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)
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.
Application of multivariate autoregressive spectrum estimation to ULF waves
NASA Technical Reports Server (NTRS)
Ioannidis, G. A.
1975-01-01
The estimation of the power spectrum of a time series by fitting a finite autoregressive model to the data has recently found widespread application in the physical sciences. The extension of this method to the analysis of vector time series is presented here through its application to ULF waves observed in the magnetosphere by the ATS 6 synchronous satellite. Autoregressive spectral estimates of the power and cross-power spectra of these waves are computed with computer programs developed by the author and are compared with the corresponding Blackman-Tukey spectral estimates. The resulting spectral density matrices are then analyzed to determine the direction of propagation and polarization of the observed waves.
Chen, L. Leon; Madhavan, Radhika; Rapoport, Benjamin I.; Anderson, William S.
2012-01-01
Neural oscillations are important features in a working central nervous system, facilitating efficient communication across large networks of neurons. They are implicated in a diverse range of processes such as synchronization and synaptic plasticity, and can be seen in a variety of cognitive processes. For example, hippocampal theta oscillations are thought to be a crucial component of memory encoding and retrieval. To better study the role of these oscillations in various cognitive processes, and to be able to build clinical applications around them, accurate and precise estimations of the instantaneous frequency and phase are required. Here, we present methodology based on autoregressive modeling to accomplish this in real time. This allows the targeting of stimulation to a specific phase of a detected oscillation. We first assess performance of the algorithm on two signals where the exact phase and frequency are known. Then, using intracranial EEG recorded from two patients performing a Sternberg memory task, we characterize our algorithm’s phase-locking performance on physiologic theta oscillations: optimizing algorithm parameters on the first patient using a genetic algorithm, we carried out cross-validation procedures on subsequent trials and electrodes within the same patient, as well as on data recorded from the second patient. PMID:21292589
MAXIMUM LIKELIHOOD ESTIMATION FOR PERIODIC AUTOREGRESSIVE MOVING AVERAGE MODELS.
Vecchia, A.V.
1985-01-01
A useful class of models for seasonal time series that cannot be filtered or standardized to achieve second-order stationarity is that of periodic autoregressive moving average (PARMA) models, which are extensions of ARMA models that allow periodic (seasonal) parameters. An approximation to the exact likelihood for Gaussian PARMA processes is developed, and a straightforward algorithm for its maximization is presented. The algorithm is tested on several periodic ARMA(1, 1) models through simulation studies and is compared to moment estimation via the seasonal Yule-Walker equations. Applicability of the technique is demonstrated through an analysis of a seasonal stream-flow series from the Rio Caroni River in Venezuela.
NASA Astrophysics Data System (ADS)
Tary, J. B.; Herrera, R. H.; van der Baan, M.
2014-01-01
Recent studies show that the frequency content of continuous passive recordings contains useful information for the study of hydraulic fracturing experiments as well as longstanding applications in volcano and global seismology. The short-time Fourier transform (STFT) is usually used to obtain the time-frequency representation of a seismic trace. Yet, this transform has two main disadvantages, namely its fixed time-frequency resolution and spectral leakage. Here, we describe two methods based on autoregressive (AR) models: the short-time autoregressive method (ST-AR) and the Kalman smoother (KS). These two methods allow for the AR coefficients to vary over time in order to follow time-varying frequency contents. The outcome of AR methods depends mainly on the number of AR coefficients. We use a robust approach to estimate the optimum order of the AR methods that best matches the spectral comparison between Fourier and AR spectra. Comparing the outcomes of the three methods on a synthetic signal, a long-period volcanic event, and microseismic data, we show that the STFT and both AR methods are able to track fast changes in frequency content. The STFT provides reasonable results even for noisy data using a simple and effective algorithm. The coefficients of the AR filter are defined at all time in the case of the KS. However, its better time resolution is slightly offset by a lower frequency resolution and its computational complexity. The ST-AR has a high spectral resolution and the lowest sensitivity to background noises, facilitating the identification of the various frequency components. The estimated AR coefficients can also be used to extract parts of the signal. The study of long-term phenomena, such as resonance frequencies, or transient events, such as long-period events, could help to gain further insight on reservoir deformation during hydraulic fracturing experiments as well as global or volcano seismological signals.
Least squares estimation of Generalized Space Time AutoRegressive (GSTAR) model and its properties
NASA Astrophysics Data System (ADS)
Ruchjana, Budi Nurani; Borovkova, Svetlana A.; Lopuhaa, H. P.
2012-05-01
In this paper we studied a least squares estimation parameters of the Generalized Space Time AutoRegressive (GSTAR) model and its properties, especially in consistency and asymptotic normality. We use R software to estimate the GSTAR parameter and apply the model toward real phenomena of data, such as an oil production data at volcanic layer.
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. PMID:25571014
Spectral estimators in elastography.
Konofagou, E E; Varghese, T; Ophir, J
2000-03-01
Like velocity, strain induces a time delay and a time scaling to the received signal. Elastography typically uses time delay techniques to indirectly (i.e. via the displacement estimate) measure tissue strain induced by an applied compression, and considers time scaling as a source of distortion. More recently, we have shown that the time scaling factor can also be spectrally estimated and used as a direct measure of strain. Strain causes a Doppler-like frequency shift and a change in bandwidth of the bandpass power spectrum of the echo signal. Two frequency shift strain estimators are described that have been proven to be more robust but less precise when compared to time delay estimators, both in simulations and experiments. The increased robustness is due to the insensitivity of the spectral techniques to phase decorrelation noise. In this paper we discuss and compare the theoretical and experimental findings obtained with traditional time delay estimators and with the newly proposed spectral methods. PMID:10829698
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 Astrophysics Data System (ADS)
Yi, Guo-Sheng; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Han, Chun-Xiao
2013-02-01
To investigate whether and how manual acupuncture (MA) modulates brain activities, we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph (EEG) signals in healthy subjects. We adopt the autoregressive (AR) Burg method to estimate the power spectrum of EEG signals and analyze the relative powers in delta (0 Hz-4 Hz), theta (4 Hz-8 Hz), alpha (8 Hz-13 Hz), and beta (13 Hz-30 Hz) bands. Our results show that MA at ST36 can significantly increase the EEG slow wave relative power (delta band) and reduce the fast wave relative powers (alpha and beta bands), while there are no statistical differences in theta band relative power between different acupuncture states. In order to quantify the ratio of slow to fast wave EEG activity, we compute the power ratio index. It is found that the MA can significantly increase the power ratio index, especially in frontal and central lobes. All the results highlight the modulation of brain activities with MA and may provide potential help for the clinical use of acupuncture. The proposed quantitative method of acupuncture signals may be further used to make MA more standardized.
Ueda, Masashi; Tomobe, Katsuma; Setoguchi, Keiichi; Endou, Akira
2002-02-15
The response of a sensor depends on its operating conditions, and thus it is desirable to develop an in-service method for response time estimation. The applicability of the autoregressive (AR) model for this purpose was examined in the case of the fuel subassembly outlet coolant thermocouples and the primary circuit electromagnetic flowmeter (EMF) of Monju, the prototype fast breeder reactor in Japan.The use of an AR model with exogenous input (ARX model) is possible when the physical variable to be sensed can be observed by an alternative means with a faster response time than that of the sensor in question. In the case of the subassembly outlet thermocouple, the temperature output from an eddy-current sensor, during pseudorandom reactor power variation, served as the exogenous input.In respect to the thermocouple response, AR and ARX modeling were shown to be applicable, and the transient responses thus derived agreed well with each other and with the results measured by means of a step change in sodium temperature. However, the primary circuit EMF response time, estimated using the AR model, decreased with increasing flow rate even when approaching the rated flow, demonstrating that the method was not completely applicable. Nevertheless, it can be concluded that the response is faster than that estimated in the rated condition.
Chon, K H; Cohen, R J; Holstein-Rathlou, N H
1997-01-01
A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre function remain with our algorithm; but, by extending the algorithm to the linear and nonlinear ARMA model, a significant reduction in the number of Laguerre functions can be made, compared with the Volterra-Wiener approach. This translates into a more compact system representation and makes the physiological interpretation of higher order kernels easier. Furthermore, simulation results show better performance of the proposed approach in estimating the system dynamics than LEK in certain cases, and it remains effective in the presence of significant additive measurement noise. PMID:9236985
Correlation analysis for long time series by robustly estimated autoregressive stochastic processes
NASA Astrophysics Data System (ADS)
Schuh, Wolf-Dieter; Brockmann, Jan-Martin; Kargoll, Boris
2015-04-01
Modern sensors and satellite missions deliver huge data sets and long time series of observations. These data sets have to be handled with care because of changing correlations, conspicuous data and possible outliers. Tailored concepts for data selection and robust techniques to estimate the correlation characteristics allow for a better/optimal exploitation of the information of these measurements. In this presentation we give an overview of standard techniques for estimating correlations occurring in long time series in the time domain as well as in the frequency domain. We discuss the pros and cons especially with the focus on the intensified occurrence of conspicuous data and outliers. We present a concept to classify the measurements and isolate conspicuous data. We propose to describe the varying correlation behavior of the measurement series by an autoregressive stochastic process and give some hints how to construct adaptive filters to decorrelate the measurement series and to handle the huge covariance matrices. As study object we use time series from gravity gradient data collected during the GOCE low orbit operation campaign (LOOC). Due to the low orbit these data from 13-Jun-2014 to 21-Oct-2014 have more or less the same potential to recover the Earth gravity field with the same accuracy than all the data from the rest of the entire mission. Therefore these data are extraordinarily valuable but hard to handle, because of conspicuous data due to maneuvers during the orbit lowering phases, overall increase in drag, saturation of ion thrusters and other (currently) unexplained effects.
Estimation of source function and medium response function by autoregressive method
NASA Astrophysics Data System (ADS)
Nair, G. J.
1983-04-01
By modelling seismograms as "low" and "high" order autoregressive (AR) processes, the source function and the medium response function are separated from a single channel seismogram. Akaike's final prediction error is used as a statistic to select the appropriate "low" and "high" AR order of the process. Case studies of synthetic data show that the recovered source and reflectivity functions compare very well with the input functions. Using this method, arrivals of the surface reflected P phases of five explosions from the Soviet region and of two earthquakes from Kamchatka, recorded at Gauribidanur Seismic Array (GBA), India, are identified. Certain features of the source and source region of these events are also inferred.
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.
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
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.
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.
A two dimensional power spectral estimate for some nonstationary processes. M.S. Thesis
NASA Technical Reports Server (NTRS)
Smith, Gregory L.
1989-01-01
A two dimensional estimate for the power spectral density of a nonstationary process is being developed. The estimate will be applied to helicopter noise data which is clearly nonstationary. The acoustic pressure from the isolated main rotor and isolated tail rotor is known to be periodically correlated (PC) and the combined noise from the main and tail rotors is assumed to be correlation autoregressive (CAR). The results of this nonstationary analysis will be compared with the current method of assuming that the data is stationary and analyzing it as such. Another method of analysis is to introduce a random phase shift into the data as shown by Papoulis to produce a time history which can then be accurately modeled as stationary. This method will also be investigated for the helicopter data. A method used to determine the period of a PC process when the period is not know is discussed. The period of a PC process must be known in order to produce an accurate spectral representation for the process. The spectral estimate is developed. The bias and variability of the estimate are also discussed. Finally, the current method for analyzing nonstationary data is compared to that of using a two dimensional spectral representation. In addition, the method of phase shifting the data is examined.
Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models
ERIC Educational Resources Information Center
Price, Larry R.
2012-01-01
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
Statistics of the Spectral Kurtosis Estimator
NASA Astrophysics Data System (ADS)
Nita, Gelu M.; Gary, Dale E.
2010-05-01
Spectral kurtosis (SK) is a statistical approach for detecting and removing radio-frequency interference (RFI) in radio astronomy data. In this article, the statistical properties of the SK estimator are investigated and all moments of its probability density function are analytically determined. These moments provide a means to determine the tail probabilities of the estimator that are essential to defining the thresholds for RFI discrimination. It is shown that, for a number of accumulated spectra M>=24, the first SK standard moments satisfy the conditions required by a Pearson type IV probability density function (pdf), which is shown to accurately reproduce the observed distributions. The cumulative function (CF) of the Pearson type IV is then found, in both analytical and numerical forms, suitable for accurate estimation of the tail probabilities of the SK estimator. This same framework is also shown to be applicable to the related time-domain kurtosis (TDK) estimator, whose pdf corresponds to Pearson type IV when the number of time-domain samples is M>=46. The pdf and CF also are determined for this case.
Alegana, Victor A.; Atkinson, Peter M.; Wright, Jim A.; Kamwi, Richard; Uusiku, Petrina; Katokele, Stark; Snow, Robert W.; Noor, Abdisalan M.
2013-01-01
As malaria transmission declines, it becomes increasingly important to monitor changes in malaria incidence rather than prevalence. Here, a spatio-temporal model was used to identify constituencies with high malaria incidence to guide malaria control. Malaria cases were assembled across all age groups along with several environmental covariates. A Bayesian conditional-autoregressive model was used to model the spatial and temporal variation of incidence after adjusting for test positivity rates and health facility utilisation. Of the 144,744 malaria cases recorded in Namibia in 2009, 134,851 were suspected and 9893 were parasitologically confirmed. The mean annual incidence based on the Bayesian model predictions was 13 cases per 1000 population with the highest incidence predicted for constituencies bordering Angola and Zambia. The smoothed maps of incidence highlight trends in disease incidence. For Namibia, the 2009 maps provide a baseline for monitoring the targets of pre-elimination. PMID:24238079
Alegana, Victor A; Atkinson, Peter M; Wright, Jim A; Kamwi, Richard; Uusiku, Petrina; Katokele, Stark; Snow, Robert W; Noor, Abdisalan M
2013-12-01
As malaria transmission declines, it becomes increasingly important to monitor changes in malaria incidence rather than prevalence. Here, a spatio-temporal model was used to identify constituencies with high malaria incidence to guide malaria control. Malaria cases were assembled across all age groups along with several environmental covariates. A Bayesian conditional-autoregressive model was used to model the spatial and temporal variation of incidence after adjusting for test positivity rates and health facility utilisation. Of the 144,744 malaria cases recorded in Namibia in 2009, 134,851 were suspected and 9893 were parasitologically confirmed. The mean annual incidence based on the Bayesian model predictions was 13 cases per 1000 population with the highest incidence predicted for constituencies bordering Angola and Zambia. The smoothed maps of incidence highlight trends in disease incidence. For Namibia, the 2009 maps provide a baseline for monitoring the targets of pre-elimination. PMID:24238079
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.
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.
Jacob, Benjamin G; Griffith, Daniel A; Muturi, Ephantus J; Caamano, Erick X; Githure, John I; Novak, Robert J
2009-01-01
Background Autoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature. Methods Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial
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.
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.
Reference data set for camera spectral sensitivity estimation.
Darrodi, Maryam Mohammadzadeh; Finlayson, Graham; Goodman, Teresa; Mackiewicz, Michal
2015-03-01
In this article, we describe a spectral sensitivity measurement procedure at the National Physical Laboratory, London, with the aim of obtaining ground truth spectral sensitivity functions for Nikon D5100 and Sigma SD1 Merill cameras. The novelty of our data is that the potential measurement errors are estimated at each wavelength. We determine how well the measured spectral sensitivity functions represent the actual camera sensitivity functions (as a function of wavelength). The second contribution of this paper is to test the performance of various leading sensor estimation techniques implemented from the literature using measured and synthetic data and also evaluate them based on ground truth data for the two cameras. We conclude that the estimation techniques tested are not sufficiently accurate when compared with our measured ground truth data and that there remains significant scope to improve estimation algorithms for spectral estimation. To help in this endeavor, we will make all our data available online for the community. PMID:26366649
Yield estimation from hyperspectral imagery using Spectral Angle Mapper (SAM)
Technology Transfer Automated Retrieval System (TEKTRAN)
Vegetation indices (VIs) derived from remotely sensed imagery are commonly used to estimate crop yields. Spectral angle mapper (SAM) provides an alternative approach to quantifying the spectral differences among all pixels in imagery and therefore has the potential for mapping yield variability. The...
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.
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
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.
Spectral Irradiance Estimation in the Near Infrared
NASA Astrophysics Data System (ADS)
Woolley, R. C.
2003-12-01
Currently, photometric data in the near IR is publicly available in only a few select wavelength bands from sources such as IRAS, 2Mass, and the MSX point source catalog. Efforts have been made to construct composite spectra using existing photometry by Martin Cohen (1995) and Michael P. Egan (1996). Astrometric and photometric databases in various wavelength bands are needed as a tool for improving pointing accuracy and calibration of space-based infrared observations. Methods for extrapolating and interpolating existing photometry in order to produce these databases are not as straightforward as previously expected. Spectral irradiance in the IR region deviates greatly from the standard Planck function and is dominated by absorption and emission in the stellar atmosphere. Since most stars that are bright in the IR are late K and M class stars (often variable), this problem is compounded. In this poster a rough method is put forth to extrapolate irradiances from existing 2Mass magnitudes and B-V temperatures. A more rigorous analysis as well as observational data are needed to verify the results.
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.
Estimation of Wheat Agronomic Parameters using New Spectral Indices
Jin, Xiu-liang; Diao, Wan-ying; Xiao, Chun-hua; Wang, Fang-yong; Chen, Bing; Wang, Ke-ru; Li, Shao-kun
2013-01-01
Crop agronomic parameters (leaf area index (LAI), nitrogen (N) uptake, total chlorophyll (Chl) content ) are very important for the prediction of crop growth. The objective of this experiment was to investigate whether the wheat LAI, N uptake, and total Chl content could be accurately predicted using spectral indices collected at different stages of wheat growth. Firstly, the product of the optimized soil-adjusted vegetation index and wheat biomass dry weight (OSAVI×BDW) were used to estimate LAI, N uptake, and total Chl content; secondly, BDW was replaced by spectral indices to establish new spectral indices (OSAVI×OSAVI, OSAVI×SIPI, OSAVI×CIred edge, OSAVI×CIgreen mode and OSAVI×EVI2); finally, we used the new spectral indices for estimating LAI, N uptake, and total Chl content. The results showed that the new spectral indices could be used to accurately estimate LAI, N uptake, and total Chl content. The highest R2 and the lowest RMSEs were 0.711 and 0.78 (OSAVI×EVI2), 0.785 and 3.98 g/m2 (OSAVI×CIred edge) and 0.846 and 0.65 g/m2 (OSAVI×CIred edge) for LAI, nitrogen uptake and total Chl content, respectively. The new spectral indices performed better than the OSAVI alone, and the problems of a lack of sensitivity at earlier growth stages and saturation at later growth stages, which are typically associated with the OSAVI, were improved. The overall results indicated that this new spectral indices provided the best approximation for the estimation of agronomic indices for all growth stages of wheat. PMID:24023639
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
Spectral estimation on a sphere in geophysics and cosmology
NASA Astrophysics Data System (ADS)
Dahlen, F. A.; Simons, Frederik J.
2008-09-01
We address the problem of estimating the spherical-harmonic power spectrum of a statistically isotropic scalar signal from noise-contaminated data on a region of the unit sphere. Three different methods of spectral estimation are considered: (i) the spherical analogue of the one-dimensional (1-D) periodogram, (ii) the maximum-likelihood method and (iii) a spherical analogue of the 1-D multitaper method. The periodogram exhibits strong spectral leakage, especially for small regions of area A << 4π, and is generally unsuitable for spherical spectral analysis applications, just as it is in 1-D. The maximum-likelihood method is particularly useful in the case of nearly-whole-sphere coverage, A ~ 4π, and has been widely used in cosmology to estimate the spectrum of the cosmic microwave background radiation from spacecraft observations. The spherical multitaper method affords easy control over the fundamental trade-off between spectral resolution and variance, and is easily implemented regardless of the region size, requiring neither non-linear iteration nor large-scale matrix inversion. As a result, the method is ideally suited for most applications in geophysics, geodesy or planetary science, where the objective is to obtain a spatially localized estimate of the spectrum of a signal from noisy data within a pre-selected and typically small region.
Estimating neugebauer primaries for multi-channel spectral printing modeling
NASA Astrophysics Data System (ADS)
Slavuj, Radovan; Coppel, Ludovic G.; Olen, Melissa; Hardeberg, Jon Yngve
2014-02-01
Multichannel printer modeling has been an active area of research in the field of spectral printing. The most commonly used models for characterization of such systems are the spectral Neugebauer (SN) and its extensions. This work addresses issues that can arise during calibration and testing of the SN model when modelling a 7-colorant printer. Since most substrates are limited in their capacity to take in large amount of ink, it is not always possible to print all colorant combinations necessary to determine the Neugebauer primaries (NP). A common solution is to estimate the nonprintable Neugebauer primaries from the single colorant primaries using the Kubelka-Munk (KM) optical model. In this work we test whether a better estimate can be obtained using general radiative transfer theory, which better represents the angular variation of the reflectance from highly absorbing media, and takes surface scattering into account. For this purpose we use the DORT2002 model. We conclude DORT2002 does not offer significant improvements over KM in the estimation of the NPs, but a significant improvement is obtained when using a simple surface scattering model. When the estimated primaries are used as inputs to the SN model instead of measured ones, it is found the SN model performs the same or better in terms of color difference and spectral error. If the mixed measured and estimated primaries are used as inputs to the SN model, it performs better than using either measured or estimated.
Induction machine faults detection using stator current parametric spectral estimation
NASA Astrophysics Data System (ADS)
El Bouchikhi, El Houssin; Choqueuse, Vincent; Benbouzid, Mohamed
2015-02-01
Current spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are required to extract a relevant fault detection criterion. Therefore, this paper proposes a new parametric spectral estimator that fully exploits the faults sensitive frequencies. The proposed technique is based on the maximum likelihood estimator (MLE) and offers high-resolution capabilities. Based on this approach, a fault criterion is derived for detecting several fault types. The proposed technique is assessed using simulation signals, issued from a coupled electromagnetic circuits approach-based simulation tool for mechanical unbalance and electrical asymmetry faults detection. It is afterward validated using experiments on a 0.75-kW induction machine test bed for the particular case of bearing faults.
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.
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.
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.
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.
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.
Seeing red in cyclic stratigraphy: Spectral noise estimation for astrochronology
NASA Astrophysics Data System (ADS)
Meyers, Stephen R.
2012-09-01
Fundamental to the development of astronomical time scales is the recognition of oscillatory variability within stratigraphic data and its evaluation relative to a null "noise" hypothesis. In this study, Monte Carlo simulations are used to investigate the suitability of two commonly used noise hypotheses (the "conventional" and "robust" AR1 approaches), and the results highlight important limitations in both for cyclostratigraphic application. Perhaps most problematic, the robust AR1 method can result in inflated confidence level estimates and excessive clumping of false positives within the low frequency portion of the spectrum, especially when the underlying noise process has a high lag-1 autocorrelation. Given typical cyclostratigraphic records, this technique will often impose "significant" eccentricity band variability, even in the case of pure AR1 noise. An alternative spectral noise estimation method is proposed to overcome these problems, which simultaneously allows for departures from the AR1 assumption, and obtains high statistical power—that is, the ability to accurately identify astronomical signals when they are present in the data. We apply the method to un-tunedδ18O data from Miocene sediments of the Ceara Rise, indicating statistically significant spectral power at frequencies that are consistent with the published orbital interpretation of Weedon et al. (1997). Furthermore, evaluation of the frequency arrangement of the significant spatial bedding periods, using the average spectral misfit method for astrochronologic testing, reveals that the null hypothesis of no orbital influence can be rejected with a high degree of confidence (the 99.8% confidence level).
Convergence of oscillator spectral estimators for counted-frequency measurements.
NASA Technical Reports Server (NTRS)
Tausworthe, R. C.
1972-01-01
A common intermediary connecting frequency-noise calibration or testing of an oscillator to useful applications is the spectral density of the frequency-deviating process. In attempting to turn test data into predicts of performance characteristics, one is naturally led to estimation of statistical values by sample-mean and sample-variance techniques. However, sample means and sample variances themselves are statistical quantities that do not necessarily converge (in the mean-square sense) to actual ensemble-average means and variances, except perhaps for excessively large sample sizes. This is especially true for the flicker noise component of oscillators. This article shows, for the various types of noises found in oscillators, how sample averages converge (or do not converge) to their statistical counterparts. The convergence rate is shown to be the same for all oscillators of a given spectral type.
Efficient spectral estimation for time series with intermittent gaps
NASA Astrophysics Data System (ADS)
Smith, L. T.; Constable, C.
2009-12-01
Data from magnetic satellites like CHAMP, Ørsted, and Swarm can be used to study electromagnetic induction in Earth’s mantle. Time series of internal and external spherical harmonic coefficients (usually those associated with the predominantly dipolar structure of ring current variations) are used to determine Earth’s electromagnetic response as a function of frequency of the external variations. Inversion of this response can yield information about electrical conductivity variations in Earth’s mantle. The inductive response depends on frequency through skin depth, so it is desirable to work with the longest time series possible. Intermittent gaps in available data complicate attempts to estimate the power or cross spectra and thus the electromagnetic response for satellite records. Complete data series are most effectively analyzed using direct multi-taper spectral estimation, either with prolate multitapers that efficiently minimize broadband bias, or with a set designed to minimize local bias. The latter group have frequently been approximated by sine tapers. Intermittent gaps in data may be patched over using custom designed interpolation. We focus on a different approach, using sets of multitapers explicitly designed to accommodate gaps in the data. The optimization problems for the prolate and minimum bias tapers are altered to allow a specific arrangement of data samples, producing a modified eigenvalue-eigenfunction problem. We have shown that the prolate tapers with gaps and the minimum bias tapers with gaps provide higher resolution spectral estimates with less leakage than spectral averaging of data sections bounded by gaps. Current work is focused on producing efficient algorithms for spectral estimation of data series with gaps. A major limitation is the time and memory needed for the solution of large eigenvalue problems used to calculate the tapers for long time series. Fortunately only a limited set of the largest eigenvalues are needed, and
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.
Spectral estimation of temporal series at unequal intervals.
Abraira, V; Ibarz, J M
1986-06-01
Many biological variables present rhythmic oscillations at different frequencies. Most common techniques, which statistically characterize temporal series and permit the study of these rhythms, require equidistant sampling. However, it is not always possible to register at regular intervals many of the variables under study, either because of the nature of the phenomenon which generates them or because of the difficulty in obtaining the samples. This paper proposes a method for spectral estimation by means of fitting to the cosine functions of sampled variables using a nonuniform point process. PMID:3754801
How Accurately Do Spectral Methods Estimate Effective Elastic Thickness?
NASA Astrophysics Data System (ADS)
Perez-Gussinye, M.; Lowry, A. R.; Watts, A. B.; Velicogna, I.
2002-12-01
The effective elastic thickness, Te, is an important parameter that has the potential to provide information on the long-term thermal and mechanical properties of the the lithosphere. Previous studies have estimated Te using both forward and inverse (spectral) methods. While there is generally good agreement between the results obtained using these methods, spectral methods are limited because they depend on the spectral estimator and the window size chosen for analysis. In order to address this problem, we have used a multitaper technique which yields optimal estimates of the bias and variance of the Bouguer coherence function relating topography and gravity anomaly data. The technique has been tested using realistic synthetic topography and gravity. Synthetic data were generated assuming surface and sub-surface (buried) loading of an elastic plate with fractal statistics consistent with real data sets. The cases of uniform and spatially varying Te are examined. The topography and gravity anomaly data consist of 2000x2000 km grids sampled at 8 km interval. The bias in the Te estimate is assessed from the difference between the true Te value and the mean from analyzing 100 overlapping windows within the 2000x2000 km data grids. For the case in which Te is uniform, the bias and variance decrease with window size and increase with increasing true Te value. In the case of a spatially varying Te, however, there is a trade-off between spatial resolution and variance. With increasing window size the variance of the Te estimate decreases, but the spatial changes in Te are smeared out. We find that for a Te distribution consisting of a strong central circular region of Te=50 km (radius 600 km) and progressively smaller Te towards its edges, the 800x800 and 1000x1000 km window gave the best compromise between spatial resolution and variance. Our studies demonstrate that assumed stationarity of the relationship between gravity and topography data yields good results even in
NASA Astrophysics Data System (ADS)
Lopes, Sílvia R. C.; Prass, Taiane S.
2014-05-01
Here we present a theoretical study on the main properties of Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedastic (FIEGARCH) processes. We analyze the conditions for the existence, the invertibility, the stationarity and the ergodicity of these processes. We prove that, if { is a FIEGARCH(p,d,q) process then, under mild conditions, { is an ARFIMA(q,d,0) with correlated innovations, that is, an autoregressive fractionally integrated moving average process. The convergence order for the polynomial coefficients that describes the volatility is presented and results related to the spectral representation and to the covariance structure of both processes { and { are discussed. Expressions for the kurtosis and the asymmetry measures for any stationary FIEGARCH(p,d,q) process are also derived. The h-step ahead forecast for the processes {, { and { are given with their respective mean square error of forecast. The work also presents a Monte Carlo simulation study showing how to generate, estimate and forecast based on six different FIEGARCH models. The forecasting performance of six models belonging to the class of autoregressive conditional heteroskedastic models (namely, ARCH-type models) and radial basis models is compared through an empirical application to Brazilian stock market exchange index.
Spectral estimates of net radiation and soil heat flux
Daughtry, C.S.T.; Kustas, W.P.; Moran, M.S.; Pinter, P. J., Jr.; 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
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.
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.
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.
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.
NASA Astrophysics Data System (ADS)
Lana, X.; Burgueño, A.; Serra, C.; Martínez, M. D.
2015-09-01
Dry spell lengths, DSL, defined as the number of consecutive days with daily rain amounts below a given threshold, may provide relevant information about drought regimes. Taking advantage of a daily pluviometric database covering a great extension of Europe, a detailed analysis of the multifractality of the dry spell regimes is achieved. At the same time, an autoregressive process is applied with the aim of predicting DSL. A set of parameters, namely Hurst exponent, H, estimated from multifractal spectrum, f(α), critical Hölder exponent, α 0, for which f(α) reaches its maximum value, spectral width, W, and spectral asymmetry, B, permits a first clustering of European rain gauges in terms of the complexity of their DSL series. This set of parameters also allows distinguishing between time series describing fine- or smooth-structure of the DSL regime by using the complexity index, CI. Results of previous monofractal analyses also permits establishing comparisons between smooth-structures, relatively low correlation dimensions, notable predictive instability and anti-persistence of DSL for European areas, sometimes submitted to long droughts. Relationships are also found between the CI and the mean absolute deviation, MAD, and the optimum autoregressive order, OAO, of an ARIMA(p,d,0) autoregressive process applied to the DSL series. The detailed analysis of the discrepancies between empiric and predicted DSL underlines the uncertainty over predictability of long DSL, particularly for the Mediterranean region.
Estimation of canopy water content with MODIS spectral index
NASA Astrophysics Data System (ADS)
Jiang, Zuoning; Li, Lin; Ustin, Susan L.
2009-08-01
Canopy water content is an important variable for forestry and agriculture management. This study was aimed at building calibration models to estimate vegetation canopy (VC) equivalent water thickness (EWT) from high temporal resolution and large areal coverage MODIS images. The models were developed for a semi-arid area in Arizona (SMEX04) and the best one was applied to MODIS images covering a forest area in Southern Indiana. EWT derived from hyperspectral data in the process of atmospheric correction was used for calibrating MODIS spectral indices. Tested in this study were four vegetation indices: Normalized Difference Water Index (NDWI), Shortwave Infrared Water Stress Index (SIWSI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI), which were designed based on either water (NDWI and SIWSI) or chlorophyll absorptions (NDVI and EVI). Validating these indices on field measured EWT for the SMEX04 site resulted in R2 correlations of 0.7547, 0.7509, 0.7299 and 0.7547, respectively. According to regression equations, however, EWT estimated using NDWI and SIWSI shows a slope more close to 1 than those using NDVI and EVI when validated with ground measured EWT, thus showing a better prediction ability than the two chlorophyll indices. The SIWSI-EWT model was chosen to apply to a time series of MODIS images covering the Southern Indiana areas and the relationship of EWT derived from these images to precipitation was examined.
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.
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
Stationary and integrated autoregressive neural network processes.
Trapletti, A; Leisch, F; Hornik, K
2000-10-01
We consider autoregressive neural network (AR-NN) processes driven by additive noise and demonstrate that the characteristic roots of the shortcuts-the standard conditions from linear time-series analysis-determine the stochastic behavior of the overall AR-NN process. If all the characteristic roots are outside the unit circle, then the process is ergodic and stationary. If at least one characteristic root lies inside the unit circle, then the process is transient. AR-NN processes with characteristic roots lying on the unit circle exhibit either ergodic, random walk, or transient behavior. We also analyze the class of integrated AR-NN (ARI-NN) processes and show that a standardized ARI-NN process "converges" to a Wiener process. Finally, least-squares estimation (training) of the stationary models and testing for nonstationarity is discussed. The estimators are shown to be consistent, and expressions on the limiting distributions are given. PMID:11032041
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.
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
Estimation of Canopy Foliar Biomass with Spectral Reflectance Measurements
Technology Transfer Automated Retrieval System (TEKTRAN)
Canopy foliar biomass, defined as the product of leaf dry matter content and leaf area index, is an important measurement for global biogeochemical cycles. This study explores the potential for retrieving foliar biomass in green canopies using a spectral index, the Normalized Dry Matter Index (NDMI)...
Assessment of spectral indicies for crop residue cover estimation
Technology Transfer Automated Retrieval System (TEKTRAN)
The quantification of surficial crop residue cover is important for assessing agricultural tillage practices, rangeland health, and brush fire hazards. The Cellulose Absorption Index (CAI) and the Shortwave Infrared Normalized Difference Residue Index (SINDRI) are two spectral indices that have show...
Correlation autoregressive processes with application to helicopter noise
NASA Technical Reports Server (NTRS)
Hardin, J. C.; Miamee, A. G.
1990-01-01
This paper introduces a new class of random processes X(t), the autocorrelations R sub x (t1, t2) of which satisfy a linear relation for all t1 and t2 in some interval of the time axis. Such random processes are denoted as 'correlation-autoregressive'. This class is shown to include the familiar stationary and periodically correlated processes as well as many other, both harmonizable and nonharmonizable, nonstationary processes. When a process is correlation-autoregressive for all times and harmonizable, its two-dimensional power spectral density is shown to take a particularly simple form. The relationship of such processes to the class of stationary processes is examined. In addition, the application of such processes in the analysis of typical helicopter noise signals is described.
USE OF SPECTRAL ANGLE MAPPER (SAM) AND HYPERSPECTRAL IMAGERY FOR YIELD ESTIMATION
Technology Transfer Automated Retrieval System (TEKTRAN)
Vegetation indices (VIs) derived from remotely sensed imagery are commonly used to estimate crop yields. Spectral angle mapper (SAM) provides an alternative approach to quantifying the spectral differences among all pixels in imagery and therefore has the potential for mapping yield variability. The...
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
ESTIMATION OF RESPONSE-SPECTRAL VALUES AS FUNCTIONS OF MAGNITUDE, DISTANCE, AND SITE CONDITIONS.
Joyner, W.B.; Boore, D.M.
1983-01-01
Horizontal pseudo-velocity response was analyzed for twelve shallow earthquakes in western North America. Estimation of response-spectral values was related to magnitude, distance and site conditions. Errors in the methods are analyzed.
Assessment of Spectral Indices for Crop Residue Cover Estimation
Technology Transfer Automated Retrieval System (TEKTRAN)
Agricultural soils are an important terrestrial carbon (C) stock, accounting for a significant portion of global C estimates. Soil tillage method is important in agricultural C sequestration models. Traditional intensive tillage systems greatly disturb the soil and have been shown to deplete the so...
Spectral Reflectance Estimates of Surface Soil Physical and Chemical Properties
Technology Transfer Automated Retrieval System (TEKTRAN)
Optical diffuse reflectance sensing in visible and near-infrared wavelength ranges is one approach to rapidly quantify soil properties for site-specific management. The objectives of this study were (1) to determine the accuracy of the reflectance approach for estimating physical and chemical proper...
Spectral responsivity estimation and noise effect analysis for digital imaging systems
NASA Astrophysics Data System (ADS)
Chang, Gao-Wei; Kuo, Hung-Zen; Tu, Chung-Fan
2004-02-01
The determination of spectral responsivities plays a significant role in analyzing and predicting the performance of digital imaging systems for remote sensing. For example, given the spectral response functions, we can readily obtain the colorimetric data from a camera corresponding to the remote illuminated objects. In this paper, we develop a filter-based optical system to estimate these functions. The design objective of this system is to effectively select a limited amount of spectral (or broadband) filters to characterize the spectral features of color imaging processes, which are contaminated with noise, so that the spectral response functions can be estimated with satisfactory accuracy. In our approach, a theoretical study is first presented to pave the way for this work, and then we propose a filter selection method based on the technique of orthogonal-triangular (QR) decomposition with column pivoting, called QRCP method. This method involves QR computations and a column permutation process, which determines a permutation matrix to conduct the subset (or filter) selection. Experimental results reveal that the proposed technique is truly consistent with the theoretical study on filter selections. As expected, the optical system with the filters selected from the QRCP method is much less sensitive to noise than those with other spectral filters from different selections. It turns out that our approach is an effective way to implement the optical system for estimating spectral responsivities of digital imaging systems.
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.
On Spectral Classification and Astrophysical Parameter Estimation for Galactic Surveys
NASA Astrophysics Data System (ADS)
Re Fiorentin, Paola; Bailer-Jones, Coryn A. L.; Beers, Timothy C.; Zwitter, Tomaž
2008-12-01
We present several strategies that are being developed in order to classify and parameterize individual stars observed by Galactic surveys, and illustrate some results obtained from spectra obtained by the RAdial Velocity Experiment (RAVE) and the Sloan Digital Sky Survey (SDSS/SEGUE). We demonstrate the efficiency of our models for discrete source classification and stellar atmospheric parameter estimation (effective temperature, surface gravity, and metallicity), which use supervised machine learning algorithms along with a principal component analysis front-end compression phase that also enables knowledge discovery.
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. PMID:26915200
Effect of spectral shape on acoustic fatigue life estimates
NASA Technical Reports Server (NTRS)
Miles, R. N.
1992-01-01
Methods for estimating fatigue life due to random loading are briefly reviewed. These methods include a probabilistic approach in which the expected value of the rate of damage accumulation is computed by integrating over the probability density of damaging events and a method which consists of analyzing the response time history to count damaging events. It is noted that it is necessary to employ a time domain approach to perform Rainflow counting, while simple peak counting may be accomplished using the probabilistic method. Data obtained indicate that Rainflow counting produces significantly different fatigue life predictions than other methods that are commonly used in acoustic fatigue predictions. When low-frequency oscillations are present in a signal along with high-frequency components, peak counting will produce substantially shorter fatigue lives than Rainflow counting. It is concluded that Rainflow counting is capable of providing reliable fatigue life predictions for acoustic fatigue studies.
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.
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.
Spectral estimation of made-up skin color under various conditions
NASA Astrophysics Data System (ADS)
Doi, Motonori; Ohtsuki, Rie; Tominaga, Shoji
2006-01-01
A method is proposed for estimating the spectral reflectance of made-up skin color under various conditions including the undesirable colored skin. The color of dark spot is caused by increasing the component of melanin. The reddish skin is caused by the increase of hemoglobin. Our method uses the Kubelka-Munk theory to calculate the surface spectral reflectance human skin. This theory calculates the reflectance and transmittance of the light passing through a turbid medium from the absorption and scattering of the medium. The spectral reflectance of made-up skin is estimated by adjusting parameters of the thickness of the makeup layer. The proposed estimation method is evaluated on an experiment in detail. First, we measure the spectral reflectance of facial skin under the three conditions of normal skin, undesirable skin, and made-up skin. The undesirable skin includes stain, suntan or ruddy skin. The made-up skin means the skin with foundation on the normal skin, the stain, the suntan and the ruddy skin. Second, we estimate the spectral reflectance of made-up skins from the reflectance of bare skins and optical characteristics of foundations. Good coincidence between the estimated reflectance and the direct measurement shows the feasibility of the proposed method.
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.
Spectral Approach to Optimal Estimation of the Global Average Temperature.
NASA Astrophysics Data System (ADS)
Shen, Samuel S. P.; North, Gerald R.; Kim, Kwang-Y.
1994-12-01
Making use of EOF analysis and statistical optimal averaging techniques, the problem of random sampling error in estimating the global average temperature by a network of surface stations has been investigated. The EOF representation makes it unnecessary to use simplified empirical models of the correlation structure of temperature anomalies. If an adjustable weight is assigned to each station according to the criterion of minimum mean-square error, a formula for this error can be derived that consists of a sum of contributions from successive EOF modes. The EOFs were calculated from both observed data and a noise-forced EBM for the problem of one-year and five-year averages. The mean square statistical sampling error depends on the spatial distribution of the stations, length of the averaging interval, and the choice of the weight for each station data stream. Examples used here include four symmetric configurations of 4 × 4, 6 × 4, 9 × 7, and 20 × 10 stations and the Angell-Korshover configuration. Comparisons with the 100-yr U.K. dataset show that correlations for the time series of the global temperature anomaly average between the full dataset and this study's sparse configurations are rather high. For example, the 63-station Angell-Korshover network with uniform weighting explains 92.7% of the total variance, whereas the same network with optimal weighting can lead to 97.8% explained total variance of the U.K. dataset.
Spectral approach to optimal estimation of the global average temperature
Shen, S.S.P.; North, G.R.; Kim, K.Y.
1994-12-01
Making use of EOF analysis and statistical optimal averaging techniques, the problem of random sampling error in estimating the global average temperature by a network of surface stations has been investigated. The EOF representation makes it unnecessary to use simplified empirical models of the correlation structure of temperature anomalies. If an adjustable weight is assigned to each station according to the criterion of minimum mean-square error, a formula for this error can be derived that consists of a sum of contributions from successive EOF modes. The EOFs were calculated from both observed data a noise-forced EBM for the problem of one-year and five-year averages. The mean square statistical sampling error depends on the spatial distribution of the stations, length of the averaging interval, and the choice of the weight for each station data stream. Examples used here include four symmetric configurations of 4 X 4, 5 X 4, 9 X 7, and 20 X 10 stations and the Angell-Korshover configuration. Comparisons with the 100-yr U.K. dataset show that correlations for the time series of the global temperature anomaly average between the full dataset and this study`s sparse configurations are rather high. For example, the 63-station Angell-Korshover network with uniform weighting explains 92.7% of the total variance, whereas the same network with optimal weighting can lead to 97.8% explained total variance of the U.K. dataset. 27 refs., 5 figs., 4 tabs.
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
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.-
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.
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
Time series autoregressive integrated moving average modeling of test-day milk yields of dairy ewes.
Macciotta, N P; Cappio-Borlino, A; Pulina, G
2000-05-01
Monthly test-day milk yields of 1200 dairy Sarda ewes were analyzed by time-series methods. Autocorrelation functions were calculated for lactations within parity classes and altitude of location of flocks. Spectral analysis of the successions of data was developed by Fourier transformation, and different Box-Jenkins autoregressive integrated moving average models were fitted. The separation of deterministic and stochastic components highlighted the autoregressive feature of milk production pattern. The forecasting power of autoregressive integrated moving average models was tested by predicting total milk production for a standardized lactation length of 225 d from only a few test-day records. Results indicated a greater forecasting capacity in comparison with standard methods and suggested further development of time-series analysis for studying lactation curves with more sophisticated methods, such as wavelet decomposition and neural network models. PMID:10821585
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. PMID:27367708
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 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.
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.
On-line estimation of myoelectric signal spectral parameters and nonstationarities detection.
D'Alessio, T; Knaflitz, M; Balestra, G; Paggi, S
1993-09-01
In this communication, we present a method for detecting nonstationarities of random time series with an approximately Gaussian distribution of amplitudes. This method is suitable for real time implementation. Here we report some results obtained by applying them to a time series of spectral parameters of surface myoelectric signals, collected during voluntary isometric contractions of human muscles. Moreover, we describe the computerized system that we used to implement our detector of nonstationarity. This system is based on the TMS 320C25 DSP chip and realizes on-line estimation and display of spectral parameters, as well as detection of their nonstationarities, featuring a sampling frequency up to 20 k samples/s. A friendly user interface, fully menu driven, allows the user to select different options during the system's operation by means of hot keys. The accuracy of the system was tested by comparing its estimates with those of an off-line system, previously characterized, which we took as a reference. The estimates of spectral parameters obtained by means of the two systems were always consistent. The on-line stationarity detector was able to recognize rates of variation of the spectral parameters as small as 1%/s during contractions lasting 10-15 s. This sensitivity makes it suitable for clinical application. The set of results herein presented has been selected to highlight the main characteristics of the system. PMID:8288289
NASA Astrophysics Data System (ADS)
Michaelsen, Kelly E.; Krishnaswamy, Venkataramanan; Pogue, Brian W.; Poplack, Steven P.; Paulsen, Keith D.
2013-03-01
X-ray image pixel intensity and optical scattering are compared for 11 normal subjects to assess the feasibility of using X-ray imaging as a surrogate for optical scattering in NIR spectral tomography. Digital breast tomosynthesis exams, as well as twenty single point reflectance measurements of optical breast scattering are compared for a wide variety of breast sizes and densities to determine if scattering can be accurately predicted based on x-ray attenuation. If implemented, x-ray based scattering estimation will decrease exam time and cost as well as simplify the design of a newly developed integrated near infrared spectral tomography and digital breast tomosynthesis imaging system.
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.
To center or not to center? Investigating inertia with a multilevel autoregressive model
Hamaker, Ellen L.; Grasman, Raoul P. P. P.
2015-01-01
Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model. PMID:25688215
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.
NASA Astrophysics Data System (ADS)
Warren, Russell E.; Vanderbeek, Richard G.
2004-08-01
Detection and estimation of materials in the atmosphere by lidar has heretofore required that the spectral dependence of the relevant cross section coefficients -- backscatter in the case of aerosols and absorptivity for vapors -- be known in advance. While this typically is a reasonable assumption in the case of vapor, the aerosol backscatter coefficients are complicated functions of particle size, shape, and refractive index, and are therefore usually not well characterized a priori. Using incorrect parameters will give biased concentration estimates and impair discrimination ability. This paper describes an approach for estimating both the spectral dependence of the aerosol backscatter and relative concentration range-dependence of a set of materials using multi-wavelength lidar. The approach is based on state-space filtering that applies a Kalman filter in range for concentration, and updates the backscatter spectral estimates through a sequential least-squares algorithm at each time step. The method is illustrated on aerosol-release data of the bio-simulant ovalbumin collected by ECBC during field tests in 2002, as well as synthetic data sets.
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.
[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 Evaluation of the Spectral Centroid Downshift Method for Attenuation Estimation
Samimi, Kayvan; Varghese, Tomy
2015-01-01
Estimation of frequency-dependent ultrasonic attenuation is an important aspect of tissue characterization. Along with other acoustic parameters studied in quantitative ultrasound, the attenuation coefficient can be used to differentiate normal and pathological tissue. The spectral centroid downshift (CDS) method is one the most common frequency-domain approaches applied to this problem. In this study, a statistical analysis of this method’s performance was carried out based on a parametric model of the signal power spectrum in the presence of electronic noise. The parametric model used for the power spectrum of received RF data assumes a Gaussian spectral profile for the transmit pulse, and incorporates effects of attenuation, windowing, and electronic noise. Spectral moments were calculated and used to estimate second-order centroid statistics. A theoretical expression for the variance of a maximum likelihood estimator of attenuation coefficient was derived in terms of the centroid statistics and other model parameters, such as transmit pulse center frequency and bandwidth, RF data window length, SNR, and number of regression points. Theoretically predicted estimation variances were compared with experimentally estimated variances on RF data sets from both computer-simulated and physical tissue-mimicking phantoms. Scan parameter ranges for this study were electronic SNR from 10 to 70 dB, transmit pulse standard deviation from 0.5 to 4.1 MHz, transmit pulse center frequency from 2 to 8 MHz, and data window length from 3 to 17 mm. Acceptable agreement was observed between theoretical predictions and experimentally estimated values with differences smaller than 0.05 dB/cm/MHz across the parameter ranges investigated. This model helps predict the best attenuation estimation variance achievable with the CDS method, in terms of said scan parameters. PMID:25965681
[A spectral unmixing method of estimating main minerals abundance of lunar soils].
Yan, Bo-Kun; Li, Jian-Zhong; Gan, Fu-Ping; Yang, Su-Ming; Wang, Run-Sheng
2012-12-01
Estimating minerals abundance from reflectance spectra is one of the fundamental goals of remote sensing lunar exploration, and the main difficulties are the complicated mixing law of minerals spectrum and spectral features being sensitive to several kinds of factors such as topography, particle size and roughness etc. A method based on spectral unmixing was put forward and tested in the present paper. Before spectra are unmixed the spectral continuum is removed for clarifying and strengthening spectral features. The absorption features and reflectance features (the upward curving parts of spectra between absorption features) are integrated for unmixing to improve the unmixing performance. The Hapke model was used to correct unmixing error due to nonlinear mixing of minerals spectra. Forty three mixed spectra of olivine, clinopyroxene, hypersthene and plagioclase were used to validate the above method. The four minerals abundance was estimated under the conditions of being unaware of endmember spectra used to mix, granularity and chemical composition of minerals. Residual error, abundance error and correlation coefficient between retrieved and true abundance were 5.0 Vol%, 14.4 Vol% and 0.92 respectively. The method and result of this paper could be referred in the lunar minerals mapping of imaging spectrometer data such as M3. PMID:23427563
Software algorithm and hardware design for real-time implementation of new spectral estimator
2014-01-01
Background Real-time spectral analyzers can be difficult to implement for PC computer-based systems because of the potential for high computational cost, and algorithm complexity. In this work a new spectral estimator (NSE) is developed for real-time analysis, and compared with the discrete Fourier transform (DFT). Method Clinical data in the form of 216 fractionated atrial electrogram sequences were used as inputs. The sample rate for acquisition was 977 Hz, or approximately 1 millisecond between digital samples. Real-time NSE power spectra were generated for 16,384 consecutive data points. The same data sequences were used for spectral calculation using a radix-2 implementation of the DFT. The NSE algorithm was also developed for implementation as a real-time spectral analyzer electronic circuit board. Results The average interval for a single real-time spectral calculation in software was 3.29 μs for NSE versus 504.5 μs for DFT. Thus for real-time spectral analysis, the NSE algorithm is approximately 150× faster than the DFT. Over a 1 millisecond sampling period, the NSE algorithm had the capability to spectrally analyze a maximum of 303 data channels, while the DFT algorithm could only analyze a single channel. Moreover, for the 8 second sequences, the NSE spectral resolution in the 3-12 Hz range was 0.037 Hz while the DFT spectral resolution was only 0.122 Hz. The NSE was also found to be implementable as a standalone spectral analyzer board using approximately 26 integrated circuits at a cost of approximately $500. The software files used for analysis are included as a supplement, please see the Additional files 1 and 2. Conclusions The NSE real-time algorithm has low computational cost and complexity, and is implementable in both software and hardware for 1 millisecond updates of multichannel spectra. The algorithm may be helpful to guide radiofrequency catheter ablation in real time. PMID:24886214
NASA Astrophysics Data System (ADS)
Osman, Abdalla; Nourledin, Aboelamgd; El-Sheimy, Naser; Theriault, Jim; Campbell, Scott
2009-06-01
The problem of target detection and tracking in the ocean environment has attracted considerable attention due to its importance in military and civilian applications. Sonobuoys are one of the capable passive sonar systems used in underwater target detection. Target detection and bearing estimation are mainly obtained through spectral analysis of received signals. The frequency resolution introduced by current techniques is limited which affects the accuracy of target detection and bearing estimation at a relatively low signal-to-noise ratio (SNR). This research investigates the development of a bearing estimation method using fast orthogonal search (FOS) for enhanced spectral estimation. FOS is employed in this research in order to improve both target detection and bearing estimation in the case of low SNR inputs. The proposed methods were tested using simulated data developed for two different scenarios under different underwater environmental conditions. The results show that the proposed method is capable of enhancing the accuracy for target detection as well as bearing estimation especially in cases of a very low SNR.
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.
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
Spectral estimation of received phase in the presence of amplitude scintillation
NASA Technical Reports Server (NTRS)
Vilnrotter, V. A.; Brown, D. H.; Hurd, W. J.
1988-01-01
A technique is demonstrated for obtaining the spectral parameters of the received carrier phase in the presence of carrier amplitude scintillation, by means of a digital phased locked loop. Since the random amplitude fluctuations generate time-varying loop characteristics, straightforward processing of the phase detector output does not provide accurate results. The method developed here performs a time-varying inverse filtering operation on the corrupted observables, thus recovering the original phase process and enabling accurate estimation of its underlying parameters.
A Real Valued Neural Network Based Autoregressive Energy Detector for Cognitive Radio Application.
Onumanyi, A J; Onwuka, E N; Aibinu, A M; Ugweje, O C; Salami, M J E
2014-01-01
A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application. PMID:27379318
A Real Valued Neural Network Based Autoregressive Energy Detector for Cognitive Radio Application
Onumanyi, A. J.; Onwuka, E. N.; Aibinu, A. M.; Ugweje, O. C.; Salami, M. J. E.
2014-01-01
A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application. PMID:27379318
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
Proper orthogonal decomposition-based spectral higher-order stochastic estimation
NASA Astrophysics Data System (ADS)
Baars, Woutijn J.; Tinney, Charles E.
2014-05-01
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.
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.
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, 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
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.
The use of spectral data in wheat yield estimation - An assessment of techniques explored in LACIE
NASA Technical Reports Server (NTRS)
Stuff, R. G.; Barnett, T. L.
1979-01-01
The object of the paper is to assess the results of the Large Area Crop Inventory Experiment (LACIE) and closely related research on yield estimation techniques based on remote sensing variables. The exploratory research conducted during LACIE substantiated the hypothesis of yield related information contained in Landsat multispectral scanner data and indicated some of its empirical characteristics. It is noted that leaf area and possibly other foliage features can be derived from spectral data for yield estimation through agrometeorological models and that multiple vegetative and grain related features may be discernable by Landsat derived wheat spectra at different points in the crop development.
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. PMID:25752061
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.
NASA Astrophysics Data System (ADS)
Behrangi, Ali
In respond to the community demands, combining microwave (MW) and infrared (IR) estimates of precipitation has been an active area of research since past two decades. The anticipated launching of NASA's Global Precipitation Measurement (GPM) mission and the increasing number of spectral bands in recently launched geostationary platforms will provide greater opportunities for investigating new approaches to combine multi-source information towards improved global high resolution precipitation retrievals. After years of the communities' efforts the limitations of the existing techniques are: (1) Drawbacks of IR-only techniques to capture warm rainfall and screen out no-rain thin cirrus clouds; (2) Grid-box- only dependency of many algorithms with not much effort to capture the cloud textures whether in local or cloud patch scale; (3) Assumption of indirect relationship between rain rate and cloud-top temperature that force high intensity precipitation to any cold cloud; (4) Neglecting the dynamics and evolution of cloud in time; (5) Inconsistent combination of MW and IR-based precipitation estimations due to the combination strategies and as a result of above described shortcomings. This PhD dissertation attempts to improve the combination of data from Geostationary Earth Orbit (GEO) and Low-Earth Orbit (LEO) satellites in manners that will allow consistent high resolution integration of the more accurate precipitation estimates, directly observed through LEO's PMW sensors, into the short-term cloud evolution process, which can be inferred from GEO images. A set of novel approaches are introduced to cope with the listed limitations and is consist of the following four consecutive components: (1) starting with the GEO part and by using an artificial-neural network based method it is demonstrated that inclusion of multi-spectral data can ameliorate existing problems associated with IR-only precipitating retrievals; (2) through development of Precipitation Estimation
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.
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.
The Performance of Multilevel Growth Curve Models under an Autoregressive Moving Average Process
ERIC Educational Resources Information Center
Murphy, Daniel L.; Pituch, Keenan A.
2009-01-01
The authors examined the robustness of multilevel linear growth curve modeling to misspecification of an autoregressive moving average process. As previous research has shown (J. Ferron, R. Dailey, & Q. Yi, 2002; O. Kwok, S. G. West, & S. B. Green, 2007; S. Sivo, X. Fan, & L. Witta, 2005), estimates of the fixed effects were unbiased, and Type I…
NASA Technical Reports Server (NTRS)
Kim, Moon S.; Daughtry, C. S. T.; Chappelle, E. W.; Mcmurtrey, J. E.; Walthall, C. L.
1994-01-01
Most remote sensing estimations of vegetation variables such as Leaf Area Index (LAI), Absorbed Photosynthetically Active Radiation (APAR), and phytomass are made using broad band sensors with a bandwidth of approximately 100 nm. However, high resolution spectrometers are available and have not been fully exploited for the purpose of improving estimates of vegetation variables. A study directed to investigate the use of high spectral resolution spectroscopy for remote sensing estimates of APAR in vegetation canopies in the presence of nonphotosynthetic background materials such as soil and leaf litter is presented. A high spectral resolution method defined as the Chlorophyll Absorption Ratio Index (CARI) was developed for minimizing the effects of nonphotosynthetic materials in the remote estimates of APAR. CARI utilizes three bands at 550, 670, and 700 nm with bandwidth of 10 nm. Simulated canopy reflectance of a range of LAI were generated with the SAIL model using measurements of 42 different soil types as canopy background. CARI obtained from the simulated canopy reflectance was compared with the broad band vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Simple Ratio (SR)). CARI reduced the effect of nonphotosynthetic background materials in the assessment of vegetation canopy APAR more effectively than broad band vegetation indices.
Practical energy response estimation of photon counting detectors for spectral X-ray imaging
NASA Astrophysics Data System (ADS)
Kang, Dong-Goo; Lee, Jongha; Sung, Younghun; Lee, SeongDeok
2010-04-01
Spectral X-ray imaging is a promising technique to drastically improve the diagnostic quality of radiography and computed tomography (CT), since it enables material decomposition and/or identification based on the energy dependency of material-specific X-ray attenuation. Unlike the charge-integration based X-ray detectors, photon counting X-ray detectors (PCXDs) can discriminate the energies of incident X-ray photons and thereby multi-energy images can be obtained in single exposure. However, the measured data are not accurate since the spectra of incident X-rays are distorted according to the energy response function (ERF) of a PCXD. Thus ERF should be properly estimated in advance for accurate spectral imaging. This paper presents a simple method for ERF estimation based on a polychromatic X-ray source that is widely used for medical imaging. The method consists of three steps: source spectra measurement, detector spectra reconstruction, and ERF inverse estimation. Real spectra of an X-ray tube are first measured at all kVs by using an X-ray spectrometer. The corresponding detector spectra are obtained by threshold scans. The ERF is then estimated by solving the inverse problem. Simulations are conducted to demonstrate the concept of the proposed method.
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 Astrophysics Data System (ADS)
Antunes, Jose; Borsoi, Laurent; Delaune, Xavier; Piteau, Philippe
2016-02-01
In this paper, we propose analytical and numerical straightforward approximate methods to estimate the unknown terms of incomplete spectral or correlation matrices, when the cross-spectra or cross-correlations available from multiple measurements do not cover all pairs of transducer locations. The proposed techniques may be applied whenever the available data includes the auto-spectra at all measurement locations, as well as selected cross-spectra which implicates all measurement locations. The suggested methods can also be used for checking the consistency between the spectral or correlation functions pertaining to measurement matrices, in cases of suspicious data. After presenting the proposed spectral estimation formulations, we discuss their merits and limitations. Then we illustrate their use on a realistic simulation of a multi-supported tube subjected to turbulence excitation from cross-flow. Finally, we show the effectiveness of the proposed techniques by extracting the modal responses of the simulated flow-excited tube, using the SOBI (Second Order Blind Identification) method, from an incomplete response matrix 1
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.
Spectral Doppler estimation utilizing 2-D spatial information and adaptive signal processing.
Ekroll, Ingvild K; Torp, Hans; Løvstakken, Lasse
2012-06-01
The trade-off between temporal and spectral resolution in conventional pulsed wave (PW) Doppler may limit duplex/triplex quality and the depiction of rapid flow events. It is therefore desirable to reduce the required observation window (OW) of the Doppler signal while preserving the frequency resolution. This work investigates how the required observation time can be reduced by adaptive spectral estimation utilizing 2-D spatial information obtained by parallel receive beamforming. Four adaptive estimation techniques were investigated, the power spectral Capon (PSC) method, the amplitude and phase estimation (APES) technique, multiple signal classification (MUSIC), and a projection-based version of the Capon technique. By averaging radially and laterally, the required covariance matrix could successfully be estimated without temporal averaging. Useful PW spectra of high resolution and contrast could be generated from ensembles corresponding to those used in color flow imaging (CFI; OW = 10). For a given OW, the frequency resolution could be increased compared with the Welch approach, in cases in which the transit time was higher or comparable to the observation time. In such cases, using short or long pulses with unfocused or focused transmit, an increase in temporal resolution of up to 4 to 6 times could be obtained in in vivo examples. It was further shown that by using adaptive signal processing, velocity spectra may be generated without high-pass filtering the Doppler signal. With the proposed approach, spectra retrospectively calculated from CFI may become useful for unfocused as well as focused imaging. This application may provide new clinical information by inspection of velocity spectra simultaneously from several spatial locations. PMID:22711413
Heasler, Patrick; Posse, Christian; Hylden, Jeff; Anderson, Kevin
2007-01-01
This paper presents a nonlinear Bayesian regression algorithm for 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 remote-sensing spectra, and the terrestrial (or atmospheric) parameters that are estimated is typically littered with many unknown “nuisance” parameters. 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 simulated data from a three-layer model for longwave infrared (LWIR) measurements from a passive instrument. The generated LWIR scenes contain plumes of varying intensities, and this allows estimation uncertainty and probability of detection to be quantified. The results show that this approach should permit more accurate estimation as well as a more reasonable description of estimate uncertainty. Specifically, the methodology produces a standard error that is more realistic than that produced by matched filter estimation.
NASA Technical Reports Server (NTRS)
Hardisky, M.; Klemas, V.
1984-01-01
Spectral radiance data were collected from the ground and from a low altitude aircraft in an attempt to gain some insight into the potential utility of actual Thematic Mapper data for biomass estimation in wetland plant communities. No attempt was made to distinguish individual plant species within brackish marsh plant associations. Rather, it was decided to lump plant species with similar canopy morphologies and then estimate from spectral radiance data the biomass of the group. The rationale for such an approach is that plants with a similar morphology will produce a similar reflecting or absorping surface (i.e., canopy) for incoming electromagnetic radiation. Variations in observed reflectance from different plant communities with a similar canopy morphology are more likely to be a result of biomass differences than a result of differences in canopy architecture. If the hypothesis that plants with a similar morphology exhibit similar reflectance characteristics is true, then biomass can be estimated based on a model for the dominant plant morphology within a plant association and the need for species discrimination has effectively been eliminated.
Modeling of uncertain spectra through stochastic autoregressive systems
NASA Astrophysics Data System (ADS)
Wang, Yiwei; Wang, X. Q.; Mignolet, Marc P.; Yang, Shuchi; Chen, P. C.
2016-03-01
The focus of this investigation is on the formulation and validation of a modeling strategy of the uncertainty that may exist on the specification of the power spectral density of scalar stationary processes and on the spectral matrices of vector ones. These processes may, for example, be forces on a structure originating from natural phenomena which are coarsely modeled (i.e., with epistemic uncertainty) or are specified by parameters unknown (i.e., with aleatoric uncertainty) in the application considered. The propagation of the uncertainty, e.g., to the response of the structure, may be carried out provided that a stochastic model of the uncertainty in the power spectral density/matrix is available from which admissible samples can be efficiently generated. Such a stochastic model will be developed here through an autoregressive-based parameterization of the specified baseline power spectral density/matrix and of its random samples. Autoregressive (AR) models are particularly well suited for this parametrization since their spectra are known to converge to a broad class of spectra (all non-pathological spectra) as the AR order increases. Note that the characterization of these models is not achieved directly in terms of their coefficients but rather in terms of their reflection coefficients which lie (or their eigenvalues in the vector process case) in the domain [0,1) as a necessary and sufficient condition for stability. Maximum entropy concepts are then employed to formulate the distribution of the reflection coefficients in both scalar and vector process case leading to a small set of hyperparameters of the uncertain model. Depending on the information available, these hyperparameters could either be varied in a parametric study format to assess the effects of uncertainty or could be identified, e.g., in a maximum likelihood format, from observed data. The validation and assessment of these concepts is finally achieved on several examples including the
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.
Remote estimation of phytoplankton size fractions using the spectral shape of light absorption.
Wang, Shengqiang; Ishizaka, Joji; Hirawake, Toru; Watanabe, Yuji; Zhu, Yuanli; Hayashi, Masataka; Yoo, Sinjae
2015-04-20
Phytoplankton size structure plays an important role in ocean biogeochemical processes. The light absorption spectra of phytoplankton provide a great potential for retrieving phytoplankton size structure because of the strong dependence on the packaging effect caused by phytoplankton cell size and on different pigment compositions related to phytoplankton taxonomy. In this study, we investigated the variability in light absorption spectra of phytoplankton in relation to the size structure. Based on this, a new approach was proposed for estimating phytoplankton size fractions. Our approach use the spectral shape of the normalized phytoplankton absorption coefficient (a(ph)(λ)) through principal component analysis (PCA). Values of a(ph)(λ) were normalized to remove biomass effects, and PCA was conducted to separate the spectral variance of normalized a(ph)(λ) into uncorrelated principal components (PCs). Spectral variations captured by the first four PC modes were used to build relationships with phytoplankton size fractions. The results showed that PCA had powerful ability to capture spectral variations in normalized a(ph)(λ), which were significantly related to phytoplankton size fractions. For both hyperspectral a(ph)(λ) and multiband a(ph)(λ), our approach is applicable. We evaluated our approach using wide in situ data collected from coastal waters and the global ocean, and the results demonstrated a good and robust performance in estimating phytoplankton size fractions in various regions. The model performance was further evaluated by a(ph)(λ) derived from in situ remote sensing reflectance (R(rs)(λ)) with a quasi-analytical algorithm. Using R(rs)(λ) only at six bands, accurate estimations of phytoplankton size fractions were obtained, with R(2) values of 0.85, 0.61, and 0.76, and root mean-square errors of 0.130, 0.126, and 0.112 for micro-, nano-, and picophytoplankton, respectively. Our approach provides practical basis for remote estimation of
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.
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.
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.
An autoregressive repeatability animal model for test-day records in multiple lactations.
Carvalheira, J; Pollak, E J; Quaas, R L; Blake, R W
2002-08-01
Test-day (TD) models are becoming a standard for genetic evaluation of production traits in dairy cattle. Various approaches to model covariances between TD records include random regression, autoregressive repeatability, orthogonal polynomials, and models based on character processing. The applicability of these models is mainly associated with the number of parameters to estimate, incorporation of multiple lactations, and the accuracy of correlations generated by the cow's repeated expression of milking performance (TD yields) within and across lactations. We define and evaluate a multiple-lactation, autoregressive-repeatability model that disentangles environmental effects due to cow within and between lactations. Simulated records either included or ignored a long-term environmental effect between lactations. Our autoregressive TD animal model correctly detected presence and the absence of this effect and accurately recovered the assumed variance components and correlations underlying the data (10 parameters for three lactations). Estimates of variance components and autocorrelation coefficients were obtained using DFREML-simplex methodology. Given the value of this approach to reduce the size of residual variance components, autoregressive animal models are a preferable alternative to classical methods based on cumulative lactation yield to improve milk production in dairy cattle. PMID:12214997
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)
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.
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.
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.
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.
Estimating probability densities from short samples: A parametric maximum likelihood approach
NASA Astrophysics Data System (ADS)
Dudok de Wit, T.; Floriani, E.
1998-10-01
A parametric method similar to autoregressive spectral estimators is proposed to determine the probability density function (PDF) of a random set. The method proceeds by maximizing the likelihood of the PDF, yielding estimates that perform equally well in the tails as in the bulk of the distribution. It is therefore well suited for the analysis of short sets drawn from smooth PDF's and stands out by the simplicity of its computational scheme. Its advantages and limitations are discussed.
On the maximum-entropy/autoregressive modeling of time series
NASA Technical Reports Server (NTRS)
Chao, B. F.
1984-01-01
The autoregressive (AR) model of a random process is interpreted in the light of the Prony's relation which relates a complex conjugate pair of poles of the AR process in the z-plane (or the z domain) on the one hand, to the complex frequency of one complex harmonic function in the time domain on the other. Thus the AR model of a time series is one that models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases. An AR model is completely determined by its z-domain pole configuration. The maximum-entropy/autogressive (ME/AR) spectrum, defined on the unit circle of the z-plane (or the frequency domain), is nothing but a convenient, but ambiguous visual representation. It is asserted that the position and shape of a spectral peak is determined by the corresponding complex frequency, and the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.
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.
Eckhard, Timo; Valero, Eva M; Hernández-Andrés, Javier; Schnitzlein, Markus
2014-02-01
The performance of learning-based spectral estimation is greatly influenced by the set of training samples selected to create the reconstruction model. Training sample selection schemes can be categorized into global and local approaches. Most of the previously proposed global training schemes aim to reduce the number of training samples, or a selection of representative samples, to maintain the generality of the training dataset. This work relates to printed ink reflectance estimation for quality assessment in in-line print inspection. We propose what we believe is a novel global training scheme that models a large population of realistic printable ink reflectances. Based on this dataset, we used a recursive top-down algorithm to reject clusters of training samples that do not enhance the performance of a linear least-square regression (pseudoinverse-based estimation) process. A set of experiments with real camera response data of a 12-channel multispectral camera system illustrate the advantages of this selection scheme over some other state-of-the-art algorithms. For our data, our method of global training sample selection outperforms other methods in terms of estimation quality and, more importantly, can quickly handle large datasets. Furthermore, we show that reflectance modeling is a reasonable, convenient tool to generate large training sets for print inspection applications. PMID:24514188
Two-Microphone Noise Reduction Using Spatial Information-Based Spectral Amplitude Estimation
NASA Astrophysics Data System (ADS)
Li, Kai; Guo, Yanmeng; Fu, Qiang; Li, Junfeng; Yan, Yonghong
Traditional two-microphone noise reduction algorithms to deal with highly nonstationary directional noises generally use the direction of arrival or phase difference information. The performance of these algorithms deteriorate when diffuse noises coexist with nonstationary directional noises in realistic adverse environments. In this paper, we present a two-channel noise reduction algorithm using a spatial information-based speech estimator and a spatial-information-controlled soft-decision noise estimator to improve the noise reduction performance in realistic non-stationary noisy environments. A target presence probability estimator based on Bayes rules using both phase difference and magnitude squared coherence is proposed for soft-decision of noise estimation, so that they can share complementary advantages when both directional noises and diffuse noises are present. Performances of the proposed two-microphone noise reduction algorithm are evaluated by noise reduction, log-spectral distance (LSD) and word recognition rate (WRR) of a distant-talking ASR system in a real room's noisy environment. Experimental results show that the proposed algorithm achieves better noises suppression without further distorting the desired signal components over the comparative dual-channel noise reduction algorithms.
[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
Estimation of glottal source features from the spectral envelope of the acoustic speech signal
NASA Astrophysics Data System (ADS)
Torres, Juan Felix
Speech communication encompasses diverse types of information, including phonetics, affective state, voice quality, and speaker identity. From a speech production standpoint, the acoustic speech signal can be mainly divided into glottal source and vocal tract components, which play distinct roles in rendering the various types of information it contains. Most deployed speech analysis systems, however, do not explicitly represent these two components as distinct entities, as their joint estimation from the acoustic speech signal becomes an ill-defined blind deconvolution problem. Nevertheless, because of the desire to understand glottal behavior and how it relates to perceived voice quality, there has been continued interest in explicitly estimating the glottal component of the speech signal. To this end, several inverse filtering (IF) algorithms have been proposed, but they are unreliable in practice because of the blind formulation of the separation problem. In an effort to develop a method that can bypass the challenging IF process, this thesis proposes a new glottal source information extraction method that relies on supervised machine learning to transform smoothed spectral representations of speech, which are already used in some of the most widely deployed and successful speech analysis applications, into a set of glottal source features. A transformation method based on Gaussian mixture regression (GMR) is presented and compared to current IF methods in terms of feature similarity, reliability, and speaker discrimination capability on a large speech corpus, and potential representations of the spectral envelope of speech are investigated for their ability represent glottal source variation in a predictable manner. The proposed system was found to produce glottal source features that reasonably matched their IF counterparts in many cases, while being less susceptible to spurious errors. The development of the proposed method entailed a study into the aspects
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)
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.
Estimation of the spectral parameter kappa in the region of the Gulf of California, Mexico
NASA Astrophysics Data System (ADS)
Castro, Raúl R.; Ávila-Barrientos, Lenin
2015-10-01
We analyzed records from the Broadband Seismological Network of the Gulf of California (RESBAN) and from stations of the NARS-Baja array, operated by CICESE, Ensenada, Baja California, Mexico, to make estimates of the spectral decay parameter kappa ( κ). This attenuation parameter is important for evaluating the seismic risk and hazard of this region. Thirteen shallow earthquakes with focal depths less than 20 km and magnitudes between 5.1 and 6.6 were selected to calculate κ and the near-site attenuation κ 0. We used three different approaches to estimate κ 0: (a) with individual measurements of κ from vector modulus of three-component spectral amplitudes at different epicentral distances and extrapolating to zero distance to estimate κ 0, (b) with individual measurements using vertical component spectra, and (c) measuring from the high-frequency part of the site transfer function determined calculating the horizontal-to-vertical spectral ratio (HVSR) method. For most stations, the three methods give similar results. At short distances (50-60 km), κ takes values close to 0.04 s at NE76, the station located in the middle of the array. κ increases with distance taking an average value of up to 0.18 s for distances close to 500 km. κ 0 at most sites is close to 0.03 s, except for GUYB (Guaymas) that has a κ 0 = 0.05 s and NE83 (Navolato) with κ 0 = 0.065 s, both stations located in the continent, on the eastern side of the gulf, where the soils are less consolidated. Finally, we analyze if κ 0 correlates with magnitude and back azimuth, and we found that for most stations, κ 0 does not correlate with either one. However, station TOPB, located on basalt, shows a moderate correlation with magnitude, with κ 0 increasing with increasing M W in a short back-azimuth range. We also found that for station NE74, located on soft soil, κ 0 correlates with back azimuth, having lower values for azimuths near 120°.
ENSO Prediction using Vector Autoregressive Models
NASA Astrophysics Data System (ADS)
Chapman, D. R.; Cane, M. A.; Henderson, N.; Lee, D.; Chen, C.
2013-12-01
A recent comparison (Barnston et al, 2012 BAMS) shows the ENSO forecasting skill of dynamical models now exceeds that of statistical models, but the best statistical models are comparable to all but the very best dynamical models. In this comparison the leading statistical model is the one based on the Empirical Model Reduction (EMR) method. Here we report on experiments with multilevel Vector Autoregressive models using only sea surface temperatures (SSTs) as predictors. VAR(L) models generalizes Linear Inverse Models (LIM), which are a VAR(1) method, as well as multilevel univariate autoregressive models. Optimal forecast skill is achieved using 12 to 14 months of prior state information (i.e 12-14 levels), which allows SSTs alone to capture the effects of other variables such as heat content as well as seasonality. The use of multiple levels allows the model advancing one month at a time to perform at least as well for a 6 month forecast as a model constructed to explicitly forecast 6 months ahead. We infer that the multilevel model has fully captured the linear dynamics (cf. Penland and Magorian, 1993 J. Climate). Finally, while VAR(L) is equivalent to L-level EMR, we show in a 150 year cross validated assessment that we can increase forecast skill by improving on the EMR initialization procedure. The greatest benefit of this change is in allowing the prediction to make effective use of information over many more months.
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.
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.
Super-resolution spectral estimation in short-time non-contact vital sign measurement
NASA Astrophysics Data System (ADS)
Sun, Li; Li, Yusheng; Hong, Hong; Xi, Feng; Cai, Weidong; Zhu, Xiaohua
2015-04-01
Non-contact techniques for measuring vital signs attract great interest due to the benefits shown in medical monitoring, military application, etc. However, the presence of respiration harmonics caused by nonlinear phase modulation will result in performance degradation. Suffering from smearing and leakage problems, conventional discrete Fourier transform (DFT) based methods cannot distinguish the heartbeat component from closely located respiration harmonics in frequency domain, especially in short-time processing. In this paper, the theory of sparse reconstruction is merged with an extended harmonic model of vital signals, aiming at achieving a super-resolution spectral estimation of vital signals by additionally exploiting the inherent sparse prior information. Both simulated and experimental results show that the proposed algorithm has superior performance to DFT-based methods and the recently applied multiple signal classification algorithm, and the required processing window length has been shortened to 5.12 s.
Super-resolution spectral estimation in short-time non-contact vital sign measurement.
Sun, Li; Li, Yusheng; Hong, Hong; Xi, Feng; Cai, Weidong; Zhu, Xiaohua
2015-04-01
Non-contact techniques for measuring vital signs attract great interest due to the benefits shown in medical monitoring, military application, etc. However, the presence of respiration harmonics caused by nonlinear phase modulation will result in performance degradation. Suffering from smearing and leakage problems, conventional discrete Fourier transform (DFT) based methods cannot distinguish the heartbeat component from closely located respiration harmonics in frequency domain, especially in short-time processing. In this paper, the theory of sparse reconstruction is merged with an extended harmonic model of vital signals, aiming at achieving a super-resolution spectral estimation of vital signals by additionally exploiting the inherent sparse prior information. Both simulated and experimental results show that the proposed algorithm has superior performance to DFT-based methods and the recently applied multiple signal classification algorithm, and the required processing window length has been shortened to 5.12 s. PMID:25933881
A robust approach to estimate stellar angular diameters from photometry and spectral type
NASA Astrophysics Data System (ADS)
Chelli, A.; Bourges, L.; Duvert, G.; Lafrasse, S.; Mella, G.; Le Bouquin, J.-B.; Chesneau, O.
2014-07-01
Observing reference stars with a known diameter is almost the only possibility to calibrate optical interferometry observations. The JMMC Calibrator Workgroup develops methods to ascertain the angular diameter of stars since 2000 and provides this expertise in the SearchCal software and associated databases. We provide on a regularly basis the JSDC, a catalogue of such stars, and an open access to our server that dynamically finds calibrators near science objects by querying CDS hosted catalogs. Here we propose a novel approach in the estimation of angular stellar diameters based on observational quantities only. It bypasses the knowledge of the visual extinction and intrinsic colors, thanks to the use of absorption free pseudo-colors (AFC) and the spectral type number on the x-axis. This new methodology allows to compute the angular diameter of 443 703 stars with a relative precision of about 1%. This calibrator set will become after filtering the next JSDC release.
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.
NASA Technical Reports Server (NTRS)
Bergstrom, Robert W.; Pilewskie, Peter; Schmid, Beat; Russell, Philip B.
2003-01-01
Using measurements of the spectral solar radiative flux and optical depth for 2 days (24 August and 6 September 2000) during the SAFARI 2000 intensive field experiment and a detailed radiative transfer model, we estimate the spectral single scattering albedo of the aerosol layer. The single scattering albedo is similar on the 2 days even though the optical depth for the aerosol layer was quite different. The aerosol single scattering albedo was between 0.85 and 0.90 at 350 nm, decreasing to 0.6 in the near infrared. The magnitude and decrease with wavelength of the single scattering albedo are consistent with the absorption properties of small black carbon particles. We estimate the uncertainty in the single scattering albedo due to the uncertainty in the measured fractional absorption and optical depths. The uncertainty in the single scattering albedo is significantly less on the high-optical-depth day (6 September) than on the low-optical-depth day (24 August). On the high-optical-depth day, the uncertainty in the single scattering albedo is 0.02 in the midvisible whereas on the low-optical-depth day the uncertainty is 0.08 in the midvisible. On both days, the uncertainty becomes larger in the near infrared. We compute the radiative effect of the aerosol by comparing calculations with and without the aerosol. The effect at the top of the atmosphere (TOA) is to cool the atmosphere by 13 W/sq m on 24 August and 17 W/sq m on 6 September. The effect on the downward flux at the surface is a reduction of 57 W/sq m on 24 August and 200 W/sq m on 6 September. The aerosol effect on the downward flux at the surface is in good agreement with the results reported from the Indian Ocean Experiment (INDOEX).
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
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
Garde, Ainara; Karlen, Walter; Ansermino, J Mark; Dumont, Guy A
2014-01-01
The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool. PMID:24466088
Zhu, Zai-Chun; Chen, Lian-Qun; Zhang, Jin-Shui; Pan, Yao-Zhong; Zhu, Wen-Quan; Hu, Tan-Gao
2012-07-01
Crop yield estimation division is the basis of crop yield estimation; it provides an important scientific basis for estimation research and practice. In the paper, MODIS EVI time-series data during winter wheat growth period is selected as the division data; JiangSu province is study area; A division method combined of advanced spectral angle mapping(SVM) and K-means clustering is presented, and tested in winter wheat yield estimation by remote sensing. The results show that: division method of spectral angle clustering can take full advantage of crop growth process that is reflected by MODIS time series data, and can fully reflect region differences of winter wheat that is brought by climate difference. Compared with the traditional division method, yield estimation result based on division result of spectral angle clustering has higher R2 (0.702 6 than 0.624 8) and lower RMSE (343.34 than 381.34 kg x hm(-2)), reflecting the advantages of the new division method in the winter wheat yield estimation. The division method in the paper only use convenient obtaining time-series remote sensing data of low-resolution as division data, can divide winter wheat into similar and well characterized region, accuracy and stability of yield estimation model is also very good, which provides an efficient way for winter wheat estimation by remote sensing, and is conducive to winter wheat yield estimation. PMID:23016349
Kepler AutoRegressive Planet Search
NASA Astrophysics Data System (ADS)
Caceres, Gabriel Antonio; Feigelson, Eric
2016-01-01
The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real
Power spectral density of velocity fluctuations estimated from phase Doppler data
NASA Astrophysics Data System (ADS)
Jedelsky, Jan; Lizal, Frantisek; Jicha, Miroslav
2012-04-01
Laser Doppler Anemometry (LDA) and its modifications such as PhaseDoppler Particle Anemometry (P/DPA) is point-wise method for optical nonintrusive measurement of particle velocity with high data rate. Conversion of the LDA velocity data from temporal to frequency domain - calculation of power spectral density (PSD) of velocity fluctuations, is a non trivial task due to nonequidistant data sampling in time. We briefly discuss possibilities for the PSD estimation and specify limitations caused by seeding density and other factors of the flow and LDA setup. Arbitrary results of LDA measurements are compared with corresponding Hot Wire Anemometry (HWA) data in the frequency domain. Slot correlation (SC) method implemented in software program Kern by Nobach (2006) is used for the PSD estimation. Influence of several input parameters on resulting PSDs is described. Optimum setup of the software for our data of particle-laden air flow in realistic human airway model is documented. Typical character of the flow is described using PSD plots of velocity fluctuations with comments on specific properties of the flow. Some recommendations for improvements of future experiments to acquire better PSD results are given.
NASA Astrophysics Data System (ADS)
Trizna, D. B.; McNeal, G. D.
1985-12-01
This work is the second paper in a series of studies of the application of spectral estimations techniques to Doppler processing of coherent radar signals. In this work, simulated high-frequency (HF) radar sea scatter time series are generated and processed by use of three different spectral estimation algorithms and the fast Fourier transform (FFT). The sea clutter is simulated by narrowband filtering a wideband Gaussian noise spectrum in the frequency domain, with filter widths appropriate to describe first-order Bragg lines and second-order continuum. Targets are introduced as sinusoids, stepped by 5 dB for eight different echo power values, and stepped in Doppler frequency for four different values relative to the clutter. These simulations identify problems that appear unique to Doppler processing of coherent radar data in the presence of broadband clutter, and are in distinction to the application of spectral estimation to processing in the spatial domain. In the latter case, the spectral contributions are generally narrowly confined in the angular power spectral estimate, and the aim is to separate these contributions in the presence of noise. The HF radar application is concerned with separation of weak targets in the presence of stronger clutter returns, which are relati vely broad compared to the target return. It appears that the Burg maximum entropy method allows the detection of targets in clutter under conditions which the FFT is incapable of detection with any degree of accuracy.
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
NASA Astrophysics Data System (ADS)
Dybus, W.; Benoit, M. H.; Ebinger, C. J.
2011-12-01
The crustal thickness beneath much of the eastern half of the US is largely unconstrained. Though there have been several controlled source seismic surveys of the region, many of these studies suffer from rays that turn in the crust above the Moho, resulting in somewhat ambiguous crustal thickness values. Furthermore, the broadband seismic station coverage east of the Mississippi has been limited, and most of the region remains largely understudied. In this study, we estimated the depth to the Moho using both spectral analysis and inversion of Bouguer gravity anomalies. We systematically estimated depths to lithospheric density contrasts from radial power spectra of Bouguer gravity within 100 km X 100 km windows eastward from the Mississippi River to the Atlantic Coast, and northward from North Carolina to Maine. The slopes and slope breaks in the radial power spectra were computed using an automated algorithm. The slope values for each window were visually inspected and then used to estimate the depth to the Moho and other lithospheric density contrasts beneath each windowed region. Additionally, we performed a standard Oldenburg-Parker inversion for lithospheric density contrasts using various reference depths and density contrasts that are realistic for the different physiographic provinces in the Eastern US. Our preliminary results suggest that the gravity-derived Moho depths are similar to those found using seismic data, and that the crust is relatively thinner (~28-33 km) than expected in beneath the Piedmont region (~35-40 km). Given the relative paucity of seismic data in the eastern US, analysis of onshore gravity data is a valuable tool for interpolating between seismic stations.
NASA Astrophysics Data System (ADS)
Qu, Liqin; Civco, Daniel; Lei, Tingwu; Yang, Xiusheng
2014-06-01
The dynamic sediment distribution in large rivers with dams constructed has often been the focus of considerable attention because of their potential adverse environmental impacts. Sedimentation modeling and environmental assessment of man-made projects are often hindered by the lack of sediment measurements with spatial details. This study aimed to investigate the method used to estimate the suspended sediment concentrations (SSCs) from on-site spectral measurements. The study investigated the spectral signature of river water from the natural channel and Sanmenxia Reservoir on the Yellow River. A field spectral survey was conducted through on-site spectral measurements by using a spectroradimeter and SSC estimation by sampling. Reectance at 750 nm to 950 nm, with all correlation coefficient (r) between SSC and reectance > 0:7, seemed to be the appropriate range for SSC estimation. Simulated Landsat Enhanced Thematic Mapper Plus Band 4 (760 nm to 900 nm) was used to build the single band model for estimating SSC. The results confirmed that the exponential model based on the relationship between SSC and reectance (R2 = 0:92, root mean square error [RMSE]= 0:241 g=l) was better than the linear model between reectance and logarithm-transformed SSC (R2 = 0:90, RMSE = 0:310 g=l). We also applied the Spectral Mixing Algorithm (SMA) from the tank experiment to the on-site spectral measurements. The result showed that the SMA models performed as well as the single band exponential model (R2 = 0:86, RMSE = 0:280 g=l). However, the valid range for application was improved from 1:99 g=l to 347 g=l. This study could provide critical instructional assistance for estimating SSC directly from remote sensing data.
NASA Astrophysics Data System (ADS)
Hansen, V.
1984-05-01
The distribution of direct and scattered solar radiant energy in the UV (295-385-nm), blue (385-495-nm), green-orange (495-630-nm), red (630-695-nm), and IR (695-2800-nm) bands commonly used in precision spectral pyranometers is estimated for clear sky conditions as a function of solar height, using a plane-parallel atmosphere model and data on the seasonal variation of the UV component at latitude 59.7 deg N. Integrated daily fluxes are also calculated for selected days of the year, and the results are compared with experimental measurements in graphs and tables. The model is found to give reasonable agreement with the observations, but fails to account for a significant blue shift with increasing solar height at heights above 15 deg. The measured distribution for March is given as UV 4.2, blue 8.9, green-orange 21.8, red 12.4, and IR 52.9 percent; for July, the respective values are 4.6, 16.1, 18.0, 10.7, and 50.6 percent.
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.
NASA Astrophysics Data System (ADS)
Chang, Gao-Wei; Jian, Hong-Da; Yeh, Zong-Mu; Cheng, Chin-Pao
2004-02-01
For estimating spectral responsivities of digital video cameras, a filter-based optical system is designed with sophisticated filter selections, in this paper. The filter consideration in the presence of noise is central to the optical systems design, since the spectral filters primarily prescribe the structure of the perturbed system. A theoretical basis is presented to confirm that sophisticated filter selections can make this system as insensitive to noise as possible. Also, we propose a filter selection method based on the orthogonal-triangular (QR) decomposition with column pivoting (QRCP). To investigate the noise effects, we assess the estimation errors between the actual and estimated spectral responsivities, with the different signal-to-noise ratio (SNR) levels of an eight-bit/channel camera. Simulation results indicate that the proposed method yields satisfactory estimation accuracy. That is, the filter-based optical system with the spectral filters selected from the QRCP-based method is much less sensitive to noise than those with other filters from different selections.
No-reference image sharpness assessment in autoregressive parameter space.
Gu, Ke; Zhai, Guangtao; Lin, Weisi; Yang, Xiaokang; Zhang, Wenjun
2015-10-01
In this paper, we propose a new no-reference (NR)/blind sharpness metric in the autoregressive (AR) parameter space. Our model is established via the analysis of AR model parameters, first calculating the energy- and contrast-differences in the locally estimated AR coefficients in a pointwise way, and then quantifying the image sharpness with percentile pooling to predict the overall score. In addition to the luminance domain, we further consider the inevitable effect of color information on visual perception to sharpness and thereby extend the above model to the widely used YIQ color space. Validation of our technique is conducted on the subsets with blurring artifacts from four large-scale image databases (LIVE, TID2008, CSIQ, and TID2013). Experimental results confirm the superiority and efficiency of our method over existing NR algorithms, the stateof-the-art blind sharpness/blurriness estimators, and classical full-reference quality evaluators. Furthermore, the proposed metric can be also extended to stereoscopic images based on binocular rivalry, and attains remarkably high performance on LIVE3D-I and LIVE3D-II databases. PMID:26054063
Image restoration using 2D autoregressive texture model and structure curve construction
NASA Astrophysics Data System (ADS)
Voronin, V. V.; Marchuk, V. I.; Petrosov, S. P.; Svirin, I.; Agaian, S.; Egiazarian, K.
2015-05-01
In this paper an image inpainting approach based on the construction of a composite curve for the restoration of the edges of objects in an image using the concepts of parametric and geometric continuity is presented. It is shown that this approach allows to restore the curved edges and provide more flexibility for curve design in damaged image by interpolating the boundaries of objects by cubic splines. After edge restoration stage, a texture restoration using 2D autoregressive texture model is carried out. The image intensity is locally modeled by a first spatial autoregressive model with support in a strongly causal prediction region on the plane. Model parameters are estimated by Yule-Walker method. Several examples considered in this paper show the effectiveness of the proposed approach for large objects removal as well as recovery of small regions on several test images.
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.
Circular Conditional Autoregressive Modeling of Vector Fields.
Modlin, Danny; Fuentes, Montse; Reich, Brian
2012-02-01
As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components. PMID:24353452
Circular Conditional Autoregressive Modeling of Vector Fields*
Modlin, Danny; Fuentes, Montse; Reich, Brian
2013-01-01
As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components. PMID:24353452
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.
Stable continuous-time autoregressive process driven by stable subordinator
NASA Astrophysics Data System (ADS)
Wyłomańska, Agnieszka; Gajda, Janusz
2016-02-01
In this paper we examine the continuous-time autoregressive moving average process driven by α-stable Lévy motion delayed by inverse stable subordinator. This process can be applied to high-frequency data with visible jumps and so-called "trapping-events". Those properties are often visible in financial time series but also in amorphous semiconductors, technical data describing the rotational speed of a machine working under various load regimes or data related to indoor air quality. We concentrate on the main characteristics of the examined subordinated process expressed in the language of the measures of dependence which are main tools used in statistical investigation of real data. However, because the analyzed system is based on the α-stable distribution therefore we cannot consider here the correlation (or covariance) as a main measure which indicates at the dependence inside the process. In the paper we examine the codifference, the more general measure of dependence defined for wide class of processes. Moreover we present the simulation procedure of the considered system and indicate how to estimate its parameters. The theoretical results we illustrate by the simulated data analysis.
A fuzzy-autoregressive model of daily river flows
NASA Astrophysics Data System (ADS)
Greco, R.
2012-04-01
A model for the identification of daily river flows has been developed, consisting of the combination of an autoregressive model with a fuzzy inference system. The AR model is devoted to the identification of base flow, supposed to be described by linear laws. The fuzzy model identifies the surface runoff, by applying a small set of linguistic statements, deriving from the knowledge of the physical features of the non linear rainfall-runoff transformation, to the inflow entering the river basin. The model has been applied to the identification of the daily flow series of river Volturno at Cancello-Arnone (Southern Italy), with a drainage basin of around 5560 km2, observed between 1970 and 1974. The inflow was estimated on the basis of daily precipitations registered during the same years at six rain gauges located throughout the basin. The first two years were used for model training, the remaining three for the validation. The obtained results show that the proposed model provides good predictions of either low river flows or high floods, although the analysis of residuals, which do not turn out to be a white noise, indicates that the cause and effect relationship between rainfall and runoff is not completely identified by the model.
A fuzzy-autoregressive model of daily river flows
NASA Astrophysics Data System (ADS)
Greco, Roberto
2012-06-01
A model for the identification of daily river flows has been developed, consisting of the combination of an autoregressive model with a fuzzy inference system. The AR model is devoted to the identification of base flow, supposed to be described by linear laws. The fuzzy model identifies the surface runoff, by applying a small set of linguistic statements, deriving from the knowledge of the physical features of the nonlinear rainfall-runoff transformation, to the inflow entering the river basin. The model has been applied to the identification of the daily flow series of river Volturno at Cancello-Arnone (Southern Italy), with a drainage basin of around 5560 km2, observed between 1970 and 1974. The inflow was estimated on the basis of daily precipitations registered during the same years at six rain gauges located throughout the basin. The first two years were used for model training, the remaining three for the validation. The obtained results show that the proposed model provides good predictions of either low river flows or high floods, although the analysis of residuals, which do not turn out to be a white noise, indicates that the cause and effect relationship between rainfall and runoff is not completely identified by the model.
ANALYSIS OF ROLLING GROUP THERAPY DATA USING CONDITIONALLY AUTOREGRESSIVE PRIORS
Paddock, Susan M.; Hunter, Sarah B.; Watkins, Katherine E.; McCaffrey, Daniel F.
2010-01-01
Group therapy is a central treatment modality for behavioral health disorders such as alcohol and other drug use (AOD) and depression. Group therapy is often delivered under a rolling (or open) admissions policy, where new clients are continuously enrolled into a group as space permits. Rolling admissions policies result in a complex correlation structure among client outcomes. Despite the ubiquity of rolling admissions in practice, little guidance on the analysis of such data is available. We discuss the limitations of previously proposed approaches in the context of a study that delivered group cognitive behavioral therapy for depression to clients in residential substance abuse treatment. We improve upon previous rolling group analytic approaches by fully modeling the interrelatedness of client depressive symptom scores using a hierarchical Bayesian model that assumes a conditionally autoregressive prior for session-level random effects. We demonstrate improved performance using our method for estimating the variance of model parameters and the enhanced ability to learn about the complex correlation structure among participants in rolling therapy groups. Our approach broadly applies to any group therapy setting where groups have changing client composition. It will lead to more efficient analyses of client-level data and improve the group therapy research community’s ability to understand how the dynamics of rolling groups lead to client outcomes. PMID:21857889
ANALYSIS OF ROLLING GROUP THERAPY DATA USING CONDITIONALLY AUTOREGRESSIVE PRIORS.
Paddock, Susan M; Hunter, Sarah B; Watkins, Katherine E; McCaffrey, Daniel F
2011-06-01
Group therapy is a central treatment modality for behavioral health disorders such as alcohol and other drug use (AOD) and depression. Group therapy is often delivered under a rolling (or open) admissions policy, where new clients are continuously enrolled into a group as space permits. Rolling admissions policies result in a complex correlation structure among client outcomes. Despite the ubiquity of rolling admissions in practice, little guidance on the analysis of such data is available. We discuss the limitations of previously proposed approaches in the context of a study that delivered group cognitive behavioral therapy for depression to clients in residential substance abuse treatment. We improve upon previous rolling group analytic approaches by fully modeling the interrelatedness of client depressive symptom scores using a hierarchical Bayesian model that assumes a conditionally autoregressive prior for session-level random effects. We demonstrate improved performance using our method for estimating the variance of model parameters and the enhanced ability to learn about the complex correlation structure among participants in rolling therapy groups. Our approach broadly applies to any group therapy setting where groups have changing client composition. It will lead to more efficient analyses of client-level data and improve the group therapy research community's ability to understand how the dynamics of rolling groups lead to client outcomes. PMID:21857889
Dealing with Multiple Solutions in Structural Vector Autoregressive Models.
Beltz, Adriene M; Molenaar, Peter C M
2016-01-01
Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior. PMID:27093380
NASA Astrophysics Data System (ADS)
Dettmer, Jan; Molnar, Sheri; Steininger, Gavin; Dosso, Stan E.; Cassidy, John F.
2012-02-01
This paper applies a general trans-dimensional Bayesian inference methodology and hierarchical autoregressive data-error models to the inversion of microtremor array dispersion data for shear wave velocity (vs) structure. This approach accounts for the limited knowledge of the optimal earth model parametrization (e.g. the number of layers in the vs profile) and of the data-error statistics in the resulting vs parameter uncertainty estimates. The assumed earth model parametrization influences estimates of parameter values and uncertainties due to different parametrizations leading to different ranges of data predictions. The support of the data for a particular model is often non-unique and several parametrizations may be supported. A trans-dimensional formulation accounts for this non-uniqueness by including a model-indexing parameter as an unknown so that groups of models (identified by the indexing parameter) are considered in the results. The earth model is parametrized in terms of a partition model with interfaces given over a depth-range of interest. In this work, the number of interfaces (layers) in the partition model represents the trans-dimensional model indexing. In addition, serial data-error correlations are addressed by augmenting the geophysical forward model with a hierarchical autoregressive error model that can account for a wide range of error processes with a small number of parameters. Hence, the limited knowledge about the true statistical distribution of data errors is also accounted for in the earth model parameter estimates, resulting in more realistic uncertainties and parameter values. Hierarchical autoregressive error models do not rely on point estimates of the model vector to estimate data-error statistics, and have no requirement for computing the inverse or determinant of a data-error covariance matrix. This approach is particularly useful for trans-dimensional inverse problems, as point estimates may not be representative of the
Best estimation of spectrum profiles for diagnosing femoral prostheses loosening.
Díaz-Pérez, Francisco; García-Nieto, Evelyn; Ros, Antonio; Claramunt, Rafael
2014-02-01
For the past few years, some authors have proposed several vibration analysis techniques to detect the prosthetic femoral stem loosening, having found some differences in the frequency response between secure and loose stems. Classical methods like periodogram have been used in most studies for the spectral estimation, and their conclusions have been reached only by visual inspection. A new metric called Non-linear Logarithmic Weighted Distance (NLWD), based on log-spectral distance is presented. As its name suggests, the spectral power is weighted in order to highlight discriminatory patterns of the spectral profiles. A Generalized Discriminant Ratio (GDR) based on NLWD metric has been also defined. In this study, experiments on a cadaveric dried bone with two kinds of fixation, Loose Stem class (LS) and Secure Stem class (SS), have been analyzed. To select the most discriminating approach to spectral estimation, five well known algorithms (Welch's, Burg's Auto-Regressive (AR), Auto-Regressive Moving Average (ARMA), Multiple Signal Classification (MUSIC) and Thomson's Multi-taper (MTM)) have been compared by using GDR. Finally, the use of the MTM method is proposed for the analysis of bone-stem interface vibratory signals, since it yields the most discriminatory profiles. PMID:24332977
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)
Hirose, Misa; Akaho, Rina; Maita, Chikashi; Sugawara, Mai; Tsumura, Norimichi
2016-02-01
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.
Tanikawa, Tomonori; Li, Wei; Kuchiki, Katsuyuki; Aoki, Teruo; Hori, Masahiro; Stamnes, Knut
2015-11-30
A new retrieval algorithm for estimation of snow grain size and impurity concentration from spectral radiation data is developed for remote sensing applications. A radiative transfer (RT) model for the coupled atmosphere-snow system is used as a forward model. This model simulates spectral radiant quantities for visible and near-infrared channels. The forward RT calculation is, however, the most time-consuming part of the forward-inverse modeling. Therefore, we replaced it with a neural network (NN) function for fast computation of radiances and Jacobians. The retrieval scheme is based on an optimal estimation method with a priori constraints. The NN function was also employed to obtain an accurate first guess in the retrieval scheme. Validation with simulation data shows that a combination of NN techniques and optimal estimation method can provide more accurate retrievals than by using only NN techniques. In addition, validation with in-situ measurements conducted by using ground-based spectral radiometer system shows that comparison between retrieved snow parameters with in-situ measurements is acceptable with satisfactory accuracy. The algorithm provides simultaneous, accurate and fast retrieval of the snow properties. The algorithm presented here is useful for airborne/satellite remote sensing. PMID:26698793
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
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.
Technology Transfer Automated Retrieval System (TEKTRAN)
Vegetation indices derived from multispectral imagery are commonly used to extract crop growth and yield information. Spectral unmixing techniques provide an alternative approach to quantifying crop canopy abundance within each image pixel and have the potential for mapping crop yield variability. T...
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,
An autoregressive growth model for longitudinal item analysis.
Jeon, Minjeong; Rabe-Hesketh, Sophia
2016-09-01
A first-order autoregressive growth model is proposed for longitudinal binary item analysis where responses to the same items are conditionally dependent across time given the latent traits. Specifically, the item response probability for a given item at a given time depends on the latent trait as well as the response to the same item at the previous time, or the lagged response. An initial conditions problem arises because there is no lagged response at the initial time period. We handle this problem by adapting solutions proposed for dynamic models in panel data econometrics. Asymptotic and finite sample power for the autoregressive parameters are investigated. The consequences of ignoring local dependence and the initial conditions problem are also examined for data simulated from a first-order autoregressive growth model. The proposed methods are applied to longitudinal data on Korean students' self-esteem. PMID:26645083
Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes
NASA Astrophysics Data System (ADS)
Wang, W.; van Gelder, P. H. A. J. M.; Vrijling, J. K.; Ma, J.
2005-01-01
Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.
NASA Technical Reports Server (NTRS)
Asrar, G.; Kanemasu, E. T.; Yoshida, M.
1985-01-01
The influence of management practices and solar illumination angle on the leaf area index (LAI) was estimated from measurements of wheat canopy reflectance evaluated by two methods, a regression formula and an indirect technique. The date of planting and the time of irrigation in relation to the stage of plant growth were found to have significant effects on the development of leaves in spring wheat. A reduction in soil moisture adversely affected both the duration and magnitude of the maximum LAI for late planting dates. In general, water stress during vegetative stages resulted in a reduction in maximum LAI, while water stress during the reproductive period shortened the duration of green LAI in spring wheat. Canopy geometry and solar angle also affected the spectral properties of the canopies, and hence the estimated LAI. Increase in solar zenith angles resulted in a general increase in estimated LAI obtained from both methods.
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.
An adaptive spectral estimation technique to detect cavitation in HIFU with high spatial resolution.
Hsieh, Chang-Yu; Probert Smith, Penny; Mayia, Fares; Ye, Guoliang
2011-07-01
In ultrasound-guided high-intensity focused ultrasound (HIFU) therapy, the changes observed on tissue are subtle during treatment; some ultrasound-guided HIFU protocols rely on the observation of significant brightness changes as the indicator of tissue lesions. The occurrence of a distinct hyperechogenic region ("bright-up") around the focus is often associated with acoustic cavitation resulting in microbubble formation, but it may indicate different physical events such as larger bubbles from boiling (known to alter acoustic impedance) or sometimes lesion formation. A reliable method to distinguish and spatially localize these causes within the tissue would assist the control of HIFU delivery, which is the subject of this paper. Spectral analysis of the radio frequency (RF) signal underlying the B-mode image provides more information on the physical cause, but the usual techniques that are methods on the Fourier transform require a long series for good spectral resolution and so they give poor spatial resolution. This paper introduces an active spectral cavitation detection method to attain high spatial resolution (0.15 × 0.15 mm per pixel) through a parametric statistical method (ARMA modeling) used on finite-length data sets, which enables local changes to be identified more easily. This technique uses the characteristics of the signal itself to optimize the model parameters and structure. Its performance is assessed using synthesized cavitation RF data, and it is then demonstrated in ex vivo bovine liver during and after HIFU exposure. The results suggest that good spatial and spectral resolution can be obtained by the design of suitable algorithms. In ultrasound-guided HIFU, the technique provides a useful supplement to B-mode analysis, with no additional time penalty in data acquisition. PMID:21684454
On estimating attenuation from the amplitude of the spectrally whitened ambient seismic field
NASA Astrophysics Data System (ADS)
Weemstra, Cornelis; Westra, Willem; Snieder, Roel; Boschi, Lapo
2014-06-01
Measuring attenuation on the basis of interferometric, receiver-receiver surface waves is a non-trivial task: the amplitude, more than the phase, of ensemble-averaged cross-correlations is strongly affected by non-uniformities in the ambient wavefield. In addition, ambient noise data are typically pre-processed in ways that affect the amplitude itself. Some authors have recently attempted to measure attenuation in receiver-receiver cross-correlations obtained after the usual pre-processing of seismic ambient-noise records, including, most notably, spectral whitening. Spectral whitening replaces the cross-spectrum with a unit amplitude spectrum. It is generally assumed that cross-terms have cancelled each other prior to spectral whitening. Cross-terms are peaks in the cross-correlation due to simultaneously acting noise sources, that is, spurious traveltime delays due to constructive interference of signal coming from different sources. Cancellation of these cross-terms is a requirement for the successful retrieval of interferometric receiver-receiver signal and results from ensemble averaging. In practice, ensemble averaging is replaced by integrating over sufficiently long time or averaging over several cross-correlation windows. Contrary to the general assumption, we show in this study that cross-terms are not required to cancel each other prior to spectral whitening, but may also cancel each other after the whitening procedure. Specifically, we derive an analytic approximation for the amplitude difference associated with the reversed order of cancellation and normalization. Our approximation shows that an amplitude decrease results from the reversed order. This decrease is predominantly non-linear at small receiver-receiver distances: at distances smaller than approximately two wavelengths, whitening prior to ensemble averaging causes a significantly stronger decay of the cross-spectrum.
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.
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
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
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.
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
NASA Technical Reports Server (NTRS)
Pearson, R. L.; Miller, L. D.; Tucker, C. J.
1976-01-01
A simple hand-held instrument has been designed and constructed to nondestructively estimate above-ground gramineous biomass using radiometric measurements. The prototype unit consists of a modified two-channel digital radiometer interfaced to a pocket calculator. A digital interface was constructed to join electronically and control the radiometer and calculator to enable the radiometer-calculator system to solve a linear conversion solution from radiometric units to estimated biomass. This instrument has been used to estimate radiometrically gramineous biomass in a more efficient fashion with a high degree of accuracy.
Estimations of Mo X-pinch plasma parameters on QiangGuang-1 facility by L-shell spectral analyses
Wu, Jian; Qiu, Aici; Li, Mo; Wang, Liangping; Wu, Gang; Ning, Guo; Qiu, Mengtong; Li, Xingwen
2013-08-15
Plasma parameters of molybdenum (Mo) X-pinches on the 1-MA QiangGuang-1 facility were estimated by L-shell spectral analysis. X-ray radiation from X-pinches had a pulsed width of 1 ns, and its spectra in 2–3 keV were measured with a time-integrated X-ray spectrometer. Relative intensities of spectral features were derived by correcting for the spectral sensitivity of the spectrometer. With an open source, atomic code FAC (flexible atomic code), ion structures, and various atomic radiative-collisional rates for O-, F-, Ne-, Na-, Mg-, and Al-like ionization stages were calculated, and synthetic spectra were constructed at given plasma parameters. By fitting the measured spectra with the modeled, Mo X-pinch plasmas on the QiangGuang-1 facility had an electron density of about 10{sup 21} cm{sup −3} and the electron temperature of about 1.2 keV.
On estimating frequency response function envelopes using the spectral element method and fuzzy sets
NASA Astrophysics Data System (ADS)
Nunes, R. F.; Klimke, A.; Arruda, J. R. F.
2006-04-01
The influence of uncertain input data on response spectra of dynamic structures is considered. Traditionally, frequency response analyses are based on finite or boundary element models of the objective structure. In the case of the mid-frequency range problem, however, a very fine mesh is required to correctly approximate the frequency response. This is particularly problematic in uncertainty modeling where the computational effort is usually increased significantly by the need for multiple runs (e.g. when conducting a Monte Carlo analysis) to achieve reliable results. In this paper, the spectral element method, combined with a fuzzy set-based uncertainty modeling approach, is presented as an appealing alternative, provided that the models are simple enough to yield a spectral element representation. To conduct the fuzzy analysis part, three different implementations of the extension principle of fuzzy arithmetic are applied and compared. The suitability of each method depends on the number of uncertain parameters, the problem characteristics, and the required accuracy of the results. The performance of the proposed approach is illustrated by two test problems, a simple coupled rod and a reinforced plate model. To verify the fuzzy-valued results, a Monte Carlo simulation has also been included.
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
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.
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
NASA Astrophysics Data System (ADS)
Molnar, S.; Dettmer, J.; Steininger, G.; Dosso, S. E.; Cassidy, J. F.
2013-12-01
This paper applies hierarchical, trans-dimensional Bayesian models for earth and residual-error parametrizations to the inversion of microtremor array dispersion data for shear-wave velocity (Vs) structure. The earth is parametrized in terms of flat-lying, homogeneous layers and residual errors are parametrized with a first-order autoregressive data-error model. The inversion accounts for the limited knowledge of the optimal earth and residual error model parametrization (e.g. the number of layers in the Vs profile) in the resulting Vs parameter uncertainty estimates. The assumed earth model parametrization influences estimates of parameter values and uncertainties due to different parametrizations leading to different ranges of data predictions. The support of the data for a particular model is often non-unique and several parametrizations may be supported. A trans-dimensional formulation accounts for this non-uniqueness by including a model-indexing parameter as an unknown so that groups of models (identified by the index) are considered in the results. In addition, serial residual-error correlations are addressed by augmenting the geophysical forward model with a hierarchical autoregressive error model that can account for a wide range of error processes with a small number of parameters. Hence, the limited knowledge about the true statistical distribution of data errors is also accounted for in the earth model parameter estimates, resulting in more realistic uncertainties and parameter values. Hierarchical autoregressive error models do not rely on point estimates of the model vector to estimate residual-error statistics, and have no requirement for computing the inverse or determinant of a covariance matrix. This approach is particularly useful for trans-dimensional inverse problems, as point estimates may not be representative of the state space that spans multiple subspaces of different dimensions. The autoregressive process is restricted to first order and
Kepler AutoRegressive Planet Search: Motivation & Methodology
NASA Astrophysics Data System (ADS)
Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian
2015-08-01
The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal
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.
Modal identification based on Gaussian continuous time autoregressive moving average model
NASA Astrophysics Data System (ADS)
Xiuli, Du; Fengquan, Wang
2010-09-01
A new time-domain modal identification method of the linear time-invariant system driven by the non-stationary Gaussian random force is presented in this paper. The proposed technique is based on the multivariate continuous time autoregressive moving average (CARMA) model. This method can identify physical parameters of a system from the response-only data. To do this, we first transform the structural dynamic equation into the CARMA model, and subsequently rewrite it in the state-space form. Second, we present the exact maximum likelihood estimators of parameters of the continuous time autoregressive (CAR) model by virtue of the Girsanov theorem, under the assumption that the uniformly modulated function is approximately equal to a constant matrix over a very short period of time. Then, based on the relation between the CAR model and the CARMA model, we present the exact maximum likelihood estimators of parameters of the CARMA model. Finally, the modal parameters are identified by the eigenvalue analysis method. Numerical results show that the method we introduced here not only has high precision and robustness, but also has very high computing efficiency. Therefore, it is suitable for real-time modal identification.
A fast, personal-computer based method of estimating molecular weights of organic compounds from low resolution mass I spectra has been thoroughly evaluated. he method is based on a rule-based pattern,recognition/expert system approach which uses empirical linear corrections whic...
Fu, Xiao-Ning; Wang, Jie; Yang, Lin
2013-01-01
It is a typical passive ranging technology that estimation of distance of an object is based on transmission characteristic of infrared radiation, it is also a hotspot in electro-optic countermeasures. Because of avoiding transmitting energy in the detection, this ranging technology will significantly enhance the penetration capability and infrared conceal capability of the missiles or unmanned aerial vehicles. With the current situation in existing passive ranging system, for overcoming the shortage in ranging an oncoming target object with small temperature difference from background, an improved distance estimation scheme was proposed. This article begins with introducing the concept of signal transfer function, makes clear the working curve of current algorithm, and points out that the estimated distance is not unique due to inherent nonlinearity of the working curve. A new distance calculation algorithm was obtained through nonlinear correction technique. It is a ranging formula by using sensing information at 3-5 and 8-12 microm combined with background temperature and field meteorological conditions. The authors' study has shown that the ranging error could be mainly kept around the level of 10% under the condition of the target and background apparent temperature difference equal to +/- 5 K, and the error in estimating background temperature is no more than +/- 15 K. PMID:23586223
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...
Combined Spectral Index to Improve Ground-Based Estimates of Nitrogen Status in Dryland Wheat
Technology Transfer Automated Retrieval System (TEKTRAN)
Recent studies have demonstrated the usefulness of the single ratio Normalized Difference Vegetation Index (NDVI) and ground-based remote sensing for estimating crop yield potential and basing in-season nitrogen (N) fertilizer application. The NDVI is positively related to crop N status and leaf ar...
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
NASA Astrophysics Data System (ADS)
Leite, Argentina; Paula Rocha, Ana; Eduarda Silva, Maria
2013-06-01
Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.
NASA Astrophysics Data System (ADS)
Privette, Jeffrey L.; Eck, Thomas F.; Deering, Donald W.
1997-12-01
In recent years, many computationally efficient bidirectional reflectance models have been developed to account for angular effects in land remote sensing data, particularly those from the NOAA advanced very high resolution radiometer (AVHRR), polarization and directionality of the Earth's reflectances (POLDER), and the planned EOS moderate-resolution imaging spectrometer (MODIS) and multi-angle imaging spectroradiometer (MISR) sensors. In this study, we assessed the relative ability of 10 such models to predict commonly used remote sensing products (nadir reflectance and albedo). Specifically, we inverted each model with ground-based data from the portable apparatus for rapid acquisition of bidirectional observations of the land and atmosphere (PARABOLA) arranged in subsets representative of satellite sampling geometries. We used data from nine land cover types, ranging from soil to grassland (First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE)) to forest (Boreal Ecosystem-Atmosphere Study (BOREAS)). Retrieved parameters were used in forward model runs to estimate nadir reflectance and spectral albedo over a wide range of solar angles. We rank the models by the accuracy of the estimated products and find results to be strongly dependent on the view azimuth angle range of the inversion data, and less dependent on the spectral band and land cover type. Overall, the nonlinear model of Rahman et al. [993] and the linear kernel-driven RossThickLiSparse model [Wanner et al., 1995] were most accurate. The latter was at least 25 times faster to invert than the former. Interestingly, we found these two models were not able to match the various bidirectional reflectance distribution function (BRDF) shapes as well as other models, suggesting their superior performance lies in their ability to be more reliably inverted with sparse data sets. These results should be useful to those interested in the computationally fast normalization
NASA Astrophysics Data System (ADS)
Ghorbanidehno, H.; Kokkinaki, A.; Darve, E. F.; Kitanidis, P. K.
2014-12-01
The Kalman Filter has been widely used for dynamic monitoring in reservoir engineering, and has recently gained popularity in hydrogeologic applications. A common characteristic of such applications is that the physical processes of interest are greatly affected by preferential flow (e.g., contaminant spreading, CO2 leakage), which can only be delineated if the problem is finely discretized into a large number of unknowns. However, for problems with large numbers of unknowns (e.g., larger than 10,000), the Kalman Filter has prohibitively expensive computation and storage costs. The EnKF, which is typically used to reduce the cost of computing the covariance in such cases converges slowly to the best estimate, and for a reasonable number of realizations, the estimate may not be accurate, especially for strongly heterogeneous systems. We present the Spectral Kalman Filter, a new Kalman Filter implementation that has a dramatically reduced computational cost compared to the full Kalman Filter, with comparable or higher accuracy than the EnKF for the same computational cost. Our algorithm's computational efficiency is achieved by a recurrence that updates small cross-covariance matrices instead of large covariance matrices, in combination with a low-rank approximation of the noise covariance matrix. In addition, instead of computing the expensive Jacobian matrix, a matrix-free method is used to obtain sensitivities. Finally, the error of our method can be explicitly controlled by reducing the time between matrix updates. The frequency of these updates is controlled independently from the data assimilation steps. We demonstrate the performance of the Spectral Kalman Filter for the joint estimation of domain properties and state evolution by assimilation of quasi-continuous data during a hypothetical CO2 injection in a heterogeneous domain.
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.
[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. PMID:26669174
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
NASA Astrophysics Data System (ADS)
Yu, Kang; Lenz-Wiedemann, Victoria; Chen, Xinping; Bareth, Georg
2014-11-01
Monitoring in situ chlorophyll (Chl) content in agricultural crop leaves is of great importance for stress detection, nutritional state diagnosis, yield prediction and studying the mechanisms of plant and environment interaction. Numerous spectral indices have been developed for chlorophyll estimation from leaf- and canopy-level reflectance. However, in most cases, these indices are negatively affected by variations in canopy structure and soil background. The objective of this study was to develop spectral indices that can reduce the effects of varied canopy structure and growth stages for the estimation of leaf Chl. Hyperspectral reflectance data was obtained through simulation by a radiative transfer model, PROSAIL, and measurements from canopies of barley comprising different cultivars across growth stages using spectroradiometers. We applied a comprehensive band-optimization algorithm to explore five types of spectral indices: reflectance difference (RD), reflectance ratio (RR), normalized reflectance difference (NRD), difference of reflectance ratio (DRR) and ratio of reflectance difference (RRD). Indirectly using the multiple scatter correction (MSC) theory, we hypothesized that RRD can eliminate adverse effects of soil background, canopy structure and multiple scattering. Published indices and multivariate models such as optimum multiple band regression (OMBR), partial least squares regression (PLSR) and support vector machines for regression (SVR) were also employed. Results showed that the ratio of reflectance difference index (RRDI) optimized for simulated data significantly improved the correlation with Chl (R2 = 0.98, p < 0.0001) and was insensitive to LAI variations (1-8), compared to widely used indices such as MCARI/OSAVI (R2 = 0.64, p < 0.0001) and TCARI/OSAVI (R2 = 0.74, p < 0.0001). The RRDI optimized for barley explained 76% of the variation in Chl and outperformed multivariate models. However, the accuracy decreased when employing the indices
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.
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.
Belghith, Akram; Bowd, Christopher; Weinreb, Robert N.; Zangwill, Linda M.
2014-01-01
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. PMID:25606299
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.
FOURIER ANALYSIS OF EXTENDED FINE STRUCTURE WITH AUTOREGRESSIVE PREDICTION
Barton, J.; Shirley, D.A.
1985-01-01
Autoregressive prediction is adapted to double the resolution of Angle-Resolved Photoemission Extended Fine Structure (ARPEFS) Fourier transforms. Even with the optimal taper (weighting function), the commonly used taper-and-transform Fourier method has limited resolution: it assumes the signal is zero beyond the limits of the measurement. By seeking the Fourier spectrum of an infinite extent oscillation consistent with the measurements but otherwise having maximum entropy, the errors caused by finite data range can be reduced. Our procedure developed to implement this concept applies autoregressive prediction to extrapolate the signal to an extent controlled by a taper width. Difficulties encountered when processing actual ARPEFS data are discussed. A key feature of this approach is the ability to convert improved measurements (signal-to-noise or point density) into improved Fourier resolution.
HIGH RESOLUTION FOURIER ANALYSIS WITH AUTO-REGRESSIVE LINEAR PREDICTION
Barton, J.; Shirley, D.A.
1984-04-01
Auto-regressive linear prediction is adapted to double the resolution of Angle-Resolved Photoemission Extended Fine Structure (ARPEFS) Fourier transforms. Even with the optimal taper (weighting function), the commonly used taper-and-transform Fourier method has limited resolution: it assumes the signal is zero beyond the limits of the measurement. By seeking the Fourier spectrum of an infinite extent oscillation consistent with the measurements but otherwise having maximum entropy, the errors caused by finite data range can be reduced. Our procedure developed to implement this concept adapts auto-regressive linear prediction to extrapolate the signal in an effective and controllable manner. Difficulties encountered when processing actual ARPEFS data are discussed. A key feature of this approach is the ability to convert improved measurements (signal-to-noise or point density) into improved Fourier resolution.
An autoregressive point source model for spatial processes
Hughes-Oliver, Jacqueline M.; Heo, Tae-Young; Ghosh, Sujit K.
2009-01-01
We suggest a parametric modeling approach for nonstationary spatial processes driven by point sources. Baseline near-stationarity, which may be reasonable in the absence of a point source, is modeled using a conditional autoregressive (CAR) Markov random field. Variability due to the point source is captured by our proposed autoregressive point source (ARPS) model. Inference proceeds according to the Bayesian hierarchical paradigm, and is implemented using Markov chain Monte Carlo (MCMC) methods. The parametric approach allows a formal test of effectiveness of the point source. Application is made to a real dataset on electric potential measurements in a field containing a metal pole and the finding is that our approach captures the pole’s impact on small-scale variability of the electric potential process. PMID:19936263
NASA Astrophysics Data System (ADS)
Nordon, R.; Lutz, D.; Genzel, R.; Berta, S.; Wuyts, S.; Magnelli, B.; Altieri, B.; Andreani, P.; Aussel, H.; Bongiovanni, A.; Cepa, J.; Cimatti, A.; Daddi, E.; Fadda, D.; Förster Schreiber, N. M.; Lagache, G.; Maiolino, R.; Pérez García, A. M.; Poglitsch, A.; Popesso, P.; Pozzi, F.; Rodighiero, G.; Rosario, D.; Saintonge, A.; Sanchez-Portal, M.; Santini, P.; Sturm, E.; Tacconi, L. J.; Valtchanov, I.; Yan, L.
2012-02-01
We combine Herschel-Photodetector Array Camera and Spectrometer (PACS) data from the PACS Evolutionary Probe (PEP) program with Spitzer 24 μm and 16 μm photometry and ultra deep Infrared Spectrograph (IRS) mid-infrared spectra to measure the mid- to far-infrared spectral energy distribution (SED) of 0.7 < z < 2.5 normal star-forming galaxies (SFGs) around the main sequence (the redshift-dependent relation of star formation rate (SFR) and stellar mass). Our very deep data confirm from individual far-infrared detections that z ~ 2 SFRs are overestimated if based on 24 μm fluxes and SED templates that are calibrated via local trends with luminosity. Galaxies with similar ratios of rest-frame νL ν(8) to 8-1000 μm infrared luminosity (LIR) tend to lie along lines of constant offset from the main sequence. We explore the relation between SED shape and offset in specific star formation rate (SSFR) from the redshift-dependent main sequence. Main-sequence galaxies tend to have a similar νL ν(8)/LIR regardless of LIR and redshift, up to z ~ 2.5, and νL ν(8)/LIR decreases with increasing offset above the main sequence in a consistent way at the studied redshifts. We provide a redshift-independent calibration of SED templates in the range of 8-60 μm as a function of Δlog(SSFR) offset from the main sequence. Redshift dependency enters only through the evolution of the main sequence with time. Ultra deep IRS spectra match these SED trends well and verify that they are mostly due to a change in ratio of polycyclic aromatic hydrocarbon (PAH) to LIR rather than continua of hidden active galactic nuclei (AGNs). Alternatively, we discuss the dependence of νL ν(8)/LIR on LIR. The same νL ν(8)/LIR is reached at increasingly higher LIR at higher redshift, with shifts relative to local by 0.5 and 0.8 dex in log(LIR) at redshifts z ~ 1 and z ~ 2. Corresponding SED template calibrations are provided for use if no stellar masses are on hand. For most of those z ~ 2 SFGs that
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)
Liu, X.; Ferrare, R. A.; Hostetler, C. A.; Burton, S. P.; Stamnes, S.; Mueller, D.; Chemyakin, E.; Sawamura, P.; Cairns, B.
2015-12-01
Knowledge of the vertical profile, composition, concentration, and size distribution of aerosols is required to quantify the impacts of aerosols on human health, global and regional climate, clouds and precipitation, and ocean ecosystems. We will describe an Optimal Estimation (OE) retrieval method that will use three wavelengths of aerosol backscattering (3β) and two wavelengths of aerosol extinction (2α). We will also describe how to use the OE framework to retrieve vertical profiles simultaneously using altitude resolved HSRL data. Finally, we will describe how to include additional measurements (e.g. polarimeter or Sun photometer) for improved aerosol microphysical property retrievals. In a traditional aerosol retrieval algorithm, one solves for aerosol size distributions under various parameter space (rmin, rmax, real and imaginary refractive index) using Tikhonov (or other) regularization and then selects physically and mathematically meaningful solutions from hundreds of thousand retrievals. In an attempt to speed up the retrieval and to provide retrieval error estimates, the OE method solves for all related aerosol microphysical parameters (e.g. number concentrations, particle size distribution, real and imaginary part of refractive indices) simultaneously in a maximum-likelihood sense by fitting the observed data. Other quantities such as effective particle radius, surface area concentration, volume concentration, and single scattering albedo are also derived from the retrieved size distribution and the number concentrations. We will show preliminary results using both simulated data and airborne measurements from HSRL-2. Coincident airborne in-situ and surface remote sensing datasets will be used to evaluate the performance of the new OE algorithm.
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.
Fabre, Sophie; Briottet, Xavier; Lesaignoux, Audrey
2015-01-01
This work aims to compare the performance of new methods to estimate the Soil Moisture Content (SMC) of bare soils from their spectral signatures in the reflective domain (0.4–2.5 μm) in comparison with widely used spectral indices like Normalized Soil Moisture Index (NSMI) and Water Index SOIL (WISOIL). Indeed, these reference spectral indices use wavelengths located in the water vapour absorption bands and their performance are thus very sensitive to the quality of the atmospheric compensation. To reduce these limitations, two new spectral indices are proposed which wavelengths are defined using the determination matrix tool by taking into account the atmospheric transmission: Normalized Index of Nswir domain for Smc estimatiOn from Linear correlation (NINSOL) and Normalized Index of Nswir domain for Smc estimatiOn from Non linear correlation (NINSON). These spectral indices are completed by two new methods based on the global shape of the soil spectral signatures. These methods are the Inverse Soil semi-Empirical Reflectance model (ISER), using the inversion of an existing empirical soil model simulating the soil spectral reflectance according to soil moisture content for a given soil class, and the convex envelope model, linking the area between the envelope and the spectral signature to the SMC. All these methods are compared using a reference database built with 32 soil samples and composed of 190 spectral signatures with five or six soil moisture contents. Half of the database is used for the calibration stage and the remaining to evaluate the performance of the SMC estimation methods. The results show that the four new methods lead to similar or better performance than the one obtained by the reference indices. The RMSE is ranging from 3.8% to 6.2% and the coefficient of determination R2 varies between 0.74 and 0.91 with the best performance obtained with the ISER model. In a second step, simulated spectral radiances at the sensor level are used to
Automating Vector Autoregression on Electronic Patient Diary Data.
Emerencia, Ando Celino; van der Krieke, Lian; Bos, Elisabeth H; de Jonge, Peter; Petkov, Nicolai; Aiello, Marco
2016-03-01
Finding the best vector autoregression model for any dataset, medical or otherwise, is a process that, to this day, is frequently performed manually in an iterative manner requiring a statistical expertize and time. Very few software solutions for automating this process exist, and they still require statistical expertize to operate. We propose a new application called Autovar, for the automation of finding vector autoregression models for time series data. The approach closely resembles the way in which experts work manually. Our proposal offers improvements over the manual approach by leveraging computing power, e.g., by considering multiple alternatives instead of choosing just one. In this paper, we describe the design and implementation of Autovar, we compare its performance against experts working manually, and we compare its features to those of the most used commercial solution available today. The main contribution of Autovar is to show that vector autoregression on a large scale is feasible. We show that an exhaustive approach for model selection can be relatively safe to use. This study forms an important step toward making adaptive, personalized treatment available and affordable for all branches of healthcare. PMID:25680221
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
Bazan, I.; Ramos, A.; Calas, H.; Ramirez, A.; Pintle, R.; Gomez, T. E.; Negreira, C.; Gallegos, F. J.; Rosales, A. J.
2012-01-01
To achieve a precise noninvasive temperature estimation, inside patient tissues, would open promising research fields, because its clinic results would provide early-diagnosis tools. In fact, detecting changes of thermal origin in ultrasonic echo spectra could be useful as an early complementary indicator of infections, inflammations, or cancer. But the effective clinic applications to diagnosis of thermometry ultrasonic techniques, proposed previously, require additional research. Before their implementations with ultrasonic probes and real-time electronic and processing systems, rigorous analyses must be still made over transient echotraces acquired from well-controlled biological and computational phantoms, to improve resolutions and evaluate clinic limitations. It must be based on computing improved signal-processing algorithms emulating tissues responses. Some related parameters in echo-traces reflected by semiregular scattering tissues must be carefully quantified to get a precise processing protocols definition. In this paper, approaches for non-invasive spectral ultrasonic detection are analyzed. Extensions of author's innovations for ultrasonic thermometry are shown and applied to computationally modeled echotraces from scattered biological phantoms, attaining high resolution (better than 0.1°C). Computer methods are provided for viability evaluation of thermal estimation from echoes with distinct noise levels, difficult to be interpreted, and its effectiveness is evaluated as possible diagnosis tool in scattered tissues like liver. PMID:22654958
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)
Zygielbaum, A. I.; Arkebauer, T. J.; Walter-Shea, E.
2013-12-01
Vegetation photoprotective responses impact the reflected spectra in the visible or photosynthetically active (PAR) spectral region. Earlier, we presented a case that the increasing PAR reflectance which accompanies increasing water stress was due to one such response, chloroplast avoidance movement. This increasing reflectance has been reported in published papers for several decades and dismissed as operator error or a result of changes in leaf turgor or optical pathway. We showed, however, that such changes in the PAR region, which occurred with no significant change in chlorophyll content, were caused by decreasing absorption, not changes in light scatter. Further, we demonstrated that the changes in reflectance were correlated with changes in ambient light (downwelling radiance). To further refine the case that chloroplast movement is the basis of these observations, excised leaves were exposed separately to either red light or white light illumination of equal photon flux densities. The transmittance observed as these leaves dried increased in the leaves exposed to white light and remained constant in the leaves exposed to red light. Since chloroplast movement is driven by blue light, our conjecture is strengthened. We have also observed distinct morning vs. afternoon differences in reflectance spectra of greenhouse-grown plants; indices derived from these spectra also vary diurnally--leading us to coin the phase 'apparent chlorophyll'. All observations previously reported were the result of greenhouse experiments. We report herein on observations of leaf and canopy reflectances under field conditions and on the impact the increasing reflectance has on estimation of chlorophyll content using spectral indices. We also present evidence that increasing reflectance which is concomitant with increasing plant stress may not correlate with stress indications using the photochemical reflectance index (PRI) and discuss the implications of that observation.
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
Chaudhary, Naveed Ishtiaq; Raja, Muhammad Asif Zahoor; Khan, Junaid Ali; Aslam, Muhammad Saeed
2013-01-01
A novel algorithm is developed based on fractional signal processing approach for parameter estimation of input nonlinear control autoregressive (INCAR) models. The design scheme consists of parameterization of INCAR systems to obtain linear-in-parameter models and to use fractional least mean square algorithm (FLMS) for adaptation of unknown parameter vectors. The performance analyses of the proposed scheme are carried out with third-order Volterra least mean square (VLMS) and kernel least mean square (KLMS) algorithms based on convergence to the true values of INCAR systems. It is found that the proposed FLMS algorithm provides most accurate and convergent results than those of VLMS and KLMS under different scenarios and by taking the low-to-high signal-to-noise ratio. PMID:23853538
NASA Astrophysics Data System (ADS)
Gautam, N.; Rasmussen, P. F.
2005-05-01
Stochastic weather generators are frequently used in climate change studies to simulate input to hydrologic models. In this presentation, we focus on the particular problem of simulating daily precipitation at multiple stations in a region for which records are available. Daily precipitation is a highly intermittent process, highly variable in space, and typically has a highly skewed distribution. A stochastic precipitation model should ideally preserve the regional pattern of intermittence, the autocorrelation, the cross-correlation, and the marginal distributions of observed precipitation. For this purpose, we employed a multivariate autoregressive model. Below zero-values were considered days with no rain. To preserve the marginal distributions of observed precipitation at different stations some prior transformation of data was required. The presentation will describe the experience gained from applying the model to precipitation records in Canada. Focus will be on analytical model properties, methods of parameter estimation, and the preservation of observed statistics in the application.
Computational problems in autoregressive moving average (ARMA) models
NASA Technical Reports Server (NTRS)
Agarwal, G. C.; Goodarzi, S. M.; Oneill, W. D.; Gottlieb, G. L.
1981-01-01
The choice of the sampling interval and the selection of the order of the model in time series analysis are considered. Band limited (up to 15 Hz) random torque perturbations are applied to the human ankle joint. The applied torque input, the angular rotation output, and the electromyographic activity using surface electrodes from the extensor and flexor muscles of the ankle joint are recorded. Autoregressive moving average models are developed. A parameter constraining technique is applied to develop more reliable models. The asymptotic behavior of the system must be taken into account during parameter optimization to develop predictive models.
Autoregressive Modeling of Physiological Tremor under Microsurgical Conditions
Becker, Brian C.; Tummala, Harsha; Riviere, Cameron N.
2010-01-01
Tremor was recorded under simulated vitreoretinal microsurgical conditions as subjects attempted to hold an instrument motionless. Several autoregressive models (AR, ARMA, multivariate, and nonlinear) are generated to predict the next value of tremor. It is shown that a sixth order ARMA model predictor can predict a tremor having an amplitude of 96.6 ± 84.5 microns RMS with an error of 8.2 ± 5.9 microns RMS, a mean improvement of 47.5% over simple last-value prediction. PMID:19163072
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.
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 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.
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 ...
Evaluation of a vector autoregressive approach for downscaling
NASA Astrophysics Data System (ADS)
Salonen, Sebastian; Sauter, Tobias
2014-05-01
Statisical downscaling has become a well-established tool in regional and local impact assessments over the last few years. Robust and universal downscaling methods are required to reliably correct the spatial and temporal structures from coarse models. In this study we set up and evaluate the application of VAR-models for automated temperature and precipitation downscaling. VAR-models belong to the vectorial regression-techniques, that include autoregressive effects of the considered time series. They might be seen as an extension of univariate time-series analysis to multivariate perspective. Including autoregressive effects is one of the great advantages of this method, but also includes some pitfalls. Before the model can be applied the structure of the data must be carfully examined and require appropriate data preprocessing. We study in detail different preprocessing techniques and the possibility of the automatization. The proposed method has been applied and evaluated to temperature and precipitation data in the Rhineland region (Germany) and Svalbard. The large-scale atmospheric data are derived from ERA-40 as NCEP/NCAR reanalysis. These datasets offer the possibility to determine the applicability of VAR-models in a downscaling approach, their need for data-preparation techniques and the possibility of an automatization of an approach based on these models.
Adaptive predictive multiplicative autoregressive model for medical image compression.
Chen, Z D; Chang, R F; Kuo, W J
1999-02-01
In this paper, an adaptive predictive multiplicative autoregressive (APMAR) method is proposed for lossless medical image coding. The adaptive predictor is used for improving the prediction accuracy of encoded image blocks in our proposed method. Each block is first adaptively predicted by one of the seven predictors of the JPEG lossless mode and a local mean predictor. It is clear that the prediction accuracy of an adaptive predictor is better than that of a fixed predictor. Then the residual values are processed by the MAR model with Huffman coding. Comparisons with other methods [MAR, SMAR, adaptive JPEG (AJPEG)] on a series of test images show that our method is suitable for reversible medical image compression. PMID:10232675
Autoregressive modelling for rolling element bearing fault diagnosis
NASA Astrophysics Data System (ADS)
Al-Bugharbee, H.; Trendafilova, I.
2015-07-01
In this study, time series analysis and pattern recognition analysis are used effectively for the purposes of rolling bearing fault diagnosis. The main part of the suggested methodology is the autoregressive (AR) modelling of the measured vibration signals. This study suggests the use of a linear AR model applied to the signals after they are stationarized. The obtained coefficients of the AR model are further used to form pattern vectors which are in turn subjected to pattern recognition for differentiating among different faults and different fault sizes. This study explores the behavior of the AR coefficients and their changes with the introduction and the growth of different faults. The idea is to gain more understanding about the process of AR modelling for roller element bearing signatures and the relation of the coefficients to the vibratory behavior of the bearings and their condition.
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
Drought Patterns Forecasting using an Auto-Regressive Logistic Model
NASA Astrophysics Data System (ADS)
del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.
2014-12-01
Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.
NASA Astrophysics Data System (ADS)
Srinath, Srikar; Poyneer, Lisa A.; Rudy, Alexander R.; Ammons, S. M.
2014-08-01
The advent of expensive, large-aperture telescopes and complex adaptive optics (AO) systems has strengthened the need for detailed simulation of such systems from the top of the atmosphere to control algorithms. The credibility of any simulation is underpinned by the quality of the atmosphere model used for introducing phase variations into the incident photons. Hitherto, simulations which incorporate wind layers have relied upon phase screen generation methods that tax the computation and memory capacities of the platforms on which they run. This places limits on parameters of a simulation, such as exposure time or resolution, thus compromising its utility. As aperture sizes and fields of view increase the problem will only get worse. We present an autoregressive method for evolving atmospheric phase that is efficient in its use of computation resources and allows for variability in the power contained in frozen flow or stochastic components of the atmosphere. Users have the flexibility of generating atmosphere datacubes in advance of runs where memory constraints allow to save on computation time or of computing the phase at each time step for long exposure times. Preliminary tests of model atmospheres generated using this method show power spectral density and rms phase in accordance with established metrics for Kolmogorov models.
Mehta, Daryush D; Rudoy, Daniel; Wolfe, Patrick J
2012-09-01
Vocal tract resonance characteristics in acoustic speech signals are classically tracked using frame-by-frame point estimates of formant frequencies followed by candidate selection and smoothing using dynamic programming methods that minimize ad hoc cost functions. The goal of the current work is to provide both point estimates and associated uncertainties of center frequencies and bandwidths in a statistically principled state-space framework. Extended Kalman (K) algorithms take advantage of a linearized mapping to infer formant and antiformant parameters from frame-based estimates of autoregressive moving average (ARMA) cepstral coefficients. Error analysis of KARMA, wavesurfer, and praat is accomplished in the all-pole case using a manually marked formant database and synthesized speech waveforms. KARMA formant tracks exhibit lower overall root-mean-square error relative to the two benchmark algorithms with the ability to modify parameters in a controlled manner to trade off bias and variance. Antiformant tracking performance of KARMA is illustrated using synthesized and spoken nasal phonemes. The simultaneous tracking of uncertainty levels enables practitioners to recognize time-varying confidence in parameters of interest and adjust algorithmic settings accordingly. PMID:22978900
NASA Astrophysics Data System (ADS)
Brzezinski, A.
2014-12-01
The methods of spectral analysis are applied to solve the following two problems concerning the free Chandler wobble (CW): 1) to estimate the CW resonance parameters, the period T and the quality factor Q, and 2) to perform the excitation balance of the observed free wobble. It appears, however, that the results depend on the algorithm of spectral analysis applied. Here we compare the following two algorithms which are frequently applied for analysis of the polar motion data, the classical discrete Fourier analysis and the maximum entropy method corresponding to the autoregressive modeling of the input time series. We start from general description of both methods and of their application to the analysis of the Earth orientation observations. Then we compare results of the analysis of the polar motion and the related excitation data.
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.
Characteristics of the transmission of autoregressive sub-patterns in financial time series
Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong
2014-01-01
There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors. PMID:25189200
Characteristics of the transmission of autoregressive sub-patterns in financial time series.
Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong
2014-01-01
There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors. PMID:25189200
Characteristics of the transmission of autoregressive sub-patterns in financial time series
NASA Astrophysics Data System (ADS)
Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong
2014-09-01
There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.
Spectral distance for ARMA models applied to electroencephalogram for early detection of hypoxia.
Löfgren, N; Lindecrantz, K; Flisberg, A; Bågenholm, R; Kjellmer, I; Thordstein, M
2006-09-01
A novel measure of spectral distance is presented, which is inspired by the prediction residual parameter presented by Itakura in 1975, but derived from frequency domain data and extended to include autoregressive moving average (ARMA) models. This new algorithm is applied to electroencephalogram (EEG) data from newborn piglets exposed to hypoxia for the purpose of early detection of hypoxia. The performance is evaluated using parameters relevant for potential clinical use, and is found to outperform the Itakura distance, which has proved to be useful for this application. Additionally, we compare the performance with various algorithms previously used for the detection of hypoxia from EEG. Our results based on EEG from newborn piglets show that some detector statistics divert significantly from a reference period less than 2 min after the start of general hypoxia. Among these successful detectors, the proposed spectral distance is the only spectral-based parameter. It therefore appears that spectral changes due to hypoxia are best described by use of an ARMA- model-based spectral estimate, but the drawback of the presented method is high computational effort. PMID:16921206
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
Analysis of Heart Rate and Blood Pressure Time Series Using a Two-Dimensional Autoregressive Model
NASA Astrophysics Data System (ADS)
Yoshida, Yutaka; Yokoyama, Kiyoko; Uehara, Akihiko; Kurata, Chinori; Takata, Kazuyuki
We analyzed the feedback relationship between short-term fluctuations in heart rate and blood pressure in healthy persons and heart failure patients. Parameters derived from the feedback relationship between heart rate and blood pressure have been proposed. The purpose of the present study is to apply these parameters in estimating autonomic function or measuring physiological and mental workload. Electrocardiographs and beat-to-beat blood pressure were recorded in supine position at rest. The blood pressure was measured using arterial tonometry. The R—R interval and systolic blood pressure were fitted to two-dimensional autoregressive models, the relative power contribution in the frequency domain was calculated. The proposed parameters are the power contribution in the low-frequency range ( 0-0.15 Hz ) [ RS_LF, SR_LF ] and the power contribution in the high-frequency range ( 0.15-0.5 Hz ) [ RS_HF, SR_HF ]. RS_LF was significantly different between healthy persons and heart failure patients ( p<0.01 ). This parameter can be used to estimate autonomic depression caused by aging and heart failure. There were no correlations between the proposed parameters and the usual indices to evaluate autonomic function. It is considered that the proposed parameters can be used to evaluate physiological states that cannot be evaluated using existing methods.
NASA Astrophysics Data System (ADS)
Hill, D.; Bell, K. R. W.; McMillan, D.; Infield, D.
2014-05-01
The growth of wind power production in the electricity portfolio is striving to meet ambitious targets set, for example by the EU, to reduce greenhouse gas emissions by 20% by 2020. Huge investments are now being made in new offshore wind farms around UK coastal waters that will have a major impact on the GB electrical supply. Representations of the UK wind field in syntheses which capture the inherent structure and correlations between different locations including offshore sites are required. Here, Vector Auto-Regressive (VAR) models are presented and extended in a novel way to incorporate offshore time series from a pan-European meteorological model called COSMO, with onshore wind speeds from the MIDAS dataset provided by the British Atmospheric Data Centre. Forecasting ability onshore is shown to be improved with the inclusion of the offshore sites with improvements of up to 25% in RMS error at 6 h ahead. In addition, the VAR model is used to synthesise time series of wind at each offshore site, which are then used to estimate wind farm capacity factors at the sites in question. These are then compared with estimates of capacity factors derived from the work of Hawkins et al. (2011). A good degree of agreement is established indicating that this synthesis tool should be useful in power system impact studies.
Generation of multivariate autoregressive sequences with emphasis on initial values
NASA Astrophysics Data System (ADS)
Ula, Taylan A.
1992-12-01
Certain aspects of data generation are studied through multivariate autoregressive (AR) models. The main emphasis is on the preservation of certain desired moments and the effect of initial values on these moments. The problem of preservation of moments is approached in a nontraditional way by starting with the initial values. For this purpose, general AR processes with a random start and with time-varying parameters are introduced to lay a foundation for the analysis of all types of AR processes, including the periodic cases. It is shown that an AR process with a random start and with parameters obtained from the moment equations is capable of generating jointly multivariate normal vectors with any specified means and covariance matrices, and with any specified autocovariance matrices up to a given lag. With a random start, there is no transition period involved for achieving these moments. A simple solution is proposed for matrix equations of the form BBT = A which appear in the moment equations. The aggregation properties of general AR process are also studied. A more detailed analysis is given for the two-period first-order periodic autoregressive model, PAR 2(1). For the PAR 2(1) process with a random start and with parameters obtained from the moment equations, it is shown that the autocovariance function depends only on the period and the lag, and therefore the process is periodic (covariance) stationary. The PAR 2(1) process with a fixed start is also studied. It is shown that the moments of this process depend on the absolute time, in addition to the period and the lag, and therefore the process is not periodic stationary. This dependence diminishes with time, and periodic stationarity is realized if the AR parameters satisfy certain conditions. In that case, the PAR 2(1) process with a fixed start converges to that with a random start, but only after a certain transition period. This proves the superiority of a random start over a fixed start.
Motaghian Nezam, S. M. R.; Joo, C; Tearney, G. J.; de Boer, J. F.
2009-01-01
Spectral-domain optical coherence phase microscopy (SD-OCPM) measures minute phase changes in transparent biological specimens using a common path interferometer and a spectrometer based optical coherence tomography system. The Fourier transform of the acquired interference spectrum in spectral-domain optical coherence tomography (SD-OCT) is complex and the phase is affected by contributions from inherent random noise. To reduce this phase noise, knowledge of the probability density function (PDF) of data becomes essential. In the present work, the intensity and phase PDFs of the complex interference signal are theoretically derived and the optical path length (OPL) PDF is experimentally validated. The full knowledge of the PDFs is exploited for optimal estimation (Maximum Likelihood estimation) of the intensity, phase, and signal-to-noise ratio (SNR) in SD-OCPM. Maximum likelihood (ML) estimates of the intensity, SNR, and OPL images are presented for two different scan modes using Bovine Pulmonary Artery Endothelial (BPAE) cells. To investigate the phase accuracy of SD-OCPM, we experimentally calculate and compare the cumulative distribution functions (CDFs) of the OPL standard deviation and the square root of the Cramér-Rao lower bound (1/2SNR) over 100 BPAE images for two different scan modes. The correction to the OPL measurement by applying ML estimation to SD-OCPM for BPAE cells is demonstrated. PMID:18957999
The Kernel Adaptive Autoregressive-Moving-Average Algorithm.
Li, Kan; Príncipe, José C
2016-02-01
In this paper, we present a novel kernel adaptive recurrent filtering algorithm based on the autoregressive-moving-average (ARMA) model, which is trained with recurrent stochastic gradient descent in the reproducing kernel Hilbert spaces. This kernelized recurrent system, the kernel adaptive ARMA (KAARMA) algorithm, brings together the theories of adaptive signal processing and recurrent neural networks (RNNs), extending the current theory of kernel adaptive filtering (KAF) using the representer theorem to include feedback. Compared with classical feedforward KAF methods, the KAARMA algorithm provides general nonlinear solutions for complex dynamical systems in a state-space representation, with a deferred teacher signal, by propagating forward the hidden states. We demonstrate its capabilities to provide exact solutions with compact structures by solving a set of benchmark nondeterministic polynomial-complete problems involving grammatical inference. Simulation results show that the KAARMA algorithm outperforms equivalent input-space recurrent architectures using first- and second-order RNNs, demonstrating its potential as an effective learning solution for the identification and synthesis of deterministic finite automata. PMID:25935049
An algebraic method for constructing stable and consistent autoregressive filters
NASA Astrophysics Data System (ADS)
Harlim, John; Hong, Hoon; Robbins, Jacob L.
2015-02-01
In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams-Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden-Julian Oscillation, a dominant tropical atmospheric wave pattern.
The comparison study among several data transformations in autoregressive modeling
NASA Astrophysics Data System (ADS)
Setiyowati, Susi; Waluyo, Ramdhani Try
2015-12-01
In finance, the adjusted close of stocks are used to observe the performance of a company. The extreme prices, which may increase or decrease drastically, are often become particular concerned since it can impact to bankruptcy. As preventing action, the investors have to observe the future (forecasting) stock prices comprehensively. For that purpose, time series analysis could be one of statistical methods that can be implemented, for both stationary and non-stationary processes. Since the variability process of stocks prices tend to large and also most of time the extreme values are always exist, then it is necessary to do data transformation so that the time series models, i.e. autoregressive model, could be applied appropriately. One of popular data transformation in finance is return model, in addition to ratio of logarithm and some others Tukey ladder transformation. In this paper these transformations are applied to AR stationary models and non-stationary ARCH and GARCH models through some simulations with varying parameters. As results, this work present the suggestion table that shows transformations behavior for some condition of parameters and models. It is confirmed that the better transformation is obtained, depends on type of data distributions. In other hands, the parameter conditions term give significant influence either.
Prediction of municipal solid waste generation using nonlinear autoregressive network.
Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A
2015-12-01
Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm. PMID:26573690
Autoregressive logistic regression applied to atmospheric circulation patterns
NASA Astrophysics Data System (ADS)
Guanche, Y.; Mínguez, R.; Méndez, F. J.
2014-01-01
Autoregressive logistic regression models have been successfully applied in medical and pharmacology research fields, and in simple models to analyze weather types. The main purpose of this paper is to introduce a general framework to study atmospheric circulation patterns capable of dealing simultaneously with: seasonality, interannual variability, long-term trends, and autocorrelation of different orders. To show its effectiveness on modeling performance, daily atmospheric circulation patterns identified from observed sea level pressure fields over the Northeastern Atlantic, have been analyzed using this framework. Model predictions are compared with probabilities from the historical database, showing very good fitting diagnostics. In addition, the fitted model is used to simulate the evolution over time of atmospheric circulation patterns using Monte Carlo method. Simulation results are statistically consistent with respect to the historical sequence in terms of (1) probability of occurrence of the different weather types, (2) transition probabilities and (3) persistence. The proposed model constitutes an easy-to-use and powerful tool for a better understanding of the climate system.
An algebraic method for constructing stable and consistent autoregressive filters
Harlim, John; Hong, Hoon; Robbins, Jacob L.
2015-02-15
In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.
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 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.
Chang, Jui-Yang; Pigorini, Andrea; Massimini, Marcello; Tononi, Giulio; Nobili, Lino; Van Veen, Barry D.
2012-01-01
A multivariate autoregressive (MVAR) model with exogenous inputs (MVARX) is developed for describing the cortical interactions excited by direct electrical current stimulation of the cortex. Current stimulation is challenging to model because it excites neurons in multiple locations both near and distant to the stimulation site. The approach presented here models these effects using an exogenous input that is passed through a bank of filters, one for each channel. The filtered input and a random input excite a MVAR system describing the interactions between cortical activity at the recording sites. The exogenous input filter coefficients, the autoregressive coefficients, and random input characteristics are estimated from the measured activity due to current stimulation. The effectiveness of the approach is demonstrated using intracranial recordings from three surgical epilepsy patients. We evaluate models for wakefulness and NREM sleep in these patients with two stimulation levels in one patient and two stimulation sites in another resulting in a total of 10 datasets. Excellent agreement between measured and model-predicted evoked responses is obtained across all datasets. Furthermore, one-step prediction is used to show that the model also describes dynamics in pre-stimulus and evoked recordings. We also compare integrated information—a measure of intracortical communication thought to reflect the capacity for consciousness—associated with the network model in wakefulness and sleep. As predicted, higher information integration is found in wakefulness than in sleep for all five cases. PMID:23226122
NASA Technical Reports Server (NTRS)
Lai, Jonathan Y.
1994-01-01
This dissertation focuses on the signal processing problems associated with the detection of hazardous windshears using airborne Doppler radar when weak weather returns are in the presence of strong clutter returns. In light of the frequent inadequacy of spectral-processing oriented clutter suppression methods, we model a clutter signal as multiple sinusoids plus Gaussian noise, and propose adaptive filtering approaches that better capture the temporal characteristics of the signal process. This idea leads to two research topics in signal processing: (1) signal modeling and parameter estimation, and (2) adaptive filtering in this particular signal environment. A high-resolution, low SNR threshold maximum likelihood (ML) frequency estimation and signal modeling algorithm is devised and proves capable of delineating both the spectral and temporal nature of the clutter return. Furthermore, the Least Mean Square (LMS) -based adaptive filter's performance for the proposed signal model is investigated, and promising simulation results have testified to its potential for clutter rejection leading to more accurate estimation of windspeed thus obtaining a better assessment of the windshear hazard.
NASA Astrophysics Data System (ADS)
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.
Likert pain score modeling: a Markov integer model and an autoregressive continuous model.
Plan, E L; Elshoff, J-P; Stockis, A; Sargentini-Maier, M L; Karlsson, M O
2012-05-01
Pain intensity is principally assessed using rating scales such as the 11-point Likert scale. In general, frequent pain assessments are serially correlated and underdispersed. The aim of this investigation was to develop population models adapted to fit the 11-point pain scale. Daily Likert scores were recorded over 18 weeks by 231 patients with neuropathic pain from a clinical trial placebo group. An integer model consisting of a truncated generalized Poisson (GP) distribution with Markovian transition probability inflation was implemented in NONMEM 7.1.0. It was compared to a logit-transformed autoregressive continuous model with correlated residual errors. In both models, the score baseline was estimated to be 6.2 and the placebo effect to be 19%. Developed models similarly retrieved consistent underlying features of the data and therefore correspond to platform models for drug effect detection. The integer model was complex but flexible, whereas the continuous model can more easily be developed, although requires longer runtimes. PMID:22433987
Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS
Barker, Jeffrey W.; Aarabi, Ardalan; Huppert, Theodore J.
2013-01-01
Systemic physiology and motion-induced artifacts represent two major sources of confounding noise in functional near infrared spectroscopy (fNIRS) imaging that can reduce the performance of analyses and inflate false positive rates (i.e., type I errors) of detecting evoked hemodynamic responses. In this work, we demonstrated a general algorithm for solving the general linear model (GLM) for both deconvolution (finite impulse response) and canonical regression models based on designing optimal pre-whitening filters using autoregressive models and employing iteratively reweighted least squares. We evaluated the performance of the new method by performing receiver operating characteristic (ROC) analyses using synthetic data, in which serial correlations, motion artifacts, and evoked responses were controlled via simulations, as well as using experimental data from children (3–5 years old) as a source baseline physiological noise and motion artifacts. The new method outperformed ordinary least squares (OLS) with no motion correction, wavelet based motion correction, or spline interpolation based motion correction in the presence of physiological and motion related noise. In the experimental data, false positive rates were as high as 37% when the estimated p-value was 0.05 for the OLS methods. The false positive rate was reduced to 5–9% with the proposed method. Overall, the method improves control of type I errors and increases performance when motion artifacts are present. PMID:24009999
A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data.
Zheng, Yin; Zhang, Yu-Jin; Larochelle, Hugo
2016-06-01
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set. PMID:26372202
NASA Technical Reports Server (NTRS)
Chao, B. F.
1983-01-01
The homogeneous set of 80-year-long (1900-1979) International Latitude Service (ILS) polar motion data is analyzed using the autoregressive method (Chao and Gilbert, 1980), which resolves and produces estimates for the complex frequency (or frequency and Q) and complex amplitude (or amplitude and phase) of each harmonic component in the data. The ILS data support the multiple-component hypothesis of the Chandler wobble. It is found that the Chandler wobble can be adequately modeled as a linear combination of four (coherent) harmonic components, each of which represents a steady, nearly circular, prograde motion. The four-component Chandler wobble model 'explains' the apparent phase reversal during 1920-1940 and the pre-1950 empirical period-amplitude relation. The annual wobble is shown to be rather stationary over the years both in amplitude and in phase, and no evidence is found to support the large variations reported by earlier investigations. The Markowitz wobble is found to be marginally retrograde and appears to have a complicated behavior which cannot be resolved because of the shortness of the data set.
Spectral estimation—What is new? What is next?
NASA Astrophysics Data System (ADS)
Tary, Jean Baptiste; Herrera, Roberto Henry; Han, Jiajun; Baan, Mirko
2014-12-01
Spectral estimation, and corresponding time-frequency representation for nonstationary signals, is a cornerstone in geophysical signal processing and interpretation. The last 10-15 years have seen the development of many new high-resolution decompositions that are often fundamentally different from Fourier and wavelet transforms. These conventional techniques, like the short-time Fourier transform and the continuous wavelet transform, show some limitations in terms of resolution (localization) due to the trade-off between time and frequency localizations and smearing due to the finite size of the time series of their template. Well-known techniques, like autoregressive methods and basis pursuit, and recently developed techniques, such as empirical mode decomposition and the synchrosqueezing transform, can achieve higher time-frequency localization due to reduced spectral smearing and leakage. We first review the theory of various established and novel techniques, pointing out their assumptions, adaptability, and expected time-frequency localization. We illustrate their performances on a provided collection of benchmark signals, including a laughing voice, a volcano tremor, a microseismic event, and a global earthquake, with the intention to provide a fair comparison of the pros and cons of each method. Finally, their outcomes are discussed and possible avenues for improvements are proposed.
Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification
Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen
2014-01-01
Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach. PMID:24595922
Kepler AutoRegressive Planet Search: Initial Results
NASA Astrophysics Data System (ADS)
Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian
2015-08-01
The statistical analysis procedures of the Kepler AutoRegressive Planet Search (KARPS) project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve, but visual inspection of the residual series shows that significant deviations from Gaussianity remain for many of them. Although the reduction in stellar signal is encouraging, it is important to note that the transit signal is also altered in the resulting residual time series. The periodogram derived from our Transit Comb Filter (TCF) is most effective for shorter period planets with quick ingress/egress times (relative to Kepler's 29-minute sample rate). We do not expect high sensitivity to periods of hundreds of days. Our findings to date on real-data tests of the KARPS methodology will be discussed including confirmation of some Kepler Team `candidate' planets, no confirmation of some `candidate' and `false positive' sytems, and suggestions of mischosen harmonics in the Kepler Team periodograms. We also present cases of new possible planetary signals.
Ambrosini, Roberto; Borgoni, Riccardo; Rubolini, Diego; Sicurella, Beatrice; Fiedler, Wolfgang; Bairlein, Franz; Baillie, Stephen R; Robinson, Robert A; Clark, Jacquie A; Spina, Fernando; Saino, Nicola
2014-01-01
Migration is a fundamental stage in the life history of several taxa, including birds, and is under strong selective pressure. At present, the only data that may allow for both an assessment of patterns of bird migration and for retrospective analyses of changes in migration timing are the databases of ring recoveries. We used ring recoveries of the Barn Swallow Hirundo rustica collected from 1908-2008 in Europe to model the calendar date at which a given proportion of birds is expected to have reached a given geographical area ('progression of migration') and to investigate the change in timing of migration over the same areas between three time periods (1908-1969, 1970-1990, 1991-2008). The analyses were conducted using binomial conditional autoregressive (CAR) mixed models. We first concentrated on data from the British Isles and then expanded the models to western Europe and north Africa. We produced maps of the progression of migration that disclosed local patterns of migration consistent with those obtained from the analyses of the movements of ringed individuals. Timing of migration estimated from our model is consistent with data on migration phenology of the Barn Swallow available in the literature, but in some cases it is later than that estimated by data collected at ringing stations, which, however, may not be representative of migration phenology over large geographical areas. The comparison of median migration date estimated over the same geographical area among time periods showed no significant advancement of spring migration over the whole of Europe, but a significant advancement of autumn migration in southern Europe. Our modelling approach can be generalized to any records of ringing date and locality of individuals including those which have not been recovered subsequently, as well as to geo-referenced databases of sightings of migratory individuals. PMID:25047331
Ambrosini, Roberto; Borgoni, Riccardo; Rubolini, Diego; Sicurella, Beatrice; Fiedler, Wolfgang; Bairlein, Franz; Baillie, Stephen R.; Robinson, Robert A.; Clark, Jacquie A.; Spina, Fernando; Saino, Nicola
2014-01-01
Migration is a fundamental stage in the life history of several taxa, including birds, and is under strong selective pressure. At present, the only data that may allow for both an assessment of patterns of bird migration and for retrospective analyses of changes in migration timing are the databases of ring recoveries. We used ring recoveries of the Barn Swallow Hirundo rustica collected from 1908–2008 in Europe to model the calendar date at which a given proportion of birds is expected to have reached a given geographical area (‘progression of migration’) and to investigate the change in timing of migration over the same areas between three time periods (1908–1969, 1970–1990, 1991–2008). The analyses were conducted using binomial conditional autoregressive (CAR) mixed models. We first concentrated on data from the British Isles and then expanded the models to western Europe and north Africa. We produced maps of the progression of migration that disclosed local patterns of migration consistent with those obtained from the analyses of the movements of ringed individuals. Timing of migration estimated from our model is consistent with data on migration phenology of the Barn Swallow available in the literature, but in some cases it is later than that estimated by data collected at ringing stations, which, however, may not be representative of migration phenology over large geographical areas. The comparison of median migration date estimated over the same geographical area among time periods showed no significant advancement of spring migration over the whole of Europe, but a significant advancement of autumn migration in southern Europe. Our modelling approach can be generalized to any records of ringing date and locality of individuals including those which have not been recovered subsequently, as well as to geo-referenced databases of sightings of migratory individuals. PMID:25047331
NASA Astrophysics Data System (ADS)
Klein, Abel
2013-11-01
We prove a unique continuation principle for spectral projections of Schrödinger operators. We consider a Schrödinger operator H = - Δ + V on , and let H Λ denote its restriction to a finite box Λ with either Dirichlet or periodic boundary condition. We prove unique continuation estimates of the type χ I ( H Λ ) W χ I ( H Λ ) ≥ κ χ I ( H Λ ) with κ > 0 for appropriate potentials W ≥ 0 and intervals I. As an application, we obtain optimal Wegner estimates at all energies for a class of non-ergodic random Schrödinger operators with alloy-type random potentials (‘crooked’ Anderson Hamiltonians). We also prove optimal Wegner estimates at the bottom of the spectrum with the expected dependence on the disorder (the Wegner estimate improves as the disorder increases), a new result even for the usual (ergodic) Anderson Hamiltonian. These estimates are applied to prove localization at high disorder for Anderson Hamiltonians in a fixed interval at the bottom of the spectrum.
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.
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.
Technology Transfer Automated Retrieval System (TEKTRAN)
A recently-launched high-resolution commercial satellite, DigitalGlobe’s WorldView-3, has 8 bands in the shortwave infrared (SWIR) wavelength region, which may be capable of estimating canopy water content at 3.7-m spatial resolution. WorldView-3 also has 8 multispectral bands at 1.24-m resolution ...
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.
Technology Transfer Automated Retrieval System (TEKTRAN)
In-season nitrogen (N) management of irrigated maize (Zea mays L.) requires frequent acquisition of plant N status estimates to timely assess the onset of crop N deficiency and its spatial variability within a field. This study compared ground-based Exotech nadir-view sensor data and QuickBird satel...
Michalareas, George; Schoffelen, Jan-Mathijs; Paterson, Gavin; Gross, Joachim
2013-01-01
Abstract In this work, we investigate the feasibility to estimating causal interactions between brain regions based on multivariate autoregressive models (MAR models) fitted to magnetoencephalographic (MEG) sensor measurements. We first demonstrate the theoretical feasibility of estimating source level causal interactions after projection of the sensor-level model coefficients onto the locations of the neural sources. Next, we show with simulated MEG data that causality, as measured by partial directed coherence (PDC), can be correctly reconstructed if the locations of the interacting brain areas are known. We further demonstrate, if a very large number of brain voxels is considered as potential activation sources, that PDC as a measure to reconstruct causal interactions is less accurate. In such case the MAR model coefficients alone contain meaningful causality information. The proposed method overcomes the problems of model nonrobustness and large computation times encountered during causality analysis by existing methods. These methods first project MEG sensor time-series onto a large number of brain locations after which the MAR model is built on this large number of source-level time-series. Instead, through this work, we demonstrate that by building the MAR model on the sensor-level and then projecting only the MAR coefficients in source space, the true casual pathways are recovered even when a very large number of locations are considered as sources. The main contribution of this work is that by this methodology entire brain causality maps can be efficiently derived without any a priori selection of regions of interest. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc. PMID:22328419
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
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.
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 to recognize main clay minerals. Hyperspectral laboratory using an ASD Fieldspec Pro spectrometer provides thus a rapid and less expensive field surface sensing that permits to measure soil spectral properties. This study presents a new laboratory reflectance spectroscopy method that used depth of clay diagnostic absorption bands (1400 nm, 1900 nm, and 2200 nm) to compare natural soils to synthetic montmorillonite-illite-kaolinite mixtures. We observe in mixtures that illite, montmorillonite, and kaolinite content respectively strongly influence the depth of absorption bands at 1400 nm (D1400), 1900 nm (D1900), and 2200 nm (D2200). To attenuate or removed effects of abundance and grain size, depth of absorption bands ratios were thus used to performed (i) 3D (using D1900/D2200, D1400/D1900, and D2200/D1400 as axis), and (ii) 2D (using D1400/D1900 and D1900/D2200 as axis) diagrams of synthetic mixtures. In this case we supposed that the overall reduction or growth of depth absorption bands should be similarly affected by the abundance and grain size of materials in soil. In 3D and 2D diagrams, the mixtures define a triangular shape formed by two clay minerals as external envelop and the three clay minerals mixtures
NASA Astrophysics Data System (ADS)
Ramachandran, S.; Jayaraman, A.
A cruise experiment was conducted in February-March 2001 to study the aerosol optical characteristics over Bay of Bengal, identify the source regions of aerosols and to estimate the anthropogenic contribution to the measured aerosol optical depths. The aerosol optical depths (AODs) exhibit significant spatial differences. The observed variations are explained by 7-days back trajectory analyses performed at different heights. The higher AODs obtained on 21 February are found influenced by the air mass at different heights originating either from Bangladesh or mainland India, indicating the anthropogenic influence. The anthropogenic influence on AOD are estimated by comparing the AODs obtained over Bay of Bengal (i) with that measured over a clean oceanic region taking into account the wind speed dependence on sea-salt aerosols and (ii) using maritime clean aerosol. From the two methods the estimated mean contribution by the anthropogenic sources to the AODs measured over Bay of Bengal are found to be in the range of 74-92% at 0.5 μm. Over Chennai, an urban station located on the eastern coastline of India, the anthropogenic contribution is estimated by comparing the measured AOD values with that of clean continental aerosol model and is found to be about 89%. This percentage contribution is higher than the contributions measured over Kaashidhoo and the northern Indian Ocean during INDOEX. INDOEX expeditions were conducted over the Arabian Sea and Indian Ocean on the western side of the Indian subcontinent, while the Bay of Bengal experiment was conducted on the eastern side. The differences in percentage contributions could possibly be due to the differences in anthropogenic activities, changes in the meteorological conditions, wind patterns, production and subsequently the transport of aerosols. The measured AOD spectra are reconstructed using OPAC to find out the possible chemical species which make up the aerosols over Bay of Bengal and Chennai. The AODs are
NASA Astrophysics Data System (ADS)
Cho, Moses Azong; Skidmore, Andrew; Corsi, Fabio; van Wieren, Sipke E.; Sobhan, Istiak
2007-12-01
The main objective was to determine whether partial least squares (PLS) regression improves grass/herb biomass estimation when compared with hyperspectral indices, that is normalised difference vegetation index (NDVI) and red-edge position (REP). To achieve this objective, fresh green grass/herb biomass and airborne images (HyMap) were collected in the Majella National Park, Italy in the summer of 2005. The predictive performances of hyperspectral indices and PLS regression models were then determined and compared using calibration ( n = 30) and test ( n = 12) data sets. The regression model derived from NDVI computed from bands at 740 and 771 nm produced a lower standard error of prediction (SEP = 264 g m -2) on the test data compared with the standard NDVI involving bands at 665 and 801 nm (SEP = 331 g m -2), but comparable results with REPs determined by various methods (SEP = 261 to 295 g m -2). PLS regression models based on original, derivative and continuum-removed spectra produced lower prediction errors (SEP = 149 to 256 g m -2) compared with NDVI and REP models. The lowest prediction error (SEP = 149 g m -2, 19% of mean) was obtained with PLS regression involving continuum-removed bands. In conclusion, PLS regression based on airborne hyperspectral imagery provides a better alternative to univariate regression involving hyperspectral indices for grass/herb biomass estimation in the Majella National Park.
NASA Technical Reports Server (NTRS)
Deland, Matthew T.; Cebula, Richard P.
1994-01-01
Quantitative assessment of the impact of solar ultraviolet irradiance variations on stratospheric ozone abundances currently requires the use of proxy indicators. The Mg II core-to-wing index has been developed as an indicator of solar UV activity between 175-400 nm that is independent of most instrument artifacts, and measures solar variability on both rotational and solar cycle time scales. Linear regression fits have been used to merge the individual Mg II index data sets from the Nimbus-7, NOAA-9, and NOAA-11 instruments onto a single reference scale. The change in 27-dayrunning average of the composite Mg II index from solar maximum to solar minimum is approximately 8 percent for solar cycle 21, and approximately 9 percent for solar cycle 22 through January 1992. Scaling factors based on the short-term variations in the Mg II index and solar irradiance data sets have been developed to estimate solar variability at mid-UV and near-UV wavelengths. Near 205 nm, where solar irradiance variations are important for stratospheric photo-chemistry and dynamics, the estimated change in irradiance during solar cycle 22 is approximately 10 percent using the composite Mg II index and scale factors.
NASA Astrophysics Data System (ADS)
Simon, Arthi; Shanmugam, Palanisamy
2016-07-01
A semi-analytical model is developed for estimating the spectral diffuse attenuation coefficient of downwelling irradiance (Kd(λ)) in inland and coastal waters. The model works as a function of the inherent optical properties (absorption and backscattering), depth, and solar zenith angle. Results of this model are validated using a large number of in-situ measurements of Kd(λ) in clear oceanic, turbid coastal and productive lagoon waters. To further evaluate its relative performance, Kd(λ) values obtained from this model are compared with results from three existing models. Validation results show that the present model is a better descriptor of Kd(λ) and shows an overall better performance compared to the existing models. The applicability of the present model is further tested on two Hyperspectral Imager for the Coastal Ocean (HICO) remote sensing images acquired simultaneously with our field measurements. The Kd(λ) spectra derived from HICO imageries have good agreement with measured data with the mean relative percent error of less than 12% which are well within the benchmark for a validated uncertainty of ±35% endorsed for the remote sensing products in oceanic waters. The model offers potential advantages for predicting changes in spectral and vertical Kd values in a wide variety of waters within inland and coastal environments.
NASA Astrophysics Data System (ADS)
Jana, Arghajit; Debnath, Dipak; Chakrabarti, Sandip Kumar; Mondal, Santanu; Chatterjee, Debjit; Molla, Aslam Ali
2016-07-01
We make a detailed analysis of the BHC using 2.5-25 keV RXTE/PCA data with two components advective flow (TCAF) model generated fits file as an additive table model in XSPEC. From the spectral analysis, we extract Keplerian disk rate, sub-Keplerian halo rate, shock location and compression ratio. We also estimate mass of the BHC from spectral analysis and we find it in the range of 7.5-11 M_{Sun}. During the entire outburst we keep the normalization in a very narrow range of 0.25-0.35 except a few days when radio jet is observed. We find quasi periodic oscillation (QPO) frequencies are in sporadic nature. From the variation of accretion rate ratio (ARR=halo rate/disk rate), QPO frequencies and photon indices, we classify the outburst in two states - hard and hard-intermediate. Unlike other BHC, soft and soft-intermediate states are absent during the entire outburst. This may be the reason due to the fact that the BHC is immersed within the excreation disk of the companion which is a Be star.
NASA Astrophysics Data System (ADS)
Kim, Dohyeong; Im, Myungshin; Kim, Ji Hoon; Jun, Hyunsung David; Woo, Jong-Hak; Lee, Hyung Mok; Lee, Myung Gyoon; Nakagawa, Takao; Matsuhara, Hideo; Wada, Takehiko; Oyabu, Shinki; Takagi, Toshinobu; Ohyama, Youichi; Lee, Seong-Kook
2015-01-01
We present 2.5-5.0 μm spectra of 83 nearby (0.002 < z < 0.48) and bright (K < 14 mag) type-1 active galactic nuclei (AGNs) taken with the Infrared Camera on board AKARI. The 2.5-5.0 μm spectral region contains emission lines such as Brβ (2.63 μm), Brα (4.05 μm), and polycyclic aromatic hydrocarbons (3.3 μm), which can be used for studying the black hole (BH) masses and star formation activity in the host galaxies of AGNs. The spectral region also suffers less dust extinction than in the ultra violet (UV) or optical wavelengths, which may provide an unobscured view of dusty AGNs. Our sample is selected from bright quasar surveys of Palomar-Green and SNUQSO, and AGNs with reverberation-mapped BH masses from Peterson et al. Using 11 AGNs with reliable detection of Brackett lines, we derive the Brackett-line-based BH mass estimators. We also find that the observed Brackett line ratios can be explained with the commonly adopted physical conditions of the broad line region. Moreover, we fit the hot and warm dust components of the dust torus by adding photometric data of SDSS, 2MASS, WISE, and ISO to the AKARI spectra, finding hot and warm dust temperatures of ~1100 K and ~220 K, respectively, rather than the commonly cited hot dust temperature of 1500 K.
Chen, Die-cong; Wang, Shao-qiang; Huang, Kun; Zhou, Lei; Yu, Quan-zhou; Wang, Hui-min; Sun, Lei-gang
2015-11-01
The photochemical reflectance index (PRI) calculated from spectral reflectance has universally become a proxy for the light-use efficiency (LUE), which significantly improves the LUE-based estimation of ecosystem gross primary productivity on a large scale through upscaling. In this study, we observed the vegetation spectral reflectance of a planted subtropical coniferous forest from the top of a flux tower at Qianyanzhou Station, one of the ChinaFLUX sites, in September and December 2013, and simultaneously measured CO2 flux and meteorological variables for correlation and regression analysis. Results showed that PRI had a better correlation with LUE (R2 = 0.20, P< 0.001) than that of normalized difference vegetation index (NDVI), i.e., PRI was preferred in LUE retrieval. During the whole observation period, PRI and soil water content (SWC)-based bivariate regression model correlated well with LUE (R2 = 0.29, P < 0.001 and R2 = 0.30, P < 0.01 for daytime and midday observation, respectively), but in autumn the bivariate regression model of PRI and vapor pressure deficit (VPD) had a higher correlation with LUE (R2 = 0.448, P < 0.001) for midday observation, which showed that environmental factors, i.e., SWC and VPD, had a potential in improving the LUE retrieval from PRI, but the choice of appropriate environmental factors depended on season. PMID:26915199
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. Two procedures for estimating alpha-1 the method of moments and maximum likelihood (ML), are developed and their statistical performance compared. It is concluded that the ML procedure attains the most desirable statistical properties and is hence the recommended statistical estimation procedure for estimating alpha-1. The ML 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 as they permit instrument developers to make important trade studies in design parameters as a function of one of the science objectives. This 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)
Olejarczyk, Elzbieta; Kaminski, Maciej; Marciniak, Radoslaw; Byrczek, Tomasz; Stasiowski, Michal; Jalowiecki, Przemyslaw; Sobieszek, Aleksander; Zmyslowski, Wojciech
2011-01-01
The aim of this study was to estimate spectral properties and propagation of the EEG signals registered during sevoflurane anaesthesia between individual EEG recording channels. The intensities of activity flows were calculated for delta, theta, alpha and beta waves using the Directed Transfer Function integration procedure. It was found that delta waves played the dominant role in the EEG signal propagation during anesthesia and it was suggested that theta and alpha waves propagation could be related to the processes participating in the wakefulness control. Data obtained with DTF method were compared with data received from the analysis of cerebral blood flow with the use of PET in other laboratory. This study showed that analysis of the EEG signal propagation is useful for better understanding and thus safer induction of anaesthesia procedure.
A new high-resolution spectral approach to noninvasively evaluate wall deformations in arteries.
Bazan, Ivonne; Negreira, Carlos; Ramos, Antonio; Brum, Javier; Ramirez, Alfredo
2014-01-01
By locally measuring changes on arterial wall thickness as a function of pressure, the related Young modulus can be evaluated. This physical magnitude has shown to be an important predictive factor for cardiovascular diseases. For evaluating those changes, imaging segmentation or time correlations of ultrasonic echoes, coming from wall interfaces, are usually employed. In this paper, an alternative low-cost technique is proposed to locally evaluate variations on arterial walls, which are dynamically measured with an improved high-resolution calculation of power spectral densities in echo-traces of the wall interfaces, by using a parametric autoregressive processing. Certain wall deformations are finely detected by evaluating the echoes overtones peaks with power spectral estimations that implement Burg and Yule Walker algorithms. Results of this spectral approach are compared with a classical cross-correlation operator, in a tube phantom and "in vitro" carotid tissue. A circulating loop, mimicking heart periods and blood pressure changes, is employed to dynamically inspect each sample with a broadband ultrasonic probe, acquiring multiple A-Scans which are windowed to isolate echo-traces packets coming from distinct walls. Then the new technique and cross-correlation operator are applied to evaluate changing parietal deformations from the detection of displacements registered on the wall faces under periodic regime. PMID:24688596
A New High-Resolution Spectral Approach to Noninvasively Evaluate Wall Deformations in Arteries
Bazan, Ivonne; Negreira, Carlos; Ramos, Antonio; Brum, Javier; Ramirez, Alfredo
2014-01-01
By locally measuring changes on arterial wall thickness as a function of pressure, the related Young modulus can be evaluated. This physical magnitude has shown to be an important predictive factor for cardiovascular diseases. For evaluating those changes, imaging segmentation or time correlations of ultrasonic echoes, coming from wall interfaces, are usually employed. In this paper, an alternative low-cost technique is proposed to locally evaluate variations on arterial walls, which are dynamically measured with an improved high-resolution calculation of power spectral densities in echo-traces of the wall interfaces, by using a parametric autoregressive processing. Certain wall deformations are finely detected by evaluating the echoes overtones peaks with power spectral estimations that implement Burg and Yule Walker algorithms. Results of this spectral approach are compared with a classical cross-correlation operator, in a tube phantom and “in vitro” carotid tissue. A circulating loop, mimicking heart periods and blood pressure changes, is employed to dynamically inspect each sample with a broadband ultrasonic probe, acquiring multiple A-Scans which are windowed to isolate echo-traces packets coming from distinct walls. Then the new technique and cross-correlation operator are applied to evaluate changing parietal deformations from the detection of displacements registered on the wall faces under periodic regime. PMID:24688596
NASA Astrophysics Data System (ADS)
McFee, J. E.; Mosquera, C. M.; Faust, A. A.
2016-08-01
An analysis of digitized pulse waveforms from experiments with LaBr3(Ce) and LaCl3(Ce) detectors is presented. Pulse waveforms from both scintillator types were captured in the presence of 22Na and 60Co sources and also background alone. Two methods to extract pulse shape discrimination (PSD) parameters and estimate energy spectra were compared. The first involved least squares fitting of the pulse waveforms to a physics-based model of one or two exponentially modified Gaussian functions. The second was the conventional gated integration method. The model fitting method produced better PSD than gated integration for LaCl3(Ce) and higher resolution energy spectra for both scintillator types. A disadvantage to the model fitting approach is that it is more computationally complex and about 5 times slower. LaBr3(Ce) waveforms had a single decay component and showed no ability for alpha/electron PSD. LaCl3(Ce) was observed to have short and long decay components and alpha/electron discrimination was observed.
Fisher, Aaron J; Reeves, Jonathan W; Chi, Cyrus
2016-07-01
Expanding on recently published methods, the current study presents an approach to estimating the dynamic, regulatory effect of the parasympathetic nervous system on heart period on a moment-to-moment basis. We estimated second-to-second variation in respiratory sinus arrhythmia (RSA) in order to estimate the contemporaneous and time-lagged relationships among RSA, interbeat interval (IBI), and respiration rate via vector autoregression. Moreover, we modeled these relationships at lags of 1 s to 10 s, in order to evaluate the optimal latency for estimating dynamic RSA effects. The IBI (t) on RSA (t-n) regression parameter was extracted from individual models as an operationalization of the regulatory effect of RSA on IBI-referred to as dynamic RSA (dRSA). Dynamic RSA positively correlated with standard averages of heart rate and negatively correlated with standard averages of RSA. We propose that dRSA reflects the active downregulation of heart period by the parasympathetic nervous system and thus represents a novel metric that provides incremental validity in the measurement of autonomic cardiac control-specifically, a method by which parasympathetic regulatory effects can be measured in process. PMID:27045609
2012-01-01
Background Generalized Additive Model (GAM) provides a flexible and effective technique for modelling nonlinear time-series in studies of the health effects of environmental factors. However, GAM assumes that errors are mutually independent, while time series can be correlated in adjacent time points. Here, a GAM with Autoregressive terms (GAMAR) is introduced to fill this gap. Methods Parameters in GAMAR are estimated by maximum partial likelihood using modified Newton’s method, and the difference between GAM and GAMAR is demonstrated using two simulation studies and a real data example. GAMM is also compared to GAMAR in simulation study 1. Results In the simulation studies, the bias of the mean estimates from GAM and GAMAR are similar but GAMAR has better coverage and smaller relative error. While the results from GAMM are similar to GAMAR, the estimation procedure of GAMM is much slower than GAMAR. In the case study, the Pearson residuals from the GAM are correlated, while those from GAMAR are quite close to white noise. In addition, the estimates of the temperature effects are different between GAM and GAMAR. Conclusions GAMAR incorporates both explanatory variables and AR terms so it can quantify the nonlinear impact of environmental factors on health outcome as well as the serial correlation between the observations. It can be a useful tool in environmental epidemiological studies. PMID:23110601
National Institute of Standards and Technology Data Gateway
SRD 117 Triatomic Spectral Database (Web, free access) All of the rotational spectral lines observed and reported in the open literature for 55 triatomic molecules have been tabulated. The isotopic molecular species, assigned quantum numbers, observed frequency, estimated measurement uncertainty and reference are given for each transition reported.
National Institute of Standards and Technology Data Gateway
SRD 115 Hydrocarbon Spectral Database (Web, free access) All of the rotational spectral lines observed and reported in the open literature for 91 hydrocarbon molecules have been tabulated. The isotopic molecular species, assigned quantum numbers, observed frequency, estimated measurement uncertainty and reference are given for each transition reported.
National Institute of Standards and Technology Data Gateway
SRD 114 Diatomic Spectral Database (Web, free access) All of the rotational spectral lines observed and reported in the open literature for 121 diatomic molecules have been tabulated. The isotopic molecular species, assigned quantum numbers, observed frequency, estimated measurement uncertainty, and reference are given for each transition reported.
Comparison of estimators of standard deviation for hydrologic time series.
Tasker, Gary D.; Gilroy, E.J.
1982-01-01
Unbiasing factors as a function of serial correlation, rho, and sample size, n for the sample standard deviation of a lag one autoregressive model were generated by random number simulation. Monte Carlo experiments were used to compare the performance of several alternative methods for estimating the standard deviation sigma of a lag one autoregressive model in terms of bias, root mean square error, probability of underestimation, and expected opportunity design loss. -from Authors
NASA Astrophysics Data System (ADS)
Mahmoud, E.; Takey, A.; Shoukry, A.
2016-07-01
We develop a galaxy cluster finding algorithm based on spectral clustering technique to identify optical counterparts and estimate optical redshifts for X-ray selected cluster candidates. As an application, we run our algorithm on a sample of X-ray cluster candidates selected from the third XMM-Newton serendipitous source catalog (3XMM-DR5) that are located in the Stripe 82 of the Sloan Digital Sky Survey (SDSS). Our method works on galaxies described in the color-magnitude feature space. We begin by examining 45 galaxy clusters with published spectroscopic redshifts in the range of 0.1-0.8 with a median of 0.36. As a result, we are able to identify their optical counterparts and estimate their photometric redshifts, which have a typical accuracy of 0.025 and agree with the published ones. Then, we investigate another 40 X-ray cluster candidates (from the same cluster survey) with no redshift information in the literature and found that 12 candidates are considered as galaxy clusters in the redshift range from 0.29 to 0.76 with a median of 0.57. These systems are newly discovered clusters in X-rays and optical data. Among them 7 clusters have spectroscopic redshifts for at least one member galaxy.
NASA Technical Reports Server (NTRS)
Shige, S.; Takayabu, Y.; Tao, W.-K.
2007-01-01
The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of precipitation formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the tropics with the associated latent heating (LH) accounting for threefourths of the total heat energy available to the Earth's atmosphere. In the last decade, it has been established that standard products of LH from satellite measurements, particularly TRMM measurements, would be a valuable resource for scientific research and applications. Such products would enable new insights and investigations concerning the complexities of convection system life cycles, the diabatic heating controls and feedbacks related to rne-sosynoptic circulations and their forecasting, the relationship of tropical patterns of LH to the global circulation and climate, and strategies for improving cloud parameterizations In environmental prediction models. However, the LH and water vapor profile or budget (called the apparent moisture sink, or Q2) is closely related. This paper presented the development of an algorithm for retrieving Q2 using 'TRMM precipitation radar. Since there is no direct measurement of LH and Q2, the validation of algorithm usually applies a method called consistency check. Consistency checking involving Cloud Resolving Model (CRM)-generated LH and 42 profiles and algorithm-reconstructed is a useful step in evaluating the performance of a given algorithm. In this process, the CRM simulation of a time-dependent precipitation process (multiple-day time series) is used to obtain the required input parameters for a given algorithm. The algorithm is then used to "econsti-LKth"e heating and moisture profiles that the CRM simulation originally produced, and finally both sets of conformal estimates (model and algorithm) are compared each other. The results indicate that discrepancies between the reconstructed and CM-simulated profiles for Q2, especially at low levels
How to compare cross-lagged associations in a multilevel autoregressive model.
Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L
2016-06-01
By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record PMID:27045851
Making of a solar spectral irradiance dataset I: observations, uncertainties, and methods
NASA Astrophysics Data System (ADS)
Schöll, Micha; Dudok de Wit, Thierry; Kretzschmar, Matthieu; Haberreiter, Margit
2016-03-01
Context. Changes in the spectral solar irradiance (SSI) are a key driver of the variability of the Earth's environment, strongly affecting the upper atmosphere, but also impacting climate. However, its measurements have been sparse and of different quality. The "First European Comprehensive Solar Irradiance Data Exploitation project" (SOLID) aims at merging the complete set of European irradiance data, complemented by archive data that include data from non-European missions. Aims: As part of SOLID, we present all available space-based SSI measurements, reference spectra, and relevant proxies in a unified format with regular temporal re-gridding, interpolation, gap-filling as well as associated uncertainty estimations. Methods: We apply a coherent methodology to all available SSI datasets. Our pipeline approach consists of the pre-processing of the data, the interpolation of missing data by utilizing the spectral coherency of SSI, the temporal re-gridding of the data, an instrumental outlier detection routine, and a proxy-based interpolation for missing and flagged values. In particular, to detect instrumental outliers, we combine an autoregressive model with proxy data. We independently estimate the precision and stability of each individual dataset and flag all changes due to processing in an accompanying quality mask. Results: We present a unified database of solar activity records with accompanying meta-data and uncertainties. Conclusions: This dataset can be used for further investigations of the long-term trend of solar activity and the construction of a homogeneous SSI record.
Kim, Dohyeong; Im, Myungshin; Kim, Ji Hoon; Jun, Hyunsung David; Lee, Seong-Kook; Woo, Jong-Hak; Lee, Hyung Mok; Lee, Myung Gyoon; Nakagawa, Takao; Matsuhara, Hideo; Wada, Takehiko; Takagi, Toshinobu; Oyabu, Shinki; Ohyama, Youichi E-mail: mim@astro.snu.ac.kr
2015-01-01
We present 2.5-5.0 μm spectra of 83 nearby (0.002 < z < 0.48) and bright (K < 14 mag) type-1 active galactic nuclei (AGNs) taken with the Infrared Camera on board AKARI. The 2.5-5.0 μm spectral region contains emission lines such as Brβ (2.63 μm), Brα (4.05 μm), and polycyclic aromatic hydrocarbons (3.3 μm), which can be used for studying the black hole (BH) masses and star formation activity in the host galaxies of AGNs. The spectral region also suffers less dust extinction than in the ultra violet (UV) or optical wavelengths, which may provide an unobscured view of dusty AGNs. Our sample is selected from bright quasar surveys of Palomar-Green and SNUQSO, and AGNs with reverberation-mapped BH masses from Peterson et al. Using 11 AGNs with reliable detection of Brackett lines, we derive the Brackett-line-based BH mass estimators. We also find that the observed Brackett line ratios can be explained with the commonly adopted physical conditions of the broad line region. Moreover, we fit the hot and warm dust components of the dust torus by adding photometric data of SDSS, 2MASS, WISE, and ISO to the AKARI spectra, finding hot and warm dust temperatures of ∼1100 K and ∼220 K, respectively, rather than the commonly cited hot dust temperature of 1500 K.
NASA Astrophysics Data System (ADS)
Wada, N.; Kawakata, H.; Murakami, O.; Doi, I.; Yoshimitsu, N.; Nakatani, M.; Yabe, Y.; Naoi, M. M.; Miyakawa, K.; Miyake, H.; Ide, S.; Igarashi, T.; Morema, G.; Pinder, E.; Ogasawara, H.
2011-12-01
Scaling relationship between corner frequencies, fc, and seismic moments, Mo is an important clue to understand the seismic source characteristics. Aki (1967) showed that Mo is proportional to fc-3 for large earthquakes (cubic law). Iio (1986) claimed breakdown of the cubic law between fc and Mo for smaller earthquakes (Mw < 2), and Gibowicz et al. (1991) also showed the breakdown for the ultra micro and small earthquakes (Mw < -2). However, it has been reported that the cubic law holds even for micro earthquakes (-1 < Mw > 4) by using high quality data observed at a deep borehole (Abercrombie, 1995; Ogasawara et al., 2001; Hiramatsu et al., 2002; Yamada et al., 2007). In order to clarify the scaling relationship for smaller earthquakes (Mw < -1), we analyzed ultra micro earthquakes using very high sampling records (48 kHz) of borehole seismometers installed within a hard rock at the Mponeng mine in South Africa. We used 4 tri-axial accelerometers of three-component that have a flat response up to 25 kHz. They were installed to be 10 to 30 meters apart from each other at 3,300 meters deep. During the period from 2008/10/14 to 2008/10/30 (17 days), 8,927 events were recorded. We estimated fc and Mo for 60 events (-3 < Mw < -1) within 200 meters from the seismometers. Assuming the Brune's source model, we estimated fc and Mo from spectral ratios. Common practice is using direct waves from adjacent events. However, there were only 5 event pairs with the distance between them less than 20 meters and Mw difference over one. In addition, the observation array is very small (radius less than 30 m), which means that effects of directivity and radiation pattern on direct waves are similar at all stations. Hence, we used spectral ratio of coda waves, since these effects are averaged and will be effectively reduced (Mayeda et al., 2007; Somei et al., 2010). Coda analysis was attempted only for relatively large 20 events (we call "coda events" hereafter) that have coda energy
NASA Technical Reports Server (NTRS)
Scholtz, P.; Smyth, P.
1992-01-01
This article describes an investigation of a statistical hypothesis testing method for detecting changes in the characteristics of an observed time series. The work is motivated by the need for practical automated methods for on-line monitoring of Deep Space Network (DSN) equipment to detect failures and changes in behavior. In particular, on-line monitoring of the motor current in a DSN 34-m beam waveguide (BWG) antenna is used as an example. The algorithm is based on a measure of the information theoretic distance between two autoregressive models: one estimated with data from a dynamic reference window and one estimated with data from a sliding reference window. The Hinkley cumulative sum stopping rule is utilized to detect a change in the mean of this distance measure, corresponding to the detection of a change in the underlying process. The basic theory behind this two-model test is presented, and the problem of practical implementation is addressed, examining windowing methods, model estimation, and detection parameter assignment. Results from the five fault-transition simulations are presented to show the possible limitations of the detection method, and suggestions for future implementation are given.
On vector autoregressive modeling in space and time
NASA Astrophysics Data System (ADS)
di Giacinto, Valter
2010-06-01
Despite the fact that it provides a potentially useful analytical tool, allowing for the joint modeling of dynamic interdependencies within a group of connected areas, until lately the VAR approach had received little attention in regional science and spatial economic analysis. This paper aims to contribute in this field by dealing with the issues of parameter identification and estimation and of structural impulse response analysis. In particular, there is a discussion of the adaptation of the recursive identification scheme (which represents one of the more common approaches in the time series VAR literature) to a space-time environment. Parameter estimation is subsequently based on the Full Information Maximum Likelihood (FIML) method, a standard approach in structural VAR analysis. As a convenient tool to summarize the information conveyed by regional dynamic multipliers with a specific emphasis on the scope of spatial spillover effects, a synthetic space-time impulse response function (STIR) is introduced, portraying average effects as a function of displacement in time and space. Asymptotic confidence bands for the STIR estimates are also derived from bootstrap estimates of the standard errors. Finally, to provide a basic illustration of the methodology, the paper presents an application of a simple bivariate fiscal model fitted to data for Italian NUTS 2 regions.
Kuo, Wen-Chuan; Kuo, Yue-Ming; Wen, Su-Ying
2016-04-01
Non-invasive and quantitative estimations for the delineation of sub-surface tumor margins could greatly aid in the early detection and monitoring of the morphological appearances of tumor growth, ensure complete tumor excision without the unnecessary sacrifice of healthy tissue, and facilitate post-operative follow-up for recurrence. In this study, a high-speed, non-invasive, and ultra-high-resolution spectral domain optical coherence tomography (UHR-SDOCT) imaging platform was developed for the quantitative measurement of human sub-surface skin mass. With a proposed robust, semi-automatic analysis, the system can rapidly quantify lesion area and shape regularity by an en-face-oriented algorithm. Various sizes of nylon sutures embedded in pork skin were used first as a phantom to verify the accuracy of our algorithm, and then in vivo, feasibility was proven using benign human angiomas and pigmented nevi. Clinically, this is the first step towards an automated skin lesion measurement system. In vivo optical coherence tomography (OCT) image of angioma (A). Thin red arrows point to a blood vessel (BV). PMID:25755214
NASA Astrophysics Data System (ADS)
Nicoleta, Melniciuc-Puica; Mihaela, Avadanei; Maria, Caprosu; Dana Ortansa, Dorohoi
2012-10-01
The dipolar compound Phthalazinium-dibenzoylmethylid (PDBM) was used as spectrally active molecule in order to analyze the molecular interactions in ternary solutions containing at least one protic solvent. In PDBM + protic solvent (1) + aprotic solvent (2) ternary solutions, PDBM can be involved both in universal and specific interactions reflected in solvatochromic effects. The protic solvent (or the solvent with the higher electric permittivity) was considered as being active from the interactions point of view. The content of the first solvation sphere of the studied ylid has been established on the basis of the statistical cell model of ternary solutions. The active solvent molecules are dominant in the first solvation sphere of the PDBM molecules. The difference between the interaction energies in the PDBM-active solvent (1) and PDBM-inactive solvent (2) molecular pairs has been determined for three binary solvents water + ethanol (W + E), propionic acid + chloroform (PA + C) and octanol + 1,2 dichloroethane (O + DCE). The hydrogen bond formation energy of the PDBM-protic solvent complex has been estimated in the binary solvents PA + C and O + DCE containing one protic (PA and O, respectively) and one aprotic solvent with close electro-optical parameters refractive index and electric permittivity of the components.
NASA Astrophysics Data System (ADS)
Deng, Chengbin; Wu, Changshan
2013-12-01
Urban impervious surface information is essential for urban and environmental applications at the regional/national scales. As a popular image processing technique, spectral mixture analysis (SMA) has rarely been applied to coarse-resolution imagery due to the difficulty of deriving endmember spectra using traditional endmember selection methods, particularly within heterogeneous urban environments. To address this problem, we derived endmember signatures through a least squares solution (LSS) technique with known abundances of sample pixels, and integrated these endmember signatures into SMA for mapping large-scale impervious surface fraction. In addition, with the same sample set, we carried out objective comparative analyses among SMA (i.e. fully constrained and unconstrained SMA) and machine learning (i.e. Cubist regression tree and Random Forests) techniques. Analysis of results suggests three major conclusions. First, with the extrapolated endmember spectra from stratified random training samples, the SMA approaches performed relatively well, as indicated by small MAE values. Second, Random Forests yields more reliable results than Cubist regression tree, and its accuracy is improved with increased sample sizes. Finally, comparative analyses suggest a tentative guide for selecting an optimal approach for large-scale fractional imperviousness estimation: unconstrained SMA might be a favorable option with a small number of samples, while Random Forests might be preferred if a large number of samples are available.
Pinault, Jean Louis; Dubus, Igor G
2008-08-20
An autoregressive approach for the prediction of water quality trends in systems subject to varying meteorological conditions and short observation periods is discussed. Under these conditions, the dynamics of the system can be reliably forecast, provided their internal processes are understood and characterized independently of the external inputs. A methodology based on stationary and non-stationary autoregressive processes with external inputs (ARX) is proposed to assess and predict trends in hydrosystems which are at risk of contamination by organic and inorganic pollutants, such as pesticides or nutrients. The procedures are exemplified for the transport of atrazine and its main metabolite deethylatrazine in a small agricultural catchment in France. The approach is expected to be of particular value to assess current and future trends in water quality as part of the European Water Framework Directive and Groundwater Directives. PMID:18554747
Medium- and Long-term Prediction of LOD Change with the Leap-step Autoregressive Model
NASA Astrophysics Data System (ADS)
Liu, Q. B.; Wang, Q. J.; Lei, M. F.
2015-09-01
It is known that the accuracies of medium- and long-term prediction of changes of length of day (LOD) based on the combined least-square and autoregressive (LS+AR) decrease gradually. The leap-step autoregressive (LSAR) model is more accurate and stable in medium- and long-term prediction, therefore it is used to forecast the LOD changes in this work. Then the LOD series from EOP 08 C04 provided by IERS (International Earth Rotation and Reference Systems Service) is used to compare the effectiveness of the LSAR and traditional AR methods. The predicted series resulted from the two models show that the prediction accuracy with the LSAR model is better than that from AR model in medium- and long-term prediction.
Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model
NASA Astrophysics Data System (ADS)
Wang, Qijie
2015-08-01
The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.
Comparisons of Four Methods for Estimating a Dynamic Factor Model
ERIC Educational Resources Information Center
Zhang, Zhiyong; Hamaker, Ellen L.; Nesselroade, John R.
2008-01-01
Four methods for estimating a dynamic factor model, the direct autoregressive factor score (DAFS) model, are evaluated and compared. The first method estimates the DAFS model using a Kalman filter algorithm based on its state space model representation. The second one employs the maximum likelihood estimation method based on the construction of a…
Population forecasts for South Pacific nations using autoregressive models, 1985-2000.
Ahlburg, D A
1987-11-01
"This paper uses an autoregressive statistical model to forecast population for Fiji, Western Samoa, Tonga, Solomon Islands, and Vanuatu and compares these forecasts with those obtained from other methods. The growth rate of population is predicted to continue to fall in Fiji and Tonga, rise a little for Western Samoa, and rise considerably in Vanuatu and the Solomon Islands. The implications of the forecasts for recent government development plans are also discussed." PMID:12314995
Schaefer, Alexander; Brach, Jennifer S.; Perera, Subashan; Sejdić, Ervin
2013-01-01
Background The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f) = 1/fβ. The scaling exponent β is thus often interpreted as a “biomarker” of relative health and decline. New Method This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. Results The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Comparison with Existing Methods: Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. Conclusions The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series. PMID:24200509
NASA Astrophysics Data System (ADS)
Chen, J.; Hubbard, S. S.; Williams, K. H.; Tuglus, C.; Flores-Orozco, A.; Kemna, A.
2010-12-01
Although in-situ bioremediation is often considered as a key approach for subsurface environmental remediation, monitoring induced biogeochemical processes, needed to evaluate the efficacy of the treatments, is challenging over field relevant scales. In this study, we develop a hierarchical Bayesian model that builds on our previous framework for estimating biogeochemical transformations using geochemical and geophysical data obtained from laboratory column experiments. The new Bayesian model treats the induced biogeochemical transformations as both spatial and temporal (rather than just temporal) processes and combines time-lapse borehole ‘point’ geochemical measurements with inverted surface- or crosshole-based spectral induced polarization (SIP) data. This model consists of three levels of statistical sub-models: (1) data model (or likelihood function), which provides links between the biogeochemical end-products and geophysical attributes, (2) process model, which describes the spatial and temporal variability of biogeochemical properties in the disturbed subsurface systems, and (3) parameter model, which describes the prior distributions of various parameters and initial conditions. The joint posterior probability distribution is explored using Markov Chain Monte Carlo sampling methods to obtain the spatial and temporal distribution of the hidden parameters. We apply the developed Bayesian model to the datasets collected from the uranium-contaminated DOE Rifle site for estimating the spatial and temporal distribution of remediation-induced end products. The datasets consist of time-lapse wellbore aqueous geochemical parameters (including Fe(II), sulfate, sulfide, acetate, uranium, chloride, and bromide concentrations) and surface SIP data collected over 13 frequencies (ranging from 0.065Hz to 256Hz). We first perform statistical analysis on the multivariate data to identify possible patterns (or ‘diagnostic signatures’) of bioremediation, and then we
Spectral and Spread Spectral Teleportation
Humble, Travis S
2010-01-01
We report how quantum information encoded into the spectral degree of freedom of a single-photon state is teleported using a finite spectrally entangled biphoton state. We further demonstrate how the bandwidth of a teleported waveform can be controllably and coherently dilated using a spread spectral variant of teleportation. We present analytical fidelities for spectral and spread spectral teleportation when complex-valued Gaussian states are prepared using a proposed experimental approach, and we discuss the utility of these techniques for integrating broad-bandwidth photonic qubits with narrow-bandwidth receivers in quantum communication systems.
Aydin, Alev Dilek; Caliskan Cavdar, Seyma
2015-01-01
The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method. PMID:26550010
Aydin, Alev Dilek; Caliskan Cavdar, Seyma
2015-01-01
The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method. PMID:26550010
A review of multitaper spectral analysis.
Babadi, Behtash; Brown, Emery N
2014-05-01
Nonparametric spectral estimation is a widely used technique in many applications ranging from radar and seismic data analysis to electroencephalography (EEG) and speech processing. Among the techniques that are used to estimate the spectral representation of a system based on finite observations, multitaper spectral estimation has many important optimality properties, but is not as widely used as it possibly could be. We give a brief overview of the standard nonparametric spectral estimation theory and the multitaper spectral estimation, and give two examples from EEG analyses of anesthesia and sleep. PMID:24759284
Finding Clocks in Genes: A Bayesian Approach to Estimate Periodicity
Ren, Yan; Hong, Christian I.; Lim, Sookkyung; Song, Seongho
2016-01-01
Identification of rhythmic gene expression from metabolic cycles to circadian rhythms is crucial for understanding the gene regulatory networks and functions of these biological processes. Recently, two algorithms, JTK_CYCLE and ARSER, have been developed to estimate periodicity of rhythmic gene expression. JTK_CYCLE performs well for long or less noisy time series, while ARSER performs well for detecting a single rhythmic category. However, observing gene expression at high temporal resolution is not always feasible, and many scientists are interested in exploring both ultradian and circadian rhythmic categories simultaneously. In this paper, a new algorithm, named autoregressive Bayesian spectral regression (ABSR), is proposed. It estimates the period of time-course experimental data and classifies gene expression profiles into multiple rhythmic categories simultaneously. Through the simulation studies, it is shown that ABSR substantially improves the accuracy of periodicity estimation and clustering of rhythmic categories as compared to JTK_CYCLE and ARSER for the data with low temporal resolution. Moreover, ABSR is insensitive to rhythmic patterns. This new scheme is applied to existing time-course mouse liver data to estimate period of rhythms and classify the genes into ultradian, circadian, and arrhythmic categories. It is observed that 49.2% of the circadian profiles detected by JTK_CYCLE with 1-hour resolution are also detected by ABSR with only 4-hour resolution. PMID:27340654
Non-contact video-based vital sign monitoring using ambient light and auto-regressive models.
Tarassenko, L; Villarroel, M; Guazzi, A; Jorge, J; Clifton, D A; Pugh, C
2014-05-01
Remote sensing of the reflectance photoplethysmogram using a video camera typically positioned 1 m away from the patient's face is a promising method for monitoring the vital signs of patients without attaching any electrodes or sensors to them. Most of the papers in the literature on non-contact vital sign monitoring report results on human volunteers in controlled environments. We have been able to obtain estimates of heart rate and respiratory rate and preliminary results on changes in oxygen saturation from double-monitored patients undergoing haemodialysis in the Oxford Kidney Unit. To achieve this, we have devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation. Secondly, we have been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model. In stable sections with minimal patient motion, the mean absolute error between the camera-derived estimate of heart rate and the reference value from a pulse oximeter is similar to the mean absolute error between two pulse oximeter measurements at different sites (finger and earlobe). The activities of daily living affect the respiratory rate, but the camera-derived estimates of this parameter are at least as accurate as those derived from a thoracic expansion sensor (chest belt). During a period of obstructive sleep apnoea, we tracked changes in oxygen saturation using the ratio of normalized reflectance changes in two colour channels (red and blue), but this required calibration against the reference data from a pulse oximeter. PMID:24681430
NASA Astrophysics Data System (ADS)
Gutierrez-Rodriguez, Mario
Scope and Method of Study. Alternative methods for selecting, detecting, and identifying higher yielding genotypes in wheat breeding programs are important for obtaining major genetic gains. The water indices can be used as an indirect selection tool because of their strong association with different physiological and yield components. Diverse spring wheat advanced lines were used, which corresponded to three international trials developed by the International Maize and Wheat Improvement Center (CIMMYT); 24th Elite Spring Wheat Yield Trial (ESWYT) with 25 lines, 11th Semi-Arid Wheat Yield Trial (SAWYT) with 40 lines, and 11th High Temperature Wheat Yield Trial (HTWYT) with 18 lines. Two other experiments also employed advanced lines for testing the relationship between water indices and water content parameters (10-16 lines) and for evaluating the influence of morphological traits (20 lines) over the water indices. Several water indices and other reflectance indices were estimated at three growth stages (booting, heading, and grain filling) using a field portable spectrometer (Analytical Spectral Devices, Boulder, CO). Field plots were planted in Northwest Mexico during three growing seasons (2006, 2007, and 2007). Grain yield, biomass, and some water status parameters were determined in diverse experiments. Findings and Conclusions. There were high correlations (phenotypic and genetic) between grain yield and the water indices showing high heritability, response to selection and correlated response, relative selection efficiency, and efficiency in selecting the higher yielding genotypes. Two water indices showed the strongest relationships (NWI-1 and NWI-3) for all the parameters determined in the well irrigated, water stress, and high temperature environments. In addition, the water indices were related with parameters commonly employed for assessing the crop water status ( i.e., water potential) during booting, anthesis and grain filling under water stress
On the Nature of SEM Estimates of ARMA Parameters.
ERIC Educational Resources Information Center
Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M.
2002-01-01
Reexamined the nature of structural equation modeling (SEM) estimates of autoregressive moving average (ARMA) models, replicated the simulation experiments of P. Molenaar, and examined the behavior of the log-likelihood ratio test. Simulation studies indicate that estimates of ARMA parameters observed with SEM software are identical to those…
Lag-One Autocorrelation in Short Series: Estimation and Hypotheses Testing
ERIC Educational Resources Information Center
Solanas, Antonio; Manolov, Rumen; Sierra, Vicenta
2010-01-01
In the first part of the study, nine estimators of the first-order autoregressive parameter are reviewed and a new estimator is proposed. The relationships and discrepancies between the estimators are discussed in order to achieve a clear differentiation. In the second part of the study, the precision in the estimation of autocorrelation is…
Application of Dynamic Grey-Linear Auto-regressive Model in Time Scale Calculation
NASA Astrophysics Data System (ADS)
Yuan, H. T.; Don, S. W.
2009-01-01
Because of the influence of different noise and the other factors, the running of an atomic clock is very complex. In order to forecast the velocity of an atomic clock accurately, it is necessary to study and design a model to calculate its velocity in the near future. By using the velocity, the clock could be used in the calculation of local atomic time and the steering of local universal time. In this paper, a new forecast model called dynamic grey-liner auto-regressive model is studied, and the precision of the new model is given. By the real data of National Time Service Center, the new model is tested.
NASA Astrophysics Data System (ADS)
Ağaç, Kübra; Koçak, Kasım; Deniz, Ali
2015-04-01
A time series approach using autoregressive model (AR), moving average model (MA) and seasonal autoregressive integrated moving average model (SARIMA) were used in this study to simulate and forecast daily PM10 concentrations in Kagithane Creek Valley, Istanbul. Hourly PM10 concentrations have been measured in Kagithane Creek Valley between 2010 and 2014 periods. Bosphorus divides the city in two parts as European and Asian parts. The historical part of the city takes place in Golden Horn. Our study area Kagithane Creek Valley is connected with this historical part. The study area is highly polluted because of its topographical structure and industrial activities. Also population density is extremely high in this site. The dispersion conditions are highly poor in this creek valley so it is necessary to calculate PM10 levels for air quality and human health. For given period there were some missing PM10 concentration values so to make an accurate calculations and to obtain exact results gap filling method was applied by Singular Spectrum Analysis (SSA). SSA is a new and efficient method for gap filling and it is an state-of-art modeling. SSA-MTM Toolkit was used for our study. SSA is considered as a noise reduction algorithm because it decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The basic SSA algorithm has stages of decomposition and reconstruction. For given period daily and monthly PM10 concentrations were calculated and episodic periods are determined. Long term and short term PM10 concentrations were analyzed according to European Union (EU) standards. For simulation and forecasting of high level PM10 concentrations, meteorological data (wind speed, pressure and temperature) were used to see the relationship between daily PM10 concentrations. Fast Fourier Transformation (FFT) was also applied to the data to see the periodicity and according to these periods models were built
Wang, Yiyi; Kockelman, Kara M
2013-11-01
This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference. Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (such as lighting conditions and local sight obstructions), along with spatially lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with higher pedestrian crash risk across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates. PMID:24036167
Simultaneous estimation of phase derivative and phase using parallel Kalman filter implementation
NASA Astrophysics Data System (ADS)
Kulkarni, Rishikesh; Rastogi, Pramod
2016-06-01
This paper proposes a technique for the simultaneous estimation of interference phase derivative and phase from a complex interferogram recorded in an optical interferometric setup. The complex interferogram is represented as a spatially varying autoregressive process in a given row or column at a time. The phase derivative is estimated from the poles of the transfer function representation of the autoregressive process. The poles are computed using the spatially varying autoregressive coefficients which are estimated by a computationally efficient Rauch–Tung–Striebel smoothing algorithm. The estimated phase derivative is used as a control input to a state space model designed for the phase estimation at each pixel. The unscented Kalman filter is utilized to deal with the nonlinear measurement process for the accurate estimation of the unwrapped phase. Numerical and experimental results substantiate the ability of the proposed method in handling noisy phase fringe patterns.
Wang, Jie; Xu, Ruisong; Yang, Shilun
2009-10-01
Vegetation water content could possibly provide widespread utility in agriculture, forestry and hydrology. In this article, three species leaves were measured radiometrically in order to determine a relationship between leaf water status and the spectral feature centered at 1,450 and 1,940 nm where there are strong water absorptions. The first step of our research is to measure leaf spectra with a FieldSpec-FR. After the spectral analysis using the continuum removal technique, the spectral absorption feature parameters: absorption band depth (D (1450), D (1940)), the normalized band depth of absorption in 1,450 and 1,940 nm (BNA(1450), BNA(1940)), the ratio of the two reflectance of continuum line (R (1450i )/R (1940i )), the ratio of the two band depth (D (1450)/D (1940)) and the ratio of the two absorption areas (A (1450)/A (1940)) in the two wavebands were extracted from each leaf spectrum. The fuel moisture content (FMC), specific leaf weight (SLW), equivalent water thickness (EWT) were measured for each leaf sample. A correlation analysis was conducted between the spectral absorption feature parameters and corresponding FMC, SLW and EWT. In addition, some existing indices for assessing water status such as WI (water index), WI/NDVI (water index/normalized difference vegetation index), MSI (moisture stress index), NDWI (normalized difference water index)were calculated and the correlation between them and water status were analyzed too. The results by comparing the correlations indicated that the spectral absorption feature indices we proposed were better. The indexes BNA(1940), D (1450)/D (1940), and A (1450)/A (1940) were well correlated with FMC, and the correlation between the indexes D (1450,) D (1940), R (1450i )/R (1940i ) and EWT were strong. The index A (1450)/A (1940) was tested to be a good indictor for evaluating plant water content, because there was strongest positive correlation between it and FMC than other indices. PMID:18853268
NASA Technical Reports Server (NTRS)
Lang, Harold R.
1991-01-01
A new approach to stratigraphic analysis is described which uses photogeologic and spectral interpretation of multispectral remote sensing data combined with topographic information to determine the attitude, thickness, and lithology of strata exposed at the surface. The new stratigraphic procedure is illustrated by examples in the literature. The published results demonstrate the potential of spectral stratigraphy for mapping strata, determining dip and strike, measuring and correlating stratigraphic sequences, defining lithofacies, mapping biofacies, and interpreting geological structures.
A new approach to simulating stream isotope dynamics using Markov switching autoregressive models
NASA Astrophysics Data System (ADS)
Birkel, Christian; Paroli, Roberta; Spezia, Luigi; Dunn, Sarah M.; Tetzlaff, Doerthe; Soulsby, Chris
2012-09-01
In this study we applied Markov switching autoregressive models (MSARMs) as a proof-of-concept to analyze the temporal dynamics and statistical characteristics of the time series of two conservative water isotopes, deuterium (δ2H) and oxygen-18 (δ18O), in daily stream water samples over two years in a small catchment in eastern Scotland. MSARMs enabled us to explicitly account for the identified non-linear, non-Normal and non-stationary isotope dynamics of both time series. The hidden states of the Markov chain could also be associated with meteorological and hydrological drivers identifying the short (event) and longer-term (inter-event) transport mechanisms for both isotopes. Inference was based on the Bayesian approach performed through Markov Chain Monte Carlo algorithms, which also allowed us to deal with a high rate of missing values (17%). Although it is usually assumed that both isotopes are conservative and exhibit similar dynamics, δ18O showed somewhat different time series characteristics. Both isotopes were best modelled with two hidden states, but δ18O demanded autoregressions of the first order, whereas δ2H of the second. Moreover, both the dynamics of observations and the hidden states of the two isotopes were explained by two different sets of covariates. Consequently use of the two tracers for transit time modelling and hydrograph separation may result in different interpretations on the functioning of a catchment system.
NASA Astrophysics Data System (ADS)
Benmouiza, Khalil; Cheknane, Ali
2016-05-01
This paper aims to introduce an approach for multi-hour forecasting (915 h ahead) of hourly global horizontal solar radiation time series and forecasting of a small-scale solar radiation database (30- and 1-s scales) for a period of 1 day (47,000 s ahead) using commonly and available measured meteorological solar radiation. Three methods are considered in this study. First, autoregressive-moving-average (ARMA) model is used to predict future values of the global solar radiation time series. However, because of the non-stationarity of solar radiation time series, a phase of detrending is needed to stationarize the irradiation data; a 6-degree polynomial model is found to be the most stationary one. Secondly, due to the nonlinearity presented in solar radiation time series, a nonlinear autoregressive (NAR) neural network model is used for prediction purposes. Taking into account the advantages of both models, the goodness of ARMA for linear problems and NAR for nonlinear problems, a hybrid method combining ARMA and NAR is introduced to produce better results. The validation process for the site of Ghardaia in Algaria shows that the hybrid model gives a normalized root mean square error (NRMSE) equals to 0.2034 compared to a NRMSE equal to 0.2634 for NAR model and 0.3241 for ARMA model.
Seifert, Michael; Abou-El-Ardat, Khalil; Friedrich, Betty; Klink, Barbara; Deutsch, Andreas
2014-01-01
Changes in gene expression programs play a central role in cancer. Chromosomal aberrations such as deletions, duplications and translocations of DNA segments can lead to highly significant positive correlations of gene expression levels of neighboring genes. This should be utilized to improve the analysis of tumor expression profiles. Here, we develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in tumor. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the simultaneous usage of higher-order state-transitions and autoregressive emissions as novel model features. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other related methods. The performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions. This benefit could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumors independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression
1998-08-01
Spectrally selective glazing is window glass that permits some portions of the solar spectrum to enter a building while blocking others. This high-performance glazing admits as much daylight as possible while preventing transmission of as much solar heat as possible. By controlling solar heat gains in summer, preventing loss of interior heat in winter, and allowing occupants to reduce electric lighting use by making maximum use of daylight, spectrally selective glazing significantly reduces building energy consumption and peak demand. Because new spectrally selective glazings can have a virtually clear appearance, they admit more daylight and permit much brighter, more open views to the outside while still providing the solar control of the dark, reflective energy-efficient glass of the past. This Federal Technology Alert provides detailed information and procedures for Federal energy managers to consider spectrally selective glazings. The principle of spectrally selective glazings is explained. Benefits related to energy efficiency and other architectural criteria are delineated. Guidelines are provided for appropriate application of spectrally selective glazing, and step-by-step instructions are given for estimating energy savings. Case studies are also presented to illustrate actual costs and energy savings. Current manufacturers, technology users, and references for further reading are included for users who have questions not fully addressed here.
NASA Astrophysics Data System (ADS)
Lang, Harold R.
1991-09-01
Stratigraphic and structural studies of the Wind River and Bighorn basins, Wyoming, and the Guerrero-Morelos basin, Mexico, have resulted in development of ''spectral stratigraphy.'' This approach to stratigraphic analysis uses photogeologic and spectral interpretation of multispectral remote sensing data combined with topographic information to determine the attitude, thickness, and lithology of strata exposed at the surface. This paper reviews selected published examples that illustrate this new stratigraphic procedure. Visible to thermal infrared laboratory, spectral measurements of sedimentary rocks are the physical basis for spectral stratigraphy. Results show that laboratory, field, and remote spectroscopy can augment conventional laboratory and field methods for petrologic analysis, stratigraphic correlation, interpretation of depositional environments, and construction of facies models. Landsat thematic mapper data are used to map strata and construct stratigraphic columns and structural cross sections at 1:24,000 scale or less. Experimental multispectral thermal infrared aircraft data facilitate lithofacies/biofacies analyses. Visible short-wavelength infrared imaging spectrometer data allow remote determination of the stratigraphic distribution of iron oxides, quartz, calcite, dolomite, gypsum, specific clay species, and other minerals diagnostic of environments of deposition. Development of a desk-top, computer-based, geologic analysis system that provides for automated application of these approaches to coregistered digital image and topographic data portends major expansion in the use of spectral stratigraphy for purely scientific (lithospheric research) or practical (resource exploration) objectives.
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
Maximum entropy spectral analysis for circadian rhythms: theory, history and practice
2013-01-01
There is an array of numerical techniques available to estimate the period of circadian and other biological rhythms. Criteria for choosing a method include accuracy of period measurement, resolution of signal embedded in noise or of multiple periodicities, and sensitivity to the presence of weak rhythms and robustness in the presence of stochastic noise. Maximum Entropy Spectral Analysis (MESA) has proven itself excellent in all regards. The MESA algorithm fits an autoregressive model to the data and extracts the spectrum from its coefficients. Entropy in this context refers to “ignorance” of the data and since this is formally maximized, no unwarranted assumptions are made. Computationally, the coefficients are calculated efficiently by solution of the Yule-Walker equations in an iterative algorithm. MESA is compared here to other common techniques. It is normal to remove high frequency noise from time series using digital filters before analysis. The Butterworth filter is demonstrated here and a danger inherent in multiple filtering passes is discussed. PMID:23844660
Detecting cycles in stratigraphic data: Spectral analysis in the presence of red noise
NASA Astrophysics Data System (ADS)
Vaughan, S.; Bailey, R. J.; Smith, D. G.
2011-12-01
We discuss the detection of cyclic signals in stratigraphic `time series' using spectral methods. The dominant source of variance in the stratigraphic record is red noise, which greatly complicates the process of searching for weak periodic signals. We highlight two issues that are more significant than generally appreciated. The first is the lack of a correction for `multiple tests' - many independent frequencies are examined for periods but using a significance test appropriate for examination of a single frequency. The second problem is the poor choice of null hypothesis used to model the spectrum of non-periodic variations. Stratigraphers commonly assume the noise is a first-order autoregressive process - the AR(1) model - which in practice often gives a very poor match to real data; a fact that goes largely unnoticed because model checking is rarely performed. These problems have the effect of raising the number of false positives far above the expected rate, to the extent that the literature on spatial stratigraphic cycles is dominated by false positives. In turn these will distort the construction of astronomically calibrated timescales, lead to inflated estimates of the physical significance of deterministic forcing of the climate and depositional processes in the pre-Neogene, and may even bias models of solar system dynamics on very long timescales. We make suggestions for controlling the false positive rate, and emphasize the value of Monte Carlo simulations to validate and calibrate analysis methods.
Covariance propagation in spectral indices
Griffin, P. J.
2015-01-09
In this study, the dosimetry community has a history of using spectral indices to support neutron spectrum characterization and cross section validation efforts. An important aspect to this type of analysis is the proper consideration of the contribution of the spectrum uncertainty to the total uncertainty in calculated spectral indices (SIs). This study identifies deficiencies in the traditional treatment of the SI uncertainty, provides simple bounds to the spectral component in the SI uncertainty estimates, verifies that these estimates are reflected in actual applications, details a methodology that rigorously captures the spectral contribution to the uncertainty in the SI, andmore » provides quantified examples that demonstrate the importance of the proper treatment the spectral contribution to the uncertainty in the SI.« less
Covariance propagation in spectral indices
Griffin, P. J.
2015-01-09
In this study, the dosimetry community has a history of using spectral indices to support neutron spectrum characterization and cross section validation efforts. An important aspect to this type of analysis is the proper consideration of the contribution of the spectrum uncertainty to the total uncertainty in calculated spectral indices (SIs). This study identifies deficiencies in the traditional treatment of the SI uncertainty, provides simple bounds to the spectral component in the SI uncertainty estimates, verifies that these estimates are reflected in actual applications, details a methodology that rigorously captures the spectral contribution to the uncertainty in the SI, and provides quantified examples that demonstrate the importance of the proper treatment the spectral contribution to the uncertainty in the SI.
Covariance Propagation in Spectral Indices
Griffin, P.J.
2015-01-15
The dosimetry community has a history of using spectral indices to support neutron spectrum characterization and cross section validation efforts. An important aspect to this type of analysis is the proper consideration of the contribution of the spectrum uncertainty to the total uncertainty in calculated spectral indices (SIs). This paper identifies deficiencies in the traditional treatment of the SI uncertainty, provides simple bounds to the spectral component in the SI uncertainty estimates, verifies that these estimates are reflected in actual applications, details a methodology that rigorously captures the spectral contribution to the uncertainty in the SI, and provides quantified examples that demonstrate the importance of the proper treatment the spectral contribution to the uncertainty in the SI.
Covariance Propagation in Spectral Indices
NASA Astrophysics Data System (ADS)
Griffin, P. J.
2015-01-01
The dosimetry community has a history of using spectral indices to support neutron spectrum characterization and cross section validation efforts. An important aspect to this type of analysis is the proper consideration of the contribution of the spectrum uncertainty to the total uncertainty in calculated spectral indices (SIs). This paper identifies deficiencies in the traditional treatment of the SI uncertainty, provides simple bounds to the spectral component in the SI uncertainty estimates, verifies that these estimates are reflected in actual applications, details a methodology that rigorously captures the spectral contribution to the uncertainty in the SI, and provides quantified examples that demonstrate the importance of the proper treatment the spectral contribution to the uncertainty in the SI.
ERIC Educational Resources Information Center
Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.
2016-01-01
The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…
Farrar, Charles; Figueiredo, Eloi; Todd, Michael; Flynn, Eric
2010-01-01
A nonlinear time series approach is presented to detect damage in systems by using a state-space reconstruction to infer the geometrical structure of a deterministic dynamical system from observed time series response at multiple locations. The unique contribution of this approach is using a Multivariate Autoregressive (MAR) model of a baseline condition to predict the state space, where the model encodes the embedding vectors rather than scalar time series. A hypothesis test is established that the MAR model will fail to predict future response if damage is present in the test condition, and this test is investigated for robustness in the context of operational and environmental variability. The applicability of this approach is demonstrated using acceleration time series from a base-excited 3-story frame structure.
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan
2014-09-12
Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
NASA Astrophysics Data System (ADS)
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan
2014-09-01
Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.
Srinath, Srikar; Poyneer, Lisa A; Rudy, Alexander R; Ammons, S Mark
2015-12-28
We present a sample-based, autoregressive (AR) method for the generation and time evolution of atmospheric phase screens that is computationally efficient and uses a single parameter per Fourier mode to vary the power contained in the frozen flow and stochastic components. We address limitations of Fourier-based methods such as screen periodicity and low spatial frequency power content. Comparisons of adaptive optics (AO) simulator performance when fed AR phase screens and translating phase screens reveal significantly elevated residual closed-loop temporal power for small increases in added stochastic content at each time step, thus displaying the importance of properly modeling atmospheric "boiling". We present preliminary evidence that our model fits to AO telemetry are better reflections of real conditions than the pure frozen flow assumption. PMID:26831998
A Continuous Time Model for Interest Rate with Autoregressive and Moving Average Components
NASA Astrophysics Data System (ADS)
Benth, F. E.; Koekebakker, S.; Zakamouline, V.
2010-09-01
In this paper we present a multi-factor continuous-time autoregressive moving-average (CARMA) model for the short and forward interest rates. This models is able to present a more adequate statistical description of the short and forward rate dynamics. We show that this is a tractable term structure model and provide closed-form solutions to bond and bond option prices, bond yields, and the forward rate volatility term structure. We demonstrate the capabilities of our model by calibrating it to market data and show that it can reproduce rather complex shapes of the empirical volatility term structure. In particular, a three-factor CARMA model can easily capture the dynamics of the level, slope, and curvature factors widely documented in term structure models.
NASA Astrophysics Data System (ADS)
Bekti, Rokhana Dwi; Nurhadiyanti, Gita; Irwansyah, Edy
2014-10-01
The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Moran's I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to support data analysis.
NASA Astrophysics Data System (ADS)
Schliep, E. M.; Gelfand, A. E.; Holland, D. M.
2015-12-01
There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.
NASA Astrophysics Data System (ADS)
Al-Bugharbee, Hussein; Trendafilova, Irina
2016-05-01
This study proposes a methodology for rolling element bearings fault diagnosis which gives a complete and highly accurate identification of the faults present. It has two main stages: signals pretreatment, which is based on several signal analysis procedures, and diagnosis, which uses a pattern-recognition process. The first stage is principally based on linear time invariant autoregressive modelling. One of the main contributions of this investigation is the development of a pretreatment signal analysis procedure which subjects the signal to noise cleaning by singular spectrum analysis and then stationarisation by differencing. So the signal is transformed to bring it close to a stationary one, rather than complicating the model to bring it closer to the signal. This type of pretreatment allows the use of a linear time invariant autoregressive model and improves its performance when the original signals are non-stationary. This contribution is at the heart of the proposed method, and the high accuracy of the diagnosis is a result of this procedure. The methodology emphasises the importance of preliminary noise cleaning and stationarisation. And it demonstrates that the information needed for fault identification is contained in the stationary part of the measured signal. The methodology is further validated using three different experimental setups, demonstrating very high accuracy for all of the applications. It is able to correctly classify nearly 100 percent of the faults with regard to their type and size. This high accuracy is the other important contribution of this methodology. Thus, this research suggests a highly accurate methodology for rolling element bearing fault diagnosis which is based on relatively simple procedures. This is also an advantage, as the simplicity of the individual processes ensures easy application and the possibility for automation of the entire process.
Lin, Yilan; Chen, Min; Chen, Guowei; Wu, Xiaoqing; Lin, Tianquan
2015-01-01
Objective Injury is currently an increasing public health problem in China. Reducing the loss due to injuries has become a main priority of public health policies. Early warning of injury mortality based on surveillance information is essential for reducing or controlling the disease burden of injuries. We conducted this study to find the possibility of applying autoregressive integrated moving average (ARIMA) models to predict mortality from injuries in Xiamen. Method The monthly mortality data on injuries in Xiamen (1 January 2002 to 31 December 2013) were used to fit the ARIMA model with the conditional least-squares method. The values p, q and d in the ARIMA (p, d, q) model refer to the numbers of autoregressive lags, moving average lags and differences, respectively. The Ljung–Box test was used to measure the ‘white noise’ and residuals. The mean absolute percentage error (MAPE) between observed and fitted values was used to evaluate the predicted accuracy of the constructed models. Results A total of 8274 injury-related deaths in Xiamen were identified during the study period; the average annual mortality rate was 40.99/100 000 persons. Three models, ARIMA (0, 1, 1), ARIMA (4, 1, 0) and ARIMA (1, 1, (2)), passed the parameter (p<0.01) and residual (p>0.05) tests, with MAPE 11.91%, 11.96% and 11.90%, respectively. We chose ARIMA (0, 1, 1) as the optimum model, the MAPE value for which was similar to that of other models but with the fewest parameters. According to the model, there would be 54 persons dying from injuries each month in Xiamen in 2014. Conclusion The ARIMA (0, 1, 1) model could be applied to predict mortality from injuries in Xiamen. PMID:26656013
NASA Astrophysics Data System (ADS)
Huan, Nai-Jen; Palaniappan, Ramaswamy
2004-09-01
In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.
A nonlinear autoregressive Volterra model of the Hodgkin-Huxley equations.
Eikenberry, Steffen E; Marmarelis, Vasilis Z
2013-02-01
We propose a new variant of Volterra-type model with a nonlinear auto-regressive (NAR) component that is a suitable framework for describing the process of AP generation by the neuron membrane potential, and we apply it to input-output data generated by the Hodgkin-Huxley (H-H) equations. Volterra models use a functional series expansion to describe the input-output relation for most nonlinear dynamic systems, and are applicable to a wide range of physiologic systems. It is difficult, however, to apply the Volterra methodology to the H-H model because is characterized by distinct subthreshold and suprathreshold dynamics. When threshold is crossed, an autonomous action potential (AP) is generated, the output becomes temporarily decoupled from the input, and the standard Volterra model fails. Therefore, in our framework, whenever membrane potential exceeds some threshold, it is taken as a second input to a dual-input Volterra model. This model correctly predicts membrane voltage deflection both within the subthreshold region and during APs. Moreover, the model naturally generates a post-AP afterpotential and refractory period. It is known that the H-H model converges to a limit cycle in response to a constant current injection. This behavior is correctly predicted by the proposed model, while the standard Volterra model is incapable of generating such limit cycle behavior. The inclusion of cross-kernels, which describe the nonlinear interactions between the exogenous and autoregressive inputs, is found to be absolutely necessary. The proposed model is general, non-parametric, and data-derived. PMID:22878687
NASA Astrophysics Data System (ADS)
Oda, Hitoshi
2016-03-01
The aspherical structure of the Earth is described in terms of lateral heterogeneity and anisotropy of the P- and S-wave velocities, density heterogeneity, ellipticity and rotation of the Earth, and undulation of the discontinuity interfaces of the seismic wave velocities. Its structure significantly influences the normal mode spectra of the Earth's free oscillation in the form of cross-coupling between toroidal and spheroidal multiplets and self-coupling between the singlets forming them. Thus, the aspherical structure must be conversely estimated from the free oscillation spectra influenced by the cross-coupling and self-coupling. In the present study, we improve a spectral fitting inversion algorithm which was developed in a previous study to retrieve the global structures of the isotropic and anisotropic velocities of the P and S waves from the free oscillation spectra. The main improvement is that the geographical distribution of the intensity of the S-wave azimuthal anisotropy is represented by a non-linear combination of structure coefficients for the anisotropic velocity structure, whereas in the previous study it was expanded into a generalized spherical harmonic series. Consequently, the improved inversion algorithm reduces the number of unknown parameters that must be determined compared to the previous inversion algorithm, and employs a one-step inversion method by which the structure coefficients for the isotropic and anisotropic velocities are directly estimated from the fee oscillation spectra. The applicability of the improved inversion is examined by several numerical experiments using synthetic spectral data, which are produced by supposing a variety of isotropic and anisotropic velocity structures, earthquake source parameters and station-event pairs. Furthermore, the robustness of the inversion algorithm is investigated with respect to the back-ground noise contaminating the spectral data as well as truncating the series expansions by finite
NASA Astrophysics Data System (ADS)
Oda, Hitoshi
2016-06-01
The aspherical structure of the Earth is described in terms of lateral heterogeneity and anisotropy of the P- and S-wave velocities, density heterogeneity, ellipticity and rotation of the Earth and undulation of the discontinuity interfaces of the seismic wave velocities. Its structure significantly influences the normal mode spectra of the Earth's free oscillation in the form of cross-coupling between toroidal and spheroidal multiplets and self-coupling between the singlets forming them. Thus, the aspherical structure must be conversely estimated from the free oscillation spectra influenced by the cross-coupling and self-coupling. In the present study, we improve a spectral fitting inversion algorithm which was developed in a previous study to retrieve the global structures of the isotropic and anisotropic velocities of the P and S waves from the free oscillation spectra. The main improvement is that the geographical distribution of the intensity of the S-wave azimuthal anisotropy is represented by a nonlinear combination of structure coefficients for the anisotropic velocity structure, whereas in the previous study it was expanded into a generalized spherical harmonic series. Consequently, the improved inversion algorithm reduces the number of unknown parameters that must be determined compared to the previous inversion algorithm and employs a one-step inversion method by which the structure coefficients for the isotropic and anisotropic velocities are directly estimated from the fee oscillation spectra. The applicability of the improved inversion is examined by several numerical experiments using synthetic spectral data, which are produced by supposing a variety of isotropic and anisotropic velocity structures, earthquake source parameters and station-event pairs. Furthermore, the robustness of the inversion algorithm is investigated with respect to the back-ground noise contaminating the spectral data as well as truncating the series expansions by finite terms
NASA Astrophysics Data System (ADS)
Huerta-Lopez, C. I.; Upegui Botero, F. M.; Pulliam, J.; Willemann, R. J.; Pasyanos, M.; Schmitz, M.; Rojas Mercedes, N.; Louie, J. N.; Moschetti, M. P.; Martinez-Cruzado, J. A.; Suárez, L.; Huerfano Moreno, V.; Polanco, E.
2013-12-01
Site characterization in civil engineering demands to know at least two of the dynamic properties of soil systems, which are: (i) dominant vibration frequency, and (ii) damping. As part of an effort to develop understanding of the principles of earthquake hazard analysis, particularly site characterization techniques using non invasive/non destructive seismic methods, a workshop (Pan-American Advanced Studies Institute: New Frontiers in Geophysical Research: Bringing New Tools and Techniques to Bear on Earthquake Hazard Analysis and Mitigation) was conducted during july 15-25, 2013 in Santo Domingo, Dominican Republic by the alliance of Pan-American Advanced Studies Institute (PASI) and Incorporated Research Institutions for Seismology (IRIS), jointly supported by Department of Energy (DOE) and National Science Foundation (NSF). Preliminary results of the site characterization in terms of fundamental vibration frequency and damping are here presented from data collected during the workshop. Three different methods were used in such estimations and later compared in order to identify the stability of estimations as well as the advantage or disadvantage among these methodologies. The used methods were the: (i) Random Decrement Method (RDM), to estimate fundamental vibration frequency and damping simultaneously; (ii) Empirical Mode Decomposition (EMD), to estimate the vibration modes, and (iii) Horizontal to Vertical Spectra ratio (HVSR), to estimate the fundamental vibration frequency. In all cases ambient vibration and induced vibration were used.
Spectral averaging techniques for Jacobi matrices
Rio, Rafael del; Martinez, Carmen; Schulz-Baldes, Hermann
2008-02-15
Spectral averaging techniques for one-dimensional discrete Schroedinger operators are revisited and extended. In particular, simultaneous averaging over several parameters is discussed. Special focus is put on proving lower bounds on the density of the averaged spectral measures. These Wegner-type estimates are used to analyze stability properties for the spectral types of Jacobi matrices under local perturbations.
Ibarria, L; Lindstrom, P; Rossignac, J
2006-11-17
Many scientific, imaging, and geospatial applications produce large high-precision scalar fields sampled on a regular grid. Lossless compression of such data is commonly done using predictive coding, in which weighted combinations of previously coded samples known to both encoder and decoder are used to predict subsequent nearby samples. In hierarchical, incremental, or selective transmission, the spatial pattern of the known neighbors is often irregular and varies from one sample to the next, which precludes prediction based on a single stencil and fixed set of weights. To handle such situations and make the best use of available neighboring samples, we propose a local spectral predictor that offers optimal prediction by tailoring the weights to each configuration of known nearby samples. These weights may be precomputed and stored in a small lookup table. We show that predictive coding using our spectral predictor improves compression for various sources of high-precision data.
Kim, Sangtae; Gupta, Nitin; Bandeira, Nuno; Pevzner, Pavel A.
2009-01-01
Database search tools identify peptides by matching tandem mass spectra against a protein database. We study an alternative approach when all plausible de novo interpretations of a spectrum (spectral dictionary) are generated and then quickly matched against the database. We present a new MS-Dictionary algorithm for efficiently generating spectral dictionaries and demonstrate that MS-Dictionary can identify spectra that are missed in the database search. We argue that MS-Dictionary enables proteogenomics searches in six-frame translation of genomic sequences that may be prohibitively time-consuming for existing database search approaches. We show that such searches allow one to correct sequencing errors and find programmed frameshifts. PMID:18703573
NASA Astrophysics Data System (ADS)
Nishidate, Izumi; Ooe, Shintaro; Todoroki, Shinsuke; Asamizu, Erika
2013-05-01
To evaluate the functional pigments in the tomato fruits nondestructively, we propose a method based on the multispectral diffuse reflectance images estimated by the Wiener estimation for a digital RGB image. Each pixel of the multispectral image is converted to the absorbance spectrum and then analyzed by the multiple regression analysis to visualize the contents of chlorophyll a, lycopene and β-carotene. The result confirms the feasibility of the method for in situ imaging of chlorophyll a, β-carotene and lycopene in the tomato fruits.
Chen, Hong-Yan; Zhao, Geng-Xing; Li, Xi-Can; Wang, Xiang-Feng; Li, Yu-Ling
2013-11-01
Taking the Qihe County in Shandong Province of East China as the study area, soil samples were collected from the field, and based on the hyperspectral reflectance measurement of the soil samples and the transformation with the first deviation, the spectra were denoised and compressed by discrete wavelet transform (DWT), the variables for the soil alkali hydrolysable nitrogen quantitative estimation models were selected by genetic algorithms (GA), and the estimation models for the soil alkali hydrolysable nitrogen content were built by using partial least squares (PLS) regression. The discrete wavelet transform and genetic algorithm in combining with partial least squares (DWT-GA-PLS) could not only compress the spectrum variables and reduce the model variables, but also improve the quantitative estimation accuracy of soil alkali hydrolysable nitrogen content. Based on the 1-2 levels low frequency coefficients of discrete wavelet transform, and under the condition of large scale decrement of spectrum variables, the calibration models could achieve the higher or the same prediction accuracy as the soil full spectra. The model based on the second level low frequency coefficients had the highest precision, with the model predicting R2 being 0.85, the RMSE being 8.11 mg x kg(-1), and RPD being 2.53, indicating the effectiveness of DWT-GA-PLS method in estimating soil alkali hydrolysable nitrogen content. PMID:24564148
NASA Astrophysics Data System (ADS)
Govindarajan, M.; Karabacak, M.
2013-04-01
In this work, the vibrational spectral analysis was carried out by using FT-Raman and FT-IR spectroscopy in the range 100-4000 cm-1 and 400-4000 cm-1 respectively, for 4-hydroxypteridine (C6H4N4O, 4HDPETN) molecule. The potential energy curve shows that 4HDPETN molecule has two stable structures. The computational results diagnose the most stable conformer of the 4HDPETN as the S1 structure. The molecular structure, fundamental vibrational frequencies and intensities of the vibrational bands were interpreted with the aid of structure optimizations and normal coordinate force field calculations based density functional theory (DFT) and ab initio HF methods and different basis sets combination. The complete vibrational assignments of wavenumbers were made on the basis of potential energy distribution (PED). The results of the calculations were applied to simulated spectra of the title compound, which show excellent agreement with observed spectra. The scaled B3LYP/6-311++G(d,p) results show the best agreement with the experimental values over the other method. The energy and oscillator strength calculated by time-dependent density functional theory (TD-DFT) complements with the experimental findings. In addition, molecular electrostatic potential, nonlinear optical and thermodynamic properties of the title compound were performed. Mulliken and natural charges of the title molecule were also calculated and interpreted.
NASA Astrophysics Data System (ADS)
Kaku, K. C.; Reid, J. S.; O'Neill, N. T.; Quinn, P. K.; Coffman, D. J.; Eck, T. F.
2014-10-01
The spectral deconvolution algorithm (SDA) and SDA+ (extended SDA) methodologies can be employed to separate the fine and coarse mode extinction coefficients from measured total aerosol extinction coefficients, but their common use is currently limited to AERONET (AErosol RObotic NETwork) aerosol optical depth (AOD). Here we provide the verification of the SDA+ methodology on a non-AERONET aerosol product, by applying it to fine and coarse mode nephelometer and particle soot absorption photometer (PSAP) data sets collected in the marine boundary layer. Using data sets collected on research vessels by NOAA-PMEL(National Oceanic and Atmospheric Administration - Pacific Marine Environmental Laboratory), we demonstrate that with accurate input, SDA+ is able to predict the fine and coarse mode scattering and extinction coefficient partition in global data sets representing a range of aerosol regimes. However, in low-extinction regimes commonly found in the clean marine boundary layer, SDA+ output accuracy is sensitive to instrumental calibration errors. This work was extended to the calculation of coarse and fine mode scattering coefficients with similar success. This effort not only verifies the application of the SDA+ method to in situ data, but by inference verifies the method as a whole for a host of applications, including AERONET. Study results open the door to much more extensive use of nephelometers and PSAPs, with the ability to calculate fine and coarse mode scattering and extinction coefficients in field campaigns that do not have the resources to explicitly measure these values.
Govindarajan, M; Karabacak, M; Udayakumar, V; Periandy, S
2012-03-01
In this work, the vibrational spectral analysis was carried out by using FT-Raman and FT-IR spectroscopy in the range 100-4000 cm(-1) and 400-4000 cm(-1) respectively, for the title molecule. The molecular structure, fundamental vibrational frequencies and intensity of the vibrational bands are interpreted with the aid of structure optimizations and normal coordinate force field calculations based on Hartree Fock (HF) and density functional theory (DFT) method and different basis sets combination. The complete vibrational assignments of wavenumbers were made on the basis of potential energy distribution (PED). The scaled B3LYP/6-311++G(d,p) results show the best agreement with the experimental values over the other methods. The effects due to the substitution of halogen bond were investigated. The results of the calculations were applied to simulated spectra of the title compound, which show excellent agreement with observed spectra. The energy and oscillator strength calculated by Time-Dependent Density Functional Theory (TD-DFT) results complements with the experimental findings. Besides, frontier molecular orbitals (FMO), molecular electrostatic potential (MEP), and thermodynamic properties were performed. The thermodynamic properties of the title compound at different temperatures have been calculated, revealing the correlations between heat capacity (C), entropy (S), and enthalpy changes (H) and temperatures. PMID:22197345
NASA Astrophysics Data System (ADS)
Govindarajan, M.; Karabacak, M.; Udayakumar, V.; Periandy, S.
2012-03-01
In this work, the vibrational spectral analysis was carried out by using FT-Raman and FT-IR spectroscopy in the range 100-4000 cm-1 and 400-4000 cm-1 respectively, for the title molecule. The molecular structure, fundamental vibrational frequencies and intensity of the vibrational bands are interpreted with the aid of structure optimizations and normal coordinate force field calculations based on Hartree Fock (HF) and density functional theory (DFT) method and different basis sets combination. The complete vibrational assignments of wavenumbers were made on the basis of potential energy distribution (PED). The scaled B3LYP/6-311++G(d,p) results show the best agreement with the experimental values over the other methods. The effects due to the substitution of halogen bond were investigated. The results of the calculations were applied to simulated spectra of the title compound, which show excellent agreement with observed spectra. The energy and oscillator strength calculated by Time-Dependent Density Functional Theory (TD-DFT) results complements with the experimental findings. Besides, frontier molecular orbitals (FMO), molecular electrostatic potential (MEP), and thermodynamic properties were performed. The thermodynamic properties of the title compound at different temperatures have been calculated, revealing the correlations between heat capacity (C), entropy (S), and enthalpy changes (H) and temperatures.
Smith, Jason F.; Chen, Kewei; Pillai, Ajay S.; Horwitz, Barry
2013-01-01
The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons. PMID:23717258
NASA Astrophysics Data System (ADS)
Natarajan, Thulasiraman; Rajendran, Kusala
2015-02-01
We investigated the site response characteristics of Kachchh rift basin over the meizoseismal area of the 2001, Mw 7.6, Bhuj (NW India) earthquake using the spectral ratio of the horizontal and vertical components of ambient vibrations. Using the available knowledge on the regional geology of Kachchh and well documented ground responses from the earthquake, we evaluated the H/V curves pattern across sediment filled valleys and uplifted areas generally characterized by weathered sandstones. Although our H/V curves showed a largely fuzzy nature, we found that the hierarchical clustering method was useful for comparing large numbers of response curves and identifying the areas with similar responses. Broad and plateau shaped peaks of a cluster of curves within the valley region suggests the possibility of basin effects within valley. Fundamental resonance frequencies (f0) are found in the narrow range of 0.1-2.3 Hz and their spatial distribution demarcated the uplifted regions from the valleys. In contrary, low H/V peak amplitudes (A0 = 2-4) were observed on the uplifted areas and varying values (2-9) were found within valleys. Compared to the amplification factors, the liquefaction indices (kg) were able to effectively indicate the areas which experienced severe liquefaction. The amplification ranges obtained in the current study were found to be comparable to those obtained from earthquake data for a limited number of seismic stations located on uplifted areas; however the values on the valley region may not reflect their true amplification potential due to basin effects. Our study highlights the practical usefulness as well as limitations of the H/V method to study complex geological settings as Kachchh.
NASA Astrophysics Data System (ADS)
Strunin, Alexander M.; Zhivoglotov, Dmitriy N.
2014-03-01
Liquid water droplets could distort aircraft temperature measurements in clouds, leading to errors in calculated heat fluxes and incorrect flux distribution pattern. The estimation of cloud droplet effect on the readings of the high-frequency aircraft thermometer employed at the Central Aerological Observatory (CAO) was based on an experimental study of the sensor in a wind tunnel, using an air flow containing liquid water droplets. Simultaneously, calculations of the distribution of speed and temperature in a flow through the sensitive element of the sensor were fulfilled. This permitted estimating the coefficient of water content effect on temperature readings. Another way of estimating cloud droplet effect was based on the analysis of data obtained during aircraft observations of cumulus clouds in a tropical zone (Cuba Island). As a result, a method of correcting air temperature and recovering true air temperature fluctuations inside clouds was developed. This method has provided consistent patterns of heat flux distribution in a cumulus area. Analysis of the results of aircraft observations of cumulus clouds with temperature correction fulfilled has permitted investigation of the spectral structure of the fields of air temperature and heat fluxes to be performed in cumulus zones based on wavelet transformation. It is shown that mesoscale eddies (over 500 m in length) were the main factor of heat exchange between a cloud and the ambient space. The role of turbulence only consisted in mixing inside the cloud.
Garland, Eric L; Geschwind, Nicole; Peeters, Frenk; Wichers, Marieke
2015-01-01
Recent theory suggests that positive psychological processes integral to health may be energized through the self-reinforcing dynamics of an upward spiral to counter emotion dysregulation. The present study examined positive emotion-cognition interactions among individuals in partial remission from depression who had been randomly assigned to treatment with mindfulness-based cognitive therapy (MBCT; n = 64) or a waitlist control condition (n = 66). We hypothesized that MBCT stimulates upward spirals by increasing positive affect and positive cognition. Experience sampling assessed changes in affect and cognition during 6 days before and after treatment, which were analyzed with a series of multilevel and autoregressive latent trajectory models. Findings suggest that MBCT was associated with significant increases in trait positive affect and momentary positive cognition, which were preserved through autoregressive and cross-lagged effects driven by global emotional tone. Findings suggest that daily positive affect and cognition are maintained by an upward spiral that might be promoted by mindfulness training. PMID:25698988
NASA Astrophysics Data System (ADS)
Williams, G. A.; Chadwick, R. A.
2009-04-01
CO2 produced at the Sleipner gas field is being injected into the Utsira Sand, a regional saline aquifer. Time-lapse seismic surveys have been acquired in 1999, 2001, 2004 and 2006 in order to monitor the growth of the plume. The plume is imaged as a sequence of high amplitude sub-horizontal reflectors within the aquifer; the reflections are thought to represent tuned responses from thin layers of CO2 trapped beneath intra-reservoir mudstone baffles. Spectral decomposition of seismic data can be used to map temporal bed thickness across a 3D seismic survey. Conventional techniques employ the Short Time Fourier Transform (STFT) using an appropriate window function to localise the frequency spectrum of the seismic trace. The resulting power spectrum represents a combination of the seismic wavelet and local thin bed effects. Time-frequency decomposition (TFD) using the STFT suffers from resolution problems: a wide analysis window gives good frequency resolution, but poor time localisation, while a narrow window localises the spectrum in time but provides poor frequency resolution. In order to overcome these limitations other time-frequency representations have been developed, these broadly fall into 2 categories: quadratic and wavelet transforms. A small analysis window is required to isolate reflections from individual CO2 layers in the Sleipner seismic data. Consequently this study explores the potential of a quadratic transform (the Wigner-Ville Distribution or WVD) and the Stockwell Transform (a pseudo-wavelet transform which preserves phase information) to quantify tuning in the top-most layer of the CO2 plume. TFD of a synthetic wedge model suggest that both techniques can be used to investigate tuning effects in the seismic data. The WVD in particular offers excellent time-frequency resolution, however cross-terms inherent in the quadratic formulation make interpretation difficult. Applying a smoothing kernel in the time-frequency plane to produce a reduced
NASA Astrophysics Data System (ADS)
Ososkov, G.; Pepelyshev, Yu.; Tsogtsaikhan, Ts.
2016-02-01
This paper presents an artificial neural network method for long-term prediction of liquid sodium flow rate through the core of the IBR-2M reactor. The nonlinear autoregressive neural network (NAR) with local feedback connection has been considered as the most appropriate tool for such a prediction. The predicted results were compared with experimental values. NAR model predicts slow changes of liquid sodium flow rate up to two days with an error less than 5%.
Taylor, Brian A.; Loeffler, Ralf B.; Song, Ruitian; McCarville, Mary E.; Hankins, Jane S.; Hillenbrand, Claudia M.
2011-01-01
Purpose To investigate the use of a complex multi-gradient echo (mGRE) acquisition and an autoregressive moving average (ARMA) model for simultaneous susceptibility and R2* measurements for the assessment of liver iron content (LIC) in patients with iron overload. Materials and Methods Fifty MR exams with magnitude and phase mGRE images are processed using the ARMA model which provides fat-separated field maps, R2* maps, and T1-W imaging. The LIC is calculated by measuring the susceptibility between the liver and the right transverse abdominal muscle from the field maps. The relationship between LIC derived from susceptibility measurements and LIC from R2* measurements is determined using linear least squares regression analysis. Results LIC measured from R2* is highly correlated to the LIC from the susceptibility method (mg/g dry = 8.99 ± 0.15 × (mg Fe/ml of wet liver) −2.38 ± 0.29, R2=0.94). The field inhomogeneity in the liver is correlated with R2* (R2=0.85). Conclusion By using the ARMA model on complex mGRE images, both susceptibility and R2*-based LIC measurements can be made simultaneously. The susceptibility measurement can be used to help verify R2* measurements in the assessment of iron overload. PMID:22180325
Gas Chromatography Data Classification Based on Complex Coefficients of an Autoregressive Model
Zhao, Weixiang; Morgan, Joshua T.; Davis, Cristina E.
2008-01-01
This paper introduces autoregressive (AR) modeling as a novel method to classify outputs from gas chromatography (GC). The inverse Fourier transformation was applied to the original sensor data, and then an AR model was applied to transform data to generate AR model complex coefficients. This series of coefficients effectively contains a compressed version of all of the information in the original GC signal output. We applied this method to chromatograms resulting from proliferating bacteria species grown in culture. Three types of neural networks were used to classify the AR coefficients: backward propagating neural network (BPNN), radial basis function-principal component analysismore » (RBF-PCA) approach, and radial basis function-partial least squares regression (RBF-PLSR) approach. This exploratory study demonstrates the feasibility of using complex root coefficient patterns to distinguish various classes of experimental data, such as those from the different bacteria species. This cognition approach also proved to be robust and potentially useful for freeing us from time alignment of GC signals.« less
NASA Astrophysics Data System (ADS)
Kosek, Wieslaw
2016-04-01
Future Earth Orientation Parameters data are needed to compute real time transformation between the celestial and terrestrial reference frames. This transformation is realized by predictions of x, y pole coordinates data, UT1-UTC data and precesion-nutation extrapolation model. This paper is focused on the pole coordinates data prediction by combination of the least-squares (LS) extrapolation and autoregressive (AR) prediction models (LS+AR). The AR prediction which is applied to the LS extrapolation residuals of pole coordinates data does not able to predict all frequency bands of them and it is mostly tuned to predict subseasonal oscillations. The absolute values of differences between pole coordinates data and their LS+AR predictions increase with prediction length and depend mostly on starting prediction epochs, thus time series of these differences for 2, 4 and 8 weeks in the future were analyzed. Time frequency spectra of these differences for different prediction lengths are very similar showing some power in the frequency band corresponding to the prograde Chandler and annual oscillations, which means that the increase of prediction errors is caused by mismodelling of these oscillations by the LS extrapolation model. Thus, the LS+AR prediction method can be modified by taking into additional AR prediction correction computed from time series of these prediction differences for different prediction lengths. This additional AR prediction is mostly tuned to the seasonal frequency band of pole coordinates data.
Assessment and prediction of air quality using fuzzy logic and autoregressive models
NASA Astrophysics Data System (ADS)
Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.
2012-12-01
In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.
Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach.
Nascimento, M; E Silva, F F; Sáfadi, T; Nascimento, A C C; Barroso, L M A; Glória, L S; de S Carvalho, B
2016-01-01
We propose and evaluate a novel approach for forecasting gene expression over non-observed times in longitudinal trials under a Bayesian viewpoint. One of the aims is to cluster genes that share similar expression patterns over time and then use this similarity to predict relative expression at time points of interest. Expression values of 106 genes expressed during the cell cycle of Saccharomyces cerevisiae were used and genes were partitioned into five distinct clusters of sizes 33, 32, 21, 16, and 4. After removing the last observed time point, the agreements of signals (upregulated or downregulated) considering the predicted expression level were 72.7, 81.3, 76.2, 68.8, and 50.0%, respectively, for each cluster. The percentage of credibility intervals that contained the true values of gene expression for a future time was ~90%. The methodology performed well, providing a valid forecast of gene expression values by fitting an autoregressive panel data model. This approach is easily implemented with other time-series models and when Poisson and negative binomial probability distributions are assumed for the gene expression data. PMID:27323205
NASA Astrophysics Data System (ADS)
Musafere, F.; Sadhu, A.; Liu, K.
2016-01-01
In the last few decades, structural health monitoring (SHM) has been an indispensable subject in the field of vibration engineering. With the aid of modern sensing technology, SHM has garnered significant attention towards diagnosis and risk management of large-scale civil structures and mechanical systems. In SHM, system identification is one of major building blocks through which unknown system parameters are extracted from vibration data of the structures. Such system information is then utilized to detect the damage instant, and its severity to rehabilitate and prolong the existing health of the structures. In recent years, blind source separation (BSS) algorithm has become one of the newly emerging advanced signal processing techniques for output-only system identification of civil structures. In this paper, a novel damage detection technique is proposed by integrating BSS with the time-varying auto-regressive modeling to identify the instant and severity of damage. The proposed method is validated using a suite of numerical studies and experimental models followed by a full-scale structure.
Mac Nally, Ralph; Thomson, James R.; Kimmerer, Wim J.; Feyrer, Frederick; Newman, Ken B.; Sih, Andy; Bennett, William A.; Brown, Larry; Fleishman, Erica; Culberson, Steven D.; Castillo, Gonzalo
2010-01-01
Four species of pelagic fish of particular management concern in the upper San Francisco Estuary, California, USA, have declined precipitously since ca. 2002: delta smelt (Hypomesus transpacificus), longfin smelt (Spirinchus thaleichthys), striped bass (Morone saxatilis), and threadfin shad (Dorosoma petenense). The estuary has been monitored since the late 1960s with extensive collection of data on the fishes, their pelagic prey, phytoplankton biomass, invasive species, and physical factors. We used multivariate autoregressive (MAR) modeling to discern the main factors responsible for the declines. An expert-elicited model was built to describe the system. Fifty-four relationships were built into the model, only one of which was of uncertain direction a priori. Twenty-eight of the proposed relationships were strongly supported by or consistent with the data, while 26 were close to zero (not supported by the data but not contrary to expectations). The position of the 2 isohaline (a measure of the physical response of the estuary to freshwater flow) and increased water clarity over the period of analyses were two factors affecting multiple declining taxa (including fishes and the fishes' main zooplankton prey). Our results were relatively robust with respect to the form of stock–recruitment model used and to inclusion of subsidiary covariates but may be enhanced by using detailed state–space models that describe more fully the life-history dynamics of the declining species.
NASA Astrophysics Data System (ADS)
Riedl, M.; Suhrbier, A.; Malberg, H.; Penzel, T.; Bretthauer, G.; Kurths, J.; Wessel, N.
2008-07-01
The parameters of heart rate variability and blood pressure variability have proved to be useful analytical tools in cardiovascular physics and medicine. Model-based analysis of these variabilities additionally leads to new prognostic information about mechanisms behind regulations in the cardiovascular system. In this paper, we analyze the complex interaction between heart rate, systolic blood pressure, and respiration by nonparametric fitted nonlinear additive autoregressive models with external inputs. Therefore, we consider measurements of healthy persons and patients suffering from obstructive sleep apnea syndrome (OSAS), with and without hypertension. It is shown that the proposed nonlinear models are capable of describing short-term fluctuations in heart rate as well as systolic blood pressure significantly better than similar linear ones, which confirms the assumption of nonlinear controlled heart rate and blood pressure. Furthermore, the comparison of the nonlinear and linear approaches reveals that the heart rate and blood pressure variability in healthy subjects is caused by a higher level of noise as well as nonlinearity than in patients suffering from OSAS. The residue analysis points at a further source of heart rate and blood pressure variability in healthy subjects, in addition to heart rate, systolic blood pressure, and respiration. Comparison of the nonlinear models within and among the different groups of subjects suggests the ability to discriminate the cohorts that could lead to a stratification of hypertension risk in OSAS patients.
Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian
2014-01-01
Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627
Summer Arctic Sea Ice Intra-Seasonal Predictability Using a Vector Auto-Regressive Model
NASA Astrophysics Data System (ADS)
Ting, M.; Wang, L.; Yuan, X.
2014-12-01
Recent Arctic sea ice changes have important societal and economic impacts: the accelerated melting of Arctic sea ice in summer provides new fishery opportunities and increases the feasibility of trans-Arctic shipping, yet it may also lead to adverse effects on the Arctic ecosystem, weather and climate. Understanding the predictability of Arctic sea ice melting is thus an important task. A Vector Auto-Regressive (VAR) model is evaluated for predicting the summer time (May through September) daily Arctic sea ice concentrations. The intra-seasonal forecast skill of the Arctic sea ice is assessed using 1979-2012 satellite data provided by the National Snow & Ice Data Center (NSIDC). The cross-validated forecast skill of the VAR model is superior over persistence and climatological seasonal cycle for a lead-time of 15~60 days, especially over marginal seas. In addition to capturing the general seasonal melt of sea ice, the VAR model is also able to capture the interannual variability of the melting, from partial melt of the marginal sea ice in the beginning of the period to almost a complete melt in the later years. While the detailed mechanism leading to the high predictability of intra-seasonal sea ice concentration needs to be further examined, the study reveals for the first time that Arctic sea ice concentration can be predicted statistically with reasonable skills at the intra-seasonal time scales.
An Optimized Autoregressive Forecast Error Generator for Wind and Load Uncertainty Study
De Mello, Phillip; Lu, Ning; Makarov, Yuri V.
2011-01-17
This paper presents a first-order autoregressive algorithm to generate real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast errors. The methodology aims at producing random wind and load forecast time series reflecting the autocorrelation and cross-correlation of historical forecast data sets. Five statistical characteristics are considered: the means, standard deviations, autocorrelations, and cross-correlations. A stochastic optimization routine is developed to minimize the differences between the statistical characteristics of the generated time series and the targeted ones. An optimal set of parameters are obtained and used to produce the RT, HA, and DA forecasts in due order of succession. This method, although implemented as the first-order regressive random forecast error generator, can be extended to higher-order. Results show that the methodology produces random series with desired statistics derived from real data sets provided by the California Independent System Operator (CAISO). The wind and load forecast error generator is currently used in wind integration studies to generate wind and load inputs for stochastic planning processes. Our future studies will focus on reflecting the diurnal and seasonal differences of the wind and load statistics and implementing them in the random forecast generator.
Offline and online detection of damage using autoregressive models and artificial neural networks
NASA Astrophysics Data System (ADS)
Omenzetter, Piotr; de Lautour, Oliver R.
2007-04-01
Developed to study long, regularly sampled streams of data, time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring. In this research, Autoregressive (AR) models are used in conjunction with Artificial Neural Networks (ANNs) for damage detection, localisation and severity assessment. In the first reported experimental exercise, AR models were used offline to fit the acceleration time histories of a 3-storey test structure in undamaged and various damaged states when excited by earthquake motion simulated on a shake table. Damage was introduced into the structure by replacing the columns with those of a thinner thickness. Analytical models of the structure in both damaged and undamaged states were also developed and updated using experimental data in order to determine structural stiffness. The coefficients of AR models were used as damage sensitive features and input into an ANN to build a relationship between them and the remaining structural stiffness. In the second, analytical exercise, a system with gradually progressing damage was numerically simulated and acceleration AR models with exogenous inputs were identified recursively. A trained ANN was then required to trace the structural stiffness online. The results for the offline and online approach showed the efficiency of using AR coefficient as damage sensitive features and good performance of the ANNs for damage detection, localization and quantification.
Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian
2014-01-01
Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627
NASA Technical Reports Server (NTRS)
Triedman, J. K.; Perrott, M. H.; Cohen, R. J.; Saul, J. P.
1995-01-01
Fourier-based techniques are mathematically noncausal and are therefore limited in their application to feedback-containing systems, such as the cardiovascular system. In this study, a mathematically causal time domain technique, autoregressive moving average (ARMA) analysis, was used to parameterize the relations of respiration and arterial blood pressure to heart rate in eight humans before and during total cardiac autonomic blockade. Impulse-response curves thus generated showed the relation of respiration to heart rate to be characterized by an immediate increase in heart rate of 9.1 +/- 1.8 beats.min-1.l-1, followed by a transient mild decrease in heart rate to -1.2 +/- 0.5 beats.min-1.l-1 below baseline. The relation of blood pressure to heart rate was characterized by a slower decrease in heart rate of -0.5 +/- 0.1 beats.min-1.mmHg-1, followed by a gradual return to baseline. Both of these relations nearly disappeared after autonomic blockade, indicating autonomic mediation. Maximum values obtained from the respiration to heart rate impulse responses were also well correlated with frequency domain measures of high-frequency "vagal" heart rate control (r = 0.88). ARMA analysis may be useful as a time domain representation of autonomic heart rate control for cardiovascular modeling.
Self-organising mixture autoregressive model for non-stationary time series modelling.
Ni, He; Yin, Hujun
2008-12-01
Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches. PMID:19145663
Qiu, J.; Gao, W.
1997-03-01
Substantial variations in surface albedo across a large area cause difficulty in estimating regional net solar radiation and atmospheric absorption of shortwave radiation when only ground point measurements of surface albedo are used to represent the whole area. Information on spatial variations and site-wide averages of surface albedo, which vary with the underlying surface type and conditions and the solar zenith angle, is important for studies of clouds and atmospheric radiation over a large surface area. In this study, a bidirectional reflectance model was used to inversely retrieve surface properties such as leaf area index and then the bidirectional reflectance distribution was calculated by using the same radiation model. The albedo was calculated by converting the narrowband reflectance to broadband reflectance and then integrating over the upper hemisphere.
NASA Astrophysics Data System (ADS)
Wang, J.; Li, C.
2011-12-01
We estimate Curie-point depths (Zb) of the western United States and northeast Pacific Ocean by analyzing radially averaged amplitude spectra of magnetic anomalies based on a fractal magnetization model. The amplitude spectrum of source magnetization is proportional to the wavenumber (k) raised to a fractal exponent (-β). We first test whether long-wavelength components are captured appropriately by using variable overlapping windows ranging in sizes from 75 × 75 km2 to 200 × 200 km2. For each sliding window, the amplitude spectrum is pre-multiplied with the factor k-β prior to computation. We then use the centroid method (Tanaka et al., 1999) to calculate Zb. We find that when the window size approaches 200 × 200 km2 the resolution of estimated Zb is too low to reveal important geological features. For our study, fractal exponents larger than 0.6 will result in overcorrection. Considering the difficulty of simultaneous inversion of the depths to the top and centroid of magnetic sources (Zt and Z0 respectively) and β, we fix β = 0.5 for the whole study area. Note that β here is defined for amplitude spectrum, which is equivalent to 1 for power spectrum of 2D magnetic sources. Our results show that the estimated Curie depths range from 4 km to 40 km. The average Zb in the northern part of the northeast Pacific Ocean is about 14 km below the sea level, and almost the same depths are found in the junction of the active and ancient Cascade arcs and remanent track of Yellowstone hotspot. Subduction beneath the North American plate and consequent magmatism can account for small Zb in the above mentioned volcanic arc regions. The Mendocino Triple Junction separates the northeast Pacific into northern (mainly consisting of the Explorer, Juan de Fuca and Gorda plates) and southern parts. Both the Zb and the thickness of magnetic layer in the southern part are larger than those in the northern part. This contrast is due to the fact that the Pacific plate to the south
Dabros, Michal; Amrhein, Michael; Bonvin, Dominique; Marison, Ian W; von Stockar, Urs
2009-01-01
Real-time data reconciliation of concentration estimates of process analytes and biomass in microbial fermentations is investigated. A Fourier-transform mid-infrared spectrometer predicting the concentrations of process metabolites is used in parallel with a dielectric spectrometer predicting the biomass concentration during a batch fermentation of the yeast Saccharomyces cerevisiae. Calibration models developed off-line for both spectrometers suffer from poor predictive capability due to instrumental and process drifts unseen during calibration. To address this problem, the predicted metabolite and biomass concentrations, along with off-gas analysis and base addition measurements, are reconciled in real-time based on the closure of mass and elemental balances. A statistical test is used to confirm the integrity of the balances, and a non-negativity constraint is used to guide the data reconciliation algorithm toward positive concentrations. It is verified experimentally that the proposed approach reduces the standard error of prediction without the need for additional off-line analysis. PMID:19334289
Submillimeter, millimeter, and microwave spectral line catalogue
NASA Technical Reports Server (NTRS)
Poynter, R. L.; Pickett, H. M.
1980-01-01
A computer accessible catalogue of submillimeter, millimeter, and microwave spectral lines in the frequency range between O and 3000 GHz (such as; wavelengths longer than 100 m) is discussed. The catalogue was used as a planning guide and as an aid in the identification and analysis of observed spectral lines. The information listed for each spectral line includes the frequency and its estimated error, the intensity, lower state energy, and quantum number assignment. The catalogue was constructed by using theoretical least squares fits of published spectral lines to accepted molecular models. The associated predictions and their estimated errors are based upon the resultant fitted parameters and their covariances.
Alwee, Razana; Hj Shamsuddin, Siti Mariyam; Sallehuddin, Roselina
2013-01-01
Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models. PMID:23766729
Iwata, Tetsuo; Ito, Ritsuki; Mizutani, Yasuhiro; Araki, Tsutomu
2009-11-01
We propose a novel method for measuring fluorescence lifetimes by use of a pulsed-excitation light source and an ordinary or a high-gain photomultiplier tube (PMT) with a high-load resistor. In order to obtain the values of fluorescence lifetimes, we adopt a normal data-processing procedure used in phase-modulation fluorometry. We apply an autoregressive (AR)-model-based data-analysis technique to fluorescence- and reference-response time-series data obtained from the PMT in order to derive plural values of phase differences at a repetition frequency of the pulsed-excitation light source and its harmonic ones. The connection of the high-load resistor enhances sensitivity in signal detection in a certain condition. Introduction of the AR-model-based data-analysis technique improves precision in estimating the values of fluorescence lifetimes. Depending on the value of the load resistor and that of the repetition frequency, plural values of fluorescence lifetimes are obtained at one time by utilizing the phase information of harmonic frequencies. Because the proposed measurement system is simple to construct, it might be effective when we need to know approximate values of fluorescence lifetimes readily, such as in the field of biochemistry for a screening purpose. PMID:19891834
Is First-Order Vector Autoregressive Model Optimal for fMRI Data?
Ting, Chee-Ming; Seghouane, Abd-Krim; Khalid, Muhammad Usman; Salleh, Sh-Hussain
2015-09-01
We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.g., the Akaike IC (AIC)) are biased and inappropriate for the high-dimensional fMRI data typically with a small sample size. We examine the mixed results on the optimal VAR orders for fMRI, especially the validity of the order-one hypothesis, by a comprehensive evaluation using different model selection criteria over three typical data types--a resting state, an event-related design, and a block design data set--with varying time series dimensions obtained from distinct functional brain networks. We use a more balanced criterion, Kullback's IC (KIC) based on Kullback's symmetric divergence combining two directed divergences. We also consider the bias-corrected versions (AICc and KICc) to improve VAR model selection in small samples. Simulation results show better small-sample selection performance of the proposed criteria over the classical ones. Both bias-corrected ICs provide more accurate and consistent model order choices than their biased counterparts, which suffer from overfitting, with KICc performing the best. Results on real data show that orders greater than one were selected by all criteria across all data sets for the small to moderate dimensions, particularly from small, specific networks such as the resting-state default mode network and the task-related motor networks, whereas low orders close to one but not necessarily one were chosen for the large dimensions of full-brain networks. PMID:26161816
2012-01-01
Background Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitations of available models in the literature and propose a new mixture model with autoregressive random effects of the first order for the clustering of time-course gene-expression profiles. Some simulations and real examples are given to demonstrate the usefulness of the proposed models. Results We illustrate the applicability of our new model using synthetic and real time-course datasets. We show that our model outperforms existing models to provide more reliable and robust clustering of time-course data. Our model provides superior results when genetic profiles are correlated. It also gives comparable results when the correlation between the gene profiles is weak. In the applications to real time-course data, relevant clusters of coregulated genes are obtained, which are supported by gene-function annotation databases. Conclusions Our new model under our extension of the EMMIX-WIRE procedure is more reliable and robust for clustering time-course data because it adopts a random effects model that allows for the correlation among observations at different time points. It postulates gene-specific random effects with an autocorrelation variance structure that models coregulation within the clusters. The developed R package is flexible in its specification of the random effects through user-input parameters that enables improved modelling and consequent clustering of time-course data. PMID:23151154
NASA Astrophysics Data System (ADS)
Sakaguchi, Kaori; Nagatsuma, Tsutomu; Reeves, Geoffrey D.; Spence, Harlan E.
2015-12-01
The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. To provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraft were used as the predictors. Model explanatory parameters were selected from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L ≧ 4.8 and L ≧ 5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≦ L ≦ 6, while for the GEO flux prediction, the KP index is better than Dst. A test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.
Gross, Kevin; Edmunds, Peter J
2015-07-01
Tropical coral reefs exemplify ecosystems imperiled by environmental change. Anticipating the future of reef ecosystems requires understanding how scleractinian corals respond to the multiple environmental disturbances that threaten their survival. We analyzed the stability of coral reefs at three habitats at different depths along the south shore of St. John, U.S. Virgin Islands, using multivariate autoregression (MAR) models and two decades of monitoring data. We quantified several measures of ecosystem stability, including the magnitude of typical stochastic fluctuations, the rate of recovery following disturbance, and the sensitivity of coral cover to hurricanes and elevated sea temperature. Our results show that, even within a -4 km shore, coral communities in different habitats display different stability properties, and that the stability of each habitat corresponds with the habitat's known synecology. Two Orbicella-dominated habitats are less prone to annual stochastic fluctuations than coral communities in shallower water, but they recover slowly from disturbance, and one habitat has suffered recent losses in scleractinian cover that will not be quickly reversed. In contrast, a shallower, low-coral-cover habitat is subject to greater stochastic fluctuations, but rebounds more quickly from disturbance and is more robust to hurricanes and seawater warming. In some sense, the shallower community is more stable, although the stability arguably arises from having little coral cover left. Our results sharpen understanding of recent changes in coral communities at these habitats, provide a more detailed understanding of how these habitats may change in future environments, and illustrate how MAR models can be used to assess stability of communities founded upon long-lived species. PMID:26378304
Nakano, M.; Kumagai, H.; Kumazawa, M.; Yamaoka, K.; Chouet, B.A.
1998-01-01
We present a method to quantify the source excitation function and characteristic frequencies of long-period volcanic events. The method is based on an inhomogeneous autoregressive (AR) model of a linear dynamic system, in which the excitation is assumed to be a time-localized function applied at the beginning of the event. The tail of an exponentially decaying harmonic waveform is used to determine the characteristic complex frequencies of the event by the Sompi method. The excitation function is then derived by operating an AR filter constructed from the characteristic frequencies to the entire seismogram of the event, including the inhomogeneous part of the signal. We apply this method to three long-period events at Kusatsu-Shirane Volcano, central Japan, whose waveforms display simple decaying monochromatic oscillations except for the beginning of the events. We recover time-localized excitation functions lasting roughly 1 s at the start of each event and find that the estimated functions are very similar to each other at all the stations of the seismic network for each event. The phases of the characteristic oscillations referred to the estimated excitation function fall within a narrow range for almost all the stations. These results strongly suggest that the excitation and mode of oscillation are both dominated by volumetric change components. Each excitation function starts with a pronounced dilatation consistent with a sudden deflation of the volumetric source which may be interpreted in terms of a choked-flow transport mechanism. The frequency and Q of the characteristic oscillation both display a temporal evolution from event to event. Assuming a crack filled with bubbly water as seismic source for these events, we apply the Van Wijngaarden-Papanicolaou model to estimate the acoustic properties of the bubbly liquid and find that the observed changes in the frequencies and Q are consistently explained by a temporal change in the radii of the bubbles
Different approaches of spectral analysis
NASA Technical Reports Server (NTRS)
Lacoume, J. L.
1977-01-01
Several approaches to the problem of the calculation of spectral power density of a random function from an estimate of the autocorrelation function were studied. A comparative study was presented of these different methods. The principles on which they are based and the hypothesis implied were pointed out. Some indications on the optimization of the length of the estimated correlation function was given. An example of application of the different methods discussed in this paper was included.
Hybrid least squares multivariate spectral analysis methods
Haaland, David M.
2002-01-01
A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.
Spectral ratio method for measuring emissivity
Watson, K.
1992-01-01
The spectral ratio method is based on the concept that although the spectral radiances are very sensitive to small changes in temperature the ratios are not. Only an approximate estimate of temperature is required thus, for example, we can determine the emissivity ratio to an accuracy of 1% with a temperature estimate that is only accurate to 12.5 K. Selecting the maximum value of the channel brightness temperatures is an unbiased estimate. Laboratory and field spectral data are easily converted into spectral ratio plots. The ratio method is limited by system signal:noise and spectral band-width. The images can appear quite noisy because ratios enhance high frequencies and may require spatial filtering. Atmospheric effects tend to rescale the ratios and require using an atmospheric model or a calibration site. ?? 1992.
Iwata, Tetsuo; Muneshige, Akitaka; Araki, Tsutomu
2007-09-01
In order to derive plural values of fluorescence lifetimes simultaneously from a multi-component sample, we formulate a mathematical method for analyzing data obtained from a frequency-multiplexed phase-modulation fluorometer (FM-PMF) using an autoregressive (AR) model. Various parameter settings necessary for performing accurate data analysis based on the AR model are studied through numerical simulations. Measurement results of fluorescence lifetimes of real samples, 10 ppm quinine sulfate in 0.1 N H(2)SO(4), 10 ppm rhodamine 6G in ethanol, and their mixture with a volume ratio of 1:1, demonstrate that the proposed method works quite well. PMID:17910791
NASA Astrophysics Data System (ADS)
Loch, Hanna; Janczura, Joanna; Weron, Aleksander
2016-04-01
In this paper we study asymptotic behavior of a dynamical functional for an α -stable autoregressive fractionally integrated moving average (ARFIMA) process. We find an analytical formula for this important statistics and show its usefulness as a diagnostic tool for ergodic properties. The obtained results point to the very fast convergence of the dynamical functional and show that even for short trajectories one may obtain reliable conclusions on the ergodic properties of the ARFIMA process. Moreover we use the obtained theoretical results to illustrate how the dynamical functional statistics can be used in the verification of the proper model for an analysis of some biophysical experimental data.
Spectral resolvability of iterated rippled noise
NASA Astrophysics Data System (ADS)
Yost, William A.
2005-04-01
A forward-masking experiment was used to estimate the spectral ripple of iterated rippled noise (IRN) that is possibly resolved by the auditory system. Tonal signals were placed at spectral peaks and valleys of IRN maskers for a wide variety of IRN conditions that included different delays, number of iterations, and stimulus durations. The differences in the forward-masked thresholds of tones at spectral peaks and valleys were used to estimate spectral resolvability, and these results were compared to estimates obtained from a gamma-tone filter bank. The IRN spectrum has spectral peaks that are harmonics of the reciprocal of the delay used to generate IRN stimuli. As the number of iterations in the generation of IRN stimuli increases so does the difference in the spectral peak-to-valley ratio. For high number of iterations, long delays, and long durations evidence for spectral resolvability existed up to the 6th harmonic. For all other conditions spectral resolvability appeared to disappear at harmonics lower than the 6th, or was not measurable at all. These data will be discussed in terms of the role spectral resolvability might play in processing the pitch, pitch strength, and timbre of IRN stimuli. [Work supported by a grant from NIDCD.
Liu, Zhuofu; Wang, Lin; Luo, Zhongming; Heusch, Andrew I; Cascioli, Vincenzo; McCarthy, Peter W
2015-11-01
There is a need to develop a greater understanding of temperature at the skin-seat interface during prolonged seating from the perspectives of both industrial design (comfort/discomfort) and medical care (skin ulcer formation). Here we test the concept of predicting temperature at the seat surface and skin interface during prolonged sitting (such as required from wheelchair users). As caregivers are usually busy, such a method would give them warning ahead of a problem. This paper describes a data-driven model capable of predicting thermal changes and thus having the potential to provide an early warning (15- to 25-min ahead prediction) of an impending temperature that may increase the risk for potential skin damages for those subject to enforced sitting and who have little or no sensory feedback from this area. Initially, the oscillations of the original signal are suppressed using the reconstruction strategy of empirical mode decomposition (EMD). Consequentially, the autoregressive data-driven model can be used to predict future thermal trends based on a shorter period of acquisition, which reduces the possibility of introducing human errors and artefacts associated with longer duration "enforced" sitting by volunteers. In this study, the method had a maximum predictive error of <0.4 °C when used to predict the temperature at the seat and skin interface 15 min ahead, but required 45 min data prior to give this accuracy. Although the 45 min front loading of data appears large (in proportion to the 15 min prediction), a relative strength derives from the fact that the same algorithm could be used on the other 4 sitting datasets created by the same individual, suggesting that the period of 45 min required to train the algorithm is transferable to other data from the same individual. This approach might be developed (along with incorporation of other measures such as movement and humidity) into a system that can give caregivers prior warning to help avoid
Spectral and spread-spectral teleportation
Humble, Travis S.
2010-06-15
We report how quantum information encoded into the spectral degree of freedom of a single-photon state may be teleported using a finite spectrally entangled biphoton state. We further demonstrate how the bandwidth of the teleported wave form can be controllably and coherently dilated using a spread-spectral variant of teleportation. We calculate analytical expressions for the fidelities of spectral and spread-spectral teleportation when complex-valued Gaussian states are transferred using a proposed experimental approach. Finally, we discuss the utility of these techniques for integrating broad-bandwidth photonic qubits with narrow-bandwidth receivers in quantum communication systems.
Undecidability of the spectral gap
NASA Astrophysics Data System (ADS)
Cubitt, Toby S.; Perez-Garcia, David; Wolf, Michael M.
2015-12-01
The spectral gap—the energy difference between the ground state and first excited state of a system—is central to quantum many-body physics. Many challenging open problems, such as the Haldane conjecture, the question of the existence of gapped topological spin liquid phases, and the Yang-Mills gap conjecture, concern spectral gaps. These and other problems are particular cases of the general spectral gap problem: given the Hamiltonian of a quantum many-body system, is it gapped or gapless? Here we prove that this is an undecidable problem. Specifically, we construct families of quantum spin systems on a two-dimensional lattice with translationally invariant, nearest-neighbour interactions, for which the spectral gap problem is undecidable. This result extends to undecidability of other low-energy properties, such as the existence of algebraically decaying ground-state correlations. The proof combines Hamiltonian complexity techniques with aperiodic tilings, to construct a Hamiltonian whose ground state encodes the evolution of a quantum phase-estimation algorithm followed by a universal Turing machine. The spectral gap depends on the outcome of the corresponding ‘halting problem’. Our result implies that there exists no algorithm to determine whether an arbitrary model is gapped or gapless, and that there exist models for which the presence or absence of a spectral gap is independent of the axioms of mathematics.
Undecidability of the spectral gap.
Cubitt, Toby S; Perez-Garcia, David; Wolf, Michael M
2015-12-10
The spectral gap--the energy difference between the ground state and first excited state of a system--is central to quantum many-body physics. Many challenging open problems, such as the Haldane conjecture, the question of the existence of gapped topological spin liquid phases, and the Yang-Mills gap conjecture, concern spectral gaps. These and other problems are particular cases of the general spectral gap problem: given the Hamiltonian of a quantum many-body system, is it gapped or gapless? Here we prove that this is an undecidable problem. Specifically, we construct families of quantum spin systems on a two-dimensional lattice with translationally invariant, nearest-neighbour interactions, for which the spectral gap problem is undecidable. This result extends to undecidability of other low-energy properties, such as the existence of algebraically decaying ground-state correlations. The proof combines Hamiltonian complexity techniques with aperiodic tilings, to construct a Hamiltonian whose ground state encodes the evolution of a quantum phase-estimation algorithm followed by a universal Turing machine. The spectral gap depends on the outcome of the corresponding 'halting problem'. Our result implies that there exists no algorithm to determine whether an arbitrary model is gapped or gapless, and that there exist models for which the presence or absence of a spectral gap is independent of the axioms of mathematics. PMID:26659181
Modeling Polio Data Using the First Order Non-Negative Integer-Valued Autoregressive, INAR(1), Model
NASA Astrophysics Data System (ADS)
Vazifedan, Turaj; Shitan, Mahendran
Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.
Hybrid least squares multivariate spectral analysis methods
Haaland, David M.
2004-03-23
A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.
The Spectral Shift Function and Spectral Flow
NASA Astrophysics Data System (ADS)
Azamov, N. A.; Carey, A. L.; Sukochev, F. A.
2007-11-01
At the 1974 International Congress, I. M. Singer proposed that eta invariants and hence spectral flow should be thought of as the integral of a one form. In the intervening years this idea has lead to many interesting developments in the study of both eta invariants and spectral flow. Using ideas of [24] Singer’s proposal was brought to an advanced level in [16] where a very general formula for spectral flow as the integral of a one form was produced in the framework of noncommutative geometry. This formula can be used for computing spectral flow in a general semifinite von Neumann algebra as described and reviewed in [5]. In the present paper we take the analytic approach to spectral flow much further by giving a large family of formulae for spectral flow between a pair of unbounded self-adjoint operators D and D + V with D having compact resolvent belonging to a general semifinite von Neumann algebra {mathcal{N}} and the perturbation V in {mathcal{N}} . In noncommutative geometry terms we remove summability hypotheses. This level of generality is made possible by introducing a new idea from [3]. There it was observed that M. G. Krein’s spectral shift function (in certain restricted cases with V trace class) computes spectral flow. The present paper extends Krein’s theory to the setting of semifinite spectral triples where D has compact resolvent belonging to {mathcal{N}} and V is any bounded self-adjoint operator in {mathcal{N}} . We give a definition of the spectral shift function under these hypotheses and show that it computes spectral flow. This is made possible by the understanding discovered in the present paper of the interplay between spectral shift function theory and the analytic theory of spectral flow. It is this interplay that enables us to take Singer’s idea much further to create a large class of one forms whose integrals calculate spectral flow. These advances depend critically on a new approach to the calculus of functions of non
Soil spectra contributions to grass canopy spectral reflectance
NASA Technical Reports Server (NTRS)
Tucker, C. J.; Miller, L. D.
1977-01-01
The soil or background spectra contribution to grass canopy spectral reflectance for the 0.35 to 0.80 micron region was investigated using in situ collected spectral reflectance data. Regression analysis was used to estimate accurately the unexposed soil spectral reflectance and to quantify maxima and minima for soil-green vegetation reflection contrasts.
Kara, Sadik; Güven, Ayşegül; Okandan, Mustafa; Dirgenali, Fatma
2006-05-01
This research is concentrated on the diagnosis of mitral heart valve stenosis through the analysis of Doppler Signals' AR power spectral density graphic with the help of ANN. Multilayer feedforward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented in the MATLAB environment. Correct classification of 94% was achieved, whereas 4 false classifications have been observed for the test group of 68 subjects in total. The designed classification structure has about 97.3% sensitivity, 90.3% specifity and positive prediction is calculated to be 92.3%. The stated results show that the proposed method can make an effective interpretation. PMID:15890326
Eastin, Matthew D; Delmelle, Eric; Casas, Irene; Wexler, Joshua; Self, Cameron
2014-09-01
Dengue fever transmission results from complex interactions between the virus, human hosts, and mosquito vectors-all of which are influenced by environmental factors. Predictive models of dengue incidence rate, based on local weather and regional climate parameters, could benefit disease mitigation efforts. Time series of epidemiological and meteorological data for the urban environment of Cali, Colombia are analyzed from January of 2000 to December of 2011. Significant dengue outbreaks generally occur during warm-dry periods with extreme daily temperatures confined between 18°C and 32°C--the optimal range for mosquito survival and viral transmission. Two environment-based, multivariate, autoregressive forecast models are developed that allow dengue outbreaks to be anticipated from 2 weeks to 6 months in advance. These models have the potential to enhance existing dengue early warning systems, ultimately supporting public health decisions on the timing and scale of vector control efforts. PMID:24957546
Schlain, B R; Lavin, P T; Hayden, C L
1993-02-01
Continuous time autoregressive (CAR(1)) and random walk models of time series data are provided for detecting non-random shifts and trends of tumour markers in breast cancer patients following resection for cure. The continuous time random walk model with observation error is extended to the case of multiple patient time series. These models can be used to monitor large numbers of patients with time series with few sampling events that are serially correlated and unequally spaced. Further, the methodologies can be used to recommend appropriate testing intervals. A Kalman filter recursive algorithm is used to calculate the likelihood functions arising from the CAR(1) and random walk models and to calculate recursive residuals, which are monitored by Shewhart-cusum schemes. PMID:8456211
NASA Astrophysics Data System (ADS)
Yusof, Fadhilah; Kane, Ibrahim Lawal; Yusop, Zulkifli
2015-02-01
Precarious circumstances related to rainfall events can be due to very intense or persistence of rainfall over a long period of time. Such events may give rise to an exceedence of the capacity of sewer systems resulting to landslides or flooding. One of the conventional ways of measuring such risk associated with persistence in rain is done through studies of long term persistence and volatility persistence. This work investigates the persistence level of Kuantan daily rainfall using the hybrid of autoregressive fractional integrated moving average (ARFIMA) and hidden Markov model (HMM). The result shows that the rainfall variability period returns quickly to its usual variability level which may not have a lasting period of extreme wet, hence relatively stable rainfall behavior is observed in Kuantan rainfall. This will enhance the understanding of the process for the successful development and implementation of water resource tools to assess engineering and environmental problems such as flood control.
NASA Astrophysics Data System (ADS)
Hoell, Simon; Omenzetter, Piotr
2016-03-01
Data-driven vibration-based damage detection techniques can be competitive because of their lower instrumentation and data analysis costs. The use of autoregressive model coefficients (ARMCs) as damage sensitive features (DSFs) is one such technique. So far, like with other DSFs, either full sets of coefficients or subsets selected by trial-and-error have been used, but this can lead to suboptimal composition of multivariate DSFs and decreased damage detection performance. This study enhances the selection of ARMCs for statistical hypothesis testing for damage presence. Two approaches for systematic ARMC selection, based on either adding or eliminating the coefficients one by one or using a genetic algorithm (GA) are proposed. The methods are applied to a numerical model of an aerodynamically excited large composite wind turbine blade with disbonding damage. The GA out performs the other selection methods and enables building multivariate DSFs that markedly enhance early damage detectability and are insensitive to measurement noise.
Wang, Kewei; Song, Wentao; Li, Jinping; Lu, Wu; Yu, Jiangang; Han, Xiaofeng
2016-05-01
The aim of this study is to forecast the incidence of bacillary dysentery with a prediction model. We collected the annual and monthly laboratory data of confirmed cases from January 2004 to December 2014. In this study, we applied an autoregressive integrated moving average (ARIMA) model to forecast bacillary dysentery incidence in Jiangsu, China. The ARIMA (1, 1, 1) × (1, 1, 2)12 model fitted exactly with the number of cases during January 2004 to December 2014. The fitted model was then used to predict bacillary dysentery incidence during the period January to August 2015, and the number of cases fell within the model's CI for the predicted number of cases during January-August 2015. This study shows that the ARIMA model fits the fluctuations in bacillary dysentery frequency, and it can be used for future forecasting when applied to bacillary dysentery prevention and control. PMID:27106828
Multitaper spectral analysis of high-frequency seismograms
NASA Astrophysics Data System (ADS)
Park, Jeffrey; Lindberg, Craig R.; Vernon, Frank L., III
1987-11-01
Spectral estimation procedures which employ several prolate spheroidal sequences as tapers have been shown to yield better results than standard single-taper spectral analysis when used on a variety of engineering data. We apply the adaptive multitaper spectral estimation method of Thomson (1982) to a number of high-resolution digital seismic records and compare the results to those obtained using standard single-taper spectral estimates. Single-taper smoothed-spectrum estimates are plagued by a trade-off between the variance of the estimate and the bias caused by spectral leakage. Applying a taper to reduce bias discards data, increasing the variance of the estimate. Using a taper also unevenly samples the record. Throwing out data from the ends of the record can result in a spectral estimate which does not adequately represent the character of the spectrum of nonstationary processes like seismic waveforms. For example, a discrete Fourier transform of an untapered record (i.e., using a boxcar taper) produces a reasonable spectral estimate of the large-amplitude portion of the seismic source spectrum but cannot be trusted to provide a good estimate of the high-frequency roll-off. A discrete Fourier transform of the record multiplied by a more severe taper (like the Hann taper) which is resistant to spectral leakage leads to a reliable estimate of high-frequency spectral roll-off, but this estimate weights the analyzed data unequally. Therefore single-taper estimators which are less affected by leakage not only have increased variance but also can misrepresent the spectra of nonstationary data. The adaptive multitaper algorithm automatically adjusts between these extremes. We demonstrate its advantages using 16-bit seismic data recorded by instruments in the Anza Telemetered Seismic Network. We also present an analysis demonstrating the superiority of the multitaper algorithm in providing low-variance spectral estimates with good leakage resistance which do not
NASA Astrophysics Data System (ADS)
Zhan, Yimin; Mechefske, Chris K.
2007-07-01
Optimal maintenance decision analysis is heavily dependent on the accuracy of condition indicators. A condition indicator that is subject to such varying operating conditions as load is unable to provide precise condition information of the monitored object for making optimal operational maintenance decisions even if the maintenance program is established within a rigorous theoretical framework. For this reason, the performance of condition monitoring techniques applied to rotating machinery under varying load conditions has been a long-term concern and has attracted intensive research interest. Part I of this study proposed a novel technique based on adaptive autoregressive modeling and hypothesis tests. The method is able to automatically search for the optimal time-series model order and establish a compromised autoregressive model fitting based on the healthy gear motion residual signals under varying load conditions. The condition of the monitored gearbox is numerically represented by a modified Kolmogorov-Smirnov test statistic. Part II of this study is devoted to applications of the proposed technique to entire lifetime condition detection of three gearboxes with distinct physical specifications, distinct load conditions, and distinct failure modes. A comprehensive and thorough comparative study is conducted between the proposed technique and several counterparts. The detection technique is further enhanced by a proposed method to automatically identify and generate fault alerts with the aid of the Wilcoxon rank-sum test and thus requires no supervision from maintenance personnel. Experimental analysis demonstrated that the proposed technique applied to automatic identification and generation of fault alerts also features two highly desirable properties, i.e. few false alerts and early alert for incipient faults. Furthermore, it is found that the proposed technique is able to identify two types of abnormalities, i.e. strong ghost components abruptly
Briët, Olivier J. T.; Amerasinghe, Priyanie H.; Vounatsou, Penelope
2013-01-01
Introduction With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions’ impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during “consolidation” and “pre-elimination” phases. Methods Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years. Results The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series. Conclusions G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low. PMID:23785448
Quantile regression applied to spectral distance decay
Rocchini, D.; Cade, B.S.
2008-01-01
Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.01), considering both OLS and quantile regressions. Nonetheless, the OLS regression estimate of the mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when the spectral distance approaches zero, was very low compared with the intercepts of the upper quantiles, which detected high species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.
ATR neutron spectral characterization
Rogers, J.W.; Anderl, R.A.
1995-11-01
The Advanced Test Reactor (ATR) at INEL provides intense neutron fields for irradiation-effects testing of reactor material samples, for production of radionuclides used in industrial and medical applications, and for scientific research. Characterization of the neutron environments in the irradiation locations of the ATR has been done by means of neutronics calculations and by means of neutron dosimetry based on the use of neutron activation monitors that are placed in the various irradiation locations. The primary purpose of this report is to present the results of an extensive characterization of several ATR irradiation locations based on neutron dosimetry measurements and on least-squares-adjustment analyses that utilize both neutron dosimetry measurements and neutronics calculations. This report builds upon the previous publications, especially the reference 4 paper. Section 2 provides a brief description of the ATR and it tabulates neutron spectral information for typical irradiation locations, as derived from the more historical neutron dosimetry measurements. Relevant details that pertain to the multigroup neutron spectral characterization are covered in section 3. This discussion includes a presentation on the dosimeter irradiation and analyses and a development of the least-squares adjustment methodology, along with a summary of the results of these analyses. Spectrum-averaged cross sections for neutron monitoring and for displacement-damage prediction in Fe, Cr, and Ni are given in section 4. In addition, section4 includes estimates of damage generation rates for these materials in selected ATR irradiation locations. In section 5, the authors present a brief discussion of the most significant conclusions of this work and comment on its relevance to the present ATR core configuration. Finally, detailed numerical and graphical results for the spectrum-characterization analyses in each irradiation location are provided in the Appendix.
NASA Astrophysics Data System (ADS)
Thiemann, E.; Eparvier, F. G.; Chamberlin, P. C.; Woods, T. N.; Peterson, W. K.; Mitchell, D. L.; Xu, S.; Liemohn, M. W.
2015-12-01
The Extreme UltraViolet Monitor (EUVM) onboard the Mars Atmosphere and Volatile EvolutioN (MAVEN) probe at Mars characterizes the solar extreme ultraviolet (EUV) and soft x-ray (SXR) input into the Martian atmosphere. EUVM measures solar irradiance at 0-7 nm, 17-22 nm and 121.6 nm at a nominal 1 second cadence. These bands were selected to capture variability originating at different heights in the solar atmosphere; and are used to drive the Flare Irradiance Solar Model at Mars (FISM-M) that is a model of the solar spectrum from 0.1-190 nm with 1 nm resolution and produced routinely as the EUVM Level 3 data product. The 0-5 nm range of the solar spectrum is of particular aeronomic interest because the primary species of the Mars upper atmosphere have Auger transitions in this range. When an Auger transition is excited by incident SXR radiation, secondary electrons are emitted with sufficient energy to further ionize the atmosphere. Because these transitions are highly structured, sub-nm resolution of the solar spectrum is needed in the 0-5 nm range to fully constrain the solar input and more accurately characterize the energetics of the upper atmosphere. At Earth, .1 nm resolution estimates of the solar 0-5 nm range are produced by the X-ray Photometer System (XPS) onboard the SOlar Radiation and Climate Experiment (SORCE) satellite by combining broad-band SXR measurements with solar flare temperature measurements to drive an atomic physics based forward model of solar coronal emissions. This spectrum has been validated with other models as well as with photo-electron and day glow measurements at Earth. Similar to XPS, the EUVM 0-7 nm and 17-22 nm bands can be used to produce an XPS-like model at Mars, but with reduced precision due to differences in the available bands. We present first results of this technique applied to a set of solar flares observed by MAVEN EUVM and Earth assets. In addition to comparing EUVM and Earth-asset derived 0-5 nm solar spectra to
NASA Astrophysics Data System (ADS)
Senthil Kumar, A.; Keerthi, V.; Manjunath, A. S.; Werff, Harald van der; Meer, Freek van der
2010-08-01
Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously.
Arosa, Yago; Lago, Elena López; Varela, Luis Miguel; de la Fuente, Raúl
2016-07-25
In this paper we apply spectrally resolved white light interferometry to measure refractive and group index over a wide spectral band from 400 to 1000 nm. The output of a Michelson interferometer is spectrally decomposed by a homemade prism spectrometer with a high resolution camera. The group index is determined directly from the phase extracted from the spectral interferogram while the refractive index is estimated once its value at a given wavelength is known. PMID:27464179
Multidimensional spectral load balancing
Hendrickson, B.; Leland, R.
1993-01-01
We describe an algorithm for the static load balancing of scientific computations that generalizes and improves upon spectral bisection. Through a novel use of multiple eigenvectors, our new spectral algorithm can divide a computation into 4 or 8 pieces at once. These multidimensional spectral partitioning algorithms generate balanced partitions that have lower communication overhead and are less expensive to compute than those produced by spectral bisection. In addition, they automatically work to minimize message contention on a hypercube or mesh architecture. These spectral partitions are further improved by a multidimensional generalization of the Kernighan-Lin graph partitioning algorithm. Results on several computational grids are given and compared with other popular methods.
PERIODIC AUTOREGRESSIVE-MOVING AVERAGE (PARMA) MODELING WITH APPLICATIONS TO WATER RESOURCES.
Vecchia, A.V.
1985-01-01
Results involving correlation properties and parameter estimation for autogressive-moving average models with periodic parameters are presented. A multivariate representation of the PARMA model is used to derive parameter space restrictions and difference equations for the periodic autocorrelations. Close approximation to the likelihood function for Gaussian PARMA processes results in efficient maximum-likelihood estimation procedures. Terms in the Fourier expansion of the parameters are sequentially included, and a selection criterion is given for determining the optimal number of harmonics to be included. Application of the techniques is demonstrated through analysis of a monthly streamflow time series.
Spectral correlations of fractional Brownian motion
Oigaard, Tor Arne; Hanssen, Alfred; Scharf, Louis L.
2006-09-15
Fractional Brownian motion (fBm) is a ubiquitous nonstationary model for many physical processes with power-law time-averaged spectra. In this paper, we exploit the nonstationarity to derive the full spectral correlation structure of fBm. Starting from the time-varying correlation function, we derive two different time-frequency spectral correlation functions (the ambiguity function and the Kirkwood-Rihaczek spectrum), and one dual-frequency spectral correlation function. The dual-frequency spectral correlation has a surprisingly simple structure, with spectral support on three discrete lines. The theoretical predictions are verified by spectrum estimates of Monte Carlo simulations and of a time series of earthquakes with a magnitude of 7 and higher.
Submillimeter, millimeter, and microwave spectral line catalogue
NASA Technical Reports Server (NTRS)
Poynter, R. L.; Pickett, H. M.
1981-01-01
A computer accessible catalogue of submillimeter, millimeter and microwave spectral lines in the frequency range between 0 and 3000 GHZ (i.e., wavelengths longer than 100 mu m) is presented which can be used a planning guide or as an aid in the identification and analysis of observed spectral lines. The information listed for each spectral line includes the frequency and its estimated error, the intensity, lower state energy, and quantum number assignment. The catalogue was constructed by using theoretical least squares fits of published spectral lines to accepted molecular models. The associated predictions and their estimated errors are based upon the resultant fitted parameters and their covariances. Future versions of this catalogue will add more atoms and molecules and update the present listings (133 species) as new data appear. The catalogue is available as a magnetic tape recorded in card images and as a set of microfiche records.
Submillimeter, millimeter, and microwave spectral line catalogue
NASA Technical Reports Server (NTRS)
Poynter, R. L.; Pickett, H. M.
1984-01-01
This report describes a computer accessible catalogue of submillimeter, millimeter, and microwave spectral lines in the frequency range between 0 and 10000 GHz (i.e., wavelengths longer than 30 micrometers). The catalogue can be used as a planning guide or as an aid in the identification and analysis of observed spectral lines. The information listed for each spectral line includes the frequency and its estimated error, the intensity, lower state energy, and quantum number assignment. The catalogue has been constructed using theoretical least squares fits of published spectral lines to accepted molecular models. The associated predictions and their estimated errors are based upon the resultant fitted parameters and their covariances. Future versions of this catalogue will add more atoms and molecules and update the present listings (151 species) as new data appear. The catalogue is available from the authors as a magnetic tape recorded in card images and as a set of microfiche records.
A study on applicability of decay ratio estimation in a pressurized water reactor
Por, G. . Central Research Inst. for Physics); Runkel, J. . Nuclear Engineering and Nondestructing Testing Inst.)
1994-03-01
Decay ratio was estimated via a simplified method from the impulse response function that had been evaluated using an unvariable autoregression method. Suggested estimation was utilized in neutron noise measurements carried out during seven fuel cycles of a 1,300-MW (electric) pressurized water reactor. Results show that such an evaluation method can be used to monitor the increasing oscillation of the neutron flux during the fuel cycle.
Estimation of FBMC/OQAM fading channels using dual Kalman filters.
Aldababseh, Mahmoud; Jamoos, Ali
2014-01-01
We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM) wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS) estimator or the minimum mean square error (MMSE) estimator with possible adaptive implementation using recursive least square (RLS) algorithm or least mean square (LMS) algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method. PMID:24701181
Estimation of FBMC/OQAM Fading Channels Using Dual Kalman Filters
Aldababseh, Mahmoud
2014-01-01
We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM) wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS) estimator or the minimum mean square error (MMSE) estimator with possible adaptive implementation using recursive least square (RLS) algorithm or least mean square (LMS) algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method. PMID:24701181
Uncertainties in Interpolated Spectral Data
Gardner, James L.
2003-01-01
Interpolation is often used to improve the accuracy of integrals over spectral data convolved with various response functions or power distributions. Formulae are developed for propagation of uncertainties through the interpolation process, specifically for Lagrangian interpolation increasing a regular data set by factors of 5 and 2, and for cubic-spline interpolation. The interpolated data are correlated; these correlations must be considered when combining the interpolated values, as in integration. Examples are given using a common spectral integral in photometry. Correlation coefficients are developed for Lagrangian interpolation where the input data are uncorrelated. It is demonstrated that in practical cases, uncertainties for the integral formed using interpolated data can be reliably estimated using the original data.
Spectral Redundancy in Tissue Characterization
NASA Astrophysics Data System (ADS)
Varghese, Tomy
1995-01-01
Ultrasonic backscattered signals from material comprised of quasi-periodic scatterers exhibit redundancy over both its phase and magnitude spectra. This dissertation addresses the problem of estimating the mean scatterer spacing and scatterer density from the backscattered ultrasound signal using spectral redundancy characterized by the spectral autocorrelation (SAC) function. The SAC function exploits characteristic differences between the phase spectrum of the resolvable quasi-periodic (regular) scatterers and the unresolvable uniformly distributed (diffuse) scatterers to improve estimator performance over other estimators that operate directly on the magnitude spectrum. Analytical, simulation, and experimental results (liver and breast tissue) indicate the potential of utilizing phase information using the SAC function. A closed form analytical expression for the SAC function is derived for gamma distributed scatterer spacings. The theoretical expression for the SAC function demonstrate the increased regular-to-diffuse scatterer signal ratio in the off-diagonal components of the SAC function, since the diffuse component contributes only to the diagonal components (power spectrum). The A-scan is modelled as a cyclostationary signal whose statistical parameters vary in time with single or multiple periodicities. A-scan models consist of a collection of regular scatterers with gamma distributed spacings embedded in diffuse scatterers with uniform distributed spacings. The model accounts for attenuation by convolving the frequency dependent backscatter coefficients of the scatterer centers with a time-varying system response. Simulation results show that SAC-based estimates converge more reliably over smaller amounts of data than previously used cepstrum-based estimates. A major reason for the performance advantage is the use of phase information by the SAC function, while the cepstnun uses a phaseless power spectral density, that is directly affected by the system
Evaluating Spectral Signals to Identify Spectral Error.
Bazar, George; Kovacs, Zoltan; Tsenkova, Roumiana
2016-01-01
Since the precision and accuracy level of a chemometric model is highly influenced by the quality of the raw spectral data, it is very important to evaluate the recorded spectra and describe the erroneous regions before qualitative and quantitative analyses or detailed band assignment. This paper provides a collection of basic spectral analytical procedures and demonstrates their applicability in detecting errors of near infrared data. Evaluation methods based on standard deviation, coefficient of variation, mean centering and smoothing techniques are presented. Applications of derivatives with various gap sizes, even below the bandpass of the spectrometer, are shown to evaluate the level of spectral errors and find their origin. The possibility for prudent measurement of the third overtone region of water is also highlighted by evaluation of a complex data recorded with various spectrometers. PMID:26731541
Evaluating Spectral Signals to Identify Spectral Error
Bazar, George; Kovacs, Zoltan; Tsenkova, Roumiana
2016-01-01
Since the precision and accuracy level of a chemometric model is highly influenced by the quality of the raw spectral data, it is very important to evaluate the recorded spectra and describe the erroneous regions before qualitative and quantitative analyses or detailed band assignment. This paper provides a collection of basic spectral analytical procedures and demonstrates their applicability in detecting errors of near infrared data. Evaluation methods based on standard deviation, coefficient of variation, mean centering and smoothing techniques are presented. Applications of derivatives with various gap sizes, even below the bandpass of the spectrometer, are shown to evaluate the level of spectral errors and find their origin. The possibility for prudent measurement of the third overtone region of water is also highlighted by evaluation of a complex data recorded with various spectrometers. PMID:26731541
NASA Technical Reports Server (NTRS)
Zang, Thomas A.; Streett, Craig L.; Hussaini, M. Yousuff
1989-01-01
One of the objectives of these notes is to provide a basic introduction to spectral methods with a particular emphasis on applications to computational fluid dynamics. Another objective is to summarize some of the most important developments in spectral methods in the last two years. The fundamentals of spectral methods for simple problems will be covered in depth, and the essential elements of several fluid dynamical applications will be sketched.
Sufficient conditions for rate-independent hysteresis in autoregressive identified models
NASA Astrophysics Data System (ADS)
Martins, Samir Angelo Milani; Aguirre, Luis Antonio
2016-06-01
This paper shows how hysteresis can be described using polynomial models and what are the sufficient conditions to be met by the model in order to have hysteresis. Such conditions are related to the model equilibria, to the forcing function and to certain term clusters in the polynomial models. The main results of the paper are used in the identification and analysis of nonlinear models estimated from data produced by a magneto-rheological damper (MRD) model with Bouc-Wen rate-independent hysteresis. A striking feature of the identified model is its simplicity and this could turn out to be a key factor in controller design.
Spectral clustering for TRUS images
Mohamed, Samar S; Salama, Magdy MA
2007-01-01
Background Identifying the location and the volume of the prostate is important for ultrasound-guided prostate brachytherapy. Prostate volume is also important for prostate cancer diagnosis. Manual outlining of the prostate border is able to determine the prostate volume accurately, however, it is time consuming and tedious. Therefore, a number of investigations have been devoted to designing algorithms that are suitable for segmenting the prostate boundary in ultrasound images. The most popular method is the deformable model (snakes), a method that involves designing an energy function and then optimizing this function. The snakes algorithm usually requires either an initial contour or some points on the prostate boundary to be estimated close enough to the original boundary which is considered a drawback to this powerful method. Methods The proposed spectral clustering segmentation algorithm is built on a totally different foundation that doesn't involve any function design or optimization. It also doesn't need any contour or any points on the boundary to be estimated. The proposed algorithm depends mainly on graph theory techniques. Results Spectral clustering is used in this paper for both prostate gland segmentation from the background and internal gland segmentation. The obtained segmented images were compared to the expert radiologist segmented images. The proposed algorithm obtained excellent gland segmentation results with 93% average overlap areas. It is also able to internally segment the gland where the segmentation showed consistency with the cancerous regions identified by the expert radiologist. Conclusion The proposed spectral clustering segmentation algorithm obtained fast excellent estimates that can give rough prostate volume and location as well as internal gland segmentation without any user interaction. PMID:17359549
Estimators of bottom reflectance spectra
NASA Technical Reports Server (NTRS)
Estep, L.; Holloway, J.
1992-01-01
Estimators of in situ bottom spectral reflectance are calculated from multi-station optical field data gathered with standard instrumentation from different sites. These spectra are then compared to reflectance spectra measured in the laboratory of the bottom sediments collected in the field for the stations at these different sites. The relative fit of the estimated spectral curves to those measured in the laboratory was measured. The most accurate absolute estimation was provided by the single scattering irradiance model.
NASA Astrophysics Data System (ADS)
Lee, Dong Eun; Chapman, David; Henderson, Naomi; Chen, Chen; Cane, Mark A.
2015-09-01
We use a multilevel vector autoregressive model (VAR-L), to forecast sea surface temperature anomalies (SSTAs) in the Atlantic hurricane Main Development Region (MDR). VAR-L is a linear regression model using global SSTA data from L prior months as predictors. In hindcasts for the recent 30 years, the multilevel VAR-L outperforms a state-of-the-art dynamic forecast model, as well as the commonly used linear inverse model (LIM). The multilevel VAR-L model shows skill in 6-12 month forecasts, with its greatest skill in the months of the active hurricane season. The optimized model for the best long-range skill score in the MDR, chosen by a cross-validation procedure, has 12 time levels and 12 empirical orthogonal function modes. We investigate the optimal initial conditions for MDR SSTA prediction using a generalized singular vector decomposition of the propagation matrix. We find that the added temporal degrees of freedom for the predictands in VAR12 as compared with a LIM model, which allow the model to capture both the local wind-evaporation-SST feedback in the Tropical Atlantic and the impact on the Atlantic of an improved medium-range ENSO forecast, elevate the long-range forecast skill in the MDR.
NASA Technical Reports Server (NTRS)
Grass, David; Jasinski, Michael F.; Govere, John
2003-01-01
There has been increasing effort in recent years to employ satellite remotely sensed data to identify and map vector habitat and malaria transmission risk in data sparse environments. In the current investigation, available satellite and other land surface climatology data products are employed in short-term forecasting of infection rates in the Mpumalanga Province of South Africa, using a multivariate autoregressive approach. The climatology variables include precipitation, air temperature and other land surface states computed by the Off-line Land-Surface Global Assimilation System (OLGA) including soil moisture and surface evaporation. Satellite data products include the Normalized Difference Vegetation Index (NDVI) and other forcing data used in the Goddard Earth Observing System (GEOS-1) model. Predictions are compared to long- term monthly records of clinical and microscopic diagnoses. The approach addresses the high degree of short-term autocorrelation in the disease and weather time series. The resulting model is able to predict 11 of the 13 months that were classified as high risk during the validation period, indicating the utility of applying antecedent climatic variables to the prediction of malaria incidence for the Mpumalanga Province.
NASA Astrophysics Data System (ADS)
Rebora, N.; Silvestro, F.; Rudari, R.; Herold, C.; Ferraris, L.
2016-06-01
Downscaling methods are used to derive stream flow at a high temporal resolution from a data series that has a coarser time resolution. These algorithms are useful for many applications, such as water management and statistical analysis, because in many cases stream flow time series are available with coarse temporal steps (monthly), especially when considering historical data; however, in many cases, data that have a finer temporal resolution are needed (daily). In this study, we considered a simple but efficient stochastic auto-regressive model that is able to downscale the available stream flow data from monthly to daily time resolution and applied it to a large dataset that covered the entire North and Central American continent. Basins with different drainage areas and different hydro-climatic characteristics were considered, and the results show the general good ability of the analysed model to downscale monthly stream flows to daily stream flows, especially regarding the reproduction of the annual maxima. If the performance in terms of the reproduction of hydrographs and duration curves is considered, better results are obtained for those cases in which the hydrologic regime is such that the annual maxima stream flow show low or medium variability, which means that they have a low or medium coefficient of variation; however, when the variability increases, the performance of the model decreases.