptation of the truncation as the optimization process progresses, improving the accuracy as the optimal value is approached. A variation on the interior method viz, the use of barrier function instead of the primal-dual approach, is found to offer considerable improvement in terms of speed and reliability. A third type of algorithm, related to the algorithm known as Fast iterative shrinkage-thresholding algorithm, is applied to the problem. This method can be efficiently formulated without the use of a matrix decomposition.
Im, Subin; Min, Soonhong
2013-04-01
Exploratory factor analyses of the Kirton Adaption-Innovation Inventory (KAI), which serves to measure individual cognitive styles, generally indicate three factors: sufficiency of originality, efficiency, and rule/group conformity. In contrast, a 2005 study by Im and Hu using confirmatory factor analysis supported a four-factor structure, dividing the sufficiency of originality dimension into two subdimensions, idea generation and preference for change. This study extends Im and Hu's (2005) study of a derived version of the KAI by providing additional evidence of the four-factor structure. Specifically, the authors test the robustness of the parameter estimates to the violation of normality assumptions in the sample using bootstrap methods. A bias-corrected confidence interval bootstrapping procedure conducted among a sample of 356 participants--members of the Arkansas Household Research Panel, with middle SES and average age of 55.6 yr. (SD = 13.9)--showed that the four-factor model with two subdimensions of sufficiency of originality fits the data significantly better than the three-factor model in non-normality conditions. PMID:23833873
Image adaptive point-spread function estimation and deconvolution for in vivo confocal microscopy.
Von Tiedemann, M; Fridberger, A; Ulfendahl, M; Tomo, I; Boutet de Monvel, J; De Monvel, J Boutet
2006-01-01
Visualizing deep inside the tissue of a thick biological sample often poses severe constraints on image conditions. Standard restoration techniques (denoising and deconvolution) can then be very useful, allowing one to increase the signal-to-noise ratio and the resolution of the images. In this paper, we consider the problem of obtaining a good determination of the point-spread function (PSF) of a confocal microscope, a prerequisite for applying deconvolution to three-dimensional image stacks acquired with this system. Because of scattering and optical distortion induced by the sample, the PSF has to be acquired anew for each experiment. To tackle this problem, we used a screening approach to estimate the PSF adaptively and automatically from the images. Small PSF-like structures were detected in the images, and a theoretical PSF model reshaped to match the geometric characteristics of these structures. We used numerical experiments to quantify the sensitivity of our detection method, and we demonstrated its usefulness by deconvolving images of the hearing organ acquired in vitro and in vivo.
ERIC Educational Resources Information Center
Wang, Shudong; Jiao, Hong; He, Wei
2011-01-01
The ability estimation procedure is one of the most important components in a computerized adaptive testing (CAT) system. Currently, all CATs that provide K-12 student scores are based on the item response theory (IRT) model(s); while such application directly violates the assumption of independent sample of a person in IRT models because ability…
ERIC Educational Resources Information Center
Klinkenberg, S.; Straatemeier, M.; van der Maas, H. L. J.
2011-01-01
In this paper we present a model for computerized adaptive practice and monitoring. This model is used in the Maths Garden, a web-based monitoring system, which includes a challenging web environment for children to practice arithmetic. Using a new item response model based on the Elo (1978) rating system and an explicit scoring rule, estimates of…
Jakeman, J.D. Wildey, T.
2015-01-01
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the physical discretization error and the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity of the sparse grid. Utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.
Jakeman, J. D.; Wildey, T.
2015-01-01
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity. We show that utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this papermore » we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.« less
Jakeman, J. D.; Wildey, T.
2015-01-01
In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity. We show that utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.
NASA Astrophysics Data System (ADS)
Telloni, D.; Bruno, R.; Trenchi, L.
2014-12-01
We exploited radial alignments between MESSENGER and WIND spacecraft to study: 1) the radial dependence of the spectral break located at the border between fluid and kinetic regimes; 2) the dependence, if any, of the spectral slope, around the frequency break, on the type of wind, either fast or slow.We found that this spectral break moves to lower and lower frequencies as heliocentric distance increases, following a power-law dependence. Moreover, we found evidence that a cyclotron-resonant dissipation mechanism must participate into the spectral energy cascade together with other possible kinetic noncyclotron-resonant mechanisms.On the other hand, the spectral slope shows a large variability between -3.75 and -1.75 with an average value around -2.8 and a robust tendency for this parameter to be steeper within the trailing edge of high speed streams and to be flatter within the subsequent slower wind, following a gradual transition between these two states. The value of the spectral index seems to depend firmly on the power associated to the fluctuations within the inertial range, higher the power steeper the slope. Research partially supported by the Agenzia Spaziale Italiana, contract ASI/INAF I/013/12/0 and by the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 313038/STORM
Adaptive quarter-pel motion estimation and motion vector coding algorithm for the H.264/AVC standard
NASA Astrophysics Data System (ADS)
Jung, Seung-Won; Park, Chun-Su; Ha, Le Thanh; Ko, Sung-Jea
2009-11-01
We present an adaptive quarter-pel (Qpel) motion estimation (ME) method for H.264/AVC. Instead of applying Qpel ME to all macroblocks (MBs), the proposed method selectively performs Qpel ME in an MB level. In order to reduce the bit rate, we also propose a motion vector (MV) encoding technique that adaptively selects a different variable length coding (VLC) table according to the accuracy of the MV. Experimental results show that the proposed method can achieve about 3% average bit rate reduction.
Estimation of Ability Level by Using Only Observable Quantities in Adaptive Testing.
ERIC Educational Resources Information Center
Kirisci, Levent; Hsu, Tse-Chi
A predictive adaptive testing (PAT) strategy was developed based on statistical predictive analysis, and its feasibility was studied by comparing PAT performance to those of the Flexilevel, Bayesian modal, and expected a posteriori (EAP) strategies in a simulated environment. The proposed adaptive test is based on the idea of using item difficulty…
NASA Technical Reports Server (NTRS)
Balas, Mark; Frost, Susan
2012-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter.
Wang, Han; Du, Wencai; Xu, Lingwei
2016-01-01
The conventional channel estimation methods based on a preamble for filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems in mobile-to-mobile sensor networks are inefficient. By utilizing the intrinsicsparsity of wireless channels, channel estimation is researched as a compressive sensing (CS) problem to improve the estimation performance. In this paper, an AdaptiveRegularized Compressive Sampling Matching Pursuit (ARCoSaMP) algorithm is proposed. Unlike anterior greedy algorithms, the new algorithm can achieve the accuracy of reconstruction by choosing the support set adaptively, and exploiting the regularization process, which realizes the second selecting of atoms in the support set although the sparsity of the channel is unknown. Simulation results show that CS-based methods obtain significant channel estimation performance improvement compared to that of conventional preamble-based methods. The proposed ARCoSaMP algorithm outperforms the conventional sparse adaptive matching pursuit (SAMP) algorithm. ARCoSaMP provides even more interesting results than the mostadvanced greedy compressive sampling matching pursuit (CoSaMP) algorithm without a prior sparse knowledge of the channel. PMID:27347967
Wang, Han; Du, Wencai; Xu, Lingwei
2016-01-01
The conventional channel estimation methods based on a preamble for filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems in mobile-to-mobile sensor networks are inefficient. By utilizing the intrinsicsparsity of wireless channels, channel estimation is researched as a compressive sensing (CS) problem to improve the estimation performance. In this paper, an AdaptiveRegularized Compressive Sampling Matching Pursuit (ARCoSaMP) algorithm is proposed. Unlike anterior greedy algorithms, the new algorithm can achieve the accuracy of reconstruction by choosing the support set adaptively, and exploiting the regularization process, which realizes the second selecting of atoms in the support set although the sparsity of the channel is unknown. Simulation results show that CS-based methods obtain significant channel estimation performance improvement compared to that of conventional preamble-based methods. The proposed ARCoSaMP algorithm outperforms the conventional sparse adaptive matching pursuit (SAMP) algorithm. ARCoSaMP provides even more interesting results than the mostadvanced greedy compressive sampling matching pursuit (CoSaMP) algorithm without a prior sparse knowledge of the channel. PMID:27347967
Wang, Han; Du, Wencai; Xu, Lingwei
2016-06-24
The conventional channel estimation methods based on a preamble for filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems in mobile-to-mobile sensor networks are inefficient. By utilizing the intrinsicsparsity of wireless channels, channel estimation is researched as a compressive sensing (CS) problem to improve the estimation performance. In this paper, an AdaptiveRegularized Compressive Sampling Matching Pursuit (ARCoSaMP) algorithm is proposed. Unlike anterior greedy algorithms, the new algorithm can achieve the accuracy of reconstruction by choosing the support set adaptively, and exploiting the regularization process, which realizes the second selecting of atoms in the support set although the sparsity of the channel is unknown. Simulation results show that CS-based methods obtain significant channel estimation performance improvement compared to that of conventional preamble-based methods. The proposed ARCoSaMP algorithm outperforms the conventional sparse adaptive matching pursuit (SAMP) algorithm. ARCoSaMP provides even more interesting results than the mostadvanced greedy compressive sampling matching pursuit (CoSaMP) algorithm without a prior sparse knowledge of the channel.
NASA Astrophysics Data System (ADS)
Rallo, Giovanni; Provenzano, Giuseppe; Jones, Hamlyn G.
2015-04-01
The Soil Plant Atmosphere Continuum (SPAC) is characterized by complex structures and biophysical processes acting over a wide range of temporal and spatial scales. Additionally, in olive grove systems, the plant adaptive strategies to respond to soil water-limited conditions make the system even more complex. One of the greatest challenges in hydrological research is to quantify changing plant water relations. A promising new technology is provided by the advent of new field spectroscopy detectors, characterized by very high resolution over the spectral range between 300 and 2500 nm, allowing the detection of narrow reflectance or absorptance peaks, to separate close lying peaks and to discover new information, hidden at lower resolutions. The general objective of the present research was to investigate a range of plant state function parameters in a non-destructive and repeatable manner and to improve methodologies aimed to parameterize hydrological models describing the entire SPAC, or each single compartment (soil or plant). We have investigated the use of hyperspectral sensing for the parameterization of the hydraulic pressure-volume curve (P-V) for olive leaf and for the indirect estimation of the two principal leaf water potential components, i.e. turgor and osmotic potentials. Experiments were carried out on an olive grove in Sicily, during the mature phase of the first vegetative flush. Leaf spectral signatures and associated P-V measurements were acquired on olive leaves collected from well-irrigated plants and from plants maintained under moderate or severe water stress. Leaf spectral reflectance was monitored with a FieldSpec 4 spectro-radiometer (Analytical Spectral Device, Inc.), in a range of wavelengths from VIS to SWIR (350-2500 nm), with sampling intervals of 1.4 nm and 2.0 nm, respectively in the regions from 350 to 1000 nm and from 1000 to 2500 nm. Measurements required the use of contact probe and leaf clip (Analytical Spectral Device, Inc
Chen, Szi-Wen; Chao, Shih-Chieh
2014-01-01
In this paper, a reweighted ℓ1-minimization based Compressed Sensing (CS) algorithm incorporating the Integral Pulse Frequency Modulation (IPFM) model for spectral estimation of HRV is introduced. Knowing as a novel sensing/sampling paradigm, the theory of CS asserts certain signals that are considered sparse or compressible can be possibly reconstructed from substantially fewer measurements than those required by traditional methods. Our study aims to employ a novel reweighted ℓ1-minimization CS method for deriving the spectrum of the modulating signal of IPFM model from incomplete RR measurements for HRV assessments. To evaluate the performance of HRV spectral estimation, a quantitative measure, referred to as the Percent Error Power (PEP) that measures the percentage of difference between the true spectrum and the spectrum derived from the incomplete RR dataset, was used. We studied the performance of spectral reconstruction from incomplete simulated and real HRV signals by experimentally truncating a number of RR data accordingly in the top portion, in the bottom portion, and in a random order from the original RR column vector. As a result, for up to 20% data truncation/loss the proposed reweighted ℓ1-minimization CS method produced, on average, 2.34%, 2.27%, and 4.55% PEP in the top, bottom, and random data-truncation cases, respectively, on Autoregressive (AR) model derived simulated HRV signals. Similarly, for up to 20% data loss the proposed method produced 5.15%, 4.33%, and 0.39% PEP in the top, bottom, and random data-truncation cases, respectively, on a real HRV database drawn from PhysioNet. Moreover, results generated by a number of intensive numerical experiments all indicated that the reweighted ℓ1-minimization CS method always achieved the most accurate and high-fidelity HRV spectral estimates in every aspect, compared with the ℓ1-minimization based method and Lomb's method used for estimating the spectrum of HRV from unevenly sampled RR
NASA Astrophysics Data System (ADS)
Trofimov, Vyacheslav A.; Peskov, Nikolay V.; Kirillov, Dmitry A.
2012-10-01
One of the problems arising in Time-Domain THz spectroscopy for the problem of security is the developing the criteria for assessment of probability for the detection and identification of the explosive and drugs. We analyze the efficiency of using the correlation function and another functional (more exactly, spectral norm) for this aim. These criteria are applied to spectral lines dynamics. For increasing the reliability of the assessment we subtract the averaged value of THz signal during time of analysis of the signal: it means deleting the constant from this part of the signal. Because of this, we can increase the contrast of assessment. We compare application of the Fourier-Gabor transform with unbounded (for example, Gaussian) window, which slides along the signal, for finding the spectral lines dynamics with application of the Fourier transform in short time interval (FTST), in which the Fourier transform is applied to parts of the signals, for the same aim. These methods are close each to other. Nevertheless, they differ by series of frequencies which they use. It is important for practice that the optimal window shape depends on chosen method for obtaining the spectral dynamics. The probability enhancements if we can find the train of pulses with different frequencies, which follow sequentially. We show that there is possibility to get pure spectral lines dynamics even under the condition of distorted spectrum of the substance response on the action of the THz pulse.
Feischl, Michael; Gantner, Gregor; Praetorius, Dirk
2015-01-01
We consider the Galerkin boundary element method (BEM) for weakly-singular integral equations of the first-kind in 2D. We analyze some residual-type a posteriori error estimator which provides a lower as well as an upper bound for the unknown Galerkin BEM error. The required assumptions are weak and allow for piecewise smooth parametrizations of the boundary, local mesh-refinement, and related standard piecewise polynomials as well as NURBS. In particular, our analysis gives a first contribution to adaptive BEM in the frame of isogeometric analysis (IGABEM), for which we formulate an adaptive algorithm which steers the local mesh-refinement and the multiplicity of the knots. Numerical experiments underline the theoretical findings and show that the proposed adaptive strategy leads to optimal convergence. PMID:26085698
NASA Astrophysics Data System (ADS)
Foffani, Guglielmo; Bianchi, Anna M.; Priori, Alberto; Baselli, Giuseppe
2004-09-01
We propose a method that combines adaptive autoregressive (AAR) identification and spectral power decomposition for the study of movement-related spectral changes in scalp EEG signals and basal ganglia local field potentials (LFPs). This approach introduces the concept of movement-related poles, allowing one to study not only the classical event-related desynchronizations (ERD) and synchronizations (ERS), which correspond to modulations of power, but also event-related modulations of frequency. We applied the method to analyze movement-related EEG signals and LFPs contemporarily recorded from the sensorimotor cortex, the globus pallidus internus (GPi) and the subthalamic nucleus (STN) in a patient with Parkinson's disease who underwent stereotactic neurosurgery for the implant of deep brain stimulation (DBS) electrodes. In the AAR identification we compared the whale and the exponential forgetting factors, showing that the whale forgetting provides a better disturbance rejection and it is therefore more suitable to investigate movement-related brain activity. Movement-related power modulations were consistent with previous studies. In addition, movement-related frequency modulations were observed from both scalp EEG signals and basal ganglia LFPs. The method therefore represents an effective approach to the study of movement-related brain activity.
NASA Technical Reports Server (NTRS)
Whitmore, S. A.
1985-01-01
The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the space shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.
Sakaguchi, Yutaka; Takano, Mitsuo
2004-09-01
This article proposes an adaptive action-selection method for a model-free reinforcement learning system, based on the concept of the 'reliability of internal prediction/estimation'. This concept is realized using an internal variable, called the Reliability Index (RI), which estimates the accuracy of the internal estimator. We define this index for a value function of a temporal difference learning system and substitute it for the temperature parameter of the Boltzmann action-selection rule. Accordingly, the weight of exploratory actions adaptively changes depending on the uncertainty of the prediction. We use this idea for tabular and weighted-sum type value functions. Moreover, we use the RI to adjust the learning coefficient in addition to the temperature parameter, meaning that the reliability becomes a general basis for meta-learning. Numerical experiments were performed to examine the behavior of the proposed method. The RI-based Q-learning system demonstrated its features when the adaptive learning coefficient and large RI-discount rate (which indicate how the RI values of future states are reflected in the RI value of the current state) were introduced. Statistical tests confirmed that the algorithm spent more time exploring in the initial phase of learning, but accelerated learning from the midpoint of learning. It is also shown that the proposed method does not work well with the actor-critic models. The limitations of the proposed method and its relationship to relevant research are discussed. PMID:15312837
NASA Astrophysics Data System (ADS)
Mirzaie, M.; Darvishzadeh, R.; Shakiba, A.; Matkan, A. A.; Atzberger, C.; Skidmore, A.
2014-02-01
Assessment of vegetation water content is critical for monitoring vegetation condition, detecting plant water stress, assessing the risk of forest fires and evaluating water status for irrigation. The main objective of this study was to investigate the performance of various mono- and multi-variate statistical methods for estimating vegetation water content (VWC) from hyper-spectral data. Hyper-spectral data is influenced by multi-collinearity because of a large number of (independent) spectral bands being modeled by a small number of (dependent) biophysical variables. Therefore, some full spectrum methods that are known to be suitable for analyzing multi-collinear data set were chosen. Canopy spectral reflectance was obtained with a GER 3700 spectro-radiometer (400-2400 nm) in a laboratory setting and VWC was measured by calculating wet/dry weight difference per unit of ground area (g/m2) of each plant canopy (n = 95). Three multivariate statistical methods were applied to estimate VWC: (1) partial least square regression, (2) artificial neural network and (3) principal component regression. They were selected to minimize the problem related to multi-collinearity. For comparison, uni-variate techniques including narrow band ratio water index (RWI), normalized difference water index (NDWI), second soil adjusted vegetation index (SAVI2) and transferred soil adjusted vegetation index (TSAVI) were applied. For each type of vegetation index, all two-band combinations were evaluated to determine the best band combination. Validation of the methods was based on the cross validation procedure and using three statistical indicators: R2, RMSE and relative RMSE. The cross-validated results identified PLSR as the regression model providing the most accurate estimates of VWC among the various methods. The result revealed that this model is highly recommended for use with multi-collinear datasets (RCV2=0.94, RRMSECV = 0.23). Principal component regression exhibited the lowest
NASA Astrophysics Data System (ADS)
Boroomand, Ameneh; Tan, Bingyao; Wong, Alexander; Bizheva, Kostadinka
2015-03-01
The axial resolution of Spectral Domain Optical Coherence Tomography (SD-OCT) images degrades with scanning depth due to the limited number of pixels and the pixel size of the camera, any aberrations in the spectrometer optics and wavelength dependent scattering and absorption in the imaged object [1]. Here we propose a novel algorithm which compensates for the blurring effect of these factors of the depth-dependent axial Point Spread Function (PSF) in SDOCT images. The proposed method is based on a Maximum A Posteriori (MAP) reconstruction framework which takes advantage of a Stochastic Fully Connected Conditional Random Field (SFCRF) model. The aim is to compensate for the depth-dependent axial blur in SD-OCT images and simultaneously suppress the speckle noise which is inherent to all OCT images. Applying the proposed depth-dependent axial resolution enhancement technique to an OCT image of cucumber considerably improved the axial resolution of the image especially at higher imaging depths and allowed for better visualization of cellular membrane and nuclei. Comparing the result of our proposed method with the conventional Lucy-Richardson deconvolution algorithm clearly demonstrates the efficiency of our proposed technique in better visualization and preservation of fine details and structures in the imaged sample, as well as better speckle noise suppression. This illustrates the potential usefulness of our proposed technique as a suitable replacement for the hardware approaches which are often very costly and complicated.
NASA Astrophysics Data System (ADS)
Flad, David; Beck, Andrea; Munz, Claus-Dieter
2016-05-01
Scale-resolving simulations of turbulent flows in complex domains demand accurate and efficient numerical schemes, as well as geometrical flexibility. For underresolved situations, the avoidance of aliasing errors is a strong demand for stability. For continuous and discontinuous Galerkin schemes, an effective way to prevent aliasing errors is to increase the quadrature precision of the projection operator to account for the non-linearity of the operands (polynomial dealiasing, overintegration). But this increases the computational costs extensively. In this work, we present a novel spatially and temporally adaptive dealiasing strategy by projection filtering. We show this to be more efficient for underresolved turbulence than the classical overintegration strategy. For this novel approach, we discuss the implementation strategy and the indicator details, show its accuracy and efficiency for a decaying homogeneous isotropic turbulence and the transitional Taylor-Green vortex and compare it to the original overintegration approach and a state of the art variational multi-scale eddy viscosity formulation.
NASA Astrophysics Data System (ADS)
Rezaei, Fatemeh; Tavassoli, Seyed Hassan
2016-11-01
In this paper, a study is performed on the spectral lines of plasma radiations created from focusing of the Nd:YAG laser on Al standard alloys at atmospheric air pressure. A new theoretical method is presented to investigate the evolution of the optical depth of the plasma based on the radiative transfer equation, in LTE condition. This work relies on the Boltzmann distribution, lines broadening equations, and as well as the self-absorption relation. Then, an experimental set-up is devised to extract some of plasma parameters such as temperature from modified line ratio analysis, electron density from Stark broadening mechanism, line intensities of two spectral lines in the same order of ionization from similar species, and the plasma length from the shadowgraphy section. In this method, the summation and the ratio of two spectral lines are considered for evaluation of the temporal variations of the plasma parameters in a LIBS homogeneous plasma. The main advantage of this method is that it comprises the both of thin and thick laser induced plasmas without straight calculation of self-absorption coefficient. Moreover, the presented model can also be utilized for evaluation the transition of plasma from the thin condition to the thick one. The results illustrated that by measuring the line intensities of two spectral lines at different evolution times, the plasma cooling and the growth of the optical depth can be followed.
Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki
2014-01-01
Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics. PMID:25089286
Fu, Zening; Chan, Shing-Chow; Di, Xin; Biswal, Bharat; Zhang, Zhiguo
2014-04-01
Time-varying covariance is an important metric to measure the statistical dependence between non-stationary biological processes. Time-varying covariance is conventionally estimated from short-time data segments within a window having a certain bandwidth, but it is difficult to choose an appropriate bandwidth to estimate covariance with different degrees of non-stationarity. This paper introduces a local polynomial regression (LPR) method to estimate time-varying covariance and performs an asymptotic analysis of the LPR covariance estimator to show that both the estimation bias and variance are functions of the bandwidth and there exists an optimal bandwidth to minimize the mean square error (MSE) locally. A data-driven variable bandwidth selection method, namely the intersection of confidence intervals (ICI), is adopted in LPR for adaptively determining the local optimal bandwidth that minimizes the MSE. Experimental results on simulated signals show that the LPR-ICI method can achieve robust and reliable performance in estimating time-varying covariance with different degrees of variations and under different noise scenarios, making it a powerful tool to study the dynamic relationship between non-stationary biomedical signals. Further, we apply the LPR-ICI method to estimate time-varying covariance of functional magnetic resonance imaging (fMRI) signals in a visual task for the inference of dynamic functional brain connectivity. The results show that the LPR-ICI method can effectively capture the transient connectivity patterns from fMRI.
Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki
2014-01-01
Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.
Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki
2014-01-01
Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics. PMID:25089286
Vasile, Gabriel; Trouvé, Emmanuel; Ciuc, Mihai; Buzuloiu, Vasile
2004-08-01
A new method for filtering the coherence map issued from synthetic aperture radar (SAR) interferometric data is presented. For each pixel of the interferogram, an adaptive neighborhood is determined by a region-growing technique driven by the information provided by the amplitude images. Then pixels in the derived adaptive neighborhood are complex averaged to yield the filtered value of the coherence, after a phase-compensation step is performed. An extension of the algorithm is proposed for polarimetric interferometric SAR images. The proposed method has been applied to both European Remote Sensing (ERS) satellite SAR images and airborne high-resolution polarimetric interferometric SAR images. Both subjective and objective performance analysis, including coherence edge detection, shows that the proposed method provides better results than the standard phase-compensated fixed multilook filter and the Lee adaptive coherence filter.
NASA Astrophysics Data System (ADS)
Moore, F.; Burke, M.
2015-12-01
A wide range of studies using a variety of methods strongly suggest that climate change will have a negative impact on agricultural production in many areas. Farmers though should be able to learn about a changing climate and to adjust what they grow and how they grow it in order to reduce these negative impacts. However, it remains unclear how effective these private (autonomous) adaptations will be, or how quickly they will be adopted. Constraining the uncertainty on this adaptation is important for understanding the impacts of climate change on agriculture. Here we review a number of empirical methods that have been proposed for understanding the rate and effectiveness of private adaptation to climate change. We compare these methods using data on agricultural yields in the United States and western Europe.
NASA Technical Reports Server (NTRS)
Tomei, B. A.; Smith, L. G.
1986-01-01
Sounding rockets equipped to monitor electron density and its fine structure were launched into the auroral and equatorial ionosphere in 1980 and 1983, respectively. The measurement electronics are based on the Langmuir probe and are described in detail. An approach to the spectral analysis of the density irregularities is addressed and a software algorithm implementing the approach is given. Preliminary results of the analysis are presented.
Mass estimation of MAXI J1659-152 during spectral and temporal analsyis with TCAF and POS models
NASA Astrophysics Data System (ADS)
Molla, Aslam Ali; Debnath, Dipak; Chakrabarti, Sandip Kumar; Mondal, Santanu; Jana, Arghajit; Chatterjee, Debjit
2016-07-01
The Galactic transient black hole candidate (BHC) MAXI J1659-152 showed its first X-ray outburst on 25th Spet. 2010. We make a detailed spectral and temporal study of this outburst with RXTE/PCA data. The spectral analysis was made with Two Component Advective Flow (TCAF) model fits file as an additive table model in XSPEC. While fitting spectra with TCAF, we note that model fitted normalization (N) remains almost constant (129.7 - 146.3) which lead us to calculate mass of the black hole (BH). We then refitted all the spectra with fixed normalization value of 139 (calculated from weighted averaging of the N values), and found that mass of the BH comes in the range of 4.69-7.75 M_Sun. It is to be noted that in TCAF model fits file, mass is an input parameter. We also calculted mass of the BH, with our study of the QPO frequency evolution during declining phase of the outburst with the Propagating Oscillatory Shock (POS) model. We observe that in the declining phase of the outburst the shock moves away from the black hole as the QPO frequency decreases. We obtain our best fit of QPO evolution by using mass of the BH at 6 M_Sun and acceptable fit (reduced chisq value < 1.5) for the mass range of 5.08-7.38 M_Sun, which lie within the range of mass obtained from our spectral fit. So, from the study of spectral and temporal variability of this source we conclude the probable mass range of the black hole to be 4.69 - 7.75 M_Sun.
NASA Astrophysics Data System (ADS)
Erasmi, Stefan; Dobers, Eike S.
2004-02-01
The use of remote sensing data in site specific crop management aims at the prediction of soil and crop factors that have an impact on yield formation processes in agriculture. Numerous methods demonstrate the potential of spectral reflectance data for the detection of qualitative and quantitative crop features but there is, however, no established methodology for the implementation of these data in operational crop production processes. The paper describes the main aspects of remote sensing based site characterization, considering major site variables (yield, soil) and plant parameters (nitrogen uptake) as key features for the description of the site specific variability in crops. Spectral reflectance data of the VIS/NIR region are transformed into different spectral indices for statistical analysis. Analyzing these indices it is found that the determination of a prediction model depends on the relevance of the suggested data fitting method (causality) as well as on the statistical significance of the interrelationship. Results point out that remote sensing data are suitable predictors for crop vitality and site characterization. Hence, the application of these data in agricultural work routines is limited by their quality and availability as well as by the influence of environmental factors on yield formation processes.
NASA Technical Reports Server (NTRS)
Miles, Jeffrey Hilton
2015-01-01
A cross-power spectrum phase based adaptive technique is discussed which iteratively determines the time delay between two digitized signals that are coherent. The adaptive delay algorithm belongs to a class of algorithms that identifies a minimum of a pattern matching function. The algorithm uses a gradient technique to find the value of the adaptive delay that minimizes a cost function based in part on the slope of a linear function that fits the measured cross power spectrum phase and in part on the standard error of the curve fit. This procedure is applied to data from a Honeywell TECH977 static-engine test. Data was obtained using a combustor probe, two turbine exit probes, and far-field microphones. Signals from this instrumentation are used estimate the post-combustion residence time in the combustor. Comparison with previous studies of the post-combustion residence time validates this approach. In addition, the procedure removes the bias due to misalignment of signals in the calculation of coherence which is a first step in applying array processing methods to the magnitude squared coherence data. The procedure also provides an estimate of the cross-spectrum phase-offset.
NASA Astrophysics Data System (ADS)
Kaydash, V.; Mall, U.; Vilenius, E.; SIR Collaboration
The infrared spectrometer SIR on board the ESA SMART-1 mission is designed for the detailed remote spectral investigation of the lunar surface in the wavelength range 0.9 - 2.4 microns with high spectral (˜6 nm) resolution [1]. Data obtained by the SIR allow a comparison of the relative spectral slope for selected lunar sites. A number of lunar features were selected as "calibration targets" for SIR [2]; among these sites is the Reiner-Gamma Swirl (RGS), widely known for its unusual spectral behavior not associated with any prominent topographic features [3]. For this first study we used data taken by SIR during SMART-1 orbit number 1781 for both RGS-tracking mode (58.51o E, 7.40o N) and adjacent mare basalt areas surrounding the swirl. All spectra were calibrated to obtain spectral values proportional to the brightness of the surface. Then we performed an averaging of separate spectra into two sets corresponding to the RGS and the mare neighborhood. After this we computed the color-indices C (1.25/2.0 µm) for the two areas and finally obtained a CRGS /Cmare value of 1.07. The same ratios for the RGS spectra were calculated using the USGS Clementine NIR mosaics [4]; we found a CRGS /Cmare value of 1.06 for that case. We also found the same inclination for the relative spectral slope and a rather good agreement in the absolute CRGS /Cmare values using data from the SIR and Clementine data sets. A slight discrepancy in two values could be explained by the very different photometric conditions which existed during the two surveys. Estimating the spectral slopes from SIR data is important for discrimination the effects of the chemical composition from effects caused by the maturation processes on the spectra in the near IR (i.e. [5]). The value of CRGS /Cmare ˜1.06 which we confirmed in the present work shows the more pronounced 2-µm depression and thus support the hypothesis of the presence of more immature material in RGS relative to its surroundings [6
NASA Astrophysics Data System (ADS)
Gitterman, Y.; Kim, S. G.; Hofstetter, R.
2016-04-01
Three underground nuclear explosions, conducted by North Korea in 2006, 2009 and 2013, are analyzed. The last two tests were recorded by the Israel Seismic Network. Pronounced coherent minima (spectral nulls) at 1.2-1.3 Hz were revealed in the spectra of teleseismic P -waves. For a ground-truth explosion with a shallow source depth, this phenomenon can be interpreted in terms of the interference between the down-going P-wave and the pP phase reflected from the Earth's surface. This effect was also observed at ISN stations for a Pakistan nuclear explosion at a different frequency 1.7 Hz and the PNE Rubin-2 in West Siberia at 1 Hz, indicating a source-effect and not a site-effect. Similar spectral minima having essentially the same frequency, as at ISN, were observed in teleseismic P-waves for all the three North Korean explosions recorded at networks and arrays in Kazakhstan (KURK), Norway (NNSN), Australia (ASAR, WRA) and Canada (YKA), covering a broad azimuthal range. Data of 2009 and 2013 tests at WRA and KURK arrays showed harmonic spectral modulation with three multiple minima frequencies, evidencing the clear interference effect. These observations support the above-mentioned interpretation. Based on the null frequency dependency on the near-surface acoustic velocity and the source depth, the depth of the North Korean tests was estimated about 2.0-2.1 km. It was shown that the observed null frequencies and the obtained source depth estimates correspond to P- pP interference phenomena in both cases of a vertical shaft or a horizontal drift in a mountain. This unusual depth estimation needs additional validation based on more stations and verification by other methods.
NASA Astrophysics Data System (ADS)
Kimlin, Michael G.; Taylor, Thomas E.; Herman, Jay R.; Rives, John E.; Cannon, Blake; Meltzer, Richard S.
2003-06-01
Most comparisons of TOMS estimates of surface UV irradiation with measured values from ground-based instruments have indicated a bias of the TOMS estimates toward larger values. A portion of this bias results from absolute uncertainties in the ground-based instruments. The comparison reported here is based on ground-based data from four sites in the UGA/EPA Brewer network. The raw data from the ground-based instruments has been corrected for (1) stray light rejection, (2) the cosine errors associated with the full sky diffuser, (3) the temperature dependence of the response of the instruments and (4) the temporal variation in the instrument response reducing the estimated errors of the absolute irradiance values of each spectral measurement to < +/-7%. Comparisons of TOMS with the surface measurements are performed both at spectrally resolved wavelengths at the time of overpass and for erythemally-weighted daily-integrated doses. These comparisons are made for all days and for clear-sky days only. The comparisons are carried out using both linear regressions of scatter plots of the two sets of data and for mean differences with respect to both TOMS and the Brewer measurements. It is found that spectrally resolved comparisons suffer from inconsistencies at some of the sites that are believed to result from wavelength uncertainties in the Brewer; they are therefore of more limited use than wavelength integrated data. A comparison based on daily-integrated doses shows only a small positive TOMS bias (4%) for clear-sky days with a somewhat larger bias (8%) for data taken from all days.
NASA Astrophysics Data System (ADS)
Mariño, Inés P.; Míguez, Joaquín; Meucci, Riccardo
2009-05-01
We propose a Monte Carlo methodology for the joint estimation of unobserved dynamic variables and unknown static parameters in chaotic systems. The technique is sequential, i.e., it updates the variable and parameter estimates recursively as new observations become available, and, hence, suitable for online implementation. We demonstrate the validity of the method by way of two examples. In the first one, we tackle the estimation of all the dynamic variables and one unknown parameter of a five-dimensional nonlinear model using a time series of scalar observations experimentally collected from a chaotic CO2 laser. In the second example, we address the estimation of the two dynamic variables and the phase parameter of a numerical model commonly employed to represent the dynamics of optoelectronic feedback loops designed for chaotic communications over fiber-optic links.
Tommasi, C.; May, C.
2010-09-30
The DKL-optimality criterion has been recently proposed for the dual problem of model discrimination and parameter estimation, for the case of two rival models. A sequential version of the DKL-optimality criterion is herein proposed in order to discriminate and efficiently estimate more than two nested non-linear models. Our sequential method is inspired by the procedure of Biswas and Chaudhuri (2002), which is however useful only in the set up of nested linear models.
Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petković, Dalibor; Mat Kiah, Miss Laiha
2015-01-01
This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes.
Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petković, Dalibor; Mat Kiah, Miss Laiha
2015-01-01
This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes. PMID:25453384
NASA Astrophysics Data System (ADS)
Jones, Reese E.; Mandadapu, Kranthi K.
2012-04-01
We present a rigorous Green-Kubo methodology for calculating transport coefficients based on on-the-fly estimates of: (a) statistical stationarity of the relevant process, and (b) error in the resulting coefficient. The methodology uses time samples efficiently across an ensemble of parallel replicas to yield accurate estimates, which is particularly useful for estimating the thermal conductivity of semi-conductors near their Debye temperatures where the characteristic decay times of the heat flux correlation functions are large. Employing and extending the error analysis of Zwanzig and Ailawadi [Phys. Rev. 182, 280 (1969)], 10.1103/PhysRev.182.280 and Frenkel [in Proceedings of the International School of Physics "Enrico Fermi", Course LXXV (North-Holland Publishing Company, Amsterdam, 1980)] to the integral of correlation, we are able to provide tight theoretical bounds for the error in the estimate of the transport coefficient. To demonstrate the performance of the method, four test cases of increasing computational cost and complexity are presented: the viscosity of Ar and water, and the thermal conductivity of Si and GaN. In addition to producing accurate estimates of the transport coefficients for these materials, this work demonstrates precise agreement of the computed variances in the estimates of the correlation and the transport coefficient with the extended theory based on the assumption that fluctuations follow a Gaussian process. The proposed algorithm in conjunction with the extended theory enables the calculation of transport coefficients with the Green-Kubo method accurately and efficiently.
Jones, Reese E; Mandadapu, Kranthi K
2012-04-21
We present a rigorous Green-Kubo methodology for calculating transport coefficients based on on-the-fly estimates of: (a) statistical stationarity of the relevant process, and (b) error in the resulting coefficient. The methodology uses time samples efficiently across an ensemble of parallel replicas to yield accurate estimates, which is particularly useful for estimating the thermal conductivity of semi-conductors near their Debye temperatures where the characteristic decay times of the heat flux correlation functions are large. Employing and extending the error analysis of Zwanzig and Ailawadi [Phys. Rev. 182, 280 (1969)] and Frenkel [in Proceedings of the International School of Physics "Enrico Fermi", Course LXXV (North-Holland Publishing Company, Amsterdam, 1980)] to the integral of correlation, we are able to provide tight theoretical bounds for the error in the estimate of the transport coefficient. To demonstrate the performance of the method, four test cases of increasing computational cost and complexity are presented: the viscosity of Ar and water, and the thermal conductivity of Si and GaN. In addition to producing accurate estimates of the transport coefficients for these materials, this work demonstrates precise agreement of the computed variances in the estimates of the correlation and the transport coefficient with the extended theory based on the assumption that fluctuations follow a Gaussian process. The proposed algorithm in conjunction with the extended theory enables the calculation of transport coefficients with the Green-Kubo method accurately and efficiently.
Ralston, J.; Ip, K.L.; Li, Y.S.; Li, C.W.
1993-12-31
Extreme wave conditions offshore of Hong Kong, which are predominantly caused by the passage of typhoons, were predicted by means of a third-generation discrete-spectral wave model. For each three-year period, the typhoon that affected Hong Kong most severely was selected, and its wind field was generated by a typhoon wind model. A total of 14 typhoons were chosen for simulation to cover a period of 42 years. An extreme value analysis was then performed for the computed maximum significant wave heights.
NASA Astrophysics Data System (ADS)
Shankar, Praveen
The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network
Franca, A.S.; Haghighi, K.
1996-06-01
This is the second of two articles concerning error estimation and adaptive refinement techniques applied to convective heat transfer problems. In the first article (Part 1), the development of the proposed methodology was presented. This article (Part 2) concerns the validation of the formulation. Examples dealing with heat and momentum transfer were used to verify the efficiency and accuracy of this technique. Applications include sterilization of food products and pasteurization of liquids contained in bottles. The desired accuracy level was always attained. Refined meshes agreed with the physical aspects of the problems. Results show significant improvements when compared with the conventional finite element approach.
An adaptive segment method for smoothing lidar signal based on noise estimation
NASA Astrophysics Data System (ADS)
Wang, Yuzhao; Luo, Pingping
2014-10-01
An adaptive segmentation smoothing method (ASSM) is introduced in the paper to smooth the signal and suppress the noise. In the ASSM, the noise is defined as the 3σ of the background signal. An integer number N is defined for finding the changing positions in the signal curve. If the difference of adjacent two points is greater than 3Nσ, the position is recorded as an end point of the smoothing segment. All the end points detected as above are recorded and the curves between them will be smoothed separately. In the traditional method, the end points of the smoothing windows in the signals are fixed. The ASSM creates changing end points in different signals and the smoothing windows could be set adaptively. The windows are always set as the half of the segmentations and then the average smoothing method will be applied in the segmentations. The Iterative process is required for reducing the end-point aberration effect in the average smoothing method and two or three times are enough. In ASSM, the signals are smoothed in the spacial area nor frequent area, that means the frequent disturbance will be avoided. A lidar echo was simulated in the experimental work. The echo was supposed to be created by a space-born lidar (e.g. CALIOP). And white Gaussian noise was added to the echo to act as the random noise resulted from environment and the detector. The novel method, ASSM, was applied to the noisy echo to filter the noise. In the test, N was set to 3 and the Iteration time is two. The results show that, the signal could be smoothed adaptively by the ASSM, but the N and the Iteration time might be optimized when the ASSM is applied in a different lidar.
NASA Astrophysics Data System (ADS)
Estrada, Antonio; Efimov, Denis; Perruquetti, Wilfrid
2016-09-01
The present work focuses on the problem of velocity and position estimation. A solution is presented for a class of oscillating systems in which position, velocity and acceleration are zero mean signals. The proposed scheme considers that the dynamic model of the system is unknown. Only noisy acceleration measurements, that may be contaminated by zero mean noise and constant bias, are considered to be available. The proposal uses the periodic nature of the signals obtaining finite-time estimations while tackling integration drift accumulation.
NASA Astrophysics Data System (ADS)
Upadhyaya, S. A.; Ramsankaran, R.
2015-12-01
A new simple geostationary satellite based hybrid rainfall estimation algorithm called Modified-INSAT Multi-Spectral Rainfall Algorithm (M-IMSRA) has been developed and evaluated in the present study. Mainly, the following two questions have been addressed and accordingly the algorithm has been developed and evaluated: Can simple geostationary satellite based SRE's perform equivalent to merged techniques which uses all the available satellite datasets? If so/not so, then how best it can perform? Whether SRE's perform differently over different climate regions? Whether incorporating topography in SRE will improve the performance equally over all climate regions? M-IMSRA incorporates topographic information in the IMSRA (INSAT Multi-Spectral Rainfall Algorithm) algorithm using 20 different variables extracted from Digital Elevation Model by using Least Absolute Shrinkage and Selection Operator (LASSO) technique. The results show that the simple algorithms like M-IMSRA can perform similar to the other highly computationally expensive merged algorithms like TRMM 3B42 and TRMM 3B42-RT over some climatic regions of India. It has been observed that, by incorporating static topographic information the estimates over orographic regions of India like Western Ghats and North-East India has significantly improved with only reduction in the additive bias over other regions. Relative performance of the tested satellite rainfall estimates like TRMM 3B42, TRMM 3B42-RT and M-IMSRA are completely different over different climatic regions, with better performance over moderate rainfall climate regions and relatively poor performance over low and high rainfall climate regions. The obtained results highlight that only one algorithm with same input variables cannot produce better rainfall estimates over all the climatic regions where the driving variables for each region will be different. Therefore, the imminent development of SRE's must give attention to this fact and consider this to
Kassianov, Evgueni I.; Barnard, James C.; Flynn, Connor J.; Riihimaki, Laura D.; Marinovici, Maria C.
2015-10-15
Areal-averaged albedos are particularly difficult to measure in coastal regions, because the surface is not homogenous, consisting of a sharp demarcation between land and water. With this difficulty in mind, we evaluate a simple retrieval of areal-averaged surface albedo using ground-based measurements of atmospheric transmission alone under fully overcast conditions. To illustrate the performance of our retrieval, we find the areal-averaged albedo using measurements from the Multi-Filter Rotating Shadowband Radiometer (MFRSR) at five wavelengths (415, 500, 615, 673, and 870 nm). These MFRSR data are collected at a coastal site in Graciosa Island, Azores supported by the U.S. Department of Energy’s (DOE’s) Atmospheric Radiation Measurement (ARM) Program. The areal-averaged albedos obtained from the MFRSR are compared with collocated and coincident Moderate Resolution Imaging Spectroradiometer (MODIS) white-sky albedo at four nominal wavelengths (470, 560, 670 and 860 nm). These comparisons are made during a 19-month period (June 2009 - December 2010). We also calculate composite-based spectral values of surface albedo by a weighted-average approach using estimated fractions of major surface types observed in an area surrounding this coastal site. Taken as a whole, these three methods of finding albedo show spectral and temporal similarities, and suggest that our simple, transmission-based technique holds promise, but with estimated errors of about ±0.03. Additional work is needed to reduce this uncertainty in areas with inhomogeneous surfaces.
NASA Astrophysics Data System (ADS)
Zhang, Min; Gong, Zhaoning; Zhao, Wenji; Pu, Ruiliang; Liu, Ke
2016-01-01
Mapping vegetation abundance by using remote sensing data is an efficient means for detecting changes of an eco-environment. With Landsat-8 operational land imager (OLI) imagery acquired on July 31, 2013, both linear spectral mixture analysis (LSMA) and multinomial logit model (MNLM) methods were applied to estimate and assess the vegetation abundance in the Wild Duck Lake Wetland in Beijing, China. To improve mapping vegetation abundance and increase the number of endmembers in spectral mixture analysis, normalized difference vegetation index was extracted from OLI imagery along with the seven reflective bands of OLI data for estimating the vegetation abundance. Five endmembers were selected, which include terrestrial plants, aquatic plants, bare soil, high albedo, and low albedo. The vegetation abundance mapping results from Landsat OLI data were finally evaluated by utilizing a WorldView-2 multispectral imagery. Similar spatial patterns of vegetation abundance produced by both fully constrained LSMA algorithm and MNLM methods were observed: higher vegetation abundance levels were distributed in agricultural and riparian areas while lower levels in urban/built-up areas. The experimental results also indicate that the MNLM model outperformed the LSMA algorithm with smaller root mean square error (0.0152 versus 0.0252) and higher coefficient of determination (0.7856 versus 0.7214) as the MNLM model could handle the nonlinear reflection phenomenon better than the LSMA with mixed pixels.
NASA Astrophysics Data System (ADS)
Kassianov, Evgueni; Barnard, James; Flynn, Connor; Riihimaki, Laura; Marinovici, Cristina
2015-10-01
Areal-averaged albedos are particularly difficult to measure in coastal regions, because the surface is not homogenous, consisting of a sharp demarcation between land and water. With this difficulty in mind, we evaluate a simple retrieval of areal-averaged surface albedo using ground-based measurements of atmospheric transmission alone under fully overcast conditions. To illustrate the performance of our retrieval, we find the areal-averaged albedo using measurements from the Multi-Filter Rotating Shadowband Radiometer (MFRSR) at five wavelengths (415, 500, 615, 673, and 870 nm). These MFRSR data are collected at a coastal site in Graciosa Island, Azores supported by the U.S. Department of Energy's (DOE's) Atmospheric Radiation Measurement (ARM) Program. The areal-averaged albedos obtained from the MFRSR are compared with collocated and coincident Moderate Resolution Imaging Spectroradiometer (MODIS) whitesky albedo at four nominal wavelengths (470, 560, 670 and 860 nm). These comparisons are made during a 19-month period (June 2009 - December 2010). We also calculate composite-based spectral values of surface albedo by a weighted-average approach using estimated fractions of major surface types observed in an area surrounding this coastal site. Taken as a whole, these three methods of finding albedo show spectral and temporal similarities, and suggest that our simple, transmission-based technique holds promise, but with estimated errors of about ±0.03. Additional work is needed to reduce this uncertainty in areas with inhomogeneous surfaces.
NASA Astrophysics Data System (ADS)
Wang, Yanjie; Liao, Qinhong; Yang, Guijun; Feng, Haikuan; Yang, Xiaodong; Yue, Jibo
2016-06-01
In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn't show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.
On the estimation algorithm used in adaptive performance optimization of turbofan engines
NASA Technical Reports Server (NTRS)
Espana, Martin D.; Gilyard, Glenn B.
1993-01-01
The performance seeking control algorithm is designed to continuously optimize the performance of propulsion systems. The performance seeking control algorithm uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated engine deviation parameters may not reflect the engine's actual off-nominal condition. This factor has a necessary impact on the overall performance seeking control scheme exacerbated by the open-loop character of the algorithm. The effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the performance seeking control algorithm to an F100 engine. An equivalence relation between the biases and engine deviation parameters stems from an observability study; therefore, it is undecided whether the estimated engine deviation parameters represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.
A Comparison of IRT Proficiency Estimation Methods under Adaptive Multistage Testing
ERIC Educational Resources Information Center
Kim, Sooyeon; Moses, Tim; Yoo, Hanwook
2015-01-01
This inquiry is an investigation of item response theory (IRT) proficiency estimators' accuracy under multistage testing (MST). We chose a two-stage MST design that includes four modules (one at Stage 1, three at Stage 2) and three difficulty paths (low, middle, high). We assembled various two-stage MST panels (i.e., forms) by manipulating two…
NASA Technical Reports Server (NTRS)
Anding, D. C.
1975-01-01
The feasibility is demonstrated of a procedure for the remote measurement of sea-surface temperature which inherently corrects for the effect of the intervening atmosphere without recourse to climatological data. The procedure relies upon the near-linear differential absorption properties of the infrared window region between 10 and 13 micrometers and requires radiometric measurements in a minimum of two spectral intervals within the infrared window which have a significant difference in absorption coefficient. The procedure was applied to Nimbus 4 infrared interferometer spectrometer (IRIS) data and to Skylab EREP S191 spectrometer data, and it is demonstrated that atmospheric effects on the observed brightness temperature can be reduced to less than 1.0 Kelvin.
NASA Technical Reports Server (NTRS)
Melick, H. C., Jr.; Ybarra, A. H.; Bencze, D. P.
1975-01-01
An inexpensive method is developed to determine the extreme values of instantaneous inlet distortion. This method also provides insight into the basic mechanics of unsteady inlet flow and the associated engine reaction. The analysis is based on fundamental fluid dynamics and statistical methods to provide an understanding of the turbulent inlet flow and quantitatively relate the rms level and power spectral density (PSD) function of the measured time variant total pressure fluctuations to the strength and size of the low pressure regions. The most probable extreme value of the instantaneous distortion is then synthesized from this information in conjunction with the steady state distortion. Results of the analysis show the extreme values to be dependent upon the steady state distortion, the measured turbulence rms level and PSD function, the time on point, and the engine response characteristics. Analytical projections of instantaneous distortion are presented and compared with data obtained by a conventional, highly time correlated, 40 probe instantaneous pressure measurement system.
NASA Technical Reports Server (NTRS)
Green, Robert O.
2001-01-01
Imaging spectroscopy offers a framework based in physics and chemistry for scientific investigation of a wide range of phenomena of interest in the Earth environment. In the scientific discipline of volcanology knowledge of lava temperature and distribution at the surface provides insight into the volcano status and subsurface processes. A remote sensing strategy to measure surface lava temperatures and distribution would support volcanology research. Hot targets such as molten lava emit spectral radiance as a function of temperature. A figure shows a series of Planck functions calculated radiance spectra for hot targets at different temperatures. A maximum Lambertian solar reflected radiance spectrum is shown as well. While similar in form, each hot target spectrum has a unique spectral shape and is distinct from the solar reflected radiance spectrum. Based on this temperature-dependent signature, imaging spectroscopy provides an innovative approach for the remote-sensing-based measurement of lava temperature. A natural site for investigation of the measurement of lava temperature is the Big Island of Hawaii where molten lava from the Kilauea vent is present at the surface. In the past, Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data sets have been used for the analysis of hot volcanic targets and hot burning fires. The research presented here builds upon and extends this earlier work. The year 2000 Hawaii AVIRIS data set has been analyzed to derive lava temperatures taking into account factors of fractional fill, solar reflected radiance, and atmospheric attenuation of the surface emitted radiance. The measurements, analyses, and current results for this research are presented here.
NASA Astrophysics Data System (ADS)
Zha, Guofeng; Wang, Hongqiang; Yang, Zhaocheng; Cheng, Yongqiang; Qin, Yuliang
2016-04-01
As a complementary imaging technology, coincidence imaging radar (CIR) achieves high resolution for stationary or low-speed targets under the assumption of ignoring the influence of the original position mismatching. As to high-speed moving targets moving from the original imaging cell to other imaging cells during imaging, it is inaccurate to reconstruct the target using the previous imaging plane. We focus on the recovery problem for high-speed moving targets in the CIR system based on the intrapulse frequency random modulation signal in a single pulse. The effects induced by the motion on the imaging performance are analyzed. Because the basis matrix in the CIR imaging equation is determined by the unknown velocity parameter of the moving target, both the target images and basis matrix should be estimated jointly. We propose an adaptive joint parametric estimation recovery algorithm based on the Tikhonov regularization method to update the target velocity and basis matrix adaptively and recover the target images synchronously. Finally, the target velocity and target images are obtained in an iterative manner. Simulation results are presented to demonstrate the efficiency of the proposed algorithm.
Insect-Inspired Self-Motion Estimation with Dense Flow Fields—An Adaptive Matched Filter Approach
Strübbe, Simon; Stürzl, Wolfgang; Egelhaaf, Martin
2015-01-01
The control of self-motion is a basic, but complex task for both technical and biological systems. Various algorithms have been proposed that allow the estimation of self-motion from the optic flow on the eyes. We show that two apparently very different approaches to solve this task, one technically and one biologically inspired, can be transformed into each other under certain conditions. One estimator of self-motion is based on a matched filter approach; it has been developed to describe the function of motion sensitive cells in the fly brain. The other estimator, the Koenderink and van Doorn (KvD) algorithm, was derived analytically with a technical background. If the distances to the objects in the environment can be assumed to be known, the two estimators are linear and equivalent, but are expressed in different mathematical forms. However, for most situations it is unrealistic to assume that the distances are known. Therefore, the depth structure of the environment needs to be determined in parallel to the self-motion parameters and leads to a non-linear problem. It is shown that the standard least mean square approach that is used by the KvD algorithm leads to a biased estimator. We derive a modification of this algorithm in order to remove the bias and demonstrate its improved performance by means of numerical simulations. For self-motion estimation it is beneficial to have a spherical visual field, similar to many flying insects. We show that in this case the representation of the depth structure of the environment derived from the optic flow can be simplified. Based on this result, we develop an adaptive matched filter approach for systems with a nearly spherical visual field. Then only eight parameters about the environment have to be memorized and updated during self-motion. PMID:26308839
Insect-Inspired Self-Motion Estimation with Dense Flow Fields--An Adaptive Matched Filter Approach.
Strübbe, Simon; Stürzl, Wolfgang; Egelhaaf, Martin
2015-01-01
The control of self-motion is a basic, but complex task for both technical and biological systems. Various algorithms have been proposed that allow the estimation of self-motion from the optic flow on the eyes. We show that two apparently very different approaches to solve this task, one technically and one biologically inspired, can be transformed into each other under certain conditions. One estimator of self-motion is based on a matched filter approach; it has been developed to describe the function of motion sensitive cells in the fly brain. The other estimator, the Koenderink and van Doorn (KvD) algorithm, was derived analytically with a technical background. If the distances to the objects in the environment can be assumed to be known, the two estimators are linear and equivalent, but are expressed in different mathematical forms. However, for most situations it is unrealistic to assume that the distances are known. Therefore, the depth structure of the environment needs to be determined in parallel to the self-motion parameters and leads to a non-linear problem. It is shown that the standard least mean square approach that is used by the KvD algorithm leads to a biased estimator. We derive a modification of this algorithm in order to remove the bias and demonstrate its improved performance by means of numerical simulations. For self-motion estimation it is beneficial to have a spherical visual field, similar to many flying insects. We show that in this case the representation of the depth structure of the environment derived from the optic flow can be simplified. Based on this result, we develop an adaptive matched filter approach for systems with a nearly spherical visual field. Then only eight parameters about the environment have to be memorized and updated during self-motion.
Performance of the JPEG Estimated Spectrum Adaptive Postfilter (JPEG-ESAP) for Low Bit Rates
NASA Technical Reports Server (NTRS)
Linares, Irving (Inventor)
2016-01-01
Frequency-based, pixel-adaptive filtering using the JPEG-ESAP algorithm for low bit rate JPEG formatted color images may allow for more compressed images while maintaining equivalent quality at a smaller file size or bitrate. For RGB, an image is decomposed into three color bands--red, green, and blue. The JPEG-ESAP algorithm is then applied to each band (e.g., once for red, once for green, and once for blue) and the output of each application of the algorithm is rebuilt as a single color image. The ESAP algorithm may be repeatedly applied to MPEG-2 video frames to reduce their bit rate by a factor of 2 or 3, while maintaining equivalent video quality, both perceptually, and objectively, as recorded in the computed PSNR values.
Technology Transfer Automated Retrieval System (TEKTRAN)
Crop nitrogen management is important world-wide, as much for small fields as it is for large operations. Developed as a non-destructive aid for estimating nitrogen content in rice crops, leaf color charts (LCC) are a numbered series of plastic panels that range from yellowgreen to dark green. By vi...
NASA Technical Reports Server (NTRS)
Baxa, Ernest G., Jr.; Lee, Jonggil
1991-01-01
The pulse pair method for spectrum parameter estimation is commonly used in pulse Doppler weather radar signal processing since it is economical to implement and can be shown to be a maximum likelihood estimator. With the use of airborne weather radar for windshear detection, the turbulent weather and strong ground clutter return spectrum differs from that assumed in its derivation, so the performance robustness of the pulse pair technique must be understood. Here, the effect of radar system pulse to pulse phase jitter and signal spectrum skew on the pulse pair algorithm performance is discussed. Phase jitter effect may be significant when the weather return signal to clutter ratio is very low and clutter rejection filtering is attempted. The analysis can be used to develop design specifications for airborne radar system phase stability. It is also shown that the weather return spectrum skew can cause a significant bias in the pulse pair mean windspeed estimates, and that the poly pulse pair algorithm can reduce this bias. It is suggested that use of a spectrum mode estimator may be more appropriate in characterizing the windspeed within a radar range resolution cell for detection of hazardous windspeed gradients.
Todorov, Emanuel
2005-05-01
Optimality principles of biological movement are conceptually appealing and straightforward to formulate. Testing them empirically, however, requires the solution to stochastic optimal control and estimation problems for reasonably realistic models of the motor task and the sensorimotor periphery. Recent studies have highlighted the importance of incorporating biologically plausible noise into such models. Here we extend the linear-quadratic-gaussian framework--currently the only framework where such problems can be solved efficiently--to include control-dependent, state-dependent, and internal noise. Under this extended noise model, we derive a coordinate-descent algorithm guaranteed to converge to a feedback control law and a nonadaptive linear estimator optimal with respect to each other. Numerical simulations indicate that convergence is exponential, local minima do not exist, and the restriction to nonadaptive linear estimators has negligible effects in the control problems of interest. The application of the algorithm is illustrated in the context of reaching movements. A Matlab implementation is available at www.cogsci.ucsd.edu/~todorov.
NASA Astrophysics Data System (ADS)
Dalkilic, Turkan Erbay; Apaydin, Aysen
2009-11-01
In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.
NASA Astrophysics Data System (ADS)
Duan, Beiping; Zheng, Zhoushun; Cao, Wen
2016-08-01
In this paper, we revisit two spectral approximations, including truncated approximation and interpolation for Caputo fractional derivative. The two approaches have been studied to approximate Riemann-Liouville (R-L) fractional derivative by Chen et al. and Zayernouri et al. respectively in their most recent work. For truncated approximation the reconsideration partly arises from the difference between fractional derivative in R-L sense and Caputo sense: Caputo fractional derivative requires higher regularity of the unknown than R-L version. Another reason for the reconsideration is that we distinguish the differential order of the unknown with the index of Jacobi polynomials, which is not presented in the previous work. Also we provide a way to choose the index when facing multi-order problems. By using generalized Hardy's inequality, the gap between the weighted Sobolev space involving Caputo fractional derivative and the classical weighted space is bridged, then the optimal projection error is derived in the non-uniformly Jacobi-weighted Sobolev space and the maximum absolute error is presented as well. For the interpolation, analysis of interpolation error was not given in their work. In this paper we build the interpolation error in non-uniformly Jacobi-weighted Sobolev space by constructing fractional inverse inequality. With combining collocation method, the approximation technique is applied to solve fractional initial-value problems (FIVPs). Numerical examples are also provided to illustrate the effectiveness of this algorithm.
Kropf, Pascal; Shmuel, Amir
2016-07-01
Estimation of current source density (CSD) from the low-frequency part of extracellular electric potential recordings is an unstable linear inverse problem. To make the estimation possible in an experimental setting where recordings are contaminated with noise, it is necessary to stabilize the inversion. Here we present a unified framework for zero- and higher-order singular-value-decomposition (SVD)-based spectral regularization of 1D (linear) CSD estimation from local field potentials. The framework is based on two general approaches commonly employed for solving inverse problems: quadrature and basis function expansion. We first show that both inverse CSD (iCSD) and kernel CSD (kCSD) fall into the category of basis function expansion methods. We then use these general categories to introduce two new estimation methods, quadrature CSD (qCSD), based on discretizing the CSD integral equation with a chosen quadrature rule, and representer CSD (rCSD), an even-determined basis function expansion method that uses the problem's data kernels (representers) as basis functions. To determine the best candidate methods to use in the analysis of experimental data, we compared the different methods on simulations under three regularization schemes (Tikhonov, tSVD, and dSVD), three regularization parameter selection methods (NCP, L-curve, and GCV), and seven different a priori spatial smoothness constraints on the CSD distribution. This resulted in a comparison of 531 estimation schemes. We evaluated the estimation schemes according to their source reconstruction accuracy by testing them using different simulated noise levels, lateral source diameters, and CSD depth profiles. We found that ranking schemes according to the average error over all tested conditions results in a reproducible ranking, where the top schemes are found to perform well in the majority of tested conditions. However, there is no single best estimation scheme that outperforms all others under all tested
Kropf, Pascal; Shmuel, Amir
2016-07-01
Estimation of current source density (CSD) from the low-frequency part of extracellular electric potential recordings is an unstable linear inverse problem. To make the estimation possible in an experimental setting where recordings are contaminated with noise, it is necessary to stabilize the inversion. Here we present a unified framework for zero- and higher-order singular-value-decomposition (SVD)-based spectral regularization of 1D (linear) CSD estimation from local field potentials. The framework is based on two general approaches commonly employed for solving inverse problems: quadrature and basis function expansion. We first show that both inverse CSD (iCSD) and kernel CSD (kCSD) fall into the category of basis function expansion methods. We then use these general categories to introduce two new estimation methods, quadrature CSD (qCSD), based on discretizing the CSD integral equation with a chosen quadrature rule, and representer CSD (rCSD), an even-determined basis function expansion method that uses the problem's data kernels (representers) as basis functions. To determine the best candidate methods to use in the analysis of experimental data, we compared the different methods on simulations under three regularization schemes (Tikhonov, tSVD, and dSVD), three regularization parameter selection methods (NCP, L-curve, and GCV), and seven different a priori spatial smoothness constraints on the CSD distribution. This resulted in a comparison of 531 estimation schemes. We evaluated the estimation schemes according to their source reconstruction accuracy by testing them using different simulated noise levels, lateral source diameters, and CSD depth profiles. We found that ranking schemes according to the average error over all tested conditions results in a reproducible ranking, where the top schemes are found to perform well in the majority of tested conditions. However, there is no single best estimation scheme that outperforms all others under all tested
Adaptive Partial Response Maximum Likelihood Detection with Tilt Estimation Using Sync Pattern
NASA Astrophysics Data System (ADS)
Lee, Kyusuk; Lee, Joohyun; Lee, Jaejin
2006-02-01
We propose an improved detection method that concurrently adjusts the coefficients of equalizer and reference branch values in Viterbi detector. For the estimation of asymmetric channel characteristics, we exploit sync patterns in each data frame. Because of using the read-only memory (ROM) table to renew the coefficients of equalizer and reference values of branches, the complexity of the hardware is reduced. The proposed partial response maximum likelihood (PRML) detector has been designed and verified by VerilogHDL and synthesized by Synopsys Design Compiler with Hynix 0.35 μm standard cell library.
Lipnikov, Konstantin; Agouzal, Abdellatif; Vassilevski, Yuri
2009-01-01
We present a new technology for generating meshes minimizing the interpolation and discretization errors or their gradients. The key element of this methodology is construction of a space metric from edge-based error estimates. For a mesh with N{sub h} triangles, the error is proportional to N{sub h}{sup -1} and the gradient of error is proportional to N{sub h}{sup -1/2} which are optimal asymptotics. The methodology is verified with numerical experiments.
An adaptive approach to computing the spectrum and mean frequency of Doppler signals.
Herment, A; Giovannelli, J F
1995-01-01
Modern ultrasound Doppler systems are facing the problem of processing increasingly shorter data sets. Spectral analysis of the strongly nonstationary Doppler signal needs to shorten the analysis window while maintaining a low variance and high resolution spectrum. Color flow imaging requires estimation of the Doppler mean frequency from even shorter Doppler data sets to obtain both a high frame rate and high spatial resolution. We reconsider these two estimation problems in light of adaptive methods. A regularized parametric method for spectral analysis as well as an adapted mean frequency estimator are developed. The choice of the adaptive criterion is then addressed and adaptive spectral and mean frequency estimators are developed to minimize the mean square error on estimation in the presence of noise. Two suboptimal spectral and mean-frequency estimators are then derived for real-time applications. Finally, their performance is compared to that of both the FFT based periodogram and the AR parametric spectral analysis for the spectral estimator, and, to both the correlation angle and the Kristoffersen's [8] estimators for the mean frequency estimator using Doppler data recorded in vitro. PMID:7638930
Goto, Ryosuke; Kumakura, Hiroo
2013-05-01
In this study we compared the power arm lengths and mechanical advantages attributed to 12 lower leg muscles across three prosimian species. The origins and insertions of the lower leg muscles in Garnett's galago, the ring-tailed lemur, and the slow loris were quantified and correlated with positional behaviour. The ankle joint of the galago has a speed-oriented mechanical system, in contrast to that of the slow loris, which exhibits more power-oriented mechanics. The lemur ankle joint exhibited intermediate power arm lengths and an intermediate mechanical advantage relative to the other primates. This result suggests that the mechanical differences in the ankle between the galago and the lemur, taxa that exhibit similar locomotory repertoires, reflect a difference in the kinematics and kinetics of leaping (i.e. generalised vs. specialised leapers). In contrast to leaping primates, lorises have developed a more power-oriented mechanical system as a foot adaptation for positional behaviours such as bridging or cantilevering in their arboreal habitat. PMID:23489408
Goto, Ryosuke; Kumakura, Hiroo
2013-05-01
In this study we compared the power arm lengths and mechanical advantages attributed to 12 lower leg muscles across three prosimian species. The origins and insertions of the lower leg muscles in Garnett's galago, the ring-tailed lemur, and the slow loris were quantified and correlated with positional behaviour. The ankle joint of the galago has a speed-oriented mechanical system, in contrast to that of the slow loris, which exhibits more power-oriented mechanics. The lemur ankle joint exhibited intermediate power arm lengths and an intermediate mechanical advantage relative to the other primates. This result suggests that the mechanical differences in the ankle between the galago and the lemur, taxa that exhibit similar locomotory repertoires, reflect a difference in the kinematics and kinetics of leaping (i.e. generalised vs. specialised leapers). In contrast to leaping primates, lorises have developed a more power-oriented mechanical system as a foot adaptation for positional behaviours such as bridging or cantilevering in their arboreal habitat.
NASA Astrophysics Data System (ADS)
Dufréchou, Grégory; Granjean, Gilles; Bourguignon, Anne
2014-05-01
Swelling soils contain clay minerals that change volume with water content and cause extensive and expensive damage on infrastructures. Presence of clay minerals is traditionally a good estimator of soils swelling and shrinking behavior. Montmorillonite (i.e. smectite group), illite, kaolinite are the most common minerals in soils and are usually associated to high, moderate, and low swelling potential when they are present in significant amount. Characterization of swelling potential and identification of clay minerals of soils using conventional analysis are slow, expensive, and does not permit integrated measurements. SWIR (1100-2500 nm) spectral domain are characterized by significant spectral absorption bands related to clay content that can be used